



'''simple docstring'''
from typing import Optional

from torch import nn

from .transformer_ad import TransformeraDModel, TransformeraDModelOutput






class 							lowercase__     (							nn.Module       ):





	def __init__(			self :    Optional[Any]			,lowerCamelCase__ :    int = 16			,lowerCamelCase__ :    int = 88			,lowerCamelCase__ :    Optional[int] = None			,lowerCamelCase__ :    int = 1			,lowerCamelCase__ :    float = 0.0			,lowerCamelCase__ :    int = 32			,lowerCamelCase__ :    Optional[int] = None			,lowerCamelCase__ :    bool = False			,lowerCamelCase__ :    Optional[int] = None			,lowerCamelCase__ :    Optional[int] = None			,lowerCamelCase__ :    str = "geglu"			,lowerCamelCase__ :    Optional[int] = None			,):







							'''simple docstring'''
							super().__init__()
							_UpperCamelCase :		Union[str, Any]			    =       nn.ModuleList(
							    [
							        TransformeraDModel(
							            num_attention_heads=lowerCamelCase__			,attention_head_dim=lowerCamelCase__			,in_channels=lowerCamelCase__			,num_layers=lowerCamelCase__			,dropout=lowerCamelCase__			,norm_num_groups=lowerCamelCase__			,cross_attention_dim=lowerCamelCase__			,attention_bias=lowerCamelCase__			,sample_size=lowerCamelCase__			,num_vector_embeds=lowerCamelCase__			,activation_fn=lowerCamelCase__			,num_embeds_ada_norm=lowerCamelCase__			,)
							        for _ in range(2			)
							    ]			)

							# Variables that can be set by a pipeline:

							# The ratio of transformer1 to transformer2's output states to be combined during inference
							_UpperCamelCase :		Tuple			    =       0.5

							# The shape of `encoder_hidden_states` is expected to be
							# `(batch_size, condition_lengths[0]+condition_lengths[1], num_features)`
							_UpperCamelCase :		str			    =       [77, 257]

							# Which transformer to use to encode which condition.
							# E.g. `(1, 0)` means that we'll use `transformers[1](conditions[0])` and `transformers[0](conditions[1])`
							_UpperCamelCase :		Tuple			    =       [1, 0]





	def      UpperCamelCase_  (			self :    str			,lowerCamelCase__ :    Union[str, Any]			,lowerCamelCase__ :    Tuple			,lowerCamelCase__ :    Dict=None			,lowerCamelCase__ :    Union[str, Any]=None			,lowerCamelCase__ :    Union[str, Any]=None			,lowerCamelCase__ :    bool = True			,):







							'''simple docstring'''
							_UpperCamelCase :		Tuple			    =       hidden_states

							_UpperCamelCase :		List[Any]			    =       []
							_UpperCamelCase :		Any			    =       0
							# attention_mask is not used yet
							for i in range(2			):
													# for each of the two transformers, pass the corresponding condition tokens
													_UpperCamelCase :		Optional[Any]			    =       encoder_hidden_states[:, tokens_start : tokens_start + self.condition_lengths[i]]
													_UpperCamelCase :		List[Any]			    =       self.transformer_index_for_condition[i]
													_UpperCamelCase :		Any			    =       self.transformers[transformer_index](
													    lowerCamelCase__			,encoder_hidden_states=lowerCamelCase__			,timestep=lowerCamelCase__			,cross_attention_kwargs=lowerCamelCase__			,return_dict=lowerCamelCase__			,)[0]
													encoded_states.append(encoded_state - input_states			)
													tokens_start += self.condition_lengths[i]

							_UpperCamelCase :		Union[str, Any]			    =       encoded_states[0] * self.mix_ratio + encoded_states[1] * (1 - self.mix_ratio)
							_UpperCamelCase :		Any			    =       output_states + input_states

							if not return_dict:
													return (output_states,)

							return TransformeraDModelOutput(sample=lowerCamelCase__			)







'''simple docstring'''
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
					import torch

if is_vision_available():
					from PIL import Image

					from transformers import MobileViTImageProcessor






class 							lowercase__     (							unittest.TestCase       ):





	def __init__(			self :    List[str]			,lowerCamelCase__ :    Optional[Any]			,lowerCamelCase__ :    Tuple=7			,lowerCamelCase__ :    Tuple=3			,lowerCamelCase__ :    Any=18			,lowerCamelCase__ :    Tuple=30			,lowerCamelCase__ :    Tuple=400			,lowerCamelCase__ :    int=True			,lowerCamelCase__ :    int=None			,lowerCamelCase__ :    str=True			,lowerCamelCase__ :    str=None			,lowerCamelCase__ :    Any=True			,):







							'''simple docstring'''
							_UpperCamelCase :		Any			    =       size if size is not None else {'shortest_edge': 20}
							_UpperCamelCase :		int			    =       crop_size if crop_size is not None else {'height': 18, 'width': 18}
							_UpperCamelCase :		List[str]			    =       parent
							_UpperCamelCase :		List[Any]			    =       batch_size
							_UpperCamelCase :		int			    =       num_channels
							_UpperCamelCase :		Optional[int]			    =       image_size
							_UpperCamelCase :		List[Any]			    =       min_resolution
							_UpperCamelCase :		List[str]			    =       max_resolution
							_UpperCamelCase :		int			    =       do_resize
							_UpperCamelCase :		Optional[int]			    =       size
							_UpperCamelCase :		str			    =       do_center_crop
							_UpperCamelCase :		List[Any]			    =       crop_size
							_UpperCamelCase :		Optional[Any]			    =       do_flip_channel_order





	def      UpperCamelCase_  (			self :    Optional[Any]			):







							'''simple docstring'''
							return {
							    "do_resize": self.do_resize,
							    "size": self.size,
							    "do_center_crop": self.do_center_crop,
							    "crop_size": self.crop_size,
							    "do_flip_channel_order": self.do_flip_channel_order,
							}






@require_torch
@require_vision
class 							lowercase__     (							lowercase						, unittest.TestCase       ):
	lowercase__      =      MobileViTImageProcessor if is_vision_available() else None





	def      UpperCamelCase_  (			self :    Optional[Any]			):







							'''simple docstring'''
							_UpperCamelCase :		Tuple			    =       MobileViTImageProcessingTester(self			)





	@property
	def      UpperCamelCase_  (			self :    List[Any]			):







							'''simple docstring'''
							return self.image_processor_tester.prepare_image_processor_dict()





	def      UpperCamelCase_  (			self :    Any			):







							'''simple docstring'''
							_UpperCamelCase :		Any			    =       self.image_processing_class(**self.image_processor_dict			)
							self.assertTrue(hasattr(lowerCamelCase__			,'do_resize'			)			)
							self.assertTrue(hasattr(lowerCamelCase__			,'size'			)			)
							self.assertTrue(hasattr(lowerCamelCase__			,'do_center_crop'			)			)
							self.assertTrue(hasattr(lowerCamelCase__			,'center_crop'			)			)
							self.assertTrue(hasattr(lowerCamelCase__			,'do_flip_channel_order'			)			)





	def      UpperCamelCase_  (			self :    List[str]			):







							'''simple docstring'''
							_UpperCamelCase :		Tuple			    =       self.image_processing_class.from_dict(self.image_processor_dict			)
							self.assertEqual(image_processor.size			,{'shortest_edge': 20}			)
							self.assertEqual(image_processor.crop_size			,{'height': 18, 'width': 18}			)

							_UpperCamelCase :		Union[str, Any]			    =       self.image_processing_class.from_dict(self.image_processor_dict			,size=42			,crop_size=84			)
							self.assertEqual(image_processor.size			,{'shortest_edge': 42}			)
							self.assertEqual(image_processor.crop_size			,{'height': 84, 'width': 84}			)





	def      UpperCamelCase_  (			self :    List[Any]			):







							'''simple docstring'''
							pass





	def      UpperCamelCase_  (			self :    List[str]			):







							'''simple docstring'''
							# Initialize image_processing
							_UpperCamelCase :		int			    =       self.image_processing_class(**self.image_processor_dict			)
							# create random PIL images
							_UpperCamelCase :		int			    =       prepare_image_inputs(self.image_processor_tester			,equal_resolution=lowerCamelCase__			)
							for image in image_inputs:
													self.assertIsInstance(lowerCamelCase__			,Image.Image			)

							# Test not batched input
							_UpperCamelCase :		Optional[Any]			    =       image_processing(image_inputs[0]			,return_tensors='pt'			).pixel_values
							self.assertEqual(
							    encoded_images.shape			,(
							        1,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.crop_size['height'],
							        self.image_processor_tester.crop_size['width'],
							    )			,)

							# Test batched
							_UpperCamelCase :		int			    =       image_processing(lowerCamelCase__			,return_tensors='pt'			).pixel_values
							self.assertEqual(
							    encoded_images.shape			,(
							        self.image_processor_tester.batch_size,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.crop_size['height'],
							        self.image_processor_tester.crop_size['width'],
							    )			,)





	def      UpperCamelCase_  (			self :    Dict			):







							'''simple docstring'''
							# Initialize image_processing
							_UpperCamelCase :		int			    =       self.image_processing_class(**self.image_processor_dict			)
							# create random numpy tensors
							_UpperCamelCase :		List[Any]			    =       prepare_image_inputs(self.image_processor_tester			,equal_resolution=lowerCamelCase__			,numpify=lowerCamelCase__			)
							for image in image_inputs:
													self.assertIsInstance(lowerCamelCase__			,np.ndarray			)

							# Test not batched input
							_UpperCamelCase :		Optional[int]			    =       image_processing(image_inputs[0]			,return_tensors='pt'			).pixel_values
							self.assertEqual(
							    encoded_images.shape			,(
							        1,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.crop_size['height'],
							        self.image_processor_tester.crop_size['width'],
							    )			,)

							# Test batched
							_UpperCamelCase :		List[str]			    =       image_processing(lowerCamelCase__			,return_tensors='pt'			).pixel_values
							self.assertEqual(
							    encoded_images.shape			,(
							        self.image_processor_tester.batch_size,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.crop_size['height'],
							        self.image_processor_tester.crop_size['width'],
							    )			,)





	def      UpperCamelCase_  (			self :    List[Any]			):







							'''simple docstring'''
							# Initialize image_processing
							_UpperCamelCase :		Optional[Any]			    =       self.image_processing_class(**self.image_processor_dict			)
							# create random PyTorch tensors
							_UpperCamelCase :		str			    =       prepare_image_inputs(self.image_processor_tester			,equal_resolution=lowerCamelCase__			,torchify=lowerCamelCase__			)
							for image in image_inputs:
													self.assertIsInstance(lowerCamelCase__			,torch.Tensor			)

							# Test not batched input
							_UpperCamelCase :		str			    =       image_processing(image_inputs[0]			,return_tensors='pt'			).pixel_values
							self.assertEqual(
							    encoded_images.shape			,(
							        1,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.crop_size['height'],
							        self.image_processor_tester.crop_size['width'],
							    )			,)

							# Test batched
							_UpperCamelCase :		Optional[int]			    =       image_processing(lowerCamelCase__			,return_tensors='pt'			).pixel_values
							self.assertEqual(
							    encoded_images.shape			,(
							        self.image_processor_tester.batch_size,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.crop_size['height'],
							        self.image_processor_tester.crop_size['width'],
							    )			,)


