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 |
|---|---|---|---|---|
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
lowercase__ : Any = {
'''configuration_ctrl''': ['''CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''CTRLConfig'''],
'''tokenizat... | 123 |
"""simple docstring"""
import argparse
import csv
import logging
import os
import random
import numpy as np
import torch
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
from tqdm import tqdm, trange
from transformers import (
CONFIG_NAME,
WEIGHTS_NAME,
AdamW,
... | 123 | 1 |
"""simple docstring"""
import argparse
import os
import re
lowercase__ : List[str] = '''src/transformers'''
# Pattern that looks at the indentation in a line.
lowercase__ : int = re.compile(r'''^(\s*)\S''')
# Pattern that matches `"key":" and puts `key` in group 0... | 123 |
"""simple docstring"""
import random
import unittest
import numpy as np
import transformers
from transformers import is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax
if is_flax_available():
import os
import jax.numpy as jnp
from ja... | 123 | 1 |
"""simple docstring"""
import json
import os
from pathlib import Path
import pytest
from datasets.download.download_config import DownloadConfig
from datasets.download.download_manager import DownloadManager
from datasets.utils.file_utils import hash_url_to_filename
lowercase__ : str = ... | 123 |
"""simple docstring"""
# 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 vocab first, and then a tiny model - so the outcome is truly tiny -
# all file... | 123 | 1 |
"""simple docstring"""
import gc
import tempfile
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device
lowercase__ : int = False
class _UpperC... | 123 |
"""simple docstring"""
import argparse
import os
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_task_guides.py
lowercase__ : Optional[int] = '''src/tr... | 123 | 1 |
"""simple docstring"""
from __future__ import annotations
def __lowercase ( _a , _a , _a , ):
if (electron_conc, hole_conc, intrinsic_conc).count(0 ) != 1:
raise ValueError('''You cannot supply more or less than 2 values''' )
elif electron_conc < 0:... | 123 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_torch_available,
is_vision_available,
)
lowercase__ : List[Any] = {'''configuration_beit''': ['''BEIT_PRETRAINED_CONFIG_AR... | 123 | 1 |
"""simple docstring"""
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_ava... | 123 |
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_lxmert import LxmertTokenizer
lowercase__ : Optional[Any] = {'''vocab_file''': '''vocab.txt'''... | 123 | 1 |
"""simple docstring"""
import os
import re
import shutil
import sys
import tempfile
import unittest
import black
lowercase__ : Optional[Any] = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, '''utils'''))
import ch... | 123 |
"""simple docstring"""
from __future__ import annotations
def __lowercase ( _a , _a , _a , ):
if (electron_conc, hole_conc, intrinsic_conc).count(0 ) != 1:
raise ValueError('''You cannot supply more or less than 2 values''' )
elif electron_conc < 0:... | 123 | 1 |
"""simple docstring"""
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
lowercase__ : str = logging.get_logger(__name__)
lowercase__ : str = {
'''ut/deta''': '''https://huggingface.... | 123 |
"""simple docstring"""
import os
import pytest
from datasets import (
get_dataset_config_info,
get_dataset_config_names,
get_dataset_infos,
get_dataset_split_names,
inspect_dataset,
inspect_metric,
)
lowercase__ : str = pytest.mark.integration
@pytest.mark.pa... | 123 | 1 |
"""simple docstring"""
import argparse
import json
import os
import time
import zipfile
from get_ci_error_statistics import download_artifact, get_artifacts_links
from transformers import logging
lowercase__ : Optional[int] = logging.get_logger(__name__)
def __lowercase ( _a ... | 123 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase__ : str = logging.get_logger(__name__)
lowercase__ : List[Any] = {
'''bigcode/gpt_bigcode-santacoder''': '''https://huggingface.co/bigcode/gpt_big... | 123 | 1 |
"""simple docstring"""
import argparse
import os
import numpy as np
import tensorflow as tf
import torch
from transformers import BertModel
def __lowercase ( _a , _a , _a ):
snake_case_ : Any = ('''dense.weight''', '''attention.self.query''', '''attention.sel... | 123 |
"""simple docstring"""
import pytest
import requests
from datasets.utils.file_utils import http_head
from .utils import OfflineSimulationMode, RequestWouldHangIndefinitelyError, offline
@pytest.mark.integration
def __lowercase ( ):
with offline(OfflineSimulationMode.CONNECTION_TIMES_OUT ):
... | 123 | 1 |
"""simple docstring"""
import contextlib
import copy
import random
from typing import Any, Dict, Iterable, Optional, Union
import numpy as np
import torch
from .utils import deprecate, is_transformers_available
if is_transformers_available():
import transformers
def __lowercase ( _a ):
... | 123 |
"""simple docstring"""
import itertools
import random
import unittest
import numpy as np
from transformers import is_speech_available
from transformers.testing_utils import require_torch, require_torchaudio
from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin
if is_speech_... | 123 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
lowercase__ : Optional[Any] = logging.get_logger(__name__)
lowercase__ : in... | 123 |
"""simple docstring"""
from math import sqrt
def __lowercase ( _a ):
assert isinstance(_a , _a ) and (
number >= 0
), "'number' must been an int and positive"
snake_case_ : List[str] = True
# 0 and 1 are none primes.
if number <= 1:
... | 123 | 1 |
"""simple docstring"""
import collections
import gzip
import os
import urllib
import numpy
from tensorflow.python.framework import dtypes, random_seed
from tensorflow.python.platform import gfile
from tensorflow.python.util.deprecation import deprecated
lowercase__ : str = collections.nam... | 123 |
"""simple docstring"""
import math
import os
import unittest
from transformers import MegatronBertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configurati... | 123 | 1 |
"""simple docstring"""
from typing import List, Union
import numpy as np
from ..tokenization_utils import TruncationStrategy
from ..utils import add_end_docstrings, logging
from .base import PIPELINE_INIT_ARGS, ArgumentHandler, ChunkPipeline
lowercase__ : Union[str, Any] = logging.get_l... | 123 |
"""simple docstring"""
import argparse
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 ... | 123 | 1 |
"""simple docstring"""
def __lowercase ( _a ):
return [
{
0: [1, 2],
1: [0, 2],
2: [0, 1, 3, 5],
3: [2, 4],
4: [3],
5: [2, 6, 8],
6: [5, 7],
7: [6, 8],
8: [5, 7],
},
... | 123 |
"""simple docstring"""
import coval # From: git+https://github.com/ns-moosavi/coval.git # noqa: F401
from coval.conll import reader, util
from coval.eval import evaluator
import datasets
lowercase__ : str = datasets.logging.get_logger(__name__)
lowercase__ : List[Any] ... | 123 | 1 |
"""simple docstring"""
import warnings
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__ : List[str] = logging.get_logge... | 123 |
"""simple docstring"""
from typing import Dict
from .base import GenericTensor, Pipeline
class _UpperCAmelCase ( lowerCAmelCase__):
def _snake_case ( self : int , lowercase_ : Optional[Any]=None , lowercase_ : List[str]=None , lowercase_ : Optional[Any]=None... | 123 | 1 |
"""simple docstring"""
from .configuration_bert_masked import MaskedBertConfig
from .modeling_bert_masked import (
MaskedBertForMultipleChoice,
MaskedBertForQuestionAnswering,
MaskedBertForSequenceClassification,
MaskedBertForTokenClassification,
MaskedBertModel,
)
from .modules import *
| 123 |
"""simple docstring"""
import numpy as np
from transformers import Pipeline
def __lowercase ( _a ):
snake_case_ : Any = np.max(_a , axis=-1 , keepdims=_a )
snake_case_ : Optional[int] = np.exp(outputs - maxes )
return shifted... | 123 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowercase__ : List[Any] = {
'''configuration_autoformer''': [
'''AUTOFORMER_PRETRAINED_CONFIG_ARCH... | 123 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast
from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandi... | 123 | 1 |
"""simple docstring"""
# limitations under the License.
# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works
from .pipelines import DiffusionPipeline, ImagePipelineOutput # noqa: F401
from .utils i... | 123 |
"""simple docstring"""
from collections import UserDict
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available()... | 123 | 1 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
EulerAncestralDiscreteScheduler,
LMSDiscreteScheduler,
... | 123 |
"""simple docstring"""
import unittest
from transformers import MraConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_atte... | 123 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
lowercase__ : str = {
'''configuration_poolformer''': [
'''POOLFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
''... | 123 |
"""simple docstring"""
import unittest
import numpy as np
from transformers import RoFormerConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import ja... | 123 | 1 |
"""simple docstring"""
from collections import Counter
from pathlib import Path
from typing import Optional, Tuple
import yaml
class _UpperCAmelCase ( yaml.SafeLoader):
def _snake_case ( self : List[Any] , lowercase_ : Any ):
snake_case_ : Optional[int] = ... | 123 |
"""simple docstring"""
from typing import List, Optional, Union
import numpy as np
from ....audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ....feature_extraction_sequence_utils import SequenceFeatureExtractor
from ....feature_extraction_utils import BatchFeature
from ..... | 123 | 1 |
"""simple docstring"""
from dataclasses import dataclass
from typing import Optional
import torch
from torch import nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .modeling_utils import ModelMixin
@dataclas... | 123 |
"""simple docstring"""
import argparse
import csv
import logging
import os
import random
import numpy as np
import torch
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
from tqdm import tqdm, trange
from transformers import (
CONFIG_NAME,
WEIGHTS_NAME,
AdamW,
... | 123 | 1 |
"""simple docstring"""
import unittest
from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
@require... | 123 |
"""simple docstring"""
import random
import unittest
import numpy as np
import transformers
from transformers import is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax
if is_flax_available():
import os
import jax.numpy as jnp
from ja... | 123 | 1 |
"""simple docstring"""
import argparse
import json
import os
from tensorflow.core.protobuf.saved_model_pba import SavedModel
# 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
lowercase__ : Optional[int] = ... | 123 |
"""simple docstring"""
# 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 vocab first, and then a tiny model - so the outcome is truly tiny -
# all file... | 123 | 1 |
"""simple docstring"""
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 (
SwiftFormerConfig,
SwiftFormerForImageClassification,
ViTImageProcessor,
)
from transformers.utils impo... | 123 |
"""simple docstring"""
import argparse
import os
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_task_guides.py
lowercase__ : Optional[int] = '''src/tr... | 123 | 1 |
"""simple docstring"""
import argparse
from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging
logging.set_verbosity_info()
def __lowercase ( _a , _a , _a ):
# Initialise PyTorch model
snake_case_ ... | 123 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_torch_available,
is_vision_available,
)
lowercase__ : List[Any] = {'''configuration_beit''': ['''BEIT_PRETRAINED_CONFIG_AR... | 123 | 1 |
"""simple docstring"""
import random
import unittest
from torch.utils.data import BatchSampler, DataLoader, IterableDataset
from accelerate import Accelerator
from accelerate.data_loader import (
BatchSamplerShard,
DataLoaderDispatcher,
DataLoaderShard,
IterableDatasetShard,
SkipBatchSampler,... | 123 |
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_lxmert import LxmertTokenizer
lowercase__ : Optional[Any] = {'''vocab_file''': '''vocab.txt'''... | 123 | 1 |
"""simple docstring"""
from __future__ import annotations
def __lowercase ( _a , _a ):
if len(_a ) == 0:
return False
snake_case_ : Optional[Any] = len(_a ) // 2
if a_list[midpoint] == item:
return True
if item < a_list[midpoint]:... | 123 |
"""simple docstring"""
from __future__ import annotations
def __lowercase ( _a , _a , _a , ):
if (electron_conc, hole_conc, intrinsic_conc).count(0 ) != 1:
raise ValueError('''You cannot supply more or less than 2 values''' )
elif electron_conc < 0:... | 123 | 1 |
"""simple docstring"""
import unittest
from diffusers import FlaxAutoencoderKL
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax
from .test_modeling_common_flax import FlaxModelTesterMixin
if is_flax_available():
import jax
@require_flax
class _Upp... | 123 |
"""simple docstring"""
import os
import pytest
from datasets import (
get_dataset_config_info,
get_dataset_config_names,
get_dataset_infos,
get_dataset_split_names,
inspect_dataset,
inspect_metric,
)
lowercase__ : str = pytest.mark.integration
@pytest.mark.pa... | 123 | 1 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class _UpperCAmelCase ( metaclass=lowerCAmelCase__):
_lowerCAmelCase : Union[str, Any] = ["""note_seq"""]
def __init__( self : Union[str, Any] , *lowercase_ : int , **lowercase_ : ... | 123 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase__ : str = logging.get_logger(__name__)
lowercase__ : List[Any] = {
'''bigcode/gpt_bigcode-santacoder''': '''https://huggingface.co/bigcode/gpt_big... | 123 | 1 |
"""simple docstring"""
import html
from ...feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from ...utils import is_bsa_available, logging, requires_backends
if is_bsa_available():
import bsa
from bsa import BeautifulSoup
lowercase__ : Dict = logging.get_l... | 123 |
"""simple docstring"""
import pytest
import requests
from datasets.utils.file_utils import http_head
from .utils import OfflineSimulationMode, RequestWouldHangIndefinitelyError, offline
@pytest.mark.integration
def __lowercase ( ):
with offline(OfflineSimulationMode.CONNECTION_TIMES_OUT ):
... | 123 | 1 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, XLMRobertaTokenizer
from diffusers import AltDiffusionPipeline, AutoencoderKL, DDIMScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.alt_diffusion.modelin... | 123 |
"""simple docstring"""
import itertools
import random
import unittest
import numpy as np
from transformers import is_speech_available
from transformers.testing_utils import require_torch, require_torchaudio
from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin
if is_speech_... | 123 | 1 |
"""simple docstring"""
import os
import tempfile
import unittest
from transformers import FlaubertConfig, is_torch_available
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelT... | 123 |
"""simple docstring"""
from math import sqrt
def __lowercase ( _a ):
assert isinstance(_a , _a ) and (
number >= 0
), "'number' must been an int and positive"
snake_case_ : List[str] = True
# 0 and 1 are none primes.
if number <= 1:
... | 123 | 1 |
"""simple docstring"""
from .testing import (
are_the_same_tensors,
execute_subprocess_async,
require_bnb,
require_cpu,
require_cuda,
require_huggingface_suite,
require_mps,
require_multi_gpu,
require_multi_xpu,
require_safetensors,
require_single_gpu,
require_single... | 123 |
"""simple docstring"""
import math
import os
import unittest
from transformers import MegatronBertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configurati... | 123 | 1 |
"""simple docstring"""
from unittest import TestCase
from datasets import Dataset
from minhash_deduplication import deduplicate_dataset, make_duplicate_clusters
def __lowercase ( ):
snake_case_ : List[str] = {
'''repo_name''': ['''test_repo1''', '''test_repo2''', '''test_repo... | 123 |
"""simple docstring"""
import argparse
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 ... | 123 | 1 |
"""simple docstring"""
from sklearn.metrics import fa_score
import datasets
lowercase__ : Tuple = '''
The F1 score is the harmonic mean of the precision and recall. It can be computed with the equation:
F1 = 2 * (precision * recall) / (precision + recall)
'''
lowercase__ : Tup... | 123 |
"""simple docstring"""
import coval # From: git+https://github.com/ns-moosavi/coval.git # noqa: F401
from coval.conll import reader, util
from coval.eval import evaluator
import datasets
lowercase__ : str = datasets.logging.get_logger(__name__)
lowercase__ : List[Any] ... | 123 | 1 |
"""simple docstring"""
from collections import OrderedDict
from ...utils import logging
from .auto_factory import _BaseAutoModelClass, _LazyAutoMapping, auto_class_update
from .configuration_auto import CONFIG_MAPPING_NAMES
lowercase__ : Tuple = logging.get_logger(__name__)
lowercase__... | 123 |
"""simple docstring"""
from typing import Dict
from .base import GenericTensor, Pipeline
class _UpperCAmelCase ( lowerCAmelCase__):
def _snake_case ( self : int , lowercase_ : Optional[Any]=None , lowercase_ : List[str]=None , lowercase_ : Optional[Any]=None... | 123 | 1 |
"""simple docstring"""
def __lowercase ( _a , _a ):
return "\n".join(
f"{number} * {i} = {number * i}" for i in range(1 , number_of_terms + 1 ) )
if __name__ == "__main__":
print(multiplication_table(number=5, number_of_terms=10))
| 123 |
"""simple docstring"""
import numpy as np
from transformers import Pipeline
def __lowercase ( _a ):
snake_case_ : Any = np.max(_a , axis=-1 , keepdims=_a )
snake_case_ : Optional[int] = np.exp(outputs - maxes )
return shifted... | 123 | 1 |
"""simple docstring"""
import copy
import os
from typing import TYPE_CHECKING, List, Union
if TYPE_CHECKING:
pass
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase__ : List[str] = logging.get_logger(__name__)
lowercase__ : str ... | 123 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast
from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandi... | 123 | 1 |
"""simple docstring"""
from __future__ import annotations
from typing import Any
def __lowercase ( _a ):
if not postfix_notation:
return 0
snake_case_ : str = {'''+''', '''-''', '''*''', '''/'''}
snake_case_ : list[Any] = []
for token in pos... | 123 |
"""simple docstring"""
from collections import UserDict
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available()... | 123 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
lowercase__ : Dict = {'''configuration_unispeech''': ['''UNISPEECH_PRETRAINED_CONFIG_A... | 123 |
"""simple docstring"""
import unittest
from transformers import MraConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_atte... | 123 | 1 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowercase__ : Any = logging.get_logger(__name__)
lowercase__ : str = ... | 123 |
"""simple docstring"""
import unittest
import numpy as np
from transformers import RoFormerConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import ja... | 123 | 1 |
"""simple docstring"""
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
WavaVecaConformerConfig,
WavaVecaConformerForCTC,
WavaVecaConformerForPreTraining,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVe... | 123 |
"""simple docstring"""
from typing import List, Optional, Union
import numpy as np
from ....audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ....feature_extraction_sequence_utils import SequenceFeatureExtractor
from ....feature_extraction_utils import BatchFeature
from ..... | 123 | 1 |
"""simple docstring"""
import collections
import json
import os
import re
from typing import TYPE_CHECKING, List, Optional, Tuple
import numpy as np
from ...tokenization_utils_fast import PreTrainedTokenizer
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational import C... | 123 |
"""simple docstring"""
import argparse
import csv
import logging
import os
import random
import numpy as np
import torch
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
from tqdm import tqdm, trange
from transformers import (
CONFIG_NAME,
WEIGHTS_NAME,
AdamW,
... | 123 | 1 |
"""simple docstring"""
def __lowercase ( _a ):
if isinstance(_a , _a ):
raise TypeError('''\'float\' object cannot be interpreted as an integer''' )
if isinstance(_a , _a ):
raise TypeError('''\'str\' object cannot be interpreted as an integ... | 123 |
"""simple docstring"""
import random
import unittest
import numpy as np
import transformers
from transformers import is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax
if is_flax_available():
import os
import jax.numpy as jnp
from ja... | 123 | 1 |
"""simple docstring"""
import pickle
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, XGLMTokenizer, XGLMTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property
from ... | 123 |
"""simple docstring"""
# 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 vocab first, and then a tiny model - so the outcome is truly tiny -
# all file... | 123 | 1 |
"""simple docstring"""
from typing import Dict
from .base import GenericTensor, Pipeline
class _UpperCAmelCase ( lowerCAmelCase__):
def _snake_case ( self : int , lowercase_ : Optional[Any]=None , lowercase_ : List[str]=None , lowercase_ : Optional[Any]=None... | 123 |
"""simple docstring"""
import argparse
import os
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_task_guides.py
lowercase__ : Optional[int] = '''src/tr... | 123 | 1 |
"""simple docstring"""
import argparse
import json
import os
import pickle
import shutil
import numpy as np
import torch
from distiller import Distiller
from lm_seqs_dataset import LmSeqsDataset
from transformers import (
BertConfig,
BertForMaskedLM,
BertTokenizer,
DistilBertConfig,
DistilBer... | 123 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_torch_available,
is_vision_available,
)
lowercase__ : List[Any] = {'''configuration_beit''': ['''BEIT_PRETRAINED_CONFIG_AR... | 123 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available, is_vision_available
from ...utils import OptionalDependencyNotAvailable
lowercase__ : Optional[int] = {'''configuration_dpt''': ['''DPT_PRETRAINED_C... | 123 |
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_lxmert import LxmertTokenizer
lowercase__ : Optional[Any] = {'''vocab_file''': '''vocab.txt'''... | 123 | 1 |
"""simple docstring"""
import os
from glob import glob
import imageio
import torch
import torchvision
import wandb
from img_processing import custom_to_pil, loop_post_process, preprocess, preprocess_vqgan
from loaders import load_vqgan
from PIL import Image
from torch import nn
from transformers import CLIPModel... | 123 |
"""simple docstring"""
from __future__ import annotations
def __lowercase ( _a , _a , _a , ):
if (electron_conc, hole_conc, intrinsic_conc).count(0 ) != 1:
raise ValueError('''You cannot supply more or less than 2 values''' )
elif electron_conc < 0:... | 123 | 1 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class _UpperCAmelCase ( metaclass=lowerCAmelCase__):
_lowerCAmelCase : Optional[int] = ["""torch""", """torchsde"""]
def __init__( self : Any , *lowercase_ : Union[str, Any] , **lowercase... | 123 |
"""simple docstring"""
import os
import pytest
from datasets import (
get_dataset_config_info,
get_dataset_config_names,
get_dataset_infos,
get_dataset_split_names,
inspect_dataset,
inspect_metric,
)
lowercase__ : str = pytest.mark.integration
@pytest.mark.pa... | 123 | 1 |
"""simple docstring"""
import argparse
import csv
import logging
import os
import random
import numpy as np
import torch
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
from tqdm import tqdm, trange
from transformers import (
CONFIG_NAME,
WEIGHTS_NAME,
AdamW,
... | 123 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase__ : str = logging.get_logger(__name__)
lowercase__ : List[Any] = {
'''bigcode/gpt_bigcode-santacoder''': '''https://huggingface.co/bigcode/gpt_big... | 123 | 1 |
"""simple docstring"""
lowercase__ : List[str] = '''Alexander Joslin'''
import operator as op
from .stack import Stack
def __lowercase ( _a ):
snake_case_ : str = {'''*''': op.mul, '''/''': op.truediv, '''+''': op.add, '''-''': op.sub}
snake_case_ ... | 123 |
"""simple docstring"""
import pytest
import requests
from datasets.utils.file_utils import http_head
from .utils import OfflineSimulationMode, RequestWouldHangIndefinitelyError, offline
@pytest.mark.integration
def __lowercase ( ):
with offline(OfflineSimulationMode.CONNECTION_TIMES_OUT ):
... | 123 | 1 |
"""simple docstring"""
from math import log
from scipy.constants import Boltzmann, physical_constants
lowercase__ : List[Any] = 3_00 # TEMPERATURE (unit = K)
def __lowercase ( _a , _a , _a , ):
if donor_conc <= 0:
raise ValueError('''Don... | 123 |
"""simple docstring"""
import itertools
import random
import unittest
import numpy as np
from transformers import is_speech_available
from transformers.testing_utils import require_torch, require_torchaudio
from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin
if is_speech_... | 123 | 1 |
"""simple docstring"""
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_t... | 123 |
"""simple docstring"""
from math import sqrt
def __lowercase ( _a ):
assert isinstance(_a , _a ) and (
number >= 0
), "'number' must been an int and positive"
snake_case_ : List[str] = True
# 0 and 1 are none primes.
if number <= 1:
... | 123 | 1 |
"""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
fro... | 123 |
"""simple docstring"""
import math
import os
import unittest
from transformers import MegatronBertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configurati... | 123 | 1 |
"""simple docstring"""
import gc
import threading
import time
import psutil
import torch
class _UpperCAmelCase :
def __init__( self : List[str] ):
snake_case_ : List[Any] = psutil.Process()
snake_case_ : int = False
def _snake_case ( self ... | 123 |
"""simple docstring"""
import argparse
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 ... | 123 | 1 |
"""simple docstring"""
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...file_utils import TensorType, is_torch_available
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfi... | 123 |
"""simple docstring"""
import coval # From: git+https://github.com/ns-moosavi/coval.git # noqa: F401
from coval.conll import reader, util
from coval.eval import evaluator
import datasets
lowercase__ : str = datasets.logging.get_logger(__name__)
lowercase__ : List[Any] ... | 123 | 1 |
"""simple docstring"""
def __lowercase ( _a ):
snake_case_ : int = 0
while num > 0:
digit_sum += num % 10
num //= 10
return digit_sum
def __lowercase ( _a = 100 ):
snake_case_ : Any = 1
snake_case_ : Optional[Any] ... | 123 |
"""simple docstring"""
from typing import Dict
from .base import GenericTensor, Pipeline
class _UpperCAmelCase ( lowerCAmelCase__):
def _snake_case ( self : int , lowercase_ : Optional[Any]=None , lowercase_ : List[str]=None , lowercase_ : Optional[Any]=None... | 123 | 1 |
"""simple docstring"""
import unittest
from transformers import is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import... | 123 |
"""simple docstring"""
import numpy as np
from transformers import Pipeline
def __lowercase ( _a ):
snake_case_ : Any = np.max(_a , axis=-1 , keepdims=_a )
snake_case_ : Optional[int] = np.exp(outputs - maxes )
return shifted... | 123 | 1 |
"""simple docstring"""
import argparse
import torch
# Step 1. clone https://github.com/microsoft/unilm
# Step 2. git checkout to https://github.com/microsoft/unilm/commit/b94ec76c36f02fb2b0bf0dcb0b8554a2185173cd
# Step 3. cd unilm
# Step 4. ln -s $(realpath wavlm/modules.py) ./ # create simlink
# import classes... | 123 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast
from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandi... | 123 | 1 |
"""simple docstring"""
import contextlib
import importlib
import io
import unittest
import transformers
# Try to import everything from transformers to ensure every object can be loaded.
from transformers import * # noqa F406
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, require_flax, require... | 123 |
"""simple docstring"""
from collections import UserDict
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available()... | 123 | 1 |
"""simple docstring"""
import pandas as pd
from matplotlib import pyplot as plt
from sklearn.linear_model import LinearRegression
# Splitting the dataset into the Training set and Test set
from sklearn.model_selection import train_test_split
# Fitting Polynomial Regression to the dataset
from sklearn.preprocessi... | 123 |
"""simple docstring"""
import unittest
from transformers import MraConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_atte... | 123 | 1 |
"""simple docstring"""
from __future__ import annotations
from functools import lru_cache
from math import ceil
lowercase__ : List[Any] = 1_00
lowercase__ : Optional[int] = set(range(3, NUM_PRIMES, 2))
primes.add(2)
lowercase__ : int
for prime in range(3... | 123 |
"""simple docstring"""
import unittest
import numpy as np
from transformers import RoFormerConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import ja... | 123 | 1 |
"""simple docstring"""
import math
import os
import unittest
from transformers import MegatronBertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configurati... | 123 |
"""simple docstring"""
from typing import List, Optional, Union
import numpy as np
from ....audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ....feature_extraction_sequence_utils import SequenceFeatureExtractor
from ....feature_extraction_utils import BatchFeature
from ..... | 123 | 1 |
"""simple docstring"""
import inspect
import unittest
import numpy as np
from transformers import ViTConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_ten... | 123 |
"""simple docstring"""
import argparse
import csv
import logging
import os
import random
import numpy as np
import torch
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
from tqdm import tqdm, trange
from transformers import (
CONFIG_NAME,
WEIGHTS_NAME,
AdamW,
... | 123 | 1 |
"""simple docstring"""
from __future__ import annotations
from fractions import Fraction
from math import gcd, sqrt
def __lowercase ( _a ):
snake_case_ : int = int(number**0.5 )
return number == sq * sq
def __lowercase ( _a , _a , _a ... | 123 |
"""simple docstring"""
import random
import unittest
import numpy as np
import transformers
from transformers import is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax
if is_flax_available():
import os
import jax.numpy as jnp
from ja... | 123 | 1 |
"""simple docstring"""
import importlib
import os
import sys
# This is required to make the module import works (when the python process is running from the root of the repo)
sys.path.append('''.''')
def __lowercase ( _a ):
snake_case_ : Dict = test_file.split(os.path.sep ... | 123 |
"""simple docstring"""
# 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 vocab first, and then a tiny model - so the outcome is truly tiny -
# all file... | 123 | 1 |
"""simple docstring"""
def __lowercase ( _a , _a ):
return 1 if input_a == input_a else 0
def __lowercase ( ):
assert xnor_gate(0 , 0 ) == 1
assert xnor_gate(0 , 1 ) == 0
assert xnor_gate(1 , 0 ) == 0
assert xnor_gate... | 123 |
"""simple docstring"""
import argparse
import os
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_task_guides.py
lowercase__ : Optional[int] = '''src/tr... | 123 | 1 |
"""simple docstring"""
from copy import deepcopy
from typing import Optional, Union
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, is_tf_available, is_torch_available
if is_torch_available():
import to... | 123 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_torch_available,
is_vision_available,
)
lowercase__ : List[Any] = {'''configuration_beit''': ['''BEIT_PRETRAINED_CONFIG_AR... | 123 | 1 |
"""simple docstring"""
import unittest
from transformers import MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING, is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
... | 123 |
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_lxmert import LxmertTokenizer
lowercase__ : Optional[Any] = {'''vocab_file''': '''vocab.txt'''... | 123 | 1 |
"""simple docstring"""
from __future__ import annotations
class _UpperCAmelCase :
def __init__( self : Any , lowercase_ : str , lowercase_ : str ):
snake_case_, snake_case_ : Tuple = text, pattern
snake_case_, snake_case_ : Tuple ... | 123 |
"""simple docstring"""
from __future__ import annotations
def __lowercase ( _a , _a , _a , ):
if (electron_conc, hole_conc, intrinsic_conc).count(0 ) != 1:
raise ValueError('''You cannot supply more or less than 2 values''' )
elif electron_conc < 0:... | 123 | 1 |
"""simple docstring"""
import warnings
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase__ : List[str] = logging.get_logger(__name__)
lowercase__ : List[Any] = {
'''xlnet-base-cased''': '''https://huggingface.co/xlnet-... | 123 |
"""simple docstring"""
import os
import pytest
from datasets import (
get_dataset_config_info,
get_dataset_config_names,
get_dataset_infos,
get_dataset_split_names,
inspect_dataset,
inspect_metric,
)
lowercase__ : str = pytest.mark.integration
@pytest.mark.pa... | 123 | 1 |
"""simple docstring"""
import math_equivalence # From: git+https://github.com/hendrycks/math.git
import datasets
lowercase__ : Dict = '''\
@article{hendrycksmath2021,
title={Measuring Mathematical Problem Solving With the MATH Dataset},
author={Dan Hendrycks
and Collin Burns
... | 123 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase__ : str = logging.get_logger(__name__)
lowercase__ : List[Any] = {
'''bigcode/gpt_bigcode-santacoder''': '''https://huggingface.co/bigcode/gpt_big... | 123 | 1 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModel,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import AutoencoderKL, DDIMScheduler... | 123 |
"""simple docstring"""
import pytest
import requests
from datasets.utils.file_utils import http_head
from .utils import OfflineSimulationMode, RequestWouldHangIndefinitelyError, offline
@pytest.mark.integration
def __lowercase ( ):
with offline(OfflineSimulationMode.CONNECTION_TIMES_OUT ):
... | 123 | 1 |
"""simple docstring"""
import numpy as np
from cva import COLOR_BGR2GRAY, CV_8UC3, cvtColor, filteraD, imread, imshow, waitKey
def __lowercase ( _a , _a , _a , _a , _a , _a ):
# prepare kernel
# the kernel size have to be odd
if (ksize % 2)... | 123 |
"""simple docstring"""
import itertools
import random
import unittest
import numpy as np
from transformers import is_speech_available
from transformers.testing_utils import require_torch, require_torchaudio
from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin
if is_speech_... | 123 | 1 |
"""simple docstring"""
from binascii import hexlify
from hashlib import shaaaa
from os import urandom
# RFC 3526 - More Modular Exponential (MODP) Diffie-Hellman groups for
# Internet Key Exchange (IKE) https://tools.ietf.org/html/rfc3526
lowercase__ : Union[str, Any] = {
# 1536-bit
... | 123 |
"""simple docstring"""
from math import sqrt
def __lowercase ( _a ):
assert isinstance(_a , _a ) and (
number >= 0
), "'number' must been an int and positive"
snake_case_ : List[str] = True
# 0 and 1 are none primes.
if number <= 1:
... | 123 | 1 |
"""simple docstring"""
from __future__ import annotations
from decimal import Decimal
from math import * # noqa: F403
from sympy import diff
def __lowercase ( _a , _a , _a = 10**-10 ):
snake_case_ : Optional[Any] = a
while True:
snake_case_ :... | 123 |
"""simple docstring"""
import math
import os
import unittest
from transformers import MegatronBertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configurati... | 123 | 1 |
"""simple docstring"""
import warnings
from typing import List, Optional, Union
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorTy... | 123 |
"""simple docstring"""
import argparse
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 ... | 123 | 1 |
"""simple docstring"""
import json
import os
import sys
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile
from huggingface_hub import HfFolder, Repository, create_repo, delete_repo
from requests.exceptions import HTTPError
import transformers
from transformers import (
CONF... | 123 |
"""simple docstring"""
import coval # From: git+https://github.com/ns-moosavi/coval.git # noqa: F401
from coval.conll import reader, util
from coval.eval import evaluator
import datasets
lowercase__ : str = datasets.logging.get_logger(__name__)
lowercase__ : List[Any] ... | 123 | 1 |
"""simple docstring"""
import math
import random
from typing import Any
from .hill_climbing import SearchProblem
def __lowercase ( _a , _a = True , _a = math.inf , _a = -math.inf , _a = math.inf , _a = -math.inf , _a = False , _a = 100 ... | 123 |
"""simple docstring"""
from typing import Dict
from .base import GenericTensor, Pipeline
class _UpperCAmelCase ( lowerCAmelCase__):
def _snake_case ( self : int , lowercase_ : Optional[Any]=None , lowercase_ : List[str]=None , lowercase_ : Optional[Any]=None... | 123 | 1 |
"""simple docstring"""
import colorsys
from PIL import Image # type: ignore
def __lowercase ( _a , _a , _a ):
snake_case_ : Optional[int] = x
snake_case_ : List[Any] = y
for step in range(_a ): # noqa: B007
snake_case_ ... | 123 |
"""simple docstring"""
import numpy as np
from transformers import Pipeline
def __lowercase ( _a ):
snake_case_ : Any = np.max(_a , axis=-1 , keepdims=_a )
snake_case_ : Optional[int] = np.exp(outputs - maxes )
return shifted... | 123 | 1 |
"""simple docstring"""
def __lowercase ( _a , _a ):
snake_case_ : Optional[int] = 1 # To kept the Calculated Value
# Since C(n, k) = C(n, n-k)
if k > (n - k):
snake_case_ : Dict = n - k
# Calculate C(n,k)
for i in range(_a ):
... | 123 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast
from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandi... | 123 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase__ : str = logging.get_logger(__name__)
lowercase__ : List[Any] = {
'''bigcode/gpt_bigcode-santacoder''': '''https://huggingface.co/bigcode/gpt_big... | 123 |
"""simple docstring"""
from collections import UserDict
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available()... | 123 | 1 |
"""simple docstring"""
from collections import Counter
import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split
lowercase__ : int = datasets.load_iris()
lowercase__ : Tuple = np.array(data['''data'''])
lowercase__ ... | 123 |
"""simple docstring"""
import unittest
from transformers import MraConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_atte... | 123 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase__ : Optional[Any] = logging.get_logger(__name__)
lowercase__ : Union[str, Any] = {
'''facebook/vit-mae-base''': '''https://huggingface.co/facebook... | 123 |
"""simple docstring"""
import unittest
import numpy as np
from transformers import RoFormerConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import ja... | 123 | 1 |
"""simple docstring"""
import argparse
import os
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_task_guides.py
lowercase__ : Optional[int] = '''src/tr... | 123 |
"""simple docstring"""
from typing import List, Optional, Union
import numpy as np
from ....audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ....feature_extraction_sequence_utils import SequenceFeatureExtractor
from ....feature_extraction_utils import BatchFeature
from ..... | 123 | 1 |
"""simple docstring"""
import random
class _UpperCAmelCase :
@staticmethod
def _snake_case ( lowercase_ : str ):
snake_case_ : str = [ord(lowercase_ ) for i in text]
snake_case_ : Tuple = []
snake_case_ : Optional[int] = ... | 123 |
"""simple docstring"""
import argparse
import csv
import logging
import os
import random
import numpy as np
import torch
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
from tqdm import tqdm, trange
from transformers import (
CONFIG_NAME,
WEIGHTS_NAME,
AdamW,
... | 123 | 1 |
"""simple docstring"""
import argparse
import requests
import torch
from PIL import Image
from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel
def __lowercase ( _a ):
# vision encoder
if "img_encoder.pos_embed" in name:
snake_case_ : Dict = name.re... | 123 |
"""simple docstring"""
import random
import unittest
import numpy as np
import transformers
from transformers import is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax
if is_flax_available():
import os
import jax.numpy as jnp
from ja... | 123 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
lowercase__ : str = {
'''configuration_llama''': ['''LLAMA_PRETRA... | 123 |
"""simple docstring"""
# 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 vocab first, and then a tiny model - so the outcome is truly tiny -
# all file... | 123 | 1 |
"""simple docstring"""
from google.protobuf import descriptor as _descriptor
from google.protobuf import descriptor_pool as _descriptor_pool
from google.protobuf import symbol_database as _symbol_database
from google.protobuf.internal import builder as _builder
# @@protoc_insertion_point(imports)
lowercase__ ... | 123 |
"""simple docstring"""
import argparse
import os
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_task_guides.py
lowercase__ : Optional[int] = '''src/tr... | 123 | 1 |
"""simple docstring"""
import logging
import numpy as np
import pytest
from scipy.linalg import eigh
logging.basicConfig(level=logging.INFO, format='''%(message)s''')
def __lowercase ( _a ):
return input_array.reshape((input_array.size, 1) )
def __lowercase ( _a , _... | 123 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_torch_available,
is_vision_available,
)
lowercase__ : List[Any] = {'''configuration_beit''': ['''BEIT_PRETRAINED_CONFIG_AR... | 123 | 1 |
"""simple docstring"""
import argparse
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 ... | 123 |
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_lxmert import LxmertTokenizer
lowercase__ : Optional[Any] = {'''vocab_file''': '''vocab.txt'''... | 123 | 1 |
"""simple docstring"""
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,
AutoModelForSeqa... | 123 |
"""simple docstring"""
from __future__ import annotations
def __lowercase ( _a , _a , _a , ):
if (electron_conc, hole_conc, intrinsic_conc).count(0 ) != 1:
raise ValueError('''You cannot supply more or less than 2 values''' )
elif electron_conc < 0:... | 123 | 1 |
"""simple docstring"""
def __lowercase ( _a = 10 , _a = 1_000 , _a = True ):
assert (
isinstance(_a , _a )
and isinstance(_a , _a )
and isinstance(_a , _a )
), "Invalid type of value(s) specified to function... | 123 |
"""simple docstring"""
import os
import pytest
from datasets import (
get_dataset_config_info,
get_dataset_config_names,
get_dataset_infos,
get_dataset_split_names,
inspect_dataset,
inspect_metric,
)
lowercase__ : str = pytest.mark.integration
@pytest.mark.pa... | 123 | 1 |
"""simple docstring"""
import unittest
from transformers import MraConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_atte... | 123 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase__ : str = logging.get_logger(__name__)
lowercase__ : List[Any] = {
'''bigcode/gpt_bigcode-santacoder''': '''https://huggingface.co/bigcode/gpt_big... | 123 | 1 |
"""simple docstring"""
import tempfile
import torch
from diffusers import (
DEISMultistepScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
UniPCMultistepScheduler,
)
from .test_schedulers import SchedulerCommonTest
class _UpperCAmelCase ( lowerCAmelCase__):
_low... | 123 |
"""simple docstring"""
import pytest
import requests
from datasets.utils.file_utils import http_head
from .utils import OfflineSimulationMode, RequestWouldHangIndefinitelyError, offline
@pytest.mark.integration
def __lowercase ( ):
with offline(OfflineSimulationMode.CONNECTION_TIMES_OUT ):
... | 123 | 1 |
"""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
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_... | 123 |
"""simple docstring"""
import itertools
import random
import unittest
import numpy as np
from transformers import is_speech_available
from transformers.testing_utils import require_torch, require_torchaudio
from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin
if is_speech_... | 123 | 1 |
"""simple docstring"""
from math import sqrt
def __lowercase ( _a ):
snake_case_ : List[str] = 0
for i in range(1 , int(sqrt(_a ) + 1 ) ):
if n % i == 0 and i != sqrt(_a ):
total += i + n // i
elif i == sqrt(_a ... | 123 |
"""simple docstring"""
from math import sqrt
def __lowercase ( _a ):
assert isinstance(_a , _a ) and (
number >= 0
), "'number' must been an int and positive"
snake_case_ : List[str] = True
# 0 and 1 are none primes.
if number <= 1:
... | 123 | 1 |
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