

import inspect
import unittest
import warnings
from math import ceil, floor

from transformers import LevitConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

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


if is_torch_available():
					import torch

					from transformers import (
					    MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
					    MODEL_MAPPING,
					    LevitForImageClassification,
					    LevitForImageClassificationWithTeacher,
					    LevitModel,
					)
					from transformers.models.levit.modeling_levit import LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
					from PIL import Image

					from transformers import LevitImageProcessor






class        A_		(       lowercase_				):
							def     lowerCAmelCase     (		self		:			List[str]):
								__lowerCamelCase				:				Any					       = self.config_class(**self.inputs_dict)
								self.parent.assertTrue(hasattr(__UpperCamelCase   ,'hidden_sizes'))
								self.parent.assertTrue(hasattr(__UpperCamelCase   ,'num_attention_heads'))






class        A_		:
							def __init__(		self		:			Tuple   ,SCREAMING_SNAKE_CASE__		:			Any   ,SCREAMING_SNAKE_CASE__		:			Optional[int]=1_3   ,SCREAMING_SNAKE_CASE__		:			List[Any]=6_4   ,SCREAMING_SNAKE_CASE__		:			Union[str, Any]=3   ,SCREAMING_SNAKE_CASE__		:			Tuple=3   ,SCREAMING_SNAKE_CASE__		:			str=2   ,SCREAMING_SNAKE_CASE__		:			Union[str, Any]=1   ,SCREAMING_SNAKE_CASE__		:			Optional[int]=1_6   ,SCREAMING_SNAKE_CASE__		:			List[str]=[1_2_8, 2_5_6, 3_8_4]   ,SCREAMING_SNAKE_CASE__		:			Optional[Any]=[4, 6, 8]   ,SCREAMING_SNAKE_CASE__		:			List[str]=[2, 3, 4]   ,SCREAMING_SNAKE_CASE__		:			Optional[int]=[1_6, 1_6, 1_6]   ,SCREAMING_SNAKE_CASE__		:			str=0   ,SCREAMING_SNAKE_CASE__		:			Optional[Any]=[2, 2, 2]   ,SCREAMING_SNAKE_CASE__		:			List[str]=[2, 2, 2]   ,SCREAMING_SNAKE_CASE__		:			Optional[int]=0.02   ,SCREAMING_SNAKE_CASE__		:			List[str]=True   ,SCREAMING_SNAKE_CASE__		:			List[str]=True   ,SCREAMING_SNAKE_CASE__		:			List[Any]=2   ,):
								__lowerCamelCase				:				Optional[Any]					       = parent
								__lowerCamelCase				:				Any					       = batch_size
								__lowerCamelCase				:				str					       = image_size
								__lowerCamelCase				:				List[Any]					       = num_channels
								__lowerCamelCase				:				Any					       = kernel_size
								__lowerCamelCase				:				Any					       = stride
								__lowerCamelCase				:				List[str]					       = padding
								__lowerCamelCase				:				Tuple					       = hidden_sizes
								__lowerCamelCase				:				List[Any]					       = num_attention_heads
								__lowerCamelCase				:				Optional[int]					       = depths
								__lowerCamelCase				:				Union[str, Any]					       = key_dim
								__lowerCamelCase				:				List[str]					       = drop_path_rate
								__lowerCamelCase				:				Dict					       = patch_size
								__lowerCamelCase				:				List[Any]					       = attention_ratio
								__lowerCamelCase				:				Optional[int]					       = mlp_ratio
								__lowerCamelCase				:				Any					       = initializer_range
								__lowerCamelCase				:				Optional[Any]					       = [
								    ['Subsample', key_dim[0], hidden_sizes[0] // key_dim[0], 4, 2, 2],
								    ['Subsample', key_dim[0], hidden_sizes[1] // key_dim[0], 4, 2, 2],
								]
								__lowerCamelCase				:				Dict					       = is_training
								__lowerCamelCase				:				Tuple					       = use_labels
								__lowerCamelCase				:				Dict					       = num_labels
								__lowerCamelCase				:				Union[str, Any]					       = initializer_range
							def     lowerCAmelCase     (		self		:			Optional[Any]):
								__lowerCamelCase				:				Union[str, Any]					       = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

								__lowerCamelCase				:				List[str]					       = None
								if self.use_labels:
									__lowerCamelCase				:				List[Any]					       = ids_tensor([self.batch_size]   ,self.num_labels)

								__lowerCamelCase				:				List[str]					       = self.get_config()
								return config, pixel_values, labels
							def     lowerCAmelCase     (		self		:			List[Any]):
								return LevitConfig(
								    image_size=self.image_size   ,num_channels=self.num_channels   ,kernel_size=self.kernel_size   ,stride=self.stride   ,padding=self.padding   ,patch_size=self.patch_size   ,hidden_sizes=self.hidden_sizes   ,num_attention_heads=self.num_attention_heads   ,depths=self.depths   ,key_dim=self.key_dim   ,drop_path_rate=self.drop_path_rate   ,mlp_ratio=self.mlp_ratio   ,attention_ratio=self.attention_ratio   ,initializer_range=self.initializer_range   ,down_ops=self.down_ops   ,)
							def     lowerCAmelCase     (		self		:			List[Any]   ,SCREAMING_SNAKE_CASE__		:			Optional[Any]   ,SCREAMING_SNAKE_CASE__		:			List[Any]   ,SCREAMING_SNAKE_CASE__		:			Optional[Any]):
								__lowerCamelCase				:				Union[str, Any]					       = LevitModel(config=__UpperCamelCase)
								model.to(__UpperCamelCase)
								model.eval()
								__lowerCamelCase				:				Optional[Any]					       = model(__UpperCamelCase)
								__lowerCamelCase				:				Union[str, Any]					       = (self.image_size, self.image_size)
								__lowerCamelCase				:				Tuple					       = image_size[0], image_size[1]
								for _ in range(4):
									__lowerCamelCase				:				Optional[int]					       = floor(((height + 2 * self.padding - self.kernel_size) / self.stride) + 1)
									__lowerCamelCase				:				Dict					       = floor(((width + 2 * self.padding - self.kernel_size) / self.stride) + 1)
								self.parent.assertEqual(
								    result.last_hidden_state.shape   ,(self.batch_size, ceil(height / 4) * ceil(width / 4), self.hidden_sizes[-1])   ,)
							def     lowerCAmelCase     (		self		:			Tuple   ,SCREAMING_SNAKE_CASE__		:			List[Any]   ,SCREAMING_SNAKE_CASE__		:			List[str]   ,SCREAMING_SNAKE_CASE__		:			List[str]):
								__lowerCamelCase				:				str					       = self.num_labels
								__lowerCamelCase				:				Optional[Any]					       = LevitForImageClassification(__UpperCamelCase)
								model.to(__UpperCamelCase)
								model.eval()
								__lowerCamelCase				:				Union[str, Any]					       = model(__UpperCamelCase   ,labels=__UpperCamelCase)
								self.parent.assertEqual(result.logits.shape   ,(self.batch_size, self.num_labels))


							def     lowerCAmelCase     (		self		:			Tuple):
								__lowerCamelCase				:				Union[str, Any]					       = self.prepare_config_and_inputs()
								__lowerCamelCase				:				Tuple					       = config_and_inputs
								__lowerCamelCase				:				int					       = {'pixel_values': pixel_values}
								return config, inputs_dict






@require_torch
class        A_		(       lowercase_			,							lowercase_			,							unittest.TestCase				):
							_UpperCAmelCase    :  Optional[Any]						      =      (
							    (LevitModel, LevitForImageClassification, LevitForImageClassificationWithTeacher)
							    if is_torch_available()
							    else ()
							)
							_UpperCAmelCase    :  Dict						      =      (
							    {
							        '''feature-extraction''': LevitModel,
							        '''image-classification''': (LevitForImageClassification, LevitForImageClassificationWithTeacher),
							    }
							    if is_torch_available()
							    else {}
							)

							_UpperCAmelCase    :  int						      =      False
							_UpperCAmelCase    :  Any						      =      False
							_UpperCAmelCase    :  Tuple						      =      False
							_UpperCAmelCase    :  str						      =      False
							_UpperCAmelCase    :  Dict						      =      False
							def     lowerCAmelCase     (		self		:			Any):
								__lowerCamelCase				:				Tuple					       = LevitModelTester(self)
								__lowerCamelCase				:				Dict					       = ConfigTester(self   ,config_class=__UpperCamelCase   ,has_text_modality=__UpperCamelCase   ,hidden_size=3_7)
							def     lowerCAmelCase     (		self		:			Tuple):
								self.create_and_test_config_common_properties()
								self.config_tester.create_and_test_config_to_json_string()
								self.config_tester.create_and_test_config_to_json_file()
								self.config_tester.create_and_test_config_from_and_save_pretrained()
								self.config_tester.create_and_test_config_with_num_labels()
								self.config_tester.check_config_can_be_init_without_params()
								self.config_tester.check_config_arguments_init()
							def     lowerCAmelCase     (		self		:			Any):
								return
							@unittest.skip(reason='Levit does not use inputs_embeds')
							def     lowerCAmelCase     (		self		:			int):
								pass
							@unittest.skip(reason='Levit does not support input and output embeddings')
							def     lowerCAmelCase     (		self		:			int):
								pass
							@unittest.skip(reason='Levit does not output attentions')
							def     lowerCAmelCase     (		self		:			str):
								pass
							def     lowerCAmelCase     (		self		:			List[str]):
								__lowerCamelCase				:				Optional[int]					       = self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
									__lowerCamelCase				:				Optional[int]					       = model_class(__UpperCamelCase)
									__lowerCamelCase				:				Dict					       = inspect.signature(model.forward)
									# signature.parameters is an OrderedDict => so arg_names order is deterministic
									__lowerCamelCase				:				Union[str, Any]					       = [*signature.parameters.keys()]

									__lowerCamelCase				:				Optional[int]					       = ['pixel_values']
									self.assertListEqual(arg_names[:1]   ,__UpperCamelCase)
							def     lowerCAmelCase     (		self		:			Union[str, Any]):


								def check_hidden_states_output(SCREAMING_SNAKE_CASE__		:			Any   ,SCREAMING_SNAKE_CASE__		:			str   ,SCREAMING_SNAKE_CASE__		:			Optional[Any]):
									__lowerCamelCase				:				List[Any]					       = model_class(__UpperCamelCase)
									model.to(__UpperCamelCase)
									model.eval()

									with torch.no_grad():
										__lowerCamelCase				:				Optional[int]					       = model(**self._prepare_for_class(__UpperCamelCase   ,__UpperCamelCase))

									__lowerCamelCase				:				Dict					       = outputs.hidden_states

									__lowerCamelCase				:				List[str]					       = len(self.model_tester.depths) + 1
									self.assertEqual(len(__UpperCamelCase)   ,__UpperCamelCase)

									__lowerCamelCase				:				Optional[int]					       = (self.model_tester.image_size, self.model_tester.image_size)
									__lowerCamelCase				:				Union[str, Any]					       = image_size[0], image_size[1]
									for _ in range(4):
										__lowerCamelCase				:				Optional[Any]					       = floor(
										    (
										        (height + 2 * self.model_tester.padding - self.model_tester.kernel_size)
										        / self.model_tester.stride
										    )
										    + 1)
										__lowerCamelCase				:				Optional[int]					       = floor(
										    (
										        (width + 2 * self.model_tester.padding - self.model_tester.kernel_size)
										        / self.model_tester.stride
										    )
										    + 1)
									# verify the first hidden states (first block)
									self.assertListEqual(
									    list(hidden_states[0].shape[-2:])   ,[
									        height * width,
									        self.model_tester.hidden_sizes[0],
									    ]   ,)

								__lowerCamelCase				:				List[str]					       = self.model_tester.prepare_config_and_inputs_for_common()

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

									# check that output_hidden_states also work using config
									del inputs_dict["output_hidden_states"]
									__lowerCamelCase				:				Optional[int]					       = True

									check_hidden_states_output(__UpperCamelCase   ,__UpperCamelCase   ,__UpperCamelCase)
							@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.')
							def     lowerCAmelCase     (		self		:			Tuple):
								pass
							def     lowerCAmelCase     (		self		:			Union[str, Any]   ,SCREAMING_SNAKE_CASE__		:			Union[str, Any]   ,SCREAMING_SNAKE_CASE__		:			List[str]   ,SCREAMING_SNAKE_CASE__		:			Any=False):
								__lowerCamelCase				:				Optional[Any]					       = super()._prepare_for_class(__UpperCamelCase   ,__UpperCamelCase   ,return_labels=__UpperCamelCase)

								if return_labels:
									if model_class.__name__ == "LevitForImageClassificationWithTeacher":
										del inputs_dict["labels"]

								return inputs_dict
							def     lowerCAmelCase     (		self		:			Dict):
								__lowerCamelCase				:				Any					       = self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*__UpperCamelCase)
							def     lowerCAmelCase     (		self		:			Optional[Any]):
								__lowerCamelCase				:				Dict					       = self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_image_classification(*__UpperCamelCase)
							def     lowerCAmelCase     (		self		:			Any):


								if not self.model_tester.is_training:
									return

								__lowerCamelCase				:				Union[str, Any]					       = self.model_tester.prepare_config_and_inputs_for_common()
								__lowerCamelCase				:				Optional[Any]					       = True

								for model_class in self.all_model_classes:
									# LevitForImageClassificationWithTeacher supports inference-only
									if (
									    model_class in get_values(__UpperCamelCase)
									    or model_class.__name__ == "LevitForImageClassificationWithTeacher"
									):
										continue
									__lowerCamelCase				:				Dict					       = model_class(__UpperCamelCase)
									model.to(__UpperCamelCase)
									model.train()
									__lowerCamelCase				:				List[Any]					       = self._prepare_for_class(__UpperCamelCase   ,__UpperCamelCase   ,return_labels=__UpperCamelCase)
									__lowerCamelCase				:				Union[str, Any]					       = model(**__UpperCamelCase).loss
									loss.backward()
							def     lowerCAmelCase     (		self		:			Optional[Any]):
								__lowerCamelCase				:				int					       = self.model_tester.prepare_config_and_inputs_for_common()
								if not self.model_tester.is_training:
									return

								__lowerCamelCase				:				Union[str, Any]					       = False
								__lowerCamelCase				:				Dict					       = True

								for model_class in self.all_model_classes:
									if model_class in get_values(__UpperCamelCase) or not model_class.supports_gradient_checkpointing:
										continue
									# LevitForImageClassificationWithTeacher supports inference-only
									if model_class.__name__ == "LevitForImageClassificationWithTeacher":
										continue
									__lowerCamelCase				:				Dict					       = model_class(__UpperCamelCase)
									model.gradient_checkpointing_enable()
									model.to(__UpperCamelCase)
									model.train()
									__lowerCamelCase				:				Tuple					       = self._prepare_for_class(__UpperCamelCase   ,__UpperCamelCase   ,return_labels=__UpperCamelCase)
									__lowerCamelCase				:				Any					       = model(**__UpperCamelCase).loss
									loss.backward()
							def     lowerCAmelCase     (		self		:			Optional[Any]):
								__lowerCamelCase				:				Any					       = self.model_tester.prepare_config_and_inputs_for_common()

								__lowerCamelCase				:				int					       = [
								    {'title': 'multi_label_classification', 'num_labels': 2, 'dtype': torch.float},
								    {'title': 'single_label_classification', 'num_labels': 1, 'dtype': torch.long},
								    {'title': 'regression', 'num_labels': 1, 'dtype': torch.float},
								]

								for model_class in self.all_model_classes:
									if (
									    model_class
									    not in [
									        *get_values(__UpperCamelCase),
									    ]
									    or model_class.__name__ == "LevitForImageClassificationWithTeacher"
									):
										continue

									for problem_type in problem_types:
										with self.subTest(msg=F"Testing {model_class} with {problem_type['title']}"):
											__lowerCamelCase				:				Any					       = problem_type['title']
											__lowerCamelCase				:				Tuple					       = problem_type['num_labels']

											__lowerCamelCase				:				Tuple					       = model_class(__UpperCamelCase)
											model.to(__UpperCamelCase)
											model.train()

											__lowerCamelCase				:				Optional[int]					       = self._prepare_for_class(__UpperCamelCase   ,__UpperCamelCase   ,return_labels=__UpperCamelCase)

											if problem_type["num_labels"] > 1:
												__lowerCamelCase				:				Any					       = inputs['labels'].unsqueeze(1).repeat(1   ,problem_type['num_labels'])

											__lowerCamelCase				:				str					       = inputs['labels'].to(problem_type['dtype'])

											# This tests that we do not trigger the warning form PyTorch "Using a target size that is different
											# to the input size. This will likely lead to incorrect results due to broadcasting. Please ensure
											# they have the same size." which is a symptom something in wrong for the regression problem.
											# See https://github.com/huggingface/transformers/issues/11780
											with warnings.catch_warnings(record=__UpperCamelCase) as warning_list:
												__lowerCamelCase				:				int					       = model(**__UpperCamelCase).loss
											for w in warning_list:
												if "Using a target size that is different to the input size" in str(w.message):
													raise ValueError(
													    F"Something is going wrong in the regression problem: intercepted {w.message}")

											loss.backward()


							@slow
							def     lowerCAmelCase     (		self		:			Tuple):


								for model_name in LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									__lowerCamelCase				:				List[Any]					       = LevitModel.from_pretrained(__UpperCamelCase)
									self.assertIsNotNone(__UpperCamelCase)






def    SCREAMING_SNAKE_CASE__       (  )  ->     List[str]:
	__lowerCamelCase				:				Any					       = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'			)
	return image






@require_torch
@require_vision
class        A_		(       unittest.TestCase				):
							@cached_property
							def     lowerCAmelCase     (		self		:			Dict):
								return LevitImageProcessor.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0])


							@slow
							def     lowerCAmelCase     (		self		:			Union[str, Any]):
								__lowerCamelCase				:				Any					       = LevitForImageClassificationWithTeacher.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]).to(
								    __UpperCamelCase)

								__lowerCamelCase				:				str					       = self.default_image_processor
								__lowerCamelCase				:				Any					       = prepare_img()
								__lowerCamelCase				:				Optional[int]					       = image_processor(images=__UpperCamelCase   ,return_tensors='pt').to(__UpperCamelCase)

								# forward pass
								with torch.no_grad():
									__lowerCamelCase				:				Union[str, Any]					       = model(**__UpperCamelCase)

								# verify the logits
								__lowerCamelCase				:				str					       = torch.Size((1, 1_0_0_0))
								self.assertEqual(outputs.logits.shape   ,__UpperCamelCase)

								__lowerCamelCase				:				Any					       = torch.tensor([1.0448, -0.3745, -1.8317]).to(__UpperCamelCase)

								self.assertTrue(torch.allclose(outputs.logits[0, :3]   ,__UpperCamelCase   ,atol=1E-4))





"""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

