




import json
import os
import tempfile

from transformers.testing_utils import check_json_file_has_correct_format





class 			lowerCamelCase_				:


 '''simple docstring'''







 lowercase_							       =       None



 def  lowerCAmelCase_    (				self    :						Dict		):
       SCREAMING_SNAKE_CASE_      		=     self.feature_extraction_class(**self.feat_extract_dict		)
       SCREAMING_SNAKE_CASE_      		=     json.loads(feat_extract.to_json_string()		)
       for key, value in self.feat_extract_dict.items():
             self.assertEqual(obj[key]							,							_lowerCAmelCase		)



 def  lowerCAmelCase_    (				self    :						Any		):
       SCREAMING_SNAKE_CASE_      		=     self.feature_extraction_class(**self.feat_extract_dict		)

       with tempfile.TemporaryDirectory() as tmpdirname:
             SCREAMING_SNAKE_CASE_      		=     os.path.join(_lowerCAmelCase							,							'feat_extract.json'		)
             feat_extract_first.to_json_file(_lowerCAmelCase		)
             SCREAMING_SNAKE_CASE_      		=     self.feature_extraction_class.from_json_file(_lowerCAmelCase		)

       self.assertEqual(feat_extract_second.to_dict()							,							feat_extract_first.to_dict()		)



 def  lowerCAmelCase_    (				self    :						Optional[Any]		):
       SCREAMING_SNAKE_CASE_      		=     self.feature_extraction_class(**self.feat_extract_dict		)

       with tempfile.TemporaryDirectory() as tmpdirname:
             SCREAMING_SNAKE_CASE_      		=     feat_extract_first.save_pretrained(_lowerCAmelCase		)[0]
             check_json_file_has_correct_format(_lowerCAmelCase		)
             SCREAMING_SNAKE_CASE_      		=     self.feature_extraction_class.from_pretrained(_lowerCAmelCase		)

       self.assertEqual(feat_extract_second.to_dict()							,							feat_extract_first.to_dict()		)




 def  lowerCAmelCase_    (				self    :						Optional[int]		):
       SCREAMING_SNAKE_CASE_      		=     self.feature_extraction_class()
       self.assertIsNotNone(_lowerCAmelCase		)





import torch
from transformers import PreTrainedModel, XLMRobertaConfig, XLMRobertaModel





class 			lowerCamelCase_				( _SCREAMING_SNAKE_CASE   ):


 '''simple docstring'''







 lowercase_							       =       "M-CLIP"



 def __init__(				self    :						Tuple							,							_lowerCAmelCase    :						List[str]=1_024							,							_lowerCAmelCase    :						str=768							,							**_lowerCAmelCase    :						Union[str, Any]		):
       SCREAMING_SNAKE_CASE_      		=     transformerDimSize
       SCREAMING_SNAKE_CASE_      		=     imageDimSize
       super().__init__(**_lowerCAmelCase		)





class 			lowerCamelCase_				( _SCREAMING_SNAKE_CASE   ):


 '''simple docstring'''







 lowercase_							       =       MCLIPConfig



 def __init__(				self    :						Dict							,							_lowerCAmelCase    :						Union[str, Any]							,							*_lowerCAmelCase    :						str							,							**_lowerCAmelCase    :						str		):
       super().__init__(_lowerCAmelCase							,							*_lowerCAmelCase							,							**_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_      		=     XLMRobertaModel(_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_      		=     torch.nn.Linear(
           in_features=config.transformerDimensions							,							out_features=config.numDims		)




 def  lowerCAmelCase_    (				self    :						List[Any]							,							_lowerCAmelCase    :						Any							,							_lowerCAmelCase    :						Union[str, Any]		):
       SCREAMING_SNAKE_CASE_      		=     self.transformer(input_ids=_lowerCAmelCase							,							attention_mask=_lowerCAmelCase		)[0]
       SCREAMING_SNAKE_CASE_      		=     (embs * attention_mask.unsqueeze(2		)).sum(dim=1		) / attention_mask.sum(dim=1		)[:, None]
       return self.LinearTransformation(_lowerCAmelCase		), embs