
import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_squeezebert import SqueezeBertTokenizer


UpperCAmelCase__		:     Dict     			=      logging.get_logger(__name__)

UpperCAmelCase__		:     Tuple     			=      {'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}

UpperCAmelCase__		:     List[Any]     			=      {
    'vocab_file': {
        'squeezebert/squeezebert-uncased': (
            'https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/vocab.txt'
        ),
        'squeezebert/squeezebert-mnli': 'https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/vocab.txt',
        'squeezebert/squeezebert-mnli-headless': (
            'https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/vocab.txt'
        ),
    },
    'tokenizer_file': {
        'squeezebert/squeezebert-uncased': (
            'https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/tokenizer.json'
        ),
        'squeezebert/squeezebert-mnli': (
            'https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/tokenizer.json'
        ),
        'squeezebert/squeezebert-mnli-headless': (
            'https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/tokenizer.json'
        ),
    },
}

UpperCAmelCase__		:     Union[str, Any]     			=      {
    'squeezebert/squeezebert-uncased': 512,
    'squeezebert/squeezebert-mnli': 512,
    'squeezebert/squeezebert-mnli-headless': 512,
}


UpperCAmelCase__		:     Union[str, Any]     			=      {
    'squeezebert/squeezebert-uncased': {'do_lower_case': True},
    'squeezebert/squeezebert-mnli': {'do_lower_case': True},
    'squeezebert/squeezebert-mnli-headless': {'do_lower_case': True},
}






class UpperCAmelCase   (						SCREAMING_SNAKE_CASE__   ):





   '''simple docstring'''
   __UpperCamelCase		:    Optional[int] 				=    VOCAB_FILES_NAMES
   __UpperCamelCase		:    List[str] 				=    PRETRAINED_VOCAB_FILES_MAP
   __UpperCamelCase		:    Any 				=    PRETRAINED_INIT_CONFIGURATION
   __UpperCamelCase		:    Optional[Any] 				=    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   __UpperCamelCase		:    int 				=    SqueezeBertTokenizer


   def __init__(					self     :							Optional[Any]			,			lowerCAmelCase_     :							Optional[int]=None			,			lowerCAmelCase_     :							int=None			,			lowerCAmelCase_     :							str=True			,			lowerCAmelCase_     :							Union[str, Any]="[UNK]"			,			lowerCAmelCase_     :							Any="[SEP]"			,			lowerCAmelCase_     :							Optional[int]="[PAD]"			,			lowerCAmelCase_     :							Dict="[CLS]"			,			lowerCAmelCase_     :							Union[str, Any]="[MASK]"			,			lowerCAmelCase_     :							Tuple=True			,			lowerCAmelCase_     :							List[Any]=None			,			**lowerCAmelCase_     :							Optional[int]			,			):

      """simple docstring"""
      super().__init__(
          lowerCAmelCase_			,			tokenizer_file=lowerCAmelCase_			,			do_lower_case=lowerCAmelCase_			,			unk_token=lowerCAmelCase_			,			sep_token=lowerCAmelCase_			,			pad_token=lowerCAmelCase_			,			cls_token=lowerCAmelCase_			,			mask_token=lowerCAmelCase_			,			tokenize_chinese_chars=lowerCAmelCase_			,			strip_accents=lowerCAmelCase_			,			**lowerCAmelCase_			,			)

      _A:  int					=       json.loads(self.backend_tokenizer.normalizer.__getstate__()     )
      if (
          normalizer_state.get('''lowercase'''			,			lowerCAmelCase_     ) != do_lower_case
          or normalizer_state.get('''strip_accents'''			,			lowerCAmelCase_     ) != strip_accents
          or normalizer_state.get('''handle_chinese_chars'''			,			lowerCAmelCase_     ) != tokenize_chinese_chars
      ):
         _A:  Any					=       getattr(lowerCAmelCase_			,			normalizer_state.pop('''type'''     )     )
         _A:  Dict					=       do_lower_case
         _A:  Any					=       strip_accents
         _A:  int					=       tokenize_chinese_chars
         _A:  List[str]					=       normalizer_class(**lowerCAmelCase_     )

      _A:  List[Any]					=       do_lower_case


   def  __magic_name__				(					self     :							Union[str, Any]			,			lowerCAmelCase_     :							Union[str, Any]			,			lowerCAmelCase_     :							Tuple=None     ):

      """simple docstring"""
      _A:  Optional[int]					=       [self.cls_token_id] + token_ids_a + [self.sep_token_id]

      if token_ids_a:
         output += token_ids_a + [self.sep_token_id]

      return output


   def  __magic_name__				(					self     :							Optional[Any]			,			lowerCAmelCase_     :							List[int]			,			lowerCAmelCase_     :							Optional[List[int]] = None     ):

      """simple docstring"""
      _A:  Optional[int]					=       [self.sep_token_id]
      _A:  Optional[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     ) * [0] + len(token_ids_a + sep     ) * [1]


   def  __magic_name__				(					self     :							Optional[Any]			,			lowerCAmelCase_     :							str			,			lowerCAmelCase_     :							Optional[str] = None     ):

      """simple docstring"""
      _A:  Tuple					=       self._tokenizer.model.save(lowerCAmelCase_			,			name=lowerCAmelCase_     )
      return tuple(lowerCAmelCase_     )





import argparse

from torch import nn

# transformers_old should correspond to branch `save_old_prophetnet_model_structure` here
# original prophetnet_checkpoints are saved under `patrickvonplaten/..._old` respectively
from transformers_old.modeling_prophetnet import (
    ProphetNetForConditionalGeneration as ProphetNetForConditionalGenerationOld,
)
from transformers_old.modeling_xlm_prophetnet import (
    XLMProphetNetForConditionalGeneration as XLMProphetNetForConditionalGenerationOld,
)

from transformers import ProphetNetForConditionalGeneration, XLMProphetNetForConditionalGeneration, logging


UpperCAmelCase__		:     List[Any]     			=      logging.get_logger(__name__)
logging.set_verbosity_info()






def   lowerCamelCase__							(					a		,		a      )      ->   Dict:
   if "xprophetnet" in prophetnet_checkpoint_path:
      _A:  List[Any]					=       XLMProphetNetForConditionalGenerationOld.from_pretrained(a      )
      _A			,  _A:  Union[str, Any]					=       XLMProphetNetForConditionalGeneration.from_pretrained(
          a		,		output_loading_info=a      )
   else:
      _A:  Dict					=       ProphetNetForConditionalGenerationOld.from_pretrained(a      )
      _A			,  _A:  Tuple					=       ProphetNetForConditionalGeneration.from_pretrained(
          a		,		output_loading_info=a      )

   _A:  Optional[int]					=       ['''key_proj''', '''value_proj''', '''query_proj''']

   _A:  List[Any]					=       {
       '''self_attn''': '''ngram_self_attn''',
       '''cross_attn''': '''encoder_attn''',
       '''cross_attn_layer_norm''': '''encoder_attn_layer_norm''',
       '''feed_forward_layer_norm''': '''final_layer_norm''',
       '''feed_forward''': '''''',
       '''intermediate''': '''fc1''',
       '''output''': '''fc2''',
       '''key_proj''': '''k_proj''',
       '''query_proj''': '''q_proj''',
       '''value_proj''': '''v_proj''',
       '''word_embeddings''': '''embed_tokens''',
       '''embeddings_layer_norm''': '''emb_layer_norm''',
       '''relative_pos_embeddings''': '''relative_linear''',
       '''ngram_embeddings''': '''ngram_input_embed''',
       '''position_embeddings''': '''embed_positions''',
   }

   for key in loading_info["missing_keys"]:
      _A:  List[str]					=       key.split('''.'''      )

      if attributes[0] == "lm_head":
         _A:  Optional[int]					=       prophet
         _A:  Tuple					=       prophet_old
      else:
         _A:  Tuple					=       prophet.prophetnet
         _A:  Any					=       prophet_old.model

      _A:  int					=       False
      for attribute in attributes:
         if attribute in mapping:
            _A:  Optional[int]					=       mapping[attribute]
            if not hasattr(a		,		a      ) and len(a      ) > 0:
               _A:  int					=       attribute
         elif hasattr(a		,		a      ):
            _A:  Tuple					=       attribute

         if attribute == "weight":
            assert old_model.weight.shape == model.weight.shape, "Shapes have to match!"
            _A:  Union[str, Any]					=       old_model.weight
            logger.info(f"""{attribute} is initialized."""      )
            _A:  Any					=       True
            break
         elif attribute == "bias":
            assert old_model.bias.shape == model.bias.shape, "Shapes have to match!"
            _A:  str					=       old_model.bias
            logger.info(f"""{attribute} is initialized"""      )
            _A:  Dict					=       True
            break
         elif attribute in special_keys and hasattr(a		,		'''in_proj_weight'''      ):
            _A:  Optional[int]					=       old_model.in_proj_weight.shape[0] // 3
            _A:  Tuple					=       getattr(a		,		a      )
            param.weight.shape == old_model.in_proj_weight[:embed_dim, :].shape, "Shapes have to match"
            param.bias.shape == old_model.in_proj_bias[:embed_dim].shape, "Shapes have to match"
            if attribute == "query_proj":
               _A:  List[str]					=       nn.Parameter(old_model.in_proj_weight[:embed_dim, :]      )
               _A:  List[Any]					=       nn.Parameter(old_model.in_proj_bias[:embed_dim]      )

            elif attribute == "key_proj":
               _A:  int					=       nn.Parameter(old_model.in_proj_weight[embed_dim : 2 * embed_dim, :]      )
               _A:  Optional[int]					=       nn.Parameter(old_model.in_proj_bias[embed_dim : 2 * embed_dim]      )
            elif attribute == "value_proj":
               _A:  List[Any]					=       nn.Parameter(old_model.in_proj_weight[2 * embed_dim :, :]      )
               _A:  int					=       nn.Parameter(old_model.in_proj_bias[2 * embed_dim :]      )
            _A:  Tuple					=       True
            break
         elif attribute == "position_embeddings":
            assert (
                model.position_embeddings.weight.shape[-1] == old_model.embed_positions.weight.shape[-1]
            ), "Hidden size has to match"
            assert model.position_embeddings.weight.shape[0] == 5_12, "We want 512 position_embeddings."
            _A:  Union[str, Any]					=       nn.Parameter(old_model.embed_positions.weight[:5_12, :]      )
            _A:  List[Any]					=       True
            break

         if attribute.isdigit():
            _A:  Tuple					=       model[int(a      )]
            _A:  int					=       old_model[int(a      )]
         else:
            _A:  Union[str, Any]					=       getattr(a		,		a      )

            if old_attribute == "":
               _A:  Union[str, Any]					=       old_model
            else:
               if not hasattr(a		,		a      ):
                  raise ValueError(f"""{old_model} does not have {old_attribute}"""      )
               _A:  List[Any]					=       getattr(a		,		a      )

      if not is_key_init:
         raise ValueError(f"""{key} was not correctly initialized!"""      )

   print(f"""Saving model to {pytorch_dump_folder_path}"""      )
   prophet.save_pretrained(a      )


if __name__ == "__main__":
   UpperCAmelCase__		:     Dict     			=      argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       '--prophetnet_checkpoint_path', default=None, type=str, required=True, help='Path the official PyTorch dump.'
   )
   parser.add_argument(
       '--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
   )
   UpperCAmelCase__		:     Tuple     			=      parser.parse_args()
   convert_prophetnet_checkpoint_to_pytorch(args.prophetnet_checkpoint_path, args.pytorch_dump_folder_path)



