


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:


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Optional[int]						)			->	Tuple:




											'''simple docstring'''

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

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

											torch.manual_seed(0						)
											__snake_case				=		UNetaDConditionModel(
											    sample_size=3_2     ,    layers_per_block=1     ,    block_out_channels=[3_2, 6_4]     ,    down_block_types=[
											        "ResnetDownsampleBlock2D",
											        "SimpleCrossAttnDownBlock2D",
											    ]     ,    mid_block_type="UNetMidBlock2DSimpleCrossAttn"     ,    up_block_types=["SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"]     ,    in_channels=3     ,    out_channels=6     ,    cross_attention_dim=3_2     ,    encoder_hid_dim=3_2     ,    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						)
											__snake_case				=		DDPMScheduler(
											    num_train_timesteps=1_0_0_0     ,    beta_schedule="squaredcos_cap_v2"     ,    beta_start=0.0001     ,    beta_end=0.02     ,    thresholding=SCREAMING_SNAKE_CASE     ,    dynamic_thresholding_ratio=0.95     ,    sample_max_value=1.0     ,    prediction_type="epsilon"     ,    variance_type="learned_range"     ,    )

											torch.manual_seed(0						)
											__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	:		List[Any]						)			->	str:




											'''simple docstring'''

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

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

											torch.manual_seed(0						)
											__snake_case				=		UNetaDConditionModel(
											    sample_size=3_2     ,    layers_per_block=[1, 2]     ,    block_out_channels=[3_2, 6_4]     ,    down_block_types=[
											        "ResnetDownsampleBlock2D",
											        "SimpleCrossAttnDownBlock2D",
											    ]     ,    mid_block_type="UNetMidBlock2DSimpleCrossAttn"     ,    up_block_types=["SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"]     ,    in_channels=6     ,    out_channels=6     ,    cross_attention_dim=3_2     ,    encoder_hid_dim=3_2     ,    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=3_2     ,    )
											unet.set_attn_processor(AttnAddedKVProcessor()						)  # For reproducibility tests

											torch.manual_seed(0						)
											__snake_case				=		DDPMScheduler(
											    num_train_timesteps=1_0_0_0     ,    beta_schedule="squaredcos_cap_v2"     ,    beta_start=0.0001     ,    beta_end=0.02     ,    thresholding=SCREAMING_SNAKE_CASE     ,    dynamic_thresholding_ratio=0.95     ,    sample_max_value=1.0     ,    prediction_type="epsilon"     ,    variance_type="learned_range"     ,    )

											torch.manual_seed(0						)
											__snake_case				=		DDPMScheduler(
											    num_train_timesteps=1_0_0_0     ,    beta_schedule="squaredcos_cap_v2"     ,    beta_start=0.0001     ,    beta_end=0.02     ,    )

											torch.manual_seed(0						)
											__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	:		Dict						)			->	str:




											'''simple docstring'''

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

											__snake_case				=		self.get_dummy_inputs(SCREAMING_SNAKE_CASE						)

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

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

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

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

											__snake_case		,					__snake_case				=		pipe.encode_prompt(SCREAMING_SNAKE_CASE						)

											# inputs with prompt converted to embeddings
											__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:
																	__snake_case				=		image

											if mask_image is not None:
																	__snake_case				=		mask_image

											if original_image is not None:
																	__snake_case				=		original_image

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

											__snake_case				=		pipe(**SCREAMING_SNAKE_CASE						)[0]

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

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

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

											__snake_case				=		self.get_dummy_inputs(SCREAMING_SNAKE_CASE						)

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

											# inputs with prompt converted to embeddings
											__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:
																	__snake_case				=		image

											if mask_image is not None:
																	__snake_case				=		mask_image

											if original_image is not None:
																	__snake_case				=		original_image

											__snake_case				=		pipe_loaded(**SCREAMING_SNAKE_CASE						)[0]

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






					def 							SCREAMING_SNAKE_CASE_  (						self	:		Tuple						)			->	List[str]:




											'''simple docstring'''

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

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

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

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

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

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









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

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


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






class     UpperCamelCase(  _a   ):


					def __init__(						self	:		Optional[int]     ,    SCREAMING_SNAKE_CASE	:		Optional[int]     ,    SCREAMING_SNAKE_CASE	:		Dict=7_6_8						)			->	int:




											'''simple docstring'''

											super().__init__(SCREAMING_SNAKE_CASE						)
											__snake_case				=		proj_size

											__snake_case				=		CLIPVisionModel(SCREAMING_SNAKE_CASE						)
											__snake_case				=		PaintByExampleMapper(SCREAMING_SNAKE_CASE						)
											__snake_case				=		nn.LayerNorm(config.hidden_size						)
											__snake_case				=		nn.Linear(config.hidden_size     ,    self.proj_size						)

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






					def 							SCREAMING_SNAKE_CASE_  (						self	:		Union[str, Any]     ,    SCREAMING_SNAKE_CASE	:		int     ,    SCREAMING_SNAKE_CASE	:		Optional[int]=False						)			->	Dict:




											'''simple docstring'''

											__snake_case				=		self.model(pixel_values=SCREAMING_SNAKE_CASE						)
											__snake_case				=		clip_output.pooler_output
											__snake_case				=		self.mapper(latent_states[:, None]						)
											__snake_case				=		self.final_layer_norm(SCREAMING_SNAKE_CASE						)
											__snake_case				=		self.proj_out(SCREAMING_SNAKE_CASE						)
											if return_uncond_vector:
																	return latent_states, self.uncond_vector

											return latent_states






class     UpperCamelCase(  nn.Module   ):


					def __init__(						self	:		List[Any]     ,    SCREAMING_SNAKE_CASE	:		List[str]						)			->	List[Any]:




											'''simple docstring'''

											super().__init__()
											__snake_case				=		(config.num_hidden_layers + 1) // 5
											__snake_case				=		config.hidden_size
											__snake_case				=		1
											__snake_case				=		nn.ModuleList(
											    [
											        BasicTransformerBlock(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE     ,    activation_fn="gelu"     ,    attention_bias=SCREAMING_SNAKE_CASE						)
											        for _ in range(SCREAMING_SNAKE_CASE						)
											    ]						)






					def 							SCREAMING_SNAKE_CASE_  (						self	:		Union[str, Any]     ,    SCREAMING_SNAKE_CASE	:		str						)			->	Optional[int]:




											'''simple docstring'''

											for block in self.blocks:
																	__snake_case				=		block(SCREAMING_SNAKE_CASE						)

											return hidden_states





