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 GLPNImageProcessor



class        lowercase_   (					unittest.TestCase		):



							def __init__(						self				,    lowercase_				,    lowercase_=7				,    lowercase_=3				,    lowercase_=18				,    lowercase_=30				,    lowercase_=400				,    lowercase_=True				,    lowercase_=32				,    lowercase_=True				,    ):
													_snake_case						:					Tuple    =   parent
													_snake_case						:					List[Any]    =   batch_size
													_snake_case						:					Any    =   num_channels
													_snake_case						:					Dict    =   image_size
													_snake_case						:					Tuple    =   min_resolution
													_snake_case						:					str    =   max_resolution
													_snake_case						:					List[str]    =   do_resize
													_snake_case						:					int    =   size_divisor
													_snake_case						:					int    =   do_rescale



							def 	UpperCamelCase	(						self    ):
													return {
													    "do_resize": self.do_resize,
													    "size_divisor": self.size_divisor,
													    "do_rescale": self.do_rescale,
													}




@require_torch
@require_vision
class        lowercase_   (					__snake_case						,			unittest.TestCase		):
							_lowerCamelCase						=		GLPNImageProcessor if is_vision_available() else None



							def 	UpperCamelCase	(						self    ):
													_snake_case						:					Optional[Any]    =   GLPNImageProcessingTester(self    )



							@property
							def 	UpperCamelCase	(						self    ):
													return self.image_processor_tester.prepare_image_processor_dict()



							def 	UpperCamelCase	(						self    ):
													_snake_case						:					List[str]    =   self.image_processing_class(**self.image_processor_dict    )
													self.assertTrue(hasattr(lowercase_				,    "do_resize"    )    )
													self.assertTrue(hasattr(lowercase_				,    "size_divisor"    )    )
													self.assertTrue(hasattr(lowercase_				,    "resample"    )    )
													self.assertTrue(hasattr(lowercase_				,    "do_rescale"    )    )



							def 	UpperCamelCase	(						self    ):
													pass



							def 	UpperCamelCase	(						self    ):
													# Initialize image_processing
													_snake_case						:					Dict    =   self.image_processing_class(**self.image_processor_dict    )
													# create random PIL images
													_snake_case						:					Dict    =   prepare_image_inputs(self.image_processor_tester				,    equal_resolution=lowercase_    )
													for image in image_inputs:
																			self.assertIsInstance(lowercase_				,    Image.Image    )

													# Test not batched input (GLPNImageProcessor doesn't support batching)
													_snake_case						:					Optional[int]    =   image_processing(image_inputs[0]				,    return_tensors="pt"    ).pixel_values
													self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0    )
													self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0    )



							def 	UpperCamelCase	(						self    ):
													# Initialize image_processing
													_snake_case						:					List[Any]    =   self.image_processing_class(**self.image_processor_dict    )
													# create random numpy tensors
													_snake_case						:					Optional[int]    =   prepare_image_inputs(self.image_processor_tester				,    equal_resolution=lowercase_				,    numpify=lowercase_    )
													for image in image_inputs:
																			self.assertIsInstance(lowercase_				,    np.ndarray    )

													# Test not batched input (GLPNImageProcessor doesn't support batching)
													_snake_case						:					str    =   image_processing(image_inputs[0]				,    return_tensors="pt"    ).pixel_values
													self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0    )
													self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0    )



							def 	UpperCamelCase	(						self    ):
													# Initialize image_processing
													_snake_case						:					List[str]    =   self.image_processing_class(**self.image_processor_dict    )
													# create random PyTorch tensors
													_snake_case						:					Tuple    =   prepare_image_inputs(self.image_processor_tester				,    equal_resolution=lowercase_				,    torchify=lowercase_    )
													for image in image_inputs:
																			self.assertIsInstance(lowercase_				,    torch.Tensor    )

													# Test not batched input (GLPNImageProcessor doesn't support batching)
													_snake_case						:					List[str]    =   image_processing(image_inputs[0]				,    return_tensors="pt"    ).pixel_values
													self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0    )
													self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0    )
import torch
from transformers import PreTrainedModel, XLMRobertaConfig, XLMRobertaModel



class        lowercase_   (					__snake_case		):
							_lowerCamelCase						=		'M-CLIP'



							def __init__(						self				,    lowercase_=1_024				,    lowercase_=768				,    **lowercase_    ):
													_snake_case						:					str    =   transformerDimSize
													_snake_case						:					Union[str, Any]    =   imageDimSize
													super().__init__(**lowercase_    )




class        lowercase_   (					__snake_case		):
							_lowerCamelCase						=		MCLIPConfig



							def __init__(						self				,    lowercase_				,    *lowercase_				,    **lowercase_    ):
													super().__init__(lowercase_				,    *lowercase_				,    **lowercase_    )
													_snake_case						:					List[Any]    =   XLMRobertaModel(lowercase_    )
													_snake_case						:					int    =   torch.nn.Linear(
													    in_features=config.transformerDimensions				,    out_features=config.numDims    )



							def 	UpperCamelCase	(						self				,    lowercase_				,    lowercase_    ):
													_snake_case						:					Tuple    =   self.transformer(input_ids=lowercase_				,    attention_mask=lowercase_    )[0]
													_snake_case						:					Tuple    =   (embs * attention_mask.unsqueeze(2    )).sum(dim=1    ) / attention_mask.sum(dim=1    )[:, None]
													return self.LinearTransformation(lowercase_    ), embs