code stringlengths 81 54k | code_codestyle int64 0 721 | style_context stringlengths 91 41.9k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__lowerCamelCase : List[str] = {
"""configuration_funnel""": ["""FUNNEL_PRETRAINED_CONFIG_ARCHIVE_MAP""", "... | 705 |
# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING, Dict, Optional
import numpy as np
import pyarrow as pa
from .. import config
from ..utils.logging import get_logger
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter
if TYPE_CHECK... | 25 | 0 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
__lo... | 706 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
__lowerCamelCase : Tuple = {
"""configuration_roberta_prelayernorm""": [
"""ROBERTA_PRELAYERNORM_PRETRAIN... | 25 | 0 |
import contextlib
import copy
import random
from typing import Any, Dict, Iterable, Optional, Union
import numpy as np
import torch
from .utils import deprecate, is_transformers_available
if is_transformers_available():
import transformers
def SCREAMING_SNAKE_CASE ( snake_case_ : ... | 707 |
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from transformers.activations import gelu_new, gelu_python, get_activation
@require_torch
class SCREAMING_SNAKE_CASE__ ( unittest.TestCase ... | 25 | 0 |
import argparse
import re
from typing import Dict
import torch
from datasets import Audio, Dataset, load_dataset, load_metric
from transformers import AutoFeatureExtractor, pipeline
def SCREAMING_SNAKE_CASE ( snake_case_ : Dataset , snake_case_ : Dict[str, str] ... | 708 |
import argparse
import fairseq
import torch
from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging
logging.set_verbosity_info()
__lowerCamelCase : int = logging.get_logger(__name__)
__lowerCamelCase : int = {
"""post_extract_proj"... | 25 | 0 |
import unittest
import numpy as np
import torch
from .utils_summarization import build_mask, compute_token_type_ids, process_story, truncate_or_pad
class SCREAMING_SNAKE_CASE__ ( unittest.TestCase ):
"""simple docstring"""
def _lowercase ( self : Tuple ):
... | 709 |
import copy
import tempfile
import unittest
from transformers import MaMaaaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from transformers.utils import cached_property
from ...generation.test_utils import Generatio... | 25 | 0 |
import pytest
from datasets.splits import SplitDict, SplitInfo
from datasets.utils.py_utils import asdict
@pytest.mark.parametrize(
"split_dict" , [
SplitDict(),
SplitDict({"train": SplitInfo(name="train" , num_bytes=1337 , num_examples=42 , dataset_name="my_dataset... | 710 |
from unittest.mock import patch
import pyspark
from datasets.packaged_modules.spark.spark import (
Spark,
SparkExamplesIterable,
_generate_iterable_examples,
)
from ..utils import (
require_dill_gt_0_3_2,
require_not_windows,
)
def SCREAMING_SNAKE_CASE ( snake_case_ ... | 25 | 0 |
from cva import destroyAllWindows, imread, imshow, waitKey
def SCREAMING_SNAKE_CASE ( snake_case_ : List[str] ):
# getting number of pixels in the image
snake_case__ : Optional[int] = img.shape[0], img.shape[1]
# converting each pixel's color to its negative
fo... | 711 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__lowerCamelCase : List[str] = {"""configuration_xlnet""": ["""XLNET_PRETRAINED... | 25 | 0 |
def SCREAMING_SNAKE_CASE ( snake_case_ : int , snake_case_ : int ):
return numa ^ numa < 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 712 |
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.du... | 25 | 0 |
import tempfile
import unittest
from transformers import TaConfig, is_torch_available
from transformers.testing_utils import (
require_sentencepiece,
require_tokenizers,
require_torch,
slow,
torch_device,
)
from ...generation.test_utils import GenerationTesterMixin
from ...test_modeling_com... | 713 |
import numpy as np
from matplotlib import pyplot as plt
from sklearn.datasets import load_iris
from sklearn.metrics import ConfusionMatrixDisplay
from sklearn.model_selection import train_test_split
from xgboost import XGBClassifier
def SCREAMING_SNAKE_CASE ( snake_case_ : dict )... | 25 | 0 |
import contextlib
from multiprocessing import Pool, RLock
from tqdm.auto import tqdm
from ..utils import experimental, logging
__lowerCamelCase : Optional[int] = logging.get_logger(__name__)
class SCREAMING_SNAKE_CASE__ :
"""simple docstring"""
a_ = None
... | 714 |
import argparse
import re
from typing import Dict
import torch
from datasets import Audio, Dataset, load_dataset, load_metric
from transformers import AutoFeatureExtractor, pipeline
def SCREAMING_SNAKE_CASE ( snake_case_ : Dataset , snake_case_ : Dict[str, str] ... | 25 | 0 |
def SCREAMING_SNAKE_CASE ( snake_case_ : int ):
snake_case__ : Dict = int(snake_case_ )
if decimal in (0, 1): # Exit cases for the recursion
return str(snake_case_ )
snake_case__ : Dict = divmod(snake_case_ , 2 )
return binary_recursi... | 715 |
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import ClassLabel, Features, Value
from .base import TaskTemplate
@dataclass(frozen=UpperCamelCase_ )
class SCREAMING_SNAKE_CASE__ ( UpperCamelCase_ ):
"""simple docstring"""
... | 25 | 0 |
__lowerCamelCase : Tuple = """ABCDEFGHIJKLMNOPQRSTUVWXYZ"""
def SCREAMING_SNAKE_CASE ( ):
snake_case__ : Optional[Any] = input("Enter message: " )
snake_case__ : Any = input("Enter key [alphanumeric]: " )
snake_case__ : Optional[int] =... | 716 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
from ...utils import logging
from ..auto import CONFIG_MAPPING
__lowerCamelCase : Union[str, Any] = logging.get_logger(__n... | 25 | 0 |
import math
def SCREAMING_SNAKE_CASE ( snake_case_ : str , snake_case_ : str ):
if 0 not in (x, y):
# We use the relation x^y = y*log10(x), where 10 is the base.
return y * math.logaa(snake_case_ )
else:
if x == 0: # 0 raised to any number is 0
re... | 717 |
def SCREAMING_SNAKE_CASE ( snake_case_ : list ):
if len(snake_case_ ) <= 1:
return lst
snake_case__ : List[Any] = 1
while i < len(snake_case_ ):
if lst[i - 1] <= lst[i]:
i += 1
else:
snake_case__, snake_case__ : Tuple = lst[i], lst[i... | 25 | 0 |
from collections.abc import Iterable
from typing import Any
class SCREAMING_SNAKE_CASE__ :
"""simple docstring"""
def __init__( self : Optional[Any] , __A : int | None = None ):
snake_case__ : Dict = value
snake_case__ : Node | N... | 718 |
from __future__ import annotations
import time
__lowerCamelCase : str = list[tuple[int, int]]
__lowerCamelCase : Optional[int] = [
[0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0]... | 25 | 0 |
def SCREAMING_SNAKE_CASE ( snake_case_ : Tuple , snake_case_ : List[Any] , snake_case_ : Tuple=False ):
if isinstance(snake_case_ , snake_case_ ) and isinstance(snake_case_ , snake_case_ ):
snake_case__ : int = len(... | 719 |
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if ... | 25 | 0 |
# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING, Dict, Optional
import numpy as np
import pyarrow as pa
from .. import config
from ..utils.logging import get_logger
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter
if TYPE_CHECKI... | 720 |
import faiss # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy # noqa: F401 # Here to have a nice missing dependency error message early on
import requests # noqa: F401 # Here to have a nice missing dependency error message early on
import sklearn # noqa: F401 # Here t... | 25 | 0 |
import argparse
import logging
from collections import namedtuple
import torch
from model_bertabs import BertAbsSummarizer
from models.model_builder import AbsSummarizer # The authors' implementation
from transformers import BertTokenizer
logging.basicConfig(level=logging.INFO)
__lowerCamelCase : An... | 721 |
# Lint as: python3
# pylint: enable=line-too-long
# pylint: disable=g-import-not-at-top,g-bad-import-order,wrong-import-position
__lowerCamelCase : Union[str, Any] = """2.13.1"""
import platform
import pyarrow
from packaging import version
if version.parse(platform.python_version()) < version.... | 25 | 0 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__lowerCamelCase : Dict = logging.get_logger(__name__)
__lowerCamelCase : Optional[int] = {
"""junnyu/rofor... | 700 |
from __future__ import annotations
def SCREAMING_SNAKE_CASE ( snake_case_ : int ):
snake_case__ : str = [True] * limit
snake_case__ : str = False
snake_case__ : str = False
snake_case__ : str = True
for i in range(3 ,... | 25 | 0 |
from arguments import InitializationArguments
from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, HfArgumentParser
# Configuration
__lowerCamelCase : Union[str, Any] = HfArgumentParser(InitializationArguments)
__lowerCamelCase : Optional[Any] = parser.parse_ar... | 701 |
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if ... | 25 | 0 |
import json
from typing import List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_mvp import MvpTokenizer
__lowe... | 702 |
import json
import os
from functools import lru_cache
from typing import Dict, List, Optional, Tuple, Union
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding, EncodedInput
from ...utils import PaddingStrategy, logging
__l... | 25 | 0 |
import argparse
from pathlib import Path
import torch
from transformers import OPTConfig, OPTModel
from transformers.utils import logging
logging.set_verbosity_info()
__lowerCamelCase : List[Any] = logging.get_logger(__name__)
def SCREAMING_SNAKE_CASE ( snake_case_ : ... | 703 |
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import sys
import warnings
from os.path import abspath, dirname, join
# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between che... | 25 | 0 |
from math import ceil, sqrt
def SCREAMING_SNAKE_CASE ( snake_case_ : int = 1000000 ):
snake_case__ : Tuple = 0
for outer_width in range(3 , (limit // 4) + 2 ):
if outer_width**2 > limit:
snake_case__ : int = max(ceil(sqrt(outer_width*... | 704 |
def SCREAMING_SNAKE_CASE ( snake_case_ : str ):
snake_case__ : Any = [0] * len(snake_case_ )
for i in range(1 , len(snake_case_ ) ):
# use last results for better performance - dynamic programming
snake_case__ : Union[str, Any] = pref... | 25 | 0 |
def SCREAMING_SNAKE_CASE ( snake_case_ : Any ):
return [
{
0: [1, 2],
1: [0, 2],
2: [0, 1, 3, 5],
3: [2, 4],
4: [3],
5: [2, 6, 8],
6: [5, 7],
7: [6, 8],
8: [5, 7],
},
{
0: [6],
... | 705 |
# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING, Dict, Optional
import numpy as np
import pyarrow as pa
from .. import config
from ..utils.logging import get_logger
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter
if TYPE_CHECK... | 25 | 0 |
from __future__ import annotations
def SCREAMING_SNAKE_CASE ( snake_case_ : list[int] , snake_case_ : int ):
if len(snake_case_ ) == 0:
return False
snake_case__ : Dict = len(snake_case_ ) // 2
if a_list[midpoint] == item:
return True... | 706 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
__lowerCamelCase : Tuple = {
"""configuration_roberta_prelayernorm""": [
"""ROBERTA_PRELAYERNORM_PRETRAIN... | 25 | 0 |
import gc
import threading
import time
import psutil
import torch
class SCREAMING_SNAKE_CASE__ :
"""simple docstring"""
def __init__( self : str ):
snake_case__ : List[str] = psutil.Process()
snake_case__ : int = False
def ... | 707 |
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from transformers.activations import gelu_new, gelu_python, get_activation
@require_torch
class SCREAMING_SNAKE_CASE__ ( unittest.TestCase ... | 25 | 0 |
def SCREAMING_SNAKE_CASE ( snake_case_ : int ):
if not isinstance(snake_case_ , snake_case_ ):
raise ValueError("multiplicative_persistence() only accepts integral values" )
if num < 0:
raise ValueError("multiplicative_persistence() does not accept negative values" ... | 708 |
import argparse
import fairseq
import torch
from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging
logging.set_verbosity_info()
__lowerCamelCase : int = logging.get_logger(__name__)
__lowerCamelCase : int = {
"""post_extract_proj"... | 25 | 0 |
__lowerCamelCase : int = range(2, 20 + 1)
__lowerCamelCase : int = [10**k for k in range(ks[-1] + 1)]
__lowerCamelCase : dict[int, dict[int, list[list[int]]]] = {}
def SCREAMING_SNAKE_CASE ( snake_case_ : List[Any] , snake_case_ : ... | 709 |
import copy
import tempfile
import unittest
from transformers import MaMaaaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from transformers.utils import cached_property
from ...generation.test_utils import Generatio... | 25 | 0 |
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import tor... | 710 |
from unittest.mock import patch
import pyspark
from datasets.packaged_modules.spark.spark import (
Spark,
SparkExamplesIterable,
_generate_iterable_examples,
)
from ..utils import (
require_dill_gt_0_3_2,
require_not_windows,
)
def SCREAMING_SNAKE_CASE ( snake_case_ ... | 25 | 0 |
from unittest import TestCase
from datasets import Sequence, Value
from datasets.arrow_dataset import Dataset
class SCREAMING_SNAKE_CASE__ ( UpperCamelCase_ ):
"""simple docstring"""
def _lowercase ( self : int ):
return [
{"col_1": 3, "col_2":... | 711 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__lowerCamelCase : List[str] = {"""configuration_xlnet""": ["""XLNET_PRETRAINED... | 25 | 0 |
import string
def SCREAMING_SNAKE_CASE ( snake_case_ : str ):
snake_case__ : Union[str, Any] = ""
for i in sequence:
snake_case__ : int = ord(snake_case_ )
if 65 <= extract <= 90:
output += chr(155 - extract )
elif 97 <= extract <= 1... | 712 |
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.du... | 25 | 0 |
import json
import os
from functools import lru_cache
from typing import Dict, List, Optional, Tuple, Union
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding, EncodedInput
from ...utils import PaddingStrategy, logging
__l... | 713 |
import numpy as np
from matplotlib import pyplot as plt
from sklearn.datasets import load_iris
from sklearn.metrics import ConfusionMatrixDisplay
from sklearn.model_selection import train_test_split
from xgboost import XGBClassifier
def SCREAMING_SNAKE_CASE ( snake_case_ : dict )... | 25 | 0 |
import inspect
import unittest
import warnings
from math import ceil, floor
from transformers import LevitConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, requ... | 714 |
import argparse
import re
from typing import Dict
import torch
from datasets import Audio, Dataset, load_dataset, load_metric
from transformers import AutoFeatureExtractor, pipeline
def SCREAMING_SNAKE_CASE ( snake_case_ : Dataset , snake_case_ : Dict[str, str] ... | 25 | 0 |
import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
__lowerCamelCase : Any = logging.get_logger(__name__)
__lowerCamelCase : Tup... | 715 |
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import ClassLabel, Features, Value
from .base import TaskTemplate
@dataclass(frozen=UpperCamelCase_ )
class SCREAMING_SNAKE_CASE__ ( UpperCamelCase_ ):
"""simple docstring"""
... | 25 | 0 |
from __future__ import annotations
def SCREAMING_SNAKE_CASE ( snake_case_ : list ):
if len(snake_case_ ) == 0:
return []
snake_case__ : Union[str, Any] = min(snake_case_ ), max(snake_case_ )
snake_case__ : Dict = int(max_value - min_valu... | 716 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
from ...utils import logging
from ..auto import CONFIG_MAPPING
__lowerCamelCase : Union[str, Any] = logging.get_logger(__n... | 25 | 0 |
import copy
import os
from typing import TYPE_CHECKING, List, Union
if TYPE_CHECKING:
pass
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowerCamelCase : int = logging.get_logger(__name__)
__lowerCamelCase : Dict = {
"""kakaobrain/ali... | 717 |
def SCREAMING_SNAKE_CASE ( snake_case_ : list ):
if len(snake_case_ ) <= 1:
return lst
snake_case__ : List[Any] = 1
while i < len(snake_case_ ):
if lst[i - 1] <= lst[i]:
i += 1
else:
snake_case__, snake_case__ : Tuple = lst[i], lst[i... | 25 | 0 |
import itertools
import os
import random
import tempfile
import unittest
import numpy as np
from transformers import TvltFeatureExtractor, is_datasets_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_utils impo... | 718 |
from __future__ import annotations
import time
__lowerCamelCase : str = list[tuple[int, int]]
__lowerCamelCase : Optional[int] = [
[0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0]... | 25 | 0 |
def SCREAMING_SNAKE_CASE ( snake_case_ : str , snake_case_ : int ):
snake_case__ : list[list[str]] = [[] for _ in range(snake_case_ )]
snake_case__ : Optional[int] = key - 1
if key <= 0:
raise ValueError("Height of grid can't be 0 o... | 719 |
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if ... | 25 | 0 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
if TYPE_CHECKING:
from ... import FeatureExtractionMixin, PreTrainedTokenizerBase, TensorType
__... | 720 |
import faiss # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy # noqa: F401 # Here to have a nice missing dependency error message early on
import requests # noqa: F401 # Here to have a nice missing dependency error message early on
import sklearn # noqa: F401 # Here t... | 25 | 0 |
import copy
import inspect
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import TimesformerConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils i... | 721 |
# Lint as: python3
# pylint: enable=line-too-long
# pylint: disable=g-import-not-at-top,g-bad-import-order,wrong-import-position
__lowerCamelCase : Union[str, Any] = """2.13.1"""
import platform
import pyarrow
from packaging import version
if version.parse(platform.python_version()) < version.... | 25 | 0 |
from __future__ import annotations
from itertools import permutations
from random import randint
from timeit import repeat
def UpperCAmelCase ( ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : List[str] = [randint(-1_000 , 1_000 ) for i in range(10... | 26 |
import json
import os
import sys
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile
from huggingface_hub import HfFolder, Repository, create_repo, delete_repo
from requests.exceptions import HTTPError
import transformers
from transformers import (
CONFIG_MAPPING,
FEATUR... | 26 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__lowercase :List[str] = {
"configuration_roberta": ["ROBERTA_PRETRAINED_CONFIG_ARCHIV... | 26 |
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class _a ( lowercase__ ):
"""simple docstring"""
snake_case_ = ["image_processor", "tokenizer"]
snake_case_ = "CLIPImageProces... | 26 | 1 |
import math
import sys
def UpperCAmelCase ( _lowerCamelCase : str ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Union[str, Any] = ""
try:
with open(_lowerCamelCase , "rb" ) as binary_file:
SCREAMING_S... | 26 |
import sys
from collections import defaultdict
class _a :
"""simple docstring"""
def __init__( self : Any ) ->Dict:
SCREAMING_SNAKE_CASE__ : Tuple = []
def A_ ( self : int , a : List[str] ) ->Dict:
... | 26 | 1 |
import os
import sys
__lowercase :int = os.path.join(os.path.dirname(__file__), "src")
sys.path.append(SRC_DIR)
from transformers import (
AutoConfig,
AutoModel,
AutoModelForCausalLM,
AutoModelForMaskedLM,
AutoModelForQuestionAnswering,
AutoModelForSequenceClassification,
... | 26 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import TensorType, logging
if TYPE_CHECKING:
from ...onnx.config import PatchingSpec
from ...tokeniz... | 26 | 1 |
import inspect
from typing import Callable, List, Optional, Union
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextModel,
CLIPTokenizer,
WhisperForConditionalGeneration,
WhisperProcessor,
)
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DiffusionPipeline... | 26 |
def UpperCAmelCase ( _lowerCamelCase : int = 4_000_000 ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Any = [0, 1]
SCREAMING_SNAKE_CASE__ : List[Any] = 0
while fib[i] <= n:
fib.append(fib[i] + fib[i + 1] )
... | 26 | 1 |
from __future__ import annotations
from typing import Any
class _a :
"""simple docstring"""
def __init__( self : Tuple , a : int , a : int , a : float = 0 ) ->None:
SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE... | 26 |
import unittest
from typing import Dict, List, Optional, Union
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image... | 26 | 1 |
import warnings
from ...utils import logging
from .image_processing_owlvit import OwlViTImageProcessor
__lowercase :Any = logging.get_logger(__name__)
class _a ( lowercase__ ):
"""simple docstring"""
def __init__( self : str , *a : O... | 26 |
def UpperCAmelCase ( _lowerCamelCase : int , _lowerCamelCase : bool = False ):
'''simple docstring'''
if n == 2:
return True
if not n % 2 or n < 2:
return False
if n > 5 and n % 10 not in (1, 3, 7, 9): # can quickly check ... | 26 | 1 |
from typing import Dict, Iterable, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
IMAGENET_STANDARD_MEA... | 26 |
import numpy
class _a :
"""simple docstring"""
def __init__( self : Optional[int] , a : numpy.ndarray , a : numpy.ndarray ) ->None:
SCREAMING_SNAKE_CASE__ : Any = input_array
# Random initial weights ar... | 26 | 1 |
def UpperCAmelCase ( _lowerCamelCase : int ):
'''simple docstring'''
return 1 if digit in (0, 1) else (digit * factorial(digit - 1 ))
def UpperCAmelCase ( _lowerCamelCase : int ):
'''simple docstring'''
SCREAMING_... | 26 |
from typing import Dict, List
from nltk.translate import gleu_score
import datasets
from datasets import MetricInfo
__lowercase :Tuple = "\\n@misc{wu2016googles,\n title={Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation},\n author={Yongh... | 26 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
__lowercase :Tuple = {"configuration_fnet": ["FNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "FNetConfig"]}
try... | 26 |
import sys
from .dependency_versions_table import deps
from .utils.versions import require_version, require_version_core
# define which module versions we always want to check at run time
# (usually the ones defined in `install_requires` in setup.py)
#
# order specific notes:
# - tqdm must be checked before to... | 26 | 1 |
from .integrations import (
is_optuna_available,
is_ray_available,
is_sigopt_available,
is_wandb_available,
run_hp_search_optuna,
run_hp_search_ray,
run_hp_search_sigopt,
run_hp_search_wandb,
)
from .trainer_utils import (
HPSearchBackend,
default_hp_space_optuna,
defau... | 26 |
from __future__ import annotations
def UpperCAmelCase ( _lowerCamelCase : list[int] , _lowerCamelCase : int ):
'''simple docstring'''
if len(_lowerCamelCase ) < k or k < 0:
raise ValueError("Invalid Input" )
SCREAMING_SNAKE_CA... | 26 | 1 |
from .constants import (
MODEL_NAME,
OPTIMIZER_NAME,
RNG_STATE_NAME,
SAFE_WEIGHTS_INDEX_NAME,
SAFE_WEIGHTS_NAME,
SCALER_NAME,
SCHEDULER_NAME,
TORCH_LAUNCH_PARAMS,
WEIGHTS_INDEX_NAME,
WEIGHTS_NAME,
)
from .dataclasses import (
BnbQuantizationConfig,
ComputeEnvironmen... | 26 |
from __future__ import annotations
def UpperCAmelCase ( _lowerCamelCase : list[int | float] , _lowerCamelCase : int , _lowerCamelCase : int ):
'''simple docstring'''
if len(_lowerCamelCase ) == 0:
raise ValueError("find_max() ... | 26 | 1 |
import copy
import inspect
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import VideoMAEConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils impor... | 26 |
import json
import multiprocessing
import os
import re
from collections import defaultdict
import torch
from accelerate import Accelerator
from accelerate.utils import set_seed
from arguments import HumanEvalArguments
from datasets import load_dataset, load_metric
from torch.utils.data import IterableDataset
fro... | 26 | 1 |
from .glue import glue_convert_examples_to_features, glue_output_modes, glue_processors, glue_tasks_num_labels
from .squad import SquadExample, SquadFeatures, SquadVaProcessor, SquadVaProcessor, squad_convert_examples_to_features
from .utils import DataProcessor, InputExample, InputFeatures, SingleSentenceClassif... | 26 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__lowercase :str = {
"configuration_upernet": ["UperNetConfig"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependen... | 26 | 1 |
import random
import timeit
from functools import wraps
from typing import Callable, Optional
from ..configuration_utils import PretrainedConfig
from ..models.auto.modeling_tf_auto import TF_MODEL_MAPPING, TF_MODEL_WITH_LM_HEAD_MAPPING
from ..utils import is_pyanvml_available, is_tf_available, logging
from .benc... | 26 |
import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional
import datasets
import numpy as np
import tensorflow as tf
from transformers import (
AutoConfig,
AutoTokenizer,
EvalPrediction,
HfArgumentParser,
PreTrainedTokenizer,
TFAutoModelForSequen... | 26 | 1 |
from __future__ import annotations
import unittest
from transformers import FunnelConfig, is_tf_available
from transformers.testing_utils import require_tf
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...... | 26 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
... | 26 | 1 |
from __future__ import annotations
from typing import Dict
from ...configuration_utils import PretrainedConfig
__lowercase :List[str] = {
"susnato/ernie-m-base_pytorch": "https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/config.json",
"susnato/ernie-m-large_pytorch": "https://... | 26 |
import gc
import unittest
from diffusers import FlaxControlNetModel, FlaxStableDiffusionControlNetPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_... | 26 | 1 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DiffusionPipeline,
EulerDiscreteScheduler,
StableDiffusionXLImgaImgPipeline,
UNeta... | 26 |
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import doctest
import sys
import warnings
from os.path import abspath, dirname, join
import _pytest
from transformers.testing_utils import HfDoctestModule, HfDocTestParser
# allow having multiple reposit... | 26 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
__lowercase :int = {"configuration_reformer": ["REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "ReformerConf... | 26 |
def UpperCAmelCase ( _lowerCamelCase : int = 1_000 ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Dict = -1
SCREAMING_SNAKE_CASE__ : str = 0
for a in range(1 , n // 3 ):
# Solving the two equations a**2+b**2... | 26 | 1 |
from typing import Dict, Iterable, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_... | 26 |
from __future__ import annotations
def UpperCAmelCase ( _lowerCamelCase : list , _lowerCamelCase : int | None = None , _lowerCamelCase : int | None = None ):
'''simple docstring'''
if start is None:
SCREAMING_SNAKE_CASE__ : ... | 26 | 1 |
import math
__lowercase :Optional[int] = 10
__lowercase :Optional[Any] = 7
__lowercase :Union[str, Any] = BALLS_PER_COLOUR * NUM_COLOURS
def UpperCAmelCase ( _lowerCamelCase : int = 20 ):
'''simple docstring'''
SCREAMING_S... | 26 |
from __future__ import annotations
from fractions import Fraction
def UpperCAmelCase ( _lowerCamelCase : int , _lowerCamelCase : int ):
'''simple docstring'''
return (
num != den and num % 10 == den // 10 and (num // 10) / (den % 10) == num ... | 26 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
__lowercase :Tuple = {
"configuration_data2vec_audio": ["DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP", "Data2VecAudioConfig"],
"configuration_data2vec_tex... | 26 |
import unittest
from datasets import load_dataset
from transformers.pipelines import pipeline
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_torch, slow
@is_pipeline_test
@require_torch
class _a ( unittest.TestCase ):
"""simple docstring"""
... | 26 | 1 |
import itertools
from dataclasses import dataclass
from typing import Any, Callable, Dict, List, Optional, Union
import pandas as pd
import pyarrow as pa
import datasets
import datasets.config
from datasets.features.features import require_storage_cast
from datasets.table import table_cast
from datasets.utils.p... | 26 |
import json
import os
import sys
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile
from huggingface_hub import HfFolder, Repository, create_repo, delete_repo
from requests.exceptions import HTTPError
import transformers
from transformers import (
CONFIG_MAPPING,
FEATUR... | 26 | 1 |
import json
import os
from datetime import date
from pathlib import Path
from tabulate import DataRow, TableFormat, tabulate
__lowercase :str = TableFormat(
lineabove=None,
linebelowheader=None,
linebetweenrows=None,
linebelow=None,
headerrow=DataRow("", "|", "|"),
datarow=... | 26 |
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class _a ( lowercase__ ):
"""simple docstring"""
snake_case_ = ["image_processor", "tokenizer"]
snake_case_ = "CLIPImageProces... | 26 | 1 |
import json
import os
import unittest
from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES, BioGptTokenizer
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTesterMixin
class _a ( lowercase__ , unittest.TestCase ):
... | 26 |
import sys
from collections import defaultdict
class _a :
"""simple docstring"""
def __init__( self : Any ) ->Dict:
SCREAMING_SNAKE_CASE__ : Tuple = []
def A_ ( self : int , a : List[str] ) ->Dict:
... | 26 | 1 |
import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional
import datasets
import numpy as np
import tensorflow as tf
from transformers import (
AutoConfig,
AutoTokenizer,
EvalPrediction,
HfArgumentParser,
PreTrainedTokenizer,
TFAutoModelForSequen... | 26 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import TensorType, logging
if TYPE_CHECKING:
from ...onnx.config import PatchingSpec
from ...tokeniz... | 26 | 1 |
import os
from argparse import ArgumentParser
from typing import List
import torch.utils.data
from datasets import Dataset, IterableDataset
from datasets.distributed import split_dataset_by_node
__lowercase :List[Any] = 4
__lowercase :Any = 3
class _a ( lowercase__ ... | 26 |
def UpperCAmelCase ( _lowerCamelCase : int = 4_000_000 ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Any = [0, 1]
SCREAMING_SNAKE_CASE__ : List[Any] = 0
while fib[i] <= n:
fib.append(fib[i] + fib[i + 1] )
... | 26 | 1 |
import numpy as np
import pandas as pd
from sklearn.preprocessing import Normalizer
from sklearn.svm import SVR
from statsmodels.tsa.statespace.sarimax import SARIMAX
def UpperCAmelCase ( _lowerCamelCase : list , _lowerCamelCase : list , _lowerCamelCase : list , ... | 26 |
import unittest
from typing import Dict, List, Optional, Union
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image... | 26 | 1 |
import unittest
from parameterized import parameterized
from transformers import LlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
fr... | 26 |
def UpperCAmelCase ( _lowerCamelCase : int , _lowerCamelCase : bool = False ):
'''simple docstring'''
if n == 2:
return True
if not n % 2 or n < 2:
return False
if n > 5 and n % 10 not in (1, 3, 7, 9): # can quickly check ... | 26 | 1 |
import torch
from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device
from .test_schedulers import SchedulerCommonTest
class _a ( lowercase__ ):
"""simple docstring"""
snake_case_ = (EulerDiscreteScheduler,)
snake_case_ ... | 26 |
import numpy
class _a :
"""simple docstring"""
def __init__( self : Optional[int] , a : numpy.ndarray , a : numpy.ndarray ) ->None:
SCREAMING_SNAKE_CASE__ : Any = input_array
# Random initial weights ar... | 26 | 1 |
__lowercase :int = "\n# Transformers installation\n! pip install transformers datasets\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/transformers.git\n"
__lowercase :Any ... | 26 |
from typing import Dict, List
from nltk.translate import gleu_score
import datasets
from datasets import MetricInfo
__lowercase :Tuple = "\\n@misc{wu2016googles,\n title={Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation},\n author={Yongh... | 26 | 1 |
import pandas as pd
from matplotlib import pyplot as plt
from sklearn.linear_model import LinearRegression
# Splitting the dataset into the Training set and Test set
from sklearn.model_selection import train_test_split
# Fitting Polynomial Regression to the dataset
from sklearn.preprocessing import PolynomialFe... | 26 |
import sys
from .dependency_versions_table import deps
from .utils.versions import require_version, require_version_core
# define which module versions we always want to check at run time
# (usually the ones defined in `install_requires` in setup.py)
#
# order specific notes:
# - tqdm must be checked before to... | 26 | 1 |
__lowercase :Optional[int] = 0 # The first color of the flag.
__lowercase :Union[str, Any] = 1 # The second color of the flag.
__lowercase :List[str] = 2 # The third color of the flag.
__lowercase :Optional[Any] = (red, white, blue)
def UpperCAme... | 26 |
from __future__ import annotations
def UpperCAmelCase ( _lowerCamelCase : list[int] , _lowerCamelCase : int ):
'''simple docstring'''
if len(_lowerCamelCase ) < k or k < 0:
raise ValueError("Invalid Input" )
SCREAMING_SNAKE_CA... | 26 | 1 |
from __future__ import annotations
from collections.abc import Iterator
class _a :
"""simple docstring"""
def __init__( self : List[Any] , a : int ) ->None:
SCREAMING_SNAKE_CASE__ : Optional[int] = value
SCREAMING_S... | 26 |
from __future__ import annotations
def UpperCAmelCase ( _lowerCamelCase : list[int | float] , _lowerCamelCase : int , _lowerCamelCase : int ):
'''simple docstring'''
if len(_lowerCamelCase ) == 0:
raise ValueError("find_max() ... | 26 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__lowercase :Tuple = {
"configuration_table_transformer": [
"TABLE_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
"TableTransformerConfig",
"TableTransform... | 26 |
import json
import multiprocessing
import os
import re
from collections import defaultdict
import torch
from accelerate import Accelerator
from accelerate.utils import set_seed
from arguments import HumanEvalArguments
from datasets import load_dataset, load_metric
from torch.utils.data import IterableDataset
fro... | 26 | 1 |
def UpperCAmelCase ( _lowerCamelCase : int ):
'''simple docstring'''
if not isinstance(_lowerCamelCase , _lowerCamelCase ):
SCREAMING_SNAKE_CASE__ : Tuple = f"""Input value of [number={number}] must be an integer"""
raise Ty... | 26 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__lowercase :str = {
"configuration_upernet": ["UperNetConfig"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependen... | 26 | 1 |
from unittest import TestCase
from datasets import Sequence, Value
from datasets.arrow_dataset import Dataset
class _a ( lowercase__ ):
"""simple docstring"""
def A_ ( self : str ) ->Any:
return [
{"col_1": 3, "col_2": "a"... | 26 |
import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional
import datasets
import numpy as np
import tensorflow as tf
from transformers import (
AutoConfig,
AutoTokenizer,
EvalPrediction,
HfArgumentParser,
PreTrainedTokenizer,
TFAutoModelForSequen... | 26 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__lowercase :str = {
"configuration_upernet": ["UperNetConfig"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependen... | 26 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
... | 26 | 1 |
from __future__ import annotations
class _a :
"""simple docstring"""
def __init__( self : int , a : list[list[int]] ) ->List[Any]:
SCREAMING_SNAKE_CASE__ : Optional[int] = TypeError(
"Matrices must be formed from... | 26 |
import gc
import unittest
from diffusers import FlaxControlNetModel, FlaxStableDiffusionControlNetPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_... | 26 | 1 |
def UpperCAmelCase ( _lowerCamelCase : int ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Optional[Any] = [0] * len(_lowerCamelCase )
SCREAMING_SNAKE_CASE__ : Dict = []
SCREAMING_SNAKE_CASE__ : int = [1] * len(... | 26 |
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import doctest
import sys
import warnings
from os.path import abspath, dirname, join
import _pytest
from transformers.testing_utils import HfDoctestModule, HfDocTestParser
# allow having multiple reposit... | 26 | 1 |
def UpperCAmelCase ( _lowerCamelCase : list[list[int | float]] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Dict = len(_lowerCamelCase )
SCREAMING_SNAKE_CASE__ : List[Any] = len(matrix[0] )
SCREAMING_SNAKE_CASE__ ... | 26 |
def UpperCAmelCase ( _lowerCamelCase : int = 1_000 ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Dict = -1
SCREAMING_SNAKE_CASE__ : str = 0
for a in range(1 , n // 3 ):
# Solving the two equations a**2+b**2... | 26 | 1 |
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import doctest
import sys
import warnings
from os.path import abspath, dirname, join
import _pytest
from transformers.testing_utils import HfDoctestModule, HfDocTestParser
# allow having multiple reposit... | 26 |
from __future__ import annotations
def UpperCAmelCase ( _lowerCamelCase : list , _lowerCamelCase : int | None = None , _lowerCamelCase : int | None = None ):
'''simple docstring'''
if start is None:
SCREAMING_SNAKE_CASE__ : ... | 26 | 1 |
def UpperCAmelCase ( _lowerCamelCase : list[int] , _lowerCamelCase : str ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Dict = int(_lowerCamelCase )
# Initialize Result
SCREAMING_SNAKE_CASE__ : Optional[Any] = []... | 26 |
from __future__ import annotations
from fractions import Fraction
def UpperCAmelCase ( _lowerCamelCase : int , _lowerCamelCase : int ):
'''simple docstring'''
return (
num != den and num % 10 == den // 10 and (num // 10) / (den % 10) == num ... | 26 | 1 |
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required b... | 26 |
import unittest
from datasets import load_dataset
from transformers.pipelines import pipeline
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_torch, slow
@is_pipeline_test
@require_torch
class _a ( unittest.TestCase ):
"""simple docstring"""
... | 26 | 1 |
import torch
def UpperCAmelCase ( ):
'''simple docstring'''
if torch.cuda.is_available():
SCREAMING_SNAKE_CASE__ : str = torch.cuda.device_count()
else:
SCREAMING_SNAKE_CASE__ : str = 0
print(f"""Successfully r... | 26 |
import json
import os
import sys
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile
from huggingface_hub import HfFolder, Repository, create_repo, delete_repo
from requests.exceptions import HTTPError
import transformers
from transformers import (
CONFIG_MAPPING,
FEATUR... | 26 | 1 |
import importlib.metadata
import warnings
from copy import deepcopy
from packaging import version
from ..utils import logging
from .import_utils import is_accelerate_available, is_bitsandbytes_available
if is_bitsandbytes_available():
import bitsandbytes as bnb
import torch
import torch.nn as nn
... | 26 |
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class _a ( lowercase__ ):
"""simple docstring"""
snake_case_ = ["image_processor", "tokenizer"]
snake_case_ = "CLIPImageProces... | 26 | 1 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__lowercase :Optional[int] = logging.get_logger(__name__)
__lowercase :Any = {
"xlm-mlm-en-2048": "https:... | 26 |
import sys
from collections import defaultdict
class _a :
"""simple docstring"""
def __init__( self : Any ) ->Dict:
SCREAMING_SNAKE_CASE__ : Tuple = []
def A_ ( self : int , a : List[str] ) ->Dict:
... | 26 | 1 |
from __future__ import annotations
def UpperCAmelCase ( _lowerCamelCase : list[int | float] , _lowerCamelCase : int , _lowerCamelCase : int ):
'''simple docstring'''
if len(_lowerCamelCase ) == 0:
raise ValueError("find_max() ... | 26 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import TensorType, logging
if TYPE_CHECKING:
from ...onnx.config import PatchingSpec
from ...tokeniz... | 26 | 1 |
import warnings
from ...utils import logging
from .image_processing_beit import BeitImageProcessor
__lowercase :Optional[int] = logging.get_logger(__name__)
class _a ( lowercase__ ):
"""simple docstring"""
def __init__( self : int , *a :... | 26 |
def UpperCAmelCase ( _lowerCamelCase : int = 4_000_000 ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Any = [0, 1]
SCREAMING_SNAKE_CASE__ : List[Any] = 0
while fib[i] <= n:
fib.append(fib[i] + fib[i + 1] )
... | 26 | 1 |
import json
import os
from pathlib import Path
import pytest
from datasets.download.download_config import DownloadConfig
from datasets.download.download_manager import DownloadManager
from datasets.utils.file_utils import hash_url_to_filename
__lowercase :Any = "http://www.mocksite.com/file1.txt... | 26 |
import unittest
from typing import Dict, List, Optional, Union
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image... | 26 | 1 |
from __future__ import annotations
import math
import random
from collections.abc import Collection
from typing import overload
class _a :
"""simple docstring"""
def __init__( self : List[str] , a : Collection[float] | None = None ) ->None:
... | 26 |
def UpperCAmelCase ( _lowerCamelCase : int , _lowerCamelCase : bool = False ):
'''simple docstring'''
if n == 2:
return True
if not n % 2 or n < 2:
return False
if n > 5 and n % 10 not in (1, 3, 7, 9): # can quickly check ... | 26 | 1 |
from typing import List, Optional, Union
import numpy as np
import torch
import torchaudio.compliance.kaldi as ta_kaldi
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging
__low... | 26 |
import numpy
class _a :
"""simple docstring"""
def __init__( self : Optional[int] , a : numpy.ndarray , a : numpy.ndarray ) ->None:
SCREAMING_SNAKE_CASE__ : Any = input_array
# Random initial weights ar... | 26 | 1 |
from sklearn.metrics import recall_score
import datasets
__lowercase :List[Any] = "\nRecall is the fraction of the positive examples that were correctly labeled by the model as positive. It can be computed with the equation:\nRecall = TP / (TP + FN)\nWhere TP is the true positives and FN is the fa... | 26 |
from typing import Dict, List
from nltk.translate import gleu_score
import datasets
from datasets import MetricInfo
__lowercase :Tuple = "\\n@misc{wu2016googles,\n title={Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation},\n author={Yongh... | 26 | 1 |
import argparse
import os
# New Code #
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import... | 26 |
import sys
from .dependency_versions_table import deps
from .utils.versions import require_version, require_version_core
# define which module versions we always want to check at run time
# (usually the ones defined in `install_requires` in setup.py)
#
# order specific notes:
# - tqdm must be checked before to... | 26 | 1 |
import gc
import unittest
from diffusers import FlaxControlNetModel, FlaxStableDiffusionControlNetPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_... | 26 |
from __future__ import annotations
def UpperCAmelCase ( _lowerCamelCase : list[int] , _lowerCamelCase : int ):
'''simple docstring'''
if len(_lowerCamelCase ) < k or k < 0:
raise ValueError("Invalid Input" )
SCREAMING_SNAKE_CA... | 26 | 1 |
import pytest
from datasets.utils.sharding import _distribute_shards, _number_of_shards_in_gen_kwargs, _split_gen_kwargs
@pytest.mark.parametrize(
"kwargs, expected" , [
({"num_shards": 0, "max_num_jobs": 1}, []),
({"num_shards": 10, "max_num_jobs": 1}, [range(10 )]),
(... | 26 |
from __future__ import annotations
def UpperCAmelCase ( _lowerCamelCase : list[int | float] , _lowerCamelCase : int , _lowerCamelCase : int ):
'''simple docstring'''
if len(_lowerCamelCase ) == 0:
raise ValueError("find_max() ... | 26 | 1 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, XLMRobertaTokenizer
from diffusers import AltDiffusionPipeline, AutoencoderKL, DDIMScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.alt_diffusion.modeling_roberta_series impor... | 26 |
import json
import multiprocessing
import os
import re
from collections import defaultdict
import torch
from accelerate import Accelerator
from accelerate.utils import set_seed
from arguments import HumanEvalArguments
from datasets import load_dataset, load_metric
from torch.utils.data import IterableDataset
fro... | 26 | 1 |
from torch import nn
def UpperCAmelCase ( _lowerCamelCase : Optional[Any] ):
'''simple docstring'''
if act_fn in ["swish", "silu"]:
return nn.SiLU()
elif act_fn == "mish":
return nn.Mish()
elif act_fn == "gelu":
re... | 26 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__lowercase :str = {
"configuration_upernet": ["UperNetConfig"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependen... | 26 | 1 |
import sys
from .dependency_versions_table import deps
from .utils.versions import require_version, require_version_core
# define which module versions we always want to check at run time
# (usually the ones defined in `install_requires` in setup.py)
#
# order specific notes:
# - tqdm must be checked before to... | 26 |
import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional
import datasets
import numpy as np
import tensorflow as tf
from transformers import (
AutoConfig,
AutoTokenizer,
EvalPrediction,
HfArgumentParser,
PreTrainedTokenizer,
TFAutoModelForSequen... | 26 | 1 |
import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import Callable, Dict, List, Tuple
import timm
import torch
import torch.nn as nn
from classy_vision.models.regnet import RegNet, RegNetParams, RegNetYaagf, RegNetYaagf, RegNetY... | 26 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
... | 26 | 1 |
import unittest
import numpy as np
from transformers import RoFormerConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax.numpy as jnp
fro... | 26 |
import gc
import unittest
from diffusers import FlaxControlNetModel, FlaxStableDiffusionControlNetPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_... | 26 | 1 |
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