


import unittest

from transformers import SPIECE_UNDERLINE
from transformers.models.speechta import SpeechTaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.tokenization_utils import AddedToken

from ...test_tokenization_common import TokenizerTesterMixin


_snake_case	:						str         =							get_tests_dir("fixtures/test_sentencepiece_bpe_char.model")

@require_sentencepiece
@require_tokenizers
class 							a						(_lowerCAmelCase	,							unittest.TestCase ):




							"""simple docstring"""


							__UpperCAmelCase       :				Any	    =    SpeechTaTokenizer
							__UpperCAmelCase       :				Any	    =    False
							__UpperCAmelCase       :				str	    =    True

							def        __snake_case   (				self			:    Optional[Any]							)      ->       Optional[int]:
														super().setUp()

														# We have a SentencePiece fixture for testing
														__snake_case						:     Tuple									=  SpeechTaTokenizer(lowerCamelCase							)

														__snake_case						:     Tuple									=  AddedToken("<mask>"       ,      lstrip=lowerCamelCase       ,      rstrip=lowerCamelCase							)
														__snake_case						:     Optional[Any]									=  mask_token
														tokenizer.add_special_tokens({"mask_token": mask_token}							)
														tokenizer.add_tokens(["<ctc_blank>"]							)

														tokenizer.save_pretrained(self.tmpdirname							)

							def        __snake_case   (				self			:    Tuple       ,      lowerCamelCase			:    Optional[int]							)      ->       Union[str, Any]:
														__snake_case						:     Any									=  "this is a test"
														__snake_case						:     Tuple									=  "this is a test"
														return input_text, output_text

							def        __snake_case   (				self			:    Optional[int]       ,      lowerCamelCase			:    Optional[int]       ,      lowerCamelCase			:    List[Any]=False       ,      lowerCamelCase			:    List[Any]=20       ,      lowerCamelCase			:    List[Any]=5							)      ->       Optional[int]:
														__snake_case					,     __snake_case						:     int									=  self.get_input_output_texts(lowerCamelCase							)
														__snake_case						:     str									=  tokenizer.encode(lowerCamelCase       ,      add_special_tokens=lowerCamelCase							)
														__snake_case						:     Optional[Any]									=  tokenizer.decode(lowerCamelCase       ,      clean_up_tokenization_spaces=lowerCamelCase							)
														return text, ids

							def        __snake_case   (				self			:    Any							)      ->       List[str]:
														__snake_case						:     Tuple									=  "<pad>"
														__snake_case						:     List[Any]									=  1

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

							def        __snake_case   (				self			:    Optional[int]							)      ->       Optional[int]:
														__snake_case						:     Tuple									=  list(self.get_tokenizer().get_vocab().keys()							)

														self.assertEqual(vocab_keys[0]       ,      "<s>"							)
														self.assertEqual(vocab_keys[1]       ,      "<pad>"							)
														self.assertEqual(vocab_keys[-4]       ,      "œ"							)
														self.assertEqual(vocab_keys[-2]       ,      "<mask>"							)
														self.assertEqual(vocab_keys[-1]       ,      "<ctc_blank>"							)
														self.assertEqual(len(lowerCamelCase							)       ,      81							)

							def        __snake_case   (				self			:    Union[str, Any]							)      ->       Optional[Any]:
														self.assertEqual(self.get_tokenizer().vocab_size       ,      79							)

							def        __snake_case   (				self			:    Union[str, Any]							)      ->       int:
														__snake_case						:     Optional[Any]									=  self.get_tokenizers(do_lower_case=lowerCamelCase							)
														for tokenizer in tokenizers:
																					with self.subTest(F'{tokenizer.__class__.__name__}'							):
																												__snake_case						:     Optional[Any]									=  tokenizer.vocab_size
																												__snake_case						:     List[Any]									=  len(lowerCamelCase							)

																												self.assertNotEqual(lowerCamelCase       ,      0							)

																												# We usually have added tokens from the start in tests because our vocab fixtures are
																												# smaller than the original vocabs - let's not assert this
																												# self.assertEqual(vocab_size, all_size)

																												__snake_case						:     List[Any]									=  ["aaaaa bbbbbb", "cccccccccdddddddd"]
																												__snake_case						:     int									=  tokenizer.add_tokens(lowerCamelCase							)
																												__snake_case						:     Optional[int]									=  tokenizer.vocab_size
																												__snake_case						:     List[Any]									=  len(lowerCamelCase							)

																												self.assertNotEqual(lowerCamelCase       ,      0							)
																												self.assertEqual(lowerCamelCase       ,      lowerCamelCase							)
																												self.assertEqual(lowerCamelCase       ,      len(lowerCamelCase							)							)
																												self.assertEqual(lowerCamelCase       ,      all_size + len(lowerCamelCase							)							)

																												__snake_case						:     Union[str, Any]									=  tokenizer.encode("aaaaa bbbbbb low cccccccccdddddddd l"       ,      add_special_tokens=lowerCamelCase							)

																												self.assertGreaterEqual(len(lowerCamelCase							)       ,      4							)
																												self.assertGreater(tokens[0]       ,      tokenizer.vocab_size - 1							)
																												self.assertGreater(tokens[-3]       ,      tokenizer.vocab_size - 1							)

																												__snake_case						:     Union[str, Any]									=  {"eos_token": ">>>>|||<||<<|<<", "pad_token": "<<<<<|||>|>>>>|>"}
																												__snake_case						:     Optional[int]									=  tokenizer.add_special_tokens(lowerCamelCase							)
																												__snake_case						:     Dict									=  tokenizer.vocab_size
																												__snake_case						:     Optional[int]									=  len(lowerCamelCase							)

																												self.assertNotEqual(lowerCamelCase       ,      0							)
																												self.assertEqual(lowerCamelCase       ,      lowerCamelCase							)
																												self.assertEqual(lowerCamelCase       ,      len(lowerCamelCase							)							)
																												self.assertEqual(lowerCamelCase       ,      all_size_a + len(lowerCamelCase							)							)

																												__snake_case						:     Optional[int]									=  tokenizer.encode(
																												    ">>>>|||<||<<|<< aaaaabbbbbb low cccccccccdddddddd <<<<<|||>|>>>>|> l"       ,      add_special_tokens=lowerCamelCase							)

																												self.assertGreaterEqual(len(lowerCamelCase							)       ,      6							)
																												self.assertGreater(tokens[0]       ,      tokenizer.vocab_size - 1							)
																												self.assertGreater(tokens[0]       ,      tokens[1]							)
																												self.assertGreater(tokens[-3]       ,      tokenizer.vocab_size - 1							)
																												self.assertGreater(tokens[-3]       ,      tokens[-4]							)
																												self.assertEqual(tokens[0]       ,      tokenizer.eos_token_id							)
																												self.assertEqual(tokens[-3]       ,      tokenizer.pad_token_id							)

							def        __snake_case   (				self			:    Optional[int]							)      ->       Optional[int]:
														pass

							def        __snake_case   (				self			:    Union[str, Any]							)      ->       Optional[Any]:
														pass

							def        __snake_case   (				self			:    List[Any]							)      ->       Optional[Any]:
														__snake_case						:     Union[str, Any]									=  self.get_tokenizer()

														__snake_case						:     Union[str, Any]									=  tokenizer.tokenize("This is a test"							)
														# fmt: off
														self.assertListEqual(lowerCamelCase       ,      [SPIECE_UNDERLINE, "T", "h", "i", "s", SPIECE_UNDERLINE, "i", "s", SPIECE_UNDERLINE, "a", SPIECE_UNDERLINE, "t", "e", "s", "t"]							)
														# fmt: on

														self.assertListEqual(
														    tokenizer.convert_tokens_to_ids(lowerCamelCase							)       ,      [4, 32, 11, 10, 12, 4, 10, 12, 4, 7, 4, 6, 5, 12, 6]       ,      )

														__snake_case						:     int									=  tokenizer.tokenize("I was born in 92000, and this is falsé."							)
														self.assertListEqual(
														    lowerCamelCase       ,      [SPIECE_UNDERLINE, "I", SPIECE_UNDERLINE, "w", "a", "s", SPIECE_UNDERLINE, "b", "o", "r", "n", SPIECE_UNDERLINE, "i", "n", SPIECE_UNDERLINE, "92000", ",", SPIECE_UNDERLINE, "a", "n", "d", SPIECE_UNDERLINE, "t", "h", "i", "s", SPIECE_UNDERLINE, "i", "s", SPIECE_UNDERLINE, "f", "a", "l", "s", "é", "."]							)

														__snake_case						:     Any									=  tokenizer.convert_tokens_to_ids(lowerCamelCase							)
														# fmt: off
														self.assertListEqual(lowerCamelCase       ,      [4, 30, 4, 20, 7, 12, 4, 25, 8, 13, 9, 4, 10, 9, 4, 3, 23, 4, 7, 9, 14, 4, 6, 11, 10, 12, 4, 10, 12, 4, 19, 7, 15, 12, 73, 26]							)
														# fmt: on

														__snake_case						:     Union[str, Any]									=  tokenizer.convert_ids_to_tokens(lowerCamelCase							)
														self.assertListEqual(
														    lowerCamelCase       ,      [SPIECE_UNDERLINE, "I", SPIECE_UNDERLINE, "w", "a", "s", SPIECE_UNDERLINE, "b", "o", "r", "n", SPIECE_UNDERLINE, "i", "n", SPIECE_UNDERLINE, "<unk>", ",", SPIECE_UNDERLINE, "a", "n", "d", SPIECE_UNDERLINE, "t", "h", "i", "s", SPIECE_UNDERLINE, "i", "s", SPIECE_UNDERLINE, "f", "a", "l", "s", "é", "."]							)






							@slow
							def        __snake_case   (				self			:    Dict							)      ->       Dict:
														# Use custom sequence because this tokenizer does not handle numbers.
														__snake_case						:     str									=  [
														    "Transformers (formerly known as pytorch-transformers and pytorch-pretrained-bert) provides "
														    "general-purpose architectures (BERT, GPT, RoBERTa, XLM, DistilBert, XLNet...) for Natural "
														    "Language Understanding (NLU) and Natural Language Generation (NLG) with over thirty-two pretrained "
														    "models in one hundred plus languages and deep interoperability between Jax, PyTorch and TensorFlow.",
														    "BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly "
														    "conditioning on both left and right context in all layers.",
														    "The quick brown fox jumps over the lazy dog.",
														]

														# fmt: off
														__snake_case						:     List[Any]									=  {
														    "input_ids": [
														        [4, 32, 13, 7, 9, 12, 19, 8, 13, 18, 5, 13, 12, 4, 64, 19, 8, 13, 18, 5, 13, 15, 22, 4, 28, 9, 8, 20, 9, 4, 7, 12, 4, 24, 22, 6, 8, 13, 17, 11, 39, 6, 13, 7, 9, 12, 19, 8, 13, 18, 5, 13, 12, 4, 7, 9, 14, 4, 24, 22, 6, 8, 13, 17, 11, 39, 24, 13, 5, 6, 13, 7, 10, 9, 5, 14, 39, 25, 5, 13, 6, 63, 4, 24, 13, 8, 27, 10, 14, 5, 12, 4, 21, 5, 9, 5, 13, 7, 15, 39, 24, 16, 13, 24, 8, 12, 5, 4, 7, 13, 17, 11, 10, 6, 5, 17, 6, 16, 13, 5, 12, 4, 64, 40, 47, 54, 32, 23, 4, 53, 49, 32, 23, 4, 54, 8, 40, 47, 54, 32, 7, 23, 4, 69, 52, 43, 23, 4, 51, 10, 12, 6, 10, 15, 40, 5, 13, 6, 23, 4, 69, 52, 48, 5, 6, 26, 26, 26, 63, 4, 19, 8, 13, 4, 48, 7, 6, 16, 13, 7, 15, 4, 52, 7, 9, 21, 16, 7, 21, 5, 4, 61, 9, 14, 5, 13, 12, 6, 7, 9, 14, 10, 9, 21, 4, 64, 48, 52, 61, 63, 4, 7, 9, 14, 4, 48, 7, 6, 16, 13, 7, 15, 4, 52, 7, 9, 21, 16, 7, 21, 5, 4, 53, 5, 9, 5, 13, 7, 6, 10, 8, 9, 4, 64, 48, 52, 53, 63, 4, 20, 10, 6, 11, 4, 8, 27, 5, 13, 4, 6, 11, 10, 13, 6, 22, 39, 6, 20, 8, 4, 24, 13, 5, 6, 13, 7, 10, 9, 5, 14, 4, 18, 8, 14, 5, 15, 12, 4, 10, 9, 4, 8, 9, 5, 4, 11, 16, 9, 14, 13, 5, 14, 4, 24, 15, 16, 12, 4, 15, 7, 9, 21, 16, 7, 21, 5, 12, 4, 7, 9, 14, 4, 14, 5, 5, 24, 4, 10, 9, 6, 5, 13, 8, 24, 5, 13, 7, 25, 10, 15, 10, 6, 22, 4, 25, 5, 6, 20, 5, 5, 9, 4, 58, 7, 37, 23, 4, 49, 22, 32, 8, 13, 17, 11, 4, 7, 9, 14, 4, 32, 5, 9, 12, 8, 13, 55, 15, 8, 20, 26, 2],
														        [4, 40, 47, 54, 32, 4, 10, 12, 4, 14, 5, 12, 10, 21, 9, 5, 14, 4, 6, 8, 4, 24, 13, 5, 39, 6, 13, 7, 10, 9, 4, 14, 5, 5, 24, 4, 25, 10, 14, 10, 13, 5, 17, 6, 10, 8, 9, 7, 15, 4, 13, 5, 24, 13, 5, 12, 5, 9, 6, 7, 6, 10, 8, 9, 12, 4, 19, 13, 8, 18, 4, 16, 9, 15, 7, 25, 5, 15, 5, 14, 4, 6, 5, 37, 6, 4, 25, 22, 4, 46, 8, 10, 9, 6, 15, 22, 4, 17, 8, 9, 14, 10, 6, 10, 8, 9, 10, 9, 21, 4, 8, 9, 4, 25, 8, 6, 11, 4, 15, 5, 19, 6, 4, 7, 9, 14, 4, 13, 10, 21, 11, 6, 4, 17, 8, 9, 6, 5, 37, 6, 4, 10, 9, 4, 7, 15, 15, 4, 15, 7, 22, 5, 13, 12, 26, 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, 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],
														        [4, 32, 11, 5, 4, 45, 16, 10, 17, 28, 4, 25, 13, 8, 20, 9, 4, 19, 8, 37, 4, 46, 16, 18, 24, 12, 4, 8, 27, 5, 13, 4, 6, 11, 5, 4, 15, 7, 57, 22, 4, 14, 8, 21, 26, 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, 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, 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],
														    ],
														    "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, 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, 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, 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, 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, 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, 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, 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, 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, 0, 0, 0, 0, 0, 0, 0, 0],
														    ]
														}
														# fmt: on

														self.tokenizer_integration_test_util(
														    expected_encoding=lowerCamelCase       ,      model_name="microsoft/speecht5_asr"       ,      revision="c5ef64c71905caeccde0e4462ef3f9077224c524"       ,      sequences=lowerCamelCase       ,      )






'''simple docstring'''





import inspect
import tempfile
import unittest

from huggingface_hub import hf_hub_download

from transformers import is_torch_available
from transformers.testing_utils import is_flaky, require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


lowercase_									=    1e-4

if is_torch_available():
  import torch

  from transformers import AutoformerConfig, AutoformerForPrediction, AutoformerModel
  from transformers.models.autoformer.modeling_autoformer import AutoformerDecoder, AutoformerEncoder






@require_torch
class __A     :



 '''simple docstring'''







 def __init__(self					,  A					,  A=16					,  A=13					,  A=7					,  A=14					,  A=10					,  A=19					,  A=5					,  A=4					,  A=True					,  A=16					,  A=2					,  A=4					,  A=4					,  A="gelu"					,  A=0.1					,  A=0.1					,  A=[1, 2, 3, 4, 5]					,  A=25					,  A=5					,  )       ->	List[str]:


      """simple docstring"""



      _a           =    d_model
      _a           =    parent
      _a           =    batch_size
      _a           =    prediction_length
      _a           =    context_length
      _a           =    cardinality
      _a           =    num_time_features
      _a           =    lags_sequence
      _a           =    embedding_dimension
      _a           =    is_training
      _a           =    hidden_size
      _a           =    num_hidden_layers
      _a           =    num_attention_heads
      _a           =    intermediate_size
      _a           =    hidden_act
      _a           =    hidden_dropout_prob
      _a           =    attention_probs_dropout_prob

      _a           =    context_length
      _a           =    prediction_length + label_length
      _a           =    label_length

      _a           =    moving_average
      _a           =    autocorrelation_factor



 def    a__    (self			)       ->	Any:


      """simple docstring"""



      return AutoformerConfig(
          d_model=self.d_model					,  encoder_layers=self.num_hidden_layers					,  decoder_layers=self.num_hidden_layers					,  encoder_attention_heads=self.num_attention_heads					,  decoder_attention_heads=self.num_attention_heads					,  encoder_ffn_dim=self.intermediate_size					,  decoder_ffn_dim=self.intermediate_size					,  dropout=self.hidden_dropout_prob					,  attention_dropout=self.attention_probs_dropout_prob					,  prediction_length=self.prediction_length					,  context_length=self.context_length					,  label_length=self.label_length					,  lags_sequence=self.lags_sequence					,  num_time_features=self.num_time_features					,  num_static_categorical_features=1					,  cardinality=[self.cardinality]					,  embedding_dimension=[self.embedding_dimension]					,  moving_average=self.moving_average					,  )



 def    a__    (self					,  A			)       ->	List[Any]:


      """simple docstring"""



      _a           =    config.context_length + max(config.lags_sequence			)

      _a           =    ids_tensor([self.batch_size, 1]					,  config.cardinality[0]			)
      _a           =    floats_tensor([self.batch_size, _past_length, config.num_time_features]			)
      _a           =    floats_tensor([self.batch_size, _past_length]			)
      _a           =    floats_tensor([self.batch_size, _past_length]			) > 0.5

      # decoder inputs
      _a           =    floats_tensor([self.batch_size, config.prediction_length, config.num_time_features]			)
      _a           =    floats_tensor([self.batch_size, config.prediction_length]			)

      _a           =    {
          '''past_values''': past_values,
          '''static_categorical_features''': static_categorical_features,
          '''past_time_features''': past_time_features,
          '''past_observed_mask''': past_observed_mask,
          '''future_time_features''': future_time_features,
          '''future_values''': future_values,
      }
      return inputs_dict



 def    a__    (self			)       ->	Any:


      """simple docstring"""



      _a           =    self.get_config()
      _a           =    self.prepare_autoformer_inputs_dict(A			)
      return config, inputs_dict



 def    a__    (self			)       ->	Optional[Any]:


      """simple docstring"""



      _a    ,       _a           =    self.prepare_config_and_inputs()
      return config, inputs_dict



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


      """simple docstring"""



      _a           =    AutoformerModel(config=A			).to(A			).eval()
      _a           =    model(**A			)

      _a           =    outputs.encoder_last_hidden_state
      _a           =    outputs.last_hidden_state

      with tempfile.TemporaryDirectory() as tmpdirname:
           _a           =    model.get_encoder()
           encoder.save_pretrained(A			)
           _a           =    AutoformerEncoder.from_pretrained(A			).to(A			)

      _a    ,       _a    ,       _a    ,       _a    ,       _a           =    model.create_network_inputs(**A			)
      _a    ,       _a           =    model.decomposition_layer(transformer_inputs[:, : config.context_length, ...]			)

      _a           =    torch.cat(
          (transformer_inputs[:, : config.context_length, ...], feature[:, : config.context_length, ...])					,  dim=-1					,  )
      _a           =    encoder(inputs_embeds=A			)[0]
      self.parent.assertTrue((encoder_last_hidden_state_a - encoder_last_hidden_state).abs().max().item() < 1E-3			)

      _a           =    (
          torch.mean(transformer_inputs[:, : config.context_length, ...]					,  dim=1			)
          .unsqueeze(1			)
          .repeat(1					,  config.prediction_length					,  1			)
      )
      _a           =    torch.zeros(
          [transformer_inputs.shape[0], config.prediction_length, transformer_inputs.shape[2]]					,  device=enc_input.device					,  )

      _a           =    torch.cat(
          (
              torch.cat((seasonal_input[:, -config.label_length :, ...], zeros)					,  dim=1			),
              feature[:, config.context_length - config.label_length :, ...],
          )					,  dim=-1					,  )
      _a           =    torch.cat(
          (
              torch.cat((trend_input[:, -config.label_length :, ...], mean)					,  dim=1			),
              feature[:, config.context_length - config.label_length :, ...],
          )					,  dim=-1					,  )

      with tempfile.TemporaryDirectory() as tmpdirname:
           _a           =    model.get_decoder()
           decoder.save_pretrained(A			)
           _a           =    AutoformerDecoder.from_pretrained(A			).to(A			)

      _a           =    decoder(
          trend=A					,  inputs_embeds=A					,  encoder_hidden_states=A					,  )[0]

      self.parent.assertTrue((last_hidden_state_a - last_hidden_state).abs().max().item() < 1E-3			)






@require_torch
class __A     (    A  ,  A  ,  unittest.TestCase		):



 '''simple docstring'''







 __lowerCamelCase       :			Dict		=			(AutoformerModel, AutoformerForPrediction) if is_torch_available() else ()
 __lowerCamelCase       :			Optional[Any]		=			(AutoformerForPrediction,) if is_torch_available() else ()
 __lowerCamelCase       :			Tuple		=			{'feature-extraction': AutoformerModel} if is_torch_available() else {}
 __lowerCamelCase       :			Tuple		=			False
 __lowerCamelCase       :			Dict		=			False
 __lowerCamelCase       :			int		=			False
 __lowerCamelCase       :			Union[str, Any]		=			False
 __lowerCamelCase       :			Optional[int]		=			False
 __lowerCamelCase       :			List[Any]		=			False



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


      """simple docstring"""



      _a           =    AutoformerModelTester(self			)
      _a           =    ConfigTester(self					,  config_class=A					,  has_text_modality=A			)



 def    a__    (self			)       ->	Dict:


      """simple docstring"""



      self.config_tester.run_common_tests()



 def    a__    (self			)       ->	Dict:


      """simple docstring"""



      _a    ,       _a           =    self.model_tester.prepare_config_and_inputs()
      for model_class in self.all_model_classes:
           _a           =    model_class(A			)

           with tempfile.TemporaryDirectory() as tmpdirname:
                model.save_pretrained(A			)
                _a    ,       _a           =    model_class.from_pretrained(A					,  output_loading_info=A			)
           self.assertEqual(info['''missing_keys''']					,  []			)



 def    a__    (self			)       ->	str:


      """simple docstring"""



      _a           =    self.model_tester.prepare_config_and_inputs_for_common()
      self.model_tester.check_encoder_decoder_model_standalone(*A			)



 @unittest.skip(reason='''Model has no tokens embeddings'''			)
 def    a__    (self			)       ->	Tuple:


      """simple docstring"""



      pass



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


      """simple docstring"""



      _a           =    inspect.signature(getattr(A					,  '''forward'''			)			)
      # The main input is the name of the argument after `self`
      _a           =    list(model_signature.parameters.keys()			)[1]
      self.assertEqual(AutoformerModel.main_input_name					,  A			)



 def    a__    (self			)       ->	Optional[int]:


      """simple docstring"""



      _a    ,       _a           =    self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
           _a           =    model_class(A			)
           _a           =    inspect.signature(model.forward			)
           # signature.parameters is an OrderedDict => so arg_names order is deterministic
           _a           =    [*signature.parameters.keys()]

           _a           =    [
               '''past_values''',
               '''past_time_features''',
               '''past_observed_mask''',
               '''static_categorical_features''',
               '''static_real_features''',
               '''future_values''',
               '''future_time_features''',
           ]

           if model.__class__.__name__ in ["AutoformerForPrediction"]:
                expected_arg_names.append('''future_observed_mask'''			)

           expected_arg_names.extend(
               [
                   '''decoder_attention_mask''',
                   '''head_mask''',
                   '''decoder_head_mask''',
                   '''cross_attn_head_mask''',
                   '''encoder_outputs''',
                   '''past_key_values''',
                   '''output_hidden_states''',
                   '''output_attentions''',
                   '''use_cache''',
                   '''return_dict''',
               ]			)

           self.assertListEqual(arg_names[: len(A			)]					,  A			)



 def    a__    (self			)       ->	Optional[int]:


      """simple docstring"""



      _a    ,       _a           =    self.model_tester.prepare_config_and_inputs_for_common()
      _a           =    True

      _a           =    getattr(self.model_tester					,  '''seq_length'''					,  A			)
      _a           =    getattr(self.model_tester					,  '''decoder_seq_length'''					,  A			)
      _a           =    getattr(self.model_tester					,  '''encoder_seq_length'''					,  A			)
      _a           =    getattr(self.model_tester					,  '''d_model'''					,  A			)
      _a           =    getattr(self.model_tester					,  '''num_attention_heads'''					,  A			)
      _a           =    d_model // num_attention_heads

      for model_class in self.all_model_classes:
           _a           =    True
           _a           =    False
           _a           =    True
           _a           =    model_class(A			)
           model.to(A			)
           model.eval()
           with torch.no_grad():
                _a           =    model(**self._prepare_for_class(A					,  A			)			)
           _a           =    outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
           self.assertEqual(len(A			)					,  self.model_tester.num_hidden_layers			)

           # check that output_attentions also work using config
           del inputs_dict["output_attentions"]
           _a           =    True
           _a           =    model_class(A			)
           model.to(A			)
           model.eval()
           with torch.no_grad():
                _a           =    model(**self._prepare_for_class(A					,  A			)			)
           _a           =    outputs.encoder_attentions
           self.assertEqual(len(A			)					,  self.model_tester.num_hidden_layers			)

           self.assertListEqual(
               list(attentions[0].shape[-3:]			)					,  [self.model_tester.num_attention_heads, encoder_seq_length, dim]					,  )
           _a           =    len(A			)

           _a           =    7

           if "last_hidden_state" in outputs:
                correct_outlen += 1

           if "trend" in outputs:
                correct_outlen += 1

           if "past_key_values" in outputs:
                correct_outlen += 1  # past_key_values have been returned

           if "loss" in outputs:
                correct_outlen += 1

           if "params" in outputs:
                correct_outlen += 1

           self.assertEqual(A					,  A			)

           # decoder attentions
           _a           =    outputs.decoder_attentions
           self.assertIsInstance(A					,  (list, tuple)			)
           self.assertEqual(len(A			)					,  self.model_tester.num_hidden_layers			)
           self.assertListEqual(
               list(decoder_attentions[0].shape[-3:]			)					,  [self.model_tester.num_attention_heads, decoder_seq_length, dim]					,  )

           # cross attentions
           _a           =    outputs.cross_attentions
           self.assertIsInstance(A					,  (list, tuple)			)
           self.assertEqual(len(A			)					,  self.model_tester.num_hidden_layers			)
           self.assertListEqual(
               list(cross_attentions[0].shape[-3:]			)					,  [self.model_tester.num_attention_heads, decoder_seq_length, dim]					,  )

      # Check attention is always last and order is fine
      _a           =    True
      _a           =    True
      _a           =    model_class(A			)
      model.to(A			)
      model.eval()
      with torch.no_grad():
           _a           =    model(**self._prepare_for_class(A					,  A			)			)

      self.assertEqual(out_len + 2					,  len(A			)			)

      _a           =    outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions

      self.assertEqual(len(A			)					,  self.model_tester.num_hidden_layers			)
      self.assertListEqual(
          list(self_attentions[0].shape[-3:]			)					,  [self.model_tester.num_attention_heads, encoder_seq_length, dim]					,  )



 @is_flaky()
 def    a__    (self			)       ->	Optional[Any]:


      """simple docstring"""



      super().test_retain_grad_hidden_states_attentions()





def lowerCAmelCase    (__A="train-batch.pt"):



     """simple docstring"""



     _a           =    hf_hub_download(repo_id='''hf-internal-testing/tourism-monthly-batch'''    ,      filename=__A    ,      repo_type='''dataset''')
     _a           =    torch.load(__A    ,      map_location=__A)
     return batch






@require_torch
@slow
class __A     (    unittest.TestCase		):



 '''simple docstring'''







 def    a__    (self			)       ->	Optional[int]:


      """simple docstring"""



      _a           =    AutoformerModel.from_pretrained('''huggingface/autoformer-tourism-monthly'''			).to(A			)
      _a           =    prepare_batch()

      with torch.no_grad():
           _a           =    model(
               past_values=batch['''past_values''']					,  past_time_features=batch['''past_time_features''']					,  past_observed_mask=batch['''past_observed_mask''']					,  static_categorical_features=batch['''static_categorical_features''']					,  future_values=batch['''future_values''']					,  future_time_features=batch['''future_time_features''']					,  )[0]

      _a           =    torch.Size(
          (64, model.config.prediction_length + model.config.label_length, model.config.feature_size)			)
      self.assertEqual(output.shape					,  A			)

      _a           =    torch.tensor(
          [[0.3593, -1.3398, 0.6330], [0.2279, 1.5396, -0.1792], [0.0450, 1.3225, -0.2335]]					,  device=A			)
      self.assertTrue(torch.allclose(output[0, :3, :3]					,  A					,  atol=A			)			)



 def    a__    (self			)       ->	Any:


      """simple docstring"""



      _a           =    AutoformerForPrediction.from_pretrained('''huggingface/autoformer-tourism-monthly'''			).to(A			)
      _a           =    prepare_batch('''val-batch.pt'''			)
      with torch.no_grad():
           _a           =    model(
               past_values=batch['''past_values''']					,  past_time_features=batch['''past_time_features''']					,  past_observed_mask=batch['''past_observed_mask''']					,  static_categorical_features=batch['''static_categorical_features''']					,  ).encoder_last_hidden_state
      _a           =    torch.Size((64, model.config.context_length, model.config.d_model)			)
      self.assertEqual(output.shape					,  A			)

      _a           =    torch.tensor(
          [[-0.0734, -0.9036, 0.8358], [4.7186, 2.4113, 1.9581], [1.7953, 2.3558, 1.2970]]					,  device=A			)
      self.assertTrue(torch.allclose(output[0, :3, :3]					,  A					,  atol=A			)			)



 def    a__    (self			)       ->	Tuple:


      """simple docstring"""



      _a           =    AutoformerForPrediction.from_pretrained('''huggingface/autoformer-tourism-monthly'''			).to(A			)
      _a           =    prepare_batch('''val-batch.pt'''			)
      with torch.no_grad():
           _a           =    model.generate(
               static_categorical_features=batch['''static_categorical_features''']					,  past_time_features=batch['''past_time_features''']					,  past_values=batch['''past_values''']					,  future_time_features=batch['''future_time_features''']					,  past_observed_mask=batch['''past_observed_mask''']					,  )
      _a           =    torch.Size((64, model.config.num_parallel_samples, model.config.prediction_length)			)
      self.assertEqual(outputs.sequences.shape					,  A			)

      _a           =    torch.tensor([3130.6763, 4056.5293, 7053.0786]					,  device=A			)
      _a           =    outputs.sequences.mean(dim=1			)
      self.assertTrue(torch.allclose(mean_prediction[0, -3:]					,  A					,  rtol=1E-1			)			)



