
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


lowercase__						:List[Any]					    =       logging.get_logger(__name__)  # pylint: disable=invalid-name



def 			UpperCamelCase       (				self					,     lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__						):
					'''simple docstring'''
					lowercase		      =		image.to(device=lowerCAmelCase__						)
					lowercase		      =		image  # take image as image_embeddings
					lowercase		      =		image_embeddings.unsqueeze(1						)

					# duplicate image embeddings for each generation per prompt, using mps friendly method
					lowercase      ,		lowercase      ,		lowercase		      =		image_embeddings.shape
					lowercase		      =		image_embeddings.repeat(1					,     lowerCAmelCase__					,     1						)
					lowercase		      =		image_embeddings.view(bs_embed * num_images_per_prompt					,     lowerCAmelCase__					,     -1						)

					if do_classifier_free_guidance:
										lowercase		      =		torch.zeros_like(lowerCAmelCase__						)

										# 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
										lowercase		      =		torch.cat([uncond_embeddings, image_embeddings]						)

					return image_embeddings







class 							lowercase						(   SCREAMING_SNAKE_CASE__						):




							def __init__(							self  ,A__  ,A__  ,A__  ,A__  ,A__ = None  ,):
												super().__init__()

												lowercase		      =		{'''image_encoder''': None} if decoder_pipe_kwargs is None else decoder_pipe_kwargs

												lowercase		      =		decoder_pipe_kwargs.get('''torch_dtype'''  ,A__) or prior.dtype

												lowercase		      =		StableDiffusionImageVariationPipeline.from_pretrained(
												    '''lambdalabs/sd-image-variations-diffusers'''  ,**A__)

												# replace `_encode_image` method
												lowercase		      =		types.MethodType(_encode_image  ,self.decoder_pipe)

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




							def 					A__ (							self  ,A__  ,A__  ,A__  ,A__  ,A__ = None  ,A__ = None  ,):
												if text_model_output is None:
																	lowercase		      =		len(A__) if isinstance(A__  ,A__) else 1
																	# get prompt text embeddings
																	lowercase		      =		self.tokenizer(
																	    A__  ,padding='''max_length'''  ,max_length=self.tokenizer.model_max_length  ,return_tensors='''pt'''  ,)
																	lowercase		      =		text_inputs.input_ids
																	lowercase		      =		text_inputs.attention_mask.bool().to(A__)

																	if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
																						lowercase		      =		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}')
																						lowercase		      =		text_input_ids[:, : self.tokenizer.model_max_length]

																	lowercase		      =		self.text_encoder(text_input_ids.to(A__))

																	lowercase		      =		text_encoder_output.text_embeds
																	lowercase		      =		text_encoder_output.last_hidden_state

												else:
																	lowercase		      =		text_model_output[0].shape[0]
																	lowercase      ,		lowercase		      =		text_model_output[0], text_model_output[1]
																	lowercase		      =		text_attention_mask

												lowercase		      =		text_embeddings.repeat_interleave(A__  ,dim=0)
												lowercase		      =		text_encoder_hidden_states.repeat_interleave(A__  ,dim=0)
												lowercase		      =		text_mask.repeat_interleave(A__  ,dim=0)

												if do_classifier_free_guidance:
																	lowercase		      =		[''''''] * batch_size

																	lowercase		      =		self.tokenizer(
																	    A__  ,padding='''max_length'''  ,max_length=self.tokenizer.model_max_length  ,truncation=A__  ,return_tensors='''pt'''  ,)
																	lowercase		      =		uncond_input.attention_mask.bool().to(A__)
																	lowercase		      =		self.text_encoder(uncond_input.input_ids.to(A__))

																	lowercase		      =		uncond_embeddings_text_encoder_output.text_embeds
																	lowercase		      =		uncond_embeddings_text_encoder_output.last_hidden_state

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

																	lowercase		      =		uncond_embeddings.shape[1]
																	lowercase		      =		uncond_embeddings.repeat(1  ,A__)
																	lowercase		      =		uncond_embeddings.view(batch_size * num_images_per_prompt  ,A__)

																	lowercase		      =		uncond_text_encoder_hidden_states.shape[1]
																	lowercase		      =		uncond_text_encoder_hidden_states.repeat(1  ,A__  ,1)
																	lowercase		      =		uncond_text_encoder_hidden_states.view(
																	    batch_size * num_images_per_prompt  ,A__  ,-1)
																	lowercase		      =		uncond_text_mask.repeat_interleave(A__  ,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
																	lowercase		      =		torch.cat([uncond_embeddings, text_embeddings])
																	lowercase		      =		torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states])

																	lowercase		      =		torch.cat([uncond_text_mask, text_mask])

												return text_embeddings, text_encoder_hidden_states, text_mask




							@property
							def 					A__ (							self):
												if self.device != torch.device('''meta''') or not hasattr(self.prior  ,'''_hf_hook'''):
																	return self.device
												for module in self.prior.modules():
																	if (
																	    hasattr(A__  ,'''_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 					A__ (							self  ,A__  ,A__  ,A__  ,A__  ,A__  ,A__):
												if latents is None:
																	lowercase		      =		randn_tensor(A__  ,generator=A__  ,device=A__  ,dtype=A__)
												else:
																	if latents.shape != shape:
																						raise ValueError(f'Unexpected latents shape, got {latents.shape}, expected {shape}')
																	lowercase		      =		latents.to(A__)

												lowercase		      =		latents * scheduler.init_noise_sigma
												return latents




							def 					A__ (							self  ,A__ = None):
												self.decoder_pipe.to(A__)
												super().to(A__)





							@torch.no_grad()
							def __call__(							self  ,A__ = None  ,A__ = None  ,A__ = None  ,A__ = 1  ,A__ = 2_5  ,A__ = None  ,A__ = None  ,A__ = None  ,A__ = None  ,A__ = 4.0  ,A__ = 8.0  ,A__ = 5_0  ,A__ = 1  ,A__ = 0.0  ,A__ = "pil"  ,A__ = True  ,):
												if prompt is not None:
																	if isinstance(A__  ,A__):
																						lowercase		      =		1
																	elif isinstance(A__  ,A__):
																						lowercase		      =		len(A__)
																	else:
																						raise ValueError(f'`prompt` has to be of type `str` or `list` but is {type(A__)}')
												else:
																	lowercase		      =		text_model_output[0].shape[0]

												lowercase		      =		self._execution_device

												lowercase		      =		batch_size * num_images_per_prompt

												lowercase		      =		prior_guidance_scale > 1.0 or decoder_guidance_scale > 1.0

												lowercase      ,		lowercase      ,		lowercase		      =		self._encode_prompt(
												    A__  ,A__  ,A__  ,A__  ,A__  ,A__)

												# prior

												self.prior_scheduler.set_timesteps(A__  ,device=A__)
												lowercase		      =		self.prior_scheduler.timesteps

												lowercase		      =		self.prior.config.embedding_dim

												lowercase		      =		self.prepare_latents(
												    (batch_size, embedding_dim)  ,text_embeddings.dtype  ,A__  ,A__  ,A__  ,self.prior_scheduler  ,)

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

																	lowercase		      =		self.prior(
																	    A__  ,timestep=A__  ,proj_embedding=A__  ,encoder_hidden_states=A__  ,attention_mask=A__  ,).predicted_image_embedding

																	if do_classifier_free_guidance:
																						lowercase      ,		lowercase		      =		predicted_image_embedding.chunk(2)
																						lowercase		      =		predicted_image_embedding_uncond + prior_guidance_scale * (
																						    predicted_image_embedding_text - predicted_image_embedding_uncond
																						)

																	if i + 1 == prior_timesteps_tensor.shape[0]:
																						lowercase		      =		None
																	else:
																						lowercase		      =		prior_timesteps_tensor[i + 1]

																	lowercase		      =		self.prior_scheduler.step(
																	    A__  ,timestep=A__  ,sample=A__  ,generator=A__  ,prev_timestep=A__  ,).prev_sample

												lowercase		      =		self.prior.post_process_latents(A__)

												lowercase		      =		prior_latents

												lowercase		      =		self.decoder_pipe(
												    image=A__  ,height=A__  ,width=A__  ,num_inference_steps=A__  ,guidance_scale=A__  ,generator=A__  ,output_type=A__  ,return_dict=A__  ,num_images_per_prompt=A__  ,eta=A__  ,)
												return output






import unittest

import torch

from diffusers import DDIMScheduler, TextToVideoZeroPipeline
from diffusers.utils import load_pt, require_torch_gpu, slow

from ..test_pipelines_common import assert_mean_pixel_difference







@slow
@require_torch_gpu
class 							lowercase						(   unittest.TestCase						):




							def 					A__ (							self):
												lowercase		      =		'''runwayml/stable-diffusion-v1-5'''
												lowercase		      =		TextToVideoZeroPipeline.from_pretrained(A__  ,torch_dtype=torch.floataa).to('''cuda''')
												lowercase		      =		DDIMScheduler.from_config(pipe.scheduler.config)
												lowercase		      =		torch.Generator(device='''cuda''').manual_seed(0)

												lowercase		      =		'''A bear is playing a guitar on Times Square'''
												lowercase		      =		pipe(prompt=A__  ,generator=A__).images

												lowercase		      =		load_pt(
												    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/text-to-video/A bear is playing a guitar on Times Square.pt''')

												assert_mean_pixel_difference(A__  ,A__)




