import json
import os
from functools import lru_cache
from typing import TYPE_CHECKING, List, Optional, Tuple

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


if TYPE_CHECKING:
					from transformers.pipelines.conversational import Conversation

__magic_name__								=logging.get_logger(__name__)


__magic_name__								={
    '''vocab_file''': '''vocab.json''',
    '''merges_file''': '''merges.txt''',
    '''tokenizer_config_file''': '''tokenizer_config.json''',
}

__magic_name__								={
    '''vocab_file''': {'''facebook/blenderbot-3B''': '''https://huggingface.co/facebook/blenderbot-3B/resolve/main/vocab.json'''},
    '''merges_file''': {'''facebook/blenderbot-3B''': '''https://huggingface.co/facebook/blenderbot-3B/resolve/main/merges.txt'''},
    '''tokenizer_config_file''': {
        '''facebook/blenderbot-3B''': '''https://huggingface.co/facebook/blenderbot-3B/resolve/main/tokenizer_config.json'''
    },
}

__magic_name__								={'''facebook/blenderbot-3B''': 128}







@lru_cache()
# Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode
def  __UpperCamelCase    (						):
					UpperCamelCase__										=      (
					    list(range(ord('''!'''							) ,							ord('''~'''							) + 1							)							) + list(range(ord('''¡'''							) ,							ord('''¬'''							) + 1							)							) + list(range(ord('''®'''							) ,							ord('''ÿ'''							) + 1							)							)
					)
					UpperCamelCase__										=      bs[:]
					UpperCamelCase__										=      0
					for b in range(2**8							):
										if b not in bs:
															bs.append(_SCREAMING_SNAKE_CASE							)
															cs.append(2**8 + n							)
															n += 1
					UpperCamelCase__										=      [chr(_SCREAMING_SNAKE_CASE							) for n in cs]
					return dict(zip(_SCREAMING_SNAKE_CASE ,							_SCREAMING_SNAKE_CASE							)							)







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



class     _A   (    __UpperCamelCase							):
		SCREAMING_SNAKE_CASE_	:	Optional[Any]						=VOCAB_FILES_NAMES
		SCREAMING_SNAKE_CASE_	:	Optional[int]						=PRETRAINED_VOCAB_FILES_MAP
		SCREAMING_SNAKE_CASE_	:	int						=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		SCREAMING_SNAKE_CASE_	:	Union[str, Any]						=['input_ids', 'attention_mask']






		def __init__(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_="replace"   ,				SCREAMING_SNAKE_CASE_="<s>"   ,				SCREAMING_SNAKE_CASE_="</s>"   ,				SCREAMING_SNAKE_CASE_="</s>"   ,				SCREAMING_SNAKE_CASE_="<s>"   ,				SCREAMING_SNAKE_CASE_="<unk>"   ,				SCREAMING_SNAKE_CASE_="<pad>"   ,				SCREAMING_SNAKE_CASE_="<mask>"   ,				SCREAMING_SNAKE_CASE_=False   ,				**SCREAMING_SNAKE_CASE_   ,				)		->      Any:





							'''simple docstring'''





							UpperCamelCase__										=      AddedToken(SCREAMING_SNAKE_CASE_   ,				lstrip=SCREAMING_SNAKE_CASE_   ,				rstrip=SCREAMING_SNAKE_CASE_	) if isinstance(SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_	) else bos_token
							UpperCamelCase__										=      AddedToken(SCREAMING_SNAKE_CASE_   ,				lstrip=SCREAMING_SNAKE_CASE_   ,				rstrip=SCREAMING_SNAKE_CASE_	) if isinstance(SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_	) else eos_token
							UpperCamelCase__										=      AddedToken(SCREAMING_SNAKE_CASE_   ,				lstrip=SCREAMING_SNAKE_CASE_   ,				rstrip=SCREAMING_SNAKE_CASE_	) if isinstance(SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_	) else sep_token
							UpperCamelCase__										=      AddedToken(SCREAMING_SNAKE_CASE_   ,				lstrip=SCREAMING_SNAKE_CASE_   ,				rstrip=SCREAMING_SNAKE_CASE_	) if isinstance(SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_	) else cls_token
							UpperCamelCase__										=      AddedToken(SCREAMING_SNAKE_CASE_   ,				lstrip=SCREAMING_SNAKE_CASE_   ,				rstrip=SCREAMING_SNAKE_CASE_	) if isinstance(SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_	) else unk_token
							UpperCamelCase__										=      AddedToken(SCREAMING_SNAKE_CASE_   ,				lstrip=SCREAMING_SNAKE_CASE_   ,				rstrip=SCREAMING_SNAKE_CASE_	) if isinstance(SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_	) else pad_token

							# Mask token behave like a normal word, i.e. include the space before it
							UpperCamelCase__										=      AddedToken(SCREAMING_SNAKE_CASE_   ,				lstrip=SCREAMING_SNAKE_CASE_   ,				rstrip=SCREAMING_SNAKE_CASE_	) if isinstance(SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_	) else mask_token

							super().__init__(
							    errors=SCREAMING_SNAKE_CASE_   ,				bos_token=SCREAMING_SNAKE_CASE_   ,				eos_token=SCREAMING_SNAKE_CASE_   ,				unk_token=SCREAMING_SNAKE_CASE_   ,				sep_token=SCREAMING_SNAKE_CASE_   ,				cls_token=SCREAMING_SNAKE_CASE_   ,				pad_token=SCREAMING_SNAKE_CASE_   ,				mask_token=SCREAMING_SNAKE_CASE_   ,				add_prefix_space=SCREAMING_SNAKE_CASE_   ,				**SCREAMING_SNAKE_CASE_   ,				)

							with open(SCREAMING_SNAKE_CASE_   ,				encoding='''utf-8'''	) as vocab_handle:
												UpperCamelCase__										=      json.load(SCREAMING_SNAKE_CASE_	)
							UpperCamelCase__										=      {v: k for k, v in self.encoder.items()}
							UpperCamelCase__										=      errors  # how to handle errors in decoding
							UpperCamelCase__										=      bytes_to_unicode()
							UpperCamelCase__										=      {v: k for k, v in self.byte_encoder.items()}
							with open(SCREAMING_SNAKE_CASE_   ,				encoding='''utf-8'''	) as merges_handle:
												UpperCamelCase__										=      merges_handle.read().split('''\n'''	)[1:-1]
							UpperCamelCase__										=      [tuple(merge.split()	) for merge in bpe_merges]
							UpperCamelCase__										=      dict(zip(SCREAMING_SNAKE_CASE_   ,				range(len(SCREAMING_SNAKE_CASE_	)	)	)	)
							UpperCamelCase__										=      {}
							UpperCamelCase__										=      add_prefix_space

							# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
							UpperCamelCase__										=      re.compile(r'''\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+'''	)






		@property
		# Copied from transformers.models.roberta.tokenization_roberta.RobertaTokenizer.vocab_size with Roberta->Blenderbot, RoBERTa->Blenderbot
		def 		_a					(self	)		->      Union[str, Any]:





							'''simple docstring'''





							return len(self.encoder	)






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





							'''simple docstring'''





							return dict(self.encoder   ,				**self.added_tokens_encoder	)






		def 		_a					(self   ,				SCREAMING_SNAKE_CASE_	)		->      Any:





							'''simple docstring'''





							if token in self.cache:
												return self.cache[token]
							UpperCamelCase__										=      tuple(SCREAMING_SNAKE_CASE_	)
							UpperCamelCase__										=      get_pairs(SCREAMING_SNAKE_CASE_	)

							if not pairs:
												return token

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

																	if word[i] == first and i < len(SCREAMING_SNAKE_CASE_	) - 1 and word[i + 1] == second:
																						new_word.append(first + second	)
																						i += 2
																	else:
																						new_word.append(word[i]	)
																						i += 1
												UpperCamelCase__										=      tuple(SCREAMING_SNAKE_CASE_	)
												UpperCamelCase__										=      new_word
												if len(SCREAMING_SNAKE_CASE_	) == 1:
																	break
												else:
																	UpperCamelCase__										=      get_pairs(SCREAMING_SNAKE_CASE_	)
							UpperCamelCase__										=      ''' '''.join(SCREAMING_SNAKE_CASE_	)
							UpperCamelCase__										=      word
							return word






		def 		_a					(self   ,				SCREAMING_SNAKE_CASE_	)		->      Tuple:





							'''simple docstring'''





							UpperCamelCase__										=      []
							for token in re.findall(self.pat   ,				SCREAMING_SNAKE_CASE_	):
												UpperCamelCase__										=      ''''''.join(
												    self.byte_encoder[b] for b in token.encode('''utf-8'''	)	)  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
												bpe_tokens.extend(bpe_token for bpe_token in self.bpe(SCREAMING_SNAKE_CASE_	).split(''' '''	)	)
							return bpe_tokens






		def 		_a					(self   ,				SCREAMING_SNAKE_CASE_	)		->      List[str]:





							'''simple docstring'''





							return self.encoder.get(SCREAMING_SNAKE_CASE_   ,				self.encoder.get(self.unk_token	)	)






		def 		_a					(self   ,				SCREAMING_SNAKE_CASE_	)		->      int:





							'''simple docstring'''





							return self.decoder.get(SCREAMING_SNAKE_CASE_	)






		def 		_a					(self   ,				SCREAMING_SNAKE_CASE_	)		->      List[str]:





							'''simple docstring'''





							UpperCamelCase__										=      ''''''.join(SCREAMING_SNAKE_CASE_	)
							UpperCamelCase__										=      bytearray([self.byte_decoder[c] for c in text]	).decode('''utf-8'''   ,				errors=self.errors	)
							return text






		def 		_a					(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_ = None	)		->      Tuple[str]:





							'''simple docstring'''





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

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

							UpperCamelCase__										=      0
							with open(SCREAMING_SNAKE_CASE_   ,				'''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 SCREAMING_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(SCREAMING_SNAKE_CASE_	) + '''\n'''	)
																	index += 1

							return vocab_file, merge_file






		def 		_a					(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_ = None   ,				SCREAMING_SNAKE_CASE_ = False	)		->      List[int]:





							'''simple docstring'''





							if already_has_special_tokens:
												return super().get_special_tokens_mask(
												    token_ids_a=SCREAMING_SNAKE_CASE_   ,				token_ids_a=SCREAMING_SNAKE_CASE_   ,				already_has_special_tokens=SCREAMING_SNAKE_CASE_	)

							if token_ids_a is None:
												return [1] + ([0] * len(SCREAMING_SNAKE_CASE_	)) + [1]
							return [1] + ([0] * len(SCREAMING_SNAKE_CASE_	)) + [1, 1] + ([0] * len(SCREAMING_SNAKE_CASE_	)) + [1]






		def 		_a					(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_ = None	)		->      List[int]:





							'''simple docstring'''





							UpperCamelCase__										=      [self.sep_token_id]
							UpperCamelCase__										=      [self.cls_token_id]

							if token_ids_a is None:
												return len(cls + token_ids_a + sep	) * [0]
							return len(cls + token_ids_a + sep + sep + token_ids_a + sep	) * [0]






		def 		_a					(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_=False   ,				**SCREAMING_SNAKE_CASE_	)		->      Union[str, Any]:





							'''simple docstring'''





							UpperCamelCase__										=      kwargs.pop('''add_prefix_space'''   ,				self.add_prefix_space	)
							if (is_split_into_words or add_prefix_space) and (len(SCREAMING_SNAKE_CASE_	) > 0 and not text[0].isspace()):
												UpperCamelCase__										=      ''' ''' + text
							return (text, kwargs)






		def 		_a					(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_ = None	)		->      Optional[Any]:





							'''simple docstring'''





							return token_ids_a + [self.eos_token_id]






		def 		_a					(self   ,				SCREAMING_SNAKE_CASE_	)		->      List[int]:





							'''simple docstring'''





							UpperCamelCase__										=      []
							for is_user, text in conversation.iter_texts():
												if is_user:
																	# We need to space prefix as it's being done within blenderbot
																	inputs.append(''' ''' + text	)
												else:
																	# Generated responses should contain them already.
																	inputs.append(SCREAMING_SNAKE_CASE_	)

							UpperCamelCase__										=      '''  '''.join(SCREAMING_SNAKE_CASE_	)
							UpperCamelCase__										=      self.encode(SCREAMING_SNAKE_CASE_	)
							if len(SCREAMING_SNAKE_CASE_	) > self.model_max_length:
												UpperCamelCase__										=      input_ids[-self.model_max_length :]
												logger.warning(F"Trimmed input from conversation as it was longer than {self.model_max_length} tokens."	)
							return input_ids




import numpy as np
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
from tensorflow.keras.layers import LSTM, Dense
from tensorflow.keras.models import Sequential

if __name__ == "__main__":
     __magic_name__								=pd.read_csv('''sample_data.csv''', header=None)
     __magic_name__								=df.shape[:1][0]
     # If you're using some other dataset input the target column
     __magic_name__								=df.iloc[:, 1:2]
     __magic_name__								=actual_data.values.reshape(len_data, 1)
     __magic_name__								=MinMaxScaler().fit_transform(actual_data)
     __magic_name__								=10
     __magic_name__								=5
     __magic_name__								=20
     __magic_name__								=len_data - periods * look_back
     __magic_name__								=actual_data[:division]
     __magic_name__								=actual_data[division - look_back :]
     __magic_name__		,  __magic_name__								=[], []
     __magic_name__		,  __magic_name__								=[], []

     for i in range(0, len(train_data) - forward_days - look_back + 1):
          train_x.append(train_data[i : i + look_back])
          train_y.append(train_data[i + look_back : i + look_back + forward_days])
     for i in range(0, len(test_data) - forward_days - look_back + 1):
          test_x.append(test_data[i : i + look_back])
          test_y.append(test_data[i + look_back : i + look_back + forward_days])
     __magic_name__								=np.array(train_x)
     __magic_name__								=np.array(test_x)
     __magic_name__								=np.array([list(i.ravel()) for i in train_y])
     __magic_name__								=np.array([list(i.ravel()) for i in test_y])

     __magic_name__								=Sequential()
     model.add(LSTM(128, input_shape=(look_back, 1), return_sequences=True))
     model.add(LSTM(64, input_shape=(128, 1)))
     model.add(Dense(forward_days))
     model.compile(loss='''mean_squared_error''', optimizer='''adam''')
     __magic_name__								=model.fit(
         x_train, y_train, epochs=150, verbose=1, shuffle=True, batch_size=4
     )
     __magic_name__								=model.predict(x_test)



