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
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DPMSolverMultistepScheduler,
TextToVideoSDPipeline,
UNetaDConditionModel,
)
from diffusers.utils impo... | 277 |
'''simple docstring'''
from pathlib import Path
from typing import List
from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import get_tests_dir, is_tool_test
from transformers.tools.agent_types import AGENT_TYPE_MAPPING, AgentAudio, AgentImage, AgentText
if is_... | 185 | 0 |
from __future__ import annotations
import requests
def __lowercase ( __lowerCAmelCase : str ):
a__ = F'https://hacker-news.firebaseio.com/v0/item/{story_id}.json?print=pretty'
return requests.get(__lowerCAmelCase ).json()
def __lowercase ( ... | 657 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
snake_case : Any = {
'''configuration_mobilevit''': ['''MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MobileViTConf... | 657 | 1 |
"""simple docstring"""
from __future__ import annotations
from collections import deque
from collections.abc import Iterator
from dataclasses import dataclass
@dataclass
class UpperCamelCase :
lowercase = 42
lowercase = 42
class UpperCamelCase :
def __init__( ... | 425 | """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 lowercase__( __SCREAMING_SNAKE_CASE : Any , __SCREAMING_SNAKE_CASE : L... | 425 | 1 |
'''simple docstring'''
import unittest
import numpy as np
from datasets import load_dataset
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, prepa... | 694 |
'''simple docstring'''
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 snake_case ( __low... | 694 | 1 |
from ..utils import is_flax_available, is_torch_available
if is_torch_available():
from .autoencoder_kl import AutoencoderKL
from .controlnet import ControlNetModel
from .dual_transformer_ad import DualTransformeraDModel
from .modeling_utils import ModelMixin
from .prior_transformer import Pr... | 10 |
"""simple docstring"""
import itertools
import json
import os
import unittest
from transformers import AddedToken, LongformerTokenizer, LongformerTokenizerFast
from transformers.models.longformer.tokenization_longformer import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, s... | 554 | 0 |
"""simple docstring"""
def UpperCAmelCase ( UpperCamelCase__ , UpperCamelCase__ ):
"""simple docstring"""
_enforce_args(UpperCamelCase__ , UpperCamelCase__ )
if n == 0:
return 0
A__ = float('-inf' )... | 536 | """simple docstring"""
import unittest
import torch
from torch import nn
from diffusers.models.activations import get_activation
class UpperCamelCase__( unittest.TestCase ):
def snake_case__ ( self ) -> str:
A__ = get_activation('swis... | 536 | 1 |
"""simple docstring"""
from .data_collator import (
DataCollatorForLanguageModeling,
DataCollatorForPermutationLanguageModeling,
DataCollatorForSeqaSeq,
DataCollatorForSOP,
DataCollatorForTokenClassification,
DataCollatorForWholeWordMask,
DataCollatorWithPadding,
Def... | 49 |
"""simple docstring"""
def lowercase__ ( snake_case_ :Dict ): # noqa: E741
__UpperCAmelCase = len(snake_case_ )
__UpperCAmelCase = 0
__UpperCAmelCase = [0] * n
__UpperCAmelCase = [False] * n
__UpperCAmelCase = [False] * n
def dfs(sn... | 49 | 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, SingleSentenceClassific... | 456 |
import unittest
from transformers import XLMConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids... | 456 | 1 |
"""simple docstring"""
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()
UpperCamelCase = logging.get_logger(__name__)
def _... | 104 |
from typing import List
from .keymap import KEYMAP, get_character
def lowercase__ ( A_: str ) -> str:
"""simple docstring"""
def decorator(A_: int ):
__UpperCAmelCase =getattr(A_ , """handle_key""" , [] )
... | 68 | 0 |
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 lowerCamelCase__ ( __lowerCAmelCase : Dict ... | 705 |
import inspect
import jax
import jax.lax as lax
import jax.numpy as jnp
from ..utils import add_start_docstrings
from ..utils.logging import get_logger
_A = get_logger(__name__)
_A = R"\n Args:\n input_ids (`jnp.ndarray` of shape `(batch_size, sequence_length)`):\n Indices of input... | 279 | 0 |
"""simple docstring"""
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, p... | 139 |
import unittest
from typing import Tuple
import torch
from diffusers.utils import floats_tensor, randn_tensor, torch_all_close, torch_device
from diffusers.utils.testing_utils import require_torch
@require_torch
class lowerCAmelCase__ :
'''simple docstring'''
@property
def _lowe... | 503 | 0 |
'''simple docstring'''
import warnings
from typing import List, Optional, Union
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils imp... | 717 |
'''simple docstring'''
import unittest
import numpy as np
from transformers import RobertaPreLayerNormConfig, 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
... | 40 | 0 |
"""simple docstring"""
import argparse
import logging
import os
import sys
import numpy as np
import onnxruntime
import torch
from bart_onnx.generation_onnx import BARTBeamSearchGenerator
from bart_onnx.reduce_onnx_size import remove_dup_initializers
import transformers
from transformers import BartForConditi... | 624 |
"""simple docstring"""
import collections
import os
import re
from pathlib import Path
lowerCamelCase__ = "src/transformers"
# Matches is_xxx_available()
lowerCamelCase__ = re.compile(R"is\_([a-z_]*)_available()")
# Catches a one-line _import_struct = {xxx}
lowerCamelCase__ = re.compile(R"^_i... | 624 | 1 |
import json
import os
from datetime import date
from pathlib import Path
from tabulate import DataRow, TableFormat, tabulate
_lowerCamelCase : Optional[Any] = TableFormat(
lineabove=None,
linebelowheader=None,
linebetweenrows=None,
linebelow=None,
headerrow=DataRow("", ... | 196 |
import cva
import numpy as np
class __snake_case :
def __init__( self : List[str] , _UpperCAmelCase : float , _UpperCAmelCase : int ) -> int:
'''simple docstring'''
if k in (0.04, 0.06):
_lowerCAmelCase : str = ... | 196 | 1 |
import math
def UpperCAmelCase_ ( UpperCAmelCase__ ):
return math.sqrt(SCREAMING_SNAKE_CASE_ ) * math.sqrt(SCREAMING_SNAKE_CASE_ ) == num
def UpperCAmelCase_ ( UpperCAmelCase__ ):
lowercase_ = 0
lowercase_ = n
while left <= right:
lowercase_... | 412 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
_SCREAMING_SNAKE_CASE = {
"tiiuae/falcon-40b": "https://huggingface.co/tiiuae/falcon-40b/resolve/main/config.json",
"tiiua... | 18 | 0 |
'''simple docstring'''
import json
import os
import pickle
import shutil
import tempfile
from unittest import TestCase
from unittest.mock import patch
import numpy as np
from datasets import Dataset
from transformers import is_faiss_available
from transformers.models.bart.conf... | 666 |
'''simple docstring'''
from dataclasses import dataclass, field
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union
import pyarrow as pa
if TYPE_CHECKING:
from .features import FeatureType
@dataclass
class SCREAMING_SNAKE_CASE :
snak... | 666 | 1 |
'''simple docstring'''
from .imports import is_tqdm_available
if is_tqdm_available():
from tqdm.auto import tqdm as _tqdm
from ..state import PartialState
def a_ ( UpperCamelCase_ = True , *UpperCamelCase_ , **UpperCamelCase_ ):
if not is_tqdm_available():
r... | 452 |
'''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,
)
if is_sentencepiece_available():
from ..ta.t... | 452 | 1 |
class _lowerCAmelCase :
"""simple docstring"""
def __init__( self : Tuple ) -> None:
"""simple docstring"""
__lowercase = {} # Mapping from char to TrieNode
__lowercase = False
def s... | 634 |
from __future__ import annotations
from collections.abc import Callable
UpperCamelCase__ = list[list[float | int]]
def UpperCAmelCase__ ( lowercase__ , lowercase__ ) -> Matrix:
__lowercase = len(lowercase__ )
__lowercase = [[0 for _ ... | 634 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
class __A ( A ):
'''simple docstring'''
__lowerCamelCase : Any = 'bert-generation'
def __init__(self , A=50_358 , A=1_024 , A=24 , A=16 , A=4_096 , A="gelu" , A... | 11 |
"""simple docstring"""
import logging
import os
import threading
import time
try:
import warnings
except ImportError:
__snake_case = None
try:
import msvcrt
except ImportError:
__snake_case = None
try:
import fcntl
except ImportError:
__snake_case = None
... | 200 | 0 |
"""simple docstring"""
import itertools
import os
import random
import tempfile
import unittest
import numpy as np
from transformers import TvltFeatureExtractor, is_datasets_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.uti... | 370 |
"""simple docstring"""
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...file_utils import TensorType, is_torch_available
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfig... | 370 | 1 |
import unicodedata
from dataclasses import dataclass
from typing import Optional, Union
import numpy as np
from transformers.data.data_collator import DataCollatorMixin
from transformers.file_utils import PaddingStrategy
from transformers.tokenization_utils_base import PreTrainedTokenizerBase
def UpperCAm... | 534 | import torch
from torch import nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class _UpperCamelCase ( _UpperCAmelCase ,_UpperCAmelCase ):
"""simple docstring"""
@register_to_config
def __init__( self , *,
lowerCAmelCase__ = 4... | 534 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A: Optional[int] = logging.get_logger(__name__)
A: Any = {
"SCUT-DLVCLab/lilt-roberta-en-base": (
"https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base/resolve/main/con... | 721 |
"""simple docstring"""
import math
import torch
from torch import nn
from ..configuration_utils import ConfigMixin, register_to_config
from .attention_processor import Attention
from .embeddings import get_timestep_embedding
from .modeling_utils import ModelMixin
class SCREAMING_SNAKE_CASE__ ( Upp... | 359 | 0 |
'''simple docstring'''
from __future__ import annotations
import matplotlib.pyplot as plt # type: ignore
import numpy
# initial triangle of Koch snowflake
__A =numpy.array([0, 0])
__A =numpy.array([0.5, 0.8_6_6_0_2_5_4])
__A =numpy.array([1, 0])
__A =[VECTOR_1, VECTOR_2, VEC... | 407 |
'''simple docstring'''
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavin... | 407 | 1 |
"""simple docstring"""
import functools
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE : list[int] , SCREAMING_SNAKE_CASE : list[int] ):
'''simple docstring'''
if not isinstance(a_ , a_ ) or not all(isinstance... | 705 |
"""simple docstring"""
from typing import Optional, Tuple, Union
import tensorflow as tf
from ...activations_tf import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
from ...modeling_tf_outputs import (
TFBaseM... | 363 | 0 |
import torch
def UpperCAmelCase__ ( ):
if torch.cuda.is_available():
__a : str = torch.cuda.device_count()
else:
__a : List[str] = 0
print(f'''Successfully ran on {num_gpus} GPUs''' )
if __name__ == "__main__":
m... | 47 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
SCREAMING_SNAKE_CASE__ = {
'''configuration_bridgetower''': [
'''BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''BridgeTowerC... | 47 | 1 |
import argparse
import glob
import logging
import os
from argparse import Namespace
from importlib import import_module
import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_score
fro... | 701 |
def _snake_case ( __snake_case , __snake_case ) -> int:
'''simple docstring'''
return int((input_a, input_a).count(0 ) != 0 )
def _snake_case ( ) -> None:
'''simple docstring'''
assert nand_gate(0 , 0 ) ... | 455 | 0 |
import collections
import os
from typing import List, Optional, Tuple
from transformers.utils import is_jieba_available, requires_backends
if is_jieba_available():
import jieba
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
snake_case__ : Any... | 23 |
import shutil
import tempfile
import unittest
from unittest.mock import patch
from transformers import (
DefaultFlowCallback,
IntervalStrategy,
PrinterCallback,
ProgressCallback,
Trainer,
TrainerCallback,
TrainingArguments,
is_torch_available,
)
from transformers.testing_utils imp... | 411 | 0 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import center_crop, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
IMAGENET_STANDARD_MEAN,
IMAG... | 594 | from __future__ import annotations
from math import pi
from typing import Protocol
import matplotlib.pyplot as plt
import numpy as np
class _a ( lowerCamelCase_ ):
"""simple docstring"""
def __lowerCAmelCase ( self , lowerCAmelCase_ ):
return 0.0
def __lowerCamelCase ... | 594 | 1 |
import numpy as np
from numpy import ndarray
from scipy.optimize import Bounds, LinearConstraint, minimize
def lowercase_ ( SCREAMING_SNAKE_CASE : ndarray ):
"""simple docstring"""
return np.dot(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
c... | 381 |
import argparse
import re
from typing import Dict
import torch
from datasets import Audio, Dataset, load_dataset, load_metric
from transformers import AutoFeatureExtractor, pipeline
def lowercase_ ( SCREAMING_SNAKE_CASE : Dataset , SCREAMING_SNAKE_CASE : Dic... | 381 | 1 |
'''simple docstring'''
from .integrations import (
is_optuna_available,
is_ray_available,
is_sigopt_available,
is_wandb_available,
run_hp_search_optuna,
run_hp_search_ray,
run_hp_search_sigopt,
run_hp_search_wandb,
)
from .trainer_utils import (
HPSearchBackend,
default_hp_spa... | 609 |
'''simple docstring'''
from argparse import ArgumentParser
from ..pipelines import Pipeline, PipelineDataFormat, get_supported_tasks, pipeline
from ..utils import logging
from . import BaseTransformersCLICommand
a : Any = logging.get_logger(__name__) # pylint: disable=invalid-name
def low... | 609 | 1 |
'''simple docstring'''
def snake_case_ ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE : Optional[Any] = """"""
for word_or_phrase in separated:
if not isinstance(A_ , A_ ):
raise Exception("""j... | 533 | '''simple docstring'''
import argparse
import os
import torch
from diffusers import (
CMStochasticIterativeScheduler,
ConsistencyModelPipeline,
UNetaDModel,
)
__snake_case : List[Any] = {
'''sample_size''': 32,
'''in_channels''': 3,
'''out_channels''': 3,
'''layers_per_block... | 660 | 0 |
import argparse
import os.path as osp
import re
import torch
from safetensors.torch import load_file, save_file
# =================#
# UNet Conversion #
# =================#
a_ : List[Any] = [
# (stable-diffusion, HF Diffusers)
("time_embed.0.weight", "time_embedding.linear_1.weight"),
... | 673 |
#
# This a `torch.distributed` diagnostics script that checks that all GPUs in the cluster (one or
# many nodes) can talk to each other via nccl and allocate gpu memory.
#
# To run first adjust the number of processes and nodes:
#
# python -m torch.distributed.run --nproc_per_node 2 --nnodes 1 torch-distributed-gpu-t... | 673 | 1 |
'''simple docstring'''
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm import create_model
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform
from trans... | 92 |
from typing import TYPE_CHECKING
from ...utils import _LazyModule
__UpperCAmelCase = {"""tokenization_wav2vec2_phoneme""": ["""Wav2Vec2PhonemeCTCTokenizer"""]}
if TYPE_CHECKING:
from .tokenization_wavaveca_phoneme import WavaVecaPhonemeCTCTokenizer
else:
import sys
__UpperCAmelC... | 651 | 0 |
"""simple docstring"""
import argparse
import re
import numpy as np
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
SamConfig,
SamImageProcessor,
SamModel,
SamProcessor,
SamVisionConfig,
)
__lowercase ... | 703 | """simple docstring"""
import sys
__lowercase = (
'''73167176531330624919225119674426574742355349194934'''
'''96983520312774506326239578318016984801869478851843'''
'''85861560789112949495459501737958331952853208805511'''
'''12540698747158523863050715693290963295227443043557'''
... | 296 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCAmelCase = logging.get_logger(__name__)
_lowerCAmelCase = {}
class _UpperCAmelCase ( _lowerCamelCase ):
a = '''llama'''
a = ['''past_key_values''']
def __in... | 569 |
import json
import os
import re
import unittest
from transformers import CodeGenTokenizer, CodeGenTokenizerFast
from transformers.models.codegen.tokenization_codegen import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMi... | 569 | 1 |
from math import pow
def lowerCAmelCase_ ( UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , ) -> Dict:
if current_sum == needed_sum:
# If the sum of the powers is equal to needed_sum, then we have a solution.
... | 712 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer
from ...utils import logging
_UpperCAmelCase = logging.get_logger(__name__)
_UpperCAmelCase =... | 371 | 0 |
"""simple docstring"""
import unittest
from knapsack import knapsack as k
class _UpperCAmelCase ( unittest.TestCase ):
"""simple docstring"""
def a__ ( self ) -> List[str]:
_lowerCamelCase : Optional[int] = 0
_lowe... | 434 | """simple docstring"""
import argparse
import json
import subprocess
def UpperCamelCase ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) ->Any:
_lowerCamelCase : List[str] = []
_lowerCamelCase : Optional[int] = (
F'''curl -H "Accept: appli... | 434 | 1 |
import unittest
from transformers.utils.backbone_utils import (
BackboneMixin,
get_aligned_output_features_output_indices,
verify_out_features_out_indices,
)
class lowercase__( unittest.TestCase ):
'''simple docstring'''
def UpperCAmelCase ( self) -> Tu... | 582 |
import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path
import torch_xla.distributed.xla_multiprocessing as xmp
def _lowerCamelCase ( ) -> int:
'''simple docstring'''
UpperCamelCase__ : str =ArgumentParser(
description=(
"PyTorch... | 582 | 1 |
"""simple docstring"""
from collections import OrderedDict
from ...utils import logging
from .auto_factory import _BaseAutoModelClass, _LazyAutoMapping, auto_class_update
from .configuration_auto import CONFIG_MAPPING_NAMES
lowerCAmelCase__ =logging.get_logger(__name__)
lowerCAmelCase__ ... | 482 |
"""simple docstring"""
import argparse
import os
import re
import packaging.version
lowerCAmelCase__ ="examples/"
lowerCAmelCase__ ={
"examples": (re.compile(r"^check_min_version\(\"[^\"]+\"\)\s*$", re.MULTILINE), "check_min_version(\"VERSION\")\n"),
"init": (re.compile(r"^__versio... | 482 | 1 |
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 UpperCAmelCase__ ( __UpperCamelCase ):
'''simple docstring'''
... | 241 |
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import numpy as np
import tensorflow as tf
from transformers import TF... | 241 | 1 |
import inspect
import unittest
from typing import List
import numpy as np
from transformers import EfficientFormerConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_config... | 61 |
"""simple docstring"""
def __lowerCamelCase ( UpperCamelCase__ ):
"""simple docstring"""
try:
_UpperCAmelCase = float(UpperCamelCase__ )
except ValueError:
raise ValueError("Please enter a valid number" )
_UpperCAmelCase = decimal - int(UpperCamelCase__ )
if fractional_part == ... | 657 | 0 |
"""simple docstring"""
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision import transforms
from transformers import BitImageProcessor, FocalNetConfig, FocalNetForImageClassification
from transformers.image_utils import IMAGEN... | 710 |
"""simple docstring"""
from math import pi, sqrt
def _snake_case ( snake_case__ : float ):
if num <= 0:
raise ValueError('math domain error' )
if num > 171.5:
raise OverflowError('math range error' )
elif num - int(snake_case__ ) not in (0, 0.5):
raise NotImplementedErr... | 22 | 0 |
'''simple docstring'''
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxSeqaSeqConfigWithPast
from ...utils import logging
if TYPE_CHECKING:
from ...feature_ex... | 212 |
'''simple docstring'''
import contextlib
import os
import sqlitea
import pytest
from datasets import Dataset, Features, Value
from datasets.io.sql import SqlDatasetReader, SqlDatasetWriter
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases, require_sqlalchemy
def lowe... | 316 | 0 |
from typing import List, Optional, Union
import numpy as np
import torch
import torchaudio.compliance.kaldi as ta_kaldi
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging
... | 712 |
"""simple docstring"""
import os
import zipfile
import requests
from get_ci_error_statistics import download_artifact, get_artifacts_links
def lowercase_ ( _snake_case ,_snake_case=7 ):
SCREAMING_SNAKE_CASE__ : Dict = None
if token is not None:
SC... | 545 | 0 |
from collections import OrderedDict
from typing import Any, List, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging
__lowercase : List[str] ... | 36 |
'''simple docstring'''
import gc
import inspect
import unittest
import torch
from parameterized import parameterized
from diffusers import PriorTransformer
from diffusers.utils import floats_tensor, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_deter... | 539 | 0 |
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
__UpperCAmelCase = ""
__UpperCAmelCase = ""
__UpperCAmelCase = ""
__UpperCAmelCase = 1 # (0 is vertical, 1 is horizontal)
def A_ ( ) ->int:
"""simple ... | 706 |
import argparse
from collections import defaultdict
import yaml
__UpperCAmelCase = "docs/source/en/_toctree.yml"
def A_ ( lowercase_ ) ->Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE = defaultdict(lowercase_ )
for doc in model_doc:
counts[doc["... | 259 | 0 |
import torch
from transformers import AutoModel
class __lowerCAmelCase ( torch.nn.Module ):
"""simple docstring"""
def __init__( self : str , _snake_case : str="sayef/fsner-bert-base-uncased" ):
"""simple docstring"""
super(Up... | 9 |
"""simple docstring"""
def lowerCamelCase__ ( UpperCAmelCase_ = 60_08_51_47_51_43 )-> int:
"""simple docstring"""
try:
UpperCamelCase = int(UpperCAmelCase_ )
except (TypeError, ValueError):
raise TypeError("P... | 554 | 0 |
'''simple docstring'''
import tempfile
import torch
from diffusers import (
DEISMultistepScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
UniPCMultistepScheduler,
)
from .test_schedulers import SchedulerCommonTest
class __UpperCAmelCase ... | 711 |
from __future__ import annotations
from decimal import Decimal
from numpy import array
def a (lowerCAmelCase__ ):
__a = Decimal
# Check if the provided matrix has 2 rows and 2 columns
# since this implementation only works for 2x2 matrices
if len(lowerCAme... | 209 | 0 |
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available
if is_vision_available():
from PIL import Image
from transformers import AutoProcessor, BertTokenizer, Blip... | 99 |
import argparse
import datetime
import json
import time
import warnings
from logging import getLogger
from pathlib import Path
from typing import Dict, List
import torch
from tqdm import tqdm
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import calculate_bleu, calculate_r... | 99 | 1 |
"""simple docstring"""
from __future__ import absolute_import, division, print_function, unicode_literals
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers import RobertaConfig
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
fro... | 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 |
import os
import socket
from contextlib import contextmanager
import torch
from ..commands.config.default import write_basic_config # noqa: F401
from ..state import PartialState
from .dataclasses import DistributedType
from .imports import is_deepspeed_available, is_tpu_available
from .transformer_engine import ... | 40 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowerCAmelCase : str =logging.get_logger(__name__)
__lowerCAmelCase : Any ={
# See all MEGATRON_BERT models at https://huggingface.co/models?filter=bert
}
class _lowercase ( A__ ):... | 696 | 0 |
import unittest
from pathlib import Path
from tempfile import TemporaryDirectory
from transformers import AutoConfig, TFGPTaLMHeadModel, is_keras_nlp_available, is_tf_available
from transformers.models.gpta.tokenization_gpta import GPTaTokenizer
from transformers.testing_utils import require_keras_nlp, require_... | 713 |
from manim import *
class lowerCAmelCase_ ( lowercase ):
"""simple docstring"""
def __a ( self :Optional[int] ):
UpperCamelCase__ :Union[str, Any] = Rectangle(height=0.5 , width=0.5 )
UpperCamelCase__ :int = Rectang... | 383 | 0 |
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 UpperCamelCase_ ( UpperCamelCase__ ... | 6 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_a : Dict = logging.get_logger(__name__)
_a : Union[str, Any] = {
"""google/vivit-b-16x2-kinetics400""": (
"""https://huggingface.co/google/vivit-b-16x2-kinetics400/resolve/main/config.json"""
... | 145 | 0 |
'''simple docstring'''
from dataclasses import dataclass
from typing import Dict, Optional, Tuple, Union
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .attention_processor import AttentionProces... | 644 |
'''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 __a (lowerCamelCase , ... | 644 | 1 |
from __future__ import annotations
def _lowerCamelCase ( lowerCamelCase_: list[int] ):
'''simple docstring'''
if len(lowerCamelCase_ ) == 0:
return array
A , A : Tuple = min(lowerCamelCase_ ), max(lowerCamelCase_ )
# Co... | 256 |
import os
import sys
import unittest
UpperCamelCase_ = 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_bac... | 256 | 1 |
from importlib import import_module
from .logging import get_logger
__lowerCamelCase : List[Any] = get_logger(__name__)
class _lowercase :
def __init__( self , a , a=None ):
snake_case__ : str =attrs or []
if module is not None:
... | 720 |
import numpy as np
# Importing the Keras libraries and packages
import tensorflow as tf
from tensorflow.keras import layers, models
if __name__ == "__main__":
# Initialising the CNN
# (Sequential- Building the model layer by layer)
__lowerCamelCase : str = models.Sequential()
... | 448 | 0 |
def _lowerCAmelCase ( __magic_name__ :str , __magic_name__ :Union[str, Any] ):
return price * (1 + tax_rate)
if __name__ == "__main__":
print(f"{price_plus_tax(100, 0.25) = }")
print(f"{price_plus_tax(125.50, 0.05) = }")
| 121 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowerCAmelCase : str =logging.get_logger(__name__)
__lowerCAmelCase : Any ={
# See all MEGATRON_BERT models at https://huggingface.co/models?filter=bert
}
class _lowercase ( A__ ):... | 696 | 0 |
'''simple docstring'''
from __future__ import annotations
def SCREAMING_SNAKE_CASE ( a_ : int , a_ : str , a_ : Any ):
if days_between_payments <= 0:
raise ValueError('days_between_payments must be > 0' )
if daily_interest_r... | 720 |
'''simple docstring'''
import os
def SCREAMING_SNAKE_CASE ( ):
with open(os.path.dirname(a_ ) + '/grid.txt' ) as f:
__a = [] # noqa: E741
for _ in range(20 ):
l.append([int(a_ ) for x in f.readline().split()] )
__a = 0
# right... | 490 | 0 |
# 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 ...utils import deprecate
from ..controlnet.multicontrolnet import MultiControlNetModel # noqa: F401
from ..controlnet.pipeline_controlnet i... | 112 |
import pickle
import numpy as np
from matplotlib import pyplot as plt
class __lowerCAmelCase :
def __init__( self , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case=0.2 , snake_case=0.2 ... | 112 | 1 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
UpperCamelCase : List[str] = loggi... | 718 | '''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
UpperCamelCase : Optional[Any] = logging.get_logger(__name__)
Upper... | 610 | 0 |
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
SCREAMING_SNAKE_CASE = loggi... | 485 |
def _lowerCamelCase ( __A : str ) -> list:
return [
txt[:a] + txt[a].upper() + txt[a + 1 :]
for a in range(len(__A ) )
if txt[a].isalpha()
]
if __name__ == "__main__":
__import__('doctest').testmod()
| 485 | 1 |
'''simple docstring'''
import argparse
from pathlib import Path
import requests
import torch
from PIL import Image
from transformers import (
RobertaTokenizer,
TrOCRConfig,
TrOCRForCausalLM,
TrOCRProcessor,
VisionEncoderDecoderModel,
ViTConfig,
ViTImageProcessor,
ViTModel,
)
from ... | 713 |
'''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 : List[Any] = "\\n\n"
_UpperCamelCase : List[Any] = "\... | 514 | 0 |
import os
import torch
from ..logging import get_logger
from .constants import FSDP_PYTORCH_VERSION, MODEL_NAME, OPTIMIZER_NAME
from .versions import is_torch_version
if is_torch_version(""">=""", FSDP_PYTORCH_VERSION):
import torch.distributed.checkpoint as dist_cp
from torch.distributed... | 23 |
import numpy as np
from cva import destroyAllWindows, imread, imshow, waitKey
class lowercase :
'''simple docstring'''
def __init__(self , __a , __a , __a ) -> Union[str, Any]:
"""simple docstring"""
if dst_width < 0 or dst_height < 0:
... | 146 | 0 |
'''simple docstring'''
from __future__ import annotations
def snake_case_ ( _lowerCAmelCase : list , _lowerCAmelCase : int ) -> List[str]:
# Checks if the entire collection has been sorted
if len(_lowerCAmelCase ) <= 1 or n <= 1:
... | 528 |
'''simple docstring'''
import contextlib
import os
import sqlitea
import pytest
from datasets import Dataset, Features, Value
from datasets.io.sql import SqlDatasetReader, SqlDatasetWriter
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases, require_sq... | 528 | 1 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, DDIMScheduler, LDMTextToImagePipeline, UNetaDConditionModel
from diffusers.utils.testing_utils import (
enable_full_d... | 288 |
'''simple docstring'''
import os
import sys
import unittest
__lowerCamelCase = 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, c... | 288 | 1 |
"""simple docstring"""
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_do... | 718 |
"""simple docstring"""
import json
import os
import tempfile
import unittest
import numpy as np
from datasets import load_dataset
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common i... | 22 | 0 |
'''simple docstring'''
import unittest
import torch
from diffusers import VQModel
from diffusers.utils import floats_tensor, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from .test_modeling_common import ModelTesterMixin, UNetTesterMixin
enable_full_determinism()
class ... | 517 |
'''simple docstring'''
from .integrations import (
is_optuna_available,
is_ray_available,
is_sigopt_available,
is_wandb_available,
run_hp_search_optuna,
run_hp_search_ray,
run_hp_search_sigopt,
run_hp_search_wandb,
)
from .trainer_utils import (
HPSearchBackend,
default_hp_s... | 517 | 1 |
from math import cos, sin, sqrt, tau
from audio_filters.iir_filter import IIRFilter
def A ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase = 1 / sqrt(2 ) ) -> IIRFilter:
A__ = tau * frequency / samplerate
A__ = sin(__UpperCamelCase )
A__ =... | 52 |
import tempfile
import unittest
from make_student import create_student_by_copying_alternating_layers
from transformers import AutoConfig
from transformers.file_utils import cached_property
from transformers.testing_utils import require_torch
SCREAMING_SNAKE_CASE__ = '''sshleifer/bart-tiny-random'''
... | 52 | 1 |
'''simple docstring'''
import random
class _lowercase :
@staticmethod
def a ( SCREAMING_SNAKE_CASE_ : str ) -> tuple[list[int], list[int]]:
__snake_case = [ord(SCREAMING_SNAKE_CASE_ ) for i in text]
__snake_case = []
... | 56 | import argparse
import json
from typing import List
from ltp import LTP
from transformers import BertTokenizer
def lowerCamelCase ( SCREAMING_SNAKE_CASE ):
'''simple docstring'''
if (
(cp >= 0X4_e_0_0 and cp <= 0X9_f_f_f)
or (cp >= 0X3_4_0_0 and cp <= 0X4_d_b_f) #
o... | 167 | 0 |
'''simple docstring'''
# This is the module that test_patching.py uses to test patch_submodule()
import os # noqa: this is just for tests
import os as renamed_os # noqa: this is just for tests
from os import path # noqa: this is just for tests
from os import path as renamed_path # noqa: this is just for... | 710 |
def snake_case (UpperCamelCase : Union[str, Any] ):
'''simple docstring'''
lowerCamelCase__ = 0
lowerCamelCase__ = len(UpperCamelCase )
for i in range(n - 1 ):
for j in range(i + 1 , UpperCamelCase ):
if arr[i] > arr[j]:
num_invers... | 235 | 0 |
import inspect
import unittest
from datasets import load_dataset
from packaging import version
from transformers import BeitConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transform... | 256 |
import os
import random
import sys
from . import cryptomath_module as cryptoMath # noqa: N812
from . import rabin_miller as rabinMiller # noqa: N812
def _lowerCamelCase ( ):
'''simple docstring'''
print('''Making key files...''' )
make_key_files(''... | 256 | 1 |
import copy
import tempfile
import unittest
from transformers import MaMaaaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from transformers.utils import cached_property
from ...generation.test_utils imp... | 703 |
import argparse
import collections
import torch
from flax import traverse_util
from tax import checkpoints
from transformers import TaConfig, TaEncoderModel, TaForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
def A ( Upper... | 278 | 0 |
import sys
def UpperCamelCase_( snake_case__: Dict ) -> Union[str, Any]:
UpperCAmelCase__ = len(__UpperCamelCase )
UpperCAmelCase__ = [[0 for x in range(__UpperCamelCase )] for x in range(__UpperCamelCase )]
UpperCAmelCase__ = [[0 for x in... | 146 |
"""simple docstring"""
def __lowerCamelCase ( __UpperCamelCase ) -> str:
"""simple docstring"""
return "".join([hex(__UpperCamelCase )[2:].zfill(2 ).upper() for byte in list(__UpperCamelCase )] )
def __lowerCamelCase ( __UpperCamelCase ) -> byte... | 610 | 0 |
"""simple docstring"""
import warnings
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase : Optional[Any] = logging.get_logger(__name__)
lowerCAmelCase : Tuple = {
'RUCAIBox/mvp': 'https://huggingface.co/RUCAIBox... | 720 |
"""simple docstring"""
import unittest
import numpy as np
import torch
from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class __magic_name__ ... | 533 | 0 |
import argparse
import json
import os
import torch
from transformers.file_utils import has_file
from diffusers import UNetaDConditionModel, UNetaDModel
lowerCamelCase__ : Union[str, Any] = False
lowerCamelCase__ : List[str] = True
lowerCamelCase__ : Optional[Any] ... | 31 |
"""simple docstring"""
import torch
from transformers import CamembertForMaskedLM, CamembertTokenizer
def __A ( a_ :Union[str, Any] , a_ :Union[str, Any] , a_ :Optional[Any] , a_ :Optional[int]=5) -> List[Any]:
# Adapted from https://github... | 52 | 0 |
def UpperCAmelCase__ ( UpperCAmelCase__ :int = 10_00 ):
'''simple docstring'''
a , a = 1, 1
a = 2
while True:
a = 0
a = fa + fa
a , a = fa, f
index += 1
for _ in str(UpperCAmelCase__ ):
i += 1
if i == n:
break
... | 32 |
from __future__ import annotations
from collections.abc import Iterable, Iterator
from dataclasses import dataclass
A_ : List[str] = (3, 9, -11, 0, 7, 5, 1, -1)
A_ : Optional[int] = (4, 6, 2, 0, 8, 10, 3, -2)
@dataclass
class _lowercase :
_UpperCAmelCase ... | 32 | 1 |
'''simple docstring'''
def lowercase__ ( __UpperCamelCase : int , __UpperCamelCase : int ):
'''simple docstring'''
return "\n".join(
F'''{number} * {i} = {number * i}''' for i in range(1 , number_of_terms + 1 ) )
if __name__ == "__main__":
print(mult... | 566 |
'''simple docstring'''
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 PreTrainedTokenizer
from ...utils import logging
snake_case : Tuple = logging.get_logger(__... | 566 | 1 |
'''simple docstring'''
from scipy.stats import pearsonr, spearmanr
from sklearn.metrics import fa_score, matthews_corrcoef
import datasets
snake_case_ : str = '\\n@inproceedings{wang2019glue,\n title={{GLUE}: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding},\n ... | 350 |
'''simple docstring'''
import os
import re
import unicodedata
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import is_torch_available, logging
if... | 350 | 1 |
from queue import PriorityQueue
from typing import Any
import numpy as np
def __snake_case ( __UpperCamelCase : dict ,__UpperCamelCase : str ,__UpperCamelCase : set ,__UpperCamelCase : set ,__UpperCamelCase : dict ,__UpperCamelCase : dict ,__UpperCamelCase : PriorityQueue ,__U... | 86 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
StableDiffusionSAGPipeline,
UNetaDConditionModel,
)
from diffusers.utils import slow, torch_d... | 555 | 0 |
'''simple docstring'''
import argparse
import os
import re
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_dummies.py
lowerCAmelCase_ : Optional[int] = '''src/diffusers'''
# Matches is_xxx_available()
lowerCA... | 156 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
lowerCAmelCase_ : List[str] = {
'''configuration_layoutlmv2''': ['''L... | 156 | 1 |
"""simple docstring"""
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL
from PIL import Image
from ...utils import (
BaseOutput,
OptionalDependencyNotAvailable,
is_flax_available,
is_k_diffusion_available,
is_k_diffusion_version,
... | 682 |
"""simple docstring"""
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,
... | 682 | 1 |
"""simple docstring"""
from .integrations import (
is_optuna_available,
is_ray_available,
is_sigopt_available,
is_wandb_available,
run_hp_search_optuna,
run_hp_search_ray,
run_hp_search_sigopt,
run_hp_search_wandb,
)
from .trainer_utils import (
HPSearch... | 702 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
SCREAMING_SNAKE_CASE = {
"""configuration_graphormer""": ["""GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GraphormerConfig... | 556 | 0 |
'''simple docstring'''
from typing import Callable, Optional
from .. import Features
from ..packaged_modules.generator.generator import Generator
from .abc import AbstractDatasetInputStream
class __lowercase ( _lowercase ):
def __init__(self , A , A = None , A = None , A = Fal... | 422 |
'''simple docstring'''
import argparse
import json
from typing import List
from ltp import LTP
from transformers import BertTokenizer
def lowercase_ ( _lowercase ) -> str:
'''simple docstring'''
if (
(cp >= 0x4_E_0_0 and cp <= 0x9_F_F_F)
or (cp >= 0x3_4_0_0 and cp <= 0... | 422 | 1 |
'''simple docstring'''
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_... | 715 |
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...file_utils import TensorType, is_torch_available
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ... | 5 | 0 |
import math
from collections.abc import Iterator
from itertools import takewhile
def UpperCAmelCase_ ( __lowerCAmelCase ) -> bool:
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, ... | 509 |
import numpy as np
class __lowerCAmelCase :
"""simple docstring"""
def __init__( self : Union[str, Any] ):
__lowercase : Optional[int] = (0, 0)
__lowercase : List[str] = None
__low... | 509 | 1 |
import torch
from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device
from .test_schedulers import SchedulerCommonTest
class __magic_name__ ( __lowerCAmelCase):
A: Optional[int] = (EulerDiscreteScheduler,)
A: List[Any] = 1_0
def UpperCAm... | 707 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_torch_available,
)
__UpperCamelCase : Dict = {
"configuration_trocr": ["TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP", ... | 106 | 0 |
"""simple docstring"""
import math
def __magic_name__ ( __snake_case : int ) -> Any:
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, ... | 361 |
from __future__ import annotations
def SCREAMING_SNAKE_CASE ( snake_case_ : float , snake_case_ : float , snake_case_ : float ):
if (voltage, current, resistance).count(0 ) != 1:
raise ValueError("One and only one argument must be 0" )
if resistance < 0:
raise Valu... | 297 | 0 |
"""simple docstring"""
import argparse
import numpy as np
import torch
from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging
logging.set_verbosity_info()
_UpperCamelCase : Optional[int] = logging.get_logger('transformers.models.speecht5')
def _SCREAMING_SNAKE_CASE ( __sna... | 708 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
fro... | 134 | 0 |
"""simple docstring"""
import random
class snake_case_ :
"""simple docstring"""
@staticmethod
def UpperCAmelCase__ ( lowerCamelCase_) -> tuple[list[int], list[int]]:
UpperCamelCase = [ord(lowerCamelCase_) for i in text]
UpperCamelCase ... | 34 |
"""simple docstring"""
# Imports
import numpy as np
class __a :
def __init__( self , a__=None , a__=None , a__=None , a__=None , a__=None ):
self.set_matricies(red=a__ , green=a__ , blue=a__ , red_edge=a__ , nir=a__ )
def snake_case_ ( self... | 650 | 0 |
"""simple docstring"""
from unittest import TestCase
from datasets import Dataset
from minhash_deduplication import deduplicate_dataset, make_duplicate_clusters
def _lowerCAmelCase ( ) -> Tuple:
_SCREAMING_SNAKE_CASE : str = {
"repo_name": ["test_repo1", "test_re... | 720 |
"""simple docstring"""
from __future__ import annotations
lowercase_ : List[str] = '''#'''
class UpperCamelCase :
def __init__( self ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE : dict = {}
def __SCREAMING_SNAKE_CASE... | 295 | 0 |
'''simple docstring'''
import numpy as np
from PIL import Image
def lowerCAmelCase_ ( snake_case_ : Optional[int] , snake_case_ : Dict , snake_case_ : Any ) -> np.ndarray:
'''simple docstring'''
UpperCAmelCase_ = np.array(snak... | 78 |
import unittest
from transformers import PegasusTokenizer, PegasusTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
lowerC... | 678 | 0 |
"""simple docstring"""
import gc
import unittest
from diffusers import FlaxStableDiffusionInpaintPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
from fl... | 296 | """simple docstring"""
from pickle import UnpicklingError
import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict
from ..utils import logging
__lowercase = logging.get_logger(__name__)
def lowe... | 296 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCamelCase_ = {
'''configuration_instructblip''': [
'''INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''InstructBlipConfig''',
'''InstructBlipQFormerConfig'''... | 513 |
from cva import destroyAllWindows, imread, imshow, waitKey
def __magic_name__ ( __a : List[Any] ):
'''simple docstring'''
UpperCamelCase__ , UpperCamelCase__ = img.shape[0], img.shape[1]
# converting each pixel's color to its negative
for i i... | 513 | 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,
)
from... | 268 |
from typing import Any, Callable, Dict, List, Optional, Union
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DiffusionPipeline,
LMSDiscreteScheduler,
PNDMScheduler,
StableDiffusionPipeline,
UNet... | 268 | 1 |
from collections.abc import Sequence
def _SCREAMING_SNAKE_CASE ( __snake_case , __snake_case ) -> float:
return sum(c * (x**i) for i, c in enumerate(__snake_case ) )
def _SCREAMING_SNAKE_CASE ( __snake_case , __snake_case ) -> float:
_Up... | 108 |
'''simple docstring'''
def UpperCamelCase__ ( lowerCAmelCase ):
"""simple docstring"""
if divisor % 5 == 0 or divisor % 2 == 0:
return 0
_lowerCAmelCase = 1
_lowerCAmelCase = 1
while repunit:
_low... | 207 | 0 |
"""simple docstring"""
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import YolosConfig, YolosForObjectDetection, YolosImageProcessor
from transformers.utils import logging
logging.set_v... | 706 |
"""simple docstring"""
from math import factorial
def a__ ( SCREAMING_SNAKE_CASE : int = 1_0_0 ):
'''simple docstring'''
return sum(int(SCREAMING_SNAKE_CASE ) for x in str(factorial(SCREAMING_SNAKE_CASE ) ) )
if __name__ == "__main__":
p... | 681 | 0 |
from typing import Optional, Tuple, Union
import tensorflow as tf
from ...activations_tf import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
from ...modeling_tf_outputs import (
TFBaseModelOutputWithNoAttention,
TFBaseModelOutp... | 137 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
snake_case__ : Dict = {
"""configuration_whisper""": ["""WHISPER_PRETRAINED_CONFIG_ARCHI... | 402 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__UpperCAmelCase = {
"configuration_altclip": [
"ALTCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP",
"AltCLIPConfig",
"AltCLIPTextConfig",
... | 703 |
import warnings
from ...utils import logging
from .image_processing_chinese_clip import ChineseCLIPImageProcessor
__UpperCAmelCase = logging.get_logger(__name__)
class A__ ( A ):
"""simple docstring"""
def __init__( self : Tuple , *A_ : Optional[int] , **A_ : int ... | 503 | 0 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_UpperCAmelCase : Dict = logging.get_logger(__name__)
_UpperCAmelCase : Optional[Any] = {
"""microsoft/git-base""": """https://huggingfa... | 295 |
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase = 200 ) -> int:
lowerCamelCase__ : Dict = [1, 2, 5, 10, 20, 50, 100, 200]
lowerCamelCase__ : Union[str, Any] = [0] * (pence + 1)
lowerCamelCase__ : List[str] = 1 # base case: 1 way to make 0 ... | 295 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase__ = logging.get_logger(__name__)
lowerCamelCase__ = {
'''edbeeching/decision-transformer-gym-hopper-medium''': (
'''https://huggingface.co/edbeeching/decision-transformer-gym-ho... | 707 |
def lowercase_ ( SCREAMING_SNAKE_CASE : str ):
"""simple docstring"""
snake_case__ : Optional[int] =len(SCREAMING_SNAKE_CASE )
snake_case__ : int =sum(SCREAMING_SNAKE_CASE )
snake_case__ : str =[[False for x in range(s + 1 )] for y in range(n + 1 )]
for i... | 408 | 0 |
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