




'''simple docstring'''




import inspect
import unittest

from transformers import DataaVecVisionConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
     import torch
     from torch import nn

     from transformers import (
         MODEL_MAPPING,
         DataaVecVisionForImageClassification,
         DataaVecVisionForSemanticSegmentation,
         DataaVecVisionModel,
     )
     from transformers.models.dataavec.modeling_dataavec_vision import DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
     from PIL import Image

     from transformers import BeitImageProcessor


class 						__UpperCamelCase :






       def __init__(							self,	lowerCAmelCase,	lowerCAmelCase=100,	lowerCAmelCase=13,	lowerCAmelCase=30,	lowerCAmelCase=2,	lowerCAmelCase=3,	lowerCAmelCase=True,	lowerCAmelCase=True,	lowerCAmelCase=32,	lowerCAmelCase=4,	lowerCAmelCase=4,	lowerCAmelCase=37,	lowerCAmelCase="gelu",	lowerCAmelCase=0.1,	lowerCAmelCase=0.1,	lowerCAmelCase=10,	lowerCAmelCase=0.0_2,	lowerCAmelCase=3,	lowerCAmelCase=None,	lowerCAmelCase=[0, 1, 2, 3],	):



              """simple docstring"""







              lowerCamelCase_							=parent
              lowerCamelCase_							=100
              lowerCamelCase_							=batch_size
              lowerCamelCase_							=image_size
              lowerCamelCase_							=patch_size
              lowerCamelCase_							=num_channels
              lowerCamelCase_							=is_training
              lowerCamelCase_							=use_labels
              lowerCamelCase_							=hidden_size
              lowerCamelCase_							=num_hidden_layers
              lowerCamelCase_							=num_attention_heads
              lowerCamelCase_							=intermediate_size
              lowerCamelCase_							=hidden_act
              lowerCamelCase_							=hidden_dropout_prob
              lowerCamelCase_							=attention_probs_dropout_prob
              lowerCamelCase_							=type_sequence_label_size
              lowerCamelCase_							=initializer_range
              lowerCamelCase_							=scope
              lowerCamelCase_							=out_indices
              lowerCamelCase_							=num_labels

              # in BeiT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
              lowerCamelCase_							=(image_size // patch_size) ** 2
              lowerCamelCase_							=num_patches + 1






       def   lowercase__  (							self						):



              """simple docstring"""







              lowerCamelCase_							=floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

              lowerCamelCase_							=None
              lowerCamelCase_							=None
              if self.use_labels:
                     lowerCamelCase_							=ids_tensor([self.batch_size],	self.type_sequence_label_size						)
                     lowerCamelCase_							=ids_tensor([self.batch_size, self.image_size, self.image_size],	self.num_labels						)

              lowerCamelCase_							=self.get_config()

              return config, pixel_values, labels, pixel_labels






       def   lowercase__  (							self						):



              """simple docstring"""







              return DataaVecVisionConfig(
                  vocab_size=self.vocab_size,	image_size=self.image_size,	patch_size=self.patch_size,	num_channels=self.num_channels,	hidden_size=self.hidden_size,	num_hidden_layers=self.num_hidden_layers,	num_attention_heads=self.num_attention_heads,	intermediate_size=self.intermediate_size,	hidden_act=self.hidden_act,	hidden_dropout_prob=self.hidden_dropout_prob,	attention_probs_dropout_prob=self.attention_probs_dropout_prob,	is_decoder=lowerCAmelCase,	initializer_range=self.initializer_range,	out_indices=self.out_indices,	)






       def   lowercase__  (							self,	lowerCAmelCase,	lowerCAmelCase,	lowerCAmelCase,	lowerCAmelCase						):



              """simple docstring"""







              lowerCamelCase_							=DataaVecVisionModel(config=lowerCAmelCase						)
              model.to(lowerCAmelCase						)
              model.eval()
              lowerCamelCase_							=model(lowerCAmelCase						)
              # expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
              lowerCamelCase_							=(self.image_size // self.patch_size) ** 2
              self.parent.assertEqual(result.last_hidden_state.shape,	(self.batch_size, num_patches + 1, self.hidden_size)						)






       def   lowercase__  (							self,	lowerCAmelCase,	lowerCAmelCase,	lowerCAmelCase,	lowerCAmelCase						):



              """simple docstring"""







              lowerCamelCase_							=self.type_sequence_label_size
              lowerCamelCase_							=DataaVecVisionForImageClassification(lowerCAmelCase						)
              model.to(lowerCAmelCase						)
              model.eval()
              lowerCamelCase_							=model(lowerCAmelCase,	labels=lowerCAmelCase						)
              self.parent.assertEqual(result.logits.shape,	(self.batch_size, self.type_sequence_label_size)						)






       def   lowercase__  (							self,	lowerCAmelCase,	lowerCAmelCase,	lowerCAmelCase,	lowerCAmelCase						):



              """simple docstring"""







              lowerCamelCase_							=self.num_labels
              lowerCamelCase_							=DataaVecVisionForSemanticSegmentation(lowerCAmelCase						)
              model.to(lowerCAmelCase						)
              model.eval()
              lowerCamelCase_							=model(lowerCAmelCase						)
              self.parent.assertEqual(
                  result.logits.shape,	(self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)						)
              lowerCamelCase_							=model(lowerCAmelCase,	labels=lowerCAmelCase						)
              self.parent.assertEqual(
                  result.logits.shape,	(self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)						)






       def   lowercase__  (							self						):



              """simple docstring"""







              lowerCamelCase_							=self.prepare_config_and_inputs()
              lowerCamelCase_,   lowerCamelCase_,   lowerCamelCase_,   lowerCamelCase_							=config_and_inputs
              lowerCamelCase_							={'''pixel_values''': pixel_values}
              return config, inputs_dict





@require_torch
class 						__UpperCamelCase (				lowerCamelCase__    ,      lowerCamelCase__    ,      unittest.TestCase       ):
       lowercase			:					Dict   		=(
           (DataaVecVisionModel, DataaVecVisionForImageClassification, DataaVecVisionForSemanticSegmentation)
           if is_torch_available()
           else ()
       )
       lowercase			:					Optional[int]   		=(
           {
               'feature-extraction': DataaVecVisionModel,
               'image-classification': DataaVecVisionForImageClassification,
               'image-segmentation': DataaVecVisionForSemanticSegmentation,
           }
           if is_torch_available()
           else {}
       )

       lowercase			:					List[Any]   		=False
       lowercase			:					Optional[int]   		=False
       lowercase			:					int   		=False






       def   lowercase__  (							self						):



              """simple docstring"""







              lowerCamelCase_							=DataaVecVisionModelTester(self						)
              lowerCamelCase_							=ConfigTester(
                  self,	config_class=lowerCAmelCase,	has_text_modality=lowerCAmelCase,	hidden_size=37						)






       def   lowercase__  (							self						):



              """simple docstring"""







              self.config_tester.run_common_tests()






       def   lowercase__  (							self						):



              """simple docstring"""







              pass






       @require_torch_multi_gpu
       @unittest.skip(
           reason='''Data2VecVision has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`'''						)
       def   lowercase__  (							self						):



              """simple docstring"""







              pass






       def   lowercase__  (							self						):



              """simple docstring"""







              lowerCamelCase_,   lowerCamelCase_							=self.model_tester.prepare_config_and_inputs_for_common()

              for model_class in self.all_model_classes:
                     lowerCamelCase_							=model_class(lowerCAmelCase						)
                     self.assertIsInstance(model.get_input_embeddings(),	(nn.Module)						)
                     lowerCamelCase_							=model.get_output_embeddings()
                     self.assertTrue(x is None or isinstance(lowerCAmelCase,	nn.Linear						)						)






       def   lowercase__  (							self						):



              """simple docstring"""







              lowerCamelCase_,   lowerCamelCase_							=self.model_tester.prepare_config_and_inputs_for_common()

              for model_class in self.all_model_classes:
                     lowerCamelCase_							=model_class(lowerCAmelCase						)
                     lowerCamelCase_							=inspect.signature(model.forward						)
                     # signature.parameters is an OrderedDict => so arg_names order is deterministic
                     lowerCamelCase_							=[*signature.parameters.keys()]

                     lowerCamelCase_							=['''pixel_values''']
                     self.assertListEqual(arg_names[:1],	lowerCAmelCase						)






       def   lowercase__  (							self						):



              """simple docstring"""







              lowerCamelCase_							=self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_model(*lowerCAmelCase						)






       def   lowercase__  (							self						):



              """simple docstring"""







              lowerCamelCase_							=self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_for_image_segmentation(*lowerCAmelCase						)






       def   lowercase__  (							self						):



              """simple docstring"""







              if not self.model_tester.is_training:
                     return

              lowerCamelCase_,   lowerCamelCase_							=self.model_tester.prepare_config_and_inputs_for_common()
              lowerCamelCase_							=True

              for model_class in self.all_model_classes:
                     if model_class in [*get_values(lowerCAmelCase						)]:
                            continue

                     lowerCamelCase_							=model_class(lowerCAmelCase						)
                     model.to(lowerCAmelCase						)
                     model.train()
                     lowerCamelCase_							=self._prepare_for_class(lowerCAmelCase,	lowerCAmelCase,	return_labels=lowerCAmelCase						)
                     lowerCamelCase_							=model(**lowerCAmelCase						).loss
                     loss.backward()






       def   lowercase__  (							self						):



              """simple docstring"""







              lowerCamelCase_,   lowerCamelCase_							=self.model_tester.prepare_config_and_inputs_for_common()
              if not self.model_tester.is_training:
                     return

              lowerCamelCase_							=False
              lowerCamelCase_							=True

              for model_class in self.all_model_classes:
                     if model_class in [*get_values(lowerCAmelCase						)] or not model_class.supports_gradient_checkpointing:
                            continue
                     # TODO: remove the following 3 lines once we have a MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING
                     # this can then be incorporated into _prepare_for_class in test_modeling_common.py
                     elif model_class.__name__ == "Data2VecVisionForSemanticSegmentation":
                            lowerCamelCase_,   lowerCamelCase_,   lowerCamelCase_,   lowerCamelCase_							=inputs_dict['''pixel_values'''].shape
                            lowerCamelCase_							=torch.zeros(
                                [self.model_tester.batch_size, height, width],	device=lowerCAmelCase						).long()
                     lowerCamelCase_							=model_class(lowerCAmelCase						)
                     model.gradient_checkpointing_enable()
                     model.to(lowerCAmelCase						)
                     model.train()
                     lowerCamelCase_							=self._prepare_for_class(lowerCAmelCase,	lowerCAmelCase,	return_labels=lowerCAmelCase						)
                     lowerCamelCase_							=model(**lowerCAmelCase						).loss
                     loss.backward()






       def   lowercase__  (							self						):



              """simple docstring"""







              lowerCamelCase_,   lowerCamelCase_							=self.model_tester.prepare_config_and_inputs_for_common()

              lowerCamelCase_							=_config_zero_init(lowerCAmelCase						)
              for model_class in self.all_model_classes:
                     lowerCamelCase_							=model_class(config=lowerCAmelCase						)
                     for name, param in model.named_parameters():
                            # we skip lambda parameters as these require special initial values
                            # determined by config.layer_scale_init_value
                            if "lambda" in name:
                                   continue
                            if param.requires_grad:
                                   self.assertIn(
                                       ((param.data.mean() * 1e9).round() / 1e9).item(),	[0.0, 1.0],	msg=f'''Parameter {name} of model {model_class} seems not properly initialized''',	)






       def   lowercase__  (							self,	lowerCAmelCase,	lowerCAmelCase,	lowerCAmelCase,	lowerCAmelCase=2e-4,	lowerCAmelCase="outputs",	lowerCAmelCase=None						):



              """simple docstring"""







              super().check_pt_tf_outputs(lowerCAmelCase,	lowerCAmelCase,	lowerCAmelCase,	lowerCAmelCase,	lowerCAmelCase,	lowerCAmelCase						)






       def   lowercase__  (							self						):



              """simple docstring"""







              lowerCamelCase_							=self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase						)






       @slow
       def   lowercase__  (							self						):



              """simple docstring"""







              for model_name in DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                     lowerCamelCase_							=DataaVecVisionModel.from_pretrained(lowerCAmelCase						)
                     self.assertIsNotNone(lowerCAmelCase						)





def   a_							(   )							->		str:



       """simple docstring"""
       lowerCamelCase_							=Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''     )
       return image


@require_torch
@require_vision
class 						__UpperCamelCase (				unittest.TestCase       ):






       @cached_property
       def   lowercase__  (							self						):



              """simple docstring"""







              return (
                  BeitImageProcessor.from_pretrained('''facebook/data2vec-vision-base-ft1k'''						) if is_vision_available() else None
              )






       @slow
       def   lowercase__  (							self						):



              """simple docstring"""







              lowerCamelCase_							=DataaVecVisionForImageClassification.from_pretrained('''facebook/data2vec-vision-base-ft1k'''						).to(
                  lowerCAmelCase						)

              lowerCamelCase_							=self.default_image_processor
              lowerCamelCase_							=prepare_img()
              lowerCamelCase_							=image_processor(images=lowerCAmelCase,	return_tensors='''pt'''						).to(lowerCAmelCase						)

              # forward pass
              with torch.no_grad():
                     lowerCamelCase_							=model(**lowerCAmelCase						)
              lowerCamelCase_							=outputs.logits

              # verify the logits
              lowerCamelCase_							=torch.Size((1, 1_000)						)
              self.assertEqual(logits.shape,	lowerCAmelCase						)

              lowerCamelCase_							=torch.tensor([0.3_2_7_7, -0.1_3_9_5, 0.0_9_1_1]						).to(lowerCAmelCase						)

              self.assertTrue(torch.allclose(logits[0, :3],	lowerCAmelCase,	atol=1e-4						)						)

              lowerCamelCase_							=[model.config.labelaid[i] for i in ['''remote control, remote''', '''tabby, tabby cat''']]
              self.assertEqual(logits[0].topk(2						).indices.cpu().tolist(),	lowerCAmelCase						)










'''simple docstring'''




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


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





def   a_							(   self							:    Union[str, Any]     , __snake_case							:    List[str]     , __snake_case							:    List[str]     , __snake_case							:    Dict     , __snake_case							:    str     )							->		str:



       """simple docstring"""
       lowerCamelCase_							=image.to(device=__snake_case     )
       lowerCamelCase_							=image  # take image as image_embeddings
       lowerCamelCase_							=image_embeddings.unsqueeze(1     )

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

       if do_classifier_free_guidance:
              lowerCamelCase_							=torch.zeros_like(__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_							=torch.cat([uncond_embeddings, image_embeddings]     )

       return image_embeddings


class 						__UpperCamelCase (				lowerCamelCase__       ):






       def __init__(							self,	lowerCAmelCase,	lowerCAmelCase,	lowerCAmelCase,	lowerCAmelCase,	lowerCAmelCase = None,	):



              """simple docstring"""







              super().__init__()

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

              lowerCamelCase_							=decoder_pipe_kwargs.get('''torch_dtype''',	lowerCAmelCase						) or prior.dtype

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

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

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






       def   lowercase__  (							self,	lowerCAmelCase,	lowerCAmelCase,	lowerCAmelCase,	lowerCAmelCase,	lowerCAmelCase = None,	lowerCAmelCase = None,	):



              """simple docstring"""







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

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

                     lowerCamelCase_							=self.text_encoder(text_input_ids.to(lowerCAmelCase						)						)

                     lowerCamelCase_							=text_encoder_output.text_embeds
                     lowerCamelCase_							=text_encoder_output.last_hidden_state

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

              lowerCamelCase_							=text_embeddings.repeat_interleave(lowerCAmelCase,	dim=0						)
              lowerCamelCase_							=text_encoder_hidden_states.repeat_interleave(lowerCAmelCase,	dim=0						)
              lowerCamelCase_							=text_mask.repeat_interleave(lowerCAmelCase,	dim=0						)

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

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

                     lowerCamelCase_							=uncond_embeddings_text_encoder_output.text_embeds
                     lowerCamelCase_							=uncond_embeddings_text_encoder_output.last_hidden_state

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

                     lowerCamelCase_							=uncond_embeddings.shape[1]
                     lowerCamelCase_							=uncond_embeddings.repeat(1,	lowerCAmelCase						)
                     lowerCamelCase_							=uncond_embeddings.view(batch_size * num_images_per_prompt,	lowerCAmelCase						)

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

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

              return text_embeddings, text_encoder_hidden_states, text_mask






       @property
       def   lowercase__  (							self						):



              """simple docstring"""







              if self.device != torch.device('''meta'''						) or not hasattr(self.prior,	'''_hf_hook'''						):
                     return self.device
              for module in self.prior.modules():
                     if (
                         hasattr(lowerCAmelCase,	'''_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,	lowerCAmelCase,	lowerCAmelCase,	lowerCAmelCase,	lowerCAmelCase,	lowerCAmelCase,	lowerCAmelCase						):



              """simple docstring"""







              if latents is None:
                     lowerCamelCase_							=randn_tensor(lowerCAmelCase,	generator=lowerCAmelCase,	device=lowerCAmelCase,	dtype=lowerCAmelCase						)
              else:
                     if latents.shape != shape:
                            raise ValueError(f'''Unexpected latents shape, got {latents.shape}, expected {shape}'''						)
                     lowerCamelCase_							=latents.to(lowerCAmelCase						)

              lowerCamelCase_							=latents * scheduler.init_noise_sigma
              return latents






       def   lowercase__  (							self,	lowerCAmelCase = None						):



              """simple docstring"""







              self.decoder_pipe.to(lowerCAmelCase						)
              super().to(lowerCAmelCase						)






       @torch.no_grad()
       def __call__(							self,	lowerCAmelCase = None,	lowerCAmelCase = None,	lowerCAmelCase = None,	lowerCAmelCase = 1,	lowerCAmelCase = 25,	lowerCAmelCase = None,	lowerCAmelCase = None,	lowerCAmelCase = None,	lowerCAmelCase = None,	lowerCAmelCase = 4.0,	lowerCAmelCase = 8.0,	lowerCAmelCase = 50,	lowerCAmelCase = 1,	lowerCAmelCase = 0.0,	lowerCAmelCase = "pil",	lowerCAmelCase = True,	):



              """simple docstring"""







              if prompt is not None:
                     if isinstance(lowerCAmelCase,	lowerCAmelCase						):
                            lowerCamelCase_							=1
                     elif isinstance(lowerCAmelCase,	lowerCAmelCase						):
                            lowerCamelCase_							=len(lowerCAmelCase						)
                     else:
                            raise ValueError(f'''`prompt` has to be of type `str` or `list` but is {type(lowerCAmelCase						)}'''						)
              else:
                     lowerCamelCase_							=text_model_output[0].shape[0]

              lowerCamelCase_							=self._execution_device

              lowerCamelCase_							=batch_size * num_images_per_prompt

              lowerCamelCase_							=prior_guidance_scale > 1.0 or decoder_guidance_scale > 1.0

              lowerCamelCase_,   lowerCamelCase_,   lowerCamelCase_							=self._encode_prompt(
                  lowerCAmelCase,	lowerCAmelCase,	lowerCAmelCase,	lowerCAmelCase,	lowerCAmelCase,	lowerCAmelCase						)

              # prior

              self.prior_scheduler.set_timesteps(lowerCAmelCase,	device=lowerCAmelCase						)
              lowerCamelCase_							=self.prior_scheduler.timesteps

              lowerCamelCase_							=self.prior.config.embedding_dim

              lowerCamelCase_							=self.prepare_latents(
                  (batch_size, embedding_dim),	text_embeddings.dtype,	lowerCAmelCase,	lowerCAmelCase,	lowerCAmelCase,	self.prior_scheduler,	)

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

                     lowerCamelCase_							=self.prior(
                         lowerCAmelCase,	timestep=lowerCAmelCase,	proj_embedding=lowerCAmelCase,	encoder_hidden_states=lowerCAmelCase,	attention_mask=lowerCAmelCase,	).predicted_image_embedding

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

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

                     lowerCamelCase_							=self.prior_scheduler.step(
                         lowerCAmelCase,	timestep=lowerCAmelCase,	sample=lowerCAmelCase,	generator=lowerCAmelCase,	prev_timestep=lowerCAmelCase,	).prev_sample

              lowerCamelCase_							=self.prior.post_process_latents(lowerCAmelCase						)

              lowerCamelCase_							=prior_latents

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




