






'''simple docstring'''







import json
import os
import shutil
import tempfile
import unittest

from transformers import BatchEncoding, CanineTokenizer
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.tokenization_utils import AddedToken
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin

class _lowercase     (    _lowercase    ,				unittest.TestCase		):
       a    				=	CanineTokenizer
       a    				=	False




       def 		lowerCamelCase_    (							self:    Optional[Any]					):
              super().setUp()
              lowerCamelCase__ :     Dict	  =	CanineTokenizer()
              tokenizer.save_pretrained(self.tmpdirname					)




       @cached_property
       def 		lowerCamelCase_    (							self:    Dict					):
              return CanineTokenizer.from_pretrained("""google/canine-s"""					)




       def 		lowerCamelCase_    (							self:    str      ,							**UpperCamelCase__:    str					):
              lowerCamelCase__ :     str	  =	self.tokenizer_class.from_pretrained(self.tmpdirname      ,							**UpperCamelCase__					)
              lowerCamelCase__ :     Dict	  =	1_024
              return tokenizer




       @require_torch
       def 		lowerCamelCase_    (							self:    Tuple					):
              lowerCamelCase__ :     List[str]	  =	self.canine_tokenizer
              lowerCamelCase__ :     Tuple	  =	["""Life is like a box of chocolates.""", """You never know what you're gonna get."""]
              # fmt: off
              lowerCamelCase__ :     Optional[Any]	  =	[57_344, 76, 105, 102, 101, 32, 105, 115, 32, 108, 105, 107, 101, 32, 97, 32, 98, 111, 120, 32, 111, 102, 32, 99, 104, 111, 99, 111, 108, 97, 116, 101, 115, 46, 57_345, 0, 0, 0, 0]
              # fmt: on
              lowerCamelCase__ :     str	  =	tokenizer(UpperCamelCase__      ,							padding=UpperCamelCase__      ,							return_tensors="""pt"""					)
              self.assertIsInstance(UpperCamelCase__      ,							UpperCamelCase__					)

              lowerCamelCase__ :     str	  =	list(batch.input_ids.numpy()[0]					)

              self.assertListEqual(UpperCamelCase__      ,							UpperCamelCase__					)

              self.assertEqual((2, 39)      ,							batch.input_ids.shape					)
              self.assertEqual((2, 39)      ,							batch.attention_mask.shape					)




       @require_torch
       def 		lowerCamelCase_    (							self:    Optional[Any]					):
              lowerCamelCase__ :     str	  =	self.canine_tokenizer
              lowerCamelCase__ :     int	  =	["""Once there was a man.""", """He wrote a test in HuggingFace Tranformers."""]
              lowerCamelCase__ :     Union[str, Any]	  =	tokenizer(UpperCamelCase__      ,							padding=UpperCamelCase__      ,							return_tensors="""pt"""					)
              # check if input_ids, attention_mask and token_type_ids are returned
              self.assertIn("""input_ids"""      ,							UpperCamelCase__					)
              self.assertIn("""attention_mask"""      ,							UpperCamelCase__					)
              self.assertIn("""token_type_ids"""      ,							UpperCamelCase__					)




       @require_torch
       def 		lowerCamelCase_    (							self:    int					):
              lowerCamelCase__ :     Optional[Any]	  =	self.canine_tokenizer
              lowerCamelCase__ :     List[str]	  =	[
                  """What's the weater?""",
                  """It's about 25 degrees.""",
              ]
              lowerCamelCase__ :     str	  =	tokenizer(
                  text_target=UpperCamelCase__      ,							max_length=32      ,							padding="""max_length"""      ,							truncation=UpperCamelCase__      ,							return_tensors="""pt"""					)
              self.assertEqual(32      ,							targets["""input_ids"""].shape[1]					)




       def 		lowerCamelCase_    (							self:    Optional[Any]					):
              # safety check on max_len default value so we are sure the test works
              lowerCamelCase__ :     Dict	  =	self.get_tokenizers()
              for tokenizer in tokenizers:
                     with self.subTest(F'''{tokenizer.__class__.__name__}'''					):
                            self.assertNotEqual(tokenizer.model_max_length      ,							42					)

        # Now let's start the test
              lowerCamelCase__ :     Optional[int]	  =	self.get_tokenizers()
              for tokenizer in tokenizers:
                     with self.subTest(F'''{tokenizer.__class__.__name__}'''					):
                            # Isolate this from the other tests because we save additional tokens/etc
                            lowerCamelCase__ :     str	  =	tempfile.mkdtemp()

                            lowerCamelCase__ :     Union[str, Any]	  =	""" He is very happy, UNwant\u00E9d,running"""
                            lowerCamelCase__ :     Optional[int]	  =	tokenizer.encode(UpperCamelCase__      ,							add_special_tokens=UpperCamelCase__					)
                            tokenizer.save_pretrained(UpperCamelCase__					)

                            lowerCamelCase__ :     Optional[int]	  =	tokenizer.__class__.from_pretrained(UpperCamelCase__					)
                            lowerCamelCase__ :     List[Any]	  =	after_tokenizer.encode(UpperCamelCase__      ,							add_special_tokens=UpperCamelCase__					)
                            self.assertListEqual(UpperCamelCase__      ,							UpperCamelCase__					)

                            shutil.rmtree(UpperCamelCase__					)

              lowerCamelCase__ :     Tuple	  =	self.get_tokenizers(model_max_length=42					)
              for tokenizer in tokenizers:
                     with self.subTest(F'''{tokenizer.__class__.__name__}'''					):
                            # Isolate this from the other tests because we save additional tokens/etc
                            lowerCamelCase__ :     Any	  =	tempfile.mkdtemp()

                            lowerCamelCase__ :     str	  =	""" He is very happy, UNwant\u00E9d,running"""

                            lowerCamelCase__ :     str	  =	tokenizer.additional_special_tokens

                            # We can add a new special token for Canine as follows:
                            lowerCamelCase__ :     int	  =	chr(0xE007					)
                            additional_special_tokens.append(UpperCamelCase__					)
                            tokenizer.add_special_tokens({"""additional_special_tokens""": additional_special_tokens}					)
                            lowerCamelCase__ :     List[Any]	  =	tokenizer.encode(UpperCamelCase__      ,							add_special_tokens=UpperCamelCase__					)
                            tokenizer.save_pretrained(UpperCamelCase__					)

                            lowerCamelCase__ :     Tuple	  =	tokenizer.__class__.from_pretrained(UpperCamelCase__					)
                            lowerCamelCase__ :     int	  =	after_tokenizer.encode(UpperCamelCase__      ,							add_special_tokens=UpperCamelCase__					)
                            self.assertListEqual(UpperCamelCase__      ,							UpperCamelCase__					)
                            self.assertIn(UpperCamelCase__      ,							after_tokenizer.additional_special_tokens					)
                            self.assertEqual(after_tokenizer.model_max_length      ,							42					)

                            lowerCamelCase__ :     Optional[int]	  =	tokenizer.__class__.from_pretrained(UpperCamelCase__      ,							model_max_length=43					)
                            self.assertEqual(tokenizer.model_max_length      ,							43					)

                            shutil.rmtree(UpperCamelCase__					)




       def 		lowerCamelCase_    (							self:    Tuple					):
              lowerCamelCase__ :     Dict	  =	self.get_tokenizers(do_lower_case=UpperCamelCase__					)
              for tokenizer in tokenizers:
                     with self.subTest(F'''{tokenizer.__class__.__name__}'''					):
                            lowerCamelCase__ :     Tuple	  =	self.get_clean_sequence(UpperCamelCase__					)

                            # a special token for Canine can be defined as follows:
                            lowerCamelCase__ :     Tuple	  =	0xE005
                            lowerCamelCase__ :     Optional[Any]	  =	chr(UpperCamelCase__					)

                            tokenizer.add_special_tokens({"""cls_token""": special_token}					)
                            lowerCamelCase__ :     List[str]	  =	tokenizer.encode(UpperCamelCase__      ,							add_special_tokens=UpperCamelCase__					)
                            self.assertEqual(len(UpperCamelCase__					)      ,							1					)

                            lowerCamelCase__ :     Optional[int]	  =	tokenizer.decode(ids + encoded_special_token      ,							clean_up_tokenization_spaces=UpperCamelCase__					)
                            lowerCamelCase__ :     str	  =	tokenizer.encode(UpperCamelCase__      ,							add_special_tokens=UpperCamelCase__					)

                            lowerCamelCase__ :     List[str]	  =	tokenizer.encode(UpperCamelCase__      ,							add_special_tokens=UpperCamelCase__					)
                            lowerCamelCase__ :     int	  =	tokenizer.encode(UpperCamelCase__      ,							add_special_tokens=UpperCamelCase__					)
                            self.assertEqual(UpperCamelCase__      ,							input_encoded + special_token_id					)

                            lowerCamelCase__ :     Dict	  =	tokenizer.decode(UpperCamelCase__      ,							skip_special_tokens=UpperCamelCase__					)
                            self.assertTrue(special_token not in decoded					)




       def 		lowerCamelCase_    (							self:    Union[str, Any]					):
              lowerCamelCase__ :     str	  =	self.get_tokenizers(do_lower_case=UpperCamelCase__					)
              for tokenizer in tokenizers:
                     with self.subTest(F'''{tokenizer.__class__.__name__}'''					):
                            lowerCamelCase__ :     Any	  =	chr(0xE005					)
                            lowerCamelCase__ :     Tuple	  =	chr(0xE006					)

                            # `add_tokens` method stores special tokens only in `tokenizer.unique_no_split_tokens`. (in tokenization_utils.py)
                            tokenizer.add_tokens([SPECIAL_TOKEN_1]      ,							special_tokens=UpperCamelCase__					)
                            # `add_special_tokens` method stores special tokens in `tokenizer.additional_special_tokens`,
                            # which also occur in `tokenizer.all_special_tokens`. (in tokenization_utils_base.py)
                            tokenizer.add_special_tokens({"""additional_special_tokens""": [SPECIAL_TOKEN_2]}					)

                            lowerCamelCase__ :     int	  =	tokenizer.tokenize(UpperCamelCase__					)
                            lowerCamelCase__ :     int	  =	tokenizer.tokenize(UpperCamelCase__					)

                            self.assertEqual(len(UpperCamelCase__					)      ,							1					)
                            self.assertEqual(len(UpperCamelCase__					)      ,							1					)
                            self.assertEqual(token_a[0]      ,							UpperCamelCase__					)
                            self.assertEqual(token_a[0]      ,							UpperCamelCase__					)




       @require_tokenizers
       def 		lowerCamelCase_    (							self:    Union[str, Any]					):
              lowerCamelCase__ :     Dict	  =	self.get_tokenizers(do_lower_case=UpperCamelCase__					)
              for tokenizer in tokenizers:
                     with self.subTest(F'''{tokenizer.__class__.__name__}'''					):
                            # a special token for Canine can be defined as follows:
                            lowerCamelCase__ :     Tuple	  =	0xE006
                            lowerCamelCase__ :     Any	  =	chr(UpperCamelCase__					)

                            lowerCamelCase__ :     Dict	  =	AddedToken(UpperCamelCase__      ,							lstrip=UpperCamelCase__					)
                            tokenizer.add_special_tokens({"""additional_special_tokens""": [new_token]}					)

                            with tempfile.TemporaryDirectory() as tmp_dir_name:
                                   tokenizer.save_pretrained(UpperCamelCase__					)
                                   tokenizer.from_pretrained(UpperCamelCase__					)




       def 		lowerCamelCase_    (							self:    Dict					):
              lowerCamelCase__ :     Union[str, Any]	  =	[]
              if self.test_slow_tokenizer:
                     tokenizer_list.append((self.tokenizer_class, self.get_tokenizer())					)

              if self.test_rust_tokenizer:
                     tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer())					)

              for tokenizer_class, tokenizer_utils in tokenizer_list:
                     with tempfile.TemporaryDirectory() as tmp_dir:
                            tokenizer_utils.save_pretrained(UpperCamelCase__					)

                            with open(os.path.join(UpperCamelCase__      ,							"""special_tokens_map.json"""					)      ,							encoding="""utf-8"""					) as json_file:
                                   lowerCamelCase__ :     Dict	  =	json.load(UpperCamelCase__					)

                            with open(os.path.join(UpperCamelCase__      ,							"""tokenizer_config.json"""					)      ,							encoding="""utf-8"""					) as json_file:
                                   lowerCamelCase__ :     Optional[int]	  =	json.load(UpperCamelCase__					)

                            # a special token for Canine can be defined as follows:
                            lowerCamelCase__ :     Union[str, Any]	  =	0xE006
                            lowerCamelCase__ :     Optional[int]	  =	chr(UpperCamelCase__					)

                            lowerCamelCase__ :     int	  =	[new_token_a]
                            lowerCamelCase__ :     Any	  =	[new_token_a]

                            with open(os.path.join(UpperCamelCase__      ,							"""special_tokens_map.json"""					)      ,							"""w"""      ,							encoding="""utf-8"""					) as outfile:
                                   json.dump(UpperCamelCase__      ,							UpperCamelCase__					)
                            with open(os.path.join(UpperCamelCase__      ,							"""tokenizer_config.json"""					)      ,							"""w"""      ,							encoding="""utf-8"""					) as outfile:
                                   json.dump(UpperCamelCase__      ,							UpperCamelCase__					)

                            # the following checks allow us to verify that our test works as expected, i.e. that the tokenizer takes
                            # into account the new value of additional_special_tokens given in the "tokenizer_config.json" and
                            # "special_tokens_map.json" files
                            lowerCamelCase__ :     str	  =	tokenizer_class.from_pretrained(UpperCamelCase__      ,							extra_ids=0					)
                            self.assertIn(UpperCamelCase__      ,							tokenizer_without_change_in_init.additional_special_tokens					)
                            # self.assertIn("an_additional_special_token",tokenizer_without_change_in_init.get_vocab()) # ByT5Tokenization no vocab
                            self.assertEqual(
                                [new_token_a]      ,							tokenizer_without_change_in_init.convert_ids_to_tokens(
                                    tokenizer_without_change_in_init.convert_tokens_to_ids([new_token_a]					)					)      ,							)

                            lowerCamelCase__ :     List[Any]	  =	0xE007
                            lowerCamelCase__ :     Optional[int]	  =	chr(UpperCamelCase__					)
                            # Now we test that we can change the value of additional_special_tokens in the from_pretrained
                            lowerCamelCase__ :     Optional[int]	  =	[AddedToken(UpperCamelCase__      ,							lstrip=UpperCamelCase__					)]
                            lowerCamelCase__ :     str	  =	tokenizer_class.from_pretrained(
                                UpperCamelCase__      ,							additional_special_tokens=UpperCamelCase__      ,							extra_ids=0					)

                            self.assertIn(UpperCamelCase__      ,							tokenizer.additional_special_tokens					)
                            # self.assertIn(new_token_2,tokenizer.get_vocab()) # ByT5Tokenization no vocab
                            self.assertEqual(
                                [new_token_a]      ,							tokenizer.convert_ids_to_tokens(tokenizer.convert_tokens_to_ids([new_token_a]					)					)					)




       @require_tokenizers
       def 		lowerCamelCase_    (							self:    Any					):
              lowerCamelCase__ :     Optional[int]	  =	self.get_tokenizers(do_lower_case=UpperCamelCase__					)
              for tokenizer in tokenizers:
                     with self.subTest(F'''{tokenizer.__class__.__name__}'''					):
                            lowerCamelCase__ :     Tuple	  =	"""hello world"""
                            if self.space_between_special_tokens:
                                   lowerCamelCase__ :     str	  =	"""[CLS] hello world [SEP]"""
                            else:
                                   lowerCamelCase__ :     str	  =	input
                            lowerCamelCase__ :     Optional[Any]	  =	tokenizer.encode(UpperCamelCase__      ,							add_special_tokens=UpperCamelCase__					)
                            lowerCamelCase__ :     List[Any]	  =	tokenizer.decode(UpperCamelCase__      ,							spaces_between_special_tokens=self.space_between_special_tokens					)
                            self.assertIn(UpperCamelCase__      ,							[output, output.lower()]					)




       def 		lowerCamelCase_    (							self:    Optional[Any]					):
              lowerCamelCase__ :     List[Any]	  =	self.get_tokenizers()
              for tokenizer in tokenizers:
                     with self.subTest(F'''{tokenizer.__class__.__name__}'''					):
                            lowerCamelCase__ :     List[Any]	  =	[
                                """bos_token""",
                                """eos_token""",
                                """unk_token""",
                                """sep_token""",
                                """pad_token""",
                                """cls_token""",
                                """mask_token""",
                            ]

              lowerCamelCase__ :     List[Any]	  =	"""a"""
              lowerCamelCase__ :     Tuple	  =	ord(UpperCamelCase__					)

              for attr in attributes_list:
                     setattr(UpperCamelCase__      ,							attr + """_id"""      ,							UpperCamelCase__					)
                     self.assertEqual(getattr(UpperCamelCase__      ,							UpperCamelCase__					)      ,							UpperCamelCase__					)
                     self.assertEqual(getattr(UpperCamelCase__      ,							attr + """_id"""					)      ,							UpperCamelCase__					)

                     setattr(UpperCamelCase__      ,							attr + """_id"""      ,							UpperCamelCase__					)
                     self.assertEqual(getattr(UpperCamelCase__      ,							UpperCamelCase__					)      ,							UpperCamelCase__					)
                     self.assertEqual(getattr(UpperCamelCase__      ,							attr + """_id"""					)      ,							UpperCamelCase__					)

              setattr(UpperCamelCase__      ,							"""additional_special_tokens_ids"""      ,							[]					)
              self.assertListEqual(getattr(UpperCamelCase__      ,							"""additional_special_tokens"""					)      ,							[]					)
              self.assertListEqual(getattr(UpperCamelCase__      ,							"""additional_special_tokens_ids"""					)      ,							[]					)

              lowerCamelCase__ :     int	  =	0xE006
              lowerCamelCase__ :     Optional[int]	  =	chr(UpperCamelCase__					)
              setattr(UpperCamelCase__      ,							"""additional_special_tokens_ids"""      ,							[additional_special_token_id]					)
              self.assertListEqual(getattr(UpperCamelCase__      ,							"""additional_special_tokens"""					)      ,							[additional_special_token]					)
              self.assertListEqual(getattr(UpperCamelCase__      ,							"""additional_special_tokens_ids"""					)      ,							[additional_special_token_id]					)




       def 		lowerCamelCase_    (							self:    Union[str, Any]					):
              pass




       def 		lowerCamelCase_    (							self:    Tuple					):
              pass




       def 		lowerCamelCase_    (							self:    str					):
              pass




       def 		lowerCamelCase_    (							self:    Optional[int]					):
              pass




       def 		lowerCamelCase_    (							self:    int					):
              pass




       def 		lowerCamelCase_    (							self:    int					):
              pass




       def 		lowerCamelCase_    (							self:    List[Any]					):
              pass




       def 		lowerCamelCase_    (							self:    Optional[Any]					):
              pass









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

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

			_A     :      Optional[Any]   					=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''')
			_A     :      Dict   					=model.fit(
			    x_train, y_train, epochs=150, verbose=1, shuffle=True, batch_size=4
			)
			_A     :      List[str]   					=model.predict(x_test)

