code stringlengths 81 54k | code_codestyle int64 0 721 | style_context stringlengths 91 41.9k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
"""simple docstring"""
import itertools
import os
from collections import Counter, defaultdict
from concurrent.futures import ThreadPoolExecutor, as_completed
import numpy as np
import datasets
from .execute import check_correctness
__SCREAMING_SNAKE_CASE : Optional[int] = '''\
@misc{chen2... | 717 |
"""simple docstring"""
from __future__ import annotations
import copy
import inspect
import json
import math
import os
import tempfile
import unittest
from importlib import import_module
import numpy as np
from transformers import ViTMAEConfig
from transformers.file_utils import cached_property, is_tf_available,... | 623 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
__SCREAMING_SNAKE_CASE : int = {
'''configuration_wav2vec2''': ['''WAV_2_VEC_2_PRETR... | 718 |
"""simple docstring"""
from itertools import zip_longest
import requests
from bsa import BeautifulSoup
from pandas import DataFrame
def lowerCAmelCase_( lowercase_ : str = "laptop" ) -> DataFrame:
_lowerCamelCase = F"""https://www.amazon.in/laptop/s?k={product}"""
... | 623 | 0 |
"""simple docstring"""
from dataclasses import dataclass
from typing import Optional
import numpy as np
import torch
import torch.nn as nn
from ..utils import BaseOutput, is_torch_version, randn_tensor
from .attention_processor import SpatialNorm
from .unet_ad_blocks import UNetMidBlockaD, get_down_block, get_up_... | 719 |
"""simple docstring"""
import logging
import torch
from accelerate import Accelerator
from arguments import EvaluationArguments
from datasets import load_dataset
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader
from transformers import AutoModelForCausalLM, AutoToken... | 623 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
__SCREAMING_SNAKE_CASE : Tuple = {
'''configuration_transfo_xl''': ['''TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''TransfoXLCo... | 720 |
"""simple docstring"""
import numpy as np
def lowerCAmelCase_( lowercase_ : np.ndarray , lowercase_ : np.ndarray , lowercase_ : float = 1e-12 , lowercase_ : int = 1_00 , ) -> tuple[float, np.ndarray]:
assert np.shape(lowercase... | 623 | 0 |
"""simple docstring"""
import argparse
import json
import os
import torch
from transformers import LukeConfig, LukeModel, LukeTokenizer, RobertaTokenizer
from transformers.tokenization_utils_base import AddedToken
@torch.no_grad()
def lowerCAmelCase_( lowercase_ : int , lowercase_ ... | 721 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_torch_available,
)
__SCREAMING_SNAKE_CASE : Union[str, Any] = {
'''configuration_speecht5''': [
'''SPEECHT5_P... | 623 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
__SCREAMING_SNAKE_CASE : Any = {'''configuration_unispeech''': ['''UNISPEECH_PRETRAINED_CO... | 700 |
"""simple docstring"""
from __future__ import annotations
import matplotlib.pyplot as plt # type: ignore
import numpy
# initial triangle of Koch snowflake
__SCREAMING_SNAKE_CASE : List[str] = numpy.array([0, 0])
__SCREAMING_SNAKE_CASE : Optional[Any] = numpy.array([0.5, ... | 623 | 0 |
"""simple docstring"""
def lowerCAmelCase_( lowercase_ : int ) -> int:
return 1 if digit in (0, 1) else (digit * factorial(digit - 1 ))
def lowerCAmelCase_( lowercase_ : int ) -> bool:
_lowerCamelCase = 0
_lowerCamelCase ... | 701 |
"""simple docstring"""
from typing import Any
class lowerCamelCase_:
'''simple docstring'''
def __init__( self , lowerCamelCase__ ):
_lowerCamelCase = data
_lowerCamelCase = None
class lowerCamelCase_:
'''simple docstr... | 623 | 0 |
"""simple docstring"""
import argparse
import collections
import json
import os
import re
import string
import sys
import numpy as np
__SCREAMING_SNAKE_CASE : List[Any] = re.compile(R'''\b(a|an|the)\b''', re.UNICODE)
__SCREAMING_SNAKE_CASE : Optional[Any] = None
def ... | 702 |
"""simple docstring"""
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import sys
import warnings
from os.path import abspath, dirname, join
# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when s... | 623 | 0 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__SCREAMING_SNAKE_CASE : Optional[int] = logging... | 703 |
"""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()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAv... | 623 | 0 |
"""simple docstring"""
import json
import os
from collections import Counter
import torch
import torchvision
import torchvision.transforms as transforms
from PIL import Image
from torch import nn
from torch.utils.data import Dataset
__SCREAMING_SNAKE_CASE = {1: (1, 1), 2: (2, 1), 3: (3, 1), 4: (2,... | 704 |
"""simple docstring"""
import unittest
from transformers import TrOCRConfig
from transformers.testing_utils import is_torch_available, require_torch, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import Mo... | 623 | 0 |
"""simple docstring"""
import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List
import git
import torch
from torch.utils.data i... | 705 |
"""simple docstring"""
import warnings
from ..trainer import Trainer
from ..utils import logging
__SCREAMING_SNAKE_CASE : Dict = logging.get_logger(__name__)
class lowerCamelCase_( A__ ):
'''simple docstring'''
def __init__( self , lowerCamelCase__... | 623 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
__SCREAMING_SNAKE_CASE : Optional[Any] = {
'''configuration_vision_encoder_decoder''':... | 706 |
"""simple docstring"""
import unittest
from transformers import BigBirdConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax
from transformers.... | 623 | 0 |
"""simple docstring"""
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.LearningRateS... | 707 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DiffusionPipeline,
EulerDiscreteScheduler,
StableDiffusionXLImgaI... | 623 | 0 |
"""simple docstring"""
import gc
import tempfile
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionTextToImagePipeline
from diffusers.utils.testing_utils import nightly, require_torch_gpu, torch_device
__SCREAMING_SNAKE_CASE : Any = False
class ... | 708 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
__SCREAMING_SNAKE_CASE : List[Any] = {
'''configuration_xlm''': ['''XLM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''XLMConfig''', '''XLMO... | 623 | 0 |
"""simple docstring"""
import string
# frequency taken from https://en.wikipedia.org/wiki/Letter_frequency
__SCREAMING_SNAKE_CASE : List[Any] = {
'''E''': 12.70,
'''T''': 9.06,
'''A''': 8.17,
'''O''': 7.51,
'''I''': 6.97,
'''N''': 6.75,
'''S''': 6.33,
'''H''': 6... | 709 |
"""simple docstring"""
import itertools
import os
import random
import tempfile
import unittest
import numpy as np
from datasets import load_dataset
from transformers import is_speech_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transf... | 623 | 0 |
import re
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
class lowerCamelCase_( A__ ):
'''simple docstring'''
lowercase__ : List[Any] = ['image_processor', 'tokenizer']
lowercase__ : Tuple =... | 710 |
"""simple docstring"""
def lowerCAmelCase_( lowercase_ : str , lowercase_ : str ) -> bool:
_lowerCamelCase = len(lowercase_ )
_lowerCamelCase = len(lowercase_ )
_lowerCamelCase = [[False for _ in range(m + 1 ... | 623 | 0 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, PNDMScheduler, StableDiffusionInpaintPipeline, UNetaDConditionModel
from diffusers.utils i... | 711 |
"""simple docstring"""
import numpy as np
def lowerCAmelCase_( lowercase_ : np.array ) -> np.array:
return 1 / (1 + np.exp(-vector ))
def lowerCAmelCase_( lowercase_ : np.array ) -> np.array:
return vector * sigmoid(1.7_0_2 * vector ... | 623 | 0 |
"""simple docstring"""
import argparse
from collections import defaultdict
import yaml
__SCREAMING_SNAKE_CASE : Any = '''docs/source/en/_toctree.yml'''
def lowerCAmelCase_( lowercase_ : int ) -> Tuple:
_lowerCamelCase = defaultdict(lowercase_ ... | 712 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
__SCREAMING_SNAKE_CASE : Optional[Any] = {
'''configuration_vision_encoder_decoder''':... | 623 | 0 |
"""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_modeling_common import ModelTest... | 713 |
"""simple docstring"""
from __future__ import annotations
from math import pow, sqrt
def lowerCAmelCase_( lowercase_ : float , lowercase_ : float , lowercase_ : float ) -> dict[str, float]:
if (resistance, reactance, impedance).count(0 )... | 623 | 0 |
import logging
import torch
from accelerate import Accelerator
from arguments import EvaluationArguments
from datasets import load_dataset
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader
from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser,... | 714 |
"""simple docstring"""
from __future__ import annotations
from typing import Any
def lowerCAmelCase_( lowercase_ : list[Any] ) -> None:
create_state_space_tree(lowercase_ , [] , 0 )
def lowerCAmelCase_( lowercase_ : list[Any] , ... | 623 | 0 |
"""simple docstring"""
import json
import os
import unittest
from transformers import AutoTokenizer, GPTaTokenizer, GPTaTokenizerFast
from transformers.models.gpta.tokenization_gpta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import Tokenizer... | 715 |
"""simple docstring"""
import warnings
from .generation import TFGenerationMixin
class lowerCamelCase_( A__ ):
'''simple docstring'''
warnings.warn(
'Importing `TFGenerationMixin` from `src/transformers/generation_tf_utils.py` is deprecated and will '
'be removed i... | 623 | 0 |
"""simple docstring"""
import tempfile
import unittest
import numpy as np
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import BertConfig, is_flax_available
from transformers.testing_utils import TOKEN, USER, is_staging_test, require_flax
if is_flax... | 716 |
"""simple docstring"""
import argparse
import json
import os
import torch
from transformers import LukeConfig, LukeModel, LukeTokenizer, RobertaTokenizer
from transformers.tokenization_utils_base import AddedToken
@torch.no_grad()
def lowerCAmelCase_( lowercase_ : int , lowercase_ ... | 623 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__SCREAMING_SNAKE_CASE : List[str] = logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE : Dict = {
'''facebook/xglm-564M''': '''https://huggingface.co/facebook/xg... | 717 |
"""simple docstring"""
from __future__ import annotations
import copy
import inspect
import json
import math
import os
import tempfile
import unittest
from importlib import import_module
import numpy as np
from transformers import ViTMAEConfig
from transformers.file_utils import cached_property, is_tf_available,... | 623 | 0 |
"""simple docstring"""
import asyncio
import os
import shutil
import subprocess
import sys
import tempfile
import unittest
from distutils.util import strtobool
from functools import partial
from pathlib import Path
from typing import List, Union
from unittest import mock
import torch
from ..state import Acceler... | 718 |
"""simple docstring"""
from itertools import zip_longest
import requests
from bsa import BeautifulSoup
from pandas import DataFrame
def lowerCAmelCase_( lowercase_ : str = "laptop" ) -> DataFrame:
_lowerCamelCase = F"""https://www.amazon.in/laptop/s?k={product}"""
... | 623 | 0 |
"""simple docstring"""
def lowerCAmelCase_( lowercase_ : int , lowercase_ : float , lowercase_ : float ) -> float:
return round(float(moles / volume ) * nfactor )
def lowerCAmelCase_( lowercase_ : float , l... | 719 |
"""simple docstring"""
import logging
import torch
from accelerate import Accelerator
from arguments import EvaluationArguments
from datasets import load_dataset
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader
from transformers import AutoModelForCausalLM, AutoToken... | 623 | 0 |
"""simple docstring"""
from scipy.stats import spearmanr
import datasets
__SCREAMING_SNAKE_CASE : Optional[Any] = '''
The Spearman rank-order correlation coefficient is a measure of the
relationship between two datasets. Like other correlation coefficients,
this one varies between -1 and +1 ... | 720 |
"""simple docstring"""
import numpy as np
def lowerCAmelCase_( lowercase_ : np.ndarray , lowercase_ : np.ndarray , lowercase_ : float = 1e-12 , lowercase_ : int = 1_00 , ) -> tuple[float, np.ndarray]:
assert np.shape(lowercase... | 623 | 0 |
"""simple docstring"""
import argparse
import os
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_task_guides.py
__SCREAMING_SNAKE_CASE : Any = '''src/trans... | 721 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_torch_available,
)
__SCREAMING_SNAKE_CASE : Union[str, Any] = {
'''configuration_speecht5''': [
'''SPEECHT5_P... | 623 | 0 |
"""simple docstring"""
import json
import os
import tempfile
import transformers
import datasets
from utils import generate_example_dataset, get_duration
__SCREAMING_SNAKE_CASE : List[str] = 5_0_0_0_0_0
__SCREAMING_SNAKE_CASE : Union[str, Any] = os.path.split(__file__)... | 700 |
"""simple docstring"""
from __future__ import annotations
import matplotlib.pyplot as plt # type: ignore
import numpy
# initial triangle of Koch snowflake
__SCREAMING_SNAKE_CASE : List[str] = numpy.array([0, 0])
__SCREAMING_SNAKE_CASE : Optional[Any] = numpy.array([0.5, ... | 623 | 0 |
"""simple docstring"""
from typing import Callable, List, Optional, Tuple, Union
import torch
from transformers import CLIPTextModel, CLIPTokenizer
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin, TransformeraDModel, VQModel
from ...schedulers import VQDiffusion... | 701 |
"""simple docstring"""
from typing import Any
class lowerCamelCase_:
'''simple docstring'''
def __init__( self , lowerCamelCase__ ):
_lowerCamelCase = data
_lowerCamelCase = None
class lowerCamelCase_:
'''simple docstr... | 623 | 0 |
"""simple docstring"""
import datasets
import faiss
import numpy as np
import streamlit as st
import torch
from elasticsearch import Elasticsearch
from elia_utils import (
embed_questions_for_retrieval,
make_qa_sas_model,
qa_sas_generate,
query_es_index,
query_qa_dense_index,
)
import transform... | 702 |
"""simple docstring"""
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import sys
import warnings
from os.path import abspath, dirname, join
# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when s... | 623 | 0 |
"""simple docstring"""
import argparse
from copy import deepcopy
import numpy as np
from datasets import ClassLabel, DatasetDict, load_dataset
from evaluate import load
from transformers import (
AutoModelForSequenceClassification,
AutoTokenizer,
DataCollatorWithPadding,
Trainer,
... | 703 |
"""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()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAv... | 623 | 0 |
"""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()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAv... | 704 |
"""simple docstring"""
import unittest
from transformers import TrOCRConfig
from transformers.testing_utils import is_torch_available, require_torch, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import Mo... | 623 | 0 |
"""simple docstring"""
import logging
import os
import sys
import warnings
from dataclasses import dataclass, field
from random import randint
from typing import Optional
import datasets
import evaluate
import numpy as np
from datasets import DatasetDict, load_dataset
import transformers
from transformers import ... | 705 |
"""simple docstring"""
import warnings
from ..trainer import Trainer
from ..utils import logging
__SCREAMING_SNAKE_CASE : Dict = logging.get_logger(__name__)
class lowerCamelCase_( A__ ):
'''simple docstring'''
def __init__( self , lowerCamelCase__... | 623 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_torch_available,
)
__SCREAMING_SNAKE_CASE : Union[str, Any] = {
'''configuration_speecht5''': [
'''SPEECHT5_P... | 706 |
"""simple docstring"""
import unittest
from transformers import BigBirdConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax
from transformers.... | 623 | 0 |
"""simple docstring"""
import itertools
import math
def lowerCAmelCase_( lowercase_ : int ) -> bool:
'''simple docstring'''
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negativ... | 707 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DiffusionPipeline,
EulerDiscreteScheduler,
StableDiffusionXLImgaI... | 623 | 0 |
"""simple docstring"""
import collections
import inspect
import unittest
from transformers import SwinvaConfig
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... | 708 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
__SCREAMING_SNAKE_CASE : List[Any] = {
'''configuration_xlm''': ['''XLM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''XLMConfig''', '''XLMO... | 623 | 0 |
"""simple docstring"""
import unittest
from transformers import TrOCRConfig
from transformers.testing_utils import is_torch_available, require_torch, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import Mo... | 709 |
"""simple docstring"""
import itertools
import os
import random
import tempfile
import unittest
import numpy as np
from datasets import load_dataset
from transformers import is_speech_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transf... | 623 | 0 |
import dataclasses
import json
import sys
import types
from argparse import ArgumentDefaultsHelpFormatter, ArgumentParser, ArgumentTypeError
from copy import copy
from enum import Enum
from inspect import isclass
from pathlib import Path
from typing import Any, Callable, Dict, Iterable, List, Literal, NewType, Opti... | 710 |
"""simple docstring"""
def lowerCAmelCase_( lowercase_ : str , lowercase_ : str ) -> bool:
_lowerCamelCase = len(lowercase_ )
_lowerCamelCase = len(lowercase_ )
_lowerCamelCase = [[False for _ in range(m + 1 ... | 623 | 0 |
"""simple docstring"""
from scipy.stats import pearsonr
import datasets
__SCREAMING_SNAKE_CASE : Dict = '''
Pearson correlation coefficient and p-value for testing non-correlation.
The Pearson correlation coefficient measures the linear relationship between two datasets. The calculation of t... | 711 |
"""simple docstring"""
import numpy as np
def lowerCAmelCase_( lowercase_ : np.array ) -> np.array:
return 1 / (1 + np.exp(-vector ))
def lowerCAmelCase_( lowercase_ : np.array ) -> np.array:
return vector * sigmoid(1.7_0_2 * vector ... | 623 | 0 |
"""simple docstring"""
from __future__ import annotations
from random import choice
def lowerCAmelCase_( lowercase_ : Tuple ) -> Optional[int]:
return choice(lowercase_ )
def lowerCAmelCase_( lowercase_ : list[int] , lowercase_ : in... | 712 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
__SCREAMING_SNAKE_CASE : Optional[Any] = {
'''configuration_vision_encoder_decoder''':... | 623 | 0 |
"""simple docstring"""
import warnings
from ..trainer import Trainer
from ..utils import logging
__SCREAMING_SNAKE_CASE : Dict = logging.get_logger(__name__)
class lowerCamelCase_( A__ ):
'''simple docstring'''
def __init__( self , lowerCamelCase__... | 713 |
"""simple docstring"""
from __future__ import annotations
from math import pow, sqrt
def lowerCAmelCase_( lowercase_ : float , lowercase_ : float , lowercase_ : float ) -> dict[str, float]:
if (resistance, reactance, impedance).count(0 )... | 623 | 0 |
from __future__ import annotations
def lowerCAmelCase_( lowercase_ : int ) -> list[int]:
_lowerCamelCase = 2
_lowerCamelCase = []
while i * i <= n:
if n % i:
i += 1
else:
n //= i
factors.append(lowe... | 714 |
"""simple docstring"""
from __future__ import annotations
from typing import Any
def lowerCAmelCase_( lowercase_ : list[Any] ) -> None:
create_state_space_tree(lowercase_ , [] , 0 )
def lowerCAmelCase_( lowercase_ : list[Any] , ... | 623 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__SCREAMING_SNAKE_CASE : str = logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE : Any = {
# See all MEGATRON_BERT models at https://huggingface.co/models?filter... | 715 |
"""simple docstring"""
import warnings
from .generation import TFGenerationMixin
class lowerCamelCase_( A__ ):
'''simple docstring'''
warnings.warn(
'Importing `TFGenerationMixin` from `src/transformers/generation_tf_utils.py` is deprecated and will '
'be removed i... | 623 | 0 |
"""simple docstring"""
def lowerCAmelCase_( lowercase_ : int = 10_00 ) -> int:
_lowerCamelCase , _lowerCamelCase = 1, 1
_lowerCamelCase = 2
while True:
_lowerCamelCase = 0
_lowerCamelCase = fa + fa
_lowerC... | 716 |
"""simple docstring"""
import argparse
import json
import os
import torch
from transformers import LukeConfig, LukeModel, LukeTokenizer, RobertaTokenizer
from transformers.tokenization_utils_base import AddedToken
@torch.no_grad()
def lowerCAmelCase_( lowercase_ : int , lowercase_ ... | 623 | 0 |
"""simple docstring"""
import unittest
from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
@require_s... | 717 |
"""simple docstring"""
from __future__ import annotations
import copy
import inspect
import json
import math
import os
import tempfile
import unittest
from importlib import import_module
import numpy as np
from transformers import ViTMAEConfig
from transformers.file_utils import cached_property, is_tf_available,... | 623 | 0 |
"""simple docstring"""
import requests
from bsa import BeautifulSoup
def lowerCAmelCase_( lowercase_ : str , lowercase_ : dict ) -> str:
_lowerCamelCase = BeautifulSoup(requests.get(lowercase_ , params=lowercase_ ).content , '''h... | 718 |
"""simple docstring"""
from itertools import zip_longest
import requests
from bsa import BeautifulSoup
from pandas import DataFrame
def lowerCAmelCase_( lowercase_ : str = "laptop" ) -> DataFrame:
_lowerCamelCase = F"""https://www.amazon.in/laptop/s?k={product}"""
... | 623 | 0 |
"""simple docstring"""
import argparse
import torch
from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging
logging.set_verbosity_info()
def lowerCAmelCase_( lowercase_ : Optional[Any] , lowercase_ ... | 719 |
"""simple docstring"""
import logging
import torch
from accelerate import Accelerator
from arguments import EvaluationArguments
from datasets import load_dataset
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader
from transformers import AutoModelForCausalLM, AutoToken... | 623 | 0 |
"""simple docstring"""
import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
__SCREAMING_SNAKE_CASE : Any = loggi... | 720 |
"""simple docstring"""
import numpy as np
def lowerCAmelCase_( lowercase_ : np.ndarray , lowercase_ : np.ndarray , lowercase_ : float = 1e-12 , lowercase_ : int = 1_00 , ) -> tuple[float, np.ndarray]:
assert np.shape(lowercase... | 623 | 0 |
"""simple docstring"""
from __future__ import annotations
from random import random
class lowerCamelCase_:
'''simple docstring'''
def __init__( self , lowerCamelCase__ = None ):
_lowerCamelCase = value
_lowerCamelCase = random()
... | 721 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_torch_available,
)
__SCREAMING_SNAKE_CASE : Union[str, Any] = {
'''configuration_speecht5''': [
'''SPEECHT5_P... | 623 | 0 |
'''simple docstring'''
def _A ( A__ = 50 ):
"""simple docstring"""
__lowercase = [1] * (length + 1)
for row_length in range(length + 1 ):
for tile_length in range(2 , 5 ):
for tile_start in range(row_length - tile_length + 1 ):
ways_number[row_length] +=... | 624 |
'''simple docstring'''
import random
from typing import Any
def _A ( A__ ):
"""simple docstring"""
for _ in range(len(A__ ) ):
__lowercase = random.randint(0 , len(A__ ) - 1 )
__lowercase = random.randint(0 , len(A__ ) - 1 )
__lo... | 624 | 1 |
'''simple docstring'''
def _A ( A__ = 1000 ):
"""simple docstring"""
__lowercase = 2**power
__lowercase = str(A__ )
__lowercase = list(A__ )
__lowercase = 0
for i in list_num:
sum_of_num += int(A__ )
return sum_of_num
... | 624 |
'''simple docstring'''
import argparse
import json
import os
import torch
from transformers.file_utils import has_file
from diffusers import UNetaDConditionModel, UNetaDModel
lowerCAmelCase__ = False
lowerCAmelCase__ = True
lowerCAmelCase__ = False
if __name__ == "__main__":
lo... | 624 | 1 |
'''simple docstring'''
lowerCAmelCase__ = '''
# Transformers installation
! pip install transformers datasets
# To install from source instead of the last release, comment the command above and uncomment the following one.
# ! pip install git+https://github.com/huggingface/transformers.git
'''
lowerCA... | 624 |
'''simple docstring'''
import inspect
import unittest
from math import floor
from transformers import CvtConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from ...test_con... | 624 | 1 |
'''simple docstring'''
def _A ( A__ ):
"""simple docstring"""
__lowercase = len(A__ )
for i in range(length - 1 ):
__lowercase = i
for k in range(i + 1 , A__ ):
if collection[k] < collection[least]:
__lowercase = k
if lea... | 624 |
'''simple docstring'''
def _A ( ):
"""simple docstring"""
for n in range(1 , 1000000 ):
yield n * (n + 1) // 2
def _A ( A__ ):
"""simple docstring"""
__lowercase = 1
__lowercase = 2
while i * i <= n:
__lowercase = ... | 624 | 1 |
'''simple docstring'''
lowerCAmelCase__ = frozenset(
[
'''prompt''',
'''height''',
'''width''',
'''guidance_scale''',
'''negative_prompt''',
'''prompt_embeds''',
'''negative_prompt_embeds''',
'''cross_attention_kwargs''',
]
)
lowe... | 624 |
'''simple docstring'''
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, rando... | 624 | 1 |
'''simple docstring'''
def _A ( A__ , A__ ):
"""simple docstring"""
if density <= 0:
raise ValueError('''Impossible fluid density''' )
if bulk_modulus <= 0:
raise ValueError('''Impossible bulk modulus''' )
return (bulk_modulus / density) ** 0.5
if __name__ == "__main__":
... | 624 |
'''simple docstring'''
import argparse
from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging
logging.set_verbosity_info()
def _A ( A__ , A__ , A__ ):
"""simple docstring"""
__lowercase = Ta... | 624 | 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_inputs
if is_torch_... | 624 |
'''simple docstring'''
from argparse import ArgumentParser
from . import BaseTransformersCLICommand
def _A ( A__ ):
"""simple docstring"""
return DownloadCommand(args.model , args.cache_dir , args.force , args.trust_remote_code )
class lowercase_ (lowerCa... | 624 | 1 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .to... | 624 |
'''simple docstring'''
import unittest
from pathlib import Path
from tempfile import TemporaryDirectory
from transformers import AutoConfig, TFGPTaLMHeadModel, is_keras_nlp_available, is_tf_available
from transformers.models.gpta.tokenization_gpta import GPTaTokenizer
from transformers.testing_utils import req... | 624 | 1 |
'''simple docstring'''
from typing import List, Optional
import numpy as np
from ...processing_utils import ProcessorMixin
from ...utils import to_numpy
class lowercase_ (lowerCamelCase__ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : str = 'EncodecFeatureExtr... | 624 |
'''simple docstring'''
from __future__ import annotations
import matplotlib.pyplot as plt # type: ignore
import numpy
# initial triangle of Koch snowflake
lowerCAmelCase__ = numpy.array([0, 0])
lowerCAmelCase__ = numpy.array([0.5, 0.8_660_254])
lowerCAmelCase__ = numpy.array([1, 0]... | 624 | 1 |
'''simple docstring'''
import darl # noqa
import gym
import tqdm
from diffusers.experimental import ValueGuidedRLPipeline
lowerCAmelCase__ = {
'''n_samples''': 64,
'''horizon''': 32,
'''num_inference_steps''': 20,
'''n_guide_steps''': 2, # can set to 0 for faster sampling, does not ... | 624 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
lowerCAmelCase__ = {'''configuration_vit_mae''': ['''VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''... | 624 | 1 |
'''simple docstring'''
import argparse
import re
import requests
import torch
# git clone https://github.com/salesforce/BLIP.git
from models.blip import blip_decoder
from models.blip_itm import blip_itm
from models.blip_vqa import blip_vqa
from PIL import Image
from torchvision import transforms
from torchvis... | 624 |
'''simple docstring'''
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
import numpy as np
# Parrameters
lowerCAmelCase__ = (720, 1280) # Height, Width
lowerCAmelCase__ = (0.4, 0.6) # if height or width lower than this scale, drop it.
lowerCAmelCase... | 624 | 1 |
'''simple docstring'''
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image
from transformers import DPTConfig, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTImageProcessor
from transformers.util... | 624 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
lowerCAmelCase__ = logging.get_logger(__name__)
lowerCAmelCase__ = {'''vocab... | 624 | 1 |
'''simple docstring'''
class lowercase_ :
"""simple docstring"""
def __init__( self : Any ):
__lowercase = 0
__lowercase = 0
__lowercase = {}
def SCREAMING_SNAKE_CASE ( self : Tuple ,lowercase__ : ... | 624 |
'''simple docstring'''
def _A ( A__ = 1000000 ):
"""simple docstring"""
__lowercase = set(range(3 , A__ , 2 ) )
primes.add(2 )
for p in range(3 , A__ , 2 ):
if p not in primes:
continue
primes.difference_update(set(range(p * p , ... | 624 | 1 |
'''simple docstring'''
def _A ( A__ , A__ ):
"""simple docstring"""
return int(input_a == input_a == 0 )
def _A ( ):
"""simple docstring"""
print('''Truth Table of NOR Gate:''' )
print('''| Input 1 | Input 2 | Output |''' )
print(F"| 0 | 0 ... | 624 |
'''simple docstring'''
import copy
import json
import os
import tempfile
from transformers import is_torch_available
from .test_configuration_utils import config_common_kwargs
class lowercase_ (lowerCamelCase__ ):
"""simple docstring"""
def __init__( self : Optional[Any] ... | 624 | 1 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaImgaImgPipeline,
KandinskyVaaPriorPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, lo... | 624 |
'''simple docstring'''
import re
def _A ( A__ ):
"""simple docstring"""
__lowercase = re.compile(
R'''^(?:0|94|\+94|0{2}94)''' R'''7(0|1|2|4|5|6|7|8)''' R'''(-| |)''' R'''\d{7}$''' )
return bool(re.search(A__ , A__ ) )
if __name__ == "__main__":
lowerCA... | 624 | 1 |
'''simple docstring'''
from collections import defaultdict
def _A ( A__ ):
"""simple docstring"""
__lowercase = 1
__lowercase = True
for v in tree[start]:
if v not in visited:
ret += dfs(A__ )
if ret % 2 == 0:
cuts.append(A__ )
return ret
def ... | 624 |
'''simple docstring'''
from __future__ import annotations
from typing import Any
class lowercase_ :
"""simple docstring"""
def __init__( self : Any ,lowercase__ : int ,lowercase__ : int ,lowercase__ : float = 0 ):
__lowercase , __lowerca... | 624 | 1 |
'''simple docstring'''
from math import factorial
def _A ( A__ , A__ ):
"""simple docstring"""
if n < k or k < 0:
raise ValueError('''Please enter positive integers for n and k where n >= k''' )
return factorial(A__ ) // (factorial(A__ ) * factorial(n - k ))
if __name__ ... | 624 |
'''simple docstring'''
def _A ( A__ = 50 ):
"""simple docstring"""
__lowercase = [1] * (length + 1)
for row_length in range(3 , length + 1 ):
for block_length in range(3 , row_length + 1 ):
for block_start in range(row_length - block_length ):
way... | 624 | 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/licenses/LICENSE-2.0
#... | 624 |
'''simple docstring'''
import argparse
import logging
from collections import namedtuple
import torch
from model_bertabs import BertAbsSummarizer
from models.model_builder import AbsSummarizer # The authors' implementation
from transformers import BertTokenizer
logging.basicConfig(level=logging.INFO)
lower... | 624 | 1 |
'''simple docstring'''
from math import factorial
class lowercase_ :
"""simple docstring"""
def __init__( self : Dict ,lowercase__ : str ,lowercase__ : Tuple ):
__lowercase = real
if isinstance(lowercase__ ,lowercase__ ):
__l... | 624 |
'''simple docstring'''
import hashlib
import unittest
from transformers import MODEL_FOR_DEPTH_ESTIMATION_MAPPING, is_torch_available, is_vision_available
from transformers.pipelines import DepthEstimationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
... | 624 | 1 |
'''simple docstring'''
from __future__ import annotations
import json
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
lowerCAmelCase__ = {'''UserAgent''': UserAgent().random}
def _A ( A__ ):
"""simple docstring"""
__lowercase = s... | 624 |
'''simple docstring'''
from collections.abc import Callable
import numpy as np
def _A ( A__ , A__ , A__ , A__ , A__ ):
"""simple docstring"""
__lowercase = int(np.ceil((x_end - xa) / step_size ) )
__lowercase = np.zeros((n + 1,... | 624 | 1 |
'''simple docstring'''
from __future__ import annotations
lowerCAmelCase__ = []
def _A ( A__ , A__ , A__ ):
"""simple docstring"""
for i in range(len(A__ ) ):
if board[row][i] == 1:
return False
for i in range(len(A__ ) ):
if board[i][column] == 1:
... | 624 |
'''simple docstring'''
def _A ( A__ ):
"""simple docstring"""
if not nums: # Makes sure that the list is not empty
raise ValueError('''List is empty''' )
__lowercase = sum(A__ ) / len(A__ ) # Calculate the average
return sum(abs(x - average ) for x in nums ) / ... | 624 | 1 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .to... | 624 |
'''simple docstring'''
from scipy.stats import spearmanr
import datasets
lowerCAmelCase__ = '''
The Spearman rank-order correlation coefficient is a measure of the
relationship between two datasets. Like other correlation coefficients,
this one varies between -1 and +1 with 0 implying no correlation... | 624 | 1 |
'''simple docstring'''
import math
from collections.abc import Iterator
from itertools import takewhile
def _A ( A__ ):
"""simple docstring"""
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even number... | 624 |
'''simple docstring'''
import random
from typing import Any
def _A ( A__ ):
"""simple docstring"""
for _ in range(len(A__ ) ):
__lowercase = random.randint(0 , len(A__ ) - 1 )
__lowercase = random.randint(0 , len(A__ ) - 1 )
__lo... | 624 | 1 |
'''simple docstring'''
import json
import os
import unittest
from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES, BioGptTokenizer
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTesterMixin
class lowercase_ (lowerCamelCase__ ... | 624 |
'''simple docstring'''
import argparse
import json
import os
import torch
from transformers.file_utils import has_file
from diffusers import UNetaDConditionModel, UNetaDModel
lowerCAmelCase__ = False
lowerCAmelCase__ = True
lowerCAmelCase__ = False
if __name__ == "__main__":
lo... | 624 | 1 |
'''simple docstring'''
from argparse import ArgumentParser
from . import BaseTransformersCLICommand
def _A ( A__ ):
"""simple docstring"""
return DownloadCommand(args.model , args.cache_dir , args.force , args.trust_remote_code )
class lowercase_ (lowerCa... | 624 |
'''simple docstring'''
import inspect
import unittest
from math import floor
from transformers import CvtConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from ...test_con... | 624 | 1 |
'''simple docstring'''
def _A ( A__ , A__ ):
"""simple docstring"""
if number < 0 or shift_amount < 0:
raise ValueError('''both inputs must be positive integers''' )
__lowercase = str(bin(A__ ) )
binary_number += "0" * shift_amount
return binary_number
def ... | 624 |
'''simple docstring'''
def _A ( ):
"""simple docstring"""
for n in range(1 , 1000000 ):
yield n * (n + 1) // 2
def _A ( A__ ):
"""simple docstring"""
__lowercase = 1
__lowercase = 2
while i * i <= n:
__lowercase = ... | 624 | 1 |
'''simple docstring'''
import os
from collections import deque
import torch
from torch.utils.data import Dataset
class lowercase_ (lowerCamelCase__ ):
"""simple docstring"""
def __init__( self : Union[str, Any] ,lowercase__ : Dict="" ,lowercase__ : Optional[... | 624 |
'''simple docstring'''
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, rando... | 624 | 1 |
'''simple docstring'''
import os
import tempfile
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from torch import nn
from transformers import (
Adafactor,
AdamW,
get_constant_schedule,
... | 624 |
'''simple docstring'''
import argparse
from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging
logging.set_verbosity_info()
def _A ( A__ , A__ , A__ ):
"""simple docstring"""
__lowercase = Ta... | 624 | 1 |
'''simple docstring'''
from __future__ import annotations
import math
from collections.abc import Callable
def _A ( A__ , A__ , A__ , A__ = 100 , ):
"""simple docstring"""
__lowercase = x_start
__lowercase = fnc(A__ )
__lowercase ... | 624 |
'''simple docstring'''
from argparse import ArgumentParser
from . import BaseTransformersCLICommand
def _A ( A__ ):
"""simple docstring"""
return DownloadCommand(args.model , args.cache_dir , args.force , args.trust_remote_code )
class lowercase_ (lowerCa... | 624 | 1 |
'''simple docstring'''
import argparse
import json
import os
import re
import shutil
import torch
from transformers import BioGptConfig, BioGptForCausalLM
from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES
from transformers.tokenization_utils_base import TOKENIZER_CONFIG_FILE
from tr... | 624 |
'''simple docstring'''
import unittest
from pathlib import Path
from tempfile import TemporaryDirectory
from transformers import AutoConfig, TFGPTaLMHeadModel, is_keras_nlp_available, is_tf_available
from transformers.models.gpta.tokenization_gpta import GPTaTokenizer
from transformers.testing_utils import req... | 624 | 1 |
'''simple docstring'''
def _A ( A__ , A__ ):
"""simple docstring"""
if not (isinstance(A__ , A__ ) and isinstance(A__ , A__ )):
raise ValueError('''longest_common_substring() takes two strings for inputs''' )
__lowercase = len(A__ )
__lowercase... | 624 |
'''simple docstring'''
from __future__ import annotations
import matplotlib.pyplot as plt # type: ignore
import numpy
# initial triangle of Koch snowflake
lowerCAmelCase__ = numpy.array([0, 0])
lowerCAmelCase__ = numpy.array([0.5, 0.8_660_254])
lowerCAmelCase__ = numpy.array([1, 0]... | 624 | 1 |
'''simple docstring'''
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import cached_download, hf_hub_download, hf_hub_url
from PIL import Image
from transformers import DetaConfig, DetaForObjectDetection, DetaImageProcessor, SwinConfig
from transformers.... | 624 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
lowerCAmelCase__ = {'''configuration_vit_mae''': ['''VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''... | 624 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase__ = logging.get_logger(__name__)
lowerCAmelCase__ = {
'''tiiuae/falcon-40b''': '''https://huggingface.co/tiiuae/falcon-40b/resolve/main/config.json''',
'''tiiuae/fa... | 624 |
'''simple docstring'''
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
import numpy as np
# Parrameters
lowerCAmelCase__ = (720, 1280) # Height, Width
lowerCAmelCase__ = (0.4, 0.6) # if height or width lower than this scale, drop it.
lowerCAmelCase... | 624 | 1 |
'''simple docstring'''
import warnings
warnings.warn(
'''memory_utils has been reorganized to utils.memory. Import `find_executable_batchsize` from the main `__init__`: '''
'''`from accelerate import find_executable_batch_size` to avoid this warning.''',
FutureWarning,
)
| 624 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
lowerCAmelCase__ = logging.get_logger(__name__)
lowerCAmelCase__ = {'''vocab... | 624 | 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_inputs
if is_torch_... | 624 |
'''simple docstring'''
def _A ( A__ = 1000000 ):
"""simple docstring"""
__lowercase = set(range(3 , A__ , 2 ) )
primes.add(2 )
for p in range(3 , A__ , 2 ):
if p not in primes:
continue
primes.difference_update(set(range(p * p , ... | 624 | 1 |
'''simple docstring'''
# Algorithm for the pigeonhole sorting
def _A ( A__ ):
"""simple docstring"""
__lowercase = min(A__ ) # min() finds the minimum value
__lowercase = max(A__ ) # max() finds the maximum value
__lowercase = max_val ... | 624 |
'''simple docstring'''
import copy
import json
import os
import tempfile
from transformers import is_torch_available
from .test_configuration_utils import config_common_kwargs
class lowercase_ (lowerCamelCase__ ):
"""simple docstring"""
def __init__( self : Optional[Any] ... | 624 | 1 |
'''simple docstring'''
import inspect
import unittest
import warnings
from transformers import DeiTConfig
from transformers.models.auto import get_values
from transformers.testing_utils import (
require_accelerate,
require_torch,
require_torch_gpu,
require_vision,
slow,
torch_device,
)
... | 624 |
'''simple docstring'''
import re
def _A ( A__ ):
"""simple docstring"""
__lowercase = re.compile(
R'''^(?:0|94|\+94|0{2}94)''' R'''7(0|1|2|4|5|6|7|8)''' R'''(-| |)''' R'''\d{7}$''' )
return bool(re.search(A__ , A__ ) )
if __name__ == "__main__":
lowerCA... | 624 | 1 |
'''simple docstring'''
import re
import tempfile
from pathlib import Path
import pytest
import yaml
from datasets.utils.readme import ReadMe
# @pytest.fixture
# def example_yaml_structure():
lowerCAmelCase__ = yaml.safe_load(
'''\
name: ""
allow_empty: false
allow_empty_text: true
subsections:... | 624 |
'''simple docstring'''
from __future__ import annotations
from typing import Any
class lowercase_ :
"""simple docstring"""
def __init__( self : Any ,lowercase__ : int ,lowercase__ : int ,lowercase__ : float = 0 ):
__lowercase , __lowerca... | 624 | 1 |
'''simple docstring'''
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, rando... | 624 |
'''simple docstring'''
def _A ( A__ = 50 ):
"""simple docstring"""
__lowercase = [1] * (length + 1)
for row_length in range(3 , length + 1 ):
for block_length in range(3 , row_length + 1 ):
for block_start in range(row_length - block_length ):
way... | 624 | 1 |
'''simple docstring'''
import argparse
from pathlib import Path
import torch
from transformers import OPTConfig, OPTModel
from transformers.utils import logging
logging.set_verbosity_info()
lowerCAmelCase__ = logging.get_logger(__name__)
def _A ( A__ ):
"""simple docstring"""
_... | 624 |
'''simple docstring'''
import argparse
import logging
from collections import namedtuple
import torch
from model_bertabs import BertAbsSummarizer
from models.model_builder import AbsSummarizer # The authors' implementation
from transformers import BertTokenizer
logging.basicConfig(level=logging.INFO)
lower... | 624 | 1 |
'''simple docstring'''
import math
import numpy as np
import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute
def _A ( A__ = 3 ):
"""simple docstring"""
if isinstance(A__ , A__ ):
raise TypeError('''number of qubits must be a integer.'''... | 624 |
'''simple docstring'''
import hashlib
import unittest
from transformers import MODEL_FOR_DEPTH_ESTIMATION_MAPPING, is_torch_available, is_vision_available
from transformers.pipelines import DepthEstimationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
... | 624 | 1 |
'''simple docstring'''
import inspect
import unittest
import warnings
from math import ceil, floor
from transformers import LevitConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils im... | 624 |
'''simple docstring'''
from collections.abc import Callable
import numpy as np
def _A ( A__ , A__ , A__ , A__ , A__ ):
"""simple docstring"""
__lowercase = int(np.ceil((x_end - xa) / step_size ) )
__lowercase = np.zeros((n + 1,... | 624 | 1 |
'''simple docstring'''
from math import factorial
lowerCAmelCase__ = {str(digit): factorial(digit) for digit in range(10)}
def _A ( A__ ):
"""simple docstring"""
if not isinstance(A__ , A__ ):
raise TypeError('''Parameter number must be int''' )
if number < 0:
rai... | 624 |
'''simple docstring'''
def _A ( A__ ):
"""simple docstring"""
if not nums: # Makes sure that the list is not empty
raise ValueError('''List is empty''' )
__lowercase = sum(A__ ) / len(A__ ) # Calculate the average
return sum(abs(x - average ) for x in nums ) / ... | 624 | 1 |
'''simple docstring'''
def _A ( A__ = 3 , A__ = 7 , A__ = 1000000 ):
"""simple docstring"""
__lowercase = 0
__lowercase = 1
for current_denominator in range(1 , limit + 1 ):
__lowercase = current_denominator * numerator ... | 624 |
'''simple docstring'''
from scipy.stats import spearmanr
import datasets
lowerCAmelCase__ = '''
The Spearman rank-order correlation coefficient is a measure of the
relationship between two datasets. Like other correlation coefficients,
this one varies between -1 and +1 with 0 implying no correlation... | 624 | 1 |
'''simple docstring'''
# 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 e... | 624 |
'''simple docstring'''
import random
from typing import Any
def _A ( A__ ):
"""simple docstring"""
for _ in range(len(A__ ) ):
__lowercase = random.randint(0 , len(A__ ) - 1 )
__lowercase = random.randint(0 , len(A__ ) - 1 )
__lo... | 624 | 1 |
'''simple docstring'''
# Lint as: python3
import os
import re
import urllib.parse
from pathlib import Path
from typing import Callable, List, Optional, Union
from zipfile import ZipFile
from ..utils.file_utils import cached_path, hf_github_url
from ..utils.logging import get_logger
from ..utils.version import ... | 624 |
'''simple docstring'''
import argparse
import json
import os
import torch
from transformers.file_utils import has_file
from diffusers import UNetaDConditionModel, UNetaDModel
lowerCAmelCase__ = False
lowerCAmelCase__ = True
lowerCAmelCase__ = False
if __name__ == "__main__":
lo... | 624 | 1 |
'''simple docstring'''
from typing import Any, Callable, Dict, List, Optional, Union
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DiffusionPipeline,
LMSDiscreteScheduler,
PNDMScheduler,
Stab... | 624 |
'''simple docstring'''
import inspect
import unittest
from math import floor
from transformers import CvtConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from ...test_con... | 624 | 1 |
'''simple docstring'''
import argparse
import torch
from transformers import YosoConfig, YosoForMaskedLM
def _A ( A__ ):
"""simple docstring"""
if "model" in orig_key:
__lowercase = orig_key.replace('''model.''' , '''''' )
if "norm1" in orig_key:
__lowercase ... | 624 |
'''simple docstring'''
def _A ( ):
"""simple docstring"""
for n in range(1 , 1000000 ):
yield n * (n + 1) // 2
def _A ( A__ ):
"""simple docstring"""
__lowercase = 1
__lowercase = 2
while i * i <= n:
__lowercase = ... | 624 | 1 |
'''simple docstring'''
def _A ( A__ ):
"""simple docstring"""
if not nums: # Makes sure that the list is not empty
raise ValueError('''List is empty''' )
__lowercase = sum(A__ ) / len(A__ ) # Calculate the average
return sum(abs(x - average ) for x in nums ) / ... | 624 |
'''simple docstring'''
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, rando... | 624 | 1 |
'''simple docstring'''
def _A ( A__ , A__ ):
"""simple docstring"""
return price * (1 + tax_rate)
if __name__ == "__main__":
print(f'{price_plus_tax(100, 0.25) = }')
print(f'{price_plus_tax(125.50, 0.05) = }')
| 624 |
'''simple docstring'''
import argparse
from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging
logging.set_verbosity_info()
def _A ( A__ , A__ , A__ ):
"""simple docstring"""
__lowercase = Ta... | 624 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase__ = logging.get_logger(__name__)
lowerCAmelCase__ = {
# See all MEGATRON_BERT models at https://huggingface.co/models?filter=bert
}
class lowercase_ (lowerC... | 624 |
'''simple docstring'''
from argparse import ArgumentParser
from . import BaseTransformersCLICommand
def _A ( A__ ):
"""simple docstring"""
return DownloadCommand(args.model , args.cache_dir , args.force , args.trust_remote_code )
class lowercase_ (lowerCa... | 624 | 1 |
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