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 numpy as np
import torch
from torch.nn import CrossEntropyLoss
from transformers import AutoModelForCausalLM, AutoTokenizer
import datasets
from datasets import logging
snake_case__ : int = """\\n\n"""
snake_case__ : Optional[Any] = """\nPerplexi... | 356 | '''simple docstring'''
import math
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import SchedulerMixin, SchedulerOutput
class __SCREAMING_SNAKE_CASE ( lowerCamelCase_ , ... | 274 | 0 |
'''simple docstring'''
def _lowerCamelCase ( lowerCamelCase_ : int , lowerCamelCase_ : int ):
"""simple docstring"""
UpperCAmelCase_ : str = 1 # To kept the Calculated Value
# Since C(n, k) = C(n, n-k)
if k > (n - k):
UpperCAm... | 357 | '''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_available
from transformers... | 274 | 0 |
import tempfile
import unittest
import numpy as np
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import BertConfig, is_flax_available
from transformers.testing_utils import TOKEN, USER, is_staging_test, require_flax
if is_flax_available():
import os
... | 358 | '''simple docstring'''
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class __SCREAMING_SNAKE_CASE ( lowerCamelCase_ ):
'''simple docstring'''
lowerCamelCase_ :Tuple = ['''image_processor''', '''tokenizer'... | 274 | 0 |
'''simple docstring'''
import random
import unittest
import torch
from diffusers import IFInpaintingPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device
from ..pipeline_params import (
... | 359 | '''simple docstring'''
snake_case__ : str = '''Tobias Carryer'''
from time import time
class __SCREAMING_SNAKE_CASE :
'''simple docstring'''
def __init__( self , snake_case_ , snake_case_ , snake_case_ , snake_case_=int(time() ) ): # no... | 274 | 0 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
snake_case__ : Optional[int] ... | 360 | '''simple docstring'''
import unittest
from parameterized import parameterized
from transformers import OpenLlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common impor... | 274 | 0 |
'''simple docstring'''
from __future__ import annotations
def _lowerCamelCase ( lowerCamelCase_ : Optional[Any] ):
"""simple docstring"""
UpperCAmelCase_ : Optional[Any] = [True] * limit
UpperCAmelCase_ : int = False
UpperCAmelC... | 361 | '''simple docstring'''
def _lowerCamelCase ( lowerCamelCase_ : str , lowerCamelCase_ : str = " " ):
"""simple docstring"""
UpperCAmelCase_ : str = []
UpperCAmelCase_ : List[Any] = 0
for index, char in enumerate(lowe... | 274 | 0 |
'''simple docstring'''
import numpy as np
def _lowerCamelCase ( lowerCamelCase_ : Tuple ):
"""simple docstring"""
return (2 / (1 + np.exp(-2 * vector ))) - 1
if __name__ == "__main__":
import doctest
doctest.testmod()
| 362 | '''simple docstring'''
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
from sagemaker.huggingface... | 274 | 0 |
from __future__ import annotations
def _lowerCamelCase ( lowerCamelCase_ : list[int | str] ):
"""simple docstring"""
create_state_space_tree(UpperCamelCase__ , [] , 0 , [0 for i in range(len(UpperCamelCase__ ) )] )
def _lowerCamelCase ... | 363 | '''simple docstring'''
import hashlib
import unittest
from transformers import MODEL_FOR_DEPTH_ESTIMATION_MAPPING, is_torch_available, is_vision_available
from transformers.pipelines import DepthEstimationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
requi... | 274 | 0 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import TransformeraDModel, VQDiffusionPipeline, VQDiffusionScheduler, VQModel
from diffusers.pipelines.vq_diffusion.pipeline_vq_diffusion import L... | 364 | '''simple docstring'''
import copy
import os
import tempfile
from unittest import TestCase
from unittest.mock import patch
import numpy as np
import pyarrow as pa
import pyarrow.parquet as pq
import pytest
from datasets.arrow_writer import ArrowWriter, OptimizedTypedSequence, ParquetWriter, TypedSequence
from datas... | 274 | 0 |
'''simple docstring'''
from abc import ABC, abstractmethod
from argparse import ArgumentParser
class __SCREAMING_SNAKE_CASE ( snake_case__ ):
'''simple docstring'''
@staticmethod
@abstractmethod
def _UpperCamelCase ( snake_case_ ):
'''simple docstring'''
... | 365 | '''simple docstring'''
snake_case__ : Optional[Any] = tuple[float, float, float]
snake_case__ : Tuple = tuple[float, float, float]
def _lowerCamelCase ( lowerCamelCase_ : Pointad , lowerCamelCase_ : Pointad ):
"""simple docstring"""
... | 274 | 0 |
'''simple docstring'''
import gc
import inspect
import unittest
import torch
from parameterized import parameterized
from diffusers import PriorTransformer
from diffusers.utils import floats_tensor, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from .test_mod... | 366 | '''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
snake_case__ : Dict = {
'''albert-base-v1''': '''https://huggingface.co/albert-base-v1/resolve/main/config.json''',
'''... | 274 | 0 |
'''simple docstring'''
import argparse
import fairseq
import torch
from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging
logging.set_verbosity_info()
snake_case__ : Dict = logging.get_logger(__name__)
snake_case__ : int ... | 367 | '''simple docstring'''
from __future__ import annotations
from collections import deque
from collections.abc import Iterator
from dataclasses import dataclass
@dataclass
class __SCREAMING_SNAKE_CASE :
'''simple docstring'''
lowerCamelCase_ :int
lowerCamelCase_ :int
class ... | 274 | 0 |
'''simple docstring'''
import argparse
import copy
def _lowerCamelCase ( lowerCamelCase_ : Union[str, Any] ):
"""simple docstring"""
UpperCAmelCase_ : Union[str, Any] = {}
with open(__lowerCamelCase ) as f:
for line in f:
... | 368 | '''simple docstring'''
from typing import List, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
snake_case__ : List[Any] = logging.get_logger(__name__)
snake_case__ : Optional[Any] = {
'''huggingface/autoformer-tourism-monthly''':... | 274 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
snake_case__ : Any = logging.get_logger(__name__)
snake_case__ : Tuple = {
"google/pegasus-large": "https://huggingface.co/google/pegasus-large/resolve/main/config.json"... | 369 | '''simple docstring'''
import unittest
from transformers.utils.backbone_utils import (
BackboneMixin,
get_aligned_output_features_output_indices,
verify_out_features_out_indices,
)
class __SCREAMING_SNAKE_CASE ( unittest.TestCase ):
'''simple docstring'''
def _UpperCamelC... | 274 | 0 |
'''simple docstring'''
from __future__ import annotations
def _lowerCamelCase ( lowerCamelCase_ : list[list[int]] ):
"""simple docstring"""
UpperCAmelCase_ : Optional[Any] = len(lowerCamelCase_ )
# We need to create solution object to save path.
... | 370 | '''simple docstring'''
import numpy as np
import torch
from torch.nn import CrossEntropyLoss
from transformers import AutoModelForCausalLM, AutoTokenizer
import datasets
from datasets import logging
snake_case__ : str = '''\
'''
snake_case__ : Union[str, Any] = '''
Perplexit... | 274 | 0 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class __SCREAMING_SNAKE_CASE ( metaclass=_a ):
'''simple docstring'''
lowerCamelCase_ :List[str] = ['''keras_nlp''']
def __init__( self , *snake_case_ , **snake_case_ ):
'... | 371 | '''simple docstring'''
from sklearn.metrics import fa_score
import datasets
snake_case__ : str = '''
The F1 score is the harmonic mean of the precision and recall. It can be computed with the equation:
F1 = 2 * (precision * recall) / (precision + recall)
'''
snake_case__ : int ... | 274 | 0 |
'''simple docstring'''
import argparse
import os
import torch
from transformers import (
XLNetConfig,
XLNetForQuestionAnswering,
XLNetForSequenceClassification,
XLNetLMHeadModel,
load_tf_weights_in_xlnet,
)
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
snake_case__ : ... | 350 | '''simple docstring'''
import os
from glob import glob
import imageio
import torch
import torchvision
import wandb
from img_processing import custom_to_pil, loop_post_process, preprocess, preprocess_vqgan
from loaders import load_vqgan
from PIL import Image
from torch import nn
from transformers import CLIPModel, C... | 274 | 0 |
'''simple docstring'''
import argparse
import gc
import json
import os
import re
import torch
from huggingface_hub import hf_hub_download
from transformers import AutoModelForCausalLM, AutoTokenizer, PreTrainedTokenizerFast, RwkvConfig
from transformers.modeling_utils import WEIGHTS_INDEX_NAME, shard_checkpoint
s... | 351 | '''simple docstring'''
def _lowerCamelCase ( lowerCamelCase_ : str ):
"""simple docstring"""
UpperCAmelCase_ : Optional[int] = [0] * len(lowerCamelCase_ )
for i in range(1 , len(lowerCamelCase_ ) ):
# use last results f... | 274 | 0 |
'''simple docstring'''
import collections
import tempfile
import unittest
import numpy as np
from transformers.testing_utils import (
is_pt_flax_cross_test,
require_flax,
require_torch,
require_vision,
slow,
torch_device,
)
from transformers.utils import is_flax_available, is_torch_available... | 352 | '''simple docstring'''
import os
import time
import numpy as np
import onnxruntime as ort
snake_case__ : Optional[int] = '''1'''
snake_case__ : str = '''0'''
snake_case__ : List[str] = '''1'''
snake_case__ : List[str] = ort.SessionOptions... | 274 | 0 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
snake_case__ : Union[str, Any] = logging.get_logger(__name__)
snake_case__ : int ... | 353 | '''simple docstring'''
def _lowerCamelCase ( lowerCamelCase_ : int , lowerCamelCase_ : int ):
"""simple docstring"""
return int(input_a == input_a == 0 )
def _lowerCamelCase ( ):
"""simple docstring"""
print('Truth Table of NOR Gate:' ... | 274 | 0 |
'''simple docstring'''
def _lowerCamelCase ( lowerCamelCase_ : Optional[Any] ):
"""simple docstring"""
if a < 0:
raise ValueError('Input value must be a positive integer' )
elif isinstance(_lowerCAmelCase , _lowerCAmelCase ):
... | 354 | '''simple docstring'''
from typing import Optional, Union
import torch
from torch import nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...models.modeling_utils import ModelMixin
class __SCREAMING_SNAKE_CASE ( lowerCamelCase_ , lowerCamelCase_ ):
'''simp... | 274 | 0 |
'''simple docstring'''
import collections
import tempfile
import unittest
import numpy as np
from transformers.testing_utils import (
is_pt_flax_cross_test,
require_flax,
require_torch,
require_vision,
slow,
torch_device,
)
from transformers.utils import is_flax_available, is_torch_available... | 355 | '''simple docstring'''
from manim import *
class __SCREAMING_SNAKE_CASE ( lowerCamelCase_ ):
'''simple docstring'''
def _UpperCamelCase ( self ):
'''simple docstring'''
UpperCAmelCase_ : Dict = Rectangle(height=0.5 , width=0.5 ... | 274 | 0 |
'''simple docstring'''
from __future__ import annotations
def _lowerCamelCase ( lowerCamelCase_ : str , lowerCamelCase_ : list[str] | None = None ):
"""simple docstring"""
UpperCAmelCase_ : Any = word_bank or []
# create a table
UpperC... | 356 | '''simple docstring'''
import math
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import SchedulerMixin, SchedulerOutput
class __SCREAMING_SNAKE_CASE ( lowerCamelCase_ , ... | 274 | 0 |
'''simple docstring'''
def _lowerCamelCase ( lowerCamelCase_ : bytes ):
"""simple docstring"""
return "".join([hex(lowerCamelCase_ )[2:].zfill(2 ).upper() for byte in list(lowerCamelCase_ )] )
def _lowerCamelCase ( lowerCamelCase_ : str )... | 357 | '''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_available
from transformers... | 274 | 0 |
from typing import Dict
from transformers import EvalPrediction, HfArgumentParser, TrainingArguments, is_torch_available
from transformers.testing_utils import (
TestCasePlus,
execute_subprocess_async,
get_torch_dist_unique_port,
require_torch_multi_gpu,
require_torch_neuroncore,
)
from transformer... | 358 | '''simple docstring'''
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class __SCREAMING_SNAKE_CASE ( lowerCamelCase_ ):
'''simple docstring'''
lowerCamelCase_ :Tuple = ['''image_processor''', '''tokenizer'... | 274 | 0 |
'''simple docstring'''
def _lowerCamelCase ( lowerCamelCase_ : Tuple ):
"""simple docstring"""
if upper_limit < 0:
raise ValueError('Limit for the Catalan sequence must be ≥ 0' )
UpperCAmelCase_ : Any = [0] * (upper_limit + 1)
# Base... | 359 | '''simple docstring'''
snake_case__ : str = '''Tobias Carryer'''
from time import time
class __SCREAMING_SNAKE_CASE :
'''simple docstring'''
def __init__( self , snake_case_ , snake_case_ , snake_case_ , snake_case_=int(time() ) ): # no... | 274 | 0 |
'''simple docstring'''
import argparse
import os.path as osp
import re
import torch
from safetensors.torch import load_file, save_file
# =================#
# UNet Conversion #
# =================#
snake_case__ : Any = [
# (stable-diffusion, HF Diffusers)
('''time_embed.0.weight'''... | 360 | '''simple docstring'''
import unittest
from parameterized import parameterized
from transformers import OpenLlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common impor... | 274 | 0 |
'''simple docstring'''
import argparse
import requests
import torch
# pip3 install salesforce-lavis
# I'm actually installing a slightly modified version: pip3 install git+https://github.com/nielsrogge/LAVIS.git@fix_lavis
from lavis.models import load_model_and_preprocess
from PIL import Image
from transformers im... | 361 | '''simple docstring'''
def _lowerCamelCase ( lowerCamelCase_ : str , lowerCamelCase_ : str = " " ):
"""simple docstring"""
UpperCAmelCase_ : str = []
UpperCAmelCase_ : List[Any] = 0
for index, char in enumerate(lowe... | 274 | 0 |
'''simple docstring'''
def _lowerCamelCase ( lowerCamelCase_ : Optional[Any] = 200 ):
"""simple docstring"""
UpperCAmelCase_ : List[str] = [1, 2, 5, 10, 20, 50, 100, 200]
UpperCAmelCase_ : Optional[Any] = [0] * (pence + 1)
UpperCA... | 362 | '''simple docstring'''
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
from sagemaker.huggingface... | 274 | 0 |
from __future__ import annotations
import unittest
from transformers import RoFormerConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from .... | 363 | '''simple docstring'''
import hashlib
import unittest
from transformers import MODEL_FOR_DEPTH_ESTIMATION_MAPPING, is_torch_available, is_vision_available
from transformers.pipelines import DepthEstimationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
requi... | 274 | 0 |
'''simple docstring'''
def _lowerCamelCase ( lowerCamelCase_ : List[Any] ):
"""simple docstring"""
if number > 0:
raise ValueError('input must be a negative integer' )
UpperCAmelCase_ : Optional[int] = len(bin(__snake_case )[3:] ... | 364 | '''simple docstring'''
import copy
import os
import tempfile
from unittest import TestCase
from unittest.mock import patch
import numpy as np
import pyarrow as pa
import pyarrow.parquet as pq
import pytest
from datasets.arrow_writer import ArrowWriter, OptimizedTypedSequence, ParquetWriter, TypedSequence
from datas... | 274 | 0 |
'''simple docstring'''
import json
import os
import sys
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile
from huggingface_hub import HfFolder, Repository, create_repo, delete_repo
from requests.exceptions import HTTPError
import transformers
from transformers import (
CONFIG_... | 365 | '''simple docstring'''
snake_case__ : Optional[Any] = tuple[float, float, float]
snake_case__ : Tuple = tuple[float, float, float]
def _lowerCamelCase ( lowerCamelCase_ : Pointad , lowerCamelCase_ : Pointad ):
"""simple docstring"""
... | 274 | 0 |
'''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
snake_case__ : str = logging.get_logger(__name__)
def _lowerCamelCase ( lowerCamelCase_ : List[Any] ... | 366 | '''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
snake_case__ : Dict = {
'''albert-base-v1''': '''https://huggingface.co/albert-base-v1/resolve/main/config.json''',
'''... | 274 | 0 |
'''simple docstring'''
from ...utils import is_torch_available, is_transformers_available
if is_transformers_available() and is_torch_available():
from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline
| 367 | '''simple docstring'''
from __future__ import annotations
from collections import deque
from collections.abc import Iterator
from dataclasses import dataclass
@dataclass
class __SCREAMING_SNAKE_CASE :
'''simple docstring'''
lowerCamelCase_ :int
lowerCamelCase_ :int
class ... | 274 | 0 |
'''simple docstring'''
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:... | 368 | '''simple docstring'''
from typing import List, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
snake_case__ : List[Any] = logging.get_logger(__name__)
snake_case__ : Optional[Any] = {
'''huggingface/autoformer-tourism-monthly''':... | 274 | 0 |
'''simple docstring'''
def _lowerCamelCase ( lowerCamelCase_ : Union[str, Any] , lowerCamelCase_ : Dict , lowerCamelCase_ : str , lowerCamelCase_ : List[Any] , lowerCamelCase_ : int , ):
"""simple docstring"""
UpperCAmelCase... | 369 | '''simple docstring'''
import unittest
from transformers.utils.backbone_utils import (
BackboneMixin,
get_aligned_output_features_output_indices,
verify_out_features_out_indices,
)
class __SCREAMING_SNAKE_CASE ( unittest.TestCase ):
'''simple docstring'''
def _UpperCamelC... | 274 | 0 |
'''simple docstring'''
import sys
snake_case__ : Tuple = (
'''73167176531330624919225119674426574742355349194934'''
'''96983520312774506326239578318016984801869478851843'''
'''85861560789112949495459501737958331952853208805511'''
'''1254069874715852386305071569329096329522744304355... | 370 | '''simple docstring'''
import numpy as np
import torch
from torch.nn import CrossEntropyLoss
from transformers import AutoModelForCausalLM, AutoTokenizer
import datasets
from datasets import logging
snake_case__ : str = '''\
'''
snake_case__ : Union[str, Any] = '''
Perplexit... | 274 | 0 |
'''simple docstring'''
import builtins
import sys
from ...utils.imports import _is_package_available
from . import cursor, input
from .helpers import Direction, clear_line, forceWrite, linebreak, move_cursor, reset_cursor, writeColor
from .keymap import KEYMAP
snake_case__ : Tuple = False
try:
... | 371 | '''simple docstring'''
from sklearn.metrics import fa_score
import datasets
snake_case__ : str = '''
The F1 score is the harmonic mean of the precision and recall. It can be computed with the equation:
F1 = 2 * (precision * recall) / (precision + recall)
'''
snake_case__ : int ... | 274 | 0 |
'''simple docstring'''
from __future__ import annotations
snake_case__ : Tuple = tuple[int, int, int]
snake_case__ : Optional[int] = tuple[str, str, str]
# used alphabet --------------------------
# from string.ascii_uppercase
snake_case__ : Tuple = "ABCDEFG... | 350 | '''simple docstring'''
import os
from glob import glob
import imageio
import torch
import torchvision
import wandb
from img_processing import custom_to_pil, loop_post_process, preprocess, preprocess_vqgan
from loaders import load_vqgan
from PIL import Image
from torch import nn
from transformers import CLIPModel, C... | 274 | 0 |
'''simple docstring'''
def _lowerCamelCase ( lowerCamelCase_ : float , lowerCamelCase_ : int ):
if digit_amount > 0:
return round(number - int(_SCREAMING_SNAKE_CASE ) , _SCREAMING_SNAKE_CASE )
return number - int(_SCREAMING_SNAKE_CASE ... | 351 | '''simple docstring'''
def _lowerCamelCase ( lowerCamelCase_ : str ):
"""simple docstring"""
UpperCAmelCase_ : Optional[int] = [0] * len(lowerCamelCase_ )
for i in range(1 , len(lowerCamelCase_ ) ):
# use last results f... | 274 | 0 |
'''simple docstring'''
import json
import os
import unittest
from typing import Tuple
from transformers import WavaVecaPhonemeCTCTokenizer
from transformers.models.wavaveca.tokenization_wavaveca import VOCAB_FILES_NAMES
from transformers.models.wavaveca_phoneme.tokenization_wavaveca_phoneme import WavaVecaPhonemeCTC... | 352 | '''simple docstring'''
import os
import time
import numpy as np
import onnxruntime as ort
snake_case__ : Optional[int] = '''1'''
snake_case__ : str = '''0'''
snake_case__ : List[str] = '''1'''
snake_case__ : List[str] = ort.SessionOptions... | 274 | 0 |
'''simple docstring'''
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_herbert import HerbertTokenizer
snake_case__ : Optional[Any] = logging.get_logger(__name__)
snake_case__ : Dic... | 353 | '''simple docstring'''
def _lowerCamelCase ( lowerCamelCase_ : int , lowerCamelCase_ : int ):
"""simple docstring"""
return int(input_a == input_a == 0 )
def _lowerCamelCase ( ):
"""simple docstring"""
print('Truth Table of NOR Gate:' ... | 274 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
snake_case__ : List[Any] = {
'''configuration_funnel''': ['''FUNNEL_P... | 354 | '''simple docstring'''
from typing import Optional, Union
import torch
from torch import nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...models.modeling_utils import ModelMixin
class __SCREAMING_SNAKE_CASE ( lowerCamelCase_ , lowerCamelCase_ ):
'''simp... | 274 | 0 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModel
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEImgaImgPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from ... | 355 | '''simple docstring'''
from manim import *
class __SCREAMING_SNAKE_CASE ( lowerCamelCase_ ):
'''simple docstring'''
def _UpperCamelCase ( self ):
'''simple docstring'''
UpperCAmelCase_ : Dict = Rectangle(height=0.5 , width=0.5 ... | 274 | 0 |
'''simple docstring'''
import argparse
import torch
from transformers import LxmertConfig, LxmertForPreTraining, load_tf_weights_in_lxmert
from transformers.utils import logging
logging.set_verbosity_info()
def _lowerCamelCase ( lowerCamelCase_ : int , lowerCamelCase_ : Dict , ... | 356 | '''simple docstring'''
import math
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import SchedulerMixin, SchedulerOutput
class __SCREAMING_SNAKE_CASE ( lowerCamelCase_ , ... | 274 | 0 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
snake_case__ : Tuple = logging.get_logger(__name__)
snake_case__ ... | 357 | '''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_available
from transformers... | 274 | 0 |
from typing import List
import datasets
from datasets.tasks import AudioClassification
from ..folder_based_builder import folder_based_builder
snake_case__ : Tuple = datasets.utils.logging.get_logger(__name__)
class __SCREAMING_SNAKE_CASE ( folder_based_builder.FolderBasedBuilderC... | 358 | '''simple docstring'''
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class __SCREAMING_SNAKE_CASE ( lowerCamelCase_ ):
'''simple docstring'''
lowerCamelCase_ :Tuple = ['''image_processor''', '''tokenizer'... | 274 | 0 |
'''simple docstring'''
from __future__ import annotations
import time
snake_case__ : Dict = list[tuple[int, int]]
snake_case__ : Optional[int] = [
[0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles
[0, 0, 0, 0, 0, 0, 0],
... | 359 | '''simple docstring'''
snake_case__ : str = '''Tobias Carryer'''
from time import time
class __SCREAMING_SNAKE_CASE :
'''simple docstring'''
def __init__( self , snake_case_ , snake_case_ , snake_case_ , snake_case_=int(time() ) ): # no... | 274 | 0 |
'''simple docstring'''
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import is_speech_available, is_vision_available
from transformers.testing_utils import require_torch
if is_vision_available():
from transformers import TvltImageProcessor
if is_speech_av... | 360 | '''simple docstring'''
import unittest
from parameterized import parameterized
from transformers import OpenLlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common impor... | 274 | 0 |
'''simple docstring'''
import doctest
import logging
import os
import unittest
from pathlib import Path
from typing import List, Union
import transformers
from transformers.testing_utils import require_tf, require_torch, slow
snake_case__ : Optional[Any] = logging.getLogger()
@unittest.ski... | 361 | '''simple docstring'''
def _lowerCamelCase ( lowerCamelCase_ : str , lowerCamelCase_ : str = " " ):
"""simple docstring"""
UpperCAmelCase_ : str = []
UpperCAmelCase_ : List[Any] = 0
for index, char in enumerate(lowe... | 274 | 0 |
'''simple docstring'''
import secrets
from random import shuffle
from string import ascii_letters, ascii_lowercase, ascii_uppercase, digits, punctuation
def _lowerCamelCase ( lowerCamelCase_ : Dict = 8 ):
"""simple docstring"""
UpperCAmelCase_ : str = ascii_l... | 362 | '''simple docstring'''
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
from sagemaker.huggingface... | 274 | 0 |
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 AutoFeatureExtractor, WavaVecaFeatureExtractor
from transformers.testing_utils import TOKEN, USER, get_t... | 363 | '''simple docstring'''
import hashlib
import unittest
from transformers import MODEL_FOR_DEPTH_ESTIMATION_MAPPING, is_torch_available, is_vision_available
from transformers.pipelines import DepthEstimationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
requi... | 274 | 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
snake_case__ : Any = logging.get_logger(__name__)
snake_case__ ... | 364 | '''simple docstring'''
import copy
import os
import tempfile
from unittest import TestCase
from unittest.mock import patch
import numpy as np
import pyarrow as pa
import pyarrow.parquet as pq
import pytest
from datasets.arrow_writer import ArrowWriter, OptimizedTypedSequence, ParquetWriter, TypedSequence
from datas... | 274 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
snake_case__ : Any = {
'''configuration_upernet''': ['''UperNetConfig'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable... | 365 | '''simple docstring'''
snake_case__ : Optional[Any] = tuple[float, float, float]
snake_case__ : Tuple = tuple[float, float, float]
def _lowerCamelCase ( lowerCamelCase_ : Pointad , lowerCamelCase_ : Pointad ):
"""simple docstring"""
... | 274 | 0 |
'''simple docstring'''
from math import isqrt, loga
def _lowerCamelCase ( lowerCamelCase_ : int ):
"""simple docstring"""
UpperCAmelCase_ : Dict = [True] * max_number
for i in range(2 , isqrt(max_number - 1 ) + 1 ):
if is_pr... | 366 | '''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
snake_case__ : Dict = {
'''albert-base-v1''': '''https://huggingface.co/albert-base-v1/resolve/main/config.json''',
'''... | 274 | 0 |
'''simple docstring'''
import csv
from collections import defaultdict
from dataclasses import dataclass, field
from typing import List, Optional
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.ticker import ScalarFormatter
from transformers import HfArgumentParser
def _lowerCame... | 367 | '''simple docstring'''
from __future__ import annotations
from collections import deque
from collections.abc import Iterator
from dataclasses import dataclass
@dataclass
class __SCREAMING_SNAKE_CASE :
'''simple docstring'''
lowerCamelCase_ :int
lowerCamelCase_ :int
class ... | 274 | 0 |
'''simple docstring'''
import torch
from diffusers import StableDiffusionPipeline
snake_case__ : Dict = "path-to-your-trained-model"
snake_case__ : Tuple = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.floataa).to('''cuda''')
snake_case__ : Unio... | 368 | '''simple docstring'''
from typing import List, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
snake_case__ : List[Any] = logging.get_logger(__name__)
snake_case__ : Optional[Any] = {
'''huggingface/autoformer-tourism-monthly''':... | 274 | 0 |
'''simple docstring'''
def _lowerCamelCase ( lowerCamelCase_ : str ):
"""simple docstring"""
UpperCAmelCase_ : Optional[Any] = 0
# if input_string is "aba" than new_input_string become "a|b|a"
UpperCAmelCase_ : Optional[Any] = ''... | 369 | '''simple docstring'''
import unittest
from transformers.utils.backbone_utils import (
BackboneMixin,
get_aligned_output_features_output_indices,
verify_out_features_out_indices,
)
class __SCREAMING_SNAKE_CASE ( unittest.TestCase ):
'''simple docstring'''
def _UpperCamelC... | 274 | 0 |
'''simple docstring'''
import numpy as np
from nltk.translate import meteor_score
import datasets
from datasets.config import importlib_metadata, version
snake_case__ : str = version.parse(importlib_metadata.version('''nltk'''))
if NLTK_VERSION >= version.Version('''3.6.4'''):
from nltk impo... | 370 | '''simple docstring'''
import numpy as np
import torch
from torch.nn import CrossEntropyLoss
from transformers import AutoModelForCausalLM, AutoTokenizer
import datasets
from datasets import logging
snake_case__ : str = '''\
'''
snake_case__ : Union[str, Any] = '''
Perplexit... | 274 | 0 |
'''simple docstring'''
import argparse
import glob
import logging
import os
from argparse import Namespace
from importlib import import_module
import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from seqeval.metrics import accuracy_score, fa_score, precision_sc... | 371 | '''simple docstring'''
from sklearn.metrics import fa_score
import datasets
snake_case__ : str = '''
The F1 score is the harmonic mean of the precision and recall. It can be computed with the equation:
F1 = 2 * (precision * recall) / (precision + recall)
'''
snake_case__ : int ... | 274 | 0 |
'''simple docstring'''
from typing import Optional
from .. import Features, NamedSplit
from ..packaged_modules.text.text import Text
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader
class __SCREAMING_SNAKE_CASE ( snake_case_ ):
'''simple doc... | 350 | '''simple docstring'''
import os
from glob import glob
import imageio
import torch
import torchvision
import wandb
from img_processing import custom_to_pil, loop_post_process, preprocess, preprocess_vqgan
from loaders import load_vqgan
from PIL import Image
from torch import nn
from transformers import CLIPModel, C... | 274 | 0 |
'''simple docstring'''
import argparse
import json
from collections import OrderedDict
from functools import partial
from pathlib import Path
import timm
import torch
from huggingface_hub import hf_hub_download
from transformers import LevitConfig, LevitForImageClassificationWithTeacher, LevitImageProcessor
from tr... | 351 | '''simple docstring'''
def _lowerCamelCase ( lowerCamelCase_ : str ):
"""simple docstring"""
UpperCAmelCase_ : Optional[int] = [0] * len(lowerCamelCase_ )
for i in range(1 , len(lowerCamelCase_ ) ):
# use last results f... | 274 | 0 |
'''simple docstring'''
def _lowerCamelCase ( lowerCamelCase_ : list , lowerCamelCase_ : list , lowerCamelCase_ : int , lowerCamelCase_ : int , lowerCamelCase_ : int ):
"""simple docstring"""
if index == number_of_items:
... | 352 | '''simple docstring'''
import os
import time
import numpy as np
import onnxruntime as ort
snake_case__ : Optional[int] = '''1'''
snake_case__ : str = '''0'''
snake_case__ : List[str] = '''1'''
snake_case__ : List[str] = ort.SessionOptions... | 274 | 0 |
'''simple docstring'''
import coval # From: git+https://github.com/ns-moosavi/coval.git # noqa: F401
from coval.conll import reader, util
from coval.eval import evaluator
import datasets
snake_case__ : List[str] = datasets.logging.get_logger(__name__)
snake_case__ : Union[str, Any] ... | 353 | '''simple docstring'''
def _lowerCamelCase ( lowerCamelCase_ : int , lowerCamelCase_ : int ):
"""simple docstring"""
return int(input_a == input_a == 0 )
def _lowerCamelCase ( ):
"""simple docstring"""
print('Truth Table of NOR Gate:' ... | 274 | 0 |
'''simple docstring'''
import multiprocessing
import os
from typing import BinaryIO, Optional, Union
import fsspec
from .. import Dataset, Features, NamedSplit, config
from ..formatting import query_table
from ..packaged_modules.json.json import Json
from ..utils import logging
from ..utils.typing impo... | 354 | '''simple docstring'''
from typing import Optional, Union
import torch
from torch import nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...models.modeling_utils import ModelMixin
class __SCREAMING_SNAKE_CASE ( lowerCamelCase_ , lowerCamelCase_ ):
'''simp... | 274 | 0 |
'''simple docstring'''
from dataclasses import dataclass
from typing import Tuple
import numpy as np
import torch
@dataclass
class __SCREAMING_SNAKE_CASE :
'''simple docstring'''
lowerCamelCase_ :torch.Tensor # [batch_size x 3]
lowerCamelCase_ :torch.Tensor # [batch_size x 3]
... | 355 | '''simple docstring'''
from manim import *
class __SCREAMING_SNAKE_CASE ( lowerCamelCase_ ):
'''simple docstring'''
def _UpperCamelCase ( self ):
'''simple docstring'''
UpperCAmelCase_ : Dict = Rectangle(height=0.5 , width=0.5 ... | 274 | 0 |
'''simple docstring'''
def _lowerCamelCase ( lowerCamelCase_ : int = 1000000 ):
"""simple docstring"""
UpperCAmelCase_ : int = set(range(3 , lowerCamelCase_ , 2 ) )
primes.add(2 )
for p in range(3 , lowerCamel... | 356 | '''simple docstring'''
import math
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import SchedulerMixin, SchedulerOutput
class __SCREAMING_SNAKE_CASE ( lowerCamelCase_ , ... | 274 | 0 |
'''simple docstring'''
import json
import re
from typing import TYPE_CHECKING, List, Optional, Tuple, Union
import numpy as np
from ...utils import is_tf_available, is_torch_available, logging
if TYPE_CHECKING:
if is_torch_available():
import torch
if is_tf_available():
import tensorflow as tf
from tokeniz... | 357 | '''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_available
from transformers... | 274 | 0 |
import torch
from transformers import AutoModel
class __SCREAMING_SNAKE_CASE ( torch.nn.Module ):
'''simple docstring'''
def __init__( self , snake_case_="sayef/fsner-bert-base-uncased" ):
'''simple docstring'''
super(__snake_case , self ).__init_... | 358 | '''simple docstring'''
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class __SCREAMING_SNAKE_CASE ( lowerCamelCase_ ):
'''simple docstring'''
lowerCamelCase_ :Tuple = ['''image_processor''', '''tokenizer'... | 274 | 0 |
'''simple docstring'''
class __SCREAMING_SNAKE_CASE :
'''simple docstring'''
def __init__( self , snake_case_ ):
'''simple docstring'''
UpperCAmelCase_ : int = len(A__ )
UpperCAmelCase_ : Optional[int] = [0]... | 359 | '''simple docstring'''
snake_case__ : str = '''Tobias Carryer'''
from time import time
class __SCREAMING_SNAKE_CASE :
'''simple docstring'''
def __init__( self , snake_case_ , snake_case_ , snake_case_ , snake_case_=int(time() ) ): # no... | 274 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_speech_available, is_torch_available
snake_case__ : Union[str, Any] = {
'''configuration_audio_spectrogram_transformer''': [
'''AUDIO_SPECTROGRAM_TRANSFO... | 360 | '''simple docstring'''
import unittest
from parameterized import parameterized
from transformers import OpenLlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common impor... | 274 | 0 |
'''simple docstring'''
def _lowerCamelCase ( lowerCamelCase_ : float , lowerCamelCase_ : float , lowerCamelCase_ : int ):
"""simple docstring"""
if principal <= 0:
raise Exception('Principal borrowed must be > 0' )
if rate_per_annum ... | 361 | '''simple docstring'''
def _lowerCamelCase ( lowerCamelCase_ : str , lowerCamelCase_ : str = " " ):
"""simple docstring"""
UpperCAmelCase_ : str = []
UpperCAmelCase_ : List[Any] = 0
for index, char in enumerate(lowe... | 274 | 0 |
'''simple docstring'''
from dataclasses import dataclass, field
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union
import pyarrow as pa
if TYPE_CHECKING:
from .features import FeatureType
@dataclass
class __SCREAMING_SNAKE_CASE :
'''simple docstring'''
lo... | 362 | '''simple docstring'''
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
from sagemaker.huggingface... | 274 | 0 |
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, XLMRobertaXLForMaskedLM, XLMRobertaXLForSequenceClassif... | 363 | '''simple docstring'''
import hashlib
import unittest
from transformers import MODEL_FOR_DEPTH_ESTIMATION_MAPPING, is_torch_available, is_vision_available
from transformers.pipelines import DepthEstimationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
requi... | 274 | 0 |
'''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
snake_case__ : Dict = logging.get_logger(__name__)
snake_case__ ... | 364 | '''simple docstring'''
import copy
import os
import tempfile
from unittest import TestCase
from unittest.mock import patch
import numpy as np
import pyarrow as pa
import pyarrow.parquet as pq
import pytest
from datasets.arrow_writer import ArrowWriter, OptimizedTypedSequence, ParquetWriter, TypedSequence
from datas... | 274 | 0 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Iterator
from typing import Any
class __SCREAMING_SNAKE_CASE :
'''simple docstring'''
def __init__( self , snake_case_ ):
'''simple docstring'''
UpperCAmelCase_ :... | 365 | '''simple docstring'''
snake_case__ : Optional[Any] = tuple[float, float, float]
snake_case__ : Tuple = tuple[float, float, float]
def _lowerCamelCase ( lowerCamelCase_ : Pointad , lowerCamelCase_ : Pointad ):
"""simple docstring"""
... | 274 | 0 |
'''simple docstring'''
from __future__ import annotations
def _lowerCamelCase ( lowerCamelCase_ : str , lowerCamelCase_ : list[str] | None = None , lowerCamelCase_ : dict[str, float] | None = None , lowerCamelCase_ : bool = False , ):
"""simple d... | 366 | '''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
snake_case__ : Dict = {
'''albert-base-v1''': '''https://huggingface.co/albert-base-v1/resolve/main/config.json''',
'''... | 274 | 0 |
'''simple docstring'''
import unittest
import numpy as np
import torch
from diffusers import DDIMPipeline, DDIMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device
from ..pipeline_params import UNCONDITIONAL_IMAGE_GENERATION_BATC... | 367 | '''simple docstring'''
from __future__ import annotations
from collections import deque
from collections.abc import Iterator
from dataclasses import dataclass
@dataclass
class __SCREAMING_SNAKE_CASE :
'''simple docstring'''
lowerCamelCase_ :int
lowerCamelCase_ :int
class ... | 274 | 0 |
'''simple docstring'''
snake_case__ : Union[str, Any] = '\n# Transformers installation\n! pip install transformers datasets\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/transform... | 368 | '''simple docstring'''
from typing import List, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
snake_case__ : List[Any] = logging.get_logger(__name__)
snake_case__ : Optional[Any] = {
'''huggingface/autoformer-tourism-monthly''':... | 274 | 0 |
'''simple docstring'''
import collections
from typing import List, Optional, Union
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert_fast import BertTokenizerFast
from .tokenization_dpr import DPRConte... | 369 | '''simple docstring'''
import unittest
from transformers.utils.backbone_utils import (
BackboneMixin,
get_aligned_output_features_output_indices,
verify_out_features_out_indices,
)
class __SCREAMING_SNAKE_CASE ( unittest.TestCase ):
'''simple docstring'''
def _UpperCamelC... | 274 | 0 |
'''simple docstring'''
import os
def _lowerCamelCase ( lowerCamelCase_ : Union[str, Any] = "matrix.txt" ):
"""simple docstring"""
with open(os.path.join(os.path.dirname(__SCREAMING_SNAKE_CASE ) , __SCREAMING_SNAKE_CASE ) ) as in_file:
UpperCAme... | 370 | '''simple docstring'''
import numpy as np
import torch
from torch.nn import CrossEntropyLoss
from transformers import AutoModelForCausalLM, AutoTokenizer
import datasets
from datasets import logging
snake_case__ : str = '''\
'''
snake_case__ : Union[str, Any] = '''
Perplexit... | 274 | 0 |
'''simple docstring'''
from typing import List
from .keymap import KEYMAP, get_character
def _lowerCamelCase ( lowerCamelCase_ : str ):
"""simple docstring"""
def decorator(lowerCamelCase_ : Optional[Any] ):
UpperCAmelCase_ : List[Any] = ... | 371 | '''simple docstring'''
from sklearn.metrics import fa_score
import datasets
snake_case__ : str = '''
The F1 score is the harmonic mean of the precision and recall. It can be computed with the equation:
F1 = 2 * (precision * recall) / (precision + recall)
'''
snake_case__ : int ... | 274 | 0 |
'''simple docstring'''
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import MaskaFormerConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch... | 350 | '''simple docstring'''
import os
from glob import glob
import imageio
import torch
import torchvision
import wandb
from img_processing import custom_to_pil, loop_post_process, preprocess, preprocess_vqgan
from loaders import load_vqgan
from PIL import Image
from torch import nn
from transformers import CLIPModel, C... | 274 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
snake_case__ : Any = logging.get_logger(__name__)
snake_case__ : List[str] = {
'''microsoft/markuplm-base''': '''https://huggingface.co/microsoft/markuplm-base/resolve/m... | 351 | '''simple docstring'''
def _lowerCamelCase ( lowerCamelCase_ : str ):
"""simple docstring"""
UpperCAmelCase_ : Optional[int] = [0] * len(lowerCamelCase_ )
for i in range(1 , len(lowerCamelCase_ ) ):
# use last results f... | 274 | 0 |
'''simple docstring'''
import argparse
import os
import re
import packaging.version
snake_case__ : Any = '''examples/'''
snake_case__ : int = {
'''examples''': (re.compile(r'''^check_min_version\("[^"]+"\)\s*$''', re.MULTILINE), '''check_min_version("VERSION")\n'''),
'... | 352 | '''simple docstring'''
import os
import time
import numpy as np
import onnxruntime as ort
snake_case__ : Optional[int] = '''1'''
snake_case__ : str = '''0'''
snake_case__ : List[str] = '''1'''
snake_case__ : List[str] = ort.SessionOptions... | 274 | 0 |
'''simple docstring'''
import math
def _lowerCamelCase ( ):
"""simple docstring"""
UpperCAmelCase_ : Tuple = input('Enter message: ' )
UpperCAmelCase_ : Dict = int(input(F'''Enter key [2-{len(lowerCamelCase_ ) - 1}]: ''' ) ... | 353 | '''simple docstring'''
def _lowerCamelCase ( lowerCamelCase_ : int , lowerCamelCase_ : int ):
"""simple docstring"""
return int(input_a == input_a == 0 )
def _lowerCamelCase ( ):
"""simple docstring"""
print('Truth Table of NOR Gate:' ... | 274 | 0 |
'''simple docstring'''
from typing import List, Optional, Union
import torch
from transformers import (
XLMRobertaTokenizer,
)
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...scheduler... | 354 | '''simple docstring'''
from typing import Optional, Union
import torch
from torch import nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...models.modeling_utils import ModelMixin
class __SCREAMING_SNAKE_CASE ( lowerCamelCase_ , lowerCamelCase_ ):
'''simp... | 274 | 0 |
'''simple docstring'''
from sklearn.metrics import fa_score
import datasets
snake_case__ : str = '''
The F1 score is the harmonic mean of the precision and recall. It can be computed with the equation:
F1 = 2 * (precision * recall) / (precision + recall)
'''
snake_case__ : int ... | 355 | '''simple docstring'''
from manim import *
class __SCREAMING_SNAKE_CASE ( lowerCamelCase_ ):
'''simple docstring'''
def _UpperCamelCase ( self ):
'''simple docstring'''
UpperCAmelCase_ : Dict = Rectangle(height=0.5 , width=0.5 ... | 274 | 0 |
'''simple docstring'''
import argparse
import csv
import logging
import os
import random
import numpy as np
import torch
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
from tqdm import tqdm, trange
from transformers import (
CONFIG_NAME,
WEIGHTS_NAME,
AdamW,
... | 356 | '''simple docstring'''
import math
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import SchedulerMixin, SchedulerOutput
class __SCREAMING_SNAKE_CASE ( lowerCamelCase_ , ... | 274 | 0 |
'''simple docstring'''
def _lowerCamelCase ( lowerCamelCase_ : int , lowerCamelCase_ : bool = False ):
"""simple docstring"""
if n == 2:
return True
if not n % 2 or n < 2:
return False
if n > 5 and n % 10 not in (1, 3, 7, 9): # can quick... | 357 | '''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_available
from transformers... | 274 | 0 |
import argparse
from argparse import Namespace
import torch
from torch import nn
from transformers import XGLMConfig, XGLMForCausalLM
def _lowerCamelCase ( lowerCamelCase_ : List[Any] ):
"""simple docstring"""
UpperCAmelCase_ : List[str] = [
'decoder... | 358 | '''simple docstring'''
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class __SCREAMING_SNAKE_CASE ( lowerCamelCase_ ):
'''simple docstring'''
lowerCamelCase_ :Tuple = ['''image_processor''', '''tokenizer'... | 274 | 0 |
'''simple docstring'''
import re
from filelock import FileLock
try:
import nltk
snake_case__ : Union[str, Any] = True
except (ImportError, ModuleNotFoundError):
snake_case__ : Any = False
if NLTK_AVAILABLE:
with FileLock('''.lock''') as lock:
nltk.download('''punkt''',... | 359 | '''simple docstring'''
snake_case__ : str = '''Tobias Carryer'''
from time import time
class __SCREAMING_SNAKE_CASE :
'''simple docstring'''
def __init__( self , snake_case_ , snake_case_ , snake_case_ , snake_case_=int(time() ) ): # no... | 274 | 0 |
'''simple docstring'''
snake_case__ : str = [0, 2, 4, 6, 8]
snake_case__ : Optional[int] = [1, 3, 5, 7, 9]
def _lowerCamelCase ( lowerCamelCase_ : int , lowerCamelCase_ : int , lowerCamelCase_ : list[int] , lowerCamelCase_ :... | 360 | '''simple docstring'''
import unittest
from parameterized import parameterized
from transformers import OpenLlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common impor... | 274 | 0 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_yolos import YolosImageProcessor
snake_case__ : Union[str, Any] = logging.get_logger(__name__)
class __SCREAMING_SNAKE_CASE ( lowerCamelCase_ ):
'''simple docstring'''
def ... | 361 | '''simple docstring'''
def _lowerCamelCase ( lowerCamelCase_ : str , lowerCamelCase_ : str = " " ):
"""simple docstring"""
UpperCAmelCase_ : str = []
UpperCAmelCase_ : List[Any] = 0
for index, char in enumerate(lowe... | 274 | 0 |
'''simple docstring'''
import pytest
from datasets.parallel import ParallelBackendConfig, parallel_backend
from datasets.utils.py_utils import map_nested
from .utils import require_dill_gt_0_3_2, require_joblibspark, require_not_windows
def _lowerCamelCase ( lowerCamelCase_ : Any ): # picklable... | 362 | '''simple docstring'''
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
from sagemaker.huggingface... | 274 | 0 |
import requests
snake_case__ : List[str] = '''''' # <-- Put your OpenWeatherMap appid here!
snake_case__ : Optional[Any] = '''https://api.openweathermap.org/data/2.5/'''
def _lowerCamelCase ( lowerCamelCase_ : str = "Chicago" , lowerCamelCase_ : str = ... | 363 | '''simple docstring'''
import hashlib
import unittest
from transformers import MODEL_FOR_DEPTH_ESTIMATION_MAPPING, is_torch_available, is_vision_available
from transformers.pipelines import DepthEstimationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
requi... | 274 | 0 |
'''simple docstring'''
from datetime import datetime
import requests
from bsa import BeautifulSoup
if __name__ == "__main__":
snake_case__ : str = input('''Enter image url: ''').strip()
print(f'''Downloading image from {url} ...''')
snake_case__ : List[Any] = BeautifulSo... | 364 | '''simple docstring'''
import copy
import os
import tempfile
from unittest import TestCase
from unittest.mock import patch
import numpy as np
import pyarrow as pa
import pyarrow.parquet as pq
import pytest
from datasets.arrow_writer import ArrowWriter, OptimizedTypedSequence, ParquetWriter, TypedSequence
from datas... | 274 | 0 |
'''simple docstring'''
import datasets
import faiss
import numpy as np
import streamlit as st
import torch
from elasticsearch import Elasticsearch
from elia_utils import (
embed_questions_for_retrieval,
make_qa_sas_model,
qa_sas_generate,
query_es_index,
query_qa_dense_index,
)
import transformer... | 365 | '''simple docstring'''
snake_case__ : Optional[Any] = tuple[float, float, float]
snake_case__ : Tuple = tuple[float, float, float]
def _lowerCamelCase ( lowerCamelCase_ : Pointad , lowerCamelCase_ : Pointad ):
"""simple docstring"""
... | 274 | 0 |
'''simple docstring'''
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING
import numpy as np
import pyarrow as pa
from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter
if TYPE_CHECKING:
import torch
class __SCREAMING_SNAKE_C... | 366 | '''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
snake_case__ : Dict = {
'''albert-base-v1''': '''https://huggingface.co/albert-base-v1/resolve/main/config.json''',
'''... | 274 | 0 |
'''simple docstring'''
snake_case__ : str = '''0.18.2'''
from .configuration_utils import ConfigMixin
from .utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_inflect_available,
is_invisible_watermark_available,
is_k_diffusion_available,
is_k_d... | 367 | '''simple docstring'''
from __future__ import annotations
from collections import deque
from collections.abc import Iterator
from dataclasses import dataclass
@dataclass
class __SCREAMING_SNAKE_CASE :
'''simple docstring'''
lowerCamelCase_ :int
lowerCamelCase_ :int
class ... | 274 | 0 |
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