


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


def 							SCREAMING_SNAKE_CASE					(  self							,     _UpperCAmelCase							,     _UpperCAmelCase							,     _UpperCAmelCase							,     _UpperCAmelCase				)    ->  Optional[Any]:
						_a     =			image.to(device=_UpperCAmelCase				)
						_a     =			image  # take image as image_embeddings
						_a     =			image_embeddings.unsqueeze(1				)

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

						if do_classifier_free_guidance:
												_a     =			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
												_a     =			torch.cat([uncond_embeddings, image_embeddings]				)

						return image_embeddings






class 					_UpperCamelCase (      lowerCamelCase__	):







							'''simple docstring'''







							def __init__(		self  :				Union[str, Any]		,				SCREAMING_SNAKE_CASE_  :				PriorTransformer		,				SCREAMING_SNAKE_CASE_  :				CLIPTokenizer		,				SCREAMING_SNAKE_CASE_  :				CLIPTextModelWithProjection		,				SCREAMING_SNAKE_CASE_  :				UnCLIPScheduler		,				SCREAMING_SNAKE_CASE_  :				Optional[dict] = None		,				):
													super().__init__()

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

													_a     =			decoder_pipe_kwargs.get('torch_dtype'		,				SCREAMING_SNAKE_CASE_		) or prior.dtype

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

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

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







							def 		_UpperCAmelCase	(		self  :				List[str]		,				SCREAMING_SNAKE_CASE_  :				List[str]		,				SCREAMING_SNAKE_CASE_  :				str		,				SCREAMING_SNAKE_CASE_  :				List[str]		,				SCREAMING_SNAKE_CASE_  :				Union[str, Any]		,				SCREAMING_SNAKE_CASE_  :				Optional[Union[CLIPTextModelOutput, Tuple]] = None		,				SCREAMING_SNAKE_CASE_  :				Optional[torch.Tensor] = None		,				):
													if text_model_output is None:
																			_a     =			len(SCREAMING_SNAKE_CASE_		) if isinstance(SCREAMING_SNAKE_CASE_		,				SCREAMING_SNAKE_CASE_		) else 1
																			# get prompt text embeddings
																			_a     =			self.tokenizer(
																			    SCREAMING_SNAKE_CASE_		,				padding='max_length'		,				max_length=self.tokenizer.model_max_length		,				return_tensors='pt'		,				)
																			_a     =			text_inputs.input_ids
																			_a     =			text_inputs.attention_mask.bool().to(SCREAMING_SNAKE_CASE_		)

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

																			_a     =			self.text_encoder(text_input_ids.to(SCREAMING_SNAKE_CASE_		)		)

																			_a     =			text_encoder_output.text_embeds
																			_a     =			text_encoder_output.last_hidden_state

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

													_a     =			text_embeddings.repeat_interleave(SCREAMING_SNAKE_CASE_		,				dim=0		)
													_a     =			text_encoder_hidden_states.repeat_interleave(SCREAMING_SNAKE_CASE_		,				dim=0		)
													_a     =			text_mask.repeat_interleave(SCREAMING_SNAKE_CASE_		,				dim=0		)

													if do_classifier_free_guidance:
																			_a     =			[''] * batch_size

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

																			_a     =			uncond_embeddings_text_encoder_output.text_embeds
																			_a     =			uncond_embeddings_text_encoder_output.last_hidden_state

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

																			_a     =			uncond_embeddings.shape[1]
																			_a     =			uncond_embeddings.repeat(1		,				SCREAMING_SNAKE_CASE_		)
																			_a     =			uncond_embeddings.view(batch_size * num_images_per_prompt		,				SCREAMING_SNAKE_CASE_		)

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

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

													return text_embeddings, text_encoder_hidden_states, text_mask







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

													_a     =			latents * scheduler.init_noise_sigma
													return latents







							def 		_UpperCAmelCase	(		self  :				List[Any]		,				SCREAMING_SNAKE_CASE_  :				Optional[Union[str, torch.device]] = None		):
													self.decoder_pipe.to(SCREAMING_SNAKE_CASE_		)
													super().to(SCREAMING_SNAKE_CASE_		)







							@torch.no_grad()
							def __call__(		self  :				Any		,				SCREAMING_SNAKE_CASE_  :				Optional[Union[str, List[str]]] = None		,				SCREAMING_SNAKE_CASE_  :				Optional[int] = None		,				SCREAMING_SNAKE_CASE_  :				Optional[int] = None		,				SCREAMING_SNAKE_CASE_  :				int = 1		,				SCREAMING_SNAKE_CASE_  :				int = 2_5		,				SCREAMING_SNAKE_CASE_  :				Optional[torch.Generator] = None		,				SCREAMING_SNAKE_CASE_  :				Optional[torch.FloatTensor] = None		,				SCREAMING_SNAKE_CASE_  :				Optional[Union[CLIPTextModelOutput, Tuple]] = None		,				SCREAMING_SNAKE_CASE_  :				Optional[torch.Tensor] = None		,				SCREAMING_SNAKE_CASE_  :				float = 4.0		,				SCREAMING_SNAKE_CASE_  :				float = 8.0		,				SCREAMING_SNAKE_CASE_  :				int = 5_0		,				SCREAMING_SNAKE_CASE_  :				Optional[int] = 1		,				SCREAMING_SNAKE_CASE_  :				float = 0.0		,				SCREAMING_SNAKE_CASE_  :				Optional[str] = "pil"		,				SCREAMING_SNAKE_CASE_  :				bool = True		,				):
													if prompt is not None:
																			if isinstance(SCREAMING_SNAKE_CASE_		,				SCREAMING_SNAKE_CASE_		):
																									_a     =			1
																			elif isinstance(SCREAMING_SNAKE_CASE_		,				SCREAMING_SNAKE_CASE_		):
																									_a     =			len(SCREAMING_SNAKE_CASE_		)
																			else:
																									raise ValueError(f"""`prompt` has to be of type `str` or `list` but is {type(SCREAMING_SNAKE_CASE_		)}"""		)
													else:
																			_a     =			text_model_output[0].shape[0]

													_a     =			self._execution_device

													_a     =			batch_size * num_images_per_prompt

													_a     =			prior_guidance_scale > 1.0 or decoder_guidance_scale > 1.0

													_a ,	_a ,	_a     =			self._encode_prompt(
													    SCREAMING_SNAKE_CASE_		,				SCREAMING_SNAKE_CASE_		,				SCREAMING_SNAKE_CASE_		,				SCREAMING_SNAKE_CASE_		,				SCREAMING_SNAKE_CASE_		,				SCREAMING_SNAKE_CASE_		)

													# prior

													self.prior_scheduler.set_timesteps(SCREAMING_SNAKE_CASE_		,				device=SCREAMING_SNAKE_CASE_		)
													_a     =			self.prior_scheduler.timesteps

													_a     =			self.prior.config.embedding_dim

													_a     =			self.prepare_latents(
													    (batch_size, embedding_dim)		,				text_embeddings.dtype		,				SCREAMING_SNAKE_CASE_		,				SCREAMING_SNAKE_CASE_		,				SCREAMING_SNAKE_CASE_		,				self.prior_scheduler		,				)

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

																			_a     =			self.prior(
																			    SCREAMING_SNAKE_CASE_		,				timestep=SCREAMING_SNAKE_CASE_		,				proj_embedding=SCREAMING_SNAKE_CASE_		,				encoder_hidden_states=SCREAMING_SNAKE_CASE_		,				attention_mask=SCREAMING_SNAKE_CASE_		,				).predicted_image_embedding

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

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

																			_a     =			self.prior_scheduler.step(
																			    SCREAMING_SNAKE_CASE_		,				timestep=SCREAMING_SNAKE_CASE_		,				sample=SCREAMING_SNAKE_CASE_		,				generator=SCREAMING_SNAKE_CASE_		,				prev_timestep=SCREAMING_SNAKE_CASE_		,				).prev_sample

													_a     =			self.prior.post_process_latents(SCREAMING_SNAKE_CASE_		)

													_a     =			prior_latents

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






def 							SCREAMING_SNAKE_CASE					(  _UpperCAmelCase				)    ->  bool:
						# must be
						_a     =			[list(_UpperCAmelCase				) for row in matrix]
						if matrix and isinstance(_UpperCAmelCase							,     _UpperCAmelCase				):
												if isinstance(matrix[0]							,     _UpperCAmelCase				):
																		_a     =			0
																		for row in matrix:
																								if prev_len == 0:
																														_a     =			len(_UpperCAmelCase				)
																														_a     =			True
																								else:
																														_a     =			prev_len == len(_UpperCAmelCase				)
												else:
																		_a     =			True
						else:
												_a     =			False

						return result


def 							SCREAMING_SNAKE_CASE					(  _UpperCAmelCase				)    ->  None:
						if check_matrix(_UpperCAmelCase				) and len(_UpperCAmelCase				) > 0:
												_a     =			[list(_UpperCAmelCase				) for row in a]
												_a     =			len(_UpperCAmelCase				)
												if isinstance(a[0]							,     _UpperCAmelCase				):
																		_a     =			len(a[0]				)
												else:
																		for dat in a:
																								print(_UpperCAmelCase				)
																		return

												# horizotal printing increasing
												for i in range(0							,     _UpperCAmelCase				):
																		print(a[0][i]				)
												# vertical printing down
												for i in range(1							,     _UpperCAmelCase				):
																		print(a[i][mat_col - 1]				)
												# horizotal printing decreasing
												if mat_row > 1:
																		for i in range(mat_col - 2							,     -1							,     -1				):
																								print(a[mat_row - 1][i]				)
        # vertical printing up
												for i in range(mat_row - 2							,     0							,     -1				):
																		print(a[i][0]				)
												_a     =			[row[1 : mat_col - 1] for row in a[1 : mat_row - 1]]
												if len(_UpperCAmelCase				) > 0:
																		spiral_print_clockwise(_UpperCAmelCase				)
												else:
																		return
						else:
												print('Not a valid matrix'				)
												return


def 							SCREAMING_SNAKE_CASE					(  _UpperCAmelCase				)    ->  list[int]:
						if matrix:
												return list(matrix.pop(0				)				) + spiral_traversal(list(zip(*_UpperCAmelCase				)				)[::-1]				)
						else:
												return []


# driver code
if __name__ == "__main__":
		import doctest

		doctest.testmod()

		lowercase_								=    [[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]]
		spiral_print_clockwise(a)


