


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   :      Dict    ,						UpperCAmelCase   :      List[str]    ,						UpperCAmelCase   :      str=None   ):
      """simple docstring"""




      assert torch_layer.weight.shape == weight.shape, f"""{torch_layer} layer.weight does not match"""
      __lowerCamelCase  :   Union[str, Any]							    =    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"""
            __lowerCamelCase  :   str							    =    nn.Parameter(SCREAMING_SNAKE_CASE_   )





def 	_UpperCAmelCase							(  UpperCAmelCase   :      List[Any]    ,						UpperCAmelCase   :      Any    ,						UpperCAmelCase   :      int   ):
      """simple docstring"""




      __lowerCamelCase  :   Any							    =    np.asarray(weights[0]   )
      __lowerCamelCase  :   Union[str, Any]							    =    np.asarray(weights[1]   )
      __lowerCamelCase  :   List[str]							    =    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   :      int    ,						UpperCAmelCase   :      Union[str, Any]    ,						UpperCAmelCase   :      Any   ):
      """simple docstring"""




      __lowerCamelCase  :   int							    =    np.asarray(weights[0]   )
      __lowerCamelCase  :   Optional[Any]							    =    np.asarray(weights[1]   )
      __lowerCamelCase  :   List[Any]							    =    np.asarray(weights[2]   )
      __lowerCamelCase  :   Optional[Any]							    =    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   :      Optional[Any]    ,						UpperCAmelCase   :      List[Any]    ,						UpperCAmelCase   :      Optional[Any]   ):
      """simple docstring"""




      __lowerCamelCase  :   str							    =    weights[0][0][0]
      __lowerCamelCase  :   Tuple							    =    np.asarray(layer_norm_a[0]   )
      __lowerCamelCase  :   Union[str, Any]							    =    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
      __lowerCamelCase  :   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
      __lowerCamelCase  :   Dict							    =    weights[2][0][1][2]

      # Chunked Feed Forward
      if len(SCREAMING_SNAKE_CASE_   ) == 4:
            __lowerCamelCase  :   Optional[Any]							    =    intermediate_weights[2]

      # layernorm 2
      __lowerCamelCase  :   Optional[int]							    =    np.asarray(intermediate_weights[0][0]   )
      __lowerCamelCase  :   int							    =    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
      __lowerCamelCase  :   Union[str, Any]							    =    np.asarray(intermediate_weights[1][0]   )
      __lowerCamelCase  :   Optional[int]							    =    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
      __lowerCamelCase  :   int							    =    np.asarray(intermediate_weights[4][0]   )
      __lowerCamelCase  :   Optional[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   :      Optional[Any]    ,						UpperCAmelCase   :      List[str]    ,						UpperCAmelCase   :      Union[str, Any]   ):
      """simple docstring"""




      __lowerCamelCase  :   Union[str, Any]							    =    torch_model.reformer

      # word embeds
      __lowerCamelCase  :   Optional[Any]							    =    np.asarray(weights[1]   )
      set_param(
          torch_model_reformer.embeddings.word_embeddings    ,						torch.tensor(SCREAMING_SNAKE_CASE_   )    ,						)

      if isinstance(weights[3]    ,						SCREAMING_SNAKE_CASE_   ):
            __lowerCamelCase  :   int							    =    torch_model_reformer.embeddings.position_embeddings
            for emb_idx in range(len(position_embeddings.weights   )   ):
                  __lowerCamelCase  :   List[Any]							    =    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"""
                  __lowerCamelCase  :   List[str]							    =    nn.Parameter(torch.tensor(SCREAMING_SNAKE_CASE_   )   )

      __lowerCamelCase  :   Tuple							    =    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   ):
            __lowerCamelCase  :   str							    =    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
      __lowerCamelCase  :   Dict							    =    np.asarray(weights[7][0]   )
      __lowerCamelCase  :   Union[str, Any]							    =    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
      __lowerCamelCase  :   Any							    =    np.asarray(weights[9][0]   )
      __lowerCamelCase  :   Tuple							    =    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   :      Dict    ,						UpperCAmelCase   :      Optional[Any]    ,						UpperCAmelCase   :      Optional[int]   ):
      """simple docstring"""




      __lowerCamelCase  :   Union[str, Any]							    =    ReformerConfig.from_json_file(SCREAMING_SNAKE_CASE_   )
      print(f"""Building PyTorch model from configuration: {config}"""   )
      __lowerCamelCase  :   List[Any]							    =    ReformerModelWithLMHead(SCREAMING_SNAKE_CASE_   )

      with open(SCREAMING_SNAKE_CASE_    ,						"""rb"""   ) as f:
            __lowerCamelCase  :   Optional[int]							    =    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__":
    __UpperCamelCase     :    List[Any] 	=				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.'
    )
    __UpperCamelCase     :    Any 	=				parser.parse_args()
    convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)





from ...processing_utils import ProcessorMixin



class    a_ (			_a    ):
			a	:			Optional[int]					   =							'''SpeechT5FeatureExtractor'''
			a	:			List[Any]					   =							'''SpeechT5Tokenizer'''






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






			def __call__(      self				,			*__UpperCamelCase				,			**__UpperCamelCase  ):
									_lowercase					       =					kwargs.pop("""audio"""				,			__UpperCamelCase  )
									_lowercase					       =					kwargs.pop("""text"""				,			__UpperCamelCase  )
									_lowercase					       =					kwargs.pop("""text_target"""				,			__UpperCamelCase  )
									_lowercase					       =					kwargs.pop("""audio_target"""				,			__UpperCamelCase  )
									_lowercase					       =					kwargs.pop("""sampling_rate"""				,			__UpperCamelCase  )

									if audio is not None and text is not None:
															raise ValueError(
															    """Cannot process both `audio` and `text` inputs. Did you mean `audio_target` or `text_target`?"""  )
									if audio_target is not None and text_target is not None:
															raise ValueError(
															    """Cannot process both `audio_target` and `text_target` inputs. Did you mean `audio` or `text`?"""  )
									if audio is None and audio_target is None and text is None and text_target is None:
															raise ValueError(
															    """You need to specify either an `audio`, `audio_target`, `text`, or `text_target` input to process."""  )

									if audio is not None:
															_lowercase					       =					self.feature_extractor(__UpperCamelCase				,			*__UpperCamelCase				,			sampling_rate=__UpperCamelCase				,			**__UpperCamelCase  )
									elif text is not None:
															_lowercase					       =					self.tokenizer(__UpperCamelCase				,			**__UpperCamelCase  )
									else:
															_lowercase					       =					None

									if audio_target is not None:
															_lowercase					       =					self.feature_extractor(audio_target=__UpperCamelCase				,			*__UpperCamelCase				,			sampling_rate=__UpperCamelCase				,			**__UpperCamelCase  )
															_lowercase					       =					targets["""input_values"""]
									elif text_target is not None:
															_lowercase					       =					self.tokenizer(__UpperCamelCase				,			**__UpperCamelCase  )
															_lowercase					       =					targets["""input_ids"""]
									else:
															_lowercase					       =					None

									if inputs is None:
															return targets

									if targets is not None:
															_lowercase					       =					labels

															_lowercase					       =					targets.get("""attention_mask"""  )
															if decoder_attention_mask is not None:
																					_lowercase					       =					decoder_attention_mask

									return inputs






			def    UpperCamelCase_				(      self				,			*__UpperCamelCase				,			**__UpperCamelCase  ):
									_lowercase					       =					kwargs.pop("""input_values"""				,			__UpperCamelCase  )
									_lowercase					       =					kwargs.pop("""input_ids"""				,			__UpperCamelCase  )
									_lowercase					       =					kwargs.pop("""labels"""				,			__UpperCamelCase  )

									if input_values is not None and input_ids is not None:
															raise ValueError("""Cannot process both `input_values` and `input_ids` inputs."""  )
									if input_values is None and input_ids is None and labels is None:
															raise ValueError(
															    """You need to specify either an `input_values`, `input_ids`, or `labels` input to be padded."""  )

									if input_values is not None:
															_lowercase					       =					self.feature_extractor.pad(__UpperCamelCase				,			*__UpperCamelCase				,			**__UpperCamelCase  )
									elif input_ids is not None:
															_lowercase					       =					self.tokenizer.pad(__UpperCamelCase				,			**__UpperCamelCase  )
									else:
															_lowercase					       =					None

									if labels is not None:
															if "input_ids" in labels or (isinstance(__UpperCamelCase				,			__UpperCamelCase  ) and "input_ids" in labels[0]):
																					_lowercase					       =					self.tokenizer.pad(__UpperCamelCase				,			**__UpperCamelCase  )
																					_lowercase					       =					targets["""input_ids"""]
															else:
																					_lowercase					       =					self.feature_extractor.feature_size
																					_lowercase					       =					self.feature_extractor.num_mel_bins
																					_lowercase					       =					self.feature_extractor.pad(__UpperCamelCase				,			*__UpperCamelCase				,			**__UpperCamelCase  )
																					_lowercase					       =					feature_size_hack
																					_lowercase					       =					targets["""input_values"""]
									else:
															_lowercase					       =					None

									if inputs is None:
															return targets

									if targets is not None:
															_lowercase					       =					labels

															_lowercase					       =					targets.get("""attention_mask"""  )
															if decoder_attention_mask is not None:
																					_lowercase					       =					decoder_attention_mask

									return inputs






			def    UpperCamelCase_				(      self				,			*__UpperCamelCase				,			**__UpperCamelCase  ):
									return self.tokenizer.batch_decode(*__UpperCamelCase				,			**__UpperCamelCase  )






			def    UpperCamelCase_				(      self				,			*__UpperCamelCase				,			**__UpperCamelCase  ):
									return self.tokenizer.decode(*__UpperCamelCase				,			**__UpperCamelCase  )