






"""simple docstring"""





import argparse
import collections

import numpy as np
import torch
from flax import traverse_util
from tax import checkpoints

from transformers import MTaConfig, UMTaEncoderModel, UMTaForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()



def     __lowercase					(    a :				List[Any]       ,		a :				List[Any]       ,		a :				List[str] )					->					Any:
      return params[f'''{prefix}/{prefix}/relpos_bias/rel_embedding'''][:, i, :]



def     __lowercase					(    a :				Optional[int]       ,		a :				Any       ,		a :				int       ,		a :				List[Any]="attention" )					->					str:
      __snake_case      :    Any  			=np.ascontiguousarray(params[f'''{prefix}/{prefix}/{layer_name}/key/kernel'''][:, i, :, :] )
      __snake_case      :    Any  			=k_tmp.reshape(k_tmp.shape[0]       ,		k_tmp.shape[1] * k_tmp.shape[2] )
      __snake_case      :    Optional[int]  			=np.ascontiguousarray(params[f'''{prefix}/{prefix}/{layer_name}/out/kernel'''][:, i, :, :] )
      __snake_case      :    Tuple  			=o_tmp.reshape(o_tmp.shape[0] * o_tmp.shape[1]       ,		o_tmp.shape[2] )
      __snake_case      :    Dict  			=np.ascontiguousarray(params[f'''{prefix}/{prefix}/{layer_name}/query/kernel'''][:, i, :, :] )
      __snake_case      :    List[Any]  			=q_tmp.reshape(q_tmp.shape[0]       ,		q_tmp.shape[1] * q_tmp.shape[2] )
      __snake_case      :    str  			=np.ascontiguousarray(params[f'''{prefix}/{prefix}/{layer_name}/value/kernel'''][:, i, :, :] )
      __snake_case      :    Optional[int]  			=v_tmp.reshape(v_tmp.shape[0]       ,		v_tmp.shape[1] * v_tmp.shape[2] )
      return k, o, q, v



def     __lowercase					(    a :				str       ,		a :				int       ,		a :				Union[str, Any]       ,		a :				str=False )					->					Tuple:
      if split_mlp_wi:
            __snake_case      :    Dict  			=params[f'''{prefix}/{prefix}/mlp/wi_0/kernel'''][:, i, :]
            __snake_case      :    List[Any]  			=params[f'''{prefix}/{prefix}/mlp/wi_1/kernel'''][:, i, :]
            __snake_case      :    str  			=(wi_a, wi_a)
      else:
            __snake_case      :    int  			=params[f'''{prefix}/{prefix}/mlp/wi/kernel'''][:, i, :]

      __snake_case      :    Union[str, Any]  			=params[f'''{prefix}/{prefix}/mlp/wo/kernel'''][:, i, :]
      return wi, wo



def     __lowercase					(    a :				str       ,		a :				List[str]       ,		a :				Tuple       ,		a :				Union[str, Any] )					->					Optional[int]:
      return params[f'''{prefix}/{prefix}/{layer_name}/scale'''][:, i]



def     __lowercase					(    a :				dict       ,		*, a :				int       ,		a :				bool       ,		a :				bool = False )					->					Dict:
      __snake_case      :    List[Any]  			=traverse_util.flatten_dict(variables['''target'''] )
      __snake_case      :    int  			={'''/'''.join(a ): v for k, v in old.items()}

      # v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
      __snake_case      :    Dict  			='''encoder/encoder/mlp/wi_0/kernel''' in old
      print('''Split MLP:'''       ,		a )

      __snake_case      :    Tuple  			=collections.OrderedDict()

      # Shared embeddings.
      __snake_case      :    List[str]  			=old['''token_embedder/embedding''']

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

            # Block i, layer 1 (MLP).
            __snake_case      :    str  			=tax_layer_norm_lookup(a       ,		a       ,		'''encoder'''       ,		'''pre_mlp_layer_norm''' )
            __snake_case			,				__snake_case      :    Dict  			=tax_mlp_lookup(a       ,		a       ,		'''encoder'''       ,		a )
            __snake_case      :    Dict  			=layer_norm
            if split_mlp_wi:
                  __snake_case      :    List[str]  			=wi[0].T
                  __snake_case      :    Union[str, Any]  			=wi[1].T
            else:
                  __snake_case      :    List[str]  			=wi.T
            __snake_case      :    str  			=wo.T
            if scalable_attention:
                  # convert the rel_embedding of each layer
                  __snake_case      :    List[Any]  			=tax_relpos_bias_lookup(
                      a       ,		a       ,		'''encoder''' ).T

      __snake_case      :    Optional[Any]  			=old['''encoder/encoder_norm/scale''']

      if not scalable_attention:
            __snake_case      :    Any  			=tax_relpos_bias_lookup(
                a       ,		0       ,		'''encoder''' ).T
            __snake_case      :    List[str]  			=tax_relpos_bias_lookup(
                a       ,		0       ,		'''decoder''' ).T

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

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

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

                  if scalable_attention:
                        # convert the rel_embedding of each layer
                        __snake_case      :    List[Any]  			=tax_relpos_bias_lookup(a       ,		a       ,		'''decoder''' ).T

            __snake_case      :    Dict  			=old['''decoder/decoder_norm/scale''']

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

      return new



def     __lowercase					(    a :				Union[str, Any]       ,		a :				bool )					->					List[str]:
      __snake_case      :    Optional[int]  			=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:
            __snake_case      :    Optional[int]  			=state_dict['''shared.weight''']

      if not is_encoder_only:
            if "decoder.embed_tokens.weight" not in state_dict:
                  __snake_case      :    Union[str, Any]  			=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.''' )
                  __snake_case      :    Dict  			=state_dict['''shared.weight''']

      return state_dict



def     __lowercase					(    a :				Optional[Any]       ,		a :				int       ,		a :				str       ,		a :				Tuple       ,		a :				List[Any] )					->					List[Any]:
      __snake_case      :    str  			=checkpoints.load_tax_checkpoint(a )
      __snake_case      :    int  			=convert_tax_to_pytorch(
          a       ,		num_layers=config.num_layers       ,		is_encoder_only=a       ,		scalable_attention=a )
      __snake_case      :    Optional[int]  			=make_state_dict(a       ,		a )
      model.load_state_dict(a       ,		strict=a )



def     __lowercase					(    a :				int       ,		a :				Optional[Any]       ,		a :				str       ,		a :				bool = False       ,		a :				bool = False       ,		)					->					Tuple:
      __snake_case      :    Dict  			=MTaConfig.from_json_file(a )
      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:
            __snake_case      :    List[Any]  			=UMTaEncoderModel(a )
      else:
            __snake_case      :    Tuple  			=UMTaForConditionalGeneration(a )

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

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

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


if __name__ == "__main__":
    UpperCamelCase_	:				Any										= 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
    )
    parser.add_argument(
        """--scalable_attention""",
        action="""store_true""",
        help="""Whether the model uses scaled attention (umt5 model)""",
        default=False,
    )
    UpperCamelCase_	:				List[str]										= parser.parse_args()
    convert_tax_checkpoint_to_pytorch(
        args.tax_checkpoint_path,
        args.config_file,
        args.pytorch_dump_path,
        args.is_encoder_only,
        args.scalable_attention,
    )










"""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


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

UpperCamelCase_	:				Union[str, Any]										= {"""vocab_file""": """sentencepiece.bpe.model"""}

UpperCamelCase_	:				Optional[Any]										= {
    """vocab_file""": {
        """moussaKam/mbarthez""": """https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model""",
        """moussaKam/barthez""": """https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model""",
        """moussaKam/barthez-orangesum-title""": (
            """https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model"""
        ),
    },
}

UpperCamelCase_	:				int										= {
    """moussaKam/mbarthez""": 1024,
    """moussaKam/barthez""": 1024,
    """moussaKam/barthez-orangesum-title""": 1024,
}

UpperCamelCase_	:				Optional[int]										= """▁"""


class      _lowercase (     lowerCAmelCase						):
      _a   :     int								=   VOCAB_FILES_NAMES
      _a   :     int								=   PRETRAINED_VOCAB_FILES_MAP
      _a   :     Union[str, Any]								=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      _a   :     Optional[int]								=   ['''input_ids''', '''attention_mask''']


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





            """simple docstring"""







            __snake_case      :    Dict  			=AddedToken(a							,   lstrip=a							,   rstrip=a						) if isinstance(a							,   a						) else mask_token

            __snake_case      :    List[str]  			={} if sp_model_kwargs is None else sp_model_kwargs

            super().__init__(
                bos_token=a							,   eos_token=a							,   unk_token=a							,   sep_token=a							,   cls_token=a							,   pad_token=a							,   mask_token=a							,   sp_model_kwargs=self.sp_model_kwargs							,   **a							,   )

            __snake_case      :    Optional[int]  			=vocab_file
            __snake_case      :    Dict  			=spm.SentencePieceProcessor(**self.sp_model_kwargs						)
            self.sp_model.Load(str(a						)						)

            __snake_case      :    Optional[Any]  			={'''<s>''': 0, '''<pad>''': 1, '''</s>''': 2, '''<unk>''': 3}

            __snake_case      :    Optional[int]  			=len(self.sp_model						) - 1
            __snake_case      :    Union[str, Any]  			={v: k for k, v in self.fairseq_tokens_to_ids.items()}


      def _UpperCamelCase						(    self				:	Tuple							,   a				:	List[int]							,   a				:	Optional[List[int]] = None						):





            """simple docstring"""







            if token_ids_a is None:
                  return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
            __snake_case      :    Dict  			=[self.cls_token_id]
            __snake_case      :    List[str]  			=[self.sep_token_id]
            return cls + token_ids_a + sep + sep + token_ids_a + sep


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





            """simple docstring"""







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

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


      def _UpperCamelCase						(    self				:	Dict							,   a				:	List[int]							,   a				:	Optional[List[int]] = None						):





            """simple docstring"""







            __snake_case      :    Optional[int]  			=[self.sep_token_id]
            __snake_case      :    int  			=[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				:	List[Any]						):





            """simple docstring"""







            return len(self.sp_model						)


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





            """simple docstring"""







            __snake_case      :    List[Any]  			={self.convert_ids_to_tokens(a						): i for i in range(self.vocab_size						)}
            vocab.update(self.added_tokens_encoder						)
            return vocab


      def _UpperCamelCase						(    self				:	str							,   a				:	str						):





            """simple docstring"""







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


      def _UpperCamelCase						(    self				:	int							,   a				:	Union[str, Any]						):





            """simple docstring"""







            if token in self.fairseq_tokens_to_ids:
                  return self.fairseq_tokens_to_ids[token]
            __snake_case      :    List[Any]  			=self.sp_model.PieceToId(a						)

            return spm_id if spm_id else self.unk_token_id


      def _UpperCamelCase						(    self				:	List[str]							,   a				:	Dict						):





            """simple docstring"""







            if index in self.fairseq_ids_to_tokens:
                  return self.fairseq_ids_to_tokens[index]
            return self.sp_model.IdToPiece(a						)


      def _UpperCamelCase						(    self				:	Optional[int]							,   a				:	int						):





            """simple docstring"""







            __snake_case      :    int  			=[]
            __snake_case      :    Optional[int]  			=''''''
            __snake_case      :    Optional[int]  			=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(a						) + token
                        __snake_case      :    str  			=True
                        __snake_case      :    int  			=[]
                  else:
                        current_sub_tokens.append(a						)
                        __snake_case      :    Tuple  			=False
            out_string += self.sp_model.decode(a						)
            return out_string.strip()


      def __getstate__(    self				:	Optional[int]						):





            """simple docstring"""







            __snake_case      :    List[Any]  			=self.__dict__.copy()
            __snake_case      :    Optional[Any]  			=None
            return state


      def __setstate__(    self				:	Optional[int]							,   a				:	Optional[Any]						):





            """simple docstring"""







            __snake_case      :    Any  			=d

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

            __snake_case      :    int  			=spm.SentencePieceProcessor(**self.sp_model_kwargs						)
            self.sp_model.Load(self.vocab_file						)







      def _UpperCamelCase						(    self				:	Optional[Any]							,   a				:	str							,   a				:	Optional[str] = None						):





            """simple docstring"""







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

            if os.path.abspath(self.vocab_file						) != os.path.abspath(a						) and os.path.isfile(self.vocab_file						):
                  copyfile(self.vocab_file							,   a						)
            elif not os.path.isfile(self.vocab_file						):
                  with open(a							,   '''wb'''						) as fi:
                        __snake_case      :    List[Any]  			=self.sp_model.serialized_model_proto()
                        fi.write(a						)

            return (out_vocab_file,)


