
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


__A							=logging.get_logger(__name__)

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

__A							={
    '''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'''
        ),
    },
}

__A							={
    '''moussaKam/mbarthez''': 1_0_2_4,
    '''moussaKam/barthez''': 1_0_2_4,
    '''moussaKam/barthez-orangesum-title''': 1_0_2_4,
}

__A							='''▁'''




class        _SCREAMING_SNAKE_CASE	( snake_case_		):
	lowerCAmelCase__   =					VOCAB_FILES_NAMES
	lowerCAmelCase__   =					PRETRAINED_VOCAB_FILES_MAP
	lowerCAmelCase__   =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	lowerCAmelCase__   =					['input_ids', 'attention_mask']



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

					lowerCamelCase_         =     {} 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_		,    sp_model_kwargs=self.sp_model_kwargs		,    **lowerCamelCase_		,    )

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

					lowerCamelCase_         =     {"<s>": 0, "<pad>": 1, "</s>": 2, "<unk>": 3}

					lowerCamelCase_         =     len(self.sp_model					) - 1
					lowerCamelCase_         =     {v: k for k, v in self.fairseq_tokens_to_ids.items()}



	def        SCREAMING_SNAKE_CASE_(       self		,    lowercase		,    lowercase = None					)							->			List[int]:

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



	def        SCREAMING_SNAKE_CASE_(       self		,    lowercase		,    lowercase = None		,    lowercase = 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        SCREAMING_SNAKE_CASE_(       self		,    lowercase		,    lowercase = None					)							->			List[int]:
					lowerCamelCase_         =     [self.sep_token_id]
					lowerCamelCase_         =     [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        SCREAMING_SNAKE_CASE_(       self					)							->			Dict:
					return len(self.sp_model					)



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



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



	def        SCREAMING_SNAKE_CASE_(       self		,    lowercase					)							->			List[str]:
					if token in self.fairseq_tokens_to_ids:
									return self.fairseq_tokens_to_ids[token]
					lowerCamelCase_         =     self.sp_model.PieceToId(lowerCamelCase_					)

					return spm_id if spm_id else self.unk_token_id



	def        SCREAMING_SNAKE_CASE_(       self		,    lowercase					)							->			List[Any]:
					if index in self.fairseq_ids_to_tokens:
									return self.fairseq_ids_to_tokens[index]
					return self.sp_model.IdToPiece(lowerCamelCase_					)



	def        SCREAMING_SNAKE_CASE_(       self		,    lowercase					)							->			Any:
					lowerCamelCase_         =     []
					lowerCamelCase_         =     ""
					lowerCamelCase_         =     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
													lowerCamelCase_         =     True
													lowerCamelCase_         =     []
									else:
													current_sub_tokens.append(lowerCamelCase_					)
													lowerCamelCase_         =     False
					out_string += self.sp_model.decode(lowerCamelCase_					)
					return out_string.strip()



	def __getstate__(       self					)							->			int:
					lowerCamelCase_         =     self.__dict__.copy()
					lowerCamelCase_         =     None
					return state



	def __setstate__(       self		,    lowercase					)							->			Tuple:
					lowerCamelCase_         =     d

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

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



	def        SCREAMING_SNAKE_CASE_(       self		,    lowercase		,    lowercase = None					)							->			Tuple[str]:
					if not os.path.isdir(lowerCamelCase_					):
									logger.error(f'Vocabulary path ({save_directory}) should be a directory'					)
									return
					lowerCamelCase_         =     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:
													lowerCamelCase_         =     self.sp_model.serialized_model_proto()
													fi.write(lowerCamelCase_					)

					return (out_vocab_file,)





"""simple docstring"""


import argparse
import re

from flax.traverse_util import flatten_dict, unflatten_dict
from tax import checkpoints

from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration
from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model
from transformers.utils import logging


logging.set_verbosity_info()


# should not include what is already done by the `from_pt` argument
UpperCAmelCase													={
    "/attention/": "/0/SelfAttention/",
    "/self_attention/": "/0/SelfAttention/",
    "/encoder_decoder_attention/": "/1/EncDecAttention/",
    "value": "v",
    "query": "q",
    "key": "k",
    "out": "o",
    "pre_self_attention_layer_norm": "0/layer_norm",
    "pre_cross_attention_layer_norm": "1/layer_norm",
    "pre_attention_layer_norm": "0/layer_norm",  # previously 1, but seems wrong
    "token_embedder": "shared",
    "encoder_norm": "final_layer_norm",
    "decoder_norm": "final_layer_norm",
    "relpos_bias/rel_embedding": "block/0/layer/0/SelfAttention/relative_attention_bias/weight",
    "router/router_weights/w/": "router/classifier/",
    "roer/roer_weights/w/": "router/classifier/",
    "logits_dense": "lm_head",
}



def   _A					(	_a	:						str				):



      """simple docstring"""






      A				  =		list(s_dict.keys()				)
      for key in keys:
            A				  =		r""".*/layers_(\d+)"""
            A				  =		key
            if re.match(_a     ,   _a				):
                  A				  =		re.sub(r"""layers_(\d+)"""     ,   r"""block/\1/layer"""     ,   _a				)

            A				  =		r"""(encoder|decoder)\/"""

            if re.match(_a     ,   _a				):
                  A				  =		re.match(_a     ,   _a				).groups()
                  if groups[0] == "encoder":
                        A				  =		re.sub(r"""/mlp/"""     ,   r"""/1/mlp/"""     ,   _a				)
                        A				  =		re.sub(r"""/pre_mlp_layer_norm/"""     ,   r"""/1/layer_norm/"""     ,   _a				)

                  elif groups[0] == "decoder":
                        A				  =		re.sub(r"""/mlp/"""     ,   r"""/2/mlp/"""     ,   _a				)
                        A				  =		re.sub(r"""/pre_mlp_layer_norm/"""     ,   r"""/2/layer_norm/"""     ,   _a				)

        # 2. Convert other classic mappings
            for old_key, temp_key in MOE_LAYER_NAME_MAPPING.items():
                  if old_key in new_key:
                        A				  =		new_key.replace(_a     ,   _a				)

            print(f'{key} -> {new_key}'				)
            A				  =		s_dict.pop(_a				)

      if "encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight" in s_dict:
            A				  =		s_dict[
                """encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight"""
            ].T
      if "decoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight" in s_dict:
            A				  =		s_dict[
                """decoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight"""
            ].T

      # 3. Take extra care of the EXPERTS layer
      for key in list(s_dict.keys()				):
            if "expert" in key:
                  A				  =		s_dict[key].shape[0]
                  A				  =		s_dict[key]
                  for idx in range(_a				):
                        A				  =		expert_weihts[idx]
                        print(f'{key} -> {key.replace("expert/"     ,   "nested fstring"				)}'				)

                  s_dict.pop(_a				)

      return s_dict


UpperCAmelCase													={
    "NUM_ENCODER_LAYERS": "num_layers",
    "NUM_DECODER_LAYERS": "num_decoder_layers",
    "NUM_HEADS": "num_heads",
    "HEAD_DIM": "d_kv",
    "EMBED_DIM": "d_model",
    "MLP_DIM": "d_ff",
    "NUM_SELECTED_EXPERTS": "num_selected_experts",
    "NUM_ENCODER_SPARSE_LAYERS": "num_sparse_encoder_layers",
    "NUM_DECODER_SPARSE_LAYERS": "num_sparse_decoder_layers",
    "dense.MlpBlock.activations": "feed_forward_proj",
}



def   _A					(	_a	:						Dict     ,   _a	:						int				):



      """simple docstring"""






      import regex as re

      with open(_a     ,   """r"""				) as f:
            A				  =		f.read()

      A				  =		re.findall(r"""(.*) = ([0-9.]*)"""     ,   _a				)
      A				  =		{}
      for param, value in regex_match:
            if param in GIN_TO_CONFIG_MAPPING and value != "":
                  A				  =		float(_a				) if """.""" in value else int(_a				)

      A				  =		re.findall(r"""(.*activations) = \(\'(.*)\',\)"""     ,   _a				)[0]
      A				  =		str(activation[1]				)

      A				  =		num_experts
      A				  =		SwitchTransformersConfig(**_a				)
      return config



def   _A					(	_a	:						Union[str, Any]     ,   _a	:						Dict     ,   _a	:						Optional[Any]=None     ,   _a	:						Dict="./"     ,   _a	:						Dict=8				):



      """simple docstring"""






      print(f'Loading flax weights from : {flax_checkpoint_path}'				)
      A				  =		checkpoints.load_tax_checkpoint(_a				)

      if gin_file is not None:
            A				  =		convert_gin_to_config(_a     ,   _a				)
      else:
            A				  =		SwitchTransformersConfig.from_pretrained(_a				)

      A				  =		SwitchTransformersForConditionalGeneration(_a				)

      A				  =		flax_params["""target"""]
      A				  =		flatten_dict(_a     ,   sep="""/"""				)
      A				  =		rename_keys(_a				)
      A				  =		unflatten_dict(_a     ,   sep="""/"""				)

      # Load the flax params in the PT model
      load_flax_weights_in_pytorch_model(_a     ,   _a				)

      print(f'Save PyTorch model to {pytorch_dump_path}'				)
      pt_model.save_pretrained(_a				)


if __name__ == "__main__":
  UpperCAmelCase													=argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      "--switch_t5x_checkpoint_path",
      default=None,
      type=str,
      required=True,
      help=(
          "The config json file corresponding to the pre-trained SwitchTransformers model. \nThis specifies the"
          " model architecture. If not provided, a `gin_file` has to be provided."
      ),
  )
  parser.add_argument(
      "--gin_file",
      default=None,
      type=str,
      required=False,
      help="Path to the gin config file. If not provided, a `config_file` has to be passed   ",
  )
  parser.add_argument(
      "--config_name", default=None, type=str, required=False, help="Config name of SwitchTransformers model."
  )
  parser.add_argument(
      "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output pytorch model."
  )
  parser.add_argument("--num_experts", default=8, type=int, required=False, help="Number of experts")
  UpperCAmelCase													=parser.parse_args()
  convert_flax_checkpoint_to_pytorch(
      args.switch_tax_checkpoint_path,
      args.config_name,
      args.gin_file,
      args.pytorch_dump_folder_path,
      args.num_experts,
  )




