




import random
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_image_inputs


if is_torch_available():
       import torch

if is_vision_available():
       import PIL

       from transformers import FlavaImageProcessor
       from transformers.image_utils import PILImageResampling
       from transformers.models.flava.image_processing_flava import (
           FLAVA_CODEBOOK_MEAN,
           FLAVA_CODEBOOK_STD,
           FLAVA_IMAGE_MEAN,
           FLAVA_IMAGE_STD,
       )
else:
       SCREAMING_SNAKE_CASE__	:				Optional[Any]  =       None




class 		UpperCamelCase__     (unittest.TestCase					):

    '''simple docstring'''



    def __init__(   self					,  UpperCamelCase__					,  UpperCamelCase__=7					,  UpperCamelCase__=3					,  UpperCamelCase__=30					,  UpperCamelCase__=400					,  UpperCamelCase__=True					,  UpperCamelCase__=None					,  UpperCamelCase__=True					,  UpperCamelCase__=None					,  UpperCamelCase__=None					,  UpperCamelCase__=True					,  UpperCamelCase__=1 / 255					,  UpperCamelCase__=True					,  UpperCamelCase__=FLAVA_IMAGE_MEAN					,  UpperCamelCase__=FLAVA_IMAGE_STD					,  UpperCamelCase__=14					,  UpperCamelCase__=75					,  UpperCamelCase__=None					,  UpperCamelCase__=16					,  UpperCamelCase__=0.3					,  UpperCamelCase__=None					,  UpperCamelCase__=True					,  UpperCamelCase__=None					,  UpperCamelCase__=None					,  UpperCamelCase__=True					,  UpperCamelCase__=None					,  UpperCamelCase__=True					,  UpperCamelCase__=True					,  UpperCamelCase__=FLAVA_CODEBOOK_MEAN					,  UpperCamelCase__=FLAVA_CODEBOOK_STD					,  )					->				Dict:
           lowerCamelCase       :				Any      			= size if size is not None else {"height": 224, "width": 224}
           lowerCamelCase       :				int      			= crop_size if crop_size is not None else {"height": 224, "width": 224}
           lowerCamelCase       :				Tuple      			= codebook_size if codebook_size is not None else {"height": 112, "width": 112}
           lowerCamelCase       :				List[str]      			= codebook_crop_size if codebook_crop_size is not None else {"height": 112, "width": 112}

           lowerCamelCase       :				str      			= parent
           lowerCamelCase       :				List[Any]      			= batch_size
           lowerCamelCase       :				List[str]      			= num_channels
           lowerCamelCase       :				str      			= do_resize
           lowerCamelCase       :				Optional[Any]      			= do_rescale
           lowerCamelCase       :				Tuple      			= rescale_factor
           lowerCamelCase       :				Any      			= min_resolution
           lowerCamelCase       :				Union[str, Any]      			= max_resolution
           lowerCamelCase       :				Dict      			= size
           lowerCamelCase       :				List[Any]      			= resample if resample is not None else PILImageResampling.BICUBIC
           lowerCamelCase       :				Dict      			= do_normalize
           lowerCamelCase       :				Optional[int]      			= image_mean
           lowerCamelCase       :				Optional[int]      			= image_std
           lowerCamelCase       :				Any      			= do_center_crop
           lowerCamelCase       :				int      			= crop_size

           lowerCamelCase       :				str      			= input_size_patches
           lowerCamelCase       :				Any      			= total_mask_patches
           lowerCamelCase       :				Tuple      			= mask_group_max_patches
           lowerCamelCase       :				Optional[int]      			= mask_group_min_patches
           lowerCamelCase       :				Union[str, Any]      			= mask_group_min_aspect_ratio
           lowerCamelCase       :				Any      			= mask_group_max_aspect_ratio

           lowerCamelCase       :				Optional[int]      			= codebook_do_resize
           lowerCamelCase       :				List[str]      			= codebook_size
           lowerCamelCase       :				Any      			= codebook_resample if codebook_resample is not None else PILImageResampling.LANCZOS
           lowerCamelCase       :				List[str]      			= codebook_do_center_crop
           lowerCamelCase       :				Any      			= codebook_crop_size
           lowerCamelCase       :				str      			= codebook_do_map_pixels
           lowerCamelCase       :				str      			= codebook_do_normalize
           lowerCamelCase       :				Tuple      			= codebook_image_mean
           lowerCamelCase       :				Union[str, Any]      			= codebook_image_std







    def    _lowercase					(   self					)					->				Optional[int]:
           return {
               "image_mean": self.image_mean,
               "image_std": self.image_std,
               "do_normalize": self.do_normalize,
               "do_resize": self.do_resize,
               "size": self.size,
               "resample": self.resample,
               "do_rescale": self.do_rescale,
               "rescale_factor": self.rescale_factor,
               "do_center_crop": self.do_center_crop,
               "crop_size": self.crop_size,
               "input_size_patches": self.input_size_patches,
               "total_mask_patches": self.total_mask_patches,
               "mask_group_max_patches": self.mask_group_max_patches,
               "mask_group_min_patches": self.mask_group_min_patches,
               "mask_group_min_aspect_ratio": self.mask_group_min_aspect_ratio,
               "mask_group_max_aspect_ratio": self.mask_group_min_aspect_ratio,
               "codebook_do_resize": self.codebook_do_resize,
               "codebook_size": self.codebook_size,
               "codebook_resample": self.codebook_resample,
               "codebook_do_center_crop": self.codebook_do_center_crop,
               "codebook_crop_size": self.codebook_crop_size,
               "codebook_do_map_pixels": self.codebook_do_map_pixels,
               "codebook_do_normalize": self.codebook_do_normalize,
               "codebook_image_mean": self.codebook_image_mean,
               "codebook_image_std": self.codebook_image_std,
           }







    def    _lowercase					(   self					)					->				List[Any]:
           return (self.size["height"], self.size["width"])







    def    _lowercase					(   self					)					->				Union[str, Any]:
           return (
               (self.input_size_patches, self.input_size_patches)
               if not isinstance(self.input_size_patches					,  UpperCamelCase__					)
               else self.input_size_patches
           )







    def    _lowercase					(   self					)					->				str:
           return (self.codebook_size["height"], self.codebook_size["width"])




@require_torch
@require_vision
class 		UpperCamelCase__     (lowerCAmelCase__   ,    unittest.TestCase					):

    '''simple docstring'''



    lowerCamelCase_				:     Union[str, Any]     					=				FlavaImageProcessor if is_vision_available() else None
    lowerCamelCase_				:     List[Any]     					=				None







    def    _lowercase					(   self					)					->				List[str]:
           lowerCamelCase       :				List[Any]      			= FlavaImageProcessingTester(self					)







    @property
    def    _lowercase					(   self					)					->				Dict:
           return self.image_processor_tester.prepare_image_processor_dict()







    def    _lowercase					(   self					)					->				List[str]:
           lowerCamelCase       :				Tuple      			= self.image_processing_class(**self.image_processor_dict					)
           self.assertTrue(hasattr(UpperCamelCase__					,  "image_mean"					)					)
           self.assertTrue(hasattr(UpperCamelCase__					,  "image_std"					)					)
           self.assertTrue(hasattr(UpperCamelCase__					,  "do_normalize"					)					)
           self.assertTrue(hasattr(UpperCamelCase__					,  "do_resize"					)					)
           self.assertTrue(hasattr(UpperCamelCase__					,  "resample"					)					)
           self.assertTrue(hasattr(UpperCamelCase__					,  "crop_size"					)					)
           self.assertTrue(hasattr(UpperCamelCase__					,  "do_center_crop"					)					)
           self.assertTrue(hasattr(UpperCamelCase__					,  "do_rescale"					)					)
           self.assertTrue(hasattr(UpperCamelCase__					,  "rescale_factor"					)					)
           self.assertTrue(hasattr(UpperCamelCase__					,  "masking_generator"					)					)
           self.assertTrue(hasattr(UpperCamelCase__					,  "codebook_do_resize"					)					)
           self.assertTrue(hasattr(UpperCamelCase__					,  "codebook_size"					)					)
           self.assertTrue(hasattr(UpperCamelCase__					,  "codebook_resample"					)					)
           self.assertTrue(hasattr(UpperCamelCase__					,  "codebook_do_center_crop"					)					)
           self.assertTrue(hasattr(UpperCamelCase__					,  "codebook_crop_size"					)					)
           self.assertTrue(hasattr(UpperCamelCase__					,  "codebook_do_map_pixels"					)					)
           self.assertTrue(hasattr(UpperCamelCase__					,  "codebook_do_normalize"					)					)
           self.assertTrue(hasattr(UpperCamelCase__					,  "codebook_image_mean"					)					)
           self.assertTrue(hasattr(UpperCamelCase__					,  "codebook_image_std"					)					)







    def    _lowercase					(   self					)					->				Optional[int]:
           lowerCamelCase       :				Optional[Any]      			= self.image_processing_class.from_dict(self.image_processor_dict					)
           self.assertEqual(image_processor.size					,  {"height": 224, "width": 224}					)
           self.assertEqual(image_processor.crop_size					,  {"height": 224, "width": 224}					)
           self.assertEqual(image_processor.codebook_size					,  {"height": 112, "width": 112}					)
           self.assertEqual(image_processor.codebook_crop_size					,  {"height": 112, "width": 112}					)

           lowerCamelCase       :				int      			= self.image_processing_class.from_dict(
               self.image_processor_dict					,  size=42					,  crop_size=84					,  codebook_size=33					,  codebook_crop_size=66					)
           self.assertEqual(image_processor.size					,  {"height": 42, "width": 42}					)
           self.assertEqual(image_processor.crop_size					,  {"height": 84, "width": 84}					)
           self.assertEqual(image_processor.codebook_size					,  {"height": 33, "width": 33}					)
           self.assertEqual(image_processor.codebook_crop_size					,  {"height": 66, "width": 66}					)







    def    _lowercase					(   self					)					->				Union[str, Any]:
           pass







    def    _lowercase					(   self					)					->				List[Any]:
           # Initialize image_processing
           lowerCamelCase       :				Optional[int]      			= self.image_processing_class(**self.image_processor_dict					)
           # create random PIL images
           lowerCamelCase       :				List[Any]      			= prepare_image_inputs(self.image_processor_tester					,  equal_resolution=UpperCamelCase__					)
           for image in image_inputs:
                  self.assertIsInstance(UpperCamelCase__					,  PIL.Image.Image					)

           # Test not batched input
           lowerCamelCase       :				str      			= image_processing(image_inputs[0]					,  return_tensors="pt"					)

           # Test no bool masked pos
           self.assertFalse("bool_masked_pos" in encoded_images					)

           lowerCamelCase			,				lowerCamelCase       :				Optional[Any]      			= self.image_processor_tester.get_expected_image_size()

           self.assertEqual(
               encoded_images.pixel_values.shape					,  (1, self.image_processor_tester.num_channels, expected_height, expected_width)					,  )

           # Test batched
           lowerCamelCase       :				str      			= image_processing(UpperCamelCase__					,  return_tensors="pt"					)
           lowerCamelCase			,				lowerCamelCase       :				Union[str, Any]      			= self.image_processor_tester.get_expected_image_size()

           # Test no bool masked pos
           self.assertFalse("bool_masked_pos" in encoded_images					)

           self.assertEqual(
               encoded_images.pixel_values.shape					,  (
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.num_channels,
                   expected_height,
                   expected_width,
               )					,  )







    def    _lowercase					(   self					,  UpperCamelCase__					,  UpperCamelCase__					)					->				List[Any]:
           # Initialize image_processing
           lowerCamelCase       :				Optional[int]      			= self.image_processing_class(**self.image_processor_dict					)
           # create random tensors
           lowerCamelCase       :				Union[str, Any]      			= prepare_image_inputs(self.image_processor_tester					,  equal_resolution=UpperCamelCase__					,  **UpperCamelCase__					)
           for image in image_inputs:
                  self.assertIsInstance(UpperCamelCase__					,  UpperCamelCase__					)

           # Test not batched input
           lowerCamelCase       :				Optional[Any]      			= image_processing(image_inputs[0]					,  return_tensors="pt"					)

           lowerCamelCase			,				lowerCamelCase       :				List[Any]      			= self.image_processor_tester.get_expected_image_size()
           self.assertEqual(
               encoded_images.pixel_values.shape					,  (1, self.image_processor_tester.num_channels, expected_height, expected_width)					,  )

           lowerCamelCase       :				Any      			= image_processing(UpperCamelCase__					,  return_image_mask=UpperCamelCase__					,  return_tensors="pt"					)

           lowerCamelCase			,				lowerCamelCase       :				Union[str, Any]      			= self.image_processor_tester.get_expected_image_size()
           self.assertEqual(
               encoded_images.pixel_values.shape					,  (
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.num_channels,
                   expected_height,
                   expected_width,
               )					,  )

           lowerCamelCase			,				lowerCamelCase       :				Optional[int]      			= self.image_processor_tester.get_expected_mask_size()
           self.assertEqual(
               encoded_images.bool_masked_pos.shape					,  (
                   self.image_processor_tester.batch_size,
                   expected_height,
                   expected_width,
               )					,  )

           # Test batched
           lowerCamelCase       :				Optional[Any]      			= image_processing(UpperCamelCase__					,  return_tensors="pt"					).pixel_values

           lowerCamelCase			,				lowerCamelCase       :				str      			= self.image_processor_tester.get_expected_image_size()
           self.assertEqual(
               encoded_images.shape					,  (
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.num_channels,
                   expected_height,
                   expected_width,
               )					,  )

           # Test masking
           lowerCamelCase       :				Any      			= image_processing(UpperCamelCase__					,  return_image_mask=UpperCamelCase__					,  return_tensors="pt"					)

           lowerCamelCase			,				lowerCamelCase       :				List[str]      			= self.image_processor_tester.get_expected_image_size()
           self.assertEqual(
               encoded_images.pixel_values.shape					,  (
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.num_channels,
                   expected_height,
                   expected_width,
               )					,  )

           lowerCamelCase			,				lowerCamelCase       :				Any      			= self.image_processor_tester.get_expected_mask_size()
           self.assertEqual(
               encoded_images.bool_masked_pos.shape					,  (
                   self.image_processor_tester.batch_size,
                   expected_height,
                   expected_width,
               )					,  )







    def    _lowercase					(   self					)					->				Tuple:
           self._test_call_framework(np.ndarray					,  prepare_kwargs={"numpify": True}					)







    def    _lowercase					(   self					)					->				Optional[Any]:
           self._test_call_framework(torch.Tensor					,  prepare_kwargs={"torchify": True}					)







    def    _lowercase					(   self					)					->				List[str]:
           # Initialize image_processing
           random.seed(1234					)
           lowerCamelCase       :				Tuple      			= self.image_processing_class(**self.image_processor_dict					)
           lowerCamelCase       :				Union[str, Any]      			= prepare_image_inputs(self.image_processor_tester					,  equal_resolution=UpperCamelCase__					,  torchify=UpperCamelCase__					)

           # Test not batched input
           lowerCamelCase       :				str      			= image_processing(image_inputs[0]					,  return_image_mask=UpperCamelCase__					,  return_tensors="pt"					)
           self.assertEqual(encoded_images.bool_masked_pos.sum().item()					,  75					)







    def    _lowercase					(   self					)					->				int:
           # Initialize image_processing
           lowerCamelCase       :				str      			= self.image_processing_class(**self.image_processor_dict					)
           # create random PIL images
           lowerCamelCase       :				Any      			= prepare_image_inputs(self.image_processor_tester					,  equal_resolution=UpperCamelCase__					)
           for image in image_inputs:
                  self.assertIsInstance(UpperCamelCase__					,  PIL.Image.Image					)

           # Test not batched input
           lowerCamelCase       :				List[Any]      			= image_processing(image_inputs[0]					,  return_codebook_pixels=UpperCamelCase__					,  return_tensors="pt"					)
           lowerCamelCase			,				lowerCamelCase       :				Dict      			= self.image_processor_tester.get_expected_codebook_image_size()
           self.assertEqual(
               encoded_images.codebook_pixel_values.shape					,  (1, self.image_processor_tester.num_channels, expected_height, expected_width)					,  )

           # Test batched
           lowerCamelCase       :				List[Any]      			= image_processing(UpperCamelCase__					,  return_codebook_pixels=UpperCamelCase__					,  return_tensors="pt"					)
           lowerCamelCase			,				lowerCamelCase       :				Tuple      			= self.image_processor_tester.get_expected_codebook_image_size()
           self.assertEqual(
               encoded_images.codebook_pixel_values.shape					,  (
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.num_channels,
                   expected_height,
                   expected_width,
               )					,  )












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

def 					A				(				self			,_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE   )     ->			str:
       lowerCamelCase       :				Dict      			= image.to(device=_SCREAMING_SNAKE_CASE   )
       lowerCamelCase       :				str      			= image  # take image as image_embeddings
       lowerCamelCase       :				List[Any]      			= image_embeddings.unsqueeze(1   )

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

       if do_classifier_free_guidance:
              lowerCamelCase       :				Dict      			= 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
              lowerCamelCase       :				Optional[Any]      			= torch.cat([uncond_embeddings, image_embeddings]   )

       return image_embeddings




class 		UpperCamelCase__     (lowerCAmelCase__					):

    '''simple docstring'''



    def __init__(   self					,  UpperCamelCase__					,  UpperCamelCase__					,  UpperCamelCase__					,  UpperCamelCase__					,  UpperCamelCase__ = None					,  )					->				Tuple:
           super().__init__()

           lowerCamelCase       :				str      			= {"image_encoder": None} if decoder_pipe_kwargs is None else decoder_pipe_kwargs

           lowerCamelCase       :				Dict      			= decoder_pipe_kwargs.get("torch_dtype"					,  UpperCamelCase__					) or prior.dtype

           lowerCamelCase       :				Dict      			= StableDiffusionImageVariationPipeline.from_pretrained(
               "lambdalabs/sd-image-variations-diffusers"					,  **UpperCamelCase__					)

           # replace `_encode_image` method
           lowerCamelCase       :				List[str]      			= types.MethodType(_encode_image					,  self.decoder_pipe					)

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







    def    _lowercase					(   self					,  UpperCamelCase__					,  UpperCamelCase__					,  UpperCamelCase__					,  UpperCamelCase__					,  UpperCamelCase__ = None					,  UpperCamelCase__ = None					,  )					->				Any:
           if text_model_output is None:
                  lowerCamelCase       :				List[str]      			= len(UpperCamelCase__					) if isinstance(UpperCamelCase__					,  UpperCamelCase__					) else 1
                  # get prompt text embeddings
                  lowerCamelCase       :				Any      			= self.tokenizer(
                      UpperCamelCase__					,  padding="max_length"					,  max_length=self.tokenizer.model_max_length					,  return_tensors="pt"					,  )
                  lowerCamelCase       :				int      			= text_inputs.input_ids
                  lowerCamelCase       :				Optional[Any]      			= text_inputs.attention_mask.bool().to(UpperCamelCase__					)

                  if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
                         lowerCamelCase       :				Optional[int]      			= 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}'''					)
                         lowerCamelCase       :				Optional[Any]      			= text_input_ids[:, : self.tokenizer.model_max_length]

                  lowerCamelCase       :				int      			= self.text_encoder(text_input_ids.to(UpperCamelCase__					)					)

                  lowerCamelCase       :				Optional[Any]      			= text_encoder_output.text_embeds
                  lowerCamelCase       :				Optional[int]      			= text_encoder_output.last_hidden_state

           else:
                  lowerCamelCase       :				Dict      			= text_model_output[0].shape[0]
                  lowerCamelCase			,				lowerCamelCase       :				int      			= text_model_output[0], text_model_output[1]
                  lowerCamelCase       :				int      			= text_attention_mask

           lowerCamelCase       :				Dict      			= text_embeddings.repeat_interleave(UpperCamelCase__					,  dim=0					)
           lowerCamelCase       :				int      			= text_encoder_hidden_states.repeat_interleave(UpperCamelCase__					,  dim=0					)
           lowerCamelCase       :				List[str]      			= text_mask.repeat_interleave(UpperCamelCase__					,  dim=0					)

           if do_classifier_free_guidance:
                  lowerCamelCase       :				Dict      			= [""] * batch_size

                  lowerCamelCase       :				List[Any]      			= self.tokenizer(
                      UpperCamelCase__					,  padding="max_length"					,  max_length=self.tokenizer.model_max_length					,  truncation=UpperCamelCase__					,  return_tensors="pt"					,  )
                  lowerCamelCase       :				str      			= uncond_input.attention_mask.bool().to(UpperCamelCase__					)
                  lowerCamelCase       :				int      			= self.text_encoder(uncond_input.input_ids.to(UpperCamelCase__					)					)

                  lowerCamelCase       :				Union[str, Any]      			= uncond_embeddings_text_encoder_output.text_embeds
                  lowerCamelCase       :				List[Any]      			= uncond_embeddings_text_encoder_output.last_hidden_state

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

                  lowerCamelCase       :				Optional[Any]      			= uncond_embeddings.shape[1]
                  lowerCamelCase       :				str      			= uncond_embeddings.repeat(1					,  UpperCamelCase__					)
                  lowerCamelCase       :				Optional[Any]      			= uncond_embeddings.view(batch_size * num_images_per_prompt					,  UpperCamelCase__					)

                  lowerCamelCase       :				Any      			= uncond_text_encoder_hidden_states.shape[1]
                  lowerCamelCase       :				int      			= uncond_text_encoder_hidden_states.repeat(1					,  UpperCamelCase__					,  1					)
                  lowerCamelCase       :				Tuple      			= uncond_text_encoder_hidden_states.view(
                      batch_size * num_images_per_prompt					,  UpperCamelCase__					,  -1					)
                  lowerCamelCase       :				List[str]      			= uncond_text_mask.repeat_interleave(UpperCamelCase__					,  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
                  lowerCamelCase       :				int      			= torch.cat([uncond_embeddings, text_embeddings]					)
                  lowerCamelCase       :				str      			= torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states]					)

                  lowerCamelCase       :				Dict      			= torch.cat([uncond_text_mask, text_mask]					)

           return text_embeddings, text_encoder_hidden_states, text_mask







    @property
    def    _lowercase					(   self					)					->				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(UpperCamelCase__					,  "_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    _lowercase					(   self					,  UpperCamelCase__					,  UpperCamelCase__					,  UpperCamelCase__					,  UpperCamelCase__					,  UpperCamelCase__					,  UpperCamelCase__					)					->				Any:
           if latents is None:
                  lowerCamelCase       :				Any      			= randn_tensor(UpperCamelCase__					,  generator=UpperCamelCase__					,  device=UpperCamelCase__					,  dtype=UpperCamelCase__					)
           else:
                  if latents.shape != shape:
                         raise ValueError(F'''Unexpected latents shape, got {latents.shape}, expected {shape}'''					)
                  lowerCamelCase       :				Any      			= latents.to(UpperCamelCase__					)

           lowerCamelCase       :				List[str]      			= latents * scheduler.init_noise_sigma
           return latents







    def    _lowercase					(   self					,  UpperCamelCase__ = None					)					->				str:
           self.decoder_pipe.to(UpperCamelCase__					)
           super().to(UpperCamelCase__					)







    @torch.no_grad()
    def __call__(   self					,  UpperCamelCase__ = None					,  UpperCamelCase__ = None					,  UpperCamelCase__ = None					,  UpperCamelCase__ = 1					,  UpperCamelCase__ = 25					,  UpperCamelCase__ = None					,  UpperCamelCase__ = None					,  UpperCamelCase__ = None					,  UpperCamelCase__ = None					,  UpperCamelCase__ = 4.0					,  UpperCamelCase__ = 8.0					,  UpperCamelCase__ = 50					,  UpperCamelCase__ = 1					,  UpperCamelCase__ = 0.0					,  UpperCamelCase__ = "pil"					,  UpperCamelCase__ = True					,  )					->				Union[str, Any]:
           if prompt is not None:
                  if isinstance(UpperCamelCase__					,  UpperCamelCase__					):
                         lowerCamelCase       :				Union[str, Any]      			= 1
                  elif isinstance(UpperCamelCase__					,  UpperCamelCase__					):
                         lowerCamelCase       :				int      			= len(UpperCamelCase__					)
                  else:
                         raise ValueError(F'''`prompt` has to be of type `str` or `list` but is {type(UpperCamelCase__					)}'''					)
           else:
                  lowerCamelCase       :				Any      			= text_model_output[0].shape[0]

           lowerCamelCase       :				str      			= self._execution_device

           lowerCamelCase       :				Optional[Any]      			= batch_size * num_images_per_prompt

           lowerCamelCase       :				int      			= prior_guidance_scale > 1.0 or decoder_guidance_scale > 1.0

           lowerCamelCase			,				lowerCamelCase			,				lowerCamelCase       :				Tuple      			= self._encode_prompt(
               UpperCamelCase__					,  UpperCamelCase__					,  UpperCamelCase__					,  UpperCamelCase__					,  UpperCamelCase__					,  UpperCamelCase__					)

           # prior

           self.prior_scheduler.set_timesteps(UpperCamelCase__					,  device=UpperCamelCase__					)
           lowerCamelCase       :				Optional[int]      			= self.prior_scheduler.timesteps

           lowerCamelCase       :				List[Any]      			= self.prior.config.embedding_dim

           lowerCamelCase       :				Dict      			= self.prepare_latents(
               (batch_size, embedding_dim)					,  text_embeddings.dtype					,  UpperCamelCase__					,  UpperCamelCase__					,  UpperCamelCase__					,  self.prior_scheduler					,  )

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

                  lowerCamelCase       :				List[Any]      			= self.prior(
                      UpperCamelCase__					,  timestep=UpperCamelCase__					,  proj_embedding=UpperCamelCase__					,  encoder_hidden_states=UpperCamelCase__					,  attention_mask=UpperCamelCase__					,  ).predicted_image_embedding

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

                  if i + 1 == prior_timesteps_tensor.shape[0]:
                         lowerCamelCase       :				Union[str, Any]      			= None
                  else:
                         lowerCamelCase       :				Dict      			= prior_timesteps_tensor[i + 1]

                  lowerCamelCase       :				Optional[int]      			= self.prior_scheduler.step(
                      UpperCamelCase__					,  timestep=UpperCamelCase__					,  sample=UpperCamelCase__					,  generator=UpperCamelCase__					,  prev_timestep=UpperCamelCase__					,  ).prev_sample

           lowerCamelCase       :				Tuple      			= self.prior.post_process_latents(UpperCamelCase__					)

           lowerCamelCase       :				Any      			= prior_latents

           lowerCamelCase       :				Union[str, Any]      			= self.decoder_pipe(
               image=UpperCamelCase__					,  height=UpperCamelCase__					,  width=UpperCamelCase__					,  num_inference_steps=UpperCamelCase__					,  guidance_scale=UpperCamelCase__					,  generator=UpperCamelCase__					,  output_type=UpperCamelCase__					,  return_dict=UpperCamelCase__					,  num_images_per_prompt=UpperCamelCase__					,  eta=UpperCamelCase__					,  )
           return output






