
import os
import re
import unicodedata
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


_lowerCamelCase :       List[Any]     					=    logging.get_logger(__name__)
_lowerCamelCase :       Tuple     					=    {'''vocab_file''': '''spiece.model'''}

_lowerCamelCase :       Dict     					=    {
    '''vocab_file''': {
        '''google/fnet-base''': '''https://huggingface.co/google/fnet-base/resolve/main/spiece.model''',
        '''google/fnet-large''': '''https://huggingface.co/google/fnet-large/resolve/main/spiece.model''',
    },
}

_lowerCamelCase :       Optional[int]     					=    {
    '''google/fnet-base''': 512,
    '''google/fnet-large''': 512,
}

_lowerCamelCase :       str     					=    '''▁'''




class      SCREAMING_SNAKE_CASE__  (			UpperCAmelCase      ):





	'''simple docstring'''



	_UpperCAmelCase :	Optional[int]					     =      VOCAB_FILES_NAMES
	_UpperCAmelCase :	Tuple					     =      PRETRAINED_VOCAB_FILES_MAP
	_UpperCAmelCase :	int					     =      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	_UpperCAmelCase :	Optional[int]					     =      ["input_ids", "token_type_ids"]



	def __init__(						self			:					Union[str, Any]    ,						lowercase			:					str    ,						lowercase			:					str=False    ,						lowercase			:					List[Any]=True    ,						lowercase			:					int=True    ,						lowercase			:					int="<unk>"    ,						lowercase			:					Dict="[SEP]"    ,						lowercase			:					Tuple="<pad>"    ,						lowercase			:					int="[CLS]"    ,						lowercase			:					Union[str, Any]="[MASK]"    ,						lowercase			:					Optional[Dict[str, Any]] = None    ,						**lowercase			:					Union[str, Any]    ,						):


				'''simple docstring'''


				_snake_case						=    (
				    AddedToken(lowercase    ,						lstrip=lowercase    ,						rstrip=lowercase    ,						normalized=lowercase			)
				    if isinstance(lowercase    ,						lowercase			)
				    else mask_token
				)

				_snake_case						=    {} if sp_model_kwargs is None else sp_model_kwargs

				super().__init__(
				    do_lower_case=lowercase    ,						remove_space=lowercase    ,						keep_accents=lowercase    ,						unk_token=lowercase    ,						sep_token=lowercase    ,						pad_token=lowercase    ,						cls_token=lowercase    ,						mask_token=lowercase    ,						sp_model_kwargs=self.sp_model_kwargs    ,						**lowercase    ,						)

				_snake_case						=    do_lower_case
				_snake_case						=    remove_space
				_snake_case						=    keep_accents
				_snake_case						=    vocab_file

				_snake_case						=    spm.SentencePieceProcessor(**self.sp_model_kwargs			)
				self.sp_model.Load(lowercase			)



	@property
	def 				A							(						self			:					Tuple			):


				'''simple docstring'''


				return len(self.sp_model			)



	def 				A							(						self			:					Any			):


				'''simple docstring'''


				_snake_case						=    {self.convert_ids_to_tokens(lowercase			): i for i in range(self.vocab_size			)}
				vocab.update(self.added_tokens_encoder			)
				return vocab



	def __getstate__(						self			:					str			):


				'''simple docstring'''


				_snake_case						=    self.__dict__.copy()
				_snake_case						=    None
				return state



	def __setstate__(						self			:					List[str]    ,						lowercase			:					Optional[Any]			):


				'''simple docstring'''


				_snake_case						=    d

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

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



	def 				A							(						self			:					str    ,						lowercase			:					Optional[Any]			):


				'''simple docstring'''


				if self.remove_space:
							_snake_case						=    ' '.join(inputs.strip().split()			)
				else:
							_snake_case						=    inputs
				_snake_case						=    outputs.replace('``'    ,						'"'			).replace('\'\''    ,						'"'			)

				if not self.keep_accents:
							_snake_case						=    unicodedata.normalize('NFKD'    ,						lowercase			)
							_snake_case						=    ''.join([c for c in outputs if not unicodedata.combining(lowercase			)]			)
				if self.do_lower_case:
							_snake_case						=    outputs.lower()

				return outputs



	def 				A							(						self			:					Optional[int]    ,						lowercase			:					str			):


				'''simple docstring'''


				_snake_case						=    self.preprocess_text(lowercase			)
				_snake_case						=    self.sp_model.encode(lowercase    ,						out_type=lowercase			)
				_snake_case						=    []
				for piece in pieces:
							if len(lowercase			) > 1 and piece[-1] == str(','			) and piece[-2].isdigit():
										_snake_case						=    self.sp_model.EncodeAsPieces(piece[:-1].replace(lowercase    ,						''			)			)
										if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
													if len(cur_pieces[0]			) == 1:
																_snake_case						=    cur_pieces[1:]
													else:
																_snake_case						=    cur_pieces[0][1:]
										cur_pieces.append(piece[-1]			)
										new_pieces.extend(lowercase			)
							else:
										new_pieces.append(lowercase			)

				return new_pieces



	def 				A							(						self			:					List[str]    ,						lowercase			:					Optional[int]			):


				'''simple docstring'''


				return self.sp_model.PieceToId(lowercase			)



	def 				A							(						self			:					Any    ,						lowercase			:					Dict			):


				'''simple docstring'''


				return self.sp_model.IdToPiece(lowercase			)



	def 				A							(						self			:					int    ,						lowercase			:					Dict			):


				'''simple docstring'''


				_snake_case						=    []
				_snake_case						=    ''
				_snake_case						=    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(lowercase			) + token
										_snake_case						=    True
										_snake_case						=    []
							else:
										current_sub_tokens.append(lowercase			)
										_snake_case						=    False
				out_string += self.sp_model.decode(lowercase			)
				return out_string.strip()



	def 				A							(						self			:					Tuple    ,						lowercase			:					List[int]    ,						lowercase			:					bool = False    ,						lowercase			:					bool = None    ,						lowercase			:					bool = True    ,						**lowercase			:					List[str]    ,						):


				'''simple docstring'''


				_snake_case						=    kwargs.pop('use_source_tokenizer'    ,						lowercase			)

				_snake_case						=    self.convert_ids_to_tokens(lowercase    ,						skip_special_tokens=lowercase			)

				# To avoid mixing byte-level and unicode for byte-level BPT
				# we need to build string separately for added tokens and byte-level tokens
				# cf. https://github.com/huggingface/transformers/issues/1133
				_snake_case						=    []
				_snake_case						=    []
				for token in filtered_tokens:
							if skip_special_tokens and token in self.all_special_ids:
										continue
							if token in self.added_tokens_encoder:
										if current_sub_text:
													sub_texts.append(self.convert_tokens_to_string(lowercase			)			)
													_snake_case						=    []
										sub_texts.append(lowercase			)
							else:
										current_sub_text.append(lowercase			)
				if current_sub_text:
							sub_texts.append(self.convert_tokens_to_string(lowercase			)			)

				# Mimic the behavior of the Rust tokenizer:
				# No space after <unk>
				if spaces_between_special_tokens:
							_snake_case						=    re.sub(R'(<unk>) '    ,						R'\1'    ,						' '.join(lowercase			)			)
				else:
							_snake_case						=    ''.join(lowercase			)

				_snake_case						=    (
				    clean_up_tokenization_spaces
				    if clean_up_tokenization_spaces is not None
				    else self.clean_up_tokenization_spaces
				)
				if clean_up_tokenization_spaces:
							_snake_case						=    self.clean_up_tokenization(lowercase			)
							return clean_text
				else:
							return text



	def 				A							(						self			:					Optional[int]    ,						lowercase			:					List[int]    ,						lowercase			:					Optional[List[int]] = None			):


				'''simple docstring'''


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



	def 				A							(						self			:					Optional[int]    ,						lowercase			:					List[int]    ,						lowercase			:					Optional[List[int]] = None    ,						lowercase			:					bool = False			):


				'''simple docstring'''


				if already_has_special_tokens:
							return super().get_special_tokens_mask(
							    token_ids_a=lowercase    ,						token_ids_a=lowercase    ,						already_has_special_tokens=lowercase			)

				if token_ids_a is not None:
							return [1] + ([0] * len(lowercase			)) + [1] + ([0] * len(lowercase			)) + [1]
				return [1] + ([0] * len(lowercase			)) + [1]



	def 				A							(						self			:					Optional[int]    ,						lowercase			:					List[int]    ,						lowercase			:					Optional[List[int]] = None			):


				'''simple docstring'''


				_snake_case						=    [self.sep_token_id]
				_snake_case						=    [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			:					List[Any]    ,						lowercase			:					str    ,						lowercase			:					Optional[str] = None			):


				'''simple docstring'''


				if not os.path.isdir(lowercase			):
							logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''			)
							return
				_snake_case						=    os.path.join(
				    lowercase    ,						(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']			)

				if os.path.abspath(self.vocab_file			) != os.path.abspath(lowercase			) and os.path.isfile(self.vocab_file			):
							copyfile(self.vocab_file    ,						lowercase			)
				elif not os.path.isfile(self.vocab_file			):
							with open(lowercase    ,						'wb'			) as fi:
										_snake_case						=    self.sp_model.serialized_model_proto()
										fi.write(lowercase			)

				return (out_vocab_file,)

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_	( __lowercase     :			Optional[Any] ,      __lowercase     :			Union[str, Any] ,      __lowercase     :			str				)				->						Any:
			# Construct configuration depending on size
			_snake_case						=    {'small': (512, 8, 24), 'medium': (1_024, 8, 36), 'large': (1_536, 16, 48)}
			_snake_case			,      _snake_case			,      _snake_case						=    MODELS[model_size]  # set model hyperparameters
			_snake_case						=    ImageGPTConfig(n_embd=__lowercase ,      n_layer=__lowercase ,      n_head=__lowercase				)
			_snake_case						=    ImageGPTForCausalLM(__lowercase				)

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

			# Save pytorch-model
			_snake_case						=    pytorch_dump_folder_path + '/' + WEIGHTS_NAME
			_snake_case						=    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 :       List[Any]     					=    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 :       Optional[Any]     					=    parser.parse_args()
			convert_imagegpt_checkpoint_to_pytorch(
			    args.imagegpt_checkpoint_path, args.model_size, args.pytorch_dump_folder_path
			)