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 sklearn.metrics import recall_score
import datasets
_lowercase = """\nRecall is the fraction of the positive examples that were correctly labeled by the model as positive. It can be computed with the equation:\nRecall = TP / (TP + FN)\nWhere TP is the true positives and FN is th... | 356 |
"""simple docstring"""
def a ( __snake_case : int, __snake_case : list ):
'''simple docstring'''
_enforce_args(__snake_case, __snake_case )
if n == 0:
return 0
UpperCAmelCase_ :Optional[int] = float('''-inf''' )
for i in range(1, ... | 608 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
__A = {
'configuration_groupvit': [
'GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP',
'GroupViTConfig',
'GroupViTOnnxConfig',
'GroupViTText... | 484 |
"""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 _snake_case ( A__ ):
'''simple docstring'''
def __init__( self : Optional[Any] , sna... | 608 | 0 |
'''simple docstring'''
def snake_case_ ( SCREAMING_SNAKE_CASE__ = 10_00 ):
'''simple docstring'''
_snake_case = -1
_snake_case = 0
for a in range(1 , n // 3 ):
# Solving the two equations a**2+b**2=c**2 and a+b+c=N eliminating c
_snake_case... | 672 |
"""simple docstring"""
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
ChannelDimension,
Image... | 608 | 0 |
"""simple docstring"""
from collections import Counter
from pathlib import Path
from typing import Optional, Tuple
import yaml
class __UpperCAmelCase ( yaml.SafeLoader ):
def UpperCAmelCase ( self : Union[str, Any] , a_ : int ) -> List[str]:
'''sim... | 642 |
"""simple docstring"""
import torch
from diffusers import CMStochasticIterativeScheduler
from .test_schedulers import SchedulerCommonTest
class _snake_case ( A__ ):
'''simple docstring'''
UpperCamelCase__ =(CMStochasticIterativeScheduler,)
UpperCamelCase__ =10
... | 608 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
lowerCamelCase__ = {'''configuration_encoder_decoder''': ['''EncoderDecoderConfig''']}
try:
if not is_torch_available... | 122 |
"""simple docstring"""
from __future__ import annotations
from math import pi, sqrt
def a ( __snake_case : float, __snake_case : float ):
'''simple docstring'''
if inductance <= 0:
raise ValueError('''Inductance cannot be 0 or negative''' )
elif capacitance... | 608 | 0 |
'''simple docstring'''
from math import isqrt
def _A ( UpperCAmelCase ):
'''simple docstring'''
A__ = [True] * max_number
for i in range(2 ,isqrt(max_number - 1 ) + 1 ):
if is_prime[i]:
for j in range(i**2 ,__snake_cas... | 531 |
"""simple docstring"""
import math
import numpy as np
import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute
def a ( __snake_case : int = 3 ):
'''simple docstring'''
if isinstance(__snake_case, __snake_case ):
raise TypeErro... | 608 | 0 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokenization_realm import RealmTokenizer
_lowerCAmelCase ... | 113 |
"""simple docstring"""
import os
import sys
import unittest
__lowerCamelCase = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, "utils"))
import check_dummies # noqa: E402
from check_dummies import create_dummy_files, create_d... | 608 | 0 |
'''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 transfor... | 320 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
__lowerCamelCase = {
"configuration_groupvit": [
"GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP",
"GroupViTConfig",
"Gro... | 608 | 0 |
from collections.abc import Sequence
def snake_case (UpperCAmelCase__ , UpperCAmelCase__ ) -> Optional[int]:
return sum(c * (x**i) for i, c in enumerate(__snake_case ) )
def snake_case (UpperCAmelCase__ , UpperCAmelCase__ ) -> Any:
... | 57 |
"""simple docstring"""
import shutil
import tempfile
import unittest
from unittest.mock import patch
from transformers import (
DefaultFlowCallback,
IntervalStrategy,
PrinterCallback,
ProgressCallback,
Trainer,
TrainerCallback,
TrainingArguments,
is_torch_available,
)
from transfo... | 608 | 0 |
from typing import Dict
import numpy as np
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline, PipelineException
if is_tf_available():
import tensorflow as tf
from ..tf_utils import stable_softmax... | 33 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
__lowerCamelCase = {"configuration_glpn": ["GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP", "GLPNConfig"]}
try:
if not is_vision_available():... | 608 | 0 |
import argparse
import json
import logging
import os
import shutil
import sys
import tempfile
import unittest
from unittest import mock
import torch
from accelerate.utils import write_basic_config
from transformers.testing_utils import TestCasePlus, get_gpu_count, run_command, slow, torch_device
f... | 684 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__lowerCamelCase = logging.get_logger(__name__)
__lowerCamelCase = {
"roberta-base":... | 608 | 0 |
'''simple docstring'''
import unittest
from transformers import SPIECE_UNDERLINE, XLNetTokenizer, XLNetTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
_lowercase = get_tests_dir... | 356 |
"""simple docstring"""
__lowerCamelCase = {
"A": ["B", "C", "E"],
"B": ["A", "D", "E"],
"C": ["A", "F", "G"],
"D": ["B"],
"E": ["A", "B", "D"],
"F": ["C"],
"G": ["C"],
}
def a ( __snake_case : dict, __snake_case : str, __snake_case : Unio... | 608 | 0 |
__A = [
[0, 16, 13, 0, 0, 0],
[0, 0, 10, 12, 0, 0],
[0, 4, 0, 0, 14, 0],
[0, 0, 9, 0, 0, 20],
[0, 0, 0, 7, 0, 4],
[0, 0, 0, 0, 0, 0],
]
def __A ( _lowercase , _lowercase , _lowercase , _lowercase ):
'''simple docstring'''
... | 484 |
"""simple docstring"""
import json
import os
import tempfile
from unittest.mock import patch
import torch
from torch.utils.data import DataLoader, TensorDataset
from accelerate import DistributedType, infer_auto_device_map, init_empty_weights
from accelerate.accelerator import Accelerator
from accelerate.state ... | 608 | 0 |
'''simple docstring'''
def snake_case_ ( SCREAMING_SNAKE_CASE__ = 10 ):
'''simple docstring'''
if not isinstance(__snake_case , __snake_case ) or n < 0:
raise ValueError("Invalid input" )
_snake_case = 10**n
_snake_case = 2_84_33 * (pow(2... | 672 |
"""simple docstring"""
from __future__ import annotations
from collections.abc import MutableSequence
class _snake_case :
'''simple docstring'''
def __init__( self : Dict , snake_case : int , snake_case : MutableSequence[float] ):
if len(snake_case ) !=... | 608 | 0 |
"""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 torc... | 642 |
"""simple docstring"""
from collections.abc import Callable
import numpy as np
def a ( __snake_case : Callable, __snake_case : float, __snake_case : float, __snake_case : float, __snake_case : float ):
'''simple docstring'''
UpperCAmelCase_ ... | 608 | 0 |
import warnings
from ...utils import logging
from .image_processing_beit import BeitImageProcessor
lowerCamelCase__ = logging.get_logger(__name__)
class __magic_name__ (A__ ):
def __init__( self , *_a , **_a ) -> str:
warnings.warn(
"The class Beit... | 122 |
"""simple docstring"""
import math
import torch
from torch import nn
from ..configuration_utils import ConfigMixin, register_to_config
from .attention_processor import Attention
from .embeddings import get_timestep_embedding
from .modeling_utils import ModelMixin
class _snake_case ( A__ , A__... | 608 | 0 |
'''simple docstring'''
import inspect
import unittest
class _snake_case( unittest.TestCase ):
def _UpperCamelCase (self : Tuple ) -> Optional[int]:
"""simple docstring"""
try:
import diffusers # noqa: F401
except ImportError:
assert ... | 531 |
"""simple docstring"""
import os
import time
import pytest
from datasets.utils.filelock import FileLock, Timeout
def a ( __snake_case : Union[str, Any] ):
'''simple docstring'''
UpperCAmelCase_ :str = FileLock(str(tmpdir / '''foo.lock''' ) )
UpperCAmelCase_ ... | 608 | 0 |
from typing import Callable, Optional
from .. import Features
from ..packaged_modules.generator.generator import Generator
from .abc import AbstractDatasetInputStream
class __UpperCamelCase ( A__ ):
'''simple docstring'''
def __init__( self , lowerCamelCase__ , lowerCam... | 113 |
"""simple docstring"""
import argparse
from typing import Dict
import tensorflow as tf
import torch
from tqdm import tqdm
from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration
__lowerCamelCase = [
# tf -> hf
("/", "."),
("layer_", "layers."),
("kerne... | 608 | 0 |
'''simple docstring'''
from math import loga
def UpperCAmelCase ( UpperCAmelCase__ : int):
if a < 0:
raise ValueError('Input value must be a positive integer')
elif isinstance(__snake_case , __snake_case):
raise TypeError('Input value mu... | 320 |
"""simple docstring"""
import socket
def a ( ):
'''simple docstring'''
UpperCAmelCase_ :Union[str, Any] = socket.socket(socket.AF_INET, socket.SOCK_STREAM )
UpperCAmelCase_ :int = socket.gethostname()
UpperCAmelCase_ :List[Any] ... | 608 | 0 |
import unittest
from transformers import AutoTokenizer, NystromformerConfig, 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, random_at... | 57 |
"""simple docstring"""
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available
if is_vision_available():
from PIL import Image
from transformers import AutoProcessor, Be... | 608 | 0 |
import inspect
from typing import Callable, List, Optional, Union
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextModel,
CLIPTokenizer,
WhisperForConditionalGeneration,
WhisperProcessor,
)
from diffusers import (
AutoencoderKL,
DDIMScheduler,
Di... | 33 |
"""simple docstring"""
from typing import Optional, Tuple
import jax
import jax.numpy as jnp
from flax import linen as nn
from flax.core.frozen_dict import FrozenDict
from transformers import CLIPConfig, FlaxPreTrainedModel
from transformers.models.clip.modeling_flax_clip import FlaxCLIPVisionModule
def ... | 608 | 0 |
import json
import os
import shutil
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import AutoConfig, BertConfig, GPTaConfig
from transform... | 684 |
"""simple docstring"""
import copy
import os
from typing import TYPE_CHECKING, List, Union
if TYPE_CHECKING:
pass
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowerCamelCase = logging.get_logger(__name__)
__lowerCamelCase = {
"kakaobrai... | 608 | 0 |
'''simple docstring'''
import datetime
import platform
import subprocess
from typing import Optional, Tuple, Union
import numpy as np
def lowerCamelCase__ ( a , a ):
__snake_case = f'{sampling_rate}'
__snake_case = '''1'''
__snake_case = '''f32le'''
... | 356 |
"""simple docstring"""
def a ( __snake_case : int, __snake_case : list ):
'''simple docstring'''
_enforce_args(__snake_case, __snake_case )
if n == 0:
return 0
UpperCAmelCase_ :Optional[int] = float('''-inf''' )
for i in range(1, ... | 608 | 0 |
import importlib.metadata
import operator
import re
import sys
from typing import Optional
from packaging import version
__A = {
'<': operator.lt,
'<=': operator.le,
'==': operator.eq,
'!=': operator.ne,
'>=': operator.ge,
'>': operator.gt,
}
def __A ( _lowercase ... | 484 |
"""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 _snake_case ( A__ ):
'''simple docstring'''
def __init__( self : Optional[Any] , sna... | 608 | 0 |
'''simple docstring'''
import argparse
from typing import Dict
import tensorflow as tf
import torch
from tqdm import tqdm
from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration
__magic_name__ : Union[str, Any] = [
# tf -> hf
("""/""", """."""),
("""laye... | 672 |
"""simple docstring"""
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
ChannelDimension,
Image... | 608 | 0 |
"""simple docstring"""
def lowercase__ ( lowerCAmelCase__ : int = 1_0_0_0 ) -> str:
'''simple docstring'''
a__ : Tuple = 2**power
a__ : Union[str, Any] = str(__snake_case )
a__ : Tuple = list(__snake_case )
a__ : Tuple = 0
for i in list_num:
... | 642 |
"""simple docstring"""
import torch
from diffusers import CMStochasticIterativeScheduler
from .test_schedulers import SchedulerCommonTest
class _snake_case ( A__ ):
'''simple docstring'''
UpperCamelCase__ =(CMStochasticIterativeScheduler,)
UpperCamelCase__ =10
... | 608 | 0 |
import sys
import webbrowser
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
if __name__ == "__main__":
print('''Googling.....''')
lowerCamelCase__ = '''https://www.google.com/search?q=''' + ''' '''.join(sys.argv[1:])
lowerCamelCase__ = requests.g... | 122 |
"""simple docstring"""
from __future__ import annotations
from math import pi, sqrt
def a ( __snake_case : float, __snake_case : float ):
'''simple docstring'''
if inductance <= 0:
raise ValueError('''Inductance cannot be 0 or negative''' )
elif capacitance... | 608 | 0 |
'''simple docstring'''
from __future__ import annotations
from math import pi
def _A ( UpperCAmelCase ,UpperCAmelCase ,UpperCAmelCase ):
'''simple docstring'''
if (inductance, frequency, reactance).count(0 ) != 1:
raise ValueError('One and only... | 531 |
"""simple docstring"""
import math
import numpy as np
import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute
def a ( __snake_case : int = 3 ):
'''simple docstring'''
if isinstance(__snake_case, __snake_case ):
raise TypeErro... | 608 | 0 |
import baseaa
def _A ( SCREAMING_SNAKE_CASE ):
return baseaa.aaaencode(string.encode("utf-8" ) )
def _A ( SCREAMING_SNAKE_CASE ):
return baseaa.aaadecode(__snake_case ).decode("utf-8" )
if __name__ == "__main__":
import doctest
doctest.testmod() | 113 |
"""simple docstring"""
import os
import sys
import unittest
__lowerCamelCase = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, "utils"))
import check_dummies # noqa: E402
from check_dummies import create_dummy_files, create_d... | 608 | 0 |
'''simple docstring'''
import math
def UpperCAmelCase ( UpperCAmelCase__ : int):
assert isinstance(__snake_case , __snake_case) and (
number >= 0
), "'number' must been an int and positive"
if 1 < number < 4:
# 2 and 3 are primes
r... | 320 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
__lowerCamelCase = {
"configuration_groupvit": [
"GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP",
"GroupViTConfig",
"Gro... | 608 | 0 |
def snake_case (UpperCAmelCase__ ) -> List[str]:
if len(__snake_case ) <= 1:
return lst
UpperCamelCase_: Union[str, Any] = 1
while i < len(__snake_case ):
if lst[i - 1] <= lst[i]:
i += 1
else:
UpperCamelCase_: Tupl... | 57 |
"""simple docstring"""
import shutil
import tempfile
import unittest
from unittest.mock import patch
from transformers import (
DefaultFlowCallback,
IntervalStrategy,
PrinterCallback,
ProgressCallback,
Trainer,
TrainerCallback,
TrainingArguments,
is_torch_available,
)
from transfo... | 608 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
lowerCamelCase__ : str = {"""configuration_glpn""": ["""GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GLPNConfig"""]}
try:
if not is_vision_... | 33 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
__lowerCamelCase = {"configuration_glpn": ["GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP", "GLPNConfig"]}
try:
if not is_vision_available():... | 608 | 0 |
import tempfile
import unittest
from transformers import TaConfig, is_torch_available
from transformers.testing_utils import (
require_sentencepiece,
require_tokenizers,
require_torch,
slow,
torch_device,
)
from ...generation.test_utils import GenerationTesterMixin
from ...test_... | 684 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__lowerCamelCase = logging.get_logger(__name__)
__lowerCamelCase = {
"roberta-base":... | 608 | 0 |
'''simple docstring'''
import unittest
from transformers import XLMConfig, 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 im... | 356 |
"""simple docstring"""
__lowerCamelCase = {
"A": ["B", "C", "E"],
"B": ["A", "D", "E"],
"C": ["A", "F", "G"],
"D": ["B"],
"E": ["A", "B", "D"],
"F": ["C"],
"G": ["C"],
}
def a ( __snake_case : dict, __snake_case : str, __snake_case : Unio... | 608 | 0 |
from collections import OrderedDict
from typing import Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...feature_extraction_utils import FeatureExtractionMixin
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...tokenization_u... | 484 |
"""simple docstring"""
import json
import os
import tempfile
from unittest.mock import patch
import torch
from torch.utils.data import DataLoader, TensorDataset
from accelerate import DistributedType, infer_auto_device_map, init_empty_weights
from accelerate.accelerator import Accelerator
from accelerate.state ... | 608 | 0 |
'''simple docstring'''
import pytest
import datasets.config
from datasets.utils.info_utils import is_small_dataset
@pytest.mark.parametrize("dataset_size" , [None, 4_00 * 2**20, 6_00 * 2**20] )
@pytest.mark.parametrize("input_in_memory_max_size" , ["default", 0, 1_00 * 2**20, 9... | 672 |
"""simple docstring"""
from __future__ import annotations
from collections.abc import MutableSequence
class _snake_case :
'''simple docstring'''
def __init__( self : Dict , snake_case : int , snake_case : MutableSequence[float] ):
if len(snake_case ) !=... | 608 | 0 |
"""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_to... | 642 |
"""simple docstring"""
from collections.abc import Callable
import numpy as np
def a ( __snake_case : Callable, __snake_case : float, __snake_case : float, __snake_case : float, __snake_case : float ):
'''simple docstring'''
UpperCAmelCase_ ... | 608 | 0 |
from collections import namedtuple
import requests
from lxml import html # type: ignore
lowerCamelCase__ = namedtuple('''covid_data''', '''cases deaths recovered''')
def A(__a: str = "https://www.worldometers.info/coronavirus/" ):
lowerCAmelCase_ = '''//div[@class = "mai... | 122 |
"""simple docstring"""
import math
import torch
from torch import nn
from ..configuration_utils import ConfigMixin, register_to_config
from .attention_processor import Attention
from .embeddings import get_timestep_embedding
from .modeling_utils import ModelMixin
class _snake_case ( A__ , A__... | 608 | 0 |
'''simple docstring'''
from __future__ import annotations
# This is the precision for this function which can be altered.
# It is recommended for users to keep this number greater than or equal to 10.
lowerCAmelCase_ = 1_0
def _A ( UpperCAmelCase ,UpperCAmelCase ,Up... | 531 |
"""simple docstring"""
import os
import time
import pytest
from datasets.utils.filelock import FileLock, Timeout
def a ( __snake_case : Union[str, Any] ):
'''simple docstring'''
UpperCAmelCase_ :str = FileLock(str(tmpdir / '''foo.lock''' ) )
UpperCAmelCase_ ... | 608 | 0 |
from collections.abc import Callable
import numpy as np
def _A ( SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE ):
UpperCAmelCase__: str = int(np.ceil((x_end - xa) / step_size ) )
UpperCAmelCase__: int = np.zero... | 113 |
"""simple docstring"""
import argparse
from typing import Dict
import tensorflow as tf
import torch
from tqdm import tqdm
from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration
__lowerCamelCase = [
# tf -> hf
("/", "."),
("layer_", "layers."),
("kerne... | 608 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
A = {'configuration_mra': ['MRA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MraConfig']}
tr... | 320 |
"""simple docstring"""
import socket
def a ( ):
'''simple docstring'''
UpperCAmelCase_ :Union[str, Any] = socket.socket(socket.AF_INET, socket.SOCK_STREAM )
UpperCAmelCase_ :int = socket.gethostname()
UpperCAmelCase_ :List[Any] ... | 608 | 0 |
import unittest
import numpy as np
import timeout_decorator # noqa
from transformers import BlenderbotSmallConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common ... | 57 |
"""simple docstring"""
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available
if is_vision_available():
from PIL import Image
from transformers import AutoProcessor, Be... | 608 | 0 |
import re
from flax.core.frozen_dict import freeze
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.experimental import PartitionSpec as P
# Sentinels
lowerCamelCase__ : Any = object()
# For specifying empty leaf dict `{}`
lowerCamelCase__ : Li... | 33 |
"""simple docstring"""
from typing import Optional, Tuple
import jax
import jax.numpy as jnp
from flax import linen as nn
from flax.core.frozen_dict import FrozenDict
from transformers import CLIPConfig, FlaxPreTrainedModel
from transformers.models.clip.modeling_flax_clip import FlaxCLIPVisionModule
def ... | 608 | 0 |
import inspect
import unittest
import warnings
from math import ceil, floor
from transformers import LevitConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torc... | 684 |
"""simple docstring"""
import copy
import os
from typing import TYPE_CHECKING, List, Union
if TYPE_CHECKING:
pass
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowerCamelCase = logging.get_logger(__name__)
__lowerCamelCase = {
"kakaobrai... | 608 | 0 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
_lowercase = """▁"""
_lowercase = {"""vocab_file""": """spiece.model"""}
_low... | 356 |
"""simple docstring"""
def a ( __snake_case : int, __snake_case : list ):
'''simple docstring'''
_enforce_args(__snake_case, __snake_case )
if n == 0:
return 0
UpperCAmelCase_ :Optional[int] = float('''-inf''' )
for i in range(1, ... | 608 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
__A = {
'configuration_xlm': ['XLM_PRETRAINED_CONFIG_ARCHIVE_MAP', 'XLMConfig', 'XLMOnnxConfig'],
'tokenization_xlm': ['XLMTokenizer'],
}
try:
if not is_t... | 484 |
"""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 _snake_case ( A__ ):
'''simple docstring'''
def __init__( self : Optional[Any] , sna... | 608 | 0 |
'''simple docstring'''
import argparse
import torch
# Step 1. clone https://github.com/microsoft/unilm
# Step 2. git checkout to https://github.com/microsoft/unilm/commit/b94ec76c36f02fb2b0bf0dcb0b8554a2185173cd
# Step 3. cd unilm
# Step 4. ln -s $(realpath wavlm/modules.py) ./ # create simlink
# import cla... | 672 |
"""simple docstring"""
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
ChannelDimension,
Image... | 608 | 0 |
"""simple docstring"""
import copy
from typing import Any, Dict, List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
... | 642 |
"""simple docstring"""
import torch
from diffusers import CMStochasticIterativeScheduler
from .test_schedulers import SchedulerCommonTest
class _snake_case ( A__ ):
'''simple docstring'''
UpperCamelCase__ =(CMStochasticIterativeScheduler,)
UpperCamelCase__ =10
... | 608 | 0 |
def A(__a: int ):
if isinstance(__snake_case , __snake_case ):
raise TypeError("\'float\' object cannot be interpreted as an integer" )
if isinstance(__snake_case , __snake_case ):
raise TypeError("\'str\' object cannot be interpreted as an integer" )
if nu... | 122 |
"""simple docstring"""
from __future__ import annotations
from math import pi, sqrt
def a ( __snake_case : float, __snake_case : float ):
'''simple docstring'''
if inductance <= 0:
raise ValueError('''Inductance cannot be 0 or negative''' )
elif capacitance... | 608 | 0 |
'''simple docstring'''
import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
FEATURE_EXTRACTOR_MAPPING,
AutoConfig,
AutoFeatureExtractor,
WavaVecaConfig,
WavaVecaFeatureExtractor,
)
from transfo... | 531 |
"""simple docstring"""
import math
import numpy as np
import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute
def a ( __snake_case : int = 3 ):
'''simple docstring'''
if isinstance(__snake_case, __snake_case ):
raise TypeErro... | 608 | 0 |
from __future__ import annotations
import string
from itertools import cycle, product
from pathlib import Path
_lowerCAmelCase : Union[str, Any] =(
string.ascii_letters + string.digits + string.punctuation + string.whitespace
)
_lowerCAmelCase : str =[ord(letter) for letter in string.ascii_low... | 113 |
"""simple docstring"""
import os
import sys
import unittest
__lowerCamelCase = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, "utils"))
import check_dummies # noqa: E402
from check_dummies import create_dummy_files, create_d... | 608 | 0 |
'''simple docstring'''
def UpperCAmelCase ( UpperCAmelCase__ : list):
def merge(UpperCAmelCase__ : list , UpperCAmelCase__ : list) -> list:
def _merge():
while left and right:
yield (left if left[0] <= right[0] else ri... | 320 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
__lowerCamelCase = {
"configuration_groupvit": [
"GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP",
"GroupViTConfig",
"Gro... | 608 | 0 |
import tempfile
import unittest
from make_student import create_student_by_copying_alternating_layers
from transformers import AutoConfig
from transformers.file_utils import cached_property
from transformers.testing_utils import require_torch
A_ : Tuple = 'sshleifer/... | 57 |
"""simple docstring"""
import shutil
import tempfile
import unittest
from unittest.mock import patch
from transformers import (
DefaultFlowCallback,
IntervalStrategy,
PrinterCallback,
ProgressCallback,
Trainer,
TrainerCallback,
TrainingArguments,
is_torch_available,
)
from transfo... | 608 | 0 |
import argparse
import collections
import torch
from flax import traverse_util
from tax import checkpoints
from transformers import TaConfig, TaEncoderModel, TaForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
def SCREAMING_SNAKE_CASE ( __... | 33 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
__lowerCamelCase = {"configuration_glpn": ["GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP", "GLPNConfig"]}
try:
if not is_vision_available():... | 608 | 0 |
import unittest
from transformers import load_tool
from transformers.utils import is_torch_available
if is_torch_available():
import torch
from transformers.testing_utils import require_torch
from .test_tools_common import ToolTesterMixin
@require_torch
class __SCREAMING_SNAKE_CASE ... | 684 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__lowerCamelCase = logging.get_logger(__name__)
__lowerCamelCase = {
"roberta-base":... | 608 | 0 |
'''simple docstring'''
import os
from typing import List, Optional, Union
from ...image_processing_utils import BatchFeature
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrate... | 356 |
"""simple docstring"""
__lowerCamelCase = {
"A": ["B", "C", "E"],
"B": ["A", "D", "E"],
"C": ["A", "F", "G"],
"D": ["B"],
"E": ["A", "B", "D"],
"F": ["C"],
"G": ["C"],
}
def a ( __snake_case : dict, __snake_case : str, __snake_case : Unio... | 608 | 0 |
import warnings
from ...utils import logging
from .image_processing_chinese_clip import ChineseCLIPImageProcessor
__A = logging.get_logger(__name__)
class SCREAMING_SNAKE_CASE ( A__ ):
"""simple docstring"""
def __init__( self: List[Any] , *__A: str , **__A... | 484 |
"""simple docstring"""
import json
import os
import tempfile
from unittest.mock import patch
import torch
from torch.utils.data import DataLoader, TensorDataset
from accelerate import DistributedType, infer_auto_device_map, init_empty_weights
from accelerate.accelerator import Accelerator
from accelerate.state ... | 608 | 0 |
'''simple docstring'''
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__magic_name__ : str = logging.get_logger(__name__)
__magic_name__ : Any = {
"""facebook/wav2vec2-base-960h""": """https://huggingface.co/facebo... | 672 |
"""simple docstring"""
from __future__ import annotations
from collections.abc import MutableSequence
class _snake_case :
'''simple docstring'''
def __init__( self : Dict , snake_case : int , snake_case : MutableSequence[float] ):
if len(snake_case ) !=... | 608 | 0 |
"""simple docstring"""
import math
def lowercase__ ( lowerCAmelCase__ : int ) -> str:
'''simple docstring'''
a__ : Union[str, Any] = 0
a__ : Optional[Any] = 0
while num > 0:
a__ : Dict = num % 8
a__ : Union[str, Any] = octal + (remaind... | 642 |
"""simple docstring"""
from collections.abc import Callable
import numpy as np
def a ( __snake_case : Callable, __snake_case : float, __snake_case : float, __snake_case : float, __snake_case : float ):
'''simple docstring'''
UpperCAmelCase_ ... | 608 | 0 |
import contextlib
import csv
import json
import os
import sqlitea
import tarfile
import textwrap
import zipfile
import pyarrow as pa
import pyarrow.parquet as pq
import pytest
import datasets
import datasets.config
@pytest.fixture(scope="session" )
def A():
lowerCAmelCase_ = 10
lower... | 122 |
"""simple docstring"""
import math
import torch
from torch import nn
from ..configuration_utils import ConfigMixin, register_to_config
from .attention_processor import Attention
from .embeddings import get_timestep_embedding
from .modeling_utils import ModelMixin
class _snake_case ( A__ , A__... | 608 | 0 |
"""simple docstring"""
import unittest
from transformers import GPTNeoXJapaneseConfig, is_torch_available
from transformers.models.gpt_neox_japanese.tokenization_gpt_neox_japanese import GPTNeoXJapaneseTokenizer
from transformers.testing_utils import require_torch, slow, torch_device
from ...test... | 609 |
"""simple docstring"""
from __future__ import annotations
def UpperCAmelCase__ (snake_case__ : int ):
"""simple docstring"""
_snake_case : int = str(snake_case__ )
return len(snake_case__ ) == 9 and set(snake_case__ ) == set("""123456789... | 609 | 1 |
"""simple docstring"""
from __future__ import annotations
def UpperCAmelCase__ (snake_case__ : list ):
"""simple docstring"""
if not nums:
raise ValueError("""List is empty""" )
return sum(snake_case__ ) / len(snake_case__ )
if __name__ == "__ma... | 609 |
"""simple docstring"""
import argparse
from collections import defaultdict
def UpperCAmelCase__ (snake_case__ : Tuple , snake_case__ : Any , snake_case__ : List[str] , snake_case__ : Union[str, Any] , snake_case__ : str ):
"""simple doc... | 609 | 1 |
"""simple docstring"""
import unittest
from pathlib import Path
from shutil import copyfile
from transformers import SPIECE_UNDERLINE, is_sentencepiece_available
from transformers.models.speech_to_text import SpeechaTextTokenizer
from transformers.models.speech_to_text.tokenization_speech_to_text ... | 609 |
"""simple docstring"""
import flax.linen as nn
import jax.numpy as jnp
from .attention_flax import FlaxTransformeraDModel
from .resnet_flax import FlaxDownsampleaD, FlaxResnetBlockaD, FlaxUpsampleaD
class lowercase( nn.Module ):
'''simple docstring'''
... | 609 | 1 |
"""simple docstring"""
def UpperCAmelCase__ (snake_case__ : Optional[Any] ):
"""simple docstring"""
return [
{
0: [1, 2],
1: [0, 2],
2: [0, 1, 3, 5],
3: [2, 4],
4: [3],
5: [2, 6, 8],
6: [5, 7],
... | 609 |
"""simple docstring"""
def UpperCAmelCase__ (snake_case__ : list[int] ):
"""simple docstring"""
if not numbers:
return 0
if not isinstance(snake_case__ , (list, tuple) ) or not all(
isinstance(snake_case__ , snake_case__ ) for number in num... | 609 | 1 |
"""simple docstring"""
def UpperCAmelCase__ (snake_case__ : float , snake_case__ : float , snake_case__ : int ):
"""simple docstring"""
if principal <= 0:
raise Exception("""Principal borrowed must be > 0""" )
if rate_per_annum < 0:
raise E... | 609 |
"""simple docstring"""
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
HubertConfig,
HubertForCTC,
HubertModel,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaProcessor,
... | 609 | 1 |
"""simple docstring"""
import math
def UpperCAmelCase__ (snake_case__ : int ):
"""simple docstring"""
_snake_case : Optional[int] = [True] * n
_snake_case : Optional[Any] = False
_snake_case : int = False
_snake_c... | 609 |
"""simple docstring"""
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
import numpy as np
# Parrameters
A_ = (7_20, 12_80) # Height, Width
A_ = (0.4, 0.6) # if height or width lower than this scale, drop it.
A_ =... | 609 | 1 |
"""simple docstring"""
import io
import os
import unicodedata
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
A_ = logging.get_logger(__name__)
A_ = ... | 609 |
"""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_scheduler''')... | 609 | 1 |
"""simple docstring"""
from __future__ import annotations
from PIL import Image
# Define glider example
A_ = [
[0, 1, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0, 0],
[1, 1, 1, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, ... | 609 |
"""simple docstring"""
from typing import Any, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from torch.utils.data import DistributedSampler, RandomSampler
from transformers import PreTrainedModel, Trainer, logging
from transformers.integrations import is_fairscale_availab... | 609 | 1 |
"""simple docstring"""
import unittest
import numpy as np
from diffusers import LMSDiscreteScheduler, OnnxStableDiffusionInpaintPipeline
from diffusers.utils.testing_utils import (
is_onnx_available,
load_image,
nightly,
require_onnxruntime,
require_torch_gpu,
)
from .... | 609 |
"""simple docstring"""
from __future__ import annotations
A_ = 10
def UpperCAmelCase__ (snake_case__ : list[int] ):
"""simple docstring"""
_snake_case : Any = 1
_snake_case : Dict = max(snake_case__ )
while plac... | 609 | 1 |
"""simple docstring"""
import os
from pathlib import Path
def UpperCAmelCase__ ():
"""simple docstring"""
from torch.utils.cpp_extension import load
_snake_case : Dict = Path(snake_case__ ).resolve().parent.parent.parent / """kernels""" / """deformable_detr... | 609 |
"""simple docstring"""
def UpperCAmelCase__ (snake_case__ : int , snake_case__ : int ):
"""simple docstring"""
if number < 0 or shift_amount < 0:
raise ValueError("""both inputs must be positive integers""" )
_snake_case : Optional[Any] = ... | 609 | 1 |
"""simple docstring"""
from queue import PriorityQueue
from typing import Any
import numpy as np
def UpperCAmelCase__ (snake_case__ : dict , snake_case__ : str , snake_case__ : set , snake_case__ : set , snake_case__ : dict , snake_case... | 609 |
"""simple docstring"""
import argparse
import json
import logging
import os
import shutil
import sys
import tempfile
import unittest
from unittest import mock
import torch
from accelerate.utils import write_basic_config
from transformers.testing_utils import TestCasePlus, get_gpu_count, ru... | 609 | 1 |
"""simple docstring"""
import unittest
from transformers import (
MODEL_FOR_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_CAUSAL_LM_MAPPING,
TextGenerationPipeline,
logging,
pipeline,
)
from transformers.testing_utils import (
CaptureLogger,
is_pipeline_test,
require_accele... | 609 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
A_ = logging.get_logger(__name__)
A_ = {
'''xlm-roberta-base... | 609 | 1 |
"""simple docstring"""
import os
import warnings
from typing import List, Optional
from ...tokenization_utils_base import BatchEncoding
from ...utils import logging
from .configuration_rag import RagConfig
A_ = logging.get_logger(__name__)
class lowercase:
... | 609 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A_ = {
'''configuration_instructblip''': [
'''INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''InstructBlipConfig''',
... | 609 | 1 |
"""simple docstring"""
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
A_ = logging.get_logger(__name__)
A_ = {
'''SenseTime/deformable-detr''': '''https://huggingface.co/sensetime... | 609 |
"""simple docstring"""
import string
import numpy
def UpperCAmelCase__ (snake_case__ : int , snake_case__ : int ):
"""simple docstring"""
return b if a == 0 else greatest_common_divisor(b % a , snake_case__ )
class lowercase:
... | 609 | 1 |
"""simple docstring"""
def UpperCAmelCase__ (snake_case__ : str , snake_case__ : str ):
"""simple docstring"""
_snake_case : List[str] = len(snake_case__ ) + 1
_snake_case : Any = len(snake_case__ ) + 1
# dp is a 2... | 609 |
"""simple docstring"""
import argparse
import json
import os
import pickle
import shutil
import numpy as np
import torch
from distiller import Distiller
from lm_seqs_dataset import LmSeqsDataset
from transformers import (
BertConfig,
BertForMaskedLM,
BertTokenizer,
Distil... | 609 | 1 |
"""simple docstring"""
def UpperCAmelCase__ (snake_case__ : int ):
"""simple docstring"""
_snake_case : List[str] = abs(snake_case__ )
_snake_case : Optional[Any] = 0
while n > 0:
res += n % 10
n //= 10
return res
d... | 609 |
"""simple docstring"""
import unicodedata
from dataclasses import dataclass
from typing import Optional, Union
import numpy as np
from transformers.data.data_collator import DataCollatorMixin
from transformers.file_utils import PaddingStrategy
from transformers.tokenization_utils_base import Pr... | 609 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A_ = {'''configuration_wavlm''': ['''WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''WavLMConfig''']}
try:
if not is_torch_available():
... | 609 |
"""simple docstring"""
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
r... | 609 | 1 |
"""simple docstring"""
import inspect
from typing import Optional, Union
import numpy as np
import PIL
import torch
from torch.nn import functional as F
from torchvision import transforms
from transformers import CLIPFeatureExtractor, CLIPModel, CLIPTextModel, CLIPTokenizer
from diffusers imp... | 609 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A_ = logging.get_logger(__name__)
A_ = {
'''s-JoL/Open-Llama-V1''': '''https://huggingface.co/s-JoL/Open-Llama-V1/blob/main/config.json''',
}
cla... | 609 | 1 |
"""simple docstring"""
import argparse
import requests
import torch
from PIL import Image
from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor
def UpperCAmelCase__ (snake_case__ : List[str] ):
"""simple docstring"""
if "cls_token" in name:... | 609 |
"""simple docstring"""
from __future__ import annotations
def UpperCAmelCase__ (snake_case__ : int ):
"""simple docstring"""
_snake_case : Any = [True] * limit
_snake_case : Optional[Any] = False
_snake_case : List[str] ... | 609 | 1 |
"""simple docstring"""
import os
from datetime import datetime as dt
from github import Github
A_ = [
'''good first issue''',
'''good second issue''',
'''good difficult issue''',
'''enhancement''',
'''new pipeline/model''',
'''new scheduler''',
''... | 609 |
"""simple docstring"""
import os
import unittest
from transformers import BatchEncoding
from transformers.models.bert.tokenization_bert import (
BasicTokenizer,
WordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitespace,
)
from transformers.models.prophetnet.tokeniza... | 609 | 1 |
"""simple docstring"""
import argparse
import collections
import json
import os
import re
import string
import sys
import numpy as np
A_ = re.compile(r'''\b(a|an|the)\b''', re.UNICODE)
A_ = None
def UpperCAmelCase__ ():
"""simple docstring"""
... | 609 |
"""simple docstring"""
from scipy.stats import spearmanr
import datasets
A_ = '''
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 correlat... | 609 | 1 |
"""simple docstring"""
from typing import Dict, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
flip_channel_order,
get_resize_output_image_size,
... | 609 |
"""simple docstring"""
A_ = 2_56
# Modulus to hash a string
A_ = 1_00_00_03
def UpperCAmelCase__ (snake_case__ : str , snake_case__ : str ):
"""simple docstring"""
_snake_case : Any = len(snake_case__ )
_snak... | 609 | 1 |
"""simple docstring"""
def UpperCAmelCase__ (snake_case__ : int = 50 ):
"""simple docstring"""
_snake_case : Optional[int] = [1] * (length + 1)
for row_length in range(length + 1 ):
for tile_length in range(2 , 5 ):
for tile_sta... | 609 |
"""simple docstring"""
from __future__ import annotations
def UpperCAmelCase__ (snake_case__ : int ):
"""simple docstring"""
_snake_case : int = str(snake_case__ )
return len(snake_case__ ) == 9 and set(snake_case__ ) == set("""123456789... | 609 | 1 |
"""simple docstring"""
import os
from pathlib import Path
from unittest.mock import patch
import pytest
import zstandard as zstd
from datasets.download.download_config import DownloadConfig
from datasets.utils.file_utils import (
OfflineModeIsEnabled,
cached_path,
fsspec_get,
... | 609 |
"""simple docstring"""
import argparse
from collections import defaultdict
def UpperCAmelCase__ (snake_case__ : Tuple , snake_case__ : Any , snake_case__ : List[str] , snake_case__ : Union[str, Any] , snake_case__ : str ):
"""simple doc... | 609 | 1 |
"""simple docstring"""
from __future__ import annotations
from collections.abc import Generator
import requests
from bsa import BeautifulSoup
A_ = '''https://www.indeed.co.in/jobs?q=mobile+app+development&l='''
def UpperCAmelCase__ (snake_case__ : str = "mumbai" ... | 609 |
"""simple docstring"""
import flax.linen as nn
import jax.numpy as jnp
from .attention_flax import FlaxTransformeraDModel
from .resnet_flax import FlaxDownsampleaD, FlaxResnetBlockaD, FlaxUpsampleaD
class lowercase( nn.Module ):
'''simple docstring'''
... | 609 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
A_ = logging.get_logger(__name__)
A_ = {
'''google/bit-50'... | 609 |
"""simple docstring"""
def UpperCAmelCase__ (snake_case__ : list[int] ):
"""simple docstring"""
if not numbers:
return 0
if not isinstance(snake_case__ , (list, tuple) ) or not all(
isinstance(snake_case__ , snake_case__ ) for number in num... | 609 | 1 |
"""simple docstring"""
def UpperCAmelCase__ (snake_case__ : list[int] ):
"""simple docstring"""
if not numbers:
return 0
if not isinstance(snake_case__ , (list, tuple) ) or not all(
isinstance(snake_case__ , snake_case__ ) for number in num... | 609 |
"""simple docstring"""
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
HubertConfig,
HubertForCTC,
HubertModel,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaProcessor,
... | 609 | 1 |
"""simple docstring"""
import json
import logging
import math
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
from datasets import Dataset, load_dataset
import transformers
from transformers import (
CONFIG_MAPPING,
MODEL_FOR_MASKED_LM_MAPP... | 609 |
"""simple docstring"""
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
import numpy as np
# Parrameters
A_ = (7_20, 12_80) # Height, Width
A_ = (0.4, 0.6) # if height or width lower than this scale, drop it.
A_ =... | 609 | 1 |
"""simple docstring"""
from __future__ import annotations
import time
import numpy as np
A_ = [8, 5, 9, 7]
A_ = [
[2, 0, 1, 1],
[0, 1, 2, 1],
[4, 0, 0, 3],
[0, 2, 1, 0],
[1, 0, 3, 0],
]
A_ = [
[3, 2, 1, 4],
[0, 2, 5,... | 609 |
"""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_scheduler''')... | 609 | 1 |
"""simple docstring"""
from dataclasses import dataclass, field
from typing import Optional
from transformers import AutoConfig, AutoImageProcessor, AutoTokenizer, FlaxVisionEncoderDecoderModel, HfArgumentParser
@dataclass
class lowercase:
'''simple docstring'''
... | 609 |
"""simple docstring"""
from typing import Any, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from torch.utils.data import DistributedSampler, RandomSampler
from transformers import PreTrainedModel, Trainer, logging
from transformers.integrations import is_fairscale_availab... | 609 | 1 |
"""simple docstring"""
import sacrebleu as scb
from packaging import version
from sacrebleu import TER
import datasets
A_ = '''\
@inproceedings{snover-etal-2006-study,
title = "A Study of Translation Edit Rate with Targeted Human Annotation",
author = "Snover, Matthew and... | 609 |
"""simple docstring"""
from __future__ import annotations
A_ = 10
def UpperCAmelCase__ (snake_case__ : list[int] ):
"""simple docstring"""
_snake_case : Any = 1
_snake_case : Dict = max(snake_case__ )
while plac... | 609 | 1 |
"""simple docstring"""
import os
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
A_ = logging.get_logger(__name__)
A_ ... | 609 |
"""simple docstring"""
def UpperCAmelCase__ (snake_case__ : int , snake_case__ : int ):
"""simple docstring"""
if number < 0 or shift_amount < 0:
raise ValueError("""both inputs must be positive integers""" )
_snake_case : Optional[Any] = ... | 609 | 1 |
"""simple docstring"""
import random
def UpperCAmelCase__ (snake_case__ : list , snake_case__ : List[str] ):
"""simple docstring"""
_snake_case , _snake_case , _snake_case : str = [], [], []
for element in data:
if element < ... | 609 |
"""simple docstring"""
import argparse
import json
import logging
import os
import shutil
import sys
import tempfile
import unittest
from unittest import mock
import torch
from accelerate.utils import write_basic_config
from transformers.testing_utils import TestCasePlus, get_gpu_count, ru... | 609 | 1 |
"""simple docstring"""
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
HubertConfig,
HubertForCTC,
HubertModel,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaProcessor,
... | 609 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
A_ = logging.get_logger(__name__)
A_ = {
'''xlm-roberta-base... | 609 | 1 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_owlvit import OwlViTImageProcessor
A_ = logging.get_logger(__name__)
class lowercase( __a ):
'''simple docstring'''
def __init__( self: ... | 609 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A_ = {
'''configuration_instructblip''': [
'''INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''InstructBlipConfig''',
... | 609 | 1 |
"""simple docstring"""
from collections.abc import Callable
class lowercase:
'''simple docstring'''
def __init__( self: Dict, a_: Callable | None = None ):
'''simple docstring'''
_snake_case : list = ... | 609 |
"""simple docstring"""
import string
import numpy
def UpperCAmelCase__ (snake_case__ : int , snake_case__ : int ):
"""simple docstring"""
return b if a == 0 else greatest_common_divisor(b % a , snake_case__ )
class lowercase:
... | 609 | 1 |
"""simple docstring"""
import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional
import numpy as np
from utils_multiple_choice import MultipleChoiceDataset, Split, processors
import transformers
from transformers import (
AutoConfig,
AutoMode... | 609 |
"""simple docstring"""
import argparse
import json
import os
import pickle
import shutil
import numpy as np
import torch
from distiller import Distiller
from lm_seqs_dataset import LmSeqsDataset
from transformers import (
BertConfig,
BertForMaskedLM,
BertTokenizer,
Distil... | 609 | 1 |
"""simple docstring"""
import torch
from diffusers import DDPMScheduler
from .test_schedulers import SchedulerCommonTest
class lowercase( __a ):
'''simple docstring'''
lowercase__ = (DDPMScheduler,)
def UpperCamelCase_ ( sel... | 609 |
"""simple docstring"""
import unicodedata
from dataclasses import dataclass
from typing import Optional, Union
import numpy as np
from transformers.data.data_collator import DataCollatorMixin
from transformers.file_utils import PaddingStrategy
from transformers.tokenization_utils_base import Pr... | 609 | 1 |
"""simple docstring"""
def UpperCAmelCase__ (snake_case__ : int = 1_00 ):
"""simple docstring"""
_snake_case : Any = (n * (n + 1) // 2) ** 2
_snake_case : Dict = n * (n + 1) * (2 * n + 1) // 6
return sum_cubes - sum_squares
if __name_... | 609 |
"""simple docstring"""
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
r... | 609 | 1 |
"""simple docstring"""
from abc import ABC, abstractmethod
from argparse import ArgumentParser
class lowercase( __a ):
'''simple docstring'''
@staticmethod
@abstractmethod
def UpperCamelCase_ ( a_: ArgumentParser ):
''... | 609 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A_ = logging.get_logger(__name__)
A_ = {
'''s-JoL/Open-Llama-V1''': '''https://huggingface.co/s-JoL/Open-Llama-V1/blob/main/config.json''',
}
cla... | 609 | 1 |
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