code stringlengths 86 54.5k | code_codestyle int64 0 371 | style_context stringlengths 87 49.2k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
'''simple docstring'''
import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import TaBlock, TaConfig, TaLayerNorm
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
clas... | 299 |
'''simple docstring'''
from collections.abc import Iterable
from typing import Any
class lowerCamelCase_ :
def __init__( self : List[Any] , _A : int | None = None ):
'''simple docstring'''
UpperCAmelCase__ : List[A... | 299 | 1 |
'''simple docstring'''
import qiskit
def a__ ( lowerCAmelCase__ = 2 ) -> qiskit.result.counts.Counts:
UpperCAmelCase__ : str = qubits
# Using Aer's simulator
UpperCAmelCase__ : List[str] = qiskit.Aer.get_backend('''aer_simulator'''... | 299 |
'''simple docstring'''
import argparse
import torch
from transformers import (
SpeechTaConfig,
SpeechTaFeatureExtractor,
SpeechTaForSpeechToSpeech,
SpeechTaForSpeechToText,
SpeechTaForTextToSpeech,
SpeechTaProcessor,
SpeechTaTokenizer,
logging,
)
from transformers.... | 299 | 1 |
'''simple docstring'''
import unittest
from transformers import DebertaVaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import... | 299 |
'''simple docstring'''
import argparse
import fairseq
import torch
from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging
logging.set_verbosity_info()
UpperCamelCase__ = logging.get_logger(__name__)
UpperCamelCase__ = {
'''post_extrac... | 299 | 1 |
'''simple docstring'''
import math
def a__ ( lowerCAmelCase__ ) -> bool:
UpperCAmelCase__ : List[str] = math.loga(math.sqrt(4 * positive_integer + 1 ) / 2 + 1 / 2 )
return exponent == int(lowerCAmelCase__ )
def a__ ( lowerCAmelCas... | 299 |
'''simple docstring'''
import itertools
import random
import unittest
import numpy as np
from transformers import ASTFeatureExtractor
from transformers.testing_utils import require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available
from ...test_sequence_feature_... | 299 | 1 |
'''simple docstring'''
import pickle
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, XLMRobertaTokenizer, XLMRobertaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import c... | 299 |
'''simple docstring'''
import os
import shutil
import sys
import tempfile
import unittest
from pathlib import Path
import pytest
import transformers
from transformers import (
BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
AutoTokenizer,
BertConfig,
B... | 299 | 1 |
'''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
UpperCamelCase__ = logging.get_logger(__name__)
UpperCamelCase... | 299 |
'''simple docstring'''
def a__ ( lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , ) -> float:
UpperCAmelCase__ : Tuple = [redshift, radiation_density, matter_density, dark_energy]
if any(p < 0 for p in parameters ):
... | 299 | 1 |
'''simple docstring'''
def a__ ( lowerCAmelCase__ ) -> str:
return "".join(chr(ord(lowerCAmelCase__ ) - 32 ) if '''a''' <= char <= '''z''' else char for char in word )
if __name__ == "__main__":
from doctest import testmod
testmod()
| 299 |
'''simple docstring'''
import gc
import math
import unittest
import torch
from diffusers import UNetaDModel
from diffusers.utils import floats_tensor, logging, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from .test_modeling_common import Mode... | 299 | 1 |
'''simple docstring'''
import math
def a__ ( lowerCAmelCase__ ) -> bool:
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
... | 299 |
'''simple docstring'''
from __future__ import annotations
def a__ ( lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ) -> tuple[float, list[float]]:
UpperCAmelCase__ : Optional[Any] = list(range(len(lowerCAmelCase__ ) ) )
UpperCAmelCase... | 299 | 1 |
'''simple docstring'''
import argparse
import fairseq
import torch
from torch import nn
from transformers import (
MBartaaTokenizer,
MBartConfig,
MBartForCausalLM,
SpeechEncoderDecoderConfig,
SpeechEncoderDecoderModel,
WavaVecaConfig,
WavaVecaFeatureExtractor,
Wava... | 299 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class lowerCamelCase_ ( metaclass=__a ):
lowerCAmelCase__ = ['torch', 'transformers', 'onnx']
def __init__( self : int , *_A : Tuple , **_A : ... | 299 | 1 |
'''simple docstring'''
import copy
import inspect
import unittest
from transformers import PretrainedConfig, SwiftFormerConfig
from transformers.testing_utils import (
require_torch,
require_vision,
slow,
torch_device,
)
from transformers.utils import cached_property, is_torch_availab... | 299 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCamelCase__ = {'''configuration_mmbt''': ['''MMBTConfig''']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except... | 299 | 1 |
'''simple docstring'''
import argparse
import math
import traceback
import dateutil.parser as date_parser
import requests
def a__ ( lowerCAmelCase__ ) -> List[str]:
UpperCAmelCase__ : Tuple = {}
UpperCAmelCase__ : Optional[int] = ... | 299 |
'''simple docstring'''
import inspect
import tempfile
from collections import OrderedDict, UserDict
from collections.abc import MutableMapping
from contextlib import ExitStack, contextmanager
from dataclasses import fields
from enum import Enum
from typing import Any, ContextManager, List, Tuple
import... | 299 | 1 |
'''simple docstring'''
import doctest
import glob
import importlib
import inspect
import os
import re
from contextlib import contextmanager
from functools import wraps
from unittest.mock import patch
import numpy as np
import pytest
from absl.testing import parameterized
import datasets
from dat... | 299 |
'''simple docstring'''
import argparse
from typing import List
import evaluate
import numpy as np
import torch
from datasets import DatasetDict, load_dataset
# New Code #
# We'll be using StratifiedKFold for this example
from sklearn.model_selection import StratifiedKFold
from torch.optim import Ada... | 299 | 1 |
'''simple docstring'''
import gc
import math
import unittest
import torch
from diffusers import UNetaDModel
from diffusers.utils import floats_tensor, logging, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from .test_modeling_common import Mode... | 299 |
'''simple docstring'''
import json
import os
import shutil
import tempfile
import unittest
from multiprocessing import get_context
from pathlib import Path
import datasets
import numpy as np
from datasets import load_dataset
from parameterized import parameterized
from transformers import AutoProc... | 299 | 1 |
'''simple docstring'''
import torch
from diffusers import DDPMScheduler
from .test_schedulers import SchedulerCommonTest
class lowerCamelCase_ ( __a ):
lowerCAmelCase__ = (DDPMScheduler,)
def lowercase_ ( self : Optional[Any] ,... | 299 |
'''simple docstring'''
# Logistic Regression from scratch
# In[62]:
# In[63]:
# importing all the required libraries
import numpy as np
from matplotlib import pyplot as plt
from sklearn import datasets
def a__ ( lowerCAmelCase__ ) -> List[Any]:
return 1 / (1 + np.exp(-z ... | 299 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase__ = logging.get_logger(__name__)
UpperCamelCase__ = {
'''google/pegasus-large''': '''https://huggingface.co/google/pegasus-large/resolve/main/config.json''',
# See... | 299 |
'''simple docstring'''
from __future__ import annotations
import copy
import tempfile
import unittest
from transformers import CONFIG_MAPPING, AutoConfig, BertConfig, GPTaConfig, TaConfig, TapasConfig, is_tf_available
from transformers.testing_utils import (
DUMMY_UNKNOWN_IDENTIFIER,
SMALL_MOD... | 299 | 1 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_owlvit import OwlViTImageProcessor
UpperCamelCase__ = logging.get_logger(__name__)
class lowerCamelCase_ ( __a ):
def __init__( self : Optional[Any] ,... | 299 |
'''simple docstring'''
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestM... | 299 | 1 |
'''simple docstring'''
import inspect
import unittest
from transformers import DecisionTransformerConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import Config... | 299 |
'''simple docstring'''
from __future__ import annotations
import math
from collections import Counter
from string import ascii_lowercase
def a__ ( lowerCAmelCase__ ) -> None:
UpperCAmelCase__ , UpperCAmelCase__ : Optional[Any] = analyze_text(lowerCA... | 299 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCamelCase__ = {
'''configuration_upernet''': ['''UperNetConfig'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvail... | 299 |
'''simple docstring'''
from typing import List, Optional
from tokenizers import ByteLevelBPETokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer
UpperCamelCase__ = logging.get_l... | 299 | 1 |
'''simple docstring'''
import json
import os
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from requests.exceptions import HTTPError
from transformers.utils import (
CONFIG_NAME,
FLAX_WEIGHTS_NAME,
TF2_WEIGHTS_NAME,
TRANSFORMERS_CACHE,
W... | 299 |
'''simple docstring'''
import pickle
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, XLMRobertaTokenizer, XLMRobertaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import c... | 299 | 1 |
'''simple docstring'''
import collections
import json
import math
import os
import re
import time
from fnmatch import fnmatch
from typing import Dict
import requests
from slack_sdk import WebClient
UpperCamelCase__ = WebClient(token=os.environ['''CI_SLACK_BOT_TOKEN'''])
def a__ ... | 299 |
'''simple docstring'''
from __future__ import annotations
import math
def a__ ( lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ) -> int:
if depth < 0:
raise ValueError('''Depth cannot be less than 0''' )
if no... | 299 | 1 |
'''simple docstring'''
import functools
from typing import Any
def a__ ( lowerCAmelCase__ , lowerCAmelCase__ ) -> bool:
# Validation
if not isinstance(lowerCAmelCase__ , lowerCAmelCase__ ) or len(lowerCAmelCase__ ) == 0:
raise ValueError('''the string shou... | 299 |
'''simple docstring'''
class lowerCamelCase_ :
def __init__( self : Union[str, Any] , _A : int ):
'''simple docstring'''
UpperCAmelCase__ : str = n
UpperCAmelCase__ : Union[str, Any] ... | 299 | 1 |
'''simple docstring'''
from __future__ import annotations
def a__ ( lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ) -> tuple[float, list[float]]:
UpperCAmelCase__ : Optional[Any] = list(range(len(lowerCAmelCase__ ) ) )
UpperCAmelCase... | 299 |
'''simple docstring'''
def a__ ( lowerCAmelCase__ ) -> Optional[Any]:
UpperCAmelCase__ : Optional[Any] = len(lowerCAmelCase__ )
for i in range(length - 1 ):
UpperCAmelCase__ : Optional[Any] = i
for k in range(i + 1 , low... | 299 | 1 |
'''simple docstring'''
from __future__ import annotations
import math
def a__ ( lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ) -> int:
if depth < 0:
raise ValueError('''Depth cannot be less than 0''' )
if le... | 299 |
'''simple docstring'''
from collections.abc import Iterable
from typing import Any
class lowerCamelCase_ :
def __init__( self : List[Any] , _A : int | None = None ):
'''simple docstring'''
UpperCAmelCase__ : List[A... | 299 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
UpperCamelCase__ = {
'''configuration_blip''': [
'''BLIP_PRETRAINED_CONFIG_... | 299 |
'''simple docstring'''
import argparse
import torch
from transformers import (
SpeechTaConfig,
SpeechTaFeatureExtractor,
SpeechTaForSpeechToSpeech,
SpeechTaForSpeechToText,
SpeechTaForTextToSpeech,
SpeechTaProcessor,
SpeechTaTokenizer,
logging,
)
from transformers.... | 299 | 1 |
'''simple docstring'''
import os
from typing import Dict, List, Union
import tensorflow as tf
from keras_nlp.tokenizers import BytePairTokenizer
from tensorflow_text import pad_model_inputs
from .tokenization_gpta import GPTaTokenizer
class lowerCamelCase_ ( tf.keras.layers.Laye... | 299 |
'''simple docstring'''
import argparse
import fairseq
import torch
from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging
logging.set_verbosity_info()
UpperCamelCase__ = logging.get_logger(__name__)
UpperCamelCase__ = {
'''post_extrac... | 299 | 1 |
'''simple docstring'''
from pathlib import PurePosixPath
from typing import Optional
import fsspec
from fsspec import AbstractFileSystem
from huggingface_hub.hf_api import DatasetInfo
from ..utils.file_utils import get_authentication_headers_for_url
from ..utils.hub import hf_hub_url
class ... | 299 |
'''simple docstring'''
import itertools
import random
import unittest
import numpy as np
from transformers import ASTFeatureExtractor
from transformers.testing_utils import require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available
from ...test_sequence_feature_... | 299 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
UpperCamelCase__ = {
'''configuration_layoutlmv2''': ['''LAYOUTLMV2_PRETRAIN... | 299 |
'''simple docstring'''
import os
import shutil
import sys
import tempfile
import unittest
from pathlib import Path
import pytest
import transformers
from transformers import (
BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
AutoTokenizer,
BertConfig,
B... | 299 | 1 |
'''simple docstring'''
from math import isclose, sqrt
def a__ ( lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ) -> tuple[float, float, float]:
UpperCAmelCase__ : Optional[int] = point_y / 4 / point_x
UpperCAmelCase__ : int = 2 ... | 299 |
'''simple docstring'''
def a__ ( lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , ) -> float:
UpperCAmelCase__ : Tuple = [redshift, radiation_density, matter_density, dark_energy]
if any(p < 0 for p in parameters ):
... | 299 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
UpperCamelCase__ = {
'''configuration_convnext''': ['''CONVNEXT_PRETRAINED_CONFIG_AR... | 299 |
'''simple docstring'''
import gc
import math
import unittest
import torch
from diffusers import UNetaDModel
from diffusers.utils import floats_tensor, logging, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from .test_modeling_common import Mode... | 299 | 1 |
'''simple docstring'''
import unittest
from queue import Empty
from threading import Thread
from transformers import AutoTokenizer, TextIteratorStreamer, TextStreamer, is_torch_available
from transformers.testing_utils import CaptureStdout, require_torch, torch_device
from ..test_modeling_common import ... | 299 |
'''simple docstring'''
from __future__ import annotations
def a__ ( lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ) -> tuple[float, list[float]]:
UpperCAmelCase__ : Optional[Any] = list(range(len(lowerCAmelCase__ ) ) )
UpperCAmelCase... | 299 | 1 |
'''simple docstring'''
UpperCamelCase__ = '''
# 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
'''
UpperCame... | 299 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class lowerCamelCase_ ( metaclass=__a ):
lowerCAmelCase__ = ['torch', 'transformers', 'onnx']
def __init__( self : int , *_A : Tuple , **_A : ... | 299 | 1 |
'''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_simplif... | 299 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCamelCase__ = {'''configuration_mmbt''': ['''MMBTConfig''']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except... | 299 | 1 |
'''simple docstring'''
from __future__ import absolute_import, division, print_function, unicode_literals
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers import RobertaConfig
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forw... | 299 |
'''simple docstring'''
import inspect
import tempfile
from collections import OrderedDict, UserDict
from collections.abc import MutableMapping
from contextlib import ExitStack, contextmanager
from dataclasses import fields
from enum import Enum
from typing import Any, ContextManager, List, Tuple
import... | 299 | 1 |
'''simple docstring'''
import re
import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey
from ..utils import logging
UpperCamelCase__ = logging.get_logger(__name__)
def a__ ( lowerCAmelCase__ ) -> Optional[int]:
... | 299 |
'''simple docstring'''
import argparse
from typing import List
import evaluate
import numpy as np
import torch
from datasets import DatasetDict, load_dataset
# New Code #
# We'll be using StratifiedKFold for this example
from sklearn.model_selection import StratifiedKFold
from torch.optim import Ada... | 299 | 1 |
'''simple docstring'''
import unittest
from transformers import AlbertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common imp... | 299 |
'''simple docstring'''
import json
import os
import shutil
import tempfile
import unittest
from multiprocessing import get_context
from pathlib import Path
import datasets
import numpy as np
from datasets import load_dataset
from parameterized import parameterized
from transformers import AutoProc... | 299 | 1 |
'''simple docstring'''
from typing import Dict, List, Optional, Union
import numpy as np
from .feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from .utils import PaddingStrategy, TensorType, is_tf_tensor, is_torch_tensor, logging, to_numpy
UpperCamelCase__ = logging.get_log... | 299 |
'''simple docstring'''
# Logistic Regression from scratch
# In[62]:
# In[63]:
# importing all the required libraries
import numpy as np
from matplotlib import pyplot as plt
from sklearn import datasets
def a__ ( lowerCAmelCase__ ) -> List[Any]:
return 1 / (1 + np.exp(-z ... | 299 | 1 |
'''simple docstring'''
import dataclasses
import re
import string
from typing import Any, Dict, Iterator, List, Mapping, Optional, Sequence, Tuple
import numpy as np
from . import residue_constants
UpperCamelCase__ = Mapping[str, np.ndarray]
UpperCamelCase__ = Mapping[str, Any] # Is a... | 299 |
'''simple docstring'''
from __future__ import annotations
import copy
import tempfile
import unittest
from transformers import CONFIG_MAPPING, AutoConfig, BertConfig, GPTaConfig, TaConfig, TapasConfig, is_tf_available
from transformers.testing_utils import (
DUMMY_UNKNOWN_IDENTIFIER,
SMALL_MOD... | 299 | 1 |
'''simple docstring'''
def a__ ( lowerCAmelCase__ ) -> Any:
UpperCAmelCase__ : Optional[Any] = 1
UpperCAmelCase__ : str = 2
while i * i <= n:
UpperCAmelCase__ : Optional[Any] = 0
while n % i == 0:
... | 299 |
'''simple docstring'''
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestM... | 299 | 1 |
'''simple docstring'''
def a__ ( lowerCAmelCase__ , lowerCAmelCase__ ) -> int:
while a != 0:
UpperCAmelCase__ , UpperCAmelCase__ : int = b % a, a
return b
def a__ ( lowerCAmelCase__ , lowerCAmelCase__ ) -> int:
if gcd... | 299 |
'''simple docstring'''
from __future__ import annotations
import math
from collections import Counter
from string import ascii_lowercase
def a__ ( lowerCAmelCase__ ) -> None:
UpperCAmelCase__ , UpperCAmelCase__ : Optional[Any] = analyze_text(lowerCA... | 299 | 1 |
'''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 (
A... | 299 |
'''simple docstring'''
from typing import List, Optional
from tokenizers import ByteLevelBPETokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer
UpperCamelCase__ = logging.get_l... | 299 | 1 |
'''simple docstring'''
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile
from transformers import MaMaaaTokenizer, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
... | 299 |
'''simple docstring'''
import pickle
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, XLMRobertaTokenizer, XLMRobertaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import c... | 299 | 1 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_beit import BeitImageProcessor
UpperCamelCase__ = logging.get_logger(__name__)
class lowerCamelCase_ ( __a ):
def __init__( self : int , *_A :... | 299 |
'''simple docstring'''
from __future__ import annotations
import math
def a__ ( lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ) -> int:
if depth < 0:
raise ValueError('''Depth cannot be less than 0''' )
if no... | 299 | 1 |
'''simple docstring'''
from abc import ABC, abstractmethod
from typing import Optional, Union
from .. import Dataset, DatasetDict, Features, IterableDataset, IterableDatasetDict, NamedSplit
from ..utils.typing import NestedDataStructureLike, PathLike
class lowerCamelCase_ ( __a )... | 299 |
'''simple docstring'''
class lowerCamelCase_ :
def __init__( self : Union[str, Any] , _A : int ):
'''simple docstring'''
UpperCAmelCase__ : str = n
UpperCAmelCase__ : Union[str, Any] ... | 299 | 1 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class lowerCamelCase_ ( metaclass=__a ):
lowerCAmelCase__ = ['torch', 'scipy']
def __init__( self : List[Any] , *_A : Optional[Any] , **_A : ... | 299 |
'''simple docstring'''
def a__ ( lowerCAmelCase__ ) -> Optional[Any]:
UpperCAmelCase__ : Optional[Any] = len(lowerCAmelCase__ )
for i in range(length - 1 ):
UpperCAmelCase__ : Optional[Any] = i
for k in range(i + 1 , low... | 299 | 1 |
'''simple docstring'''
from __future__ import annotations
import time
from math import sqrt
# 1 for manhattan, 0 for euclidean
UpperCamelCase__ = 0
UpperCamelCase__ = [
[0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles
[0, 0, 0, 0, 0,... | 299 |
'''simple docstring'''
from collections.abc import Iterable
from typing import Any
class lowerCamelCase_ :
def __init__( self : List[Any] , _A : int | None = None ):
'''simple docstring'''
UpperCAmelCase__ : List[A... | 299 | 1 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random... | 299 |
'''simple docstring'''
import argparse
import torch
from transformers import (
SpeechTaConfig,
SpeechTaFeatureExtractor,
SpeechTaForSpeechToSpeech,
SpeechTaForSpeechToText,
SpeechTaForTextToSpeech,
SpeechTaProcessor,
SpeechTaTokenizer,
logging,
)
from transformers.... | 299 | 1 |
'''simple docstring'''
def a__ ( lowerCAmelCase__ , lowerCAmelCase__ ) -> bool:
UpperCAmelCase__ : Optional[int] = len(lowerCAmelCase__ )
UpperCAmelCase__ : List[Any] = [[False] * (required_sum + 1) for _ in range(arr_len + 1 )]
# ... | 299 |
'''simple docstring'''
import argparse
import fairseq
import torch
from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging
logging.set_verbosity_info()
UpperCamelCase__ = logging.get_logger(__name__)
UpperCamelCase__ = {
'''post_extrac... | 299 | 1 |
'''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 lowerCamelCase_ ... | 299 |
'''simple docstring'''
import itertools
import random
import unittest
import numpy as np
from transformers import ASTFeatureExtractor
from transformers.testing_utils import require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available
from ...test_sequence_feature_... | 299 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCamelCase__ = {'''configuration_vit_msn''': ['''VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ViTMSNConfig''']}
try:
if not is_torch_available():
... | 299 |
'''simple docstring'''
import os
import shutil
import sys
import tempfile
import unittest
from pathlib import Path
import pytest
import transformers
from transformers import (
BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
AutoTokenizer,
BertConfig,
B... | 299 | 1 |
'''simple docstring'''
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
from .tokenization_wavaveca import WavaVecaCTCTokenizer
class lowerCamelCase_ ( __a ):
... | 299 |
'''simple docstring'''
def a__ ( lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , ) -> float:
UpperCAmelCase__ : Tuple = [redshift, radiation_density, matter_density, dark_energy]
if any(p < 0 for p in parameters ):
... | 299 | 1 |
'''simple docstring'''
import heapq as hq
import math
from collections.abc import Iterator
class lowerCamelCase_ :
def __init__( self : str , _A : Optional[int] ):
'''simple docstring'''
UpperCAmelCase__ : str ... | 299 |
'''simple docstring'''
import gc
import math
import unittest
import torch
from diffusers import UNetaDModel
from diffusers.utils import floats_tensor, logging, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from .test_modeling_common import Mode... | 299 | 1 |
'''simple docstring'''
from typing import List, Optional, Union
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
... | 299 |
'''simple docstring'''
from __future__ import annotations
def a__ ( lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ) -> tuple[float, list[float]]:
UpperCAmelCase__ : Optional[Any] = list(range(len(lowerCAmelCase__ ) ) )
UpperCAmelCase... | 299 | 1 |
'''simple docstring'''
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestM... | 299 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class lowerCamelCase_ ( metaclass=__a ):
lowerCAmelCase__ = ['torch', 'transformers', 'onnx']
def __init__( self : int , *_A : Tuple , **_A : ... | 299 | 1 |
'''simple docstring'''
import warnings
from diffusers import StableDiffusionImgaImgPipeline # noqa F401
warnings.warn(
'''The `image_to_image.py` script is outdated. Please use directly `from diffusers import'''
''' StableDiffusionImg2ImgPipeline` instead.'''
)
| 299 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCamelCase__ = {'''configuration_mmbt''': ['''MMBTConfig''']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except... | 299 | 1 |
'''simple docstring'''
def a__ ( lowerCAmelCase__ ) -> Optional[Any]:
UpperCAmelCase__ : Optional[Any] = len(lowerCAmelCase__ )
for i in range(length - 1 ):
UpperCAmelCase__ : Optional[Any] = i
for k in range(i + 1 , low... | 299 |
'''simple docstring'''
import inspect
import tempfile
from collections import OrderedDict, UserDict
from collections.abc import MutableMapping
from contextlib import ExitStack, contextmanager
from dataclasses import fields
from enum import Enum
from typing import Any, ContextManager, List, Tuple
import... | 299 | 1 |
'''simple docstring'''
import argparse
import gc
import json
import os
import shutil
import warnings
import torch
from transformers import LlamaConfig, LlamaForCausalLM, LlamaTokenizer
try:
from transformers import LlamaTokenizerFast
except ImportError as e:
warnings.warn(e)
warnings.wa... | 299 |
'''simple docstring'''
import argparse
from typing import List
import evaluate
import numpy as np
import torch
from datasets import DatasetDict, load_dataset
# New Code #
# We'll be using StratifiedKFold for this example
from sklearn.model_selection import StratifiedKFold
from torch.optim import Ada... | 299 | 1 |
'''simple docstring'''
import unittest
import numpy as np
import torch
from torch import nn
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModelWithProjection,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import K... | 299 |
'''simple docstring'''
import json
import os
import shutil
import tempfile
import unittest
from multiprocessing import get_context
from pathlib import Path
import datasets
import numpy as np
from datasets import load_dataset
from parameterized import parameterized
from transformers import AutoProc... | 299 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCamelCase__ = {
'''configuration_lilt''': ['''LILT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''LiltConfig'''],
}
try:
if not is_torch_available():
... | 299 |
'''simple docstring'''
# Logistic Regression from scratch
# In[62]:
# In[63]:
# importing all the required libraries
import numpy as np
from matplotlib import pyplot as plt
from sklearn import datasets
def a__ ( lowerCAmelCase__ ) -> List[Any]:
return 1 / (1 + np.exp(-z ... | 299 | 1 |
'''simple docstring'''
import collections
import os
from typing import List, Optional, Tuple
from transformers.utils import is_jieba_available, requires_backends
if is_jieba_available():
import jieba
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
UpperCam... | 299 |
'''simple docstring'''
from __future__ import annotations
import copy
import tempfile
import unittest
from transformers import CONFIG_MAPPING, AutoConfig, BertConfig, GPTaConfig, TaConfig, TapasConfig, is_tf_available
from transformers.testing_utils import (
DUMMY_UNKNOWN_IDENTIFIER,
SMALL_MOD... | 299 | 1 |
'''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 t... | 299 |
'''simple docstring'''
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestM... | 299 | 1 |
'''simple docstring'''
import unittest
import numpy as np
from transformers.testing_utils import require_flax, require_tf, require_torch
from transformers.utils import (
expand_dims,
flatten_dict,
is_flax_available,
is_tf_available,
is_torch_available,
reshape,
squeeze,
... | 299 |
'''simple docstring'''
from __future__ import annotations
import math
from collections import Counter
from string import ascii_lowercase
def a__ ( lowerCAmelCase__ ) -> None:
UpperCAmelCase__ , UpperCAmelCase__ : Optional[Any] = analyze_text(lowerCA... | 299 | 1 |
'''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 V... | 299 |
'''simple docstring'''
from typing import List, Optional
from tokenizers import ByteLevelBPETokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer
UpperCamelCase__ = logging.get_l... | 299 | 1 |
'''simple docstring'''
from math import ceil, sqrt
def a__ ( lowerCAmelCase__ = 1_00_00_00 ) -> int:
UpperCAmelCase__ : Optional[int] = 0
for outer_width in range(3 , (limit // 4) + 2 ):
if outer_width**2 > limit:
UpperCAmelCase_... | 299 |
'''simple docstring'''
import pickle
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, XLMRobertaTokenizer, XLMRobertaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import c... | 299 | 1 |
'''simple docstring'''
from typing import List, Optional
from tokenizers import ByteLevelBPETokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer
UpperCamelCase__ = logging.get_l... | 299 |
'''simple docstring'''
from __future__ import annotations
import math
def a__ ( lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ) -> int:
if depth < 0:
raise ValueError('''Depth cannot be less than 0''' )
if no... | 299 | 1 |
'''simple docstring'''
import argparse
import json
import os
import numpy as np
import PIL
import requests
import tensorflow.keras.applications.efficientnet as efficientnet
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from tensorflow.keras.preprocessing import image
... | 299 |
'''simple docstring'''
class lowerCamelCase_ :
def __init__( self : Union[str, Any] , _A : int ):
'''simple docstring'''
UpperCAmelCase__ : str = n
UpperCAmelCase__ : Union[str, Any] ... | 299 | 1 |
'''simple docstring'''
import copy
from typing import Any, Dict, List, Optional, Union
import numpy as np
import torch
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils impo... | 299 |
'''simple docstring'''
def a__ ( lowerCAmelCase__ ) -> Optional[Any]:
UpperCAmelCase__ : Optional[Any] = len(lowerCAmelCase__ )
for i in range(length - 1 ):
UpperCAmelCase__ : Optional[Any] = i
for k in range(i + 1 , low... | 299 | 1 |
'''simple docstring'''
def a__ ( lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , ) -> float:
UpperCAmelCase__ : Tuple = [redshift, radiation_density, matter_density, dark_energy]
if any(p < 0 for p in parameters ):
... | 299 |
'''simple docstring'''
from collections.abc import Iterable
from typing import Any
class lowerCamelCase_ :
def __init__( self : List[Any] , _A : int | None = None ):
'''simple docstring'''
UpperCAmelCase__ : List[A... | 299 | 1 |
'''simple docstring'''
def a__ ( lowerCAmelCase__ = 50 ) -> int:
UpperCAmelCase__ : Any = [[0] * 3 for _ in range(length + 1 )]
for row_length in range(length + 1 ):
for tile_length in range(2 , 5 ):
for tile_start in range(row_le... | 299 |
'''simple docstring'''
import argparse
import torch
from transformers import (
SpeechTaConfig,
SpeechTaFeatureExtractor,
SpeechTaForSpeechToSpeech,
SpeechTaForSpeechToText,
SpeechTaForTextToSpeech,
SpeechTaProcessor,
SpeechTaTokenizer,
logging,
)
from transformers.... | 299 | 1 |
'''simple docstring'''
def a__ ( lowerCAmelCase__ = 3 , lowerCAmelCase__ = 7 , lowerCAmelCase__ = 1_00_00_00 ) -> int:
UpperCAmelCase__ : List[str] = 0
UpperCAmelCase__ : Optional[int] = 1
for current_denominator in range(1 , limit + 1 ... | 299 |
'''simple docstring'''
import argparse
import fairseq
import torch
from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging
logging.set_verbosity_info()
UpperCamelCase__ = logging.get_logger(__name__)
UpperCamelCase__ = {
'''post_extrac... | 299 | 1 |
'''simple docstring'''
import functools
def a__ ( lowerCAmelCase__ , lowerCAmelCase__ ) -> int:
# Validation
if not isinstance(lowerCAmelCase__ , lowerCAmelCase__ ) or not all(isinstance(lowerCAmelCase__ , lowerCAmelCase__ ) for day in days ):
raise V... | 299 |
'''simple docstring'''
import itertools
import random
import unittest
import numpy as np
from transformers import ASTFeatureExtractor
from transformers.testing_utils import require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available
from ...test_sequence_feature_... | 299 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
UpperCamelCase__ = {
'''configuration_roc_bert''': ['''ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''RoCBertConfig'''],
'''... | 299 |
'''simple docstring'''
import os
import shutil
import sys
import tempfile
import unittest
from pathlib import Path
import pytest
import transformers
from transformers import (
BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
AutoTokenizer,
BertConfig,
B... | 299 | 1 |
'''simple docstring'''
import argparse
import fairseq
import torch
from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging
logging.set_verbosity_info()
UpperCamelCase__ = logging.get_logger(__name__)
UpperCamelCase__ = {
'''post_extrac... | 299 |
'''simple docstring'''
def a__ ( lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , ) -> float:
UpperCAmelCase__ : Tuple = [redshift, radiation_density, matter_density, dark_energy]
if any(p < 0 for p in parameters ):
... | 299 | 1 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_mobilebert import MobileBertTokenizer
UpperCamelCase__ = logging.get_lo... | 299 |
'''simple docstring'''
import gc
import math
import unittest
import torch
from diffusers import UNetaDModel
from diffusers.utils import floats_tensor, logging, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from .test_modeling_common import Mode... | 299 | 1 |
'''simple docstring'''
import argparse
import pathlib
import fairseq
import torch
from fairseq.models.roberta import RobertaModel as FairseqRobertaModel
from fairseq.modules import TransformerSentenceEncoderLayer
from packaging import version
from transformers import XLMRobertaConfig, XLMRobertaXLForM... | 299 |
'''simple docstring'''
from __future__ import annotations
def a__ ( lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ) -> tuple[float, list[float]]:
UpperCAmelCase__ : Optional[Any] = list(range(len(lowerCAmelCase__ ) ) )
UpperCAmelCase... | 299 | 1 |
'''simple docstring'''
from typing import Dict, List
from nltk.translate import gleu_score
import datasets
from datasets import MetricInfo
UpperCamelCase__ = '''\
@misc{wu2016googles,
title={Google\'s Neural Machine Translation System: Bridging the Gap between Human and Machine Translatio... | 299 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class lowerCamelCase_ ( metaclass=__a ):
lowerCAmelCase__ = ['torch', 'transformers', 'onnx']
def __init__( self : int , *_A : Tuple , **_A : ... | 299 | 1 |
'''simple docstring'''
import argparse
from typing import List
import evaluate
import numpy as np
import torch
from datasets import DatasetDict, load_dataset
# New Code #
# We'll be using StratifiedKFold for this example
from sklearn.model_selection import StratifiedKFold
from torch.optim import Ada... | 299 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCamelCase__ = {'''configuration_mmbt''': ['''MMBTConfig''']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except... | 299 | 1 |
'''simple docstring'''
import inspect
import tempfile
from collections import OrderedDict, UserDict
from collections.abc import MutableMapping
from contextlib import ExitStack, contextmanager
from dataclasses import fields
from enum import Enum
from typing import Any, ContextManager, List, Tuple
import... | 299 |
'''simple docstring'''
import inspect
import tempfile
from collections import OrderedDict, UserDict
from collections.abc import MutableMapping
from contextlib import ExitStack, contextmanager
from dataclasses import fields
from enum import Enum
from typing import Any, ContextManager, List, Tuple
import... | 299 | 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... | 299 |
'''simple docstring'''
import argparse
from typing import List
import evaluate
import numpy as np
import torch
from datasets import DatasetDict, load_dataset
# New Code #
# We'll be using StratifiedKFold for this example
from sklearn.model_selection import StratifiedKFold
from torch.optim import Ada... | 299 | 1 |
'''simple docstring'''
import json
import os
import shutil
import tempfile
import unittest
from multiprocessing import get_context
from pathlib import Path
import datasets
import numpy as np
from datasets import load_dataset
from parameterized import parameterized
from transformers import AutoProc... | 299 |
'''simple docstring'''
import json
import os
import shutil
import tempfile
import unittest
from multiprocessing import get_context
from pathlib import Path
import datasets
import numpy as np
from datasets import load_dataset
from parameterized import parameterized
from transformers import AutoProc... | 299 | 1 |
'''simple docstring'''
import copy
from typing import TYPE_CHECKING, Any, Mapping, Optional, OrderedDict
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig
if... | 299 |
'''simple docstring'''
# Logistic Regression from scratch
# In[62]:
# In[63]:
# importing all the required libraries
import numpy as np
from matplotlib import pyplot as plt
from sklearn import datasets
def a__ ( lowerCAmelCase__ ) -> List[Any]:
return 1 / (1 + np.exp(-z ... | 299 | 1 |
'''simple docstring'''
from datetime import datetime
import requests
from bsa import BeautifulSoup
if __name__ == "__main__":
UpperCamelCase__ = input('''Enter image url: ''').strip()
print(F"""Downloading image from {url} ...""")
UpperCamelCase__ = BeautifulSoup(requests.get(url).con... | 299 |
'''simple docstring'''
from __future__ import annotations
import copy
import tempfile
import unittest
from transformers import CONFIG_MAPPING, AutoConfig, BertConfig, GPTaConfig, TaConfig, TapasConfig, is_tf_available
from transformers.testing_utils import (
DUMMY_UNKNOWN_IDENTIFIER,
SMALL_MOD... | 299 | 1 |
'''simple docstring'''
from diffusers.utils.testing_utils import require_onnxruntime
@require_onnxruntime
class lowerCamelCase_ :
pass
| 299 |
'''simple docstring'''
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestM... | 299 | 1 |
'''simple docstring'''
from argparse import ArgumentParser
from ..pipelines import Pipeline, PipelineDataFormat, get_supported_tasks, pipeline
from ..utils import logging
from . import BaseTransformersCLICommand
UpperCamelCase__ = logging.get_logger(__name__) # pylint: disable=invalid-name
... | 299 |
'''simple docstring'''
from __future__ import annotations
import math
from collections import Counter
from string import ascii_lowercase
def a__ ( lowerCAmelCase__ ) -> None:
UpperCAmelCase__ , UpperCAmelCase__ : Optional[Any] = analyze_text(lowerCA... | 299 | 1 |
'''simple docstring'''
import argparse
import os
import torch
from diffusers import (
CMStochasticIterativeScheduler,
ConsistencyModelPipeline,
UNetaDModel,
)
UpperCamelCase__ = {
'''sample_size''': 3_2,
'''in_channels''': 3,
'''out_channels''': 3,
'''layers_... | 299 |
'''simple docstring'''
from typing import List, Optional
from tokenizers import ByteLevelBPETokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer
UpperCamelCase__ = logging.get_l... | 299 | 1 |
'''simple docstring'''
from __future__ import annotations
UpperCamelCase__ = [
[-1, 0], # left
[0, -1], # down
[1, 0], # right
[0, 1], # up
]
def a__ ( lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , ) -> tupl... | 299 |
'''simple docstring'''
import pickle
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, XLMRobertaTokenizer, XLMRobertaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import c... | 299 | 1 |
'''simple docstring'''
import math
def a__ ( ) -> None:
UpperCAmelCase__ : Tuple = input('''Enter message: ''' )
UpperCAmelCase__ : Tuple = int(input(F"""Enter key [2-{len(lowerCAmelCase__ ) - 1}]: """ ) )
UpperCAmelCa... | 299 |
'''simple docstring'''
from __future__ import annotations
import math
def a__ ( lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ) -> int:
if depth < 0:
raise ValueError('''Depth cannot be less than 0''' )
if no... | 299 | 1 |
'''simple docstring'''
from typing import Optional, Tuple, Union
import flax
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict
from ..configuration_utils import ConfigMixin, flax_register_to_config
from ..utils import BaseOutput
from .embeddings_f... | 299 |
'''simple docstring'''
class lowerCamelCase_ :
def __init__( self : Union[str, Any] , _A : int ):
'''simple docstring'''
UpperCAmelCase__ : str = n
UpperCAmelCase__ : Union[str, Any] ... | 299 | 1 |
'''simple docstring'''
import os
import tempfile
import unittest
from transformers.models.marian.convert_marian_tatoeba_to_pytorch import DEFAULT_REPO, TatoebaConverter
from transformers.testing_utils import slow
from transformers.utils import cached_property
@unittest.skipUnless(os.path.exists(_... | 299 |
'''simple docstring'''
def a__ ( lowerCAmelCase__ ) -> Optional[Any]:
UpperCAmelCase__ : Optional[Any] = len(lowerCAmelCase__ )
for i in range(length - 1 ):
UpperCAmelCase__ : Optional[Any] = i
for k in range(i + 1 , low... | 299 | 1 |
'''simple docstring'''
from __future__ import annotations
import collections
import pprint
from pathlib import Path
def a__ ( lowerCAmelCase__ ) -> str:
return "".join(sorted(lowerCAmelCase__ ) )
def a__ ( lowerCAmelCase__ ) -> list[str]:
ret... | 299 |
'''simple docstring'''
from collections.abc import Iterable
from typing import Any
class lowerCamelCase_ :
def __init__( self : List[Any] , _A : int | None = None ):
'''simple docstring'''
UpperCAmelCase__ : List[A... | 299 | 1 |
'''simple docstring'''
UpperCamelCase__ = 8.314_462 # Unit - J mol-1 K-1
def a__ ( lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ) -> float:
if moles < 0 or kelvin < 0 or volume < 0:
raise ValueError('''Invalid inputs. Enter positive value.''' )
re... | 299 |
'''simple docstring'''
import argparse
import torch
from transformers import (
SpeechTaConfig,
SpeechTaFeatureExtractor,
SpeechTaForSpeechToSpeech,
SpeechTaForSpeechToText,
SpeechTaForTextToSpeech,
SpeechTaProcessor,
SpeechTaTokenizer,
logging,
)
from transformers.... | 299 | 1 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_dpt import DPTImageProcessor
UpperCamelCase__ = logging.get_logger(__name__)
class lowerCamelCase_ ( __a ):
def __init__( self : str , *_A : ... | 299 |
'''simple docstring'''
import argparse
import fairseq
import torch
from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging
logging.set_verbosity_info()
UpperCamelCase__ = logging.get_logger(__name__)
UpperCamelCase__ = {
'''post_extrac... | 299 | 1 |
'''simple docstring'''
def a__ ( lowerCAmelCase__ , lowerCAmelCase__ ) -> Any:
print('''\nThe shortest path matrix using Floyd Warshall algorithm\n''' )
for i in range(lowerCAmelCase__ ):
for j in range(lowerCAmelCase__ ):
if dist[i][j] != float('''inf'... | 299 |
'''simple docstring'''
import itertools
import random
import unittest
import numpy as np
from transformers import ASTFeatureExtractor
from transformers.testing_utils import require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available
from ...test_sequence_feature_... | 299 | 1 |
'''simple docstring'''
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import requir... | 299 |
'''simple docstring'''
import os
import shutil
import sys
import tempfile
import unittest
from pathlib import Path
import pytest
import transformers
from transformers import (
BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
AutoTokenizer,
BertConfig,
B... | 299 | 1 |
'''simple docstring'''
# Logistic Regression from scratch
# In[62]:
# In[63]:
# importing all the required libraries
import numpy as np
from matplotlib import pyplot as plt
from sklearn import datasets
def a__ ( lowerCAmelCase__ ) -> List[Any]:
return 1 / (1 + np.exp(-z ... | 299 |
'''simple docstring'''
def a__ ( lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , ) -> float:
UpperCAmelCase__ : Tuple = [redshift, radiation_density, matter_density, dark_energy]
if any(p < 0 for p in parameters ):
... | 299 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
UpperCamelCase__ = {
'''configuration_conditional_detr''': [
'''CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP''',
... | 299 |
'''simple docstring'''
import gc
import math
import unittest
import torch
from diffusers import UNetaDModel
from diffusers.utils import floats_tensor, logging, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from .test_modeling_common import Mode... | 299 | 1 |
'''simple docstring'''
from sklearn.metrics import fa_score, matthews_corrcoef
import datasets
from .record_evaluation import evaluate as evaluate_record
UpperCamelCase__ = '''\
@article{wang2019superglue,
title={SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding System... | 299 |
'''simple docstring'''
from __future__ import annotations
def a__ ( lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ) -> tuple[float, list[float]]:
UpperCAmelCase__ : Optional[Any] = list(range(len(lowerCAmelCase__ ) ) )
UpperCAmelCase... | 299 | 1 |
'''simple docstring'''
def a__ ( lowerCAmelCase__ = 10_00 ) -> int:
return sum(2 * a * ((a - 1) // 2) for a in range(3 , n + 1 ) )
if __name__ == "__main__":
print(solution())
| 299 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class lowerCamelCase_ ( metaclass=__a ):
lowerCAmelCase__ = ['torch', 'transformers', 'onnx']
def __init__( self : int , *_A : Tuple , **_A : ... | 299 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tensorflow_text_available, is_torch_available
UpperCamelCase__ = {
'''configuration_ernie''': ['''ERNIE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ErnieConfig''', '''ErnieOnnx... | 299 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCamelCase__ = {'''configuration_mmbt''': ['''MMBTConfig''']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except... | 299 | 1 |
'''simple docstring'''
import argparse
import collections
import os
import re
import tempfile
import pandas as pd
from datasets import Dataset
from huggingface_hub import hf_hub_download, upload_folder
from transformers.utils import direct_transformers_import
# All paths are set with the intent ... | 299 |
'''simple docstring'''
import inspect
import tempfile
from collections import OrderedDict, UserDict
from collections.abc import MutableMapping
from contextlib import ExitStack, contextmanager
from dataclasses import fields
from enum import Enum
from typing import Any, ContextManager, List, Tuple
import... | 299 | 1 |
'''simple docstring'''
from pickle import UnpicklingError
import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict
from ..utils import logging
UpperCamelCase__ = logging.get_logger(__name__)
def a_... | 299 |
'''simple docstring'''
import argparse
from typing import List
import evaluate
import numpy as np
import torch
from datasets import DatasetDict, load_dataset
# New Code #
# We'll be using StratifiedKFold for this example
from sklearn.model_selection import StratifiedKFold
from torch.optim import Ada... | 299 | 1 |
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