




import torch
from torch import nn
from transformers import CLIPPreTrainedModel, CLIPVisionModel

from ...models.attention import BasicTransformerBlock
from ...utils import logging


__lowerCAmelCase		:				Union[str, Any]       			=		logging.get_logger(__name__)  # pylint: disable=invalid-name




class     snake_case__							(_UpperCamelCase   ):
				"""simple docstring"""



				def __init__(      self				:     List[Any]					,      __lowerCamelCase				:     Tuple					,      __lowerCamelCase				:     List[str]=7_68							)					->     Any:
							super().__init__(__lowerCamelCase							)
							a    =			proj_size

							a    =			CLIPVisionModel(__lowerCamelCase							)
							a    =			PaintByExampleMapper(__lowerCamelCase							)
							a    =			nn.LayerNorm(config.hidden_size							)
							a    =			nn.Linear(config.hidden_size					,      self.proj_size							)

							# uncondition for scaling
							a    =			nn.Parameter(torch.randn((1, 1, self.proj_size)							)							)





				def 							__UpperCAmelCase							(      self				:     Union[str, Any]					,      __lowerCamelCase				:     Tuple					,      __lowerCamelCase				:     List[str]=False							)					->     Tuple:
							a    =			self.model(pixel_values=__lowerCamelCase							)
							a    =			clip_output.pooler_output
							a    =			self.mapper(latent_states[:, None]							)
							a    =			self.final_layer_norm(__lowerCamelCase							)
							a    =			self.proj_out(__lowerCamelCase							)
							if return_uncond_vector:
										return latent_states, self.uncond_vector

							return latent_states




class     snake_case__							(nn.Module   ):
				"""simple docstring"""



				def __init__(      self				:     Dict					,      __lowerCamelCase				:     List[Any]							)					->     Any:
							super().__init__()
							a    =			(config.num_hidden_layers + 1) // 5
							a    =			config.hidden_size
							a    =			1
							a    =			nn.ModuleList(
							    [
							        BasicTransformerBlock(__lowerCamelCase					,      __lowerCamelCase					,      __lowerCamelCase					,      activation_fn="gelu"					,      attention_bias=__lowerCamelCase							)
							        for _ in range(__lowerCamelCase							)
							    ]							)





				def 							__UpperCAmelCase							(      self				:     Tuple					,      __lowerCamelCase				:     int							)					->     Any:
							for block in self.blocks:
										a    =			block(__lowerCamelCase							)

							return hidden_states














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 ViTImageProcessor

class     _SCREAMING_SNAKE_CASE       (			unittest.TestCase      ):





	def __init__(		self				:					List[str] ,  __lowerCamelCase				:					Optional[Any] ,  __lowerCamelCase				:					Any=13 ,  __lowerCamelCase				:					Dict=3 ,  __lowerCamelCase				:					int=224 ,  __lowerCamelCase				:					Any=30 ,  __lowerCamelCase				:					Tuple=400 ,  __lowerCamelCase				:					int=True ,  __lowerCamelCase				:					List[str]=None ,  __lowerCamelCase				:					Any=True ,  __lowerCamelCase				:					Dict=[0.5, 0.5, 0.5] ,  __lowerCamelCase				:					List[Any]=[0.5, 0.5, 0.5] ,  ):
		UpperCamelCase     :List[Any]          =	size if size is not None else {"""height""": 18, """width""": 18}
		UpperCamelCase     :str          =	parent
		UpperCamelCase     :Optional[int]          =	batch_size
		UpperCamelCase     :Dict          =	num_channels
		UpperCamelCase     :str          =	image_size
		UpperCamelCase     :Dict          =	min_resolution
		UpperCamelCase     :str          =	max_resolution
		UpperCamelCase     :Union[str, Any]          =	do_resize
		UpperCamelCase     :Optional[Any]          =	size
		UpperCamelCase     :Any          =	do_normalize
		UpperCamelCase     :Optional[Any]          =	image_mean
		UpperCamelCase     :Tuple          =	image_std





	def 						_A (		self				:					int	):
		return {
		    "image_mean": self.image_mean,
		    "image_std": self.image_std,
		    "do_normalize": self.do_normalize,
		    "do_resize": self.do_resize,
		    "size": self.size,
		}

@require_torch
@require_vision
class     _SCREAMING_SNAKE_CASE       (			_a		,   unittest.TestCase      ):
	snake_case__							:     List[Any]     	=  ViTImageProcessor if is_vision_available() else None





	def 						_A (		self				:					str	):
		UpperCamelCase     :Tuple          =	EfficientFormerImageProcessorTester(self	)





	@property
	def 						_A (		self				:					List[str]	):
		return self.image_proc_tester.prepare_image_processor_dict()





	def 						_A (		self				:					int	):
		UpperCamelCase     :List[Any]          =	self.image_processing_class(**self.image_processor_dict	)
		self.assertTrue(hasattr(__lowerCamelCase ,  """image_mean"""	)	)
		self.assertTrue(hasattr(__lowerCamelCase ,  """image_std"""	)	)
		self.assertTrue(hasattr(__lowerCamelCase ,  """do_normalize"""	)	)
		self.assertTrue(hasattr(__lowerCamelCase ,  """do_resize"""	)	)
		self.assertTrue(hasattr(__lowerCamelCase ,  """size"""	)	)





	def 						_A (		self				:					Optional[int]	):
		pass





	def 						_A (		self				:					str	):
		# Initialize image_processor
		UpperCamelCase     :Union[str, Any]          =	self.image_processing_class(**self.image_processor_dict	)
		# create random PIL images
		UpperCamelCase     :Union[str, Any]          =	prepare_image_inputs(self.image_proc_tester ,  equal_resolution=__lowerCamelCase	)
		for image in image_inputs:
			self.assertIsInstance(__lowerCamelCase ,  Image.Image	)

		# Test not batched input
		UpperCamelCase     :List[str]          =	image_processor(image_inputs[0] ,  return_tensors="""pt"""	).pixel_values
		self.assertEqual(
		    encoded_images.shape ,  (
		        1,
		        self.image_proc_tester.num_channels,
		        self.image_proc_tester.size["""height"""],
		        self.image_proc_tester.size["""width"""],
		    ) ,  )

		# Test batched
		UpperCamelCase     :List[Any]          =	image_processor(__lowerCamelCase ,  return_tensors="""pt"""	).pixel_values
		self.assertEqual(
		    encoded_images.shape ,  (
		        self.image_proc_tester.batch_size,
		        self.image_proc_tester.num_channels,
		        self.image_proc_tester.size["""height"""],
		        self.image_proc_tester.size["""width"""],
		    ) ,  )





	def 						_A (		self				:					Union[str, Any]	):
		# Initialize image_processor
		UpperCamelCase     :Optional[int]          =	self.image_processing_class(**self.image_processor_dict	)
		# create random numpy tensors
		UpperCamelCase     :List[Any]          =	prepare_image_inputs(self.image_proc_tester ,  equal_resolution=__lowerCamelCase ,  numpify=__lowerCamelCase	)
		for image in image_inputs:
			self.assertIsInstance(__lowerCamelCase ,  np.ndarray	)

		# Test not batched input
		UpperCamelCase     :Dict          =	image_processor(image_inputs[0] ,  return_tensors="""pt"""	).pixel_values
		self.assertEqual(
		    encoded_images.shape ,  (
		        1,
		        self.image_proc_tester.num_channels,
		        self.image_proc_tester.size["""height"""],
		        self.image_proc_tester.size["""width"""],
		    ) ,  )

		# Test batched
		UpperCamelCase     :Tuple          =	image_processor(__lowerCamelCase ,  return_tensors="""pt"""	).pixel_values
		self.assertEqual(
		    encoded_images.shape ,  (
		        self.image_proc_tester.batch_size,
		        self.image_proc_tester.num_channels,
		        self.image_proc_tester.size["""height"""],
		        self.image_proc_tester.size["""width"""],
		    ) ,  )





	def 						_A (		self				:					List[Any]	):
		# Initialize image_processor
		UpperCamelCase     :List[str]          =	self.image_processing_class(**self.image_processor_dict	)
		# create random PyTorch tensors
		UpperCamelCase     :Any          =	prepare_image_inputs(self.image_proc_tester ,  equal_resolution=__lowerCamelCase ,  torchify=__lowerCamelCase	)
		for image in image_inputs:
			self.assertIsInstance(__lowerCamelCase ,  torch.Tensor	)

		# Test not batched input
		UpperCamelCase     :List[Any]          =	image_processor(image_inputs[0] ,  return_tensors="""pt"""	).pixel_values
		self.assertEqual(
		    encoded_images.shape ,  (
		        1,
		        self.image_proc_tester.num_channels,
		        self.image_proc_tester.size["""height"""],
		        self.image_proc_tester.size["""width"""],
		    ) ,  )

		# Test batched
		UpperCamelCase     :str          =	image_processor(__lowerCamelCase ,  return_tensors="""pt"""	).pixel_values
		self.assertEqual(
		    encoded_images.shape ,  (
		        self.image_proc_tester.batch_size,
		        self.image_proc_tester.num_channels,
		        self.image_proc_tester.size["""height"""],
		        self.image_proc_tester.size["""width"""],
		    ) ,  )
