






import json
import os
import unittest

from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES, BioGptTokenizer
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin


class        __UpperCamelCase    (     _lowerCAmelCase   ,			unittest.TestCase   ):
	__snake_case	:int   =       BioGptTokenizer
	__snake_case	:List[Any]   =       False







	def  _a   (  self      :							List[str]			)		-> Tuple:
		"""simple docstring"""







		super().setUp()

		# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
		__lowercase             =  [
		    """l""",
		    """o""",
		    """w""",
		    """e""",
		    """r""",
		    """s""",
		    """t""",
		    """i""",
		    """d""",
		    """n""",
		    """w</w>""",
		    """r</w>""",
		    """t</w>""",
		    """lo""",
		    """low""",
		    """er</w>""",
		    """low</w>""",
		    """lowest</w>""",
		    """newer</w>""",
		    """wider</w>""",
		    """<unk>""",
		]
		__lowercase             =  dict(zip(_lowerCAmelCase       ,				range(len(_lowerCAmelCase			)			)			)			)
		__lowercase             =  ["""l o 123""", """lo w 1456""", """e r</w> 1789""", """"""]

		__lowercase             =  os.path.join(self.tmpdirname       ,				VOCAB_FILES_NAMES["""vocab_file"""]			)
		__lowercase             =  os.path.join(self.tmpdirname       ,				VOCAB_FILES_NAMES["""merges_file"""]			)
		with open(self.vocab_file       ,				"""w"""			) as fp:
			fp.write(json.dumps(_lowerCAmelCase			)			)
		with open(self.merges_file       ,				"""w"""			) as fp:
			fp.write("""\n""".join(_lowerCAmelCase			)			)







	def  _a   (  self      :							List[str]       ,				_lowerCAmelCase      :							Any			)		-> Dict:
		"""simple docstring"""







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







	def  _a   (  self      :							Tuple			)		-> List[Any]:
		"""simple docstring"""







		__lowercase             =  BioGptTokenizer(self.vocab_file       ,				self.merges_file			)

		__lowercase             =  """lower"""
		__lowercase             =  ["""low""", """er</w>"""]
		__lowercase             =  tokenizer.tokenize(_lowerCAmelCase			)
		self.assertListEqual(_lowerCAmelCase       ,				_lowerCAmelCase			)

		__lowercase             =  tokens + ["""<unk>"""]
		__lowercase             =  [14, 15, 20]
		self.assertListEqual(tokenizer.convert_tokens_to_ids(_lowerCAmelCase			)       ,				_lowerCAmelCase			)







	@slow
	def  _a   (  self      :							Any			)		-> Dict:
		"""simple docstring"""







		__lowercase             =  BioGptTokenizer.from_pretrained("""microsoft/biogpt"""			)

		__lowercase             =  tokenizer.encode("""sequence builders"""       ,				add_special_tokens=_lowerCAmelCase			)
		__lowercase             =  tokenizer.encode("""multi-sequence build"""       ,				add_special_tokens=_lowerCAmelCase			)

		__lowercase             =  tokenizer.build_inputs_with_special_tokens(_lowerCAmelCase			)
		__lowercase             =  tokenizer.build_inputs_with_special_tokens(_lowerCAmelCase       ,				_lowerCAmelCase			)

		self.assertTrue(encoded_sentence == [2] + text			)
		self.assertTrue(encoded_pair == [2] + text + [2] + text_a			)









# Copyright 2023 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.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


__UpperCamelCase      :      Any     						=						{"""configuration_mra""": ["""MRA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MraConfig"""]}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__UpperCamelCase      :      Dict     						=						[
						    """MRA_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """MraForMaskedLM""",
						    """MraForMultipleChoice""",
						    """MraForQuestionAnswering""",
						    """MraForSequenceClassification""",
						    """MraForTokenClassification""",
						    """MraLayer""",
						    """MraModel""",
						    """MraPreTrainedModel""",
						]


if TYPE_CHECKING:
						from .configuration_mra import MRA_PRETRAINED_CONFIG_ARCHIVE_MAP, MraConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_mra import (
												    MRA_PRETRAINED_MODEL_ARCHIVE_LIST,
												    MraForMaskedLM,
												    MraForMultipleChoice,
												    MraForQuestionAnswering,
												    MraForSequenceClassification,
												    MraForTokenClassification,
												    MraLayer,
												    MraModel,
												    MraPreTrainedModel,
												)
else:
						import sys

						__UpperCamelCase      :      List[str]     						=						_LazyModule(__name__, globals()["""__file__"""], _import_structure)

