





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        SCREAMING_SNAKE_CASE__						(							self		:					str )  ->	Any:




           '''simple docstring'''


           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=0.0001		,beta_end=0.02		,thresholding=lowerCamelCase__		,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		:					Optional[int] )  ->	Any:




           '''simple docstring'''


           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=1.414		,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=0.0001		,beta_end=0.02		,thresholding=lowerCamelCase__		,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=0.0001		,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		:					Optional[int] )  ->	List[Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		self.get_dummy_components()
           SCREAMING_SNAKE_CASE	=		self.pipeline_class(**lowerCamelCase__ )
           pipe.to(lowerCamelCase__ )
           pipe.set_progress_bar_config(disable=lowerCamelCase__ )

           SCREAMING_SNAKE_CASE	=		self.get_dummy_inputs(lowerCamelCase__ )

           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(lowerCamelCase__ )

           # 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(lowerCamelCase__		,lowerCamelCase__		,lowerCamelCase__ )

           SCREAMING_SNAKE_CASE	=		pipe(**lowerCamelCase__ )[0]

           with tempfile.TemporaryDirectory() as tmpdir:
                 pipe.save_pretrained(lowerCamelCase__ )
                 SCREAMING_SNAKE_CASE	=		self.pipeline_class.from_pretrained(lowerCamelCase__ )
                 pipe_loaded.to(lowerCamelCase__ )
                 pipe_loaded.set_progress_bar_config(disable=lowerCamelCase__ )

           pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor() )  # For reproducibility tests

           for optional_component in pipe._optional_components:
                 self.assertTrue(
                     getattr(lowerCamelCase__		,lowerCamelCase__ ) is None		,F"""`{optional_component}` did not stay set to None after loading."""		,)

           SCREAMING_SNAKE_CASE	=		self.get_dummy_inputs(lowerCamelCase__ )

           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(**lowerCamelCase__ )[0]

           SCREAMING_SNAKE_CASE	=		np.abs(to_np(lowerCamelCase__ ) - to_np(lowerCamelCase__ ) ).max()
           self.assertLess(lowerCamelCase__		,1e-4 )






     def        SCREAMING_SNAKE_CASE__						(							self		:					Union[str, Any] )  ->	List[str]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		self.get_dummy_components()
           SCREAMING_SNAKE_CASE	=		self.pipeline_class(**lowerCamelCase__ )
           pipe.to(lowerCamelCase__ )
           pipe.set_progress_bar_config(disable=lowerCamelCase__ )

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

           with tempfile.TemporaryDirectory() as tmpdir:
                 pipe.save_pretrained(lowerCamelCase__ )
                 SCREAMING_SNAKE_CASE	=		self.pipeline_class.from_pretrained(lowerCamelCase__ )
                 pipe_loaded.to(lowerCamelCase__ )
                 pipe_loaded.set_progress_bar_config(disable=lowerCamelCase__ )

           pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor() )  # For reproducibility tests

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

           SCREAMING_SNAKE_CASE	=		np.abs(to_np(lowerCamelCase__ ) - to_np(lowerCamelCase__ ) ).max()
           self.assertLess(lowerCamelCase__		,1e-4 )









import warnings

from ...utils import logging
from .image_processing_deformable_detr import DeformableDetrImageProcessor


SCREAMING_SNAKE_CASE_		     = logging.get_logger(__name__)

class 	UpperCamelCase__				(   lowerCAmelCase_     ):



     '''simple docstring'''

     def __init__(							self		:					int		,*lowerCamelCase__		:					int		,**lowerCamelCase__		:					List[Any] )  ->	None:




           '''simple docstring'''


           warnings.warn(
               """The class DeformableDetrFeatureExtractor is deprecated and will be removed in version 5 of Transformers."""
               """ Please use DeformableDetrImageProcessor instead."""		,lowerCamelCase__		,)
           super().__init__(*lowerCamelCase__		,**lowerCamelCase__ )


