





from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


UpperCAmelCase_           =	logging.get_logger(__name__)


if is_vision_available():
   import PIL




class 			__UpperCamelCase    (							A__    ):
     __A	:  Optional[int]							  =   ["""pixel_values"""]





     def __init__(     self				,							_UpperCamelCase = True				,							_UpperCamelCase = None				,							_UpperCamelCase = PILImageResampling.BICUBIC				,							_UpperCamelCase = True				,							_UpperCamelCase = None				,							_UpperCamelCase = True				,							_UpperCamelCase = 1 / 255				,							_UpperCamelCase = True				,							_UpperCamelCase = None				,							_UpperCamelCase = None				,							_UpperCamelCase = True				,							**_UpperCamelCase				,							):
        super().__init__(**_UpperCamelCase					)
        _UpperCAmelCase           =   size if size is not None else {'''shortest_edge''': 224}
        _UpperCAmelCase           =   get_size_dict(_UpperCamelCase				,							default_to_square=_UpperCamelCase					)
        _UpperCAmelCase           =   crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
        _UpperCAmelCase           =   get_size_dict(_UpperCamelCase				,							default_to_square=_UpperCamelCase				,							param_name='''crop_size'''					)

        _UpperCAmelCase           =   do_resize
        _UpperCAmelCase           =   size
        _UpperCAmelCase           =   resample
        _UpperCAmelCase           =   do_center_crop
        _UpperCAmelCase           =   crop_size
        _UpperCAmelCase           =   do_rescale
        _UpperCAmelCase           =   rescale_factor
        _UpperCAmelCase           =   do_normalize
        _UpperCAmelCase           =   image_mean if image_mean is not None else OPENAI_CLIP_MEAN
        _UpperCAmelCase           =   image_std if image_std is not None else OPENAI_CLIP_STD
        _UpperCAmelCase           =   do_convert_rgb





     def 		UpperCamelCase(     self				,							_UpperCamelCase				,							_UpperCamelCase				,							_UpperCamelCase = PILImageResampling.BICUBIC				,							_UpperCamelCase = None				,							**_UpperCamelCase				,							):
        _UpperCAmelCase           =   get_size_dict(_UpperCamelCase				,							default_to_square=_UpperCamelCase					)
        if "shortest_edge" not in size:
           raise ValueError(f'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'''					)
        _UpperCAmelCase           =   get_resize_output_image_size(_UpperCamelCase				,							size=size['''shortest_edge''']				,							default_to_square=_UpperCamelCase					)
        return resize(_UpperCamelCase				,							size=_UpperCamelCase				,							resample=_UpperCamelCase				,							data_format=_UpperCamelCase				,							**_UpperCamelCase					)





     def 		UpperCamelCase(     self				,							_UpperCamelCase				,							_UpperCamelCase				,							_UpperCamelCase = None				,							**_UpperCamelCase				,							):
        _UpperCAmelCase           =   get_size_dict(_UpperCamelCase					)
        if "height" not in size or "width" not in size:
           raise ValueError(f'''The `size` parameter must contain the keys (height, width). Got {size.keys()}'''					)
        return center_crop(_UpperCamelCase				,							size=(size['''height'''], size['''width'''])				,							data_format=_UpperCamelCase				,							**_UpperCamelCase					)





     def 		UpperCamelCase(     self				,							_UpperCamelCase				,							_UpperCamelCase				,							_UpperCamelCase = None				,							**_UpperCamelCase				,							):
        return rescale(_UpperCamelCase				,							scale=_UpperCamelCase				,							data_format=_UpperCamelCase				,							**_UpperCamelCase					)





     def 		UpperCamelCase(     self				,							_UpperCamelCase				,							_UpperCamelCase				,							_UpperCamelCase				,							_UpperCamelCase = None				,							**_UpperCamelCase				,							):
        return normalize(_UpperCamelCase				,							mean=_UpperCamelCase				,							std=_UpperCamelCase				,							data_format=_UpperCamelCase				,							**_UpperCamelCase					)





     def 		UpperCamelCase(     self				,							_UpperCamelCase				,							_UpperCamelCase = None				,							_UpperCamelCase = None				,							_UpperCamelCase = None				,							_UpperCamelCase = None				,							_UpperCamelCase = None				,							_UpperCamelCase = None				,							_UpperCamelCase = None				,							_UpperCamelCase = None				,							_UpperCamelCase = None				,							_UpperCamelCase = None				,							_UpperCamelCase = None				,							_UpperCamelCase = None				,							_UpperCamelCase = ChannelDimension.FIRST				,							**_UpperCamelCase				,							):
        _UpperCAmelCase           =   do_resize if do_resize is not None else self.do_resize
        _UpperCAmelCase           =   size if size is not None else self.size
        _UpperCAmelCase           =   get_size_dict(_UpperCamelCase				,							param_name='''size'''				,							default_to_square=_UpperCamelCase					)
        _UpperCAmelCase           =   resample if resample is not None else self.resample
        _UpperCAmelCase           =   do_center_crop if do_center_crop is not None else self.do_center_crop
        _UpperCAmelCase           =   crop_size if crop_size is not None else self.crop_size
        _UpperCAmelCase           =   get_size_dict(_UpperCamelCase				,							param_name='''crop_size'''				,							default_to_square=_UpperCamelCase					)
        _UpperCAmelCase           =   do_rescale if do_rescale is not None else self.do_rescale
        _UpperCAmelCase           =   rescale_factor if rescale_factor is not None else self.rescale_factor
        _UpperCAmelCase           =   do_normalize if do_normalize is not None else self.do_normalize
        _UpperCAmelCase           =   image_mean if image_mean is not None else self.image_mean
        _UpperCAmelCase           =   image_std if image_std is not None else self.image_std
        _UpperCAmelCase           =   do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

        _UpperCAmelCase           =   make_list_of_images(_UpperCamelCase					)

        if not valid_images(_UpperCamelCase					):
           raise ValueError(
               '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
               '''torch.Tensor, tf.Tensor or jax.ndarray.'''					)

        if do_resize and size is None:
           raise ValueError('''Size must be specified if do_resize is True.'''					)

        if do_center_crop and crop_size is None:
           raise ValueError('''Crop size must be specified if do_center_crop is True.'''					)

        if do_rescale and rescale_factor is None:
           raise ValueError('''Rescale factor must be specified if do_rescale is True.'''					)

        if do_normalize and (image_mean is None or image_std is None):
           raise ValueError('''Image mean and std must be specified if do_normalize is True.'''					)

        # PIL RGBA images are converted to RGB
        if do_convert_rgb:
           _UpperCAmelCase           =   [convert_to_rgb(_UpperCamelCase					) for image in images]

        # All transformations expect numpy arrays.
        _UpperCAmelCase           =   [to_numpy_array(_UpperCamelCase					) for image in images]

        if do_resize:
           _UpperCAmelCase           =   [self.resize(image=_UpperCamelCase				,							size=_UpperCamelCase				,							resample=_UpperCamelCase					) for image in images]

        if do_center_crop:
           _UpperCAmelCase           =   [self.center_crop(image=_UpperCamelCase				,							size=_UpperCamelCase					) for image in images]

        if do_rescale:
           _UpperCAmelCase           =   [self.rescale(image=_UpperCamelCase				,							scale=_UpperCamelCase					) for image in images]

        if do_normalize:
           _UpperCAmelCase           =   [self.normalize(image=_UpperCamelCase				,							mean=_UpperCamelCase				,							std=_UpperCamelCase					) for image in images]

        _UpperCAmelCase           =   [to_channel_dimension_format(_UpperCamelCase				,							_UpperCamelCase					) for image in images]

        _UpperCAmelCase           =   {'''pixel_values''': images}
        return BatchFeature(data=_UpperCamelCase				,							tensor_type=_UpperCamelCase					)






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"
)