code stringlengths 87 55.2k | code_codestyle int64 0 349 | style_context stringlengths 135 49.1k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
import unittest
from transformers import load_tool
from .test_tools_common import ToolTesterMixin
class A_ ( unittest.TestCase , _a ):
'''simple docstring'''
def lowerCAmelCase_ (self ) -> List[Any]:
__UpperCAmelCase = load_tool('''te... | 333 |
import doctest
from collections import deque
import numpy as np
class A_ :
'''simple docstring'''
def __init__(self ) -> None:
__UpperCAmelCase = [2, 1, 2, -1]
__UpperCAmelCase = [1, 2, 3, 4]
def lowerCAmelCase_ (self ... | 333 | 1 |
from math import factorial, pi
def __a ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = 3_0 ) -> float:
'''simple docstring'''
if not isinstance(SCREAMING_SNAKE_CASE , (int, float) ):
raise ValueError('''maclaurin_sin() requires either ... | 333 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A_ : Any = logging.get_logger(__name__)
A_ : Optional[Any] = {
'google/pegasus-large': 'https://huggingface.co/google/pegasus-large/resolve/main/config.json',
# See all PEGASUS models at https... | 333 | 1 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A_ : Any = logging.get_logger(__name__)
A_ : Optional[int] = {
'BAAI/AltCLIP': 'https://huggingface.co/BAAI/AltCLIP/resolve/main/config.json',
... | 333 |
import itertools
import json
import os
import unittest
from transformers import AddedToken, LongformerTokenizer, LongformerTokenizerFast
from transformers.models.longformer.tokenization_longformer import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokeni... | 333 | 1 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_... | 333 |
import tempfile
import torch
from diffusers import IPNDMScheduler
from .test_schedulers import SchedulerCommonTest
class A_ ( _a ):
'''simple docstring'''
a__ = (IPNDMScheduler,)
a__ = (("num_inference_steps", 50),)
def lower... | 333 | 1 |
import datasets
import faiss
import numpy as np
import streamlit as st
import torch
from elasticsearch import Elasticsearch
from elia_utils import (
embed_questions_for_retrieval,
make_qa_sas_model,
qa_sas_generate,
query_es_index,
query_qa_dense_index,
)
import transformers
from... | 333 |
import copy
import inspect
import unittest
from transformers import PretrainedConfig, SwiftFormerConfig
from transformers.testing_utils import (
require_torch,
require_vision,
slow,
torch_device,
)
from transformers.utils import cached_property, is_torch_available, is_vision_available
... | 333 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
A_ : int = {
'configuration_graphormer': ['GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GraphormerConfig'],
}
try:
if not is_torch_availa... | 333 |
from collections import OrderedDict
from ...utils import logging
from .auto_factory import _BaseAutoModelClass, _LazyAutoMapping, auto_class_update
from .configuration_auto import CONFIG_MAPPING_NAMES
A_ : str = logging.get_logger(__name__)
A_ : str = OrderedDict(
[
... | 333 | 1 |
from typing import List, Union
import numpy as np
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_im... | 333 |
import math
from enum import Enum
from typing import Optional, Union
from torch.optim import Optimizer
from torch.optim.lr_scheduler import LambdaLR
from .utils import logging
A_ : Tuple = logging.get_logger(__name__)
class A_ ( _a ):
'''simple docstring'''
... | 333 | 1 |
def __a ( SCREAMING_SNAKE_CASE ) -> int:
'''simple docstring'''
__UpperCAmelCase = 0
while num > 0:
digit_sum += num % 1_0
num //= 1_0
return digit_sum
def __a ( SCREAMING_SNAKE_CASE = 1_0_0 ) -> int:
... | 333 |
def __a ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) -> list:
'''simple docstring'''
__UpperCAmelCase = len(SCREAMING_SNAKE_CASE )
__UpperCAmelCase = [[0] * n for i in range(SCREAMING_SNAKE_CASE )]
for i... | 333 | 1 |
import math
import qiskit
def __a ( SCREAMING_SNAKE_CASE = 1 , SCREAMING_SNAKE_CASE = 1 , SCREAMING_SNAKE_CASE = 1 ) -> qiskit.result.counts.Counts:
'''simple docstring'''
if (
isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CAS... | 333 |
def __a ( SCREAMING_SNAKE_CASE ) -> set:
'''simple docstring'''
__UpperCAmelCase = set()
# edges = list of graph's edges
__UpperCAmelCase = get_edges(SCREAMING_SNAKE_CASE )
# While there are still elements in edges list, take an arbitrary edg... | 333 | 1 |
import unittest
from transformers import AutoConfig, AutoTokenizer, BertConfig, TensorType, is_flax_available
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, require_flax, slow
if is_flax_available():
import jax
from transformers.models.auto.modeling_flax_auto import Flax... | 333 |
A_ : List[Any] = {'a': ['c', 'b'], 'b': ['d', 'e'], 'c': [], 'd': [], 'e': []}
A_ : int = ['a', 'b', 'c', 'd', 'e']
def __a ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) -> List[Any]:
'''simple docstring''... | 333 | 1 |
A_ : Dict = {
0: '0',
1: '1',
2: '2',
3: '3',
4: '4',
5: '5',
6: '6',
7: '7',
8: '8',
9: '9',
10: 'a',
11: 'b',
12: 'c',
13: 'd',
14: 'e',
15: 'f',
}
def __a ( SCREAMING_SNAKE_CASE ) -> str:
... | 333 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
A_ : int = {
'configuration_graphormer': ['GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GraphormerConfig'],
}
try:
if not is_torch_availa... | 333 | 1 |
from .configuration_bert_masked import MaskedBertConfig
from .modeling_bert_masked import (
MaskedBertForMultipleChoice,
MaskedBertForQuestionAnswering,
MaskedBertForSequenceClassification,
MaskedBertForTokenClassification,
MaskedBertModel,
)
from .modules import *
| 333 |
from copy import deepcopy
import torch
import torch.nn.functional as F
from torch.optim import AdamW
from torch.optim.lr_scheduler import LambdaLR
from torch.utils.data import DataLoader
from accelerate.accelerator import Accelerator
from accelerate.state import GradientState
from accelerate.test_utils ... | 333 | 1 |
def __a ( SCREAMING_SNAKE_CASE = 1_0_0_0 ) -> int:
'''simple docstring'''
__UpperCAmelCase = 2**power
__UpperCAmelCase = str(SCREAMING_SNAKE_CASE )
__UpperCAmelCase = list(SCREAMING_SNAKE_CASE )
__UpperCAmelCase = 0
for i in ... | 333 |
import os
try:
from .build_directory_md import good_file_paths
except ImportError:
from build_directory_md import good_file_paths # type: ignore
A_ : Optional[Any] = list(good_file_paths())
assert filepaths, "good_file_paths() failed!"
A_ : Optional[Any] = [file f... | 333 | 1 |
from collections.abc import Callable
def __a ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) -> float:
'''simple docstring'''
__UpperCAmelCase = a
__UpperCAmelCase = b
if function(SCREAMING_SNAKE_CASE ) ==... | 333 |
def __a ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) -> str:
'''simple docstring'''
__UpperCAmelCase = [[] for _ in range(SCREAMING_SNAKE_CASE )]
__UpperCAmelCase = key - 1
if key <= 0:
raise ValueError('''Height of grid can\'t... | 333 | 1 |
import inspect
import unittest
from transformers import BitConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_backbone_common import BackboneTesterMixin
from ..... | 333 |
import gc
import unittest
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DDPMScheduler,
PriorTransformer,
StableUnCLIPPipeline,
UNetaDConditionModel,
)
from ... | 333 | 1 |
import doctest
from collections import deque
import numpy as np
class A_ :
'''simple docstring'''
def __init__(self ) -> None:
__UpperCAmelCase = [2, 1, 2, -1]
__UpperCAmelCase = [1, 2, 3, 4]
def lowerCAmelCase_ (self ... | 333 |
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers
from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelin... | 333 | 1 |
import argparse
import os
from pathlib import Path
import torch
from bark.generation import _load_model as _bark_load_model
from huggingface_hub import hf_hub_download
from transformers import EncodecConfig, EncodecModel, set_seed
from transformers.models.bark.configuration_bark import (
BarkCoarseC... | 333 |
import math
import sys
def __a ( SCREAMING_SNAKE_CASE ) -> int:
'''simple docstring'''
if number != int(SCREAMING_SNAKE_CASE ):
raise ValueError('''the value of input must be a natural number''' )
if number < 0:
raise ValueError('''th... | 333 | 1 |
from collections.abc import Sequence
def __a ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = False ) -> float:
'''simple docstring'''
if not arr:
return 0
__UpperCAmelCase = 0 if allow_empty_subarrays else float('''-inf''' )
__Up... | 333 |
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 rescale, resize, to_channel_dimension_format
from ...image_utils import (
ChannelDimension,
ImageInput,
PILIm... | 333 | 1 |
import math
from datetime import datetime, timedelta
def __a ( SCREAMING_SNAKE_CASE ) -> datetime:
'''simple docstring'''
__UpperCAmelCase = year % 1_9
__UpperCAmelCase = year % 4
__UpperCAmelCase = year % 7
__UpperCAmelCase = math.fl... | 333 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
A_ : Optional[int] = {
'configuration_poolformer': [
'POOLFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
'PoolFormerConfig',
'PoolFor... | 333 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
A_ : Any = {
'configuration_mask2former': [
'MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
'Mask2FormerConfig',
],
}
try:
... | 333 |
import math
def __a ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) -> float:
'''simple docstring'''
if (
not isinstance(SCREAMING_SNAKE_CASE , (int, float) )
or power_factor < -1
or power_factor > 1
):
ra... | 333 | 1 |
from ..utils import DummyObject, requires_backends
class A_ ( metaclass=_a ):
'''simple docstring'''
a__ = ["transformers", "torch", "note_seq"]
def __init__(self , *lowercase__ , **lowercase__ ) -> int:
requires_backends(s... | 333 |
def __a ( ) -> list[list[int]]:
'''simple docstring'''
return [list(range(1_0_0_0 - i , -1_0_0_0 - i , -1 ) ) for i in range(1_0_0_0 )]
A_ : Union[str, Any] = generate_large_matrix()
A_ : Union[str, Any] = (
[[4,... | 333 | 1 |
from ...configuration_utils import PretrainedConfig
class A_ ( _a ):
'''simple docstring'''
a__ = "bert-generation"
def __init__(self , lowercase__=50_358 , lowercase__=1_024 , lowercase__=24 , lowercase__=16 , lowercase__=4... | 333 |
import argparse
import json
import os
import sys
import tempfile
import unittest
from argparse import Namespace
from dataclasses import dataclass, field
from enum import Enum
from pathlib import Path
from typing import List, Literal, Optional
import yaml
from transformers import HfArgumentParser, Tr... | 333 | 1 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import load_... | 333 |
import doctest
from collections import deque
import numpy as np
class A_ :
'''simple docstring'''
def __init__(self ) -> None:
__UpperCAmelCase = [2, 1, 2, -1]
__UpperCAmelCase = [1, 2, 3, 4]
def lowerCAmelCase_ (self ... | 333 | 1 |
import argparse
A_ : Optional[Any] = 'docs/source/_static/js/custom.js'
def __a ( SCREAMING_SNAKE_CASE ) -> Dict:
'''simple docstring'''
with open(SCREAMING_SNAKE_CASE , encoding='''utf-8''' , newline='''\n''' ) as f:
__... | 333 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A_ : Any = logging.get_logger(__name__)
A_ : Optional[Any] = {
'google/pegasus-large': 'https://huggingface.co/google/pegasus-large/resolve/main/config.json',
# See all PEGASUS models at https... | 333 | 1 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
A_ : Dict = {
'albert-base-v1': 'https://huggingface.co/albert-base-v1/resolve/main/config.json',
'albert-large-v1': 'https://huggingface.... | 333 |
import itertools
import json
import os
import unittest
from transformers import AddedToken, LongformerTokenizer, LongformerTokenizerFast
from transformers.models.longformer.tokenization_longformer import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokeni... | 333 | 1 |
import gc
import unittest
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DDPMScheduler,
PriorTransformer,
StableUnCLIPPipeline,
UNetaDConditionModel,
)
from ... | 333 |
import tempfile
import torch
from diffusers import IPNDMScheduler
from .test_schedulers import SchedulerCommonTest
class A_ ( _a ):
'''simple docstring'''
a__ = (IPNDMScheduler,)
a__ = (("num_inference_steps", 50),)
def lower... | 333 | 1 |
# this script reports modified .py files under the desired list of top-level sub-dirs passed as a list of arguments, e.g.:
# python ./utils/get_modified_files.py utils src tests examples
#
# it uses git to find the forking point and which files were modified - i.e. files not under git won't be considered
# si... | 333 |
import copy
import inspect
import unittest
from transformers import PretrainedConfig, SwiftFormerConfig
from transformers.testing_utils import (
require_torch,
require_vision,
slow,
torch_device,
)
from transformers.utils import cached_property, is_torch_available, is_vision_available
... | 333 | 1 |
import warnings
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A_ : int = logging.get_logger(__name__)
A_ : Dict = {
'RUCAIBox/mvp': 'https://huggingface.co/RUCAIBox/mvp/resolve/main/config.json',
}
class A_ ( _a ):
... | 333 |
from collections import OrderedDict
from ...utils import logging
from .auto_factory import _BaseAutoModelClass, _LazyAutoMapping, auto_class_update
from .configuration_auto import CONFIG_MAPPING_NAMES
A_ : str = logging.get_logger(__name__)
A_ : str = OrderedDict(
[
... | 333 | 1 |
import json
import os
from typing import Dict, List, Optional, Tuple
import regex as re
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
A_ : Any = logging.get_logger(__name__)
A_ : Tuple = {
'vocab_file': 'vocab.json',
'merges_f... | 333 |
import math
from enum import Enum
from typing import Optional, Union
from torch.optim import Optimizer
from torch.optim.lr_scheduler import LambdaLR
from .utils import logging
A_ : Tuple = logging.get_logger(__name__)
class A_ ( _a ):
'''simple docstring'''
... | 333 | 1 |
def __a ( SCREAMING_SNAKE_CASE ) -> bool:
'''simple docstring'''
__UpperCAmelCase = n ** (1 / 3)
return (val * val * val) == n
if __name__ == "__main__":
print(perfect_cube(27))
print(perfect_cube(4))
| 333 |
def __a ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) -> list:
'''simple docstring'''
__UpperCAmelCase = len(SCREAMING_SNAKE_CASE )
__UpperCAmelCase = [[0] * n for i in range(SCREAMING_SNAKE_CASE )]
for i... | 333 | 1 |
import math
def __a ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) -> float:
'''simple docstring'''
if (
not isinstance(SCREAMING_SNAKE_CASE , (int, float) )
or power_factor < -1
or power_factor > 1
):
ra... | 333 |
def __a ( SCREAMING_SNAKE_CASE ) -> set:
'''simple docstring'''
__UpperCAmelCase = set()
# edges = list of graph's edges
__UpperCAmelCase = get_edges(SCREAMING_SNAKE_CASE )
# While there are still elements in edges list, take an arbitrary edg... | 333 | 1 |
import unittest
from transformers import XLMConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMix... | 333 |
A_ : List[Any] = {'a': ['c', 'b'], 'b': ['d', 'e'], 'c': [], 'd': [], 'e': []}
A_ : int = ['a', 'b', 'c', 'd', 'e']
def __a ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) -> List[Any]:
'''simple docstring''... | 333 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A_ : str = logging.get_logger(__name__)
A_ : Any = {
'RWKV/rwkv-4-169m-pile': 'https://huggingface.co/RWKV/rwkv-4-169m-pile/resolve/main/config.json',
'RWKV/rwkv-4-430m-pile': 'https://hugging... | 333 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
A_ : int = {
'configuration_graphormer': ['GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GraphormerConfig'],
}
try:
if not is_torch_availa... | 333 | 1 |
import itertools
import random
import unittest
import numpy as np
from transformers import WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST, WavaVecaConfig, WavaVecaFeatureExtractor
from transformers.testing_utils import require_torch, slow
from ...test_sequence_feature_extraction_common import SequenceFeature... | 333 |
from copy import deepcopy
import torch
import torch.nn.functional as F
from torch.optim import AdamW
from torch.optim.lr_scheduler import LambdaLR
from torch.utils.data import DataLoader
from accelerate.accelerator import Accelerator
from accelerate.state import GradientState
from accelerate.test_utils ... | 333 | 1 |
import os
from distutils.util import strtobool
def __a ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) -> Optional[Any]:
'''simple docstring'''
for e in env_keys:
__UpperCAmelCase = int(os.environ.get(SCREAMING_SNAKE_CASE , -1 ) ... | 333 |
import os
try:
from .build_directory_md import good_file_paths
except ImportError:
from build_directory_md import good_file_paths # type: ignore
A_ : Optional[Any] = list(good_file_paths())
assert filepaths, "good_file_paths() failed!"
A_ : Optional[Any] = [file f... | 333 | 1 |
import unittest
from datasets import load_dataset
from transformers import BloomTokenizerFast
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class A_ ( _a , unittest.TestCase ):
'''... | 333 |
def __a ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) -> str:
'''simple docstring'''
__UpperCAmelCase = [[] for _ in range(SCREAMING_SNAKE_CASE )]
__UpperCAmelCase = key - 1
if key <= 0:
raise ValueError('''Height of grid can\'t... | 333 | 1 |
import math
from numpy import inf
from scipy.integrate import quad
def __a ( SCREAMING_SNAKE_CASE ) -> float:
'''simple docstring'''
if num <= 0:
raise ValueError('''math domain error''' )
return quad(SCREAMING_SNAKE_CASE , 0 , ... | 333 |
import gc
import unittest
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DDPMScheduler,
PriorTransformer,
StableUnCLIPPipeline,
UNetaDConditionModel,
)
from ... | 333 | 1 |
def __a ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) -> int:
'''simple docstring'''
while second != 0:
__UpperCAmelCase = first & second
first ^= second
__UpperCAmelCase = c << 1
return first
if __name__ == "__main_... | 333 |
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers
from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelin... | 333 | 1 |
from ....configuration_utils import PretrainedConfig
from ....utils import logging
A_ : Any = logging.get_logger(__name__)
A_ : Dict = {
'CarlCochet/trajectory-transformer-halfcheetah-medium-v2': (
'https://huggingface.co/CarlCochet/trajectory-transformer-halfcheetah-m... | 333 |
import math
import sys
def __a ( SCREAMING_SNAKE_CASE ) -> int:
'''simple docstring'''
if number != int(SCREAMING_SNAKE_CASE ):
raise ValueError('''the value of input must be a natural number''' )
if number < 0:
raise ValueError('''th... | 333 | 1 |
import argparse
import logging
import os
from pathlib import Path
from typing import Any, Dict
import pytorch_lightning as pl
from pytorch_lightning.utilities import rank_zero_info
from transformers import (
AdamW,
AutoConfig,
AutoModel,
AutoModelForPreTraining,
AutoMod... | 0 |
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 rescale, resize, to_channel_dimension_format
from ...image_utils import (
ChannelDimension,
ImageInput,
PILIm... | 333 | 0 |
'''simple docstring'''
def lowerCAmelCase_ ( snake_case_ : str ) -> int:
'''simple docstring'''
UpperCAmelCase_ = 0
UpperCAmelCase_ = len(snake_case_ )
for i in range(n - 1 ):
for j in range(i + 1 , snake_case_ ):
... | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
A_ : Optional[int] = {
'configuration_poolformer': [
'POOLFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
'PoolFormerConfig',
'PoolFor... | 333 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCamelCase : Union[str, Any] = {'configuration_vit_msn': ['VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ViTMSNConfig']}
try:
if not is_torch_available():
... | 2 |
import math
def __a ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) -> float:
'''simple docstring'''
if (
not isinstance(SCREAMING_SNAKE_CASE , (int, float) )
or power_factor < -1
or power_factor > 1
):
ra... | 333 | 0 |
'''simple docstring'''
def lowerCAmelCase_ ( snake_case__ , snake_case__ ):
'''simple docstring'''
print('''\nThe shortest path matrix using Floyd Warshall algorithm\n''' )
for i in range(snake_case__ ):
for j in range(snake_case__ ):
... | 3 |
def __a ( ) -> list[list[int]]:
'''simple docstring'''
return [list(range(1_0_0_0 - i , -1_0_0_0 - i , -1 ) ) for i in range(1_0_0_0 )]
A_ : Union[str, Any] = generate_large_matrix()
A_ : Union[str, Any] = (
[[4,... | 333 | 0 |
'''simple docstring'''
import os
import random
import sys
from . import cryptomath_module as cryptomath
from . import rabin_miller
__snake_case =3
def a_ ( lowerCamelCase : int ):
print('Generating primitive root of p' )
while True:
lowerCAmelCas... | 4 |
import argparse
import json
import os
import sys
import tempfile
import unittest
from argparse import Namespace
from dataclasses import dataclass, field
from enum import Enum
from pathlib import Path
from typing import List, Literal, Optional
import yaml
from transformers import HfArgumentParser, Tr... | 333 | 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
UpperCAmelCase__ = logging.get_logger(__name__)
UpperCAmelCase__ = {
'''google/vit-ba... | 5 |
import doctest
from collections import deque
import numpy as np
class A_ :
'''simple docstring'''
def __init__(self ) -> None:
__UpperCAmelCase = [2, 1, 2, -1]
__UpperCAmelCase = [1, 2, 3, 4]
def lowerCAmelCase_ (self ... | 333 | 0 |
import os
from typing import Dict, List, Tuple, TypeVar, Union
A : str = TypeVar('T')
A : Dict = Union[List[T], Tuple[T, ...]]
A : Union[str, Any] = Union[T, List[T], Dict[str, T]]
A : int = Union[str, bytes, os.PathLike] | 6 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A_ : Any = logging.get_logger(__name__)
A_ : Optional[Any] = {
'google/pegasus-large': 'https://huggingface.co/google/pegasus-large/resolve/main/config.json',
# See all PEGASUS models at https... | 333 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available
lowercase_ = {"tokenization_herbert": ["HerbertTokenizer"]}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDe... | 7 |
import itertools
import json
import os
import unittest
from transformers import AddedToken, LongformerTokenizer, LongformerTokenizerFast
from transformers.models.longformer.tokenization_longformer import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokeni... | 333 | 0 |
def __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__ = 200 ):
snake_case_ = [1, 2, 5, 10, 20, 50, 100, 200]
snake_case_ = [0] * (pence + 1)
snake_case_ = 1 # base case: 1 way to make 0 pence
for coin in coins:
for i in range(SCREAMING_SNAKE_CASE__ , penc... | 8 |
import tempfile
import torch
from diffusers import IPNDMScheduler
from .test_schedulers import SchedulerCommonTest
class A_ ( _a ):
'''simple docstring'''
a__ = (IPNDMScheduler,)
a__ = (("num_inference_steps", 50),)
def lower... | 333 | 0 |
from __future__ import annotations
import unittest
from transformers import AutoTokenizer, PegasusConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_common import Confi... | 9 |
import copy
import inspect
import unittest
from transformers import PretrainedConfig, SwiftFormerConfig
from transformers.testing_utils import (
require_torch,
require_vision,
slow,
torch_device,
)
from transformers.utils import cached_property, is_torch_available, is_vision_available
... | 333 | 0 |
import os
import re
import shutil
from argparse import ArgumentParser, Namespace
from datasets.commands import BaseDatasetsCLICommand
from datasets.utils.logging import get_logger
__A = "<<<<<<< This should probably be modified because it mentions: "
__A = "=======\n>>>>>>>\n"
__A ... | 10 |
from collections import OrderedDict
from ...utils import logging
from .auto_factory import _BaseAutoModelClass, _LazyAutoMapping, auto_class_update
from .configuration_auto import CONFIG_MAPPING_NAMES
A_ : str = logging.get_logger(__name__)
A_ : str = OrderedDict(
[
... | 333 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
lowerCAmelCase__ = {
'configuration_convnext': ['CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ConvNextConfig', 'ConvNextOnnxC... | 11 |
import math
from enum import Enum
from typing import Optional, Union
from torch.optim import Optimizer
from torch.optim.lr_scheduler import LambdaLR
from .utils import logging
A_ : Tuple = logging.get_logger(__name__)
class A_ ( _a ):
'''simple docstring'''
... | 333 | 0 |
from typing import Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
from ...image_utils import ChannelDimension, ImageInput, make_list_of_images, to_numpy_array, vali... | 12 |
def __a ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) -> list:
'''simple docstring'''
__UpperCAmelCase = len(SCREAMING_SNAKE_CASE )
__UpperCAmelCase = [[0] * n for i in range(SCREAMING_SNAKE_CASE )]
for i... | 333 | 0 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import... | 13 |
def __a ( SCREAMING_SNAKE_CASE ) -> set:
'''simple docstring'''
__UpperCAmelCase = set()
# edges = list of graph's edges
__UpperCAmelCase = get_edges(SCREAMING_SNAKE_CASE )
# While there are still elements in edges list, take an arbitrary edg... | 333 | 0 |
import copy
from collections import OrderedDict
from typing import Dict, Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
_lowerCamelCase : List[Any] = logging.... | 14 |
A_ : List[Any] = {'a': ['c', 'b'], 'b': ['d', 'e'], 'c': [], 'd': [], 'e': []}
A_ : int = ['a', 'b', 'c', 'd', 'e']
def __a ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) -> List[Any]:
'''simple docstring''... | 333 | 0 |
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available
if is_vision_available():
from PIL import Image
from transformers import AutoProcessor, BertTokenizer, BlipImageProcesso... | 15 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
A_ : int = {
'configuration_graphormer': ['GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GraphormerConfig'],
}
try:
if not is_torch_availa... | 333 | 0 |
"""simple docstring"""
from __future__ import annotations
import unittest
import numpy as np
from transformers import BlipTextConfig
from transformers.testing_utils import require_tf, slow
from transformers.utils import is_tf_available
from ...test_configuration_common import Config... | 16 |
from copy import deepcopy
import torch
import torch.nn.functional as F
from torch.optim import AdamW
from torch.optim.lr_scheduler import LambdaLR
from torch.utils.data import DataLoader
from accelerate.accelerator import Accelerator
from accelerate.state import GradientState
from accelerate.test_utils ... | 333 | 0 |
"""simple docstring"""
import os
import tempfile
import unittest
import numpy as np
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax, slow
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_utils import replicate
from flax.... | 17 |
import os
try:
from .build_directory_md import good_file_paths
except ImportError:
from build_directory_md import good_file_paths # type: ignore
A_ : Optional[Any] = list(good_file_paths())
assert filepaths, "good_file_paths() failed!"
A_ : Optional[Any] = [file f... | 333 | 0 |
import json
import os
import unittest
from transformers import BatchEncoding, LEDTokenizer, LEDTokenizerFast
from transformers.models.led.tokenization_led import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property
from ...test_token... | 18 |
def __a ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) -> str:
'''simple docstring'''
__UpperCAmelCase = [[] for _ in range(SCREAMING_SNAKE_CASE )]
__UpperCAmelCase = key - 1
if key <= 0:
raise ValueError('''Height of grid can\'t... | 333 | 0 |
import json
from typing import TYPE_CHECKING, 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_blenderbot import Blend... | 19 |
import gc
import unittest
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DDPMScheduler,
PriorTransformer,
StableUnCLIPPipeline,
UNetaDConditionModel,
)
from ... | 333 | 0 |
import unittest
from huggingface_hub import hf_hub_download
from transformers import MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING, VideoMAEFeatureExtractor
from transformers.pipelines import VideoClassificationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
requir... | 20 |
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers
from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelin... | 333 | 0 |
import copy
import tempfile
import unittest
from huggingface_hub import HfFolder, delete_repo
from parameterized import parameterized
from requests.exceptions import HTTPError
from transformers import AutoConfig, GenerationConfig
from transformers.testing_utils import TOKEN, USER, is_staging_test
class _lowe... | 21 |
import math
import sys
def __a ( SCREAMING_SNAKE_CASE ) -> int:
'''simple docstring'''
if number != int(SCREAMING_SNAKE_CASE ):
raise ValueError('''the value of input must be a natural number''' )
if number < 0:
raise ValueError('''th... | 333 | 0 |
'''simple docstring'''
import warnings
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class A_ ( lowerCAme... | 22 |
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 rescale, resize, to_channel_dimension_format
from ...image_utils import (
ChannelDimension,
ImageInput,
PILIm... | 333 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
UpperCamelCase__: Any = {"configuration_glpn": ["GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP", "GLPNConfig"]}
try:
if... | 23 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
A_ : Optional[int] = {
'configuration_poolformer': [
'POOLFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
'PoolFormerConfig',
'PoolFor... | 333 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
snake_case_ = logging.get_logger(__name__)
snake_case_ = {
'tanreinama/GPTSAN-2.8B-spout_is_uniform': (
'https://huggingface.co/tanreinama/GPTSAN-2.8B-spout_is_uniform/resolve/main/config.json'
... | 24 |
import math
def __a ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) -> float:
'''simple docstring'''
if (
not isinstance(SCREAMING_SNAKE_CASE , (int, float) )
or power_factor < -1
or power_factor > 1
):
ra... | 333 | 0 |
"""simple docstring"""
import unittest
import numpy as np
from diffusers import LMSDiscreteScheduler, OnnxStableDiffusionInpaintPipeline
from diffusers.utils.testing_utils import (
is_onnx_available,
load_image,
nightly,
require_onnxruntime,
require_torch_gpu,
)
from ..tes... | 25 |
def __a ( ) -> list[list[int]]:
'''simple docstring'''
return [list(range(1_0_0_0 - i , -1_0_0_0 - i , -1 ) ) for i in range(1_0_0_0 )]
A_ : Union[str, Any] = generate_large_matrix()
A_ : Union[str, Any] = (
[[4,... | 333 | 0 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import YolosConfig, YolosForObjectDetection, YolosImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
_snake_case ... | 26 |
import argparse
import json
import os
import sys
import tempfile
import unittest
from argparse import Namespace
from dataclasses import dataclass, field
from enum import Enum
from pathlib import Path
from typing import List, Literal, Optional
import yaml
from transformers import HfArgumentParser, Tr... | 333 | 0 |
'''simple docstring'''
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
fro... | 27 |
import doctest
from collections import deque
import numpy as np
class A_ :
'''simple docstring'''
def __init__(self ) -> None:
__UpperCAmelCase = [2, 1, 2, -1]
__UpperCAmelCase = [1, 2, 3, 4]
def lowerCAmelCase_ (self ... | 333 | 0 |
'''simple docstring'''
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,
res... | 28 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A_ : Any = logging.get_logger(__name__)
A_ : Optional[Any] = {
'google/pegasus-large': 'https://huggingface.co/google/pegasus-large/resolve/main/config.json',
# See all PEGASUS models at https... | 333 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCAmelCase = logging.get_logger(__name__)
__UpperCAmelCase = {
'abeja/gpt-neox-japanese-2.7b': 'https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/config.json',
}
... | 29 |
import itertools
import json
import os
import unittest
from transformers import AddedToken, LongformerTokenizer, LongformerTokenizerFast
from transformers.models.longformer.tokenization_longformer import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokeni... | 333 | 0 |
from __future__ import annotations
def a ( snake_case__: list , snake_case__: int ):
'''simple docstring'''
# Checks if the entire collection has been sorted
if len(snake_case__ ) <= 1 or n <= 1:
return
insert_next(snake_case__ , ... | 30 |
import tempfile
import torch
from diffusers import IPNDMScheduler
from .test_schedulers import SchedulerCommonTest
class A_ ( _a ):
'''simple docstring'''
a__ = (IPNDMScheduler,)
a__ = (("num_inference_steps", 50),)
def lower... | 333 | 0 |
'''simple docstring'''
def UpperCamelCase_ ( _UpperCAmelCase : int ) -> int:
"""simple docstring"""
if n == 1 or not isinstance(_UpperCAmelCase , _UpperCAmelCase ):
return 0
elif n == 2:
return 1
else:
_Upp... | 31 |
import copy
import inspect
import unittest
from transformers import PretrainedConfig, SwiftFormerConfig
from transformers.testing_utils import (
require_torch,
require_vision,
slow,
torch_device,
)
from transformers.utils import cached_property, is_torch_available, is_vision_available
... | 333 | 0 |
import unittest
from typing import Tuple
import torch
from diffusers.utils import floats_tensor, randn_tensor, torch_all_close, torch_device
from diffusers.utils.testing_utils import require_torch
@require_torch
class SCREAMING_SNAKE_CASE__ :
@property
def SCREAMING_SNAKE_CASE ... | 32 |
from collections import OrderedDict
from ...utils import logging
from .auto_factory import _BaseAutoModelClass, _LazyAutoMapping, auto_class_update
from .configuration_auto import CONFIG_MAPPING_NAMES
A_ : str = logging.get_logger(__name__)
A_ : str = OrderedDict(
[
... | 333 | 0 |
"""simple docstring"""
# Lint as: python3
import dataclasses
import re
from dataclasses import dataclass
from functools import total_ordering
from typing import Optional, Union
__A : List[str] = re.compile(R'''^(?P<major>\d+)''' R'''\.(?P<minor>\d+)''' R'''\.(?P<patch>\d+)$''... | 33 |
import math
from enum import Enum
from typing import Optional, Union
from torch.optim import Optimizer
from torch.optim.lr_scheduler import LambdaLR
from .utils import logging
A_ : Tuple = logging.get_logger(__name__)
class A_ ( _a ):
'''simple docstring'''
... | 333 | 0 |
'''simple docstring'''
from typing import Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_torch_available():
... | 34 |
def __a ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) -> list:
'''simple docstring'''
__UpperCAmelCase = len(SCREAMING_SNAKE_CASE )
__UpperCAmelCase = [[0] * n for i in range(SCREAMING_SNAKE_CASE )]
for i... | 333 | 0 |
'''simple docstring'''
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 che... | 35 |
def __a ( SCREAMING_SNAKE_CASE ) -> set:
'''simple docstring'''
__UpperCAmelCase = set()
# edges = list of graph's edges
__UpperCAmelCase = get_edges(SCREAMING_SNAKE_CASE )
# While there are still elements in edges list, take an arbitrary edg... | 333 | 0 |
from maths.prime_factors import prime_factors
def A ( _lowerCamelCase ):
'''simple docstring'''
if not isinstance(_lowerCamelCase , _lowerCamelCase ):
_lowerCAmelCase : int = F"Input value of [number={number}] must be an integer"
... | 36 |
A_ : List[Any] = {'a': ['c', 'b'], 'b': ['d', 'e'], 'c': [], 'd': [], 'e': []}
A_ : int = ['a', 'b', 'c', 'd', 'e']
def __a ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) -> List[Any]:
'''simple docstring''... | 333 | 0 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_lowerCAmelCase = logging.get_logger(__name__)
_lowerCAmelCase = {
'''junnyu/roform... | 37 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
A_ : int = {
'configuration_graphormer': ['GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GraphormerConfig'],
}
try:
if not is_torch_availa... | 333 | 0 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bert import BertTokenizer
UpperCAmelCase_ : Optional[Any] = logging.get_logger(__name__)
Upper... | 38 |
from copy import deepcopy
import torch
import torch.nn.functional as F
from torch.optim import AdamW
from torch.optim.lr_scheduler import LambdaLR
from torch.utils.data import DataLoader
from accelerate.accelerator import Accelerator
from accelerate.state import GradientState
from accelerate.test_utils ... | 333 | 0 |
class __lowerCamelCase :
"""simple docstring"""
def __init__( self , UpperCAmelCase ):
"""simple docstring"""
_UpperCAmelCase = val
_UpperCAmelCase = None
_UpperCAmelCase = None
def ... | 39 |
import os
try:
from .build_directory_md import good_file_paths
except ImportError:
from build_directory_md import good_file_paths # type: ignore
A_ : Optional[Any] = list(good_file_paths())
assert filepaths, "good_file_paths() failed!"
A_ : Optional[Any] = [file f... | 333 | 0 |
"""simple docstring"""
import unittest
from transformers import SPIECE_UNDERLINE
from transformers.models.speechta import SpeechTaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.tokenization_utils import AddedTo... | 40 |
def __a ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) -> str:
'''simple docstring'''
__UpperCAmelCase = [[] for _ in range(SCREAMING_SNAKE_CASE )]
__UpperCAmelCase = key - 1
if key <= 0:
raise ValueError('''Height of grid can\'t... | 333 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_torch_available
from ...utils import OptionalDependencyNotAvailable
_A : str ={
'''configuration_gpt_neox_japanese''': ['''GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MA... | 41 |
import gc
import unittest
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DDPMScheduler,
PriorTransformer,
StableUnCLIPPipeline,
UNetaDConditionModel,
)
from ... | 333 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_speech_available, is_torch_available
lowercase : Optional[Any] = {
"configuration_audio_spectrogram_transformer": [
"AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAI... | 42 |
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers
from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelin... | 333 | 0 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import logging
logging.set_verbosity_info()
__lowerca... | 43 |
import math
import sys
def __a ( SCREAMING_SNAKE_CASE ) -> int:
'''simple docstring'''
if number != int(SCREAMING_SNAKE_CASE ):
raise ValueError('''the value of input must be a natural number''' )
if number < 0:
raise ValueError('''th... | 333 | 0 |
"""simple docstring"""
import os
import re
import warnings
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
if TYPE_CHECKING:
from ...tokenization_utils_base import TextInput
from... | 44 |
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 rescale, resize, to_channel_dimension_format
from ...image_utils import (
ChannelDimension,
ImageInput,
PILIm... | 333 | 0 |
"""simple docstring"""
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
lowercase_ = logging.get_logger(__name__)
lowercase_... | 45 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
A_ : Optional[int] = {
'configuration_poolformer': [
'POOLFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
'PoolFormerConfig',
'PoolFor... | 333 | 0 |
"""simple docstring"""
import math
from typing import Optional
import numpy as np
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE__ = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ = {
"facebook/encodec_24khz": "https://huggingfa... | 46 |
import math
def __a ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) -> float:
'''simple docstring'''
if (
not isinstance(SCREAMING_SNAKE_CASE , (int, float) )
or power_factor < -1
or power_factor > 1
):
ra... | 333 | 0 |
'''simple docstring'''
def _lowerCAmelCase ( ) -> Tuple:
"""simple docstring"""
_SCREAMING_SNAKE_CASE =[31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31]
_SCREAMING_SNAKE_CASE =6
_SCREAMING_SNAKE_CASE =1
_SCREAMING_SNAKE_CASE =... | 47 |
def __a ( ) -> list[list[int]]:
'''simple docstring'''
return [list(range(1_0_0_0 - i , -1_0_0_0 - i , -1 ) ) for i in range(1_0_0_0 )]
A_ : Union[str, Any] = generate_large_matrix()
A_ : Union[str, Any] = (
[[4,... | 333 | 0 |
from typing import Dict
from .base import GenericTensor, Pipeline
class UpperCamelCase__ (lowerCAmelCase__ ):
'''simple docstring'''
def _lowercase ( self , UpperCamelCase__=None , UpperCamelCase__=None , UpperCamelCase__=None , **UpperCamelCa... | 48 |
import argparse
import json
import os
import sys
import tempfile
import unittest
from argparse import Namespace
from dataclasses import dataclass, field
from enum import Enum
from pathlib import Path
from typing import List, Literal, Optional
import yaml
from transformers import HfArgumentParser, Tr... | 333 | 0 |
import warnings
from typing import Dict, List, Optional, Tuple
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
__snake_case :int = logging.get_logger(__name__)
class _A ( __UpperCAmelCase ):
UpperCamelCase__ : int =... | 49 |
import doctest
from collections import deque
import numpy as np
class A_ :
'''simple docstring'''
def __init__(self ) -> None:
__UpperCAmelCase = [2, 1, 2, -1]
__UpperCAmelCase = [1, 2, 3, 4]
def lowerCAmelCase_ (self ... | 333 | 0 |
import os
import pickle
import unittest
from transformers import AutoTokenizer
from transformers.models.bert.tokenization_bert import BertTokenizer
from transformers.models.bert_japanese.tokenization_bert_japanese import (
VOCAB_FILES_NAMES,
BertJapaneseTokenizer,
CharacterTokenizer,
JumanppToken... | 50 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A_ : Any = logging.get_logger(__name__)
A_ : Optional[Any] = {
'google/pegasus-large': 'https://huggingface.co/google/pegasus-large/resolve/main/config.json',
# See all PEGASUS models at https... | 333 | 0 |
import argparse
import os
import pickle
import sys
import torch
from transformers import TransfoXLConfig, TransfoXLLMHeadModel, load_tf_weights_in_transfo_xl
from transformers.models.transfo_xl import tokenization_transfo_xl as data_utils
from transformers.models.transfo_xl.tokenization_transfo_xl ... | 51 |
import itertools
import json
import os
import unittest
from transformers import AddedToken, LongformerTokenizer, LongformerTokenizerFast
from transformers.models.longformer.tokenization_longformer import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokeni... | 333 | 0 |
import argparse
import hashlib # hashlib is only used inside the Test class
import struct
class A__ :
def __init__( self , A_ ):
'''simple docstring'''
UpperCamelCase : Any = data
UpperCamelCase : Union[str, Any] = [0x6_7_4_5_2... | 52 |
import tempfile
import torch
from diffusers import IPNDMScheduler
from .test_schedulers import SchedulerCommonTest
class A_ ( _a ):
'''simple docstring'''
a__ = (IPNDMScheduler,)
a__ = (("num_inference_steps", 50),)
def lower... | 333 | 0 |
'''simple docstring'''
def lowercase__ ( __lowercase : Union[str, Any] ) -> Union[str, Any]: # noqa: E741
"""simple docstring"""
__UpperCamelCase = len(__lowercase )
__UpperCamelCase = 0
__UpperCamelCase = [0] * n
__Upper... | 53 |
import copy
import inspect
import unittest
from transformers import PretrainedConfig, SwiftFormerConfig
from transformers.testing_utils import (
require_torch,
require_vision,
slow,
torch_device,
)
from transformers.utils import cached_property, is_torch_available, is_vision_available
... | 333 | 0 |
"""simple docstring"""
import inspect
import unittest
from transformers import DPTConfig
from transformers.file_utils import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from .... | 54 |
from collections import OrderedDict
from ...utils import logging
from .auto_factory import _BaseAutoModelClass, _LazyAutoMapping, auto_class_update
from .configuration_auto import CONFIG_MAPPING_NAMES
A_ : str = logging.get_logger(__name__)
A_ : str = OrderedDict(
[
... | 333 | 0 |
'''simple docstring'''
def __snake_case ( UpperCAmelCase_ : int ):
lowerCamelCase_ = [[0 for _ in range(UpperCAmelCase_ )] for _ in range(m + 1 )]
for i in range(m + 1 ):
lowerCamelCase_ = 1
for n in range(m + 1 ):
for k in range(1 , UpperCAmelCase_ ):
... | 55 |
import math
from enum import Enum
from typing import Optional, Union
from torch.optim import Optimizer
from torch.optim.lr_scheduler import LambdaLR
from .utils import logging
A_ : Tuple = logging.get_logger(__name__)
class A_ ( _a ):
'''simple docstring'''
... | 333 | 0 |
'''simple docstring'''
import contextlib
import os
import sqlitea
import pytest
from datasets import Dataset, Features, Value
from datasets.io.sql import SqlDatasetReader, SqlDatasetWriter
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases, require_sqlalchemy
def __magic... | 56 |
def __a ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) -> list:
'''simple docstring'''
__UpperCAmelCase = len(SCREAMING_SNAKE_CASE )
__UpperCAmelCase = [[0] * n for i in range(SCREAMING_SNAKE_CASE )]
for i... | 333 | 0 |
"""simple docstring"""
from ....utils import logging
A : Optional[int] = logging.get_logger(__name__)
class _UpperCamelCase ( lowerCAmelCase__ ):
'''simple docstring'''
def __init__( self , __a , __a=None , __a=20_48 ):
__lowerCAmelCase ... | 57 |
def __a ( SCREAMING_SNAKE_CASE ) -> set:
'''simple docstring'''
__UpperCAmelCase = set()
# edges = list of graph's edges
__UpperCAmelCase = get_edges(SCREAMING_SNAKE_CASE )
# While there are still elements in edges list, take an arbitrary edg... | 333 | 0 |
'''simple docstring'''
from math import log
from scipy.constants import Boltzmann, physical_constants
lowercase_ = 300 # TEMPERATURE (unit = K)
def lowerCamelCase ( __lowerCamelCase : float , __lowerCamelCase : float , __lowerCamelCase : float , ) ... | 58 |
A_ : List[Any] = {'a': ['c', 'b'], 'b': ['d', 'e'], 'c': [], 'd': [], 'e': []}
A_ : int = ['a', 'b', 'c', 'd', 'e']
def __a ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) -> List[Any]:
'''simple docstring''... | 333 | 0 |
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,
TFAutoMod... | 59 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
A_ : int = {
'configuration_graphormer': ['GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GraphormerConfig'],
}
try:
if not is_torch_availa... | 333 | 0 |
"""simple docstring"""
import contextlib
import os
import sqlitea
import pytest
from datasets import Dataset, Features, Value
from datasets.io.sql import SqlDatasetReader, SqlDatasetWriter
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases, require_sqlalchemy
def _sna... | 60 |
from copy import deepcopy
import torch
import torch.nn.functional as F
from torch.optim import AdamW
from torch.optim.lr_scheduler import LambdaLR
from torch.utils.data import DataLoader
from accelerate.accelerator import Accelerator
from accelerate.state import GradientState
from accelerate.test_utils ... | 333 | 0 |
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