





'''simple docstring'''




import unittest

from transformers import GPTSwaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


_A							=				get_tests_dir("""fixtures/test_sentencepiece_with_bytefallback.model""")






@require_sentencepiece
@require_tokenizers
class UpperCAmelCase							(		UpperCAmelCase_					,			unittest.TestCase							):
    _A :			Union[str, Any]						   =       GPTSwaTokenizer
    _A :			Optional[int]						   =       False
    _A :			Optional[int]						   =       True
    _A :			List[Any]						   =       False



    def 					__lowerCamelCase						(  self					):
     super().setUp()

     # We have a SentencePiece fixture for testing
     __UpperCAmelCase					=   GPTSwaTokenizer(__A     ,			eos_token='<unk>'     ,			bos_token='<unk>'     ,			pad_token='<unk>'					)

     tokenizer.save_pretrained(self.tmpdirname					)



    def 					__lowerCamelCase						(  self     ,			__A					):
     __UpperCAmelCase					=   'This is a test'
     __UpperCAmelCase					=   'This is a test'
     return input_text, output_text



    def 					__lowerCamelCase						(  self					):
     __UpperCAmelCase					=   '<s>'
     __UpperCAmelCase					=   1

     self.assertEqual(self.get_tokenizer()._convert_token_to_id(__A					)     ,			__A					)
     self.assertEqual(self.get_tokenizer()._convert_id_to_token(__A					)     ,			__A					)



    def 					__lowerCamelCase						(  self					):
     __UpperCAmelCase					=   list(self.get_tokenizer().get_vocab().keys()					)

     self.assertEqual(vocab_keys[0]     ,			'<unk>'					)
     self.assertEqual(vocab_keys[1]     ,			'<s>'					)
     self.assertEqual(vocab_keys[-1]     ,			'j'					)
     self.assertEqual(len(__A					)     ,			2_000					)



    def 					__lowerCamelCase						(  self					):
     self.assertEqual(self.get_tokenizer().vocab_size     ,			2_000					)



    def 					__lowerCamelCase						(  self					):
     __UpperCAmelCase					=   GPTSwaTokenizer(__A					)

     __UpperCAmelCase					=   tokenizer.tokenize('This is a test'					)
     self.assertListEqual(__A     ,			['▁This', '▁is', '▁a', '▁t', 'est']					)

     self.assertListEqual(tokenizer.convert_tokens_to_ids(__A					)     ,			[465, 287, 265, 631, 842]					)

     __UpperCAmelCase					=   tokenizer.tokenize('I was born in 92000, and this is falsé.'					)
     # fmt: off
     self.assertListEqual(
         __A     ,			['▁I', '▁was', '▁bor', 'n', '▁in', '▁', '<0x39>', '2', '0', '0', '0', ',', '▁and', '▁this', '▁is', '▁f', 'al', 's', '<0xC3>', '<0xA9>', '.']     ,			)
     # fmt: on

     __UpperCAmelCase					=   tokenizer.convert_tokens_to_ids(__A					)
     self.assertListEqual(
         __A     ,			[262, 272, 1_525, 286, 271, 268, 60, 916, 633, 633, 633, 259, 266, 301, 287, 384, 367, 263, 198, 172, 260]     ,			)

     __UpperCAmelCase					=   tokenizer.convert_ids_to_tokens(__A					)
     # fmt: off
     self.assertListEqual(
         __A     ,			['▁I', '▁was', '▁bor', 'n', '▁in', '▁', '<0x39>', '2', '0', '0', '0', ',', '▁and', '▁this', '▁is', '▁f', 'al', 's', '<0xC3>', '<0xA9>', '.']					)
     # fmt: on



    def 					__lowerCamelCase						(  self					):
     __UpperCAmelCase					=   GPTSwaTokenizer(__A					)
     __UpperCAmelCase					=   ['This is a test', 'I was born in 92000, and this is falsé.']
     __UpperCAmelCase					=   [
         [465, 287, 265, 631, 842],
         [262, 272, 1_525, 286, 271, 268, 60, 916, 633, 633, 633, 259, 266, 301, 287, 384, 367, 263, 198, 172, 260],
     ]

     # Test that encode_fast returns the same as tokenize + convert_tokens_to_ids
     for text, expected_ids in zip(__A     ,			__A					):
      self.assertListEqual(tokenizer.encode_fast(__A					)     ,			__A					)

     # Test that decode_fast returns the input text
     for text, token_ids in zip(__A     ,			__A					):
      self.assertEqual(tokenizer.decode_fast(__A					)     ,			__A					)




    @slow
    def 					__lowerCamelCase						(  self					):
     __UpperCAmelCase					=   [
         '<|python|>def fibonacci(n)\n    if n < 0:\n        print(\'Incorrect input\')',
         'Hey there, how are you doing this fine day?',
         'This is a text with a trailing spaces followed by a dot     .',
         'Häj sväjs lillebrör! =)',
         'Det är inget fel på Mr. Cool',
     ]

     # fmt: off
     __UpperCAmelCase					=   {'input_ids': [[63_423, 5, 6_811, 14_954, 282, 816, 3_821, 63_466, 63_425, 63_462, 18, 63_978, 678, 301, 1_320, 63_423, 63_455, 63_458, 18, 63_982, 4_246, 3_940, 1_901, 47_789, 5_547, 18_994], [19_630, 1_100, 63_446, 1_342, 633, 544, 4_488, 593, 5_102, 2_416, 63_495, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1_652, 428, 268, 1_936, 515, 268, 58_593, 22_413, 9_106, 546, 268, 33_213, 63_979, 698, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [55_130, 63_450, 924, 63_449, 2_249, 4_062, 1_558, 318, 63_504, 21_498, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [509, 377, 2_827, 2_559, 332, 6_575, 63_443, 26_801, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'token_type_ids': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}
     # fmt: on
     self.tokenizer_integration_test_util(
         expected_encoding=__A     ,			model_name='AI-Sweden/gpt-sw3-126m'     ,			sequences=__A     ,			)












'''simple docstring'''




from typing import List, Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging


_A:					str							=				logging.get_logger(__name__)

_A:					Optional[Any]							=				{
    """huggingface/autoformer-tourism-monthly""": """https://huggingface.co/huggingface/autoformer-tourism-monthly/resolve/main/config.json""",
}






class UpperCAmelCase							(		UpperCAmelCase_							):
    _A :			Tuple						   =       """autoformer"""
    _A :			str						   =       {
        """hidden_size""": """d_model""",
        """num_attention_heads""": """encoder_attention_heads""",
        """num_hidden_layers""": """encoder_layers""",
    }



    def __init__(  self     ,			__A = None     ,			__A = None     ,			__A = "student_t"     ,			__A = "nll"     ,			__A = 1     ,			__A = [1, 2, 3, 4, 5, 6, 7]     ,			__A = True     ,			__A = 0     ,			__A = 0     ,			__A = 0     ,			__A = 0     ,			__A = None     ,			__A = None     ,			__A = 64     ,			__A = 2     ,			__A = 2     ,			__A = 2     ,			__A = 2     ,			__A = 32     ,			__A = 32     ,			__A = "gelu"     ,			__A = 0.1     ,			__A = 0.1     ,			__A = 0.1     ,			__A = 0.1     ,			__A = 0.1     ,			__A = 100     ,			__A = 0.0_2     ,			__A = True     ,			__A=True     ,			__A = 10     ,			__A = 25     ,			__A = 3     ,			**__A     ,			):
     # time series specific configuration
     __UpperCAmelCase					=   prediction_length
     __UpperCAmelCase					=   context_length if context_length is not None else prediction_length
     __UpperCAmelCase					=   distribution_output
     __UpperCAmelCase					=   loss
     __UpperCAmelCase					=   input_size
     __UpperCAmelCase					=   num_time_features
     __UpperCAmelCase					=   lags_sequence
     __UpperCAmelCase					=   scaling
     __UpperCAmelCase					=   num_dynamic_real_features
     __UpperCAmelCase					=   num_static_real_features
     __UpperCAmelCase					=   num_static_categorical_features
     if cardinality is not None and num_static_categorical_features > 0:
      if len(__A					) != num_static_categorical_features:
       raise ValueError(
           'The cardinality should be a list of the same length as `num_static_categorical_features`'					)
      __UpperCAmelCase					=   cardinality
     else:
      __UpperCAmelCase					=   [0]
     if embedding_dimension is not None and num_static_categorical_features > 0:
      if len(__A					) != num_static_categorical_features:
       raise ValueError(
           'The embedding dimension should be a list of the same length as `num_static_categorical_features`'					)
      __UpperCAmelCase					=   embedding_dimension
     else:
      __UpperCAmelCase					=   [min(50     ,			(cat + 1) // 2					) for cat in self.cardinality]
     __UpperCAmelCase					=   num_parallel_samples

     # Transformer architecture configuration
     __UpperCAmelCase					=   input_size * len(self.lags_sequence					) + self._number_of_features
     __UpperCAmelCase					=   d_model
     __UpperCAmelCase					=   encoder_attention_heads
     __UpperCAmelCase					=   decoder_attention_heads
     __UpperCAmelCase					=   encoder_ffn_dim
     __UpperCAmelCase					=   decoder_ffn_dim
     __UpperCAmelCase					=   encoder_layers
     __UpperCAmelCase					=   decoder_layers

     __UpperCAmelCase					=   dropout
     __UpperCAmelCase					=   attention_dropout
     __UpperCAmelCase					=   activation_dropout
     __UpperCAmelCase					=   encoder_layerdrop
     __UpperCAmelCase					=   decoder_layerdrop

     __UpperCAmelCase					=   activation_function
     __UpperCAmelCase					=   init_std

     __UpperCAmelCase					=   use_cache

     # Autoformer
     __UpperCAmelCase					=   label_length
     __UpperCAmelCase					=   moving_average
     __UpperCAmelCase					=   autocorrelation_factor

     super().__init__(is_encoder_decoder=__A     ,			**__A					)




    @property
    def 					__lowerCamelCase						(  self					):
     return (
         sum(self.embedding_dimension					)
         + self.num_dynamic_real_features
         + self.num_time_features
         + self.num_static_real_features
         + self.input_size * 2  # the log1p(abs(loc)) and log(scale) features
     )





