





'''simple docstring'''


import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device


lowerCAmelCase					:     List[Any]												=False
class       a_   (				unittest.TestCase							):
     pass







@nightly
@require_torch_gpu
class       a_   (				unittest.TestCase							):





     def 							lowercase__      ( self		:					Dict						):





        """simple docstring"""




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





     def 							lowercase__      ( self		:					List[Any]						):





        """simple docstring"""




        lowercase_			:Optional[Any]    			= VersatileDiffusionPipeline.from_pretrained("shi-labs/versatile-diffusion"	,      torch_dtype=torch.floataa						)
        pipe.to(lowercase						)
        pipe.set_progress_bar_config(disable=lowercase						)

        lowercase_			:str    			= load_image(
            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"						)

        lowercase_			:Optional[Any]    			= torch.manual_seed(0						)
        lowercase_			:Optional[int]    			= pipe.dual_guided(
            prompt="first prompt"	,      image=lowercase	,      text_to_image_strength=0.75	,      generator=lowercase	,      guidance_scale=7.5	,      num_inference_steps=2	,      output_type="numpy"	,      ).images

        with tempfile.TemporaryDirectory() as tmpdirname:
           pipe.save_pretrained(lowercase						)
           lowercase_			:Dict    			= VersatileDiffusionPipeline.from_pretrained(lowercase	,      torch_dtype=torch.floataa						)
        pipe.to(lowercase						)
        pipe.set_progress_bar_config(disable=lowercase						)

        lowercase_			:int    			= generator.manual_seed(0						)
        lowercase_			:Optional[Any]    			= pipe.dual_guided(
            prompt="first prompt"	,      image=lowercase	,      text_to_image_strength=0.75	,      generator=lowercase	,      guidance_scale=7.5	,      num_inference_steps=2	,      output_type="numpy"	,      ).images

        assert np.abs(image - new_image						).sum() < 1e-5, "Models don't have the same forward pass"







     def 							lowercase__      ( self		:					List[str]						):





        """simple docstring"""




        lowercase_			:Optional[int]    			= VersatileDiffusionPipeline.from_pretrained("shi-labs/versatile-diffusion"	,      torch_dtype=torch.floataa						)
        pipe.to(lowercase						)
        pipe.set_progress_bar_config(disable=lowercase						)

        lowercase_			:Tuple    			= "cyberpunk 2077"
        lowercase_			:Dict    			= load_image(
            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"						)
        lowercase_			:Dict    			= torch.manual_seed(0						)
        lowercase_			:int    			= pipe.dual_guided(
            prompt=lowercase	,      image=lowercase	,      text_to_image_strength=0.75	,      generator=lowercase	,      guidance_scale=7.5	,      num_inference_steps=50	,      output_type="numpy"	,      ).images

        lowercase_			:Optional[int]    			= image[0, 253:256, 253:256, -1]

        assert image.shape == (1, 512, 512, 3)
        lowercase_			:int    			= np.array([0.14_48, 0.16_19, 0.17_41, 0.10_86, 0.11_47, 0.11_28, 0.11_99, 0.11_65, 0.10_01]						)

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

        lowercase_			:List[Any]    			= "A painting of a squirrel eating a burger "
        lowercase_			:Any    			= torch.manual_seed(0						)
        lowercase_			:str    			= pipe.text_to_image(
            prompt=lowercase	,      generator=lowercase	,      guidance_scale=7.5	,      num_inference_steps=50	,      output_type="numpy"						).images

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

        assert image.shape == (1, 512, 512, 3)
        lowercase_			:Optional[Any]    			= np.array([0.33_67, 0.31_69, 0.26_56, 0.38_70, 0.47_90, 0.37_96, 0.40_09, 0.48_78, 0.47_78]						)

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

        lowercase_			:Dict    			= pipe.image_variation(lowercase	,      generator=lowercase	,      output_type="numpy"						).images

        lowercase_			:str    			= image[0, 253:256, 253:256, -1]

        assert image.shape == (1, 512, 512, 3)
        lowercase_			:Optional[int]    			= np.array([0.30_76, 0.31_23, 0.32_84, 0.37_82, 0.37_70, 0.38_94, 0.42_97, 0.43_31, 0.44_56]						)

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









'''simple docstring'''


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


lowerCAmelCase					:     Tuple												=logging.get_logger(__name__)


lowerCAmelCase					:     List[str]												={'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}

# See all MVP models at https://huggingface.co/models?filter=mvp
lowerCAmelCase					:     Optional[int]												={
    '''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''',
    },
}

lowerCAmelCase					:     List[Any]												={
    '''RUCAIBox/mvp''': 1_024,
}
class       a_   (				_lowerCAmelCase							):
     __A      =			VOCAB_FILES_NAMES
     __A      =			PRETRAINED_VOCAB_FILES_MAP
     __A      =			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     __A      =			["input_ids", "attention_mask"]
     __A      =			MvpTokenizer





     def __init__( self		:					Optional[Any]	,      lowercase		:					Any=None	,      lowercase		:					List[Any]=None	,      lowercase		:					Dict=None	,      lowercase		:					int="replace"	,      lowercase		:					int="<s>"	,      lowercase		:					List[str]="</s>"	,      lowercase		:					Optional[Any]="</s>"	,      lowercase		:					List[str]="<s>"	,      lowercase		:					List[str]="<unk>"	,      lowercase		:					List[str]="<pad>"	,      lowercase		:					Tuple="<mask>"	,      lowercase		:					Tuple=False	,      lowercase		:					Dict=True	,      **lowercase		:					List[str]	,      ):





        """simple docstring"""




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

        lowercase_			:Dict    			= json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()						)
        if pre_tok_state.get("add_prefix_space"	,      lowercase						) != add_prefix_space:
           lowercase_			:List[str]    			= getattr(lowercase	,      pre_tok_state.pop("type"						)						)
           lowercase_			:int    			= add_prefix_space
           lowercase_			:Optional[int]    			= pre_tok_class(**lowercase						)

        lowercase_			:Any    			= add_prefix_space

        # the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
        lowercase_			:List[Any]    			= "post_processor"
        lowercase_			:str    			= getattr(self.backend_tokenizer	,      lowercase	,      lowercase						)
        if tokenizer_component_instance:
           lowercase_			:Any    			= 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:
              lowercase_			:int    			= tuple(state["sep"]						)
           if "cls" in state:
              lowercase_			:Any    			= tuple(state["cls"]						)

           lowercase_			:int    			= False

           if state.get("add_prefix_space"	,      lowercase						) != add_prefix_space:
              lowercase_			:Union[str, Any]    			= add_prefix_space
              lowercase_			:int    			= True

           if state.get("trim_offsets"	,      lowercase						) != trim_offsets:
              lowercase_			:Any    			= trim_offsets
              lowercase_			:int    			= True

           if changes_to_apply:
              lowercase_			:Tuple    			= getattr(lowercase	,      state.pop("type"						)						)
              lowercase_			:Any    			= component_class(**lowercase						)
              setattr(self.backend_tokenizer	,      lowercase	,      lowercase						)





     @property
     def 							lowercase__      ( self		:					Optional[int]						):





        """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		:					int	,      lowercase		:					Dict						):





        """simple docstring"""




        lowercase_			:List[str]    			= AddedToken(lowercase	,      lstrip=lowercase	,      rstrip=lowercase						) if isinstance(lowercase	,      lowercase						) else value
        lowercase_			:Union[str, Any]    			= value





     def 							lowercase__      ( self		:					Optional[Any]	,      *lowercase		:					List[Any]	,      **lowercase		:					Any						):





        """simple docstring"""




        lowercase_			:Any    			= kwargs.get("is_split_into_words"	,      lowercase						)

        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(*lowercase	,      **lowercase						)





     def 							lowercase__      ( self		:					Optional[Any]	,      *lowercase		:					Optional[int]	,      **lowercase		:					int						):





        """simple docstring"""




        lowercase_			:Any    			= kwargs.get("is_split_into_words"	,      lowercase						)

        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(*lowercase	,      **lowercase						)





     def 							lowercase__      ( self		:					Dict	,      lowercase		:					str	,      lowercase		:					Optional[str] = None						):





        """simple docstring"""




        lowercase_			:str    			= self._tokenizer.model.save(lowercase	,      name=lowercase						)
        return tuple(lowercase						)





     def 							lowercase__      ( self		:					Tuple	,      lowercase		:					Dict	,      lowercase		:					int=None						):





        """simple docstring"""




        lowercase_			:List[str]    			= [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		:					int	,      lowercase		:					List[int]	,      lowercase		:					Optional[List[int]] = None						):





        """simple docstring"""




        lowercase_			:Union[str, Any]    			= [self.sep_token_id]
        lowercase_			:Tuple    			= [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]


