






import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


a__       =     logging.get_logger(__name__)

a__       =     {"""vocab_file""": """spm_char.model"""}

a__       =     {
    """vocab_file""": {
        """microsoft/speecht5_asr""": """https://huggingface.co/microsoft/speecht5_asr/resolve/main/spm_char.model""",
        """microsoft/speecht5_tts""": """https://huggingface.co/microsoft/speecht5_tts/resolve/main/spm_char.model""",
        """microsoft/speecht5_vc""": """https://huggingface.co/microsoft/speecht5_vc/resolve/main/spm_char.model""",
    }
}

a__       =     {
    """microsoft/speecht5_asr""": 1_0_2_4,
    """microsoft/speecht5_tts""": 1_0_2_4,
    """microsoft/speecht5_vc""": 1_0_2_4,
}


class    _lowerCAmelCase			(		lowercase_      ):



				"""simple docstring"""



				_lowercase				:   Optional[int]      =  VOCAB_FILES_NAMES
				_lowercase				:   List[str]      =  PRETRAINED_VOCAB_FILES_MAP
				_lowercase				:   List[str]      =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				_lowercase				:   Any      =  ['''input_ids''', '''attention_mask''']



				def __init__(      self    :    int			,		UpperCamelCase__    :    Optional[int]			,		UpperCamelCase__    :    Optional[Any]="<s>"			,		UpperCamelCase__    :    List[str]="</s>"			,		UpperCamelCase__    :    Any="<unk>"			,		UpperCamelCase__    :    Optional[int]="<pad>"			,		UpperCamelCase__    :    Optional[Dict[str, Any]] = None			,		**UpperCamelCase__    :    Optional[int]			,		):






											'''simple docstring'''







											snake_case__	  =       {} if sp_model_kwargs is None else sp_model_kwargs

											super().__init__(
											    bos_token=UpperCamelCase__			,		eos_token=UpperCamelCase__			,		unk_token=UpperCamelCase__			,		pad_token=UpperCamelCase__			,		sp_model_kwargs=self.sp_model_kwargs			,		**UpperCamelCase__			,		)

											snake_case__	  =       vocab_file

											snake_case__	  =       spm.SentencePieceProcessor(**self.sp_model_kwargs)
											self.sp_model.Load(UpperCamelCase__)



				@property
				def 					__magic_name__							(      self    :    Tuple):






											'''simple docstring'''







											return self.sp_model.get_piece_size()



				def 					__magic_name__							(      self    :    Tuple):






											'''simple docstring'''







											snake_case__	  =       {self.convert_ids_to_tokens(UpperCamelCase__): i for i in range(self.vocab_size)}
											vocab.update(self.added_tokens_encoder)
											return vocab



				def __getstate__(      self    :    List[Any]):






											'''simple docstring'''







											snake_case__	  =       self.__dict__.copy()
											snake_case__	  =       None
											return state



				def __setstate__(      self    :    Union[str, Any]			,		UpperCamelCase__    :    Dict):






											'''simple docstring'''







											snake_case__	  =       d

											# for backward compatibility
											if not hasattr(self			,		"""sp_model_kwargs"""):
																		snake_case__	  =       {}

											snake_case__	  =       spm.SentencePieceProcessor(**self.sp_model_kwargs)
											self.sp_model.Load(self.vocab_file)



				def 					__magic_name__							(      self    :    str			,		UpperCamelCase__    :    str):






											'''simple docstring'''







											return self.sp_model.encode(UpperCamelCase__			,		out_type=UpperCamelCase__)



				def 					__magic_name__							(      self    :    str			,		UpperCamelCase__    :    Optional[Any]):






											'''simple docstring'''







											return self.sp_model.piece_to_id(UpperCamelCase__)



				def 					__magic_name__							(      self    :    int			,		UpperCamelCase__    :    List[str]):






											'''simple docstring'''







											snake_case__	  =       self.sp_model.IdToPiece(UpperCamelCase__)
											return token



				def 					__magic_name__							(      self    :    List[Any]			,		UpperCamelCase__    :    Optional[int]):






											'''simple docstring'''







											snake_case__	  =       []
											snake_case__	  =       """"""
											for token in tokens:
																		# make sure that special tokens are not decoded using sentencepiece model
																		if token in self.all_special_tokens:
																									out_string += self.sp_model.decode(UpperCamelCase__) + token
																									snake_case__	  =       []
																		else:
																									current_sub_tokens.append(UpperCamelCase__)
											out_string += self.sp_model.decode(UpperCamelCase__)
											return out_string.strip()



				def 					__magic_name__							(      self    :    Any			,		UpperCamelCase__    :    Tuple			,		UpperCamelCase__    :    Union[str, Any]=None):






											'''simple docstring'''







											if token_ids_a is None:
																		return token_ids_a + [self.eos_token_id]
											# We don't expect to process pairs, but leave the pair logic for API consistency
											return token_ids_a + token_ids_a + [self.eos_token_id]



				def 					__magic_name__							(      self    :    List[str]			,		UpperCamelCase__    :    List[int]			,		UpperCamelCase__    :    Optional[List[int]] = None			,		UpperCamelCase__    :    bool = False):






											'''simple docstring'''







											if already_has_special_tokens:
																		return super().get_special_tokens_mask(
																		    token_ids_a=UpperCamelCase__			,		token_ids_a=UpperCamelCase__			,		already_has_special_tokens=UpperCamelCase__)

											snake_case__	  =       [1]
											if token_ids_a is None:
																		return ([0] * len(UpperCamelCase__)) + suffix_ones
											return ([0] * len(UpperCamelCase__)) + ([0] * len(UpperCamelCase__)) + suffix_ones



				def 					__magic_name__							(      self    :    Any			,		UpperCamelCase__    :    str			,		UpperCamelCase__    :    Optional[str] = None):






											'''simple docstring'''







											if not os.path.isdir(UpperCamelCase__):
																		logger.error(F'''Vocabulary path ({save_directory}) should be a directory''')
																		return
											snake_case__	  =       os.path.join(
											    UpperCamelCase__			,		(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""])

											if os.path.abspath(self.vocab_file) != os.path.abspath(UpperCamelCase__) and os.path.isfile(self.vocab_file):
																		copyfile(self.vocab_file			,		UpperCamelCase__)
											elif not os.path.isfile(self.vocab_file):
																		with open(UpperCamelCase__			,		"""wb""") as fi:
																									snake_case__	  =       self.sp_model.serialized_model_proto()
																									fi.write(UpperCamelCase__)

											return (out_vocab_file,)








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   (	a						:	Optional[Any]  ,					a						:	Dict  ,					a						:	Union[str, Any]=None ):
							# set parameter of one layer
							assert torch_layer.weight.shape == weight.shape, F'''{torch_layer} layer.weight does not match'''
							snake_case__	  =       nn.Parameter(a )
							if bias is not None:
														assert torch_layer.bias.shape == bias.shape, F'''{torch_layer} layer.bias does not match'''
														snake_case__	  =       nn.Parameter(a )

def _UpperCAmelCase   (	a						:	List[Any]  ,					a						:	List[str]  ,					a						:	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(a ).transpose(1  ,					2 ).contiguous().view(-1  ,					a )  ,					)
							set_param(
							    torch_layer.self_attention.value  ,					torch.tensor(a ).transpose(1  ,					2 ).contiguous().view(-1  ,					a )  ,					)
							set_param(
							    torch_layer.output.dense  ,					torch.tensor(a ).view(-1  ,					a ).contiguous().transpose(0  ,					1 )  ,					)

def _UpperCAmelCase   (	a						:	Optional[int]  ,					a						:	Dict  ,					a						:	Optional[int] ):
							# 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(a ).transpose(1  ,					2 ).contiguous().view(-1  ,					a )  ,					)
							set_param(
							    torch_layer.self_attention.key  ,					torch.tensor(a ).transpose(1  ,					2 ).contiguous().view(-1  ,					a )  ,					)
							set_param(
							    torch_layer.self_attention.value  ,					torch.tensor(a ).transpose(1  ,					2 ).contiguous().view(-1  ,					a )  ,					)
							set_param(
							    torch_layer.output.dense  ,					torch.tensor(a ).view(-1  ,					a ).contiguous().transpose(0  ,					1 )  ,					)

def _UpperCAmelCase   (	a						:	List[Any]  ,					a						:	Tuple  ,					a						:	List[Any] ):
							# 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(a )  ,					torch.tensor(a )  ,					)

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

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

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

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

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

def _UpperCAmelCase   (	a						:	List[Any]  ,					a						:	Dict  ,					a						:	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(a )  ,					)

							if isinstance(weights[3]  ,					a ):
														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(a ) )

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

							# 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(a )  ,					torch.tensor(a )  ,					)

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

def _UpperCAmelCase   (	a						:	Any  ,					a						:	str  ,					a						:	str ):
							# Initialise PyTorch model
							snake_case__	  =       ReformerConfig.from_json_file(a )
							print(F'''Building PyTorch model from configuration: {config}''' )
							snake_case__	  =       ReformerModelWithLMHead(a )

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

							set_model_weights_in_torch(a  ,					a  ,					config.hidden_size )

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


if __name__ == "__main__":
	a__       =     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__       =     parser.parse_args()
	convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)
