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 json
from typing import Iterator, List, Union
from tokenizers import AddedToken, Regex, Tokenizer, decoders, normalizers, pre_tokenizers, trainers
from tokenizers.implementations.base_tokenizer import BaseTokenizer
from tokenizers.models import Unigram
from tokenizers.processors import TemplateProcessing
... | 112 |
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 | 1 |
def _A ( lowerCamelCase = 100_0000 ):
a__ : Optional[Any] = limit + 1
a__ : Dict = [0] * limit
for first_term in range(1 , lowerCamelCase ):
for n in range(lowerCamelCase , lowerCamelCase , lowerCamelCase ):
a__ : List[Any] = first_te... | 112 |
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 | 1 |
from typing import Any
def _A ( lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , ):
_validation(
lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , )
# Creates data struc... | 112 |
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 | 1 |
from typing import Tuple, Union
from ...modeling_outputs import BackboneOutput
from ...modeling_utils import PreTrainedModel
from ...utils import is_timm_available, is_torch_available, requires_backends
from ...utils.backbone_utils import BackboneMixin
from .configuration_timm_backbone import TimmBackboneConfig
... | 112 |
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 | 1 |
from unittest import TestCase
from datasets import Dataset
from minhash_deduplication import deduplicate_dataset, make_duplicate_clusters
def _A ( ):
a__ : List[str] = {
"repo_name": ["test_repo1", "test_repo2", "test_repo3"],
"path": ["test_1.py", "test_2.py", "unit_test.... | 112 |
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 | 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 i... | 112 |
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 | 1 |
import os
import time
import warnings
from dataclasses import dataclass, field
from enum import Enum
from typing import List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import loggin... | 112 |
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 | 1 |
from torch import nn
def _A ( lowerCamelCase ):
if act_fn in ["swish", "silu"]:
return nn.SiLU()
elif act_fn == "mish":
return nn.Mish()
elif act_fn == "gelu":
return nn.GELU()
else:
raise ValueError(F"""Unsupported activation function: {act_fn}""" )
| 112 |
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 | 1 |
import itertools
import random
import unittest
import numpy as np
from transformers import BatchFeature, SpeechTaFeatureExtractor
from transformers.testing_utils import require_torch
from transformers.utils.import_utils import is_torch_available
from ...test_sequence_feature_extraction_common import SequenceFe... | 112 |
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 | 1 |
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 |
# 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 | 1 |
import torch
from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device
from .test_schedulers import SchedulerCommonTest
class __lowerCAmelCase ( _UpperCamelCase ):
_UpperCamelCase : Any = (EulerDiscreteScheduler,)
_UpperCamelCase : Dict = ... | 112 |
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 | 1 |
from collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean
def _A ( lowerCamelCase ):
# A local function to see if a dot lands in the circle.
def is_in_circle(lowerCamelCase , lowerCamelCase ) -> bool:
a__ : Any = ... | 112 |
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 | 1 |
import requests
from bsa import BeautifulSoup
def _A ( lowerCamelCase = "https://www.worldometers.info/coronavirus" ):
a__ : List[str] = BeautifulSoup(requests.get(lowerCamelCase ).text , "html.parser" )
a__ : List[Any] = soup.findAll("h1" )
a__ : List[str]... | 112 |
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 | 1 |
from transformers import BertTokenizerFast
from .custom_tokenization import CustomTokenizer
class __lowerCAmelCase ( _UpperCamelCase ):
_UpperCamelCase : Optional[int] = CustomTokenizer
pass
| 112 |
import pickle
import numpy as np
from matplotlib import pyplot as plt
class __lowerCAmelCase :
def __init__( self , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case=0.2 , snake_case=0.2 ... | 112 | 1 |
import re
from typing import Callable, List, Optional, Union
import tensorflow as tf
try:
from tensorflow.keras.optimizers.legacy import Adam
except ImportError:
from tensorflow.keras.optimizers import Adam
class __lowerCAmelCase ( tf.keras.optimizers.schedules.LearningRateSchedule ):
... | 112 |
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 | 1 |
import os
import textwrap
import pyarrow as pa
import pytest
from datasets import ClassLabel, Features, Image
from datasets.packaged_modules.csv.csv import Csv
from ..utils import require_pil
@pytest.fixture
def _A ( lowerCamelCase ):
a__ : int = tmp_path / "file.csv"
a__ : ... | 112 |
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 | 1 |
import copy
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
SCREAMING_SNAKE_CASE__ : Union[str, Any] ... | 112 |
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 | 1 |
# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team.
#
# 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 appl... | 112 |
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 | 1 |
from __future__ import annotations
def _A ( lowerCamelCase ):
create_state_space_tree(lowerCamelCase , [] , 0 , [0 for i in range(len(lowerCamelCase ) )] )
def _A ( lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , ):
if index == le... | 112 |
# 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 | 1 |
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 |
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 | 1 |
from __future__ import annotations
SCREAMING_SNAKE_CASE__ : Optional[int] = []
def _A ( lowerCamelCase , lowerCamelCase , lowerCamelCase ):
for i in range(len(lowerCamelCase ) ):
if board[row][i] == 1:
return False
for i in range(len(lowerCamelCase ... | 112 |
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 | 1 |
import inspect
import os
import sys
import unittest
import accelerate
from accelerate.test_utils import execute_subprocess_async, require_tpu
class __lowerCAmelCase ( unittest.TestCase ):
def _snake_case ( self ) -> int:
"""simple docstring"""
a__ ... | 112 |
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 | 1 |
import unittest
from transformers import SPIECE_UNDERLINE, XLNetTokenizer, XLNetTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
SCREAMING_SNAKE_CASE__ : str = ... | 112 |
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 | 1 |
import json
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prep... | 112 |
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 | 1 |
from multiprocessing import Lock, Pipe, Process
# lock used to ensure that two processes do not access a pipe at the same time
SCREAMING_SNAKE_CASE__ : Optional[Any] = Lock()
def _A ( lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCam... | 112 |
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 | 1 |
import json
import os
from functools import lru_cache
from typing import Dict, List, Optional, Tuple, Union
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding, EncodedInput
from ...utils import PaddingStrategy, logging
SCRE... | 112 |
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 | 1 |
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 |
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 | 1 |
from __future__ import annotations
import bisect
def _A ( lowerCamelCase , lowerCamelCase , lowerCamelCase = 0 , lowerCamelCase = -1 ):
if hi < 0:
a__ : Union[str, Any] = len(lowerCamelCase )
while lo < hi:
a__ : Optional[int] = lo + (hi - lo) ... | 112 |
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 | 1 |
from __future__ import annotations
import unittest
from transformers import EsmConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor, random_atten... | 112 |
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 | 1 |
from ..utils import DummyObject, requires_backends
class __lowerCAmelCase ( metaclass=_UpperCamelCase ):
_UpperCamelCase : Any = ["""flax"""]
def __init__( self , *snake_case , **snake_case ) -> Tuple:
"""simple docstring"""
... | 112 |
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 | 1 |
from math import loga
def _A ( lowerCamelCase ):
if a < 0:
raise ValueError("Input value must be a positive integer" )
elif isinstance(lowerCamelCase , lowerCamelCase ):
raise TypeError("Input value must be a 'int' type" )
return 0 if (a == 0) else int(loga(a & -a ) )
if __n... | 112 |
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 | 1 |
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile
from transformers import MaMaaaTokenizer, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_torch,
slow,
)
from... | 112 |
# 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 | 1 |
# 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 |
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 | 1 |
import os
import shutil
import tempfile
import unittest
import numpy as np
from transformers import AutoTokenizer, BarkProcessor
from transformers.testing_utils import require_torch, slow
@require_torch
class __lowerCAmelCase ( unittest.TestCase ):
def _snake_case ( self )... | 112 |
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 | 1 |
import os
import pytest
from attr import dataclass
SCREAMING_SNAKE_CASE__ : Any = """us-east-1""" # defaults region
@dataclass
class __lowerCAmelCase :
_UpperCamelCase : str
_UpperCamelCase : List[Any] = """arn:aws:iam::558105141721:role/sagemaker_exec... | 112 |
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 | 1 |
from __future__ import annotations
def _A ( lowerCamelCase ):
if not nums:
raise ValueError("List is empty" )
return sum(lowerCamelCase ) / len(lowerCamelCase )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 112 |
import pickle
import numpy as np
from matplotlib import pyplot as plt
class __lowerCAmelCase :
def __init__( self , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case=0.2 , snake_case=0.2 ... | 112 | 1 |
import copy
import unittest
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling... | 112 |
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 | 1 |
import ast
import os
import re
import shutil
import tempfile
import unittest
from unittest import mock
import torch
from accelerate.test_utils.examples import compare_against_test
from accelerate.test_utils.testing import TempDirTestCase, require_trackers, run_command, slow
from accelerate.utils import write_ba... | 112 |
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 | 1 |
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
if is_torch_available():
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
@require_torch
@require_sentencepiece
@... | 112 |
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 | 1 |
import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import List
import timm
import torch
import torch.nn as nn
from huggingface_hub import hf_hub_download
from torch import Tensor
from transformers import AutoImageProcessor, Res... | 112 |
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 | 1 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import load_numpy, ... | 112 |
# 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 | 1 |
import operator as op
def _A ( lowerCamelCase ):
a__ : Any = []
a__ : Union[str, Any] = lambda lowerCamelCase , lowerCamelCase : int(x / y ) # noqa: E731 integer division operation
a__ : Any = {
"^": op.pow,
"*": op.mul,
"/": div,
... | 112 |
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 | 1 |
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 |
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 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE__ : Optional[Any] = {
"""configuration_trocr""": ["""TROCR_PRETRAINED_CO... | 112 |
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 | 1 |
import shutil
import tempfile
import unittest
import numpy as np
from transformers.testing_utils import (
is_pt_tf_cross_test,
require_tf,
require_torch,
require_torchvision,
require_vision,
)
from transformers.utils import is_tf_available, is_torch_available, is_vision_available
if is_vis... | 112 |
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 | 1 |
import numpy as np
from cva import destroyAllWindows, imread, imshow, waitKey
class __lowerCAmelCase :
def __init__( self , snake_case , snake_case , snake_case ) -> str:
"""simple docstring"""
if dst_width < 0 or dst_height < 0:
... | 112 |
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 | 1 |
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 |
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 | 1 |
from __future__ import annotations
def _A ( lowerCamelCase ):
return len(set(lowerCamelCase ) ) == len(lowerCamelCase )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 112 |
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 | 1 |
# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch
import math
from typing import Union
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import randn_tensor
from .scheduling_utils import SchedulerMixin
class __lowe... | 112 |
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 | 1 |
from typing import Optional, Union
import torch
from torch import nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...models.modeling_utils import ModelMixin
class __lowerCAmelCase ( _UpperCamelCase ,_UpperCamelCase ):
@register_to_config
def __init__( ... | 112 |
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 | 1 |
import argparse
import torch
from safetensors.torch import load_file
from diffusers import StableDiffusionPipeline
def _A ( lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase ):
# load base model
a__ : Union[str, Any] = StableDiffu... | 112 |
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 | 1 |
class __lowerCAmelCase :
def __init__( self , snake_case ) -> str:
"""simple docstring"""
a__ : List[Any] = arr.split("," )
def _snake_case ( self ) -> Dict:
"""simple docstring"""
a__ : Union[str, Any... | 112 |
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 | 1 |
import argparse
import os
import jax as jnp
import numpy as onp
import torch
import torch.nn as nn
from music_spectrogram_diffusion import inference
from tax import checkpoints
from diffusers import DDPMScheduler, OnnxRuntimeModel, SpectrogramDiffusionPipeline
from diffusers.pipelines.spectrogram_diffusion impo... | 112 |
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 | 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 |
# 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 | 1 |
import heapq
def _A ( lowerCamelCase ):
a__ : list[list] = []
# for each node and his adjacency list add them and the rank of the node to queue
# using heapq module the queue will be filled like a Priority Queue
# heapq works with a min priority queue, so I used -1*len(v) to bu... | 112 |
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 | 1 |
def _A ( ):
return 1
def _A ( lowerCamelCase ):
return 0 if x < 0 else two_pence(x - 2 ) + one_pence()
def _A ( lowerCamelCase ):
return 0 if x < 0 else five_pence(x - 5 ) + two_pence(lowerCamelCase )
def _A ( lowerCamelCase ):
return 0 if x < 0 else ten_pence(x -... | 112 |
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 | 1 |
from __future__ import annotations
from collections.abc import Sequence
from typing import Literal
def _A ( lowerCamelCase , lowerCamelCase ):
a__ : Tuple = list(lowerCamelCase )
a__ : Optional[Any] = list(lowerCamelCase )
a__ : Any = 0
for i in ran... | 112 |
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 | 1 |
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_herbert import HerbertTokenizer
SCREAMING_SNAKE_CASE__ : Optional[int] = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ : ... | 112 |
import pickle
import numpy as np
from matplotlib import pyplot as plt
class __lowerCAmelCase :
def __init__( self , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case=0.2 , snake_case=0.2 ... | 112 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
SCREAMING_SNAKE_CASE__ : Dict = {
"""configuration_timesformer""": ["""TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """TimesformerConfig"""],
}
try:
if not is_torc... | 112 |
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 | 1 |
from typing import List, Optional, Union
import torch
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
is_accelerate_available,
is_acce... | 112 |
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 | 1 |
from __future__ import annotations
from PIL import Image
# Define glider example
SCREAMING_SNAKE_CASE__ : str = [
[0, 1, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0, 0],
[1, 1, 1, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, ... | 112 |
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 | 1 |
from ..utils import DummyObject, requires_backends
class __lowerCAmelCase ( metaclass=_UpperCamelCase ):
_UpperCamelCase : Tuple = ["""flax""", """transformers"""]
def __init__( self , *snake_case , **snake_case ) -> Union[str, Any]:
""... | 112 |
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 | 1 |
from __future__ import annotations
SCREAMING_SNAKE_CASE__ : Optional[Any] = list[tuple[int, int]]
SCREAMING_SNAKE_CASE__ : str = [
[0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles
[0, 0, 0, 0, 0, 0, 0],
[0, 0, ... | 112 |
# 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 | 1 |
from math import isqrt
def _A ( lowerCamelCase ):
return all(number % divisor != 0 for divisor in range(2 , isqrt(lowerCamelCase ) + 1 ) )
def _A ( lowerCamelCase = 10**6 ):
a__ : Dict = 0
a__ : Optional[int] = 1
a__ : List[Any] = 7
whi... | 112 |
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 | 1 |
import os
import random
import sys
from . import cryptomath_module as cryptomath
from . import rabin_miller
SCREAMING_SNAKE_CASE__ : Any = 3
def _A ( lowerCamelCase ):
print("Generating primitive root of p" )
while True:
a__ : Union[str, Any] = random.ran... | 112 |
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 | 1 |
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 |
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 | 1 |
import inspect
import unittest
from transformers import YolosConfig
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_configuration_common import ConfigTester
from ...test_... | 112 |
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 | 1 |
import warnings
from ...utils import logging
from .image_processing_clip import CLIPImageProcessor
SCREAMING_SNAKE_CASE__ : str = logging.get_logger(__name__)
class __lowerCAmelCase ( _UpperCamelCase ):
def __init__( self , *snake_case , ... | 112 |
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 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
SCREAMING_SNAKE_CASE__ : List[str] = {
"""configuration_gpt_neo""": ["""GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTNeoConfig""", """GPTNeoOnnxC... | 112 |
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 | 1 |
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import numpy as np
import tensorflow as tf
from transformers import TFXLMRobertaModel
... | 112 |
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 | 1 |
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
SCREAMING_SNAKE_CASE__ : str = {"""configuration_mmbt""": ["""MMBTConfig"""]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except Optio... | 112 |
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 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE__ : List[Any] = {
"""configuration_mobilebert""": [
"""MOBILEBERT_PRETR... | 112 |
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 | 1 |
from transformers import DistilBertTokenizer, DistilBertTokenizerFast
from transformers.testing_utils import require_tokenizers, slow
from ..bert.test_tokenization_bert import BertTokenizationTest
@require_tokenizers
class __lowerCAmelCase ( _UpperCamelCase ):
_UpperCamelCase : List[An... | 112 |
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 | 1 |
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 im... | 112 |
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 | 1 |
from typing import Optional, Tuple, Union
import tensorflow as tf
from ...activations_tf import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
from ...modeling_tf_outputs import (
TFBaseModelOutputWithNoAttention,
TFBaseModelOutpu... | 112 |
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 | 1 |
import os
import pytest
from datasets import (
get_dataset_config_info,
get_dataset_config_names,
get_dataset_infos,
get_dataset_split_names,
inspect_dataset,
inspect_metric,
)
SCREAMING_SNAKE_CASE__ : Dict = pytest.mark.integration
@pytest.mark.parametrize(... | 112 |
# 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 | 1 |
import logging
import math
from functools import partial
from typing import Any, Callable, Dict, Iterable, List, Optional, Sequence, Tuple, Union
import torch
from .tensor_utils import tensor_tree_map, tree_map
def _A ( lowerCamelCase ):
a__ : List[str] = []
if isinstance(lowerCamel... | 112 |
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 | 1 |
from collections import Counter
from timeit import timeit
def _A ( lowerCamelCase = "" , ):
return sum(c % 2 for c in Counter(input_str.replace(" " , "" ).lower() ).values() ) < 2
def _A ( lowerCamelCase = "" ):
if len(lowerCamelCase ) == 0:
return True
a__ : ... | 112 |
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 | 1 |
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
# Register SEW's fairseq modules
from sew_asapp import tasks # noqa: F401
from transformers import (
SEWConfig,
SEWForCTC,
SEWModel,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
Wav... | 112 |
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 | 1 |
from __future__ import annotations
def _A ( lowerCamelCase = 4 ):
a__ : Union[str, Any] = abs(lowerCamelCase ) or 4
return [[1 + x + y * row_size for x in range(lowerCamelCase )] for y in range(lowerCamelCase )]
def _A ( lowerCamelCase ):
return reverse_row(transpose(low... | 112 |
import pickle
import numpy as np
from matplotlib import pyplot as plt
class __lowerCAmelCase :
def __init__( self , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case=0.2 , snake_case=0.2 ... | 112 | 1 |
# this script reports modified .py files under the desired list of top-level sub-dirs passed as a list of arguments, e.g.:
# python ./utils/get_modified_files.py utils src tests examples
#
# it uses git to find the forking point and which files were modified - i.e. files not under git won't be considered
# sinc... | 112 |
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 | 1 |
import pickle
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, XLMRobertaTokenizer, XLMRobertaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_to... | 112 |
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 | 1 |
def _A ( lowerCamelCase , lowerCamelCase ):
return (pointa[0] - pointa[0]) ** 2 + (pointa[1] - pointa[1]) ** 2
def _A ( lowerCamelCase , lowerCamelCase=0 ):
return sorted(lowerCamelCase , key=lambda lowerCamelCase : x[column] )
def _A ( lowerCamelCase , lowerCa... | 112 |
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 | 1 |
import json
import os
import unittest
from transformers.models.ctrl.tokenization_ctrl import VOCAB_FILES_NAMES, CTRLTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class __lowerCAmelCase ( _UpperCamelCase ,unittest.TestCase ):
_UpperCamelCase : Union[str, Any] ... | 112 |
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 | 1 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
SCREAMING_SNAKE_CASE__ : Tuple = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ : List[str] ... | 112 |
# 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 | 1 |
import unittest
from transformers import is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, require_torch, slow
if is_flax_available():
import optax
from flax.training.common_utils import onehot
from transformers import AutoTokenizer, FlaxMT... | 112 |
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 | 1 |
import os
from bleurt import score # From: git+https://github.com/google-research/bleurt.git
import datasets
SCREAMING_SNAKE_CASE__ : Optional[Any] = datasets.logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ : str = """\
@inproceedings{bleurt,
title={BLEURT:... | 112 |
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 | 1 |
def _A ( lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase ):
if index == number_of_items:
return 0
a__ : Union[str, Any] = 0
a__ : str = 0
a__ : List[str] = knapsack(lowerCamelCase , lowerCamelCas... | 112 |
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 | 1 |
import json
import os
import tempfile
import transformers
import datasets
from utils import generate_example_dataset, get_duration
SCREAMING_SNAKE_CASE__ : Tuple = 5_0_0_0_0_0
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Union[str, Any] = os.path.split(__fi... | 112 |
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 | 1 |
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class __lowerCAmelCase ( _UpperCamelCase ):
_UpperC... | 112 |
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 | 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 _A ( lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase ):
a__ : Dict... | 112 |
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 | 1 |
from __future__ import annotations
class __lowerCAmelCase :
def __init__( self , snake_case ) -> None:
"""simple docstring"""
a__ : Dict = data
a__ : Node | None = None
a__ : Node | None = None
def _... | 112 |
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 | 1 |
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 |
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 | 1 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
ChannelDimension,
ImageInput,
PILImageResa... | 112 |
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 | 1 |
import argparse
import torch
from ...utils import logging
from . import AlbertConfig, AlbertForPreTraining, load_tf_weights_in_albert
logging.set_verbosity_info()
def _A ( lowerCamelCase , lowerCamelCase , lowerCamelCase ):
# Initialise PyTorch model
a__ : Any = AlbertCo... | 112 |
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 | 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... | 112 |
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 | 1 |
# HF Trainer benchmarking tool
#
# This tool can be used to run and compare multiple dimensions of the HF Trainers args.
#
# It then prints a report once in github format with all the information that needs to be shared
# with others and second time in a console-friendly format, so it's easier to use for tuning t... | 112 |
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 | 1 |
from __future__ import annotations
import unittest
from transformers import FunnelConfig, is_tf_available
from transformers.testing_utils import require_tf
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...... | 112 |
# 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 | 1 |
from __future__ import annotations
def _A ( lowerCamelCase , lowerCamelCase , lowerCamelCase ):
a__ : List[str] = list(range(len(lowerCamelCase ) ) )
a__ : Dict = [v / w for v, w in zip(lowerCamelCase , lowerCamelCase )]
index.sort(key=lambda lowerCamelCa... | 112 |
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 | 1 |
import argparse
from transformers import BigBirdConfig, BigBirdForPreTraining, BigBirdForQuestionAnswering, load_tf_weights_in_big_bird
from transformers.utils import logging
logging.set_verbosity_info()
def _A ( lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase ):
... | 112 |
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 | 1 |
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