code stringlengths 86 54.5k | code_codestyle int64 0 371 | style_context stringlengths 87 49.2k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
from __future__ import annotations
import unittest
from transformers import DebertaVaConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_at... | 308 |
'''simple docstring'''
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import DetrConfig, MaskFormerConfig, SwinConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, requi... | 341 | 0 |
"""simple docstring"""
import argparse
import logging
import pickle
from collections import Counter
logging.basicConfig(
format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""", datefmt="""%m/%d/%Y %H:%M:%S""", level=logging.INFO
)
_lowerCAmelCase : Optional[Any] = logging.get... | 370 |
"""simple docstring"""
import qiskit
def SCREAMING_SNAKE_CASE__ ( snake_case : int , snake_case : int )-> qiskit.result.counts.Counts:
'''simple docstring'''
UpperCAmelCase__ : str = qiskit.Aer.get_backend("aer_simulator" )
UpperCAme... | 298 | 0 |
"""simple docstring"""
def lowerCamelCase ( _UpperCamelCase : List[str] ) -> Optional[int]:
'''simple docstring'''
return [
txt[:a] + txt[a].upper() + txt[a + 1 :]
for a in range(len(_UpperCamelCase ) )
if txt[a].i... | 115 |
from math import cos, sin, sqrt, tau
from audio_filters.iir_filter import IIRFilter
def A_ ( a , a , a = 1 / sqrt(2 ) ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : str = tau * frequency / samplerate
SCREAMING_SNAKE_CASE_ : List[Any] ... | 253 | 0 |
import argparse
import json
from pathlib import Path
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import DeiTConfig, DeiTForImageClassificationWithTeacher, DeiTImageProcessor
from transformers.utils import logging
logging.set_verbosi... | 173 |
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, Di... | 173 | 1 |
from .glue import glue_convert_examples_to_features, glue_output_modes, glue_processors, glue_tasks_num_labels
from .squad import SquadExample, SquadFeatures, SquadVaProcessor, SquadVaProcessor, squad_convert_examples_to_features
from .utils import DataProcessor, InputExample, InputFeatures, SingleSentenceClassificat... | 182 |
'''simple docstring'''
import importlib
import shutil
import threading
import warnings
from typing import List
import fsspec
import fsspec.asyn
from . import compression
from .hffilesystem import HfFileSystem
SCREAMING_SNAKE_CASE__ = importlib.util.find_spec('s3fs') is not None
... | 321 | 0 |
"""simple docstring"""
import argparse
import json
import os
from pathlib import Path
import requests
import torch
from transformers import JukeboxConfig, JukeboxModel
from transformers.utils import logging
logging.set_verbosity_info()
__A = logging.get_logger(__name__)
_... | 365 |
"""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
__A = logging.get_logger(__name__)
__A ... | 108 | 0 |
from __future__ import annotations
import inspect
import unittest
import numpy as np
from transformers import DeiTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuration_... | 143 |
import flax.linen as nn
import jax
import jax.numpy as jnp
class SCREAMING_SNAKE_CASE__ ( nn.Module ):
snake_case__ : int
snake_case__ : jnp.dtype = jnp.floataa
def SCREAMING_SNAKE_CASE ( self : str ) -> int:
... | 32 | 0 |
import argparse
import pickle
import numpy as np
import torch
from torch import nn
from transformers import ReformerConfig, ReformerModelWithLMHead
from transformers.utils import logging
logging.set_verbosity_info()
def _snake_case( SCREAMING_SNAKE_CASE__ : Optional[int] , SC... | 282 |
# XXX: we want transformers master here - in the absense of conftest manipulating sys.path:
# hack it in for now:
import sys
from pathlib import Path
lowercase_ = Path(__file__).resolve().parents[3] / "src"
sys.path.insert(1, str(git_repo_path))
import dataclasses # noqa
import io # noqa
import i... | 282 | 1 |
'''simple docstring'''
import argparse
import json
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
fro... | 141 |
'''simple docstring'''
import math
class lowerCAmelCase :
def snake_case ( self : Optional[int] , __lowercase : list[list[float]] , __lowercase : list[int] ):
"""simple docstring"""
__lowercase =0.0
... | 141 | 1 |
'''simple docstring'''
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import ClassLabel, Features, Image
from .base import TaskTemplate
@dataclass(frozen=__SCREAMING_SNAKE_CASE )
class lowercase ( __SCREAMING_SNAKE_CASE ):
"""simple docstri... | 351 |
'''simple docstring'''
import os
import unittest
from huggingface_hub.utils import are_progress_bars_disabled
import transformers.models.bart.tokenization_bart
from transformers import logging
from transformers.testing_utils import CaptureLogger, mockenv, mockenv_context
from transformers.utils.logging import disa... | 219 | 0 |
from random import randint, random
def lowerCAmelCase_ ( __a , __a , __a , __a = False , __a = False , __a = 5 , ) -> list:
"""simple docstring"""
lowerCamelCase__: Tuple =[[-1] * number_of_cells] # Create a highway without any car
lowerCamelCase_... | 10 | """simple docstring"""
import unittest
from pathlib import Path
from tempfile import NamedTemporaryFile, TemporaryDirectory
from transformers import BertConfig, BertTokenizerFast, FeatureExtractionPipeline
from transformers.convert_graph_to_onnx import (
convert,
ensure_valid_input,
genera... | 221 | 0 |
def lowerCamelCase__ (_UpperCAmelCase):
SCREAMING_SNAKE_CASE = []
SCREAMING_SNAKE_CASE = set({'(', '[', '{'})
SCREAMING_SNAKE_CASE = set({')', ']', '}'})
SCREAMING_SNAKE_CASE = {'''{''': '''}''', '''[''': ''']''', '''(''': ''')'''}
for i in range(len(__a)):
... | 365 |
import absl # noqa: F401 # Here to have a nice missing dependency error message early on
import nltk # 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 six # noqa: F401 # Here to have a ... | 327 | 0 |
'''simple docstring'''
import gc
import importlib.metadata
import tempfile
import unittest
from packaging import version
from transformers import (
AutoModel,
AutoModelForCausalLM,
AutoModelForSeqaSeqLM,
AutoModelForSequenceClassification,
AutoTokenizer,
BitsAndBytesConfig,
... | 229 |
import argparse
import os
import re
SCREAMING_SNAKE_CASE__ : List[Any] = 'src/transformers/models/auto'
# re pattern that matches mapping introductions:
# SUPER_MODEL_MAPPING_NAMES = OrderedDict or SUPER_MODEL_MAPPING = OrderedDict
SCREAMING_SNAKE_CASE__ : Optional[in... | 48 | 0 |
from __future__ import annotations
from math import gcd
def lowercase_ ( _lowerCamelCase: int , _lowerCamelCase: int = 2 , _lowerCamelCase: int = 1 , _lowerCamelCase: int = 3 , ) -> int | None:
'''simple docstring'''
if num < 2:
raise ValueError("The inpu... | 365 | """simple docstring"""
import os
import zipfile
import requests
from get_ci_error_statistics import download_artifact, get_artifacts_links
def lowercase_ ( _lowerCamelCase: str , _lowerCamelCase: Optional[int]=7 ) -> int:
'''simple docstring'''
__lowerCamelCase : List[st... | 64 | 0 |
from math import pi, sqrt, tan
def snake_case_ ( snake_case ) -> float:
if side_length < 0:
raise ValueError('surface_area_cube() only accepts non-negative values' )
return 6 * side_length**2
def snake_case_ ( snake_case , snake_... | 196 |
from __future__ import annotations
from collections.abc import Callable
__lowerCAmelCase = list[list[float | int]]
def snake_case_ ( snake_case , snake_case ) -> Matrix:
lowercase__: int = len(snake_case )
lowercase__: Matrix =... | 196 | 1 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
... | 273 |
from typing import Callable, Dict, Optional, Tuple
import torch
from torch import nn
from torch.distributions import (
AffineTransform,
Distribution,
Independent,
NegativeBinomial,
Normal,
StudentT,
TransformedDistribution,
)
class _SCREAMING_SNAKE_CASE ( __SCREAMIN... | 273 | 1 |
'''simple docstring'''
def a ( lowerCamelCase__ , lowerCamelCase__ ):
'''simple docstring'''
return abs(lowerCamelCase__ ) if a == 0 else greatest_common_divisor(b % a , lowerCamelCase__ )
def a ( lowerCamelCase__ , lowerCamelCase__ ):
'''simple d... | 206 |
'''simple docstring'''
import fire
from torch.utils.data import DataLoader
from tqdm import tqdm
from transformers import AutoTokenizer
from utils import SeqaSeqDataset, pickle_save
def a ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__=10_24 , lowerCamelCase__=10_24... | 206 | 1 |
"""simple docstring"""
import operator
def _A (__a , __a = False , __a = None ) -> list:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : List[str] = operator.lt if reverse else operator.gt
SCREAMING_SNAKE_CASE_ : List[Any] = solution or ... | 318 |
"""simple docstring"""
from ...processing_utils import ProcessorMixin
class lowerCAmelCase__ ( UpperCAmelCase__ ):
'''simple docstring'''
__UpperCamelCase = "SpeechT5FeatureExtractor"
__UpperCamelCase = "SpeechT5Tokenizer"
def __init__( ... | 318 | 1 |
from typing import Dict, Optional
import numpy as np
import datasets
__snake_case = """
IoU is the area of overlap between the predicted segmentation and the ground truth divided by the area of union
between the predicted segmentation and the ground truth. For binary (two classes) or multi-class ... | 259 |
from __future__ import annotations
from typing import Any
def _A ( SCREAMING_SNAKE_CASE__ : list[Any] ):
create_state_space_tree(SCREAMING_SNAKE_CASE__ , [] , 0 )
def _A ( SCREAMING_SNAKE_CASE__ : list[Any] , SCREAMING_SNAKE_CASE__ : list[Any] , SCREAMING... | 259 | 1 |
import math
def __lowerCAmelCase ()-> None:
"""simple docstring"""
snake_case_ = input('''Enter message: ''' )
snake_case_ = int(input(f'''Enter key [2-{len(SCREAMING_SNAKE_CASE ) - 1}]: ''' ) )
snake_case_ = input('''Encryption/Decryption [e/d]: ''' )
if mode.l... | 267 |
def __lowerCAmelCase (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )-> list[int]:
"""simple docstring"""
snake_case_ = int(SCREAMING_SNAKE_CASE )
# Initialize Result
snake_case_ = []
# Traverse through all denomination
for denomination in reversed(SCREAMING_SNAKE_C... | 267 | 1 |
import argparse
import json
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import AutoImageProcessor, SwinConfig, SwinForImageClassification
def lowerCamelCase ( SCREAMING_SNAKE_CASE ):
'''simple docstring'''
__UpperCam... | 43 |
'''simple docstring'''
import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch
import datasets
import datasets.config
from .utils import require_beam
class A ( datasets.BeamBasedBuilder ):
'''simpl... | 298 | 0 |
"""simple docstring"""
import json
import os
import unittest
from transformers.models.blenderbot_small.tokenization_blenderbot_small import (
VOCAB_FILES_NAMES,
BlenderbotSmallTokenizer,
)
from ...test_tokenization_common import TokenizerTesterMixin
class SCREAMING_SNAKE_CASE_ (... | 360 |
"""simple docstring"""
import inspect
import unittest
class SCREAMING_SNAKE_CASE_ ( unittest.TestCase ):
"""simple docstring"""
def snake_case_ ( self):
try:
import diffusers # noqa: F401
except ImportError:
assert False
def snake_case_ ... | 255 | 0 |
"""simple docstring"""
import os
_UpperCAmelCase = {"""I""": 1, """V""": 5, """X""": 1_0, """L""": 5_0, """C""": 1_0_0, """D""": 5_0_0, """M""": 1_0_0_0}
def __magic_name__ ( lowercase ):
SCREAMING_SNAKE_CASE_: str =0
SCREAMING_SNAKE_CASE_: int =0
... | 173 |
"""simple docstring"""
from argparse import ArgumentParser
from datasets.commands.convert import ConvertCommand
from datasets.commands.dummy_data import DummyDataCommand
from datasets.commands.env import EnvironmentCommand
from datasets.commands.run_beam import RunBeamCommand
from datasets.commands.test... | 173 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowerCAmelCase_ : Dict = {
'''configuration_distilbert... | 371 |
'''simple docstring'''
import PIL.Image
import PIL.ImageOps
from packaging import version
from PIL import Image
if version.parse(version.parse(PIL.__version__).base_version) >= version.parse('9.1.0'):
lowerCAmelCase_ : str = {
'linear': PIL.Image.Resampling.BILINEAR,
... | 346 | 0 |
"""simple docstring"""
from math import pow
def _lowerCAmelCase ( UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , ):
if current_sum == needed_sum:
# If the sum of the powers is equal to needed_sum, then we have a solution.
solu... | 100 |
"""simple docstring"""
from __future__ import annotations
from functools import lru_cache
from math import ceil
lowerCAmelCase__ = 100
lowerCAmelCase__ = set(range(3, NUM_PRIMES, 2))
primes.add(2)
lowerCAmelCase__ = 42
for prime in range(3, ceil(NUM_PRIMES**0.5), 2):
... | 108 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
__A : Any = {
'configuration_ctrl': ['CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP', 'CTRLConfig'],
'tokenization_ctrl': ['CTRLTokenizer']... | 8 |
'''simple docstring'''
from __future__ import annotations
import unittest
import numpy as np
from transformers import BlipTextConfig
from transformers.testing_utils import require_tf, slow
from transformers.utils import is_tf_available
from ...test_configuration_common import ConfigTester
from ...test_mode... | 8 | 1 |
from itertools import count
def a_ ( __lowercase : int = 50 ) -> int:
_snake_case = [1] * min_block_length
for n in count(__lowercase ):
fill_count_functions.append(1 )
for block_length in range(__lowercase , n + 1 ):
for block_start in range(n - block_l... | 282 |
from __future__ import annotations
import time
from collections.abc import Sequence
from random import randint
from matplotlib import pyplot as plt
def a_ ( __lowercase : Sequence[float] , __lowercase : int , __lowercase : int ) -> tuple[int | None, int | None, float]:
... | 282 | 1 |
from __future__ import annotations
from typing import Dict
from ...configuration_utils import PretrainedConfig
UpperCamelCase__ = {
"""susnato/ernie-m-base_pytorch""": """https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/config.json""",
"""susnato/ernie-m-large_pytorch""": """h... | 102 |
import tempfile
import unittest
import numpy as np
import transformers
from transformers import GPTaTokenizer, GPTJConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, tooslow
from ...generation.test_flax_utils import FlaxGenerationTesterMixin... | 102 | 1 |
import argparse
import glob
import importlib.util
import os
import re
import black
from doc_builder.style_doc import style_docstrings_in_code
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
_A = 'src/diffusers'
_... | 62 | import warnings
from ..trainer import Trainer
from ..utils import logging
__lowerCamelCase : List[Any] = logging.get_logger(__name__)
class __snake_case ( lowerCamelCase_ ):
def __init__( self : Tuple , _lowercase : Optional[int]=None , *... | 219 | 0 |
import unittest
from transformers import BigBirdConfig, 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
from transformers.mode... | 14 |
import warnings
from ...utils import logging
from .image_processing_poolformer import PoolFormerImageProcessor
lowerCamelCase_ = logging.get_logger(__name__)
class a_ ( a_ ):
'''simple docstring'''
def __init__( self , ... | 14 | 1 |
import argparse
import json
from pathlib import Path
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import AutoImageProcessor, SwinvaConfig, SwinvaForImageClassification
def UpperCAmelCase ( a_ ) -> Optional[Any]:
... | 15 |
import unittest
from typing import Dict, List, Optional, Union
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin,... | 327 | 0 |
import os
import re
import shutil
import sys
import tempfile
import unittest
import black
snake_case = 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_copies # noqa: E402
# This is the referenc... | 363 |
import argparse
import os
import torch
from transformers.utils import WEIGHTS_NAME
snake_case = ["""small""", """medium""", """large"""]
snake_case = """lm_head.decoder.weight"""
snake_case = """lm_head.weight"""
def lowerCamelCase__ ( lowercase , ... | 319 | 0 |
"""simple docstring"""
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, BatchEncoding, PLBartTokenizer, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_torch,
)
from ...test_... | 102 |
"""simple docstring"""
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import add_start_docstrings
A_ = r'''
[`RagConfig`] stores the configuration of a *RagModel*. Configuration objects inherit from [`PretrainedConfig`] and
can be used to control th... | 64 | 0 |
import contextlib
import copy
import random
from typing import Any, Dict, Iterable, Optional, Union
import numpy as np
import torch
from .utils import deprecate, is_transformers_available
if is_transformers_available():
import transformers
def UpperCAmelCase__ ( UpperCAmelCase__ ) -> Optiona... | 370 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from transformers import XLMRobertaTokenizer
from diffusers import (
AltDiffusionImgaImgPipeline,
AutoencoderKL,
PNDMScheduler,
UNetaDConditionModel,
)
from diffusers.image_processor import VaeImage... | 101 | 0 |
from typing import TYPE_CHECKING
from ...utils import _LazyModule
__A : List[str] = {"tokenization_bertweet": ["BertweetTokenizer"]}
if TYPE_CHECKING:
from .tokenization_bertweet import BertweetTokenizer
else:
import sys
__A : List[Any] = _LazyModule(__name__, global... | 273 |
def __SCREAMING_SNAKE_CASE ( UpperCamelCase__ ) -> list[int]:
'''simple docstring'''
if length <= 0 or not isinstance(UpperCamelCase__ , UpperCamelCase__ ):
raise ValueError('''Length must be a positive integer.''' )
return [n * (2 * n - 1) for n in range(UpperCamelCase__ ... | 273 | 1 |
"""simple docstring"""
import random
def UpperCAmelCase__ ( lowerCAmelCase__ :Tuple , lowerCAmelCase__ :Tuple , lowerCAmelCase__ :Tuple ) -> Optional[int]:
'''simple docstring'''
lowercase = a[left_index]
lowercase = ... | 32 | """simple docstring"""
from unittest.mock import Mock, patch
from file_transfer.send_file import send_file
@patch("""socket.socket""" )
@patch("""builtins.open""" )
def UpperCAmelCase__ ( lowerCAmelCase__ :Tuple , lowerCAmelCase__ :List[str] ) -> Union[str, Any]:
'... | 32 | 1 |
'''simple docstring'''
import operator
def lowercase_ ( _lowercase , _lowercase = False , _lowercase = None ) -> list:
'''simple docstring'''
lowerCamelCase_ : Optional[Any] = operator.lt if reverse else operator.gt
lowerCamelCase_ : Optional[int] = ... | 318 |
'''simple docstring'''
import os
import unittest
from transformers import LayoutLMTokenizer, LayoutLMTokenizerFast
from transformers.models.layoutlm.tokenization_layoutlm import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
... | 318 | 1 |
'''simple docstring'''
import numpy as np
import torch
from torch.nn import CrossEntropyLoss
from transformers import AutoModelForCausalLM, AutoTokenizer
import datasets
from datasets import logging
lowerCAmelCase_ : List[Any] = '\\n\n'
lowerCAmelCase_ : Optional[Any] = '\... | 346 |
'''simple docstring'''
def _lowerCamelCase ( lowercase : int = 6008_5147_5143 ) -> int:
try:
_a = int(lowercase )
except (TypeError, ValueError):
raise TypeError("Parameter n must be int or castable to int." )
if n <= 0:
... | 346 | 1 |
'''simple docstring'''
from . import __version__
# Backward compatibility imports, to make sure all those objects can be found in file_utils
from .utils import (
CLOUDFRONT_DISTRIB_PREFIX,
CONFIG_NAME,
DISABLE_TELEMETRY,
DUMMY_INPUTS,
DUMMY_MASK,
ENV_VARS_TRUE_AND_AUTO_VALUES,
ENV_VARS... | 267 |
'''simple docstring'''
import unittest
import numpy as np
import torch
from diffusers import PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class lowerCAmelCase__ ( unitt... | 267 | 1 |
import unittest
import numpy as np
import torch
from diffusers import PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class __lowerCAmelCase ( unittest.TestCase ):
@pro... | 358 |
import os
from typing import List, Optional, Union
from ...image_processing_utils import BatchFeature
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import ... | 279 | 0 |
from collections.abc import Iterator, MutableMapping
from dataclasses import dataclass
from typing import Generic, TypeVar
__A = TypeVar("KEY")
__A = TypeVar("VAL")
@dataclass(frozen=SCREAMING_SNAKE_CASE__ , slots=SCREAMING_SNAKE_CASE__ )
class __lowerCAmelCa... | 90 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import tensorflow as tf
from transformers... | 255 | 0 |
import baseaa
def UpperCAmelCase_ (_lowerCAmelCase : str ):
return baseaa.baaencode(string.encode("utf-8" ) )
def UpperCAmelCase_ (_lowerCAmelCase : bytes ):
return baseaa.baadecode(a_ ).decode("utf-8" )
if __name__ == "__main__":
lowercase ... | 367 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
lowercase : Union[str, Any] = {"configuration_glpn": ["GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP", "GLPNConfig"]}
try:
if not is_vision_available():
r... | 171 | 0 |
"""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.kandin... | 109 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase_ = logging.get_logger(__name__)
UpperCAmelCase_ = {
'google/vivit-b-16x2-kinetics400': (
'https://huggingface.co/google/vivit-b-16x2-kinetics400/resolve/main/con... | 346 | 0 |
import os
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from ...models.controlnet import ControlNetModel, ControlNetOutput
from ...models.modeling_utils import ModelMixin
from ...utils import logging
lowercase : str = logging.get_log... | 151 |
def _SCREAMING_SNAKE_CASE ( _lowerCamelCase : List[Any] , _lowerCamelCase : str) -> Any:
'''simple docstring'''
__UpperCamelCase : Dict = 0
while b > 0:
if b & 1:
res += a
... | 151 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
lowerCAmelCase_ = {
'''configuration_ctrl''': ['''CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''CTRLConfig'''],
'''tokenization_ctrl''': ['''CTRLTokenizer'''],
... | 8 |
import functools
import gc
import inspect
import torch
from .imports import is_npu_available, is_xpu_available
def __SCREAMING_SNAKE_CASE (*SCREAMING_SNAKE_CASE__ ):
if not isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
snake_case_ = list(SCREAMI... | 8 | 1 |
"""simple docstring"""
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import is_speech_available, is_vision_available
from transformers.testing_utils import require_torch
if is_vision_available():
from transformers import TvltImage... | 362 |
"""simple docstring"""
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import is_speech_available, is_vision_available
from transformers.testing_utils import require_torch
if is_vision_available():
from transformers import TvltImageProcessor
if is_spee... | 230 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
SCREAMING_SNAKE_CASE : Dict = {
"""configuration_rag""": ["""RagConfig"""],
"""retrieval_rag""": ["""RagRetriever"""],
"""tokeniza... | 102 |
"""simple docstring"""
from __future__ import annotations
def lowercase ( _snake_case : int | str ) ->bool:
"""simple docstring"""
__snake_case : List[str] = str(_snake_case )
return n == n[::-1]
def lowercase ( _snake_case : in... | 102 | 1 |
"""simple docstring"""
import unittest
import numpy as np
import torch
from .utils_summarization import build_mask, compute_token_type_ids, process_story, truncate_or_pad
class UpperCamelCase_ ( unittest.TestCase ):
def UpperCamelCase_ ( self ) -> Tuple:
... | 248 |
"""simple docstring"""
import os
def _lowerCAmelCase ( lowerCAmelCase ):
'''simple docstring'''
UpperCAmelCase = len(grid[0] )
UpperCAmelCase = len(lowerCAmelCase )
UpperCAmelCase = 0
UpperCAmelCase ... | 248 | 1 |
import unittest
from transformers import BigBirdConfig, 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
from transformers.models.big_bird.modeling... | 14 |
_lowerCamelCase : Optional[int] = 65521
def SCREAMING_SNAKE_CASE ( lowercase_ ) -> int:
"""simple docstring"""
A__ = 1
A__ = 0
for plain_chr in plain_text:
A__ = (a + ord(lowercase_ )) % MOD_A... | 14 | 1 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class UpperCamelCase_ ( metaclass=UpperCamelCase):
"""simple docstring"""
snake_case__ : Any = ["flax"]
def __init__( self : str , *UpperCAmelCase__ : Optional[int] ... | 195 |
"""simple docstring"""
def UpperCAmelCase__ (lowerCAmelCase_ ):
'''simple docstring'''
return " ".join(
"".join(word[::-1] ) if len(lowerCAmelCase_ ) > 4 else word for word in sentence.split() )
if __name__ == "__main__":
import doctest
doctest.te... | 195 | 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... | 57 |
'''simple docstring'''
import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
UpperCamelCase = logging.get_logger(__name__)
... | 319 | 0 |
'''simple docstring'''
import unittest
from transformers import is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, require_torch, slow
if is_flax_available():
import optax
from flax.training.common_utils import onehot
... | 31 |
'''simple docstring'''
from __future__ import annotations
def SCREAMING_SNAKE_CASE__( _UpperCamelCase : float , _UpperCamelCase : float , _UpperCamelCase : float , ) -> tuple:
'''simple docstring'''
if (electron_conc, ho... | 31 | 1 |
"""simple docstring"""
import math
from typing import Optional
import numpy as np
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowercase = logging.get_logger(__name__)
__lowercase = {
"""facebook/encodec_24khz""": ... | 40 |
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__ :int = logging.get_logger(__name__)
lowercase__ :Any = {
"sail/poolformer_s12": "... | 101 | 0 |
'''simple docstring'''
import jax.numpy as jnp
from ...utils import logging
from ..ta.modeling_flax_ta import FlaxTaEncoderModel, FlaxTaForConditionalGeneration, FlaxTaModel
from .configuration_mta import MTaConfig
lowerCamelCase_ = logging.get_logger(__name__)
lowerCamelCase_ = '''T5Config... | 360 |
'''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_ = {
'''camembert-base''': ... | 174 | 0 |
import os
import pickle
import unittest
from transformers import AutoTokenizer
from transformers.models.bert.tokenization_bert import BertTokenizer
from transformers.models.bert_japanese.tokenization_bert_japanese import (
VOCAB_FILES_NAMES,
BertJapaneseTokenizer,
CharacterTokenizer,
Ju... | 32 |
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_t... | 32 | 1 |
"""simple docstring"""
import pyarrow.parquet as pq
import pytest
from datasets import Audio, Dataset, DatasetDict, Features, NamedSplit, Sequence, Value, config
from datasets.features.image import Image
from datasets.io.parquet import ParquetDatasetReader, ParquetDatasetWriter, get_writer_batch_size
... | 163 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
a_ = {
"co... | 163 | 1 |
'''simple docstring'''
import numpy as np
import torch
from torch.nn import CrossEntropyLoss
from transformers import AutoModelForCausalLM, AutoTokenizer
import datasets
from datasets import logging
UpperCAmelCase_ = '\\n\n'
UpperCAmelCase_ = '\nPerplexity (PPL) is one of the most common me... | 346 |
'''simple docstring'''
import itertools
import os
from collections import Counter, defaultdict
from concurrent.futures import ThreadPoolExecutor, as_completed
import numpy as np
import datasets
from .execute import check_correctness
UpperCAmelCase_ = '\\n@misc{chen2021evaluating,\n title={Eva... | 346 | 1 |
"""simple docstring"""
__lowercase = 0 # The first color of the flag.
__lowercase = 1 # The second color of the flag.
__lowercase = 2 # The third color of the flag.
__lowercase = (red, white, blue)
def lowerCAmelCase (__UpperCamelCase : list ):
"""simple docstri... | 353 | """simple docstring"""
from __future__ import annotations
from collections.abc import Iterator
from typing import Any
class _lowercase :
"""simple docstring"""
def __init__( self : int , UpperCamelCase__ : Any ) -> Optional... | 85 | 0 |
import math
def a__ ( _UpperCamelCase : List[Any] ):
__lowerCamelCase = [True] * n
__lowerCamelCase = False
__lowerCamelCase = False
__lowerCamelCase = True
for i in range(3 ,int(n**0.5 + 1 ) ,2 ):
__lo... | 330 |
import json
import os
import unittest
from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES, BioGptTokenizer
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTesterMixin
class __lowerCAmelCase ( _a, unittest.TestCase ):
... | 279 | 0 |
'''simple docstring'''
from __future__ import annotations
from PIL import Image
# Define glider example
__SCREAMING_SNAKE_CASE :List[Any] = [
[0, 1, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0, 0],
[1, 1, 1, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
... | 363 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__SCREAMING_SNAKE_CASE :Dict = {
'''configuration_upernet''': ['''UperNetConfig'''],
}
try:
if not is_torch_available():
raise OptionalDepende... | 156 | 0 |
import warnings
from ...utils import logging
from .image_processing_beit import BeitImageProcessor
_lowerCamelCase : int = logging.get_logger(__name__)
class UpperCamelCase_ ( UpperCAmelCase__ ):
'''simple docstring'''
def __init__( self : Dict , *UpperCAme... | 14 |
"""simple docstring"""
import unittest
import numpy as np
from transformers import RobertaConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available():
... | 171 | 0 |
"""simple docstring"""
import argparse
import gc
import json
import os
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 a... | 112 |
"""simple docstring"""
import argparse
import re
from flax.traverse_util import flatten_dict, unflatten_dict
from tax import checkpoints
from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration
from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch... | 112 | 1 |
'''simple docstring'''
from __future__ import annotations
import inspect
import unittest
import numpy as np
from transformers import ResNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_availabl... | 151 |
'''simple docstring'''
import datasets
from .nmt_bleu import compute_bleu # From: https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py
lowercase__ = "\\n@INPROCEEDINGS{Papineni02bleu:a,\n author = {Kishore Papineni and Salim Roukos and Todd Ward and Wei-jing Zhu},\n title = {B... | 151 | 1 |
import itertools
import random
import unittest
import numpy as np
from transformers import ASTFeatureExtractor
from transformers.testing_utils import require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available
from ...test_sequence_feature_extraction_common import SequenceFeatu... | 369 |
import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union
import sentencepiece
from ...tokenization_utils import BatchEncoding, PreTrainedTokenizer
from ...utils import logging
__A = logging.get_logger(__name__)
__A = ... | 278 | 0 |
"""simple docstring"""
import argparse
import pickle
import numpy as np
import torch
from torch import nn
from transformers import ReformerConfig, ReformerModelWithLMHead
from transformers.utils import logging
logging.set_verbosity_info()
def lowerCAmelCase__ ( _UpperCamelCase : Op... | 150 |
import copy
from typing import Any, Dict, List, Optional, Union
import numpy as np
import torch
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ... | 230 | 0 |
import os
import sys
import unittest
snake_case : 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, create_dummy_object, find_backend,... | 41 |
import argparse
from pathlib import Path
from typing import Dict, OrderedDict, Tuple
import torch
from audiocraft.models import MusicGen
from transformers import (
AutoFeatureExtractor,
AutoTokenizer,
EncodecModel,
MusicgenDecoderConfig,
MusicgenForConditionalGeneration,
MusicgenProcessor,
... | 41 | 1 |
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 Accelerator, DistributedTy... | 248 |
import itertools
import os
import random
import tempfile
import unittest
import numpy as np
from datasets import load_dataset
from transformers import is_speech_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_utils... | 248 | 1 |
from collections import defaultdict
class lowerCamelCase :
def __init__(self : Tuple , _A : Optional[int] , _A : List[str] ) -> Union[str, Any]:
snake_case = total # total no of tasks (N)
# DP table will have a... | 137 |
from __future__ import annotations
from decimal import Decimal
from numpy import array
def lowercase_ ( A__ ) -> list[list[float]]:
"""simple docstring"""
snake_case = Decimal
# Check if the provided matrix has 2 rows and 2 columns
# since this... | 137 | 1 |
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE = 200_0000 ):
lowercase = [0 for i in range(n + 1 )]
lowercase = 1
lowercase = 1
for i in range(2 , int(n**0.5 ) + 1 ):
if primality_list[i] == 0:
for j in range(i * i , n + 1 , __S... | 195 |
import pprint
import requests
UpperCAmelCase = '''https://zenquotes.io/api'''
def UpperCAmelCase_ ( ):
return requests.get(API_ENDPOINT_URL + '/today' ).json()
def UpperCAmelCase_ ( ):
return requests.get(API_ENDPOINT_URL + '/random' ).json()
if __name__ ... | 195 | 1 |
'''simple docstring'''
def _lowerCAmelCase ( _UpperCamelCase : int = 50 ) -> int:
"""simple docstring"""
_SCREAMING_SNAKE_CASE =[[0] * 3 for _ in range(length + 1 )]
for row_length in range(length + 1 ):
for tile_length in range(2 , 5 )... | 114 |
'''simple docstring'''
def _lowerCAmelCase ( _UpperCamelCase : str ) -> bool:
"""simple docstring"""
_SCREAMING_SNAKE_CASE =0
for ch in input_str:
_SCREAMING_SNAKE_CASE =ord(_UpperCamelCase )
_SCREAMING_SNAKE_CASE =pow(2 ... | 114 | 1 |
'''simple docstring'''
import argparse
import hashlib
import os
import urllib
import warnings
import torch
from torch import nn
from tqdm import tqdm
from transformers import WhisperConfig, WhisperForConditionalGeneration
__SCREAMING_SNAKE_CASE : List[Any] = {
"""tiny.en""": """https://openai... | 31 | '''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 transforme... | 31 | 1 |
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required... | 359 | 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, CLIPTokenizerF... | 81 | 0 |
from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments
def lowerCAmelCase__ ( ):
"""simple docstring"""
__a = HfArgumentParser(_SCREAMING_SNAKE_CASE )
__a = parser.parse_args_into_dataclasses()[0]
__a = T... | 302 |
'''simple docstring'''
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,... | 174 | 0 |
import random
import unittest
import numpy as np
from diffusers import (
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionImgaImgPipeline,
PNDMScheduler,
)
from diffusers.utils import floats_tensor
from diffusers.... | 370 |
from ...utils import is_note_seq_available, is_transformers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy... | 173 | 0 |
'''simple docstring'''
from arguments import InitializationArguments
from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, HfArgumentParser
# Configuration
__A =HfArgumentParser(InitializationArguments)
__A =parser.parse_args()
# Load codeparrot tokenizer trained for Pyth... | 163 |
'''simple docstring'''
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import ConvNextConfig, SegformerImageProcessor, UperNetConfig, UperNetForSemanticSegmentation
def _UpperCamelCase ... | 163 | 1 |
from math import atan, cos, radians, sin, tan
from .haversine_distance import haversine_distance
lowerCAmelCase_ = 637_8137.0
lowerCAmelCase_ = 635_6752.31_4245
lowerCAmelCase_ = 637_8137
def lowerCamelCase_ ( lowerCAmelCase: float , lowerCAmelCase: float ... | 260 |
import argparse
import torch
from transformers import RemBertConfig, RemBertModel, load_tf_weights_in_rembert
from transformers.utils import logging
logging.set_verbosity_info()
def lowerCamelCase_ ( lowerCAmelCase: List[str] , lowerCAmelCase: int , lowerCAmelCase: List[Any] ... | 260 | 1 |
'''simple docstring'''
import pytest
import datasets.config
from datasets.utils.info_utils import is_small_dataset
@pytest.mark.parametrize('''dataset_size''' , [None, 4_0_0 * 2**2_0, 6_0_0 * 2**2_0] )
@pytest.mark.parametrize('''input_in_memory_max_size''' , ['''default''', 0, 1_0_0... | 304 |
'''simple docstring'''
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversation
... | 85 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
A: int = {"configuration_xglm": ... | 76 |
"""simple docstring"""
def _snake_case ( UpperCamelCase : list , UpperCamelCase : list ):
_validate_point(UpperCamelCase )
_validate_point(UpperCamelCase )
if len(UpperCamelCase ) != len(UpperCamelCase ):
raise ValueError("""Both points must be in the sa... | 76 | 1 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class snake_case ( metaclass=lowerCAmelCase_ ):
SCREAMING_SNAKE_CASE_ : Tuple = ['''speech''']
def __init__( self : List[Any] , *UpperCamelCase__ : str , **UpperC... | 217 |
import numpy as np
from PIL import Image
def UpperCAmelCase_ ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> np.ndarray:
__lowercase : Optional[int] = np.array(__lowerCAmelCase )
if arr.shape[0] != arr.shape[1]:
raise ValueError('''The input ... | 156 | 0 |
"""simple docstring"""
import gc
import unittest
import torch
from parameterized import parameterized
from diffusers import AutoencoderKL
from diffusers.utils import floats_tensor, load_hf_numpy, require_torch_gpu, slow, torch_all_close, torch_device
from diffusers.utils.import_utils import is_xformers_avai... | 361 |
from .glue import glue_convert_examples_to_features, glue_output_modes, glue_processors, glue_tasks_num_labels
from .squad import SquadExample, SquadFeatures, SquadVaProcessor, SquadVaProcessor, squad_convert_examples_to_features
from .utils import DataProcessor, InputExample, InputFeatures, SingleSentenceClassif... | 262 | 0 |
'''simple docstring'''
import sacrebleu as scb
from packaging import version
from sacrebleu import CHRF
import datasets
UpperCamelCase__ : int = '''\
@inproceedings{popovic-2015-chrf,
title = "chr{F}: character n-gram {F}-score for automatic {MT} evaluation",
author = "Popovi{\'c}, Maja",
... | 112 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCamelCase__ : Any = {
'''configuration_instructblip''': [
'''INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''InstructBlipConfig''',
... | 112 | 1 |
'''simple docstring'''
lowercase : Tuple = '0.21.0'
from .accelerator import Accelerator
from .big_modeling import (
cpu_offload,
cpu_offload_with_hook,
disk_offload,
dispatch_model,
init_empty_weights,
init_on_device,
load_checkpoint_and_dispatch,
)
... | 311 |
'''simple docstring'''
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase : Optional[int] = logging.get_logger(__name__)
lowercase : Tuple = {
'google/pix2struct-textcap... | 311 | 1 |
a ={
"""Pillow""": """Pillow<10.0.0""",
"""accelerate""": """accelerate>=0.20.3""",
"""av""": """av==9.2.0""",
"""beautifulsoup4""": """beautifulsoup4""",
"""black""": """black~=23.1""",
"""codecarbon""": """codecarbon==1.2.0""",
"""cookiecutter""": """cookiecutter==1.7.3""",
... | 73 |
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,
... | 278 | 0 |
'''simple docstring'''
class __magic_name__ :
def __init__( self , snake_case , snake_case , snake_case) -> Optional[Any]:
'''simple docstring'''
_UpperCAmelCase : int =None
_UpperCAmelCase : Op... | 362 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase =logging.get_logger(__name__)
lowercase ={
'vinvino02/glpn-kitti': 'https://huggingface.co/vinvino02/glpn-kitti/resolve/main/config.json',
# See all GLPN models at https://hu... | 242 | 0 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_owlvit import OwlViTImageProcessor
_A : Dict =logging.get_logger(__name__)
class _lowercase ( _lowercase ):
def __init__( self: Optional[int] , ... | 41 |
'''simple docstring'''
import argparse
from pathlib import Path
from typing import Dict, OrderedDict, Tuple
import torch
from audiocraft.models import MusicGen
from transformers import (
AutoFeatureExtractor,
AutoTokenizer,
EncodecModel,
MusicgenDecoderConfig,
... | 41 | 1 |
"""simple docstring"""
import argparse
import torch
from safetensors.torch import load_file
from diffusers import StableDiffusionPipeline
def _lowerCAmelCase ( lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ ):
# load base model
... | 359 |
"""simple docstring"""
from collections import deque
class A_ :
"""simple docstring"""
def __init__( self :Any , lowercase_ :str , lowercase_ :int , lowercase_ :int ) -> None:
UpperCAmelCase = pr... | 181 | 0 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image
from transformers import DPTConfig, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTImageProcessor
from transformers.utils import logging
loggi... | 137 |
import logging
import math
import os
from dataclasses import dataclass, field
from glob import glob
from typing import Optional
from torch.utils.data import ConcatDataset
import transformers
from transformers import (
CONFIG_MAPPING,
MODEL_WITH_LM_HEAD_MAPPING,
AutoConfig,
AutoModelWithLMHead,
... | 137 | 1 |
"""simple docstring"""
def _lowerCAmelCase ( UpperCAmelCase__ : list[list] ) ->list[list]:
A__ : Tuple = current_set.copy()
for row_index, row in enumerate(UpperCAmelCase__ ):
A__ : Union[str, Any] = row[0]
f... | 296 |
"""simple docstring"""
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image
from transformers import DPTConfig, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTImageProcessor
from transformers.ut... | 296 | 1 |
import flax.linen as nn
import jax
import jax.numpy as jnp
class a ( nn.Module ):
"""simple docstring"""
a : int
a : jnp.dtype = jnp.floataa
def UpperCAmelCase ( self : Optional[int] ) -> str:
__Upper... | 114 |
from functools import lru_cache
@lru_cache
def lowerCamelCase__ ( __lowerCamelCase : int ):
if num < 0:
raise ValueError("""Number should not be negative.""" )
return 1 if num in (0, 1) else num * factorial(num - 1 )
if __name__ == "__main__":
i... | 114 | 1 |
'''simple docstring'''
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING
__lowerCAmelCase : Any =logging.get_logger(__name__)
class UpperCAmelCase ( UpperCamelCase__ ):
__lowerc... | 353 |
'''simple docstring'''
import argparse
from pathlib import Path
import torch
from transformers import OPTConfig, OPTModel
from transformers.utils import logging
logging.set_verbosity_info()
__lowerCAmelCase : List[str] =logging.get_logger(__name__)
def UpperCamelCase ( _lo... | 123 | 0 |
UpperCAmelCase__ = """0.18.2"""
from .configuration_utils import ConfigMixin
from .utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_inflect_available,
is_invisible_watermark_available,
is_k_diffusion_available,
is_k_diffusion_version,
is_librosa_available,
... | 339 |
"""simple docstring"""
import copy
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 ..auto import CONFIG_MAPPING
lowerCame... | 81 | 0 |
import sys
import webbrowser
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
if __name__ == "__main__":
print('''Googling.....''')
lowerCAmelCase_ : Optional[int] = '''https://www.google.com/search?q=''' + ''' '''.join(sys.argv[1:])
lowerCAmelCase_ : ... | 361 |
"""simple docstring"""
import math
import random
from typing import Any
from .hill_climbing import SearchProblem
def _lowerCAmelCase ( lowerCAmelCase , lowerCAmelCase = True , lowerCAmelCase = math.inf , lowerCAmelCase = -math.inf , lowerCAmelCase = math.inf , lowerCAmelCase = -math.inf ,... | 248 | 0 |
import requests
from bsa import BeautifulSoup
def lowerCAmelCase ( _lowerCAmelCase : Optional[Any] = "https://www.worldometers.info/coronavirus" ):
"""simple docstring"""
UpperCAmelCase__ = BeautifulSoup(requests.get(_lowerCAmelCase ).text , "html.parse... | 169 |
"""simple docstring"""
# Copyright 2023 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/LICENS... | 173 | 0 |
from __future__ import annotations
import csv
import requests
from bsa import BeautifulSoup
def UpperCamelCase( lowercase_ = "" ) -> dict[str, float]:
'''simple docstring'''
snake_case_ = url or """https://www.imdb.com/chart/top/?ref_=nv_mv_250"""
snake_case_ = ... | 34 |
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_ = logging.get_logger(__name__)
lowerCamelCase_ = {
'''google/mobilenet... | 34 | 1 |
"""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... | 260 |
"""simple docstring"""
import pytest
import datasets
# Import fixture modules as plugins
__A : int = ["tests.fixtures.files", "tests.fixtures.hub", "tests.fixtures.fsspec"]
def lowercase ( _SCREAMING_SNAKE_CASE : Any , _SCREAMING_SNAKE_CASE ... | 260 | 1 |
'''simple docstring'''
from collections import deque
from math import floor
from random import random
from time import time
class __lowerCAmelCase :
def __init__(self ):
_UpperCAmelCase : Tuple = {}
def snake_case_ (self , ... | 352 |
'''simple docstring'''
from typing import List, Optional, Tuple, Union
import torch
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class __lowerCAmelCase ( __a ):
def __init_... | 170 | 0 |
import multiprocessing
import time
from arguments import PretokenizationArguments
from datasets import load_dataset
from transformers import AutoTokenizer, HfArgumentParser
def lowerCamelCase__ ( _a):
SCREAMING_SNAKE_CASE : int = {}
SCREAMING_SNAKE_CASE : Any = token... | 76 |
from math import factorial
def lowerCamelCase__ ( _a , _a , _a):
if successes > trials:
raise ValueError("successes must be lower or equal to trials")
if trials < 0 or successes < 0:
raise ValueError("the function is defined for non-negative integers")
if not isinstance(_a , ... | 76 | 1 |
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__... | 371 |
def SCREAMING_SNAKE_CASE__ ( __a = 60_08_51_47_51_43 ):
try:
snake_case_ : Optional[Any] = int(__a )
except (TypeError, ValueError):
raise TypeError('Parameter n must be int or castable to int.' )
if n <= 0:
raise ValueError('Parameter n must be greater t... | 88 | 0 |
"""simple docstring"""
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set... | 255 |
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,
)
fro... | 262 | 0 |
def UpperCAmelCase__ ( lowerCamelCase ):
lowercase :list[list[float]] = []
for data in source_data:
for i, el in enumerate(lowerCamelCase ):
if len(lowerCamelCase ) < i + 1:
data_lists.append([] )
data_lists[i].append(float(lowerCamelCase ) )
retur... | 350 |
import pytest
_UpperCAmelCase : List[Any] = "__dummy_dataset1__"
_UpperCAmelCase : Union[str, Any] = "\nimport json\nimport os\n\nimport datasets\n\n\nREPO_URL = \"https://huggingface.co/datasets/albertvillanova/tests-raw-jsonl/resolve/main/\"\nURLS = {\"train\": REPO_URL + \"wikiann-bn-... | 158 | 0 |
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