




'''simple docstring'''


import argparse
import pickle

import numpy as np
import torch
from torch import nn

from transformers import ReformerConfig, ReformerModelWithLMHead
from transformers.utils import logging


logging.set_verbosity_info()
def 						snake_case_       (UpperCamelCase			:    Optional[Any]					,		UpperCamelCase			:    int					,		UpperCamelCase			:    Union[str, Any]=None ):





							'''simple docstring'''


							assert torch_layer.weight.shape == weight.shape, f'{torch_layer} layer.weight does not match'
							_a      	=							nn.Parameter(UpperCamelCase )
							if bias is not None:
														assert torch_layer.bias.shape == bias.shape, f'{torch_layer} layer.bias does not match'
														_a      	=							nn.Parameter(UpperCamelCase )
def 						snake_case_       (UpperCamelCase			:    Dict					,		UpperCamelCase			:    Optional[int]					,		UpperCamelCase			:    Optional[int] ):





							'''simple docstring'''


							_a      	=							np.asarray(weights[0] )
							_a      	=							np.asarray(weights[1] )
							_a      	=							np.asarray(weights[2] )

							set_param(
							    torch_layer.self_attention.query_key					,		torch.tensor(UpperCamelCase ).transpose(1					,		2 ).contiguous().view(-1					,		UpperCamelCase )					,		)
							set_param(
							    torch_layer.self_attention.value					,		torch.tensor(UpperCamelCase ).transpose(1					,		2 ).contiguous().view(-1					,		UpperCamelCase )					,		)
							set_param(
							    torch_layer.output.dense					,		torch.tensor(UpperCamelCase ).view(-1					,		UpperCamelCase ).contiguous().transpose(0					,		1 )					,		)
def 						snake_case_       (UpperCamelCase			:    List[Any]					,		UpperCamelCase			:    Union[str, Any]					,		UpperCamelCase			:    int ):





							'''simple docstring'''


							_a      	=							np.asarray(weights[0] )
							_a      	=							np.asarray(weights[1] )
							_a      	=							np.asarray(weights[2] )
							_a      	=							np.asarray(weights[3] )

							set_param(
							    torch_layer.self_attention.query					,		torch.tensor(UpperCamelCase ).transpose(1					,		2 ).contiguous().view(-1					,		UpperCamelCase )					,		)
							set_param(
							    torch_layer.self_attention.key					,		torch.tensor(UpperCamelCase ).transpose(1					,		2 ).contiguous().view(-1					,		UpperCamelCase )					,		)
							set_param(
							    torch_layer.self_attention.value					,		torch.tensor(UpperCamelCase ).transpose(1					,		2 ).contiguous().view(-1					,		UpperCamelCase )					,		)
							set_param(
							    torch_layer.output.dense					,		torch.tensor(UpperCamelCase ).view(-1					,		UpperCamelCase ).contiguous().transpose(0					,		1 )					,		)
def 						snake_case_       (UpperCamelCase			:    int					,		UpperCamelCase			:    Any					,		UpperCamelCase			:    int ):





							'''simple docstring'''


							_a      	=							weights[0][0][0]
							_a      	=							np.asarray(layer_norm_a[0] )
							_a      	=							np.asarray(layer_norm_a[1] )
							set_param(
							    torch_block.attention.layer_norm					,		torch.tensor(UpperCamelCase )					,		torch.tensor(UpperCamelCase )					,		)

							# lsh weights + output
							_a      	=							weights[0][1]
							if len(UpperCamelCase ) < 4:
														set_layer_weights_in_torch_lsh(UpperCamelCase					,		torch_block.attention					,		UpperCamelCase )
							else:
														set_layer_weights_in_torch_local(UpperCamelCase					,		torch_block.attention					,		UpperCamelCase )

							# intermediate weighs
							_a      	=							weights[2][0][1][2]

							# Chunked Feed Forward
							if len(UpperCamelCase ) == 4:
														_a      	=							intermediate_weights[2]

							# layernorm 2
							_a      	=							np.asarray(intermediate_weights[0][0] )
							_a      	=							np.asarray(intermediate_weights[0][1] )
							set_param(
							    torch_block.feed_forward.layer_norm					,		torch.tensor(UpperCamelCase )					,		torch.tensor(UpperCamelCase )					,		)

							# intermediate dense
							_a      	=							np.asarray(intermediate_weights[1][0] )
							_a      	=							np.asarray(intermediate_weights[1][1] )
							set_param(
							    torch_block.feed_forward.dense.dense					,		torch.tensor(UpperCamelCase ).transpose(0					,		1 ).contiguous()					,		torch.tensor(UpperCamelCase )					,		)

							# intermediate out
							_a      	=							np.asarray(intermediate_weights[4][0] )
							_a      	=							np.asarray(intermediate_weights[4][1] )
							set_param(
							    torch_block.feed_forward.output.dense					,		torch.tensor(UpperCamelCase ).transpose(0					,		1 ).contiguous()					,		torch.tensor(UpperCamelCase )					,		)
def 						snake_case_       (UpperCamelCase			:    Dict					,		UpperCamelCase			:    str					,		UpperCamelCase			:    List[Any] ):





							'''simple docstring'''


							_a      	=							torch_model.reformer

							# word embeds
							_a      	=							np.asarray(weights[1] )
							set_param(
							    torch_model_reformer.embeddings.word_embeddings					,		torch.tensor(UpperCamelCase )					,		)

							if isinstance(weights[3]					,		UpperCamelCase ):
														_a      	=							torch_model_reformer.embeddings.position_embeddings
														for emb_idx in range(len(position_embeddings.weights ) ):
																					_a      	=							np.asarray(weights[3][emb_idx][0] )
																					assert (
																					    position_embeddings.weights[emb_idx].shape == emb_weights.shape
																					), f'{position_embeddings[emb_idx]} emb does not match'
																					_a      	=							nn.Parameter(torch.tensor(UpperCamelCase ) )

							_a      	=							weights[5]
							assert len(torch_model_reformer.encoder.layers ) * 4 == len(
							    UpperCamelCase ), "HF and trax model do not have the same number of layers"
							for layer_idx, layer in enumerate(torch_model_reformer.encoder.layers ):
														_a      	=							trax_layer_weights[4 * layer_idx : 4 * (layer_idx + 1)]
														set_block_weights_in_torch(UpperCamelCase					,		UpperCamelCase					,		UpperCamelCase )

							# output layer norm
							_a      	=							np.asarray(weights[7][0] )
							_a      	=							np.asarray(weights[7][1] )
							set_param(
							    torch_model_reformer.encoder.layer_norm					,		torch.tensor(UpperCamelCase )					,		torch.tensor(UpperCamelCase )					,		)

							# output embeddings
							_a      	=							np.asarray(weights[9][0] )
							_a      	=							np.asarray(weights[9][1] )
							set_param(
							    torch_model.lm_head.decoder					,		torch.tensor(UpperCamelCase ).transpose(0					,		1 ).contiguous()					,		torch.tensor(UpperCamelCase )					,		)
def 						snake_case_       (UpperCamelCase			:    str					,		UpperCamelCase			:    Optional[Any]					,		UpperCamelCase			:    Tuple ):





							'''simple docstring'''


							_a      	=							ReformerConfig.from_json_file(UpperCamelCase )
							print(f'Building PyTorch model from configuration: {config}' )
							_a      	=							ReformerModelWithLMHead(UpperCamelCase )

							with open(UpperCamelCase					,		'''rb''' ) as f:
														_a      	=							pickle.load(UpperCamelCase )['''weights''']

							set_model_weights_in_torch(UpperCamelCase					,		UpperCamelCase					,		config.hidden_size )

							# Save pytorch-model
							print(f'Save PyTorch model to {pytorch_dump_path}' )
							torch.save(model.state_dict()					,		UpperCamelCase )


if __name__ == "__main__":
				_snake_case : List[str]  				=						argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    '--trax_model_pkl_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
				)
				parser.add_argument(
				    '--config_file',
				    default=None,
				    type=str,
				    required=True,
				    help=(
				        'The config json file corresponding to the pre-trained Reformer model. \n'
				        'This specifies the model architecture.'
				    ),
				)
				parser.add_argument(
				    '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
				)
				_snake_case : Tuple  				=						parser.parse_args()
				convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)







'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


_snake_case : str  				=						{
    'configuration_layoutlmv3': [
        'LAYOUTLMV3_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'LayoutLMv3Config',
        'LayoutLMv3OnnxConfig',
    ],
    'processing_layoutlmv3': ['LayoutLMv3Processor'],
    'tokenization_layoutlmv3': ['LayoutLMv3Tokenizer'],
}

try:
				if not is_tokenizers_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_snake_case : List[str]  				=						['LayoutLMv3TokenizerFast']

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_snake_case : Optional[int]  				=						[
				    'LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'LayoutLMv3ForQuestionAnswering',
				    'LayoutLMv3ForSequenceClassification',
				    'LayoutLMv3ForTokenClassification',
				    'LayoutLMv3Model',
				    'LayoutLMv3PreTrainedModel',
				]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_snake_case : Tuple  				=						[
				    'TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'TFLayoutLMv3ForQuestionAnswering',
				    'TFLayoutLMv3ForSequenceClassification',
				    'TFLayoutLMv3ForTokenClassification',
				    'TFLayoutLMv3Model',
				    'TFLayoutLMv3PreTrainedModel',
				]

try:
				if not is_vision_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_snake_case : List[Any]  				=						['LayoutLMv3FeatureExtractor']
				_snake_case : Tuple  				=						['LayoutLMv3ImageProcessor']


if TYPE_CHECKING:
				from .configuration_layoutlmva import (
				    LAYOUTLMV3_PRETRAINED_CONFIG_ARCHIVE_MAP,
				    LayoutLMvaConfig,
				    LayoutLMvaOnnxConfig,
				)
				from .processing_layoutlmva import LayoutLMvaProcessor
				from .tokenization_layoutlmva import LayoutLMvaTokenizer

				try:
								if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_layoutlmva_fast import LayoutLMvaTokenizerFast

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_layoutlmva import (
								    LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
								    LayoutLMvaForQuestionAnswering,
								    LayoutLMvaForSequenceClassification,
								    LayoutLMvaForTokenClassification,
								    LayoutLMvaModel,
								    LayoutLMvaPreTrainedModel,
								)

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_layoutlmva import (
								    TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TFLayoutLMvaForQuestionAnswering,
								    TFLayoutLMvaForSequenceClassification,
								    TFLayoutLMvaForTokenClassification,
								    TFLayoutLMvaModel,
								    TFLayoutLMvaPreTrainedModel,
								)

				try:
								if not is_vision_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .feature_extraction_layoutlmva import LayoutLMvaFeatureExtractor
								from .image_processing_layoutlmva import LayoutLMvaImageProcessor

else:
				import sys

				_snake_case : Any  				=						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

