




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


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







@require_sentencepiece
@require_tokenizers
class 		lowercase							(     SCREAMING_SNAKE_CASE_,unittest.TestCase	):
     _a											=		PegasusTokenizer
     _a											=		PegasusTokenizerFast
     _a											=		True
     _a											=		True

     def       a__			(						self       )		->							Dict:
           super().setUp()

           # We have a SentencePiece fixture for testing
           _A							:  List[str]    =		PegasusTokenizer(UpperCamelCase__       )
           tokenizer.save_pretrained(self.tmpdirname       )

     @cached_property
     def       a__			(						self       )		->							Dict:
           return PegasusTokenizer.from_pretrained("""google/pegasus-large"""       )

     def       a__			(						self	,  **_a       )		->							Any:
           return PegasusTokenizer.from_pretrained(self.tmpdirname	,  **UpperCamelCase__       )

     def       a__			(						self	,  _a       )		->							Dict:
           return ("This is a test", "This is a test")

     def       a__			(						self       )		->							List[str]:
           _A							:  List[Any]    =		'''</s>'''
           _A							:  List[Any]    =		1

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

     def       a__			(						self       )		->							Dict:
           _A							:  Optional[int]    =		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__       )	,  1103       )

     def       a__			(						self       )		->							Optional[Any]:
           self.assertEqual(self.get_tokenizer().vocab_size	,  1103       )

     def       a__			(						self       )		->							Optional[Any]:
           _A							:  Tuple    =		self.rust_tokenizer_class.from_pretrained(self.tmpdirname       )
           _A							:  List[str]    =		self.tokenizer_class.from_pretrained(self.tmpdirname       )
           _A							:  List[Any]    =		(
               '''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>'''
           )
           _A							:  str    =		rust_tokenizer([raw_input_str]	,  return_tensors=UpperCamelCase__	,  add_special_tokens=UpperCamelCase__       ).input_ids[0]
           _A							:  List[str]    =		py_tokenizer([raw_input_str]	,  return_tensors=UpperCamelCase__	,  add_special_tokens=UpperCamelCase__       ).input_ids[0]
           self.assertListEqual(UpperCamelCase__	,  UpperCamelCase__       )

     def       a__			(						self       )		->							Optional[int]:
           _A							:  List[str]    =		self._large_tokenizer
           # <mask_1> masks whole sentence while <mask_2> masks single word
           _A							:  List[str]    =		'''<mask_1> To ensure a <mask_2> flow of bank resolutions.'''
           _A							:  List[Any]    =		[2, 413, 615, 114, 3, 1971, 113, 1679, 1_0710, 107, 1]
           _A							:  Union[str, Any]    =		tokenizer([raw_input_str]	,  return_tensors=UpperCamelCase__       ).input_ids[0]
           self.assertListEqual(UpperCamelCase__	,  UpperCamelCase__       )

     def       a__			(						self       )		->							Union[str, Any]:
           _A							:  Any    =		self._large_tokenizer
           # The tracebacks for the following asserts are **better** without messages or self.assertEqual
           assert tokenizer.vocab_size == 9_6103
           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
           _A							:  List[Any]    =		'''To ensure a smooth flow of bank resolutions.'''
           _A							:  Union[str, Any]    =		[413, 615, 114, 2291, 1971, 113, 1679, 1_0710, 107, 1]
           _A							:  List[str]    =		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       a__			(						self       )		->							int:
           _A							:  Union[str, Any]    =		['''This is going to be way too long.''' * 150, '''short example''']
           _A							:  Tuple    =		['''not super long but more than 5 tokens''', '''tiny''']
           _A							:  Tuple    =		self._large_tokenizer(UpperCamelCase__	,  padding=UpperCamelCase__	,  truncation=UpperCamelCase__	,  return_tensors="""pt"""       )
           _A							:  Any    =		self._large_tokenizer(
               text_target=UpperCamelCase__	,  max_length=5	,  padding=UpperCamelCase__	,  truncation=UpperCamelCase__	,  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(UpperCamelCase__       ) == 2  # input_ids, attention_mask.



     @slow
     def       a__			(						self       )		->							Union[str, Any]:
           # fmt: off
           _A							:  Optional[Any]    =		{'''input_ids''': [[3_8979, 143, 1_8485, 606, 130, 2_6669, 8_7686, 121, 5_4189, 1129, 111, 2_6669, 8_7686, 121, 9114, 1_4787, 121, 1_3249, 158, 592, 956, 121, 1_4621, 3_1576, 143, 6_2613, 108, 9688, 930, 4_3430, 1_1562, 6_2613, 304, 108, 1_1443, 897, 108, 9314, 1_7415, 6_3399, 108, 1_1443, 7614, 1_8316, 118, 4284, 7148, 1_2430, 143, 1400, 2_5703, 158, 111, 4284, 7148, 1_1772, 143, 2_1297, 1064, 158, 122, 204, 3506, 1754, 1133, 1_4787, 1581, 115, 3_3224, 4482, 111, 1355, 110, 2_9173, 317, 5_0833, 108, 2_0147, 9_4665, 111, 7_7198, 107, 1], [110, 6_2613, 117, 638, 112, 1133, 121, 2_0098, 1355, 7_9050, 1_3872, 135, 1596, 5_3541, 1352, 141, 1_3039, 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, 1_8289, 1_7780, 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=UpperCamelCase__	,  model_name="""google/bigbird-pegasus-large-arxiv"""	,  revision="""ba85d0851d708441f91440d509690f1ab6353415"""	,  )







@require_sentencepiece
@require_tokenizers
class 		lowercase							(     SCREAMING_SNAKE_CASE_,unittest.TestCase	):
     _a											=		PegasusTokenizer
     _a											=		PegasusTokenizerFast
     _a											=		True
     _a											=		True

     def       a__			(						self       )		->							int:
           super().setUp()

           # We have a SentencePiece fixture for testing
           _A							:  Optional[int]    =		PegasusTokenizer(UpperCamelCase__	,  offset=0	,  mask_token_sent=UpperCamelCase__	,  mask_token="""[MASK]"""       )
           tokenizer.save_pretrained(self.tmpdirname       )

     @cached_property
     def       a__			(						self       )		->							str:
           return PegasusTokenizer.from_pretrained("""google/bigbird-pegasus-large-arxiv"""       )

     def       a__			(						self	,  **_a       )		->							List[Any]:
           return PegasusTokenizer.from_pretrained(self.tmpdirname	,  **UpperCamelCase__       )

     def       a__			(						self	,  _a       )		->							List[Any]:
           return ("This is a test", "This is a test")

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

     @require_torch
     def       a__			(						self       )		->							List[str]:
           _A							:  Dict    =		['''This is going to be way too long.''' * 1000, '''short example''']
           _A							:  str    =		['''not super long but more than 5 tokens''', '''tiny''']
           _A							:  List[Any]    =		self._large_tokenizer(UpperCamelCase__	,  padding=UpperCamelCase__	,  truncation=UpperCamelCase__	,  return_tensors="""pt"""       )
           _A							:  Dict    =		self._large_tokenizer(
               text_target=UpperCamelCase__	,  max_length=5	,  padding=UpperCamelCase__	,  truncation=UpperCamelCase__	,  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(UpperCamelCase__       ) == 2  # input_ids, attention_mask.



     def       a__			(						self       )		->							List[Any]:
           _A							:  Tuple    =		(
               '''This is an example string that is used to test the original TF implementation against the HF'''
               ''' implementation'''
           )

           _A							:  List[str]    =		self._large_tokenizer(UpperCamelCase__       ).input_ids

           self.assertListEqual(
               UpperCamelCase__	,  [182, 117, 142, 587, 4211, 120, 117, 263, 112, 804, 109, 856, 2_5016, 3137, 464, 109, 2_6955, 3137, 1]	,  )







import json
import os
import shutil
import tempfile
from unittest import TestCase

from transformers import BartTokenizer, BartTokenizerFast, DPRQuestionEncoderTokenizer, DPRQuestionEncoderTokenizerFast
from transformers.models.bart.configuration_bart import BartConfig
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES as DPR_VOCAB_FILES_NAMES
from transformers.models.dpr.configuration_dpr import DPRConfig
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES as BART_VOCAB_FILES_NAMES
from transformers.testing_utils import require_faiss, require_tokenizers, require_torch, slow
from transformers.utils import is_datasets_available, is_faiss_available, is_torch_available


if is_torch_available() and is_datasets_available() and is_faiss_available():
  from transformers.models.rag.configuration_rag import RagConfig
  from transformers.models.rag.tokenization_rag import RagTokenizer







@require_faiss
@require_torch
class 		lowercase							(     UpperCamelCase__	):

     def       a__			(						self       )		->							int:
           _A							:  int    =		tempfile.mkdtemp()
           _A							:  Union[str, Any]    =		8

           # DPR tok
           _A							:  List[str]    =		[
               """[UNK]""",
               """[CLS]""",
               """[SEP]""",
               """[PAD]""",
               """[MASK]""",
               """want""",
               """##want""",
               """##ed""",
               """wa""",
               """un""",
               """runn""",
               """##ing""",
               """,""",
               """low""",
               """lowest""",
           ]
           _A							:  List[str]    =		os.path.join(self.tmpdirname	,  """dpr_tokenizer"""       )
           os.makedirs(_a	,  exist_ok=_a       )
           _A							:  str    =		os.path.join(_a	,  DPR_VOCAB_FILES_NAMES["""vocab_file"""]       )
           with open(self.vocab_file	,  """w"""	,  encoding="""utf-8"""       ) as vocab_writer:
                 vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens]       )       )

           # BART tok
           _A							:  Dict    =		[
               """l""",
               """o""",
               """w""",
               """e""",
               """r""",
               """s""",
               """t""",
               """i""",
               """d""",
               """n""",
               """\u0120""",
               """\u0120l""",
               """\u0120n""",
               """\u0120lo""",
               """\u0120low""",
               """er""",
               """\u0120lowest""",
               """\u0120newer""",
               """\u0120wider""",
               """<unk>""",
           ]
           _A							:  Optional[Any]    =		dict(zip(_a	,  range(len(_a       )       )       )       )
           _A							:  Tuple    =		["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
           _A							:  Dict    =		{"""unk_token""": """<unk>"""}

           _A							:  Optional[Any]    =		os.path.join(self.tmpdirname	,  """bart_tokenizer"""       )
           os.makedirs(_a	,  exist_ok=_a       )
           _A							:  str    =		os.path.join(_a	,  BART_VOCAB_FILES_NAMES["""vocab_file"""]       )
           _A							:  List[Any]    =		os.path.join(_a	,  BART_VOCAB_FILES_NAMES["""merges_file"""]       )
           with open(self.vocab_file	,  """w"""	,  encoding="""utf-8"""       ) as fp:
                 fp.write(json.dumps(_a       ) + """\n"""       )
           with open(self.merges_file	,  """w"""	,  encoding="""utf-8"""       ) as fp:
                 fp.write("""\n""".join(_a       )       )

     def       a__			(						self       )		->							DPRQuestionEncoderTokenizer:
           return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname	,  """dpr_tokenizer"""       )       )

     def       a__			(						self       )		->							BartTokenizer:
           return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname	,  """bart_tokenizer"""       )       )

     def       a__			(						self       )		->							Optional[Any]:
           shutil.rmtree(self.tmpdirname       )

     @require_tokenizers
     def       a__			(						self       )		->							str:
           _A							:  Optional[Any]    =		os.path.join(self.tmpdirname	,  """rag_tokenizer"""       )
           _A							:  int    =		RagConfig(question_encoder=DPRConfig().to_dict()	,  generator=BartConfig().to_dict()       )
           _A							:  Any    =		RagTokenizer(question_encoder=self.get_dpr_tokenizer()	,  generator=self.get_bart_tokenizer()       )
           rag_config.save_pretrained(_a       )
           rag_tokenizer.save_pretrained(_a       )
           _A							:  Optional[Any]    =		RagTokenizer.from_pretrained(_a	,  config=_a       )
           self.assertIsInstance(new_rag_tokenizer.question_encoder	,  _a       )
           self.assertEqual(new_rag_tokenizer.question_encoder.get_vocab()	,  rag_tokenizer.question_encoder.get_vocab()       )
           self.assertIsInstance(new_rag_tokenizer.generator	,  _a       )
           self.assertEqual(new_rag_tokenizer.generator.get_vocab()	,  rag_tokenizer.generator.get_vocab()       )

     @slow
     def       a__			(						self       )		->							str:
           _A							:  Union[str, Any]    =		RagTokenizer.from_pretrained("""facebook/rag-token-nq"""       )
           _A							:  Tuple    =		[
               """who got the first nobel prize in physics""",
               """when is the next deadpool movie being released""",
               """which mode is used for short wave broadcast service""",
               """who is the owner of reading football club""",
               """when is the next scandal episode coming out""",
               """when is the last time the philadelphia won the superbowl""",
               """what is the most current adobe flash player version""",
               """how many episodes are there in dragon ball z""",
               """what is the first step in the evolution of the eye""",
               """where is gall bladder situated in human body""",
               """what is the main mineral in lithium batteries""",
               """who is the president of usa right now""",
               """where do the greasers live in the outsiders""",
               """panda is a national animal of which country""",
               """what is the name of manchester united stadium""",
           ]
           _A							:  Tuple    =		tokenizer(_a       )
           self.assertIsNotNone(_a       )



     @slow
     def       a__			(						self       )		->							Dict:
           _A							:  Dict    =		RagTokenizer.from_pretrained("""facebook/rag-sequence-nq"""       )
           _A							:  str    =		[
               """who got the first nobel prize in physics""",
               """when is the next deadpool movie being released""",
               """which mode is used for short wave broadcast service""",
               """who is the owner of reading football club""",
               """when is the next scandal episode coming out""",
               """when is the last time the philadelphia won the superbowl""",
               """what is the most current adobe flash player version""",
               """how many episodes are there in dragon ball z""",
               """what is the first step in the evolution of the eye""",
               """where is gall bladder situated in human body""",
               """what is the main mineral in lithium batteries""",
               """who is the president of usa right now""",
               """where do the greasers live in the outsiders""",
               """panda is a national animal of which country""",
               """what is the name of manchester united stadium""",
           ]
           _A							:  Optional[Any]    =		tokenizer(_a       )
           self.assertIsNotNone(_a       )

