
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


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







@require_sentencepiece
@require_tokenizers
class 					lowerCamelCase		(  __lowerCamelCase							,			unittest.TestCase  ):
     UpperCAmelCase__    :							List[str]									=		SpeechTaTokenizer
     UpperCAmelCase__    :							Optional[int]									=		False
     UpperCAmelCase__    :							Dict									=		True





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

         # We have a SentencePiece fixture for testing
         snake_case								= SpeechTaTokenizer(UpperCamelCase_       )

         snake_case								= AddedToken("<mask>"    ,   lstrip=UpperCamelCase_    ,   rstrip=UpperCamelCase_       )
         snake_case								= mask_token
         tokenizer.add_special_tokens({"mask_token": mask_token}       )
         tokenizer.add_tokens(["<ctc_blank>"]       )

         tokenizer.save_pretrained(self.tmpdirname       )





     def 		UpperCAmelCase(self				:   Optional[Any]    ,   _A				:   str       )						->						Any:
         snake_case								= "this is a test"
         snake_case								= "this is a test"
         return input_text, output_text





     def 		UpperCAmelCase(self				:   Optional[Any]    ,   _A				:   List[Any]    ,   _A				:   List[str]=False    ,   _A				:   Any=2_0    ,   _A				:   Tuple=5       )						->						int:
         snake_case					,							snake_case								= self.get_input_output_texts(UpperCamelCase_       )
         snake_case								= tokenizer.encode(UpperCamelCase_    ,   add_special_tokens=UpperCamelCase_       )
         snake_case								= tokenizer.decode(UpperCamelCase_    ,   clean_up_tokenization_spaces=UpperCamelCase_       )
         return text, ids





     def 		UpperCAmelCase(self				:   Tuple       )						->						int:
         snake_case								= "<pad>"
         snake_case								= 1

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





     def 		UpperCAmelCase(self				:   int       )						->						Optional[int]:
         snake_case								= 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(UpperCamelCase_       )    ,   8_1       )





     def 		UpperCAmelCase(self				:   str       )						->						List[Any]:
         self.assertEqual(self.get_tokenizer().vocab_size    ,   7_9       )





     def 		UpperCAmelCase(self				:   Any       )						->						Union[str, Any]:
         snake_case								= self.get_tokenizers(do_lower_case=UpperCamelCase_       )
         for tokenizer in tokenizers:
             with self.subTest(f'{tokenizer.__class__.__name__}'       ):
                 snake_case								= tokenizer.vocab_size
                 snake_case								= len(UpperCamelCase_       )

                 self.assertNotEqual(UpperCamelCase_    ,   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								= ["aaaaa bbbbbb", "cccccccccdddddddd"]
                 snake_case								= tokenizer.add_tokens(UpperCamelCase_       )
                 snake_case								= tokenizer.vocab_size
                 snake_case								= len(UpperCamelCase_       )

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

                 snake_case								= tokenizer.encode("aaaaa bbbbbb low cccccccccdddddddd l"    ,   add_special_tokens=UpperCamelCase_       )

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

                 snake_case								= {"eos_token": ">>>>|||<||<<|<<", "pad_token": "<<<<<|||>|>>>>|>"}
                 snake_case								= tokenizer.add_special_tokens(UpperCamelCase_       )
                 snake_case								= tokenizer.vocab_size
                 snake_case								= len(UpperCamelCase_       )

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

                 snake_case								= tokenizer.encode(
                     ">>>>|||<||<<|<< aaaaabbbbbb low cccccccccdddddddd <<<<<|||>|>>>>|> l"    ,   add_special_tokens=UpperCamelCase_       )

                 self.assertGreaterEqual(len(UpperCamelCase_       )    ,   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 		UpperCAmelCase(self				:   Union[str, Any]       )						->						int:
         pass





     def 		UpperCAmelCase(self				:   str       )						->						int:
         pass





     def 		UpperCAmelCase(self				:   Any       )						->						int:
         snake_case								= self.get_tokenizer()

         snake_case								= tokenizer.tokenize("This is a test"       )
         # fmt: off
         self.assertListEqual(UpperCamelCase_    ,   [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(UpperCamelCase_       )    ,   [4, 3_2, 1_1, 1_0, 1_2, 4, 1_0, 1_2, 4, 7, 4, 6, 5, 1_2, 6]    ,   )

         snake_case								= tokenizer.tokenize("I was born in 92000, and this is falsé."       )
         self.assertListEqual(
             UpperCamelCase_    ,   [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								= tokenizer.convert_tokens_to_ids(UpperCamelCase_       )
         # fmt: off
         self.assertListEqual(UpperCamelCase_    ,   [4, 3_0, 4, 2_0, 7, 1_2, 4, 2_5, 8, 1_3, 9, 4, 1_0, 9, 4, 3, 2_3, 4, 7, 9, 1_4, 4, 6, 1_1, 1_0, 1_2, 4, 1_0, 1_2, 4, 1_9, 7, 1_5, 1_2, 7_3, 2_6]       )
         # fmt: on

         snake_case								= tokenizer.convert_ids_to_tokens(UpperCamelCase_       )
         self.assertListEqual(
             UpperCamelCase_    ,   [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 		UpperCAmelCase(self				:   str       )						->						str:
         # Use custom sequence because this tokenizer does not handle numbers.
         snake_case								= [
             "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								= {
             "input_ids": [
                 [4, 3_2, 1_3, 7, 9, 1_2, 1_9, 8, 1_3, 1_8, 5, 1_3, 1_2, 4, 6_4, 1_9, 8, 1_3, 1_8, 5, 1_3, 1_5, 2_2, 4, 2_8, 9, 8, 2_0, 9, 4, 7, 1_2, 4, 2_4, 2_2, 6, 8, 1_3, 1_7, 1_1, 3_9, 6, 1_3, 7, 9, 1_2, 1_9, 8, 1_3, 1_8, 5, 1_3, 1_2, 4, 7, 9, 1_4, 4, 2_4, 2_2, 6, 8, 1_3, 1_7, 1_1, 3_9, 2_4, 1_3, 5, 6, 1_3, 7, 1_0, 9, 5, 1_4, 3_9, 2_5, 5, 1_3, 6, 6_3, 4, 2_4, 1_3, 8, 2_7, 1_0, 1_4, 5, 1_2, 4, 2_1, 5, 9, 5, 1_3, 7, 1_5, 3_9, 2_4, 1_6, 1_3, 2_4, 8, 1_2, 5, 4, 7, 1_3, 1_7, 1_1, 1_0, 6, 5, 1_7, 6, 1_6, 1_3, 5, 1_2, 4, 6_4, 4_0, 4_7, 5_4, 3_2, 2_3, 4, 5_3, 4_9, 3_2, 2_3, 4, 5_4, 8, 4_0, 4_7, 5_4, 3_2, 7, 2_3, 4, 6_9, 5_2, 4_3, 2_3, 4, 5_1, 1_0, 1_2, 6, 1_0, 1_5, 4_0, 5, 1_3, 6, 2_3, 4, 6_9, 5_2, 4_8, 5, 6, 2_6, 2_6, 2_6, 6_3, 4, 1_9, 8, 1_3, 4, 4_8, 7, 6, 1_6, 1_3, 7, 1_5, 4, 5_2, 7, 9, 2_1, 1_6, 7, 2_1, 5, 4, 6_1, 9, 1_4, 5, 1_3, 1_2, 6, 7, 9, 1_4, 1_0, 9, 2_1, 4, 6_4, 4_8, 5_2, 6_1, 6_3, 4, 7, 9, 1_4, 4, 4_8, 7, 6, 1_6, 1_3, 7, 1_5, 4, 5_2, 7, 9, 2_1, 1_6, 7, 2_1, 5, 4, 5_3, 5, 9, 5, 1_3, 7, 6, 1_0, 8, 9, 4, 6_4, 4_8, 5_2, 5_3, 6_3, 4, 2_0, 1_0, 6, 1_1, 4, 8, 2_7, 5, 1_3, 4, 6, 1_1, 1_0, 1_3, 6, 2_2, 3_9, 6, 2_0, 8, 4, 2_4, 1_3, 5, 6, 1_3, 7, 1_0, 9, 5, 1_4, 4, 1_8, 8, 1_4, 5, 1_5, 1_2, 4, 1_0, 9, 4, 8, 9, 5, 4, 1_1, 1_6, 9, 1_4, 1_3, 5, 1_4, 4, 2_4, 1_5, 1_6, 1_2, 4, 1_5, 7, 9, 2_1, 1_6, 7, 2_1, 5, 1_2, 4, 7, 9, 1_4, 4, 1_4, 5, 5, 2_4, 4, 1_0, 9, 6, 5, 1_3, 8, 2_4, 5, 1_3, 7, 2_5, 1_0, 1_5, 1_0, 6, 2_2, 4, 2_5, 5, 6, 2_0, 5, 5, 9, 4, 5_8, 7, 3_7, 2_3, 4, 4_9, 2_2, 3_2, 8, 1_3, 1_7, 1_1, 4, 7, 9, 1_4, 4, 3_2, 5, 9, 1_2, 8, 1_3, 5_5, 1_5, 8, 2_0, 2_6, 2],
                 [4, 4_0, 4_7, 5_4, 3_2, 4, 1_0, 1_2, 4, 1_4, 5, 1_2, 1_0, 2_1, 9, 5, 1_4, 4, 6, 8, 4, 2_4, 1_3, 5, 3_9, 6, 1_3, 7, 1_0, 9, 4, 1_4, 5, 5, 2_4, 4, 2_5, 1_0, 1_4, 1_0, 1_3, 5, 1_7, 6, 1_0, 8, 9, 7, 1_5, 4, 1_3, 5, 2_4, 1_3, 5, 1_2, 5, 9, 6, 7, 6, 1_0, 8, 9, 1_2, 4, 1_9, 1_3, 8, 1_8, 4, 1_6, 9, 1_5, 7, 2_5, 5, 1_5, 5, 1_4, 4, 6, 5, 3_7, 6, 4, 2_5, 2_2, 4, 4_6, 8, 1_0, 9, 6, 1_5, 2_2, 4, 1_7, 8, 9, 1_4, 1_0, 6, 1_0, 8, 9, 1_0, 9, 2_1, 4, 8, 9, 4, 2_5, 8, 6, 1_1, 4, 1_5, 5, 1_9, 6, 4, 7, 9, 1_4, 4, 1_3, 1_0, 2_1, 1_1, 6, 4, 1_7, 8, 9, 6, 5, 3_7, 6, 4, 1_0, 9, 4, 7, 1_5, 1_5, 4, 1_5, 7, 2_2, 5, 1_3, 1_2, 2_6, 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, 3_2, 1_1, 5, 4, 4_5, 1_6, 1_0, 1_7, 2_8, 4, 2_5, 1_3, 8, 2_0, 9, 4, 1_9, 8, 3_7, 4, 4_6, 1_6, 1_8, 2_4, 1_2, 4, 8, 2_7, 5, 1_3, 4, 6, 1_1, 5, 4, 1_5, 7, 5_7, 2_2, 4, 1_4, 8, 2_1, 2_6, 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=UpperCamelCase_    ,   model_name="microsoft/speecht5_asr"    ,   revision="c5ef64c71905caeccde0e4462ef3f9077224c524"    ,   sequences=UpperCamelCase_    ,   )



import os
from pathlib import Path

import numpy as np
import pytest
from pack_dataset import pack_data_dir
from parameterized import parameterized
from save_len_file import save_len_file
from torch.utils.data import DataLoader

from transformers import AutoTokenizer
from transformers.models.mbart.modeling_mbart import shift_tokens_right
from transformers.testing_utils import TestCasePlus, slow
from utils import FAIRSEQ_AVAILABLE, DistributedSortishSampler, LegacySeqaSeqDataset, SeqaSeqDataset


_A   =			"bert-base-cased"
_A   =			"google/pegasus-xsum"
_A   =			[" Sam ate lunch today.", "Sams lunch ingredients."]
_A   =			["A very interesting story about what I ate for lunch.", "Avocado, celery, turkey, coffee"]
_A   =			"patrickvonplaten/t5-tiny-random"
_A   =			"sshleifer/bart-tiny-random"
_A   =			"sshleifer/tiny-mbart"
_A   =			"sshleifer/tiny-marian-en-de"



def 						lowercase_      (       A__			,		A__		)							->							Optional[int]:
    """simple docstring"""

    snake_case								= "\n".join(A__		)
    Path(A__		).open("w"		).writelines(A__		)



def 						lowercase_      (       A__		)							->							List[Any]:
    """simple docstring"""

    for split in ["train", "val", "test"]:
        _dump_articles(os.path.join(A__			,		F'{split}.source'		)			,		A__		)
        _dump_articles(os.path.join(A__			,		F'{split}.target'		)			,		A__		)
    return tmp_dir







class 					lowerCamelCase		(  A_  ):





     @parameterized.expand(
         [
             MBART_TINY,
             MARIAN_TINY,
             T5_TINY,
             BART_TINY,
             PEGASUS_XSUM,
         ]    ,   )
     @slow
     def 		UpperCAmelCase(self				:   Tuple    ,   _A				:   List[str]       )						->						Optional[int]:
         snake_case								= AutoTokenizer.from_pretrained(_A       )
         snake_case								= make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()       )
         snake_case								= max(len(tokenizer.encode(_A       )       ) for a in ARTICLES       )
         snake_case								= max(len(tokenizer.encode(_A       )       ) for a in SUMMARIES       )
         snake_case								= 4
         snake_case								= 8
         assert max_len_target > max_src_len  # Will be truncated
         assert max_len_source > max_src_len  # Will be truncated
         snake_case					,							snake_case								= "ro_RO", "de_DE"  # ignored for all but mbart, but never causes error.
         snake_case								= SeqaSeqDataset(
             _A    ,   data_dir=_A    ,   type_path="train"    ,   max_source_length=_A    ,   max_target_length=_A    ,   src_lang=_A    ,   tgt_lang=_A    ,   )
         snake_case								= DataLoader(_A    ,   batch_size=2    ,   collate_fn=train_dataset.collate_fn       )
         for batch in dataloader:
             assert isinstance(_A    ,   _A       )
             assert batch["attention_mask"].shape == batch["input_ids"].shape
             # show that articles were trimmed.
             assert batch["input_ids"].shape[1] == max_src_len
             # show that targets are the same len
             assert batch["labels"].shape[1] == max_tgt_len
             if tok_name != MBART_TINY:
                 continue
             # check language codes in correct place
             snake_case								= shift_tokens_right(batch["labels"]    ,   tokenizer.pad_token_id       )
             assert batch["decoder_input_ids"][0, 0].item() == tokenizer.lang_code_to_id[tgt_lang]
             assert batch["decoder_input_ids"][0, -1].item() == tokenizer.eos_token_id
             assert batch["input_ids"][0, -2].item() == tokenizer.eos_token_id
             assert batch["input_ids"][0, -1].item() == tokenizer.lang_code_to_id[src_lang]

             break  # No need to test every batch





     @parameterized.expand([BART_TINY, BERT_BASE_CASED]       )
     def 		UpperCAmelCase(self				:   str    ,   _A				:   Dict       )						->						Dict:
         snake_case								= AutoTokenizer.from_pretrained(_A       )
         snake_case								= make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()       )
         snake_case								= max(len(tokenizer.encode(_A       )       ) for a in ARTICLES       )
         snake_case								= max(len(tokenizer.encode(_A       )       ) for a in SUMMARIES       )
         snake_case								= 4
         snake_case								= LegacySeqaSeqDataset(
             _A    ,   data_dir=_A    ,   type_path="train"    ,   max_source_length=2_0    ,   max_target_length=_A    ,   )
         snake_case								= DataLoader(_A    ,   batch_size=2    ,   collate_fn=train_dataset.collate_fn       )
         for batch in dataloader:
             assert batch["attention_mask"].shape == batch["input_ids"].shape
             # show that articles were trimmed.
             assert batch["input_ids"].shape[1] == max_len_source
             assert 2_0 >= batch["input_ids"].shape[1]  # trimmed significantly
             # show that targets were truncated
             assert batch["labels"].shape[1] == trunc_target  # Truncated
             assert max_len_target > trunc_target  # Truncated
             break  # No need to test every batch





     def 		UpperCAmelCase(self				:   Union[str, Any]       )						->						Optional[Any]:
         snake_case								= AutoTokenizer.from_pretrained("facebook/mbart-large-cc25"       )

         snake_case								= Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()       )       )
         snake_case								= tmp_dir.joinpath("train.source"       ).open().readlines()
         snake_case								= Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()       )       )
         pack_data_dir(_A    ,   _A    ,   1_2_8    ,   _A       )
         snake_case								= {x.name for x in tmp_dir.iterdir()}
         snake_case								= {x.name for x in save_dir.iterdir()}
         snake_case								= save_dir.joinpath("train.source"       ).open().readlines()
         # orig: [' Sam ate lunch today.\n', 'Sams lunch ingredients.']
         # desired_packed: [' Sam ate lunch today.\n Sams lunch ingredients.']
         assert len(_A       ) < len(_A       )
         assert len(_A       ) == 1
         assert len(packed_examples[0]       ) == sum(len(_A       ) for x in orig_examples       )
         assert orig_paths == new_paths





     @pytest.mark.skipif(not FAIRSEQ_AVAILABLE    ,   reason="This test requires fairseq"       )
     def 		UpperCAmelCase(self				:   Optional[int]       )						->						Union[str, Any]:
         if not FAIRSEQ_AVAILABLE:
             return
         snake_case					,							snake_case					,							snake_case								= self._get_dataset(max_len=6_4       )
         snake_case								= 6_4
         snake_case								= ds.make_dynamic_sampler(_A    ,   required_batch_size_multiple=_A       )
         snake_case								= [len(_A       ) for x in batch_sampler]
         assert len(set(_A       )       ) > 1  # it's not dynamic batch size if every batch is the same length
         assert sum(_A       ) == len(_A       )  # no dropped or added examples
         snake_case								= DataLoader(_A    ,   batch_sampler=_A    ,   collate_fn=ds.collate_fn    ,   num_workers=2       )
         snake_case								= []
         snake_case								= []
         for batch in data_loader:
             snake_case								= batch["input_ids"].shape
             snake_case								= src_shape[0]
             assert bs % required_batch_size_multiple == 0 or bs < required_batch_size_multiple
             snake_case								= np.product(batch["input_ids"].shape       )
             num_src_per_batch.append(_A       )
             if num_src_tokens > (max_tokens * 1.1):
                 failures.append(_A       )
         assert num_src_per_batch[0] == max(_A       )
         if failures:
             raise AssertionError(f'too many tokens in {len(_A       )} batches'       )





     def 		UpperCAmelCase(self				:   int       )						->						str:
         snake_case					,							snake_case					,							snake_case								= self._get_dataset(max_len=5_1_2       )
         snake_case								= 2
         snake_case								= ds.make_sortish_sampler(_A    ,   shuffle=_A       )

         snake_case								= DataLoader(_A    ,   batch_size=_A    ,   collate_fn=ds.collate_fn    ,   num_workers=2       )
         snake_case								= DataLoader(_A    ,   batch_size=_A    ,   collate_fn=ds.collate_fn    ,   num_workers=2    ,   sampler=_A       )

         snake_case								= tokenizer.pad_token_id

         def count_pad_tokens(_A				:   Dict    ,   _A				:   Union[str, Any]="input_ids"       ):
             return [batch[k].eq(_A       ).sum().item() for batch in data_loader]

         assert sum(count_pad_tokens(_A    ,   k="labels"       )       ) < sum(count_pad_tokens(_A    ,   k="labels"       )       )
         assert sum(count_pad_tokens(_A       )       ) < sum(count_pad_tokens(_A       )       )
         assert len(_A       ) == len(_A       )





     def 		UpperCAmelCase(self				:   Union[str, Any]    ,   _A				:   Union[str, Any]=1_0_0_0    ,   _A				:   Optional[int]=1_2_8       )						->						List[Any]:
         if os.getenv("USE_REAL_DATA"    ,   _A       ):
             snake_case								= "examples/seq2seq/wmt_en_ro"
             snake_case								= max_len * 2 * 6_4
             if not Path(_A       ).joinpath("train.len"       ).exists():
                 save_len_file(_A    ,   _A       )
         else:
             snake_case								= "examples/seq2seq/test_data/wmt_en_ro"
             snake_case								= max_len * 4
             save_len_file(_A    ,   _A       )

         snake_case								= AutoTokenizer.from_pretrained(_A       )
         snake_case								= SeqaSeqDataset(
             _A    ,   data_dir=_A    ,   type_path="train"    ,   max_source_length=_A    ,   max_target_length=_A    ,   n_obs=_A    ,   )
         return ds, max_tokens, tokenizer





     def 		UpperCAmelCase(self				:   List[Any]       )						->						Union[str, Any]:
         snake_case					,							snake_case					,							snake_case								= self._get_dataset()
         snake_case								= set(DistributedSortishSampler(_A    ,   2_5_6    ,   num_replicas=2    ,   rank=0    ,   add_extra_examples=_A       )       )
         snake_case								= set(DistributedSortishSampler(_A    ,   2_5_6    ,   num_replicas=2    ,   rank=1    ,   add_extra_examples=_A       )       )
         assert idsa.intersection(_A       ) == set()





     @parameterized.expand(
         [
             MBART_TINY,
             MARIAN_TINY,
             T5_TINY,
             BART_TINY,
             PEGASUS_XSUM,
         ]    ,   )
     def 		UpperCAmelCase(self				:   Any    ,   _A				:   Optional[Any]       )						->						Union[str, Any]:
         snake_case								= AutoTokenizer.from_pretrained(_A    ,   use_fast=_A       )
         if tok_name == MBART_TINY:
             snake_case								= SeqaSeqDataset(
                 _A    ,   data_dir=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()       )    ,   type_path="train"    ,   max_source_length=4    ,   max_target_length=8    ,   src_lang="EN"    ,   tgt_lang="FR"    ,   )
             snake_case								= train_dataset.dataset_kwargs
             assert "src_lang" in kwargs and "tgt_lang" in kwargs
         else:
             snake_case								= SeqaSeqDataset(
                 _A    ,   data_dir=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()       )    ,   type_path="train"    ,   max_source_length=4    ,   max_target_length=8    ,   )
             snake_case								= train_dataset.dataset_kwargs
             assert "add_prefix_space" not in kwargs if tok_name != BART_TINY else "add_prefix_space" in kwargs
             assert len(_A       ) == 1 if tok_name == BART_TINY else len(_A       ) == 0

