



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


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







def 				A			(		self							,					__UpperCAmelCase							,					__UpperCAmelCase							,					__UpperCAmelCase							,					__UpperCAmelCase	)       ->       Union[str, Any]:



						'''simple docstring'''






						UpperCAmelCase_     							=     image.to(device=__UpperCAmelCase	)
						UpperCAmelCase_     							=     image  # take image as image_embeddings
						UpperCAmelCase_     							=     image_embeddings.unsqueeze(1	)

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

						if do_classifier_free_guidance:
												UpperCAmelCase_     							=     torch.zeros_like(__UpperCAmelCase	)

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

						return image_embeddings







class 							a_  (		_snake_case				):

						def __init__(    self	:List[str]   ,						_lowercase	:PriorTransformer   ,						_lowercase	:CLIPTokenizer   ,						_lowercase	:CLIPTextModelWithProjection   ,						_lowercase	:UnCLIPScheduler   ,						_lowercase	:Optional[dict] = None   ,						)		->					Dict:
												super().__init__()

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

												UpperCAmelCase_     							=     decoder_pipe_kwargs.get('''torch_dtype'''   ,						_lowercase) or prior.dtype

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

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

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

						def 	__a       (    self	:str   ,						_lowercase	:str   ,						_lowercase	:Dict   ,						_lowercase	:Optional[int]   ,						_lowercase	:Dict   ,						_lowercase	:Optional[Union[CLIPTextModelOutput, Tuple]] = None   ,						_lowercase	:Optional[torch.Tensor] = None   ,						)		->					List[str]:
												if text_model_output is None:
																		UpperCAmelCase_     							=     len(_lowercase) if isinstance(_lowercase   ,						_lowercase) else 1
																		# get prompt text embeddings
																		UpperCAmelCase_     							=     self.tokenizer(
																		    _lowercase   ,						padding='''max_length'''   ,						max_length=self.tokenizer.model_max_length   ,						return_tensors='''pt'''   ,						)
																		UpperCAmelCase_     							=     text_inputs.input_ids
																		UpperCAmelCase_     							=     text_inputs.attention_mask.bool().to(_lowercase)

																		if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
																								UpperCAmelCase_     							=     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_     							=     text_input_ids[:, : self.tokenizer.model_max_length]

																		UpperCAmelCase_     							=     self.text_encoder(text_input_ids.to(_lowercase))

																		UpperCAmelCase_     							=     text_encoder_output.text_embeds
																		UpperCAmelCase_     							=     text_encoder_output.last_hidden_state

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

												UpperCAmelCase_     							=     text_embeddings.repeat_interleave(_lowercase   ,						dim=0)
												UpperCAmelCase_     							=     text_encoder_hidden_states.repeat_interleave(_lowercase   ,						dim=0)
												UpperCAmelCase_     							=     text_mask.repeat_interleave(_lowercase   ,						dim=0)

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

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

																		UpperCAmelCase_     							=     uncond_embeddings_text_encoder_output.text_embeds
																		UpperCAmelCase_     							=     uncond_embeddings_text_encoder_output.last_hidden_state

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

																		UpperCAmelCase_     							=     uncond_embeddings.shape[1]
																		UpperCAmelCase_     							=     uncond_embeddings.repeat(1   ,						_lowercase)
																		UpperCAmelCase_     							=     uncond_embeddings.view(batch_size * num_images_per_prompt   ,						_lowercase)

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

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

												return text_embeddings, text_encoder_hidden_states, text_mask

						@property
						def 	__a       (    self	:Tuple)		->					Dict:
												if self.device != torch.device('''meta''') or not hasattr(self.prior   ,						'''_hf_hook'''):
																		return self.device
												for module in self.prior.modules():
																		if (
																		    hasattr(_lowercase   ,						'''_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	:Union[str, Any]   ,						_lowercase	:int   ,						_lowercase	:List[str]   ,						_lowercase	:List[str]   ,						_lowercase	:Tuple   ,						_lowercase	:List[str]   ,						_lowercase	:Optional[int])		->					List[Any]:
												if latents is None:
																		UpperCAmelCase_     							=     randn_tensor(_lowercase   ,						generator=_lowercase   ,						device=_lowercase   ,						dtype=_lowercase)
												else:
																		if latents.shape != shape:
																								raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {shape}")
																		UpperCAmelCase_     							=     latents.to(_lowercase)

												UpperCAmelCase_     							=     latents * scheduler.init_noise_sigma
												return latents

						def 	__a       (    self	:int   ,						_lowercase	:Optional[Union[str, torch.device]] = None)		->					List[str]:
												self.decoder_pipe.to(_lowercase)
												super().to(_lowercase)




						@torch.no_grad()
						def __call__(    self	:Any   ,						_lowercase	:Optional[Union[str, List[str]]] = None   ,						_lowercase	:Optional[int] = None   ,						_lowercase	:Optional[int] = None   ,						_lowercase	:int = 1   ,						_lowercase	:int = 25   ,						_lowercase	:Optional[torch.Generator] = None   ,						_lowercase	:Optional[torch.FloatTensor] = None   ,						_lowercase	:Optional[Union[CLIPTextModelOutput, Tuple]] = None   ,						_lowercase	:Optional[torch.Tensor] = None   ,						_lowercase	:float = 4.0   ,						_lowercase	:float = 8.0   ,						_lowercase	:int = 50   ,						_lowercase	:Optional[int] = 1   ,						_lowercase	:float = 0.0   ,						_lowercase	:Optional[str] = "pil"   ,						_lowercase	:bool = True   ,						)		->					Dict:
												if prompt is not None:
																		if isinstance(_lowercase   ,						_lowercase):
																								UpperCAmelCase_     							=     1
																		elif isinstance(_lowercase   ,						_lowercase):
																								UpperCAmelCase_     							=     len(_lowercase)
																		else:
																								raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(_lowercase)}")
												else:
																		UpperCAmelCase_     							=     text_model_output[0].shape[0]

												UpperCAmelCase_     							=     self._execution_device

												UpperCAmelCase_     							=     batch_size * num_images_per_prompt

												UpperCAmelCase_     							=     prior_guidance_scale > 1.0 or decoder_guidance_scale > 1.0

												UpperCAmelCase_							,      UpperCAmelCase_							,      UpperCAmelCase_     							=     self._encode_prompt(
												    _lowercase   ,						_lowercase   ,						_lowercase   ,						_lowercase   ,						_lowercase   ,						_lowercase)

												# prior

												self.prior_scheduler.set_timesteps(_lowercase   ,						device=_lowercase)
												UpperCAmelCase_     							=     self.prior_scheduler.timesteps

												UpperCAmelCase_     							=     self.prior.config.embedding_dim

												UpperCAmelCase_     							=     self.prepare_latents(
												    (batch_size, embedding_dim)   ,						text_embeddings.dtype   ,						_lowercase   ,						_lowercase   ,						_lowercase   ,						self.prior_scheduler   ,						)

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

																		UpperCAmelCase_     							=     self.prior(
																		    _lowercase   ,						timestep=_lowercase   ,						proj_embedding=_lowercase   ,						encoder_hidden_states=_lowercase   ,						attention_mask=_lowercase   ,						).predicted_image_embedding

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

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

																		UpperCAmelCase_     							=     self.prior_scheduler.step(
																		    _lowercase   ,						timestep=_lowercase   ,						sample=_lowercase   ,						generator=_lowercase   ,						prev_timestep=_lowercase   ,						).prev_sample

												UpperCAmelCase_     							=     self.prior.post_process_latents(_lowercase)

												UpperCAmelCase_     							=     prior_latents

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












'''simple docstring'''







import unittest
from pathlib import Path
from tempfile import TemporaryDirectory
from unittest.mock import Mock, patch

import diffusers.utils.hub_utils





class     _UpperCAmelCase							(						unittest.TestCase   ):
		@patch("""diffusers.utils.hub_utils.get_full_repo_name"""			)
		def   lowerCamelCase__			(  self,__SCREAMING_SNAKE_CASE			):



					'''simple docstring'''

					__lowerCAmelCase											=				"""full_repo_name"""
					with TemporaryDirectory() as tmpdir:
								# Dummy args values
								__lowerCAmelCase											=				Mock()
								__lowerCAmelCase											=				tmpdir
								__lowerCAmelCase											=				0
								__lowerCAmelCase											=				"""hub_token"""
								__lowerCAmelCase											=				"""dataset_name"""
								__lowerCAmelCase											=				0.01
								__lowerCAmelCase											=				10_00_00
								__lowerCAmelCase											=				1_00_00
								__lowerCAmelCase											=				0.01
								__lowerCAmelCase											=				0.02
								__lowerCAmelCase											=				0.03
								__lowerCAmelCase											=				0.0005
								__lowerCAmelCase											=				0.00_0001
								__lowerCAmelCase											=				1
								__lowerCAmelCase											=				10
								__lowerCAmelCase											=				0.001
								__lowerCAmelCase											=				0.1
								__lowerCAmelCase											=				0.2
								__lowerCAmelCase											=				True

								# Model card mush be rendered and saved
								diffusers.utils.hub_utils.create_model_card(__SCREAMING_SNAKE_CASE,model_name="""model_name"""			)
								self.assertTrue((Path(__SCREAMING_SNAKE_CASE			) / """README.md""").is_file()			)

