
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


__UpperCAmelCase							=			logging.get_logger(__name__)

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

__UpperCAmelCase							=			{
    """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""",
    }
}

__UpperCAmelCase							=			{
    """microsoft/speecht5_asr""": 10_24,
    """microsoft/speecht5_tts""": 10_24,
    """microsoft/speecht5_vc""": 10_24,
}





class 							UpperCamelCase__						(     _UpperCAmelCase     ):






       """simple docstring"""


       UpperCAmelCase_							    =VOCAB_FILES_NAMES
       UpperCAmelCase_							    =PRETRAINED_VOCAB_FILES_MAP
       UpperCAmelCase_							    =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       UpperCAmelCase_							    =['input_ids', 'attention_mask']



       def __init__(				self      ,			_A      ,			_A="<s>"      ,			_A="</s>"      ,			_A="<unk>"      ,			_A="<pad>"      ,			_A = None      ,			**_A      ,			)							->							None:
          SCREAMING_SNAKE_CASE_     =	{} if sp_model_kwargs is None else sp_model_kwargs

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

          SCREAMING_SNAKE_CASE_     =	vocab_file

          SCREAMING_SNAKE_CASE_     =	spm.SentencePieceProcessor(**self.sp_model_kwargs     )
          self.sp_model.Load(SCREAMING_SNAKE_CASE_     )



       @property
       def   _UpperCamelCase     (				self     )							->							List[str]:
          return self.sp_model.get_piece_size()



       def   _UpperCamelCase     (				self     )							->							Tuple:
          SCREAMING_SNAKE_CASE_     =	{self.convert_ids_to_tokens(SCREAMING_SNAKE_CASE_     ): i for i in range(self.vocab_size     )}
          vocab.update(self.added_tokens_encoder     )
          return vocab



       def __getstate__(				self     )							->							Optional[Any]:
          SCREAMING_SNAKE_CASE_     =	self.__dict__.copy()
          SCREAMING_SNAKE_CASE_     =	None
          return state



       def __setstate__(				self      ,			_A     )							->							List[str]:
          SCREAMING_SNAKE_CASE_     =	d

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

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



       def   _UpperCamelCase     (				self      ,			_A     )							->							List[str]:
          return self.sp_model.encode(SCREAMING_SNAKE_CASE_      ,			out_type=SCREAMING_SNAKE_CASE_     )



       def   _UpperCamelCase     (				self      ,			_A     )							->							str:
          return self.sp_model.piece_to_id(SCREAMING_SNAKE_CASE_     )



       def   _UpperCamelCase     (				self      ,			_A     )							->							Dict:
          SCREAMING_SNAKE_CASE_     =	self.sp_model.IdToPiece(SCREAMING_SNAKE_CASE_     )
          return token



       def   _UpperCamelCase     (				self      ,			_A     )							->							List[Any]:
          SCREAMING_SNAKE_CASE_     =	[]
          SCREAMING_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(SCREAMING_SNAKE_CASE_     ) + token
                SCREAMING_SNAKE_CASE_     =	[]
             else:
                current_sub_tokens.append(SCREAMING_SNAKE_CASE_     )
          out_string += self.sp_model.decode(SCREAMING_SNAKE_CASE_     )
          return out_string.strip()



       def   _UpperCamelCase     (				self      ,			_A      ,			_A=None     )							->							List[int]:
          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   _UpperCamelCase     (				self      ,			_A      ,			_A = None      ,			_A = False     )							->							List[int]:
          if already_has_special_tokens:
             return super().get_special_tokens_mask(
                 token_ids_a=SCREAMING_SNAKE_CASE_      ,			token_ids_a=SCREAMING_SNAKE_CASE_      ,			already_has_special_tokens=SCREAMING_SNAKE_CASE_     )

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




       def   _UpperCamelCase     (				self      ,			_A      ,			_A = None     )							->							Tuple[str]:
          if not os.path.isdir(SCREAMING_SNAKE_CASE_     ):
             logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''     )
             return
          SCREAMING_SNAKE_CASE_     =	os.path.join(
              SCREAMING_SNAKE_CASE_      ,			(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']     )

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

          return (out_vocab_file,)






'''simple docstring'''



import argparse
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration


A__		:					str												=  [
    # tf -> hf
    ("""/""", """."""),
    ("""layer_""", """layers."""),
    ("""kernel""", """weight"""),
    ("""beta""", """bias"""),
    ("""gamma""", """weight"""),
    ("""pegasus""", """model"""),
]
A__		:					Dict												=  [
    (""".output.dense""", """.fc2"""),
    ("""intermediate.LayerNorm""", """final_layer_norm"""),
    ("""intermediate.dense""", """fc1"""),
]

A__		:					int												=  (
    INIT_COMMON
    + [
        ("""attention.self.LayerNorm""", """self_attn_layer_norm"""),
        ("""attention.output.dense""", """self_attn.out_proj"""),
        ("""attention.self""", """self_attn"""),
        ("""attention.encdec.LayerNorm""", """encoder_attn_layer_norm"""),
        ("""attention.encdec_output.dense""", """encoder_attn.out_proj"""),
        ("""attention.encdec""", """encoder_attn"""),
        ("""key""", """k_proj"""),
        ("""value""", """v_proj"""),
        ("""query""", """q_proj"""),
        ("""decoder.LayerNorm""", """decoder.layernorm_embedding"""),
    ]
    + END_COMMON
)

A__		:					Tuple												=  (
    INIT_COMMON
    + [
        ("""embeddings.word_embeddings""", """shared.weight"""),
        ("""embeddings.position_embeddings""", """embed_positions.weight"""),
        ("""attention.self.LayerNorm""", """self_attn_layer_norm"""),
        ("""attention.output.dense""", """self_attn.output"""),
        ("""attention.self""", """self_attn.self"""),
        ("""encoder.LayerNorm""", """encoder.layernorm_embedding"""),
    ]
    + END_COMMON
)

A__		:					Tuple												=  [
    """encdec/key/bias""",
    """encdec/query/bias""",
    """encdec/value/bias""",
    """self/key/bias""",
    """self/query/bias""",
    """self/value/bias""",
    """encdec_output/dense/bias""",
    """attention/output/dense/bias""",
]



def 			UpperCAmelCase__					(	UpperCAmelCase_    :				Union[str, Any]      ,    UpperCAmelCase_    :				Any							)     ->		List[str]:
     for tf_name, hf_name in patterns:
          __lowerCamelCase   :	Optional[int]								=		k.replace(UpperCAmelCase_      ,    UpperCAmelCase_							)
     return k



def 			UpperCAmelCase__					(	UpperCAmelCase_    :				dict      ,    UpperCAmelCase_    :				dict							)     ->		BigBirdPegasusForConditionalGeneration:
     __lowerCamelCase   :	int								=		BigBirdPegasusConfig(**UpperCAmelCase_							)
     __lowerCamelCase   :	Any								=		BigBirdPegasusForConditionalGeneration(UpperCAmelCase_							)
     __lowerCamelCase   :	Union[str, Any]								=		torch_model.state_dict()
     __lowerCamelCase   :	Tuple								=		{}

     # separating decoder weights
     __lowerCamelCase   :	Dict								=		{k: tf_weights[k] for k in tf_weights if k.startswith('pegasus/decoder'							)}
     __lowerCamelCase   :	str								=		{k: tf_weights[k] for k in tf_weights if not k.startswith('pegasus/decoder'							)}

     for k, v in tqdm(decoder_weights.items()      ,    'tf -> hf conversion'							):
          __lowerCamelCase   :	Tuple								=		[k.endswith(UpperCAmelCase_							) for ending in KEYS_TO_IGNORE]
          if any(UpperCAmelCase_							):
               continue
          __lowerCamelCase   :	Tuple								=		DECODER_PATTERNS
          __lowerCamelCase   :	Optional[int]								=		rename_state_dict_key(UpperCAmelCase_      ,    UpperCAmelCase_							)
          if new_k not in state_dict:
               raise ValueError(F'could not find new key {new_k} in state dict. (converted from {k})'							)
          if any(True if i in k else False for i in ['dense', 'query', 'key', 'value']							):
               __lowerCamelCase   :	Union[str, Any]								=		v.T
          __lowerCamelCase   :	str								=		torch.from_numpy(UpperCAmelCase_							)
          assert v.shape == state_dict[new_k].shape, F'{new_k}, {k}, {v.shape}, {state_dict[new_k].shape}'

     for k, v in tqdm(remaining_weights.items()      ,    'tf -> hf conversion'							):
          __lowerCamelCase   :	Optional[Any]								=		[k.endswith(UpperCAmelCase_							) for ending in KEYS_TO_IGNORE]
          if any(UpperCAmelCase_							):
               continue
          __lowerCamelCase   :	Dict								=		REMAINING_PATTERNS
          __lowerCamelCase   :	List[str]								=		rename_state_dict_key(UpperCAmelCase_      ,    UpperCAmelCase_							)
          if new_k not in state_dict and k != "pegasus/embeddings/position_embeddings":
               raise ValueError(F'could not find new key {new_k} in state dict. (converted from {k})'							)
          if any(True if i in k else False for i in ['dense', 'query', 'key', 'value']							):
               __lowerCamelCase   :	List[str]								=		v.T
          __lowerCamelCase   :	List[str]								=		torch.from_numpy(UpperCAmelCase_							)
          if k != "pegasus/embeddings/position_embeddings":
               assert v.shape == state_dict[new_k].shape, F'{new_k}, {k}, {v.shape}, {state_dict[new_k].shape}'

     __lowerCamelCase   :	Any								=		mapping['model.embed_positions.weight']
     __lowerCamelCase   :	Union[str, Any]								=		mapping.pop('model.embed_positions.weight'							)
     __lowerCamelCase				,       __lowerCamelCase   :	List[str]								=		torch_model.load_state_dict(UpperCAmelCase_      ,    strict=UpperCAmelCase_							)
     __lowerCamelCase   :	Any								=		[
         k
         for k in missing
         if k
         not in [
             'final_logits_bias',
             'model.encoder.embed_tokens.weight',
             'model.decoder.embed_tokens.weight',
             'lm_head.weight',
         ]
     ]
     assert unexpected_missing == [], F'no matches found for the following torch keys {unexpected_missing}'
     assert extra == [], F'no matches found for the following tf keys {extra}'
     return torch_model



def 			UpperCAmelCase__					(	UpperCAmelCase_    :				Union[str, Any]							)     ->		Dict:
     __lowerCamelCase   :	int								=		tf.train.list_variables(UpperCAmelCase_							)
     __lowerCamelCase   :	List[str]								=		{}
     __lowerCamelCase   :	List[Any]								=		['global_step']
     for name, shape in tqdm(UpperCAmelCase_      ,    desc='converting tf checkpoint to dict'							):
          __lowerCamelCase   :	Any								=		any(pat in name for pat in ignore_name							)
          if skip_key:
               continue
          __lowerCamelCase   :	Dict								=		tf.train.load_variable(UpperCAmelCase_      ,    UpperCAmelCase_							)
          __lowerCamelCase   :	List[Any]								=		array
     return tf_weights



def 			UpperCAmelCase__					(	UpperCAmelCase_    :				str      ,    UpperCAmelCase_    :				str      ,    UpperCAmelCase_    :				dict							)     ->		Union[str, Any]:
     __lowerCamelCase   :	Optional[int]								=		get_tf_weights_as_numpy(UpperCAmelCase_							)
     __lowerCamelCase   :	List[str]								=		convert_bigbird_pegasus(UpperCAmelCase_      ,    UpperCAmelCase_							)
     torch_model.save_pretrained(UpperCAmelCase_							)


if __name__ == "__main__":
      A__		:					Any												=  argparse.ArgumentParser()
      parser.add_argument("""--tf_ckpt_path""", type=str, help="""passed to tf.train.list_variables""")
      parser.add_argument("""--save_dir""", default=None, type=str, help="""Path to the output PyTorch model.""")
      A__		:					List[str]												=  parser.parse_args()
      A__		:					Optional[int]												=  {}
      convert_bigbird_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir, config_update=config_update)






