
"""simple docstring"""


import json
import os
import unittest

from transformers import HerbertTokenizer, HerbertTokenizerFast
from transformers.models.herbert.tokenization_herbert import VOCAB_FILES_NAMES
from transformers.testing_utils import get_tests_dir, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin







@require_tokenizers
class 			lowerCAmelCase_			( _lowercase						,   unittest.TestCase      ):



						'''simple docstring'''


						_lowerCamelCase:      List[str] =      HerbertTokenizer
						_lowerCamelCase:      Any =      HerbertTokenizerFast
						_lowerCamelCase:      Optional[int] =      True


						def   _SCREAMING_SNAKE_CASE (	self       : Dict )       ->				Tuple:
							super().setUp()

							# Use a simpler test file without japanese/chinese characters
							with open(F'{get_tests_dir()}/fixtures/sample_text_no_unicode.txt'					,encoding='utf-8' ) as f_data:
								A  				=				f_data.read().replace('\n\n'					,'\n' ).strip()

							A  				=				[
							    '<s>',
							    '</s>',
							    '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>',
							    ',</w>',
							    '<unk>',
							]
							A  				=				dict(zip(A_					,range(len(A_ ) ) ) )
							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(A_ ) )
							with open(self.merges_file					,'w' ) as fp:
								fp.write('\n'.join(A_ ) )


						def   _SCREAMING_SNAKE_CASE (	self       : Tuple					,A_       : List[str] )       ->				int:
							A  				=				'lower newer'
							A  				=				'lower newer'
							return input_text, output_text


						def   _SCREAMING_SNAKE_CASE (	self       : List[Any] )       ->				int:
							A  				=				self.tokenizer_class(vocab_file=self.vocab_file					,merges_file=self.merges_file )

							A  				=				'lower'
							A  				=				['low', 'er</w>']
							A  				=				tokenizer.tokenize(A_ )
							self.assertListEqual(A_					,A_ )

							A  				=				tokens + ['<unk>']
							A  				=				[16, 17, 23]
							self.assertListEqual(tokenizer.convert_tokens_to_ids(A_ )					,A_ )


						def   _SCREAMING_SNAKE_CASE (	self       : Dict )       ->				Optional[int]:
							if not self.test_rust_tokenizer:
								return

							A  				=				self.get_tokenizer()
							A  				=				self.get_rust_tokenizer()

							A  				=				'lower,newer'

							A  				=				tokenizer.tokenize(A_ )
							A  				=				rust_tokenizer.tokenize(A_ )
							self.assertListEqual(A_					,A_ )

							A  				=				tokenizer.encode(A_					,add_special_tokens=A_ )
							A  				=				rust_tokenizer.encode(A_					,add_special_tokens=A_ )
							self.assertListEqual(A_					,A_ )

							A  				=				self.get_rust_tokenizer()
							A  				=				tokenizer.encode(A_ )
							A  				=				rust_tokenizer.encode(A_ )
							self.assertListEqual(A_					,A_ )




						@slow
						def   _SCREAMING_SNAKE_CASE (	self       : str )       ->				List[Any]:
							A  				=				self.tokenizer_class.from_pretrained('allegro/herbert-base-cased' )

							A  				=				tokenizer.encode('konstruowanie sekwencji'					,add_special_tokens=A_ )
							A  				=				tokenizer.encode('konstruowanie wielu sekwencji'					,add_special_tokens=A_ )

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

							assert encoded_sentence == [0] + text + [2]
							assert encoded_pair == [0] + text + [2] + text_a + [2]

"""simple docstring"""


from typing import TYPE_CHECKING

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


_lowercase											=			{
    '''auto_factory''': ['''get_values'''],
    '''configuration_auto''': ['''ALL_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''CONFIG_MAPPING''', '''MODEL_NAMES_MAPPING''', '''AutoConfig'''],
    '''feature_extraction_auto''': ['''FEATURE_EXTRACTOR_MAPPING''', '''AutoFeatureExtractor'''],
    '''image_processing_auto''': ['''IMAGE_PROCESSOR_MAPPING''', '''AutoImageProcessor'''],
    '''processing_auto''': ['''PROCESSOR_MAPPING''', '''AutoProcessor'''],
    '''tokenization_auto''': ['''TOKENIZER_MAPPING''', '''AutoTokenizer'''],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase											=			[
		    '''MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING''',
		    '''MODEL_FOR_AUDIO_FRAME_CLASSIFICATION_MAPPING''',
		    '''MODEL_FOR_AUDIO_XVECTOR_MAPPING''',
		    '''MODEL_FOR_BACKBONE_MAPPING''',
		    '''MODEL_FOR_CAUSAL_IMAGE_MODELING_MAPPING''',
		    '''MODEL_FOR_CAUSAL_LM_MAPPING''',
		    '''MODEL_FOR_CTC_MAPPING''',
		    '''MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING''',
		    '''MODEL_FOR_DEPTH_ESTIMATION_MAPPING''',
		    '''MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING''',
		    '''MODEL_FOR_IMAGE_SEGMENTATION_MAPPING''',
		    '''MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING''',
		    '''MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING''',
		    '''MODEL_FOR_MASKED_LM_MAPPING''',
		    '''MODEL_FOR_MASK_GENERATION_MAPPING''',
		    '''MODEL_FOR_MULTIPLE_CHOICE_MAPPING''',
		    '''MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING''',
		    '''MODEL_FOR_OBJECT_DETECTION_MAPPING''',
		    '''MODEL_FOR_PRETRAINING_MAPPING''',
		    '''MODEL_FOR_QUESTION_ANSWERING_MAPPING''',
		    '''MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING''',
		    '''MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING''',
		    '''MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING''',
		    '''MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING''',
		    '''MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING''',
		    '''MODEL_FOR_TEXT_ENCODING_MAPPING''',
		    '''MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING''',
		    '''MODEL_FOR_UNIVERSAL_SEGMENTATION_MAPPING''',
		    '''MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING''',
		    '''MODEL_FOR_VISION_2_SEQ_MAPPING''',
		    '''MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING''',
		    '''MODEL_MAPPING''',
		    '''MODEL_WITH_LM_HEAD_MAPPING''',
		    '''MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING''',
		    '''MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING''',
		    '''AutoModel''',
		    '''AutoBackbone''',
		    '''AutoModelForAudioClassification''',
		    '''AutoModelForAudioFrameClassification''',
		    '''AutoModelForAudioXVector''',
		    '''AutoModelForCausalLM''',
		    '''AutoModelForCTC''',
		    '''AutoModelForDepthEstimation''',
		    '''AutoModelForImageClassification''',
		    '''AutoModelForImageSegmentation''',
		    '''AutoModelForInstanceSegmentation''',
		    '''AutoModelForMaskGeneration''',
		    '''AutoModelForTextEncoding''',
		    '''AutoModelForMaskedImageModeling''',
		    '''AutoModelForMaskedLM''',
		    '''AutoModelForMultipleChoice''',
		    '''AutoModelForNextSentencePrediction''',
		    '''AutoModelForObjectDetection''',
		    '''AutoModelForPreTraining''',
		    '''AutoModelForQuestionAnswering''',
		    '''AutoModelForSemanticSegmentation''',
		    '''AutoModelForSeq2SeqLM''',
		    '''AutoModelForSequenceClassification''',
		    '''AutoModelForSpeechSeq2Seq''',
		    '''AutoModelForTableQuestionAnswering''',
		    '''AutoModelForTokenClassification''',
		    '''AutoModelForUniversalSegmentation''',
		    '''AutoModelForVideoClassification''',
		    '''AutoModelForVision2Seq''',
		    '''AutoModelForVisualQuestionAnswering''',
		    '''AutoModelForDocumentQuestionAnswering''',
		    '''AutoModelWithLMHead''',
		    '''AutoModelForZeroShotImageClassification''',
		    '''AutoModelForZeroShotObjectDetection''',
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase											=			[
		    '''TF_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING''',
		    '''TF_MODEL_FOR_CAUSAL_LM_MAPPING''',
		    '''TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING''',
		    '''TF_MODEL_FOR_MASK_GENERATION_MAPPING''',
		    '''TF_MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING''',
		    '''TF_MODEL_FOR_MASKED_LM_MAPPING''',
		    '''TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING''',
		    '''TF_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING''',
		    '''TF_MODEL_FOR_PRETRAINING_MAPPING''',
		    '''TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING''',
		    '''TF_MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING''',
		    '''TF_MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING''',
		    '''TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING''',
		    '''TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING''',
		    '''TF_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING''',
		    '''TF_MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING''',
		    '''TF_MODEL_FOR_TEXT_ENCODING_MAPPING''',
		    '''TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING''',
		    '''TF_MODEL_FOR_VISION_2_SEQ_MAPPING''',
		    '''TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING''',
		    '''TF_MODEL_MAPPING''',
		    '''TF_MODEL_WITH_LM_HEAD_MAPPING''',
		    '''TFAutoModel''',
		    '''TFAutoModelForAudioClassification''',
		    '''TFAutoModelForCausalLM''',
		    '''TFAutoModelForImageClassification''',
		    '''TFAutoModelForMaskedImageModeling''',
		    '''TFAutoModelForMaskedLM''',
		    '''TFAutoModelForMaskGeneration''',
		    '''TFAutoModelForMultipleChoice''',
		    '''TFAutoModelForNextSentencePrediction''',
		    '''TFAutoModelForPreTraining''',
		    '''TFAutoModelForDocumentQuestionAnswering''',
		    '''TFAutoModelForQuestionAnswering''',
		    '''TFAutoModelForSemanticSegmentation''',
		    '''TFAutoModelForSeq2SeqLM''',
		    '''TFAutoModelForSequenceClassification''',
		    '''TFAutoModelForSpeechSeq2Seq''',
		    '''TFAutoModelForTableQuestionAnswering''',
		    '''TFAutoModelForTextEncoding''',
		    '''TFAutoModelForTokenClassification''',
		    '''TFAutoModelForVision2Seq''',
		    '''TFAutoModelForZeroShotImageClassification''',
		    '''TFAutoModelWithLMHead''',
		]

try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase											=			[
		    '''FLAX_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING''',
		    '''FLAX_MODEL_FOR_CAUSAL_LM_MAPPING''',
		    '''FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING''',
		    '''FLAX_MODEL_FOR_MASKED_LM_MAPPING''',
		    '''FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING''',
		    '''FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING''',
		    '''FLAX_MODEL_FOR_PRETRAINING_MAPPING''',
		    '''FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING''',
		    '''FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING''',
		    '''FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING''',
		    '''FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING''',
		    '''FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING''',
		    '''FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING''',
		    '''FLAX_MODEL_MAPPING''',
		    '''FlaxAutoModel''',
		    '''FlaxAutoModelForCausalLM''',
		    '''FlaxAutoModelForImageClassification''',
		    '''FlaxAutoModelForMaskedLM''',
		    '''FlaxAutoModelForMultipleChoice''',
		    '''FlaxAutoModelForNextSentencePrediction''',
		    '''FlaxAutoModelForPreTraining''',
		    '''FlaxAutoModelForQuestionAnswering''',
		    '''FlaxAutoModelForSeq2SeqLM''',
		    '''FlaxAutoModelForSequenceClassification''',
		    '''FlaxAutoModelForSpeechSeq2Seq''',
		    '''FlaxAutoModelForTokenClassification''',
		    '''FlaxAutoModelForVision2Seq''',
		]


if TYPE_CHECKING:
		from .auto_factory import get_values
		from .configuration_auto import ALL_PRETRAINED_CONFIG_ARCHIVE_MAP, CONFIG_MAPPING, MODEL_NAMES_MAPPING, AutoConfig
		from .feature_extraction_auto import FEATURE_EXTRACTOR_MAPPING, AutoFeatureExtractor
		from .image_processing_auto import IMAGE_PROCESSOR_MAPPING, AutoImageProcessor
		from .processing_auto import PROCESSOR_MAPPING, AutoProcessor
		from .tokenization_auto import TOKENIZER_MAPPING, AutoTokenizer

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_auto import (
				    MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING,
				    MODEL_FOR_AUDIO_FRAME_CLASSIFICATION_MAPPING,
				    MODEL_FOR_AUDIO_XVECTOR_MAPPING,
				    MODEL_FOR_BACKBONE_MAPPING,
				    MODEL_FOR_CAUSAL_IMAGE_MODELING_MAPPING,
				    MODEL_FOR_CAUSAL_LM_MAPPING,
				    MODEL_FOR_CTC_MAPPING,
				    MODEL_FOR_DEPTH_ESTIMATION_MAPPING,
				    MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING,
				    MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
				    MODEL_FOR_IMAGE_SEGMENTATION_MAPPING,
				    MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING,
				    MODEL_FOR_MASK_GENERATION_MAPPING,
				    MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING,
				    MODEL_FOR_MASKED_LM_MAPPING,
				    MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
				    MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING,
				    MODEL_FOR_OBJECT_DETECTION_MAPPING,
				    MODEL_FOR_PRETRAINING_MAPPING,
				    MODEL_FOR_QUESTION_ANSWERING_MAPPING,
				    MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING,
				    MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
				    MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
				    MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING,
				    MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING,
				    MODEL_FOR_TEXT_ENCODING_MAPPING,
				    MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
				    MODEL_FOR_UNIVERSAL_SEGMENTATION_MAPPING,
				    MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING,
				    MODEL_FOR_VISION_2_SEQ_MAPPING,
				    MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING,
				    MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING,
				    MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING,
				    MODEL_MAPPING,
				    MODEL_WITH_LM_HEAD_MAPPING,
				    AutoBackbone,
				    AutoModel,
				    AutoModelForAudioClassification,
				    AutoModelForAudioFrameClassification,
				    AutoModelForAudioXVector,
				    AutoModelForCausalLM,
				    AutoModelForCTC,
				    AutoModelForDepthEstimation,
				    AutoModelForDocumentQuestionAnswering,
				    AutoModelForImageClassification,
				    AutoModelForImageSegmentation,
				    AutoModelForInstanceSegmentation,
				    AutoModelForMaskedImageModeling,
				    AutoModelForMaskedLM,
				    AutoModelForMaskGeneration,
				    AutoModelForMultipleChoice,
				    AutoModelForNextSentencePrediction,
				    AutoModelForObjectDetection,
				    AutoModelForPreTraining,
				    AutoModelForQuestionAnswering,
				    AutoModelForSemanticSegmentation,
				    AutoModelForSeqaSeqLM,
				    AutoModelForSequenceClassification,
				    AutoModelForSpeechSeqaSeq,
				    AutoModelForTableQuestionAnswering,
				    AutoModelForTextEncoding,
				    AutoModelForTokenClassification,
				    AutoModelForUniversalSegmentation,
				    AutoModelForVideoClassification,
				    AutoModelForVisionaSeq,
				    AutoModelForVisualQuestionAnswering,
				    AutoModelForZeroShotImageClassification,
				    AutoModelForZeroShotObjectDetection,
				    AutoModelWithLMHead,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_auto import (
				    TF_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING,
				    TF_MODEL_FOR_CAUSAL_LM_MAPPING,
				    TF_MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING,
				    TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
				    TF_MODEL_FOR_MASK_GENERATION_MAPPING,
				    TF_MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING,
				    TF_MODEL_FOR_MASKED_LM_MAPPING,
				    TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
				    TF_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING,
				    TF_MODEL_FOR_PRETRAINING_MAPPING,
				    TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
				    TF_MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING,
				    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
				    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
				    TF_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING,
				    TF_MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING,
				    TF_MODEL_FOR_TEXT_ENCODING_MAPPING,
				    TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
				    TF_MODEL_FOR_VISION_2_SEQ_MAPPING,
				    TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING,
				    TF_MODEL_MAPPING,
				    TF_MODEL_WITH_LM_HEAD_MAPPING,
				    TFAutoModel,
				    TFAutoModelForAudioClassification,
				    TFAutoModelForCausalLM,
				    TFAutoModelForDocumentQuestionAnswering,
				    TFAutoModelForImageClassification,
				    TFAutoModelForMaskedImageModeling,
				    TFAutoModelForMaskedLM,
				    TFAutoModelForMaskGeneration,
				    TFAutoModelForMultipleChoice,
				    TFAutoModelForNextSentencePrediction,
				    TFAutoModelForPreTraining,
				    TFAutoModelForQuestionAnswering,
				    TFAutoModelForSemanticSegmentation,
				    TFAutoModelForSeqaSeqLM,
				    TFAutoModelForSequenceClassification,
				    TFAutoModelForSpeechSeqaSeq,
				    TFAutoModelForTableQuestionAnswering,
				    TFAutoModelForTextEncoding,
				    TFAutoModelForTokenClassification,
				    TFAutoModelForVisionaSeq,
				    TFAutoModelForZeroShotImageClassification,
				    TFAutoModelWithLMHead,
				)

		try:
				if not is_flax_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_flax_auto import (
				    FLAX_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING,
				    FLAX_MODEL_FOR_CAUSAL_LM_MAPPING,
				    FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
				    FLAX_MODEL_FOR_MASKED_LM_MAPPING,
				    FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
				    FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING,
				    FLAX_MODEL_FOR_PRETRAINING_MAPPING,
				    FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
				    FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
				    FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
				    FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING,
				    FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
				    FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING,
				    FLAX_MODEL_MAPPING,
				    FlaxAutoModel,
				    FlaxAutoModelForCausalLM,
				    FlaxAutoModelForImageClassification,
				    FlaxAutoModelForMaskedLM,
				    FlaxAutoModelForMultipleChoice,
				    FlaxAutoModelForNextSentencePrediction,
				    FlaxAutoModelForPreTraining,
				    FlaxAutoModelForQuestionAnswering,
				    FlaxAutoModelForSeqaSeqLM,
				    FlaxAutoModelForSequenceClassification,
				    FlaxAutoModelForSpeechSeqaSeq,
				    FlaxAutoModelForTokenClassification,
				    FlaxAutoModelForVisionaSeq,
				)

else:
		import sys

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