



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:
						UpperCAmelCase_				:     Optional[Any]              =    None


UpperCAmelCase_				:     Any              =    logging.get_logger(__name__)

UpperCAmelCase_				:     Optional[Any]              =    {"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}

UpperCAmelCase_				:     Tuple              =    {
    "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
UpperCAmelCase_				:     Union[str, Any]              =    {
    "t5-small": 512,
    "t5-base": 512,
    "t5-large": 512,
    "t5-3b": 512,
    "t5-11b": 512,
}






class       UpperCamelCase							(		_a      ):
	lowerCAmelCase			:       Optional[int]								=			VOCAB_FILES_NAMES
	lowerCAmelCase			:       Any								=			PRETRAINED_VOCAB_FILES_MAP
	lowerCAmelCase			:       Optional[int]								=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	lowerCAmelCase			:       Any								=			["""input_ids""", """attention_mask"""]
	lowerCAmelCase			:       str								=			TaTokenizer

	lowerCAmelCase			:       List[str]								=			[]



	def __init__(  self					,			UpperCAmelCase__=None					,			UpperCAmelCase__=None					,			UpperCAmelCase__="</s>"					,			UpperCAmelCase__="<unk>"					,			UpperCAmelCase__="<pad>"					,			UpperCAmelCase__=100					,			UpperCAmelCase__=None					,			**UpperCAmelCase__					,			):
							# Add extra_ids to the special token list
							if extra_ids > 0 and additional_special_tokens is None:
													A__					=  [F"""<extra_id_{i}>""" for i in range(snake_case_						)]
							elif extra_ids > 0 and additional_special_tokens is not None:
													# Check that we have the right number of extra special tokens
													A__					=  len(set(filter(lambda UpperCAmelCase__						: bool("extra_id_" in str(snake_case_						)						)					,			snake_case_						)						)						)
													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__(
							    snake_case_					,			tokenizer_file=snake_case_					,			eos_token=snake_case_					,			unk_token=snake_case_					,			pad_token=snake_case_					,			extra_ids=snake_case_					,			additional_special_tokens=snake_case_					,			**snake_case_					,			)

							A__					=  vocab_file
							A__					=  False if not self.vocab_file else True
							A__					=  extra_ids



	@staticmethod
	def 					__A				(  UpperCAmelCase__					,			UpperCAmelCase__					,			UpperCAmelCase__						):
							if pretrained_model_name_or_path in TaTokenizerFast.max_model_input_sizes:
													A__					=  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."					,			snake_case_					,			)

							return max_model_length



	def 					__A				(  self					,			UpperCAmelCase__					,			UpperCAmelCase__ = None						):
							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(snake_case_						):
													logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""						)
													return
							A__					=  os.path.join(
							    snake_case_					,			(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]						)

							if os.path.abspath(self.vocab_file						) != os.path.abspath(snake_case_						):
													copyfile(self.vocab_file					,			snake_case_						)
													logger.info(F"""Copy vocab file to {out_vocab_file}"""						)

							return (out_vocab_file,)



	def 					__A				(  self					,			UpperCAmelCase__					,			UpperCAmelCase__ = None						):
							A__					=  token_ids_a + [self.eos_token_id]
							if token_ids_a is None:
													return self.prefix_tokens + token_ids_a
							else:
													A__					=  token_ids_a + [self.eos_token_id]
													return self.prefix_tokens + token_ids_a + token_ids_a



	def 					__A				(  self					,			UpperCAmelCase__					,			UpperCAmelCase__ = None						):
							A__					=  [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 					__A				(  self						):
							return list(
							    set(filter(lambda UpperCAmelCase__						: bool(re.search(R"<extra_id_\d+>"					,			snake_case_						)						) is not None					,			self.additional_special_tokens						)						)						)



	def 					__A				(  self						):
							return [self.convert_tokens_to_ids(snake_case_						) for token in self.get_sentinel_tokens()]










'''simple docstring'''





from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


__a  			=						logging.get_logger(__name__)

__a  			=						{
    "shi-labs/dinat-mini-in1k-224": "https://huggingface.co/shi-labs/dinat-mini-in1k-224/resolve/main/config.json",
    # See all Dinat models at https://huggingface.co/models?filter=dinat
}







class        UpperCAmelCase_			(			_a						,						_a      ):
    """simple docstring"""







    lowercase   =						"dinat"

    lowercase   =						{
        "num_attention_heads": "num_heads",
        "num_hidden_layers": "num_layers",
    }




    def __init__(       self   :	Optional[int]					,	snake_case_   :	Optional[Any]=4					,	snake_case_   :	List[str]=3					,	snake_case_   :	Tuple=64					,	snake_case_   :	List[Any]=[3, 4, 6, 5]					,	snake_case_   :	List[Any]=[2, 4, 8, 16]					,	snake_case_   :	List[str]=7					,	snake_case_   :	List[str]=[[1, 8, 1], [1, 4, 1, 4], [1, 2, 1, 2, 1, 2], [1, 1, 1, 1, 1]]					,	snake_case_   :	List[Any]=3.0					,	snake_case_   :	Dict=True					,	snake_case_   :	int=0.0					,	snake_case_   :	Optional[int]=0.0					,	snake_case_   :	str=0.1					,	snake_case_   :	Optional[Any]="gelu"					,	snake_case_   :	Union[str, Any]=0.02					,	snake_case_   :	Optional[int]=1E-5					,	snake_case_   :	str=0.0					,	snake_case_   :	Dict=None					,	snake_case_   :	Dict=None					,	**snake_case_   :	Dict					,	):
          super().__init__(**snake_case_		)

          snake_case__    :       Optional[Any]       		= patch_size
          snake_case__    :       Union[str, Any]       		= num_channels
          snake_case__    :       int       		= embed_dim
          snake_case__    :       Dict       		= depths
          snake_case__    :       Dict       		= len(snake_case_		)
          snake_case__    :       Optional[int]       		= num_heads
          snake_case__    :       Any       		= kernel_size
          snake_case__    :       str       		= dilations
          snake_case__    :       Any       		= mlp_ratio
          snake_case__    :       int       		= qkv_bias
          snake_case__    :       int       		= hidden_dropout_prob
          snake_case__    :       int       		= attention_probs_dropout_prob
          snake_case__    :       Optional[Any]       		= drop_path_rate
          snake_case__    :       int       		= hidden_act
          snake_case__    :       Tuple       		= layer_norm_eps
          snake_case__    :       Any       		= initializer_range
          # we set the hidden_size attribute in order to make Dinat work with VisionEncoderDecoderModel
          # this indicates the channel dimension after the last stage of the model
          snake_case__    :       Optional[Any]       		= int(embed_dim * 2 ** (len(snake_case_		) - 1)		)
          snake_case__    :       int       		= layer_scale_init_value
          snake_case__    :       Dict       		= ["""stem"""] + [f"stage{idx}" for idx in range(1					,	len(snake_case_		) + 1		)]
          snake_case__		,							snake_case__    :       Tuple       		= get_aligned_output_features_output_indices(
              out_features=snake_case_					,	out_indices=snake_case_					,	stage_names=self.stage_names		)
