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

from packaging import version

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


if TYPE_CHECKING:
					from ...processing_utils import ProcessorMixin
					from ...utils import TensorType


_UpperCAmelCase					: Any      =       logging.get_logger(__name__)

_UpperCAmelCase					: List[str]      =       {
    "microsoft/layoutlmv3-base": "https://huggingface.co/microsoft/layoutlmv3-base/resolve/main/config.json",
}


class 				lowercase					(		_SCREAMING_SNAKE_CASE   ):
		__lowercase       :       Union[str, Any]	     =				"layoutlmv3"



		def __init__( self				,      A_=50_265				,      A_=768				,      A_=12				,      A_=12				,      A_=3_072				,      A_="gelu"				,      A_=0.1				,      A_=0.1				,      A_=512				,      A_=2				,      A_=0.02				,      A_=1e-5				,      A_=1				,      A_=0				,      A_=2				,      A_=1_024				,      A_=128				,      A_=128				,      A_=True				,      A_=32				,      A_=128				,      A_=64				,      A_=256				,      A_=True				,      A_=True				,      A_=True				,      A_=224				,      A_=3				,      A_=16				,      A_=None				,      **A_				,      )  ->					Dict:
				"""simple docstring"""
				super().__init__(
				    vocab_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_				,      pad_token_id=A_				,      bos_token_id=A_				,      eos_token_id=A_				,      **A_				,      )
				UpperCamelCase		=     max_ad_position_embeddings
				UpperCamelCase		=     coordinate_size
				UpperCamelCase		=     shape_size
				UpperCamelCase		=     has_relative_attention_bias
				UpperCamelCase		=     rel_pos_bins
				UpperCamelCase		=     max_rel_pos
				UpperCamelCase		=     has_spatial_attention_bias
				UpperCamelCase		=     rel_ad_pos_bins
				UpperCamelCase		=     max_rel_ad_pos
				UpperCamelCase		=     text_embed
				UpperCamelCase		=     visual_embed
				UpperCamelCase		=     input_size
				UpperCamelCase		=     num_channels
				UpperCamelCase		=     patch_size
				UpperCamelCase		=     classifier_dropout






class 				lowercase					(		_SCREAMING_SNAKE_CASE   ):
		__lowercase       :       Union[str, Any]	     =				version.parse("1.12"   )



		@property
		def __UpperCamelCase ( self       )  ->					Mapping[str, Mapping[int, str]]:
				"""simple docstring"""
				# The order of inputs is different for question answering and sequence classification
				if self.task in ["question-answering", "sequence-classification"]:
						return OrderedDict(
						    [
						        ('input_ids', {0: 'batch', 1: 'sequence'}),
						        ('attention_mask', {0: 'batch', 1: 'sequence'}),
						        ('bbox', {0: 'batch', 1: 'sequence'}),
						        ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
						    ]       )
				else:
						return OrderedDict(
						    [
						        ('input_ids', {0: 'batch', 1: 'sequence'}),
						        ('bbox', {0: 'batch', 1: 'sequence'}),
						        ('attention_mask', {0: 'batch', 1: 'sequence'}),
						        ('pixel_values', {0: 'batch', 1: 'num_channels'}),
						    ]       )



		@property
		def __UpperCamelCase ( self       )  ->					float:
				"""simple docstring"""
				return 1e-5



		@property
		def __UpperCamelCase ( self       )  ->					int:
				"""simple docstring"""
				return 12



		def __UpperCamelCase ( self				,      A_				,      A_ = -1				,      A_ = -1				,      A_ = False				,      A_ = None				,      A_ = 3				,      A_ = 40				,      A_ = 40				,      )  ->					Mapping[str, Any]:
				"""simple docstring"""
				setattr(processor.image_processor				,      'apply_ocr'				,      A_       )

				# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
				UpperCamelCase		=     compute_effective_axis_dimension(
				    A_				,      fixed_dimension=OnnxConfig.default_fixed_batch				,      num_token_to_add=0       )
				# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
				UpperCamelCase		=     processor.tokenizer.num_special_tokens_to_add(A_       )
				UpperCamelCase		=     compute_effective_axis_dimension(
				    A_				,      fixed_dimension=OnnxConfig.default_fixed_sequence				,      num_token_to_add=A_       )
				# Generate dummy inputs according to compute batch and sequence
				UpperCamelCase		=     [[' '.join([processor.tokenizer.unk_token]       ) * seq_length]] * batch_size

				# Generate dummy bounding boxes
				UpperCamelCase		=     [[[48, 84, 73, 128]]] * batch_size

				# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
				# batch_size = compute_effective_axis_dimension(batch_size, fixed_dimension=OnnxConfig.default_fixed_batch)
				UpperCamelCase		=     self._generate_dummy_images(A_				,      A_				,      A_				,      A_       )

				UpperCamelCase		=     dict(
				    processor(
				        A_				,      text=A_				,      boxes=A_				,      return_tensors=A_				,      )       )

				return inputs


import argparse

import torch
from torch import nn

from transformers import SpeechaTextConfig, SpeechaTextForConditionalGeneration


def       A       (					lowercase )   ->  Optional[Any]:
		'''simple docstring'''

		UpperCamelCase		=     [
		    'encoder.version',
		    'decoder.version',
		    'model.encoder.version',
		    'model.decoder.version',
		    'decoder.output_projection.weight',
		    '_float_tensor',
		    'encoder.embed_positions._float_tensor',
		    'decoder.embed_positions._float_tensor',
		]
		for k in ignore_keys:
				state_dict.pop(lowercase    ,       lowercase )


def       A       (					lowercase )   ->  List[Any]:
		'''simple docstring'''

		UpperCamelCase		=     list(s_dict.keys() )
		for key in keys:
				if "transformer_layers" in key:
						UpperCamelCase		=     s_dict.pop(lowercase )
				elif "subsample" in key:
						UpperCamelCase		=     s_dict.pop(lowercase )


def       A       (					lowercase )   ->  Tuple:
		'''simple docstring'''

		UpperCamelCase	,  UpperCamelCase		=     emb.weight.shape
		UpperCamelCase		=     nn.Linear(lowercase    ,       lowercase    ,       bias=lowercase )
		UpperCamelCase		=     emb.weight.data
		return lin_layer


def       A       (					lowercase    ,       lowercase )   ->  Dict:
		'''simple docstring'''

		UpperCamelCase		=     torch.load(lowercase    ,       map_location='cpu' )
		UpperCamelCase		=     mam_aaa['args']
		UpperCamelCase		=     mam_aaa['model']
		UpperCamelCase		=     state_dict['decoder.output_projection.weight']

		remove_ignore_keys_(lowercase )
		rename_keys(lowercase )

		UpperCamelCase		=     state_dict['decoder.embed_tokens.weight'].shape[0]

		UpperCamelCase		=     args.share_decoder_input_output_embed

		UpperCamelCase		=     [int(lowercase ) for i in args.conv_kernel_sizes.split(',' )]
		UpperCamelCase		=     SpeechaTextConfig(
		    vocab_size=lowercase    ,       max_source_positions=args.max_source_positions    ,       max_target_positions=args.max_target_positions    ,       encoder_layers=args.encoder_layers    ,       decoder_layers=args.decoder_layers    ,       encoder_attention_heads=args.encoder_attention_heads    ,       decoder_attention_heads=args.decoder_attention_heads    ,       encoder_ffn_dim=args.encoder_ffn_embed_dim    ,       decoder_ffn_dim=args.decoder_ffn_embed_dim    ,       d_model=args.encoder_embed_dim    ,       dropout=args.dropout    ,       attention_dropout=args.attention_dropout    ,       activation_dropout=args.activation_dropout    ,       activation_function='relu'    ,       num_conv_layers=len(lowercase )    ,       conv_channels=args.conv_channels    ,       conv_kernel_sizes=lowercase    ,       input_feat_per_channel=args.input_feat_per_channel    ,       input_channels=args.input_channels    ,       tie_word_embeddings=lowercase    ,       num_beams=5    ,       max_length=200    ,       use_cache=lowercase    ,       decoder_start_token_id=2    ,       early_stopping=lowercase    ,       )

		UpperCamelCase		=     SpeechaTextForConditionalGeneration(lowercase )
		UpperCamelCase	,  UpperCamelCase		=     model.model.load_state_dict(lowercase    ,       strict=lowercase )
		if len(lowercase ) > 0 and not set(lowercase ) <= {
		    "encoder.embed_positions.weights",
		    "decoder.embed_positions.weights",
		}:
				raise ValueError(
				    'Only `encoder.embed_positions.weights` and `decoder.embed_positions.weights`  are allowed to be missing,'
				    f''' but all the following weights are missing {missing}''' )

		if tie_embeds:
				UpperCamelCase		=     make_linear_from_emb(model.model.decoder.embed_tokens )
		else:
				UpperCamelCase		=     lm_head_weights

		model.save_pretrained(lowercase )


if __name__ == "__main__":
					_UpperCAmelCase					: Optional[Any]      =       argparse.ArgumentParser()
					# Required parameters
					parser.add_argument("--fairseq_path", type=str, help="Path to the fairseq model (.pt) file.")
					parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
					_UpperCAmelCase					: Dict      =       parser.parse_args()
					convert_fairseq_sat_checkpoint_to_tfms(args.fairseq_path, args.pytorch_dump_folder_path)
