



"""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    lowercase__     :







       '''simple docstring'''


       def   lowercase__       (							self    :						List[Any]					)				->		Optional[Any]:


             '''simple docstring'''




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

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

             torch.manual_seed(0					)
             UpperCAmelCase_    				=							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					)
             UpperCAmelCase_    				=							DDPMScheduler(
                 num_train_timesteps=1000	,       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					)
             UpperCAmelCase_    				=							IFWatermarker()

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



       def   lowercase__       (							self    :						Optional[Any]					)				->		List[str]:


             '''simple docstring'''




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

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

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

             torch.manual_seed(0					)
             UpperCAmelCase_    				=							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   lowercase__       (							self    :						List[str]					)				->		Optional[int]:


             '''simple docstring'''




             UpperCAmelCase_    				=							self.get_dummy_components()
             UpperCAmelCase_    				=							self.pipeline_class(**_UpperCAmelCase					)
             pipe.to(_UpperCAmelCase					)
             pipe.set_progress_bar_config(disable=_UpperCAmelCase					)

             UpperCAmelCase_    				=							self.get_dummy_inputs(_UpperCAmelCase					)

             UpperCAmelCase_    				=							inputs["prompt"]
             UpperCAmelCase_    				=							inputs["generator"]
             UpperCAmelCase_    				=							inputs["num_inference_steps"]
             UpperCAmelCase_    				=							inputs["output_type"]

             if "image" in inputs:
                   UpperCAmelCase_    				=							inputs["image"]
             else:
                   UpperCAmelCase_    				=							None

             if "mask_image" in inputs:
                   UpperCAmelCase_    				=							inputs["mask_image"]
             else:
                   UpperCAmelCase_    				=							None

             if "original_image" in inputs:
                   UpperCAmelCase_    				=							inputs["original_image"]
             else:
                   UpperCAmelCase_    				=							None

             UpperCAmelCase_				,					UpperCAmelCase_    				=							pipe.encode_prompt(_UpperCAmelCase					)

             # inputs with prompt converted to embeddings
             UpperCAmelCase_    				=							{
                 "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:
                   UpperCAmelCase_    				=							image

             if mask_image is not None:
                   UpperCAmelCase_    				=							mask_image

             if original_image is not None:
                   UpperCAmelCase_    				=							original_image

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

             UpperCAmelCase_    				=							pipe(**_UpperCAmelCase					)[0]

             with tempfile.TemporaryDirectory() as tmpdir:
                   pipe.save_pretrained(_UpperCAmelCase					)
                   UpperCAmelCase_    				=							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."""	,       )

             UpperCAmelCase_    				=							self.get_dummy_inputs(_UpperCAmelCase					)

             UpperCAmelCase_    				=							inputs["generator"]
             UpperCAmelCase_    				=							inputs["num_inference_steps"]
             UpperCAmelCase_    				=							inputs["output_type"]

             # inputs with prompt converted to embeddings
             UpperCAmelCase_    				=							{
                 "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:
                   UpperCAmelCase_    				=							image

             if mask_image is not None:
                   UpperCAmelCase_    				=							mask_image

             if original_image is not None:
                   UpperCAmelCase_    				=							original_image

             UpperCAmelCase_    				=							pipe_loaded(**_UpperCAmelCase					)[0]

             UpperCAmelCase_    				=							np.abs(to_np(_UpperCAmelCase					) - to_np(_UpperCAmelCase					)					).max()
             self.assertLess(_UpperCAmelCase	,       1e-4					)







       def   lowercase__       (							self    :						Optional[int]					)				->		int:


             '''simple docstring'''




             UpperCAmelCase_    				=							self.get_dummy_components()
             UpperCAmelCase_    				=							self.pipeline_class(**_UpperCAmelCase					)
             pipe.to(_UpperCAmelCase					)
             pipe.set_progress_bar_config(disable=_UpperCAmelCase					)

             UpperCAmelCase_    				=							self.get_dummy_inputs(_UpperCAmelCase					)
             UpperCAmelCase_    				=							pipe(**_UpperCAmelCase					)[0]

             with tempfile.TemporaryDirectory() as tmpdir:
                   pipe.save_pretrained(_UpperCAmelCase					)
                   UpperCAmelCase_    				=							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

             UpperCAmelCase_    				=							self.get_dummy_inputs(_UpperCAmelCase					)
             UpperCAmelCase_    				=							pipe_loaded(**_UpperCAmelCase					)[0]

             UpperCAmelCase_    				=							np.abs(to_np(_UpperCAmelCase					) - to_np(_UpperCAmelCase					)					).max()
             self.assertLess(_UpperCAmelCase	,       1e-4					)









"""simple docstring"""





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 (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_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


lowerCamelCase      =		logging.get_logger(__name__)
class    lowercase__     (					SCREAMING_SNAKE_CASE   ):







       '''simple docstring'''


       UpperCamelCase		=  ['''pixel_values''']



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


             '''simple docstring'''




             super().__init__(**_UpperCAmelCase					)
             UpperCAmelCase_    				=							size if size is not None else {"shortest_edge": 384}
             UpperCAmelCase_    				=							get_size_dict(_UpperCAmelCase	,       default_to_square=_UpperCAmelCase					)

             UpperCAmelCase_    				=							do_resize
             UpperCAmelCase_    				=							size
             # Default value set here for backwards compatibility where the value in config is None
             UpperCAmelCase_    				=							crop_pct if crop_pct is not None else 224 / 256
             UpperCAmelCase_    				=							resample
             UpperCAmelCase_    				=							do_rescale
             UpperCAmelCase_    				=							rescale_factor
             UpperCAmelCase_    				=							do_normalize
             UpperCAmelCase_    				=							image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
             UpperCAmelCase_    				=							image_std if image_std is not None else IMAGENET_STANDARD_STD



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


             '''simple docstring'''




             UpperCAmelCase_    				=							get_size_dict(_UpperCAmelCase	,       default_to_square=_UpperCAmelCase					)
             if "shortest_edge" not in size:
                   raise ValueError(F"""Size dictionary must contain 'shortest_edge' key. Got {size.keys()}"""					)
             UpperCAmelCase_    				=							size["shortest_edge"]

             if shortest_edge < 384:
                   # maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
                   UpperCAmelCase_    				=							int(shortest_edge / crop_pct					)
                   UpperCAmelCase_    				=							get_resize_output_image_size(_UpperCAmelCase	,       size=_UpperCAmelCase	,       default_to_square=_UpperCAmelCase					)
                   UpperCAmelCase_    				=							resize(image=_UpperCAmelCase	,       size=_UpperCAmelCase	,       resample=_UpperCAmelCase	,       data_format=_UpperCAmelCase	,       **_UpperCAmelCase					)
                   # then crop to (shortest_edge, shortest_edge)
                   return center_crop(image=_UpperCAmelCase	,       size=(shortest_edge, shortest_edge)	,       data_format=_UpperCAmelCase	,       **_UpperCAmelCase					)
             else:
                   # warping (no cropping) when evaluated at 384 or larger
                   return resize(
                       _UpperCAmelCase	,       size=(shortest_edge, shortest_edge)	,       resample=_UpperCAmelCase	,       data_format=_UpperCAmelCase	,       **_UpperCAmelCase					)



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


             '''simple docstring'''




             return rescale(_UpperCAmelCase	,       scale=_UpperCAmelCase	,       data_format=_UpperCAmelCase	,       **_UpperCAmelCase					)



       def   lowercase__       (							self    :						Dict	,       _UpperCAmelCase    :						np.ndarray	,       _UpperCAmelCase    :						Union[float, List[float]]	,       _UpperCAmelCase    :						Union[float, List[float]]	,       _UpperCAmelCase    :						Optional[Union[str, ChannelDimension]] = None	,       **_UpperCAmelCase    :						List[str]	,       )				->		np.ndarray:


             '''simple docstring'''




             return normalize(_UpperCAmelCase	,       mean=_UpperCAmelCase	,       std=_UpperCAmelCase	,       data_format=_UpperCAmelCase	,       **_UpperCAmelCase					)







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


             '''simple docstring'''




             UpperCAmelCase_    				=							do_resize if do_resize is not None else self.do_resize
             UpperCAmelCase_    				=							crop_pct if crop_pct is not None else self.crop_pct
             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_    				=							size if size is not None else self.size
             UpperCAmelCase_    				=							get_size_dict(_UpperCAmelCase	,       default_to_square=_UpperCAmelCase					)

             UpperCAmelCase_    				=							make_list_of_images(_UpperCAmelCase					)

             if not valid_images(_UpperCAmelCase					):
                   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_resize and size["shortest_edge"] < 384 and crop_pct is None:
                   raise ValueError("crop_pct must be specified if size < 384."					)

             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."					)

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

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

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

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

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

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




