



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


UpperCAmelCase__  =			logging.get_logger(__name__)

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

UpperCAmelCase__  =			{
    "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"
        ),
    },
}

UpperCAmelCase__  =			{
    "microsoft/layoutlm-base-uncased": 512,
    "microsoft/layoutlm-large-uncased": 512,
}

UpperCAmelCase__  =			{
    "microsoft/layoutlm-base-uncased": {"do_lower_case": True},
    "microsoft/layoutlm-large-uncased": {"do_lower_case": True},
}







class      __lowerCAmelCase       (     A		):
			UpperCamelCase     =	VOCAB_FILES_NAMES
			UpperCamelCase     =	PRETRAINED_VOCAB_FILES_MAP
			UpperCamelCase     =	PRETRAINED_INIT_CONFIGURATION
			UpperCamelCase     =	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			UpperCamelCase     =	LayoutLMTokenizer



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



						"""simple docstring"""






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

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

						_UpperCAmelCase									=  do_lower_case



			def        _lowerCamelCase			(		self  :    Dict					,						A  :    Optional[Any]					,						A  :    Union[str, Any]=None)  ->       Any:



						"""simple docstring"""






						_UpperCAmelCase									=  [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        _lowerCamelCase			(		self  :    int					,						A  :    List[int]					,						A  :    Optional[List[int]] = None)  ->       List[int]:



						"""simple docstring"""






						_UpperCAmelCase									=  [self.sep_token_id]
						_UpperCAmelCase									=  [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        _lowerCamelCase			(		self  :    List[Any]					,						A  :    str					,						A  :    Optional[str] = None)  ->       Tuple[str]:



						"""simple docstring"""






						_UpperCAmelCase									=  self._tokenizer.model.save(A					,						name=A)
						return tuple(A)





import argparse

import torch

from transformers import ImageGPTConfig, ImageGPTForCausalLM, load_tf_weights_in_imagegpt
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()





def 				A      (	_UpperCAmelCase							:  Tuple				,  _UpperCAmelCase							:  Any				,  _UpperCAmelCase							:  str       )							->					Union[str, Any]:







			'''simple docstring'''
			# Construct configuration depending on size
			_UpperCAmelCase									=  {'small': (512, 8, 24), 'medium': (1_024, 8, 36), 'large': (1_536, 16, 48)}
			_UpperCAmelCase			,	_UpperCAmelCase			,	_UpperCAmelCase									=  MODELS[model_size]  # set model hyperparameters
			_UpperCAmelCase									=  ImageGPTConfig(n_embd=_UpperCAmelCase				,  n_layer=_UpperCAmelCase				,  n_head=_UpperCAmelCase       )
			_UpperCAmelCase									=  ImageGPTForCausalLM(_UpperCAmelCase       )

			# Load weights from numpy
			load_tf_weights_in_imagegpt(_UpperCAmelCase				,  _UpperCAmelCase				,  _UpperCAmelCase       )

			# Save pytorch-model
			_UpperCAmelCase									=  pytorch_dump_folder_path + '/' + WEIGHTS_NAME
			_UpperCAmelCase									=  pytorch_dump_folder_path + '/' + CONFIG_NAME
			print(F"Save PyTorch model to {pytorch_weights_dump_path}"       )
			torch.save(model.state_dict()				,  _UpperCAmelCase       )
			print(F"Save configuration file to {pytorch_config_dump_path}"       )
			with open(_UpperCAmelCase				,  'w'				,  encoding='utf-8'       ) as f:
						f.write(config.to_json_string()       )


if __name__ == "__main__":
							UpperCAmelCase__  =			argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    "--imagegpt_checkpoint_path",
							    default=None,
							    type=str,
							    required=True,
							    help="Path to the TensorFlow checkpoint path.",
							)
							parser.add_argument(
							    "--model_size",
							    default=None,
							    type=str,
							    required=True,
							    help="Size of the model (can be either 'small', 'medium' or 'large').",
							)
							parser.add_argument(
							    "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
							)
							UpperCAmelCase__  =			parser.parse_args()
							convert_imagegpt_checkpoint_to_pytorch(
							    args.imagegpt_checkpoint_path, args.model_size, args.pytorch_dump_folder_path
							)
