


import os
import zipfile

import pytest

from datasets.utils.extract import (
    BzipaExtractor,
    Extractor,
    GzipExtractor,
    LzaExtractor,
    SevenZipExtractor,
    TarExtractor,
    XzExtractor,
    ZipExtractor,
    ZstdExtractor,
)

from .utils import require_lza, require_pyazr, require_zstandard




@pytest.mark.parametrize(
    '''compression_format, is_archive'''      ,    [
        ('''7z''', True),
        ('''bz2''', False),
        ('''gzip''', False),
        ('''lz4''', False),
        ('''tar''', True),
        ('''xz''', False),
        ('''zip''', True),
        ('''zstd''', False),
    ]      ,    )
def    a   (							A__      ,    A__      ,    A__      ,    A__      ,    A__      ,    A__      ,    A__      ,    A__      ,    A__      ,    A__      ,    A__      ,    A__      ,    )	-> Union[str, Any]:




      '''simple docstring'''

      SCREAMING_SNAKE_CASE__       :  List[Any]						     =		{
          '''7z''': (seven_zip_file, SevenZipExtractor),
          '''bz2''': (bza_file, BzipaExtractor),
          '''gzip''': (gz_file, GzipExtractor),
          '''lz4''': (lza_file, LzaExtractor),
          '''tar''': (tar_file, TarExtractor),
          '''xz''': (xz_file, XzExtractor),
          '''zip''': (zip_file, ZipExtractor),
          '''zstd''': (zstd_file, ZstdExtractor),
      }
      SCREAMING_SNAKE_CASE__     ,       SCREAMING_SNAKE_CASE__       :  Optional[int]						     =		input_paths_and_base_extractors[compression_format]
      if input_path is None:
            SCREAMING_SNAKE_CASE__       :  Union[str, Any]						     =		f"""for '{compression_format}' compression_format, """
            if compression_format == "7z":
                  reason += require_pyazr.kwargs["reason"]
            elif compression_format == "lz4":
                  reason += require_lza.kwargs["reason"]
            elif compression_format == "zstd":
                  reason += require_zstandard.kwargs["reason"]
            pytest.skip(A__							)
      assert base_extractor.is_extractable(A__							)
      SCREAMING_SNAKE_CASE__       :  Tuple						     =		tmp_path / ('''extracted''' if is_archive else '''extracted.txt''')
      base_extractor.extract(A__      ,    A__							)
      if is_archive:
            assert output_path.is_dir()
            for file_path in output_path.iterdir():
                  assert file_path.name == text_file.name
                  SCREAMING_SNAKE_CASE__       :  List[str]						     =		file_path.read_text(encoding='''utf-8'''							)
      else:
            SCREAMING_SNAKE_CASE__       :  Tuple						     =		output_path.read_text(encoding='''utf-8'''							)
      SCREAMING_SNAKE_CASE__       :  List[str]						     =		text_file.read_text(encoding='''utf-8'''							)
      assert extracted_file_content == expected_file_content




@pytest.mark.parametrize(
    '''compression_format, is_archive'''      ,    [
        ('''7z''', True),
        ('''bz2''', False),
        ('''gzip''', False),
        ('''lz4''', False),
        ('''tar''', True),
        ('''xz''', False),
        ('''zip''', True),
        ('''zstd''', False),
    ]      ,    )
def    a   (							A__      ,    A__      ,    A__      ,    A__      ,    A__      ,    A__      ,    A__      ,    A__      ,    A__      ,    A__      ,    A__      ,    A__      ,    )	-> Optional[Any]:




      '''simple docstring'''

      SCREAMING_SNAKE_CASE__       :  Union[str, Any]						     =		{
          '''7z''': seven_zip_file,
          '''bz2''': bza_file,
          '''gzip''': gz_file,
          '''lz4''': lza_file,
          '''tar''': tar_file,
          '''xz''': xz_file,
          '''zip''': zip_file,
          '''zstd''': zstd_file,
      }
      SCREAMING_SNAKE_CASE__       :  int						     =		input_paths[compression_format]
      if input_path is None:
            SCREAMING_SNAKE_CASE__       :  int						     =		f"""for '{compression_format}' compression_format, """
            if compression_format == "7z":
                  reason += require_pyazr.kwargs["reason"]
            elif compression_format == "lz4":
                  reason += require_lza.kwargs["reason"]
            elif compression_format == "zstd":
                  reason += require_zstandard.kwargs["reason"]
            pytest.skip(A__							)
      SCREAMING_SNAKE_CASE__       :  Any						     =		Extractor.infer_extractor_format(A__							)
      assert extractor_format is not None
      SCREAMING_SNAKE_CASE__       :  Any						     =		tmp_path / ('''extracted''' if is_archive else '''extracted.txt''')
      Extractor.extract(A__      ,    A__      ,    A__							)
      if is_archive:
            assert output_path.is_dir()
            for file_path in output_path.iterdir():
                  assert file_path.name == text_file.name
                  SCREAMING_SNAKE_CASE__       :  List[Any]						     =		file_path.read_text(encoding='''utf-8'''							)
      else:
            SCREAMING_SNAKE_CASE__       :  Optional[int]						     =		output_path.read_text(encoding='''utf-8'''							)
      SCREAMING_SNAKE_CASE__       :  List[Any]						     =		text_file.read_text(encoding='''utf-8'''							)
      assert extracted_file_content == expected_file_content




@pytest.fixture
def    a   (							A__      ,    A__							)	-> str:




      '''simple docstring'''

      import tarfile

      SCREAMING_SNAKE_CASE__       :  Optional[int]						     =		tmp_path / '''data_dot_dot'''
      directory.mkdir()
      SCREAMING_SNAKE_CASE__       :  str						     =		directory / '''tar_file_with_dot_dot.tar'''
      with tarfile.TarFile(A__      ,    '''w'''							) as f:
            f.add(A__      ,    arcname=os.path.join('''..'''      ,    text_file.name							)							)
      return path




@pytest.fixture
def    a   (							A__							)	-> Union[str, Any]:




      '''simple docstring'''

      import tarfile

      SCREAMING_SNAKE_CASE__       :  Optional[int]						     =		tmp_path / '''data_sym_link'''
      directory.mkdir()
      SCREAMING_SNAKE_CASE__       :  Tuple						     =		directory / '''tar_file_with_sym_link.tar'''
      os.symlink('''..'''      ,    directory / '''subdir'''      ,    target_is_directory=A__							)
      with tarfile.TarFile(A__      ,    '''w'''							) as f:
            f.add(str(directory / '''subdir'''							)      ,    arcname='''subdir'''							)  # str required by os.readlink on Windows and Python < 3.8
      return path




@pytest.mark.parametrize(
    '''insecure_tar_file, error_log'''      ,    [('''tar_file_with_dot_dot''', '''illegal path'''), ('''tar_file_with_sym_link''', '''Symlink''')]      ,    )
def    a   (							A__      ,    A__      ,    A__      ,    A__      ,    A__      ,    A__							)	-> Optional[Any]:




      '''simple docstring'''

      SCREAMING_SNAKE_CASE__       :  List[str]						     =		{
          '''tar_file_with_dot_dot''': tar_file_with_dot_dot,
          '''tar_file_with_sym_link''': tar_file_with_sym_link,
      }
      SCREAMING_SNAKE_CASE__       :  Tuple						     =		insecure_tar_files[insecure_tar_file]
      SCREAMING_SNAKE_CASE__       :  List[Any]						     =		tmp_path / '''extracted'''
      TarExtractor.extract(A__      ,    A__							)
      assert caplog.text
      for record in caplog.records:
            assert record.levelname == "ERROR"
            assert error_log in record.msg





def    a   (							A__							)	-> str:




      '''simple docstring'''

      SCREAMING_SNAKE_CASE__       :  Optional[Any]						     =		tmpdir / '''not_a_zip_file'''
      # From: https://github.com/python/cpython/pull/5053
      SCREAMING_SNAKE_CASE__       :  Tuple						     =		(
          b'''\x89PNG\r\n\x1a\n\x00\x00\x00\rIHDR\x00\x00\x00\x01\x00\x00'''
          b'''\x00\x02\x08\x06\x00\x00\x00\x99\x81\xb6\'\x00\x00\x00\x15I'''
          b'''DATx\x01\x01\n\x00\xf5\xff\x00PK\x05\x06\x00PK\x06\x06\x07'''
          b'''\xac\x01N\xc6|a\r\x00\x00\x00\x00IEND\xaeB`\x82'''
      )
      with not_a_zip_file.open('''wb'''							) as f:
            f.write(A__							)
      assert zipfile.is_zipfile(str(A__							)							)  # is a false positive for `zipfile`
      assert not ZipExtractor.is_extractable(A__							)  # but we're right













from collections import OrderedDict

from ...utils import logging
from .auto_factory import _BaseAutoModelClass, _LazyAutoMapping, auto_class_update
from .configuration_auto import CONFIG_MAPPING_NAMES


UpperCAmelCase_           =	logging.get_logger(__name__)


UpperCAmelCase_           =	OrderedDict(
    [
        # Base model mapping
        ("albert", "FlaxAlbertModel"),
        ("bart", "FlaxBartModel"),
        ("beit", "FlaxBeitModel"),
        ("bert", "FlaxBertModel"),
        ("big_bird", "FlaxBigBirdModel"),
        ("blenderbot", "FlaxBlenderbotModel"),
        ("blenderbot-small", "FlaxBlenderbotSmallModel"),
        ("clip", "FlaxCLIPModel"),
        ("distilbert", "FlaxDistilBertModel"),
        ("electra", "FlaxElectraModel"),
        ("gpt-sw3", "FlaxGPT2Model"),
        ("gpt2", "FlaxGPT2Model"),
        ("gpt_neo", "FlaxGPTNeoModel"),
        ("gptj", "FlaxGPTJModel"),
        ("longt5", "FlaxLongT5Model"),
        ("marian", "FlaxMarianModel"),
        ("mbart", "FlaxMBartModel"),
        ("mt5", "FlaxMT5Model"),
        ("opt", "FlaxOPTModel"),
        ("pegasus", "FlaxPegasusModel"),
        ("regnet", "FlaxRegNetModel"),
        ("resnet", "FlaxResNetModel"),
        ("roberta", "FlaxRobertaModel"),
        ("roberta-prelayernorm", "FlaxRobertaPreLayerNormModel"),
        ("roformer", "FlaxRoFormerModel"),
        ("t5", "FlaxT5Model"),
        ("vision-text-dual-encoder", "FlaxVisionTextDualEncoderModel"),
        ("vit", "FlaxViTModel"),
        ("wav2vec2", "FlaxWav2Vec2Model"),
        ("whisper", "FlaxWhisperModel"),
        ("xglm", "FlaxXGLMModel"),
        ("xlm-roberta", "FlaxXLMRobertaModel"),
    ]
)

UpperCAmelCase_           =	OrderedDict(
    [
        # Model for pre-training mapping
        ("albert", "FlaxAlbertForPreTraining"),
        ("bart", "FlaxBartForConditionalGeneration"),
        ("bert", "FlaxBertForPreTraining"),
        ("big_bird", "FlaxBigBirdForPreTraining"),
        ("electra", "FlaxElectraForPreTraining"),
        ("longt5", "FlaxLongT5ForConditionalGeneration"),
        ("mbart", "FlaxMBartForConditionalGeneration"),
        ("mt5", "FlaxMT5ForConditionalGeneration"),
        ("roberta", "FlaxRobertaForMaskedLM"),
        ("roberta-prelayernorm", "FlaxRobertaPreLayerNormForMaskedLM"),
        ("roformer", "FlaxRoFormerForMaskedLM"),
        ("t5", "FlaxT5ForConditionalGeneration"),
        ("wav2vec2", "FlaxWav2Vec2ForPreTraining"),
        ("whisper", "FlaxWhisperForConditionalGeneration"),
        ("xlm-roberta", "FlaxXLMRobertaForMaskedLM"),
    ]
)

UpperCAmelCase_           =	OrderedDict(
    [
        # Model for Masked LM mapping
        ("albert", "FlaxAlbertForMaskedLM"),
        ("bart", "FlaxBartForConditionalGeneration"),
        ("bert", "FlaxBertForMaskedLM"),
        ("big_bird", "FlaxBigBirdForMaskedLM"),
        ("distilbert", "FlaxDistilBertForMaskedLM"),
        ("electra", "FlaxElectraForMaskedLM"),
        ("mbart", "FlaxMBartForConditionalGeneration"),
        ("roberta", "FlaxRobertaForMaskedLM"),
        ("roberta-prelayernorm", "FlaxRobertaPreLayerNormForMaskedLM"),
        ("roformer", "FlaxRoFormerForMaskedLM"),
        ("xlm-roberta", "FlaxXLMRobertaForMaskedLM"),
    ]
)

UpperCAmelCase_           =	OrderedDict(
    [
        # Model for Seq2Seq Causal LM mapping
        ("bart", "FlaxBartForConditionalGeneration"),
        ("blenderbot", "FlaxBlenderbotForConditionalGeneration"),
        ("blenderbot-small", "FlaxBlenderbotSmallForConditionalGeneration"),
        ("encoder-decoder", "FlaxEncoderDecoderModel"),
        ("longt5", "FlaxLongT5ForConditionalGeneration"),
        ("marian", "FlaxMarianMTModel"),
        ("mbart", "FlaxMBartForConditionalGeneration"),
        ("mt5", "FlaxMT5ForConditionalGeneration"),
        ("pegasus", "FlaxPegasusForConditionalGeneration"),
        ("t5", "FlaxT5ForConditionalGeneration"),
    ]
)

UpperCAmelCase_           =	OrderedDict(
    [
        # Model for Image-classsification
        ("beit", "FlaxBeitForImageClassification"),
        ("regnet", "FlaxRegNetForImageClassification"),
        ("resnet", "FlaxResNetForImageClassification"),
        ("vit", "FlaxViTForImageClassification"),
    ]
)

UpperCAmelCase_           =	OrderedDict(
    [
        ("vision-encoder-decoder", "FlaxVisionEncoderDecoderModel"),
    ]
)

UpperCAmelCase_           =	OrderedDict(
    [
        # Model for Causal LM mapping
        ("bart", "FlaxBartForCausalLM"),
        ("bert", "FlaxBertForCausalLM"),
        ("big_bird", "FlaxBigBirdForCausalLM"),
        ("electra", "FlaxElectraForCausalLM"),
        ("gpt-sw3", "FlaxGPT2LMHeadModel"),
        ("gpt2", "FlaxGPT2LMHeadModel"),
        ("gpt_neo", "FlaxGPTNeoForCausalLM"),
        ("gptj", "FlaxGPTJForCausalLM"),
        ("opt", "FlaxOPTForCausalLM"),
        ("roberta", "FlaxRobertaForCausalLM"),
        ("roberta-prelayernorm", "FlaxRobertaPreLayerNormForCausalLM"),
        ("xglm", "FlaxXGLMForCausalLM"),
        ("xlm-roberta", "FlaxXLMRobertaForCausalLM"),
    ]
)

UpperCAmelCase_           =	OrderedDict(
    [
        # Model for Sequence Classification mapping
        ("albert", "FlaxAlbertForSequenceClassification"),
        ("bart", "FlaxBartForSequenceClassification"),
        ("bert", "FlaxBertForSequenceClassification"),
        ("big_bird", "FlaxBigBirdForSequenceClassification"),
        ("distilbert", "FlaxDistilBertForSequenceClassification"),
        ("electra", "FlaxElectraForSequenceClassification"),
        ("mbart", "FlaxMBartForSequenceClassification"),
        ("roberta", "FlaxRobertaForSequenceClassification"),
        ("roberta-prelayernorm", "FlaxRobertaPreLayerNormForSequenceClassification"),
        ("roformer", "FlaxRoFormerForSequenceClassification"),
        ("xlm-roberta", "FlaxXLMRobertaForSequenceClassification"),
    ]
)

UpperCAmelCase_           =	OrderedDict(
    [
        # Model for Question Answering mapping
        ("albert", "FlaxAlbertForQuestionAnswering"),
        ("bart", "FlaxBartForQuestionAnswering"),
        ("bert", "FlaxBertForQuestionAnswering"),
        ("big_bird", "FlaxBigBirdForQuestionAnswering"),
        ("distilbert", "FlaxDistilBertForQuestionAnswering"),
        ("electra", "FlaxElectraForQuestionAnswering"),
        ("mbart", "FlaxMBartForQuestionAnswering"),
        ("roberta", "FlaxRobertaForQuestionAnswering"),
        ("roberta-prelayernorm", "FlaxRobertaPreLayerNormForQuestionAnswering"),
        ("roformer", "FlaxRoFormerForQuestionAnswering"),
        ("xlm-roberta", "FlaxXLMRobertaForQuestionAnswering"),
    ]
)

UpperCAmelCase_           =	OrderedDict(
    [
        # Model for Token Classification mapping
        ("albert", "FlaxAlbertForTokenClassification"),
        ("bert", "FlaxBertForTokenClassification"),
        ("big_bird", "FlaxBigBirdForTokenClassification"),
        ("distilbert", "FlaxDistilBertForTokenClassification"),
        ("electra", "FlaxElectraForTokenClassification"),
        ("roberta", "FlaxRobertaForTokenClassification"),
        ("roberta-prelayernorm", "FlaxRobertaPreLayerNormForTokenClassification"),
        ("roformer", "FlaxRoFormerForTokenClassification"),
        ("xlm-roberta", "FlaxXLMRobertaForTokenClassification"),
    ]
)

UpperCAmelCase_           =	OrderedDict(
    [
        # Model for Multiple Choice mapping
        ("albert", "FlaxAlbertForMultipleChoice"),
        ("bert", "FlaxBertForMultipleChoice"),
        ("big_bird", "FlaxBigBirdForMultipleChoice"),
        ("distilbert", "FlaxDistilBertForMultipleChoice"),
        ("electra", "FlaxElectraForMultipleChoice"),
        ("roberta", "FlaxRobertaForMultipleChoice"),
        ("roberta-prelayernorm", "FlaxRobertaPreLayerNormForMultipleChoice"),
        ("roformer", "FlaxRoFormerForMultipleChoice"),
        ("xlm-roberta", "FlaxXLMRobertaForMultipleChoice"),
    ]
)

UpperCAmelCase_           =	OrderedDict(
    [
        ("bert", "FlaxBertForNextSentencePrediction"),
    ]
)

UpperCAmelCase_           =	OrderedDict(
    [
        ("speech-encoder-decoder", "FlaxSpeechEncoderDecoderModel"),
        ("whisper", "FlaxWhisperForConditionalGeneration"),
    ]
)

UpperCAmelCase_           =	OrderedDict(
    [
        ("whisper", "FlaxWhisperForAudioClassification"),
    ]
)

UpperCAmelCase_           =	_LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_MAPPING_NAMES)
UpperCAmelCase_           =	_LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_PRETRAINING_MAPPING_NAMES)
UpperCAmelCase_           =	_LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_MASKED_LM_MAPPING_NAMES)
UpperCAmelCase_           =	_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES
)
UpperCAmelCase_           =	_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES
)
UpperCAmelCase_           =	_LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING_NAMES)
UpperCAmelCase_           =	_LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_CAUSAL_LM_MAPPING_NAMES)
UpperCAmelCase_           =	_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES
)
UpperCAmelCase_           =	_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES
)
UpperCAmelCase_           =	_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES
)
UpperCAmelCase_           =	_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES
)
UpperCAmelCase_           =	_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMES
)
UpperCAmelCase_           =	_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES
)
UpperCAmelCase_           =	_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES
)




class 			__UpperCamelCase    (							_BaseAutoModelClass    ):
     __A	:  List[str]							  =   FLAX_MODEL_MAPPING




UpperCAmelCase_           =	auto_class_update(FlaxAutoModel)




class 			__UpperCamelCase    (							_BaseAutoModelClass    ):
     __A	:  Optional[Any]							  =   FLAX_MODEL_FOR_PRETRAINING_MAPPING




UpperCAmelCase_           =	auto_class_update(FlaxAutoModelForPreTraining, head_doc="pretraining")




class 			__UpperCamelCase    (							_BaseAutoModelClass    ):
     __A	:  List[Any]							  =   FLAX_MODEL_FOR_CAUSAL_LM_MAPPING




UpperCAmelCase_           =	auto_class_update(FlaxAutoModelForCausalLM, head_doc="causal language modeling")




class 			__UpperCamelCase    (							_BaseAutoModelClass    ):
     __A	:  List[str]							  =   FLAX_MODEL_FOR_MASKED_LM_MAPPING




UpperCAmelCase_           =	auto_class_update(FlaxAutoModelForMaskedLM, head_doc="masked language modeling")




class 			__UpperCamelCase    (							_BaseAutoModelClass    ):
     __A	:  Dict							  =   FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING




UpperCAmelCase_           =	auto_class_update(
    FlaxAutoModelForSeqaSeqLM, head_doc="sequence-to-sequence language modeling", checkpoint_for_example="t5-base"
)




class 			__UpperCamelCase    (							_BaseAutoModelClass    ):
     __A	:  List[str]							  =   FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING




UpperCAmelCase_           =	auto_class_update(
    FlaxAutoModelForSequenceClassification, head_doc="sequence classification"
)




class 			__UpperCamelCase    (							_BaseAutoModelClass    ):
     __A	:  Dict							  =   FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING




UpperCAmelCase_           =	auto_class_update(FlaxAutoModelForQuestionAnswering, head_doc="question answering")




class 			__UpperCamelCase    (							_BaseAutoModelClass    ):
     __A	:  Union[str, Any]							  =   FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING




UpperCAmelCase_           =	auto_class_update(
    FlaxAutoModelForTokenClassification, head_doc="token classification"
)




class 			__UpperCamelCase    (							_BaseAutoModelClass    ):
     __A	:  Union[str, Any]							  =   FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING




UpperCAmelCase_           =	auto_class_update(FlaxAutoModelForMultipleChoice, head_doc="multiple choice")




class 			__UpperCamelCase    (							_BaseAutoModelClass    ):
     __A	:  Any							  =   FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING




UpperCAmelCase_           =	auto_class_update(
    FlaxAutoModelForNextSentencePrediction, head_doc="next sentence prediction"
)




class 			__UpperCamelCase    (							_BaseAutoModelClass    ):
     __A	:  Dict							  =   FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING




UpperCAmelCase_           =	auto_class_update(
    FlaxAutoModelForImageClassification, head_doc="image classification"
)




class 			__UpperCamelCase    (							_BaseAutoModelClass    ):
     __A	:  Optional[int]							  =   FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING




UpperCAmelCase_           =	auto_class_update(FlaxAutoModelForVisionaSeq, head_doc="vision-to-text modeling")




class 			__UpperCamelCase    (							_BaseAutoModelClass    ):
     __A	:  str							  =   FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING




UpperCAmelCase_           =	auto_class_update(
    FlaxAutoModelForSpeechSeqaSeq, head_doc="sequence-to-sequence speech-to-text modeling"
)