


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:
	A	:		List[str]    =       None


A	:		Optional[int]    =       logging.get_logger(__name__)

A	:		Optional[int]    =       {'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}

A	:		str    =       {
    '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
A	:		Dict    =       {
    '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 							A   (	UpperCAmelCase__							):
					'''simple docstring'''






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

					A__      =			[]

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




							"""simple docstring"""


							if extra_ids > 0 and additional_special_tokens is None:
									lowercase__					      =		[f'''<extra_id_{i}>''' for i in range(_UpperCAmelCase		)]
							elif extra_ids > 0 and additional_special_tokens is not None:
									# Check that we have the right number of extra special tokens
									lowercase__					      =		len(set(filter(lambda _UpperCAmelCase		: bool("""extra_id_""" in str(_UpperCAmelCase		)		)				, _UpperCAmelCase		)		)		)
									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__(
							    _UpperCAmelCase				, tokenizer_file=_UpperCAmelCase				, eos_token=_UpperCAmelCase				, unk_token=_UpperCAmelCase				, pad_token=_UpperCAmelCase				, extra_ids=_UpperCAmelCase				, additional_special_tokens=_UpperCAmelCase				, **_UpperCAmelCase				, )

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

					@staticmethod
					def   lowerCamelCase__					(_UpperCAmelCase   :						int				, _UpperCAmelCase   :						Optional[int]				, _UpperCAmelCase   :						List[str]		)						->       Optional[Any]:




							"""simple docstring"""


							if pretrained_model_name_or_path in TaTokenizerFast.max_model_input_sizes:
									lowercase__					      =		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."""				, _UpperCAmelCase				, )

							return max_model_length

					def   lowerCamelCase__					(self   :						Optional[Any]				, _UpperCAmelCase   :						str				, _UpperCAmelCase   :						Optional[str] = None		)						->       Tuple[str]:




							"""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(_UpperCAmelCase		):
									logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''		)
									return
							lowercase__					      =		os.path.join(
							    _UpperCAmelCase				, (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]		)

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

							return (out_vocab_file,)

					def   lowerCamelCase__					(self   :						Any				, _UpperCAmelCase   :						List[int]				, _UpperCAmelCase   :						Optional[List[int]] = None		)						->       List[int]:




							"""simple docstring"""


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

					def   lowerCamelCase__					(self   :						str				, _UpperCAmelCase   :						List[int]				, _UpperCAmelCase   :						Optional[List[int]] = None		)						->       List[int]:




							"""simple docstring"""


							lowercase__					      =		[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   :						Dict		)						->       str:




							"""simple docstring"""


							return list(
							    set(filter(lambda _UpperCAmelCase		: bool(re.search(r"""<extra_id_\d+>"""				, _UpperCAmelCase		)		) is not None				, self.additional_special_tokens		)		)		)


					def   lowerCamelCase__					(self   :						List[Any]		)						->       int:




							"""simple docstring"""


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





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


A	:		Dict    =       {'configuration_glpn': ['GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GLPNConfig']}

try:
	if not is_vision_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A	:		Tuple    =       ['GLPNFeatureExtractor']
	A	:		List[str]    =       ['GLPNImageProcessor']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A	:		Optional[int]    =       [
	    'GLPN_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'GLPNForDepthEstimation',
	    'GLPNLayer',
	    'GLPNModel',
	    'GLPNPreTrainedModel',
	]


if TYPE_CHECKING:
	from .configuration_glpn import GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP, GLPNConfig

	try:
		if not is_vision_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .feature_extraction_glpn import GLPNFeatureExtractor
		from .image_processing_glpn import GLPNImageProcessor

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_glpn import (
		    GLPN_PRETRAINED_MODEL_ARCHIVE_LIST,
		    GLPNForDepthEstimation,
		    GLPNLayer,
		    GLPNModel,
		    GLPNPreTrainedModel,
		)


else:
	import sys

	A	:		Tuple    =       _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

