code stringlengths 87 55.2k | code_codestyle int64 0 349 | style_context stringlengths 135 49.1k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
import warnings
from ...utils import is_sklearn_available, requires_backends
if is_sklearn_available():
from scipy.stats import pearsonr, spearmanr
from sklearn.metrics import fa_score, matthews_corrcoef
a__ : Optional[int] = (
'''This metric will be removed from the library soon, m... | 313 |
from __future__ import annotations
import math
def UpperCAmelCase_( a__ , a__ ):
"""simple docstring"""
if len(a__ ) != 2 or len(a[0] ) != 2 or len(a__ ) != 2 or len(b[0] ) != 2:
raise Exception('''Matrices are not 2x2''' )
SCREAMING_SNAKE_CASE ... | 313 | 1 |
import torch
from transformers import PreTrainedModel, XLMRobertaConfig, XLMRobertaModel
class a_ ( a__ ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Optional[Any] = 'M-CLIP'
def __init__( self , _lowerCamelCase=1024 , _lowerCamelCase=... | 313 |
# We ignore warnings about stepping the scheduler since we step it ourselves during gradient accumulation
import warnings
from .state import AcceleratorState, GradientState
warnings.filterwarnings('''ignore''', category=UserWarning, module='''torch.optim.lr_scheduler''')
class a_ :
"""simple d... | 313 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
a__ : int = {
'''configuration_layoutlmv2''': ['''LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''LayoutLMv2... | 313 |
import argparse
import os
from pathlib import Path
from typing import Dict
import tensorflow as tf
import torch
from tqdm import tqdm
from transformers import PegasusConfig, PegasusForConditionalGeneration, PegasusTokenizer
from transformers.models.pegasus.configuration_pegasus import DEFAULTS, task_specific_params
... | 313 | 1 |
import csv
import tweepy
# Twitter API credentials
a__ : Union[str, Any] = ''''''
a__ : List[str] = ''''''
a__ : Any = ''''''
a__ : List[str] = ''''''
def UpperCAmelCase_( a__ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE ... | 313 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast
from diffusers import DDIMScheduler, KandinskyImgaImgPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MC... | 313 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tensorflow_text_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
a__ : List[str] = {
'''configuration_bert''': ['''BERT_... | 313 |
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import ViTImageProcessor, ViTMSNConfig, ViTMSNModel
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
torch.set_grad_enabled(False)
def ... | 313 | 1 |
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,
... | 313 |
import csv
import tweepy
# Twitter API credentials
a__ : Union[str, Any] = ''''''
a__ : List[str] = ''''''
a__ : Any = ''''''
a__ : List[str] = ''''''
def UpperCAmelCase_( a__ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE ... | 313 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
a__ : List[Any] = logging.get_logger(__name__)
a__ : List[Any] = {
'''google/bit-50''': '''https://hu... | 313 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
a__ : Optional[Any] = logging.get_logger(__name__)
a__ : List[str] = {
'''kssteven/ibert-roberta-base''': ... | 313 | 1 |
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, prepare_image_inputs
if is_tor... | 313 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
if TYPE_CHECKING:
from ... import FeatureExtractionMixin, TensorType
a__ : Any = logging.... | 313 | 1 |
import argparse
import json
import os
import pickle
import shutil
import numpy as np
import torch
from distiller import Distiller
from lm_seqs_dataset import LmSeqsDataset
from transformers import (
BertConfig,
BertForMaskedLM,
BertTokenizer,
DistilBertConfig,
DistilBertForMaskedLM,
DistilBer... | 313 |
from maths.prime_check import is_prime
def UpperCAmelCase_( a__ ):
"""simple docstring"""
if not isinstance(a__ , a__ ):
SCREAMING_SNAKE_CASE : List[Any] = F"""Input value of [number={number}] must be an integer"""
raise TypeError(a__ ... | 313 | 1 |
import json
from typing import Dict, List, Optional, Tuple, Union
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding, EncodedInput
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokeniz... | 313 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaControlnetImgaImgPipeline,
KandinskyVaaPriorEmbaEmbPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, load_image, loa... | 313 | 1 |
import string
import numpy
def UpperCAmelCase_( a__ , a__ ):
"""simple docstring"""
return b if a == 0 else greatest_common_divisor(b % a , a__ )
class a_ :
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Any = string.ascii_uppercase +... | 313 |
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,
UNeta... | 313 | 1 |
import os
from itertools import chain
from random import randrange, shuffle
import pytest
from .sola import PokerHand
a__ : int = (
'''4S 3H 2C 7S 5H''',
'''9D 8H 2C 6S 7H''',
'''2D 6D 9D TH 7D''',
'''TC 8C 2S JH 6C''',
'''JH 8S TH AH QH''',
'''TS KS 5S 9S AC''',
'''KD 6S ... | 313 |
from typing import Optional, Tuple, Union
import flax
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict
from ..configuration_utils import ConfigMixin, flax_register_to_config
from ..utils import BaseOutput
from .embeddings_flax import FlaxTimestepEmbedding, FlaxT... | 313 | 1 |
def UpperCAmelCase_( a__ = 100 ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[Any] = set()
SCREAMING_SNAKE_CASE : Optional[Any] = 0
SCREAMING_SNAKE_CASE : Optional[int] = n + 1 # maximum limit
f... | 313 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModel,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableUnCL... | 313 | 1 |
import math
def UpperCAmelCase_( a__ , a__ = 0 , a__ = 0 ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[int] = end or len(a__ )
for i in range(a__ , a__ ):
SCREAMING_SNAKE_CASE : int = i
... | 313 |
from abc import ABC, abstractmethod
from typing import List, Optional
class a_ ( a__ ):
"""simple docstring"""
def __init__( self ) ->List[str]:
# test for the above condition
self.test()
def __lowerCAmelCase ( self ... | 313 | 1 |
import math
def UpperCAmelCase_( a__ ):
"""simple docstring"""
if not isinstance(a__ , a__ ):
SCREAMING_SNAKE_CASE : List[str] = F"""Input value of [number={number}] must be an integer"""
raise TypeError(a__ )
if number < 1:
... | 313 |
import argparse
import random
import joblib
import numpy as np
import torch
from igf.igf import (
SecondaryLearner,
collect_objective_set,
compute_perplexity,
generate_datasets,
load_gpta,
recopy_gpta,
set_seed,
train_secondary_learner,
)
from torch.utils.data import DataLoader, Random... | 313 | 1 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
a__ : Optional[Any] = logging.get_logger(__name__)
a__ : List[str] = {
'''kssteven/ibert-roberta-base''': ... | 313 |
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 UpperCAmelCase_( a__ ):
"""... | 313 | 1 |
a__ : List[Any] = {str(digit): digit**5 for digit in range(10)}
def UpperCAmelCase_( a__ ):
"""simple docstring"""
return sum(DIGITS_FIFTH_POWER[digit] for digit in str(a__ ) )
def UpperCAmelCase_( ):
"""simple docstring"""
return sum... | 313 |
import argparse
import json
from typing import List
from ltp import LTP
from transformers import BertTokenizer
def UpperCAmelCase_( a__ ):
"""simple docstring"""
if (
(cp >= 0x4_E00 and cp <= 0x9_FFF)
or (cp >= 0x3_400 and cp <= 0x4_DBF) #
or (cp >= 0x20_000 ... | 313 | 1 |
from unittest.mock import patch
import pyspark
from datasets.packaged_modules.spark.spark import (
Spark,
SparkExamplesIterable,
_generate_iterable_examples,
)
from ..utils import (
require_dill_gt_0_3_2,
require_not_windows,
)
def UpperCAmelCase_( a__ , a__ ):
""... | 313 |
import datetime
import platform
import subprocess
from typing import Optional, Tuple, Union
import numpy as np
def UpperCAmelCase_( a__ , a__ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[Any] = F"""{sampling_rate}"""
SCREAMING_SNAKE_CASE ... | 313 | 1 |
import argparse
import os
from pathlib import Path
from typing import Dict
import tensorflow as tf
import torch
from tqdm import tqdm
from transformers import PegasusConfig, PegasusForConditionalGeneration, PegasusTokenizer
from transformers.models.pegasus.configuration_pegasus import DEFAULTS, task_specific_params
... | 313 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a__ : Tuple = {'''configuration_wavlm''': ['''WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''WavLMConfig''']}
try:
if not is_torch_available():
raise OptionalDependency... | 313 | 1 |
def UpperCAmelCase_( a__ = 50 ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[Any] = [1] * (length + 1)
for row_length in range(3 , length + 1 ):
for block_length in range(3 , row_length + 1 ):
for block_start in range... | 313 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
a__ : int = logging.get_logger(__name__)
a__ : Optional[Any] = {
'''SenseTime/deformable-detr''': '''https://huggingface.co/sensetime/deformable-detr/reso... | 313 | 1 |
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 a_ ( a__ ,... | 313 |
import torch
from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device
from .test_schedulers import SchedulerCommonTest
class a_ ( a__ ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Optional[Any] = (EulerDiscreteScheduler,)
__SCREAMING... | 313 | 1 |
from sympy import diff, lambdify, symbols
from sympy.functions import * # noqa: F403
def UpperCAmelCase_( a__ , a__ , a__ = "x" , a__ = 10**-10 , a__ = 1 , ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[Any] = symbols(a__ )
... | 313 |
import json
from typing import Dict, List, Optional, Tuple, Union
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding, EncodedInput
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokeniz... | 313 | 1 |
import argparse
import os
import torch
from transformers import (
XLNetConfig,
XLNetForQuestionAnswering,
XLNetForSequenceClassification,
XLNetLMHeadModel,
load_tf_weights_in_xlnet,
)
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
a__ : Dict = {
'''cola''':... | 313 |
from __future__ import annotations
import math
def UpperCAmelCase_( a__ , a__ ):
"""simple docstring"""
if len(a__ ) != 2 or len(a[0] ) != 2 or len(a__ ) != 2 or len(b[0] ) != 2:
raise Exception('''Matrices are not 2x2''' )
SCREAMING_SNAKE_CASE ... | 313 | 1 |
import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import TaBlock, TaConfig, TaLayerNorm
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class a_ ( a__ , a__ ... | 313 |
# We ignore warnings about stepping the scheduler since we step it ourselves during gradient accumulation
import warnings
from .state import AcceleratorState, GradientState
warnings.filterwarnings('''ignore''', category=UserWarning, module='''torch.optim.lr_scheduler''')
class a_ :
"""simple d... | 313 | 1 |
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 VaeImageProcessor
from diffusers.pipeli... | 313 |
import argparse
import os
from pathlib import Path
from typing import Dict
import tensorflow as tf
import torch
from tqdm import tqdm
from transformers import PegasusConfig, PegasusForConditionalGeneration, PegasusTokenizer
from transformers.models.pegasus.configuration_pegasus import DEFAULTS, task_specific_params
... | 313 | 1 |
import argparse
import math
import traceback
import dateutil.parser as date_parser
import requests
def UpperCAmelCase_( a__ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[int] = {}
SCREAMING_SNAKE_CASE : int = job['''star... | 313 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast
from diffusers import DDIMScheduler, KandinskyImgaImgPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MC... | 313 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a__ : Union[str, Any] = {
'''configuration_upernet''': ['''UperNetConfig'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except ... | 313 |
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import ViTImageProcessor, ViTMSNConfig, ViTMSNModel
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
torch.set_grad_enabled(False)
def ... | 313 | 1 |
# Function to print upper half of diamond (pyramid)
def UpperCAmelCase_( a__ ):
"""simple docstring"""
for i in range(0 , a__ ):
for _ in range(0 , n - i - 1 ): # printing spaces
print(''' ''' , end='''''' )
for _ in range(0 ... | 313 |
import csv
import tweepy
# Twitter API credentials
a__ : Union[str, Any] = ''''''
a__ : List[str] = ''''''
a__ : Any = ''''''
a__ : List[str] = ''''''
def UpperCAmelCase_( a__ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE ... | 313 | 1 |
import inspect
import unittest
from transformers import MobileViTVaConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ... | 313 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
a__ : Optional[Any] = logging.get_logger(__name__)
a__ : List[str] = {
'''kssteven/ibert-roberta-base''': ... | 313 | 1 |
import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union
# Build the list of all image processors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_remote_code
from ...image_pr... | 313 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
if TYPE_CHECKING:
from ... import FeatureExtractionMixin, TensorType
a__ : Any = logging.... | 313 | 1 |
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_available
from ...test_configuration_common i... | 313 |
from maths.prime_check import is_prime
def UpperCAmelCase_( a__ ):
"""simple docstring"""
if not isinstance(a__ , a__ ):
SCREAMING_SNAKE_CASE : List[Any] = F"""Input value of [number={number}] must be an integer"""
raise TypeError(a__ ... | 313 | 1 |
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 transformers import BitConfig, Bit... | 313 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaControlnetImgaImgPipeline,
KandinskyVaaPriorEmbaEmbPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, load_image, loa... | 313 | 1 |
import copy
import inspect
import unittest
from transformers import AutoBackbone
from transformers.configuration_utils import PretrainedConfig
from transformers.testing_utils import require_timm, require_torch, torch_device
from transformers.utils.import_utils import is_torch_available
from ...test_backbone_common i... | 313 |
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,
UNeta... | 313 | 1 |
import math
import os
import unittest
from transformers import MegatronBertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTest... | 313 |
from typing import Optional, Tuple, Union
import flax
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict
from ..configuration_utils import ConfigMixin, flax_register_to_config
from ..utils import BaseOutput
from .embeddings_flax import FlaxTimestepEmbedding, FlaxT... | 313 | 1 |
import numpy as np
from matplotlib import pyplot as plt
from sklearn.datasets import load_iris
from sklearn.metrics import ConfusionMatrixDisplay
from sklearn.model_selection import train_test_split
from xgboost import XGBClassifier
def UpperCAmelCase_( a__ ):
"""simple docstring"""
re... | 313 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModel,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableUnCL... | 313 | 1 |
import copy
from typing import Any, Dict, List, Optional, Union
import numpy as np
import torch
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import ... | 313 |
from abc import ABC, abstractmethod
from typing import List, Optional
class a_ ( a__ ):
"""simple docstring"""
def __init__( self ) ->List[str]:
# test for the above condition
self.test()
def __lowerCAmelCase ( self ... | 313 | 1 |
import numpy as np
import pandas as pd
from sklearn.preprocessing import Normalizer
from sklearn.svm import SVR
from statsmodels.tsa.statespace.sarimax import SARIMAX
def UpperCAmelCase_( a__ , a__ , a__ , a__ , a__ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE... | 313 |
import argparse
import random
import joblib
import numpy as np
import torch
from igf.igf import (
SecondaryLearner,
collect_objective_set,
compute_perplexity,
generate_datasets,
load_gpta,
recopy_gpta,
set_seed,
train_secondary_learner,
)
from torch.utils.data import DataLoader, Random... | 313 | 1 |
import unittest
from transformers import EsmConfig, is_torch_available
from transformers.testing_utils import TestCasePlus, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipe... | 313 |
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 UpperCAmelCase_( a__ ):
"""... | 313 | 1 |
from random import shuffle
import tensorflow as tf
from numpy import array
def UpperCAmelCase_( a__ , a__ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[Any] = int(a__ )
assert noofclusters < len(a__ )
# Find out the dimensionality... | 313 |
import argparse
import json
from typing import List
from ltp import LTP
from transformers import BertTokenizer
def UpperCAmelCase_( a__ ):
"""simple docstring"""
if (
(cp >= 0x4_E00 and cp <= 0x9_FFF)
or (cp >= 0x3_400 and cp <= 0x4_DBF) #
or (cp >= 0x20_000 ... | 313 | 1 |
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
from transformers.models.robert... | 313 |
import datetime
import platform
import subprocess
from typing import Optional, Tuple, Union
import numpy as np
def UpperCAmelCase_( a__ , a__ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[Any] = F"""{sampling_rate}"""
SCREAMING_SNAKE_CASE ... | 313 | 1 |
import inspect
import unittest
from huggingface_hub import hf_hub_download
from transformers import ConvNextConfig, UperNetConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import is_torch_available, is_vision_available
f... | 313 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a__ : Tuple = {'''configuration_wavlm''': ['''WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''WavLMConfig''']}
try:
if not is_torch_available():
raise OptionalDependency... | 313 | 1 |
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import ViTImageProcessor, ViTMSNConfig, ViTMSNModel
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
torch.set_grad_enabled(False)
def ... | 313 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
a__ : int = logging.get_logger(__name__)
a__ : Optional[Any] = {
'''SenseTime/deformable-detr''': '''https://huggingface.co/sensetime/deformable-detr/reso... | 313 | 1 |
import numpy as np
from scipy.spatial.distance import cdist
from sklearn.metrics import fa_score
import datasets
a__ : Any = '''\
@inproceedings{kakwani2020indicnlpsuite,
title={{IndicNLPSuite: Monolingual Corpora, Evaluation Benchmarks and Pre-trained Multilingual Language Models for Indian ... | 313 |
import torch
from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device
from .test_schedulers import SchedulerCommonTest
class a_ ( a__ ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Optional[Any] = (EulerDiscreteScheduler,)
__SCREAMING... | 313 | 1 |
import argparse
import json
from typing import List
from ltp import LTP
from transformers import BertTokenizer
def UpperCAmelCase_( a__ ):
"""simple docstring"""
if (
(cp >= 0x4_E00 and cp <= 0x9_FFF)
or (cp >= 0x3_400 and cp <= 0x4_DBF) #
or (cp >= 0x20_000 ... | 313 |
import json
from typing import Dict, List, Optional, Tuple, Union
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding, EncodedInput
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokeniz... | 313 | 1 |
from typing import Callable, List, Optional, Union
import PIL
import torch
from transformers import (
CLIPImageProcessor,
CLIPSegForImageSegmentation,
CLIPSegProcessor,
CLIPTextModel,
CLIPTokenizer,
)
from diffusers import DiffusionPipeline
from diffusers.configuration_utils import FrozenDict
fro... | 313 |
from __future__ import annotations
import math
def UpperCAmelCase_( a__ , a__ ):
"""simple docstring"""
if len(a__ ) != 2 or len(a[0] ) != 2 or len(a__ ) != 2 or len(b[0] ) != 2:
raise Exception('''Matrices are not 2x2''' )
SCREAMING_SNAKE_CASE ... | 313 | 1 |
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version('''>=''', '''4.25.0''')):
raise OptionalDependencyNotAvailab... | 313 |
# We ignore warnings about stepping the scheduler since we step it ourselves during gradient accumulation
import warnings
from .state import AcceleratorState, GradientState
warnings.filterwarnings('''ignore''', category=UserWarning, module='''torch.optim.lr_scheduler''')
class a_ :
"""simple d... | 313 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a__ : Tuple = {'''configuration_wavlm''': ['''WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''WavLMConfig''']}
try:
if not is_torch_available():
raise OptionalDependency... | 313 |
import argparse
import os
from pathlib import Path
from typing import Dict
import tensorflow as tf
import torch
from tqdm import tqdm
from transformers import PegasusConfig, PegasusForConditionalGeneration, PegasusTokenizer
from transformers.models.pegasus.configuration_pegasus import DEFAULTS, task_specific_params
... | 313 | 1 |
import inspect
import unittest
from datasets import load_dataset
from packaging import version
from transformers import BeitConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils ... | 313 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast
from diffusers import DDIMScheduler, KandinskyImgaImgPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MC... | 313 | 1 |
import torch
from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device
from .test_schedulers import SchedulerCommonTest
class a_ ( a__ ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Optional[Any] = (EulerDiscreteScheduler,)
__SCREAMING... | 313 |
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import ViTImageProcessor, ViTMSNConfig, ViTMSNModel
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
torch.set_grad_enabled(False)
def ... | 313 | 1 |
import unittest
import numpy as np
import timeout_decorator # noqa
from transformers import BlenderbotConfig, is_flax_available
from transformers.testing_utils import jax_device, require_flax, slow
from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxMode... | 313 |
import csv
import tweepy
# Twitter API credentials
a__ : Union[str, Any] = ''''''
a__ : List[str] = ''''''
a__ : Any = ''''''
a__ : List[str] = ''''''
def UpperCAmelCase_( a__ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE ... | 313 | 1 |
import unittest
from transformers import (
MODEL_FOR_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_CAUSAL_LM_MAPPING,
TextGenerationPipeline,
logging,
pipeline,
)
from transformers.testing_utils import (
CaptureLogger,
is_pipeline_test,
require_accelerate,
require_tf,
require_torch,
requ... | 313 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
a__ : Optional[Any] = logging.get_logger(__name__)
a__ : List[str] = {
'''kssteven/ibert-roberta-base''': ... | 313 | 1 |
import inspect
import unittest
import warnings
from math import ceil, floor
from transformers import LevitConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vi... | 313 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
if TYPE_CHECKING:
from ... import FeatureExtractionMixin, TensorType
a__ : Any = logging.... | 313 | 1 |
def UpperCAmelCase_( a__ ):
"""simple docstring"""
return " ".join(input_str.split()[::-1] )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 313 |
from maths.prime_check import is_prime
def UpperCAmelCase_( a__ ):
"""simple docstring"""
if not isinstance(a__ , a__ ):
SCREAMING_SNAKE_CASE : List[Any] = F"""Input value of [number={number}] must be an integer"""
raise TypeError(a__ ... | 313 | 1 |
import argparse
import os
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_task_guides.py
a__ : Optional[Any] = '''src/transformers'''
a__ : Dict = ''... | 313 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaControlnetImgaImgPipeline,
KandinskyVaaPriorEmbaEmbPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, load_image, loa... | 313 | 1 |
# limitations under the License.
# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works
from .pipelines import DiffusionPipeline, ImagePipelineOutput # noqa: F401
from .utils import deprecate
deprecate... | 313 |
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,
UNeta... | 313 | 1 |
import inspect
import logging
import os
import random
import shutil
import tempfile
import unittest
import pytest
import torch
from torch import nn
from torch.utils.data import DataLoader, TensorDataset
from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_cuda
from a... | 313 |
from typing import Optional, Tuple, Union
import flax
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict
from ..configuration_utils import ConfigMixin, flax_register_to_config
from ..utils import BaseOutput
from .embeddings_flax import FlaxTimestepEmbedding, FlaxT... | 313 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a__ : Dict = logging.get_logger(__name__)
a__ : Union[str, Any] = {
'''google/switch-base-8''': '''https://huggingface.co/google/switch-base-8/blob/main/config.json''',
}
class a_ ( a_... | 313 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModel,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableUnCL... | 313 | 1 |
import contextlib
import importlib
import io
import unittest
import transformers
# Try to import everything from transformers to ensure every object can be loaded.
from transformers import * # noqa F406
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, require_flax, require_tf, require_torch
from tra... | 313 |
from abc import ABC, abstractmethod
from typing import List, Optional
class a_ ( a__ ):
"""simple docstring"""
def __init__( self ) ->List[str]:
# test for the above condition
self.test()
def __lowerCAmelCase ( self ... | 313 | 1 |
import json
import os
import sys
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile
from huggingface_hub import HfFolder, Repository, create_repo, delete_repo
from requests.exceptions import HTTPError
import transformers
from transformers import (
CONFIG_MAPPING,
FEATURE_EXT... | 313 |
import argparse
import random
import joblib
import numpy as np
import torch
from igf.igf import (
SecondaryLearner,
collect_objective_set,
compute_perplexity,
generate_datasets,
load_gpta,
recopy_gpta,
set_seed,
train_secondary_learner,
)
from torch.utils.data import DataLoader, Random... | 313 | 1 |
import collections
import json
import os
import re
from typing import TYPE_CHECKING, List, Optional, Tuple
import numpy as np
from ...tokenization_utils_fast import PreTrainedTokenizer
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversation
a__ : ... | 313 |
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 UpperCAmelCase_( a__ ):
"""... | 313 | 1 |
import numpy as np
def UpperCAmelCase_( a__ ):
"""simple docstring"""
return 1 / (1 + np.exp(-vector ))
def UpperCAmelCase_( a__ ):
"""simple docstring"""
return vector * sigmoid(1.702 * vector )
if __name__ == "__main__":
import doctest... | 313 |
import argparse
import json
from typing import List
from ltp import LTP
from transformers import BertTokenizer
def UpperCAmelCase_( a__ ):
"""simple docstring"""
if (
(cp >= 0x4_E00 and cp <= 0x9_FFF)
or (cp >= 0x3_400 and cp <= 0x4_DBF) #
or (cp >= 0x20_000 ... | 313 | 1 |
import unittest
from transformers import load_tool
from transformers.utils import is_torch_available
if is_torch_available():
import torch
from transformers.testing_utils import require_torch
from .test_tools_common import ToolTesterMixin
@require_torch
class a_ ( unittest.TestCase , ... | 313 |
import datetime
import platform
import subprocess
from typing import Optional, Tuple, Union
import numpy as np
def UpperCAmelCase_( a__ , a__ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[Any] = F"""{sampling_rate}"""
SCREAMING_SNAKE_CASE ... | 313 | 1 |
def UpperCAmelCase_( a__ = 100 ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[Any] = 0
SCREAMING_SNAKE_CASE : List[str] = 0
for i in range(1 , n + 1 ):
sum_of_squares += i**2
sum_of_ints += i
return s... | 313 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a__ : Tuple = {'''configuration_wavlm''': ['''WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''WavLMConfig''']}
try:
if not is_torch_available():
raise OptionalDependency... | 313 | 1 |
def UpperCAmelCase_( a__ ):
"""simple docstring"""
if not numbers:
return 0
if not isinstance(a__ , (list, tuple) ) or not all(
isinstance(a__ , a__ ) for number in numbers ):
raise ValueError('''numbers must be an iterable of integers''' ... | 313 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
a__ : int = logging.get_logger(__name__)
a__ : Optional[Any] = {
'''SenseTime/deformable-detr''': '''https://huggingface.co/sensetime/deformable-detr/reso... | 313 | 1 |
import os
import zipfile
import pytest
from datasets.utils.extract import (
BzipaExtractor,
Extractor,
GzipExtractor,
LzaExtractor,
SevenZipExtractor,
TarExtractor,
XzExtractor,
ZipExtractor,
ZstdExtractor,
)
from .utils import require_lza, require_pyazr, require_zstandard
... | 313 |
import torch
from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device
from .test_schedulers import SchedulerCommonTest
class a_ ( a__ ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Optional[Any] = (EulerDiscreteScheduler,)
__SCREAMING... | 313 | 1 |
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
a__ : List[str] = logging.get_logger(__name... | 313 |
import json
from typing import Dict, List, Optional, Tuple, Union
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding, EncodedInput
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokeniz... | 313 | 1 |
import logging
import os
from typing import List, Tuple
import numpy as np
import psutil
import torch
import torch.distributed as dist
from transformers import RagRetriever
a__ : Tuple = logging.getLogger(__name__)
class a_ ( a__ ):
"""simple docstring"""
d... | 313 |
from __future__ import annotations
import math
def UpperCAmelCase_( a__ , a__ ):
"""simple docstring"""
if len(a__ ) != 2 or len(a[0] ) != 2 or len(a__ ) != 2 or len(b[0] ) != 2:
raise Exception('''Matrices are not 2x2''' )
SCREAMING_SNAKE_CASE ... | 313 | 1 |
import argparse
import random
import joblib
import numpy as np
import torch
from igf.igf import (
SecondaryLearner,
collect_objective_set,
compute_perplexity,
generate_datasets,
load_gpta,
recopy_gpta,
set_seed,
train_secondary_learner,
)
from torch.utils.data import DataLoader, Random... | 313 |
# We ignore warnings about stepping the scheduler since we step it ourselves during gradient accumulation
import warnings
from .state import AcceleratorState, GradientState
warnings.filterwarnings('''ignore''', category=UserWarning, module='''torch.optim.lr_scheduler''')
class a_ :
"""simple d... | 313 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
a__ : str = logging.get_logger(__name__)
a__ : Union[str, Any] = {
'''shi-labs/nat-mini-in1k-224''': ... | 313 |
import argparse
import os
from pathlib import Path
from typing import Dict
import tensorflow as tf
import torch
from tqdm import tqdm
from transformers import PegasusConfig, PegasusForConditionalGeneration, PegasusTokenizer
from transformers.models.pegasus.configuration_pegasus import DEFAULTS, task_specific_params
... | 313 | 1 |
# Note: if you intend to run this script make sure you look under scripts/fsmt/
# to locate the appropriate script to do the work correctly. There is a set of scripts to:
# - download and prepare data and run the conversion script
# - perform eval to get the best hparam into the config
# - generate model_cards - usefu... | 313 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast
from diffusers import DDIMScheduler, KandinskyImgaImgPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MC... | 313 | 1 |
from __future__ import annotations
from collections.abc import Callable
from typing import Generic, TypeVar
a__ : Dict = TypeVar('''T''')
a__ : Optional[int] = TypeVar('''U''')
class a_ ( Generic[T, U] ):
"""simple docstring"""
def __init__( ... | 313 |
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import ViTImageProcessor, ViTMSNConfig, ViTMSNModel
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
torch.set_grad_enabled(False)
def ... | 313 | 1 |
import unittest
from .lib import (
Matrix,
Vector,
axpy,
square_zero_matrix,
unit_basis_vector,
zero_vector,
)
class a_ ( unittest.TestCase ):
"""simple docstring"""
def __lowerCAmelCase ( self ) ->None:
SCREAMING_SNAKE_CASE... | 313 |
import csv
import tweepy
# Twitter API credentials
a__ : Union[str, Any] = ''''''
a__ : List[str] = ''''''
a__ : Any = ''''''
a__ : List[str] = ''''''
def UpperCAmelCase_( a__ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE ... | 313 | 1 |
import json
import multiprocessing as mp
import re
from collections import defaultdict
from functools import partial
from typing import Dict, List, Optional, Set, Tuple, Type
from datasets import Dataset
from datasketch import MinHash, MinHashLSH
from dpu_utils.utils.iterators import ThreadedIterator
from tqdm import... | 313 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
a__ : Optional[Any] = logging.get_logger(__name__)
a__ : List[str] = {
'''kssteven/ibert-roberta-base''': ... | 313 | 1 |
import inspect
import unittest
from transformers import SegformerConfig, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common ... | 313 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
if TYPE_CHECKING:
from ... import FeatureExtractionMixin, TensorType
a__ : Any = logging.... | 313 | 1 |
import unittest
from transformers.testing_utils import require_bsa
from transformers.utils import is_bsa_available
from ...test_feature_extraction_common import FeatureExtractionSavingTestMixin
if is_bsa_available():
from transformers import MarkupLMFeatureExtractor
class a_ ( unittest.TestC... | 313 |
from maths.prime_check import is_prime
def UpperCAmelCase_( a__ ):
"""simple docstring"""
if not isinstance(a__ , a__ ):
SCREAMING_SNAKE_CASE : List[Any] = F"""Input value of [number={number}] must be an integer"""
raise TypeError(a__ ... | 313 | 1 |
import warnings
from ...utils import logging
from .image_processing_donut import DonutImageProcessor
a__ : str = logging.get_logger(__name__)
class a_ ( a__ ):
"""simple docstring"""
def __init__( self , *_lowerCamelCase , **_lowerCame... | 313 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaControlnetImgaImgPipeline,
KandinskyVaaPriorEmbaEmbPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, load_image, loa... | 313 | 1 |
import json
import logging
import math
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
from datasets import Dataset, load_dataset
import transformers
from transformers import (
CONFIG_MAPPING,
MODEL_FOR_MASKED_LM_MAPPING,
AutoConfig,
AutoModelForMaskedLM,
... | 313 |
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,
UNeta... | 313 | 1 |
from typing import List
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a__ : str = logging.get_logger(__name__)
a__ : str = {
'''snap-research/efficientformer-l1-300''': (
'''https://huggingface.co/snap-research/efficientformer-l1-300/reso... | 313 |
from typing import Optional, Tuple, Union
import flax
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict
from ..configuration_utils import ConfigMixin, flax_register_to_config
from ..utils import BaseOutput
from .embeddings_flax import FlaxTimestepEmbedding, FlaxT... | 313 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a__ : Optional[int] = logging.get_logger(__name__)
a__ : List[Any] = {
'''funnel-transformer/small''': '''https://huggingface.co/funnel-transformer/small/resolve/main/config.json''',
'''funnel-tran... | 313 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModel,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableUnCL... | 313 | 1 |
from __future__ import annotations
def UpperCAmelCase_( a__ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[int] = 2
SCREAMING_SNAKE_CASE : Optional[Any] = []
while i * i <= n:
if n % i:
i += 1
... | 313 |
from abc import ABC, abstractmethod
from typing import List, Optional
class a_ ( a__ ):
"""simple docstring"""
def __init__( self ) ->List[str]:
# test for the above condition
self.test()
def __lowerCAmelCase ( self ... | 313 | 1 |
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel
from diffusers import DDIMScheduler, LDMPipeline, UNetaDModel, VQModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class ... | 313 |
import argparse
import random
import joblib
import numpy as np
import torch
from igf.igf import (
SecondaryLearner,
collect_objective_set,
compute_perplexity,
generate_datasets,
load_gpta,
recopy_gpta,
set_seed,
train_secondary_learner,
)
from torch.utils.data import DataLoader, Random... | 313 | 1 |
import random
from typing import Any
def UpperCAmelCase_( a__ ):
"""simple docstring"""
for _ in range(len(a__ ) ):
SCREAMING_SNAKE_CASE : str = random.randint(0 , len(a__ ) - 1 )
SCREAMING_SNAKE_CASE : int ... | 313 |
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 UpperCAmelCase_( a__ ):
"""... | 313 | 1 |
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.checkpoint.de... | 313 |
import argparse
import json
from typing import List
from ltp import LTP
from transformers import BertTokenizer
def UpperCAmelCase_( a__ ):
"""simple docstring"""
if (
(cp >= 0x4_E00 and cp <= 0x9_FFF)
or (cp >= 0x3_400 and cp <= 0x4_DBF) #
or (cp >= 0x20_000 ... | 313 | 1 |
def UpperCAmelCase_( a__ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[int] = abs(a__ )
SCREAMING_SNAKE_CASE : int = 0
while n > 0:
res += n % 10
n //= 10
return res
def UpperCAmelCase_( ... | 313 |
import datetime
import platform
import subprocess
from typing import Optional, Tuple, Union
import numpy as np
def UpperCAmelCase_( a__ , a__ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[Any] = F"""{sampling_rate}"""
SCREAMING_SNAKE_CASE ... | 313 | 1 |
import unittest
from transformers import is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import ANY
if is_vision_availab... | 313 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a__ : Tuple = {'''configuration_wavlm''': ['''WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''WavLMConfig''']}
try:
if not is_torch_available():
raise OptionalDependency... | 313 | 1 |
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicab... | 313 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
a__ : int = logging.get_logger(__name__)
a__ : Optional[Any] = {
'''SenseTime/deformable-detr''': '''https://huggingface.co/sensetime/deformable-detr/reso... | 313 | 1 |
import argparse
import torch
from transformers import (
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVecaForAudioFrameClassification,
WavaVecaForSequenceClassification,
WavaVecaForXVector,
logging,
)
logging.set_verbosity_info()
a__ : str = logging.get_logger(__name__)
... | 313 |
import torch
from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device
from .test_schedulers import SchedulerCommonTest
class a_ ( a__ ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Optional[Any] = (EulerDiscreteScheduler,)
__SCREAMING... | 313 | 1 |
from ...processing_utils import ProcessorMixin
class a_ ( a__ ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Dict = 'SpeechT5FeatureExtractor'
__SCREAMING_SNAKE_CASE : int = 'SpeechT5Tokenizer'
def __init__( self , _lowerCamelCase ... | 313 |
import json
from typing import Dict, List, Optional, Tuple, Union
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding, EncodedInput
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokeniz... | 313 | 1 |
from __future__ import annotations
import math
def UpperCAmelCase_( a__ , a__ ):
"""simple docstring"""
if len(a__ ) != 2 or len(a[0] ) != 2 or len(a__ ) != 2 or len(b[0] ) != 2:
raise Exception('''Matrices are not 2x2''' )
SCREAMING_SNAKE_CASE ... | 313 |
from __future__ import annotations
import math
def UpperCAmelCase_( a__ , a__ ):
"""simple docstring"""
if len(a__ ) != 2 or len(a[0] ) != 2 or len(a__ ) != 2 or len(b[0] ) != 2:
raise Exception('''Matrices are not 2x2''' )
SCREAMING_SNAKE_CASE ... | 313 | 1 |
def UpperCAmelCase_( a__ ):
"""simple docstring"""
if any(not isinstance(a__ , a__ ) or x < 0 for x in sequence ):
raise TypeError('''Sequence must be list of non-negative integers''' )
for _ in range(len(a__ ) ):
for i, (rod_upper, rod_lower) in ... | 313 |
# We ignore warnings about stepping the scheduler since we step it ourselves during gradient accumulation
import warnings
from .state import AcceleratorState, GradientState
warnings.filterwarnings('''ignore''', category=UserWarning, module='''torch.optim.lr_scheduler''')
class a_ :
"""simple d... | 313 | 1 |
from abc import ABC, abstractmethod
from typing import List, Optional
class a_ ( a__ ):
"""simple docstring"""
def __init__( self ) ->List[str]:
# test for the above condition
self.test()
def __lowerCAmelCase ( self ... | 313 |
import argparse
import os
from pathlib import Path
from typing import Dict
import tensorflow as tf
import torch
from tqdm import tqdm
from transformers import PegasusConfig, PegasusForConditionalGeneration, PegasusTokenizer
from transformers.models.pegasus.configuration_pegasus import DEFAULTS, task_specific_params
... | 313 | 1 |
import operator as op
a__ : Dict = '''scaler.pt'''
a__ : Dict = '''pytorch_model'''
a__ : Any = '''random_states'''
a__ : Optional[int] = '''optimizer'''
a__ : Tuple = '''scheduler'''
a__ : int = '''pytorch_model.bin'''
a__ ... | 313 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast
from diffusers import DDIMScheduler, KandinskyImgaImgPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MC... | 313 | 1 |
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
import evaluate
import numpy as np
import torch
from datasets import load_dataset
from PIL import Image
from torchvision.transforms import (
CenterCrop,
Compose,
Normalize,
RandomHorizontalFlip,
... | 313 |
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import ViTImageProcessor, ViTMSNConfig, ViTMSNModel
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
torch.set_grad_enabled(False)
def ... | 313 | 1 |
import random
import unittest
import torch
from diffusers import IFInpaintingSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device
from ..pipeline_params import (
TEXT_GUID... | 313 |
import csv
import tweepy
# Twitter API credentials
a__ : Union[str, Any] = ''''''
a__ : List[str] = ''''''
a__ : Any = ''''''
a__ : List[str] = ''''''
def UpperCAmelCase_( a__ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE ... | 313 | 1 |
from typing import Optional, Union
import torch
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import BaseModelOutputWithPoolingAndNoAttention, ImageClassifierOutputWithNoAttention
from ...modeling_utils import PreTrai... | 313 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
a__ : Optional[Any] = logging.get_logger(__name__)
a__ : List[str] = {
'''kssteven/ibert-roberta-base''': ... | 313 | 1 |
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicab... | 313 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
if TYPE_CHECKING:
from ... import FeatureExtractionMixin, TensorType
a__ : Any = logging.... | 313 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
a__ : int = {
'''configuration_vision_text_dual_encoder''': ['''VisionTextDualEncoderConfig'''],
'''processing_vis... | 313 |
from maths.prime_check import is_prime
def UpperCAmelCase_( a__ ):
"""simple docstring"""
if not isinstance(a__ , a__ ):
SCREAMING_SNAKE_CASE : List[Any] = F"""Input value of [number={number}] must be an integer"""
raise TypeError(a__ ... | 313 | 1 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import ... | 313 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaControlnetImgaImgPipeline,
KandinskyVaaPriorEmbaEmbPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, load_image, loa... | 313 | 1 |
from __future__ import annotations
import numpy as np
def UpperCAmelCase_( a__ ):
"""simple docstring"""
return np.maximum(0 , a__ )
if __name__ == "__main__":
print(np.array(relu([-1, 0, 5]))) # --> [0, 0, 5]
| 313 |
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,
UNeta... | 313 | 1 |
from random import randint
from tempfile import TemporaryFile
import numpy as np
def UpperCAmelCase_( a__ , a__ , a__ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Tuple = 0
if start < end:
SCREAMING_SNAKE_CASE : List[str]... | 313 |
from typing import Optional, Tuple, Union
import flax
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict
from ..configuration_utils import ConfigMixin, flax_register_to_config
from ..utils import BaseOutput
from .embeddings_flax import FlaxTimestepEmbedding, FlaxT... | 313 | 1 |
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