


import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_mvp import MvpTokenizer


__UpperCAmelCase : List[Any] 			=				logging.get_logger(__name__)


__UpperCAmelCase : Optional[int] 			=				{'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}

# See all MVP models at https://huggingface.co/models?filter=mvp
__UpperCAmelCase : Any 			=				{
    '''vocab_file''': {
        '''RUCAIBox/mvp''': '''https://huggingface.co/RUCAIBox/mvp/resolve/main/vocab.json''',
    },
    '''added_tokens.json''': {
        '''RUCAIBox/mvp''': '''https://huggingface.co/RUCAIBox/mvp/resolve/main/added_tokens.json''',
    },
    '''merges_file''': {
        '''RUCAIBox/mvp''': '''https://huggingface.co/RUCAIBox/mvp/resolve/main/merges.txt''',
    },
    '''tokenizer_file''': {
        '''RUCAIBox/mvp''': '''https://huggingface.co/RUCAIBox/mvp/resolve/main/tokenizer.json''',
    },
}

__UpperCAmelCase : Any 			=				{
    '''RUCAIBox/mvp''': 1024,
}



class    UpperCAmelCase_	(							__UpperCAmelCase):
       '''simple docstring'''
       __UpperCamelCase      :     Tuple							=					VOCAB_FILES_NAMES
       __UpperCamelCase      :     Union[str, Any]							=					PRETRAINED_VOCAB_FILES_MAP
       __UpperCamelCase      :     Any							=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       __UpperCamelCase      :     Dict							=					['''input_ids''', '''attention_mask''']
       __UpperCamelCase      :     Optional[int]							=					MvpTokenizer






       def __init__(     self ,					__SCREAMING_SNAKE_CASE=None ,					__SCREAMING_SNAKE_CASE=None ,					__SCREAMING_SNAKE_CASE=None ,					__SCREAMING_SNAKE_CASE="replace" ,					__SCREAMING_SNAKE_CASE="<s>" ,					__SCREAMING_SNAKE_CASE="</s>" ,					__SCREAMING_SNAKE_CASE="</s>" ,					__SCREAMING_SNAKE_CASE="<s>" ,					__SCREAMING_SNAKE_CASE="<unk>" ,					__SCREAMING_SNAKE_CASE="<pad>" ,					__SCREAMING_SNAKE_CASE="<mask>" ,					__SCREAMING_SNAKE_CASE=False ,					__SCREAMING_SNAKE_CASE=True ,					**__SCREAMING_SNAKE_CASE ,					):




              """simple docstring"""




              super().__init__(
                  __SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE ,					tokenizer_file=__SCREAMING_SNAKE_CASE ,					errors=__SCREAMING_SNAKE_CASE ,					bos_token=__SCREAMING_SNAKE_CASE ,					eos_token=__SCREAMING_SNAKE_CASE ,					sep_token=__SCREAMING_SNAKE_CASE ,					cls_token=__SCREAMING_SNAKE_CASE ,					unk_token=__SCREAMING_SNAKE_CASE ,					pad_token=__SCREAMING_SNAKE_CASE ,					mask_token=__SCREAMING_SNAKE_CASE ,					add_prefix_space=__SCREAMING_SNAKE_CASE ,					trim_offsets=__SCREAMING_SNAKE_CASE ,					**__SCREAMING_SNAKE_CASE ,					)

              UpperCamelCase    :	Union[str, Any]   	=		json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()						)
              if pre_tok_state.get('''add_prefix_space''' ,					__SCREAMING_SNAKE_CASE						) != add_prefix_space:
                     UpperCamelCase    :	Any   	=		getattr(__SCREAMING_SNAKE_CASE ,					pre_tok_state.pop('''type'''						)						)
                     UpperCamelCase    :	List[str]   	=		add_prefix_space
                     UpperCamelCase    :	Tuple   	=		pre_tok_class(**__SCREAMING_SNAKE_CASE						)

              UpperCamelCase    :	Optional[Any]   	=		add_prefix_space

              # the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
              UpperCamelCase    :	Any   	=		'''post_processor'''
              UpperCamelCase    :	Union[str, Any]   	=		getattr(self.backend_tokenizer ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE						)
              if tokenizer_component_instance:
                     UpperCamelCase    :	Dict   	=		json.loads(tokenizer_component_instance.__getstate__()						)

                     # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
                     if "sep" in state:
                            UpperCamelCase    :	Optional[Any]   	=		tuple(state['''sep''']						)
                     if "cls" in state:
                            UpperCamelCase    :	Dict   	=		tuple(state['''cls''']						)

                     UpperCamelCase    :	Optional[int]   	=		False

                     if state.get('''add_prefix_space''' ,					__SCREAMING_SNAKE_CASE						) != add_prefix_space:
                            UpperCamelCase    :	List[str]   	=		add_prefix_space
                            UpperCamelCase    :	Dict   	=		True

                     if state.get('''trim_offsets''' ,					__SCREAMING_SNAKE_CASE						) != trim_offsets:
                            UpperCamelCase    :	List[Any]   	=		trim_offsets
                            UpperCamelCase    :	List[str]   	=		True

                     if changes_to_apply:
                            UpperCamelCase    :	str   	=		getattr(__SCREAMING_SNAKE_CASE ,					state.pop('''type'''						)						)
                            UpperCamelCase    :	List[Any]   	=		component_class(**__SCREAMING_SNAKE_CASE						)
                            setattr(self.backend_tokenizer ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE						)






       @property
       def  _lowercase   (     self						):




              """simple docstring"""




              if self._mask_token is None:
                     if self.verbose:
                            logger.error('''Using mask_token, but it is not set yet.'''						)
                     return None
              return str(self._mask_token						)






       @mask_token.setter
       def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE						):




              """simple docstring"""




              UpperCamelCase    :	int   	=		AddedToken(__SCREAMING_SNAKE_CASE ,					lstrip=__SCREAMING_SNAKE_CASE ,					rstrip=__SCREAMING_SNAKE_CASE						) if isinstance(__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE						) else value
              UpperCamelCase    :	List[str]   	=		value






       def  _lowercase   (     self ,					*__SCREAMING_SNAKE_CASE ,					**__SCREAMING_SNAKE_CASE						):




              """simple docstring"""




              UpperCamelCase    :	Any   	=		kwargs.get('''is_split_into_words''' ,					__SCREAMING_SNAKE_CASE						)

              if is_split_into_words and not self.add_prefix_space:
                     raise ValueError(
                         f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
                         '''to use it with pretokenized inputs.'''						)

              return super()._batch_encode_plus(*__SCREAMING_SNAKE_CASE ,					**__SCREAMING_SNAKE_CASE						)






       def  _lowercase   (     self ,					*__SCREAMING_SNAKE_CASE ,					**__SCREAMING_SNAKE_CASE						):




              """simple docstring"""




              UpperCamelCase    :	Optional[int]   	=		kwargs.get('''is_split_into_words''' ,					__SCREAMING_SNAKE_CASE						)

              if is_split_into_words and not self.add_prefix_space:
                     raise ValueError(
                         f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
                         '''to use it with pretokenized inputs.'''						)

              return super()._encode_plus(*__SCREAMING_SNAKE_CASE ,					**__SCREAMING_SNAKE_CASE						)






       def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE = None						):




              """simple docstring"""




              UpperCamelCase    :	List[str]   	=		self._tokenizer.model.save(__SCREAMING_SNAKE_CASE ,					name=__SCREAMING_SNAKE_CASE						)
              return tuple(__SCREAMING_SNAKE_CASE						)






       def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE=None						):




              """simple docstring"""




              UpperCamelCase    :	Tuple   	=		[self.bos_token_id] + token_ids_a + [self.eos_token_id]
              if token_ids_a is None:
                     return output

              return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]






       def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE = None						):




              """simple docstring"""




              UpperCamelCase    :	Any   	=		[self.sep_token_id]
              UpperCamelCase    :	Union[str, Any]   	=		[self.cls_token_id]

              if token_ids_a is None:
                     return len(cls + token_ids_a + sep						) * [0]
              return len(cls + token_ids_a + sep + sep + token_ids_a + sep						) * [0]





import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionImageVariationPipeline
from diffusers.utils.testing_utils import load_image, require_torch_gpu, slow, torch_device


__UpperCAmelCase : Dict 			=				False



class    UpperCAmelCase_	(							unittest.TestCase):
       '''simple docstring'''
       pass




@slow
@require_torch_gpu
class    UpperCAmelCase_	(							unittest.TestCase):
       '''simple docstring'''






       def  _lowercase   (     self						):




              """simple docstring"""




              UpperCamelCase    :	Dict   	=		VersatileDiffusionImageVariationPipeline.from_pretrained('''shi-labs/versatile-diffusion'''						)
              pipe.to(__SCREAMING_SNAKE_CASE						)
              pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE						)

              UpperCamelCase    :	Union[str, Any]   	=		load_image(
                  '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'''						)
              UpperCamelCase    :	str   	=		torch.manual_seed(0						)
              UpperCamelCase    :	Union[str, Any]   	=		pipe(
                  image=__SCREAMING_SNAKE_CASE ,					generator=__SCREAMING_SNAKE_CASE ,					guidance_scale=7.5 ,					num_inference_steps=50 ,					output_type='''numpy''' ,					).images

              UpperCamelCase    :	List[Any]   	=		image[0, 253:256, 253:256, -1]

              assert image.shape == (1, 512, 512, 3)
              UpperCamelCase    :	Dict   	=		np.array([0.0_441, 0.0_469, 0.0_507, 0.0_575, 0.0_632, 0.0_650, 0.0_865, 0.0_909, 0.0_945]						)

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

