


import json
import os
import unittest

from transformers import DebertaTokenizer, DebertaTokenizerFast
from transformers.models.deberta.tokenization_deberta import VOCAB_FILES_NAMES
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin







class        __lowercase       (							a_			,							unittest.TestCase  ):



		"""simple docstring"""

		UpperCamelCase     :					Dict			       =		DebertaTokenizer
		UpperCamelCase     :					str			       =		True
		UpperCamelCase     :					Any			       =		DebertaTokenizerFast







		def 					__A   ( self	)  -> str:







					'''simple docstring'''







					super().setUp()

					# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
					lowerCamelCase    		=  [
					    """l""",
					    """o""",
					    """w""",
					    """e""",
					    """r""",
					    """s""",
					    """t""",
					    """i""",
					    """d""",
					    """n""",
					    """\u0120""",
					    """\u0120l""",
					    """\u0120n""",
					    """\u0120lo""",
					    """\u0120low""",
					    """er""",
					    """\u0120lowest""",
					    """\u0120newer""",
					    """\u0120wider""",
					    """[UNK]""",
					]
					lowerCamelCase    		=  dict(zip(A		,      range(len(A	)	)	)	)
					lowerCamelCase    		=  ["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
					lowerCamelCase    		=  {"""unk_token""": """[UNK]"""}

					lowerCamelCase    		=  os.path.join(self.tmpdirname		,      VOCAB_FILES_NAMES["""vocab_file"""]	)
					lowerCamelCase    		=  os.path.join(self.tmpdirname		,      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		,      **A	)  -> Any:







					'''simple docstring'''







					kwargs.update(self.special_tokens_map	)
					return self.tokenizer_class.from_pretrained(self.tmpdirname		,      **A	)







		def 					__A   ( self		,      A	)  -> List[Any]:







					'''simple docstring'''







					lowerCamelCase    		=  """lower newer"""
					lowerCamelCase    		=  """lower newer"""
					return input_text, output_text







		def 					__A   ( self	)  -> Dict:







					'''simple docstring'''







					lowerCamelCase    		=  self.get_tokenizer()
					lowerCamelCase    		=  """lower newer"""
					lowerCamelCase    		=  ["""l""", """o""", """w""", """er""", """\u0120""", """n""", """e""", """w""", """er"""]
					lowerCamelCase    		=  tokenizer.tokenize(A	)
					self.assertListEqual(A		,      A	)

					lowerCamelCase    		=  tokens + [tokenizer.unk_token]
					lowerCamelCase    		=  [0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
					self.assertListEqual(tokenizer.convert_tokens_to_ids(A	)		,      A	)







		def 					__A   ( self	)  -> Dict:







					'''simple docstring'''







					lowerCamelCase    		=  self.get_tokenizer()
					lowerCamelCase    		=  tokenizer("""Hello"""		,      """World"""	)
					lowerCamelCase    		=  [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1]
					self.assertListEqual(tokd["""token_type_ids"""]		,      A	)







		@slow
		def 					__A   ( self	)  -> Any:







					'''simple docstring'''







					lowerCamelCase    		=  self.tokenizer_class.from_pretrained("""microsoft/deberta-base"""	)

					lowerCamelCase    		=  tokenizer.encode("""sequence builders"""		,      add_special_tokens=A	)
					lowerCamelCase    		=  tokenizer.encode("""multi-sequence build"""		,      add_special_tokens=A	)

					lowerCamelCase    		=  tokenizer.encode(
					    """sequence builders"""		,      add_special_tokens=A		,      add_prefix_space=A	)
					lowerCamelCase    		=  tokenizer.encode(
					    """sequence builders"""		,      """multi-sequence build"""		,      add_special_tokens=A		,      add_prefix_space=A	)

					lowerCamelCase    		=  tokenizer.build_inputs_with_special_tokens(A	)
					lowerCamelCase    		=  tokenizer.build_inputs_with_special_tokens(A		,      A	)

					assert encoded_sentence == encoded_text_from_decode
					assert encoded_pair == encoded_pair_from_decode







		@slow
		def 					__A   ( self	)  -> Optional[int]:







					'''simple docstring'''







					lowerCamelCase    		=  [self.tokenizer_class]
					if self.test_rust_tokenizer:
								tokenizer_classes.append(self.rust_tokenizer_class	)

					for tokenizer_class in tokenizer_classes:
								lowerCamelCase    		=  tokenizer_class.from_pretrained("""microsoft/deberta-base"""	)

								lowerCamelCase    		=  [
								    """ALBERT: A Lite BERT for Self-supervised Learning of Language Representations""",
								    """ALBERT incorporates two parameter reduction techniques""",
								    """The first one is a factorized embedding parameterization. By decomposing the large vocabulary"""
								    """ embedding matrix into two small matrices, we separate the size of the hidden layers from the size of"""
								    """ vocabulary embedding.""",
								]

								lowerCamelCase    		=  tokenizer(A		,      padding=A	)
								lowerCamelCase    		=  [tokenizer.decode(A		,      skip_special_tokens=A	) for seq in encoding["""input_ids"""]]

								# fmt: off
								lowerCamelCase    		=  {
								    """input_ids""": [
								        [1, 21_18, 1_11_26, 5_65, 35, 83, 2_51_91, 1_63, 1_88_54, 13, 1_21_56, 12, 1_61_01, 2_53_76, 1_38_07, 9, 2_22_05, 2_78_93, 16_35, 2, 0, 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, 21_18, 1_11_26, 5_65, 2_45_36, 80, 4_37_97, 48_78, 73_73, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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_33, 78, 65, 16, 10, 37_24, 15_38, 3_31_83, 1_13_03, 4_37_97, 19_38, 4, 8_70, 2_41_65, 2_91_05, 5, 7_39, 3_26_44, 3_31_83, 1_13_03, 3_61_73, 88, 80, 6_50, 78_21, 4_59_40, 6, 52, 25_59, 5, 18_36, 9, 5, 73_97, 1_31_71, 31, 5, 18_36, 9, 3_26_44, 3_31_83, 1_13_03, 4, 2]
								    ],
								    """token_type_ids""": [
								        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
								        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
								        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 0, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
								    ]
								}
								# fmt: on

								lowerCamelCase    		=  [
								    """ALBERT: A Lite BERT for Self-supervised Learning of Language Representations""",
								    """ALBERT incorporates two parameter reduction techniques""",
								    """The first one is a factorized embedding parameterization. By decomposing the large vocabulary"""
								    """ embedding matrix into two small matrices, we separate the size of the hidden layers from the size of"""
								    """ vocabulary embedding.""",
								]

								self.assertDictEqual(encoding.data		,      A	)

								for expected, decoded in zip(A		,      A	):
											self.assertEqual(A		,      A	)





# 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						(							lowerCamelCase__		:						Any=None      ):





			'''simple docstring'''




			lowerCamelCase    		=  argparse.ArgumentParser(add_help=lowerCamelCase__  ,							allow_abbrev=lowerCamelCase__      )
			# The main config parser
			lowerCamelCase    		=  config_command_parser(lowerCamelCase__      )
			# The subparser to add commands to
			lowerCamelCase    		=  config_parser.add_subparsers(title="""subcommands"""  ,							dest="""subcommand"""      )

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

			return config_parser






def 	__lowerCamelCase						(							):





			'''simple docstring'''




			lowerCamelCase    		=  get_config_parser()
			lowerCamelCase    		=  config_parser.parse_args()

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

			# Run
			args.func(lowerCamelCase__      )


if __name__ == "__main__":
		main()

