





"""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  _lowerCamelCase		(						self     ):
									torch.manual_seed(0     )
									__a :						str			       =  TaEncoderModel.from_pretrained('''hf-internal-testing/tiny-random-t5'''     )

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

									torch.manual_seed(0     )
									__a :						Dict			       =  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 :						str			       =  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 :						Any			       =  IFWatermarker()

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







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

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

									torch.manual_seed(0     )
									__a :						Tuple			       =  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 :						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 :						Dict			       =  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 :						List[Any]			       =  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  _lowerCamelCase		(						self     ):
									__a :						Union[str, Any]			       =  self.get_dummy_components()
									__a :						Union[str, Any]			       =  self.pipeline_class(**_UpperCAmelCase     )
									pipe.to(_UpperCAmelCase     )
									pipe.set_progress_bar_config(disable=_UpperCAmelCase     )

									__a :						Optional[int]			       =  self.get_dummy_inputs(_UpperCAmelCase     )

									__a :						Any			       =  inputs['''prompt''']
									__a :						Any			       =  inputs['''generator''']
									__a :						Tuple			       =  inputs['''num_inference_steps''']
									__a :						Tuple			       =  inputs['''output_type''']

									if "image" in inputs:
													__a :						Any			       =  inputs['''image''']
									else:
													__a :						Optional[int]			       =  None

									if "mask_image" in inputs:
													__a :						Optional[Any]			       =  inputs['''mask_image''']
									else:
													__a :						str			       =  None

									if "original_image" in inputs:
													__a :						List[str]			       =  inputs['''original_image''']
									else:
													__a :						Optional[int]			       =  None

									__a       ,						__a :						Dict			       =  pipe.encode_prompt(_UpperCAmelCase     )

									# inputs with prompt converted to embeddings
									__a :						List[Any]			       =  {
									    '''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 :						Optional[int]			       =  image

									if mask_image is not None:
													__a :						Optional[Any]			       =  mask_image

									if original_image is not None:
													__a :						Dict			       =  original_image

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

									__a :						Optional[int]			       =  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

									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 :						Optional[int]			       =  self.get_dummy_inputs(_UpperCAmelCase     )

									__a :						int			       =  inputs['''generator''']
									__a :						List[Any]			       =  inputs['''num_inference_steps''']
									__a :						List[str]			       =  inputs['''output_type''']

									# inputs with prompt converted to embeddings
									__a :						int			       =  {
									    '''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 :						Union[str, Any]			       =  image

									if mask_image is not None:
													__a :						Dict			       =  mask_image

									if original_image is not None:
													__a :						List[Any]			       =  original_image

									__a :						Optional[int]			       =  pipe_loaded(**_UpperCAmelCase     )[0]

									__a :						List[Any]			       =  np.abs(to_np(_UpperCAmelCase     ) - to_np(_UpperCAmelCase     )     ).max()
									self.assertLess(_UpperCAmelCase	,			1e-4     )







					def  _lowerCamelCase		(						self     ):
									__a :						Optional[int]			       =  self.get_dummy_components()
									__a :						Dict			       =  self.pipeline_class(**_UpperCAmelCase     )
									pipe.to(_UpperCAmelCase     )
									pipe.set_progress_bar_config(disable=_UpperCAmelCase     )

									__a :						Dict			       =  self.get_dummy_inputs(_UpperCAmelCase     )
									__a :						Optional[Any]			       =  pipe(**_UpperCAmelCase     )[0]

									with tempfile.TemporaryDirectory() as tmpdir:
													pipe.save_pretrained(_UpperCAmelCase     )
													__a :						str			       =  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 :						Optional[Any]			       =  self.get_dummy_inputs(_UpperCAmelCase     )
									__a :						int			       =  pipe_loaded(**_UpperCAmelCase     )[0]

									__a :						Union[str, Any]			       =  np.abs(to_np(_UpperCAmelCase     ) - to_np(_UpperCAmelCase     )     ).max()
									self.assertLess(_UpperCAmelCase	,			1e-4     )






"""simple docstring"""






import torch
from torch import nn
from transformers import CLIPPreTrainedModel, CLIPVisionModel

from ...models.attention import BasicTransformerBlock
from ...utils import logging


A 			=  logging.get_logger(__name__)  # pylint: disable=invalid-name




class 				__lowercase			(			_UpperCamelCase							):







					'''simple docstring'''




					def __init__(						self	,			_UpperCAmelCase	,			_UpperCAmelCase=768     ):
									super().__init__(_UpperCAmelCase     )
									__a :						str			       =  proj_size

									__a :						Optional[Any]			       =  CLIPVisionModel(_UpperCAmelCase     )
									__a :						List[Any]			       =  PaintByExampleMapper(_UpperCAmelCase     )
									__a :						int			       =  nn.LayerNorm(config.hidden_size     )
									__a :						List[Any]			       =  nn.Linear(config.hidden_size	,			self.proj_size     )

									# uncondition for scaling
									__a :						int			       =  nn.Parameter(torch.randn((1, 1, self.proj_size)     )     )







					def  _lowerCamelCase		(						self	,			_UpperCAmelCase	,			_UpperCAmelCase=False     ):
									__a :						str			       =  self.model(pixel_values=_UpperCAmelCase     )
									__a :						Union[str, Any]			       =  clip_output.pooler_output
									__a :						Optional[int]			       =  self.mapper(latent_states[:, None]     )
									__a :						int			       =  self.final_layer_norm(_UpperCAmelCase     )
									__a :						Optional[Any]			       =  self.proj_out(_UpperCAmelCase     )
									if return_uncond_vector:
													return latent_states, self.uncond_vector

									return latent_states






class 				__lowercase			(			nn.Module							):







					'''simple docstring'''




					def __init__(						self	,			_UpperCAmelCase     ):
									super().__init__()
									__a :						List[str]			       =  (config.num_hidden_layers + 1) // 5
									__a :						Optional[Any]			       =  config.hidden_size
									__a :						str			       =  1
									__a :						Union[str, Any]			       =  nn.ModuleList(
									    [
									        BasicTransformerBlock(_UpperCAmelCase	,			_UpperCAmelCase	,			_UpperCAmelCase	,			activation_fn='''gelu'''	,			attention_bias=_UpperCAmelCase     )
									        for _ in range(_UpperCAmelCase     )
									    ]     )







					def  _lowerCamelCase		(						self	,			_UpperCAmelCase     ):
									for block in self.blocks:
													__a :						Union[str, Any]			       =  block(_UpperCAmelCase     )

									return hidden_states