






"""simple docstring"""



from collections import OrderedDict
from typing import Any, List, Mapping, Optional

from ... import PretrainedConfig, PreTrainedTokenizer
from ...onnx import OnnxConfig, PatchingSpec
from ...utils import TensorType, is_torch_available, logging


__A		:	List[Any]      =		logging.get_logger(__name__)

__A		:	Optional[Any]      =		{
    '''microsoft/layoutlm-base-uncased''': (
        '''https://huggingface.co/microsoft/layoutlm-base-uncased/resolve/main/config.json'''
    ),
    '''microsoft/layoutlm-large-uncased''': (
        '''https://huggingface.co/microsoft/layoutlm-large-uncased/resolve/main/config.json'''
    ),
}





class  _UpperCAmelCase	(			_A							):
					SCREAMING_SNAKE_CASE_		:    Optional[int]							   =       "layoutlm"




					def __init__(    self					:						List[str]   ,						A					:						Union[str, Any]=3_05_22   ,						A					:						Optional[Any]=7_68   ,						A					:						str=12   ,						A					:						str=12   ,						A					:						Optional[Any]=30_72   ,						A					:						List[str]="gelu"   ,						A					:						List[str]=0.1   ,						A					:						int=0.1   ,						A					:						List[str]=5_12   ,						A					:						Union[str, Any]=2   ,						A					:						str=0.02   ,						A					:						List[Any]=1e-12   ,						A					:						Tuple=0   ,						A					:						List[Any]="absolute"   ,						A					:						int=True   ,						A					:						int=10_24   ,						**A					:						Dict   ,						)  ->  Optional[int]:
							super().__init__(pad_token_id=A   ,						**A       )
							lowercase_			:			Optional[Any]								=       vocab_size
							lowercase_			:			Union[str, Any]								=       hidden_size
							lowercase_			:			Tuple								=       num_hidden_layers
							lowercase_			:			str								=       num_attention_heads
							lowercase_			:			Optional[Any]								=       hidden_act
							lowercase_			:			int								=       intermediate_size
							lowercase_			:			Optional[Any]								=       hidden_dropout_prob
							lowercase_			:			Union[str, Any]								=       attention_probs_dropout_prob
							lowercase_			:			int								=       max_position_embeddings
							lowercase_			:			List[Any]								=       type_vocab_size
							lowercase_			:			Optional[Any]								=       initializer_range
							lowercase_			:			Optional[int]								=       layer_norm_eps
							lowercase_			:			Optional[Any]								=       position_embedding_type
							lowercase_			:			int								=       use_cache
							lowercase_			:			int								=       max_ad_position_embeddings





class  _UpperCAmelCase	(			_A							):




					def __init__(    self					:						str   ,						A					:						PretrainedConfig   ,						A					:						str = "default"   ,						A					:						List[PatchingSpec] = None   ,						)  ->  Union[str, Any]:
							super().__init__(A   ,						task=A   ,						patching_specs=A       )
							lowercase_			:			int								=       config.max_ad_position_embeddings - 1




					@property
					def   A  (    self					:						Any       )  ->  Mapping[str, Mapping[int, str]]:
							return OrderedDict(
							    [
							        ('''input_ids''', {0: '''batch''', 1: '''sequence'''}),
							        ('''bbox''', {0: '''batch''', 1: '''sequence'''}),
							        ('''attention_mask''', {0: '''batch''', 1: '''sequence'''}),
							        ('''token_type_ids''', {0: '''batch''', 1: '''sequence'''}),
							    ]       )






					def   A  (    self					:						List[str]   ,						A					:						PreTrainedTokenizer   ,						A					:						int = -1   ,						A					:						int = -1   ,						A					:						bool = False   ,						A					:						Optional[TensorType] = None   ,						)  ->  Mapping[str, Any]:
							lowercase_			:			Tuple								=       super().generate_dummy_inputs(
							    A   ,						batch_size=A   ,						seq_length=A   ,						is_pair=A   ,						framework=A       )

							# Generate a dummy bbox
							lowercase_			:			Optional[Any]								=       [48, 84, 73, 1_28]

							if not framework == TensorType.PYTORCH:
									raise NotImplementedError('''Exporting LayoutLM to ONNX is currently only supported for PyTorch.'''       )

							if not is_torch_available():
									raise ValueError('''Cannot generate dummy inputs without PyTorch installed.'''       )
							import torch

							lowercase_	, lowercase_			:			Any								=       input_dict['''input_ids'''].shape
							lowercase_			:			List[str]								=       torch.tensor([*[box] * seq_length]       ).tile(A   ,						1   ,						1       )
							return input_dict











"""simple docstring"""



def 						lowercase   (    __snake_case    :			str = "mytext.txt"		, __snake_case    :			bool = False     ):
		import socket

		lowercase_			:			List[str]								=       1_2_3_1_2  # Reserve a port for your service.
		lowercase_			:			Dict								=       socket.socket()  # Create a socket object
		lowercase_			:			int								=       socket.gethostname()  # Get local machine name
		sock.bind((host, port)     )  # Bind to the port
		sock.listen(5     )  # Now wait for client connection.

		print('''Server listening....'''     )

		while True:
				lowercase_	, lowercase_			:			int								=       sock.accept()  # Establish connection with client.
				print(F'''Got connection from {addr}'''     )
				lowercase_			:			List[Any]								=       conn.recv(1_0_2_4     )
				print(F'''Server received: {data = }'''     )

				with open(__snake_case		, '''rb'''     ) as in_file:
						lowercase_			:			List[Any]								=       in_file.read(1_0_2_4     )
						while data:
								conn.send(__snake_case     )
								print(F'''Sent {data!r}'''     )
								lowercase_			:			List[Any]								=       in_file.read(1_0_2_4     )

				print('''Done sending'''     )
				conn.close()
				if testing:  # Allow the test to complete
						break

		sock.shutdown(1     )
		sock.close()


if __name__ == "__main__":
						send_file()



