
import gc
import random
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DiffusionPipeline,
    EulerDiscreteScheduler,
    StableDiffusionXLImgaImgPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import floats_tensor, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()


class        lowercase      (	_SCREAMING_SNAKE_CASE						,     _SCREAMING_SNAKE_CASE						,     unittest.TestCase     ):
  __lowercase : str			  =       StableDiffusionXLImgaImgPipeline
  __lowercase : List[str]			  =       TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"height", "width"}
  __lowercase : Any			  =       PipelineTesterMixin.required_optional_params - {"latents"}
  __lowercase : str			  =       TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
  __lowercase : Optional[Any]			  =       IMAGE_TO_IMAGE_IMAGE_PARAMS
  __lowercase : Tuple			  =       IMAGE_TO_IMAGE_IMAGE_PARAMS



  def __UpperCamelCase				(				self )						->      List[str]:
    """simple docstring"""
    torch.manual_seed(0 )
    UpperCamelCase						=  UNetaDConditionModel(
        block_out_channels=(32, 64)       ,   layers_per_block=2       ,   sample_size=32       ,   in_channels=4       ,   out_channels=4       ,   down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')       ,   up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')       ,   attention_head_dim=(2, 4)       ,   use_linear_projection=A_       ,   addition_embed_type='text_time'       ,   addition_time_embed_dim=8       ,   transformer_layers_per_block=(1, 2)       ,   projection_class_embeddings_input_dim=80       ,   cross_attention_dim=64       ,   )
    UpperCamelCase						=  EulerDiscreteScheduler(
        beta_start=0.0_0085       ,   beta_end=0.012       ,   steps_offset=1       ,   beta_schedule='scaled_linear'       ,   timestep_spacing='leading'       ,   )
    torch.manual_seed(0 )
    UpperCamelCase						=  AutoencoderKL(
        block_out_channels=[32, 64]       ,   in_channels=3       ,   out_channels=3       ,   down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']       ,   up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']       ,   latent_channels=4       ,   sample_size=128       ,   )
    torch.manual_seed(0 )
    UpperCamelCase						=  CLIPTextConfig(
        bos_token_id=0       ,   eos_token_id=2       ,   hidden_size=32       ,   intermediate_size=37       ,   layer_norm_eps=1e-05       ,   num_attention_heads=4       ,   num_hidden_layers=5       ,   pad_token_id=1       ,   vocab_size=1_000       ,   hidden_act='gelu'       ,   projection_dim=32       ,   )
    UpperCamelCase						=  CLIPTextModel(A_ )
    UpperCamelCase						=  CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'       ,   local_files_only=A_ )

    UpperCamelCase						=  CLIPTextModelWithProjection(A_ )
    UpperCamelCase						=  CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'       ,   local_files_only=A_ )

    UpperCamelCase						=  {
        'unet': unet,
        'scheduler': scheduler,
        'vae': vae,
        'text_encoder': text_encoder,
        'tokenizer': tokenizer,
        'text_encoder_2': text_encoder_a,
        'tokenizer_2': tokenizer_a,
        # "safety_checker": None,
        # "feature_extractor": None,
    }
    return components



  def __UpperCamelCase				(				self       ,   A_       ,   A_=0 )						->      Optional[Any]:
    """simple docstring"""
    UpperCamelCase						=  floats_tensor((1, 3, 32, 32)       ,   rng=random.Random(A_ ) ).to(A_ )
    UpperCamelCase						=  image / 2 + 0.5
    if str(A_ ).startswith('mps' ):
      UpperCamelCase						=  torch.manual_seed(A_ )
    else:
      UpperCamelCase						=  torch.Generator(device=A_ ).manual_seed(A_ )
    UpperCamelCase						=  {
        'prompt': 'A painting of a squirrel eating a burger',
        'image': image,
        'generator': generator,
        'num_inference_steps': 2,
        'guidance_scale': 5.0,
        'output_type': 'numpy',
        'strength': 0.75,
    }
    return inputs



  def __UpperCamelCase				(				self )						->      Optional[Any]:
    """simple docstring"""
    UpperCamelCase						=  'cpu'  # ensure determinism for the device-dependent torch.Generator
    UpperCamelCase						=  self.get_dummy_components()
    UpperCamelCase						=  StableDiffusionXLImgaImgPipeline(**A_ )
    UpperCamelCase						=  sd_pipe.to(A_ )
    sd_pipe.set_progress_bar_config(disable=A_ )

    UpperCamelCase						=  self.get_dummy_inputs(A_ )
    UpperCamelCase						=  sd_pipe(**A_ ).images
    UpperCamelCase						=  image[0, -3:, -3:, -1]

    assert image.shape == (1, 32, 32, 3)

    UpperCamelCase						=  np.array([0.4656, 0.4840, 0.4439, 0.6698, 0.5574, 0.4524, 0.5799, 0.5943, 0.5165] )

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



  def __UpperCamelCase				(				self )						->      Optional[Any]:
    """simple docstring"""
    super().test_attention_slicing_forward_pass(expected_max_diff=3e-3 )



  def __UpperCamelCase				(				self )						->      Optional[Any]:
    """simple docstring"""
    super().test_inference_batch_single_identical(expected_max_diff=3e-3 )



  def __UpperCamelCase				(				self )						->      Optional[Any]:
    """simple docstring"""
    pass



  def __UpperCamelCase				(				self )						->      str:
    """simple docstring"""
    UpperCamelCase						=  self.get_dummy_components()
    UpperCamelCase						=  StableDiffusionXLImgaImgPipeline(**A_ )
    UpperCamelCase						=  sd_pipe.to(A_ )
    UpperCamelCase						=  sd_pipe.to(A_ )
    sd_pipe.set_progress_bar_config(disable=A_ )

    # forward without prompt embeds
    UpperCamelCase						=  self.get_dummy_inputs(A_ )
    UpperCamelCase						=  3 * ['this is a negative prompt']
    UpperCamelCase						=  negative_prompt
    UpperCamelCase						=  3 * [inputs['prompt']]

    UpperCamelCase						=  sd_pipe(**A_ )
    UpperCamelCase						=  output.images[0, -3:, -3:, -1]

    # forward with prompt embeds
    UpperCamelCase						=  self.get_dummy_inputs(A_ )
    UpperCamelCase						=  3 * ['this is a negative prompt']
    UpperCamelCase						=  3 * [inputs.pop('prompt' )]

    (
        (
        UpperCamelCase						
    )			,						(
        UpperCamelCase						
    )			,						(
        UpperCamelCase						
    )			,						(
        UpperCamelCase						
    )			,												
    )						=  sd_pipe.encode_prompt(A_       ,   negative_prompt=A_ )

    UpperCamelCase						=  sd_pipe(
        **A_       ,   prompt_embeds=A_       ,   negative_prompt_embeds=A_       ,   pooled_prompt_embeds=A_       ,   negative_pooled_prompt_embeds=A_       ,   )
    UpperCamelCase						=  output.images[0, -3:, -3:, -1]

    # make sure that it's equal
    assert np.abs(image_slice_a.flatten() - image_slice_a.flatten() ).max() < 1e-4






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



  def __UpperCamelCase				(				self )						->      Tuple:
    """simple docstring"""
    super().tearDown()
    gc.collect()
    torch.cuda.empty_cache()



  def __UpperCamelCase				(				self       ,   A_       ,   A_="cpu"       ,   A_=torch.floataa       ,   A_=0 )						->      Any:
    """simple docstring"""
    UpperCamelCase						=  torch.Generator(device=A_ ).manual_seed(A_ )
    UpperCamelCase						=  np.random.RandomState(A_ ).standard_normal((1, 4, 64, 64) )
    UpperCamelCase						=  torch.from_numpy(A_ ).to(device=A_       ,   dtype=A_ )
    UpperCamelCase						=  {
        'prompt': 'a photograph of an astronaut riding a horse',
        'latents': latents,
        'generator': generator,
        'num_inference_steps': 3,
        'guidance_scale': 7.5,
        'output_type': 'numpy',
    }
    return inputs



  def __UpperCamelCase				(				self )						->      List[Any]:
    """simple docstring"""
    UpperCamelCase						=  DiffusionPipeline.from_pretrained('stabilityai/stable-diffusion-2-base' )
    pipe.to(A_ )
    pipe.set_progress_bar_config(disable=A_ )

    UpperCamelCase						=  self.get_inputs(A_ )
    UpperCamelCase						=  pipe(**A_ ).images
    UpperCamelCase						=  image[0, -3:, -3:, -1].flatten()

    assert image.shape == (1, 512, 512, 3)
    UpperCamelCase						=  np.array([0.4_9493, 0.4_7896, 0.4_0798, 0.5_4214, 0.5_3212, 0.4_8202, 0.4_7656, 0.4_6329, 0.4_8506] )
    assert np.abs(image_slice - expected_slice ).max() < 7e-3


import unittest

import numpy as np

from transformers.testing_utils import is_flaky, require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
					import torch

if is_vision_available():
					from PIL import Image

					from transformers import DonutImageProcessor


class        lowercase      (	unittest.TestCase     ):



		def __init__(				self       ,   A_       ,   A_=7       ,   A_=3       ,   A_=18       ,   A_=30       ,   A_=400       ,   A_=True       ,   A_=None       ,   A_=True       ,   A_=False       ,   A_=True       ,   A_=True       ,   A_=[0.5, 0.5, 0.5]       ,   A_=[0.5, 0.5, 0.5]       ,   )						->      Tuple:
				"""simple docstring"""
				UpperCamelCase						=  parent
				UpperCamelCase						=  batch_size
				UpperCamelCase						=  num_channels
				UpperCamelCase						=  image_size
				UpperCamelCase						=  min_resolution
				UpperCamelCase						=  max_resolution
				UpperCamelCase						=  do_resize
				UpperCamelCase						=  size if size is not None else {'height': 18, 'width': 20}
				UpperCamelCase						=  do_thumbnail
				UpperCamelCase						=  do_align_axis
				UpperCamelCase						=  do_pad
				UpperCamelCase						=  do_normalize
				UpperCamelCase						=  image_mean
				UpperCamelCase						=  image_std



		def __UpperCamelCase				(				self )						->      Tuple:
				"""simple docstring"""
				return {
				    "do_resize": self.do_resize,
				    "size": self.size,
				    "do_thumbnail": self.do_thumbnail,
				    "do_align_long_axis": self.do_align_axis,
				    "do_pad": self.do_pad,
				    "do_normalize": self.do_normalize,
				    "image_mean": self.image_mean,
				    "image_std": self.image_std,
				}






@require_torch
@require_vision
class        lowercase      (	_SCREAMING_SNAKE_CASE						,     unittest.TestCase     ):
		__lowercase : Optional[int]			  =       DonutImageProcessor if is_vision_available() else None



		def __UpperCamelCase				(				self )						->      List[Any]:
				"""simple docstring"""
				UpperCamelCase						=  DonutImageProcessingTester(self )



		@property
		def __UpperCamelCase				(				self )						->      Any:
				"""simple docstring"""
				return self.image_processor_tester.prepare_image_processor_dict()



		def __UpperCamelCase				(				self )						->      int:
				"""simple docstring"""
				UpperCamelCase						=  self.image_processing_class(**self.image_processor_dict )
				self.assertTrue(hasattr(A_       ,   'do_resize' ) )
				self.assertTrue(hasattr(A_       ,   'size' ) )
				self.assertTrue(hasattr(A_       ,   'do_thumbnail' ) )
				self.assertTrue(hasattr(A_       ,   'do_align_long_axis' ) )
				self.assertTrue(hasattr(A_       ,   'do_pad' ) )
				self.assertTrue(hasattr(A_       ,   'do_normalize' ) )
				self.assertTrue(hasattr(A_       ,   'image_mean' ) )
				self.assertTrue(hasattr(A_       ,   'image_std' ) )



		def __UpperCamelCase				(				self )						->      str:
				"""simple docstring"""
				UpperCamelCase						=  self.image_processing_class.from_dict(self.image_processor_dict )
				self.assertEqual(image_processor.size       ,   {'height': 18, 'width': 20} )

				UpperCamelCase						=  self.image_processing_class.from_dict(self.image_processor_dict       ,   size=42 )
				self.assertEqual(image_processor.size       ,   {'height': 42, 'width': 42} )

				# Previous config had dimensions in (width, height) order
				UpperCamelCase						=  self.image_processing_class.from_dict(self.image_processor_dict       ,   size=(42, 84) )
				self.assertEqual(image_processor.size       ,   {'height': 84, 'width': 42} )



		def __UpperCamelCase				(				self )						->      Optional[Any]:
				"""simple docstring"""
				pass



		@is_flaky()
		def __UpperCamelCase				(				self )						->      int:
				"""simple docstring"""
				# Initialize image_processing
				UpperCamelCase						=  self.image_processing_class(**self.image_processor_dict )
				# create random PIL images
				UpperCamelCase						=  prepare_image_inputs(self.image_processor_tester       ,   equal_resolution=A_ )
				for image in image_inputs:
						self.assertIsInstance(A_       ,   Image.Image )

				# Test not batched input
				UpperCamelCase						=  image_processing(image_inputs[0]       ,   return_tensors='pt' ).pixel_values
				self.assertEqual(
				    encoded_images.shape       ,   (
				        1,
				        self.image_processor_tester.num_channels,
				        self.image_processor_tester.size['height'],
				        self.image_processor_tester.size['width'],
				    )       ,   )

				# Test batched
				UpperCamelCase						=  image_processing(A_       ,   return_tensors='pt' ).pixel_values
				self.assertEqual(
				    encoded_images.shape       ,   (
				        self.image_processor_tester.batch_size,
				        self.image_processor_tester.num_channels,
				        self.image_processor_tester.size['height'],
				        self.image_processor_tester.size['width'],
				    )       ,   )



		@is_flaky()
		def __UpperCamelCase				(				self )						->      Any:
				"""simple docstring"""
				# Initialize image_processing
				UpperCamelCase						=  self.image_processing_class(**self.image_processor_dict )
				# create random numpy tensors
				UpperCamelCase						=  prepare_image_inputs(self.image_processor_tester       ,   equal_resolution=A_       ,   numpify=A_ )
				for image in image_inputs:
						self.assertIsInstance(A_       ,   np.ndarray )

				# Test not batched input
				UpperCamelCase						=  image_processing(image_inputs[0]       ,   return_tensors='pt' ).pixel_values
				self.assertEqual(
				    encoded_images.shape       ,   (
				        1,
				        self.image_processor_tester.num_channels,
				        self.image_processor_tester.size['height'],
				        self.image_processor_tester.size['width'],
				    )       ,   )

				# Test batched
				UpperCamelCase						=  image_processing(A_       ,   return_tensors='pt' ).pixel_values
				self.assertEqual(
				    encoded_images.shape       ,   (
				        self.image_processor_tester.batch_size,
				        self.image_processor_tester.num_channels,
				        self.image_processor_tester.size['height'],
				        self.image_processor_tester.size['width'],
				    )       ,   )



		@is_flaky()
		def __UpperCamelCase				(				self )						->      Union[str, Any]:
				"""simple docstring"""
				# Initialize image_processing
				UpperCamelCase						=  self.image_processing_class(**self.image_processor_dict )
				# create random PyTorch tensors
				UpperCamelCase						=  prepare_image_inputs(self.image_processor_tester       ,   equal_resolution=A_       ,   torchify=A_ )
				for image in image_inputs:
						self.assertIsInstance(A_       ,   torch.Tensor )

				# Test not batched input
				UpperCamelCase						=  image_processing(image_inputs[0]       ,   return_tensors='pt' ).pixel_values
				self.assertEqual(
				    encoded_images.shape       ,   (
				        1,
				        self.image_processor_tester.num_channels,
				        self.image_processor_tester.size['height'],
				        self.image_processor_tester.size['width'],
				    )       ,   )

				# Test batched
				UpperCamelCase						=  image_processing(A_       ,   return_tensors='pt' ).pixel_values
				self.assertEqual(
				    encoded_images.shape       ,   (
				        self.image_processor_tester.batch_size,
				        self.image_processor_tester.num_channels,
				        self.image_processor_tester.size['height'],
				        self.image_processor_tester.size['width'],
				    )       ,   )
