





'''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__						:      Tuple	=    None


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

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

lowercase__						:      Dict	=    {
    '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__						:      Optional[Any]	=    {
    't5-small': 5_12,
    't5-base': 5_12,
    't5-large': 5_12,
    't5-3b': 5_12,
    't5-11b': 5_12,
}
class     __lowerCAmelCase					(   a__			):






							"""simple docstring"""
							_snake_case     :					Optional[Any]						    =   VOCAB_FILES_NAMES
							_snake_case     :					Optional[int]						    =   PRETRAINED_VOCAB_FILES_MAP
							_snake_case     :					List[str]						    =   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							_snake_case     :					List[Any]						    =   ['input_ids', 'attention_mask']
							_snake_case     :					Union[str, Any]						    =   TaTokenizer

							_snake_case     :					List[str]						    =   []





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

												'''simple docstring'''




												if extra_ids > 0 and additional_special_tokens is None:
																	_UpperCamelCase       =	[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
																	_UpperCamelCase       =	len(set(filter(lambda lowerCAmelCase__					: 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__	,			)

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





							@staticmethod
							def snake_case__						(							lowerCAmelCase__       :		Optional[int]	,			lowerCAmelCase__       :		List[str]	,			lowerCAmelCase__       :		Dict					)   ->					int:

												'''simple docstring'''




												if pretrained_model_name_or_path in TaTokenizerFast.max_model_input_sizes:
																	_UpperCamelCase       =	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 snake_case__						(							self       :		Dict	,			lowerCAmelCase__       :		str	,			lowerCAmelCase__       :		Optional[str] = None					)   ->					Optional[int]:

												'''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
												_UpperCamelCase       =	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 snake_case__						(							self       :		Union[str, Any]	,			lowerCAmelCase__       :		List[int]	,			lowerCAmelCase__       :		Optional[List[int]] = None					)   ->					Optional[int]:

												'''simple docstring'''




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





							def snake_case__						(							self       :		Any	,			lowerCAmelCase__       :		List[int]	,			lowerCAmelCase__       :		Optional[List[int]] = None					)   ->					Tuple:

												'''simple docstring'''




												_UpperCamelCase       =	[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 snake_case__						(							self       :		Tuple					)   ->					str:

												'''simple docstring'''




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







							def snake_case__						(							self       :		Union[str, Any]					)   ->					int:

												'''simple docstring'''




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










'''simple docstring'''

import unittest

from diffusers.pipelines.pipeline_utils import is_safetensors_compatible
class     __lowerCAmelCase					(   unittest.TestCase			):






       """simple docstring"""





       def snake_case__						(							self       :		Tuple					)   ->					int:

            '''simple docstring'''




            _UpperCamelCase       =	[
                '''safety_checker/pytorch_model.bin''',
                '''safety_checker/model.safetensors''',
                '''vae/diffusion_pytorch_model.bin''',
                '''vae/diffusion_pytorch_model.safetensors''',
                '''text_encoder/pytorch_model.bin''',
                '''text_encoder/model.safetensors''',
                '''unet/diffusion_pytorch_model.bin''',
                '''unet/diffusion_pytorch_model.safetensors''',
            ]
            self.assertTrue(is_safetensors_compatible(lowerCAmelCase__					)					)





       def snake_case__						(							self       :		int					)   ->					Tuple:

            '''simple docstring'''




            _UpperCamelCase       =	[
                '''unet/diffusion_pytorch_model.bin''',
                '''unet/diffusion_pytorch_model.safetensors''',
            ]
            self.assertTrue(is_safetensors_compatible(lowerCAmelCase__					)					)





       def snake_case__						(							self       :		Optional[Any]					)   ->					List[str]:

            '''simple docstring'''




            _UpperCamelCase       =	[
                '''safety_checker/pytorch_model.bin''',
                '''safety_checker/model.safetensors''',
                '''vae/diffusion_pytorch_model.bin''',
                '''vae/diffusion_pytorch_model.safetensors''',
                '''text_encoder/pytorch_model.bin''',
                '''text_encoder/model.safetensors''',
                '''unet/diffusion_pytorch_model.bin''',
                # Removed: 'unet/diffusion_pytorch_model.safetensors',
            ]
            self.assertFalse(is_safetensors_compatible(lowerCAmelCase__					)					)





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

            '''simple docstring'''




            _UpperCamelCase       =	[
                '''text_encoder/pytorch_model.bin''',
                '''text_encoder/model.safetensors''',
            ]
            self.assertTrue(is_safetensors_compatible(lowerCAmelCase__					)					)





       def snake_case__						(							self       :		Dict					)   ->					Dict:

            '''simple docstring'''




            _UpperCamelCase       =	[
                '''safety_checker/pytorch_model.bin''',
                '''safety_checker/model.safetensors''',
                '''vae/diffusion_pytorch_model.bin''',
                '''vae/diffusion_pytorch_model.safetensors''',
                '''text_encoder/pytorch_model.bin''',
                # Removed: 'text_encoder/model.safetensors',
                '''unet/diffusion_pytorch_model.bin''',
                '''unet/diffusion_pytorch_model.safetensors''',
            ]
            self.assertFalse(is_safetensors_compatible(lowerCAmelCase__					)					)





       def snake_case__						(							self       :		Any					)   ->					Optional[int]:

            '''simple docstring'''




            _UpperCamelCase       =	[
                '''safety_checker/pytorch_model.fp16.bin''',
                '''safety_checker/model.fp16.safetensors''',
                '''vae/diffusion_pytorch_model.fp16.bin''',
                '''vae/diffusion_pytorch_model.fp16.safetensors''',
                '''text_encoder/pytorch_model.fp16.bin''',
                '''text_encoder/model.fp16.safetensors''',
                '''unet/diffusion_pytorch_model.fp16.bin''',
                '''unet/diffusion_pytorch_model.fp16.safetensors''',
            ]
            _UpperCamelCase       =	'''fp16'''
            self.assertTrue(is_safetensors_compatible(lowerCAmelCase__	,			variant=lowerCAmelCase__					)					)





       def snake_case__						(							self       :		Optional[Any]					)   ->					Dict:

            '''simple docstring'''




            _UpperCamelCase       =	[
                '''unet/diffusion_pytorch_model.fp16.bin''',
                '''unet/diffusion_pytorch_model.fp16.safetensors''',
            ]
            _UpperCamelCase       =	'''fp16'''
            self.assertTrue(is_safetensors_compatible(lowerCAmelCase__	,			variant=lowerCAmelCase__					)					)





       def snake_case__						(							self       :		Tuple					)   ->					Optional[Any]:

            '''simple docstring'''




            _UpperCamelCase       =	[
                '''unet/diffusion_pytorch_model.bin''',
                '''unet/diffusion_pytorch_model.safetensors''',
            ]
            _UpperCamelCase       =	'''fp16'''
            self.assertTrue(is_safetensors_compatible(lowerCAmelCase__	,			variant=lowerCAmelCase__					)					)





       def snake_case__						(							self       :		Tuple					)   ->					Union[str, Any]:

            '''simple docstring'''




            _UpperCamelCase       =	[
                '''safety_checker/pytorch_model.fp16.bin''',
                '''safety_checker/model.fp16.safetensors''',
                '''vae/diffusion_pytorch_model.fp16.bin''',
                '''vae/diffusion_pytorch_model.fp16.safetensors''',
                '''text_encoder/pytorch_model.fp16.bin''',
                '''text_encoder/model.fp16.safetensors''',
                '''unet/diffusion_pytorch_model.fp16.bin''',
                # Removed: 'unet/diffusion_pytorch_model.fp16.safetensors',
            ]
            _UpperCamelCase       =	'''fp16'''
            self.assertFalse(is_safetensors_compatible(lowerCAmelCase__	,			variant=lowerCAmelCase__					)					)





       def snake_case__						(							self       :		Optional[int]					)   ->					Dict:

            '''simple docstring'''




            _UpperCamelCase       =	[
                '''text_encoder/pytorch_model.fp16.bin''',
                '''text_encoder/model.fp16.safetensors''',
            ]
            _UpperCamelCase       =	'''fp16'''
            self.assertTrue(is_safetensors_compatible(lowerCAmelCase__	,			variant=lowerCAmelCase__					)					)





       def snake_case__						(							self       :		Optional[Any]					)   ->					str:

            '''simple docstring'''




            _UpperCamelCase       =	[
                '''text_encoder/pytorch_model.bin''',
                '''text_encoder/model.safetensors''',
            ]
            _UpperCamelCase       =	'''fp16'''
            self.assertTrue(is_safetensors_compatible(lowerCAmelCase__	,			variant=lowerCAmelCase__					)					)







       def snake_case__						(							self       :		List[str]					)   ->					Optional[int]:

            '''simple docstring'''




            _UpperCamelCase       =	[
                '''safety_checker/pytorch_model.fp16.bin''',
                '''safety_checker/model.fp16.safetensors''',
                '''vae/diffusion_pytorch_model.fp16.bin''',
                '''vae/diffusion_pytorch_model.fp16.safetensors''',
                '''text_encoder/pytorch_model.fp16.bin''',
                # 'text_encoder/model.fp16.safetensors',
                '''unet/diffusion_pytorch_model.fp16.bin''',
                '''unet/diffusion_pytorch_model.fp16.safetensors''',
            ]
            _UpperCamelCase       =	'''fp16'''
            self.assertFalse(is_safetensors_compatible(lowerCAmelCase__	,			variant=lowerCAmelCase__					)					)



