


import os
import time

import pytest

from datasets.utils.filelock import FileLock, Timeout






def 		lowercase_(  SCREAMING_SNAKE_CASE_	):


  '''simple docstring'''

  lowerCamelCase :					Optional[int]    =				FileLock(str(tmpdir / "foo.lock"	)	)
  lowerCamelCase :					Optional[int]    =				FileLock(str(tmpdir / "foo.lock"	)	)
  lowerCamelCase :					Union[str, Any]    =				0.01
  with locka.acquire():
    with pytest.raises(SCREAMING_SNAKE_CASE_	):
      lowerCamelCase :					int    =				time.time()
      locka.acquire(SCREAMING_SNAKE_CASE_	)
      assert time.time() - _start > timeout







def 		lowercase_(  SCREAMING_SNAKE_CASE_	):


  '''simple docstring'''

  lowerCamelCase :					List[Any]    =				"a" * 1000 + ".lock"
  lowerCamelCase :					Optional[Any]    =				FileLock(str(tmpdir / filename	)	)
  assert locka._lock_file.endswith(".lock"	)
  assert not locka._lock_file.endswith(SCREAMING_SNAKE_CASE_	)
  assert len(os.path.basename(locka._lock_file	)	) <= 255
  lowerCamelCase :					Any    =				FileLock(tmpdir / filename	)
  with locka.acquire():
    with pytest.raises(SCREAMING_SNAKE_CASE_	):
      locka.acquire(0	)







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



