





'''simple docstring'''







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 							__SCREAMING_SNAKE_CASE    (SCREAMING_SNAKE_CASE__			,				unittest.TestCase ):






				"""simple docstring"""







				__a  =BioGptTokenizer
				__a  =False




				def     UpperCamelCase__  (    self	:   List[str]   ):
								super().setUp()

								# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
								_a				      =					[
								    "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>",
								]
								_a				      =					dict(zip(_lowercase   ,							range(len(_lowercase   )   )   )   )
								_a				      =					["l o 123", "lo w 1456", "e r</w> 1789", ""]

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




				def     UpperCamelCase__  (    self	:   List[Any]   ,							__a	:   Tuple   ):
								_a				      =					"lower newer"
								_a				      =					"lower newer"
								return input_text, output_text




				def     UpperCamelCase__  (    self	:   List[str]   ):
								_a				      =					BioGptTokenizer(self.vocab_file   ,							self.merges_file   )

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

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




				@slow
				def     UpperCamelCase__  (    self	:   List[str]   ):
								_a				      =					BioGptTokenizer.from_pretrained("microsoft/biogpt"   )

								_a				      =					tokenizer.encode("sequence builders"   ,							add_special_tokens=_lowercase   )
								_a				      =					tokenizer.encode("multi-sequence build"   ,							add_special_tokens=_lowercase   )

								_a				      =					tokenizer.build_inputs_with_special_tokens(_lowercase   )
								_a				      =					tokenizer.build_inputs_with_special_tokens(_lowercase   ,							_lowercase   )

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





"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


A		=						{'configuration_vit': ['VIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ViTConfig', 'ViTOnnxConfig']}

try:
			if not is_vision_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			A		=						['ViTFeatureExtractor']
			A		=						['ViTImageProcessor']

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			A		=						[
			    'VIT_PRETRAINED_MODEL_ARCHIVE_LIST',
			    'ViTForImageClassification',
			    'ViTForMaskedImageModeling',
			    'ViTModel',
			    'ViTPreTrainedModel',
			]

try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			A		=						[
			    'TFViTForImageClassification',
			    'TFViTModel',
			    'TFViTPreTrainedModel',
			]

try:
			if not is_flax_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			A		=						[
			    'FlaxViTForImageClassification',
			    'FlaxViTModel',
			    'FlaxViTPreTrainedModel',
			]

if TYPE_CHECKING:
			from .configuration_vit import VIT_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTConfig, ViTOnnxConfig

			try:
						if not is_vision_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .feature_extraction_vit import ViTFeatureExtractor
						from .image_processing_vit import ViTImageProcessor

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_vit import (
						    VIT_PRETRAINED_MODEL_ARCHIVE_LIST,
						    ViTForImageClassification,
						    ViTForMaskedImageModeling,
						    ViTModel,
						    ViTPreTrainedModel,
						)

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_vit import TFViTForImageClassification, TFViTModel, TFViTPreTrainedModel

			try:
						if not is_flax_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel, FlaxViTPreTrainedModel


else:
			import sys

			A		=						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



