




'''simple docstring'''




import numpy as np


class 						__UpperCamelCase :






       def __init__(							self,	lowerCAmelCase=5,	lowerCAmelCase=5						):



              """simple docstring"""







              lowerCamelCase_							=depth
              lowerCamelCase_							=0
              lowerCamelCase_							=None
              lowerCamelCase_							=None
              lowerCamelCase_							=min_leaf_size
              lowerCamelCase_							=None






       def   lowercase__  (							self,	lowerCAmelCase,	lowerCAmelCase						):



              """simple docstring"""







              if labels.ndim != 1:
                     print('''Error: Input labels must be one dimensional'''						)

              return np.mean((labels - prediction) ** 2						)






       def   lowercase__  (							self,	lowerCAmelCase,	lowerCAmelCase						):



              """simple docstring"""







              if x.ndim != 1:
                     print('''Error: Input data set must be one dimensional'''						)
                     return
              if len(lowerCAmelCase						) != len(lowerCAmelCase						):
                     print('''Error: X and y have different lengths'''						)
                     return
              if y.ndim != 1:
                     print('''Error: Data set labels must be one dimensional'''						)
                     return

              if len(lowerCAmelCase						) < 2 * self.min_leaf_size:
                     lowerCamelCase_							=np.mean(lowerCAmelCase						)
                     return

              if self.depth == 1:
                     lowerCamelCase_							=np.mean(lowerCAmelCase						)
                     return

              lowerCamelCase_							=0
              lowerCamelCase_							=self.mean_squared_error(lowerCAmelCase,	np.mean(lowerCAmelCase						)						) * 2
              for i in range(len(lowerCAmelCase						)						):
                     if len(x[:i]						) < self.min_leaf_size:
                            continue
                     elif len(x[i:]						) < self.min_leaf_size:
                            continue
                     else:
                            lowerCamelCase_							=self.mean_squared_error(x[:i],	np.mean(y[:i]						)						)
                            lowerCamelCase_							=self.mean_squared_error(x[i:],	np.mean(y[i:]						)						)
                            lowerCamelCase_							=error_left + error_right
                            if error < min_error:
                                   lowerCamelCase_							=i
                                   lowerCamelCase_							=error

              if best_split != 0:
                     lowerCamelCase_							=x[:best_split]
                     lowerCamelCase_							=y[:best_split]
                     lowerCamelCase_							=x[best_split:]
                     lowerCamelCase_							=y[best_split:]

                     lowerCamelCase_							=x[best_split]
                     lowerCamelCase_							=DecisionTree(
                         depth=self.depth - 1,	min_leaf_size=self.min_leaf_size						)
                     lowerCamelCase_							=DecisionTree(
                         depth=self.depth - 1,	min_leaf_size=self.min_leaf_size						)
                     self.left.train(lowerCAmelCase,	lowerCAmelCase						)
                     self.right.train(lowerCAmelCase,	lowerCAmelCase						)
              else:
                     lowerCamelCase_							=np.mean(lowerCAmelCase						)

              return






       def   lowercase__  (							self,	lowerCAmelCase						):



              """simple docstring"""







              if self.prediction is not None:
                     return self.prediction
              elif self.left or self.right is not None:
                     if x >= self.decision_boundary:
                            return self.right.predict(lowerCAmelCase						)
                     else:
                            return self.left.predict(lowerCAmelCase						)
              else:
                     print('''Error: Decision tree not yet trained'''						)
                     return None





class 						__UpperCamelCase :






       @staticmethod
       def   lowercase__  (							lowerCAmelCase,	lowerCAmelCase						):



              """simple docstring"""







              lowerCamelCase_							=float(0						)
              for label in labels:
                     squared_error_sum += (label - prediction) ** 2

              return float(squared_error_sum / labels.size						)





def   a_							(   )							->		Optional[Any]:



       """simple docstring"""
       lowerCamelCase_							=np.arange(-1.0     , 1.0     , 0.0_0_5     )
       lowerCamelCase_							=np.sin(__snake_case     )

       lowerCamelCase_							=DecisionTree(depth=10     , min_leaf_size=10     )
       tree.train(__snake_case     , __snake_case     )

       lowerCamelCase_							=(np.random.rand(10     ) * 2) - 1
       lowerCamelCase_							=np.array([tree.predict(__snake_case     ) for x in test_cases]     )
       lowerCamelCase_							=np.mean((predictions - test_cases) ** 2     )

       print('''Test values: ''' + str(__snake_case     )     )
       print('''Predictions: ''' + str(__snake_case     )     )
       print('''Average error: ''' + str(__snake_case     )     )


if __name__ == "__main__":
     main()
     import doctest

     doctest.testmod(name="""mean_squarred_error""", verbose=True)










'''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


a_   :	Tuple							   = logging.get_logger(__name__)

a_   :	str							   = {"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}

a_   :	Tuple							   = {
    """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"""
        ),
    },
}

a_   :	str							   = {
    """openai/clip-vit-base-patch32""": 77,
}


class 						__UpperCamelCase (				lowerCamelCase__       ):
       lowercase			:					Union[str, Any]   		=VOCAB_FILES_NAMES
       lowercase			:					Optional[int]   		=PRETRAINED_VOCAB_FILES_MAP
       lowercase			:					Any   		=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       lowercase			:					str   		=['input_ids', 'attention_mask']
       lowercase			:					List[Any]   		=CLIPTokenizer






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



              """simple docstring"""







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

              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   lowercase__  (							self						):



              """simple docstring"""







              lowerCamelCase_							=self.backend_tokenizer.decode

              def new_decode_method(*lowerCAmelCase,	**lowerCAmelCase						):
                     lowerCamelCase_							=orig_decode_method(*lowerCAmelCase,	**lowerCAmelCase						)
                     lowerCamelCase_							=text.replace(self.backend_tokenizer.model.end_of_word_suffix,	''' '''						).strip()
                     return text

              lowerCamelCase_							=new_decode_method






       def   lowercase__  (							self,	lowerCAmelCase,	lowerCAmelCase = None						):



              """simple docstring"""







              lowerCamelCase_							=[self.bos_token_id]
              lowerCamelCase_							=[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   lowercase__  (							self,	lowerCAmelCase,	lowerCAmelCase = None						):



              """simple docstring"""







              lowerCamelCase_							=[self.bos_token_id]
              lowerCamelCase_							=[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   lowercase__  (							self,	lowerCAmelCase,	lowerCAmelCase = None						):



              """simple docstring"""







              lowerCamelCase_							=self._tokenizer.model.save(lowerCAmelCase,	name=lowerCAmelCase						)
              return tuple(lowerCAmelCase						)




