code stringlengths 82 54.1k | code_codestyle int64 0 699 | style_context stringlengths 111 35.6k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class lowerCamelCase_ ( lowerCamelCase ):
a__ = ['''image_processor''', '''tokenizer''']
a__ = '''ChineseCLIPImageProcessor'''
a__ = ... | 0 |
from sklearn.metrics import mean_squared_error
import datasets
SCREAMING_SNAKE_CASE__ : List[str] = """\
@article{scikit-learn,
title={Scikit-learn: Machine Learning in {P}ython},
author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
and Thirion, B. and Grisel... | 112 | 0 |
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import DetrConfig, MaskFormerConfig, SwinConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, ... | 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 OptionalDependencyNotAvailable()
ex... | 112 | 0 |
import itertools
import math
def SCREAMING_SNAKE_CASE_ ( _snake_case :int ) -> 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, 1, all even numbers, all multiples of 3 are not primes... | 2 |
def _A ( lowerCamelCase ):
a__ : Tuple = []
a__ : str = set({"(", "[", "{"} )
a__ : List[str] = set({")", "]", "}"} )
a__ : int = {"{": "}", "[": "]", "(": ")"}
for i in range(len(lowerCamelCase ) ):
if s[i] in open_brackets:
stac... | 112 | 0 |
'''simple docstring'''
import gc
import unittest
from parameterized import parameterized
from diffusers import FlaxUNetaDConditionModel
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import load_hf_numpy, require_flax, slow
if is_flax_available():
import jax
... | 3 |
import sacrebleu as scb
from packaging import version
from sacrebleu import TER
import datasets
SCREAMING_SNAKE_CASE__ : List[Any] = """\
@inproceedings{snover-etal-2006-study,
title = \"A Study of Translation Edit Rate with Targeted Human Annotation\",
author = \"Snover, Matthew ... | 112 | 0 |
"""simple docstring"""
import collections
import tempfile
import unittest
import numpy as np
from transformers.testing_utils import (
is_pt_flax_cross_test,
require_flax,
require_torch,
require_vision,
slow,
torch_device,
)
from transformers.utils import is_flax_available, is_torch_avail... | 4 |
def _A ( lowerCamelCase = 200 ):
a__ : List[str] = [1, 2, 5, 10, 20, 50, 100, 200]
a__ : Dict = [0] * (pence + 1)
a__ : int = 1 # base case: 1 way to make 0 pence
for coin in coins:
for i in range(lowerCamelCase , pence + 1 , 1 ):
num... | 112 | 0 |
'''simple docstring'''
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
WavaVecaConfig,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaForCTC,
WavaVecaForPreTraining,
WavaVecaProcessor,
logging,
)
... | 5 |
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
SCREAMING_SNAKE_CASE__ : Union[str, Any] = {
"""configuration_autoformer""": [
"""AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP... | 112 | 0 |
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 Confi... | 6 |
import argparse
import os
import re
SCREAMING_SNAKE_CASE__ : Any = """src/transformers/models/auto"""
# re pattern that matches mapping introductions:
# SUPER_MODEL_MAPPING_NAMES = OrderedDict or SUPER_MODEL_MAPPING = OrderedDict
SCREAMING_SNAKE_CASE__ : Union[str, Any] ... | 112 | 0 |
"""simple docstring"""
a = '''0.18.2'''
from .configuration_utils import ConfigMixin
from .utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_inflect_available,
is_invisible_watermark_available,
is_k_diffusion_available,
is_k_diffusion_version,
is_lib... | 7 |
# Function to print upper half of diamond (pyramid)
def _A ( lowerCamelCase ):
for i in range(0 , lowerCamelCase ):
for _ in range(0 , n - i - 1 ): # printing spaces
print(" " , end="" )
for _ in range(0 , i + 1 ): # printing stars
print("* " , ... | 112 | 0 |
'''simple docstring'''
import argparse
import logging
import os
from datetime import datetime
import numpy as np
import torch
from torch import nn
from torch.utils.data import DataLoader, RandomSampler, TensorDataset
from tqdm import tqdm
from transformers import GPTaLMHeadModel
l... | 8 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
SCREAMING_SNAKE_CASE__ : Tuple = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ ... | 112 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
SCREAMING_SNAKE_CASE__ = {'''configuration_deit''': ['''DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DeiTConfig''', '''DeiTOnnxC... | 9 |
import os
import string
import sys
SCREAMING_SNAKE_CASE__ : int = 1 << 8
SCREAMING_SNAKE_CASE__ : List[str] = {
"""tab""": ord("""\t"""),
"""newline""": ord("""\r"""),
"""esc""": 2_7,
"""up""": 6_5 + ARROW_KEY_FLAG,
"""down""": 6_6 + ARROW_KEY_FLA... | 112 | 0 |
from typing import List, Optional, Union
import numpy as np
import tensorflow as tf
from .utils import logging
_lowerCAmelCase = logging.get_logger(__name__)
def _snake_case ( __snake_case ):
if isinstance(__snake_case , np.ndarray ):
return list(tensor.shape )
... | 10 |
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_extraction_utils import FeatureExtra... | 112 | 0 |
'''simple docstring'''
from ...processing_utils import ProcessorMixin
class __A ( A ):
'''simple docstring'''
__lowerCamelCase : List[Any] = ['image_processor', 'feature_extractor']
__lowerCamelCase : int = 'TvltImageProcessor'
__lowerCamelCase : Option... | 11 |
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 | 0 |
import numpy as np
def UpperCamelCase ( lowercase_ , lowercase_ , lowercase_ = 1E-12 , lowercase_ = 1_00 , ) -> tuple[float, np.ndarray]:
'''simple docstring'''
assert np.shape(lowercase_ )[0] == np.shape(lowercase_ )[1]
# Ensure proper dimensionality.
ass... | 12 |
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,
is_onnx_available,
is... | 112 | 0 |
'''simple docstring'''
import multiprocessing
import os
from typing import BinaryIO, Optional, Union
import fsspec
from .. import Dataset, Features, NamedSplit, config
from ..formatting import query_table
from ..packaged_modules.json.json import Json
from ..utils import logging
from ..utils.typing import Nested... | 13 |
import warnings
from typing import Any, Dict, List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ... | 112 | 0 |
a__ = '''Input must be a string of 8 numbers plus letter'''
a__ = '''TRWAGMYFPDXBNJZSQVHLCKE'''
def __UpperCAmelCase ( __a : str ) -> bool:
"""simple docstring"""
if not isinstance(__a ,__a ):
_a : List[s... | 14 |
from packaging import version
from .import_utils import is_accelerate_available
if is_accelerate_available():
import accelerate
def _A ( lowerCamelCase ):
if not is_accelerate_available():
return method
a__ : List[Any] = version.parse(accelerate.__version__ ).base_version
... | 112 | 0 |
import absl # noqa: F401 # Here to have a nice missing dependency error message early on
import nltk # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy # noqa: F401 # Here to have a nice missing dependency error message early on
import six # noqa: F401 # Here to have ... | 15 |
import argparse
import intel_extension_for_pytorch as ipex
import torch
from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline
SCREAMING_SNAKE_CASE__ : List[str] = argparse.ArgumentParser("""Stable Diffusion script with intel optimization""", add_help=False)
parser.ad... | 112 | 0 |
import unittest
import torch
from torch import nn
from diffusers.models.activations import get_activation
class _SCREAMING_SNAKE_CASE ( unittest.TestCase ):
'''simple docstring'''
def _snake_case ( self : Optional[int] ):
SCREAMING_SN... | 16 |
# XXX: we want transformers master here - in the absense of conftest manipulating sys.path:
# hack it in for now:
import sys
from pathlib import Path
SCREAMING_SNAKE_CASE__ : List[str] = Path(__file__).resolve().parents[3] / """src"""
sys.path.insert(1, str(git_repo_path))
import dataclas... | 112 | 0 |
import argparse
import torch
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_from_original_stable_diffusion_ckpt
if __name__ == "__main__":
UpperCAmelCase_ : str = argparse.ArgumentParser()
parser.add_argument(
'''--checkpoint_path''', default=None, type=str... | 17 |
from __future__ import annotations
import unittest
from transformers import DebertaVaConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask... | 112 | 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, pr... | 18 |
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 convert_to_rgb, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
OPENAI_CLIP_MEAN,
O... | 112 | 0 |
"""simple docstring"""
def lowerCamelCase__ ( __snake_case, __snake_case ) -> int:
"""simple docstring"""
_UpperCamelCase = ''''''
for i in table:
res += inp[i - 1]
return res
def lowerCamelCase__ ( __snake_... | 19 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE__ : Tuple = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ : int = {
"""google/pix2struct-textcaps-base""": ... | 112 | 0 |
import argparse
import json
from collections import OrderedDict
from functools import partial
from pathlib import Path
import timm
import torch
from huggingface_hub import hf_hub_download
from transformers import LevitConfig, LevitForImageClassificationWithTeacher, LevitImageProcessor
from transformers.utils impor... | 20 |
def _A ( lowerCamelCase ):
a__ : Optional[Any] = 1
for i in range(1 , num + 1 ):
fact *= i
return fact
def _A ( lowerCamelCase ):
a__ : List[Any] = 0
while number > 0:
a__ : str = number % 10
sum_of_digits += last_digit
... | 112 | 0 |
import functools
import gc
import inspect
import torch
from .imports import is_npu_available, is_xpu_available
def lowerCAmelCase_ ( *lowerCamelCase ):
if not isinstance(lowerCamelCase , lowerCamelCase ):
__magic_name__ : Union[str, Any] =list(low... | 21 |
import argparse
import pytorch_lightning as pl
import torch
from torch import nn
from transformers import LongformerForQuestionAnswering, LongformerModel
class __lowerCAmelCase ( pl.LightningModule ):
def __init__( self , snake_case ) -> Dict:
"""simple docstr... | 112 | 0 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
_snake_case : List[Any] = logging.get_logger(__name__)
... | 22 |
from sklearn.metrics import mean_squared_error
import datasets
SCREAMING_SNAKE_CASE__ : List[str] = """\
@article{scikit-learn,
title={Scikit-learn: Machine Learning in {P}ython},
author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
and Thirion, B. and Grisel... | 112 | 0 |
import tensorflow as tf
from ...tf_utils import shape_list
class _a ( tf.keras.layers.Layer ):
"""simple docstring"""
def __init__( self , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase=1 , _UpperCAmelC... | 23 |
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 OptionalDependencyNotAvailable()
ex... | 112 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
UpperCAmelCase_ : Any = {
'''configuration_rag''': ['''RagConfig'''],
'''retrieval_rag''': ['''RagRetriever'''],
'''tokenizatio... | 24 |
def _A ( lowerCamelCase ):
a__ : Tuple = []
a__ : str = set({"(", "[", "{"} )
a__ : List[str] = set({")", "]", "}"} )
a__ : int = {"{": "}", "[": "]", "(": ")"}
for i in range(len(lowerCamelCase ) ):
if s[i] in open_brackets:
stac... | 112 | 0 |
import unittest
import numpy as np
import torch
from .utils_summarization import build_mask, compute_token_type_ids, process_story, truncate_or_pad
class _UpperCamelCase ( unittest.TestCase ):
'''simple docstring'''
def __UpperCamelCase ( self : Dict ) -> Tuple... | 25 |
import sacrebleu as scb
from packaging import version
from sacrebleu import TER
import datasets
SCREAMING_SNAKE_CASE__ : List[Any] = """\
@inproceedings{snover-etal-2006-study,
title = \"A Study of Translation Edit Rate with Targeted Human Annotation\",
author = \"Snover, Matthew ... | 112 | 0 |
'''simple docstring'''
from __future__ import annotations
import math
def _a ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ) -> int:
"""simple docstring"""
if depth < 0:
raise V... | 26 |
def _A ( lowerCamelCase = 200 ):
a__ : List[str] = [1, 2, 5, 10, 20, 50, 100, 200]
a__ : Dict = [0] * (pence + 1)
a__ : int = 1 # base case: 1 way to make 0 pence
for coin in coins:
for i in range(lowerCamelCase , pence + 1 , 1 ):
num... | 112 | 0 |
import argparse
import os
import re
import packaging.version
__A : str = "examples/"
__A : Optional[int] = {
"examples": (re.compile(r"^check_min_version\(\"[^\"]+\"\)\s*$", re.MULTILINE), "check_min_version(\"VERSION\")\n"),
"init": (re.compile(r"^__... | 27 |
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
SCREAMING_SNAKE_CASE__ : Union[str, Any] = {
"""configuration_autoformer""": [
"""AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP... | 112 | 0 |
'''simple docstring'''
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_visi... | 28 |
import argparse
import os
import re
SCREAMING_SNAKE_CASE__ : Any = """src/transformers/models/auto"""
# re pattern that matches mapping introductions:
# SUPER_MODEL_MAPPING_NAMES = OrderedDict or SUPER_MODEL_MAPPING = OrderedDict
SCREAMING_SNAKE_CASE__ : Union[str, Any] ... | 112 | 0 |
"""simple docstring"""
import os
from datetime import datetime as dt
from github import Github
A_ = [
"""good first issue""",
"""feature request""",
"""wip""",
]
def lowercase ( ):
lowerCamelCase_ = Github(os.environ['''GITHUB_TOKEN'''] )
lowerCamelCase_ =... | 29 |
# Function to print upper half of diamond (pyramid)
def _A ( lowerCamelCase ):
for i in range(0 , lowerCamelCase ):
for _ in range(0 , n - i - 1 ): # printing spaces
print(" " , end="" )
for _ in range(0 , i + 1 ): # printing stars
print("* " , ... | 112 | 0 |
import argparse
from typing import Dict
import tensorflow as tf
import torch
from tqdm import tqdm
from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration
__a = [
# tf -> hf
('/', '.'),
('layer_', 'layers.'),
('kernel', 'weight'),
('beta', 'bias'... | 30 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
SCREAMING_SNAKE_CASE__ : Tuple = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ ... | 112 | 0 |
import unittest
from diffusers.models.unet_ad_blocks import * # noqa F403
from diffusers.utils import torch_device
from .test_unet_blocks_common import UNetBlockTesterMixin
class lowerCamelCase_ ( _SCREAMING_SNAKE_CASE , unittest.TestCase ):
'''simple docstring'''
lowercase_ ... | 31 |
import os
import string
import sys
SCREAMING_SNAKE_CASE__ : int = 1 << 8
SCREAMING_SNAKE_CASE__ : List[str] = {
"""tab""": ord("""\t"""),
"""newline""": ord("""\r"""),
"""esc""": 2_7,
"""up""": 6_5 + ARROW_KEY_FLAG,
"""down""": 6_6 + ARROW_KEY_FLA... | 112 | 0 |
def A__ ( SCREAMING_SNAKE_CASE_ : Union[str, Any] ) -> Optional[Any]:
"""simple docstring"""
if not head:
return True
# split the list to two parts
_UpperCAmelCase , _UpperCAmelCase = head.next, head
while fast and fast.next:
... | 32 |
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_extraction_utils import FeatureExtra... | 112 | 0 |
def SCREAMING_SNAKE_CASE ( __lowerCAmelCase ) -> bool:
if number < 0:
raise ValueError('''number must not be negative''' )
return number & (number - 1) == 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 33 |
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 | 0 |
"""simple docstring"""
from __future__ import annotations
import unittest
import numpy as np
from transformers import OPTConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_c... | 34 |
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,
is_onnx_available,
is... | 112 | 0 |
import inspect
from typing import Callable, List, Optional, Union
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
from diffusers import DiffusionPipeline
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion import StableDi... | 35 |
import warnings
from typing import Any, Dict, List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ... | 112 | 0 |
import os
import pytest
from attr import dataclass
__lowercase : Optional[int] = '''us-east-1''' # defaults region
@dataclass
class _A :
'''simple docstring'''
__lowerCamelCase : str
__lowerCamelCase : Dict = '''arn:aws:iam::558105141721:role/sagemaker... | 36 |
from packaging import version
from .import_utils import is_accelerate_available
if is_accelerate_available():
import accelerate
def _A ( lowerCamelCase ):
if not is_accelerate_available():
return method
a__ : List[Any] = version.parse(accelerate.__version__ ).base_version
... | 112 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase : Optional[Any] = logging.get_logger(__name__)
UpperCamelCase : str = {
"""EleutherAI/gpt-neox-20b""": """https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/config.j... | 37 |
import argparse
import intel_extension_for_pytorch as ipex
import torch
from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline
SCREAMING_SNAKE_CASE__ : List[str] = argparse.ArgumentParser("""Stable Diffusion script with intel optimization""", add_help=False)
parser.ad... | 112 | 0 |
'''simple docstring'''
import numpy as np
import torch
from torch.nn import CrossEntropyLoss
from transformers import AutoModelForCausalLM, AutoTokenizer
import datasets
from datasets import logging
A_ : Optional[Any] = "\\n\n"
A_ : int = "\nPerplexity (PPL) is one of the most common m... | 38 |
# XXX: we want transformers master here - in the absense of conftest manipulating sys.path:
# hack it in for now:
import sys
from pathlib import Path
SCREAMING_SNAKE_CASE__ : List[str] = Path(__file__).resolve().parents[3] / """src"""
sys.path.insert(1, str(git_repo_path))
import dataclas... | 112 | 0 |
from math import factorial
def __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
# If either of the conditions are true, the function is being asked
# to calculate a factorial of a negative number, which is not possible
if n < k or k < 0:
... | 39 |
from __future__ import annotations
import unittest
from transformers import DebertaVaConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask... | 112 | 0 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING
__UpperCAmelCase = logging.get_logger(__name__)
class lowerCAmelCase_ ( a__ ):
UpperCAmelCase__ : int = "upernet"
... | 40 |
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 convert_to_rgb, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
OPENAI_CLIP_MEAN,
O... | 112 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
lowerCAmelCase__ = {
'''configuration_tapas''': ['''TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''TapasConfig'''],
'''tokenization_tapas'... | 41 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE__ : Tuple = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ : int = {
"""google/pix2struct-textcaps-base""": ... | 112 | 0 |
'''simple docstring'''
import itertools
import os
import random
import tempfile
import unittest
import numpy as np
from datasets import load_dataset
from transformers import is_speech_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from tra... | 42 |
def _A ( lowerCamelCase ):
a__ : Optional[Any] = 1
for i in range(1 , num + 1 ):
fact *= i
return fact
def _A ( lowerCamelCase ):
a__ : List[Any] = 0
while number > 0:
a__ : str = number % 10
sum_of_digits += last_digit
... | 112 | 0 |
# using dfs for finding eulerian path traversal
def _a ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE=None ):
"""simple docstring"""
lowercase__ = (path or []) + [u]
for v in graph[u]:
if visited_edge[u][v] is... | 43 |
import argparse
import pytorch_lightning as pl
import torch
from torch import nn
from transformers import LongformerForQuestionAnswering, LongformerModel
class __lowerCAmelCase ( pl.LightningModule ):
def __init__( self , snake_case ) -> Dict:
"""simple docstr... | 112 | 0 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import RoFormerConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor... | 44 |
from sklearn.metrics import mean_squared_error
import datasets
SCREAMING_SNAKE_CASE__ : List[str] = """\
@article{scikit-learn,
title={Scikit-learn: Machine Learning in {P}ython},
author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
and Thirion, B. and Grisel... | 112 | 0 |
# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny model through reduction of a normal pre-trained model, but keeping the
# full vocab, merges file, a... | 45 |
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 OptionalDependencyNotAvailable()
ex... | 112 | 0 |
"""simple docstring"""
from __future__ import annotations
from typing import TypedDict
class A_ ( _a ):
lowerCAmelCase__ = 42
lowerCAmelCase__ = 42
def lowerCamelCase_( _lowerCamelCase ) -> list[str]:
'''simple docstring'''
if no... | 46 |
def _A ( lowerCamelCase ):
a__ : Tuple = []
a__ : str = set({"(", "[", "{"} )
a__ : List[str] = set({")", "]", "}"} )
a__ : int = {"{": "}", "[": "]", "(": ")"}
for i in range(len(lowerCamelCase ) ):
if s[i] in open_brackets:
stac... | 112 | 0 |
from google.protobuf import descriptor as _descriptor
from google.protobuf import descriptor_pool as _descriptor_pool
from google.protobuf import symbol_database as _symbol_database
from google.protobuf.internal import builder as _builder
# @@protoc_insertion_point(imports)
SCREAMING_SNAKE_CASE... | 47 |
import sacrebleu as scb
from packaging import version
from sacrebleu import TER
import datasets
SCREAMING_SNAKE_CASE__ : List[Any] = """\
@inproceedings{snover-etal-2006-study,
title = \"A Study of Translation Edit Rate with Targeted Human Annotation\",
author = \"Snover, Matthew ... | 112 | 0 |
'''simple docstring'''
import argparse
import math
import traceback
import dateutil.parser as date_parser
import requests
def A ( UpperCamelCase_ : Any ) -> Optional[Any]:
'''simple docstring'''
lowerCAmelCase__ = {}
lowerCAmelCase__ = job["... | 48 |
def _A ( lowerCamelCase = 200 ):
a__ : List[str] = [1, 2, 5, 10, 20, 50, 100, 200]
a__ : Dict = [0] * (pence + 1)
a__ : int = 1 # base case: 1 way to make 0 pence
for coin in coins:
for i in range(lowerCamelCase , pence + 1 , 1 ):
num... | 112 | 0 |
"""simple docstring"""
from __future__ import annotations
def lowercase__ ( snake_case_ :list[int] ): # This function is recursive
__UpperCAmelCase = len(snake_case_ )
# If the array contains only one element, we return it (it's the stop condition of
# recu... | 49 |
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
SCREAMING_SNAKE_CASE__ : Union[str, Any] = {
"""configuration_autoformer""": [
"""AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP... | 112 | 0 |
'''simple docstring'''
class UpperCamelCase__ :
'''simple docstring'''
def __init__( self ,_lowerCAmelCase ):
lowerCamelCase__ = size
lowerCamelCase__ = [0] * size
lowerCamelCase__ = [0] * size
@staticmethod
def UpperC... | 50 |
import argparse
import os
import re
SCREAMING_SNAKE_CASE__ : Any = """src/transformers/models/auto"""
# re pattern that matches mapping introductions:
# SUPER_MODEL_MAPPING_NAMES = OrderedDict or SUPER_MODEL_MAPPING = OrderedDict
SCREAMING_SNAKE_CASE__ : Union[str, Any] ... | 112 | 0 |
'''simple docstring'''
import hashlib
import unittest
from typing import Dict
import numpy as np
from transformers import (
MODEL_FOR_MASK_GENERATION_MAPPING,
TF_MODEL_FOR_MASK_GENERATION_MAPPING,
is_vision_available,
pipeline,
)
from transformers.pipelines import MaskGenerationPipeline
from t... | 51 |
# Function to print upper half of diamond (pyramid)
def _A ( lowerCamelCase ):
for i in range(0 , lowerCamelCase ):
for _ in range(0 , n - i - 1 ): # printing spaces
print(" " , end="" )
for _ in range(0 , i + 1 ): # printing stars
print("* " , ... | 112 | 0 |
"""simple docstring"""
from __future__ import annotations
def __A ( a_ :list[int]) -> int:
if not nums:
return 0
__a : Any = nums[0]
__a : Optional[Any] = 0
for num in nums[1:]:
__a , __a : ... | 52 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
SCREAMING_SNAKE_CASE__ : Tuple = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ ... | 112 | 0 |
from math import factorial
def a_ ( lowerCAmelCase_ : int = 100 ):
return sum(int(lowerCAmelCase_ ) for x in str(factorial(lowerCAmelCase_ ) ) )
if __name__ == "__main__":
print(solution(int(input('Enter the Number: ').strip())))
| 53 |
import os
import string
import sys
SCREAMING_SNAKE_CASE__ : int = 1 << 8
SCREAMING_SNAKE_CASE__ : List[str] = {
"""tab""": ord("""\t"""),
"""newline""": ord("""\r"""),
"""esc""": 2_7,
"""up""": 6_5 + ARROW_KEY_FLAG,
"""down""": 6_6 + ARROW_KEY_FLA... | 112 | 0 |
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 transfor... | 54 |
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_extraction_utils import FeatureExtra... | 112 | 0 |
import csv
import tweepy
# Twitter API credentials
SCREAMING_SNAKE_CASE :List[str] = ''
SCREAMING_SNAKE_CASE :List[str] = ''
SCREAMING_SNAKE_CASE :Tuple = ''
SCREAMING_SNAKE_CASE :List[str] = ''
def UpperCAmelCase ( a_ ) -> N... | 55 |
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 | 0 |
'''simple docstring'''
import argparse
import torch
from huggingface_hub import hf_hub_download
from transformers import AutoTokenizer, RobertaPreLayerNormConfig, RobertaPreLayerNormForMaskedLM
from transformers.utils import logging
logging.set_verbosity_info()
_a : Dict = logging.get_logger... | 56 |
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,
is_onnx_available,
is... | 112 | 0 |
from typing import Optional
import numpy as np
import torch
from torch import nn
from transformers import GPTaConfig, GPTaLMHeadModel
from transformers.modeling_utils import ModuleUtilsMixin
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
... | 57 |
import warnings
from typing import Any, Dict, List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ... | 112 | 0 |
"""simple docstring"""
import baseaa
def __lowerCAmelCase ( __UpperCamelCase : str ):
'''simple docstring'''
return baseaa.baaencode(string.encode("""utf-8""" ) )
def __lowerCAmelCase ( __UpperCamelCase : bytes ):
... | 58 |
from packaging import version
from .import_utils import is_accelerate_available
if is_accelerate_available():
import accelerate
def _A ( lowerCamelCase ):
if not is_accelerate_available():
return method
a__ : List[Any] = version.parse(accelerate.__version__ ).base_version
... | 112 | 0 |
import argparse
import torch
from torch import nn
from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration
def lowerCAmelCase_ ( __a ) -> Any:
"""simple docstring"""
lowerCamelCase__: List[Any] =[
"encoder.version",
"decoder.version",
... | 59 |
import argparse
import intel_extension_for_pytorch as ipex
import torch
from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline
SCREAMING_SNAKE_CASE__ : List[str] = argparse.ArgumentParser("""Stable Diffusion script with intel optimization""", add_help=False)
parser.ad... | 112 | 0 |
import warnings
from ..trainer import Trainer
from ..utils import logging
lowerCAmelCase_ = logging.get_logger(__name__)
class __lowerCAmelCase ( _a ):
def __init__(self , __magic_name__=None , **__magic_name__ ) -> Dict:
'''simple docstring'''
... | 60 |
# XXX: we want transformers master here - in the absense of conftest manipulating sys.path:
# hack it in for now:
import sys
from pathlib import Path
SCREAMING_SNAKE_CASE__ : List[str] = Path(__file__).resolve().parents[3] / """src"""
sys.path.insert(1, str(git_repo_path))
import dataclas... | 112 | 0 |
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unles... | 61 |
from __future__ import annotations
import unittest
from transformers import DebertaVaConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask... | 112 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
snake_case = {
"""configuration_blip""": [
"""BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""BlipConfig"... | 62 |
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 convert_to_rgb, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
OPENAI_CLIP_MEAN,
O... | 112 | 0 |
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,
)
a : Any = ... | 63 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE__ : Tuple = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ : int = {
"""google/pix2struct-textcaps-base""": ... | 112 | 0 |
import importlib
import os
import sys
# This is required to make the module import works (when the python process is running from the root of the repo)
sys.path.append('.')
def A__ ( snake_case_ : Union[str, Any] ):
SCREAMING_SNAKE_CASE__: Dict= test_file.split(os.path.sep )
if componen... | 64 |
def _A ( lowerCamelCase ):
a__ : Optional[Any] = 1
for i in range(1 , num + 1 ):
fact *= i
return fact
def _A ( lowerCamelCase ):
a__ : List[Any] = 0
while number > 0:
a__ : str = number % 10
sum_of_digits += last_digit
... | 112 | 0 |
"""simple docstring"""
import os
import pytest
from attr import dataclass
__UpperCAmelCase = 'us-east-1' # defaults region
@dataclass
class __lowercase :
snake_case_ = 42
snake_case_ = """arn:aws:iam::558105141721:role/sagemaker... | 65 |
import argparse
import pytorch_lightning as pl
import torch
from torch import nn
from transformers import LongformerForQuestionAnswering, LongformerModel
class __lowerCAmelCase ( pl.LightningModule ):
def __init__( self , snake_case ) -> Dict:
"""simple docstr... | 112 | 0 |
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class lowerCAmelCase_ ( __snake_case ):
_UpperCamelCase : str = ["image_processor", "tokenizer"]
_UpperCamelCase : Union[str, Any] = "AutoImageProcesso... | 66 |
from sklearn.metrics import mean_squared_error
import datasets
SCREAMING_SNAKE_CASE__ : List[str] = """\
@article{scikit-learn,
title={Scikit-learn: Machine Learning in {P}ython},
author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
and Thirion, B. and Grisel... | 112 | 0 |
import inspect
import unittest
from transformers import BitConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_backbone_common import BackboneTesterMixin
... | 67 |
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 OptionalDependencyNotAvailable()
ex... | 112 | 0 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_distilbert import DistilBertTokenizer
__A = logging.get_logger(__name__)
__A = {"vocab_file": "vocab.... | 68 |
def _A ( lowerCamelCase ):
a__ : Tuple = []
a__ : str = set({"(", "[", "{"} )
a__ : List[str] = set({")", "]", "}"} )
a__ : int = {"{": "}", "[": "]", "(": ")"}
for i in range(len(lowerCamelCase ) ):
if s[i] in open_brackets:
stac... | 112 | 0 |
'''simple docstring'''
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from transformers.activations import gelu_new, gelu_python, get_activation
@require_torch
class SC... | 69 |
import sacrebleu as scb
from packaging import version
from sacrebleu import TER
import datasets
SCREAMING_SNAKE_CASE__ : List[Any] = """\
@inproceedings{snover-etal-2006-study,
title = \"A Study of Translation Edit Rate with Targeted Human Annotation\",
author = \"Snover, Matthew ... | 112 | 0 |
from dataclasses import dataclass
from typing import Tuple
import numpy as np
import torch
@dataclass
class A:
'''simple docstring'''
UpperCamelCase = 42 # [batch_size x 3]
UpperCamelCase = 42 # [batch_size x 3]
UpperCamelCase = 42 # [batch_size x 3]
... | 70 |
def _A ( lowerCamelCase = 200 ):
a__ : List[str] = [1, 2, 5, 10, 20, 50, 100, 200]
a__ : Dict = [0] * (pence + 1)
a__ : int = 1 # base case: 1 way to make 0 pence
for coin in coins:
for i in range(lowerCamelCase , pence + 1 , 1 ):
num... | 112 | 0 |
'''simple docstring'''
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor
from torchvision.... | 71 |
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
SCREAMING_SNAKE_CASE__ : Union[str, Any] = {
"""configuration_autoformer""": [
"""AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP... | 112 | 0 |
'''simple docstring'''
import argparse
import os
import re
import packaging.version
_UpperCAmelCase : Tuple = '''examples/'''
_UpperCAmelCase : Union[str, Any] = {
'''examples''': (re.compile(r'''^check_min_version\("[^"]+"\)\s*$''', re.MULTILINE), '''check_min_version("VERSION")\n'''),
'''init''':... | 72 |
import argparse
import os
import re
SCREAMING_SNAKE_CASE__ : Any = """src/transformers/models/auto"""
# re pattern that matches mapping introductions:
# SUPER_MODEL_MAPPING_NAMES = OrderedDict or SUPER_MODEL_MAPPING = OrderedDict
SCREAMING_SNAKE_CASE__ : Union[str, Any] ... | 112 | 0 |
from __future__ import annotations
def lowerCamelCase__ (_UpperCAmelCase = 4):
SCREAMING_SNAKE_CASE = abs(_UpperCAmelCase) or 4
return [[1 + x + y * row_size for x in range(_UpperCAmelCase)] for y in range(_UpperCAmelCase)]
def lowerCamelCase__ (_UpperCAmelCase):
re... | 73 |
# Function to print upper half of diamond (pyramid)
def _A ( lowerCamelCase ):
for i in range(0 , lowerCamelCase ):
for _ in range(0 , n - i - 1 ): # printing spaces
print(" " , end="" )
for _ in range(0 , i + 1 ): # printing stars
print("* " , ... | 112 | 0 |
import unittest
from parameterized import parameterized
from transformers import OpenLlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...t... | 74 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
SCREAMING_SNAKE_CASE__ : Tuple = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ ... | 112 | 0 |
'''simple docstring'''
import json
import sys
def a__ ( lowerCAmelCase__ , lowerCAmelCase__ ) -> List[str]:
with open(lowerCAmelCase__ , encoding='''utf-8''' ) as f:
UpperCAmelCase__ : Union[str, Any] = json.load(lowerCAmelCase__ )
... | 75 |
import os
import string
import sys
SCREAMING_SNAKE_CASE__ : int = 1 << 8
SCREAMING_SNAKE_CASE__ : List[str] = {
"""tab""": ord("""\t"""),
"""newline""": ord("""\r"""),
"""esc""": 2_7,
"""up""": 6_5 + ARROW_KEY_FLAG,
"""down""": 6_6 + ARROW_KEY_FLA... | 112 | 0 |
"""simple docstring"""
def __UpperCAmelCase ( __UpperCamelCase ):
return sum(i for i in range(1 , number // 2 + 1 ) if number % i == 0 ) == number
if __name__ == "__main__":
print('Program to check whether a number is a Perfect number or not...')
a_ = int(inp... | 76 |
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_extraction_utils import FeatureExtra... | 112 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available
A = {"""tokenization_herbert""": ["""HerbertTokenizer"""]}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()... | 77 |
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 | 0 |
'''simple docstring'''
def lowerCAmelCase_ ( snake_case_ : int = 60_08_51_47_51_43 ) -> int:
'''simple docstring'''
try:
UpperCAmelCase_ = int(snake_case_ )
except (TypeError, ValueError):
raise TypeError("Parameter n must be int ... | 78 |
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,
is_onnx_available,
is... | 112 | 0 |
import copy
import re
class UpperCAmelCase_ :
__lowerCamelCase = 'hp'
__lowerCamelCase = {}
__lowerCamelCase = None
@classmethod
def __UpperCAmelCase ( cls , _lowerCAmelCase , _lowerCAmelCase ):
... | 79 |
import warnings
from typing import Any, Dict, List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ... | 112 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCamelCase : List[Any] = logging.get_logger(__name__)
__UpperCamelCase : str = {
"""sayakpaul/vit-msn-base""": """https://huggingface.co/sayakpaul/vit-msn-base/resolve/main/config.json""",
# ... | 80 |
from packaging import version
from .import_utils import is_accelerate_available
if is_accelerate_available():
import accelerate
def _A ( lowerCamelCase ):
if not is_accelerate_available():
return method
a__ : List[Any] = version.parse(accelerate.__version__ ).base_version
... | 112 | 0 |
from math import cos, sin, sqrt, tau
from audio_filters.iir_filter import IIRFilter
def lowerCAmelCase_ ( __lowerCamelCase , __lowerCamelCase , __lowerCamelCase = 1 / sqrt(2 ) ):
__snake_case : List[Any] = tau * frequency / samplerate
__snake... | 81 |
import argparse
import intel_extension_for_pytorch as ipex
import torch
from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline
SCREAMING_SNAKE_CASE__ : List[str] = argparse.ArgumentParser("""Stable Diffusion script with intel optimization""", add_help=False)
parser.ad... | 112 | 0 |
"""simple docstring"""
import os
from distutils.util import strtobool
def a__ ( lowerCAmelCase__ , lowerCAmelCase__ ):
for e in env_keys:
UpperCAmelCase_ = int(os.environ.get(lowerCAmelCase__ , -1 ) )
if val >= 0:
... | 82 |
# XXX: we want transformers master here - in the absense of conftest manipulating sys.path:
# hack it in for now:
import sys
from pathlib import Path
SCREAMING_SNAKE_CASE__ : List[str] = Path(__file__).resolve().parents[3] / """src"""
sys.path.insert(1, str(git_repo_path))
import dataclas... | 112 | 0 |
"""simple docstring"""
from __future__ import annotations
from typing import Generic, TypeVar
lowerCAmelCase__ = TypeVar('''T''')
class __snake_case ( Generic[T]):
def __init__( self : Optional[int] , __lowerCAmelCase : T ):
"""simple doc... | 83 |
from __future__ import annotations
import unittest
from transformers import DebertaVaConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask... | 112 | 0 |
import argparse
import torch
from transformers import GPTaConfig, GPTaModel, load_tf_weights_in_gpta
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNA... | 84 |
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 convert_to_rgb, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
OPENAI_CLIP_MEAN,
O... | 112 | 0 |
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
SCREAMING_SNAKE_CASE__ : List[Any] = ""
SCREAMING_SNAKE_CASE__ : Optional[int] = ""
SCREAMING_SNAKE_CASE__ : Optional[Any] = ""
SCREAMING_SNAKE_CASE__ : ... | 85 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE__ : Tuple = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ : int = {
"""google/pix2struct-textcaps-base""": ... | 112 | 0 |
import fire
from transformers import AutoConfig, AutoModelForSeqaSeqLM, AutoTokenizer
def __snake_case ( __UpperCamelCase : str ,__UpperCamelCase : str ,**__UpperCamelCase : str ):
"""simple docstring"""
A_ = AutoConfig.from_pretrained(__UpperCamelCase ,*... | 86 |
def _A ( lowerCamelCase ):
a__ : Optional[Any] = 1
for i in range(1 , num + 1 ):
fact *= i
return fact
def _A ( lowerCamelCase ):
a__ : List[Any] = 0
while number > 0:
a__ : str = number % 10
sum_of_digits += last_digit
... | 112 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
_lowerCamelCase : List[Any] = {
"""configuration_gpt_bigcode""": ["""GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTBigCodeConfig"""],
}
try:
if not... | 87 |
import argparse
import pytorch_lightning as pl
import torch
from torch import nn
from transformers import LongformerForQuestionAnswering, LongformerModel
class __lowerCAmelCase ( pl.LightningModule ):
def __init__( self , snake_case ) -> Dict:
"""simple docstr... | 112 | 0 |
"""simple docstring"""
import os
from pathlib import Path
def _snake_case ( ):
"""simple docstring"""
from torch.utils.cpp_extension import load
_lowerCamelCase : Optional[Any] = Path(__snake_case ).resolve().parent.parent.parent / """kernels""" / """de... | 88 |
from sklearn.metrics import mean_squared_error
import datasets
SCREAMING_SNAKE_CASE__ : List[str] = """\
@article{scikit-learn,
title={Scikit-learn: Machine Learning in {P}ython},
author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
and Thirion, B. and Grisel... | 112 | 0 |
def UpperCamelCase_( lowerCamelCase_ ) -> Tuple:
_lowercase : Tuple = 0
_lowercase : Any = len(lowerCamelCase_ )
for i in range(n - 1 ):
for j in range(i + 1 , lowerCamelCase_ ):
if arr[i] > arr[j]:
... | 89 |
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 OptionalDependencyNotAvailable()
ex... | 112 | 0 |
'''simple docstring'''
from unittest.mock import Mock, patch
from file_transfer.send_file import send_file
@patch('''socket.socket''' )
@patch('''builtins.open''' )
def _snake_case ( A , A ) -> List[Any]:
# ===== initialization =====
lowerCAmelCase... | 90 |
def _A ( lowerCamelCase ):
a__ : Tuple = []
a__ : str = set({"(", "[", "{"} )
a__ : List[str] = set({")", "]", "}"} )
a__ : int = {"{": "}", "[": "]", "(": ")"}
for i in range(len(lowerCamelCase ) ):
if s[i] in open_brackets:
stac... | 112 | 0 |
"""simple docstring"""
import argparse
import requests
import torch
# pip3 install salesforce-lavis
# I'm actually installing a slightly modified version: pip3 install git+https://github.com/nielsrogge/LAVIS.git@fix_lavis_float32 (there's also the fix_lavis branch)
# also note: to convert Vicuna checkpoints, we ha... | 91 |
import sacrebleu as scb
from packaging import version
from sacrebleu import TER
import datasets
SCREAMING_SNAKE_CASE__ : List[Any] = """\
@inproceedings{snover-etal-2006-study,
title = \"A Study of Translation Edit Rate with Targeted Human Annotation\",
author = \"Snover, Matthew ... | 112 | 0 |
'''simple docstring'''
import doctest
from collections import deque
import numpy as np
class __SCREAMING_SNAKE_CASE :
def __init__( self : int ):
'''simple docstring'''
lowercase : List[str] =[2, 1, 2, -1]
lowercase : Tuple ... | 92 |
def _A ( lowerCamelCase = 200 ):
a__ : List[str] = [1, 2, 5, 10, 20, 50, 100, 200]
a__ : Dict = [0] * (pence + 1)
a__ : int = 1 # base case: 1 way to make 0 pence
for coin in coins:
for i in range(lowerCamelCase , pence + 1 , 1 ):
num... | 112 | 0 |
"""simple docstring"""
from typing import Optional, Tuple
import jax
import jax.numpy as jnp
from flax import linen as nn
from flax.core.frozen_dict import FrozenDict
from transformers import CLIPConfig, FlaxPreTrainedModel
from transformers.models.clip.modeling_flax_clip import FlaxCLIPVisionModule
def ... | 93 |
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
SCREAMING_SNAKE_CASE__ : Union[str, Any] = {
"""configuration_autoformer""": [
"""AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP... | 112 | 0 |
'''simple docstring'''
from scipy.stats import pearsonr, spearmanr
from sklearn.metrics import fa_score, matthews_corrcoef
import datasets
SCREAMING_SNAKE_CASE = '\\n@inproceedings{wang2019glue,\n title={{GLUE}: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding},\n... | 94 |
import argparse
import os
import re
SCREAMING_SNAKE_CASE__ : Any = """src/transformers/models/auto"""
# re pattern that matches mapping introductions:
# SUPER_MODEL_MAPPING_NAMES = OrderedDict or SUPER_MODEL_MAPPING = OrderedDict
SCREAMING_SNAKE_CASE__ : Union[str, Any] ... | 112 | 0 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from torch.backends.cuda import sdp_kernel
from diffusers import (
CMStochasticIterativeScheduler,
ConsistencyModelPipeline,
UNetaDModel,
)
from diffusers.utils import randn_tensor, slow, torch_device
from diffusers.ut... | 95 |
# Function to print upper half of diamond (pyramid)
def _A ( lowerCamelCase ):
for i in range(0 , lowerCamelCase ):
for _ in range(0 , n - i - 1 ): # printing spaces
print(" " , end="" )
for _ in range(0 , i + 1 ): # printing stars
print("* " , ... | 112 | 0 |
"""simple docstring"""
def a ( __UpperCAmelCase : str ) -> str:
return " ".join(input_str.split()[::-1] )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 96 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
SCREAMING_SNAKE_CASE__ : Tuple = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ ... | 112 | 0 |
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_common import Tokenizer... | 97 |
import os
import string
import sys
SCREAMING_SNAKE_CASE__ : int = 1 << 8
SCREAMING_SNAKE_CASE__ : List[str] = {
"""tab""": ord("""\t"""),
"""newline""": ord("""\r"""),
"""esc""": 2_7,
"""up""": 6_5 + ARROW_KEY_FLAG,
"""down""": 6_6 + ARROW_KEY_FLA... | 112 | 0 |
'''simple docstring'''
from math import factorial
lowercase__ : dict[str, int] = {str(digit): factorial(digit) for digit in range(10)}
def a__ ( lowercase : int ) -> int:
"""simple docstring"""
if not isinstance(lowercase, lowercase ):
r... | 98 |
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_extraction_utils import FeatureExtra... | 112 | 0 |
import argparse
import json
import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download
from transformers import (
VideoMAEConfig,
VideoMAEForPreTraining,
VideoMAEForVideoClassification,
VideoMAEImageProcessor,
)
def a (lowerCAmelCase__ ):
... | 99 |
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 | 0 |
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