


'''simple docstring'''



from typing import List, Optional, Union

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





class 							__UpperCamelCase				(		lowerCAmelCase_					):
	A_				=    ["image_processor", "tokenizer"]
	A_				=    "BlipImageProcessor"
	A_				=    "AutoTokenizer"




	def __init__(   self						,				__a						,				__a							):

						'''simple docstring'''





						__a			:       Tuple					    =  False
						super().__init__(__a						,				__a							)
						__a			:       Any					    =  self.image_processor




	def __call__(   self						,				__a = None						,				__a = None						,				__a = True						,				__a = False						,				__a = None						,				__a = None						,				__a = 0						,				__a = None						,				__a = None						,				__a = False						,				__a = False						,				__a = False						,				__a = False						,				__a = False						,				__a = True						,				__a = None						,				**__a						,				):

						'''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:
											__a			:       Any					    =  self.tokenizer
											__a			:       str					    =  self.tokenizer(
											    text=__a						,				add_special_tokens=__a						,				padding=__a						,				truncation=__a						,				max_length=__a						,				stride=__a						,				pad_to_multiple_of=__a						,				return_attention_mask=__a						,				return_overflowing_tokens=__a						,				return_special_tokens_mask=__a						,				return_offsets_mapping=__a						,				return_token_type_ids=__a						,				return_length=__a						,				verbose=__a						,				return_tensors=__a						,				**__a						,				)
											return text_encoding

						# add pixel_values
						__a			:       Tuple					    =  self.image_processor(__a						,				return_tensors=__a							)

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

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

						return encoding_image_processor




	def       __UpperCAmelCase							(   self						,				*__a						,				**__a							):

						'''simple docstring'''





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




	def       __UpperCAmelCase							(   self						,				*__a						,				**__a							):

						'''simple docstring'''





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





	@property
	# Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a			:       int					    =  self.tokenizer.model_input_names
						__a			:       Tuple					    =  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

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 							__UpperCamelCase				(		unittest.TestCase					):




	@property
	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





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




	@property
	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a			:       Dict					    =  ort.SessionOptions()
						__a			:       Dict					    =  False
						return options





	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a			:       Tuple					    =  load_image(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
						    '/in_paint/overture-creations-5sI6fQgYIuo.png'							)
						__a			:       int					    =  load_image(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
						    '/in_paint/overture-creations-5sI6fQgYIuo_mask.png'							)
						__a			:       Dict					    =  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
						__a			:       str					    =  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							)

						__a			:       Tuple					    =  'A red cat sitting on a park bench'

						__a			:       int					    =  np.random.RandomState(0							)
						__a			:       Tuple					    =  pipe(
						    prompt=__a						,				image=__a						,				mask_image=__a						,				strength=0.75						,				guidance_scale=7.5						,				num_inference_steps=15						,				generator=__a						,				output_type='np'						,				)

						__a			:       Tuple					    =  output.images[0]

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



