





'''simple docstring'''



import json
import os
import unittest

from transformers import BatchEncoding, MvpTokenizer, MvpTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin, filter_roberta_detectors


@require_tokenizers
class       __SCREAMING_SNAKE_CASE	(						__UpperCamelCase					,    unittest.TestCase			):



     '''simple docstring'''






     UpperCAmelCase__       :						Tuple      =   MvpTokenizer
     UpperCAmelCase__       :						Any      =   MvpTokenizerFast
     UpperCAmelCase__       :						Optional[Any]      =   True
     UpperCAmelCase__       :						Dict      =   filter_roberta_detectors

     def 	UpperCamelCase(					self	):
       super().setUp()
       _snake_case     		=				[
           "l",
           "o",
           "w",
           "e",
           "r",
           "s",
           "t",
           "i",
           "d",
           "n",
           "\u0120",
           "\u0120l",
           "\u0120n",
           "\u0120lo",
           "\u0120low",
           "er",
           "\u0120lowest",
           "\u0120newer",
           "\u0120wider",
           "<unk>",
       ]
       _snake_case     		=				dict(zip(lowerCamelCase						,   range(len(lowerCamelCase	)	)	)	)
       _snake_case     		=				["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
       _snake_case     		=				{"unk_token": "<unk>"}

       _snake_case     		=				os.path.join(self.tmpdirname						,   VOCAB_FILES_NAMES["vocab_file"]	)
       _snake_case     		=				os.path.join(self.tmpdirname						,   VOCAB_FILES_NAMES["merges_file"]	)
       with open(self.vocab_file						,   "w"						,   encoding="utf-8"	) as fp:
         fp.write(json.dumps(lowerCamelCase	) + "\n"	)
       with open(self.merges_file						,   "w"						,   encoding="utf-8"	) as fp:
         fp.write("\n".join(lowerCamelCase	)	)

     def 	UpperCamelCase(					self						,   **lowerCamelCase	):
       kwargs.update(self.special_tokens_map	)
       return self.tokenizer_class.from_pretrained(self.tmpdirname						,   **lowerCamelCase	)

     def 	UpperCamelCase(					self						,   **lowerCamelCase	):
       kwargs.update(self.special_tokens_map	)
       return self.rust_tokenizer_class.from_pretrained(self.tmpdirname						,   **lowerCamelCase	)

     def 	UpperCamelCase(					self						,   lowerCamelCase	):
       return "lower newer", "lower newer"

     @cached_property
     def 	UpperCamelCase(					self	):
       return MvpTokenizer.from_pretrained("RUCAIBox/mvp"	)

     @cached_property
     def 	UpperCamelCase(					self	):
       return MvpTokenizerFast.from_pretrained("RUCAIBox/mvp"	)

     @require_torch
     def 	UpperCamelCase(					self	):
       _snake_case     		=				["A long paragraph for summarization.", "Another paragraph for summarization."]
       _snake_case     		=				[0, 250, 251, 17_818, 13, 39_186, 1_938, 4, 2]

       for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
         _snake_case     		=				tokenizer(lowerCamelCase						,   max_length=len(lowerCamelCase	)						,   padding=lowerCamelCase						,   return_tensors="pt"	)
         self.assertIsInstance(lowerCamelCase						,   lowerCamelCase	)

         self.assertEqual((2, 9)						,   batch.input_ids.shape	)
         self.assertEqual((2, 9)						,   batch.attention_mask.shape	)
         _snake_case     		=				batch.input_ids.tolist()[0]
         self.assertListEqual(lowerCamelCase						,   lowerCamelCase	)
         # Test that special tokens are reset

     @require_torch
     def 	UpperCamelCase(					self	):
       _snake_case     		=				["A long paragraph for summarization.", "Another paragraph for summarization."]
       for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
         _snake_case     		=				tokenizer(lowerCamelCase						,   padding=lowerCamelCase						,   return_tensors="pt"	)
         # check if input_ids are returned and no labels
         self.assertIn("input_ids"						,   lowerCamelCase	)
         self.assertIn("attention_mask"						,   lowerCamelCase	)
         self.assertNotIn("labels"						,   lowerCamelCase	)
         self.assertNotIn("decoder_attention_mask"						,   lowerCamelCase	)

     @require_torch
     def 	UpperCamelCase(					self	):
       _snake_case     		=				[
           "Summary of the text.",
           "Another summary.",
       ]
       for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
         _snake_case     		=				tokenizer(text_target=lowerCamelCase						,   max_length=32						,   padding="max_length"						,   return_tensors="pt"	)
         self.assertEqual(32						,   targets["input_ids"].shape[1]	)

     @require_torch
     def 	UpperCamelCase(					self	):
       for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
         _snake_case     		=				tokenizer(
             ["I am a small frog" * 1_024, "I am a small frog"]						,   padding=lowerCamelCase						,   truncation=lowerCamelCase						,   return_tensors="pt"	)
         self.assertIsInstance(lowerCamelCase						,   lowerCamelCase	)
         self.assertEqual(batch.input_ids.shape						,   (2, 1_024)	)

     @require_torch
     def 	UpperCamelCase(					self	):
       _snake_case     		=				["A long paragraph for summarization."]
       _snake_case     		=				[
           "Summary of the text.",
       ]
       for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
         _snake_case     		=				tokenizer(lowerCamelCase						,   text_target=lowerCamelCase						,   return_tensors="pt"	)
         _snake_case     		=				inputs["input_ids"]
         _snake_case     		=				inputs["labels"]
         self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item()	)
         self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item()	)
         self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item()	)
         self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item()	)

     def 	UpperCamelCase(					self	):
       pass






     def 	UpperCamelCase(					self	):
       for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
         with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''	):
           _snake_case     		=				self.rust_tokenizer_class.from_pretrained(lowerCamelCase						,   **lowerCamelCase	)
           _snake_case     		=				self.tokenizer_class.from_pretrained(lowerCamelCase						,   **lowerCamelCase	)
           _snake_case     		=				"A, <mask> AllenNLP sentence."
           _snake_case     		=				tokenizer_r.encode_plus(lowerCamelCase						,   add_special_tokens=lowerCamelCase						,   return_token_type_ids=lowerCamelCase	)
           _snake_case     		=				tokenizer_p.encode_plus(lowerCamelCase						,   add_special_tokens=lowerCamelCase						,   return_token_type_ids=lowerCamelCase	)

           # token_type_ids should put 0 everywhere
           self.assertEqual(sum(tokens_r["token_type_ids"]	)						,   sum(tokens_p["token_type_ids"]	)	)

           # attention_mask should put 1 everywhere, so sum over length should be 1
           self.assertEqual(
               sum(tokens_r["attention_mask"]	) / len(tokens_r["attention_mask"]	)						,   sum(tokens_p["attention_mask"]	) / len(tokens_p["attention_mask"]	)						,   )

           _snake_case     		=				tokenizer_r.convert_ids_to_tokens(tokens_r["input_ids"]	)
           _snake_case     		=				tokenizer_p.convert_ids_to_tokens(tokens_p["input_ids"]	)

           # Rust correctly handles the space before the mask while python doesnt
           self.assertSequenceEqual(tokens_p["input_ids"]						,   [0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2]	)
           self.assertSequenceEqual(tokens_r["input_ids"]						,   [0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2]	)

           self.assertSequenceEqual(
               lowerCamelCase						,   ["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"]	)
           self.assertSequenceEqual(
               lowerCamelCase						,   ["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"]	)









'''simple docstring'''



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

 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__   :						List[str]    =			np.array(train_x)
 __magic_name__   :						List[str]    =			np.array(test_x)
 __magic_name__   :						int    =			np.array([list(i.ravel()) for i in train_y])
 __magic_name__   :						int    =			np.array([list(i.ravel()) for i in test_y])

 __magic_name__   :						Tuple    =			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__   :						int    =			model.fit(
     x_train, y_train, epochs=150, verbose=1, shuffle=True, batch_size=4
 )
 __magic_name__   :						int    =			model.predict(x_test)


