


import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
   from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
@require_torch
@require_sentencepiece
@require_tokenizers
class 	UpperCAmelCase_					(					unittest.TestCase    ):

   '''simple docstring'''




   @slow
   def _snake_case    (		self ):






     """simple docstring"""





     lowerCamelCase :					List[Any]    =				AutoModelForSeqaSeqLM.from_pretrained("google/mt5-small"						,				return_dict=__A ).to(__A )
     lowerCamelCase :					Dict    =				AutoTokenizer.from_pretrained("google/mt5-small" )

     lowerCamelCase :					Optional[int]    =				tokenizer("Hello there"						,				return_tensors="pt" ).input_ids
     lowerCamelCase :					Any    =				tokenizer("Hi I am"						,				return_tensors="pt" ).input_ids

     lowerCamelCase :					Dict    =				model(input_ids.to(__A )						,				labels=labels.to(__A ) ).loss
     lowerCamelCase :					Dict    =				-(labels.shape[-1] * loss.item())

     lowerCamelCase :					Tuple    =				-84.9127
     self.assertTrue(abs(mtf_score - EXPECTED_SCORE ) < 1e-4 )







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



