


'''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     _UpperCamelCase				(			UpperCamelCase__  , UpperCamelCase__  , UpperCamelCase__=None		):






				"""simple docstring"""



				assert torch_layer.weight.shape == weight.shape, F"""{torch_layer} layer.weight does not match"""
				__magic_name__					:       Optional[int]    							=							nn.Parameter(SCREAMING_SNAKE_CASE_		)
				if bias is not None:
								assert torch_layer.bias.shape == bias.shape, F"""{torch_layer} layer.bias does not match"""
								__magic_name__					:       Union[str, Any]    							=							nn.Parameter(SCREAMING_SNAKE_CASE_		)



def     _UpperCamelCase				(			UpperCamelCase__  , UpperCamelCase__  , UpperCamelCase__		):






				"""simple docstring"""



				__magic_name__					:       Optional[Any]    							=							np.asarray(weights[0]		)
				__magic_name__					:       int    							=							np.asarray(weights[1]		)
				__magic_name__					:       Optional[int]    							=							np.asarray(weights[2]		)

				set_param(
				    torch_layer.self_attention.query_key  , torch.tensor(SCREAMING_SNAKE_CASE_		).transpose(1  , 2		).contiguous().view(-1  , SCREAMING_SNAKE_CASE_		)  , )
				set_param(
				    torch_layer.self_attention.value  , torch.tensor(SCREAMING_SNAKE_CASE_		).transpose(1  , 2		).contiguous().view(-1  , SCREAMING_SNAKE_CASE_		)  , )
				set_param(
				    torch_layer.output.dense  , torch.tensor(SCREAMING_SNAKE_CASE_		).view(-1  , SCREAMING_SNAKE_CASE_		).contiguous().transpose(0  , 1		)  , )



def     _UpperCamelCase				(			UpperCamelCase__  , UpperCamelCase__  , UpperCamelCase__		):






				"""simple docstring"""



				__magic_name__					:       List[str]    							=							np.asarray(weights[0]		)
				__magic_name__					:       str    							=							np.asarray(weights[1]		)
				__magic_name__					:       int    							=							np.asarray(weights[2]		)
				__magic_name__					:       Tuple    							=							np.asarray(weights[3]		)

				set_param(
				    torch_layer.self_attention.query  , torch.tensor(SCREAMING_SNAKE_CASE_		).transpose(1  , 2		).contiguous().view(-1  , SCREAMING_SNAKE_CASE_		)  , )
				set_param(
				    torch_layer.self_attention.key  , torch.tensor(SCREAMING_SNAKE_CASE_		).transpose(1  , 2		).contiguous().view(-1  , SCREAMING_SNAKE_CASE_		)  , )
				set_param(
				    torch_layer.self_attention.value  , torch.tensor(SCREAMING_SNAKE_CASE_		).transpose(1  , 2		).contiguous().view(-1  , SCREAMING_SNAKE_CASE_		)  , )
				set_param(
				    torch_layer.output.dense  , torch.tensor(SCREAMING_SNAKE_CASE_		).view(-1  , SCREAMING_SNAKE_CASE_		).contiguous().transpose(0  , 1		)  , )



def     _UpperCamelCase				(			UpperCamelCase__  , UpperCamelCase__  , UpperCamelCase__		):






				"""simple docstring"""



				__magic_name__					:       List[str]    							=							weights[0][0][0]
				__magic_name__					:       Union[str, Any]    							=							np.asarray(layer_norm_a[0]		)
				__magic_name__					:       int    							=							np.asarray(layer_norm_a[1]		)
				set_param(
				    torch_block.attention.layer_norm  , torch.tensor(SCREAMING_SNAKE_CASE_		)  , torch.tensor(SCREAMING_SNAKE_CASE_		)  , )

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

				# intermediate weighs
				__magic_name__					:       Dict    							=							weights[2][0][1][2]

				# Chunked Feed Forward
				if len(SCREAMING_SNAKE_CASE_		) == 4:
								__magic_name__					:       Union[str, Any]    							=							intermediate_weights[2]

				# layernorm 2
				__magic_name__					:       Dict    							=							np.asarray(intermediate_weights[0][0]		)
				__magic_name__					:       Tuple    							=							np.asarray(intermediate_weights[0][1]		)
				set_param(
				    torch_block.feed_forward.layer_norm  , torch.tensor(SCREAMING_SNAKE_CASE_		)  , torch.tensor(SCREAMING_SNAKE_CASE_		)  , )

				# intermediate dense
				__magic_name__					:       Dict    							=							np.asarray(intermediate_weights[1][0]		)
				__magic_name__					:       Dict    							=							np.asarray(intermediate_weights[1][1]		)
				set_param(
				    torch_block.feed_forward.dense.dense  , torch.tensor(SCREAMING_SNAKE_CASE_		).transpose(0  , 1		).contiguous()  , torch.tensor(SCREAMING_SNAKE_CASE_		)  , )

				# intermediate out
				__magic_name__					:       Optional[int]    							=							np.asarray(intermediate_weights[4][0]		)
				__magic_name__					:       List[Any]    							=							np.asarray(intermediate_weights[4][1]		)
				set_param(
				    torch_block.feed_forward.output.dense  , torch.tensor(SCREAMING_SNAKE_CASE_		).transpose(0  , 1		).contiguous()  , torch.tensor(SCREAMING_SNAKE_CASE_		)  , )



def     _UpperCamelCase				(			UpperCamelCase__  , UpperCamelCase__  , UpperCamelCase__		):






				"""simple docstring"""



				__magic_name__					:       int    							=							torch_model.reformer

				# word embeds
				__magic_name__					:       List[str]    							=							np.asarray(weights[1]		)
				set_param(
				    torch_model_reformer.embeddings.word_embeddings  , torch.tensor(SCREAMING_SNAKE_CASE_		)  , )

				if isinstance(weights[3]  , SCREAMING_SNAKE_CASE_		):
								__magic_name__					:       Optional[int]    							=							torch_model_reformer.embeddings.position_embeddings
								for emb_idx in range(len(position_embeddings.weights		)		):
												__magic_name__					:       Dict    							=							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"""
												__magic_name__					:       Optional[int]    							=							nn.Parameter(torch.tensor(SCREAMING_SNAKE_CASE_		)		)

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

				# output layer norm
				__magic_name__					:       Optional[int]    							=							np.asarray(weights[7][0]		)
				__magic_name__					:       List[str]    							=							np.asarray(weights[7][1]		)
				set_param(
				    torch_model_reformer.encoder.layer_norm  , torch.tensor(SCREAMING_SNAKE_CASE_		)  , torch.tensor(SCREAMING_SNAKE_CASE_		)  , )

				# output embeddings
				__magic_name__					:       Optional[Any]    							=							np.asarray(weights[9][0]		)
				__magic_name__					:       Union[str, Any]    							=							np.asarray(weights[9][1]		)
				set_param(
				    torch_model.lm_head.decoder  , torch.tensor(SCREAMING_SNAKE_CASE_		).transpose(0  , 1		).contiguous()  , torch.tensor(SCREAMING_SNAKE_CASE_		)  , )



def     _UpperCamelCase				(			UpperCamelCase__  , UpperCamelCase__  , UpperCamelCase__		):






				"""simple docstring"""



				__magic_name__					:       str    							=							ReformerConfig.from_json_file(SCREAMING_SNAKE_CASE_		)
				print(F"""Building PyTorch model from configuration: {config}"""		)
				__magic_name__					:       Any    							=							ReformerModelWithLMHead(SCREAMING_SNAKE_CASE_		)

				with open(SCREAMING_SNAKE_CASE_  , "rb"		) as f:
								__magic_name__					:       Dict    							=							pickle.load(SCREAMING_SNAKE_CASE_		)["weights"]

				set_model_weights_in_torch(SCREAMING_SNAKE_CASE_  , SCREAMING_SNAKE_CASE_  , config.hidden_size		)

				# Save pytorch-model
				print(F"""Save PyTorch model to {pytorch_dump_path}"""		)
				torch.save(model.state_dict()  , SCREAMING_SNAKE_CASE_		)


if __name__ == "__main__":
		_SCREAMING_SNAKE_CASE							: Dict										=			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."
		)
		_SCREAMING_SNAKE_CASE							: List[str]										=			parser.parse_args()
		convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)






from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType




class 			__UpperCamelCase    (							A__    ):
     __A	:  Dict							  =   ["""image_processor""", """tokenizer"""]
     __A	:  List[str]							  =   """BridgeTowerImageProcessor"""
     __A	:  str							  =   ("""RobertaTokenizer""", """RobertaTokenizerFast""")





     def __init__(     self				,							_UpperCamelCase				,							_UpperCamelCase					):
        super().__init__(_UpperCamelCase				,							_UpperCamelCase					)





     def __call__(     self				,							_UpperCamelCase				,							_UpperCamelCase = None				,							_UpperCamelCase = True				,							_UpperCamelCase = False				,							_UpperCamelCase = None				,							_UpperCamelCase = None				,							_UpperCamelCase = 0				,							_UpperCamelCase = None				,							_UpperCamelCase = None				,							_UpperCamelCase = None				,							_UpperCamelCase = False				,							_UpperCamelCase = False				,							_UpperCamelCase = False				,							_UpperCamelCase = False				,							_UpperCamelCase = True				,							_UpperCamelCase = None				,							**_UpperCamelCase				,							):
        _UpperCAmelCase           =   self.tokenizer(
            text=_UpperCamelCase				,							add_special_tokens=_UpperCamelCase				,							padding=_UpperCamelCase				,							truncation=_UpperCamelCase				,							max_length=_UpperCamelCase				,							stride=_UpperCamelCase				,							pad_to_multiple_of=_UpperCamelCase				,							return_token_type_ids=_UpperCamelCase				,							return_attention_mask=_UpperCamelCase				,							return_overflowing_tokens=_UpperCamelCase				,							return_special_tokens_mask=_UpperCamelCase				,							return_offsets_mapping=_UpperCamelCase				,							return_length=_UpperCamelCase				,							verbose=_UpperCamelCase				,							return_tensors=_UpperCamelCase				,							**_UpperCamelCase				,							)
        # add pixel_values + pixel_mask
        _UpperCAmelCase           =   self.image_processor(
            _UpperCamelCase				,							return_tensors=_UpperCamelCase				,							do_normalize=_UpperCamelCase				,							do_center_crop=_UpperCamelCase				,							**_UpperCamelCase					)
        encoding.update(_UpperCamelCase					)

        return encoding





     def 		UpperCamelCase(     self				,							*_UpperCamelCase				,							**_UpperCamelCase					):
        return self.tokenizer.batch_decode(*_UpperCamelCase				,							**_UpperCamelCase					)





     def 		UpperCamelCase(     self				,							*_UpperCamelCase				,							**_UpperCamelCase					):
        return self.tokenizer.decode(*_UpperCamelCase				,							**_UpperCamelCase					)





     @property
     def 		UpperCamelCase(     self					):
        _UpperCAmelCase           =   self.tokenizer.model_input_names
        _UpperCAmelCase           =   self.image_processor.model_input_names
        return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names					)					)