


'''simple docstring'''
import os
import shutil
import tempfile
import unittest

import numpy as np

from transformers import AutoTokenizer, BarkProcessor
from transformers.testing_utils import require_torch, slow




@require_torch
class      lowerCamelCase_  (      unittest.TestCase ):






      def lowercase_				(   self							:       Union[str, Any]					):


          '''simple docstring'''


          UpperCAmelCase__     :						Dict						=							'''ylacombe/bark-small'''
          UpperCAmelCase__     :						int						=							tempfile.mkdtemp()
          UpperCAmelCase__     :						Union[str, Any]						=							'''en_speaker_1'''
          UpperCAmelCase__     :						Union[str, Any]						=							'''This is a test string'''
          UpperCAmelCase__     :						Any						=							'''speaker_embeddings_path.json'''
          UpperCAmelCase__     :						Dict						=							'''speaker_embeddings'''






      def lowercase_				(   self							:       str		,		**_A							:       Any					):


          '''simple docstring'''


          return AutoTokenizer.from_pretrained(self.checkpoint		,		**UpperCamelCase_					)






      def lowercase_				(   self							:       int					):


          '''simple docstring'''


          shutil.rmtree(self.tmpdirname					)






      def lowercase_				(   self							:       int					):


          '''simple docstring'''


          UpperCAmelCase__     :						int						=							self.get_tokenizer()

          UpperCAmelCase__     :						Optional[int]						=							BarkProcessor(tokenizer=UpperCamelCase_					)

          processor.save_pretrained(self.tmpdirname					)
          UpperCAmelCase__     :						List[str]						=							BarkProcessor.from_pretrained(self.tmpdirname					)

          self.assertEqual(processor.tokenizer.get_vocab()		,		tokenizer.get_vocab()					)






      @slow
      def lowercase_				(   self							:       Optional[Any]					):


          '''simple docstring'''


          UpperCAmelCase__     :						Dict						=							BarkProcessor.from_pretrained(
              pretrained_processor_name_or_path=self.checkpoint		,		speaker_embeddings_dict_path=self.speaker_embeddings_dict_path		,		)
          processor.save_pretrained(
              self.tmpdirname		,		speaker_embeddings_dict_path=self.speaker_embeddings_dict_path		,		speaker_embeddings_directory=self.speaker_embeddings_directory		,		)

          UpperCAmelCase__     :						str						=							self.get_tokenizer(bos_token='''(BOS)'''		,		eos_token='''(EOS)'''					)

          UpperCAmelCase__     :						int						=							BarkProcessor.from_pretrained(
              self.tmpdirname		,		self.speaker_embeddings_dict_path		,		bos_token='''(BOS)'''		,		eos_token='''(EOS)'''		,		)

          self.assertEqual(processor.tokenizer.get_vocab()		,		tokenizer_add_kwargs.get_vocab()					)






      def lowercase_				(   self							:       Optional[Any]					):


          '''simple docstring'''


          UpperCAmelCase__     :						int						=							BarkProcessor.from_pretrained(
              pretrained_processor_name_or_path=self.checkpoint		,		speaker_embeddings_dict_path=self.speaker_embeddings_dict_path		,		)

          UpperCAmelCase__     :						List[Any]						=							35
          UpperCAmelCase__     :						Dict						=							2
          UpperCAmelCase__     :						List[Any]						=							8

          UpperCAmelCase__     :						Union[str, Any]						=							{
              '''semantic_prompt''': np.ones(UpperCamelCase_					),
              '''coarse_prompt''': np.ones((nb_codebooks_coarse, seq_len)					),
              '''fine_prompt''': np.ones((nb_codebooks_total, seq_len)					),
          }

          # test providing already loaded voice_preset
          UpperCAmelCase__     :						int						=							processor(text=self.input_string		,		voice_preset=UpperCamelCase_					)

          UpperCAmelCase__     :						int						=							inputs['''history_prompt''']
          for key in voice_preset:
              self.assertListEqual(voice_preset[key].tolist()		,		processed_voice_preset.get(UpperCamelCase_		,		np.array([]					)					).tolist()					)

          # test loading voice preset from npz file
          UpperCAmelCase__     :						List[str]						=							os.path.join(self.tmpdirname		,		'''file.npz'''					)
          np.savez(UpperCamelCase_		,		**UpperCamelCase_					)
          UpperCAmelCase__     :						List[str]						=							processor(text=self.input_string		,		voice_preset=UpperCamelCase_					)
          UpperCAmelCase__     :						str						=							inputs['''history_prompt''']

          for key in voice_preset:
              self.assertListEqual(voice_preset[key].tolist()		,		processed_voice_preset.get(UpperCamelCase_		,		np.array([]					)					).tolist()					)

          # test loading voice preset from the hub
          UpperCAmelCase__     :						Tuple						=							processor(text=self.input_string		,		voice_preset=self.voice_preset					)






      def lowercase_				(   self							:       Tuple					):


          '''simple docstring'''


          UpperCAmelCase__     :						List[str]						=							self.get_tokenizer()

          UpperCAmelCase__     :						int						=							BarkProcessor(tokenizer=UpperCamelCase_					)

          UpperCAmelCase__     :						Optional[int]						=							processor(text=self.input_string					)

          UpperCAmelCase__     :						List[str]						=							tokenizer(
              self.input_string		,		padding='''max_length'''		,		max_length=256		,		add_special_tokens=UpperCamelCase_		,		return_attention_mask=UpperCamelCase_		,		return_token_type_ids=UpperCamelCase_		,		)

          for key in encoded_tok.keys():
              self.assertListEqual(encoded_tok[key]		,		encoded_processor[key].squeeze().tolist()					)







'''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 						__lowerCamelCase			(	UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_=None							)   ->List[str]:
	# set parameter of one layer
	assert torch_layer.weight.shape == weight.shape, f'''{torch_layer} layer.weight does not match'''
	snake_case__	       =   nn.Parameter(UpperCAmelCase_							)
	if bias is not None:
		assert torch_layer.bias.shape == bias.shape, f'''{torch_layer} layer.bias does not match'''
		snake_case__	       =   nn.Parameter(UpperCAmelCase_							)




def 						__lowerCamelCase			(	UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_							)   ->List[Any]:
	# set torch weights for 1-to-1 comparison
	snake_case__	       =   np.asarray(weights[0]							)
	snake_case__	       =   np.asarray(weights[1]							)
	snake_case__	       =   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 						__lowerCamelCase			(	UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_							)   ->List[Any]:
	# set torch weights for 1-to-1 comparison
	snake_case__	       =   np.asarray(weights[0]							)
	snake_case__	       =   np.asarray(weights[1]							)
	snake_case__	       =   np.asarray(weights[2]							)
	snake_case__	       =   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 						__lowerCamelCase			(	UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_							)   ->Dict:
	# layernorm 1
	snake_case__	       =   weights[0][0][0]
	snake_case__	       =   np.asarray(layer_norm_a[0]							)
	snake_case__	       =   np.asarray(layer_norm_a[1]							)
	set_param(
	    torch_block.attention.layer_norm					,				torch.tensor(UpperCAmelCase_							)					,				torch.tensor(UpperCAmelCase_							)					,				)

	# lsh weights + output
	snake_case__	       =   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
	snake_case__	       =   weights[2][0][1][2]

	# Chunked Feed Forward
	if len(UpperCAmelCase_							) == 4:
		snake_case__	       =   intermediate_weights[2]

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

	# intermediate dense
	snake_case__	       =   np.asarray(intermediate_weights[1][0]							)
	snake_case__	       =   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
	snake_case__	       =   np.asarray(intermediate_weights[4][0]							)
	snake_case__	       =   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 						__lowerCamelCase			(	UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_							)   ->Union[str, Any]:
	# reformer model
	snake_case__	       =   torch_model.reformer

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

	if isinstance(weights[3]					,				UpperCAmelCase_							):
		snake_case__	       =   torch_model_reformer.embeddings.position_embeddings
		for emb_idx in range(len(position_embeddings.weights							)							):
			snake_case__	       =   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'''
			snake_case__	       =   nn.Parameter(torch.tensor(UpperCAmelCase_							)							)

	snake_case__	       =   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							):
		snake_case__	       =   trax_layer_weights[4 * layer_idx : 4 * (layer_idx + 1)]
		set_block_weights_in_torch(UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_							)

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

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




def 						__lowerCamelCase			(	UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_							)   ->List[Any]:
	# Initialise PyTorch model
	snake_case__	       =   ReformerConfig.from_json_file(UpperCAmelCase_							)
	print(f'''Building PyTorch model from configuration: {config}'''							)
	snake_case__	       =   ReformerModelWithLMHead(UpperCAmelCase_							)

	with open(UpperCAmelCase_					,				'rb'							) as f:
		snake_case__	       =   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__":
			a__					:			Optional[int]          =     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.'''
			)
			a__					:			Dict          =     parser.parse_args()
			convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)



