




# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import argparse

from .config import config_command_parser
from .config_args import default_config_file, load_config_from_file  # noqa: F401
from .default import default_command_parser
from .update import update_command_parser







def        lowerCAmelCase_   (  snake_case_=None  ):
      _A							:  List[str]    =		argparse.ArgumentParser(add_help=snake_case_,allow_abbrev=snake_case_  )
      # The main config parser
      _A							:  Union[str, Any]    =		config_command_parser(snake_case_  )
      # The subparser to add commands to
      _A							:  str    =		config_parser.add_subparsers(title="""subcommands""",dest="""subcommand"""  )

      # Then add other parsers with the parent parser
      default_command_parser(snake_case_,parents=[parent_parser]  )
      update_command_parser(snake_case_,parents=[parent_parser]  )

      return config_parser







def        lowerCAmelCase_   (  ):
      _A							:  Optional[int]    =		get_config_parser()
      _A							:  Union[str, Any]    =		config_parser.parse_args()

      if not hasattr(snake_case_,"""func"""  ):
            config_parser.print_help()
            exit(1  )

      # Run
      args.func(snake_case_  )


if __name__ == "__main__":
  main()







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							(     UpperCamelCase__,unittest.TestCase	):
     _a											=		PegasusTokenizer
     _a											=		PegasusTokenizerFast
     _a											=		True
     _a											=		True

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

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

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

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

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

     def       a__			(						self       )		->							int:
           _A							:  Dict    =		"""</s>"""
           _A							:  Dict    =		1

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

     def       a__			(						self       )		->							Dict:
           _A							:  List[str]    =		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(_a       )	,  1103       )

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

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

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

     def       a__			(						self       )		->							List[str]:
           _A							:  Optional[int]    =		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							:  Any    =		"""To ensure a smooth flow of bank resolutions."""
           _A							:  Optional[int]    =		[413, 615, 114, 2291, 1971, 113, 1679, 1_0710, 107, 1]
           _A							:  Optional[Any]    =		tokenizer([raw_input_str]	,  return_tensors=_a       ).input_ids[0]
           self.assertListEqual(_a	,  _a       )
           assert tokenizer.convert_ids_to_tokens([0, 1, 2, 3]       ) == ["<pad>", "</s>", "<mask_1>", "<mask_2>"]

     @require_torch
     def       a__			(						self       )		->							List[str]:
           _A							:  Union[str, Any]    =		["""This is going to be way too long.""" * 150, """short example"""]
           _A							:  Optional[Any]    =		["""not super long but more than 5 tokens""", """tiny"""]
           _A							:  Union[str, Any]    =		self._large_tokenizer(_a	,  padding=_a	,  truncation=_a	,  return_tensors="""pt"""       )
           _A							:  Tuple    =		self._large_tokenizer(
               text_target=_a	,  max_length=5	,  padding=_a	,  truncation=_a	,  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(_a       ) == 2  # input_ids, attention_mask.



     @slow
     def       a__			(						self       )		->							Optional[Any]:
           # fmt: off
           _A							:  List[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=_a	,  model_name="""google/bigbird-pegasus-large-arxiv"""	,  revision="""ba85d0851d708441f91440d509690f1ab6353415"""	,  )







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

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

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

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

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

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

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

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



     def       a__			(						self       )		->							Dict:
           _A							:  Optional[int]    =		(
               """This is an example string that is used to test the original TF implementation against the HF"""
               """ implementation"""
           )

           _A							:  Any    =		self._large_tokenizer(_a       ).input_ids

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

