


import unittest

import numpy as np

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

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 DPTImageProcessor
class 	UpperCAmelCase_					(					unittest.TestCase    ):

   '''simple docstring'''




   def __init__(		self						,				__A						,				__A=7						,				__A=3						,				__A=18						,				__A=30						,				__A=400						,				__A=True						,				__A=None						,				__A=True						,				__A=[0.5, 0.5, 0.5]						,				__A=[0.5, 0.5, 0.5]						,				):






     """simple docstring"""





     lowerCamelCase :					Optional[int]    =				size if size is not None else {"height": 18, "width": 18}
     lowerCamelCase :					List[Any]    =				parent
     lowerCamelCase :					Any    =				batch_size
     lowerCamelCase :					str    =				num_channels
     lowerCamelCase :					int    =				image_size
     lowerCamelCase :					Optional[int]    =				min_resolution
     lowerCamelCase :					List[Any]    =				max_resolution
     lowerCamelCase :					str    =				do_resize
     lowerCamelCase :					Optional[Any]    =				size
     lowerCamelCase :					Optional[int]    =				do_normalize
     lowerCamelCase :					List[str]    =				image_mean
     lowerCamelCase :					Tuple    =				image_std






   def _snake_case    (		self ):






     """simple docstring"""





     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 	UpperCAmelCase_					(					UpperCamelCase		, unittest.TestCase    ):

   '''simple docstring'''




   __A :      str     		=		DPTImageProcessor if is_vision_available() else None






   def _snake_case    (		self ):






     """simple docstring"""





     lowerCamelCase :					List[Any]    =				DPTImageProcessingTester(self )






   @property
   def _snake_case    (		self ):






     """simple docstring"""





     return self.image_processor_tester.prepare_image_processor_dict()






   def _snake_case    (		self ):






     """simple docstring"""





     lowerCamelCase :					List[str]    =				self.image_processing_class(**self.image_processor_dict )
     self.assertTrue(hasattr(__A						,				"image_mean" ) )
     self.assertTrue(hasattr(__A						,				"image_std" ) )
     self.assertTrue(hasattr(__A						,				"do_normalize" ) )
     self.assertTrue(hasattr(__A						,				"do_resize" ) )
     self.assertTrue(hasattr(__A						,				"size" ) )






   def _snake_case    (		self ):






     """simple docstring"""





     lowerCamelCase :					int    =				self.image_processing_class.from_dict(self.image_processor_dict )
     self.assertEqual(image_processor.size						,				{"height": 18, "width": 18} )

     lowerCamelCase :					List[str]    =				self.image_processing_class.from_dict(self.image_processor_dict						,				size=42 )
     self.assertEqual(image_processor.size						,				{"height": 42, "width": 42} )






   def _snake_case    (		self ):






     """simple docstring"""





     lowerCamelCase :					Optional[int]    =				self.image_processing_class(**self.image_processor_dict )
     # create random PIL images
     lowerCamelCase :					int    =				prepare_image_inputs(self.image_processor_tester						,				equal_resolution=__A )
     for image in image_inputs:
       self.assertIsInstance(__A						,				Image.Image )

     # Test not batched input
     lowerCamelCase :					Tuple    =				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.size["height"],
             self.image_processor_tester.size["width"],
         )						,				)

     # Test batched
     lowerCamelCase :					str    =				image_processing(__A						,				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.size["height"],
             self.image_processor_tester.size["width"],
         )						,				)






   def _snake_case    (		self ):






     """simple docstring"""





     lowerCamelCase :					Union[str, Any]    =				self.image_processing_class(**self.image_processor_dict )
     # create random numpy tensors
     lowerCamelCase :					str    =				prepare_image_inputs(self.image_processor_tester						,				equal_resolution=__A						,				numpify=__A )
     for image in image_inputs:
       self.assertIsInstance(__A						,				np.ndarray )

     # Test not batched input
     lowerCamelCase :					List[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.size["height"],
             self.image_processor_tester.size["width"],
         )						,				)

     # Test batched
     lowerCamelCase :					List[str]    =				image_processing(__A						,				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.size["height"],
             self.image_processor_tester.size["width"],
         )						,				)






   def _snake_case    (		self ):






     """simple docstring"""





     lowerCamelCase :					Tuple    =				self.image_processing_class(**self.image_processor_dict )
     # create random PyTorch tensors
     lowerCamelCase :					List[Any]    =				prepare_image_inputs(self.image_processor_tester						,				equal_resolution=__A						,				torchify=__A )
     for image in image_inputs:
       self.assertIsInstance(__A						,				torch.Tensor )

     # Test not batched input
     lowerCamelCase :					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.size["height"],
             self.image_processor_tester.size["width"],
         )						,				)

     # Test batched
     lowerCamelCase :					List[Any]    =				image_processing(__A						,				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.size["height"],
             self.image_processor_tester.size["width"],
         )						,				)







import torch
from transformers import PreTrainedModel, XLMRobertaConfig, XLMRobertaModel
class 	UpperCAmelCase_					(					UpperCamelCase    ):

   '''simple docstring'''




   __A :      Optional[int]     		=		"M-CLIP"






   def __init__(		self						,				__A=1024						,				__A=768						,				**__A ):






     """simple docstring"""





     lowerCamelCase :					str    =				transformerDimSize
     lowerCamelCase :					Any    =				imageDimSize
     super().__init__(**__A )



class 	UpperCAmelCase_					(					UpperCamelCase    ):

   '''simple docstring'''




   __A :      Tuple     		=		MCLIPConfig






   def __init__(		self						,				__A						,				*__A						,				**__A ):






     """simple docstring"""





     super().__init__(__A						,				*__A						,				**__A )
     lowerCamelCase :					Tuple    =				XLMRobertaModel(__A )
     lowerCamelCase :					Optional[Any]    =				torch.nn.Linear(
         in_features=config.transformerDimensions						,				out_features=config.numDims )






   def _snake_case    (		self						,				__A						,				__A ):






     """simple docstring"""





     lowerCamelCase :					Any    =				self.transformer(input_ids=__A						,				attention_mask=__A )[0]
     lowerCamelCase :					int    =				(embs * attention_mask.unsqueeze(2 )).sum(dim=1 ) / attention_mask.sum(dim=1 )[:, None]
     return self.LinearTransformation(__A ), embs



