




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

from ...tokenization_utils import PreTrainedTokenizer, _is_control, _is_punctuation, _is_whitespace
from ...utils import logging


a_ 		=  logging.get_logger(__name__)

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

a_ 		=  {
    """vocab_file""": {"""openai-gpt""": """https://huggingface.co/openai-gpt/resolve/main/vocab.json"""},
    """merges_file""": {"""openai-gpt""": """https://huggingface.co/openai-gpt/resolve/main/merges.txt"""},
}

a_ 		=  {
    """openai-gpt""": 512,
}




def        __SCREAMING_SNAKE_CASE (	lowercase_   )							->						List[str]:

    """simple docstring"""


    __UpperCamelCase			=    text.strip()
    if not text:
        return []
    __UpperCamelCase			=    text.split()
    return tokens

class 			_lowerCamelCase    (      A_ ):

      """simple docstring"""

      def __init__(  self       :			str			,							snake_case       :			List[str]=True			,							snake_case       :			str=None			,							snake_case       :			Dict=True			,							snake_case       :			Union[str, Any]=None			,							snake_case       :			int=True			,							):







          if never_split is None:
              __UpperCamelCase			=    []
          __UpperCamelCase			=    do_lower_case
          __UpperCamelCase			=    set(__lowercase					)
          __UpperCamelCase			=    tokenize_chinese_chars
          __UpperCamelCase			=    strip_accents
          __UpperCamelCase			=    do_split_on_punc







      def    snake_case						(  self       :			List[Any]			,							snake_case       :			Union[str, Any]			,							snake_case       :			Any=None					):
          __UpperCamelCase			=    self.never_split.union(set(__lowercase					)					) if never_split else self.never_split
          __UpperCamelCase			=    self._clean_text(__lowercase					)

          # This was added on November 1st, 2018 for the multilingual and Chinese
          # models. This is also applied to the English models now, but it doesn't
          # matter since the English models were not trained on any Chinese data
          # and generally don't have any Chinese data in them (there are Chinese
          # characters in the vocabulary because Wikipedia does have some Chinese
          # words in the English Wikipedia.).
          if self.tokenize_chinese_chars:
              __UpperCamelCase			=    self._tokenize_chinese_chars(__lowercase					)
          # prevents treating the same character with different unicode codepoints as different characters
          __UpperCamelCase			=    unicodedata.normalize('''NFC'''			,							__lowercase					)
          __UpperCamelCase			=    whitespace_tokenize(__lowercase					)
          __UpperCamelCase			=    []
          for token in orig_tokens:
              if token not in never_split:
                  if self.do_lower_case:
                      __UpperCamelCase			=    token.lower()
                      if self.strip_accents is not False:
                          __UpperCamelCase			=    self._run_strip_accents(__lowercase					)
                  elif self.strip_accents:
                      __UpperCamelCase			=    self._run_strip_accents(__lowercase					)
              split_tokens.extend(self._run_split_on_punc(__lowercase			,							__lowercase					)					)

          __UpperCamelCase			=    whitespace_tokenize(''' '''.join(__lowercase					)					)
          return output_tokens







      def    snake_case						(  self       :			Optional[Any]			,							snake_case       :			int					):
          __UpperCamelCase			=    unicodedata.normalize('''NFD'''			,							__lowercase					)
          __UpperCamelCase			=    []
          for char in text:
              __UpperCamelCase			=    unicodedata.category(__lowercase					)
              if cat == "Mn":
                  continue
              output.append(__lowercase					)
          return "".join(__lowercase					)







      def    snake_case						(  self       :			Dict			,							snake_case       :			List[str]			,							snake_case       :			int=None					):







          if not self.do_split_on_punc or (never_split is not None and text in never_split):
              return [text]
          __UpperCamelCase			=    list(__lowercase					)
          __UpperCamelCase			=    0
          __UpperCamelCase			=    True
          __UpperCamelCase			=    []
          while i < len(__lowercase					):
              __UpperCamelCase			=    chars[i]
              if _is_punctuation(__lowercase					):
                  output.append([char]					)
                  __UpperCamelCase			=    True
              else:
                  if start_new_word:
                      output.append([]					)
                  __UpperCamelCase			=    False
                  output[-1].append(__lowercase					)
              i += 1

          return ["".join(__lowercase					) for x in output]







      def    snake_case						(  self       :			List[Any]			,							snake_case       :			Optional[int]					):
          __UpperCamelCase			=    []
          for char in text:
              __UpperCamelCase			=    ord(__lowercase					)
              if self._is_chinese_char(__lowercase					):
                  output.append(''' '''					)
                  output.append(__lowercase					)
                  output.append(''' '''					)
              else:
                  output.append(__lowercase					)
          return "".join(__lowercase					)







      def    snake_case						(  self       :			Union[str, Any]			,							snake_case       :			Dict					):







          if (
              (cp >= 0x4_e_0_0 and cp <= 0x9_f_f_f)
              or (cp >= 0x3_4_0_0 and cp <= 0x4_d_b_f)  #
              or (cp >= 0x2_0_0_0_0 and cp <= 0x2_a_6_d_f)  #
              or (cp >= 0x2_a_7_0_0 and cp <= 0x2_b_7_3_f)  #
              or (cp >= 0x2_b_7_4_0 and cp <= 0x2_b_8_1_f)  #
              or (cp >= 0x2_b_8_2_0 and cp <= 0x2_c_e_a_f)  #
              or (cp >= 0xf_9_0_0 and cp <= 0xf_a_f_f)
              or (cp >= 0x2_f_8_0_0 and cp <= 0x2_f_a_1_f)  #
          ):  #
              return True

          return False







      def    snake_case						(  self       :			Optional[Any]			,							snake_case       :			Optional[Any]					):
          __UpperCamelCase			=    []
          for char in text:
              __UpperCamelCase			=    ord(__lowercase					)
              if cp == 0 or cp == 0xf_f_f_d or _is_control(__lowercase					):
                  continue
              if _is_whitespace(__lowercase					):
                  output.append(''' '''					)
              else:
                  output.append(__lowercase					)
          return "".join(__lowercase					)







def        __SCREAMING_SNAKE_CASE (	lowercase_   )							->						Dict:

    """simple docstring"""


    __UpperCamelCase			=    set()
    __UpperCamelCase			=    word[0]
    for char in word[1:]:
        pairs.add((prev_char, char)   )
        __UpperCamelCase			=    char
    return pairs




def        __SCREAMING_SNAKE_CASE (	lowercase_   )							->						Union[str, Any]:

    """simple docstring"""


    __UpperCamelCase			=    text.replace('''—'''       ,  '''-'''   )
    __UpperCamelCase			=    text.replace('''–'''       ,  '''-'''   )
    __UpperCamelCase			=    text.replace('''―'''       ,  '''-'''   )
    __UpperCamelCase			=    text.replace('''…'''       ,  '''...'''   )
    __UpperCamelCase			=    text.replace('''´'''       ,  '''\''''   )
    __UpperCamelCase			=    re.sub(r'''(-+|~+|!+|\"+|;+|\?+|\++|,+|\)+|\(+|\\+|\/+|\*+|\[+|\]+|}+|{+|\|+|_+)'''       ,  r''' \1 '''       ,  lowercase_   )
    __UpperCamelCase			=    re.sub(r'''\s*\n\s*'''       ,  ''' \n '''       ,  lowercase_   )
    __UpperCamelCase			=    re.sub(r'''[^\S\n]+'''       ,  ''' '''       ,  lowercase_   )
    return text.strip()


class 			_lowerCamelCase    (      A_ ):

      """simple docstring"""

      lowerCAmelCase__      :			Dict     =       VOCAB_FILES_NAMES
      lowerCAmelCase__      :			Optional[int]     =       PRETRAINED_VOCAB_FILES_MAP
      lowerCAmelCase__      :			List[str]     =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      lowerCAmelCase__      :			List[str]     =       ['input_ids', 'attention_mask']







      def __init__(  self       :			Tuple			,							snake_case       :			List[Any]			,							snake_case       :			Optional[Any]			,							snake_case       :			List[Any]="<unk>"			,							**snake_case       :			Optional[Any]					):
          super().__init__(unk_token=__lowercase			,							**__lowercase					)

          try:
              import ftfy
              from spacy.lang.en import English

              __UpperCamelCase			=    English()
              __UpperCamelCase			=    _nlp.tokenizer
              __UpperCamelCase			=    ftfy.fix_text
          except ImportError:
              logger.warning('''ftfy or spacy is not installed using BERT BasicTokenizer instead of SpaCy & ftfy.'''					)
              __UpperCamelCase			=    BasicTokenizer(do_lower_case=__lowercase					)
              __UpperCamelCase			=    None

          with open(__lowercase			,							encoding='''utf-8'''					) as vocab_handle:
              __UpperCamelCase			=    json.load(__lowercase					)
          __UpperCamelCase			=    {v: k for k, v in self.encoder.items()}
          with open(__lowercase			,							encoding='''utf-8'''					) as merges_handle:
              __UpperCamelCase			=    merges_handle.read().split('''\n'''					)[1:-1]
          __UpperCamelCase			=    [tuple(merge.split()					) for merge in merges]
          __UpperCamelCase			=    dict(zip(__lowercase			,							range(len(__lowercase					)					)					)					)
          __UpperCamelCase			=    {}







      @property
      def    snake_case						(  self       :			Tuple					):
          return True







      @property
      def    snake_case						(  self       :			int					):
          return len(self.encoder					)







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







      def    snake_case						(  self       :			Tuple			,							snake_case       :			Dict					):
          __UpperCamelCase			=    tuple(token[:-1]					) + (token[-1] + '''</w>''',)
          if token in self.cache:
              return self.cache[token]
          __UpperCamelCase			=    get_pairs(__lowercase					)

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

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

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







      def    snake_case						(  self       :			Union[str, Any]			,							snake_case       :			Any					):
          __UpperCamelCase			=    []
          if self.fix_text is None:
              # Using BERT's BasicTokenizer
              __UpperCamelCase			=    self.nlp.tokenize(__lowercase					)
              for token in text:
                  split_tokens.extend(list(self.bpe(__lowercase					).split(''' '''					)					)					)
          else:
              # Using SpaCy & ftfy (original tokenization process of OpenAI GPT)
              __UpperCamelCase			=    self.nlp(text_standardize(self.fix_text(__lowercase					)					)					)
              for token in text:
                  split_tokens.extend(list(self.bpe(token.text.lower()					).split(''' '''					)					)					)
          return split_tokens







      def    snake_case						(  self       :			List[str]			,							snake_case       :			Optional[int]					):
          return self.encoder.get(__lowercase			,							self.encoder.get(self.unk_token					)					)







      def    snake_case						(  self       :			List[str]			,							snake_case       :			Union[str, Any]					):
          return self.decoder.get(__lowercase			,							self.unk_token					)







      def    snake_case						(  self       :			str			,							snake_case       :			str					):
          __UpperCamelCase			=    ''''''.join(__lowercase					).replace('''</w>'''			,							''' '''					).strip()
          return out_string







      def    snake_case						(  self       :			Optional[int]			,							snake_case       :			str			,							snake_case       :			Optional[str] = None					):







          if not os.path.isdir(__lowercase					):
              logger.error(F"Vocabulary path ({save_directory}) should be a directory"					)
              return
          __UpperCamelCase			=    os.path.join(
              __lowercase			,							(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']					)
          __UpperCamelCase			=    os.path.join(
              __lowercase			,							(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file''']					)

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

          __UpperCamelCase			=    0
          with open(__lowercase			,							'''w'''			,							encoding='''utf-8'''					) as writer:
              writer.write('''#version: 0.2\n'''					)
              for bpe_tokens, token_index in sorted(self.bpe_ranks.items()			,							key=lambda snake_case					: kv[1]					):
                  if index != token_index:
                      logger.warning(
                          F"Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."
                          ''' Please check that the tokenizer is not corrupted!'''					)
                      __UpperCamelCase			=    token_index
                  writer.write(''' '''.join(__lowercase					) + '''\n'''					)
                  index += 1

          return vocab_file, merge_file













import matplotlib.pyplot as plt
import numpy as np




def  A_(      A					,   A					,   A ):
	UpperCAmelCase_		    =  len(A )  # Number of training samples
	UpperCAmelCase_		    =  np.eye(A )  # Initialize weights as identity matrix
	for j in range(A ):
		UpperCAmelCase_		    =  point - x_train[j]
		UpperCAmelCase_		    =  np.exp(diff @ diff.T / (-2.0 * tau**2) )

	return weights




def  A_(      A					,   A					,   A					,   A ):
	UpperCAmelCase_		    =  weight_matrix(A					,   A					,   A )
	UpperCAmelCase_		    =  np.linalg.inv(x_train.T @ weight_mat @ x_train ) @ (
	    x_train.T @ weight_mat @ y_train.T
	)

	return weight




def  A_(      A					,   A					,   A ):
	UpperCAmelCase_		    =  np.zeros(len(A ) )  # Initialize array of predictions
	for i, item in enumerate(A ):
		UpperCAmelCase_		    =  item @ local_weight(A					,   A					,   A					,   A )

	return y_pred




def  A_(      A					,   A					,   A ):
	import seaborn as sns

	UpperCAmelCase_		    =  sns.load_dataset(A )
	UpperCAmelCase_		    =  np.array(data[x_name] )
	UpperCAmelCase_		    =  np.array(data[y_name] )

	UpperCAmelCase_		    =  np.ones(len(A ) )

	# pairing elements of one and x_data
	UpperCAmelCase_		    =  np.column_stack((one, x_data) )

	return x_train, x_data, y_data




def  A_(      A					,   A					,   A					,   A					,   A					,   A					,   ):
	UpperCAmelCase_		    =  np.sort(A					,   axis=0 )
	plt.scatter(A					,   A					,   color="""blue""" )
	plt.plot(
	    x_train_sorted[:, 1]					,   preds[x_train[:, 1].argsort(0 )]					,   color="""yellow"""					,   linewidth=5					,   )
	plt.title("""Local Weighted Regression""" )
	plt.xlabel(A )
	plt.ylabel(A )
	plt.show()


if __name__ == "__main__":
				import doctest

				doctest.testmod()

				# Demo with a dataset from the seaborn module
				UpperCamelCase__	,      UpperCamelCase__	,      UpperCamelCase__ :			Tuple       	=			load_data("""tips""", """total_bill""", """tip""")
				UpperCamelCase__ :			Tuple       	=			local_weight_regression(training_data_x, tip, 5)
				plot_preds(training_data_x, predictions, total_bill, tip, """total_bill""", """tip""")


