code stringlengths 82 53.2k | code_codestyle int64 0 721 | style_context stringlengths 91 41.9k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
'''simple docstring'''
import unittest
from pathlib import Path
from shutil import copyfile
from transformers import SPIECE_UNDERLINE, is_sentencepiece_available
from transformers.models.speech_to_text import SpeechaTextTokenizer
from transformers.models.speech_to_text.tokenization_speech_to_text import VOCAB_FILES_... | 48 |
def __lowerCamelCase ( UpperCAmelCase_ : int , UpperCAmelCase_ : int ):
"""simple docstring"""
while b:
a , a :int = b, a % b
return a
def __lowerCamelCase ( UpperCAmelCase_ : int , UpperCAmelCase_ : ... | 445 | 0 |
"""simple docstring"""
from __future__ import annotations
from math import gcd
def __A ( a_ : int , a_ : int = 2 , a_ : int = 1 , a_ : int = 3 , )-> int | None:
'''simple docstring'''
if num < 2:
raise ValueError('''The input value cannot be le... | 709 |
"""simple docstring"""
import copy
import unittest
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTest... | 18 | 0 |
import unittest
from accelerate import debug_launcher
from accelerate.test_utils import require_cpu, test_ops, test_script
@require_cpu
class UpperCamelCase__ (unittest.TestCase ):
'''simple docstring'''
def _lowercase ( self ) -> str:
debug_... | 311 |
import unittest
import numpy as np
from transformers import DistilBertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax.numpy a... | 352 | 0 |
import math
import tensorflow as tf
from packaging import version
def _SCREAMING_SNAKE_CASE ( _lowerCamelCase : Optional[int]) -> Dict:
'''simple docstring'''
__UpperCamelCase : int = tf.convert_to_tensor(_lowerCamelC... | 705 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowercase : Optional[Any] = {
'configuration_blip_2': [
'BLIP_2_PRETRAINED_CONFIG_ARCHIVE_MAP',
'Blip2Config',
'Blip2QFormerConfig',
... | 94 | 0 |
from math import isclose, sqrt
def _lowerCamelCase ( snake_case , snake_case , snake_case ):
_lowerCAmelCase = point_y / 4 / point_x
_lowerCAmelCase = 2 * normal_gradient / (1 + normal_gradient * normal_gradient)
_lowerCAmelCase = (1 - normal_gra... | 192 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase_ = logging.get_logger(__name__)
UpperCAmelCase_ = {
"tiiuae/falcon-40b": "https://huggingface.co/tiiuae/falcon-40b/resolve/main/config.json",
"tiiuae/falcon-7b": "https://huggingface.co/t... | 32 | 0 |
'''simple docstring'''
from typing import Any, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from torch.utils.data import DistributedSampler, RandomSampler
from transformers import PreTrainedModel, Trainer, logging
from transformers.integrations import is_fairscale_available
from tra... | 233 |
'''simple docstring'''
from math import sqrt
def SCREAMING_SNAKE_CASE__ ( SCREAMING_SNAKE_CASE = 100_0000 ):
SCREAMING_SNAKE_CASE_ :int = 0
SCREAMING_SNAKE_CASE_ :int = 0
SCREAMING_SNAKE_CASE_ :int
while num_cuboids <= limit:
max_cuboid_size += 1
for su... | 233 | 1 |
import asyncio
import os
import shutil
import subprocess
import sys
import tempfile
import unittest
from distutils.util import strtobool
from functools import partial
from pathlib import Path
from typing import List, Union
from unittest import mock
import torch
from ..state import AcceleratorState, P... | 443 |
def SCREAMING_SNAKE_CASE_ ( UpperCAmelCase_ : int | float | str ) -> tuple[int, int]:
try:
SCREAMING_SNAKE_CASE_ : int =float(UpperCAmelCase_ )
except ValueError:
raise ValueError('''Please enter a valid number''' )
SCREAMI... | 443 | 1 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_A = logging.get_logger(__name__)
_A = {
'xlm-roberta-base': 'https://huggingface.co/xlm-roberta-base/resolve/main/config.json',
... | 718 |
import logging
import random
import ray
from transformers import RagConfig, RagRetriever, RagTokenizer
from transformers.models.rag.retrieval_rag import CustomHFIndex
_A = logging.getLogger(__name__)
class UpperCAmelCase__ :
"""simple docstring"""
def __init__( self ) ->... | 682 | 0 |
lowerCAmelCase_ = [
[0, 1_6, 1_3, 0, 0, 0],
[0, 0, 1_0, 1_2, 0, 0],
[0, 4, 0, 0, 1_4, 0],
[0, 0, 9, 0, 0, 2_0],
[0, 0, 0, 7, 0, 4],
[0, 0, 0, 0, 0, 0],
]
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _Up... | 60 |
import copy
import os
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Dict, Mapping, Optional, Union
if TYPE_CHECKING:
from ...processing_utils import ProcessorMixin
from ...utils import TensorType
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
f... | 60 | 1 |
'''simple docstring'''
def lowercase_ ( __A : Tuple , __A : str ) -> Union[str, Any]:
"""simple docstring"""
lowercase : List[Any] =''''''
for i in table:
res += inp[i - 1]
return res
def lowercase_ ( __A : Tuple ... | 702 |
'''simple docstring'''
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_herbert import HerbertTokenizer
SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE = {'vocab_file':... | 8 | 0 |
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
f... | 252 |
import unittest
import numpy as np
from transformers import AlbertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax.numpy as j... | 252 | 1 |
'''simple docstring'''
import functools
import logging
import os
import sys
import threading
from logging import (
CRITICAL, # NOQA
DEBUG, # NOQA
ERROR, # NOQA
FATAL, # NOQA
INFO, # NOQA
NOTSET, # NOQA
WARN, # NOQA
WARNING, # NOQA
)
from typing import Optional
import hu... | 712 |
import pytest
import datasets
# Import fixture modules as plugins
SCREAMING_SNAKE_CASE__ = ['''tests.fixtures.files''', '''tests.fixtures.hub''', '''tests.fixtures.fsspec''']
def A ( __UpperCamelCase , __UpperCamelCase ) -> Optional[int]:
# Mark tests as "unit" by default... | 52 | 0 |
from __future__ import annotations
from collections.abc import Iterator
class lowerCAmelCase_ :
def __init__( self ,snake_case__ ):
SCREAMING_SNAKE_CASE_ : Union[str, Any] = value
SCREAMING_SNAKE_CASE_ : Node | None = None
SCREAMING_... | 105 |
import inspect
import re
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_config_docstrings.py
__a = '''src/transformers'''
# This is to make sure the transformers modul... | 319 | 0 |
from __future__ import annotations
from collections.abc import Iterator
class snake_case__:
'''simple docstring'''
def __init__( self , __lowercase ) -> None:
lowerCAmelCase_ : str = value
lowerCAmelCase_ : List[str] ... | 721 |
from __future__ import annotations
# This is the precision for this function which can be altered.
# It is recommended for users to keep this number greater than or equal to 10.
_UpperCAmelCase : Tuple =10
def lowerCAmelCase ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_... | 619 | 0 |
import warnings
from ...utils import logging
from .image_processing_segformer import SegformerImageProcessor
lowerCAmelCase : int = logging.get_logger(__name__)
class a ( __lowercase ):
def __init__( self , *_lowerCAmelCase , **_lowerCAmelCase ):
... | 202 |
import argparse
from argparse import Namespace
import torch
from torch import nn
from transformers import XGLMConfig, XGLMForCausalLM
def lowerCAmelCase ( UpperCamelCase__ : Union[str, Any] ) -> int:
"""simple docstring"""
__SCREAMING_SNAKE_CASE: List[Any] ... | 202 | 1 |
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 torch
if is_tf_available()... | 704 |
import argparse
import os
import re
import torch
from flax.traverse_util import flatten_dict
from tax import checkpoints
from transformers import (
AutoTokenizer,
PixaStructConfig,
PixaStructForConditionalGeneration,
PixaStructImageProcessor,
PixaStructProcessor,
PixaStructTextConfig,
... | 362 | 0 |
import inspect
import unittest
from huggingface_hub import hf_hub_download
from transformers import ConvNextConfig, UperNetConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import is_torch_available, is_vision_available... | 354 |
from graphs.minimum_spanning_tree_kruskal import kruskal
def _SCREAMING_SNAKE_CASE ( ) -> Any:
_UpperCAmelCase = 9
_UpperCAmelCase = [
[0, 1, 4],
[0, 7, 8],
[1, 2, 8],
[7, 8, 7],
[7, 6, 1],
... | 518 | 0 |
'''simple docstring'''
from maths.prime_factors import prime_factors
def UpperCAmelCase ( A : Union[str, Any] ):
if not isinstance(A , A ):
SCREAMING_SNAKE_CASE : int = F"""Input value of [number={number}] must be an integer"""
... | 720 |
'''simple docstring'''
import os
from pickle import UnpicklingError
from typing import Dict, Tuple
import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict, unflatten_dict
import transformers
from .utils imp... | 464 | 0 |
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartTokenizer, MBartTokenizerFast, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_torch,
)
from .... | 537 |
import inspect
import unittest
import warnings
from transformers import DeiTConfig
from transformers.models.auto import get_values
from transformers.testing_utils import (
require_accelerate,
require_torch,
require_torch_gpu,
require_vision,
slow,
torch_device,
)
from transformers.utils import... | 611 | 0 |
import os
import unittest
from transformers.models.phobert.tokenization_phobert import VOCAB_FILES_NAMES, PhobertTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class A ( _UpperCAmelCase , unittest.TestCase ):
"""simple docstring"""
... | 715 |
from __future__ import annotations
from fractions import Fraction
def _snake_case( SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : int ) -> bool:
'''simple docstring'''
return (
num != den and num % 10 == den // 10 and (num // 10)... | 586 | 0 |
'''simple docstring'''
import doctest
import glob
import importlib
import inspect
import os
import re
from contextlib import contextmanager
from functools import wraps
from unittest.mock import patch
import numpy as np
import pytest
from absl.testing import parameterized
import datasets... | 28 |
import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
IMAGE_PROCESSOR_MAPPING,
AutoConfig,
AutoImageProcessor,
CLIPConfig,
CLIPImageProcessor,
)
from transformers.testing_utils import DUM... | 14 | 0 |
import os
from pickle import UnpicklingError
from typing import Dict, Tuple
import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict, unflatten_dict
import transformers
from .utils import logging
__Upper... | 372 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
__UpperCamelCase : str = {"""processing_layoutxlm""": ["""... | 372 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
A_ = {"configuration_plbart": ["PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP", "PLB... | 391 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
A_ = logging.get_logger(__name__)
A_ = {
"bert-base-uncased": "https://huggingfac... | 391 | 1 |
'''simple docstring'''
def a_ ( __snake_case ) -> bool:
'''simple docstring'''
if num < 0:
return False
UpperCamelCase_ = num
UpperCamelCase_ = 0
while num > 0:
UpperCamelCase_ = rev_num * 1_0 + (num % 1_0)
... | 718 |
def a_ ( __snake_case ) -> Union[str, Any]:
'''simple docstring'''
if not head:
return True
# split the list to two parts
UpperCamelCase_ , UpperCamelCase_ = head.next, head
while fast and fast.next:
UpperCamelCase_ = fas... | 559 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
__a :List[Any] = {
'configuration_swiftformer': [
'SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
'SwiftFormerConfig',
'SwiftForme... | 86 |
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 _a ( unittest.TestCase ):
"""simple docstring"""
def __A ( self : ... | 86 | 1 |
"""simple docstring"""
import os
import shutil
from pathlib import Path
from typing import Optional, Union
import numpy as np
from huggingface_hub import hf_hub_download
from ..utils import ONNX_EXTERNAL_WEIGHTS_NAME, ONNX_WEIGHTS_NAME, is_onnx_available, logging
if is_onnx_available():
import onnxruntime as o... | 66 |
"""simple docstring"""
from ....configuration_utils import PretrainedConfig
from ....utils import logging
__lowercase : Optional[Any] = logging.get_logger(__name__)
__lowercase : Optional[Any] = {
"""Visual-Attention-Network/van-base""": (
"""https://huggingface.co/Visual-At... | 66 | 1 |
"""simple docstring"""
import math
def lowerCamelCase_(__SCREAMING_SNAKE_CASE )-> list[int]:
_SCREAMING_SNAKE_CASE : Optional[int] = []
_SCREAMING_SNAKE_CASE : List[str] = 2
_SCREAMING_SNAKE_CASE : Optional[Any] = int(math.sqrt(__SCREAMING_SNA... | 338 | """simple docstring"""
import argparse
import collections
import json
import os
import re
import string
import sys
import numpy as np
lowerCAmelCase_ = re.compile(R'''\b(a|an|the)\b''', re.UNICODE)
lowerCAmelCase_ = None
def lowerCamelCase_()-> List[str]:
_SCREAMING_SNAK... | 338 | 1 |
'''simple docstring'''
def UpperCAmelCase_ ( lowercase__ , lowercase__ ):
'''simple docstring'''
return int(input_a == input_a == 0 )
def UpperCAmelCase_ ( ):
'''simple docstring'''
print("Truth Table... | 717 |
'''simple docstring'''
import torch
from transformers import CamembertForMaskedLM, CamembertTokenizer
def UpperCAmelCase_ ( lowercase__ , lowercase__ , lowercase__ , lowercase__=5 ):
'''simple docstring'''
assert masked_input.cou... | 41 | 0 |
"""simple docstring"""
from __future__ import annotations
import math
def __snake_case ( SCREAMING_SNAKE_CASE: Any , SCREAMING_SNAKE_CASE: List[str] , SCREAMING_SNAKE_CASE: Optional[Any] , SCREAMING_SNAKE_CASE: Any , SCREAMING_SNAKE_CASE: List[Any] ... | 580 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowerCAmelCase : Optional[int] = {'configuration_xlnet': ['XLNET_PRETRAINED_CONFIG_ARCHIVE_M... | 511 | 0 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
SCREAMING_SNAKE_CASE__ : List[Any] = logging.get_logger(__name__)
... | 703 |
"""simple docstring"""
from io import BytesIO
from typing import List, Union
import requests
from ..utils import add_end_docstrings, is_decord_available, is_torch_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_decord_available():
import numpy as np
from decord ... | 509 | 0 |
'''simple docstring'''
from typing import List
from .keymap import KEYMAP, get_character
def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ ) -> Union[str, Any]:
'''simple docstring'''
def decorator(SCREAMING_SNAKE_CASE__ ):
snake_case : List[Any] = ... | 638 |
'''simple docstring'''
import numpy as np
def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ ) -> np.ndarray:
'''simple docstring'''
return 1 / (1 + np.exp(-vector ))
def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ ) -> np.ndarray:
'''simp... | 638 | 1 |
'''simple docstring'''
def _lowerCAmelCase ( __magic_name__ : int ) -> int:
if divisor % 5 == 0 or divisor % 2 == 0:
return 0
lowercase : List[str] =1
lowercase : Union[str, Any] =1
while repunit:
lowercase : ... | 718 |
'''simple docstring'''
import argparse
import logging
import os
import re
import tensorflow as tf
from transformers import (
AutoConfig,
AutoTokenizer,
DataCollatorForLanguageModeling,
PushToHubCallback,
TFAutoModelForMaskedLM,
create_optimizer,
)
UpperCamelCase_ = logging.g... | 88 | 0 |
"""simple docstring"""
import logging
from pathlib import Path
import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only
from utils_rag import save_json
def _SCREAMING_SNAKE_CASE ... | 4 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
_lowerCAmelCase : Optional[int] = logging.get_logger(__name__)
_lowerCAmelCase : ... | 46 | 0 |
from __future__ import annotations
from random import random
from typing import Generic, TypeVar
__snake_case :Any = TypeVar('''KT''')
__snake_case :List[str] = TypeVar('''VT''')
class _A ( Generic[KT, VT] ):
def __init__( self : Dict , __SCREAMING_SNAKE_CASE : KT | ... | 721 |
from __future__ import annotations
__snake_case :Optional[Any] = []
def __snake_case ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ):
for i in range(len(_UpperCAmelCase ) ):
if board[row][i] == 1:
return False
for i in range(len(_... | 60 | 0 |
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import AutoFeatureExtractor, WavaVecaFeatureExtractor
from transformers.testing_utils import TOKE... | 246 |
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
_lowerCAmelCase : str = logging.get_logger(__name__)
_lowerCAmelCase : ... | 246 | 1 |
"""simple docstring"""
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision... | 720 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_UpperCamelCase : int = {
'configuration_blip_2': [
'BLIP_2_PRETRAINED_CONFIG_ARCHIVE_MAP',
'Blip2Config',
'Blip2QFormerConfig',
... | 134 | 0 |
'''simple docstring'''
from typing import Dict
from transformers import EvalPrediction, HfArgumentParser, TrainingArguments, is_torch_available
from transformers.testing_utils import (
TestCasePlus,
execute_subprocess_async,
get_torch_dist_unique_port,
require_torch_multi_gpu,
require_torch... | 24 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
__lowerCAmelCase : Any ={
'configuration_llama': ['LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'Llama... | 696 | 0 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaInpaintPipeline,
KandinskyVaaPriorPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow... | 704 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
_lowerCamelCase = {
'''configuration_roberta_prelayernorm''': [
'''ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIV... | 321 | 0 |
class UpperCAmelCase_ :
'''simple docstring'''
def __init__( self , __A ):
"""simple docstring"""
lowerCamelCase : Union[str, Any] = size
lowerCamelCase : List[str] = [0] * size
lowerCamelCa... | 340 |
from dataclasses import asdict, dataclass
from typing import Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A = logging.get_logger(__name__)
# TODO Update this
__A = {
'''facebook/esm-1b''': '''https://huggingface.co/facebook/esm-1b/re... | 593 | 0 |
import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
FEATURE_EXTRACTOR_MAPPING,
AutoConfig,
AutoFeatureExtractor,
WavaVecaConfig,
WavaVecaFeatureExtractor,
)
from transformers.testing_utils import D... | 590 |
import io
import itertools
import json
from dataclasses import dataclass
from typing import Optional
import pyarrow as pa
import pyarrow.json as paj
import datasets
from datasets.table import table_cast
from datasets.utils.file_utils import readline
UpperCAmelCase_ : Dict = datasets.utils.log... | 590 | 1 |
"""simple docstring"""
import os
import tempfile
import unittest
import uuid
from pathlib import Path
from transformers.testing_utils import get_tests_dir, require_soundfile, require_torch, require_vision
from transformers.tools.agent_types import AgentAudio, AgentImage, AgentText
from transformers.ut... | 224 |
"""simple docstring"""
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import flax
import jax.numpy as jnp
from jax import random
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .scheduling_utils_flax import FlaxSchedu... | 224 | 1 |
import argparse
from transformers import (
TapasConfig,
TapasForMaskedLM,
TapasForQuestionAnswering,
TapasForSequenceClassification,
TapasModel,
TapasTokenizer,
load_tf_weights_in_tapas,
)
from transformers.utils import logging
logging.set_verbosity_info()
def a ( ... | 380 |
from __future__ import annotations
import collections
import tempfile
import unittest
import numpy as np
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import is_tf_available, is_vision_available
from ...test_modeling_tf_common import floats_tensor, id... | 380 | 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 .... | 38 |
'''simple docstring'''
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
... | 527 | 0 |
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import sys
import warnings
from os.path import abspath, dirname, join
# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switc... | 57 |
import os
import sys
import unittest
__UpperCAmelCase : str = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, "utils"))
import check_dummies # noqa: E402
from check_dummies import create_dummy_files, creat... | 57 | 1 |
from __future__ import annotations
import matplotlib.pyplot as plt # type: ignore
import numpy
# initial triangle of Koch snowflake
__lowercase : Optional[Any] =numpy.array([0, 0])
__lowercase : str =numpy.array([0.5, 0.866_0254])
__lowercase : Optional[Any] =numpy.array([1, 0])... | 54 |
"""simple docstring"""
from scipy.stats import pearsonr
import datasets
SCREAMING_SNAKE_CASE_ = '''
Pearson correlation coefficient and p-value for testing non-correlation.
The Pearson correlation coefficient measures the linear relationship between two datasets. The calculation of the p-value relie... | 465 | 0 |
import math
lowerCamelCase__ : Optional[int] = 1_0
lowerCamelCase__ : List[Any] = 7
lowerCamelCase__ : int = BALLS_PER_COLOUR * NUM_COLOURS
def SCREAMING_SNAKE_CASE ( __lowerCAmelCase = 20 ) -> str:
snake_case_... | 208 |
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
lowerCamelCase__ : Union[str, Any] = datasets.logging.get_logger(__name__)
lowerCamelCase__ : Dict ... | 208 | 1 |
"""simple docstring"""
import inspect
import unittest
from transformers import RegNetConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from ...test_configuration_common im... | 434 |
from typing import List
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowercase : Any = logging.get_logger(__name__)
_lowercase : Union[str, Any] = {
"snap-research/efficientformer-l1-300": (
"https://huggingface.co/snap-research... | 641 | 0 |
'''simple docstring'''
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 lowerCamelCase__ ( unittest.TestCase ):
'''simple docstring'''
de... | 4 |
'''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 jax.n... | 4 | 1 |
"""simple docstring"""
import faiss # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy # noqa: F401 # Here to have a nice missing dependency error message early on
import requests # noqa: F401 # Here to have a nice missing dependency error message early on
imp... | 118 |
"""simple docstring"""
import os
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from huggingface_hub.file_download import http_get
from requests.exceptions import HTTPError
from transformers i... | 118 | 1 |
'''simple docstring'''
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING, Dict, Optional
import numpy as np
import pyarrow as pa
from .. import config
from ..utils.logging import get_logger
from ..utils.py_utils import map_nested
from .formatting import TensorFormatt... | 718 |
'''simple docstring'''
import unittest
from transformers import (
MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
Pipeline,
ZeroShotClassificationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simpli... | 343 | 0 |
from itertools import product
def _lowerCamelCase ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
'''simple docstring'''
A_ = sides_number
A_ = max_face_number * dice_number
A_ = [0] * (max_total + 1)
A_ ... | 203 |
# this script reports modified .py files under the desired list of top-level sub-dirs passed as a list of arguments, e.g.:
# python ./utils/get_modified_files.py utils src tests examples
#
# it uses git to find the forking point and which files were modified - i.e. files not under git won't be considered
# since th... | 203 | 1 |
def lowerCamelCase_ ( UpperCAmelCase_ : list[int] , UpperCAmelCase_ : int ) -> bool:
'''simple docstring'''
_UpperCamelCase : List[Any] = len(UpperCAmelCase_ )
_UpperCamelCase : List[Any] = [[False] * (required_sum + 1) for _ in ran... | 648 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase__ = logging.get_logger(__name__)
lowerCAmelCase__ = {
"""facebook/vit-mae-base""": """https://huggingface.co/facebook/vit-mae-base/resolve/main/config.json""",
# See all ViT MAE models at https:/... | 648 | 1 |
def A_ ( A__ = 5000_0000 ) -> int:
a__ : Any = set()
a__ : Any = int((limit - 24) ** (1 / 2) )
a__ : Optional[Any] = set(range(3 , prime_square_limit + 1 , 2 ) )
primes.add(2 )
... | 302 |
from collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean
def A_ ( A__ ) -> Union[str, Any]:
# A local function to see if a dot lands in the circle.
def is_in_circle(A__ , A__ ) -> bool:
... | 302 | 1 |
import argparse
import struct
import unittest
class a :
def __init__( self : List[Any] , snake_case__ : bytes ):
"""simple docstring"""
__lowerCAmelCase = data
# Initialize hash values
__lowerCAmelCase = [
... | 376 |
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
UpperCamelCase_ = {
# 1536-bit
5: {
"prime": int(
"FFFFFFFFFFFFFFFFC90... | 376 | 1 |
import argparse
import json
import os
from collections import OrderedDict
import torch
from transformers import LukeConfig, LukeForMaskedLM, MLukeTokenizer, XLMRobertaTokenizer
from transformers.tokenization_utils_base import AddedToken
@torch.no_grad()
def _a ( UpperCAmelCase , UpperCAmelCa... | 315 |
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... | 315 | 1 |
'''simple docstring'''
import collections
import inspect
import unittest
from transformers import FocalNetConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_availa... | 44 |
'''simple docstring'''
from ...processing_utils import ProcessorMixin
class _lowercase ( __a ):
_UpperCAmelCase = '''WhisperFeatureExtractor'''
_UpperCAmelCase = '''WhisperTokenizer'''
def __init__( self , A__ ... | 44 | 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.preproce... | 650 |
from math import sqrt
def UpperCAmelCase ( a_ ) -> bool:
"""simple docstring"""
assert isinstance(a_ , a_ ) and (
number >= 0
), "'number' must been an int and positive"
__A = True
# 0 and 1 are none primes.
if number <= 1:
__... | 55 | 0 |
from __future__ import annotations
from math import pow, sqrt
def UpperCAmelCase__ ( UpperCAmelCase__ :float , UpperCAmelCase__ :float , UpperCAmelCase__ :float ):
'''simple docstring'''
if (resistance, reactance, impedance).count(0 ) != 1:
raise ValueErro... | 32 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A_ : Optional[int] = {
'''configuration_instructblip''': [
'''INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''InstructBlipConfig''',
'''... | 32 | 1 |
"""simple docstring"""
import argparse
import random
import joblib
import numpy as np
import torch
from igf.igf import (
SecondaryLearner,
collect_objective_set,
compute_perplexity,
generate_datasets,
load_gpta,
recopy_gpta,
set_seed,
train_secondary_learner,
)
from torch.u... | 589 |
from manim import *
class A__ ( __snake_case ):
'''simple docstring'''
def _SCREAMING_SNAKE_CASE ( self : Union[str, Any] ):
"""simple docstring"""
UpperCamelCase = Rectangle(height=0.5 ... | 280 | 0 |
"""simple docstring"""
import logging
import numpy as np
import pytest
from scipy.linalg import eigh
logging.basicConfig(level=logging.INFO, format="""%(message)s""")
def UpperCamelCase ( _lowerCAmelCase : np.ndarray ):
return input_array.reshape((input_array.size, 1) )
def U... | 173 | """simple docstring"""
from __future__ import annotations
class a :
def __init__( self : List[str] , lowerCamelCase_ : list[list[int]] ) -> Any:
__a = TypeError(
"""Matrices must be formed from a list of zero or more lists containing... | 173 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase__ : Any = logging.get_logger(__name__)
lowerCamelCase__ : Optional[Any] = {
"""edbeeching/decision-transformer-gym-hopper-medium""": (
"""https://huggingface.co/edbeeching/dec... | 12 |
"""simple docstring"""
import argparse
import json
import os
import numpy as np
import PIL
import requests
import tensorflow.keras.applications.efficientnet as efficientnet
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from tensorflow.keras.preprocessing import image
from transfor... | 160 | 0 |
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import SegformerImageProcessor, SwinConfig, UperNetConfig, UperNetForSemanticSegmentation
def lowerCamelCase ( SCREAMING_SNAKE_CASE ):
'''simple docstring'''
... | 452 | import numpy as np
from transformers import BatchFeature
from transformers.testing_utils import require_tf, require_torch
from .test_feature_extraction_common import FeatureExtractionSavingTestMixin
class lowerCamelCase_ ( UpperCAmelCase_ ):
'''simple docstring'''
a__ : Union[str, ... | 452 | 1 |
import unittest
from parameterized import parameterized
from transformers import AutoTokenizer, GPTNeoXConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common i... | 443 |
import gc
import unittest
import numpy as np
import torch
import torch.nn.functional as F
from transformers import (
ClapTextConfig,
ClapTextModelWithProjection,
RobertaTokenizer,
SpeechTaHifiGan,
SpeechTaHifiGanConfig,
)
from diffusers import (
AudioLDMPipeline,
AutoencoderKL,
D... | 302 | 0 |
import random
import unittest
import numpy as np
import torch
from diffusers import (
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionUpscalePipeline,
PNDMScheduler,
)
from diffusers.utils im... | 175 |
from typing import Union
import fire
import torch
from tqdm import tqdm
def _SCREAMING_SNAKE_CASE ( snake_case , snake_case = "cpu" , snake_case = None ) -> None:
_UpperCAmelCase = torch.load(snake_case , map_location=snake_case )
... | 175 | 1 |
import os
import sys
import unittest
__a: Optional[int] = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, '''utils'''))
import get_test_info # noqa: E402
from get_test_info import ( # noqa: E402
get_model_to_test_map... | 108 |
"""simple docstring"""
from typing import List
from .keymap import KEYMAP, get_character
def __UpperCamelCase ( SCREAMING_SNAKE_CASE ) -> Dict:
"""simple docstring"""
def decorator(SCREAMING_SNAKE_CASE ):
__snake_case = getattr(SCREAMING_SNAKE_CASE ... | 163 | 0 |
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
... | 705 |
from __future__ import annotations
from fractions import Fraction
from math import gcd, sqrt
def _a ( __UpperCamelCase ):
a_ : int = int(number**0.5 )
return number == sq * sq
def _a ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ... | 478 | 0 |
import torch
from ..models.speechta import SpeechTaForTextToSpeech, SpeechTaHifiGan, SpeechTaProcessor
from ..utils import is_datasets_available
from .base import PipelineTool
if is_datasets_available():
from datasets import load_dataset
class __lowerCAmelCase ( __UpperCAmelCase ):
lowerCAmelCase_... | 175 |
"""simple docstring"""
def _lowerCamelCase ( lowerCamelCase__ : str ):
assert column_title.isupper()
lowercase__ : int = 0
lowercase__ : Optional[int] = len(lowerCamelCase__ ) - 1
lowercase__ : int = 0
while index >= 0:
lowercase_... | 200 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowercase_ = {
"configuration_lilt": ["LILT_PRETRAINED_CONFIG_ARCHIVE_MAP", "LiltConfig"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvai... | 718 |
from ..utils import DummyObject, requires_backends
class A ( metaclass=_UpperCAmelCase ):
"""simple docstring"""
lowerCamelCase = ['transformers', 'torch', 'note_seq']
def __init__( self : Tuple,*lowercase_ : Any,**lowercase_ : Dict )... | 586 | 0 |
'''simple docstring'''
import argparse
from collections import defaultdict
def SCREAMING_SNAKE_CASE__ ( __A , __A , __A , __A , __A ) -> List[Any]:
_snake_case = F'{file}_{class_name}_{test_name}'
done_test[_id] += 1
with open(_... | 495 |
"""simple docstring"""
from manim import *
class __lowerCAmelCase ( UpperCAmelCase ):
'''simple docstring'''
def UpperCamelCase__ ( self: int ):
UpperCamelCase_ =Rectangle(height=0.5 , width=0.5 )
UpperCamelCase_ =Rectangle(... | 391 | 0 |
'''simple docstring'''
import timeit
import numpy as np
import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features.features import _ArrayXD
def __SCREAMING_SNAKE_CASE ( lowercase_ ) -> Optional[int]:
"""simple docstring"""
def wrapper(*lowercase_ ... | 717 |
from pathlib import PurePosixPath
from typing import Optional
import fsspec
from fsspec import AbstractFileSystem
from huggingface_hub.hf_api import DatasetInfo
from ..utils.file_utils import get_authentication_headers_for_url
from ..utils.hub import hf_hub_url
class _lowerCamelCase ( _SCREAMING_SNAKE_C... | 375 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import _LazyModule
UpperCamelCase__ : List[str] = {'tokenization_byt5': ['ByT5Tokenizer']}
if TYPE_CHECKING:
from .tokenization_byta import ByTaTokenizer
else:
import sys
UpperCamelCase__ : List[str] = _Lazy... | 614 | '''simple docstring'''
import numpy as np
def __UpperCamelCase( _A : np.ndarray , _A : np.ndarray , _A : float = 1e-12 , _A : int = 1_00 , ):
'''simple docstring'''
assert np.shape(_A )[0] == np.shape(_A )[1]
# Ensure proper dimensiona... | 614 | 1 |
"""simple docstring"""
class _UpperCAmelCase :
'''simple docstring'''
def __init__( self ):
"""simple docstring"""
A_ : Union[str, Any] = 0
A_ : List[str] = 0
A_ : Any = {}
def l... | 302 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_layoutlmva import LayoutLMvaImageProcessor
lowerCamelCase_ : int = logging.get_logger(__name__)
class _UpperCAmelCase ( UpperCAmelCase__ ):
'''simple docstring'''
def __in... | 302 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
_SCREAMING_SNAKE_CASE : Any = {
'configuration_vision_encoder_decoder': ['VisionEncoderDecoderConfig', 'VisionEn... | 226 |
import unittest
from transformers import PegasusConfig, PegasusTokenizer, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor
if is_flax_available():
impo... | 226 | 1 |
from collections.abc import Sequence
def _snake_case (_snake_case : Sequence[int] | None = None) -> int:
if nums is None or not nums:
raise ValueError('Input sequence should not be empty')
_lowercase =nums[0]
for i in range(1 , len(_snake_case)):
... | 557 |
from math import isqrt, loga
def _snake_case (_snake_case : int) -> list[int]:
_lowercase =[True] * max_number
for i in range(2 , isqrt(max_number - 1) + 1):
if is_prime[i]:
for j in range(i**2 , _snake_case , _snake_case):
... | 557 | 1 |
import math
def lowercase ( __A : float , __A : float ) -> float:
'''simple docstring'''
if initial_intensity < 0:
raise ValueError("""The value of intensity cannot be negative""" )
# handling of negative values of initial intensity
if angle < 0 ... | 36 |
from .imports import is_rich_available
if is_rich_available():
from rich.traceback import install
install(show_locals=False)
else:
raise ModuleNotFoundError('''To use the rich extension, install rich with `pip install rich`''')
| 36 | 1 |
'''simple docstring'''
import argparse
import logging
import sys
from unittest.mock import patch
import run_glue_deebert
from transformers.testing_utils import TestCasePlus, get_gpu_count, require_torch_non_multi_gpu, slow
logging.basicConfig(level=logging.DEBUG)
lowerCAmelCase_ : str = ... | 716 |
'''simple docstring'''
import argparse
import torch
from transformers import FunnelBaseModel, FunnelConfig, FunnelModel, load_tf_weights_in_funnel
from transformers.utils import logging
logging.set_verbosity_info()
def _lowerCamelCase ( lowercase : str , lowercase : i... | 521 | 0 |
import argparse
import torch
from torch import nn
from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration
def lowerCamelCase__ ( __lowerCAmelCase : str ):
"""simple docstring"""
lowerCAmelCase_ = [
"encoder.version",
"decoder.versio... | 290 |
import argparse
from pathlib import Path
import torch
from transformers import OPTConfig, OPTModel
from transformers.utils import logging
logging.set_verbosity_info()
_A = logging.get_logger(__name__)
def lowerCamelCase__ ( __lowerCAmelCase : Any ):
"""simple docstring"""
... | 290 | 1 |
'''simple docstring'''
from __future__ import annotations
def _UpperCAmelCase ( __A : Tuple ):
a_ : Optional[Any] = str(__lowerCAmelCase )
return len(__lowerCAmelCase ) == 9 and set(__lowerCAmelCase ) == set('''1234... | 718 |
'''simple docstring'''
import unittest
import numpy as np
import torch
from diffusers import DDIMPipeline, DDIMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device
from ..pipeline_params import UNCONDITION... | 666 | 0 |
'''simple docstring'''
import inspect
import unittest
import torch
import torch.nn as nn
from accelerate.hooks import (
AlignDevicesHook,
ModelHook,
SequentialHook,
add_hook_to_module,
attach_align_device_hook,
remove_hook_from_module,
remove_hook_from_submodules,
)
from acceler... | 270 |
'''simple docstring'''
def UpperCamelCase__ ( __SCREAMING_SNAKE_CASE ) -> str:
snake_case__ , snake_case__ : Optional[int] = [], []
while len(__SCREAMING_SNAKE_CASE ) > 1:
snake_case__ , snake_case__ : Tuple = min(... | 270 | 1 |
def _A ( __magic_name__ , __magic_name__ ):
if a < 0 or b < 0:
raise ValueError("the value of both inputs must be positive" )
lowercase__ = str(bin(__magic_name__ ) )[2:] # remove the leading "0b"
lowercase__ = str(bin(__magic_name__ ) )[2:] # remove t... | 611 |
import argparse
import os
import re
import tensorflow as tf
import torch
from transformers import BertConfig, BertModel
from transformers.utils import logging
logging.set_verbosity_info()
_snake_case = logging.get_logger(__name__)
def _A ( __magic_name__ , __magic_name__ , ... | 611 | 1 |
"""simple docstring"""
import argparse
import collections
import os
import re
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_table.py
A = 's... | 449 |
"""simple docstring"""
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional
from packaging import version
if TYPE_CHECKING:
from ... import PreTrainedTokenizer, TensorType
from ...configuration_utils import PretrainedConfig
from ...on... | 449 | 1 |
'''simple docstring'''
from collections import Counter
from pathlib import Path
from typing import Optional, Tuple
import yaml
class __SCREAMING_SNAKE_CASE ( yaml.SafeLoader ):
def __magic_name__ ( self : int , __lowercase : Any ... | 665 |
'''simple docstring'''
import unittest
from transformers import JukeboxTokenizer
from transformers.testing_utils import require_torch
class __SCREAMING_SNAKE_CASE ( unittest.TestCase ):
snake_case_ = JukeboxTokenizer
snake_case_ = {
""... | 665 | 1 |
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
__snake_case = logging.get_logger(__name__)
__snake_case = {
... | 1 |
import string
def _A ( _lowercase ) -> None:
"""simple docstring"""
for key in range(len(string.ascii_uppercase ) ):
__UpperCamelCase = ''
for symbol in message:
if symbol in string.ascii_uppercase:
__UpperCamelC... | 1 | 1 |
"""simple docstring"""
# This code is adapted from OpenAI's release
# https://github.com/openai/human-eval/blob/master/human_eval/execution.py
import contextlib
import faulthandler
import io
import multiprocessing
import os
import platform
import signal
import tempfile
def _SCR... | 701 |
"""simple docstring"""
import os
import shutil
from pathlib import Path
from typing import Optional, Union
import numpy as np
from huggingface_hub import hf_hub_download
from ..utils import ONNX_EXTERNAL_WEIGHTS_NAME, ONNX_WEIGHTS_NAME, is_onnx_available, logging
if is_onnx_available... | 31 | 0 |
from __future__ import annotations
from math import ceil, floor, sqrt
def UpperCamelCase ( _A = 2000000 ):
"""simple docstring"""
__magic_name__ : list[int] = [0]
__magic_name__ : int
for idx in range(1, ceil(sqrt(target * 2 ) * 1.1 ... | 324 |
from collections import deque
def UpperCamelCase ( _A ):
"""simple docstring"""
__magic_name__ : Union[str, Any] = len(_A )
__magic_name__ : Optional[int] = deque()
__magic_name__ : Tuple = [False for _ in range(_A )]
... | 324 | 1 |
import warnings
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class _A ( lowercase_ ):
snake_ca... | 713 | """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/LI... | 197 | 0 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
__lowerCamelCase = {
'albert-base-v1': 'https://huggingface.co/albert-base-v1/resolve/main/config.json',... | 96 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCAmelCase__ = logging.get_logger(__name__)
lowerCAmelCase__ = {
... | 83 | 0 |
from math import log
from scipy.constants import Boltzmann, physical_constants
snake_case_ : Optional[int] = 300 # TEMPERATURE (unit = K)
def A (__A : float , __A : float , __A : float , ) -> float:
""... | 169 |
from __future__ import annotations
from typing import Any
class __snake_case :
def __init__( self : Dict , _snake_case : int):
"""simple docstring"""
UpperCAmelCase_ = num_of_nodes
UpperCAmelCase_ = []
... | 169 | 1 |
import inspect
import os
import re
from transformers.configuration_utils import PretrainedConfig
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_config_docstring... | 468 |
from bisect import bisect
from itertools import accumulate
def __A ( __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase ) -> Dict:
a = sorted(zip(__lowerCamelCase , __lowerCamelCase ) , key=lambda ... | 468 | 1 |
"""simple docstring"""
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version(""">=""", """4.25.0""")):
raise OptionalDep... | 87 |
"""simple docstring"""
from __future__ import annotations
from collections import namedtuple
def a__ ( a : float , a : float , a : float ):
"""simple docstring"""
_snake_case : Optional[Any] = namedtuple("result" , "name value" )
if ... | 87 | 1 |
import unittest
from knapsack import knapsack as k
class __lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
def snake_case_ ( self : List[Any] ):
__lowercase : int = 0
__lowercase : ... | 509 |
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 ...test_tokenizat... | 509 | 1 |
import fire
from transformers import AutoConfig, AutoModelForSeqaSeqLM, AutoTokenizer
def UpperCamelCase (lowercase_: int , lowercase_: Dict , **lowercase_: Dict ) -> Union[str, Any]:
A__ : int = AutoConfig.from_pretrained(__lowerCAmelCase , **__lowerCAmelC... | 718 |
def UpperCamelCase (lowercase_: str , lowercase_: str ) -> bool:
A__ : Union[str, Any] = len(lowercase_ )
A__ : List[Any] = len(lowercase_ )
A__ : List[Any] = [[False for _ in range(m + 1 )] for _ in range(n + 1 )]
A__ ... | 64 | 0 |
"""simple docstring"""
def __UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ ):
if n == 0:
return 1
elif n % 2 == 1:
return (binary_exponentiation(UpperCamelCase_ , n - 1 , UpperCamelCase_ ) * a) % mod
else:
A_ : Union[str, Any] = binary_exponentiation... | 180 |
from collections import deque
from .hash_table import HashTable
class __snake_case (_a ):
def __init__( self : int , *_UpperCAmelCase : str , **_UpperCAmelCase : Union[str, Any] ) -> Tuple:
'''simple docstring'''
super().__init__(*_UpperCAmelCas... | 429 | 0 |
"""simple docstring"""
import unittest
from transformers import DebertaVaTokenizer, DebertaVaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
lowerCAmelCase__ : int = ... | 632 | """simple docstring"""
import unittest
from transformers.models.xlm_prophetnet.tokenization_xlm_prophetnet import SPIECE_UNDERLINE, XLMProphetNetTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import cached_property
from ...test_tokenization_co... | 632 | 1 |
'''simple docstring'''
import torch
from torch import nn
from transformers import CLIPPreTrainedModel, CLIPVisionModel
from ...models.attention import BasicTransformerBlock
from ...utils import logging
UpperCamelCase__ = logging.get_logger(__name__) # pylint: disable=invalid-name
class _Upper... | 620 |
'''simple docstring'''
import argparse
import os
# New Code #
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed... | 620 | 1 |
'''simple docstring'''
import os
from datetime import datetime as dt
from github import Github
SCREAMING_SNAKE_CASE_ = [
'good first issue',
'feature request',
'wip',
]
def UpperCamelCase__ ( ) -> str:
__UpperCAmelCase: Optional[int] = Github(os.environ["""GITHUB_TO... | 466 | '''simple docstring'''
import numpy
# List of input, output pairs
SCREAMING_SNAKE_CASE_ = (
((5, 2, 3), 15),
((6, 5, 9), 25),
((11, 12, 13), 41),
((1, 1, 1), 8),
((11, 12, 13), 41),
)
SCREAMING_SNAKE_CASE_ = (((5_15, 22, 13), 5_55), ((61, 35, 49), 1_50))
SCREAMING_SNAKE_CASE_ = ... | 466 | 1 |
'''simple docstring'''
from __future__ import annotations
from scipy.special import comb # type: ignore
class lowercase_ :
"""simple docstring"""
def __init__( self : Tuple, UpperCamelCase__ : list[tuple[float, float]] ) -> Any:
_A = list_of_poi... | 107 |
import warnings
from ...utils import logging
from .image_processing_glpn import GLPNImageProcessor
a__ : Optional[Any] = logging.get_logger(__name__)
class UpperCAmelCase__( lowerCamelCase ):
'''simple docstring'''
def __init__( self : Optional[Any] , *l... | 622 | 0 |
import os
import sys
import unittest
_lowercase = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, "utils"))
import get_test_info # noqa: E402
from get_test_info import ( # noqa: E402
get_model_to_test_mapping,
get_model_to... | 526 |
# Copyright 2022 The HuggingFace Team and The OpenBMB 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
#
# Unl... | 526 | 1 |
'''simple docstring'''
import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional
import datasets
import numpy as np
import tensorflow as tf
from transformers import (
AutoConfig,
AutoTokenizer,
EvalPrediction,
HfArgumentParser,
PreTrainedTokenize... | 185 |
def UpperCAmelCase_ ( ):
return [
a * b * (1_000 - a - b)
for a in range(1, 999 )
for b in range(__UpperCamelCase, 999 )
if (a * a + b * b == (1_000 - a - b) ** 2)
][0]
if __name__ == "__main__":
print(f"""{solution() = }""")
... | 151 | 0 |
"""simple docstring"""
import os
def _snake_case ( ):
_lowerCamelCase : List[Any] = os.path.join(os.path.dirname(A_ ) , 'num.txt' )
with open(A_ ) as file_hand:
return str(sum(int(A_ ) for line in file_hand ) )[:10]
if __name... | 710 |
"""simple docstring"""
from typing import Dict
from transformers import EvalPrediction, HfArgumentParser, TrainingArguments, is_torch_available
from transformers.testing_utils import (
TestCasePlus,
execute_subprocess_async,
get_torch_dist_unique_port,
require_torch_multi_... | 492 | 0 |
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