import warnings
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 lowerCAmelCase_		(				__lowerCamelCase ):

 '''simple docstring'''

 __snake_case				=						["image_processor", "tokenizer"]
 __snake_case				=						"FlavaImageProcessor"
 __snake_case				=						("BertTokenizer", "BertTokenizerFast")



 def __init__(			self	,   _UpperCAmelCase=None	,   _UpperCAmelCase=None	,   **_UpperCAmelCase      ):
       snake_case_ =   None
       if "feature_extractor" in kwargs:
             warnings.warn(
                 '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
                 ''' instead.'''	,   UpperCAmelCase_	,   )
             snake_case_ =   kwargs.pop('''feature_extractor'''      )

       snake_case_ =   image_processor if image_processor is not None else feature_extractor
       if image_processor is None:
             raise ValueError('''You need to specify an `image_processor`.'''      )
       if tokenizer is None:
             raise ValueError('''You need to specify a `tokenizer`.'''      )

       super().__init__(UpperCAmelCase_	,   UpperCAmelCase_      )
       snake_case_ =   self.image_processor



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

       if text is None and images is None:
             raise ValueError('''You have to specify either text or images. Both cannot be none.'''      )

       if text is not None:
             snake_case_ =   self.tokenizer(
                 text=UpperCAmelCase_	,   add_special_tokens=UpperCAmelCase_	,   padding=UpperCAmelCase_	,   truncation=UpperCAmelCase_	,   max_length=UpperCAmelCase_	,   stride=UpperCAmelCase_	,   pad_to_multiple_of=UpperCAmelCase_	,   return_token_type_ids=UpperCAmelCase_	,   return_attention_mask=UpperCAmelCase_	,   return_overflowing_tokens=UpperCAmelCase_	,   return_special_tokens_mask=UpperCAmelCase_	,   return_offsets_mapping=UpperCAmelCase_	,   return_length=UpperCAmelCase_	,   verbose=UpperCAmelCase_	,   return_tensors=UpperCAmelCase_	,   **UpperCAmelCase_	,   )
       if images is not None:
             snake_case_ =   self.image_processor(
                 UpperCAmelCase_	,   return_image_mask=UpperCAmelCase_	,   return_codebook_pixels=UpperCAmelCase_	,   return_tensors=UpperCAmelCase_	,   **UpperCAmelCase_	,   )

       if text is not None and images is not None:
             encoding.update(UpperCAmelCase_      )
             return encoding
       elif text is not None:
             return encoding
       else:
             return BatchEncoding(data=dict(**UpperCAmelCase_      )	,   tensor_type=UpperCAmelCase_      )



 def 		UpperCamelCase__  (			self	,   *_UpperCAmelCase	,   **_UpperCAmelCase      ):
       return self.tokenizer.batch_decode(*UpperCAmelCase_	,   **UpperCAmelCase_      )



 def 		UpperCamelCase__  (			self	,   *_UpperCAmelCase	,   **_UpperCAmelCase      ):
       return self.tokenizer.decode(*UpperCAmelCase_	,   **UpperCAmelCase_      )



 @property
 def 		UpperCamelCase__  (			self      ):
       snake_case_ =   self.tokenizer.model_input_names
       snake_case_ =   self.image_processor.model_input_names
       return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names      )      )



 @property
 def 		UpperCamelCase__  (			self      ):
       warnings.warn(
           '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''	,   UpperCAmelCase_	,   )
       return self.image_processor_class





 @property
 def 		UpperCamelCase__  (			self      ):
       warnings.warn(
           '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''	,   UpperCAmelCase_	,   )
       return self.image_processor



import unittest

import numpy as np

from diffusers import LMSDiscreteScheduler, OnnxStableDiffusionInpaintPipeline
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)

from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin


if is_onnx_available():
   import onnxruntime as ort







class lowerCAmelCase_		(				lowerCamelCase__      ,		unittest.TestCase ):

 '''simple docstring'''

 pass







@nightly
@require_onnxruntime
@require_torch_gpu
class lowerCAmelCase_		(				unittest.TestCase ):

 '''simple docstring'''

 @property
 def 		UpperCamelCase__  (			self      ):
       return (
           "CUDAExecutionProvider",
           {
               "gpu_mem_limit": "15000000000",  # 15GB
               "arena_extend_strategy": "kSameAsRequested",
           },
       )



 @property
 def 		UpperCamelCase__  (			self      ):
       snake_case_ =   ort.SessionOptions()
       snake_case_ =   False
       return options



 def 		UpperCamelCase__  (			self      ):
       snake_case_ =   load_image(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
           '''/in_paint/overture-creations-5sI6fQgYIuo.png'''      )
       snake_case_ =   load_image(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
           '''/in_paint/overture-creations-5sI6fQgYIuo_mask.png'''      )
       snake_case_ =   OnnxStableDiffusionInpaintPipeline.from_pretrained(
           '''runwayml/stable-diffusion-inpainting'''	,   revision='''onnx'''	,   safety_checker=_UpperCAmelCase	,   feature_extractor=_UpperCAmelCase	,   provider=self.gpu_provider	,   sess_options=self.gpu_options	,   )
       pipe.set_progress_bar_config(disable=_UpperCAmelCase      )

       snake_case_ =   '''A red cat sitting on a park bench'''

       snake_case_ =   np.random.RandomState(0      )
       snake_case_ =   pipe(
           prompt=_UpperCAmelCase	,   image=_UpperCAmelCase	,   mask_image=_UpperCAmelCase	,   guidance_scale=7.5	,   num_inference_steps=10	,   generator=_UpperCAmelCase	,   output_type='''np'''	,   )
       snake_case_ =   output.images
       snake_case_ =   images[0, 2_55:2_58, 2_55:2_58, -1]

       assert images.shape == (1, 5_12, 5_12, 3)
       snake_case_ =   np.array([0.2_514, 0.3_007, 0.3_517, 0.1_790, 0.2_382, 0.3_167, 0.1_944, 0.2_273, 0.2_464]      )

       assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1E-3





 def 		UpperCamelCase__  (			self      ):
       snake_case_ =   load_image(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
           '''/in_paint/overture-creations-5sI6fQgYIuo.png'''      )
       snake_case_ =   load_image(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
           '''/in_paint/overture-creations-5sI6fQgYIuo_mask.png'''      )
       snake_case_ =   LMSDiscreteScheduler.from_pretrained(
           '''runwayml/stable-diffusion-inpainting'''	,   subfolder='''scheduler'''	,   revision='''onnx'''      )
       snake_case_ =   OnnxStableDiffusionInpaintPipeline.from_pretrained(
           '''runwayml/stable-diffusion-inpainting'''	,   revision='''onnx'''	,   scheduler=_UpperCAmelCase	,   safety_checker=_UpperCAmelCase	,   feature_extractor=_UpperCAmelCase	,   provider=self.gpu_provider	,   sess_options=self.gpu_options	,   )
       pipe.set_progress_bar_config(disable=_UpperCAmelCase      )

       snake_case_ =   '''A red cat sitting on a park bench'''

       snake_case_ =   np.random.RandomState(0      )
       snake_case_ =   pipe(
           prompt=_UpperCAmelCase	,   image=_UpperCAmelCase	,   mask_image=_UpperCAmelCase	,   guidance_scale=7.5	,   num_inference_steps=20	,   generator=_UpperCAmelCase	,   output_type='''np'''	,   )
       snake_case_ =   output.images
       snake_case_ =   images[0, 2_55:2_58, 2_55:2_58, -1]

       assert images.shape == (1, 5_12, 5_12, 3)
       snake_case_ =   np.array([0.0_086, 0.0_077, 0.0_083, 0.0_093, 0.0_107, 0.0_139, 0.0_094, 0.0_097, 0.0_125]      )

       assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1E-3