code stringlengths 82 54.1k | code_codestyle int64 0 699 | style_context stringlengths 111 35.6k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
import unittest
from transformers import (
MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TextaTextGenerationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch
from transformers.utils import is_torch_available
... | 671 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
lowerCAmelCase : str = {
"""configuration_xlm""": ["""XLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """XLMConfig""", """XLMOnnxConfig"""],
"""tokenization_xlm""": [""... | 671 | 1 |
class __lowercase :
"""simple docstring"""
def __init__( self : Union[str, Any] , lowerCAmelCase__ : List[str]):
# we need a list not a string, so do something to change the type
SCREAMING_SNAKE_CASE_: List[str] = arr.split(",")
def _SC... | 671 |
lowerCAmelCase : List[str] = {
"""A""": ["""B""", """C""", """E"""],
"""B""": ["""A""", """D""", """E"""],
"""C""": ["""A""", """F""", """G"""],
"""D""": ["""B"""],
"""E""": ["""A""", """B""", """D"""],
"""F""": ["""C"""],
"""G""": ["""C"""],
}
def A_ ( _Upp... | 671 | 1 |
import json
import os
import tempfile
import transformers
import datasets
from utils import generate_example_dataset, get_duration
lowerCAmelCase : Tuple = 500000
lowerCAmelCase , lowerCAmelCase : Optional[Any] = os.path.split(__file__)
lowerCAmelCase : Dict = o... | 671 |
from __future__ import annotations
from math import pi
from typing import Protocol
import matplotlib.pyplot as plt
import numpy as np
class __lowercase ( UpperCAmelCase_ ):
"""simple docstring"""
def _SCREAMING_SNAKE_CASE ( self : List[Any] , lowerCAmelCase__ ... | 671 | 1 |
import os
lowerCAmelCase : Tuple = {"""I""": 1, """V""": 5, """X""": 10, """L""": 50, """C""": 100, """D""": 500, """M""": 1000}
def A_ ( _UpperCAmelCase ):
SCREAMING_SNAKE_CASE_: Optional[int] = 0
SCREAMING_SNAKE_CASE_: Dict = 0
... | 671 |
from __future__ import annotations
from math import ceil, floor, sqrt
def A_ ( _UpperCAmelCase = 2_00_00_00 ):
SCREAMING_SNAKE_CASE_: list[int] = [0]
SCREAMING_SNAKE_CASE_: int
for idx in range(1 , ceil(sqrt(target * 2 ) * 1.1 ) ... | 671 | 1 |
# Lint as: python3
import itertools
import os
import re
lowerCAmelCase : Dict = re.compile(R"""([A-Z]+)([A-Z][a-z])""")
lowerCAmelCase : str = re.compile(R"""([a-z\d])([A-Z])""")
lowerCAmelCase : List[Any] = re.compile(R"""(?<!_)_(?!_)""")
lowerCAmelCase : Optional[A... | 671 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowerCAmelCase : Optional[int] = {
"""configuration_longformer""": [
"""LONGFORMER_PRETRAINED_CONFIG_ARC... | 671 | 1 |
import json
from typing import Iterator, List, Union
from tokenizers import AddedToken, Regex, Tokenizer, decoders, normalizers, pre_tokenizers, trainers
from tokenizers.implementations.base_tokenizer import BaseTokenizer
from tokenizers.models import Unigram
from tokenizers.processors import TemplateProcessing
c... | 671 |
import argparse
import os.path as osp
import re
import torch
from safetensors.torch import load_file, save_file
# =================#
# UNet Conversion #
# =================#
lowerCAmelCase : Optional[int] = [
# (stable-diffusion, HF Diffusers)
("""time_embed.0.weight""", """time_embedding.li... | 671 | 1 |
import unittest
from transformers import SqueezeBertConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, r... | 671 |
from typing import Callable, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase : int = logging.get_logger(__name__)
lowerCAmelCase : Dict = {
"""microsoft/xprophetnet-large-wiki100-cased""": (
"""https://huggingf... | 671 | 1 |
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase : Any = logging.get_logger(__name__)
lowerCAmelCase : Dict = {
"""microsoft/unispeech-sat-base-100h-libri-ft""": (
"""https://huggingface.co/micr... | 671 |
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,
PILImageResampli... | 671 | 1 |
import time
import warnings
from abc import ABC
from copy import deepcopy
from typing import Optional
import torch
from ..utils import add_start_docstrings, logging
lowerCAmelCase : Any = logging.get_logger(__name__)
lowerCAmelCase : List[str] = R"""
Args:
input_ids (`tor... | 671 |
import collections
from typing import List, Optional, Union
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert import BertTokenizer
lowerCAmelCase : Optional[int] = logging.get_logger(__... | 671 | 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, SingleSentenceClassificat... | 671 |
from transformers import DistilBertTokenizer, DistilBertTokenizerFast
from transformers.testing_utils import require_tokenizers, slow
from ..bert.test_tokenization_bert import BertTokenizationTest
@require_tokenizers
class __lowercase ( UpperCAmelCase_ ):
"""simple docstring"""
... | 671 | 1 |
import importlib
import inspect
import json
import os
import re
import shutil
import sys
from pathlib import Path
from typing import Dict, Optional, Union
from urllib import request
from huggingface_hub import HfFolder, cached_download, hf_hub_download, model_info
from packaging import version
from .. import __vers... | 671 |
import collections
import json
import math
import os
import re
import time
from fnmatch import fnmatch
from typing import Dict
import requests
from slack_sdk import WebClient
lowerCAmelCase : List[Any] = WebClient(token=os.environ["""CI_SLACK_BOT_TOKEN"""])
def A_ ( _UpperCAmelCase ... | 671 | 1 |
from __future__ import annotations
from typing import Any
class __lowercase :
"""simple docstring"""
def __init__( self : List[str] , lowerCAmelCase__ : int = 6):
SCREAMING_SNAKE_CASE_: Node | None = None
SCREAMING_SNAKE_CASE_: Node | Non... | 671 |
import argparse
import os
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 Accelerator, Dis... | 671 | 1 |
def A_ ( _UpperCAmelCase = 50 ):
SCREAMING_SNAKE_CASE_: int = [1] * (length + 1)
for row_length in range(length + 1 ):
for tile_length in range(2 , 5 ):
for tile_start in range(row_length - tile_length + 1 ):
... | 671 |
from math import asin, atan, cos, radians, sin, sqrt, tan
lowerCAmelCase : Union[str, Any] = 637_8137.0
lowerCAmelCase : int = 635_6752.31_4245
lowerCAmelCase : Union[str, Any] = 6378137
def A_ ( _UpperCAmelCase , _UpperCAmelCase , _Up... | 671 | 1 |
import collections
from typing import List, Optional, Union
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert import BertTokenizer
lowerCAmelCase : Optional[int] = logging.get_logger(__... | 671 |
import argparse
import torch
from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging
logging.set_verbosity_info()
def A_ ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ):
# Initialise PyTorch... | 671 | 1 |
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
from seqaseq_trainer import SeqaSeqTrainer
from seqaseq_training_args import SeqaSeqTrainingArguments
import transformers
from transformers import (
AutoConfig,
AutoModelForSeqaSeqLM,
AutoTokenizer,
... | 671 |
import math
def A_ ( _UpperCAmelCase ):
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
... | 671 | 1 |
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 __lowercase ( UpperCAmelCase_ ):
"""simple... | 671 |
import re
def A_ ( _UpperCAmelCase ):
return [char.split() for char in re.split(R"[^ a-z A-Z 0-9 \s]" , str_ )]
def A_ ( _UpperCAmelCase ):
SCREAMING_SNAKE_CASE_: int = split_input(str_ )
return "".join(
... | 671 | 1 |
from ..utils import DummyObject, requires_backends
class __lowercase ( metaclass=UpperCAmelCase_ ):
"""simple docstring"""
_UpperCAmelCase : Optional[int] = ['''flax''']
def __init__( self : Union[str, Any] , *lowerCAmelCase__ : int , **lowerCAmel... | 671 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING
lowerCAmelCase : Union[str, Any] = logging.get_logger(__name__)
class __lowercase ( UpperCAmelCase_ ):
"""simple docstring"""
... | 671 | 1 |
def A_ ( _UpperCAmelCase ):
if not all(x.isalpha() for x in string ):
raise ValueError("String must only contain alphabetic characters." )
SCREAMING_SNAKE_CASE_: Any = sorted(string.lower() )
return len(_UpperCAmelCase ) == len(set... | 671 |
import pickle
import unittest
import torch
from accelerate import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils import require_cpu
@require_cpu
class __lowercase ( unittest.TestCase ):
"""simple docstring"""
def _SCREAMING_SNAKE_CASE ... | 671 | 1 |
from __future__ import annotations
from scipy.special import comb # type: ignore
class __lowercase :
"""simple docstring"""
def __init__( self : List[Any] , lowerCAmelCase__ : list[tuple[float, float]]):
SCREAMING_SNAKE_CASE_: Optional[int] = list_of... | 671 |
from itertools import count
def A_ ( _UpperCAmelCase = 50 ):
SCREAMING_SNAKE_CASE_: Union[str, Any] = [1] * min_block_length
for n in count(_UpperCAmelCase ):
fill_count_functions.append(1 )
for block_length in range(_UpperCAmelC... | 671 | 1 |
import json
import logging
import math
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
from datasets import Dataset, load_dataset
import transformers
from transformers import (
CONFIG_MAPPING,
MODEL_FOR_MASKED_LM_MAPPING,
AutoConfig,
AutoModelForMaskedLM,
... | 671 |
def A_ ( _UpperCAmelCase ):
if not isinstance(_UpperCAmelCase , _UpperCAmelCase ):
raise TypeError("only integers accepted as input" )
else:
SCREAMING_SNAKE_CASE_: List[Any] = str(abs(_UpperCAmelCase ) )
... | 671 | 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,
AutoModelForQuestionAnswering,
... | 671 |
from __future__ import annotations
from collections.abc import Iterator
from typing import Any
class __lowercase :
"""simple docstring"""
def __init__( self : List[str] , lowerCAmelCase__ : Any):
SCREAMING_SNAKE_CASE_: Any = data
SCREAMING_SN... | 671 | 1 |
from __future__ import annotations
import unittest
from transformers import RoFormerConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from... | 671 |
from collections import defaultdict
from math import ceil, sqrt
def A_ ( _UpperCAmelCase = 1_00_00_00 , _UpperCAmelCase = 10 ):
SCREAMING_SNAKE_CASE_: defaultdict = defaultdict(_UpperCAmelCase )
for outer_width in range(3 , (t_limit // 4)... | 671 | 1 |
import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch
import numpy as np
import pytest
from datasets.arrow_dataset import Dataset
from datasets.search import ElasticSearchIndex, FaissIndex, MissingIndex
from .utils import require_elasticsearch, requir... | 671 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
lowerCAmelCase : str = {
"""configuration_xlm""": ["""XLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """XLMConfig""", """XLMOnnxConfig"""],
"""tokenization_xlm""": [""... | 671 | 1 |
import argparse
import os.path as osp
import re
import torch
from safetensors.torch import load_file, save_file
# =================#
# UNet Conversion #
# =================#
lowerCAmelCase : Optional[int] = [
# (stable-diffusion, HF Diffusers)
("""time_embed.0.weight""", """time_embedding.li... | 671 |
lowerCAmelCase : List[str] = {
"""A""": ["""B""", """C""", """E"""],
"""B""": ["""A""", """D""", """E"""],
"""C""": ["""A""", """F""", """G"""],
"""D""": ["""B"""],
"""E""": ["""A""", """B""", """D"""],
"""F""": ["""C"""],
"""G""": ["""C"""],
}
def A_ ( _Upp... | 671 | 1 |
import collections
import json
import math
import os
import re
import time
from fnmatch import fnmatch
from typing import Dict
import requests
from slack_sdk import WebClient
lowerCAmelCase : List[Any] = WebClient(token=os.environ["""CI_SLACK_BOT_TOKEN"""])
def A_ ( _UpperCAmelCase ... | 671 |
from __future__ import annotations
from math import pi
from typing import Protocol
import matplotlib.pyplot as plt
import numpy as np
class __lowercase ( UpperCAmelCase_ ):
"""simple docstring"""
def _SCREAMING_SNAKE_CASE ( self : List[Any] , lowerCAmelCase__ ... | 671 | 1 |
def A_ ( _UpperCAmelCase ):
if not nums: # Makes sure that the list is not empty
raise ValueError("List is empty" )
SCREAMING_SNAKE_CASE_: Union[str, Any] = sum(_UpperCAmelCase ) / len(_UpperCAmelCase ) # Calculate the average
return s... | 671 |
from __future__ import annotations
from math import ceil, floor, sqrt
def A_ ( _UpperCAmelCase = 2_00_00_00 ):
SCREAMING_SNAKE_CASE_: list[int] = [0]
SCREAMING_SNAKE_CASE_: int
for idx in range(1 , ceil(sqrt(target * 2 ) * 1.1 ) ... | 671 | 1 |
import re
def A_ ( _UpperCAmelCase ):
return [char.split() for char in re.split(R"[^ a-z A-Z 0-9 \s]" , str_ )]
def A_ ( _UpperCAmelCase ):
SCREAMING_SNAKE_CASE_: int = split_input(str_ )
return "".join(
... | 671 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowerCAmelCase : Optional[int] = {
"""configuration_longformer""": [
"""LONGFORMER_PRETRAINED_CONFIG_ARC... | 671 | 1 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaControlnetImgaImgPipeline,
KandinskyVaaPriorEmbaEmbPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, load_image, lo... | 671 |
import argparse
import os.path as osp
import re
import torch
from safetensors.torch import load_file, save_file
# =================#
# UNet Conversion #
# =================#
lowerCAmelCase : Optional[int] = [
# (stable-diffusion, HF Diffusers)
("""time_embed.0.weight""", """time_embedding.li... | 671 | 1 |
import argparse
import json
import logging
import os
import sys
from unittest.mock import patch
from transformers.testing_utils import TestCasePlus, get_gpu_count, slow
lowerCAmelCase : Dict = [
os.path.join(os.path.dirname(__file__), dirname)
for dirname in [
"""text-classification""... | 671 |
from typing import Callable, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase : int = logging.get_logger(__name__)
lowerCAmelCase : Dict = {
"""microsoft/xprophetnet-large-wiki100-cased""": (
"""https://huggingf... | 671 | 1 |
import inspect
import unittest
from transformers import YolosConfig
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_configuration_common import ConfigTester
from ...test_mode... | 671 |
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,
PILImageResampli... | 671 | 1 |
def A_ ( _UpperCAmelCase=2_81_23 ):
SCREAMING_SNAKE_CASE_: int = [1] * (limit + 1)
for i in range(2 , int(limit**0.5 ) + 1 ):
sum_divs[i * i] += i
for k in range(i + 1 , limit // i + 1 ):
sum... | 671 |
import collections
from typing import List, Optional, Union
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert import BertTokenizer
lowerCAmelCase : Optional[int] = logging.get_logger(__... | 671 | 1 |
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 : int = {
"""roberta-base""": """https:... | 671 |
from transformers import DistilBertTokenizer, DistilBertTokenizerFast
from transformers.testing_utils import require_tokenizers, slow
from ..bert.test_tokenization_bert import BertTokenizationTest
@require_tokenizers
class __lowercase ( UpperCAmelCase_ ):
"""simple docstring"""
... | 671 | 1 |
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_tf
if is_tf_available():
import tensorflow as tf
from tensorflow.python.eager import context
from tensorflow.python.framework import ops
from transformers import GradientAccumulator, cre... | 671 |
import collections
import json
import math
import os
import re
import time
from fnmatch import fnmatch
from typing import Dict
import requests
from slack_sdk import WebClient
lowerCAmelCase : List[Any] = WebClient(token=os.environ["""CI_SLACK_BOT_TOKEN"""])
def A_ ( _UpperCAmelCase ... | 671 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowerCAmelCase : Tuple = {
"""configuration_lxmert""": ["""LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LxmertCon... | 671 |
import argparse
import os
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 Accelerator, Dis... | 671 | 1 |
from typing import Dict, List, Optional, Tuple, Union
import torch
from ...models import AutoencoderKL, TransformeraDModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class __lowercase ( Up... | 671 |
from math import asin, atan, cos, radians, sin, sqrt, tan
lowerCAmelCase : Union[str, Any] = 637_8137.0
lowerCAmelCase : int = 635_6752.31_4245
lowerCAmelCase : Union[str, Any] = 6378137
def A_ ( _UpperCAmelCase , _UpperCAmelCase , _Up... | 671 | 1 |
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion_safe i... | 671 |
import argparse
import torch
from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging
logging.set_verbosity_info()
def A_ ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ):
# Initialise PyTorch... | 671 | 1 |
import warnings
from ...utils import logging
from .image_processing_beit import BeitImageProcessor
lowerCAmelCase : Tuple = logging.get_logger(__name__)
class __lowercase ( UpperCAmelCase_ ):
"""simple docstring"""
def __init__( self : Optional[Any] , *... | 671 |
import math
def A_ ( _UpperCAmelCase ):
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
... | 671 | 1 |
def A_ ( _UpperCAmelCase , _UpperCAmelCase ):
if len(_UpperCAmelCase ) != len(_UpperCAmelCase ):
raise ValueError("String lengths must match!" )
SCREAMING_SNAKE_CASE_: Any = 0
for chara, chara in zip(_UpperCAmelCase , ... | 671 |
import re
def A_ ( _UpperCAmelCase ):
return [char.split() for char in re.split(R"[^ a-z A-Z 0-9 \s]" , str_ )]
def A_ ( _UpperCAmelCase ):
SCREAMING_SNAKE_CASE_: int = split_input(str_ )
return "".join(
... | 671 | 1 |
import os
def A_ ( ):
SCREAMING_SNAKE_CASE_: Dict = os.path.join(os.path.dirname(_UpperCAmelCase ) , "num.txt" )
with open(_UpperCAmelCase ) as file_hand:
return str(sum(int(_UpperCAmelCase ) for line in file_hand ) ... | 671 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING
lowerCAmelCase : Union[str, Any] = logging.get_logger(__name__)
class __lowercase ( UpperCAmelCase_ ):
"""simple docstring"""
... | 671 | 1 |
import itertools
import random
import unittest
import numpy as np
from transformers import BatchFeature, SpeechTaFeatureExtractor
from transformers.testing_utils import require_torch
from transformers.utils.import_utils import is_torch_available
from ...test_sequence_feature_extraction_common import SequenceFeatur... | 671 |
import pickle
import unittest
import torch
from accelerate import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils import require_cpu
@require_cpu
class __lowercase ( unittest.TestCase ):
"""simple docstring"""
def _SCREAMING_SNAKE_CASE ... | 671 | 1 |
import inspect
import unittest
from datasets import load_dataset
from packaging import version
from transformers import BeitConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils... | 671 |
from itertools import count
def A_ ( _UpperCAmelCase = 50 ):
SCREAMING_SNAKE_CASE_: Union[str, Any] = [1] * min_block_length
for n in count(_UpperCAmelCase ):
fill_count_functions.append(1 )
for block_length in range(_UpperCAmelC... | 671 | 1 |
import warnings
warnings.warn(
"""memory_utils has been reorganized to utils.memory. Import `find_executable_batchsize` from the main `__init__`: """
"""`from accelerate import find_executable_batch_size` to avoid this warning.""",
FutureWarning,
)
| 671 |
def A_ ( _UpperCAmelCase ):
if not isinstance(_UpperCAmelCase , _UpperCAmelCase ):
raise TypeError("only integers accepted as input" )
else:
SCREAMING_SNAKE_CASE_: List[Any] = str(abs(_UpperCAmelCase ) )
... | 671 | 1 |
# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny model through reduction of a normal pre-trained model, but keeping the
# full vocab, merges file, and th... | 671 |
from __future__ import annotations
from collections.abc import Iterator
from typing import Any
class __lowercase :
"""simple docstring"""
def __init__( self : List[str] , lowerCAmelCase__ : Any):
SCREAMING_SNAKE_CASE_: Any = data
SCREAMING_SN... | 671 | 1 |
from functools import reduce
lowerCAmelCase : Optional[int] = (
"""73167176531330624919225119674426574742355349194934"""
"""96983520312774506326239578318016984801869478851843"""
"""85861560789112949495459501737958331952853208805511"""
"""1254069874715852386305071569329096329522744304355... | 671 |
from collections import defaultdict
from math import ceil, sqrt
def A_ ( _UpperCAmelCase = 1_00_00_00 , _UpperCAmelCase = 10 ):
SCREAMING_SNAKE_CASE_: defaultdict = defaultdict(_UpperCAmelCase )
for outer_width in range(3 , (t_limit // 4)... | 671 | 1 |
import math
def A_ ( _UpperCAmelCase ):
assert isinstance(_UpperCAmelCase , _UpperCAmelCase ) and (
number >= 0
), "'number' must been an int and positive"
if 1 < number < 4:
# 2 and 3 are primes
return True
elif numbe... | 671 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
lowerCAmelCase : str = {
"""configuration_xlm""": ["""XLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """XLMConfig""", """XLMOnnxConfig"""],
"""tokenization_xlm""": [""... | 671 | 1 |
import argparse
import re
import requests
import torch
# git clone https://github.com/salesforce/BLIP.git
from models.blip import blip_decoder
from models.blip_itm import blip_itm
from models.blip_vqa import blip_vqa
from PIL import Image
from torchvision import transforms
from torchvision.transforms.functional imp... | 671 |
lowerCAmelCase : List[str] = {
"""A""": ["""B""", """C""", """E"""],
"""B""": ["""A""", """D""", """E"""],
"""C""": ["""A""", """F""", """G"""],
"""D""": ["""B"""],
"""E""": ["""A""", """B""", """D"""],
"""F""": ["""C"""],
"""G""": ["""C"""],
}
def A_ ( _Upp... | 671 | 1 |
def A_ ( _UpperCAmelCase ):
SCREAMING_SNAKE_CASE_: set[int] = set()
# To detect a back edge, keep track of vertices currently in the recursion stack
SCREAMING_SNAKE_CASE_: set[int] = set()
return any(
node not in visited and depth_first_... | 671 |
from __future__ import annotations
from math import pi
from typing import Protocol
import matplotlib.pyplot as plt
import numpy as np
class __lowercase ( UpperCAmelCase_ ):
"""simple docstring"""
def _SCREAMING_SNAKE_CASE ( self : List[Any] , lowerCAmelCase__ ... | 671 | 1 |
# Copyright 2021 The HuggingFace 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 by applica... | 671 |
from __future__ import annotations
from math import ceil, floor, sqrt
def A_ ( _UpperCAmelCase = 2_00_00_00 ):
SCREAMING_SNAKE_CASE_: list[int] = [0]
SCREAMING_SNAKE_CASE_: int
for idx in range(1 , ceil(sqrt(target * 2 ) * 1.1 ) ... | 671 | 1 |
def A_ ( _UpperCAmelCase , _UpperCAmelCase ):
if digit_amount > 0:
return round(number - int(_UpperCAmelCase ) , _UpperCAmelCase )
return number - int(_UpperCAmelCase )
if __name__ == "__main__":
print(decimal_isolate(1.53, 0))
... | 671 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowerCAmelCase : Optional[int] = {
"""configuration_longformer""": [
"""LONGFORMER_PRETRAINED_CONFIG_ARC... | 671 | 1 |
import argparse
import logging
import pickle
import random
import time
import numpy as np
from transformers import BertTokenizer, GPTaTokenizer, RobertaTokenizer
logging.basicConfig(
format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""", datefmt="""%m/%d/%Y %H:%M:%S""", level=logging.INFO
)
lowerC... | 671 |
import argparse
import os.path as osp
import re
import torch
from safetensors.torch import load_file, save_file
# =================#
# UNet Conversion #
# =================#
lowerCAmelCase : Optional[int] = [
# (stable-diffusion, HF Diffusers)
("""time_embed.0.weight""", """time_embedding.li... | 671 | 1 |
import math
def A_ ( _UpperCAmelCase ):
return math.sqrt(_UpperCAmelCase ) * math.sqrt(_UpperCAmelCase ) == num
def A_ ( _UpperCAmelCase ):
SCREAMING_SNAKE_CASE_: Any = 0
SCREAMING_SNAKE_CASE_: List[str] = n
... | 671 |
from typing import Callable, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase : int = logging.get_logger(__name__)
lowerCAmelCase : Dict = {
"""microsoft/xprophetnet-large-wiki100-cased""": (
"""https://huggingf... | 671 | 1 |
import gc
import unittest
import numpy as np
import torch
from diffusers import (
AudioDiffusionPipeline,
AutoencoderKL,
DDIMScheduler,
DDPMScheduler,
DiffusionPipeline,
Mel,
UNetaDConditionModel,
UNetaDModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.test... | 671 |
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,
PILImageResampli... | 671 | 1 |
def A_ ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ):
if exponent == 1:
return base
if exponent % 2 == 0:
SCREAMING_SNAKE_CASE_: Dict = _modexpt(_UpperCAmelCase , exponent // 2 , _UpperCAmelCase ... | 671 |
import collections
from typing import List, Optional, Union
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert import BertTokenizer
lowerCAmelCase : Optional[int] = logging.get_logger(__... | 671 | 1 |
import unittest
import torch
from diffusers import DDIMScheduler, DDPMScheduler, UNetaDModel
from diffusers.training_utils import set_seed
from diffusers.utils.testing_utils import slow
lowerCAmelCase : Any = False
class __lowercase ( unittest.TestCase ):
"""simple docst... | 671 |
from transformers import DistilBertTokenizer, DistilBertTokenizerFast
from transformers.testing_utils import require_tokenizers, slow
from ..bert.test_tokenization_bert import BertTokenizationTest
@require_tokenizers
class __lowercase ( UpperCAmelCase_ ):
"""simple docstring"""
... | 671 | 1 |
import json
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
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_... | 671 |
import collections
import json
import math
import os
import re
import time
from fnmatch import fnmatch
from typing import Dict
import requests
from slack_sdk import WebClient
lowerCAmelCase : List[Any] = WebClient(token=os.environ["""CI_SLACK_BOT_TOKEN"""])
def A_ ( _UpperCAmelCase ... | 671 | 1 |
import copy
from typing import Any, Dict, List, Optional, Union
import numpy as np
import torch
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import... | 671 |
import argparse
import os
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 Accelerator, Dis... | 671 | 1 |
import os
import numpy
import onnx
def A_ ( _UpperCAmelCase , _UpperCAmelCase ):
SCREAMING_SNAKE_CASE_: Dict = a.name
SCREAMING_SNAKE_CASE_: Optional[int] = b.name
SCREAMING_SNAKE_CASE_: Tuple = ""
SCREAMING_SNAK... | 671 |
from math import asin, atan, cos, radians, sin, sqrt, tan
lowerCAmelCase : Union[str, Any] = 637_8137.0
lowerCAmelCase : int = 635_6752.31_4245
lowerCAmelCase : Union[str, Any] = 6378137
def A_ ( _UpperCAmelCase , _UpperCAmelCase , _Up... | 671 | 1 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
lowerCAmelCase : int = """▁"""
lowerCAmelCase : Optional[Any] = {"""vocab_file""": "... | 671 |
import argparse
import torch
from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging
logging.set_verbosity_info()
def A_ ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ):
# Initialise PyTorch... | 671 | 1 |
import os
import textwrap
import pyarrow as pa
import pytest
from datasets import ClassLabel, Features, Image
from datasets.packaged_modules.csv.csv import Csv
from ..utils import require_pil
@pytest.fixture
def A_ ( _UpperCAmelCase ):
SCREAMING_SNAKE_CASE_: Optional[int] ... | 671 |
import math
def A_ ( _UpperCAmelCase ):
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
... | 671 | 1 |
from transformers import DistilBertTokenizer, DistilBertTokenizerFast
from transformers.testing_utils import require_tokenizers, slow
from ..bert.test_tokenization_bert import BertTokenizationTest
@require_tokenizers
class __lowercase ( UpperCAmelCase_ ):
"""simple docstring"""
... | 671 |
import re
def A_ ( _UpperCAmelCase ):
return [char.split() for char in re.split(R"[^ a-z A-Z 0-9 \s]" , str_ )]
def A_ ( _UpperCAmelCase ):
SCREAMING_SNAKE_CASE_: int = split_input(str_ )
return "".join(
... | 671 | 1 |
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Value
from .base import TaskTemplate
@dataclass(frozen=lowerCamelCase )
class lowerCamelCase_ ( lowerCamelCase ):
a__ = field(default='''language-modeling''' , meta... | 0 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING
lowerCAmelCase : Union[str, Any] = logging.get_logger(__name__)
class __lowercase ( UpperCAmelCase_ ):
"""simple docstring"""
... | 671 | 0 |
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import numpy as np
import tensorflow as tf
from transformers import TFCa... | 1 |
import pickle
import unittest
import torch
from accelerate import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils import require_cpu
@require_cpu
class __lowercase ( unittest.TestCase ):
"""simple docstring"""
def _SCREAMING_SNAKE_CASE ... | 671 | 0 |
import os
from distutils.util import strtobool
def SCREAMING_SNAKE_CASE_ ( _snake_case :Any , _snake_case :List[Any] ) -> Optional[Any]:
for e in env_keys:
_A = int(os.environ.get(_snake_case , -1 ) )
if val >= 0:
return val
return default
... | 2 |
from itertools import count
def A_ ( _UpperCAmelCase = 50 ):
SCREAMING_SNAKE_CASE_: Union[str, Any] = [1] * min_block_length
for n in count(_UpperCAmelCase ):
fill_count_functions.append(1 )
for block_length in range(_UpperCAmelC... | 671 | 0 |
'''simple docstring'''
import pytest
from datasets import inspect_metric, list_metrics, load_metric
@pytest.fixture
def A_( A : Union[str, Any]):
monkeypatch.setattr('datasets.utils.deprecation_utils._emitted_deprecation_warnings' , set())
@pytest.fixture
d... | 3 |
def A_ ( _UpperCAmelCase ):
if not isinstance(_UpperCAmelCase , _UpperCAmelCase ):
raise TypeError("only integers accepted as input" )
else:
SCREAMING_SNAKE_CASE_: List[Any] = str(abs(_UpperCAmelCase ) )
... | 671 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
__UpperCamelCase : Union[str, Any] = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable... | 4 |
from __future__ import annotations
from collections.abc import Iterator
from typing import Any
class __lowercase :
"""simple docstring"""
def __init__( self : List[str] , lowerCAmelCase__ : Any):
SCREAMING_SNAKE_CASE_: Any = data
SCREAMING_SN... | 671 | 0 |
'''simple docstring'''
import unittest
from transformers import load_tool
from transformers.utils import is_torch_available
if is_torch_available():
import torch
from transformers.testing_utils import require_torch
from .test_tools_common import ToolTesterMixin
@require_torch
class UpperCAmelCase... | 5 |
from collections import defaultdict
from math import ceil, sqrt
def A_ ( _UpperCAmelCase = 1_00_00_00 , _UpperCAmelCase = 10 ):
SCREAMING_SNAKE_CASE_: defaultdict = defaultdict(_UpperCAmelCase )
for outer_width in range(3 , (t_limit // 4)... | 671 | 0 |
import argparse
import glob
import importlib.util
import os
import re
import black
from doc_builder.style_doc import style_docstrings_in_code
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
_lowerCamelCase = 'src... | 6 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
lowerCAmelCase : str = {
"""configuration_xlm""": ["""XLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """XLMConfig""", """XLMOnnxConfig"""],
"""tokenization_xlm""": [""... | 671 | 0 |
"""simple docstring"""
def _snake_case ( _snake_case : str , _snake_case : str ) -> float:
'''simple docstring'''
def get_matched_characters(_snake_case : str , _snake_case : str ) -> str:
_A = []
_A = mi... | 7 |
lowerCAmelCase : List[str] = {
"""A""": ["""B""", """C""", """E"""],
"""B""": ["""A""", """D""", """E"""],
"""C""": ["""A""", """F""", """G"""],
"""D""": ["""B"""],
"""E""": ["""A""", """B""", """D"""],
"""F""": ["""C"""],
"""G""": ["""C"""],
}
def A_ ( _Upp... | 671 | 0 |
'''simple docstring'''
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_... | 8 |
from __future__ import annotations
from math import pi
from typing import Protocol
import matplotlib.pyplot as plt
import numpy as np
class __lowercase ( UpperCAmelCase_ ):
"""simple docstring"""
def _SCREAMING_SNAKE_CASE ( self : List[Any] , lowerCAmelCase__ ... | 671 | 0 |
import csv
import tweepy
# Twitter API credentials
SCREAMING_SNAKE_CASE__ = ''''''
SCREAMING_SNAKE_CASE__ = ''''''
SCREAMING_SNAKE_CASE__ = ''''''
SCREAMING_SNAKE_CASE__ = ''''''
def A ( __UpperCamelCase ) -> None:
# authorize twitter, initialize tweepy
A__ ... | 9 |
from __future__ import annotations
from math import ceil, floor, sqrt
def A_ ( _UpperCAmelCase = 2_00_00_00 ):
SCREAMING_SNAKE_CASE_: list[int] = [0]
SCREAMING_SNAKE_CASE_: int
for idx in range(1 , ceil(sqrt(target * 2 ) * 1.1 ) ... | 671 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCAmelCase = logging.get_logger(__name__)
_lowerCAmelCase = {
"facebook/s2t-wav2vec2-large-en-de": (
"https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/config.json"
),
# See all... | 10 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowerCAmelCase : Optional[int] = {
"""configuration_longformer""": [
"""LONGFORMER_PRETRAINED_CONFIG_ARC... | 671 | 0 |
'''simple docstring'''
from importlib import import_module
from .logging import get_logger
lowercase_ = get_logger(__name__)
class __A :
'''simple docstring'''
def __init__(self , A , A=None ) -> List[str]:
"""simple docstring"""
_a ... | 11 |
import argparse
import os.path as osp
import re
import torch
from safetensors.torch import load_file, save_file
# =================#
# UNet Conversion #
# =================#
lowerCAmelCase : Optional[int] = [
# (stable-diffusion, HF Diffusers)
("""time_embed.0.weight""", """time_embedding.li... | 671 | 0 |
import shutil
import tempfile
import unittest
from unittest.mock import patch
from transformers import (
DefaultFlowCallback,
IntervalStrategy,
PrinterCallback,
ProgressCallback,
Trainer,
TrainerCallback,
TrainingArguments,
is_torch_available,
)
from transformers.testin... | 12 |
from typing import Callable, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase : int = logging.get_logger(__name__)
lowerCAmelCase : Dict = {
"""microsoft/xprophetnet-large-wiki100-cased""": (
"""https://huggingf... | 671 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
A__ : Tuple = {
"""configuration_convbert""": ["""CONVBERT_PRETRAINED_CONFIG_ARCHIVE_M... | 13 |
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,
PILImageResampli... | 671 | 0 |
from datetime import datetime
import requests
from bsa import BeautifulSoup
if __name__ == "__main__":
a__ = input('''Enter image url: ''').strip()
print(f'''Downloading image from {url} ...''')
a__ = BeautifulSoup(requests.get(url).content, '''html.parser''')
... | 14 |
import collections
from typing import List, Optional, Union
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert import BertTokenizer
lowerCAmelCase : Optional[int] = logging.get_logger(__... | 671 | 0 |
import gc
import random
import unittest
import torch
from diffusers import (
IFImgaImgPipeline,
IFImgaImgSuperResolutionPipeline,
IFInpaintingPipeline,
IFInpaintingSuperResolutionPipeline,
IFPipeline,
IFSuperResolutionPipeline,
)
from diffusers.models.attention_processor im... | 15 |
from transformers import DistilBertTokenizer, DistilBertTokenizerFast
from transformers.testing_utils import require_tokenizers, slow
from ..bert.test_tokenization_bert import BertTokenizationTest
@require_tokenizers
class __lowercase ( UpperCAmelCase_ ):
"""simple docstring"""
... | 671 | 0 |
import inspect
import os
import unittest
import torch
import accelerate
from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_multi_gpu
from accelerate.utils import patch_environment
class _SCREAMING_SNAKE_CASE ( unittest.TestCase ):
... | 16 |
import collections
import json
import math
import os
import re
import time
from fnmatch import fnmatch
from typing import Dict
import requests
from slack_sdk import WebClient
lowerCAmelCase : List[Any] = WebClient(token=os.environ["""CI_SLACK_BOT_TOKEN"""])
def A_ ( _UpperCAmelCase ... | 671 | 0 |
import time
import warnings
from abc import ABC
from copy import deepcopy
from typing import Optional
import torch
from ..utils import add_start_docstrings, logging
UpperCAmelCase_ : List[str] = logging.get_logger(__name__)
UpperCAmelCase_ : Union[str, Any] = r'''
Args:
i... | 17 |
import argparse
import os
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 Accelerator, Dis... | 671 | 0 |
'''simple docstring'''
import re
import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey
from ..utils import logging
_SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
def __a(SCREAMING_SNAKE_CASE_ : Union[str, Any] ):... | 18 |
from math import asin, atan, cos, radians, sin, sqrt, tan
lowerCAmelCase : Union[str, Any] = 637_8137.0
lowerCAmelCase : int = 635_6752.31_4245
lowerCAmelCase : Union[str, Any] = 6378137
def A_ ( _UpperCAmelCase , _UpperCAmelCase , _Up... | 671 | 0 |
"""simple docstring"""
from typing import Callable, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_a = logging.get_logger(__name__)
_a = {
"""microsoft/xprophetnet-large-wiki100-cased""": (
"""https://huggi... | 19 |
import argparse
import torch
from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging
logging.set_verbosity_info()
def A_ ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ):
# Initialise PyTorch... | 671 | 0 |
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow
if is_torch_available():
import torch
from transformers import XLMRobertaModel
@require_sentencepiece
@require_tokenizers
@require_torch
class ... | 20 |
import math
def A_ ( _UpperCAmelCase ):
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
... | 671 | 0 |
import json
import os
import shutil
import tempfile
import unittest
from transformers import BatchEncoding, CanineTokenizer
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.tokenization_utils import AddedToken
from transformers.utils import cached_property
... | 21 |
import re
def A_ ( _UpperCAmelCase ):
return [char.split() for char in re.split(R"[^ a-z A-Z 0-9 \s]" , str_ )]
def A_ ( _UpperCAmelCase ):
SCREAMING_SNAKE_CASE_: int = split_input(str_ )
return "".join(
... | 671 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_snake_case : Optional[int] = {
'configuration_mobilebert': [
... | 22 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING
lowerCAmelCase : Union[str, Any] = logging.get_logger(__name__)
class __lowercase ( UpperCAmelCase_ ):
"""simple docstring"""
... | 671 | 0 |
from pathlib import Path
import cva
import numpy as np
from matplotlib import pyplot as plt
def _snake_case (__lowercase , __lowercase , __lowercase , __lowercase , __lowercase):
UpperCamelCase_ = cva.getAffineTransform(__lowercase , __low... | 23 |
import pickle
import unittest
import torch
from accelerate import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils import require_cpu
@require_cpu
class __lowercase ( unittest.TestCase ):
"""simple docstring"""
def _SCREAMING_SNAKE_CASE ... | 671 | 0 |
'''simple docstring'''
import math
from numpy import inf
from scipy.integrate import quad
def _UpperCamelCase (_lowerCamelCase : float )-> float:
'''simple docstring'''
if num <= 0:
raise ValueError('''math domain error''' )
return quad(_lowerCame... | 24 |
from itertools import count
def A_ ( _UpperCAmelCase = 50 ):
SCREAMING_SNAKE_CASE_: Union[str, Any] = [1] * min_block_length
for n in count(_UpperCAmelCase ):
fill_count_functions.append(1 )
for block_length in range(_UpperCAmelC... | 671 | 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
a_ ... | 25 |
def A_ ( _UpperCAmelCase ):
if not isinstance(_UpperCAmelCase , _UpperCAmelCase ):
raise TypeError("only integers accepted as input" )
else:
SCREAMING_SNAKE_CASE_: List[Any] = str(abs(_UpperCAmelCase ) )
... | 671 | 0 |
'''simple docstring'''
def _a ( _lowerCamelCase ) -> int:
"""simple docstring"""
__snake_case : List[str] = 0
while num > 0:
digit_sum += num % 10
num //= 10
return digit_sum
def _a ( ... | 26 |
from __future__ import annotations
from collections.abc import Iterator
from typing import Any
class __lowercase :
"""simple docstring"""
def __init__( self : List[str] , lowerCAmelCase__ : Any):
SCREAMING_SNAKE_CASE_: Any = data
SCREAMING_SN... | 671 | 0 |
import argparse
import requests
import torch
from PIL import Image
from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel
def __lowerCAmelCase( _SCREAMING_SNAKE_CASE ) -> Tuple:
"""simple docstring"""
if "img_encoder.pos_embed" i... | 27 |
from collections import defaultdict
from math import ceil, sqrt
def A_ ( _UpperCAmelCase = 1_00_00_00 , _UpperCAmelCase = 10 ):
SCREAMING_SNAKE_CASE_: defaultdict = defaultdict(_UpperCAmelCase )
for outer_width in range(3 , (t_limit // 4)... | 671 | 0 |
'''simple docstring'''
import argparse
import os
import torch
from transformers import FlavaConfig, FlavaForPreTraining
from transformers.models.flava.convert_dalle_to_flava_codebook import convert_dalle_checkpoint
def lowercase__( __UpperCamelCase: Any ):
"""simple... | 28 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
lowerCAmelCase : str = {
"""configuration_xlm""": ["""XLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """XLMConfig""", """XLMOnnxConfig"""],
"""tokenization_xlm""": [""... | 671 | 0 |
"""simple docstring"""
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import is_speech_available, is_vision_available
from transformers.testing_utils import require_torch
if is_vision_available():
from transformers import TvltImageProcessor
if is_speech_av... | 29 |
lowerCAmelCase : List[str] = {
"""A""": ["""B""", """C""", """E"""],
"""B""": ["""A""", """D""", """E"""],
"""C""": ["""A""", """F""", """G"""],
"""D""": ["""B"""],
"""E""": ["""A""", """B""", """D"""],
"""F""": ["""C"""],
"""G""": ["""C"""],
}
def A_ ( _Upp... | 671 | 0 |
import torch
from diffusers import DDIMParallelScheduler
from .test_schedulers import SchedulerCommonTest
class __a( _a ):
"""simple docstring"""
lowerCAmelCase = (DDIMParallelScheduler,)
lowerCAmelCase = (('''eta''', 0.0), ('''num_inference_steps''', 50))
de... | 30 |
from __future__ import annotations
from math import pi
from typing import Protocol
import matplotlib.pyplot as plt
import numpy as np
class __lowercase ( UpperCAmelCase_ ):
"""simple docstring"""
def _SCREAMING_SNAKE_CASE ( self : List[Any] , lowerCAmelCase__ ... | 671 | 0 |
import fire
from utils import calculate_rouge, save_json
def UpperCAmelCase_ ( __UpperCAmelCase : Dict , __UpperCAmelCase : List[Any] , __UpperCAmelCase : Tuple=None , **__UpperCAmelCase : List[str] ) -> Any:
SCREAMING_SNAKE_CASE_ = ... | 31 |
from __future__ import annotations
from math import ceil, floor, sqrt
def A_ ( _UpperCAmelCase = 2_00_00_00 ):
SCREAMING_SNAKE_CASE_: list[int] = [0]
SCREAMING_SNAKE_CASE_: int
for idx in range(1 , ceil(sqrt(target * 2 ) * 1.1 ) ... | 671 | 0 |
import os
import shutil
import tempfile
from unittest import TestCase
from unittest.mock import patch
import numpy as np
from datasets import Dataset
from transformers.models.realm.configuration_realm import RealmConfig
from transformers.models.realm.retrieval_realm import _REALM_BLOCK_RECORDS_FILENAME, RealmRe... | 32 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowerCAmelCase : Optional[int] = {
"""configuration_longformer""": [
"""LONGFORMER_PRETRAINED_CONFIG_ARC... | 671 | 0 |
def SCREAMING_SNAKE_CASE ( __lowerCAmelCase , __lowerCAmelCase ) -> str:
if a < 0 or b < 0:
raise ValueError('''the value of both inputs must be positive''' )
snake_case__ = str(bin(__lowerCAmelCase ) )[2:] # remove the leading "0b"
snake_case__ = str(bin(__l... | 33 |
import argparse
import os.path as osp
import re
import torch
from safetensors.torch import load_file, save_file
# =================#
# UNet Conversion #
# =================#
lowerCAmelCase : Optional[int] = [
# (stable-diffusion, HF Diffusers)
("""time_embed.0.weight""", """time_embedding.li... | 671 | 0 |
"""simple docstring"""
# Copyright 2021 The HuggingFace 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... | 34 |
from typing import Callable, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase : int = logging.get_logger(__name__)
lowerCAmelCase : Dict = {
"""microsoft/xprophetnet-large-wiki100-cased""": (
"""https://huggingf... | 671 | 0 |
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import numpy as np
import tensorflow as tf
from transformers import TFXLMRobertaModel
... | 35 |
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,
PILImageResampli... | 671 | 0 |
__lowercase : List[str] = '''
# Transformers installation
! pip install transformers datasets
# To install from source instead of the last release, comment the command above and uncomment the following one.
# ! pip install git+https://github.com/huggingface/transformers.git
'''
__lowercase : str ... | 36 |
import collections
from typing import List, Optional, Union
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert import BertTokenizer
lowerCAmelCase : Optional[int] = logging.get_logger(__... | 671 | 0 |
UpperCamelCase : Optional[int] = """Input must be a string of 8 numbers plus letter"""
UpperCamelCase : Tuple = """TRWAGMYFPDXBNJZSQVHLCKE"""
def UpperCamelCase_ ( __a ) -> bool:
if not isinstance(__a , __a ):
a__ : Dict = f'... | 37 |
from transformers import DistilBertTokenizer, DistilBertTokenizerFast
from transformers.testing_utils import require_tokenizers, slow
from ..bert.test_tokenization_bert import BertTokenizationTest
@require_tokenizers
class __lowercase ( UpperCAmelCase_ ):
"""simple docstring"""
... | 671 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
A_ : int = logging.get_logger(__name__)
A_ : Dict = {
"google/bit-50": "https:/... | 38 |
import collections
import json
import math
import os
import re
import time
from fnmatch import fnmatch
from typing import Dict
import requests
from slack_sdk import WebClient
lowerCAmelCase : List[Any] = WebClient(token=os.environ["""CI_SLACK_BOT_TOKEN"""])
def A_ ( _UpperCAmelCase ... | 671 | 0 |
from ..utils import DummyObject, requires_backends
class snake_case_ ( metaclass=__A ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Tuple = ["flax", "transformers"]
def __init__( self : Union[str, Any] , *_UpperCamelCase... | 39 |
import argparse
import os
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 Accelerator, Dis... | 671 | 0 |
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