


"""simple docstring"""


import tempfile
import unittest
from pathlib import Path
from shutil import copyfile

from transformers import MaMaaaTokenizer, is_torch_available
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
    slow,
)
from transformers.utils import is_sentencepiece_available


if is_sentencepiece_available():
    from transformers.models.mam_aaa.tokenization_mam_aaa import VOCAB_FILES_NAMES, save_json

from ...test_tokenization_common import TokenizerTesterMixin


if is_sentencepiece_available():
    __lowerCamelCase      :Dict	    =		get_tests_dir('fixtures/test_sentencepiece.model')


if is_torch_available():
    from transformers.models.mam_aaa.modeling_mam_aaa import shift_tokens_right

__lowerCamelCase      :Union[str, Any]	    =		128_022
__lowerCamelCase      :Union[str, Any]	    =		128_028


@require_sentencepiece
class 			A__	(						__lowercase ,     unittest.TestCase):


     """simple docstring"""
     snake_case__	:					Optional[Any]													=MaMaaaTokenizer
     snake_case__	:					Optional[Any]													=False
     snake_case__	:					Any													=False
     snake_case__	:					int													=True

     def 		a__						(	self:						str	)->    Optional[int]:
           super().setUp()

           lowerCamelCase					:					Any        =	["""</s>""", """<unk>""", """▁This""", """▁is""", """▁a""", """▁t""", """est""", """\u0120""", """<pad>"""]
           lowerCamelCase					:					Tuple        =	dict(zip(__a     ,		range(len(__a	)	)	)	)
           lowerCamelCase					:					str        =	Path(self.tmpdirname	)
           save_json(__a     ,		save_dir / VOCAB_FILES_NAMES["""vocab_file"""]	)
           if not (save_dir / VOCAB_FILES_NAMES["spm_file"]).exists():
                 copyfile(__a     ,		save_dir / VOCAB_FILES_NAMES["""spm_file"""]	)

           lowerCamelCase					:					List[str]        =	MaMaaaTokenizer.from_pretrained(self.tmpdirname	)
           tokenizer.save_pretrained(self.tmpdirname	)

     def 		a__						(	self:						Optional[Any]     ,		**__a:						Any	)->    str:
           return MaMaaaTokenizer.from_pretrained(self.tmpdirname     ,		**__a	)

     def 		a__						(	self:						Union[str, Any]     ,		__a:						List[Any]	)->    Union[str, Any]:
           return (
               "This is a test",
               "This is a test",
           )

     def 		a__						(	self:						Tuple	)->    int:
           lowerCamelCase					:					List[str]        =	"""</s>"""
           lowerCamelCase					:					Union[str, Any]        =	0

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

     def 		a__						(	self:						Optional[Any]	)->    Dict:
           lowerCamelCase					:					Optional[Any]        =	self.get_tokenizer()
           lowerCamelCase					:					List[Any]        =	list(tokenizer.get_vocab().keys()	)

           self.assertEqual(vocab_keys[0]     ,		"""</s>"""	)
           self.assertEqual(vocab_keys[1]     ,		"""<unk>"""	)
           self.assertEqual(vocab_keys[-1]     ,		"""<s>"""	)
           self.assertEqual(len(__a	)     ,		tokenizer.vocab_size + len(tokenizer.get_added_vocab()	)	)

     @unittest.skip("""Skip this test while all models are still to be uploaded."""	)
     def 		a__						(	self:						str	)->    str:
           pass

     def 		a__						(	self:						Tuple	)->    Dict:
           lowerCamelCase					:					List[Any]        =	self.get_tokenizer()

           lowerCamelCase					:					List[str]        =	tokenizer.tokenize("""This is a test"""	)
           self.assertListEqual(__a     ,		["""▁This""", """▁is""", """▁a""", """▁t""", """est"""]	)

           self.assertListEqual(
               tokenizer.convert_tokens_to_ids(__a	)     ,		[2, 3, 4, 5, 6]     ,		)

           lowerCamelCase					:					str        =	tokenizer.convert_ids_to_tokens([2, 3, 4, 5, 6]	)
           self.assertListEqual(__a     ,		["""▁This""", """▁is""", """▁a""", """▁t""", """est"""]	)

           lowerCamelCase					:					List[Any]        =	tokenizer.convert_tokens_to_string(__a	)
           self.assertEqual(__a     ,		"""This is a test"""	)







     @slow
     def 		a__						(	self:						Any	)->    List[str]:
           # fmt: off
           lowerCamelCase					:					Union[str, Any]        =	{"""input_ids""": [[128_022, 110_108, 397, 11, 38_272, 2_247, 124_811, 285, 18_105, 1_586, 207, 7, 39_534, 4_428, 397, 1_019, 18_105, 1_586, 207, 7, 41_337, 16_786, 241, 7, 20_214, 17, 125_690, 10_398, 7, 44_378, 58_069, 68_342, 7_798, 7_343, 11, 299, 33_310, 4, 158, 37_350, 94_077, 4_569, 299, 33_310, 90, 4, 52_840, 290, 4, 31_270, 112, 299, 682, 4, 52_840, 39_953, 14_079, 193, 52_519, 90_894, 17_894, 120_697, 11, 40_445, 551, 17, 1_019, 52_519, 90_894, 17_756, 963, 11, 40_445, 480, 17, 9_792, 1_120, 5_173, 1_393, 6_240, 16_786, 241, 120_996, 28, 1_245, 1_393, 118_240, 11_123, 1_019, 93_612, 2_691, 10_618, 98_058, 120_409, 1_928, 279, 4, 40_683, 367, 178, 207, 1_019, 103, 103_121, 506, 65_296, 5, 2], [128_022, 21_217, 367, 117, 125_450, 128, 719, 7, 7_308, 40, 93_612, 12_669, 1_116, 16_704, 71, 17_785, 3_699, 15_592, 35, 144, 9_584, 241, 11_943, 713, 950, 799, 2_247, 88_427, 150, 149, 118_813, 120_706, 1_019, 106_906, 81_518, 28, 1_224, 22_799, 397, 5, 2, 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, 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], [128_022, 1_658, 123_311, 5_155, 5_578, 4_722, 279, 14_947, 2_366, 1_120, 1_197, 14, 1_348, 9_232, 5, 2, 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, 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, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], """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, 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, 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, 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, 1, 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], [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, 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]]}  # noqa: E501
           # fmt: on

           self.tokenizer_integration_test_util(
               expected_encoding=__a     ,		model_name="""facebook/m2m100_418M"""     ,		revision="""c168bae485c864188cf9aa0e4108b0b6934dc91e"""     ,		)


@require_torch
@require_sentencepiece
@require_tokenizers
class 			A__	(						unittest.TestCase):


     """simple docstring"""
     snake_case__	:					List[str]													='''facebook/m2m100_418M'''
     snake_case__	:					Union[str, Any]													=[
         '''In my opinion, there are two levels of response from the French government.''',
         '''NSA Affair Emphasizes Complete Lack of Debate on Intelligence''',
     ]
     snake_case__	:					Union[str, Any]													=[
         '''Selon moi, il y a deux niveaux de réponse de la part du gouvernement français.''',
         '''L\'affaire NSA souligne l\'absence totale de débat sur le renseignement''',
     ]

     # fmt: off
     snake_case__	:					List[Any]													=[EN_CODE, 5_93, 19_49, 11_57_81, 4, 7_15_86, 42_34, 6_06_33, 12_62_33, 4_32, 12_38_08, 1_55_92, 11_97, 11_71_32, 12_06_18, 5, 2]

     @classmethod
     def 		a__						(	cls:						Union[str, Any]	)->    List[str]:
           lowerCamelCase					:					MaMaaaTokenizer        =	MaMaaaTokenizer.from_pretrained(
               cls.checkpoint_name     ,		src_lang="""en"""     ,		tgt_lang="""fr"""	)
           lowerCamelCase					:					List[str]        =	1
           return cls

     def 		a__						(	self:						int	)->    Any:
           self.assertEqual(self.tokenizer.get_lang_id("""ar"""	)     ,		128_006	)
           self.assertEqual(self.tokenizer.get_lang_id("""en"""	)     ,		128_022	)
           self.assertEqual(self.tokenizer.get_lang_id("""ro"""	)     ,		128_076	)
           self.assertEqual(self.tokenizer.get_lang_id("""mr"""	)     ,		128_063	)

     def 		a__						(	self:						List[Any]	)->    Union[str, Any]:
           lowerCamelCase					:					Optional[Any]        =	self.tokenizer.get_vocab()
           self.assertEqual(len(__a	)     ,		self.tokenizer.vocab_size	)
           self.assertEqual(vocab["""<unk>"""]     ,		3	)
           self.assertIn(self.tokenizer.get_lang_token("""en"""	)     ,		__a	)

     def 		a__						(	self:						Tuple	)->    Optional[Any]:
           lowerCamelCase					:					str        =	"""en"""
           lowerCamelCase					:					List[Any]        =	self.tokenizer.batch_encode_plus(self.src_text	).input_ids[0]
           self.assertListEqual(self.expected_src_tokens     ,		__a	)

     def 		a__						(	self:						List[Any]	)->    int:
           self.assertIn(__a     ,		self.tokenizer.all_special_ids	)
           # fmt: off
           lowerCamelCase					:					Optional[int]        =	[FR_CODE, 5_364, 82, 8_642, 4, 294, 47, 8, 14_028, 136, 3_286, 9_706, 6, 90_797, 6, 144_012, 162, 88_128, 30_061, 5, 2]
           # fmt: on
           lowerCamelCase					:					Any        =	self.tokenizer.decode(__a     ,		skip_special_tokens=__a	)
           lowerCamelCase					:					Union[str, Any]        =	self.tokenizer.decode(generated_ids[1:]     ,		skip_special_tokens=__a	)
           self.assertEqual(__a     ,		__a	)
           self.assertNotIn(self.tokenizer.eos_token     ,		__a	)

     def 		a__						(	self:						List[str]	)->    List[str]:
           lowerCamelCase					:					Optional[int]        =	tempfile.mkdtemp()
           lowerCamelCase					:					Any        =	self.tokenizer.lang_token_to_id
           self.tokenizer.save_pretrained(__a	)
           lowerCamelCase					:					Tuple        =	MaMaaaTokenizer.from_pretrained(__a	)
           self.assertDictEqual(new_tok.lang_token_to_id     ,		__a	)

     @require_torch
     def 		a__						(	self:						Tuple	)->    str:
           lowerCamelCase					:					Optional[Any]        =	"""en"""
           lowerCamelCase					:					List[str]        =	"""fr"""

           lowerCamelCase					:					int        =	self.tokenizer(self.src_text     ,		text_target=self.tgt_text     ,		padding=__a     ,		return_tensors="""pt"""	)

           lowerCamelCase					:					str        =	shift_tokens_right(
               batch["""labels"""]     ,		self.tokenizer.pad_token_id     ,		self.tokenizer.eos_token_id	)

           for k in batch:
                 lowerCamelCase					:					Optional[Any]        =	batch[k].tolist()
           # batch = {k: v.tolist() for k,v in batch.items()}
           # fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
           # batch.decoder_inputs_ids[0][0] ==
           assert batch.input_ids[1][0] == EN_CODE
           assert batch.input_ids[1][-1] == 2
           assert batch.labels[1][0] == FR_CODE
           assert batch.labels[1][-1] == 2
           assert batch.decoder_input_ids[1][:2] == [2, FR_CODE]

     @require_torch
     def 		a__						(	self:						Union[str, Any]	)->    Union[str, Any]:
           lowerCamelCase					:					Optional[Any]        =	"""mr"""
           self.assertListEqual(self.tokenizer.prefix_tokens     ,		[self.tokenizer.get_lang_id("""mr"""	)]	)
           self.assertListEqual(self.tokenizer.suffix_tokens     ,		[self.tokenizer.eos_token_id]	)

           lowerCamelCase					:					Tuple        =	"""zh"""
           self.assertListEqual(self.tokenizer.prefix_tokens     ,		[self.tokenizer.get_lang_id("""zh"""	)]	)
           self.assertListEqual(self.tokenizer.suffix_tokens     ,		[self.tokenizer.eos_token_id]	)

     @require_torch
     def 		a__						(	self:						Any	)->    Optional[int]:
           lowerCamelCase					:					Any        =	"""mr"""
           self.tokenizer._switch_to_target_mode()
           self.assertListEqual(self.tokenizer.prefix_tokens     ,		[self.tokenizer.get_lang_id("""mr"""	)]	)
           self.assertListEqual(self.tokenizer.suffix_tokens     ,		[self.tokenizer.eos_token_id]	)
           self.tokenizer._switch_to_input_mode()
           self.assertListEqual(self.tokenizer.prefix_tokens     ,		[self.tokenizer.get_lang_id(self.tokenizer.src_lang	)]	)

           lowerCamelCase					:					Any        =	"""zh"""
           self.tokenizer._switch_to_target_mode()
           self.assertListEqual(self.tokenizer.prefix_tokens     ,		[self.tokenizer.get_lang_id("""zh"""	)]	)
           self.assertListEqual(self.tokenizer.suffix_tokens     ,		[self.tokenizer.eos_token_id]	)
           self.tokenizer._switch_to_input_mode()
           self.assertListEqual(self.tokenizer.prefix_tokens     ,		[self.tokenizer.get_lang_id(self.tokenizer.src_lang	)]	)







     @require_torch
     def 		a__						(	self:						Optional[int]	)->    Optional[Any]:
           lowerCamelCase					:					Optional[Any]        =	self.tokenizer._build_translation_inputs("""A test"""     ,		return_tensors="""pt"""     ,		src_lang="""en"""     ,		tgt_lang="""ar"""	)

           self.assertEqual(
               nested_simplify(__a	)     ,		{
                   # en_XX, A, test, EOS
                   """input_ids""": [[128_022, 58, 4_183, 2]],
                   """attention_mask""": [[1, 1, 1, 1]],
                   # ar_AR
                   """forced_bos_token_id""": 128_006,
               }     ,		)








"""simple docstring"""


from typing import List, Optional, Union

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


__lowerCamelCase      :str	    =		logging.get_logger(__name__)

__lowerCamelCase      :Any	    =		{
    'huggingface/time-series-transformer-tourism-monthly': (
        'https://huggingface.co/huggingface/time-series-transformer-tourism-monthly/resolve/main/config.json'
    ),
    # See all TimeSeriesTransformer models at https://huggingface.co/models?filter=time_series_transformer
}


class 			A__	(						__lowercase):


     """simple docstring"""
     snake_case__	:					List[Any]													='''time_series_transformer'''
     snake_case__	:					List[Any]													={
         '''hidden_size''': '''d_model''',
         '''num_attention_heads''': '''encoder_attention_heads''',
         '''num_hidden_layers''': '''encoder_layers''',
     }

     def __init__(	self:						List[str]     ,		__a:						Optional[int] = None     ,		__a:						Optional[int] = None     ,		__a:						str = "student_t"     ,		__a:						str = "nll"     ,		__a:						int = 1     ,		__a:						List[int] = [1, 2, 3, 4, 5, 6, 7]     ,		__a:						Optional[Union[str, bool]] = "mean"     ,		__a:						int = 0     ,		__a:						int = 0     ,		__a:						int = 0     ,		__a:						int = 0     ,		__a:						Optional[List[int]] = None     ,		__a:						Optional[List[int]] = None     ,		__a:						int = 32     ,		__a:						int = 32     ,		__a:						int = 2     ,		__a:						int = 2     ,		__a:						int = 2     ,		__a:						int = 2     ,		__a:						bool = True     ,		__a:						str = "gelu"     ,		__a:						int = 64     ,		__a:						float = 0.1     ,		__a:						float = 0.1     ,		__a:						float = 0.1     ,		__a:						float = 0.1     ,		__a:						float = 0.1     ,		__a:						int = 100     ,		__a:						float = 0.02     ,		__a:						Tuple=True     ,		**__a:						str     ,		)->    Any:
           # time series specific configuration
           lowerCamelCase					:					str        =	prediction_length
           lowerCamelCase					:					Optional[Any]        =	context_length or prediction_length
           lowerCamelCase					:					Tuple        =	distribution_output
           lowerCamelCase					:					Any        =	loss
           lowerCamelCase					:					List[Any]        =	input_size
           lowerCamelCase					:					int        =	num_time_features
           lowerCamelCase					:					Dict        =	lags_sequence
           lowerCamelCase					:					Optional[int]        =	scaling
           lowerCamelCase					:					int        =	num_dynamic_real_features
           lowerCamelCase					:					Tuple        =	num_static_real_features
           lowerCamelCase					:					Any        =	num_static_categorical_features
           if cardinality 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`"""	)
                 lowerCamelCase					:					int        =	cardinality
           else:
                 lowerCamelCase					:					Dict        =	[0]
           if embedding_dimension 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`"""	)
                 lowerCamelCase					:					str        =	embedding_dimension
           else:
                 lowerCamelCase					:					str        =	[min(50     ,		(cat + 1) // 2	) for cat in self.cardinality]
           lowerCamelCase					:					Any        =	num_parallel_samples

           # Transformer architecture configuration
           lowerCamelCase					:					Any        =	input_size * len(__a	) + self._number_of_features
           lowerCamelCase					:					List[str]        =	d_model
           lowerCamelCase					:					Tuple        =	encoder_attention_heads
           lowerCamelCase					:					Optional[int]        =	decoder_attention_heads
           lowerCamelCase					:					Union[str, Any]        =	encoder_ffn_dim
           lowerCamelCase					:					str        =	decoder_ffn_dim
           lowerCamelCase					:					str        =	encoder_layers
           lowerCamelCase					:					Any        =	decoder_layers

           lowerCamelCase					:					Optional[int]        =	dropout
           lowerCamelCase					:					List[str]        =	attention_dropout
           lowerCamelCase					:					Tuple        =	activation_dropout
           lowerCamelCase					:					Optional[int]        =	encoder_layerdrop
           lowerCamelCase					:					int        =	decoder_layerdrop

           lowerCamelCase					:					Optional[int]        =	activation_function
           lowerCamelCase					:					Optional[Any]        =	init_std

           lowerCamelCase					:					Optional[Any]        =	use_cache

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







     @property
     def 		a__						(	self:						int	)->    int:
           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
           )




