



'''simple docstring'''


import argparse
import json
import os
from collections import OrderedDict

import numpy as np
import tensorflow as tf
import torch







def      SCREAMING_SNAKE_CASE				(				lowercase_    :      Union[str, Any]						):
      lowercase        =							os.path.join(args.tf_model_dir ,  """parameters.json"""						)
      lowercase        =							json.loads(open(lowercase_						).read()						)
      if not params:
            raise ValueError(
                F"""It seems that the json file at {parameter_file} is empty. Make sure you have a correct json file."""						)
      if not args.output.endswith(""".pt"""						):
            lowercase        =							args.output + """.pt"""
      lowercase        =							OrderedDict()
      with tf.device("""/CPU:0"""						):
            lowercase        =							tf.train.load_checkpoint(args.tf_model_dir						)
            lowercase        =							reader.get_variable_to_shape_map()
            for key_name in shapes.keys():
                  lowercase        =							reader.get_tensor(lowercase_						).astype(np.floataa						)
                  if key_name.endswith("""/adam_m"""						) or key_name.endswith("""/adam_v"""						):
                        continue
                  if key_name.startswith("""pasts/"""						):
                        if key_name.startswith("""pasts/mlp"""						):
                              lowercase        =							int(key_name[9]						)
                        elif key_name.startswith("""pasts/out"""						):
                              lowercase        =							8
                        lowercase        =							"""model.sqout.%d.weight""" % (player * 2)  # enter to nn.Sequencial with Tanh, so 2 at a time
                        lowercase        =							vnp.transpose([1, 0]						).copy()  # Mesh-Tensorflow is a diagonal matrix
                        lowercase        =							torch.tensor(lowercase_						)
                  elif key_name.startswith("""model/moe"""						):
                        lowercase        =							int(key_name[9:].split("""/"""						)[0]						)
                        if key_name.endswith("""/switch_gating/kernel"""						):
                              lowercase        =							"""model.blocks.%d.feed_forward.mlp.router.classifier.weight""" % player
                              lowercase        =							vnp.transpose([1, 0]						).copy()  # Mesh-Tensorflow is a diagonal matrix
                              lowercase        =							torch.tensor(lowercase_						)
                        elif key_name.endswith("""/softmlp/kernel"""						):
                              lowercase        =							"""model.blocks.%d.feed_forward.soft_bypass_mlp.weight""" % player
                              lowercase        =							vnp.transpose([1, 0]						).copy()  # Mesh-Tensorflow is a diagonal matrix
                              lowercase        =							torch.tensor(lowercase_						)
                        elif key_name.endswith("""/wo/kernel"""						) or key_name.endswith("""/wi/kernel"""						):
                              lowercase        =							key_name[-9:-7]
                              for i in range(16						):
                                    lowercase        =							"""model.blocks.%d.feed_forward.mlp.experts.expert_%d.%s.weight""" % (player, i, nlayer)
                                    lowercase        =							(
                                        vnp[i].transpose([1, 0]						).copy()
                                    )  # In Mesh-Tensorflow, it is one array, so it is divided
                                    lowercase        =							torch.tensor(lowercase_						)
                  elif key_name.startswith("""model/mlp"""						):
                        lowercase        =							int(key_name[9:].split("""/"""						)[0]						)
                        if key_name.endswith("""/p1/kernel"""						):
                              lowercase        =							"""model.blocks.%d.feed_forward.mlp.wi.weight""" % player
                              lowercase        =							vnp.transpose([1, 0]						).copy()  # Mesh-Tensorflow is a diagonal matrix
                              lowercase        =							torch.tensor(lowercase_						)
                        elif key_name.endswith("""/p1/bias"""						):
                              lowercase        =							"""model.blocks.%d.feed_forward.mlp.wi.bias""" % player
                              lowercase        =							vnp.copy()  # same because it is one dimensional
                              lowercase        =							torch.tensor(lowercase_						)
                        elif key_name.endswith("""/p2/kernel"""						):
                              lowercase        =							"""model.blocks.%d.feed_forward.mlp.wo.weight""" % player
                              lowercase        =							vnp.transpose([1, 0]						).copy()  # Mesh-Tensorflow is a diagonal matrix
                              lowercase        =							torch.tensor(lowercase_						)
                        elif key_name.endswith("""/p2/bias"""						):
                              lowercase        =							"""model.blocks.%d.feed_forward.mlp.wo.bias""" % player
                              lowercase        =							vnp.copy()  # same because it is one dimensional
                              lowercase        =							torch.tensor(lowercase_						)
                  elif key_name.startswith("""model/ln"""						):
                        lowercase        =							int(key_name[8:].split("""/"""						)[0]						)
                        if key_name.endswith("""/b"""						):
                              lowercase        =							"""model.blocks.%d.feed_forward.norm.bias""" % player
                              lowercase        =							vnp.copy()  # same because it is one dimensional
                              lowercase        =							torch.tensor(lowercase_						)
                        elif key_name.endswith("""/g"""						):
                              lowercase        =							"""model.blocks.%d.feed_forward.norm.weight""" % player
                              lowercase        =							vnp.copy()  # same because it is one dimensional
                              lowercase        =							torch.tensor(lowercase_						)
                  elif key_name.startswith("""model/att"""						):
                        lowercase        =							int(key_name[9:].split("""/"""						)[0]						)
                        if key_name.endswith("""/qkv/kernel"""						):
                              lowercase        =							vnp.copy()  # Compute same dimension as Mesh-tensorflow using einsum
                              lowercase        =							state[:, 0, :, :]
                              lowercase        =							state[:, 1, :, :]
                              lowercase        =							state[:, 2, :, :]
                              lowercase        =							(
                                  state_q.reshape([state_q.shape[0], state_q.shape[1] * state_q.shape[2]]						)
                                  .transpose([1, 0]						)
                                  .copy()
                              )  # Mesh-Tensorflow is a diagonal matrix
                              lowercase        =							(
                                  state_k.reshape([state_k.shape[0], state_k.shape[1] * state_k.shape[2]]						)
                                  .transpose([1, 0]						)
                                  .copy()
                              )  # Mesh-Tensorflow is a diagonal matrix
                              lowercase        =							(
                                  state_v.reshape([state_v.shape[0], state_v.shape[1] * state_v.shape[2]]						)
                                  .transpose([1, 0]						)
                                  .copy()
                              )  # Mesh-Tensorflow is a diagonal matrix
                              lowercase        =							"""model.blocks.%d.self_attn.self_attn.q_proj.weight""" % player
                              lowercase        =							torch.tensor(lowercase_						)
                              lowercase        =							"""model.blocks.%d.self_attn.self_attn.k_proj.weight""" % player
                              lowercase        =							torch.tensor(lowercase_						)
                              lowercase        =							"""model.blocks.%d.self_attn.self_attn.v_proj.weight""" % player
                              lowercase        =							torch.tensor(lowercase_						)
                        elif key_name.endswith("""/o/kernel"""						):
                              lowercase        =							"""model.blocks.%d.self_attn.self_attn.out_proj.weight""" % player
                              lowercase        =							(
                                  vnp.reshape([vnp.shape[0] * vnp.shape[1], vnp.shape[2]]						).transpose([1, 0]						).copy()
                              )  # Mesh-Tensorflow is a diagonal matrix
                              lowercase        =							torch.tensor(lowercase_						)
                  elif key_name.startswith("""model/an"""						):
                        lowercase        =							int(key_name[8:].split("""/"""						)[0]						)
                        if key_name.endswith("""/b"""						):
                              lowercase        =							"""model.blocks.%d.self_attn.norm.bias""" % player
                              lowercase        =							vnp.copy()  # same because it is one dimensional
                              lowercase        =							torch.tensor(lowercase_						)
                        elif key_name.endswith("""/g"""						):
                              lowercase        =							"""model.blocks.%d.self_attn.norm.weight""" % player
                              lowercase        =							vnp.copy()  # same because it is one dimensional
                              lowercase        =							torch.tensor(lowercase_						)
                  elif (
                      key_name.startswith("""model/wte"""						)
                      or key_name.startswith("""model/wpe"""						)
                      or key_name.startswith("""model/ete"""						)
                  ):
                        lowercase        =							{"""wte""": """embed_tokens""", """wpe""": """position_embeddings""", """ete""": """extra_position_embeddings"""}[
                            key_name[-3:]
                        ]
                        lowercase        =							"""model.%s.weight""" % nlayer
                        lowercase        =							vnp.copy()  # same in embedded
                        lowercase        =							torch.tensor(lowercase_						)
                        if key_name.startswith("""model/wte"""						):
                              lowercase        =							"""lm_head.weight"""
                              lowercase        =							vnp.copy()  # same in embedded
                              lowercase        =							torch.tensor(lowercase_						)
                  elif key_name.startswith("""model/wob"""						):
                        lowercase        =							"""final_logits_bias"""
                        lowercase        =							vnp.copy()  # same in embedded
                        lowercase        =							state.reshape((1, -1)						)
                        lowercase        =							torch.tensor(lowercase_						)
                  elif key_name == "model/dense/kernel":
                        lowercase        =							"""model.last_project.weight"""
                        lowercase        =							vnp.transpose([1, 0]						).copy()  # Mesh-Tensorflow is a diagonal matrix
                        lowercase        =							torch.tensor(lowercase_						)
                  elif key_name == "model/dense_1/bias":
                        lowercase        =							"""model.last_project.bias"""
                        lowercase        =							vnp.copy()  # same because it is one dimensional
                        lowercase        =							torch.tensor(lowercase_						)
      torch.save(lowercase_ ,  args.output						)


if __name__ == "__main__":
      lowercase_      :				int   					=    argparse.ArgumentParser(
          description='''model converter.''', formatter_class=argparse.ArgumentDefaultsHelpFormatter
      )
      parser.add_argument('''--tf_model_dir''', metavar='''PATH''', type=str, required=True, help='''import model''')
      parser.add_argument('''--output''', metavar='''PATH''', type=str, required=True, help='''output model''')
      lowercase_      :				str   					=    parser.parse_args()
      convert_tf_gptsan_to_pt(args)







'''simple docstring'''


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


lowercase_      :				Optional[Any]   					=    logging.get_logger(__name__)

lowercase_      :				int   					=    {'''vocab_file''': '''spm_char.model'''}

lowercase_      :				int   					=    {
    '''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''',
    }
}

lowercase_      :				Optional[Any]   					=    {
    '''microsoft/speecht5_asr''': 1024,
    '''microsoft/speecht5_tts''': 1024,
    '''microsoft/speecht5_vc''': 1024,
}




class      __UpperCamelCase       (_UpperCAmelCase			):
	__A			  =					VOCAB_FILES_NAMES
	__A			  =					PRETRAINED_VOCAB_FILES_MAP
	__A			  =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	__A			  =					['''input_ids''', '''attention_mask''']







	def __init__(    self ,				_lowerCAmelCase ,				_lowerCAmelCase="<s>" ,				_lowerCAmelCase="</s>" ,				_lowerCAmelCase="<unk>" ,				_lowerCAmelCase="<pad>" ,				_lowerCAmelCase = None ,				**_lowerCAmelCase ,				)    ->    None:
							'''simple docstring'''






							lowercase        =							{} if sp_model_kwargs is None else sp_model_kwargs

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

							lowercase        =							vocab_file

							lowercase        =							spm.SentencePieceProcessor(**self.sp_model_kwargs						)
							self.sp_model.Load(_lowerCAmelCase						)







	@property
	def   _a   (    self						)    ->    List[Any]:
							'''simple docstring'''






							return self.sp_model.get_piece_size()







	def   _a   (    self						)    ->    str:
							'''simple docstring'''






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







	def __getstate__(    self						)    ->    Union[str, Any]:
							'''simple docstring'''






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







	def __setstate__(    self ,				_lowerCAmelCase						)    ->    Optional[int]:
							'''simple docstring'''






							lowercase        =							d

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

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







	def   _a   (    self ,				_lowerCAmelCase						)    ->    List[str]:
							'''simple docstring'''






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







	def   _a   (    self ,				_lowerCAmelCase						)    ->    List[Any]:
							'''simple docstring'''






							return self.sp_model.piece_to_id(_lowerCAmelCase						)







	def   _a   (    self ,				_lowerCAmelCase						)    ->    str:
							'''simple docstring'''






							lowercase        =							self.sp_model.IdToPiece(_lowerCAmelCase						)
							return token







	def   _a   (    self ,				_lowerCAmelCase						)    ->    Optional[Any]:
							'''simple docstring'''






							lowercase        =							[]
							lowercase        =							""""""
							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(_lowerCAmelCase						) + token
																			lowercase        =							[]
													else:
																			current_sub_tokens.append(_lowerCAmelCase						)
							out_string += self.sp_model.decode(_lowerCAmelCase						)
							return out_string.strip()







	def   _a   (    self ,				_lowerCAmelCase ,				_lowerCAmelCase=None						)    ->    List[int]:
							'''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   _a   (    self ,				_lowerCAmelCase ,				_lowerCAmelCase = None ,				_lowerCAmelCase = False						)    ->    List[int]:
							'''simple docstring'''






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

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







	def   _a   (    self ,				_lowerCAmelCase ,				_lowerCAmelCase = None						)    ->    Tuple[str]:
							'''simple docstring'''






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

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

							return (out_vocab_file,)


