

import json
import os
import unittest

from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin


@require_tokenizers
class SCREAMING_SNAKE_CASE  (		__snake_case							,						unittest.TestCase							):






			"""simple docstring"""




			__A						  =  MgpstrTokenizer
			__A						  =  False
			__A						  =  {}
			__A						  =  False


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				super().setUp()

				# fmt: off
				snake_case_      		= ['[GO]', '[s]', '0', '1', '2', '3', '4', '5', '6', '7', '8', '9', 'a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 't', 'u', 'v', 'w', 'x', 'y', 'z']
				# fmt: on
				snake_case_      		= dict(zip(__UpperCamelCase ,				range(len(__UpperCamelCase							)							)							)							)

				snake_case_      		= os.path.join(self.tmpdirname ,				VOCAB_FILES_NAMES['vocab_file']							)
				with open(self.vocab_file ,				'w' ,				encoding='utf-8'							) as fp:
					fp.write(json.dumps(__UpperCamelCase							) + '\n'							)


			def 			__lowerCAmelCase (    self ,				**__UpperCamelCase							):

				"""simple docstring"""


				return MgpstrTokenizer.from_pretrained(self.tmpdirname ,				**__UpperCamelCase							)


			def 			__lowerCAmelCase (    self ,				__UpperCamelCase							):

				"""simple docstring"""


				snake_case_      		= 'tester'
				snake_case_      		= 'tester'
				return input_text, output_text


			@unittest.skip('MGP-STR always lower cases letters.'							)
			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				pass


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= self.get_tokenizers(do_lower_case=__UpperCamelCase							)
				for tokenizer in tokenizers:
					with self.subTest(f"""{tokenizer.__class__.__name__}"""							):
						snake_case_      		= '[SPECIAL_TOKEN]'

						tokenizer.add_special_tokens({'cls_token': special_token}							)
						snake_case_      		= tokenizer.encode([special_token] ,				add_special_tokens=__UpperCamelCase							)
						self.assertEqual(len(__UpperCamelCase							) ,				1							)

						snake_case_      		= tokenizer.decode(__UpperCamelCase ,				skip_special_tokens=__UpperCamelCase							)
						self.assertTrue(special_token not in decoded							)


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= self.get_tokenizers()
				for tokenizer in tokenizers:
					with self.subTest(f"""{tokenizer.__class__.__name__}"""							):
						snake_case_		,       snake_case_      		= self.get_input_output_texts(__UpperCamelCase							)

						snake_case_      		= tokenizer.tokenize(__UpperCamelCase							)
						snake_case_      		= tokenizer.convert_tokens_to_ids(__UpperCamelCase							)
						snake_case_      		= tokenizer.encode(__UpperCamelCase ,				add_special_tokens=__UpperCamelCase							)
						self.assertListEqual(__UpperCamelCase ,				__UpperCamelCase							)

						snake_case_      		= tokenizer.convert_ids_to_tokens(__UpperCamelCase							)
						self.assertNotEqual(len(__UpperCamelCase							) ,				0							)
						snake_case_      		= tokenizer.decode(__UpperCamelCase							)
						self.assertIsInstance(__UpperCamelCase ,				__UpperCamelCase							)

						self.assertEqual(text_a.replace(' ' ,				''							) ,				__UpperCamelCase							)


			@unittest.skip('MGP-STR tokenizer only handles one sequence.'							)
			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				pass







			@unittest.skip('inputs cannot be pretokenized in MgpstrTokenizer'							)
			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				pass



from typing import TYPE_CHECKING

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


A											=     {
    'configuration_audio_spectrogram_transformer': [
        'AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'ASTConfig',
    ]
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		A											=     [
		    'AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'ASTForAudioClassification',
		    'ASTModel',
		    'ASTPreTrainedModel',
		]

try:
		if not is_speech_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		A											=     ['ASTFeatureExtractor']

if TYPE_CHECKING:
		from .configuration_audio_spectrogram_transformer import (
		    AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    ASTConfig,
		)

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_audio_spectrogram_transformer import (
				    AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
				    ASTForAudioClassification,
				    ASTModel,
				    ASTPreTrainedModel,
				)

		try:
				if not is_speech_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_audio_spectrogram_transformer import ASTFeatureExtractor


else:
		import sys

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