
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 							UpperCAmelCase_		(							self			,							__SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE							):
	lowercase					  =   image.to(device=__SCREAMING_SNAKE_CASE							)
	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			,							__SCREAMING_SNAKE_CASE			,							1							)
	lowercase					  =   image_embeddings.view(bs_embed * num_images_per_prompt			,							__SCREAMING_SNAKE_CASE			,							-1							)

	if do_classifier_free_guidance:
		lowercase					  =   torch.zeros_like(__SCREAMING_SNAKE_CASE							)

		# 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 	A_       (      __lowerCamelCase       ):




	'''simple docstring'''


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

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

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

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

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

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





	def      SCREAMING_SNAKE_CASE__						(	self		,   snake_case		,   snake_case		,   snake_case		,   snake_case		,   snake_case = None		,   snake_case = None		,   ):
		if text_model_output is None:
			lowercase					  =   len(snake_case	) if isinstance(snake_case		,   snake_case	) else 1
			# get prompt text embeddings
			lowercase					  =   self.tokenizer(
			    snake_case		,   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(snake_case	)

			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(snake_case	)	)

			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(snake_case		,   dim=0	)
		lowercase					  =   text_encoder_hidden_states.repeat_interleave(snake_case		,   dim=0	)
		lowercase					  =   text_mask.repeat_interleave(snake_case		,   dim=0	)

		if do_classifier_free_guidance:
			lowercase					  =   [''] * batch_size

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

			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		,   snake_case	)
			lowercase					  =   uncond_embeddings.view(batch_size * num_images_per_prompt		,   snake_case	)

			lowercase					  =   uncond_text_encoder_hidden_states.shape[1]
			lowercase					  =   uncond_text_encoder_hidden_states.repeat(1		,   snake_case		,   1	)
			lowercase					  =   uncond_text_encoder_hidden_states.view(
			    batch_size * num_images_per_prompt		,   snake_case		,   -1	)
			lowercase					  =   uncond_text_mask.repeat_interleave(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
			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      SCREAMING_SNAKE_CASE__						(	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(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      SCREAMING_SNAKE_CASE__						(	self		,   snake_case		,   snake_case		,   snake_case		,   snake_case		,   snake_case		,   snake_case	):
		if latents is None:
			lowercase					  =   randn_tensor(snake_case		,   generator=snake_case		,   device=snake_case		,   dtype=snake_case	)
		else:
			if latents.shape != shape:
				raise ValueError(F'''Unexpected latents shape, got {latents.shape}, expected {shape}'''	)
			lowercase					  =   latents.to(snake_case	)

		lowercase					  =   latents * scheduler.init_noise_sigma
		return latents





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





	@torch.no_grad()
	def __call__(	self		,   snake_case = None		,   snake_case = None		,   snake_case = None		,   snake_case = 1		,   snake_case = 25		,   snake_case = None		,   snake_case = None		,   snake_case = None		,   snake_case = None		,   snake_case = 4.0		,   snake_case = 8.0		,   snake_case = 50		,   snake_case = 1		,   snake_case = 0.0		,   snake_case = "pil"		,   snake_case = True		,   ):
		if prompt is not None:
			if isinstance(snake_case		,   snake_case	):
				lowercase					  =   1
			elif isinstance(snake_case		,   snake_case	):
				lowercase					  =   len(snake_case	)
			else:
				raise ValueError(F'''`prompt` has to be of type `str` or `list` but is {type(snake_case	)}'''	)
		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(
		    snake_case		,   snake_case		,   snake_case		,   snake_case		,   snake_case		,   snake_case	)

		# prior

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

		lowercase					  =   self.prior.config.embedding_dim

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

		for i, t in enumerate(self.progress_bar(snake_case	)	):
			# 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(
			    snake_case		,   timestep=snake_case		,   proj_embedding=snake_case		,   encoder_hidden_states=snake_case		,   attention_mask=snake_case		,   ).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(
			    snake_case		,   timestep=snake_case		,   sample=snake_case		,   generator=snake_case		,   prev_timestep=snake_case		,   ).prev_sample

		lowercase					  =   self.prior.post_process_latents(snake_case	)

		lowercase					  =   prior_latents

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



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, prepare_video_inputs


if is_torch_available():
	import torch

if is_vision_available():
	from PIL import Image

	from transformers import VideoMAEImageProcessor







class 	A_       (      unittest.TestCase       ):




	'''simple docstring'''


	def __init__(	self		,   snake_case		,   snake_case=7		,   snake_case=3		,   snake_case=10		,   snake_case=18		,   snake_case=30		,   snake_case=400		,   snake_case=True		,   snake_case=None		,   snake_case=True		,   snake_case=[0.5, 0.5, 0.5]		,   snake_case=[0.5, 0.5, 0.5]		,   snake_case=None		,   ):
		lowercase					  =   size if size is not None else {'shortest_edge': 18}
		lowercase					  =   crop_size if crop_size is not None else {'height': 18, 'width': 18}

		lowercase					  =   parent
		lowercase					  =   batch_size
		lowercase					  =   num_channels
		lowercase					  =   num_frames
		lowercase					  =   image_size
		lowercase					  =   min_resolution
		lowercase					  =   max_resolution
		lowercase					  =   do_resize
		lowercase					  =   size
		lowercase					  =   do_normalize
		lowercase					  =   image_mean
		lowercase					  =   image_std
		lowercase					  =   crop_size





	def      SCREAMING_SNAKE_CASE__						(	self	):
		return {
		    "image_mean": self.image_mean,
		    "image_std": self.image_std,
		    "do_normalize": self.do_normalize,
		    "do_resize": self.do_resize,
		    "size": self.size,
		    "crop_size": self.crop_size,
		}







@require_torch
@require_vision
class 	A_       (      __lowerCamelCase				,     unittest.TestCase       ):




	'''simple docstring'''


	_UpperCamelCase					:					List[str]										=	VideoMAEImageProcessor if is_vision_available() else None





	def      SCREAMING_SNAKE_CASE__						(	self	):
		lowercase					  =   VideoMAEImageProcessingTester(self	)





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





	def      SCREAMING_SNAKE_CASE__						(	self	):
		lowercase					  =   self.image_processing_class(**self.image_processor_dict	)
		self.assertTrue(hasattr(snake_case		,   'image_mean'	)	)
		self.assertTrue(hasattr(snake_case		,   'image_std'	)	)
		self.assertTrue(hasattr(snake_case		,   'do_normalize'	)	)
		self.assertTrue(hasattr(snake_case		,   'do_resize'	)	)
		self.assertTrue(hasattr(snake_case		,   'do_center_crop'	)	)
		self.assertTrue(hasattr(snake_case		,   'size'	)	)





	def      SCREAMING_SNAKE_CASE__						(	self	):
		lowercase					  =   self.image_processing_class.from_dict(self.image_processor_dict	)
		self.assertEqual(image_processor.size		,   {'shortest_edge': 18}	)
		self.assertEqual(image_processor.crop_size		,   {'height': 18, 'width': 18}	)

		lowercase					  =   self.image_processing_class.from_dict(self.image_processor_dict		,   size=42		,   crop_size=84	)
		self.assertEqual(image_processor.size		,   {'shortest_edge': 42}	)
		self.assertEqual(image_processor.crop_size		,   {'height': 84, 'width': 84}	)





	def      SCREAMING_SNAKE_CASE__						(	self	):
		pass





	def      SCREAMING_SNAKE_CASE__						(	self	):
		# Initialize image_processing
		lowercase					  =   self.image_processing_class(**self.image_processor_dict	)
		# create random PIL videos
		lowercase					  =   prepare_video_inputs(self.image_processor_tester		,   equal_resolution=snake_case	)
		for video in video_inputs:
			self.assertIsInstance(snake_case		,   snake_case	)
			self.assertIsInstance(video[0]		,   Image.Image	)

		# Test not batched input
		lowercase					  =   image_processing(video_inputs[0]		,   return_tensors='pt'	).pixel_values
		self.assertEqual(
		    encoded_videos.shape		,   (
		        1,
		        self.image_processor_tester.num_frames,
		        self.image_processor_tester.num_channels,
		        self.image_processor_tester.crop_size['height'],
		        self.image_processor_tester.crop_size['width'],
		    )		,   )

		# Test batched
		lowercase					  =   image_processing(snake_case		,   return_tensors='pt'	).pixel_values
		self.assertEqual(
		    encoded_videos.shape		,   (
		        self.image_processor_tester.batch_size,
		        self.image_processor_tester.num_frames,
		        self.image_processor_tester.num_channels,
		        self.image_processor_tester.crop_size['height'],
		        self.image_processor_tester.crop_size['width'],
		    )		,   )





	def      SCREAMING_SNAKE_CASE__						(	self	):
		# Initialize image_processing
		lowercase					  =   self.image_processing_class(**self.image_processor_dict	)
		# create random numpy tensors
		lowercase					  =   prepare_video_inputs(self.image_processor_tester		,   equal_resolution=snake_case		,   numpify=snake_case	)
		for video in video_inputs:
			self.assertIsInstance(snake_case		,   snake_case	)
			self.assertIsInstance(video[0]		,   np.ndarray	)

		# Test not batched input
		lowercase					  =   image_processing(video_inputs[0]		,   return_tensors='pt'	).pixel_values
		self.assertEqual(
		    encoded_videos.shape		,   (
		        1,
		        self.image_processor_tester.num_frames,
		        self.image_processor_tester.num_channels,
		        self.image_processor_tester.crop_size['height'],
		        self.image_processor_tester.crop_size['width'],
		    )		,   )

		# Test batched
		lowercase					  =   image_processing(snake_case		,   return_tensors='pt'	).pixel_values
		self.assertEqual(
		    encoded_videos.shape		,   (
		        self.image_processor_tester.batch_size,
		        self.image_processor_tester.num_frames,
		        self.image_processor_tester.num_channels,
		        self.image_processor_tester.crop_size['height'],
		        self.image_processor_tester.crop_size['width'],
		    )		,   )





	def      SCREAMING_SNAKE_CASE__						(	self	):
		# Initialize image_processing
		lowercase					  =   self.image_processing_class(**self.image_processor_dict	)
		# create random PyTorch tensors
		lowercase					  =   prepare_video_inputs(self.image_processor_tester		,   equal_resolution=snake_case		,   torchify=snake_case	)
		for video in video_inputs:
			self.assertIsInstance(snake_case		,   snake_case	)
			self.assertIsInstance(video[0]		,   torch.Tensor	)

		# Test not batched input
		lowercase					  =   image_processing(video_inputs[0]		,   return_tensors='pt'	).pixel_values
		self.assertEqual(
		    encoded_videos.shape		,   (
		        1,
		        self.image_processor_tester.num_frames,
		        self.image_processor_tester.num_channels,
		        self.image_processor_tester.crop_size['height'],
		        self.image_processor_tester.crop_size['width'],
		    )		,   )

		# Test batched
		lowercase					  =   image_processing(snake_case		,   return_tensors='pt'	).pixel_values
		self.assertEqual(
		    encoded_videos.shape		,   (
		        self.image_processor_tester.batch_size,
		        self.image_processor_tester.num_frames,
		        self.image_processor_tester.num_channels,
		        self.image_processor_tester.crop_size['height'],
		        self.image_processor_tester.crop_size['width'],
		    )		,   )

