


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  a   (lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__=None   ):
   # set parameter of one layer
   assert torch_layer.weight.shape == weight.shape, f'''{torch_layer} layer.weight does not match'''
   __a					=  nn.Parameter(lowerCAmelCase__   )
   if bias is not None:
      assert torch_layer.bias.shape == bias.shape, f'''{torch_layer} layer.bias does not match'''
      __a					=  nn.Parameter(lowerCAmelCase__   )




def  a   (lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__   ):
   # set torch weights for 1-to-1 comparison
   __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(lowerCAmelCase__   ).transpose(1		,				2   ).contiguous().view(-1		,				lowerCAmelCase__   )		,				)
   set_param(
       torch_layer.self_attention.value		,				torch.tensor(lowerCAmelCase__   ).transpose(1		,				2   ).contiguous().view(-1		,				lowerCAmelCase__   )		,				)
   set_param(
       torch_layer.output.dense		,				torch.tensor(lowerCAmelCase__   ).view(-1		,				lowerCAmelCase__   ).contiguous().transpose(0		,				1   )		,				)




def  a   (lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__   ):
   # set torch weights for 1-to-1 comparison
   __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(lowerCAmelCase__   ).transpose(1		,				2   ).contiguous().view(-1		,				lowerCAmelCase__   )		,				)
   set_param(
       torch_layer.self_attention.key		,				torch.tensor(lowerCAmelCase__   ).transpose(1		,				2   ).contiguous().view(-1		,				lowerCAmelCase__   )		,				)
   set_param(
       torch_layer.self_attention.value		,				torch.tensor(lowerCAmelCase__   ).transpose(1		,				2   ).contiguous().view(-1		,				lowerCAmelCase__   )		,				)
   set_param(
       torch_layer.output.dense		,				torch.tensor(lowerCAmelCase__   ).view(-1		,				lowerCAmelCase__   ).contiguous().transpose(0		,				1   )		,				)




def  a   (lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__   ):
   # layernorm 1
   __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(lowerCAmelCase__   )		,				torch.tensor(lowerCAmelCase__   )		,				)

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

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

   # Chunked Feed Forward
   if len(lowerCAmelCase__   ) == 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(lowerCAmelCase__   )		,				torch.tensor(lowerCAmelCase__   )		,				)

   # 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(lowerCAmelCase__   ).transpose(0		,				1   ).contiguous()		,				torch.tensor(lowerCAmelCase__   )		,				)

   # 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(lowerCAmelCase__   ).transpose(0		,				1   ).contiguous()		,				torch.tensor(lowerCAmelCase__   )		,				)




def  a   (lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__   ):
   # reformer model
   __a					=  torch_model.reformer

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

   if isinstance(weights[3]		,				lowerCAmelCase__   ):
      __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(lowerCAmelCase__   )   )

   __a					=  weights[5]
   assert len(torch_model_reformer.encoder.layers   ) * 4 == len(
       lowerCAmelCase__   ), "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(lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__   )

   # 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(lowerCAmelCase__   )		,				torch.tensor(lowerCAmelCase__   )		,				)

   # output embeddings
   __a					=  np.asarray(weights[9][0]   )
   __a					=  np.asarray(weights[9][1]   )
   set_param(
       torch_model.lm_head.decoder		,				torch.tensor(lowerCAmelCase__   ).transpose(0		,				1   ).contiguous()		,				torch.tensor(lowerCAmelCase__   )		,				)




def  a   (lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__   ):
   # Initialise PyTorch model
   __a					=  ReformerConfig.from_json_file(lowerCAmelCase__   )
   print(f'''Building PyTorch model from configuration: {config}'''   )
   __a					=  ReformerModelWithLMHead(lowerCAmelCase__   )

   with open(lowerCAmelCase__		,				"""rb"""   ) as f:
      __a					=  pickle.load(lowerCAmelCase__   )["""weights"""]

   set_model_weights_in_torch(lowerCAmelCase__		,				lowerCAmelCase__		,				config.hidden_size   )

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


if __name__ == "__main__":
  SCREAMING_SNAKE_CASE							    =							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							    =							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

import numpy as np

from ...processing_utils import ProcessorMixin
from ...utils import to_numpy






class      lowerCamelCase_      (	_lowercase	):
	_lowercase		:   Union[str, Any]     					=				'''EncodecFeatureExtractor'''
	_lowercase		:   Any     					=				('''T5Tokenizer''', '''T5TokenizerFast''')




	def __init__(     self						:     List[Any]					,      __A						:     Any					,      __A						:     Tuple							):
		super().__init__(__A					,      __A							)
		__A :			Dict		    =			self.feature_extractor
		__A :			List[str]		    =			False




	def        lowerCAmelCase_       (     self						:     Union[str, Any]					,      __A						:     str=None					,      __A						:     Tuple=None					,      __A						:     Dict=True							):
		return self.tokenizer.get_decoder_prompt_ids(task=__A					,      language=__A					,      no_timestamps=__A							)




	def __call__(     self						:     Optional[Any]					,      *__A						:     Tuple					,      **__A						:     Tuple							):
		# For backward compatibility
		if self._in_target_context_manager:
			return self.current_processor(*__A					,      **__A							)

		__A :			str		    =			kwargs.pop("""audio"""					,      __A							)
		__A :			Optional[Any]		    =			kwargs.pop("""sampling_rate"""					,      __A							)
		__A :			int		    =			kwargs.pop("""text"""					,      __A							)
		if len(__A							) > 0:
			__A :			int		    =			args[0]
			__A :			Dict		    =			args[1:]

		if audio is None and text is None:
			raise ValueError("""You need to specify either an `audio` or `text` input to process."""							)

		if text is not None:
			__A :			Dict		    =			self.tokenizer(__A					,      **__A							)

		if audio is not None:
			__A :			Optional[int]		    =			self.feature_extractor(__A					,      *__A					,      sampling_rate=__A					,      **__A							)

		if audio is None:
			return inputs

		elif text is None:
			return audio_inputs

		else:
			__A :			List[Any]		    =			audio_inputs["""input_values"""]
			if "padding_mask" in audio_inputs:
				__A :			int		    =			audio_inputs["""padding_mask"""]
			return inputs




	def        lowerCAmelCase_       (     self						:     List[str]					,      *__A						:     int					,      **__A						:     Tuple							):
		__A :			Optional[int]		    =			kwargs.pop("""audio"""					,      __A							)
		__A :			List[str]		    =			kwargs.pop("""padding_mask"""					,      __A							)

		if len(__A							) > 0:
			__A :			Dict		    =			args[0]
			__A :			Optional[int]		    =			args[1:]

		if audio_values is not None:
			return self._decode_audio(__A					,      padding_mask=__A							)
		else:
			return self.tokenizer.batch_decode(*__A					,      **__A							)




	def        lowerCAmelCase_       (     self						:     Optional[Any]					,      *__A						:     Dict					,      **__A						:     Any							):
		return self.tokenizer.decode(*__A					,      **__A							)




	def        lowerCAmelCase_       (     self						:     Tuple					,      __A						:     Union[str, Any]					,      __A						:     Optional = None							):
		__A :			List[str]		    =			to_numpy(__A							)
		__A    ,				__A    ,				__A :			Tuple		    =			audio_values.shape

		if padding_mask is None:
			return list(__A							)

		__A :			Union[str, Any]		    =			to_numpy(__A							)

		# match the sequence length of the padding mask to the generated audio arrays by padding with the **non-padding**
		# token (so that the generated audio values are **not** treated as padded tokens)
		__A :			List[str]		    =			seq_len - padding_mask.shape[-1]
		__A :			Tuple		    =			1 - self.feature_extractor.padding_value
		__A :			Optional[int]		    =			np.pad(__A					,      ((0, 0), (0, difference))					,      """constant"""					,      constant_values=__A							)

		__A :			int		    =			audio_values.tolist()
		for i in range(__A							):
			__A :			str		    =			np.asarray(audio_values[i]							)[
			    padding_mask[i][None, :] != self.feature_extractor.padding_value
			]
			__A :			List[Any]		    =			sliced_audio.reshape(__A					,      -1							)

		return audio_values





