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

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

					torch.manual_seed(0					)
					__A       =					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       =					DDPMScheduler(
					    num_train_timesteps=10_00				,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					)
					__A       =					IFWatermarker()

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






	def 							UpperCamelCase_    (			self   :					int					):
					torch.manual_seed(0					)
					__A       =					TaEncoderModel.from_pretrained("hf-internal-testing/tiny-random-t5"					)

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

					torch.manual_seed(0					)
					__A       =					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					)
					__A       =					DDPMScheduler(
					    num_train_timesteps=10_00				,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					)
					__A       =					DDPMScheduler(
					    num_train_timesteps=10_00				,beta_schedule="squaredcos_cap_v2"				,beta_start=0.00_01				,beta_end=0.02				,)

					torch.manual_seed(0					)
					__A       =					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[str]					):
					__A       =					self.get_dummy_components()
					__A       =					self.pipeline_class(**A					)
					pipe.to(A					)
					pipe.set_progress_bar_config(disable=A					)

					__A       =					self.get_dummy_inputs(A					)

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

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

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

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

					__A     ,				__A       =					pipe.encode_prompt(A					)

					# inputs with prompt converted to embeddings
					__A       =					{
					    "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       =					image

					if mask_image is not None:
									__A       =					mask_image

					if original_image is not None:
									__A       =					original_image

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

					__A       =					pipe(**A					)[0]

					with tempfile.TemporaryDirectory() as tmpdir:
									pipe.save_pretrained(A					)
									__A       =					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       =					self.get_dummy_inputs(A					)

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

					# inputs with prompt converted to embeddings
					__A       =					{
					    "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       =					image

					if mask_image is not None:
									__A       =					mask_image

					if original_image is not None:
									__A       =					original_image

					__A       =					pipe_loaded(**A					)[0]

					__A       =					np.abs(to_np(A					) - to_np(A					)					).max()
					self.assertLess(A				,1E-4					)







	def 							UpperCamelCase_    (			self   :					Any					):
					__A       =					self.get_dummy_components()
					__A       =					self.pipeline_class(**A					)
					pipe.to(A					)
					pipe.set_progress_bar_config(disable=A					)

					__A       =					self.get_dummy_inputs(A					)
					__A       =					pipe(**A					)[0]

					with tempfile.TemporaryDirectory() as tmpdir:
									pipe.save_pretrained(A					)
									__A       =					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       =					self.get_dummy_inputs(A					)
					__A       =					pipe_loaded(**A					)[0]

					__A       =					np.abs(to_np(A					) - to_np(A					)					).max()
					self.assertLess(A				,1E-4					)




"""simple docstring"""


import warnings

from ...utils import logging
from .image_processing_yolos import YolosImageProcessor


A	:  Optional[Any]            =     logging.get_logger(__name__)
class     _UpperCamelCase    (  lowerCAmelCase__						):
							'''simple docstring'''







							def __init__(	self       ,  *__a       ,  **__a      ):
										warnings.warn(
										    "The class YolosFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"
										    " use YolosImageProcessor instead."       ,  __a       ,  )
										super().__init__(*__a       ,  **__a      )




