

from typing import Dict, List, Optional, Tuple

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


lowerCamelCase 	=   logging.get_logger(__name__)







class 		__magic_name__     (				__a				):

   '''simple docstring'''




   lowerCamelCase__					:      Dict        =  ['input_ids', 'attention_mask']



   def __init__(							self,       lowercase_="[PAD]",       lowercase_="[BOS]",       lowercase_="[EOS]",       lowercase_="[MASK]",       lowercase_="[CLS]",       lowercase_="[SEP]",       lowercase_=2048,       **lowercase_,       )				->       None:






     """simple docstring"""






     a__    =AddedToken(UpperCamelCase__,       lstrip=UpperCamelCase__,       rstrip=UpperCamelCase__			) if isinstance(UpperCamelCase__,       UpperCamelCase__			) else pad_token
     a__    =AddedToken(UpperCamelCase__,       lstrip=UpperCamelCase__,       rstrip=UpperCamelCase__			) if isinstance(UpperCamelCase__,       UpperCamelCase__			) else bos_token
     a__    =AddedToken(UpperCamelCase__,       lstrip=UpperCamelCase__,       rstrip=UpperCamelCase__			) if isinstance(UpperCamelCase__,       UpperCamelCase__			) else eos_token
     a__    =AddedToken(UpperCamelCase__,       lstrip=UpperCamelCase__,       rstrip=UpperCamelCase__			) if isinstance(UpperCamelCase__,       UpperCamelCase__			) else mask_token
     a__    =AddedToken(UpperCamelCase__,       lstrip=UpperCamelCase__,       rstrip=UpperCamelCase__			) if isinstance(UpperCamelCase__,       UpperCamelCase__			) else cls_token
     a__    =AddedToken(UpperCamelCase__,       lstrip=UpperCamelCase__,       rstrip=UpperCamelCase__			) if isinstance(UpperCamelCase__,       UpperCamelCase__			) else sep_token

     super().__init__(
         pad_token=UpperCamelCase__,       bos_token=UpperCamelCase__,       eos_token=UpperCamelCase__,       mask_token=UpperCamelCase__,       cls_token=UpperCamelCase__,       sep_token=UpperCamelCase__,       model_max_length=UpperCamelCase__,       **UpperCamelCase__,       )

     a__    =2**8  # utf is 8 bits

     # define special tokens dict
     a__    ={
         self.pad_token: 0,
         self.bos_token: 1,
         self.eos_token: 2,
         self.mask_token: 3,
         self.cls_token: 4,
         self.sep_token: 5,
     }
     a__    =len(self.special_tokens_encoder			)
     a__    ={v: k for k, v in self.special_tokens_encoder.items()}



   def 					_UpperCAmelCase					(							self			)				->       Dict[str, int]:






     """simple docstring"""






     a__    =self.special_tokens_encoder.copy()
     vocab.update(self.added_tokens_encoder			)
     for i in range(self._utf_vocab_size			):
       a__    =chr(UpperCamelCase__			)
       a__    =i + len(self.special_tokens_encoder			)
     return vocab



   @property
   def 					_UpperCAmelCase					(							self			)				->       int:






     """simple docstring"""






     return self._utf_vocab_size + self._num_special_tokens



   def 					_UpperCAmelCase					(							self,       lowercase_,       lowercase_ = None,       lowercase_ = False			)				->       List[int]:






     """simple docstring"""






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

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



   def 					_UpperCAmelCase					(							self,       lowercase_,       lowercase_ = None			)				->       List[int]:






     """simple docstring"""






     if token_ids_a is None:
       return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
     else:
       return [self.cls_token_id] + token_ids_a + [self.sep_token_id] + token_ids_a + [self.sep_token_id]



   def 					_UpperCAmelCase					(							self,       lowercase_			)				->       List[str]:






     """simple docstring"""






     a__    =[chr(UpperCamelCase__			) for i in text.encode('''utf-8'''			)]
     return tokens



   def 					_UpperCAmelCase					(							self,       lowercase_			)				->       Union[str, Any]:






     """simple docstring"""






     if token in self.special_tokens_encoder:
       a__    =self.special_tokens_encoder[token]
     elif token in self.added_tokens_encoder:
       a__    =self.added_tokens_encoder[token]
     elif len(UpperCamelCase__			) != 1:
       a__    =self.unk_token_id
     else:
       a__    =ord(UpperCamelCase__			) + self._num_special_tokens
     return token_id



   def 					_UpperCAmelCase					(							self,       lowercase_			)				->       Optional[Any]:






     """simple docstring"""






     if index in self.special_tokens_decoder:
       a__    =self.special_tokens_decoder[index]
     elif index in self.added_tokens_decoder:
       a__    =self.added_tokens_decoder[index]
     else:
       a__    =chr(index - self._num_special_tokens			)
     return token



   def 					_UpperCAmelCase					(							self,       lowercase_			)				->       Optional[Any]:






     """simple docstring"""






     a__    =b''''''
     for token in tokens:
       if token in self.special_tokens_decoder:
         a__    =self.special_tokens_decoder[token].encode('''utf-8'''			)
       elif token in self.added_tokens_decoder:
         a__    =self.special_tokens_decoder[token].encode('''utf-8'''			)
       elif token in self.special_tokens_encoder:
         a__    =token.encode('''utf-8'''			)
       elif token in self.added_tokens_encoder:
         a__    =token.encode('''utf-8'''			)
       else:
         a__    =bytes([ord(UpperCamelCase__			)]			)
       bstring += tok_string
     a__    =bstring.decode('''utf-8''',       errors='''replace'''			)
     return string



   def 					_UpperCAmelCase					(							self,       lowercase_,       lowercase_ = None			)				->       Tuple[str]:






     """simple docstring"""






     return ()

"""simple docstring"""

import gc
import tempfile
import unittest

import numpy as np
import torch

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


__lowercase    =				False






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



						"""simple docstring"""




						def   UpperCAmelCase_ (				self							:    List[str]				)				->				List[Any]:
													'''simple docstring'''



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





						def   UpperCAmelCase_ (				self							:    List[Any]				)				->				List[Any]:
													'''simple docstring'''



													__UpperCamelCase							=VersatileDiffusionDualGuidedPipeline.from_pretrained('''shi-labs/versatile-diffusion'''				)
													# remove text_unet
													pipe.remove_unused_weights()
													pipe.to(UpperCamelCase__				)
													pipe.set_progress_bar_config(disable=UpperCamelCase__				)

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

													__UpperCamelCase							=torch.manual_seed(0				)
													__UpperCamelCase							=pipe(
													    prompt='''first prompt'''     , image=UpperCamelCase__     , text_to_image_strength=0.75     , generator=UpperCamelCase__     , guidance_scale=7.5     , num_inference_steps=2     , output_type='''numpy'''     , ).images

													with tempfile.TemporaryDirectory() as tmpdirname:
																				pipe.save_pretrained(UpperCamelCase__				)
																				__UpperCamelCase							=VersatileDiffusionDualGuidedPipeline.from_pretrained(UpperCamelCase__				)

													pipe.to(UpperCamelCase__				)
													pipe.set_progress_bar_config(disable=UpperCamelCase__				)

													__UpperCamelCase							=generator.manual_seed(0				)
													__UpperCamelCase							=pipe(
													    prompt='''first prompt'''     , image=UpperCamelCase__     , text_to_image_strength=0.75     , generator=UpperCamelCase__     , 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   UpperCAmelCase_ (				self							:    Optional[int]				)				->				Optional[int]:
													'''simple docstring'''



													__UpperCamelCase							=VersatileDiffusionDualGuidedPipeline.from_pretrained('''shi-labs/versatile-diffusion'''				)
													pipe.remove_unused_weights()
													pipe.to(UpperCamelCase__				)
													pipe.set_progress_bar_config(disable=UpperCamelCase__				)

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

													__UpperCamelCase							=image[0, 253:256, 253:256, -1]

													assert image.shape == (1, 512, 512, 3)
													__UpperCamelCase							=np.array([0.07_87, 0.08_49, 0.08_26, 0.08_12, 0.08_07, 0.07_95, 0.08_18, 0.07_98, 0.07_79]				)

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



