





'''simple docstring'''
import inspect
import unittest

from transformers import YolosConfig
from transformers.testing_utils import require_torch, 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, floats_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch
    from torch import nn

    from transformers import YolosForObjectDetection, YolosModel
    from transformers.models.yolos.modeling_yolos import YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
    from PIL import Image

    from transformers import AutoImageProcessor

class      snake_case     :




 """simple docstring"""



 def __init__(       self       :							Union[str, Any]							,	__A       :							str							,	__A       :							Tuple=1_3							,	__A       :							Optional[int]=[3_0, 3_0]							,	__A       :							str=2							,	__A       :							List[Any]=3							,	__A       :							Dict=True							,	__A       :							Union[str, Any]=True							,	__A       :							Tuple=3_2							,	__A       :							str=5							,	__A       :							Dict=4							,	__A       :							Optional[int]=3_7							,	__A       :							Tuple="gelu"							,	__A       :							Tuple=0.1							,	__A       :							List[str]=0.1							,	__A       :							List[str]=1_0							,	__A       :							Optional[int]=0.02							,	__A       :							str=3							,	__A       :							Dict=None							,	__A       :							List[str]=8							,	__A       :							Any=1_0							,	):
        __UpperCamelCase       =  parent
        __UpperCamelCase       =  batch_size
        __UpperCamelCase       =  image_size
        __UpperCamelCase       =  patch_size
        __UpperCamelCase       =  num_channels
        __UpperCamelCase       =  is_training
        __UpperCamelCase       =  use_labels
        __UpperCamelCase       =  hidden_size
        __UpperCamelCase       =  num_hidden_layers
        __UpperCamelCase       =  num_attention_heads
        __UpperCamelCase       =  intermediate_size
        __UpperCamelCase       =  hidden_act
        __UpperCamelCase       =  hidden_dropout_prob
        __UpperCamelCase       =  attention_probs_dropout_prob
        __UpperCamelCase       =  type_sequence_label_size
        __UpperCamelCase       =  initializer_range
        __UpperCamelCase       =  num_labels
        __UpperCamelCase       =  scope
        __UpperCamelCase       =  n_targets
        __UpperCamelCase       =  num_detection_tokens
        # we set the expected sequence length (which is used in several tests)
        # expected sequence length = num_patches + 1 (we add 1 for the [CLS] token) + num_detection_tokens
        __UpperCamelCase       =  (image_size[1] // patch_size) * (image_size[0] // patch_size)
        __UpperCamelCase       =  num_patches + 1 + self.num_detection_tokens






 def        _lowerCamelCase					(       self       :							Optional[Any]	):
        __UpperCamelCase       =  floats_tensor([self.batch_size, self.num_channels, self.image_size[0], self.image_size[1]]	)

        __UpperCamelCase       =  None
        if self.use_labels:
               # labels is a list of Dict (each Dict being the labels for a given example in the batch)
               __UpperCamelCase       =  []
               for i in range(self.batch_size	):
                      __UpperCamelCase       =  {}
                      __UpperCamelCase       =  torch.randint(
                          high=self.num_labels							,	size=(self.n_targets,)							,	device=__A	)
                      __UpperCamelCase       =  torch.rand(self.n_targets							,	4							,	device=__A	)
                      labels.append(__A	)

        __UpperCamelCase       =  self.get_config()

        return config, pixel_values, labels






 def        _lowerCamelCase					(       self       :							Tuple	):
        return YolosConfig(
            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=__A							,	initializer_range=self.initializer_range							,	num_detection_tokens=self.num_detection_tokens							,	num_labels=self.num_labels							,	)






 def        _lowerCamelCase					(       self       :							Optional[Any]							,	__A       :							str							,	__A       :							Dict							,	__A       :							Dict	):
        __UpperCamelCase       =  YolosModel(config=__A	)
        model.to(__A	)
        model.eval()
        __UpperCamelCase       =  model(__A	)
        self.parent.assertEqual(
            result.last_hidden_state.shape							,	(self.batch_size, self.expected_seq_len, self.hidden_size)	)






 def        _lowerCamelCase					(       self       :							Optional[Any]							,	__A       :							Union[str, Any]							,	__A       :							Dict							,	__A       :							Optional[Any]	):
        __UpperCamelCase       =  YolosForObjectDetection(__A	)
        model.to(__A	)
        model.eval()

        __UpperCamelCase       =  model(pixel_values=__A	)
        __UpperCamelCase       =  model(__A	)

        self.parent.assertEqual(result.logits.shape							,	(self.batch_size, self.num_detection_tokens, self.num_labels + 1)	)
        self.parent.assertEqual(result.pred_boxes.shape							,	(self.batch_size, self.num_detection_tokens, 4)	)

        __UpperCamelCase       =  model(pixel_values=__A							,	labels=__A	)

        self.parent.assertEqual(result.loss.shape							,	()	)
        self.parent.assertEqual(result.logits.shape							,	(self.batch_size, self.num_detection_tokens, self.num_labels + 1)	)
        self.parent.assertEqual(result.pred_boxes.shape							,	(self.batch_size, self.num_detection_tokens, 4)	)






 def        _lowerCamelCase					(       self       :							List[Any]	):
        __UpperCamelCase       =  self.prepare_config_and_inputs()
        __UpperCamelCase       ,						__UpperCamelCase       ,						__UpperCamelCase       =  config_and_inputs
        __UpperCamelCase       =  {'pixel_values': pixel_values}
        return config, inputs_dict






@require_torch
class      snake_case     (    __lowerCamelCase			,       __lowerCamelCase			,       unittest.TestCase       ):




 """simple docstring"""



 SCREAMING_SNAKE_CASE_	:					Dict					=(YolosModel, YolosForObjectDetection) if is_torch_available() else ()
 SCREAMING_SNAKE_CASE_	:					str					=(
     {"feature-extraction": YolosModel, "object-detection": YolosForObjectDetection} if is_torch_available() else {}
 )

 SCREAMING_SNAKE_CASE_	:					Dict					=False
 SCREAMING_SNAKE_CASE_	:					Tuple					=False
 SCREAMING_SNAKE_CASE_	:					Optional[int]					=False
 SCREAMING_SNAKE_CASE_	:					Tuple					=False






 def        _lowerCamelCase					(       self       :							Optional[Any]							,	__A       :							Optional[int]							,	__A       :							Dict							,	__A       :							str=False	):
        __UpperCamelCase       =  super()._prepare_for_class(__A							,	__A							,	return_labels=__A	)

        if return_labels:
               if model_class.__name__ == "YolosForObjectDetection":
                      __UpperCamelCase       =  []
                      for i in range(self.model_tester.batch_size	):
                             __UpperCamelCase       =  {}
                             __UpperCamelCase       =  torch.ones(
                                 size=(self.model_tester.n_targets,)							,	device=__A							,	dtype=torch.long	)
                             __UpperCamelCase       =  torch.ones(
                                 self.model_tester.n_targets							,	4							,	device=__A							,	dtype=torch.float	)
                             labels.append(__A	)
                      __UpperCamelCase       =  labels

        return inputs_dict






 def        _lowerCamelCase					(       self       :							Tuple	):
        __UpperCamelCase       =  YolosModelTester(self	)
        __UpperCamelCase       =  ConfigTester(self							,	config_class=__A							,	has_text_modality=__A							,	hidden_size=3_7	)






 def        _lowerCamelCase					(       self       :							List[str]	):
        self.config_tester.run_common_tests()






 def        _lowerCamelCase					(       self       :							Union[str, Any]	):
        # YOLOS does not use inputs_embeds
        pass






 def        _lowerCamelCase					(       self       :							Any	):
        __UpperCamelCase       ,						__UpperCamelCase       =  self.model_tester.prepare_config_and_inputs_for_common()

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






 def        _lowerCamelCase					(       self       :							Any	):
        __UpperCamelCase       ,						__UpperCamelCase       =  self.model_tester.prepare_config_and_inputs_for_common()

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

               __UpperCamelCase       =  ['pixel_values']
               self.assertListEqual(arg_names[:1]							,	__A	)






 def        _lowerCamelCase					(       self       :							Optional[Any]	):
        __UpperCamelCase       =  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*__A	)






 def        _lowerCamelCase					(       self       :							List[str]	):
        __UpperCamelCase       ,						__UpperCamelCase       =  self.model_tester.prepare_config_and_inputs_for_common()
        __UpperCamelCase       =  True

        # in YOLOS, the seq_len is different
        __UpperCamelCase       =  self.model_tester.expected_seq_len
        for model_class in self.all_model_classes:
               __UpperCamelCase       =  True
               __UpperCamelCase       =  False
               __UpperCamelCase       =  True
               __UpperCamelCase       =  model_class(__A	)
               model.to(__A	)
               model.eval()
               with torch.no_grad():
                      __UpperCamelCase       =  model(**self._prepare_for_class(__A							,	__A	)	)
               __UpperCamelCase       =  outputs.attentions
               self.assertEqual(len(__A	)							,	self.model_tester.num_hidden_layers	)

               # check that output_attentions also work using config
               del inputs_dict["output_attentions"]
               __UpperCamelCase       =  True
               __UpperCamelCase       =  model_class(__A	)
               model.to(__A	)
               model.eval()
               with torch.no_grad():
                      __UpperCamelCase       =  model(**self._prepare_for_class(__A							,	__A	)	)
               __UpperCamelCase       =  outputs.attentions
               self.assertEqual(len(__A	)							,	self.model_tester.num_hidden_layers	)

               self.assertListEqual(
                   list(attentions[0].shape[-3:]	)							,	[self.model_tester.num_attention_heads, seq_len, seq_len]							,	)
               __UpperCamelCase       =  len(__A	)

               # Check attention is always last and order is fine
               __UpperCamelCase       =  True
               __UpperCamelCase       =  True
               __UpperCamelCase       =  model_class(__A	)
               model.to(__A	)
               model.eval()
               with torch.no_grad():
                      __UpperCamelCase       =  model(**self._prepare_for_class(__A							,	__A	)	)

               __UpperCamelCase       =  1
               self.assertEqual(out_len + added_hidden_states							,	len(__A	)	)

               __UpperCamelCase       =  outputs.attentions

               self.assertEqual(len(__A	)							,	self.model_tester.num_hidden_layers	)
               self.assertListEqual(
                   list(self_attentions[0].shape[-3:]	)							,	[self.model_tester.num_attention_heads, seq_len, seq_len]							,	)






 def        _lowerCamelCase					(       self       :							str	):
        def check_hidden_states_output(__A       :							List[str]							,	__A       :							int							,	__A       :							Tuple	):
               __UpperCamelCase       =  model_class(__A	)
               model.to(__A	)
               model.eval()

               with torch.no_grad():
                      __UpperCamelCase       =  model(**self._prepare_for_class(__A							,	__A	)	)

               __UpperCamelCase       =  outputs.hidden_states

               __UpperCamelCase       =  getattr(
                   self.model_tester							,	'expected_num_hidden_layers'							,	self.model_tester.num_hidden_layers + 1	)
               self.assertEqual(len(__A	)							,	__A	)

               # YOLOS has a different seq_length
               __UpperCamelCase       =  self.model_tester.expected_seq_len

               self.assertListEqual(
                   list(hidden_states[0].shape[-2:]	)							,	[seq_length, self.model_tester.hidden_size]							,	)

        __UpperCamelCase       ,						__UpperCamelCase       =  self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
               __UpperCamelCase       =  True
               check_hidden_states_output(__A							,	__A							,	__A	)

               # check that output_hidden_states also work using config
               del inputs_dict["output_hidden_states"]
               __UpperCamelCase       =  True

               check_hidden_states_output(__A							,	__A							,	__A	)






 def        _lowerCamelCase					(       self       :							Optional[Any]	):
        __UpperCamelCase       =  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_object_detection(*__A	)






 @slow
 def        _lowerCamelCase					(       self       :							List[str]	):
        for model_name in YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               __UpperCamelCase       =  YolosModel.from_pretrained(__A	)
               self.assertIsNotNone(__A	)






def 				lowercase__   (   ) ->      Union[str, Any]:



       """simple docstring"""






       __UpperCamelCase       =  Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'     )
       return image


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




 """simple docstring"""



 @cached_property
 def        _lowerCamelCase					(       self       :							Any	):
        return AutoImageProcessor.from_pretrained('hustvl/yolos-small'	) if is_vision_available() else None






 @slow
 def        _lowerCamelCase					(       self       :							Tuple	):
        __UpperCamelCase       =  YolosForObjectDetection.from_pretrained('hustvl/yolos-small'	).to(__A	)

        __UpperCamelCase       =  self.default_image_processor
        __UpperCamelCase       =  prepare_img()
        __UpperCamelCase       =  image_processor(images=__A							,	return_tensors='pt'	).to(__A	)

        # forward pass
        with torch.no_grad():
               __UpperCamelCase       =  model(inputs.pixel_values	)

        # verify outputs
        __UpperCamelCase       =  torch.Size((1, 1_0_0, 9_2)	)
        self.assertEqual(outputs.logits.shape							,	__A	)

        __UpperCamelCase       =  torch.tensor(
            [[-24.0248, -10.3024, -14.8290], [-42.0392, -16.8200, -27.4334], [-27.2743, -11.8154, -18.7148]]							,	device=__A							,	)
        __UpperCamelCase       =  torch.tensor(
            [[0.2559, 0.5455, 0.4706], [0.2989, 0.7279, 0.1875], [0.7732, 0.4017, 0.4462]]							,	device=__A	)
        self.assertTrue(torch.allclose(outputs.logits[0, :3, :3]							,	__A							,	atol=1e-4	)	)
        self.assertTrue(torch.allclose(outputs.pred_boxes[0, :3, :3]							,	__A							,	atol=1e-4	)	)

        # verify postprocessing
        __UpperCamelCase       =  image_processor.post_process_object_detection(
            __A							,	threshold=0.3							,	target_sizes=[image.size[::-1]]	)[0]
        __UpperCamelCase       =  torch.tensor([0.9994, 0.9790, 0.9964, 0.9972, 0.9861]	).to(__A	)
        __UpperCamelCase       =  [7_5, 7_5, 1_7, 6_3, 1_7]
        __UpperCamelCase       =  torch.tensor([335.0609, 79.3848, 375.4216, 187.2495]	).to(__A	)

        self.assertEqual(len(results['scores']	)							,	5	)
        self.assertTrue(torch.allclose(results['scores']							,	__A							,	atol=1e-4	)	)
        self.assertSequenceEqual(results['labels'].tolist()							,	__A	)
        self.assertTrue(torch.allclose(results['boxes'][0, :]							,	__A	)	)

"""simple docstring"""







from dataclasses import dataclass
from typing import Dict, Optional, Union

import torch
import torch.nn.functional as F
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .attention_processor import AttentionProcessor, AttnProcessor
from .embeddings import TimestepEmbedding, Timesteps
from .modeling_utils import ModelMixin







@dataclass
class        UpperCamelCase		(			lowercase_  ):
   lowercase							=  42







class        UpperCamelCase		(			lowercase_						,      lowercase_  ):
   @register_to_config
   def __init__(			self     ,__UpperCamelCase = 32     ,__UpperCamelCase = 64     ,__UpperCamelCase = 20     ,__UpperCamelCase = 768     ,__UpperCamelCase=77     ,__UpperCamelCase=4     ,__UpperCamelCase = 0.0     ,__UpperCamelCase = "silu"     ,__UpperCamelCase = None     ,__UpperCamelCase = None     ,__UpperCamelCase = "linear"     ,__UpperCamelCase = "prd"     ,__UpperCamelCase = None     ,__UpperCamelCase = None     ,__UpperCamelCase = None     ,)	->      List[str]:


        '''simple docstring'''


        super().__init__()
        lowercase_	:				Dict													=	num_attention_heads
        lowercase_	:				Dict													=	attention_head_dim
        lowercase_	:				Any													=	num_attention_heads * attention_head_dim
        lowercase_	:				Optional[Any]													=	additional_embeddings

        lowercase_	:				int													=	time_embed_dim or inner_dim
        lowercase_	:				Optional[Any]													=	embedding_proj_dim or embedding_dim
        lowercase_	:				List[str]													=	clip_embed_dim or embedding_dim

        lowercase_	:				Union[str, Any]													=	Timesteps(__UpperCamelCase     ,__UpperCamelCase     ,0			)
        lowercase_	:				Tuple													=	TimestepEmbedding(__UpperCamelCase     ,__UpperCamelCase     ,out_dim=__UpperCamelCase     ,act_fn=__UpperCamelCase			)

        lowercase_	:				Optional[Any]													=	nn.Linear(__UpperCamelCase     ,__UpperCamelCase			)

        if embedding_proj_norm_type is None:
             lowercase_	:				Dict													=	None
        elif embedding_proj_norm_type == "layer":
             lowercase_	:				str													=	nn.LayerNorm(__UpperCamelCase			)
        else:
             raise ValueError(f'''unsupported embedding_proj_norm_type: {embedding_proj_norm_type}'''			)

        lowercase_	:				Tuple													=	nn.Linear(__UpperCamelCase     ,__UpperCamelCase			)

        if encoder_hid_proj_type is None:
             lowercase_	:				str													=	None
        elif encoder_hid_proj_type == "linear":
             lowercase_	:				Optional[int]													=	nn.Linear(__UpperCamelCase     ,__UpperCamelCase			)
        else:
             raise ValueError(f'''unsupported encoder_hid_proj_type: {encoder_hid_proj_type}'''			)

        lowercase_	:				List[Any]													=	nn.Parameter(torch.zeros(1     ,num_embeddings + additional_embeddings     ,__UpperCamelCase			)			)

        if added_emb_type == "prd":
             lowercase_	:				Union[str, Any]													=	nn.Parameter(torch.zeros(1     ,1     ,__UpperCamelCase			)			)
        elif added_emb_type is None:
             lowercase_	:				List[Any]													=	None
        else:
             raise ValueError(
                 f'''`added_emb_type`: {added_emb_type} is not supported. Make sure to choose one of `\'prd\'` or `None`.'''			)

        lowercase_	:				Dict													=	nn.ModuleList(
            [
                BasicTransformerBlock(
                    __UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase     ,dropout=__UpperCamelCase     ,activation_fn='gelu'     ,attention_bias=__UpperCamelCase     ,)
                for d in range(__UpperCamelCase			)
            ]			)

        if norm_in_type == "layer":
             lowercase_	:				Optional[int]													=	nn.LayerNorm(__UpperCamelCase			)
        elif norm_in_type is None:
             lowercase_	:				Dict													=	None
        else:
             raise ValueError(f'''Unsupported norm_in_type: {norm_in_type}.'''			)

        lowercase_	:				List[Any]													=	nn.LayerNorm(__UpperCamelCase			)

        lowercase_	:				Optional[int]													=	nn.Linear(__UpperCamelCase     ,__UpperCamelCase			)

        lowercase_	:				Dict													=	torch.full(
            [num_embeddings + additional_embeddings, num_embeddings + additional_embeddings]     ,-1_0000.0			)
        causal_attention_mask.triu_(1			)
        lowercase_	:				int													=	causal_attention_mask[None, ...]
        self.register_buffer('causal_attention_mask'     ,__UpperCamelCase     ,persistent=__UpperCamelCase			)

        lowercase_	:				List[Any]													=	nn.Parameter(torch.zeros(1     ,__UpperCamelCase			)			)
        lowercase_	:				Optional[int]													=	nn.Parameter(torch.zeros(1     ,__UpperCamelCase			)			)
   @property
   # Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors
   def 			_UpperCAmelCase   (			self			)	->      Dict[str, AttentionProcessor]:


        '''simple docstring'''


        lowercase_	:				Tuple													=	{}

        def fn_recursive_add_processors(__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase			):
             if hasattr(__UpperCamelCase     ,'set_processor'			):
                  lowercase_	:				List[str]													=	module.processor

             for sub_name, child in module.named_children():
                  fn_recursive_add_processors(f'''{name}.{sub_name}'''     ,__UpperCamelCase     ,__UpperCamelCase			)

             return processors

        for name, module in self.named_children():
             fn_recursive_add_processors(__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase			)

        return processors
   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase			)	->      Optional[Any]:


        '''simple docstring'''


        lowercase_	:				Dict													=	len(self.attn_processors.keys()			)

        if isinstance(__UpperCamelCase     ,__UpperCamelCase			) and len(__UpperCamelCase			) != count:
             raise ValueError(
                 f'''A dict of processors was passed, but the number of processors {len(__UpperCamelCase			)} does not match the'''
                 f''' number of attention layers: {count}. Please make sure to pass {count} processor classes.'''			)

        def fn_recursive_attn_processor(__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase			):
             if hasattr(__UpperCamelCase     ,'set_processor'			):
                  if not isinstance(__UpperCamelCase     ,__UpperCamelCase			):
                       module.set_processor(__UpperCamelCase			)
                  else:
                       module.set_processor(processor.pop(f'''{name}.processor'''			)			)

             for sub_name, child in module.named_children():
                  fn_recursive_attn_processor(f'''{name}.{sub_name}'''     ,__UpperCamelCase     ,__UpperCamelCase			)

        for name, module in self.named_children():
             fn_recursive_attn_processor(__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase			)
   def 			_UpperCAmelCase   (			self			)	->      List[str]:


        '''simple docstring'''


        self.set_attn_processor(AttnProcessor()			)
   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase = None     ,__UpperCamelCase = None     ,__UpperCamelCase = True     ,)	->      Any:


        '''simple docstring'''


        lowercase_	:				Any													=	hidden_states.shape[0]

        lowercase_	:				str													=	timestep
        if not torch.is_tensor(__UpperCamelCase			):
             lowercase_	:				Any													=	torch.tensor([timesteps]     ,dtype=torch.long     ,device=hidden_states.device			)
        elif torch.is_tensor(__UpperCamelCase			) and len(timesteps.shape			) == 0:
             lowercase_	:				List[str]													=	timesteps[None].to(hidden_states.device			)

        # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
        lowercase_	:				Optional[int]													=	timesteps * torch.ones(__UpperCamelCase     ,dtype=timesteps.dtype     ,device=timesteps.device			)

        lowercase_	:				Any													=	self.time_proj(__UpperCamelCase			)

        # timesteps does not contain any weights and will always return f32 tensors
        # but time_embedding might be fp16, so we need to cast here.
        lowercase_	:				List[Any]													=	timesteps_projected.to(dtype=self.dtype			)
        lowercase_	:				Any													=	self.time_embedding(__UpperCamelCase			)

        if self.embedding_proj_norm is not None:
             lowercase_	:				List[str]													=	self.embedding_proj_norm(__UpperCamelCase			)

        lowercase_	:				Any													=	self.embedding_proj(__UpperCamelCase			)
        if self.encoder_hidden_states_proj is not None and encoder_hidden_states is not None:
             lowercase_	:				Optional[int]													=	self.encoder_hidden_states_proj(__UpperCamelCase			)
        elif self.encoder_hidden_states_proj is not None and encoder_hidden_states is None:
             raise ValueError('`encoder_hidden_states_proj` requires `encoder_hidden_states` to be set'			)

        lowercase_	:				List[Any]													=	self.proj_in(__UpperCamelCase			)

        lowercase_	:				List[str]													=	self.positional_embedding.to(hidden_states.dtype			)

        lowercase_	:				Dict													=	[]
        lowercase_	:				Tuple													=	0

        if encoder_hidden_states is not None:
             additional_embeds.append(__UpperCamelCase			)
             additional_embeddings_len += encoder_hidden_states.shape[1]

        if len(proj_embeddings.shape			) == 2:
             lowercase_	:				List[Any]													=	proj_embeddings[:, None, :]

        if len(hidden_states.shape			) == 2:
             lowercase_	:				List[Any]													=	hidden_states[:, None, :]

        lowercase_	:				str													=	additional_embeds + [
            proj_embeddings,
            time_embeddings[:, None, :],
            hidden_states,
        ]

        if self.prd_embedding is not None:
             lowercase_	:				int													=	self.prd_embedding.to(hidden_states.dtype			).expand(__UpperCamelCase     ,-1     ,-1			)
             additional_embeds.append(__UpperCamelCase			)

        lowercase_	:				int													=	torch.cat(
            __UpperCamelCase     ,dim=1     ,)

        # Allow positional_embedding to not include the `addtional_embeddings` and instead pad it with zeros for these additional tokens
        lowercase_	:				Optional[Any]													=	additional_embeddings_len + proj_embeddings.shape[1] + 1
        if positional_embeddings.shape[1] < hidden_states.shape[1]:
             lowercase_	:				Any													=	F.pad(
                 __UpperCamelCase     ,(
                     0,
                     0,
                     additional_embeddings_len,
                     self.prd_embedding.shape[1] if self.prd_embedding is not None else 0,
                 )     ,value=0.0     ,)

        lowercase_	:				Dict													=	hidden_states + positional_embeddings

        if attention_mask is not None:
             lowercase_	:				Any													=	(1 - attention_mask.to(hidden_states.dtype			)) * -1_0000.0
             lowercase_	:				str													=	F.pad(__UpperCamelCase     ,(0, self.additional_embeddings)     ,value=0.0			)
             lowercase_	:				Optional[Any]													=	(attention_mask[:, None, :] + self.causal_attention_mask).to(hidden_states.dtype			)
             lowercase_	:				Tuple													=	attention_mask.repeat_interleave(self.config.num_attention_heads     ,dim=0			)

        if self.norm_in is not None:
             lowercase_	:				Optional[Any]													=	self.norm_in(__UpperCamelCase			)

        for block in self.transformer_blocks:
             lowercase_	:				int													=	block(__UpperCamelCase     ,attention_mask=__UpperCamelCase			)

        lowercase_	:				str													=	self.norm_out(__UpperCamelCase			)

        if self.prd_embedding is not None:
             lowercase_	:				Tuple													=	hidden_states[:, -1]
        else:
             lowercase_	:				List[Any]													=	hidden_states[:, additional_embeddings_len:]

        lowercase_	:				str													=	self.proj_to_clip_embeddings(__UpperCamelCase			)

        if not return_dict:
             return (predicted_image_embedding,)

        return PriorTransformerOutput(predicted_image_embedding=__UpperCamelCase			)







   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase			)	->      int:


        '''simple docstring'''


        lowercase_	:				Optional[Any]													=	(prior_latents * self.clip_std) + self.clip_mean
        return prior_latents

