




import timeit

import numpy as np

import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features.features import _ArrayXD




def  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						Tuple				)  ->							Tuple:
    def wrapper(*__UpperCamelCase      :						int	,					**__UpperCamelCase      :						Optional[Any]				):
        UpperCAmelCase_										=					timeit.default_timer()
        UpperCAmelCase_										=					func(*__UpperCamelCase	,					**__UpperCamelCase				)
        UpperCAmelCase_										=					timeit.default_timer() - starttime
        return delta

    UpperCAmelCase_										=					func.__name__

    return wrapper




def  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						dict	,					__UpperCamelCase      :						List[str]=100	,					__UpperCamelCase      :						List[Any]=None				)  ->							Union[str, Any]:
    UpperCAmelCase_										=					[]
    UpperCAmelCase_										=					seq_shapes or {}
    for i in range(__UpperCamelCase				):
        UpperCAmelCase_										=					{}
        for col_id, (k, v) in enumerate(features.items()				):
            if isinstance(__UpperCamelCase	,					_ArrayXD				):
                UpperCAmelCase_										=					np.random.rand(*v.shape				).astype(v.dtype				)
            elif isinstance(__UpperCamelCase	,					datasets.Value				):
                if v.dtype == "string":
                    UpperCAmelCase_										=					'''The small grey turtle was surprisingly fast when challenged.'''
                else:
                    UpperCAmelCase_										=					np.random.randint(10	,					size=1				).astype(v.dtype				).item()
            elif isinstance(__UpperCamelCase	,					datasets.Sequence				):
                while isinstance(__UpperCamelCase	,					datasets.Sequence				):
                    UpperCAmelCase_										=					v.feature
                UpperCAmelCase_										=					seq_shapes[k]
                UpperCAmelCase_										=					np.random.rand(*__UpperCamelCase				).astype(v.dtype				)
            UpperCAmelCase_										=					data

        dummy_data.append((i, example)				)

    return dummy_data




def  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						List[str]	,					__UpperCamelCase      :						Optional[int]	,					__UpperCamelCase      :						List[Any]=100	,					__UpperCamelCase      :						str=None				)  ->							Union[str, Any]:
    UpperCAmelCase_										=					generate_examples(__UpperCamelCase	,					num_examples=__UpperCamelCase	,					seq_shapes=__UpperCamelCase				)

    with ArrowWriter(features=__UpperCamelCase	,					path=__UpperCamelCase				) as writer:
        for key, record in dummy_data:
            UpperCAmelCase_										=					features.encode_example(__UpperCamelCase				)
            writer.write(__UpperCamelCase				)

        UpperCAmelCase_			,							UpperCAmelCase_										=					writer.finalize()

    if not num_final_examples == num_examples:
        raise ValueError(
            f'Error writing the dataset, wrote {num_final_examples} examples but should have written {num_examples}.'				)

    UpperCAmelCase_										=					datasets.Dataset.from_file(filename=__UpperCamelCase	,					info=datasets.DatasetInfo(features=__UpperCamelCase				)				)

    return dataset











from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_lowerCamelCase      			=						logging.get_logger(__name__)

_lowerCamelCase      			=						{
    'facebook/data2vec-text-base': 'https://huggingface.co/data2vec/resolve/main/config.json',
}



class       a					(       _A      ):

     '''simple docstring'''





     lowerCAmelCase    :	Optional[Any]       		=  'data2vec-text'
     def __init__(     self       :      Optional[Any] ,   __snake_case       :      Optional[int]=3_05_22 ,   __snake_case       :      List[str]=7_68 ,   __snake_case       :      Tuple=12 ,   __snake_case       :      int=12 ,   __snake_case       :      Union[str, Any]=30_72 ,   __snake_case       :      List[Any]="gelu" ,   __snake_case       :      Any=0.1 ,   __snake_case       :      Union[str, Any]=0.1 ,   __snake_case       :      Tuple=5_12 ,   __snake_case       :      str=2 ,   __snake_case       :      str=0.02 ,   __snake_case       :      List[Any]=1E-12 ,   __snake_case       :      Any=1 ,   __snake_case       :      List[Any]=0 ,   __snake_case       :      Dict=2 ,   __snake_case       :      Any="absolute" ,   __snake_case       :      Union[str, Any]=True ,   __snake_case       :      Any=None ,   **__snake_case       :      List[Any] ,   ):
         super().__init__(pad_token_id=__snake_case ,   bos_token_id=__snake_case ,   eos_token_id=__snake_case ,   **__snake_case    )

         UpperCAmelCase_										=					vocab_size
         UpperCAmelCase_										=					hidden_size
         UpperCAmelCase_										=					num_hidden_layers
         UpperCAmelCase_										=					num_attention_heads
         UpperCAmelCase_										=					hidden_act
         UpperCAmelCase_										=					intermediate_size
         UpperCAmelCase_										=					hidden_dropout_prob
         UpperCAmelCase_										=					attention_probs_dropout_prob
         UpperCAmelCase_										=					max_position_embeddings
         UpperCAmelCase_										=					type_vocab_size
         UpperCAmelCase_										=					initializer_range
         UpperCAmelCase_										=					layer_norm_eps
         UpperCAmelCase_										=					position_embedding_type
         UpperCAmelCase_										=					use_cache
         UpperCAmelCase_										=					classifier_dropout



class       a					(       _A      ):

     '''simple docstring'''





     @property
     def   lowerCamelCase_       (     self       :      str    ):
         if self.task == "multiple-choice":
             UpperCAmelCase_										=					{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
         else:
             UpperCAmelCase_										=					{0: '''batch''', 1: '''sequence'''}
         return OrderedDict(
             [
                 ('''input_ids''', dynamic_axis),
                 ('''attention_mask''', dynamic_axis),
             ]    )





