



'''simple docstring'''

import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_longformer import LongformerTokenizer


UpperCAmelCase			=    logging.get_logger(__name__)

UpperCAmelCase			=    {'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

UpperCAmelCase			=    {
    'vocab_file': {
        'allenai/longformer-base-4096': 'https://huggingface.co/allenai/longformer-base-4096/resolve/main/vocab.json',
        'allenai/longformer-large-4096': (
            'https://huggingface.co/allenai/longformer-large-4096/resolve/main/vocab.json'
        ),
        'allenai/longformer-large-4096-finetuned-triviaqa': (
            'https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/vocab.json'
        ),
        'allenai/longformer-base-4096-extra.pos.embd.only': (
            'https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/vocab.json'
        ),
        'allenai/longformer-large-4096-extra.pos.embd.only': (
            'https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/vocab.json'
        ),
    },
    'merges_file': {
        'allenai/longformer-base-4096': 'https://huggingface.co/allenai/longformer-base-4096/resolve/main/merges.txt',
        'allenai/longformer-large-4096': (
            'https://huggingface.co/allenai/longformer-large-4096/resolve/main/merges.txt'
        ),
        'allenai/longformer-large-4096-finetuned-triviaqa': (
            'https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/merges.txt'
        ),
        'allenai/longformer-base-4096-extra.pos.embd.only': (
            'https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/merges.txt'
        ),
        'allenai/longformer-large-4096-extra.pos.embd.only': (
            'https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/merges.txt'
        ),
    },
    'tokenizer_file': {
        'allenai/longformer-base-4096': (
            'https://huggingface.co/allenai/longformer-base-4096/resolve/main/tokenizer.json'
        ),
        'allenai/longformer-large-4096': (
            'https://huggingface.co/allenai/longformer-large-4096/resolve/main/tokenizer.json'
        ),
        'allenai/longformer-large-4096-finetuned-triviaqa': (
            'https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/tokenizer.json'
        ),
        'allenai/longformer-base-4096-extra.pos.embd.only': (
            'https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/tokenizer.json'
        ),
        'allenai/longformer-large-4096-extra.pos.embd.only': (
            'https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/tokenizer.json'
        ),
    },
}

UpperCAmelCase			=    {
    'allenai/longformer-base-4096': 4096,
    'allenai/longformer-large-4096': 4096,
    'allenai/longformer-large-4096-finetuned-triviaqa': 4096,
    'allenai/longformer-base-4096-extra.pos.embd.only': 4096,
    'allenai/longformer-large-4096-extra.pos.embd.only': 4096,
}

class __snake_case(      _UpperCamelCase					):


						'''simple docstring'''



						UpperCAmelCase							:    Dict           =		VOCAB_FILES_NAMES
						UpperCAmelCase							:    Dict           =		PRETRAINED_VOCAB_FILES_MAP
						UpperCAmelCase							:    Optional[int]           =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						UpperCAmelCase							:    Dict           =		["input_ids", "attention_mask"]
						UpperCAmelCase							:    Any           =		LongformerTokenizer


						def __init__(					self	,       A_=None	,       A_=None	,       A_=None	,       A_="replace"	,       A_="<s>"	,       A_="</s>"	,       A_="</s>"	,       A_="<s>"	,       A_="<unk>"	,       A_="<pad>"	,       A_="<mask>"	,       A_=False	,       A_=True	,       **A_	,       )	->	Optional[Any]:
							super().__init__(
							    __a	,       __a	,       tokenizer_file=__a	,       errors=__a	,       bos_token=__a	,       eos_token=__a	,       sep_token=__a	,       cls_token=__a	,       unk_token=__a	,       pad_token=__a	,       mask_token=__a	,       add_prefix_space=__a	,       trim_offsets=__a	,       **__a	,       )

							lowerCAmelCase    =	json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()      )
							if pre_tok_state.get("""add_prefix_space"""	,       __a      ) != add_prefix_space:
								lowerCAmelCase    =	getattr(__a	,       pre_tok_state.pop("""type"""      )      )
								lowerCAmelCase    =	add_prefix_space
								lowerCAmelCase    =	pre_tok_class(**__a      )

							lowerCAmelCase    =	add_prefix_space

							lowerCAmelCase    =	"post_processor"
							lowerCAmelCase    =	getattr(self.backend_tokenizer	,       __a	,       __a      )
							if tokenizer_component_instance:
								lowerCAmelCase    =	json.loads(tokenizer_component_instance.__getstate__()      )

								# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
								if "sep" in state:
									lowerCAmelCase    =	tuple(state["""sep"""]      )
								if "cls" in state:
									lowerCAmelCase    =	tuple(state["""cls"""]      )

								lowerCAmelCase    =	False

								if state.get("""add_prefix_space"""	,       __a      ) != add_prefix_space:
									lowerCAmelCase    =	add_prefix_space
									lowerCAmelCase    =	True

								if state.get("""trim_offsets"""	,       __a      ) != trim_offsets:
									lowerCAmelCase    =	trim_offsets
									lowerCAmelCase    =	True

								if changes_to_apply:
									lowerCAmelCase    =	getattr(__a	,       state.pop("""type"""      )      )
									lowerCAmelCase    =	component_class(**__a      )
									setattr(self.backend_tokenizer	,       __a	,       __a      )


						@property
						def 				__snake_case    (					self      )	->	str:
							if self._mask_token is None:
								if self.verbose:
									logger.error("""Using mask_token, but it is not set yet."""      )
								return None
							return str(self._mask_token      )


						@mask_token.setter
						def 				__snake_case    (					self	,       A_      )	->	str:
							lowerCAmelCase    =	AddedToken(__a	,       lstrip=__a	,       rstrip=__a      ) if isinstance(__a	,       __a      ) else value
							lowerCAmelCase    =	value


						def 				__snake_case    (					self	,       *A_	,       **A_      )	->	BatchEncoding:
							lowerCAmelCase    =	kwargs.get("""is_split_into_words"""	,       __a      )
							assert self.add_prefix_space or not is_split_into_words, (
							    f'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
							    "to use it with pretokenized inputs."
							)

							return super()._batch_encode_plus(*__a	,       **__a      )


						def 				__snake_case    (					self	,       *A_	,       **A_      )	->	BatchEncoding:
							lowerCAmelCase    =	kwargs.get("""is_split_into_words"""	,       __a      )

							assert self.add_prefix_space or not is_split_into_words, (
							    f'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
							    "to use it with pretokenized inputs."
							)

							return super()._encode_plus(*__a	,       **__a      )


						def 				__snake_case    (					self	,       A_	,       A_ = None      )	->	Tuple[str]:
							lowerCAmelCase    =	self._tokenizer.model.save(__a	,       name=__a      )
							return tuple(__a      )


						def 				__snake_case    (					self	,       A_	,       A_=None      )	->	List[Any]:
							lowerCAmelCase    =	[self.bos_token_id] + token_ids_a + [self.eos_token_id]
							if token_ids_a is None:
								return output

							return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]




						def 				__snake_case    (					self	,       A_	,       A_ = 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]






"""simple docstring"""


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     lowercase__			(    lowercase_						,lowercase_						,lowercase_	)    ->						Union[str, Any]:




      """simple docstring"""

      _UpperCamelCase					:		Optional[int]  =				{"small": (512, 8, 24), "medium": (1_024, 8, 36), "large": (1_536, 16, 48)}
      _UpperCamelCase,     _UpperCamelCase,     _UpperCamelCase					:		List[Any]  =				MODELS[model_size]  # set model hyperparameters
      _UpperCamelCase					:		str  =				ImageGPTConfig(n_embd=lowercase_						,n_layer=lowercase_						,n_head=lowercase_	)
      _UpperCamelCase					:		Dict  =				ImageGPTForCausalLM(lowercase_	)

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

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


if __name__ == "__main__":
  lowerCamelCase__      =      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."
  )
  lowerCamelCase__      =      parser.parse_args()
  convert_imagegpt_checkpoint_to_pytorch(
      args.imagegpt_checkpoint_path, args.model_size, args.pytorch_dump_folder_path
  )
