
"""simple docstring"""

import os
import tempfile
import unittest
from typing import List

from transformers.models.esm.tokenization_esm import VOCAB_FILES_NAMES, EsmTokenizer
from transformers.testing_utils import require_tokenizers
from transformers.tokenization_utils import PreTrainedTokenizer
from transformers.tokenization_utils_base import PreTrainedTokenizerBase






@require_tokenizers
class 			_lowercase  (							unittest.TestCase						):
	lowercase_								=      EsmTokenizer




	def      _UpperCamelCase       (					self   )						->				Union[str, Any]:
			super().setUp()
			lowerCamelCase	:     Union[str, Any]            =       tempfile.mkdtemp()
			# fmt: off
			lowerCamelCase	:     Dict            =       ['<cls>', '<pad>', '<eos>', '<unk>', 'L', 'A', 'G', 'V', 'S', 'E', 'R', 'T', 'I', 'D', 'P', 'K', 'Q', 'N', 'F', 'Y', 'M', 'H', 'W', 'C', 'X', 'B', 'U', 'Z', 'O', '.', '-', '<null_1>', '<mask>']  # noqa: E501
			# fmt: on
			lowerCamelCase	:     Tuple            =       os.path.join(self.tmpdirname						,					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]   )   )




	def      _UpperCamelCase       (					self						,					**UpperCAmelCase_   )						->				int:
			return [self.get_tokenizer(**UpperCAmelCase_   )]




	def      _UpperCamelCase       (					self						,					**UpperCAmelCase_   )						->				Tuple:
			return self.tokenizer_class.from_pretrained(self.tmpdirname						,					**UpperCAmelCase_   )




	def      _UpperCamelCase       (					self   )						->				str:
			lowerCamelCase	:     int            =       self.tokenizer_class(self.vocab_file   )

			lowerCamelCase	:     Any            =       tokenizer.tokenize('LAGVS'   )
			self.assertListEqual(UpperCAmelCase_						,					['L', 'A', 'G', 'V', 'S']   )
			self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCAmelCase_   )						,					[4, 5, 6, 7, 8]   )




	def      _UpperCamelCase       (					self   )						->				str:
			lowerCamelCase	:     Any            =       self.tokenizer_class(self.vocab_file   )

			lowerCamelCase	:     Union[str, Any]            =       'LAGVS'
			self.assertListEqual(tokenizer.encode(UpperCAmelCase_   )						,					[0, 4, 5, 6, 7, 8, 2]   )




	def      _UpperCamelCase       (					self   )						->				Optional[Any]:
			lowerCamelCase	:     Union[str, Any]            =       self.tokenizer_class(self.vocab_file   )

			lowerCamelCase	:     List[str]            =       ['LAGVS', 'WCB']
			lowerCamelCase	:     List[str]            =       tokenizer(UpperCAmelCase_						,					padding=UpperCAmelCase_   )['input_ids']

			self.assertListEqual(UpperCAmelCase_						,					[[0, 4, 5, 6, 7, 8, 2], [0, 22, 23, 25, 2]]   )




	def      _UpperCamelCase       (					self   )						->				List[str]:
			lowerCamelCase	:     Union[str, Any]            =       self.tokenizer_class(self.vocab_file   )

			lowerCamelCase	:     Any            =       ['LAGVS', 'WCB']
			lowerCamelCase	:     Optional[Any]            =       tokenizer(UpperCAmelCase_						,					padding=UpperCAmelCase_   )['input_ids']

			self.assertListEqual(UpperCAmelCase_						,					[[0, 4, 5, 6, 7, 8, 2], [0, 22, 23, 25, 2, 1, 1]]   )




	def      _UpperCamelCase       (					self   )						->				str:
			lowerCamelCase	:     List[Any]            =       self.get_tokenizers(fast=UpperCAmelCase_   )
			for tokenizer in tokenizers:
					with self.subTest(F"""{tokenizer.__class__.__name__}"""   ):
							lowerCamelCase	:     List[str]            =       '<unk>'
							lowerCamelCase	:     Tuple            =       '<mask>'

							lowerCamelCase	:     Tuple            =       tokenizer.tokenize(UpperCAmelCase_   )
							lowerCamelCase	:     Tuple            =       tokenizer.tokenize(UpperCAmelCase_   )

							self.assertEqual(len(UpperCAmelCase_   )						,					1   )
							self.assertEqual(len(UpperCAmelCase_   )						,					1   )
							self.assertEqual(token_a[0]						,					UpperCAmelCase_   )
							self.assertEqual(token_a[0]						,					UpperCAmelCase_   )












from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


UpperCAmelCase_    ={"""configuration_vilt""": ["""VILT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ViltConfig"""]}

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		UpperCAmelCase_    =["""ViltFeatureExtractor"""]
		UpperCAmelCase_    =["""ViltImageProcessor"""]
		UpperCAmelCase_    =["""ViltProcessor"""]

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		UpperCAmelCase_    =[
		    """VILT_PRETRAINED_MODEL_ARCHIVE_LIST""",
		    """ViltForImageAndTextRetrieval""",
		    """ViltForImagesAndTextClassification""",
		    """ViltForTokenClassification""",
		    """ViltForMaskedLM""",
		    """ViltForQuestionAnswering""",
		    """ViltLayer""",
		    """ViltModel""",
		    """ViltPreTrainedModel""",
		]


if TYPE_CHECKING:
		from .configuration_vilt import VILT_PRETRAINED_CONFIG_ARCHIVE_MAP, ViltConfig

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_vilt import ViltFeatureExtractor
				from .image_processing_vilt import ViltImageProcessor
				from .processing_vilt import ViltProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_vilt import (
				    VILT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    ViltForImageAndTextRetrieval,
				    ViltForImagesAndTextClassification,
				    ViltForMaskedLM,
				    ViltForQuestionAnswering,
				    ViltForTokenClassification,
				    ViltLayer,
				    ViltModel,
				    ViltPreTrainedModel,
				)


else:
		import sys

		UpperCAmelCase_    =_LazyModule(__name__, globals()["""__file__"""], _import_structure)



