




from copy import deepcopy
from typing import Optional, Union

import numpy as np

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, is_tf_available, is_torch_available


if is_torch_available():
       import torch

if is_tf_available():
       import tensorflow as tf


class 				SCREAMING_SNAKE_CASE__ (__snake_case					):
    __lowerCamelCase						:	Tuple														=					["""image_processor"""]
    __lowerCamelCase						:	Tuple														=					"""SamImageProcessor"""





    def __init__(				self						,       a):
         super().__init__(a)
         lowercase__   :       Dict				=	self.image_processor
         lowercase__   :       str				=	-10
         lowercase__   :       str				=	self.image_processor.size['longest_edge']





    def __call__(				self						,       a=None						,       a=None						,       a=None						,       a=None						,       a = None						,       **a						,       ):
         lowercase__   :       Optional[Any]				=	self.image_processor(
             a						,       return_tensors=a						,       **a						,       )

         # pop arguments that are not used in the foward but used nevertheless
         lowercase__   :       str				=	encoding_image_processor['original_sizes']

         if hasattr(a						,       'numpy'):  # Checks if Torch or TF tensor
              lowercase__   :       Tuple				=	original_sizes.numpy()

         lowercase__   :       Optional[Any]				=	self._check_and_preprocess_points(
             input_points=a						,       input_labels=a						,       input_boxes=a						,       )

         lowercase__   :       List[str]				=	self._normalize_and_convert(
             a						,       a						,       input_points=a						,       input_labels=a						,       input_boxes=a						,       return_tensors=a						,       )

         return encoding_image_processor





    def 				snake_case_    (				self						,       a						,       a						,       a=None						,       a=None						,       a=None						,       a="pt"						,       ):
         if input_points is not None:
              if len(a) != len(a):
                   lowercase__   :       Union[str, Any]				=	[
                       self._normalize_coordinates(self.target_size						,       a						,       original_sizes[0]) for point in input_points
                   ]
              else:
                   lowercase__   :       List[Any]				=	[
                       self._normalize_coordinates(self.target_size						,       a						,       a)
                       for point, original_size in zip(a						,       a)
                   ]
              # check that all arrays have the same shape
              if not all(point.shape == input_points[0].shape for point in input_points):
                   if input_labels is not None:
                        lowercase__   :       List[str]				=	self._pad_points_and_labels(a						,       a)

              lowercase__   :       Union[str, Any]				=	np.array(a)

         if input_labels is not None:
              lowercase__   :       Union[str, Any]				=	np.array(a)

         if input_boxes is not None:
              if len(a) != len(a):
                   lowercase__   :       Union[str, Any]				=	[
                       self._normalize_coordinates(self.target_size						,       a						,       original_sizes[0]						,       is_bounding_box=a)
                       for box in input_boxes
                   ]
              else:
                   lowercase__   :       Tuple				=	[
                       self._normalize_coordinates(self.target_size						,       a						,       a						,       is_bounding_box=a)
                       for box, original_size in zip(a						,       a)
                   ]
              lowercase__   :       Optional[Any]				=	np.array(a)

         if input_boxes is not None:
              if return_tensors == "pt":
                   lowercase__   :       Union[str, Any]				=	torch.from_numpy(a)
                   # boxes batch size of 1 by default
                   lowercase__   :       Dict				=	input_boxes.unsqueeze(1) if len(input_boxes.shape) != 3 else input_boxes
              elif return_tensors == "tf":
                   lowercase__   :       Dict				=	tf.convert_to_tensor(a)
                   # boxes batch size of 1 by default
                   lowercase__   :       str				=	tf.expand_dims(a						,       1) if len(input_boxes.shape) != 3 else input_boxes
              encoding_image_processor.update({'input_boxes': input_boxes})
         if input_points is not None:
              if return_tensors == "pt":
                   lowercase__   :       int				=	torch.from_numpy(a)
                   # point batch size of 1 by default
                   lowercase__   :       Union[str, Any]				=	input_points.unsqueeze(1) if len(input_points.shape) != 4 else input_points
              elif return_tensors == "tf":
                   lowercase__   :       Optional[int]				=	tf.convert_to_tensor(a)
                   # point batch size of 1 by default
                   lowercase__   :       str				=	tf.expand_dims(a						,       1) if len(input_points.shape) != 4 else input_points
              encoding_image_processor.update({'input_points': input_points})
         if input_labels is not None:
              if return_tensors == "pt":
                   lowercase__   :       int				=	torch.from_numpy(a)
                   # point batch size of 1 by default
                   lowercase__   :       str				=	input_labels.unsqueeze(1) if len(input_labels.shape) != 3 else input_labels
              elif return_tensors == "tf":
                   lowercase__   :       Optional[int]				=	tf.convert_to_tensor(a)
                   # point batch size of 1 by default
                   lowercase__   :       List[str]				=	tf.expand_dims(a						,       1) if len(input_labels.shape) != 3 else input_labels
              encoding_image_processor.update({'input_labels': input_labels})

         return encoding_image_processor





    def 				snake_case_    (				self						,       a						,       a):
         lowercase__   :       Tuple				=	max([point.shape[0] for point in input_points])
         lowercase__   :       Optional[int]				=	[]
         for i, point in enumerate(a):
              if point.shape[0] != expected_nb_points:
                   lowercase__   :       Optional[Any]				=	np.concatenate(
                       [point, np.zeros((expected_nb_points - point.shape[0], 2)) + self.point_pad_value]						,       axis=0)
                   lowercase__   :       Optional[Any]				=	np.append(input_labels[i]						,       [self.point_pad_value])
              processed_input_points.append(a)
         lowercase__   :       int				=	processed_input_points
         return input_points, input_labels





    def 				snake_case_    (				self						,       a						,       a						,       a						,       a=False):
         lowercase__   :       Tuple				=	original_size
         lowercase__   :       Optional[int]				=	self.image_processor._get_preprocess_shape(a						,       longest_edge=a)
         lowercase__   :       Any				=	deepcopy(a).astype(a)

         if is_bounding_box:
              lowercase__   :       int				=	coords.reshape(-1						,       2						,       2)

         lowercase__   :       str				=	coords[..., 0] * (new_w / old_w)
         lowercase__   :       List[Any]				=	coords[..., 1] * (new_h / old_h)

         if is_bounding_box:
              lowercase__   :       int				=	coords.reshape(-1						,       4)

         return coords





    def 				snake_case_    (				self						,       a=None						,       a=None						,       a=None						,       ):
         if input_points is not None:
              if hasattr(a						,       'numpy'):  # Checks for TF or Torch tensor
                   lowercase__   :       List[str]				=	input_points.numpy().tolist()

              if not isinstance(a						,       a) or not isinstance(input_points[0]						,       a):
                   raise ValueError('Input points must be a list of list of floating points.')
              lowercase__   :       Tuple				=	[np.array(a) for input_point in input_points]
         else:
              lowercase__   :       List[Any]				=	None

         if input_labels is not None:
              if hasattr(a						,       'numpy'):
                   lowercase__   :       Any				=	input_labels.numpy().tolist()

              if not isinstance(a						,       a) or not isinstance(input_labels[0]						,       a):
                   raise ValueError('Input labels must be a list of list integers.')
              lowercase__   :       Union[str, Any]				=	[np.array(a) for label in input_labels]
         else:
              lowercase__   :       List[Any]				=	None

         if input_boxes is not None:
              if hasattr(a						,       'numpy'):
                   lowercase__   :       List[str]				=	input_boxes.numpy().tolist()

              if (
                  not isinstance(a						,       a)
                  or not isinstance(input_boxes[0]						,       a)
                  or not isinstance(input_boxes[0][0]						,       a)
              ):
                   raise ValueError('Input boxes must be a list of list of list of floating points.')
              lowercase__   :       Optional[Any]				=	[np.array(a).astype(np.floataa) for box in input_boxes]
         else:
              lowercase__   :       Optional[int]				=	None

         return input_points, input_labels, input_boxes





    @property
    def 				snake_case_    (				self):
         lowercase__   :       str				=	self.image_processor.model_input_names
         return list(dict.fromkeys(a))





    def 				snake_case_    (				self						,       *a						,       **a):
         return self.image_processor.post_process_masks(*a						,       **a)




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'''
)

