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 |
|---|---|---|---|---|
"""simple docstring"""
from __future__ import annotations
import math
import random
from typing import Any
class lowerCAmelCase__ :
'''simple docstring'''
def __init__( self ):
_lowerCamelCase : list[Any] = []
... | 630 |
"""simple docstring"""
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 ... | 630 | 1 |
"""simple docstring"""
lowercase__ = [sum(int(c, 10) ** 2 for c in i.__str__()) for i in range(10_0000)]
def _snake_case ( lowercase__ ):
_lowerCamelCase : List[str] = 0
while number:
# Increased Speed Slightly by checking every... | 630 |
"""simple docstring"""
from typing import Any
def _snake_case ( lowercase__ , lowercase__ , lowercase__ , lowercase__ , lowercase__ , ):
_validation(
lowercase__ , lowercase__ , lowercase__ ,... | 630 | 1 |
"""simple docstring"""
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
... | 630 |
"""simple docstring"""
import os
from typing import Dict, List, Union
import tensorflow as tf
from keras_nlp.tokenizers import BytePairTokenizer
from tensorflow_text import pad_model_inputs
from .tokenization_gpta import GPTaTokenizer
class lowerCAmelCase__ ( tf.keras.l... | 630 | 1 |
"""simple docstring"""
import collections
import json
import os
import re
from typing import TYPE_CHECKING, List, Optional, Tuple
import numpy as np
from ...tokenization_utils_fast import PreTrainedTokenizer
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipe... | 630 |
"""simple docstring"""
from diffusers.utils.testing_utils import require_onnxruntime
@require_onnxruntime
class lowerCAmelCase__ :
'''simple docstring'''
pass | 630 | 1 |
"""simple docstring"""
def _snake_case ( lowercase__ , lowercase__ , lowercase__ ):
if exponent == 1:
return base
if exponent % 2 == 0:
_lowerCamelCase : Optional[int] = _modexpt(lowercase__ , exponent // 2 ... | 630 |
"""simple docstring"""
import qiskit
def _snake_case ( lowercase__ = 2 ):
_lowerCamelCase : Optional[Any] = qubits
# Using Aer's simulator
_lowerCamelCase : Dict = qiskit.Aer.get_backend('aer_simulator' )
... | 630 | 1 |
"""simple docstring"""
def _snake_case ( lowercase__ ):
_lowerCamelCase : int = (1 + 24 * n) ** 0.5
return ((1 + root) / 6) % 1 == 0
def _snake_case ( lowercase__ = 5000 ):
_lowerCamelCase : Tuple = [(i ... | 630 |
"""simple docstring"""
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
... | 630 | 1 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowercase__ = logging.get_logger(__name__)
lowercase__ = {
"""facebook/dat... | 630 |
"""simple docstring"""
import unittest
from parameterized import parameterized
from transformers import LlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...... | 630 | 1 |
"""simple docstring"""
import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple
from transformers import AddedToken, BatchEncoding, PerceiverTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available
fro... | 630 |
"""simple docstring"""
from __future__ import annotations
from fractions import Fraction
from math import gcd, sqrt
def _snake_case ( lowercase__ ):
_lowerCamelCase : int = int(number**0.5 )
return number == sq * sq
... | 630 | 1 |
"""simple docstring"""
def _snake_case ( ):
return [list(range(1000 - i , -1000 - i , -1 ) ) for i in range(1000 )]
lowercase__ = generate_large_matrix()
lowercase__ = (
[[4, 3, 2, -1], [3, 2, 1, -1], [1, 1, -1, -2], [-1, -1, -2, -... | 630 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase__ = logging.get_logger(__name__)
lowercase__ = {"""openai-gpt""": """https://huggingface.co/openai-gpt/resolve/main/config.json"""}
class lowerCAmelCase__ ... | 630 | 1 |
"""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 ImageProce... | 630 |
"""simple docstring"""
from __future__ import annotations
def _snake_case ( lowercase__ ): # This function is recursive
_lowerCamelCase : Optional[Any] = len(lowercase__ )
# If the array contains only one element, we return it (it's t... | 630 | 1 |
"""simple docstring"""
import argparse
import intel_extension_for_pytorch as ipex
import torch
from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline
lowercase__ = argparse.ArgumentParser("""Stable Diffusion script with intel optimization""", add_help=False)
pa... | 630 |
"""simple docstring"""
from __future__ import annotations
def _snake_case ( lowercase__ ):
_lowerCamelCase : int = len(lowercase__ )
# We need to create solution object to save path.
_lowerCamelCase : Tuple = [[0 f... | 630 | 1 |
"""simple docstring"""
from dataclasses import dataclass, field
from typing import Optional
@dataclass
class lowerCAmelCase__ :
'''simple docstring'''
lowerCamelCase__ = field(
default="""codeparrot/codeparrot""", metadata={"""help"""... | 630 |
"""simple docstring"""
import operator as op
def _snake_case ( lowercase__ ):
_lowerCamelCase : Dict = []
_lowerCamelCase : List[str] = lambda lowercase__ , lowercase__ : int(x / y ) # noqa: E731 integer ... | 630 | 1 |
"""simple docstring"""
from __future__ import annotations
from random import random
from typing import Generic, TypeVar
lowercase__ = TypeVar("""KT""")
lowercase__ = TypeVar("""VT""")
class lowerCAmelCase__ ( Generic[KT, VT] ):
'''simple docstring'... | 630 |
"""simple docstring"""
def _snake_case ( lowercase__ = 1 , lowercase__ = 1000 ):
_lowerCamelCase : Optional[int] = 1
_lowerCamelCase : List[Any] = 0
for divide_by_number in range(lowercase__ , digit + 1 ):
... | 630 | 1 |
"""simple docstring"""
from __future__ import annotations
def _snake_case ( lowercase__ = 4 ):
_lowerCamelCase : int = abs(lowercase__ ) or 4
return [[1 + x + y * row_size for x in range(lowercase__ )] for y in range(lowercase__ ... | 630 |
"""simple docstring"""
import os
import unittest
from transformers import MobileBertTokenizer, MobileBertTokenizerFast
from transformers.models.bert.tokenization_bert import (
VOCAB_FILES_NAMES,
BasicTokenizer,
WordpieceTokenizer,
_is_control,
_is_punctuation,
_... | 630 | 1 |
"""simple docstring"""
import os
import unittest
from transformers import MobileBertTokenizer, MobileBertTokenizerFast
from transformers.models.bert.tokenization_bert import (
VOCAB_FILES_NAMES,
BasicTokenizer,
WordpieceTokenizer,
_is_control,
_is_punctuation,
_... | 630 |
"""simple docstring"""
import unittest
from transformers import MPNetConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, ... | 630 | 1 |
"""simple docstring"""
import json
import os
import unittest
from typing import Tuple
from transformers import WavaVecaPhonemeCTCTokenizer
from transformers.models.wavaveca.tokenization_wavaveca import VOCAB_FILES_NAMES
from transformers.models.wavaveca_phoneme.tokenization_wavaveca_phone... | 630 |
"""simple docstring"""
import argparse
import intel_extension_for_pytorch as ipex
import torch
from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline
lowercase__ = argparse.ArgumentParser("""Stable Diffusion script with intel optimization""", add_help=False)
pa... | 630 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
lowercase__ = {
"""configuration_pix2struct""": [
"""PIX2STRUCT_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""... | 630 |
"""simple docstring"""
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""... | 630 | 1 |
"""simple docstring"""
import json
import os
from typing import Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
lowercase__ = logging.get_logger(__name__)
lowercase__ = {"""vocab_file""": """vocab.json"""}
lowercase__ = ... | 630 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase__ = logging.get_logger(__name__)
lowercase__ = {
"""facebook/dpr-ctx_encoder-single-nq-base""": (
"""https://huggingface.co/facebook/dpr-ctx_encoder-singl... | 630 | 1 |
"""simple docstring"""
class lowerCAmelCase__ :
'''simple docstring'''
def __init__( self , lowercase ):
_lowerCamelCase : Any = len(lowercase )
_lowerCamelCase : Optional[int] = [0] * len_array
i... | 630 |
"""simple docstring"""
import re
def _snake_case ( lowercase__ ):
if len(re.findall('[ATCG]' , lowercase__ ) ) != len(lowercase__ ):
raise ValueError('Invalid Strand' )
return dna.translate(dna.maketrans('ATCG' ... | 630 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
lowercase__ = {
"""configuration_ctrl""": ["""CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP""", """CTRLConfig"""],
"""tokenizatio... | 630 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from diffusers import (
AudioDiffusionPipeline,
AutoencoderKL,
DDIMScheduler,
DDPMScheduler,
DiffusionPipeline,
Mel,
UNetaDConditionModel,
UNetaDModel,
)
from diffuse... | 630 | 1 |
"""simple docstring"""
import torch
from diffusers import UnCLIPScheduler
from .test_schedulers import SchedulerCommonTest
class lowerCAmelCase__ ( lowercase ):
'''simple docstring'''
lowerCamelCase__ = (UnCLIPScheduler,)
def A_ ... | 630 |
"""simple docstring"""
# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
# and https://github.com/hojonathanho/diffusion
import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union
import numpy as np
import torc... | 630 | 1 |
"""simple docstring"""
import unittest
from transformers import AutoConfig, AutoTokenizer, BertConfig, TensorType, is_flax_available
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, require_flax, slow
if is_flax_available():
import jax
from transformers.models.a... | 630 |
"""simple docstring"""
import itertools
import json
import os
import unittest
from transformers import AddedToken, RobertaTokenizer, RobertaTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizer... | 630 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase__ = logging.get_logger(__name__)
lowercase__ = {
"""funnel-transformer/small""": """https://huggingface.co/funnel-transformer/small/resolve/main/config.json""",
... | 630 |
"""simple docstring"""
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 ... | 630 | 1 |
"""simple docstring"""
import pytest
import datasets
# Import fixture modules as plugins
lowercase__ = ["""tests.fixtures.files""", """tests.fixtures.hub""", """tests.fixtures.fsspec"""]
def _snake_case ( lowercase__ , lowercase__ ):
# Mark... | 630 |
"""simple docstring"""
from typing import Any
def _snake_case ( lowercase__ , lowercase__ , lowercase__ , lowercase__ , lowercase__ , ):
_validation(
lowercase__ , lowercase__ , lowercase__ ,... | 630 | 1 |
"""simple docstring"""
import inspect
import os
import torch
from transformers import AutoModel
from transformers.testing_utils import mockenv_context
from transformers.trainer_utils import set_seed
import accelerate
from accelerate.accelerator import Accelerator
from accelerate.state... | 630 |
"""simple docstring"""
import os
from typing import Dict, List, Union
import tensorflow as tf
from keras_nlp.tokenizers import BytePairTokenizer
from tensorflow_text import pad_model_inputs
from .tokenization_gpta import GPTaTokenizer
class lowerCAmelCase__ ( tf.keras.l... | 630 | 1 |
"""simple docstring"""
from __future__ import annotations
import time
import numpy as np
lowercase__ = [8, 5, 9, 7]
lowercase__ = [
[2, 0, 1, 1],
[0, 1, 2, 1],
[4, 0, 0, 3],
[0, 2, 1, 0],
[1, 0, 3, 0],
]
lowercase__ = [
[3, 2, 1, 4],
[0, 2, ... | 630 |
"""simple docstring"""
from diffusers.utils.testing_utils import require_onnxruntime
@require_onnxruntime
class lowerCAmelCase__ :
'''simple docstring'''
pass | 630 | 1 |
"""simple docstring"""
from __future__ import annotations
def _snake_case ( lowercase__ , lowercase__ ):
print(f'''Vertex\tShortest Distance from vertex {src}''' )
for i, d in enumerate(lowercase__ ):
print(f'''{i}\t\t{d}''' ... | 630 |
"""simple docstring"""
import qiskit
def _snake_case ( lowercase__ = 2 ):
_lowerCamelCase : Optional[Any] = qubits
# Using Aer's simulator
_lowerCamelCase : Dict = qiskit.Aer.get_backend('aer_simulator' )
... | 630 | 1 |
"""simple docstring"""
import os
import re
import unicodedata
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import is_torch_available,... | 630 |
"""simple docstring"""
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
... | 630 | 1 |
"""simple docstring"""
def _snake_case ( lowercase__ , lowercase__ ):
# "extended trapezoidal rule"
# int(f) = dx/2 * (f1 + 2f2 + ... + fn)
_lowerCamelCase : Optional[Any] = (boundary[1] - boundary[0]) / steps
_lowerCamelCase : Dict... | 630 |
"""simple docstring"""
import unittest
from parameterized import parameterized
from transformers import LlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...... | 630 | 1 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import DistilBertConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFMo... | 630 |
"""simple docstring"""
from __future__ import annotations
from fractions import Fraction
from math import gcd, sqrt
def _snake_case ( lowercase__ ):
_lowerCamelCase : int = int(number**0.5 )
return number == sq * sq
... | 630 | 1 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaInpaintPipeline,
KandinskyVaaPriorPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.... | 630 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase__ = logging.get_logger(__name__)
lowercase__ = {"""openai-gpt""": """https://huggingface.co/openai-gpt/resolve/main/config.json"""}
class lowerCAmelCase__ ... | 630 | 1 |
"""simple docstring"""
import os
from pathlib import Path
def _snake_case ( ):
from torch.utils.cpp_extension import load
_lowerCamelCase : Any = Path(lowercase__ ).resolve().parent.parent.parent / 'kernels' / 'deformable_detr'
_low... | 630 |
"""simple docstring"""
from __future__ import annotations
def _snake_case ( lowercase__ ): # This function is recursive
_lowerCamelCase : Optional[Any] = len(lowercase__ )
# If the array contains only one element, we return it (it's t... | 630 | 1 |
"""simple docstring"""
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 ... | 630 |
"""simple docstring"""
from __future__ import annotations
def _snake_case ( lowercase__ ):
_lowerCamelCase : int = len(lowercase__ )
# We need to create solution object to save path.
_lowerCamelCase : Tuple = [[0 f... | 630 | 1 |
"""simple docstring"""
import argparse
import torch
from transformers import OpenAIGPTConfig, OpenAIGPTModel, load_tf_weights_in_openai_gpt
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def _snake_case ( lowerc... | 630 |
"""simple docstring"""
import operator as op
def _snake_case ( lowercase__ ):
_lowerCamelCase : Dict = []
_lowerCamelCase : List[str] = lambda lowercase__ , lowercase__ : int(x / y ) # noqa: E731 integer ... | 630 | 1 |
"""simple docstring"""
from diffusers.utils.testing_utils import require_onnxruntime
@require_onnxruntime
class lowerCAmelCase__ :
'''simple docstring'''
pass | 630 |
"""simple docstring"""
def _snake_case ( lowercase__ = 1 , lowercase__ = 1000 ):
_lowerCamelCase : Optional[int] = 1
_lowerCamelCase : List[Any] = 0
for divide_by_number in range(lowercase__ , digit + 1 ):
... | 630 | 1 |
"""simple docstring"""
from abc import ABC, abstractmethod
from typing import List, Optional
class lowerCAmelCase__ ( lowercase ):
'''simple docstring'''
def __init__( self ):
# test for the above condition
self.test()
def ... | 630 |
"""simple docstring"""
import os
import unittest
from transformers import MobileBertTokenizer, MobileBertTokenizerFast
from transformers.models.bert.tokenization_bert import (
VOCAB_FILES_NAMES,
BasicTokenizer,
WordpieceTokenizer,
_is_control,
_is_punctuation,
_... | 630 | 1 |
"""simple docstring"""
import argparse
import collections
import json
import os
import re
import string
import sys
import numpy as np
lowercase__ = re.compile(R"""\b(a|an|the)\b""", re.UNICODE)
lowercase__ = None
def _snake_case ( ):
_lowerCam... | 630 |
"""simple docstring"""
import unittest
from transformers import MPNetConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, ... | 630 | 1 |
"""simple docstring"""
import re
def _snake_case ( lowercase__ ):
if len(re.findall('[ATCG]' , lowercase__ ) ) != len(lowercase__ ):
raise ValueError('Invalid Strand' )
return dna.translate(dna.maketrans('ATCG' ... | 630 |
"""simple docstring"""
import argparse
import intel_extension_for_pytorch as ipex
import torch
from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline
lowercase__ = argparse.ArgumentParser("""Stable Diffusion script with intel optimization""", add_help=False)
pa... | 630 | 1 |
"""simple docstring"""
import fire
from utils import calculate_rouge, save_json
def _snake_case ( lowercase__ , lowercase__ , lowercase__=None , **lowercase__ ):
_lowerCamelCase : Tuple = [x.strip() for x in open(lo... | 630 |
"""simple docstring"""
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""... | 630 | 1 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_chinese_clip import ChineseCLIPImageProcessor
lowercase__ = logging.get_logger(__name__)
class lowerCAmelCase__ ( lowercase ):
'''simple docstring'''
... | 630 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase__ = logging.get_logger(__name__)
lowercase__ = {
"""facebook/dpr-ctx_encoder-single-nq-base""": (
"""https://huggingface.co/facebook/dpr-ctx_encoder-singl... | 630 | 1 |
"""simple docstring"""
from __future__ import annotations
def _snake_case ( lowercase__ ):
_lowerCamelCase : str = 2
_lowerCamelCase : List[Any] = []
while i * i <= n:
if n % i:
i += 1
els... | 630 |
"""simple docstring"""
import re
def _snake_case ( lowercase__ ):
if len(re.findall('[ATCG]' , lowercase__ ) ) != len(lowercase__ ):
raise ValueError('Invalid Strand' )
return dna.translate(dna.maketrans('ATCG' ... | 630 | 1 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, DDIMScheduler, LDMTextToImagePipeline, UNetaDConditionModel
from diffusers.utils.testing_utils impo... | 630 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from diffusers import (
AudioDiffusionPipeline,
AutoencoderKL,
DDIMScheduler,
DDPMScheduler,
DiffusionPipeline,
Mel,
UNetaDConditionModel,
UNetaDModel,
)
from diffuse... | 630 | 1 |
"""simple docstring"""
def _snake_case ( lowercase__ ):
stooge(lowercase__ , 0 , len(lowercase__ ) - 1 )
return arr
def _snake_case ( lowercase__ , lowercase__ , lowercase__ ):
if i >= h:
... | 630 |
"""simple docstring"""
# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
# and https://github.com/hojonathanho/diffusion
import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union
import numpy as np
import torc... | 630 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowercase__ = {
"""configuration_bigbird_pegasus""": [
"""BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""BigBirdPega... | 630 |
"""simple docstring"""
import itertools
import json
import os
import unittest
from transformers import AddedToken, RobertaTokenizer, RobertaTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizer... | 630 | 1 |
"""simple docstring"""
def _snake_case ( lowercase__ = 100 ):
_lowerCamelCase : Optional[int] = n * (n + 1) * (2 * n + 1) / 6
_lowerCamelCase : List[Any] = (n * (n + 1) / 2) ** 2
return int(square_of_sum - sum_of_squares )
i... | 630 |
"""simple docstring"""
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 ... | 630 | 1 |
"""simple docstring"""
from __future__ import annotations
from collections.abc import Generator
import requests
from bsa import BeautifulSoup
lowercase__ = """https://www.indeed.co.in/jobs?q=mobile+app+development&l="""
def _snake_case ( lowercase__ = "mumba... | 630 |
"""simple docstring"""
from typing import Any
def _snake_case ( lowercase__ , lowercase__ , lowercase__ , lowercase__ , lowercase__ , ):
_validation(
lowercase__ , lowercase__ , lowercase__ ,... | 630 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
lowercase__ = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependenc... | 630 |
"""simple docstring"""
import os
from typing import Dict, List, Union
import tensorflow as tf
from keras_nlp.tokenizers import BytePairTokenizer
from tensorflow_text import pad_model_inputs
from .tokenization_gpta import GPTaTokenizer
class lowerCAmelCase__ ( tf.keras.l... | 630 | 1 |
"""simple docstring"""
# We ignore warnings about stepping the scheduler since we step it ourselves during gradient accumulation
import warnings
from .state import AcceleratorState, GradientState
warnings.filterwarnings("""ignore""", category=UserWarning, module="""torch.optim.lr_sched... | 630 |
"""simple docstring"""
from diffusers.utils.testing_utils import require_onnxruntime
@require_onnxruntime
class lowerCAmelCase__ :
'''simple docstring'''
pass | 630 | 1 |
"""simple docstring"""
from sklearn.metrics import recall_score
import datasets
lowercase__ = """
Recall is the fraction of the positive examples that were correctly labeled by the model as positive. It can be computed with the equation:
Recall = TP / (TP + FN)
Where TP is the true pos... | 630 |
"""simple docstring"""
import qiskit
def _snake_case ( lowercase__ = 2 ):
_lowerCamelCase : Optional[Any] = qubits
# Using Aer's simulator
_lowerCamelCase : Dict = qiskit.Aer.get_backend('aer_simulator' )
... | 630 | 1 |
"""simple docstring"""
from __future__ import annotations
import inspect
import unittest
from transformers import ViTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from... | 630 |
"""simple docstring"""
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
... | 630 | 1 |
"""simple docstring"""
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import add_start_docstrings
lowercase__ = R"""
[`RagConfig`] stores the configuration of a *RagModel*. Configuration objects inherit from [`PretrainedConfig`] and
can be used t... | 630 |
"""simple docstring"""
import unittest
from parameterized import parameterized
from transformers import LlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...... | 630 | 1 |
"""simple docstring"""
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeli... | 630 |
"""simple docstring"""
from __future__ import annotations
from fractions import Fraction
from math import gcd, sqrt
def _snake_case ( lowercase__ ):
_lowerCamelCase : int = int(number**0.5 )
return number == sq * sq
... | 630 | 1 |
"""simple docstring"""
from __future__ import annotations
from collections.abc import Iterator
from typing import Any
class lowerCAmelCase__ :
'''simple docstring'''
def __init__( self , lowercase ):
_lowerCamelCase : Any ... | 630 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase__ = logging.get_logger(__name__)
lowercase__ = {"""openai-gpt""": """https://huggingface.co/openai-gpt/resolve/main/config.json"""}
class lowerCAmelCase__ ... | 630 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowercase__ = {
"""configuration_mctct""": ["""MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MCTCTConfig"""],
"""feature_extraction_mctct... | 630 |
"""simple docstring"""
from __future__ import annotations
def _snake_case ( lowercase__ ): # This function is recursive
_lowerCamelCase : Optional[Any] = len(lowercase__ )
# If the array contains only one element, we return it (it's t... | 630 | 1 |
"""simple docstring"""
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
UNetaDConditionModel,
VideoToVideoSDPipeline,
)
from diff... | 630 |
"""simple docstring"""
from __future__ import annotations
def _snake_case ( lowercase__ ):
_lowerCamelCase : int = len(lowercase__ )
# We need to create solution object to save path.
_lowerCamelCase : Tuple = [[0 f... | 630 | 1 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_pegasus ... | 630 |
"""simple docstring"""
import operator as op
def _snake_case ( lowercase__ ):
_lowerCamelCase : Dict = []
_lowerCamelCase : List[str] = lambda lowercase__ , lowercase__ : int(x / y ) # noqa: E731 integer ... | 630 | 1 |
"""simple docstring"""
import time
from dataclasses import dataclass
from multiprocessing import Pool
from unittest import TestCase
from unittest.mock import patch
import multiprocess
import numpy as np
import pytest
from datasets.utils.py_utils import (
NestedDataStructure,
... | 630 |
"""simple docstring"""
def _snake_case ( lowercase__ = 1 , lowercase__ = 1000 ):
_lowerCamelCase : Optional[int] = 1
_lowerCamelCase : List[Any] = 0
for divide_by_number in range(lowercase__ , digit + 1 ):
... | 630 | 1 |
"""simple docstring"""
import numpy as np
from transformers import Pipeline
def _snake_case ( lowercase__ ):
_lowerCamelCase : Dict = np.max(lowercase__ , axis=-1 , keepdims=lowercase__ )
_lowerCamelCase : Opti... | 630 |
"""simple docstring"""
import os
import unittest
from transformers import MobileBertTokenizer, MobileBertTokenizerFast
from transformers.models.bert.tokenization_bert import (
VOCAB_FILES_NAMES,
BasicTokenizer,
WordpieceTokenizer,
_is_control,
_is_punctuation,
_... | 630 | 1 |
"""simple docstring"""
from __future__ import annotations
import math
def _snake_case ( lowercase__ , lowercase__ ):
_lowerCamelCase : Any = u
for i in range(1 , lowercase__ ):
_lowerCamelCase : Tuple ... | 630 |
"""simple docstring"""
import unittest
from transformers import MPNetConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, ... | 630 | 1 |
"""simple docstring"""
import inspect
import re
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
lowercase__ = """src/transfor... | 630 |
"""simple docstring"""
import argparse
import intel_extension_for_pytorch as ipex
import torch
from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline
lowercase__ = argparse.ArgumentParser("""Stable Diffusion script with intel optimization""", add_help=False)
pa... | 630 | 1 |
"""simple docstring"""
from __future__ import annotations
from fractions import Fraction
from math import gcd, sqrt
def _snake_case ( lowercase__ ):
_lowerCamelCase : int = int(number**0.5 )
return number == sq * sq
... | 630 |
"""simple docstring"""
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""... | 630 | 1 |
"""simple docstring"""
import unittest
from knapsack import greedy_knapsack as kp
class lowerCAmelCase__ ( unittest.TestCase ):
'''simple docstring'''
def A_ ( self ):
_lowerCamelCase : Dict = [10, 20, 30, 40, ... | 630 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase__ = logging.get_logger(__name__)
lowercase__ = {
"""facebook/dpr-ctx_encoder-single-nq-base""": (
"""https://huggingface.co/facebook/dpr-ctx_encoder-singl... | 630 | 1 |
"""simple docstring"""
from typing import Dict, Optional
import numpy as np
import datasets
lowercase__ = """
IoU is the area of overlap between the predicted segmentation and the ground truth divided by the area of union
between the predicted segmentation and the ground truth. For ... | 630 |
"""simple docstring"""
import re
def _snake_case ( lowercase__ ):
if len(re.findall('[ATCG]' , lowercase__ ) ) != len(lowercase__ ):
raise ValueError('Invalid Strand' )
return dna.translate(dna.maketrans('ATCG' ... | 630 | 1 |
"""simple docstring"""
from typing import Any
def _snake_case ( lowercase__ , lowercase__ , lowercase__ , lowercase__ , lowercase__ , ):
_validation(
lowercase__ , lowercase__ , lowercase__ ,... | 630 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from diffusers import (
AudioDiffusionPipeline,
AutoencoderKL,
DDIMScheduler,
DDPMScheduler,
DiffusionPipeline,
Mel,
UNetaDConditionModel,
UNetaDModel,
)
from diffuse... | 630 | 1 |
"""simple docstring"""
def _snake_case ( lowercase__ = 1000 ):
_lowerCamelCase, _lowerCamelCase : Union[str, Any] = 1, 1
_lowerCamelCase : Union[str, Any] = 2
while True:
_lowerCamelCase : str = 0
... | 630 |
"""simple docstring"""
# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
# and https://github.com/hojonathanho/diffusion
import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union
import numpy as np
import torc... | 630 | 1 |
"""simple docstring"""
import itertools
import string
from collections.abc import Generator, Iterable
def _snake_case ( lowercase__ , lowercase__ ):
_lowerCamelCase : str = iter(lowercase__ )
while True:
_lowerCamel... | 630 |
"""simple docstring"""
import itertools
import json
import os
import unittest
from transformers import AddedToken, RobertaTokenizer, RobertaTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizer... | 630 | 1 |
"""simple docstring"""
import socket
def _snake_case ( ):
_lowerCamelCase : Optional[int] = socket.socket(socket.AF_INET , socket.SOCK_STREAM )
_lowerCamelCase : Optional[int] = socket.gethostname()
_lowerCamel... | 630 |
"""simple docstring"""
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 ... | 630 | 1 |
"""simple docstring"""
def _snake_case ( lowercase__ = 1 , lowercase__ = 1000 ):
_lowerCamelCase : Optional[int] = 1
_lowerCamelCase : List[Any] = 0
for divide_by_number in range(lowercase__ , digit + 1 ):
... | 630 |
"""simple docstring"""
from typing import Any
def _snake_case ( lowercase__ , lowercase__ , lowercase__ , lowercase__ , lowercase__ , ):
_validation(
lowercase__ , lowercase__ , lowercase__ ,... | 630 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowercase__ = {
"""configuration_luke""": ["""LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LukeConfig"""],
"""tokenization_luke""": ["""Lu... | 630 |
"""simple docstring"""
import os
from typing import Dict, List, Union
import tensorflow as tf
from keras_nlp.tokenizers import BytePairTokenizer
from tensorflow_text import pad_model_inputs
from .tokenization_gpta import GPTaTokenizer
class lowerCAmelCase__ ( tf.keras.l... | 630 | 1 |
"""simple docstring"""
from math import ceil
def _snake_case ( lowercase__ , lowercase__ ):
_lowerCamelCase : Tuple = list(range(0 , lowercase__ ) )
_lowerCamelCase : Dict = [item for sublist in... | 630 |
"""simple docstring"""
from diffusers.utils.testing_utils import require_onnxruntime
@require_onnxruntime
class lowerCAmelCase__ :
'''simple docstring'''
pass | 630 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowercase__ = {
"""configuration_megatron_bert""": ["""MEGATRON_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MegatronBertConfig"""],
}
try:... | 630 |
"""simple docstring"""
import qiskit
def _snake_case ( lowercase__ = 2 ):
_lowerCamelCase : Optional[Any] = qubits
# Using Aer's simulator
_lowerCamelCase : Dict = qiskit.Aer.get_backend('aer_simulator' )
... | 630 | 1 |
"""simple docstring"""
from __future__ import annotations
from collections.abc import Callable
from typing import Generic, TypeVar
lowercase__ = TypeVar("""T""")
lowercase__ = TypeVar("""U""")
class lowerCAmelCase__ ( Generic[T, U] ):
'''simple doc... | 630 |
"""simple docstring"""
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
... | 630 | 1 |
"""simple docstring"""
import inspect
import unittest
import torch
import torch.nn as nn
from accelerate.hooks import (
AlignDevicesHook,
ModelHook,
SequentialHook,
add_hook_to_module,
attach_align_device_hook,
remove_hook_from_module,
remove_hook_from_s... | 630 |
"""simple docstring"""
import unittest
from parameterized import parameterized
from transformers import LlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...... | 630 | 1 |
"""simple docstring"""
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image... | 630 |
"""simple docstring"""
from __future__ import annotations
from fractions import Fraction
from math import gcd, sqrt
def _snake_case ( lowercase__ ):
_lowerCamelCase : int = int(number**0.5 )
return number == sq * sq
... | 630 | 1 |
"""simple docstring"""
import warnings
from typing import List, Optional, Union
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ... | 630 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase__ = logging.get_logger(__name__)
lowercase__ = {"""openai-gpt""": """https://huggingface.co/openai-gpt/resolve/main/config.json"""}
class lowerCAmelCase__ ... | 630 | 1 |
"""simple docstring"""
from __future__ import annotations
from collections.abc import Callable
lowercase__ = list[list[float | int]]
def _snake_case ( lowercase__ , lowercase__ ):
_lowerCamelCase : int = len(lowercase__ )
... | 630 |
"""simple docstring"""
from __future__ import annotations
def _snake_case ( lowercase__ ): # This function is recursive
_lowerCamelCase : Optional[Any] = len(lowercase__ )
# If the array contains only one element, we return it (it's t... | 630 | 1 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from diffusers import (
AudioDiffusionPipeline,
AutoencoderKL,
DDIMScheduler,
DDPMScheduler,
DiffusionPipeline,
Mel,
UNetaDConditionModel,
UNetaDModel,
)
from diffuse... | 630 |
"""simple docstring"""
from __future__ import annotations
def _snake_case ( lowercase__ ):
_lowerCamelCase : int = len(lowercase__ )
# We need to create solution object to save path.
_lowerCamelCase : Tuple = [[0 f... | 630 | 1 |
"""simple docstring"""
import argparse
import os
import re
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_dummies.py
lowercase__ = """src/diffusers"""
# Matches is_xxx_available()
lowercase__ = re.c... | 630 |
"""simple docstring"""
import operator as op
def _snake_case ( lowercase__ ):
_lowerCamelCase : Dict = []
_lowerCamelCase : List[str] = lambda lowercase__ , lowercase__ : int(x / y ) # noqa: E731 integer ... | 630 | 1 |
"""simple docstring"""
import logging
import re
import pytorch_quantization
import pytorch_quantization.nn as quant_nn
import torch
from pytorch_quantization import calib
from pytorch_quantization.tensor_quant import QuantDescriptor
lowercase__ = logging.getLogger(__name__)
low... | 630 |
"""simple docstring"""
def _snake_case ( lowercase__ = 1 , lowercase__ = 1000 ):
_lowerCamelCase : Optional[int] = 1
_lowerCamelCase : List[Any] = 0
for divide_by_number in range(lowercase__ , digit + 1 ):
... | 630 | 1 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from tokenizers import processors
from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sent... | 630 |
"""simple docstring"""
import os
import unittest
from transformers import MobileBertTokenizer, MobileBertTokenizerFast
from transformers.models.bert.tokenization_bert import (
VOCAB_FILES_NAMES,
BasicTokenizer,
WordpieceTokenizer,
_is_control,
_is_punctuation,
_... | 630 | 1 |
"""simple docstring"""
lowercase__ = [
"""Audio""",
"""Array2D""",
"""Array3D""",
"""Array4D""",
"""Array5D""",
"""ClassLabel""",
"""Features""",
"""Sequence""",
"""Value""",
"""Image""",
"""Translation""",
"""TranslationVariableLangu... | 630 |
"""simple docstring"""
import unittest
from transformers import MPNetConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, ... | 630 | 1 |
"""simple docstring"""
import gc
import tempfile
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device
lowercase__ = False
class... | 630 |
"""simple docstring"""
import argparse
import intel_extension_for_pytorch as ipex
import torch
from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline
lowercase__ = argparse.ArgumentParser("""Stable Diffusion script with intel optimization""", add_help=False)
pa... | 630 | 1 |
"""simple docstring"""
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase__ = logging.get_logger(__name__)
lowercase__ = {
"""microsoft/unispeech-sat-base-100h-libri-ft""": (
"""https://huggi... | 630 |
"""simple docstring"""
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""... | 630 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_torch_available,
)
lowercase__ = {
"""configuration_trocr""": ["""TROCR_PRETRAI... | 630 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase__ = logging.get_logger(__name__)
lowercase__ = {
"""facebook/dpr-ctx_encoder-single-nq-base""": (
"""https://huggingface.co/facebook/dpr-ctx_encoder-singl... | 630 | 1 |
"""simple docstring"""
import json
import os
import unittest
from transformers import BatchEncoding, MvpTokenizer, MvpTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from ... | 630 |
"""simple docstring"""
import re
def _snake_case ( lowercase__ ):
if len(re.findall('[ATCG]' , lowercase__ ) ) != len(lowercase__ ):
raise ValueError('Invalid Strand' )
return dna.translate(dna.maketrans('ATCG' ... | 630 | 1 |
"""simple docstring"""
from __future__ import annotations
import inspect
import unittest
from typing import List, Tuple
from transformers import RegNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_ava... | 630 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from diffusers import (
AudioDiffusionPipeline,
AutoencoderKL,
DDIMScheduler,
DDPMScheduler,
DiffusionPipeline,
Mel,
UNetaDConditionModel,
UNetaDModel,
)
from diffuse... | 630 | 1 |
"""simple docstring"""
import unittest
from transformers import AutoTokenizer, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow
if is_flax_available():
import jax.numpy as jnp
from transformers import FlaxXLM... | 630 |
"""simple docstring"""
# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
# and https://github.com/hojonathanho/diffusion
import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union
import numpy as np
import torc... | 630 | 1 |
"""simple docstring"""
from typing import Any, Dict, Optional
import torch
import torch.nn.functional as F
from torch import nn
from ..utils import maybe_allow_in_graph
from .activations import get_activation
from .attention_processor import Attention
from .embeddings import CombinedTi... | 630 |
"""simple docstring"""
import itertools
import json
import os
import unittest
from transformers import AddedToken, RobertaTokenizer, RobertaTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizer... | 630 | 1 |
"""simple docstring"""
import pytest
from datasets.parallel import ParallelBackendConfig, parallel_backend
from datasets.utils.py_utils import map_nested
from .utils import require_dill_gt_0_3_2, require_joblibspark, require_not_windows
def _snake_case ( lowercase... | 630 |
"""simple docstring"""
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 ... | 630 | 1 |
"""simple docstring"""
from __future__ import annotations
def _snake_case ( lowercase__ ):
_lowerCamelCase : int = len(lowercase__ )
# We need to create solution object to save path.
_lowerCamelCase : Tuple = [[0 f... | 630 |
"""simple docstring"""
from typing import Any
def _snake_case ( lowercase__ , lowercase__ , lowercase__ , lowercase__ , lowercase__ , ):
_validation(
lowercase__ , lowercase__ , lowercase__ ,... | 630 | 1 |
"""simple docstring"""
def _snake_case ( lowercase__ , lowercase__ ):
_enforce_args(lowercase__ , lowercase__ )
if n == 0:
return 0
_lowerCamelCase : Optional[Any] = float('-inf' )
for i in range(1 , ... | 630 |
"""simple docstring"""
import os
from typing import Dict, List, Union
import tensorflow as tf
from keras_nlp.tokenizers import BytePairTokenizer
from tensorflow_text import pad_model_inputs
from .tokenization_gpta import GPTaTokenizer
class lowerCAmelCase__ ( tf.keras.l... | 630 | 1 |
"""simple docstring"""
import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union
import sentencepiece
from ...tokenization_utils import BatchEncoding, PreTrainedTokenizer
from ...utils import logging
lower... | 630 |
"""simple docstring"""
from diffusers.utils.testing_utils import require_onnxruntime
@require_onnxruntime
class lowerCAmelCase__ :
'''simple docstring'''
pass | 630 | 1 |
"""simple docstring"""
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized, parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Sessio... | 630 |
"""simple docstring"""
import qiskit
def _snake_case ( lowercase__ = 2 ):
_lowerCamelCase : Optional[Any] = qubits
# Using Aer's simulator
_lowerCamelCase : Dict = qiskit.Aer.get_backend('aer_simulator' )
... | 630 | 1 |
"""simple docstring"""
import unittest
from transformers import MPNetConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, ... | 630 |
"""simple docstring"""
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
... | 630 | 1 |
"""simple docstring"""
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/l... | 630 |
"""simple docstring"""
import unittest
from parameterized import parameterized
from transformers import LlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...... | 630 | 1 |
"""simple docstring"""
import unittest
import numpy as np
from transformers import BertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
... | 630 |
"""simple docstring"""
from __future__ import annotations
from fractions import Fraction
from math import gcd, sqrt
def _snake_case ( lowercase__ ):
_lowerCamelCase : int = int(number**0.5 )
return number == sq * sq
... | 630 | 1 |
"""simple docstring"""
# using dfs for finding eulerian path traversal
def _snake_case ( lowercase__ , lowercase__ , lowercase__ , lowercase__=None ):
_lowerCamelCase : Union[str, Any] = (path or []) + [u]
for v i... | 630 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase__ = logging.get_logger(__name__)
lowercase__ = {"""openai-gpt""": """https://huggingface.co/openai-gpt/resolve/main/config.json"""}
class lowerCAmelCase__ ... | 630 | 1 |
"""simple docstring"""
def _snake_case ( lowercase__ ):
_lowerCamelCase : Dict = [0] * len(lowercase__ )
for i in range(1 , len(lowercase__ ) ):
# use last results for better performance - dynamic programming
_l... | 630 |
"""simple docstring"""
from __future__ import annotations
def _snake_case ( lowercase__ ): # This function is recursive
_lowerCamelCase : Optional[Any] = len(lowercase__ )
# If the array contains only one element, we return it (it's t... | 630 | 1 |
"""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
... | 630 |
"""simple docstring"""
from __future__ import annotations
def _snake_case ( lowercase__ ):
_lowerCamelCase : int = len(lowercase__ )
# We need to create solution object to save path.
_lowerCamelCase : Tuple = [[0 f... | 630 | 1 |
"""simple docstring"""
from cva import destroyAllWindows, imread, imshow, waitKey
def _snake_case ( lowercase__ ):
# getting number of pixels in the image
_lowerCamelCase, _lowerCamelCase : List[Any] = img.shape[0], img.shape[1]
... | 630 |
"""simple docstring"""
import operator as op
def _snake_case ( lowercase__ ):
_lowerCamelCase : Dict = []
_lowerCamelCase : List[str] = lambda lowercase__ , lowercase__ : int(x / y ) # noqa: E731 integer ... | 630 | 1 |
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