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():
     snake_case__				= get_tests_dir('fixtures/test_sentencepiece.model')


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

snake_case__				= 12_8022
snake_case__				= 12_8028



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

     '''simple docstring'''

     A_										=  MaMaaaTokenizer
     A_										=  False
     A_										=  False
     A_										=  True



     def 		UpperCamelCase_						(					self						) ->				str:






           """simple docstring"""
           super().setUp()

           _lowerCamelCase			    =				['''</s>''', '''<unk>''', '''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''', '''\u0120''', '''<pad>''']
           _lowerCamelCase			    =				dict(zip(A_				,				range(len(A_						)						)						)						)
           _lowerCamelCase			    =				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			    =				MaMaaaTokenizer.from_pretrained(self.tmpdirname						)
           tokenizer.save_pretrained(self.tmpdirname						)



     def 		UpperCamelCase_						(					self				,				**A_						) ->				Union[str, Any]:






           """simple docstring"""
           return MaMaaaTokenizer.from_pretrained(self.tmpdirname				,				**A_						)



     def 		UpperCamelCase_						(					self				,				A_						) ->				List[str]:






           """simple docstring"""
           return (
               "This is a test",
               "This is a test",
           )



     def 		UpperCamelCase_						(					self						) ->				Dict:






           """simple docstring"""
           _lowerCamelCase			    =				'''</s>'''
           _lowerCamelCase			    =				0

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



     def 		UpperCamelCase_						(					self						) ->				Tuple:






           """simple docstring"""
           _lowerCamelCase			    =				self.get_tokenizer()
           _lowerCamelCase			    =				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 		UpperCamelCase_						(					self						) ->				Optional[Any]:






           """simple docstring"""
           pass



     def 		UpperCamelCase_						(					self						) ->				Dict:






           """simple docstring"""
           _lowerCamelCase			    =				self.get_tokenizer()

           _lowerCamelCase			    =				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			    =				tokenizer.convert_ids_to_tokens([2, 3, 4, 5, 6]						)
           self.assertListEqual(A_				,				['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''']						)

           _lowerCamelCase			    =				tokenizer.convert_tokens_to_string(A_						)
           self.assertEqual(A_				,				'''This is a test'''						)





     @slow
     def 		UpperCamelCase_						(					self						) ->				Tuple:






           """simple docstring"""
           # fmt: off
           _lowerCamelCase			    =				{'''input_ids''': [[12_80_22, 11_01_08, 3_97, 11, 3_82_72, 22_47, 12_48_11, 2_85, 1_81_05, 15_86, 2_07, 7, 3_95_34, 44_28, 3_97, 10_19, 1_81_05, 15_86, 2_07, 7, 4_13_37, 1_67_86, 2_41, 7, 2_02_14, 17, 12_56_90, 1_03_98, 7, 4_43_78, 5_80_69, 6_83_42, 77_98, 73_43, 11, 2_99, 3_33_10, 4, 1_58, 3_73_50, 9_40_77, 45_69, 2_99, 3_33_10, 90, 4, 5_28_40, 2_90, 4, 3_12_70, 1_12, 2_99, 6_82, 4, 5_28_40, 3_99_53, 1_40_79, 1_93, 5_25_19, 9_08_94, 1_78_94, 12_06_97, 11, 4_04_45, 5_51, 17, 10_19, 5_25_19, 9_08_94, 1_77_56, 9_63, 11, 4_04_45, 4_80, 17, 97_92, 11_20, 51_73, 13_93, 62_40, 1_67_86, 2_41, 12_09_96, 28, 12_45, 13_93, 11_82_40, 1_11_23, 10_19, 9_36_12, 26_91, 1_06_18, 9_80_58, 12_04_09, 19_28, 2_79, 4, 4_06_83, 3_67, 1_78, 2_07, 10_19, 1_03, 10_31_21, 5_06, 6_52_96, 5, 2], [12_80_22, 2_12_17, 3_67, 1_17, 12_54_50, 1_28, 7_19, 7, 73_08, 40, 9_36_12, 1_26_69, 11_16, 1_67_04, 71, 1_77_85, 36_99, 1_55_92, 35, 1_44, 95_84, 2_41, 1_19_43, 7_13, 9_50, 7_99, 22_47, 8_84_27, 1_50, 1_49, 11_88_13, 12_07_06, 10_19, 10_69_06, 8_15_18, 28, 12_24, 2_27_99, 3_97, 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], [12_80_22, 16_58, 12_33_11, 51_55, 55_78, 47_22, 2_79, 1_49_47, 23_66, 11_20, 11_97, 14, 13_48, 92_32, 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  UpperCamelCase				( unittest.TestCase ):

     '''simple docstring'''

     A_										=  'facebook/m2m100_418M'
     A_										=  [
         'In my opinion, there are two levels of response from the French government.',
         'NSA Affair Emphasizes Complete Lack of Debate on Intelligence',
     ]
     A_										=  [
         '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
     A_										=  [EN_CODE, 593, 1_949, 115_781, 4, 71_586, 4_234, 60_633, 126_233, 432, 123_808, 15_592, 1_197, 117_132, 120_618, 5, 2]



     @classmethod
     def 		UpperCamelCase_						(					cls						) ->				str:






           """simple docstring"""
           _lowerCamelCase			    =				MaMaaaTokenizer.from_pretrained(
               cls.checkpoint_name				,				src_lang='''en'''				,				tgt_lang='''fr'''						)
           _lowerCamelCase			    =				1
           return cls



     def 		UpperCamelCase_						(					self						) ->				int:






           """simple docstring"""
           self.assertEqual(self.tokenizer.get_lang_id('''ar'''						)				,				12_80_06						)
           self.assertEqual(self.tokenizer.get_lang_id('''en'''						)				,				12_80_22						)
           self.assertEqual(self.tokenizer.get_lang_id('''ro'''						)				,				12_80_76						)
           self.assertEqual(self.tokenizer.get_lang_id('''mr'''						)				,				12_80_63						)



     def 		UpperCamelCase_						(					self						) ->				int:






           """simple docstring"""
           _lowerCamelCase			    =				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 		UpperCamelCase_						(					self						) ->				Any:






           """simple docstring"""
           _lowerCamelCase			    =				'''en'''
           _lowerCamelCase			    =				self.tokenizer.batch_encode_plus(self.src_text						).input_ids[0]
           self.assertListEqual(self.expected_src_tokens				,				A_						)



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






           """simple docstring"""
           self.assertIn(A_				,				self.tokenizer.all_special_ids						)
           # fmt: off
           _lowerCamelCase			    =				[FR_CODE, 53_64, 82, 86_42, 4, 2_94, 47, 8, 1_40_28, 1_36, 32_86, 97_06, 6, 9_07_97, 6, 14_40_12, 1_62, 8_81_28, 3_00_61, 5, 2]
           # fmt: on
           _lowerCamelCase			    =				self.tokenizer.decode(A_				,				skip_special_tokens=A_						)
           _lowerCamelCase			    =				self.tokenizer.decode(generated_ids[1:]				,				skip_special_tokens=A_						)
           self.assertEqual(A_				,				A_						)
           self.assertNotIn(self.tokenizer.eos_token				,				A_						)



     def 		UpperCamelCase_						(					self						) ->				List[Any]:






           """simple docstring"""
           _lowerCamelCase			    =				tempfile.mkdtemp()
           _lowerCamelCase			    =				self.tokenizer.lang_token_to_id
           self.tokenizer.save_pretrained(A_						)
           _lowerCamelCase			    =				MaMaaaTokenizer.from_pretrained(A_						)
           self.assertDictEqual(new_tok.lang_token_to_id				,				A_						)



     @require_torch
     def 		UpperCamelCase_						(					self						) ->				Union[str, Any]:






           """simple docstring"""
           _lowerCamelCase			    =				'''en'''
           _lowerCamelCase			    =				'''fr'''

           _lowerCamelCase			    =				self.tokenizer(self.src_text				,				text_target=self.tgt_text				,				padding=A_				,				return_tensors='''pt'''						)

           _lowerCamelCase			    =				shift_tokens_right(
               batch['''labels''']				,				self.tokenizer.pad_token_id				,				self.tokenizer.eos_token_id						)

           for k in batch:
                 _lowerCamelCase			    =				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 		UpperCamelCase_						(					self						) ->				List[Any]:






           """simple docstring"""
           _lowerCamelCase			    =				'''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			    =				'''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 		UpperCamelCase_						(					self						) ->				Union[str, Any]:






           """simple docstring"""
           _lowerCamelCase			    =				'''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			    =				'''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 		UpperCamelCase_						(					self						) ->				List[Any]:






           """simple docstring"""
           _lowerCamelCase			    =				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''': [[12_80_22, 58, 41_83, 2]],
                   '''attention_mask''': [[1, 1, 1, 1]],
                   # ar_AR
                   '''forced_bos_token_id''': 12_80_06,
               }				,				)
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


snake_case__				= {
    'configuration_whisper': ['WHISPER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'WhisperConfig', 'WhisperOnnxConfig'],
    'feature_extraction_whisper': ['WhisperFeatureExtractor'],
    'processing_whisper': ['WhisperProcessor'],
    'tokenization_whisper': ['WhisperTokenizer'],
}

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     snake_case__				= ['WhisperTokenizerFast']

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     snake_case__				= [
         'WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST',
         'WhisperForConditionalGeneration',
         'WhisperModel',
         'WhisperPreTrainedModel',
         'WhisperForAudioClassification',
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     snake_case__				= [
         'TF_WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST',
         'TFWhisperForConditionalGeneration',
         'TFWhisperModel',
         'TFWhisperPreTrainedModel',
     ]

try:
     if not is_flax_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     snake_case__				= [
         'FlaxWhisperForConditionalGeneration',
         'FlaxWhisperModel',
         'FlaxWhisperPreTrainedModel',
         'FlaxWhisperForAudioClassification',
     ]


if TYPE_CHECKING:
     from .configuration_whisper import WHISPER_PRETRAINED_CONFIG_ARCHIVE_MAP, WhisperConfig, WhisperOnnxConfig
     from .feature_extraction_whisper import WhisperFeatureExtractor
     from .processing_whisper import WhisperProcessor
     from .tokenization_whisper import WhisperTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_whisper_fast import WhisperTokenizerFast

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_whisper import (
              WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST,
              WhisperForAudioClassification,
              WhisperForConditionalGeneration,
              WhisperModel,
              WhisperPreTrainedModel,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_whisper import (
              TF_WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFWhisperForConditionalGeneration,
              TFWhisperModel,
              TFWhisperPreTrainedModel,
          )

     try:
          if not is_flax_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_flax_whisper import (
              FlaxWhisperForAudioClassification,
              FlaxWhisperForConditionalGeneration,
              FlaxWhisperModel,
              FlaxWhisperPreTrainedModel,
          )

else:
     import sys

     snake_case__				= _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)