






from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import convert_to_rgb, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
  import PIL


__a:     Dict   					=					logging.get_logger(__name__)
class 		SCREAMING_SNAKE_CASE__	(  UpperCAmelCase     ):

  '''simple docstring'''







  _lowerCamelCase    =  ['''pixel_values''']


  def __init__(      self		:			Any       ,				lowerCamelCase		:			bool = True       ,				lowerCamelCase		:			Dict[str, int] = None       ,				lowerCamelCase		:			PILImageResampling = PILImageResampling.BICUBIC       ,				lowerCamelCase		:			bool = True       ,				lowerCamelCase		:			Union[int, float] = 1 / 255       ,				lowerCamelCase		:			bool = True       ,				lowerCamelCase		:			Optional[Union[float, List[float]]] = None       ,				lowerCamelCase		:			Optional[Union[float, List[float]]] = None       ,				lowerCamelCase		:			bool = True       ,				**lowerCamelCase		:			List[Any]       ,				)						->	None:


         """simple docstring"""
         super().__init__(**lowerCamelCase							)
         _UpperCAmelCase							=    size if size is not None else {"""height""": 384, """width""": 384}
         _UpperCAmelCase							=    get_size_dict(lowerCamelCase       ,				default_to_square=lowerCamelCase							)

         _UpperCAmelCase							=    do_resize
         _UpperCAmelCase							=    size
         _UpperCAmelCase							=    resample
         _UpperCAmelCase							=    do_rescale
         _UpperCAmelCase							=    rescale_factor
         _UpperCAmelCase							=    do_normalize
         _UpperCAmelCase							=    image_mean if image_mean is not None else OPENAI_CLIP_MEAN
         _UpperCAmelCase							=    image_std if image_std is not None else OPENAI_CLIP_STD
         _UpperCAmelCase							=    do_convert_rgb


  def  lowerCamelCase       (      self		:			Any       ,				lowerCamelCase		:			np.ndarray       ,				lowerCamelCase		:			Dict[str, int]       ,				lowerCamelCase		:			PILImageResampling = PILImageResampling.BICUBIC       ,				lowerCamelCase		:			Optional[Union[str, ChannelDimension]] = None       ,				**lowerCamelCase		:			str       ,				)						->	np.ndarray:


         """simple docstring"""
         _UpperCAmelCase							=    get_size_dict(lowerCamelCase       ,				default_to_square=lowerCamelCase							)
         if "height" not in size or "width" not in size:
                raise ValueError(f"""The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}"""							)
         _UpperCAmelCase							=    (size["""height"""], size["""width"""])
         return resize(lowerCamelCase       ,				size=lowerCamelCase       ,				resample=lowerCamelCase       ,				data_format=lowerCamelCase       ,				**lowerCamelCase							)


  def  lowerCamelCase       (      self		:			Tuple       ,				lowerCamelCase		:			np.ndarray       ,				lowerCamelCase		:			Union[int, float]       ,				lowerCamelCase		:			Optional[Union[str, ChannelDimension]] = None       ,				**lowerCamelCase		:			Dict       ,				)						->	Union[str, Any]:


         """simple docstring"""
         return rescale(lowerCamelCase       ,				scale=lowerCamelCase       ,				data_format=lowerCamelCase       ,				**lowerCamelCase							)


  def  lowerCamelCase       (      self		:			Tuple       ,				lowerCamelCase		:			np.ndarray       ,				lowerCamelCase		:			Union[float, List[float]]       ,				lowerCamelCase		:			Union[float, List[float]]       ,				lowerCamelCase		:			Optional[Union[str, ChannelDimension]] = None       ,				**lowerCamelCase		:			List[Any]       ,				)						->	np.ndarray:


         """simple docstring"""
         return normalize(lowerCamelCase       ,				mean=lowerCamelCase       ,				std=lowerCamelCase       ,				data_format=lowerCamelCase       ,				**lowerCamelCase							)





  def  lowerCamelCase       (      self		:			Dict       ,				lowerCamelCase		:			ImageInput       ,				lowerCamelCase		:			Optional[bool] = None       ,				lowerCamelCase		:			Optional[Dict[str, int]] = None       ,				lowerCamelCase		:			PILImageResampling = None       ,				lowerCamelCase		:			Optional[bool] = None       ,				lowerCamelCase		:			Optional[float] = None       ,				lowerCamelCase		:			Optional[bool] = None       ,				lowerCamelCase		:			Optional[Union[float, List[float]]] = None       ,				lowerCamelCase		:			Optional[Union[float, List[float]]] = None       ,				lowerCamelCase		:			Optional[Union[str, TensorType]] = None       ,				lowerCamelCase		:			bool = None       ,				lowerCamelCase		:			ChannelDimension = ChannelDimension.FIRST       ,				**lowerCamelCase		:			List[str]       ,				)						->	PIL.Image.Image:


         """simple docstring"""
         _UpperCAmelCase							=    do_resize if do_resize is not None else self.do_resize
         _UpperCAmelCase							=    resample if resample is not None else self.resample
         _UpperCAmelCase							=    do_rescale if do_rescale is not None else self.do_rescale
         _UpperCAmelCase							=    rescale_factor if rescale_factor is not None else self.rescale_factor
         _UpperCAmelCase							=    do_normalize if do_normalize is not None else self.do_normalize
         _UpperCAmelCase							=    image_mean if image_mean is not None else self.image_mean
         _UpperCAmelCase							=    image_std if image_std is not None else self.image_std
         _UpperCAmelCase							=    do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

         _UpperCAmelCase							=    size if size is not None else self.size
         _UpperCAmelCase							=    get_size_dict(lowerCamelCase       ,				default_to_square=lowerCamelCase							)

         _UpperCAmelCase							=    make_list_of_images(lowerCamelCase							)

         if not valid_images(lowerCamelCase							):
                raise ValueError(
                    """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
                    """torch.Tensor, tf.Tensor or jax.ndarray."""							)

         if do_resize and size is None or resample is None:
                raise ValueError("""Size and resample must be specified if do_resize is True."""							)

         if do_rescale and rescale_factor is None:
                raise ValueError("""Rescale factor must be specified if do_rescale is True."""							)

         if do_normalize and (image_mean is None or image_std is None):
                raise ValueError("""Image mean and std must be specified if do_normalize is True."""							)

         # PIL RGBA images are converted to RGB
         if do_convert_rgb:
                _UpperCAmelCase							=    [convert_to_rgb(lowerCamelCase							) for image in images]

         # All transformations expect numpy arrays.
         _UpperCAmelCase							=    [to_numpy_array(lowerCamelCase							) for image in images]

         if do_resize:
                _UpperCAmelCase							=    [self.resize(image=lowerCamelCase       ,				size=lowerCamelCase       ,				resample=lowerCamelCase							) for image in images]

         if do_rescale:
                _UpperCAmelCase							=    [self.rescale(image=lowerCamelCase       ,				scale=lowerCamelCase							) for image in images]

         if do_normalize:
                _UpperCAmelCase							=    [self.normalize(image=lowerCamelCase       ,				mean=lowerCamelCase       ,				std=lowerCamelCase							) for image in images]

         _UpperCAmelCase							=    [to_channel_dimension_format(lowerCamelCase       ,				lowerCamelCase							) for image in images]

         _UpperCAmelCase							=    BatchFeature(data={"""pixel_values""": images}       ,				tensor_type=lowerCamelCase							)

         return encoded_outputs






"""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 					__UpperCAmelCase							:
				def       UpperCAmelCase      (		self    :			List[str]    )	->	List[str]:



							'''simple docstring'''



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

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

							torch.manual_seed(0    )
							a__  :	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    )
							a__  :	str		 =			DDPMScheduler(
							    num_train_timesteps=10_00	, 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    )
							a__  :	Optional[int]		 =			IFWatermarker()

							return {
							    "text_encoder": text_encoder,
							    "tokenizer": tokenizer,
							    "unet": unet,
							    "scheduler": scheduler,
							    "watermarker": watermarker,
							    "safety_checker": None,
							    "feature_extractor": None,
							}
				def       UpperCAmelCase      (		self    :			Optional[Any]    )	->	Union[str, Any]:



							'''simple docstring'''



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

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

							torch.manual_seed(0    )
							a__  :	Dict		 =			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    )
							a__  :	Optional[int]		 =			DDPMScheduler(
							    num_train_timesteps=10_00	, 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    )
							a__  :	Tuple		 =			DDPMScheduler(
							    num_train_timesteps=10_00	, beta_schedule="squaredcos_cap_v2"	, beta_start=0.0001	, beta_end=0.02	, )

							torch.manual_seed(0    )
							a__  :	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       UpperCAmelCase      (		self    :			List[Any]    )	->	Tuple:



							'''simple docstring'''



							a__  :	Dict		 =			self.get_dummy_components()
							a__  :	Any		 =			self.pipeline_class(**a_    )
							pipe.to(a_    )
							pipe.set_progress_bar_config(disable=a_    )

							a__  :	Any		 =			self.get_dummy_inputs(a_    )

							a__  :	Optional[int]		 =			inputs["prompt"]
							a__  :	List[Any]		 =			inputs["generator"]
							a__  :	Optional[int]		 =			inputs["num_inference_steps"]
							a__  :	Any		 =			inputs["output_type"]

							if "image" in inputs:
										a__  :	Any		 =			inputs["image"]
							else:
										a__  :	Dict		 =			None

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

							if "original_image" in inputs:
										a__  :	List[Any]		 =			inputs["original_image"]
							else:
										a__  :	str		 =			None

							a__ ,	a__  :	Optional[int]		 =			pipe.encode_prompt(a_    )

							# inputs with prompt converted to embeddings
							a__  :	Union[str, 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:
										a__  :	Dict		 =			image

							if mask_image is not None:
										a__  :	Any		 =			mask_image

							if original_image is not None:
										a__  :	Optional[int]		 =			original_image

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

							a__  :	int		 =			pipe(**a_    )[0]

							with tempfile.TemporaryDirectory() as tmpdir:
										pipe.save_pretrained(a_    )
										a__  :	List[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

							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."	, )

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

							a__  :	str		 =			inputs["generator"]
							a__  :	Dict		 =			inputs["num_inference_steps"]
							a__  :	Optional[int]		 =			inputs["output_type"]

							# inputs with prompt converted to embeddings
							a__  :	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:
										a__  :	Dict		 =			image

							if mask_image is not None:
										a__  :	Any		 =			mask_image

							if original_image is not None:
										a__  :	Dict		 =			original_image

							a__  :	Optional[Any]		 =			pipe_loaded(**a_    )[0]

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






				def       UpperCAmelCase      (		self    :			int    )	->	Any:



							'''simple docstring'''



							a__  :	Dict		 =			self.get_dummy_components()
							a__  :	Dict		 =			self.pipeline_class(**a_    )
							pipe.to(a_    )
							pipe.set_progress_bar_config(disable=a_    )

							a__  :	List[str]		 =			self.get_dummy_inputs(a_    )
							a__  :	Dict		 =			pipe(**a_    )[0]

							with tempfile.TemporaryDirectory() as tmpdir:
										pipe.save_pretrained(a_    )
										a__  :	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

							a__  :	Optional[int]		 =			self.get_dummy_inputs(a_    )
							a__  :	Optional[int]		 =			pipe_loaded(**a_    )[0]

							a__  :	List[Any]		 =			np.abs(to_np(a_    ) - to_np(a_    )    ).max()
							self.assertLess(a_	, 1E-4    )