



"""simple docstring"""

import os
import re
import warnings
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer


if TYPE_CHECKING:
							from ...tokenization_utils_base import TextInput
from ...utils import logging


A_						:    int				=      logging.get_logger(__name__)

A_						:    List[str]				=      {'vocab_file': 'spiece.model'}

A_						:    List[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',
    }
}


# TODO(PVP) - this should be removed in Transformers v5
A_						:    Optional[Any]				=      {
    't5-small': 512,
    't5-base': 512,
    't5-large': 512,
    't5-3b': 512,
    't5-11b': 512,
}

A_						:    Optional[Any]				=      '▁'
class     lowerCAmelCase__						(	_lowerCamelCase ):


	'''simple docstring'''







	_SCREAMING_SNAKE_CASE	:		Union[str, Any]  						= VOCAB_FILES_NAMES
	_SCREAMING_SNAKE_CASE	:		str  						= PRETRAINED_VOCAB_FILES_MAP
	_SCREAMING_SNAKE_CASE	:		int  						= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	_SCREAMING_SNAKE_CASE	:		int  						= ['''input_ids''', '''attention_mask''']





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





					"""simple docstring"""


					if extra_ids > 0 and additional_special_tokens is None:
									SCREAMING_SNAKE_CASE						:							Dict							=     [f"""<extra_id_{i}>""" for i in range(_SCREAMING_SNAKE_CASE    )]
					elif extra_ids > 0 and additional_special_tokens is not None:
									# Check that we have the right number of extra_id special tokens
									SCREAMING_SNAKE_CASE						:							Optional[int]							=     len(set(filter(lambda _SCREAMING_SNAKE_CASE    : bool('extra_id' in str(_SCREAMING_SNAKE_CASE    )    )     ,       _SCREAMING_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'    )
					if legacy:
									logger.warning_once(
									    f"""You are using the legacy behaviour of the {self.__class__}. This means that tokens that come after special tokens will not be properly handled. We recommend you to"""
									    ' read the related pull request available at https://github.com/huggingface/transformers/pull/24565'    )

					SCREAMING_SNAKE_CASE						:							Optional[Any]							=     legacy
					SCREAMING_SNAKE_CASE						:							List[str]							=     {} if sp_model_kwargs is None else sp_model_kwargs

					super().__init__(
					    eos_token=_SCREAMING_SNAKE_CASE     ,       unk_token=_SCREAMING_SNAKE_CASE     ,       pad_token=_SCREAMING_SNAKE_CASE     ,       extra_ids=_SCREAMING_SNAKE_CASE     ,       additional_special_tokens=_SCREAMING_SNAKE_CASE     ,       sp_model_kwargs=self.sp_model_kwargs     ,       legacy=_SCREAMING_SNAKE_CASE     ,       **_SCREAMING_SNAKE_CASE     ,       )

					SCREAMING_SNAKE_CASE						:							List[str]							=     vocab_file
					SCREAMING_SNAKE_CASE						:							Dict							=     extra_ids

					SCREAMING_SNAKE_CASE						:							Any							=     spm.SentencePieceProcessor(**self.sp_model_kwargs    )
					self.sp_model.Load(_SCREAMING_SNAKE_CASE    )





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





					"""simple docstring"""


					if pretrained_model_name_or_path in TaTokenizer.max_model_input_sizes:
									SCREAMING_SNAKE_CASE						:							List[Any]							=     TaTokenizer.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.'     ,       _SCREAMING_SNAKE_CASE     ,       )

					return max_model_length





	@property
	def 			_lowerCAmelCase     (      self				:       Optional[Any]    )						->	Any:





					"""simple docstring"""


					return self.sp_model.get_piece_size() + self._extra_ids





	def 			_lowerCAmelCase     (      self				:       Any    )						->	Tuple:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							List[str]							=     {self.convert_ids_to_tokens(_SCREAMING_SNAKE_CASE    ): i for i in range(self.vocab_size    )}
					vocab.update(self.added_tokens_encoder    )
					return vocab





	def 			_lowerCAmelCase     (      self				:       str     ,       _SCREAMING_SNAKE_CASE				:       List[int]     ,       _SCREAMING_SNAKE_CASE				:       Optional[List[int]] = None     ,       _SCREAMING_SNAKE_CASE				:       bool = False    )						->	List[int]:





					"""simple docstring"""


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

					# normal case: some special tokens
					if token_ids_a is None:
									return ([0] * len(_SCREAMING_SNAKE_CASE    )) + [1]
					return ([0] * len(_SCREAMING_SNAKE_CASE    )) + [1] + ([0] * len(_SCREAMING_SNAKE_CASE    )) + [1]





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





					"""simple docstring"""


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





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





					"""simple docstring"""


					return [self._convert_token_to_id(_SCREAMING_SNAKE_CASE    ) for token in self.get_sentinel_tokens()]





	def 			_lowerCAmelCase     (      self				:       Optional[int]     ,       _SCREAMING_SNAKE_CASE				:       List[int]    )						->	List[int]:





					"""simple docstring"""


					if len(_SCREAMING_SNAKE_CASE    ) > 0 and token_ids[-1] == self.eos_token_id:
									warnings.warn(
									    f"""This sequence already has {self.eos_token}. In future versions this behavior may lead to duplicated"""
									    ' eos tokens being added.'    )
									return token_ids
					else:
									return token_ids + [self.eos_token_id]





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





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							Optional[int]							=     [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				:       Optional[int]     ,       _SCREAMING_SNAKE_CASE				:       List[int]     ,       _SCREAMING_SNAKE_CASE				:       Optional[List[int]] = None    )						->	List[int]:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     self._add_eos_if_not_present(_SCREAMING_SNAKE_CASE    )
					if token_ids_a is None:
									return token_ids_a
					else:
									SCREAMING_SNAKE_CASE						:							Tuple							=     self._add_eos_if_not_present(_SCREAMING_SNAKE_CASE    )
									return token_ids_a + token_ids_a





	def __getstate__(      self				:       List[str]    )						->	str:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							Any							=     self.__dict__.copy()
					SCREAMING_SNAKE_CASE						:							Dict							=     None
					return state





	def __setstate__(      self				:       Optional[Any]     ,       _SCREAMING_SNAKE_CASE				:       Union[str, Any]    )						->	str:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							Tuple							=     d

					# for backward compatibility
					if not hasattr(self     ,       'sp_model_kwargs'    ):
									SCREAMING_SNAKE_CASE						:							Optional[Any]							=     {}

					SCREAMING_SNAKE_CASE						:							int							=     spm.SentencePieceProcessor(**self.sp_model_kwargs    )
					self.sp_model.Load(self.vocab_file    )





	def 			_lowerCAmelCase     (      self				:       Optional[int]     ,       _SCREAMING_SNAKE_CASE				:       "TextInput"     ,       **_SCREAMING_SNAKE_CASE				:       str    )						->	List[str]:





					"""simple docstring"""


					if not self.legacy:
									SCREAMING_SNAKE_CASE						:							List[Any]							=     SPIECE_UNDERLINE + text.replace(_SCREAMING_SNAKE_CASE     ,       ' '    )
					return super().tokenize(_SCREAMING_SNAKE_CASE     ,       **_SCREAMING_SNAKE_CASE    )





	def 			_lowerCAmelCase     (      self				:       Optional[int]     ,       _SCREAMING_SNAKE_CASE				:       Optional[int]     ,       **_SCREAMING_SNAKE_CASE				:       List[str]    )						->	Any:





					"""simple docstring"""


					if not self.legacy:
									SCREAMING_SNAKE_CASE						:							int							=     text.startswith(_SCREAMING_SNAKE_CASE    )
									if is_first:
													SCREAMING_SNAKE_CASE						:							Tuple							=     text[1:]

					SCREAMING_SNAKE_CASE						:							Any							=     self.sp_model.encode(_SCREAMING_SNAKE_CASE     ,       out_type=_SCREAMING_SNAKE_CASE    )

					if not self.legacy and not is_first and not text.startswith(' '    ) and tokens[0].startswith(_SCREAMING_SNAKE_CASE    ):
									SCREAMING_SNAKE_CASE						:							Dict							=     ([tokens[0][1:]] if len(tokens[0]    ) > 1 else []) + tokens[1:]
					return tokens





	def 			_lowerCAmelCase     (      self				:       List[Any]     ,       _SCREAMING_SNAKE_CASE				:       List[str]    )						->	Tuple:





					"""simple docstring"""


					if token.startswith('<extra_id_'    ):
									SCREAMING_SNAKE_CASE						:							Any							=     re.match(R'<extra_id_(\d+)>'     ,       _SCREAMING_SNAKE_CASE    )
									SCREAMING_SNAKE_CASE						:							str							=     int(match.group(1    )    )
									return self.vocab_size - num - 1
					return self.sp_model.piece_to_id(_SCREAMING_SNAKE_CASE    )





	def 			_lowerCAmelCase     (      self				:       str     ,       _SCREAMING_SNAKE_CASE				:       Dict    )						->	Tuple:





					"""simple docstring"""


					if index < self.sp_model.get_piece_size():
									SCREAMING_SNAKE_CASE						:							List[str]							=     self.sp_model.IdToPiece(_SCREAMING_SNAKE_CASE    )
					else:
									SCREAMING_SNAKE_CASE						:							Optional[Any]							=     f"""<extra_id_{self.vocab_size - 1 - index}>"""
					return token





	def 			_lowerCAmelCase     (      self				:       Optional[Any]     ,       _SCREAMING_SNAKE_CASE				:       Dict    )						->	Union[str, Any]:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							int							=     []
					SCREAMING_SNAKE_CASE						:							Optional[Any]							=     ''
					SCREAMING_SNAKE_CASE						:							int							=     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(_SCREAMING_SNAKE_CASE    ) + token
													SCREAMING_SNAKE_CASE						:							str							=     True
													SCREAMING_SNAKE_CASE						:							Optional[int]							=     []
									else:
													current_sub_tokens.append(_SCREAMING_SNAKE_CASE    )
													SCREAMING_SNAKE_CASE						:							Dict							=     False
					out_string += self.sp_model.decode(_SCREAMING_SNAKE_CASE    )
					return out_string.strip()







	def 			_lowerCAmelCase     (      self				:       List[str]     ,       _SCREAMING_SNAKE_CASE				:       str     ,       _SCREAMING_SNAKE_CASE				:       Optional[str] = None    )						->	Tuple[str]:





					"""simple docstring"""


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

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

					return (out_vocab_file,)










"""simple docstring"""

import gc
import unittest

import numpy as np
import torch

from diffusers import StableDiffusionKDiffusionPipeline
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()
@slow
@require_torch_gpu
class     lowerCAmelCase__						(	unittest.TestCase ):


	'''simple docstring'''







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





					"""simple docstring"""


					super().tearDown()
					gc.collect()
					torch.cuda.empty_cache()





	def 			_lowerCAmelCase     (      self				:       Any    )						->	str:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							int							=     StableDiffusionKDiffusionPipeline.from_pretrained('CompVis/stable-diffusion-v1-4'    )
					SCREAMING_SNAKE_CASE						:							Dict							=     sd_pipe.to(_SCREAMING_SNAKE_CASE    )
					sd_pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE    )

					sd_pipe.set_scheduler('sample_euler'    )

					SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     'A painting of a squirrel eating a burger'
					SCREAMING_SNAKE_CASE						:							Optional[Any]							=     torch.manual_seed(0    )
					SCREAMING_SNAKE_CASE						:							Optional[Any]							=     sd_pipe([prompt]     ,       generator=_SCREAMING_SNAKE_CASE     ,       guidance_scale=9.0     ,       num_inference_steps=20     ,       output_type='np'    )

					SCREAMING_SNAKE_CASE						:							str							=     output.images

					SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     image[0, -3:, -3:, -1]

					assert image.shape == (1, 512, 512, 3)
					SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     np.array([0.0_4_4_7, 0.0_4_9_2, 0.0_4_6_8, 0.0_4_0_8, 0.0_3_8_3, 0.0_4_0_8, 0.0_3_5_4, 0.0_3_8_0, 0.0_3_3_9]    )

					assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1E-2





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





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							Optional[Any]							=     StableDiffusionKDiffusionPipeline.from_pretrained('stabilityai/stable-diffusion-2-1-base'    )
					SCREAMING_SNAKE_CASE						:							List[Any]							=     sd_pipe.to(_SCREAMING_SNAKE_CASE    )
					sd_pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE    )

					sd_pipe.set_scheduler('sample_euler'    )

					SCREAMING_SNAKE_CASE						:							List[str]							=     'A painting of a squirrel eating a burger'
					SCREAMING_SNAKE_CASE						:							Optional[int]							=     torch.manual_seed(0    )
					SCREAMING_SNAKE_CASE						:							Dict							=     sd_pipe([prompt]     ,       generator=_SCREAMING_SNAKE_CASE     ,       guidance_scale=9.0     ,       num_inference_steps=20     ,       output_type='np'    )

					SCREAMING_SNAKE_CASE						:							Optional[int]							=     output.images

					SCREAMING_SNAKE_CASE						:							List[str]							=     image[0, -3:, -3:, -1]

					assert image.shape == (1, 512, 512, 3)
					SCREAMING_SNAKE_CASE						:							List[Any]							=     np.array([0.1_2_3_7, 0.1_3_2_0, 0.1_4_3_8, 0.1_3_5_9, 0.1_3_9_0, 0.1_1_3_2, 0.1_2_7_7, 0.1_1_7_5, 0.1_1_1_2]    )

					assert np.abs(image_slice.flatten() - expected_slice    ).max() < 5E-1







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





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							Optional[int]							=     StableDiffusionKDiffusionPipeline.from_pretrained('stabilityai/stable-diffusion-2-1-base'    )
					SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     sd_pipe.to(_SCREAMING_SNAKE_CASE    )
					sd_pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE    )

					sd_pipe.set_scheduler('sample_dpmpp_2m'    )

					SCREAMING_SNAKE_CASE						:							Optional[int]							=     'A painting of a squirrel eating a burger'
					SCREAMING_SNAKE_CASE						:							str							=     torch.manual_seed(0    )
					SCREAMING_SNAKE_CASE						:							Optional[Any]							=     sd_pipe(
					    [prompt]     ,       generator=_SCREAMING_SNAKE_CASE     ,       guidance_scale=7.5     ,       num_inference_steps=15     ,       output_type='np'     ,       use_karras_sigmas=_SCREAMING_SNAKE_CASE     ,       )

					SCREAMING_SNAKE_CASE						:							int							=     output.images

					SCREAMING_SNAKE_CASE						:							List[Any]							=     image[0, -3:, -3:, -1]

					assert image.shape == (1, 512, 512, 3)
					SCREAMING_SNAKE_CASE						:							int							=     np.array(
					    [0.1_1_3_8_1_6_8_9, 0.1_2_1_1_2_9_2_1, 0.1_3_8_9_4_5_7, 0.1_2_5_4_9_6_0_6, 0.1_2_4_4_9_6_4, 0.1_0_8_3_1_5_1_7, 0.1_1_5_6_2_8_6_6, 0.1_0_8_6_7_8_1_6, 0.1_0_4_9_9_0_4_8]    )

					assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1E-2





