




import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_video_inputs


if is_torch_available():
       import torch

if is_vision_available():
       from PIL import Image

       from transformers import VivitImageProcessor


class 				SCREAMING_SNAKE_CASE__ (unittest.TestCase					):





    def __init__(				self						,       a						,       a=7						,       a=3						,       a=10						,       a=18						,       a=30						,       a=400						,       a=True						,       a=None						,       a=True						,       a=[0.5, 0.5, 0.5]						,       a=[0.5, 0.5, 0.5]						,       a=None						,       ):
         lowercase__   :       str				=	size if size is not None else {'shortest_edge': 18}
         lowercase__   :       Dict				=	crop_size if crop_size is not None else {'height': 18, 'width': 18}

         lowercase__   :       Any				=	parent
         lowercase__   :       Optional[Any]				=	batch_size
         lowercase__   :       Optional[Any]				=	num_channels
         lowercase__   :       List[str]				=	num_frames
         lowercase__   :       Union[str, Any]				=	image_size
         lowercase__   :       Any				=	min_resolution
         lowercase__   :       Any				=	max_resolution
         lowercase__   :       str				=	do_resize
         lowercase__   :       Tuple				=	size
         lowercase__   :       Any				=	do_normalize
         lowercase__   :       Any				=	image_mean
         lowercase__   :       str				=	image_std
         lowercase__   :       List[str]				=	crop_size





    def 				snake_case_    (				self):
         return {
             "image_mean": self.image_mean,
             "image_std": self.image_std,
             "do_normalize": self.do_normalize,
             "do_resize": self.do_resize,
             "size": self.size,
             "crop_size": self.crop_size,
         }



@require_torch
@require_vision
class 				SCREAMING_SNAKE_CASE__ (__snake_case		,	unittest.TestCase					):
    __lowerCamelCase						:	List[str]														=					VivitImageProcessor if is_vision_available() else None





    def 				snake_case_    (				self):
         lowercase__   :       Optional[Any]				=	VivitImageProcessingTester(self)





    @property
    def 				snake_case_    (				self):
         return self.image_processor_tester.prepare_image_processor_dict()





    def 				snake_case_    (				self):
         lowercase__   :       Any				=	self.image_processing_class(**self.image_processor_dict)
         self.assertTrue(hasattr(a						,       'image_mean'))
         self.assertTrue(hasattr(a						,       'image_std'))
         self.assertTrue(hasattr(a						,       'do_normalize'))
         self.assertTrue(hasattr(a						,       'do_resize'))
         self.assertTrue(hasattr(a						,       'do_center_crop'))
         self.assertTrue(hasattr(a						,       'size'))





    def 				snake_case_    (				self):
         lowercase__   :       str				=	self.image_processing_class.from_dict(self.image_processor_dict)
         self.assertEqual(image_processor.size						,       {'shortest_edge': 18})
         self.assertEqual(image_processor.crop_size						,       {'height': 18, 'width': 18})

         lowercase__   :       List[str]				=	self.image_processing_class.from_dict(self.image_processor_dict						,       size=42						,       crop_size=84)
         self.assertEqual(image_processor.size						,       {'shortest_edge': 42})
         self.assertEqual(image_processor.crop_size						,       {'height': 84, 'width': 84})





    def 				snake_case_    (				self):
         # Initialize image_processing
         lowercase__   :       List[str]				=	self.image_processing_class(**self.image_processor_dict)
         # create random PIL videos
         lowercase__   :       List[str]				=	prepare_video_inputs(self.image_processor_tester						,       equal_resolution=a)
         for video in video_inputs:
              self.assertIsInstance(a						,       a)
              self.assertIsInstance(video[0]						,       Image.Image)

         # Test not batched input
         lowercase__   :       Union[str, Any]				=	image_processing(video_inputs[0]						,       return_tensors='pt').pixel_values
         self.assertEqual(
             encoded_videos.shape						,       (
                 1,
                 self.image_processor_tester.num_frames,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.crop_size['height'],
                 self.image_processor_tester.crop_size['width'],
             )						,       )

         # Test batched
         lowercase__   :       Optional[Any]				=	image_processing(a						,       return_tensors='pt').pixel_values
         self.assertEqual(
             encoded_videos.shape						,       (
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_frames,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.crop_size['height'],
                 self.image_processor_tester.crop_size['width'],
             )						,       )





    def 				snake_case_    (				self):
         # Initialize image_processing
         lowercase__   :       int				=	self.image_processing_class(**self.image_processor_dict)
         # create random numpy tensors
         lowercase__   :       Optional[int]				=	prepare_video_inputs(self.image_processor_tester						,       equal_resolution=a						,       numpify=a)
         for video in video_inputs:
              self.assertIsInstance(a						,       a)
              self.assertIsInstance(video[0]						,       np.ndarray)

         # Test not batched input
         lowercase__   :       Dict				=	image_processing(video_inputs[0]						,       return_tensors='pt').pixel_values
         self.assertEqual(
             encoded_videos.shape						,       (
                 1,
                 self.image_processor_tester.num_frames,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.crop_size['height'],
                 self.image_processor_tester.crop_size['width'],
             )						,       )

         # Test batched
         lowercase__   :       str				=	image_processing(a						,       return_tensors='pt').pixel_values
         self.assertEqual(
             encoded_videos.shape						,       (
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_frames,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.crop_size['height'],
                 self.image_processor_tester.crop_size['width'],
             )						,       )





    def 				snake_case_    (				self):
         # Initialize image_processing
         lowercase__   :       List[str]				=	self.image_processing_class(**self.image_processor_dict)
         # create random PyTorch tensors
         lowercase__   :       List[str]				=	prepare_video_inputs(self.image_processor_tester						,       equal_resolution=a						,       torchify=a)
         for video in video_inputs:
              self.assertIsInstance(a						,       a)
              self.assertIsInstance(video[0]						,       torch.Tensor)

         # Test not batched input
         lowercase__   :       List[str]				=	image_processing(video_inputs[0]						,       return_tensors='pt').pixel_values
         self.assertEqual(
             encoded_videos.shape						,       (
                 1,
                 self.image_processor_tester.num_frames,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.crop_size['height'],
                 self.image_processor_tester.crop_size['width'],
             )						,       )

         # Test batched
         lowercase__   :       Any				=	image_processing(a						,       return_tensors='pt').pixel_values
         self.assertEqual(
             encoded_videos.shape						,       (
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_frames,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.crop_size['height'],
                 self.image_processor_tester.crop_size['width'],
             )						,       )




from collections import OrderedDict

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


snake_case_        =			logging.get_logger(__name__)


snake_case_        =			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'''),
    ]
)

snake_case_        =			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'''),
    ]
)

snake_case_        =			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'''),
    ]
)

snake_case_        =			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'''),
    ]
)

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

snake_case_        =			OrderedDict(
    [
        ('''vision-encoder-decoder''', '''FlaxVisionEncoderDecoderModel'''),
    ]
)

snake_case_        =			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'''),
    ]
)

snake_case_        =			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'''),
    ]
)

snake_case_        =			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'''),
    ]
)

snake_case_        =			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'''),
    ]
)

snake_case_        =			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'''),
    ]
)

snake_case_        =			OrderedDict(
    [
        ('''bert''', '''FlaxBertForNextSentencePrediction'''),
    ]
)

snake_case_        =			OrderedDict(
    [
        ('''speech-encoder-decoder''', '''FlaxSpeechEncoderDecoderModel'''),
        ('''whisper''', '''FlaxWhisperForConditionalGeneration'''),
    ]
)

snake_case_        =			OrderedDict(
    [
        ('''whisper''', '''FlaxWhisperForAudioClassification'''),
    ]
)

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


class 				SCREAMING_SNAKE_CASE__ (_BaseAutoModelClass					):
    __lowerCamelCase						:	str														=					FLAX_MODEL_MAPPING



snake_case_        =			auto_class_update(FlaxAutoModel)


class 				SCREAMING_SNAKE_CASE__ (_BaseAutoModelClass					):
    __lowerCamelCase						:	Dict														=					FLAX_MODEL_FOR_PRETRAINING_MAPPING



snake_case_        =			auto_class_update(FlaxAutoModelForPreTraining, head_doc='''pretraining''')


class 				SCREAMING_SNAKE_CASE__ (_BaseAutoModelClass					):
    __lowerCamelCase						:	List[Any]														=					FLAX_MODEL_FOR_CAUSAL_LM_MAPPING



snake_case_        =			auto_class_update(FlaxAutoModelForCausalLM, head_doc='''causal language modeling''')


class 				SCREAMING_SNAKE_CASE__ (_BaseAutoModelClass					):
    __lowerCamelCase						:	Optional[int]														=					FLAX_MODEL_FOR_MASKED_LM_MAPPING



snake_case_        =			auto_class_update(FlaxAutoModelForMaskedLM, head_doc='''masked language modeling''')


class 				SCREAMING_SNAKE_CASE__ (_BaseAutoModelClass					):
    __lowerCamelCase						:	Optional[int]														=					FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING



snake_case_        =			auto_class_update(
    FlaxAutoModelForSeqaSeqLM, head_doc='''sequence-to-sequence language modeling''', checkpoint_for_example='''t5-base'''
)


class 				SCREAMING_SNAKE_CASE__ (_BaseAutoModelClass					):
    __lowerCamelCase						:	Dict														=					FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING



snake_case_        =			auto_class_update(
    FlaxAutoModelForSequenceClassification, head_doc='''sequence classification'''
)


class 				SCREAMING_SNAKE_CASE__ (_BaseAutoModelClass					):
    __lowerCamelCase						:	Tuple														=					FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING



snake_case_        =			auto_class_update(FlaxAutoModelForQuestionAnswering, head_doc='''question answering''')


class 				SCREAMING_SNAKE_CASE__ (_BaseAutoModelClass					):
    __lowerCamelCase						:	Tuple														=					FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING



snake_case_        =			auto_class_update(
    FlaxAutoModelForTokenClassification, head_doc='''token classification'''
)


class 				SCREAMING_SNAKE_CASE__ (_BaseAutoModelClass					):
    __lowerCamelCase						:	str														=					FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING



snake_case_        =			auto_class_update(FlaxAutoModelForMultipleChoice, head_doc='''multiple choice''')


class 				SCREAMING_SNAKE_CASE__ (_BaseAutoModelClass					):
    __lowerCamelCase						:	Union[str, Any]														=					FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING



snake_case_        =			auto_class_update(
    FlaxAutoModelForNextSentencePrediction, head_doc='''next sentence prediction'''
)


class 				SCREAMING_SNAKE_CASE__ (_BaseAutoModelClass					):
    __lowerCamelCase						:	Union[str, Any]														=					FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING



snake_case_        =			auto_class_update(
    FlaxAutoModelForImageClassification, head_doc='''image classification'''
)


class 				SCREAMING_SNAKE_CASE__ (_BaseAutoModelClass					):
    __lowerCamelCase						:	Tuple														=					FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING



snake_case_        =			auto_class_update(FlaxAutoModelForVisionaSeq, head_doc='''vision-to-text modeling''')


class 				SCREAMING_SNAKE_CASE__ (_BaseAutoModelClass					):
    __lowerCamelCase						:	List[str]														=					FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING



snake_case_        =			auto_class_update(
    FlaxAutoModelForSpeechSeqaSeq, head_doc='''sequence-to-sequence speech-to-text modeling'''
)

