






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 		_snake_case  :






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

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

						torch.manual_seed(0)
						SCREAMING_SNAKE_CASE						= 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						= DDPMScheduler(
						    num_train_timesteps=1000		,					beta_schedule='squaredcos_cap_v2'		,					beta_start=0.00_01		,					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)
						SCREAMING_SNAKE_CASE						= IFWatermarker()

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






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

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

						torch.manual_seed(0)
						SCREAMING_SNAKE_CASE						= 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=1.4_14		,					time_embedding_act_fn='gelu'		,					time_embedding_dim=32		,					)
						unet.set_attn_processor(AttnAddedKVProcessor())  # For reproducibility tests

						torch.manual_seed(0)
						SCREAMING_SNAKE_CASE						= DDPMScheduler(
						    num_train_timesteps=1000		,					beta_schedule='squaredcos_cap_v2'		,					beta_start=0.00_01		,					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)
						SCREAMING_SNAKE_CASE						= DDPMScheduler(
						    num_train_timesteps=1000		,					beta_schedule='squaredcos_cap_v2'		,					beta_start=0.00_01		,					beta_end=0.02		,					)

						torch.manual_seed(0)
						SCREAMING_SNAKE_CASE						= 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    SCREAMING_SNAKE_CASE__    (       self)      ->       List[Any]:
						SCREAMING_SNAKE_CASE						= self.get_dummy_components()
						SCREAMING_SNAKE_CASE						= self.pipeline_class(**a)
						pipe.to(a)
						pipe.set_progress_bar_config(disable=a)

						SCREAMING_SNAKE_CASE						= self.get_dummy_inputs(a)

						SCREAMING_SNAKE_CASE						= inputs['prompt']
						SCREAMING_SNAKE_CASE						= inputs['generator']
						SCREAMING_SNAKE_CASE						= inputs['num_inference_steps']
						SCREAMING_SNAKE_CASE						= inputs['output_type']

						if "image" in inputs:
									SCREAMING_SNAKE_CASE						= inputs['image']
						else:
									SCREAMING_SNAKE_CASE						= None

						if "mask_image" in inputs:
									SCREAMING_SNAKE_CASE						= inputs['mask_image']
						else:
									SCREAMING_SNAKE_CASE						= None

						if "original_image" in inputs:
									SCREAMING_SNAKE_CASE						= inputs['original_image']
						else:
									SCREAMING_SNAKE_CASE						= None

						SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						= pipe.encode_prompt(a)

						# inputs with prompt converted to embeddings
						SCREAMING_SNAKE_CASE						= {
						    '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						= image

						if mask_image is not None:
									SCREAMING_SNAKE_CASE						= mask_image

						if original_image is not None:
									SCREAMING_SNAKE_CASE						= original_image

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

						SCREAMING_SNAKE_CASE						= pipe(**a)[0]

						with tempfile.TemporaryDirectory() as tmpdir:
									pipe.save_pretrained(a)
									SCREAMING_SNAKE_CASE						= 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.'''		,					)

						SCREAMING_SNAKE_CASE						= self.get_dummy_inputs(a)

						SCREAMING_SNAKE_CASE						= inputs['generator']
						SCREAMING_SNAKE_CASE						= inputs['num_inference_steps']
						SCREAMING_SNAKE_CASE						= inputs['output_type']

						# inputs with prompt converted to embeddings
						SCREAMING_SNAKE_CASE						= {
						    '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						= image

						if mask_image is not None:
									SCREAMING_SNAKE_CASE						= mask_image

						if original_image is not None:
									SCREAMING_SNAKE_CASE						= original_image

						SCREAMING_SNAKE_CASE						= pipe_loaded(**a)[0]

						SCREAMING_SNAKE_CASE						= np.abs(to_np(a) - to_np(a)).max()
						self.assertLess(a		,					1E-4)







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

						SCREAMING_SNAKE_CASE						= self.get_dummy_inputs(a)
						SCREAMING_SNAKE_CASE						= pipe(**a)[0]

						with tempfile.TemporaryDirectory() as tmpdir:
									pipe.save_pretrained(a)
									SCREAMING_SNAKE_CASE						= 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

						SCREAMING_SNAKE_CASE						= self.get_dummy_inputs(a)
						SCREAMING_SNAKE_CASE						= pipe_loaded(**a)[0]

						SCREAMING_SNAKE_CASE						= np.abs(to_np(a) - to_np(a)).max()
						self.assertLess(a		,					1E-4)












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 rescale, resize, to_channel_dimension_format
from ...image_utils import (
    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_		:						Optional[int]             =    logging.get_logger(__name__)






def 				lowerCamelCase__				(_UpperCAmelCase							,       _UpperCAmelCase):
			SCREAMING_SNAKE_CASE						= b.T
			SCREAMING_SNAKE_CASE						= np.sum(np.square(_UpperCAmelCase)							,       axis=1)
			SCREAMING_SNAKE_CASE						= np.sum(np.square(_UpperCAmelCase)							,       axis=0)
			SCREAMING_SNAKE_CASE						= np.matmul(_UpperCAmelCase							,       _UpperCAmelCase)
			SCREAMING_SNAKE_CASE						= aa[:, None] - 2 * ab + ba[None, :]
			return d






def 				lowerCamelCase__				(_UpperCAmelCase							,       _UpperCAmelCase):
			SCREAMING_SNAKE_CASE						= x.reshape(-1							,       3)
			SCREAMING_SNAKE_CASE						= squared_euclidean_distance(_UpperCAmelCase							,       _UpperCAmelCase)
			return np.argmin(_UpperCAmelCase							,       axis=1)







class 		_snake_case  (  A__					):
			_lowercase		:    str			     =							['''pixel_values''']






			def __init__(       self		,					a = None		,					a = True		,					a = None		,					a = PILImageResampling.BILINEAR		,					a = True		,					a = True		,					**a		,					)      ->       None:
						super().__init__(**a)
						SCREAMING_SNAKE_CASE						= size if size is not None else {'height': 256, 'width': 256}
						SCREAMING_SNAKE_CASE						= get_size_dict(a)
						SCREAMING_SNAKE_CASE						= np.array(a) if clusters is not None else None
						SCREAMING_SNAKE_CASE						= do_resize
						SCREAMING_SNAKE_CASE						= size
						SCREAMING_SNAKE_CASE						= resample
						SCREAMING_SNAKE_CASE						= do_normalize
						SCREAMING_SNAKE_CASE						= do_color_quantize






			def    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a		,					a = PILImageResampling.BILINEAR		,					a = None		,					**a		,					)      ->       np.ndarray:
						SCREAMING_SNAKE_CASE						= get_size_dict(a)
						if "height" not in size or "width" not in size:
									raise ValueError(f'''Size dictionary must contain both height and width keys. Got {size.keys()}''')
						return resize(
						    a		,					size=(size['height'], size['width'])		,					resample=a		,					data_format=a		,					**a)






			def    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a = None		,					)      ->       np.ndarray:
						SCREAMING_SNAKE_CASE						= rescale(image=a		,					scale=1 / 1_27.5		,					data_format=a)
						SCREAMING_SNAKE_CASE						= image - 1
						return image







			def    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a = None		,					a = None		,					a = None		,					a = None		,					a = None		,					a = None		,					a = None		,					a = ChannelDimension.FIRST		,					**a		,					)      ->       PIL.Image.Image:
						SCREAMING_SNAKE_CASE						= do_resize if do_resize is not None else self.do_resize
						SCREAMING_SNAKE_CASE						= size if size is not None else self.size
						SCREAMING_SNAKE_CASE						= get_size_dict(a)
						SCREAMING_SNAKE_CASE						= resample if resample is not None else self.resample
						SCREAMING_SNAKE_CASE						= do_normalize if do_normalize is not None else self.do_normalize
						SCREAMING_SNAKE_CASE						= do_color_quantize if do_color_quantize is not None else self.do_color_quantize
						SCREAMING_SNAKE_CASE						= clusters if clusters is not None else self.clusters
						SCREAMING_SNAKE_CASE						= np.array(a)

						SCREAMING_SNAKE_CASE						= make_list_of_images(a)

						if not valid_images(a):
									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_color_quantize and clusters is None:
									raise ValueError('Clusters must be specified if do_color_quantize is True.')

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

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

						if do_normalize:
									SCREAMING_SNAKE_CASE						= [self.normalize(image=a) for image in images]

						if do_color_quantize:
									SCREAMING_SNAKE_CASE						= [to_channel_dimension_format(a		,					ChannelDimension.LAST) for image in images]
									# color quantize from (batch_size, height, width, 3) to (batch_size, height, width)
									SCREAMING_SNAKE_CASE						= np.array(a)
									SCREAMING_SNAKE_CASE						= color_quantize(a		,					a).reshape(images.shape[:-1])

									# flatten to (batch_size, height*width)
									SCREAMING_SNAKE_CASE						= images.shape[0]
									SCREAMING_SNAKE_CASE						= images.reshape(a		,					-1)

									# We need to convert back to a list of images to keep consistent behaviour across processors.
									SCREAMING_SNAKE_CASE						= list(a)
						else:
									SCREAMING_SNAKE_CASE						= [to_channel_dimension_format(a		,					a) for image in images]

						SCREAMING_SNAKE_CASE						= {'input_ids': images}
						return BatchFeature(data=a		,					tensor_type=a)




