





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


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

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



     '''simple docstring'''

     __snake_case					:			str				=    PegasusTokenizer
     __snake_case					:			int				=    PegasusTokenizerFast
     __snake_case					:			int				=    True
     __snake_case					:			int				=    True






     def        SCREAMING_SNAKE_CASE__						(							self		:					str )  ->	List[Any]:




           '''simple docstring'''


           super().setUp()

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






     @cached_property
     def        SCREAMING_SNAKE_CASE__						(							self		:					Optional[Any] )  ->	Optional[Any]:




           '''simple docstring'''


           return PegasusTokenizer.from_pretrained("""google/pegasus-large""" )






     def        SCREAMING_SNAKE_CASE__						(							self		:					Any		,**lowerCamelCase__		:					Tuple )  ->	PegasusTokenizer:




           '''simple docstring'''


           return PegasusTokenizer.from_pretrained(self.tmpdirname		,**lowerCamelCase__ )






     def        SCREAMING_SNAKE_CASE__						(							self		:					Optional[int]		,lowerCamelCase__		:					Optional[int] )  ->	int:




           '''simple docstring'''


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






     def        SCREAMING_SNAKE_CASE__						(							self		:					Optional[Any] )  ->	str:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		"""</s>"""
           SCREAMING_SNAKE_CASE	=		1

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






     def        SCREAMING_SNAKE_CASE__						(							self		:					Dict )  ->	Dict:




           '''simple docstring'''


           SCREAMING_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(lowerCamelCase__ )		,1103 )






     def        SCREAMING_SNAKE_CASE__						(							self		:					Tuple )  ->	Dict:




           '''simple docstring'''


           self.assertEqual(self.get_tokenizer().vocab_size		,1103 )






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




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		self.rust_tokenizer_class.from_pretrained(self.tmpdirname )
           SCREAMING_SNAKE_CASE	=		self.tokenizer_class.from_pretrained(self.tmpdirname )
           SCREAMING_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>"""
           )
           SCREAMING_SNAKE_CASE	=		rust_tokenizer([raw_input_str]		,return_tensors=lowerCamelCase__		,add_special_tokens=lowerCamelCase__ ).input_ids[0]
           SCREAMING_SNAKE_CASE	=		py_tokenizer([raw_input_str]		,return_tensors=lowerCamelCase__		,add_special_tokens=lowerCamelCase__ ).input_ids[0]
           self.assertListEqual(lowerCamelCase__		,lowerCamelCase__ )






     def        SCREAMING_SNAKE_CASE__						(							self		:					Optional[Any] )  ->	List[Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		self._large_tokenizer
           # <mask_1> masks whole sentence while <mask_2> masks single word
           SCREAMING_SNAKE_CASE	=		"""<mask_1> To ensure a <mask_2> flow of bank resolutions."""
           SCREAMING_SNAKE_CASE	=		[2, 413, 615, 114, 3, 1971, 113, 1679, 10710, 107, 1]
           SCREAMING_SNAKE_CASE	=		tokenizer([raw_input_str]		,return_tensors=lowerCamelCase__ ).input_ids[0]
           self.assertListEqual(lowerCamelCase__		,lowerCamelCase__ )






     def        SCREAMING_SNAKE_CASE__						(							self		:					Any )  ->	str:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		self._large_tokenizer
           # The tracebacks for the following asserts are **better** without messages or self.assertEqual
           assert tokenizer.vocab_size == 96103
           assert tokenizer.pad_token_id == 0
           assert tokenizer.eos_token_id == 1
           assert tokenizer.offset == 103
           assert tokenizer.unk_token_id == tokenizer.offset + 2 == 105
           assert tokenizer.unk_token == "<unk>"
           assert tokenizer.model_max_length == 1024
           SCREAMING_SNAKE_CASE	=		"""To ensure a smooth flow of bank resolutions."""
           SCREAMING_SNAKE_CASE	=		[413, 615, 114, 2291, 1971, 113, 1679, 10710, 107, 1]
           SCREAMING_SNAKE_CASE	=		tokenizer([raw_input_str]		,return_tensors=lowerCamelCase__ ).input_ids[0]
           self.assertListEqual(lowerCamelCase__		,lowerCamelCase__ )
           assert tokenizer.convert_ids_to_tokens([0, 1, 2, 3] ) == ["<pad>", "</s>", "<mask_1>", "<mask_2>"]






     @require_torch
     def        SCREAMING_SNAKE_CASE__						(							self		:					str )  ->	Any:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		["""This is going to be way too long.""" * 150, """short example"""]
           SCREAMING_SNAKE_CASE	=		["""not super long but more than 5 tokens""", """tiny"""]
           SCREAMING_SNAKE_CASE	=		self._large_tokenizer(lowerCamelCase__		,padding=lowerCamelCase__		,truncation=lowerCamelCase__		,return_tensors="""pt""" )
           SCREAMING_SNAKE_CASE	=		self._large_tokenizer(
               text_target=lowerCamelCase__		,max_length=5		,padding=lowerCamelCase__		,truncation=lowerCamelCase__		,return_tensors="""pt""" )

           assert batch.input_ids.shape == (2, 1024)
           assert batch.attention_mask.shape == (2, 1024)
           assert targets["input_ids"].shape == (2, 5)
           assert len(lowerCamelCase__ ) == 2  # input_ids, attention_mask.






     @slow
     def        SCREAMING_SNAKE_CASE__						(							self		:					Tuple )  ->	List[str]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		{"""input_ids""": [[38979, 143, 18485, 606, 130, 26669, 87686, 121, 54189, 1129, 111, 26669, 87686, 121, 9114, 14787, 121, 13249, 158, 592, 956, 121, 14621, 31576, 143, 62613, 108, 9688, 930, 43430, 11562, 62613, 304, 108, 11443, 897, 108, 9314, 17415, 63399, 108, 11443, 7614, 18316, 118, 4284, 7148, 12430, 143, 1400, 25703, 158, 111, 4284, 7148, 11772, 143, 21297, 1064, 158, 122, 204, 3506, 1754, 1133, 14787, 1581, 115, 33224, 4482, 111, 1355, 110, 29173, 317, 50833, 108, 20147, 94665, 111, 77198, 107, 1], [110, 62613, 117, 638, 112, 1133, 121, 20098, 1355, 79050, 13872, 135, 1596, 53541, 1352, 141, 13039, 5542, 124, 302, 518, 111, 268, 2956, 115, 149, 4427, 107, 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], [139, 1235, 2799, 18289, 17780, 204, 109, 9474, 1296, 107, 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=lowerCamelCase__		,model_name="""google/bigbird-pegasus-large-arxiv"""		,revision="""ba85d0851d708441f91440d509690f1ab6353415"""		,)

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



     '''simple docstring'''

     __snake_case					:			Tuple				=    PegasusTokenizer
     __snake_case					:			Optional[Any]				=    PegasusTokenizerFast
     __snake_case					:			Union[str, Any]				=    True
     __snake_case					:			Dict				=    True






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




           '''simple docstring'''


           super().setUp()

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






     @cached_property
     def        SCREAMING_SNAKE_CASE__						(							self		:					Dict )  ->	Dict:




           '''simple docstring'''


           return PegasusTokenizer.from_pretrained("""google/bigbird-pegasus-large-arxiv""" )






     def        SCREAMING_SNAKE_CASE__						(							self		:					List[str]		,**lowerCamelCase__		:					str )  ->	PegasusTokenizer:




           '''simple docstring'''


           return PegasusTokenizer.from_pretrained(self.tmpdirname		,**lowerCamelCase__ )






     def        SCREAMING_SNAKE_CASE__						(							self		:					Optional[int]		,lowerCamelCase__		:					str )  ->	Optional[int]:




           '''simple docstring'''


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






     def        SCREAMING_SNAKE_CASE__						(							self		:					int )  ->	Dict:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		self.rust_tokenizer_class.from_pretrained(self.tmpdirname )
           SCREAMING_SNAKE_CASE	=		self.tokenizer_class.from_pretrained(self.tmpdirname )
           SCREAMING_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>"""
           )
           SCREAMING_SNAKE_CASE	=		rust_tokenizer([raw_input_str]		,return_tensors=lowerCamelCase__		,add_special_tokens=lowerCamelCase__ ).input_ids[0]
           SCREAMING_SNAKE_CASE	=		py_tokenizer([raw_input_str]		,return_tensors=lowerCamelCase__		,add_special_tokens=lowerCamelCase__ ).input_ids[0]
           self.assertListEqual(lowerCamelCase__		,lowerCamelCase__ )






     @require_torch
     def        SCREAMING_SNAKE_CASE__						(							self		:					Optional[int] )  ->	Any:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		["""This is going to be way too long.""" * 1000, """short example"""]
           SCREAMING_SNAKE_CASE	=		["""not super long but more than 5 tokens""", """tiny"""]
           SCREAMING_SNAKE_CASE	=		self._large_tokenizer(lowerCamelCase__		,padding=lowerCamelCase__		,truncation=lowerCamelCase__		,return_tensors="""pt""" )
           SCREAMING_SNAKE_CASE	=		self._large_tokenizer(
               text_target=lowerCamelCase__		,max_length=5		,padding=lowerCamelCase__		,truncation=lowerCamelCase__		,return_tensors="""pt""" )

           assert batch.input_ids.shape == (2, 4096)
           assert batch.attention_mask.shape == (2, 4096)
           assert targets["input_ids"].shape == (2, 5)
           assert len(lowerCamelCase__ ) == 2  # input_ids, attention_mask.






     def        SCREAMING_SNAKE_CASE__						(							self		:					Tuple )  ->	Tuple:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		(
               """This is an example string that is used to test the original TF implementation against the HF"""
               """ implementation"""
           )

           SCREAMING_SNAKE_CASE	=		self._large_tokenizer(lowerCamelCase__ ).input_ids

           self.assertListEqual(
               lowerCamelCase__		,[182, 117, 142, 587, 4211, 120, 117, 263, 112, 804, 109, 856, 25016, 3137, 464, 109, 26955, 3137, 1]		,)









import inspect
import re
from hashlib import shaaaa
from typing import Dict, List

from .arrow import arrow
from .audiofolder import audiofolder
from .csv import csv
from .imagefolder import imagefolder
from .json import json
from .pandas import pandas
from .parquet import parquet
from .sql import sql  # noqa F401
from .text import text



def      __lowercase     (   _SCREAMING_SNAKE_CASE			)				->     str:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE	=		[]
      for line in lines:
            SCREAMING_SNAKE_CASE	=		re.sub(r"""#.*"""				,		""""""				,		_SCREAMING_SNAKE_CASE			)  # remove comments
            if line:
                  filtered_lines.append(_SCREAMING_SNAKE_CASE			)
      SCREAMING_SNAKE_CASE	=		"""\n""".join(_SCREAMING_SNAKE_CASE			)

      # Make a hash from all this code
      SCREAMING_SNAKE_CASE	=		full_str.encode("""utf-8"""			)
      return shaaaa(_SCREAMING_SNAKE_CASE			).hexdigest()


# get importable module names and hash for caching
SCREAMING_SNAKE_CASE_		     = {
    """csv""": (csv.__name__, _hash_python_lines(inspect.getsource(csv).splitlines())),
    """json""": (json.__name__, _hash_python_lines(inspect.getsource(json).splitlines())),
    """pandas""": (pandas.__name__, _hash_python_lines(inspect.getsource(pandas).splitlines())),
    """parquet""": (parquet.__name__, _hash_python_lines(inspect.getsource(parquet).splitlines())),
    """arrow""": (arrow.__name__, _hash_python_lines(inspect.getsource(arrow).splitlines())),
    """text""": (text.__name__, _hash_python_lines(inspect.getsource(text).splitlines())),
    """imagefolder""": (imagefolder.__name__, _hash_python_lines(inspect.getsource(imagefolder).splitlines())),
    """audiofolder""": (audiofolder.__name__, _hash_python_lines(inspect.getsource(audiofolder).splitlines())),
}

# Used to infer the module to use based on the data files extensions
SCREAMING_SNAKE_CASE_		     = {
    """.csv""": ("""csv""", {}),
    """.tsv""": ("""csv""", {"""sep""": """\t"""}),
    """.json""": ("""json""", {}),
    """.jsonl""": ("""json""", {}),
    """.parquet""": ("""parquet""", {}),
    """.arrow""": ("""arrow""", {}),
    """.txt""": ("""text""", {}),
}
_EXTENSION_TO_MODULE.update({ext: ("""imagefolder""", {}) for ext in imagefolder.ImageFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext.upper(): ("""imagefolder""", {}) for ext in imagefolder.ImageFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext: ("""audiofolder""", {}) for ext in audiofolder.AudioFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext.upper(): ("""audiofolder""", {}) for ext in audiofolder.AudioFolder.EXTENSIONS})
SCREAMING_SNAKE_CASE_		     = {"""imagefolder""", """audiofolder"""}

# Used to filter data files based on extensions given a module name
SCREAMING_SNAKE_CASE_		     = {}
for _ext, (_module, _) in _EXTENSION_TO_MODULE.items():
      _MODULE_TO_EXTENSIONS.setdefault(_module, []).append(_ext)

_MODULE_TO_EXTENSIONS["imagefolder"].append(""".zip""")



_MODULE_TO_EXTENSIONS["audiofolder"].append(""".zip""")


