



'''simple docstring'''






import numpy as np



class 				lowercase			:


 """simple docstring"""


 def __init__(				self    ,a_=5    ,a_=5				)						->  Tuple:
        _UpperCAmelCase    :		Dict     	=					depth
        _UpperCAmelCase    :		List[str]     	=					0
        _UpperCAmelCase    :		List[str]     	=					None
        _UpperCAmelCase    :		Optional[int]     	=					None
        _UpperCAmelCase    :		Tuple     	=					min_leaf_size
        _UpperCAmelCase    :		int     	=					None







 def _snake_case					(				self    ,a_    ,a_				)						->  Optional[int]:
        if labels.ndim != 1:
               print("""Error: Input labels must be one dimensional"""				)

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







 def _snake_case					(				self    ,a_    ,a_				)						->  int:
        if x.ndim != 1:
               print("""Error: Input data set must be one dimensional"""				)
               return
        if len(a_				) != len(a_				):
               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(a_				) < 2 * self.min_leaf_size:
               _UpperCAmelCase    :		Union[str, Any]     	=					np.mean(a_				)
               return

        if self.depth == 1:
               _UpperCAmelCase    :		Dict     	=					np.mean(a_				)
               return

        _UpperCAmelCase    :		Union[str, Any]     	=					0
        _UpperCAmelCase    :		Optional[Any]     	=					self.mean_squared_error(a_    ,np.mean(a_				)				) * 2
        for i in range(len(a_				)				):
               if len(x[:i]				) < self.min_leaf_size:
                      continue
               elif len(x[i:]				) < self.min_leaf_size:
                      continue
               else:
                      _UpperCAmelCase    :		Dict     	=					self.mean_squared_error(x[:i]    ,np.mean(y[:i]				)				)
                      _UpperCAmelCase    :		Any     	=					self.mean_squared_error(x[i:]    ,np.mean(y[i:]				)				)
                      _UpperCAmelCase    :		Optional[Any]     	=					error_left + error_right
                      if error < min_error:
                             _UpperCAmelCase    :		Union[str, Any]     	=					i
                             _UpperCAmelCase    :		Optional[int]     	=					error

        if best_split != 0:
               _UpperCAmelCase    :		int     	=					x[:best_split]
               _UpperCAmelCase    :		Optional[Any]     	=					y[:best_split]
               _UpperCAmelCase    :		Optional[int]     	=					x[best_split:]
               _UpperCAmelCase    :		Dict     	=					y[best_split:]

               _UpperCAmelCase    :		List[str]     	=					x[best_split]
               _UpperCAmelCase    :		List[Any]     	=					DecisionTree(
                   depth=self.depth - 1    ,min_leaf_size=self.min_leaf_size				)
               _UpperCAmelCase    :		Union[str, Any]     	=					DecisionTree(
                   depth=self.depth - 1    ,min_leaf_size=self.min_leaf_size				)
               self.left.train(a_    ,a_				)
               self.right.train(a_    ,a_				)
        else:
               _UpperCAmelCase    :		Any     	=					np.mean(a_				)

        return







 def _snake_case					(				self    ,a_				)						->  List[Any]:
        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(a_				)
               else:
                      return self.left.predict(a_				)
        else:
               print("""Error: Decision tree not yet trained"""				)
               return None







class 				lowercase			:


 """simple docstring"""


 @staticmethod
 def _snake_case					(				a_    ,a_				)						->  int:
        _UpperCAmelCase    :		Union[str, Any]     	=					float(0				)
        for label in labels:
               squared_error_sum += (label - prediction) ** 2

        return float(squared_error_sum / labels.size				)







def        snake_case_   (	)->						Optional[int]:





       '''simple docstring'''


       _UpperCAmelCase    :		Tuple     	=					np.arange(-1.0			,  1.0			,  0.0_0_5						)
       _UpperCAmelCase    :		Optional[int]     	=					np.sin(lowerCAmelCase_						)

       _UpperCAmelCase    :		int     	=					DecisionTree(depth=10			,  min_leaf_size=10						)
       tree.train(lowerCAmelCase_			,  lowerCAmelCase_						)

       _UpperCAmelCase    :		Any     	=					(np.random.rand(10						) * 2) - 1
       _UpperCAmelCase    :		Dict     	=					np.array([tree.predict(lowerCAmelCase_						) for x in test_cases]						)
       _UpperCAmelCase    :		Optional[Any]     	=					np.mean((predictions - test_cases) ** 2						)

       print("""Test values: """ + str(lowerCAmelCase_						)						)
       print("""Predictions: """ + str(lowerCAmelCase_						)						)
       print("""Average error: """ + str(lowerCAmelCase_						)						)


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

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





import json
import os
import re
import unicodedata
from typing import Dict, List, Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


lowercase__						:Tuple					    =       logging.get_logger(__name__)

lowercase__						:List[str]					    =       {
    "src_vocab_file": "vocab-src.json",
    "tgt_vocab_file": "vocab-tgt.json",
    "merges_file": "merges.txt",
}

lowercase__						:List[str]					    =       {
    "src_vocab_file": {
        "stas/tiny-wmt19-en-de": "https://huggingface.co/stas/tiny-wmt19-en-de/resolve/main/vocab-src.json"
    },
    "tgt_vocab_file": {
        "stas/tiny-wmt19-en-de": "https://huggingface.co/stas/tiny-wmt19-en-de/resolve/main/vocab-tgt.json"
    },
    "merges_file": {"stas/tiny-wmt19-en-de": "https://huggingface.co/stas/tiny-wmt19-en-de/resolve/main/merges.txt"},
}

lowercase__						:Dict					    =       {"stas/tiny-wmt19-en-de": 1024}
lowercase__						:str					    =       {
    "stas/tiny-wmt19-en-de": {
        "langs": ["en", "de"],
        "model_max_length": 1024,
        "special_tokens_map_file": None,
        "full_tokenizer_file": None,
    }
}



def 			UpperCamelCase       (				lowerCAmelCase__						):
					'''simple docstring'''
					lowercase		      =		set()
					lowercase		      =		word[0]
					for char in word[1:]:
										pairs.add((prev_char, char)						)
										lowercase		      =		char
					return pairs



def 			UpperCamelCase       (				lowerCAmelCase__						):
					'''simple docstring'''
					lowercase		      =		text.replace('''，'''					,     ''','''						)
					lowercase		      =		re.sub(R'''。\s*'''					,     '''. '''					,     lowerCAmelCase__						)
					lowercase		      =		text.replace('''、'''					,     ''','''						)
					lowercase		      =		text.replace('''”'''					,     '''"'''						)
					lowercase		      =		text.replace('''“'''					,     '''"'''						)
					lowercase		      =		text.replace('''∶'''					,     ''':'''						)
					lowercase		      =		text.replace('''：'''					,     ''':'''						)
					lowercase		      =		text.replace('''？'''					,     '''?'''						)
					lowercase		      =		text.replace('''《'''					,     '''"'''						)
					lowercase		      =		text.replace('''》'''					,     '''"'''						)
					lowercase		      =		text.replace('''）'''					,     ''')'''						)
					lowercase		      =		text.replace('''！'''					,     '''!'''						)
					lowercase		      =		text.replace('''（'''					,     '''('''						)
					lowercase		      =		text.replace('''；'''					,     ''';'''						)
					lowercase		      =		text.replace('''１'''					,     '''1'''						)
					lowercase		      =		text.replace('''」'''					,     '''"'''						)
					lowercase		      =		text.replace('''「'''					,     '''"'''						)
					lowercase		      =		text.replace('''０'''					,     '''0'''						)
					lowercase		      =		text.replace('''３'''					,     '''3'''						)
					lowercase		      =		text.replace('''２'''					,     '''2'''						)
					lowercase		      =		text.replace('''５'''					,     '''5'''						)
					lowercase		      =		text.replace('''６'''					,     '''6'''						)
					lowercase		      =		text.replace('''９'''					,     '''9'''						)
					lowercase		      =		text.replace('''７'''					,     '''7'''						)
					lowercase		      =		text.replace('''８'''					,     '''8'''						)
					lowercase		      =		text.replace('''４'''					,     '''4'''						)
					lowercase		      =		re.sub(R'''．\s*'''					,     '''. '''					,     lowerCAmelCase__						)
					lowercase		      =		text.replace('''～'''					,     '''~'''						)
					lowercase		      =		text.replace('''’'''					,     '''\''''						)
					lowercase		      =		text.replace('''…'''					,     '''...'''						)
					lowercase		      =		text.replace('''━'''					,     '''-'''						)
					lowercase		      =		text.replace('''〈'''					,     '''<'''						)
					lowercase		      =		text.replace('''〉'''					,     '''>'''						)
					lowercase		      =		text.replace('''【'''					,     '''['''						)
					lowercase		      =		text.replace('''】'''					,     ''']'''						)
					lowercase		      =		text.replace('''％'''					,     '''%'''						)
					return text



def 			UpperCamelCase       (				lowerCAmelCase__						):
					'''simple docstring'''
					lowercase		      =		[]
					for char in text:
										lowercase		      =		unicodedata.category(lowerCAmelCase__						)
										if cat.startswith('''C'''						):
															continue
										output.append(lowerCAmelCase__						)
					return "".join(lowerCAmelCase__						)







class 							lowercase						(   SCREAMING_SNAKE_CASE__						):
							lowercase_			:			Any			   =VOCAB_FILES_NAMES
							lowercase_			:			List[str]			   =PRETRAINED_VOCAB_FILES_MAP
							lowercase_			:			Any			   =PRETRAINED_INIT_CONFIGURATION
							lowercase_			:			List[str]			   =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							lowercase_			:			Optional[Any]			   =['''input_ids''', '''attention_mask''']




							def __init__(							self  ,A__=None  ,A__=None  ,A__=None  ,A__=None  ,A__=False  ,A__="<unk>"  ,A__="<s>"  ,A__="</s>"  ,A__="<pad>"  ,**A__  ,):
												super().__init__(
												    langs=A__  ,src_vocab_file=A__  ,tgt_vocab_file=A__  ,merges_file=A__  ,do_lower_case=A__  ,unk_token=A__  ,bos_token=A__  ,sep_token=A__  ,pad_token=A__  ,**A__  ,)

												try:
																	import sacremoses
												except ImportError:
																	raise ImportError(
																	    '''You need to install sacremoses to use XLMTokenizer. '''
																	    '''See https://pypi.org/project/sacremoses/ for installation.''')

												lowercase		      =		sacremoses

												lowercase		      =		src_vocab_file
												lowercase		      =		tgt_vocab_file
												lowercase		      =		merges_file
												lowercase		      =		do_lower_case

												# cache of sm.MosesPunctNormalizer instance
												lowercase		      =		{}
												# cache of sm.MosesTokenizer instance
												lowercase		      =		{}
												lowercase		      =		{}

												if langs and len(A__) == 2:
																	lowercase      ,		lowercase		      =		langs
												else:
																	raise ValueError(
																	    f'arg `langs` needs to be a list of 2 langs, e.g. [\'en\', \'ru\'], but got {langs}. '
																	    '''Usually that means that tokenizer can\'t find a mapping for the given model path '''
																	    '''in PRETRAINED_VOCAB_FILES_MAP, and other maps of this tokenizer.''')

												with open(A__  ,encoding='''utf-8''') as src_vocab_handle:
																	lowercase		      =		json.load(A__)
												with open(A__  ,encoding='''utf-8''') as tgt_vocab_handle:
																	lowercase		      =		json.load(A__)
																	lowercase		      =		{v: k for k, v in tgt_vocab.items()}
												with open(A__  ,encoding='''utf-8''') as merges_handle:
																	lowercase		      =		merges_handle.read().split('''\n''')[:-1]
												lowercase		      =		[tuple(merge.split()[:2]) for merge in merges]
												lowercase		      =		dict(zip(A__  ,range(len(A__))))
												lowercase		      =		{}




							def 					A__ (							self):
												return self.get_src_vocab()




							@property
							def 					A__ (							self):
												return self.src_vocab_size




							def 					A__ (							self  ,A__  ,A__):
												if lang not in self.cache_moses_punct_normalizer:
																	lowercase		      =		self.sm.MosesPunctNormalizer(lang=A__)
																	lowercase		      =		punct_normalizer
												return self.cache_moses_punct_normalizer[lang].normalize(A__)




							def 					A__ (							self  ,A__  ,A__):
												if lang not in self.cache_moses_tokenizer:
																	lowercase		      =		self.sm.MosesTokenizer(lang=A__)
																	lowercase		      =		moses_tokenizer
												return self.cache_moses_tokenizer[lang].tokenize(
												    A__  ,aggressive_dash_splits=A__  ,return_str=A__  ,escape=A__)




							def 					A__ (							self  ,A__  ,A__):
												if lang not in self.cache_moses_detokenizer:
																	lowercase		      =		self.sm.MosesDetokenizer(lang=A__)
																	lowercase		      =		moses_detokenizer
												return self.cache_moses_detokenizer[lang].detokenize(A__)




							def 					A__ (							self  ,A__  ,A__):
												lowercase		      =		replace_unicode_punct(A__)
												lowercase		      =		self.moses_punct_norm(A__  ,A__)
												lowercase		      =		remove_non_printing_char(A__)
												return text




							@property
							def 					A__ (							self):
												return len(self.encoder)




							@property
							def 					A__ (							self):
												return len(self.decoder)




							def 					A__ (							self):
												return dict(self.encoder  ,**self.added_tokens_encoder)




							def 					A__ (							self):
												return dict(self.decoder  ,**self.added_tokens_decoder)




							def 					A__ (							self  ,A__):
												lowercase		      =		tuple(token[:-1]) + (token[-1] + '''</w>''',)
												if token in self.cache:
																	return self.cache[token]
												lowercase		      =		get_pairs(A__)

												if not pairs:
																	return token + "</w>"

												while True:
																	lowercase		      =		min(A__  ,key=lambda A__: self.bpe_ranks.get(A__  ,float('''inf''')))
																	if bigram not in self.bpe_ranks:
																						break
																	lowercase      ,		lowercase		      =		bigram
																	lowercase		      =		[]
																	lowercase		      =		0
																	while i < len(A__):
																						try:
																											lowercase		      =		word.index(A__  ,A__)
																						except ValueError:
																											new_word.extend(word[i:])
																											break
																						else:
																											new_word.extend(word[i:j])
																											lowercase		      =		j

																						if word[i] == first and i < len(A__) - 1 and word[i + 1] == second:
																											new_word.append(first + second)
																											i += 2
																						else:
																											new_word.append(word[i])
																											i += 1
																	lowercase		      =		tuple(A__)
																	lowercase		      =		new_word
																	if len(A__) == 1:
																						break
																	else:
																						lowercase		      =		get_pairs(A__)
												lowercase		      =		''' '''.join(A__)
												if word == "\n  </w>":
																	lowercase		      =		'''\n</w>'''
												lowercase		      =		word
												return word




							def 					A__ (							self  ,A__  ,A__="en"  ,A__=False):
												lowercase		      =		self.src_lang

												if self.do_lower_case:
																	lowercase		      =		text.lower()

												if bypass_tokenizer:
																	lowercase		      =		text.split()
												else:
																	lowercase		      =		self.moses_pipeline(A__  ,lang=A__)
																	lowercase		      =		self.moses_tokenize(A__  ,lang=A__)

												lowercase		      =		[]
												for token in text:
																	if token:
																						split_tokens.extend(list(self.bpe(A__).split(''' ''')))

												return split_tokens




							def 					A__ (							self  ,A__):
												return self.encoder.get(A__  ,self.encoder.get(self.unk_token))




							def 					A__ (							self  ,A__):
												return self.decoder.get(A__  ,self.unk_token)




							def 					A__ (							self  ,A__):
												lowercase		      =		[t.replace(''' '''  ,'''''').replace('''</w>'''  ,''' ''') for t in tokens]
												lowercase		      =		''''''.join(A__).split()
												# detokenize
												lowercase		      =		self.moses_detokenize(A__  ,self.tgt_lang)
												return text




							def 					A__ (							self  ,A__  ,A__ = None):
												lowercase		      =		[self.sep_token_id]

												# no bos used in fairseq
												if token_ids_a is None:
																	return token_ids_a + sep
												return token_ids_a + sep + token_ids_a + sep




							def 					A__ (							self  ,A__  ,A__ = None  ,A__ = False):

												if already_has_special_tokens:
																	return super().get_special_tokens_mask(
																	    token_ids_a=A__  ,token_ids_a=A__  ,already_has_special_tokens=A__)
												# no bos used in fairseq
												if token_ids_a is not None:
																	return ([0] * len(A__)) + [1] + ([0] * len(A__)) + [1]
												return ([0] * len(A__)) + [1]




							def 					A__ (							self  ,A__  ,A__ = None):
												lowercase		      =		[self.sep_token_id]

												# no bos used in fairseq
												if token_ids_a is None:
																	return len(token_ids_a + sep) * [0]
												return len(token_ids_a + sep) * [0] + len(token_ids_a + sep) * [1]




							def 					A__ (							self  ,A__  ,A__ = None):
												if not os.path.isdir(A__):
																	logger.error(f'Vocabulary path ({save_directory}) should be a directory')
																	return

												lowercase		      =		os.path.join(
												    A__  ,(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''src_vocab_file'''])
												lowercase		      =		os.path.join(
												    A__  ,(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''tgt_vocab_file'''])
												lowercase		      =		os.path.join(
												    A__  ,(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file'''])

												with open(A__  ,'''w'''  ,encoding='''utf-8''') as f:
																	f.write(json.dumps(self.encoder  ,indent=2  ,sort_keys=A__  ,ensure_ascii=A__) + '''\n''')

												with open(A__  ,'''w'''  ,encoding='''utf-8''') as f:
																	lowercase		      =		{v: k for k, v in self.decoder.items()}
																	f.write(json.dumps(A__  ,indent=2  ,sort_keys=A__  ,ensure_ascii=A__) + '''\n''')

												lowercase		      =		0
												with open(A__  ,'''w'''  ,encoding='''utf-8''') as writer:
																	for bpe_tokens, token_index in sorted(self.bpe_ranks.items()  ,key=lambda A__: kv[1]):
																						if index != token_index:
																											logger.warning(
																											    f'Saving vocabulary to {merges_file}: BPE merge indices are not consecutive.'
																											    ''' Please check that the tokenizer is not corrupted!''')
																											lowercase		      =		token_index
																						writer.write(''' '''.join(A__) + '''\n''')
																						index += 1

												return src_vocab_file, tgt_vocab_file, merges_file




							def __getstate__(							self):
												lowercase		      =		self.__dict__.copy()
												lowercase		      =		None
												return state





							def __setstate__(							self  ,A__):
												lowercase		      =		d

												try:
																	import sacremoses
												except ImportError:
																	raise ImportError(
																	    '''You need to install sacremoses to use XLMTokenizer. '''
																	    '''See https://pypi.org/project/sacremoses/ for installation.''')

												lowercase		      =		sacremoses




