
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__		:







	'''simple docstring'''





	def   __snake_case				(			self		):
					torch.manual_seed(0		)
					A__  :	int         =							TaEncoderModel.from_pretrained('''hf-internal-testing/tiny-random-t5'''		)

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

					torch.manual_seed(0		)
					A__  :	Tuple         =							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__  :	Any         =							DDPMScheduler(
					    num_train_timesteps=1000				,       beta_schedule='''squaredcos_cap_v2'''				,       beta_start=0.0_0_0_1				,       beta_end=0.0_2				,       thresholding=UpperCamelCase__				,       dynamic_thresholding_ratio=0.9_5				,       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   __snake_case				(			self		):
					torch.manual_seed(0		)
					A__  :	Dict         =							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__  :	str         =							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_1_4				,       time_embedding_act_fn='''gelu'''				,       time_embedding_dim=32				,       )
					unet.set_attn_processor(AttnAddedKVProcessor()		)  # For reproducibility tests

					torch.manual_seed(0		)
					A__  :	int         =							DDPMScheduler(
					    num_train_timesteps=1000				,       beta_schedule='''squaredcos_cap_v2'''				,       beta_start=0.0_0_0_1				,       beta_end=0.0_2				,       thresholding=UpperCamelCase__				,       dynamic_thresholding_ratio=0.9_5				,       sample_max_value=1.0				,       prediction_type='''epsilon'''				,       variance_type='''learned_range'''				,       )

					torch.manual_seed(0		)
					A__  :	List[Any]         =							DDPMScheduler(
					    num_train_timesteps=1000				,       beta_schedule='''squaredcos_cap_v2'''				,       beta_start=0.0_0_0_1				,       beta_end=0.0_2				,       )

					torch.manual_seed(0		)
					A__  :	str         =							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   __snake_case				(			self		):
					A__  :	int         =							self.get_dummy_components()
					A__  :	Tuple         =							self.pipeline_class(**UpperCamelCase__		)
					pipe.to(UpperCamelCase__		)
					pipe.set_progress_bar_config(disable=UpperCamelCase__		)

					A__  :	List[str]         =							self.get_dummy_inputs(UpperCamelCase__		)

					A__  :	Optional[int]         =							inputs['''prompt''']
					A__  :	Optional[int]         =							inputs['''generator''']
					A__  :	int         =							inputs['''num_inference_steps''']
					A__  :	Optional[Any]         =							inputs['''output_type''']

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

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

					if "original_image" in inputs:
									A__  :	Optional[int]         =							inputs['''original_image''']
					else:
									A__  :	Tuple         =							None

					A__       ,							A__  :	int         =							pipe.encode_prompt(UpperCamelCase__		)

					# inputs with prompt converted to embeddings
					A__  :	List[str]         =							{
					    '''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__  :	str         =							mask_image

					if original_image is not None:
									A__  :	List[str]         =							original_image

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

					A__  :	int         =							pipe(**UpperCamelCase__		)[0]

					with tempfile.TemporaryDirectory() as tmpdir:
									pipe.save_pretrained(UpperCamelCase__		)
									A__  :	Union[str, 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

					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."				,       )

					A__  :	int         =							self.get_dummy_inputs(UpperCamelCase__		)

					A__  :	Optional[int]         =							inputs['''generator''']
					A__  :	str         =							inputs['''num_inference_steps''']
					A__  :	Optional[int]         =							inputs['''output_type''']

					# inputs with prompt converted to embeddings
					A__  :	List[str]         =							{
					    '''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__  :	List[Any]         =							image

					if mask_image is not None:
									A__  :	Union[str, Any]         =							mask_image

					if original_image is not None:
									A__  :	List[str]         =							original_image

					A__  :	Any         =							pipe_loaded(**UpperCamelCase__		)[0]

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





	def   __snake_case				(			self		):
					A__  :	Optional[Any]         =							self.get_dummy_components()
					A__  :	List[Any]         =							self.pipeline_class(**UpperCamelCase__		)
					pipe.to(UpperCamelCase__		)
					pipe.set_progress_bar_config(disable=UpperCamelCase__		)

					A__  :	int         =							self.get_dummy_inputs(UpperCamelCase__		)
					A__  :	str         =							pipe(**UpperCamelCase__		)[0]

					with tempfile.TemporaryDirectory() as tmpdir:
									pipe.save_pretrained(UpperCamelCase__		)
									A__  :	Optional[int]         =							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

					A__  :	List[Any]         =							self.get_dummy_inputs(UpperCamelCase__		)
					A__  :	str         =							pipe_loaded(**UpperCamelCase__		)[0]

					A__  :	Optional[int]         =							np.abs(to_np(UpperCamelCase__		) - to_np(UpperCamelCase__		)		).max()
					self.assertLess(UpperCamelCase__				,       1e-4		)

from abc import ABC, abstractmethod
from argparse import ArgumentParser







class  UpperCamelCase__		(    SCREAMING_SNAKE_CASE_   ):







	'''simple docstring'''





	@staticmethod
	@abstractmethod
	def   __snake_case				(			UpperCamelCase__		):
					raise NotImplementedError()





	@abstractmethod
	def   __snake_case				(			self		):
					raise NotImplementedError()