'''simple docstring'''







import tempfile

import numpy as np
import torch
from transformers import AutoTokenizer, TaEncoderModel

from diffusers import DDPMScheduler, UNetaDConditionModel
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.pipelines.deepfloyd_if import IFWatermarker
from diffusers.utils.testing_utils import torch_device

from ..test_pipelines_common import to_np




class _SCREAMING_SNAKE_CASE:




						def      __lowerCamelCase       (    self :       Union[str, Any]      )     ->				Optional[Any]:
													torch.manual_seed(0      )
													SCREAMING_SNAKE_CASE__     :str						    = TaEncoderModel.from_pretrained('hf-internal-testing/tiny-random-t5'      )

													torch.manual_seed(0      )
													SCREAMING_SNAKE_CASE__     :Dict						    = AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-t5'      )

													torch.manual_seed(0      )
													SCREAMING_SNAKE_CASE__     :List[Any]						    = UNetaDConditionModel(
													    sample_size=32							,							layers_per_block=1							,							block_out_channels=[32, 64]							,							down_block_types=[
													        'ResnetDownsampleBlock2D',
													        'SimpleCrossAttnDownBlock2D',
													    ]							,							mid_block_type='UNetMidBlock2DSimpleCrossAttn'							,							up_block_types=['SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D']							,							in_channels=3							,							out_channels=6							,							cross_attention_dim=32							,							encoder_hid_dim=32							,							attention_head_dim=8							,							addition_embed_type='text'							,							addition_embed_type_num_heads=2							,							cross_attention_norm='group_norm'							,							resnet_time_scale_shift='scale_shift'							,							act_fn='gelu'							,							)
													unet.set_attn_processor(AttnAddedKVProcessor()      )  # For reproducibility tests

													torch.manual_seed(0      )
													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = DDPMScheduler(
													    num_train_timesteps=10_00							,							beta_schedule='squaredcos_cap_v2'							,							beta_start=0.0001							,							beta_end=0.02							,							thresholding=UpperCamelCase_							,							dynamic_thresholding_ratio=0.95							,							sample_max_value=1.0							,							prediction_type='epsilon'							,							variance_type='learned_range'							,							)

													torch.manual_seed(0      )
													SCREAMING_SNAKE_CASE__     :Any						    = IFWatermarker()

													return {
													    "text_encoder": text_encoder,
													    "tokenizer": tokenizer,
													    "unet": unet,
													    "scheduler": scheduler,
													    "watermarker": watermarker,
													    "safety_checker": None,
													    "feature_extractor": None,
													}




						def      __lowerCamelCase       (    self :       Tuple      )     ->				Optional[Any]:
													torch.manual_seed(0      )
													SCREAMING_SNAKE_CASE__     :List[Any]						    = TaEncoderModel.from_pretrained('hf-internal-testing/tiny-random-t5'      )

													torch.manual_seed(0      )
													SCREAMING_SNAKE_CASE__     :int						    = AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-t5'      )

													torch.manual_seed(0      )
													SCREAMING_SNAKE_CASE__     :Any						    = UNetaDConditionModel(
													    sample_size=32							,							layers_per_block=[1, 2]							,							block_out_channels=[32, 64]							,							down_block_types=[
													        'ResnetDownsampleBlock2D',
													        'SimpleCrossAttnDownBlock2D',
													    ]							,							mid_block_type='UNetMidBlock2DSimpleCrossAttn'							,							up_block_types=['SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D']							,							in_channels=6							,							out_channels=6							,							cross_attention_dim=32							,							encoder_hid_dim=32							,							attention_head_dim=8							,							addition_embed_type='text'							,							addition_embed_type_num_heads=2							,							cross_attention_norm='group_norm'							,							resnet_time_scale_shift='scale_shift'							,							act_fn='gelu'							,							class_embed_type='timestep'							,							mid_block_scale_factor=1.414							,							time_embedding_act_fn='gelu'							,							time_embedding_dim=32							,							)
													unet.set_attn_processor(AttnAddedKVProcessor()      )  # For reproducibility tests

													torch.manual_seed(0      )
													SCREAMING_SNAKE_CASE__     :Dict						    = DDPMScheduler(
													    num_train_timesteps=10_00							,							beta_schedule='squaredcos_cap_v2'							,							beta_start=0.0001							,							beta_end=0.02							,							thresholding=UpperCamelCase_							,							dynamic_thresholding_ratio=0.95							,							sample_max_value=1.0							,							prediction_type='epsilon'							,							variance_type='learned_range'							,							)

													torch.manual_seed(0      )
													SCREAMING_SNAKE_CASE__     :Tuple						    = DDPMScheduler(
													    num_train_timesteps=10_00							,							beta_schedule='squaredcos_cap_v2'							,							beta_start=0.0001							,							beta_end=0.02							,							)

													torch.manual_seed(0      )
													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = IFWatermarker()

													return {
													    "text_encoder": text_encoder,
													    "tokenizer": tokenizer,
													    "unet": unet,
													    "scheduler": scheduler,
													    "image_noising_scheduler": image_noising_scheduler,
													    "watermarker": watermarker,
													    "safety_checker": None,
													    "feature_extractor": None,
													}




						def      __lowerCamelCase       (    self :       str      )     ->				int:
													SCREAMING_SNAKE_CASE__     :List[Any]						    = self.get_dummy_components()
													SCREAMING_SNAKE_CASE__     :Any						    = self.pipeline_class(**UpperCamelCase_      )
													pipe.to(UpperCamelCase_      )
													pipe.set_progress_bar_config(disable=UpperCamelCase_      )

													SCREAMING_SNAKE_CASE__     :Any						    = self.get_dummy_inputs(UpperCamelCase_      )

													SCREAMING_SNAKE_CASE__     :str						    = inputs['prompt']
													SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = inputs['generator']
													SCREAMING_SNAKE_CASE__     :str						    = inputs['num_inference_steps']
													SCREAMING_SNAKE_CASE__     :str						    = inputs['output_type']

													if "image" in inputs:
																				SCREAMING_SNAKE_CASE__     :Optional[Any]						    = inputs['image']
													else:
																				SCREAMING_SNAKE_CASE__     :Optional[Any]						    = None

													if "mask_image" in inputs:
																				SCREAMING_SNAKE_CASE__     :Optional[int]						    = inputs['mask_image']
													else:
																				SCREAMING_SNAKE_CASE__     :Any						    = None

													if "original_image" in inputs:
																				SCREAMING_SNAKE_CASE__     :Dict						    = inputs['original_image']
													else:
																				SCREAMING_SNAKE_CASE__     :List[Any]						    = None

													SCREAMING_SNAKE_CASE__    ,      SCREAMING_SNAKE_CASE__     :Dict						    = pipe.encode_prompt(UpperCamelCase_      )

													# inputs with prompt converted to embeddings
													SCREAMING_SNAKE_CASE__     :List[Any]						    = {
													    'prompt_embeds': prompt_embeds,
													    'negative_prompt_embeds': negative_prompt_embeds,
													    'generator': generator,
													    'num_inference_steps': num_inference_steps,
													    'output_type': output_type,
													}

													if image is not None:
																				SCREAMING_SNAKE_CASE__     :List[str]						    = image

													if mask_image is not None:
																				SCREAMING_SNAKE_CASE__     :Dict						    = mask_image

													if original_image is not None:
																				SCREAMING_SNAKE_CASE__     :Any						    = original_image

													# set all optional components to None
													for optional_component in pipe._optional_components:
																				setattr(UpperCamelCase_							,							UpperCamelCase_							,							UpperCamelCase_      )

													SCREAMING_SNAKE_CASE__     :str						    = pipe(**UpperCamelCase_      )[0]

													with tempfile.TemporaryDirectory() as tmpdir:
																				pipe.save_pretrained(UpperCamelCase_      )
																				SCREAMING_SNAKE_CASE__     :str						    = self.pipeline_class.from_pretrained(UpperCamelCase_      )
																				pipe_loaded.to(UpperCamelCase_      )
																				pipe_loaded.set_progress_bar_config(disable=UpperCamelCase_      )

													pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor()      )  # For reproducibility tests

													for optional_component in pipe._optional_components:
																				self.assertTrue(
																				    getattr(UpperCamelCase_							,							UpperCamelCase_      ) is None							,							f'''`{optional_component}` did not stay set to None after loading.'''							,							)

													SCREAMING_SNAKE_CASE__     :List[str]						    = self.get_dummy_inputs(UpperCamelCase_      )

													SCREAMING_SNAKE_CASE__     :str						    = inputs['generator']
													SCREAMING_SNAKE_CASE__     :Tuple						    = inputs['num_inference_steps']
													SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = inputs['output_type']

													# inputs with prompt converted to embeddings
													SCREAMING_SNAKE_CASE__     :Optional[int]						    = {
													    'prompt_embeds': prompt_embeds,
													    'negative_prompt_embeds': negative_prompt_embeds,
													    'generator': generator,
													    'num_inference_steps': num_inference_steps,
													    'output_type': output_type,
													}

													if image is not None:
																				SCREAMING_SNAKE_CASE__     :Tuple						    = image

													if mask_image is not None:
																				SCREAMING_SNAKE_CASE__     :Dict						    = mask_image

													if original_image is not None:
																				SCREAMING_SNAKE_CASE__     :Tuple						    = original_image

													SCREAMING_SNAKE_CASE__     :List[str]						    = pipe_loaded(**UpperCamelCase_      )[0]

													SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = np.abs(to_np(UpperCamelCase_      ) - to_np(UpperCamelCase_      )      ).max()
													self.assertLess(UpperCamelCase_							,							1e-4      )




						def      __lowerCamelCase       (    self :       List[Any]      )     ->				Any:
													SCREAMING_SNAKE_CASE__     :str						    = self.get_dummy_components()
													SCREAMING_SNAKE_CASE__     :List[str]						    = self.pipeline_class(**UpperCamelCase_      )
													pipe.to(UpperCamelCase_      )
													pipe.set_progress_bar_config(disable=UpperCamelCase_      )

													SCREAMING_SNAKE_CASE__     :Tuple						    = self.get_dummy_inputs(UpperCamelCase_      )
													SCREAMING_SNAKE_CASE__     :List[str]						    = pipe(**UpperCamelCase_      )[0]

													with tempfile.TemporaryDirectory() as tmpdir:
																				pipe.save_pretrained(UpperCamelCase_      )
																				SCREAMING_SNAKE_CASE__     :Optional[Any]						    = self.pipeline_class.from_pretrained(UpperCamelCase_      )
																				pipe_loaded.to(UpperCamelCase_      )
																				pipe_loaded.set_progress_bar_config(disable=UpperCamelCase_      )

													pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor()      )  # For reproducibility tests

													SCREAMING_SNAKE_CASE__     :Tuple						    = self.get_dummy_inputs(UpperCamelCase_      )
													SCREAMING_SNAKE_CASE__     :str						    = pipe_loaded(**UpperCamelCase_      )[0]

													SCREAMING_SNAKE_CASE__     :Any						    = np.abs(to_np(UpperCamelCase_      ) - to_np(UpperCamelCase_      )      ).max()
													self.assertLess(UpperCamelCase_							,							1e-4      )







'''simple docstring'''







import unittest
from typing import Dict, List, Optional, Union

import numpy as np

from transformers.testing_utils import 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 BridgeTowerImageProcessor




class _SCREAMING_SNAKE_CASE(							unittest.TestCase					):




						def __init__(    self :       Any							,							UpperCamelCase_ :       List[Any]							,							UpperCamelCase_ :       bool = True							,							UpperCamelCase_ :       Dict[str, int] = None							,							UpperCamelCase_ :       int = 32							,							UpperCamelCase_ :       bool = True							,							UpperCamelCase_ :       Union[int, float] = 1 / 2_55							,							UpperCamelCase_ :       bool = True							,							UpperCamelCase_ :       bool = True							,							UpperCamelCase_ :       Optional[Union[float, List[float]]] = [0.4814_5466, 0.457_8275, 0.4082_1073]							,							UpperCamelCase_ :       Optional[Union[float, List[float]]] = [0.2686_2954, 0.2613_0258, 0.2757_7711]							,							UpperCamelCase_ :       bool = True							,							UpperCamelCase_ :       int=7							,							UpperCamelCase_ :       Dict=30							,							UpperCamelCase_ :       Tuple=4_00							,							UpperCamelCase_ :       List[Any]=3							,							)     ->				List[Any]:
													SCREAMING_SNAKE_CASE__     :List[str]						    = parent
													SCREAMING_SNAKE_CASE__     :Tuple						    = do_resize
													SCREAMING_SNAKE_CASE__     :List[Any]						    = size if size is not None else {'shortest_edge': 2_88}
													SCREAMING_SNAKE_CASE__     :str						    = size_divisor
													SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = do_rescale
													SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = rescale_factor
													SCREAMING_SNAKE_CASE__     :str						    = do_normalize
													SCREAMING_SNAKE_CASE__     :int						    = do_center_crop
													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = image_mean
													SCREAMING_SNAKE_CASE__     :str						    = image_std
													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = do_pad
													SCREAMING_SNAKE_CASE__     :Tuple						    = batch_size
													SCREAMING_SNAKE_CASE__     :List[str]						    = num_channels
													SCREAMING_SNAKE_CASE__     :Optional[int]						    = min_resolution
													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = max_resolution




						def      __lowerCamelCase       (    self :       Tuple      )     ->				Optional[int]:
													return {
													    "image_mean": self.image_mean,
													    "image_std": self.image_std,
													    "do_normalize": self.do_normalize,
													    "do_resize": self.do_resize,
													    "size": self.size,
													    "size_divisor": self.size_divisor,
													}




						def      __lowerCamelCase       (    self :       int							,							UpperCamelCase_ :       str							,							UpperCamelCase_ :       str=False      )     ->				Optional[int]:
													if not batched:
																				SCREAMING_SNAKE_CASE__     :Dict						    = self.size['shortest_edge']
																				SCREAMING_SNAKE_CASE__     :List[Any]						    = image_inputs[0]
																				if isinstance(UpperCamelCase_							,							Image.Image      ):
																											SCREAMING_SNAKE_CASE__    ,      SCREAMING_SNAKE_CASE__     :Optional[int]						    = image.size
																				else:
																											SCREAMING_SNAKE_CASE__    ,      SCREAMING_SNAKE_CASE__     :str						    = image.shape[1], image.shape[2]
																				SCREAMING_SNAKE_CASE__     :Optional[int]						    = size / min(UpperCamelCase_							,							UpperCamelCase_      )
																				if h < w:
																											SCREAMING_SNAKE_CASE__    ,      SCREAMING_SNAKE_CASE__     :List[Any]						    = size, scale * w
																				else:
																											SCREAMING_SNAKE_CASE__    ,      SCREAMING_SNAKE_CASE__     :str						    = scale * h, size

																				SCREAMING_SNAKE_CASE__     :Any						    = int((13_33 / 8_00) * size      )
																				if max(UpperCamelCase_							,							UpperCamelCase_      ) > max_size:
																											SCREAMING_SNAKE_CASE__     :Tuple						    = max_size / max(UpperCamelCase_							,							UpperCamelCase_      )
																											SCREAMING_SNAKE_CASE__     :str						    = newh * scale
																											SCREAMING_SNAKE_CASE__     :Any						    = neww * scale

																				SCREAMING_SNAKE_CASE__    ,      SCREAMING_SNAKE_CASE__     :int						    = int(newh + 0.5      ), int(neww + 0.5      )
																				SCREAMING_SNAKE_CASE__    ,      SCREAMING_SNAKE_CASE__     :Optional[Any]						    = (
																				    newh // self.size_divisor * self.size_divisor,
																				    neww // self.size_divisor * self.size_divisor,
																				)

													else:
																				SCREAMING_SNAKE_CASE__     :Any						    = []
																				for image in image_inputs:
																											SCREAMING_SNAKE_CASE__    ,      SCREAMING_SNAKE_CASE__     :str						    = self.get_expected_values([image]      )
																											expected_values.append((expected_height, expected_width)      )
																				SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = max(UpperCamelCase_							,							key=lambda UpperCamelCase_      : item[0]      )[0]
																				SCREAMING_SNAKE_CASE__     :Optional[int]						    = max(UpperCamelCase_							,							key=lambda UpperCamelCase_      : item[1]      )[1]

													return expected_height, expected_width




@require_torch
@require_vision
class _SCREAMING_SNAKE_CASE(							_SCREAMING_SNAKE_CASE						,						unittest.TestCase					):
						A_			:							List[Any]     		= BridgeTowerImageProcessor if is_vision_available() else None




						def      __lowerCamelCase       (    self :       Optional[Any]      )     ->				str:
													SCREAMING_SNAKE_CASE__     :List[str]						    = BridgeTowerImageProcessingTester(self      )




						@property
						def      __lowerCamelCase       (    self :       int      )     ->				Any:
													return self.image_processor_tester.prepare_image_processor_dict()




						def      __lowerCamelCase       (    self :       int      )     ->				Union[str, Any]:
													SCREAMING_SNAKE_CASE__     :Optional[int]						    = self.image_processing_class(**self.image_processor_dict      )
													self.assertTrue(hasattr(UpperCamelCase_							,							'image_mean'      )      )
													self.assertTrue(hasattr(UpperCamelCase_							,							'image_std'      )      )
													self.assertTrue(hasattr(UpperCamelCase_							,							'do_normalize'      )      )
													self.assertTrue(hasattr(UpperCamelCase_							,							'do_resize'      )      )
													self.assertTrue(hasattr(UpperCamelCase_							,							'size'      )      )
													self.assertTrue(hasattr(UpperCamelCase_							,							'size_divisor'      )      )




						def      __lowerCamelCase       (    self :       Union[str, Any]      )     ->				List[str]:
													pass




						def      __lowerCamelCase       (    self :       int      )     ->				Dict:
													# Initialize image processor
													SCREAMING_SNAKE_CASE__     :Optional[int]						    = self.image_processing_class(**self.image_processor_dict      )
													# create random PIL images
													SCREAMING_SNAKE_CASE__     :Any						    = prepare_image_inputs(self.image_processor_tester							,							equal_resolution=UpperCamelCase_      )
													for image in image_inputs:
																				self.assertIsInstance(UpperCamelCase_							,							Image.Image      )

													# Test not batched input
													SCREAMING_SNAKE_CASE__     :str						    = image_processing(image_inputs[0]							,							return_tensors='pt'      ).pixel_values

													SCREAMING_SNAKE_CASE__    ,      SCREAMING_SNAKE_CASE__     :Dict						    = self.image_processor_tester.get_expected_values(UpperCamelCase_      )
													self.assertEqual(
													    encoded_images.shape							,							(1, self.image_processor_tester.num_channels, expected_height, expected_width)							,							)

													# Test batched
													SCREAMING_SNAKE_CASE__     :Any						    = image_processing(UpperCamelCase_							,							return_tensors='pt'      ).pixel_values

													SCREAMING_SNAKE_CASE__    ,      SCREAMING_SNAKE_CASE__     :int						    = self.image_processor_tester.get_expected_values(UpperCamelCase_							,							batched=UpperCamelCase_      )
													self.assertEqual(
													    encoded_images.shape							,							(
													        self.image_processor_tester.batch_size,
													        self.image_processor_tester.num_channels,
													        expected_height,
													        expected_width,
													    )							,							)




						def      __lowerCamelCase       (    self :       List[Any]      )     ->				Tuple:
													# Initialize image processor
													SCREAMING_SNAKE_CASE__     :Dict						    = self.image_processing_class(**self.image_processor_dict      )
													# create random numpy tensors
													SCREAMING_SNAKE_CASE__     :Tuple						    = prepare_image_inputs(self.image_processor_tester							,							equal_resolution=UpperCamelCase_							,							numpify=UpperCamelCase_      )
													for image in image_inputs:
																				self.assertIsInstance(UpperCamelCase_							,							np.ndarray      )

													# Test not batched input
													SCREAMING_SNAKE_CASE__     :List[str]						    = image_processing(image_inputs[0]							,							return_tensors='pt'      ).pixel_values

													SCREAMING_SNAKE_CASE__    ,      SCREAMING_SNAKE_CASE__     :int						    = self.image_processor_tester.get_expected_values(UpperCamelCase_      )
													self.assertEqual(
													    encoded_images.shape							,							(1, self.image_processor_tester.num_channels, expected_height, expected_width)							,							)

													# Test batched
													SCREAMING_SNAKE_CASE__     :str						    = image_processing(UpperCamelCase_							,							return_tensors='pt'      ).pixel_values

													SCREAMING_SNAKE_CASE__    ,      SCREAMING_SNAKE_CASE__     :Any						    = self.image_processor_tester.get_expected_values(UpperCamelCase_							,							batched=UpperCamelCase_      )
													self.assertEqual(
													    encoded_images.shape							,							(
													        self.image_processor_tester.batch_size,
													        self.image_processor_tester.num_channels,
													        expected_height,
													        expected_width,
													    )							,							)




						def      __lowerCamelCase       (    self :       List[str]      )     ->				List[Any]:
													# Initialize image processor
													SCREAMING_SNAKE_CASE__     :List[str]						    = self.image_processing_class(**self.image_processor_dict      )
													# create random PyTorch tensors
													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = prepare_image_inputs(self.image_processor_tester							,							equal_resolution=UpperCamelCase_							,							torchify=UpperCamelCase_      )
													for image in image_inputs:
																				self.assertIsInstance(UpperCamelCase_							,							torch.Tensor      )

													# Test not batched input
													SCREAMING_SNAKE_CASE__     :List[str]						    = image_processing(image_inputs[0]							,							return_tensors='pt'      ).pixel_values

													SCREAMING_SNAKE_CASE__    ,      SCREAMING_SNAKE_CASE__     :Optional[int]						    = self.image_processor_tester.get_expected_values(UpperCamelCase_      )
													self.assertEqual(
													    encoded_images.shape							,							(1, self.image_processor_tester.num_channels, expected_height, expected_width)							,							)

													# Test batched
													SCREAMING_SNAKE_CASE__     :List[Any]						    = image_processing(UpperCamelCase_							,							return_tensors='pt'      ).pixel_values

													SCREAMING_SNAKE_CASE__    ,      SCREAMING_SNAKE_CASE__     :List[Any]						    = self.image_processor_tester.get_expected_values(UpperCamelCase_							,							batched=UpperCamelCase_      )
													self.assertEqual(
													    encoded_images.shape							,							(
													        self.image_processor_tester.batch_size,
													        self.image_processor_tester.num_channels,
													        expected_height,
													        expected_width,
													    )							,							)






