




'''simple docstring'''



from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_clip import CLIPTokenizer


UpperCAmelCase_       :  Union[str, Any]      =		logging.get_logger(__name__)

UpperCAmelCase_       :  List[str]      =		{'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}

UpperCAmelCase_       :  Optional[int]      =		{
    '''vocab_file''': {
        '''openai/clip-vit-base-patch32''': '''https://huggingface.co/openai/clip-vit-base-patch32/resolve/main/vocab.json''',
    },
    '''merges_file''': {
        '''openai/clip-vit-base-patch32''': '''https://huggingface.co/openai/clip-vit-base-patch32/resolve/main/merges.txt''',
    },
    '''tokenizer_file''': {
        '''openai/clip-vit-base-patch32''': (
            '''https://huggingface.co/openai/clip-vit-base-patch32/resolve/main/tokenizer.json'''
        ),
    },
}

UpperCAmelCase_       :  Union[str, Any]      =		{
    '''openai/clip-vit-base-patch32''': 7_7,
}





class     lowerCAmelCase   ( lowercase__):
       __lowercase  :      List[Any]        =			VOCAB_FILES_NAMES
       __lowercase  :      Union[str, Any]        =			PRETRAINED_VOCAB_FILES_MAP
       __lowercase  :      Optional[Any]        =			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       __lowercase  :      Tuple        =			["""input_ids""", """attention_mask"""]
       __lowercase  :      Dict        =			CLIPTokenizer


       def __init__(   self    ,  __SCREAMING_SNAKE_CASE=None    ,  __SCREAMING_SNAKE_CASE=None    ,  __SCREAMING_SNAKE_CASE=None    ,  __SCREAMING_SNAKE_CASE="<|endoftext|>"    ,  __SCREAMING_SNAKE_CASE="<|startoftext|>"    ,  __SCREAMING_SNAKE_CASE="<|endoftext|>"    ,  __SCREAMING_SNAKE_CASE="<|endoftext|>"    ,  **__SCREAMING_SNAKE_CASE    ,  )      ->  str:



            '''simple docstring'''




            super().__init__(
                _A    ,  _A    ,  tokenizer_file=_A    ,  unk_token=_A    ,  bos_token=_A    ,  eos_token=_A    ,  pad_token=_A    ,  **_A    ,  )

            if not isinstance(self.backend_tokenizer.pre_tokenizer    ,  pre_tokenizers.Sequence  ):
                 raise ValueError(
                     '''The `backend_tokenizer` provided does not match the expected format. The CLIP tokenizer has been'''
                     ''' heavily modified from transformers version 4.17.0. You need to convert the tokenizer you are using'''
                     ''' to be compatible with this version.The easiest way to do so is'''
                     ''' `CLIPTokenizerFast.from_pretrained("path_to_local_folder_or_hub_repo, from_slow=True)`. If you want'''
                     ''' to use your existing tokenizer, you will have to revert to a version prior to 4.17.0 of'''
                     ''' transformers.'''  )

            self._wrap_decode_method_backend_tokenizer()


       def       lowerCAmelCase      (   self  )      ->  Union[str, Any]:



            '''simple docstring'''




            __snake_case    					=  self.backend_tokenizer.decode

            def new_decode_method(*__SCREAMING_SNAKE_CASE    ,  **__SCREAMING_SNAKE_CASE  ):
                 __snake_case    					=  orig_decode_method(*_A    ,  **_A  )
                 __snake_case    					=  text.replace(self.backend_tokenizer.model.end_of_word_suffix    ,  ''' '''  ).strip()
                 return text

            __snake_case    					=  new_decode_method


       def       lowerCAmelCase      (   self    ,  __SCREAMING_SNAKE_CASE    ,  __SCREAMING_SNAKE_CASE = None  )      ->  List[int]:



            '''simple docstring'''




            __snake_case    					=  [self.bos_token_id]
            __snake_case    					=  [self.eos_token_id]

            if token_ids_a is None:
                 return bos_token + token_ids_a + eos_token
            return bos_token + token_ids_a + eos_token + eos_token + token_ids_a + eos_token


       def       lowerCAmelCase      (   self    ,  __SCREAMING_SNAKE_CASE    ,  __SCREAMING_SNAKE_CASE = None  )      ->  List[int]:



            '''simple docstring'''




            __snake_case    					=  [self.bos_token_id]
            __snake_case    					=  [self.eos_token_id]

            if token_ids_a is None:
                 return len(bos_token + token_ids_a + eos_token  ) * [0]
            return len(bos_token + token_ids_a + eos_token + eos_token + token_ids_a + eos_token  ) * [0]




       def       lowerCAmelCase      (   self    ,  __SCREAMING_SNAKE_CASE    ,  __SCREAMING_SNAKE_CASE = None  )      ->  Tuple[str]:



            '''simple docstring'''




            __snake_case    					=  self._tokenizer.model.save(_A    ,  name=_A  )
            return tuple(_A  )












'''simple docstring'''



from typing import Any

import numpy as np







def 			_UpperCamelCase (_lowerCamelCase							:     np.ndarray	)->   bool:




     '''simple docstring'''





     assert np.shape(_lowerCamelCase	)[0] == np.shape(_lowerCamelCase	)[1]

     # If matrix not symmetric, exit right away.
     if np.allclose(_lowerCamelCase   ,	matrix.T	) is False:
          return False

     # Get eigenvalues and eignevectors for a symmetric matrix.
     __snake_case					,    __snake_case    					=  np.linalg.eigh(_lowerCamelCase	)

     # Check sign of all eigenvalues.
     # np.all returns a value of type np.bool_
     return bool(np.all(eigen_values > 0	)	)







def 			_UpperCamelCase (_lowerCamelCase							:     int	)->   Any:




     '''simple docstring'''





     __snake_case    					=  np.random.randn(_lowerCamelCase   ,	_lowerCamelCase	)
     __snake_case    					=  np.dot(_lowerCamelCase   ,	random_matrix.T	)
     assert _is_matrix_spd(_lowerCamelCase	)
     return spd_matrix







def 			_UpperCamelCase (_lowerCamelCase							:     np.ndarray   ,	_lowerCamelCase							:     np.ndarray   ,	_lowerCamelCase							:     int = 10_00   ,	_lowerCamelCase							:     float = 1E-8   ,	)->   Any:




     '''simple docstring'''





     assert np.shape(_lowerCamelCase	)[0] == np.shape(_lowerCamelCase	)[1]
     assert np.shape(_lowerCamelCase	)[0] == np.shape(_lowerCamelCase	)[0]
     assert _is_matrix_spd(_lowerCamelCase	)

     # Initialize solution guess, residual, search direction.
     __snake_case    					=  np.zeros((np.shape(_lowerCamelCase	)[0], 1)	)
     __snake_case    					=  np.copy(_lowerCamelCase	)
     __snake_case    					=  np.copy(_lowerCamelCase	)

     # Set initial errors in solution guess and residual.
     __snake_case    					=  1E9
     __snake_case    					=  1E9
     __snake_case    					=  1E9

     # Set iteration counter to threshold number of iterations.
     __snake_case    					=  0

     while error > tol:
          # Save this value so we only calculate the matrix-vector product once.
          __snake_case    					=  np.dot(_lowerCamelCase   ,	_lowerCamelCase	)

          # The main algorithm.

          # Update search direction magnitude.
          __snake_case    					=  np.dot(ra.T   ,	_lowerCamelCase	) / np.dot(pa.T   ,	_lowerCamelCase	)
          # Update solution guess.
          __snake_case    					=  xa + alpha * pa
          # Calculate new residual.
          __snake_case    					=  ra - alpha * w
          # Calculate new Krylov subspace scale.
          __snake_case    					=  np.dot(r.T   ,	_lowerCamelCase	) / np.dot(ra.T   ,	_lowerCamelCase	)
          # Calculate new A conjuage search direction.
          __snake_case    					=  r + beta * pa

          # Calculate errors.
          __snake_case    					=  np.linalg.norm(r - ra	)
          __snake_case    					=  np.linalg.norm(x - xa	)
          __snake_case    					=  np.maximum(_lowerCamelCase   ,	_lowerCamelCase	)

          # Update variables.
          __snake_case    					=  np.copy(_lowerCamelCase	)
          __snake_case    					=  np.copy(_lowerCamelCase	)
          __snake_case    					=  np.copy(_lowerCamelCase	)

          # Update number of iterations.
          iterations += 1
          if iterations > max_iterations:
               break

     return x







def 			_UpperCamelCase ()->   None:




     '''simple docstring'''





     __snake_case    					=  3
     __snake_case    					=  _create_spd_matrix(_lowerCamelCase	)
     __snake_case    					=  np.random.randn(_lowerCamelCase   ,	1	)
     __snake_case    					=  np.dot(_lowerCamelCase   ,	_lowerCamelCase	)

     # Numpy solution.
     __snake_case    					=  np.linalg.solve(_lowerCamelCase   ,	_lowerCamelCase	)

     # Our implementation.
     __snake_case    					=  conjugate_gradient(_lowerCamelCase   ,	_lowerCamelCase	)

     # Ensure both solutions are close to x_true (and therefore one another).
     assert np.linalg.norm(x_numpy - x_true	) <= 1E-6
     assert np.linalg.norm(x_conjugate_gradient - x_true	) <= 1E-6


if __name__ == "__main__":
     import doctest

     doctest.testmod()
     test_conjugate_gradient()






