


import json
import sys
import tempfile
import unittest
from pathlib import Path

import transformers
from transformers import (
    CONFIG_MAPPING,
    FEATURE_EXTRACTOR_MAPPING,
    AutoConfig,
    AutoFeatureExtractor,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
)
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, get_tests_dir


sys.path.append(str(Path(__file__).parent.parent.parent.parent / """utils"""))

from test_module.custom_configuration import CustomConfig  # noqa E402
from test_module.custom_feature_extraction import CustomFeatureExtractor  # noqa E402


UpperCAmelCase      =   get_tests_dir("""fixtures""")
UpperCAmelCase      =   get_tests_dir("""fixtures/dummy_feature_extractor_config.json""")
UpperCAmelCase      =   get_tests_dir("""fixtures/dummy-config.json""")







class lowerCAmelCase_						(			unittest.TestCase	):

 '''simple docstring'''

 def 						UpperCamelCase__					(     self ):
       snake_case_	=				0



 def 						UpperCamelCase__					(     self ):
       snake_case_	=				AutoFeatureExtractor.from_pretrained('''facebook/wav2vec2-base-960h''' )
       self.assertIsInstance(_UpperCAmelCase		,				_UpperCAmelCase )



 def 						UpperCamelCase__					(     self ):
       snake_case_	=				AutoFeatureExtractor.from_pretrained(_UpperCAmelCase )
       self.assertIsInstance(_UpperCAmelCase		,				_UpperCAmelCase )



 def 						UpperCamelCase__					(     self ):
       with tempfile.TemporaryDirectory() as tmpdirname:
             snake_case_	=				WavaVecaConfig()

             # remove feature_extractor_type to make sure config.json alone is enough to load feature processor locally
             snake_case_	=				AutoFeatureExtractor.from_pretrained(_UpperCAmelCase ).to_dict()

             config_dict.pop('''feature_extractor_type''' )
             snake_case_	=				WavaVecaFeatureExtractor(**_UpperCAmelCase )

             # save in new folder
             model_config.save_pretrained(_UpperCAmelCase )
             config.save_pretrained(_UpperCAmelCase )

             snake_case_	=				AutoFeatureExtractor.from_pretrained(_UpperCAmelCase )

             # make sure private variable is not incorrectly saved
             snake_case_	=				json.loads(config.to_json_string() )
             self.assertTrue('''_processor_class''' not in dict_as_saved )

       self.assertIsInstance(_UpperCAmelCase		,				_UpperCAmelCase )



 def 						UpperCamelCase__					(     self ):
       snake_case_	=				AutoFeatureExtractor.from_pretrained(_UpperCAmelCase )
       self.assertIsInstance(_UpperCAmelCase		,				_UpperCAmelCase )



 def 						UpperCamelCase__					(     self ):
       with self.assertRaisesRegex(
           _UpperCAmelCase		,				'''bert-base is not a local folder and is not a valid model identifier''' ):
             snake_case_	=				AutoFeatureExtractor.from_pretrained('''bert-base''' )



 def 						UpperCamelCase__					(     self ):
       with self.assertRaisesRegex(
           _UpperCAmelCase		,				R'''aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)''' ):
             snake_case_	=				AutoFeatureExtractor.from_pretrained(_UpperCAmelCase		,				revision='''aaaaaa''' )



 def 						UpperCamelCase__					(     self ):
       with self.assertRaisesRegex(
           _UpperCAmelCase		,				'''hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json.'''		,				):
             snake_case_	=				AutoFeatureExtractor.from_pretrained('''hf-internal-testing/config-no-model''' )



 def 						UpperCamelCase__					(     self ):
       # If remote code is not set, we will time out when asking whether to load the model.
       with self.assertRaises(_UpperCAmelCase ):
             snake_case_	=				AutoFeatureExtractor.from_pretrained(
                 '''hf-internal-testing/test_dynamic_feature_extractor''' )
       # If remote code is disabled, we can't load this config.
       with self.assertRaises(_UpperCAmelCase ):
             snake_case_	=				AutoFeatureExtractor.from_pretrained(
                 '''hf-internal-testing/test_dynamic_feature_extractor'''		,				trust_remote_code=_UpperCAmelCase )

       snake_case_	=				AutoFeatureExtractor.from_pretrained(
           '''hf-internal-testing/test_dynamic_feature_extractor'''		,				trust_remote_code=_UpperCAmelCase )
       self.assertEqual(feature_extractor.__class__.__name__		,				'''NewFeatureExtractor''' )

       # Test feature extractor can be reloaded.
       with tempfile.TemporaryDirectory() as tmp_dir:
             feature_extractor.save_pretrained(_UpperCAmelCase )
             snake_case_	=				AutoFeatureExtractor.from_pretrained(_UpperCAmelCase		,				trust_remote_code=_UpperCAmelCase )
       self.assertEqual(reloaded_feature_extractor.__class__.__name__		,				'''NewFeatureExtractor''' )



 def 						UpperCamelCase__					(     self ):
       try:
             AutoConfig.register('''custom'''		,				_UpperCAmelCase )
             AutoFeatureExtractor.register(_UpperCAmelCase		,				_UpperCAmelCase )
             # Trying to register something existing in the Transformers library will raise an error
             with self.assertRaises(_UpperCAmelCase ):
                   AutoFeatureExtractor.register(_UpperCAmelCase		,				_UpperCAmelCase )

             # Now that the config is registered, it can be used as any other config with the auto-API
             snake_case_	=				CustomFeatureExtractor.from_pretrained(_UpperCAmelCase )
             with tempfile.TemporaryDirectory() as tmp_dir:
                   feature_extractor.save_pretrained(_UpperCAmelCase )
                   snake_case_	=				AutoFeatureExtractor.from_pretrained(_UpperCAmelCase )
                   self.assertIsInstance(_UpperCAmelCase		,				_UpperCAmelCase )

       finally:
             if "custom" in CONFIG_MAPPING._extra_content:
                   del CONFIG_MAPPING._extra_content["custom"]
             if CustomConfig in FEATURE_EXTRACTOR_MAPPING._extra_content:
                   del FEATURE_EXTRACTOR_MAPPING._extra_content[CustomConfig]





 def 						UpperCamelCase__					(     self ):







       class lowerCAmelCase_						(			lowerCamelCase__	):

        '''simple docstring'''

        __snake_case												=  True

       try:
             AutoConfig.register('''custom'''		,				_UpperCAmelCase )
             AutoFeatureExtractor.register(_UpperCAmelCase		,				_UpperCAmelCase )
             # If remote code is not set, the default is to use local
             snake_case_	=				AutoFeatureExtractor.from_pretrained(
                 '''hf-internal-testing/test_dynamic_feature_extractor''' )
             self.assertEqual(feature_extractor.__class__.__name__		,				'''NewFeatureExtractor''' )
             self.assertTrue(feature_extractor.is_local )

             # If remote code is disabled, we load the local one.
             snake_case_	=				AutoFeatureExtractor.from_pretrained(
                 '''hf-internal-testing/test_dynamic_feature_extractor'''		,				trust_remote_code=_UpperCAmelCase )
             self.assertEqual(feature_extractor.__class__.__name__		,				'''NewFeatureExtractor''' )
             self.assertTrue(feature_extractor.is_local )

             # If remote is enabled, we load from the Hub
             snake_case_	=				AutoFeatureExtractor.from_pretrained(
                 '''hf-internal-testing/test_dynamic_feature_extractor'''		,				trust_remote_code=_UpperCAmelCase )
             self.assertEqual(feature_extractor.__class__.__name__		,				'''NewFeatureExtractor''' )
             self.assertTrue(not hasattr(_UpperCAmelCase		,				'''is_local''' ) )

       finally:
             if "custom" in CONFIG_MAPPING._extra_content:
                   del CONFIG_MAPPING._extra_content["custom"]
             if CustomConfig in FEATURE_EXTRACTOR_MAPPING._extra_content:
                   del FEATURE_EXTRACTOR_MAPPING._extra_content[CustomConfig]



import unittest

from transformers import PegasusTokenizer, PegasusTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


UpperCAmelCase      =   get_tests_dir("""fixtures/test_sentencepiece_no_bos.model""")







@require_sentencepiece
@require_tokenizers
class lowerCAmelCase_						(			lowerCamelCase__ ,						unittest.TestCase	):

 '''simple docstring'''

 __snake_case												=  PegasusTokenizer
 __snake_case												=  PegasusTokenizerFast
 __snake_case												=  True
 __snake_case												=  True



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

       # We have a SentencePiece fixture for testing
       snake_case_	=				PegasusTokenizer(_UpperCAmelCase )
       tokenizer.save_pretrained(self.tmpdirname )



 @cached_property
 def 						UpperCamelCase__					(     self ):
       return PegasusTokenizer.from_pretrained('''google/pegasus-large''' )



 def 						UpperCamelCase__					(     self		,				**_UpperCAmelCase ):
       return PegasusTokenizer.from_pretrained(self.tmpdirname		,				**_UpperCAmelCase )



 def 						UpperCamelCase__					(     self		,				_UpperCAmelCase ):
       return ("This is a test", "This is a test")



 def 						UpperCamelCase__					(     self ):
       snake_case_	=				'''</s>'''
       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 ):
       snake_case_	=				list(self.get_tokenizer().get_vocab().keys() )

       self.assertEqual(vocab_keys[0]		,				'''<pad>''' )
       self.assertEqual(vocab_keys[1]		,				'''</s>''' )
       self.assertEqual(vocab_keys[-1]		,				'''v''' )
       self.assertEqual(len(_UpperCAmelCase )		,				11_03 )



 def 						UpperCamelCase__					(     self ):
       self.assertEqual(self.get_tokenizer().vocab_size		,				11_03 )



 def 						UpperCamelCase__					(     self ):
       snake_case_	=				self.rust_tokenizer_class.from_pretrained(self.tmpdirname )
       snake_case_	=				self.tokenizer_class.from_pretrained(self.tmpdirname )
       snake_case_	=				(
           '''Let\'s see which <unk> is the better <unk_token_11> one <mask_1> It seems like this <mask_2> was important'''
           ''' </s> <pad> <pad> <pad>'''
       )
       snake_case_	=				rust_tokenizer([raw_input_str]		,				return_tensors=_UpperCAmelCase		,				add_special_tokens=_UpperCAmelCase ).input_ids[0]
       snake_case_	=				py_tokenizer([raw_input_str]		,				return_tensors=_UpperCAmelCase		,				add_special_tokens=_UpperCAmelCase ).input_ids[0]
       self.assertListEqual(_UpperCAmelCase		,				_UpperCAmelCase )



 def 						UpperCamelCase__					(     self ):
       snake_case_	=				self._large_tokenizer
       # <mask_1> masks whole sentence while <mask_2> masks single word
       snake_case_	=				'''<mask_1> To ensure a <mask_2> flow of bank resolutions.'''
       snake_case_	=				[2, 4_13, 6_15, 1_14, 3, 19_71, 1_13, 16_79, 1_07_10, 1_07, 1]
       snake_case_	=				tokenizer([raw_input_str]		,				return_tensors=_UpperCAmelCase ).input_ids[0]
       self.assertListEqual(_UpperCAmelCase		,				_UpperCAmelCase )



 def 						UpperCamelCase__					(     self ):
       snake_case_	=				self._large_tokenizer
       # The tracebacks for the following asserts are **better** without messages or self.assertEqual
       assert tokenizer.vocab_size == 9_61_03
       assert tokenizer.pad_token_id == 0
       assert tokenizer.eos_token_id == 1
       assert tokenizer.offset == 1_03
       assert tokenizer.unk_token_id == tokenizer.offset + 2 == 1_05
       assert tokenizer.unk_token == "<unk>"
       assert tokenizer.model_max_length == 10_24
       snake_case_	=				'''To ensure a smooth flow of bank resolutions.'''
       snake_case_	=				[4_13, 6_15, 1_14, 22_91, 19_71, 1_13, 16_79, 1_07_10, 1_07, 1]
       snake_case_	=				tokenizer([raw_input_str]		,				return_tensors=_UpperCAmelCase ).input_ids[0]
       self.assertListEqual(_UpperCAmelCase		,				_UpperCAmelCase )
       assert tokenizer.convert_ids_to_tokens([0, 1, 2, 3] ) == ["<pad>", "</s>", "<mask_1>", "<mask_2>"]



 @require_torch
 def 						UpperCamelCase__					(     self ):
       snake_case_	=				['''This is going to be way too long.''' * 1_50, '''short example''']
       snake_case_	=				['''not super long but more than 5 tokens''', '''tiny''']
       snake_case_	=				self._large_tokenizer(_UpperCAmelCase		,				padding=_UpperCAmelCase		,				truncation=_UpperCAmelCase		,				return_tensors='''pt''' )
       snake_case_	=				self._large_tokenizer(
           text_target=_UpperCAmelCase		,				max_length=5		,				padding=_UpperCAmelCase		,				truncation=_UpperCAmelCase		,				return_tensors='''pt''' )

       assert batch.input_ids.shape == (2, 10_24)
       assert batch.attention_mask.shape == (2, 10_24)
       assert targets["input_ids"].shape == (2, 5)
       assert len(_UpperCAmelCase ) == 2  # input_ids, attention_mask.





 @slow
 def 						UpperCamelCase__					(     self ):
       # fmt: off
       snake_case_	=				{'''input_ids''': [[3_89_79, 1_43, 1_84_85, 6_06, 1_30, 2_66_69, 8_76_86, 1_21, 5_41_89, 11_29, 1_11, 2_66_69, 8_76_86, 1_21, 91_14, 1_47_87, 1_21, 1_32_49, 1_58, 5_92, 9_56, 1_21, 1_46_21, 3_15_76, 1_43, 6_26_13, 1_08, 96_88, 9_30, 4_34_30, 1_15_62, 6_26_13, 3_04, 1_08, 1_14_43, 8_97, 1_08, 93_14, 1_74_15, 6_33_99, 1_08, 1_14_43, 76_14, 1_83_16, 1_18, 42_84, 71_48, 1_24_30, 1_43, 14_00, 2_57_03, 1_58, 1_11, 42_84, 71_48, 1_17_72, 1_43, 2_12_97, 10_64, 1_58, 1_22, 2_04, 35_06, 17_54, 11_33, 1_47_87, 15_81, 1_15, 3_32_24, 44_82, 1_11, 13_55, 1_10, 2_91_73, 3_17, 5_08_33, 1_08, 2_01_47, 9_46_65, 1_11, 7_71_98, 1_07, 1], [1_10, 6_26_13, 1_17, 6_38, 1_12, 11_33, 1_21, 2_00_98, 13_55, 7_90_50, 1_38_72, 1_35, 15_96, 5_35_41, 13_52, 1_41, 1_30_39, 55_42, 1_24, 3_02, 5_18, 1_11, 2_68, 29_56, 1_15, 1_49, 44_27, 1_07, 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], [1_39, 12_35, 27_99, 1_82_89, 1_77_80, 2_04, 1_09, 94_74, 12_96, 1_07, 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]], '''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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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=_UpperCAmelCase		,				model_name='''google/bigbird-pegasus-large-arxiv'''		,				revision='''ba85d0851d708441f91440d509690f1ab6353415'''		,				)







@require_sentencepiece
@require_tokenizers
class lowerCAmelCase_						(			lowerCamelCase__ ,						unittest.TestCase	):

 '''simple docstring'''

 __snake_case												=  PegasusTokenizer
 __snake_case												=  PegasusTokenizerFast
 __snake_case												=  True
 __snake_case												=  True



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

       # We have a SentencePiece fixture for testing
       snake_case_	=				PegasusTokenizer(_UpperCAmelCase		,				offset=0		,				mask_token_sent=_UpperCAmelCase		,				mask_token='''[MASK]''' )
       tokenizer.save_pretrained(self.tmpdirname )



 @cached_property
 def 						UpperCamelCase__					(     self ):
       return PegasusTokenizer.from_pretrained('''google/bigbird-pegasus-large-arxiv''' )



 def 						UpperCamelCase__					(     self		,				**_UpperCAmelCase ):
       return PegasusTokenizer.from_pretrained(self.tmpdirname		,				**_UpperCAmelCase )



 def 						UpperCamelCase__					(     self		,				_UpperCAmelCase ):
       return ("This is a test", "This is a test")



 def 						UpperCamelCase__					(     self ):
       snake_case_	=				self.rust_tokenizer_class.from_pretrained(self.tmpdirname )
       snake_case_	=				self.tokenizer_class.from_pretrained(self.tmpdirname )
       snake_case_	=				(
           '''Let\'s see which <unk> is the better <unk_token> one [MASK] It seems like this [MASK] was important </s>'''
           ''' <pad> <pad> <pad>'''
       )
       snake_case_	=				rust_tokenizer([raw_input_str]		,				return_tensors=_UpperCAmelCase		,				add_special_tokens=_UpperCAmelCase ).input_ids[0]
       snake_case_	=				py_tokenizer([raw_input_str]		,				return_tensors=_UpperCAmelCase		,				add_special_tokens=_UpperCAmelCase ).input_ids[0]
       self.assertListEqual(_UpperCAmelCase		,				_UpperCAmelCase )



 @require_torch
 def 						UpperCamelCase__					(     self ):
       snake_case_	=				['''This is going to be way too long.''' * 10_00, '''short example''']
       snake_case_	=				['''not super long but more than 5 tokens''', '''tiny''']
       snake_case_	=				self._large_tokenizer(_UpperCAmelCase		,				padding=_UpperCAmelCase		,				truncation=_UpperCAmelCase		,				return_tensors='''pt''' )
       snake_case_	=				self._large_tokenizer(
           text_target=_UpperCAmelCase		,				max_length=5		,				padding=_UpperCAmelCase		,				truncation=_UpperCAmelCase		,				return_tensors='''pt''' )

       assert batch.input_ids.shape == (2, 40_96)
       assert batch.attention_mask.shape == (2, 40_96)
       assert targets["input_ids"].shape == (2, 5)
       assert len(_UpperCAmelCase ) == 2  # input_ids, attention_mask.





 def 						UpperCamelCase__					(     self ):
       snake_case_	=				(
           '''This is an example string that is used to test the original TF implementation against the HF'''
           ''' implementation'''
       )

       snake_case_	=				self._large_tokenizer(_UpperCAmelCase ).input_ids

       self.assertListEqual(
           _UpperCAmelCase		,				[1_82, 1_17, 1_42, 5_87, 42_11, 1_20, 1_17, 2_63, 1_12, 8_04, 1_09, 8_56, 2_50_16, 31_37, 4_64, 1_09, 2_69_55, 31_37, 1]		,				)