


"""simple docstring"""


import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_layoutlm import LayoutLMTokenizer


a_											=     logging.get_logger(__name__)

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

a_											=     {
    'vocab_file': {
        'microsoft/layoutlm-base-uncased': (
            'https://huggingface.co/microsoft/layoutlm-base-uncased/resolve/main/vocab.txt'
        ),
        'microsoft/layoutlm-large-uncased': (
            'https://huggingface.co/microsoft/layoutlm-large-uncased/resolve/main/vocab.txt'
        ),
    },
    'tokenizer_file': {
        'microsoft/layoutlm-base-uncased': (
            'https://huggingface.co/microsoft/layoutlm-base-uncased/resolve/main/tokenizer.json'
        ),
        'microsoft/layoutlm-large-uncased': (
            'https://huggingface.co/microsoft/layoutlm-large-uncased/resolve/main/tokenizer.json'
        ),
    },
}

a_											=     {
    'microsoft/layoutlm-base-uncased': 5_12,
    'microsoft/layoutlm-large-uncased': 5_12,
}

a_											=     {
    'microsoft/layoutlm-base-uncased': {'do_lower_case': True},
    'microsoft/layoutlm-large-uncased': {'do_lower_case': True},
}
class snake_case   (	__snake_case):
		__UpperCamelCase							     =      VOCAB_FILES_NAMES
		__UpperCamelCase							     =      PRETRAINED_VOCAB_FILES_MAP
		__UpperCamelCase							     =      PRETRAINED_INIT_CONFIGURATION
		__UpperCamelCase							     =      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		__UpperCamelCase							     =      LayoutLMTokenizer







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





						'''simple docstring'''







						super().__init__(
						    __UpperCamelCase     , tokenizer_file=__UpperCamelCase     , do_lower_case=__UpperCamelCase     , unk_token=__UpperCamelCase     , sep_token=__UpperCamelCase     , pad_token=__UpperCamelCase     , cls_token=__UpperCamelCase     , mask_token=__UpperCamelCase     , tokenize_chinese_chars=__UpperCamelCase     , strip_accents=__UpperCamelCase     , **__UpperCamelCase     , )

						_A       	=      json.loads(self.backend_tokenizer.normalizer.__getstate__()		)
						if (
						    normalizer_state.get("lowercase"     , __UpperCamelCase		) != do_lower_case
						    or normalizer_state.get("strip_accents"     , __UpperCamelCase		) != strip_accents
						    or normalizer_state.get("handle_chinese_chars"     , __UpperCamelCase		) != tokenize_chinese_chars
						):
										_A       	=      getattr(__UpperCamelCase     , normalizer_state.pop("type"		)		)
										_A       	=      do_lower_case
										_A       	=      strip_accents
										_A       	=      tokenize_chinese_chars
										_A       	=      normalizer_class(**__UpperCamelCase		)

						_A       	=      do_lower_case







		def 			a_			(       self   :							Union[str, Any]     , a__   :							Tuple     , a__   :							Any=None		)							->    List[str]:





						'''simple docstring'''







						_A       	=      [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 			a_			(       self   :							List[Any]     , a__   :							Tuple     , a__   :							int = None		)							->    int:





						'''simple docstring'''







						_A       	=      [self.sep_token_id]
						_A       	=      [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 			a_			(       self   :							int     , a__   :							Tuple     , a__   :							List[Any] = None		)							->    Union[str, Any]:





						'''simple docstring'''







						_A       	=      self._tokenizer.model.save(__UpperCamelCase     , name=__UpperCamelCase		)
						return tuple(__UpperCamelCase		)

import argparse

import torch
from torch import nn

from transformers import PLBartConfig, PLBartForConditionalGeneration, PLBartForSequenceClassification





def        lowerCAmelCase       (				snake_case__   :      Union[str, Any] )->			List[str]:
    A_								=					[
        "encoder.version",
        "decoder.version",
        "model.encoder.version",
        "model.decoder.version",
        "_float_tensor",
        "decoder.output_projection.weight",
    ]
    for k in ignore_keys:
        state_dict.pop(snake_case__				,					snake_case__ )





def        lowerCAmelCase       (				snake_case__   :      Dict )->			str:
    A_	,	A_								=					emb.weight.shape
    A_								=					nn.Linear(snake_case__				,					snake_case__				,					bias=snake_case__ )
    A_								=					emb.weight.data
    return lin_layer





def        lowerCAmelCase       (				snake_case__   :      Any				,					snake_case__   :      Optional[int]="uclanlp/plbart-base"				,					snake_case__   :      Any=False				,					snake_case__   :      str=False )->			str:
    A_								=					torch.load(snake_case__				,					map_location="cpu" )["model"]
    remove_ignore_keys_(snake_case__ )
    A_								=					state_dict["encoder.embed_tokens.weight"].shape[0]

    A_								=					PLBartConfig.from_pretrained(snake_case__				,					vocab_size=snake_case__ )

    A_								=					state_dict["decoder.embed_tokens.weight"]
    if not classification:
        A_								=					PLBartForConditionalGeneration(snake_case__ )
        model.model.load_state_dict(snake_case__ )
        if finetuned:
            A_								=					make_linear_from_emb(model.model.shared )

    else:
        A_								=					{}
        for key, value in state_dict.copy().items():
            if key.startswith("classification_heads.sentence_classification_head" ):
                A_								=					value
                state_dict.pop(snake_case__ )
        A_								=					PLBartForSequenceClassification(snake_case__ )
        model.model.load_state_dict(snake_case__ )
        model.classification_head.load_state_dict(snake_case__ )

    return model


if __name__ == "__main__":
     __magic_name__  :  int     			=   argparse.ArgumentParser()
     # Required parameters
     parser.add_argument('fairseq_path', type=str, help='model.pt on local filesystem.')
     parser.add_argument('pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
     parser.add_argument(
         '--hf_config',
         default='uclanlp/plbart-base',
         type=str,
         help='Which huggingface architecture to use: plbart-base',
     )
     parser.add_argument('--finetuned', action='store_true', help='whether the model is a fine-tuned checkpoint')
     parser.add_argument(
         '--classification', action='store_true', help='whether the model is a classification checkpoint'
     )
     __magic_name__  :  Union[str, Any]     			=   parser.parse_args()
     __magic_name__  :  List[str]     			=   convert_fairseq_plbart_checkpoint_from_disk(
         args.fairseq_path,
         hf_config_path=args.hf_config,
         finetuned=args.finetuned,
         classification=args.classification,
     )
     model.save_pretrained(args.pytorch_dump_folder_path)






