
from collections import OrderedDict
from typing import Mapping

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


A  :			List[str]     				=				logging.get_logger(__name__)

A  :			Optional[Any]     				=				{
    'google/electra-small-generator': 'https://huggingface.co/google/electra-small-generator/resolve/main/config.json',
    'google/electra-base-generator': 'https://huggingface.co/google/electra-base-generator/resolve/main/config.json',
    'google/electra-large-generator': 'https://huggingface.co/google/electra-large-generator/resolve/main/config.json',
    'google/electra-small-discriminator': (
        'https://huggingface.co/google/electra-small-discriminator/resolve/main/config.json'
    ),
    'google/electra-base-discriminator': (
        'https://huggingface.co/google/electra-base-discriminator/resolve/main/config.json'
    ),
    'google/electra-large-discriminator': (
        'https://huggingface.co/google/electra-large-discriminator/resolve/main/config.json'
    ),
}







class     __A(				a    ):
			snake_case_		     =					'''electra'''




			def __init__(  self	,  _snake_case=30_522	,  _snake_case=128	,  _snake_case=256	,  _snake_case=12	,  _snake_case=4	,  _snake_case=1_024	,  _snake_case="gelu"	,  _snake_case=0.1	,  _snake_case=0.1	,  _snake_case=512	,  _snake_case=2	,  _snake_case=0.02	,  _snake_case=1E-12	,  _snake_case="first"	,  _snake_case=True	,  _snake_case="gelu"	,  _snake_case=0.1	,  _snake_case=0	,  _snake_case="absolute"	,  _snake_case=True	,  _snake_case=None	,  **_snake_case	,  )					->   Dict:


							'''simple docstring'''




							super().__init__(pad_token_id=_snake_case	,  **_snake_case       )

							__a    							=					vocab_size
							__a    							=					embedding_size
							__a    							=					hidden_size
							__a    							=					num_hidden_layers
							__a    							=					num_attention_heads
							__a    							=					intermediate_size
							__a    							=					hidden_act
							__a    							=					hidden_dropout_prob
							__a    							=					attention_probs_dropout_prob
							__a    							=					max_position_embeddings
							__a    							=					type_vocab_size
							__a    							=					initializer_range
							__a    							=					layer_norm_eps

							__a    							=					summary_type
							__a    							=					summary_use_proj
							__a    							=					summary_activation
							__a    							=					summary_last_dropout
							__a    							=					position_embedding_type
							__a    							=					use_cache
							__a    							=					classifier_dropout







class     __A(				a    ):




			@property
			def        SCREAMING_SNAKE_CASE_	(  self       )					->   Mapping[str, Mapping[int, str]]:


							'''simple docstring'''




							if self.task == "multiple-choice":
											__a    							=					{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
							else:
											__a    							=					{0: '''batch''', 1: '''sequence'''}
							return OrderedDict(
							    [
							        ('''input_ids''', dynamic_axis),
							        ('''attention_mask''', dynamic_axis),
							        ('''token_type_ids''', dynamic_axis),
							    ]       )

import os

from apache_beam.io.filesystems import FileSystems
from apache_beam.pipeline import Pipeline

from .logging import get_logger


A  :			Optional[int]     				=				2 << 2_0  # 2mb
A  :			Optional[int]     				=				get_logger(__name__)







class     __A(				a    ):




				def        SCREAMING_SNAKE_CASE_	(  self       )					->   Union[str, Any]:


								'''simple docstring'''




								__a    							=					self._options.get_all_options().get('''runner'''       )
								return runner in [None, "DirectRunner", "PortableRunner"]



def 					__lowerCAmelCase    (    a__		,					a__		,					a__=False						) ->						Union[str, Any]:
				__a    							=					FileSystems
				if fs.exists(a__						):
								if force_upload:
												logger.info(F"""Remote path already exist: {remote_file_path}. Overwriting it as force_upload=True."""						)
								else:
												logger.info(F"""Remote path already exist: {remote_file_path}. Skipping it as force_upload=False."""						)
												return
				with fs.create(a__						) as remote_file:
								with open(a__		,					'''rb'''						) as local_file:
												__a    							=					local_file.read(a__						)
												while chunk:
																remote_file.write(a__						)
																__a    							=					local_file.read(a__						)






def 					__lowerCAmelCase    (    a__		,					a__		,					a__=False						) ->						Dict:
				__a    							=					FileSystems
				if os.path.exists(a__						):
								if force_download:
												logger.info(F"""Local path already exist: {remote_file_path}. Overwriting it as force_upload=True."""						)
								else:
												logger.info(F"""Local path already exist: {remote_file_path}. Skipping it as force_upload=False."""						)
												return
				with fs.open(a__						) as remote_file:
								with open(a__		,					'''wb'''						) as local_file:
												__a    							=					remote_file.read(a__						)
												while chunk:
																local_file.write(a__						)
																__a    							=					remote_file.read(a__						)