


"""simple docstring"""



from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType







class _lowerCAmelCase				(      _lowercase	):
   __UpperCAmelCase :       int							  =  ['''image_processor''', '''tokenizer''']
   __UpperCAmelCase :       List[str]							  =  '''Pix2StructImageProcessor'''
   __UpperCAmelCase :       List[str]							  =  ('''T5Tokenizer''', '''T5TokenizerFast''')







   def __init__(	self						,	UpperCamelCase__						,	UpperCamelCase__						)	->		Optional[int]:

       '''simple docstring'''




       snake_case    :      Optional[int]      					=    False
       super().__init__(UpperCamelCase__						,	UpperCamelCase__						)







   def __call__(	self						,	UpperCamelCase__=None						,	UpperCamelCase__ = None						,	UpperCamelCase__ = True						,	UpperCamelCase__ = False						,	UpperCamelCase__ = None						,	UpperCamelCase__ = None						,	UpperCamelCase__ = 2048						,	UpperCamelCase__ = 0						,	UpperCamelCase__ = None						,	UpperCamelCase__ = None						,	UpperCamelCase__ = False						,	UpperCamelCase__ = False						,	UpperCamelCase__ = False						,	UpperCamelCase__ = False						,	UpperCamelCase__ = False						,	UpperCamelCase__ = True						,	UpperCamelCase__ = None						,	**UpperCamelCase__						,	)	->		Union[str, Any]:

       '''simple docstring'''




       if images is None and text is None:
           raise ValueError("You have to specify either images or text."						)

       # Get only text
       if images is None and not self.image_processor.is_vqa:
           snake_case    :      Dict      					=    self.tokenizer
           snake_case    :      Any      					=    self.tokenizer(
               text=UpperCamelCase__						,	add_special_tokens=UpperCamelCase__						,	padding=UpperCamelCase__						,	truncation=UpperCamelCase__						,	max_length=UpperCamelCase__						,	stride=UpperCamelCase__						,	pad_to_multiple_of=UpperCamelCase__						,	return_attention_mask=UpperCamelCase__						,	return_overflowing_tokens=UpperCamelCase__						,	return_special_tokens_mask=UpperCamelCase__						,	return_offsets_mapping=UpperCamelCase__						,	return_token_type_ids=UpperCamelCase__						,	return_length=UpperCamelCase__						,	verbose=UpperCamelCase__						,	return_tensors=UpperCamelCase__						,	**UpperCamelCase__						,	)
           return text_encoding

       if not self.image_processor.is_vqa:
           # add pixel_values
           snake_case    :      List[Any]      					=    self.image_processor(
               UpperCamelCase__						,	return_tensors=UpperCamelCase__						,	max_patches=UpperCamelCase__						,	**UpperCamelCase__						)
       else:
           # add pixel_values and bbox
           snake_case    :      List[str]      					=    self.image_processor(
               UpperCamelCase__						,	return_tensors=UpperCamelCase__						,	max_patches=UpperCamelCase__						,	header_text=UpperCamelCase__						,	**UpperCamelCase__						)

       if text is not None and not self.image_processor.is_vqa:
           snake_case    :      Optional[Any]      					=    self.tokenizer(
               text=UpperCamelCase__						,	add_special_tokens=UpperCamelCase__						,	padding=UpperCamelCase__						,	truncation=UpperCamelCase__						,	max_length=UpperCamelCase__						,	stride=UpperCamelCase__						,	pad_to_multiple_of=UpperCamelCase__						,	return_attention_mask=UpperCamelCase__						,	return_overflowing_tokens=UpperCamelCase__						,	return_special_tokens_mask=UpperCamelCase__						,	return_offsets_mapping=UpperCamelCase__						,	return_token_type_ids=UpperCamelCase__						,	return_length=UpperCamelCase__						,	verbose=UpperCamelCase__						,	return_tensors=UpperCamelCase__						,	**UpperCamelCase__						,	)

           if "attention_mask" in text_encoding:
               snake_case    :      Dict      					=    text_encoding.pop("attention_mask"						)
           if "input_ids" in text_encoding:
               snake_case    :      int      					=    text_encoding.pop("input_ids"						)
       else:
           snake_case    :      Optional[int]      					=    None

       if text_encoding is not None:
           encoding_image_processor.update(UpperCamelCase__						)

       return encoding_image_processor







   def  lowerCamelCase		(	self						,	*UpperCamelCase__						,	**UpperCamelCase__						)	->		Optional[Any]:

       '''simple docstring'''




       return self.tokenizer.batch_decode(*UpperCamelCase__						,	**UpperCamelCase__						)







   def  lowerCamelCase		(	self						,	*UpperCamelCase__						,	**UpperCamelCase__						)	->		List[Any]:

       '''simple docstring'''




       return self.tokenizer.decode(*UpperCamelCase__						,	**UpperCamelCase__						)







   @property
   def  lowerCamelCase		(	self						)	->		Any:

       '''simple docstring'''




       snake_case    :      Union[str, Any]      					=    self.tokenizer.model_input_names
       snake_case    :      List[Any]      					=    self.image_processor.model_input_names
       return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names						)						)













'''simple docstring'''







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


_A	:			Union[str, Any]         =False

class _lowercase	(				unittest.TestCase		):
							pass



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




							def 		lowerCamelCase_				(							self:				Optional[int]     ):
														lowerCamelCase__					:	Tuple         =       VersatileDiffusionImageVariationPipeline.from_pretrained("""shi-labs/versatile-diffusion"""     )
														pipe.to(UpperCamelCase__     )
														pipe.set_progress_bar_config(disable=UpperCamelCase__     )

														lowerCamelCase__					:	Optional[int]         =       load_image(
														    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"""     )
														lowerCamelCase__					:	List[Any]         =       torch.manual_seed(0     )
														lowerCamelCase__					:	List[Any]         =       pipe(
														    image=UpperCamelCase__			,							generator=UpperCamelCase__			,							guidance_scale=7.5			,							num_inference_steps=50			,							output_type="""numpy"""			,							).images

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

														assert image.shape == (1, 512, 512, 3)
														lowerCamelCase__					:	Tuple         =       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

