
'''simple docstring'''





from collections import OrderedDict
from typing import Mapping

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


UpperCAmelCase            = logging.get_logger(__name__)

UpperCAmelCase            = {
    "google/mobilebert-uncased": "https://huggingface.co/google/mobilebert-uncased/resolve/main/config.json"
}

class     a (					__magic_name__     ):
				_snake_case		     =			MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP
				_snake_case		     =			'''mobilebert'''




				def __init__(				self						:					Any,     SCREAMING_SNAKE_CASE_						:					int=3_05_22,     SCREAMING_SNAKE_CASE_						:					List[Any]=5_12,     SCREAMING_SNAKE_CASE_						:					str=24,     SCREAMING_SNAKE_CASE_						:					List[Any]=4,     SCREAMING_SNAKE_CASE_						:					Optional[Any]=5_12,     SCREAMING_SNAKE_CASE_						:					int="relu",     SCREAMING_SNAKE_CASE_						:					Tuple=0.0,     SCREAMING_SNAKE_CASE_						:					Dict=0.1,     SCREAMING_SNAKE_CASE_						:					Optional[Any]=5_12,     SCREAMING_SNAKE_CASE_						:					int=2,     SCREAMING_SNAKE_CASE_						:					Dict=0.02,     SCREAMING_SNAKE_CASE_						:					List[str]=1e-12,     SCREAMING_SNAKE_CASE_						:					List[Any]=0,     SCREAMING_SNAKE_CASE_						:					Any=1_28,     SCREAMING_SNAKE_CASE_						:					Tuple=True,     SCREAMING_SNAKE_CASE_						:					Dict=True,     SCREAMING_SNAKE_CASE_						:					List[str]=1_28,     SCREAMING_SNAKE_CASE_						:					Optional[Any]=False,     SCREAMING_SNAKE_CASE_						:					List[Any]=True,     SCREAMING_SNAKE_CASE_						:					Any=4,     SCREAMING_SNAKE_CASE_						:					Dict="no_norm",     SCREAMING_SNAKE_CASE_						:					Any=True,     SCREAMING_SNAKE_CASE_						:					Optional[int]=None,     **SCREAMING_SNAKE_CASE_						:					str,     ):
										super().__init__(pad_token_id=SCREAMING_SNAKE_CASE_,     **SCREAMING_SNAKE_CASE_   )

										snake_case							:  str			=     vocab_size
										snake_case							:  Optional[Any]			=     hidden_size
										snake_case							:  Union[str, Any]			=     num_hidden_layers
										snake_case							:  str			=     num_attention_heads
										snake_case							:  Tuple			=     hidden_act
										snake_case							:  Optional[int]			=     intermediate_size
										snake_case							:  str			=     hidden_dropout_prob
										snake_case							:  Any			=     attention_probs_dropout_prob
										snake_case							:  Dict			=     max_position_embeddings
										snake_case							:  List[str]			=     type_vocab_size
										snake_case							:  int			=     initializer_range
										snake_case							:  List[Any]			=     layer_norm_eps
										snake_case							:  Any			=     embedding_size
										snake_case							:  Tuple			=     trigram_input
										snake_case							:  str			=     use_bottleneck
										snake_case							:  Union[str, Any]			=     intra_bottleneck_size
										snake_case							:  Any			=     use_bottleneck_attention
										snake_case							:  Tuple			=     key_query_shared_bottleneck
										snake_case							:  int			=     num_feedforward_networks
										snake_case							:  Union[str, Any]			=     normalization_type
										snake_case							:  Union[str, Any]			=     classifier_activation

										if self.use_bottleneck:
																snake_case							:  Optional[int]			=     intra_bottleneck_size
										else:
																snake_case							:  Optional[int]			=     hidden_size

										snake_case							:  Optional[Any]			=     classifier_dropout




class     a (					__magic_name__     ):




				@property
				def 						__snake_case				(				self						:					List[str]   ):
										if self.task == "multiple-choice":
																snake_case							:  Any			=     {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
										else:
																snake_case							:  List[str]			=     {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


lowerCamelCase   					=  2 << 2_0  # 2mb
lowerCamelCase   					=  get_logger(__name__)





class      snake_case_      (					_a				):


							"""simple docstring"""





							def 							A__	(			self				):
												__lowerCAmelCase       =				self._options.get_all_options().get('runner'				)
												return runner in [None, "DirectRunner", "PortableRunner"]


def 							__lowercase     (	UpperCAmelCase__		,       UpperCAmelCase__		,       UpperCAmelCase__=False   ):

					"""simple docstring"""






					__lowerCAmelCase       =				FileSystems
					if fs.exists(UpperCAmelCase__   ):
										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(UpperCAmelCase__   ) as remote_file:
										with open(UpperCAmelCase__		,       'rb'   ) as local_file:
															__lowerCAmelCase       =				local_file.read(UpperCAmelCase__   )
															while chunk:
																				remote_file.write(UpperCAmelCase__   )
																				__lowerCAmelCase       =				local_file.read(UpperCAmelCase__   )


def 							__lowercase     (	UpperCAmelCase__		,       UpperCAmelCase__		,       UpperCAmelCase__=False   ):

					"""simple docstring"""






					__lowerCAmelCase       =				FileSystems
					if os.path.exists(UpperCAmelCase__   ):
										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(UpperCAmelCase__   ) as remote_file:
										with open(UpperCAmelCase__		,       'wb'   ) as local_file:
															__lowerCAmelCase       =				remote_file.read(UpperCAmelCase__   )
															while chunk:
																				local_file.write(UpperCAmelCase__   )
																				__lowerCAmelCase       =				remote_file.read(UpperCAmelCase__   )






