

import types
from typing import List, Optional, Tuple, Union

import torch
from transformers import CLIPTextModelWithProjection, CLIPTokenizer
from transformers.models.clip.modeling_clip import CLIPTextModelOutput

from diffusers.models import PriorTransformer
from diffusers.pipelines import DiffusionPipeline, StableDiffusionImageVariationPipeline
from diffusers.schedulers import UnCLIPScheduler
from diffusers.utils import logging, randn_tensor


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

def 							__SCREAMING_SNAKE_CASE							( self:     str     ,					lowerCAmelCase:     str     ,					lowerCAmelCase:     Union[str, Any]     ,					lowerCAmelCase:     int     ,					lowerCAmelCase:     List[str]       )      ->				Optional[int]:
				_UpperCAmelCase				:						Dict       						=			image.to(device=lowercase_       )
				_UpperCAmelCase				:						int       						=			image  # take image as image_embeddings
				_UpperCAmelCase				:						Union[str, Any]       						=			image_embeddings.unsqueeze(1       )

				# duplicate image embeddings for each generation per prompt, using mps friendly method
				_UpperCAmelCase						,							_UpperCAmelCase						,							_UpperCAmelCase				:						Optional[int]       						=			image_embeddings.shape
				_UpperCAmelCase				:						Tuple       						=			image_embeddings.repeat(1     ,					lowercase_     ,					1       )
				_UpperCAmelCase				:						Dict       						=			image_embeddings.view(bs_embed * num_images_per_prompt     ,					lowercase_     ,					-1       )

				if do_classifier_free_guidance:
								_UpperCAmelCase				:						str       						=			torch.zeros_like(lowercase_       )

								# For classifier free guidance, we need to do two forward passes.
								# Here we concatenate the unconditional and text embeddings into a single batch
								# to avoid doing two forward passes
								_UpperCAmelCase				:						Tuple       						=			torch.cat([uncond_embeddings, image_embeddings]       )

				return image_embeddings





class  a     (		A__ ):


						def __init__(				self  ,    A_  ,    A_  ,    A_  ,    A_  ,    A_ = None  ,    ):







										'''simple docstring'''


										super().__init__()

										_UpperCAmelCase				:						List[str]       						=			{"image_encoder": None} if decoder_pipe_kwargs is None else decoder_pipe_kwargs

										_UpperCAmelCase				:						Dict       						=			decoder_pipe_kwargs.get("torch_dtype"  ,    lowerCamelCase__		) or prior.dtype

										_UpperCAmelCase				:						str       						=			StableDiffusionImageVariationPipeline.from_pretrained(
										    "lambdalabs/sd-image-variations-diffusers"  ,    **lowerCamelCase__		)

										# replace `_encode_image` method
										_UpperCAmelCase				:						Dict       						=			types.MethodType(_encode_image  ,    self.decoder_pipe		)

										self.register_modules(
										    prior=lowerCamelCase__  ,    tokenizer=lowerCamelCase__  ,    text_encoder=lowerCamelCase__  ,    prior_scheduler=lowerCamelCase__  ,    )


						def 				_UpperCAmelCase (				self  ,    A_  ,    A_  ,    A_  ,    A_  ,    A_ = None  ,    A_ = None  ,    ):







										'''simple docstring'''


										if text_model_output is None:
														_UpperCAmelCase				:						List[str]       						=			len(lowerCamelCase__		) if isinstance(lowerCamelCase__  ,    lowerCamelCase__		) else 1
														# get prompt text embeddings
														_UpperCAmelCase				:						Dict       						=			self.tokenizer(
														    lowerCamelCase__  ,    padding="max_length"  ,    max_length=self.tokenizer.model_max_length  ,    return_tensors="pt"  ,    )
														_UpperCAmelCase				:						Any       						=			text_inputs.input_ids
														_UpperCAmelCase				:						List[str]       						=			text_inputs.attention_mask.bool().to(lowerCamelCase__		)

														if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
																		_UpperCAmelCase				:						List[str]       						=			self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :]		)
																		logger.warning(
																		    "The following part of your input was truncated because CLIP can only handle sequences up to"
																		    f' {self.tokenizer.model_max_length} tokens: {removed_text}'		)
																		_UpperCAmelCase				:						int       						=			text_input_ids[:, : self.tokenizer.model_max_length]

														_UpperCAmelCase				:						Dict       						=			self.text_encoder(text_input_ids.to(lowerCamelCase__		)		)

														_UpperCAmelCase				:						List[Any]       						=			text_encoder_output.text_embeds
														_UpperCAmelCase				:						Optional[int]       						=			text_encoder_output.last_hidden_state

										else:
														_UpperCAmelCase				:						int       						=			text_model_output[0].shape[0]
														_UpperCAmelCase						,							_UpperCAmelCase				:						Any       						=			text_model_output[0], text_model_output[1]
														_UpperCAmelCase				:						Tuple       						=			text_attention_mask

										_UpperCAmelCase				:						Union[str, Any]       						=			text_embeddings.repeat_interleave(lowerCamelCase__  ,    dim=0		)
										_UpperCAmelCase				:						List[Any]       						=			text_encoder_hidden_states.repeat_interleave(lowerCamelCase__  ,    dim=0		)
										_UpperCAmelCase				:						List[Any]       						=			text_mask.repeat_interleave(lowerCamelCase__  ,    dim=0		)

										if do_classifier_free_guidance:
														_UpperCAmelCase				:						Optional[int]       						=			[""] * batch_size

														_UpperCAmelCase				:						Optional[Any]       						=			self.tokenizer(
														    lowerCamelCase__  ,    padding="max_length"  ,    max_length=self.tokenizer.model_max_length  ,    truncation=lowerCamelCase__  ,    return_tensors="pt"  ,    )
														_UpperCAmelCase				:						Optional[Any]       						=			uncond_input.attention_mask.bool().to(lowerCamelCase__		)
														_UpperCAmelCase				:						str       						=			self.text_encoder(uncond_input.input_ids.to(lowerCamelCase__		)		)

														_UpperCAmelCase				:						List[str]       						=			uncond_embeddings_text_encoder_output.text_embeds
														_UpperCAmelCase				:						List[Any]       						=			uncond_embeddings_text_encoder_output.last_hidden_state

														# duplicate unconditional embeddings for each generation per prompt, using mps friendly method

														_UpperCAmelCase				:						Tuple       						=			uncond_embeddings.shape[1]
														_UpperCAmelCase				:						Union[str, Any]       						=			uncond_embeddings.repeat(1  ,    lowerCamelCase__		)
														_UpperCAmelCase				:						str       						=			uncond_embeddings.view(batch_size * num_images_per_prompt  ,    lowerCamelCase__		)

														_UpperCAmelCase				:						Optional[Any]       						=			uncond_text_encoder_hidden_states.shape[1]
														_UpperCAmelCase				:						Tuple       						=			uncond_text_encoder_hidden_states.repeat(1  ,    lowerCamelCase__  ,    1		)
														_UpperCAmelCase				:						Tuple       						=			uncond_text_encoder_hidden_states.view(
														    batch_size * num_images_per_prompt  ,    lowerCamelCase__  ,    -1		)
														_UpperCAmelCase				:						List[Any]       						=			uncond_text_mask.repeat_interleave(lowerCamelCase__  ,    dim=0		)

														# done duplicates

														# For classifier free guidance, we need to do two forward passes.
														# Here we concatenate the unconditional and text embeddings into a single batch
														# to avoid doing two forward passes
														_UpperCAmelCase				:						Any       						=			torch.cat([uncond_embeddings, text_embeddings]		)
														_UpperCAmelCase				:						Tuple       						=			torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states]		)

														_UpperCAmelCase				:						str       						=			torch.cat([uncond_text_mask, text_mask]		)

										return text_embeddings, text_encoder_hidden_states, text_mask


						@property
						def 				_UpperCAmelCase (				self		):







										'''simple docstring'''


										if self.device != torch.device("meta"		) or not hasattr(self.prior  ,    "_hf_hook"		):
														return self.device
										for module in self.prior.modules():
														if (
														    hasattr(lowerCamelCase__  ,    "_hf_hook"		)
														    and hasattr(module._hf_hook  ,    "execution_device"		)
														    and module._hf_hook.execution_device is not None
														):
																		return torch.device(module._hf_hook.execution_device		)
										return self.device


						def 				_UpperCAmelCase (				self  ,    A_  ,    A_  ,    A_  ,    A_  ,    A_  ,    A_		):







										'''simple docstring'''


										if latents is None:
														_UpperCAmelCase				:						Tuple       						=			randn_tensor(lowerCamelCase__  ,    generator=lowerCamelCase__  ,    device=lowerCamelCase__  ,    dtype=lowerCamelCase__		)
										else:
														if latents.shape != shape:
																		raise ValueError(f'Unexpected latents shape, got {latents.shape}, expected {shape}'		)
														_UpperCAmelCase				:						Any       						=			latents.to(lowerCamelCase__		)

										_UpperCAmelCase				:						Dict       						=			latents * scheduler.init_noise_sigma
										return latents


						def 				_UpperCAmelCase (				self  ,    A_ = None		):







										'''simple docstring'''


										self.decoder_pipe.to(lowerCamelCase__		)
										super().to(lowerCamelCase__		)





						@torch.no_grad()
						def __call__(				self  ,    A_ = None  ,    A_ = None  ,    A_ = None  ,    A_ = 1  ,    A_ = 25  ,    A_ = None  ,    A_ = None  ,    A_ = None  ,    A_ = None  ,    A_ = 4.0  ,    A_ = 8.0  ,    A_ = 50  ,    A_ = 1  ,    A_ = 0.0  ,    A_ = "pil"  ,    A_ = True  ,    ):







										'''simple docstring'''


										if prompt is not None:
														if isinstance(lowerCamelCase__  ,    lowerCamelCase__		):
																		_UpperCAmelCase				:						int       						=			1
														elif isinstance(lowerCamelCase__  ,    lowerCamelCase__		):
																		_UpperCAmelCase				:						Any       						=			len(lowerCamelCase__		)
														else:
																		raise ValueError(f'`prompt` has to be of type `str` or `list` but is {type(lowerCamelCase__		)}'		)
										else:
														_UpperCAmelCase				:						str       						=			text_model_output[0].shape[0]

										_UpperCAmelCase				:						Optional[int]       						=			self._execution_device

										_UpperCAmelCase				:						Optional[int]       						=			batch_size * num_images_per_prompt

										_UpperCAmelCase				:						Optional[Any]       						=			prior_guidance_scale > 1.0 or decoder_guidance_scale > 1.0

										_UpperCAmelCase						,							_UpperCAmelCase						,							_UpperCAmelCase				:						str       						=			self._encode_prompt(
										    lowerCamelCase__  ,    lowerCamelCase__  ,    lowerCamelCase__  ,    lowerCamelCase__  ,    lowerCamelCase__  ,    lowerCamelCase__		)

										# prior

										self.prior_scheduler.set_timesteps(lowerCamelCase__  ,    device=lowerCamelCase__		)
										_UpperCAmelCase				:						Optional[int]       						=			self.prior_scheduler.timesteps

										_UpperCAmelCase				:						Dict       						=			self.prior.config.embedding_dim

										_UpperCAmelCase				:						Any       						=			self.prepare_latents(
										    (batch_size, embedding_dim)  ,    text_embeddings.dtype  ,    lowerCamelCase__  ,    lowerCamelCase__  ,    lowerCamelCase__  ,    self.prior_scheduler  ,    )

										for i, t in enumerate(self.progress_bar(lowerCamelCase__		)		):
														# expand the latents if we are doing classifier free guidance
														_UpperCAmelCase				:						List[Any]       						=			torch.cat([prior_latents] * 2		) if do_classifier_free_guidance else prior_latents

														_UpperCAmelCase				:						Union[str, Any]       						=			self.prior(
														    lowerCamelCase__  ,    timestep=lowerCamelCase__  ,    proj_embedding=lowerCamelCase__  ,    encoder_hidden_states=lowerCamelCase__  ,    attention_mask=lowerCamelCase__  ,    ).predicted_image_embedding

														if do_classifier_free_guidance:
																		_UpperCAmelCase						,							_UpperCAmelCase				:						str       						=			predicted_image_embedding.chunk(2		)
																		_UpperCAmelCase				:						List[str]       						=			predicted_image_embedding_uncond + prior_guidance_scale * (
																		    predicted_image_embedding_text - predicted_image_embedding_uncond
																		)

														if i + 1 == prior_timesteps_tensor.shape[0]:
																		_UpperCAmelCase				:						Tuple       						=			None
														else:
																		_UpperCAmelCase				:						Dict       						=			prior_timesteps_tensor[i + 1]

														_UpperCAmelCase				:						List[str]       						=			self.prior_scheduler.step(
														    lowerCamelCase__  ,    timestep=lowerCamelCase__  ,    sample=lowerCamelCase__  ,    generator=lowerCamelCase__  ,    prev_timestep=lowerCamelCase__  ,    ).prev_sample

										_UpperCAmelCase				:						List[Any]       						=			self.prior.post_process_latents(lowerCamelCase__		)

										_UpperCAmelCase				:						str       						=			prior_latents

										_UpperCAmelCase				:						List[str]       						=			self.decoder_pipe(
										    image=lowerCamelCase__  ,    height=lowerCamelCase__  ,    width=lowerCamelCase__  ,    num_inference_steps=lowerCamelCase__  ,    guidance_scale=lowerCamelCase__  ,    generator=lowerCamelCase__  ,    output_type=lowerCamelCase__  ,    return_dict=lowerCamelCase__  ,    num_images_per_prompt=lowerCamelCase__  ,    eta=lowerCamelCase__  ,    )
										return output







"""simple docstring"""
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin


if is_torch_available():
			import torch

if is_vision_available():
			from PIL import Image

			from transformers import BlipImageProcessor


class 							lowerCamelCase_(      unittest.TestCase			):





					'''simple docstring'''



					def __init__(			self ,				lowerCamelCase__ ,				lowerCamelCase__=7 ,				lowerCamelCase__=3 ,				lowerCamelCase__=1_8 ,				lowerCamelCase__=3_0 ,				lowerCamelCase__=4_0_0 ,				lowerCamelCase__=True ,				lowerCamelCase__=None ,				lowerCamelCase__=True ,				lowerCamelCase__=False ,				lowerCamelCase__=[0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3] ,				lowerCamelCase__=[0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1] ,				lowerCamelCase__=True ,				):
									_lowerCamelCase					     = size if size is not None else {'''height''': 2_0, '''width''': 2_0}
									_lowerCamelCase					     = parent
									_lowerCamelCase					     = batch_size
									_lowerCamelCase					     = num_channels
									_lowerCamelCase					     = image_size
									_lowerCamelCase					     = min_resolution
									_lowerCamelCase					     = max_resolution
									_lowerCamelCase					     = do_resize
									_lowerCamelCase					     = size
									_lowerCamelCase					     = do_normalize
									_lowerCamelCase					     = image_mean
									_lowerCamelCase					     = image_std
									_lowerCamelCase					     = do_pad
									_lowerCamelCase					     = do_convert_rgb

					def 	snake_case__					(			self      ):
									return {
									    "do_resize": self.do_resize,
									    "size": self.size,
									    "do_normalize": self.do_normalize,
									    "image_mean": self.image_mean,
									    "image_std": self.image_std,
									    "do_convert_rgb": self.do_convert_rgb,
									    "do_pad": self.do_pad,
									}





					def 	snake_case__					(			self ,				lowerCamelCase__=False ,				lowerCamelCase__=False ,				lowerCamelCase__=False      ):
									assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

									if equal_resolution:
													_lowerCamelCase					     = []
													for i in range(self.batch_size      ):
																	image_inputs.append(
																	    np.random.randint(
																	        2_5_5 ,				size=(self.num_channels, self.max_resolution, self.max_resolution) ,				dtype=np.uinta      )      )
									else:
													_lowerCamelCase					     = []
													for i in range(self.batch_size      ):
																	_lowerCamelCase     ,   _lowerCamelCase					     = np.random.choice(np.arange(self.min_resolution ,				self.max_resolution      ) ,				2      )
																	image_inputs.append(np.random.randint(2_5_5 ,				size=(self.num_channels, width, height) ,				dtype=np.uinta      )      )

									if not numpify and not torchify:
													# PIL expects the channel dimension as last dimension
													_lowerCamelCase					     = [Image.fromarray(np.moveaxis(lowerCamelCase__ ,				0 ,				-1      )      ) for x in image_inputs]

									if torchify:
													_lowerCamelCase					     = [torch.from_numpy(lowerCamelCase__      ) for x in image_inputs]

									return image_inputs


@require_torch
@require_vision
class 							lowerCamelCase_(      A__, unittest.TestCase			):





					'''simple docstring'''



					lowercase__      :				Any        =       BlipImageProcessor if is_vision_available() else None

					def 	snake_case__					(			self      ):
									_lowerCamelCase					     = BlipImageProcessingTester(self      )

					@property
					def 	snake_case__					(			self      ):
									return self.image_processor_tester.prepare_image_processor_dict()

					def 	snake_case__					(			self      ):
									_lowerCamelCase					     = self.image_processing_class(**self.image_processor_dict      )
									self.assertTrue(hasattr(lowerCamelCase__ ,				'''do_resize'''      )      )
									self.assertTrue(hasattr(lowerCamelCase__ ,				'''size'''      )      )
									self.assertTrue(hasattr(lowerCamelCase__ ,				'''do_normalize'''      )      )
									self.assertTrue(hasattr(lowerCamelCase__ ,				'''image_mean'''      )      )
									self.assertTrue(hasattr(lowerCamelCase__ ,				'''image_std'''      )      )
									self.assertTrue(hasattr(lowerCamelCase__ ,				'''do_convert_rgb'''      )      )

					def 	snake_case__					(			self      ):
									pass

					def 	snake_case__					(			self      ):
									# Initialize image_processor
									_lowerCamelCase					     = self.image_processing_class(**self.image_processor_dict      )
									# create random PIL images
									_lowerCamelCase					     = self.image_processor_tester.prepare_inputs(equal_resolution=lowerCamelCase__      )
									for image in image_inputs:
													self.assertIsInstance(lowerCamelCase__ ,				Image.Image      )

									# Test not batched input
									_lowerCamelCase					     = image_processor(image_inputs[0] ,				return_tensors='''pt'''      ).pixel_values
									self.assertEqual(
									    encoded_images.shape ,				(
									        1,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.size['''height'''],
									        self.image_processor_tester.size['''width'''],
									    ) ,				)

									# Test batched
									_lowerCamelCase					     = image_processor(lowerCamelCase__ ,				return_tensors='''pt'''      ).pixel_values
									self.assertEqual(
									    encoded_images.shape ,				(
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.size['''height'''],
									        self.image_processor_tester.size['''width'''],
									    ) ,				)

					def 	snake_case__					(			self      ):
									# Initialize image_processor
									_lowerCamelCase					     = self.image_processing_class(**self.image_processor_dict      )
									# create random numpy tensors
									_lowerCamelCase					     = self.image_processor_tester.prepare_inputs(equal_resolution=lowerCamelCase__ ,				numpify=lowerCamelCase__      )
									for image in image_inputs:
													self.assertIsInstance(lowerCamelCase__ ,				np.ndarray      )

									# Test not batched input
									_lowerCamelCase					     = image_processor(image_inputs[0] ,				return_tensors='''pt'''      ).pixel_values
									self.assertEqual(
									    encoded_images.shape ,				(
									        1,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.size['''height'''],
									        self.image_processor_tester.size['''width'''],
									    ) ,				)

									# Test batched
									_lowerCamelCase					     = image_processor(lowerCamelCase__ ,				return_tensors='''pt'''      ).pixel_values
									self.assertEqual(
									    encoded_images.shape ,				(
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.size['''height'''],
									        self.image_processor_tester.size['''width'''],
									    ) ,				)





					def 	snake_case__					(			self      ):
									# Initialize image_processor
									_lowerCamelCase					     = self.image_processing_class(**self.image_processor_dict      )
									# create random PyTorch tensors
									_lowerCamelCase					     = self.image_processor_tester.prepare_inputs(equal_resolution=lowerCamelCase__ ,				torchify=lowerCamelCase__      )
									for image in image_inputs:
													self.assertIsInstance(lowerCamelCase__ ,				torch.Tensor      )

									# Test not batched input
									_lowerCamelCase					     = image_processor(image_inputs[0] ,				return_tensors='''pt'''      ).pixel_values
									self.assertEqual(
									    encoded_images.shape ,				(
									        1,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.size['''height'''],
									        self.image_processor_tester.size['''width'''],
									    ) ,				)

									# Test batched
									_lowerCamelCase					     = image_processor(lowerCamelCase__ ,				return_tensors='''pt'''      ).pixel_values
									self.assertEqual(
									    encoded_images.shape ,				(
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.size['''height'''],
									        self.image_processor_tester.size['''width'''],
									    ) ,				)


@require_torch
@require_vision
class 							lowerCamelCase_(      A__, unittest.TestCase			):





					'''simple docstring'''



					lowercase__      :				Tuple        =       BlipImageProcessor if is_vision_available() else None

					def 	snake_case__					(			self      ):
									_lowerCamelCase					     = BlipImageProcessingTester(self ,				num_channels=4      )
									_lowerCamelCase					     = 3

					@property
					def 	snake_case__					(			self      ):
									return self.image_processor_tester.prepare_image_processor_dict()

					def 	snake_case__					(			self      ):
									_lowerCamelCase					     = self.image_processing_class(**self.image_processor_dict      )
									self.assertTrue(hasattr(lowerCamelCase__ ,				'''do_resize'''      )      )
									self.assertTrue(hasattr(lowerCamelCase__ ,				'''size'''      )      )
									self.assertTrue(hasattr(lowerCamelCase__ ,				'''do_normalize'''      )      )
									self.assertTrue(hasattr(lowerCamelCase__ ,				'''image_mean'''      )      )
									self.assertTrue(hasattr(lowerCamelCase__ ,				'''image_std'''      )      )
									self.assertTrue(hasattr(lowerCamelCase__ ,				'''do_convert_rgb'''      )      )

					def 	snake_case__					(			self      ):
									pass





					def 	snake_case__					(			self      ):
									# Initialize image_processor
									_lowerCamelCase					     = self.image_processing_class(**self.image_processor_dict      )
									# create random PIL images
									_lowerCamelCase					     = self.image_processor_tester.prepare_inputs(equal_resolution=lowerCamelCase__      )
									for image in image_inputs:
													self.assertIsInstance(lowerCamelCase__ ,				Image.Image      )

									# Test not batched input
									_lowerCamelCase					     = image_processor(image_inputs[0] ,				return_tensors='''pt'''      ).pixel_values
									self.assertEqual(
									    encoded_images.shape ,				(
									        1,
									        self.expected_encoded_image_num_channels,
									        self.image_processor_tester.size['''height'''],
									        self.image_processor_tester.size['''width'''],
									    ) ,				)

									# Test batched
									_lowerCamelCase					     = image_processor(lowerCamelCase__ ,				return_tensors='''pt'''      ).pixel_values
									self.assertEqual(
									    encoded_images.shape ,				(
									        self.image_processor_tester.batch_size,
									        self.expected_encoded_image_num_channels,
									        self.image_processor_tester.size['''height'''],
									        self.image_processor_tester.size['''width'''],
									    ) ,				)
