
'''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    lowerCAmelCase       (     unittest.TestCase  ):


 def __init__(    self  : List[Any]			,							a__  : int			,							a__  : bool = True			,							a__  : Dict[str, int] = None			,							a__  : int = 32			,							a__  : bool = True			,							a__  : Union[int, float] = 1 / 255			,							a__  : bool = True			,							a__  : bool = True			,							a__  : Optional[Union[float, List[float]]] = [0.48145466, 0.4578275, 0.40821073]			,							a__  : Optional[Union[float, List[float]]] = [0.26862954, 0.26130258, 0.27577711]			,							a__  : bool = True			,							a__  : Any=7			,							a__  : int=30			,							a__  : List[Any]=400			,							a__  : int=3			,							):
  '''simple docstring'''

  lowerCAmelCase__     :   str     		=	parent
  lowerCAmelCase__     :   List[str]     		=	do_resize
  lowerCAmelCase__     :   Union[str, Any]     		=	size if size is not None else {"shortest_edge": 288}
  lowerCAmelCase__     :   Any     		=	size_divisor
  lowerCAmelCase__     :   Optional[int]     		=	do_rescale
  lowerCAmelCase__     :   Any     		=	rescale_factor
  lowerCAmelCase__     :   List[str]     		=	do_normalize
  lowerCAmelCase__     :   Any     		=	do_center_crop
  lowerCAmelCase__     :   int     		=	image_mean
  lowerCAmelCase__     :   List[Any]     		=	image_std
  lowerCAmelCase__     :   List[str]     		=	do_pad
  lowerCAmelCase__     :   Any     		=	batch_size
  lowerCAmelCase__     :   Optional[Any]     		=	num_channels
  lowerCAmelCase__     :   Dict     		=	min_resolution
  lowerCAmelCase__     :   Any     		=	max_resolution


 def    _A	(    self  : Any    ):
  '''simple docstring'''

  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    _A	(    self  : Dict			,							a__  : Optional[int]			,							a__  : Optional[int]=False    ):
  '''simple docstring'''

  if not batched:
   lowerCAmelCase__     :   Optional[Any]     		=	self.size["shortest_edge"]
   lowerCAmelCase__     :   List[Any]     		=	image_inputs[0]
   if isinstance(a__			,							Image.Image    ):
    lowerCAmelCase__		,     lowerCAmelCase__     :   List[Any]     		=	image.size
   else:
    lowerCAmelCase__		,     lowerCAmelCase__     :   Union[str, Any]     		=	image.shape[1], image.shape[2]
   lowerCAmelCase__     :   Any     		=	size / min(a__			,							a__    )
   if h < w:
    lowerCAmelCase__		,     lowerCAmelCase__     :   int     		=	size, scale * w
   else:
    lowerCAmelCase__		,     lowerCAmelCase__     :   Any     		=	scale * h, size

   lowerCAmelCase__     :   Union[str, Any]     		=	int((1333 / 800) * size    )
   if max(a__			,							a__    ) > max_size:
    lowerCAmelCase__     :   Union[str, Any]     		=	max_size / max(a__			,							a__    )
    lowerCAmelCase__     :   str     		=	newh * scale
    lowerCAmelCase__     :   List[Any]     		=	neww * scale

   lowerCAmelCase__		,     lowerCAmelCase__     :   int     		=	int(newh + 0.5    ), int(neww + 0.5    )
   lowerCAmelCase__		,     lowerCAmelCase__     :   Any     		=	(
       newh // self.size_divisor * self.size_divisor,
       neww // self.size_divisor * self.size_divisor,
   )

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

  return expected_height, expected_width




@require_torch
@require_vision
class    lowerCAmelCase       (     UpperCamelCase_    ,  unittest.TestCase  ):
 A_				:				str	=			BridgeTowerImageProcessor if is_vision_available() else None


 def    _A	(    self  : str    ):
  '''simple docstring'''

  lowerCAmelCase__     :   Optional[Any]     		=	BridgeTowerImageProcessingTester(self    )


 @property
 def    _A	(    self  : Tuple    ):
  '''simple docstring'''

  return self.image_processor_tester.prepare_image_processor_dict()


 def    _A	(    self  : List[str]    ):
  '''simple docstring'''

  lowerCAmelCase__     :   Tuple     		=	self.image_processing_class(**self.image_processor_dict    )
  self.assertTrue(hasattr(a__			,							"image_mean"    )    )
  self.assertTrue(hasattr(a__			,							"image_std"    )    )
  self.assertTrue(hasattr(a__			,							"do_normalize"    )    )
  self.assertTrue(hasattr(a__			,							"do_resize"    )    )
  self.assertTrue(hasattr(a__			,							"size"    )    )
  self.assertTrue(hasattr(a__			,							"size_divisor"    )    )


 def    _A	(    self  : str    ):
  '''simple docstring'''

  pass


 def    _A	(    self  : Any    ):
  '''simple docstring'''

  lowerCAmelCase__     :   Dict     		=	self.image_processing_class(**self.image_processor_dict    )
  # create random PIL images
  lowerCAmelCase__     :   List[Any]     		=	prepare_image_inputs(self.image_processor_tester			,							equal_resolution=a__    )
  for image in image_inputs:
   self.assertIsInstance(a__			,							Image.Image    )

  # Test not batched input
  lowerCAmelCase__     :   Tuple     		=	image_processing(image_inputs[0]			,							return_tensors="pt"    ).pixel_values

  lowerCAmelCase__		,     lowerCAmelCase__     :   List[str]     		=	self.image_processor_tester.get_expected_values(a__    )
  self.assertEqual(
      encoded_images.shape			,							(1, self.image_processor_tester.num_channels, expected_height, expected_width)			,							)

  # Test batched
  lowerCAmelCase__     :   int     		=	image_processing(a__			,							return_tensors="pt"    ).pixel_values

  lowerCAmelCase__		,     lowerCAmelCase__     :   str     		=	self.image_processor_tester.get_expected_values(a__			,							batched=a__    )
  self.assertEqual(
      encoded_images.shape			,							(
          self.image_processor_tester.batch_size,
          self.image_processor_tester.num_channels,
          expected_height,
          expected_width,
      )			,							)


 def    _A	(    self  : int    ):
  '''simple docstring'''

  lowerCAmelCase__     :   int     		=	self.image_processing_class(**self.image_processor_dict    )
  # create random numpy tensors
  lowerCAmelCase__     :   str     		=	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
  lowerCAmelCase__     :   str     		=	image_processing(image_inputs[0]			,							return_tensors="pt"    ).pixel_values

  lowerCAmelCase__		,     lowerCAmelCase__     :   List[Any]     		=	self.image_processor_tester.get_expected_values(a__    )
  self.assertEqual(
      encoded_images.shape			,							(1, self.image_processor_tester.num_channels, expected_height, expected_width)			,							)

  # Test batched
  lowerCAmelCase__     :   int     		=	image_processing(a__			,							return_tensors="pt"    ).pixel_values

  lowerCAmelCase__		,     lowerCAmelCase__     :   Optional[Any]     		=	self.image_processor_tester.get_expected_values(a__			,							batched=a__    )
  self.assertEqual(
      encoded_images.shape			,							(
          self.image_processor_tester.batch_size,
          self.image_processor_tester.num_channels,
          expected_height,
          expected_width,
      )			,							)


 def    _A	(    self  : Optional[int]    ):
  '''simple docstring'''

  lowerCAmelCase__     :   List[str]     		=	self.image_processing_class(**self.image_processor_dict    )
  # create random PyTorch tensors
  lowerCAmelCase__     :   int     		=	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
  lowerCAmelCase__     :   Any     		=	image_processing(image_inputs[0]			,							return_tensors="pt"    ).pixel_values

  lowerCAmelCase__		,     lowerCAmelCase__     :   Tuple     		=	self.image_processor_tester.get_expected_values(a__    )
  self.assertEqual(
      encoded_images.shape			,							(1, self.image_processor_tester.num_channels, expected_height, expected_width)			,							)

  # Test batched
  lowerCAmelCase__     :   Optional[Any]     		=	image_processing(a__			,							return_tensors="pt"    ).pixel_values

  lowerCAmelCase__		,     lowerCAmelCase__     :   Dict     		=	self.image_processor_tester.get_expected_values(a__			,							batched=a__    )
  self.assertEqual(
      encoded_images.shape			,							(
          self.image_processor_tester.batch_size,
          self.image_processor_tester.num_channels,
          expected_height,
          expected_width,
      )			,							)







'''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    lowerCAmelCase       :


 def    _A	(    self  : Union[str, Any]    ):
  '''simple docstring'''

  torch.manual_seed(0    )
  lowerCAmelCase__     :   List[str]     		=	TaEncoderModel.from_pretrained("hf-internal-testing/tiny-random-t5"    )

  torch.manual_seed(0    )
  lowerCAmelCase__     :   Optional[int]     		=	AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-t5"    )

  torch.manual_seed(0    )
  lowerCAmelCase__     :   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    )
  lowerCAmelCase__     :   Union[str, Any]     		=	DDPMScheduler(
      num_train_timesteps=1000			,							beta_schedule="squaredcos_cap_v2"			,							beta_start=0.0001			,							beta_end=0.02			,							thresholding=a__			,							dynamic_thresholding_ratio=0.95			,							sample_max_value=1.0			,							prediction_type="epsilon"			,							variance_type="learned_range"			,							)

  torch.manual_seed(0    )
  lowerCAmelCase__     :   Tuple     		=	IFWatermarker()

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


 def    _A	(    self  : Dict    ):
  '''simple docstring'''

  torch.manual_seed(0    )
  lowerCAmelCase__     :   Any     		=	TaEncoderModel.from_pretrained("hf-internal-testing/tiny-random-t5"    )

  torch.manual_seed(0    )
  lowerCAmelCase__     :   List[Any]     		=	AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-t5"    )

  torch.manual_seed(0    )
  lowerCAmelCase__     :   int     		=	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    )
  lowerCAmelCase__     :   Tuple     		=	DDPMScheduler(
      num_train_timesteps=1000			,							beta_schedule="squaredcos_cap_v2"			,							beta_start=0.0001			,							beta_end=0.02			,							thresholding=a__			,							dynamic_thresholding_ratio=0.95			,							sample_max_value=1.0			,							prediction_type="epsilon"			,							variance_type="learned_range"			,							)

  torch.manual_seed(0    )
  lowerCAmelCase__     :   Tuple     		=	DDPMScheduler(
      num_train_timesteps=1000			,							beta_schedule="squaredcos_cap_v2"			,							beta_start=0.0001			,							beta_end=0.02			,							)

  torch.manual_seed(0    )
  lowerCAmelCase__     :   Dict     		=	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    _A	(    self  : Optional[Any]    ):
  '''simple docstring'''

  lowerCAmelCase__     :   Union[str, Any]     		=	self.get_dummy_components()
  lowerCAmelCase__     :   Dict     		=	self.pipeline_class(**a__    )
  pipe.to(a__    )
  pipe.set_progress_bar_config(disable=a__    )

  lowerCAmelCase__     :   Tuple     		=	self.get_dummy_inputs(a__    )

  lowerCAmelCase__     :   str     		=	inputs["prompt"]
  lowerCAmelCase__     :   Optional[Any]     		=	inputs["generator"]
  lowerCAmelCase__     :   str     		=	inputs["num_inference_steps"]
  lowerCAmelCase__     :   int     		=	inputs["output_type"]

  if "image" in inputs:
   lowerCAmelCase__     :   Optional[int]     		=	inputs["image"]
  else:
   lowerCAmelCase__     :   str     		=	None

  if "mask_image" in inputs:
   lowerCAmelCase__     :   List[str]     		=	inputs["mask_image"]
  else:
   lowerCAmelCase__     :   str     		=	None

  if "original_image" in inputs:
   lowerCAmelCase__     :   Optional[int]     		=	inputs["original_image"]
  else:
   lowerCAmelCase__     :   Union[str, Any]     		=	None

  lowerCAmelCase__		,     lowerCAmelCase__     :   Dict     		=	pipe.encode_prompt(a__    )

  # inputs with prompt converted to embeddings
  lowerCAmelCase__     :   Dict     		=	{
      "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:
   lowerCAmelCase__     :   Optional[Any]     		=	image

  if mask_image is not None:
   lowerCAmelCase__     :   Optional[int]     		=	mask_image

  if original_image is not None:
   lowerCAmelCase__     :   Optional[Any]     		=	original_image

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

  lowerCAmelCase__     :   Optional[int]     		=	pipe(**a__    )[0]

  with tempfile.TemporaryDirectory() as tmpdir:
   pipe.save_pretrained(a__    )
   lowerCAmelCase__     :   Tuple     		=	self.pipeline_class.from_pretrained(a__    )
   pipe_loaded.to(a__    )
   pipe_loaded.set_progress_bar_config(disable=a__    )

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

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

  lowerCAmelCase__     :   Union[str, Any]     		=	self.get_dummy_inputs(a__    )

  lowerCAmelCase__     :   Any     		=	inputs["generator"]
  lowerCAmelCase__     :   Union[str, Any]     		=	inputs["num_inference_steps"]
  lowerCAmelCase__     :   str     		=	inputs["output_type"]

  # inputs with prompt converted to embeddings
  lowerCAmelCase__     :   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:
   lowerCAmelCase__     :   Tuple     		=	image

  if mask_image is not None:
   lowerCAmelCase__     :   int     		=	mask_image

  if original_image is not None:
   lowerCAmelCase__     :   List[Any]     		=	original_image

  lowerCAmelCase__     :   Dict     		=	pipe_loaded(**a__    )[0]

  lowerCAmelCase__     :   int     		=	np.abs(to_np(a__    ) - to_np(a__    )    ).max()
  self.assertLess(a__			,							1e-4    )


 def    _A	(    self  : Any    ):
  '''simple docstring'''

  lowerCAmelCase__     :   str     		=	self.get_dummy_components()
  lowerCAmelCase__     :   int     		=	self.pipeline_class(**a__    )
  pipe.to(a__    )
  pipe.set_progress_bar_config(disable=a__    )

  lowerCAmelCase__     :   Any     		=	self.get_dummy_inputs(a__    )
  lowerCAmelCase__     :   Union[str, Any]     		=	pipe(**a__    )[0]

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

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

  lowerCAmelCase__     :   str     		=	self.get_dummy_inputs(a__    )
  lowerCAmelCase__     :   Union[str, Any]     		=	pipe_loaded(**a__    )[0]

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





