




"""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:
     A_						=				None


A_						=				logging.get_logger(__name__)

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

A_						=				{
    '''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_						=				{
    '''t5-small''': 5_12,
    '''t5-base''': 5_12,
    '''t5-large''': 5_12,
    '''t5-3b''': 5_12,
    '''t5-11b''': 5_12,
}






class 					lowercase(     __a ):







 '''simple docstring'''





 lowercase__ 					=					VOCAB_FILES_NAMES
 lowercase__ 					=					PRETRAINED_VOCAB_FILES_MAP
 lowercase__ 					=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 lowercase__ 					=					["input_ids", "attention_mask"]
 lowercase__ 					=					TaTokenizer

 lowercase__ 					=					[]


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




   '''simple docstring'''



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

   _snake_case							:		str								=  vocab_file
   _snake_case							:		Dict								=  False if not self.vocab_file else True
   _snake_case							:		Dict								=  extra_ids


 @staticmethod
 def  UpperCamelCase_   (				a_:		Union[str, Any],      a_:		List[Any],      a_:		Optional[int]		):




   '''simple docstring'''



   if pretrained_model_name_or_path in TaTokenizerFast.max_model_input_sizes:
     _snake_case							:		Union[str, Any]								=  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.""",      a_,      )

   return max_model_length


 def  UpperCamelCase_   (				self:		Dict,      a_:		str,      a_:		Optional[str] = None		):




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

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

   return (out_vocab_file,)


 def  UpperCamelCase_   (				self:		int,      a_:		List[int],      a_:		Optional[List[int]] = None		):




   '''simple docstring'''



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


 def  UpperCamelCase_   (				self:		Tuple,      a_:		List[int],      a_:		Optional[List[int]] = None		):




   '''simple docstring'''



   _snake_case							:		List[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  UpperCamelCase_   (				self:		int		):




   '''simple docstring'''



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


 def  UpperCamelCase_   (				self:		Union[str, Any]		):




   '''simple docstring'''



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








"""simple docstring"""


import unittest

import numpy as np

from diffusers import OnnxStableDiffusionInpaintPipelineLegacy
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    load_numpy,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)


if is_onnx_available():
     import onnxruntime as ort






@nightly
@require_onnxruntime
@require_torch_gpu
class 					lowercase(     unittest.TestCase ):







 '''simple docstring'''





 @property
 def  UpperCamelCase_   (				self:		Optional[Any]		):




   '''simple docstring'''



   return (
       "CUDAExecutionProvider",
       {
           "gpu_mem_limit": "15000000000",  # 15GB
           "arena_extend_strategy": "kSameAsRequested",
       },
   )


 @property
 def  UpperCamelCase_   (				self:		int		):




   '''simple docstring'''



   _snake_case							:		Any								=  ort.SessionOptions()
   _snake_case							:		Union[str, Any]								=  False
   return options


 def  UpperCamelCase_   (				self:		List[Any]		):




   '''simple docstring'''



   _snake_case							:		Any								=  load_image(
       """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
       """/in_paint/overture-creations-5sI6fQgYIuo.png"""		)
   _snake_case							:		Union[str, Any]								=  load_image(
       """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
       """/in_paint/overture-creations-5sI6fQgYIuo_mask.png"""		)
   _snake_case							:		Union[str, Any]								=  load_numpy(
       """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
       """/in_paint/red_cat_sitting_on_a_park_bench_onnx.npy"""		)

   # using the PNDM scheduler by default
   _snake_case							:		Optional[Any]								=  OnnxStableDiffusionInpaintPipelineLegacy.from_pretrained(
       """CompVis/stable-diffusion-v1-4""",      revision="""onnx""",      safety_checker=a_,      feature_extractor=a_,      provider=self.gpu_provider,      sess_options=self.gpu_options,      )
   pipe.set_progress_bar_config(disable=a_		)

   _snake_case							:		Optional[Any]								=  """A red cat sitting on a park bench"""

   _snake_case							:		Optional[int]								=  np.random.RandomState(0		)
   _snake_case							:		Any								=  pipe(
       prompt=a_,      image=a_,      mask_image=a_,      strength=0.75,      guidance_scale=7.5,      num_inference_steps=15,      generator=a_,      output_type="""np""",      )

   _snake_case							:		Dict								=  output.images[0]

   assert image.shape == (512, 512, 3)
   assert np.abs(expected_image - image		).max() < 1E-2


