
from collections import OrderedDict
from typing import Mapping

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


__SCREAMING_SNAKE_CASE			:   Dict         =     logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE			:   Dict         =     {
    'squeezebert/squeezebert-uncased': (
        'https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/config.json'
    ),
    'squeezebert/squeezebert-mnli': 'https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/config.json',
    'squeezebert/squeezebert-mnli-headless': (
        'https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/config.json'
    ),
}


class 		__magic_name__   (						snake_case    ):
		_lowerCAmelCase		=	SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP
		_lowerCAmelCase		=	"squeezebert"





		def __init__(							self    :	Tuple						,  lowerCamelCase__    :	List[str]=3_0_5_2_2						,  lowerCamelCase__    :	Any=7_6_8						,  lowerCamelCase__    :	Any=1_2						,  lowerCamelCase__    :	Optional[Any]=1_2						,  lowerCamelCase__    :	int=3_0_7_2						,  lowerCamelCase__    :	List[Any]="gelu"						,  lowerCamelCase__    :	str=0.1						,  lowerCamelCase__    :	Union[str, Any]=0.1						,  lowerCamelCase__    :	Optional[Any]=5_1_2						,  lowerCamelCase__    :	Any=2						,  lowerCamelCase__    :	List[Any]=0.0_2						,  lowerCamelCase__    :	Optional[int]=1E-12						,  lowerCamelCase__    :	Any=0						,  lowerCamelCase__    :	Dict=7_6_8						,  lowerCamelCase__    :	List[str]=4						,  lowerCamelCase__    :	Optional[int]=4						,  lowerCamelCase__    :	List[str]=4						,  lowerCamelCase__    :	int=1						,  lowerCamelCase__    :	Union[str, Any]=4						,  lowerCamelCase__    :	Tuple=4						,  **lowerCamelCase__    :	List[Any]						,  ):
							super().__init__(pad_token_id=lowerCamelCase__						,  **lowerCamelCase__				)

							lowerCAmelCase		:					Any  	=     vocab_size
							lowerCAmelCase		:					List[str]  	=     hidden_size
							lowerCAmelCase		:					Union[str, Any]  	=     num_hidden_layers
							lowerCAmelCase		:					Any  	=     num_attention_heads
							lowerCAmelCase		:					Dict  	=     hidden_act
							lowerCAmelCase		:					str  	=     intermediate_size
							lowerCAmelCase		:					int  	=     hidden_dropout_prob
							lowerCAmelCase		:					Any  	=     attention_probs_dropout_prob
							lowerCAmelCase		:					int  	=     max_position_embeddings
							lowerCAmelCase		:					List[str]  	=     type_vocab_size
							lowerCAmelCase		:					Optional[Any]  	=     initializer_range
							lowerCAmelCase		:					List[str]  	=     layer_norm_eps
							lowerCAmelCase		:					Union[str, Any]  	=     embedding_size
							lowerCAmelCase		:					Union[str, Any]  	=     q_groups
							lowerCAmelCase		:					Any  	=     k_groups
							lowerCAmelCase		:					str  	=     v_groups
							lowerCAmelCase		:					Optional[Any]  	=     post_attention_groups
							lowerCAmelCase		:					Union[str, Any]  	=     intermediate_groups
							lowerCAmelCase		:					List[Any]  	=     output_groups



class 		__magic_name__   (						snake_case    ):





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


import json
import os
from dataclasses import dataclass

import numpy as np
import pyarrow as pa

import datasets
from utils import get_duration


__SCREAMING_SNAKE_CASE			:   List[Any]         =     10_00_00_00_00_00
__SCREAMING_SNAKE_CASE			:   Optional[Any]         =     1_00_00

__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE			:   Any         =     os.path.split(__file__)
__SCREAMING_SNAKE_CASE			:   List[str]         =     os.path.join(RESULTS_BASEPATH, 'results', RESULTS_FILENAME.replace('.py', '.json'))
def     UpperCAmelCase__							(       __magic_name__						:     int							,  __magic_name__						:     int  ):


					'''simple docstring'''

					lowerCAmelCase		:					Optional[int]  	=     pa.Table.from_pydict({'''col''': [0] * chunk_size}  )
					lowerCAmelCase		:					int  	=     pa.concat_tables([table] * (num_examples // chunk_size)  )
					return datasets.Dataset(__magic_name__							,  fingerprint='''table_100B'''  )


@dataclass
class 		__magic_name__   :
		_lowerCAmelCase		=	42
		_lowerCAmelCase		=	42
		_lowerCAmelCase		=	42
		_lowerCAmelCase		=	42





		def       _A							(							self    :	Optional[int]				):
							lowerCAmelCase		:					Union[str, Any]  	=     np.random.default_rng(self.seed				)
							lowerCAmelCase		:					Tuple  	=     rng.integers(low=self.low						,  high=self.high						,  size=self.size				).tolist()





		def __iter__(							self    :	Dict				):
							return iter(self._sampled_values				)





		def __len__(							self    :	Tuple				):
							return self.size



@get_duration
def     UpperCAmelCase__							(       __magic_name__						:     datasets.Dataset  ):


					'''simple docstring'''

					lowerCAmelCase		:					Union[str, Any]  	=     dataset[0]
@get_duration
def     UpperCAmelCase__							(       __magic_name__						:     datasets.Dataset  ):


					'''simple docstring'''

					lowerCAmelCase		:					Any  	=     dataset[-1]
@get_duration
def     UpperCAmelCase__							(       __magic_name__						:     datasets.Dataset  ):


					'''simple docstring'''

					lowerCAmelCase		:					int  	=     dataset[range(len(__magic_name__  ) // 2							,  len(__magic_name__  ) // 2 + 10_24  )]
@get_duration
def     UpperCAmelCase__							(       __magic_name__						:     datasets.Dataset  ):


					'''simple docstring'''

					lowerCAmelCase		:					List[Any]  	=     dataset[RandIter(0							,  len(__magic_name__  )							,  10_24							,  seed=42  )]
def     UpperCAmelCase__							(       ):


					'''simple docstring'''

					lowerCAmelCase		:					Dict  	=     {'''num examples''': SPEED_TEST_N_EXAMPLES}
					lowerCAmelCase		:					Tuple  	=     (get_first_row, get_last_row, get_batch_of_aaaa_rows, get_batch_of_aaaa_random_rows)
					print('''generating dataset'''  )
					lowerCAmelCase		:					Dict  	=     generate_100B_dataset(num_examples=__magic_name__							,  chunk_size=__magic_name__  )
					print('''Functions'''  )
					for func in functions:
										print(func.__name__  )
										lowerCAmelCase		:					List[Any]  	=     func(__magic_name__  )

					with open(__magic_name__							,  '''wb'''  ) as f:
										f.write(json.dumps(__magic_name__  ).encode('''utf-8'''  )  )


if __name__ == "__main__":  # useful to run the profiler
		benchmark_table_100B()
