






import argparse
import collections

import torch
from flax import traverse_util
from tax import checkpoints

from transformers import TaConfig, TaEncoderModel, TaForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()

def   __lowercase       (			snake_case,	snake_case,	snake_case,	snake_case="attention"			):







				"""simple docstring"""






				__magic_name__		:Union[str, Any]   =   params[f'''{prefix}/layers_{i}/{layer_name}/key/kernel''']
				__magic_name__		:Union[str, Any]   =   params[f'''{prefix}/layers_{i}/{layer_name}/out/kernel''']
				__magic_name__		:int   =   params[f'''{prefix}/layers_{i}/{layer_name}/query/kernel''']
				__magic_name__		:List[Any]   =   params[f'''{prefix}/layers_{i}/{layer_name}/value/kernel''']
				return k, o, q, v

def   __lowercase       (			snake_case,	snake_case,	snake_case,	snake_case=False			):







				"""simple docstring"""






				if split_mlp_wi:
								__magic_name__		:List[str]   =   params[f'''{prefix}/layers_{i}/mlp/wi_0/kernel''']
								__magic_name__		:Union[str, Any]   =   params[f'''{prefix}/layers_{i}/mlp/wi_1/kernel''']
								__magic_name__		:List[Any]   =   (wi_a, wi_a)
				else:
								__magic_name__		:List[Any]   =   params[f'''{prefix}/layers_{i}/mlp/wi/kernel''']

				__magic_name__		:Dict   =   params[f'''{prefix}/layers_{i}/mlp/wo/kernel''']
				return wi, wo

def   __lowercase       (			snake_case,	snake_case,	snake_case,	snake_case			):







				"""simple docstring"""






				return params[f'''{prefix}/layers_{i}/{layer_name}/scale''']

def   __lowercase       (			snake_case,	*, snake_case,	snake_case			):







				"""simple docstring"""






				__magic_name__		:Tuple   =   traverse_util.flatten_dict(variables['''target''']			)
				__magic_name__		:Dict   =   {'''/'''.join(snake_case			): v for k, v in old.items()}

				# v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
				__magic_name__		:Optional[int]   =   '''encoder/layers_0/mlp/wi_0/kernel''' in old
				print('''Split MLP:''',	snake_case			)

				__magic_name__		:Optional[int]   =   collections.OrderedDict()

				# Shared embeddings.
				__magic_name__		:Union[str, Any]   =   old['''token_embedder/embedding''']

				# Encoder.
				for i in range(snake_case			):
								# Block i, layer 0 (Self Attention).
								__magic_name__		:int   =   tax_layer_norm_lookup(snake_case,	snake_case,	'''encoder''',	'''pre_attention_layer_norm'''			)
								__magic_name__  ,					__magic_name__  ,					__magic_name__  ,					__magic_name__		:Optional[int]   =   tax_attention_lookup(snake_case,	snake_case,	'''encoder''',	'''attention'''			)
								__magic_name__		:List[Any]   =   layer_norm
								__magic_name__		:List[Any]   =   k.T
								__magic_name__		:Dict   =   o.T
								__magic_name__		:Union[str, Any]   =   q.T
								__magic_name__		:int   =   v.T

								# Block i, layer 1 (MLP).
								__magic_name__		:Union[str, Any]   =   tax_layer_norm_lookup(snake_case,	snake_case,	'''encoder''',	'''pre_mlp_layer_norm'''			)
								__magic_name__  ,					__magic_name__		:List[Any]   =   tax_mlp_lookup(snake_case,	snake_case,	'''encoder''',	snake_case			)
								__magic_name__		:Optional[int]   =   layer_norm
								if split_mlp_wi:
												__magic_name__		:Optional[int]   =   wi[0].T
												__magic_name__		:Any   =   wi[1].T
								else:
												__magic_name__		:str   =   wi.T
								__magic_name__		:Tuple   =   wo.T

				__magic_name__		:List[str]   =   old[
				    '''encoder/relpos_bias/rel_embedding'''
				].T
				__magic_name__		:str   =   old['''encoder/encoder_norm/scale''']

				if not is_encoder_only:
								# Decoder.
								for i in range(snake_case			):
												# Block i, layer 0 (Self Attention).
												__magic_name__		:List[Any]   =   tax_layer_norm_lookup(snake_case,	snake_case,	'''decoder''',	'''pre_self_attention_layer_norm'''			)
												__magic_name__  ,					__magic_name__  ,					__magic_name__  ,					__magic_name__		:List[str]   =   tax_attention_lookup(snake_case,	snake_case,	'''decoder''',	'''self_attention'''			)
												__magic_name__		:Any   =   layer_norm
												__magic_name__		:List[Any]   =   k.T
												__magic_name__		:Any   =   o.T
												__magic_name__		:List[str]   =   q.T
												__magic_name__		:Optional[int]   =   v.T

												# Block i, layer 1 (Cross Attention).
												__magic_name__		:int   =   tax_layer_norm_lookup(snake_case,	snake_case,	'''decoder''',	'''pre_cross_attention_layer_norm'''			)
												__magic_name__  ,					__magic_name__  ,					__magic_name__  ,					__magic_name__		:Any   =   tax_attention_lookup(snake_case,	snake_case,	'''decoder''',	'''encoder_decoder_attention'''			)
												__magic_name__		:Union[str, Any]   =   layer_norm
												__magic_name__		:int   =   k.T
												__magic_name__		:Tuple   =   o.T
												__magic_name__		:int   =   q.T
												__magic_name__		:Union[str, Any]   =   v.T

												# Block i, layer 2 (MLP).
												__magic_name__		:Any   =   tax_layer_norm_lookup(snake_case,	snake_case,	'''decoder''',	'''pre_mlp_layer_norm'''			)
												__magic_name__  ,					__magic_name__		:int   =   tax_mlp_lookup(snake_case,	snake_case,	'''decoder''',	snake_case			)
												__magic_name__		:Union[str, Any]   =   layer_norm
												if split_mlp_wi:
																__magic_name__		:List[str]   =   wi[0].T
																__magic_name__		:Dict   =   wi[1].T
												else:
																__magic_name__		:Tuple   =   wi.T
												__magic_name__		:Dict   =   wo.T

								__magic_name__		:Dict   =   old['''decoder/decoder_norm/scale''']
								__magic_name__		:List[str]   =   old[
								    '''decoder/relpos_bias/rel_embedding'''
								].T

								# LM Head (only in v1.1 checkpoints, in v1.0 embeddings are used instead)
								if "decoder/logits_dense/kernel" in old:
												__magic_name__		:Optional[int]   =   old['''decoder/logits_dense/kernel'''].T

				return new

def   __lowercase       (			snake_case,	snake_case			):







				"""simple docstring"""






				__magic_name__		:Dict   =   collections.OrderedDict([(k, torch.from_numpy(v.copy()			)) for (k, v) in converted_params.items()]			)

				# Add what is missing.
				if "encoder.embed_tokens.weight" not in state_dict:
								__magic_name__		:int   =   state_dict['''shared.weight''']

				if not is_encoder_only:
								if "decoder.embed_tokens.weight" not in state_dict:
												__magic_name__		:Optional[int]   =   state_dict['''shared.weight''']

								if "lm_head.weight" not in state_dict:  # For old 1.0 models.
												print('''Using shared word embeddings as lm_head.'''			)
												__magic_name__		:Any   =   state_dict['''shared.weight''']

				return state_dict

def   __lowercase       (			snake_case,	snake_case,	snake_case,	snake_case			):







				"""simple docstring"""






				__magic_name__		:int   =   checkpoints.load_tax_checkpoint(snake_case			)
				__magic_name__		:Dict   =   convert_tax_to_pytorch(snake_case,	num_layers=config.num_layers,	is_encoder_only=snake_case			)
				__magic_name__		:str   =   make_state_dict(snake_case,	snake_case			)
				model.load_state_dict(snake_case,	strict=snake_case			)

def   __lowercase       (			snake_case,	snake_case,	snake_case,	snake_case = False			):







				"""simple docstring"""






				__magic_name__		:Optional[Any]   =   TaConfig.from_json_file(snake_case			)
				print(f'''Building PyTorch model from configuration: {config}'''			)
				# Non-v1.1 checkpoints could also use T5Model, but this works for all.
				# The v1.0 checkpoints will simply have an LM head that is the word embeddings.
				if is_encoder_only:
								__magic_name__		:Optional[Any]   =   TaEncoderModel(snake_case			)
				else:
								__magic_name__		:Union[str, Any]   =   TaForConditionalGeneration(snake_case			)

				# Load weights from tf checkpoint
				load_tax_weights_in_ta(snake_case,	snake_case,	snake_case,	snake_case			)

				# Save pytorch-model
				print(f'''Save PyTorch model to {pytorch_dump_path}'''			)
				model.save_pretrained(snake_case			)

				# Verify that we can load the checkpoint.
				model.from_pretrained(snake_case			)
				print('''Done'''			)


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE__   :      List[str]				=		argparse.ArgumentParser(description="""Converts a native T5X checkpoint into a PyTorch checkpoint.""")
					# Required parameters
					parser.add_argument(
					    """--t5x_checkpoint_path""", default=None, type=str, required=True, help="""Path to the T5X checkpoint."""
					)
					parser.add_argument(
					    """--config_file""",
					    default=None,
					    type=str,
					    required=True,
					    help="""The config json file corresponding to the pre-trained T5 model.\nThis specifies the model architecture.""",
					)
					parser.add_argument(
					    """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
					)
					parser.add_argument(
					    """--is_encoder_only""", action="""store_true""", help="""Check if the model is encoder-decoder model""", default=False
					)
					SCREAMING_SNAKE_CASE__   :      Optional[Any]				=		parser.parse_args()
					convert_tax_checkpoint_to_pytorch(
					    args.tax_checkpoint_path, args.config_file, args.pytorch_dump_path, args.is_encoder_only
					)










"""simple docstring"""


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

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


__snake_case    					=					logging.get_logger(__name__)

__snake_case    					=					{'vocab_file': 'sentencepiece.bpe.model'}

__snake_case    					=					{
    'vocab_file': {
        'camembert-base': 'https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model',
    }
}

__snake_case    					=					{
    'camembert-base': 512,
}

__snake_case    					=					'▁'

class 				_SCREAMING_SNAKE_CASE				(							__UpperCAmelCase  ):
   """simple docstring"""



   _a   :       List[Any]									=    VOCAB_FILES_NAMES
   _a   :       List[Any]									=    PRETRAINED_VOCAB_FILES_MAP
   _a   :       List[str]									=    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   _a   :       Union[str, Any]									=    ['''input_ids''', '''attention_mask''']


   def __init__(						self ,     lowerCamelCase__ ,     lowerCamelCase__="<s>" ,     lowerCamelCase__="</s>" ,     lowerCamelCase__="</s>" ,     lowerCamelCase__="<s>" ,     lowerCamelCase__="<unk>" ,     lowerCamelCase__="<pad>" ,     lowerCamelCase__="<mask>" ,     lowerCamelCase__=["<s>NOTUSED", "</s>NOTUSED"] ,     lowerCamelCase__ = None ,     **lowerCamelCase__ ,     )      ->							None:
    # Mask token behave like a normal word, i.e. include the space before it
    lowercase__       :       Optional[int]				=						AddedToken(lowerCamelCase__ ,     lstrip=lowerCamelCase__ ,     rstrip=lowerCamelCase__						) if isinstance(lowerCamelCase__ ,     lowerCamelCase__						) else mask_token

    lowercase__       :       Dict				=						{} if sp_model_kwargs is None else sp_model_kwargs

    super().__init__(
        bos_token=lowerCamelCase__ ,     eos_token=lowerCamelCase__ ,     unk_token=lowerCamelCase__ ,     sep_token=lowerCamelCase__ ,     cls_token=lowerCamelCase__ ,     pad_token=lowerCamelCase__ ,     mask_token=lowerCamelCase__ ,     additional_special_tokens=lowerCamelCase__ ,     sp_model_kwargs=self.sp_model_kwargs ,     **lowerCamelCase__ ,     )
    lowercase__       :       Optional[int]				=						spm.SentencePieceProcessor(**self.sp_model_kwargs						)
    self.sp_model.Load(str(lowerCamelCase__						)						)
    lowercase__       :       str				=						vocab_file
    # HACK: These tokens were added by fairseq but don't seem to be actually used when duplicated in the actual
    # sentencepiece vocabulary (this is the case for <s> and </s>
    lowercase__       :       Optional[int]				=						{"""<s>NOTUSED""": 0, """<pad>""": 1, """</s>NOTUSED""": 2, """<unk>""": 3}
    lowercase__       :       int				=						len(self.fairseq_tokens_to_ids						)
    lowercase__       :       Dict				=						len(self.sp_model						) + len(self.fairseq_tokens_to_ids						)
    lowercase__       :       List[Any]				=						{v: k for k, v in self.fairseq_tokens_to_ids.items()}


   def 					UpperCAmelCase__(						self ,     lowerCamelCase__ ,     lowerCamelCase__ = None						)      ->							List[int]:

    if token_ids_a is None:
     return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
    lowercase__       :       Any				=						[self.cls_token_id]
    lowercase__       :       str				=						[self.sep_token_id]
    return cls + token_ids_a + sep + sep + token_ids_a + sep


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

    if token_ids_a is None:
     return [1] + ([0] * len(lowerCamelCase__						)) + [1]
    return [1] + ([0] * len(lowerCamelCase__						)) + [1, 1] + ([0] * len(lowerCamelCase__						)) + [1]


   def 					UpperCAmelCase__(						self ,     lowerCamelCase__ ,     lowerCamelCase__ = None						)      ->							List[int]:
    lowercase__       :       int				=						[self.sep_token_id]
    lowercase__       :       Tuple				=						[self.cls_token_id]

    if token_ids_a is None:
     return len(cls + token_ids_a + sep						) * [0]
    return len(cls + token_ids_a + sep + sep + token_ids_a + sep						) * [0]


   @property
   def 					UpperCAmelCase__(						self						)      ->							Optional[Any]:
    return len(self.fairseq_tokens_to_ids						) + len(self.sp_model						)


   def 					UpperCAmelCase__(						self						)      ->							Dict:
    lowercase__       :       Dict				=						{self.convert_ids_to_tokens(lowerCamelCase__						): i for i in range(self.vocab_size						)}
    vocab.update(self.added_tokens_encoder						)
    return vocab


   def 					UpperCAmelCase__(						self ,     lowerCamelCase__						)      ->							List[str]:
    return self.sp_model.encode(lowerCamelCase__ ,     out_type=lowerCamelCase__						)


   def 					UpperCAmelCase__(						self ,     lowerCamelCase__						)      ->							str:
    if token in self.fairseq_tokens_to_ids:
     return self.fairseq_tokens_to_ids[token]
    elif self.sp_model.PieceToId(lowerCamelCase__						) == 0:
     # Convert sentence piece unk token to fairseq unk token index
     return self.unk_token_id
    return self.fairseq_offset + self.sp_model.PieceToId(lowerCamelCase__						)


   def 					UpperCAmelCase__(						self ,     lowerCamelCase__						)      ->							int:
    if index in self.fairseq_ids_to_tokens:
     return self.fairseq_ids_to_tokens[index]
    return self.sp_model.IdToPiece(index - self.fairseq_offset						)


   def 					UpperCAmelCase__(						self ,     lowerCamelCase__						)      ->							str:
    lowercase__       :       Dict				=						[]
    lowercase__       :       Dict				=						""""""
    lowercase__       :       Tuple				=						False
    for token in tokens:
     # make sure that special tokens are not decoded using sentencepiece model
     if token in self.all_special_tokens:
      if not prev_is_special:
       out_string += " "
      out_string += self.sp_model.decode(lowerCamelCase__						) + token
      lowercase__       :       List[Any]				=						True
      lowercase__       :       str				=						[]
     else:
      current_sub_tokens.append(lowerCamelCase__						)
      lowercase__       :       Optional[Any]				=						False
    out_string += self.sp_model.decode(lowerCamelCase__						)
    return out_string.strip()


   def __getstate__(						self						)      ->							int:
    lowercase__       :       Dict				=						self.__dict__.copy()
    lowercase__       :       Union[str, Any]				=						None
    return state


   def __setstate__(						self ,     lowerCamelCase__						)      ->							int:
    lowercase__       :       Any				=						d

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

    lowercase__       :       Optional[Any]				=						spm.SentencePieceProcessor(**self.sp_model_kwargs						)
    self.sp_model.Load(self.vocab_file						)







   def 					UpperCAmelCase__(						self ,     lowerCamelCase__ ,     lowerCamelCase__ = None						)      ->							Tuple[str]:
    if not os.path.isdir(lowerCamelCase__						):
     logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''						)
     return
    lowercase__       :       Any				=						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__       :       int				=						self.sp_model.serialized_model_proto()
      fi.write(lowerCamelCase__						)

    return (out_vocab_file,)