
'''simple docstring'''
import os
import re
import warnings
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
						from .tokenization_ta import TaTokenizer
else:
						lowercase__						       =      None


lowercase__						       =      logging.get_logger(__name__)

lowercase__						       =      {'''vocab_file''': '''spiece.model''', '''tokenizer_file''': '''tokenizer.json'''}

lowercase__						       =      {
    '''vocab_file''': {
        '''t5-small''': '''https://huggingface.co/t5-small/resolve/main/spiece.model''',
        '''t5-base''': '''https://huggingface.co/t5-base/resolve/main/spiece.model''',
        '''t5-large''': '''https://huggingface.co/t5-large/resolve/main/spiece.model''',
        '''t5-3b''': '''https://huggingface.co/t5-3b/resolve/main/spiece.model''',
        '''t5-11b''': '''https://huggingface.co/t5-11b/resolve/main/spiece.model''',
    },
    '''tokenizer_file''': {
        '''t5-small''': '''https://huggingface.co/t5-small/resolve/main/tokenizer.json''',
        '''t5-base''': '''https://huggingface.co/t5-base/resolve/main/tokenizer.json''',
        '''t5-large''': '''https://huggingface.co/t5-large/resolve/main/tokenizer.json''',
        '''t5-3b''': '''https://huggingface.co/t5-3b/resolve/main/tokenizer.json''',
        '''t5-11b''': '''https://huggingface.co/t5-11b/resolve/main/tokenizer.json''',
    },
}


# TODO(PVP) - this should be removed in Transformers v5
lowercase__						       =      {
    '''t5-small''': 5_1_2,
    '''t5-base''': 5_1_2,
    '''t5-large''': 5_1_2,
    '''t5-3b''': 5_1_2,
    '''t5-11b''': 5_1_2,
}






class 	snake_case__       (      __a					):







		"""simple docstring"""

		lowerCamelCase     							=						VOCAB_FILES_NAMES
		lowerCamelCase     							=						PRETRAINED_VOCAB_FILES_MAP
		lowerCamelCase     							=						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		lowerCamelCase     							=						['input_ids', 'attention_mask']
		lowerCamelCase     							=						TaTokenizer

		lowerCamelCase     							=						[]



		def __init__(      self				:			int  ,	UpperCamelCase__				:			Optional[int]=None  ,	UpperCamelCase__				:			Tuple=None  ,	UpperCamelCase__				:			int="</s>"  ,	UpperCamelCase__				:			Dict="<unk>"  ,	UpperCamelCase__				:			Optional[int]="<pad>"  ,	UpperCamelCase__				:			Union[str, Any]=100  ,	UpperCamelCase__				:			Optional[int]=None  ,	**UpperCamelCase__				:			Optional[Any]  ,	)						->			Dict:






				"""simple docstring"""







				if extra_ids > 0 and additional_special_tokens is None:
						snake_case  :       int     			=						[f'<extra_id_{i}>' for i in range(_lowerCamelCase      )]
				elif extra_ids > 0 and additional_special_tokens is not None:
						# Check that we have the right number of extra special tokens
						snake_case  :       Any     			=						len(set(filter(lambda UpperCamelCase__      : bool('''extra_id_''' in str(_lowerCamelCase      )      )  ,	_lowerCamelCase      )      )      )
						if extra_tokens != extra_ids:
								raise ValueError(
								    f'Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are'
								    ''' provided to T5Tokenizer. In this case the additional_special_tokens must include the extra_ids'''
								    ''' tokens'''      )

				super().__init__(
				    _lowerCamelCase  ,	tokenizer_file=_lowerCamelCase  ,	eos_token=_lowerCamelCase  ,	unk_token=_lowerCamelCase  ,	pad_token=_lowerCamelCase  ,	extra_ids=_lowerCamelCase  ,	additional_special_tokens=_lowerCamelCase  ,	**_lowerCamelCase  ,	)

				snake_case  :       int     			=						vocab_file
				snake_case  :       Union[str, Any]     			=						False if not self.vocab_file else True
				snake_case  :       int     			=						extra_ids



		@staticmethod
		def       lowerCAmelCase				(      UpperCamelCase__				:			List[Any]  ,	UpperCamelCase__				:			int  ,	UpperCamelCase__				:			Optional[Any]      )						->			Any:






				"""simple docstring"""







				if pretrained_model_name_or_path in TaTokenizerFast.max_model_input_sizes:
						snake_case  :       int     			=						TaTokenizerFast.max_model_input_sizes[pretrained_model_name_or_path]
						if init_max_model_length is not None and init_max_model_length != max_model_length:
								return init_max_model_length
						elif init_max_model_length is None:
								warnings.warn(
								    '''This tokenizer was incorrectly instantiated with a model max length of'''
								    f' {deprecated_max_model_length} which will be corrected in Transformers v5.\nFor now, this'
								    ''' behavior is kept to avoid breaking backwards compatibility when padding/encoding with'''
								    ''' `truncation is True`.\n- Be aware that you SHOULD NOT rely on'''
								    f' {pretrained_model_name_or_path} automatically truncating your input to'
								    f' {deprecated_max_model_length} when padding/encoding.\n- If you want to encode/pad to sequences'
								    f' longer than {deprecated_max_model_length} you can either instantiate this tokenizer with'
								    ''' `model_max_length` or pass `max_length` when encoding/padding.\n- To avoid this warning, please'''
								    ''' instantiate this tokenizer with `model_max_length` set to your preferred value.'''  ,	_lowerCamelCase  ,	)

				return max_model_length



		def       lowerCAmelCase				(      self				:			Optional[int]  ,	UpperCamelCase__				:			str  ,	UpperCamelCase__				:			Optional[int] = None      )						->			Tuple:






				"""simple docstring"""







				if not self.can_save_slow_tokenizer:
						raise ValueError(
						    '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
						    '''tokenizer.'''      )

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

				if os.path.abspath(self.vocab_file      ) != os.path.abspath(_lowerCamelCase      ):
						copyfile(self.vocab_file  ,	_lowerCamelCase      )
						logger.info(f'Copy vocab file to {out_vocab_file}'      )

				return (out_vocab_file,)



		def       lowerCAmelCase				(      self				:			Tuple  ,	UpperCamelCase__				:			int  ,	UpperCamelCase__				:			Tuple = None      )						->			Tuple:






				"""simple docstring"""







				snake_case  :       Dict     			=						token_ids_a + [self.eos_token_id]
				if token_ids_a is None:
						return self.prefix_tokens + token_ids_a
				else:
						snake_case  :       Dict     			=						token_ids_a + [self.eos_token_id]
						return self.prefix_tokens + token_ids_a + token_ids_a



		def       lowerCAmelCase				(      self				:			Optional[Any]  ,	UpperCamelCase__				:			Optional[int]  ,	UpperCamelCase__				:			Union[str, Any] = None      )						->			List[str]:






				"""simple docstring"""







				snake_case  :       Union[str, Any]     			=						[self.eos_token_id]

				if token_ids_a is None:
						return len(token_ids_a + eos      ) * [0]
				return len(token_ids_a + eos + token_ids_a + eos      ) * [0]



		def       lowerCAmelCase				(      self				:			Any      )						->			List[Any]:






				"""simple docstring"""







				return list(
				    set(filter(lambda UpperCamelCase__      : bool(re.search(R'''<extra_id_\d+>'''  ,	_lowerCamelCase      )      ) is not None  ,	self.additional_special_tokens      )      )      )



		def       lowerCAmelCase				(      self				:			Union[str, Any]      )						->			List[str]:






				"""simple docstring"""







				return [self.convert_tokens_to_ids(_lowerCamelCase      ) for token in self.get_sentinel_tokens()]



'''simple docstring'''

import importlib

import torch
import yaml
from omegaconf import OmegaConf
from taming.models.vqgan import VQModel



def snake_case_      (					__snake_case  :							Union[str, Any]	,   __snake_case  :							Union[str, Any]=False)			->					Optional[int]:
   lowerCAmelCase_			=      OmegaConf.load(__snake_case)
   if display:
      print(yaml.dump(OmegaConf.to_container(__snake_case)))
   return config



def snake_case_      (					__snake_case  :							Any	,   __snake_case  :							Optional[int]=None	,   __snake_case  :							str=None)			->					str:
   if conf_path is None:
      lowerCAmelCase_			=      '''./model_checkpoints/vqgan_only.yaml'''
   lowerCAmelCase_			=      load_config(__snake_case	,   display=__snake_case)
   lowerCAmelCase_			=      VQModel(**config.model.params)
   if ckpt_path is None:
      lowerCAmelCase_			=      '''./model_checkpoints/vqgan_only.pt'''
   lowerCAmelCase_			=      torch.load(__snake_case	,   map_location=__snake_case)
   if ".ckpt" in ckpt_path:
      lowerCAmelCase_			=      sd['''state_dict''']
   model.load_state_dict(__snake_case	,   strict=__snake_case)
   model.to(__snake_case)
   del sd
   return model



def snake_case_      (					__snake_case  :							Union[str, Any]	,   __snake_case  :							str)			->					Optional[int]:
   lowerCAmelCase_	,lowerCAmelCase_	,lowerCAmelCase_			=      model.encode(__snake_case)
   print(F'''VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}''')
   lowerCAmelCase_			=      model.decode(__snake_case)
   return xrec



def snake_case_      (					__snake_case  :							int	,   __snake_case  :							List[Any]=False)			->					Union[str, Any]:
   lowerCAmelCase_	,lowerCAmelCase_			=      string.rsplit('''.'''	,   1)
   if reload:
      lowerCAmelCase_			=      importlib.import_module(__snake_case)
      importlib.reload(__snake_case)
   return getattr(importlib.import_module(__snake_case	,   package=__snake_case)	,   cls)



def snake_case_      (					__snake_case  :							Tuple)			->					Union[str, Any]:
   if "target" not in config:
      raise KeyError('''Expected key `target` to instantiate.''')
   return get_obj_from_str(config['''target'''])(**config.get('''params'''	,   {}))



def snake_case_      (					__snake_case  :							Optional[int]	,   __snake_case  :							Any	,   __snake_case  :							Optional[int]=True	,   __snake_case  :							Any=True)			->					List[str]:
   lowerCAmelCase_			=      instantiate_from_config(__snake_case)
   if sd is not None:
      model.load_state_dict(__snake_case)
   if gpu:
      model.cuda()
   if eval_mode:
      model.eval()
   return {"model": model}






def snake_case_      (					__snake_case  :							Any	,   __snake_case  :							Optional[int]	,   __snake_case  :							Dict	,   __snake_case  :							str)			->					Any:
   # load the specified checkpoint
   if ckpt:
      lowerCAmelCase_			=      torch.load(__snake_case	,   map_location='''cpu''')
      lowerCAmelCase_			=      pl_sd['''global_step''']
      print(F'''loaded model from global step {global_step}.''')
   else:
      lowerCAmelCase_			=      {'''state_dict''': None}
      lowerCAmelCase_			=      None
   lowerCAmelCase_			=      load_model_from_config(config.model	,   pl_sd['''state_dict''']	,   gpu=__snake_case	,   eval_mode=__snake_case)['''model''']
   return model, global_step





