


import copy
import os
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Dict, Mapping, Optional, Union


if TYPE_CHECKING:
   from ...processing_utils import ProcessorMixin
   from ...utils import TensorType

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


UpperCAmelCase_      : List[str]  					=       logging.get_logger(__name__)

UpperCAmelCase_      : Union[str, Any]  					=       {
    'google/owlvit-base-patch32': 'https://huggingface.co/google/owlvit-base-patch32/resolve/main/config.json',
    'google/owlvit-base-patch16': 'https://huggingface.co/google/owlvit-base-patch16/resolve/main/config.json',
    'google/owlvit-large-patch14': 'https://huggingface.co/google/owlvit-large-patch14/resolve/main/config.json',
}
class  SCREAMING_SNAKE_CASE__  (				lowercase__			):
      snake_case__				:     List[str]        =						'''owlvit_text_model'''
      def __init__(       self				:       List[str]     ,							SCREAMING_SNAKE_CASE__				:       Tuple=4_9_4_0_8     ,							SCREAMING_SNAKE_CASE__				:       Optional[int]=5_1_2     ,							SCREAMING_SNAKE_CASE__				:       Union[str, Any]=2_0_4_8     ,							SCREAMING_SNAKE_CASE__				:       Optional[Any]=1_2     ,							SCREAMING_SNAKE_CASE__				:       List[str]=8     ,							SCREAMING_SNAKE_CASE__				:       str=1_6     ,							SCREAMING_SNAKE_CASE__				:       Any="quick_gelu"     ,							SCREAMING_SNAKE_CASE__				:       Any=1E-5     ,							SCREAMING_SNAKE_CASE__				:       Optional[Any]=0.0     ,							SCREAMING_SNAKE_CASE__				:       Tuple=0.02     ,							SCREAMING_SNAKE_CASE__				:       int=1.0     ,							SCREAMING_SNAKE_CASE__				:       Optional[int]=0     ,							SCREAMING_SNAKE_CASE__				:       Union[str, Any]=4_9_4_0_6     ,							SCREAMING_SNAKE_CASE__				:       Dict=4_9_4_0_7     ,							**SCREAMING_SNAKE_CASE__				:       str     ,							)		->			Union[str, Any]:
             super().__init__(pad_token_id=SCREAMING_SNAKE_CASE__     ,							bos_token_id=SCREAMING_SNAKE_CASE__     ,							eos_token_id=SCREAMING_SNAKE_CASE__     ,							**SCREAMING_SNAKE_CASE__     )

             a_					:		Union[str, Any]							=  vocab_size
             a_					:		List[Any]							=  hidden_size
             a_					:		Dict							=  intermediate_size
             a_					:		Dict							=  num_hidden_layers
             a_					:		List[str]							=  num_attention_heads
             a_					:		Any							=  max_position_embeddings
             a_					:		str							=  hidden_act
             a_					:		Optional[int]							=  layer_norm_eps
             a_					:		Tuple							=  attention_dropout
             a_					:		Any							=  initializer_range
             a_					:		Optional[Any]							=  initializer_factor

      @classmethod
      def        SCREAMING_SNAKE_CASE							(       cls				:       Optional[Any]     ,							SCREAMING_SNAKE_CASE__				:       Union[str, os.PathLike]     ,							**SCREAMING_SNAKE_CASE__				:       Optional[int]     )		->			"PretrainedConfig":
             cls._set_token_in_kwargs(SCREAMING_SNAKE_CASE__     )

             a_							,		a_					:		Optional[Any]							=  cls.get_config_dict(SCREAMING_SNAKE_CASE__     ,							**SCREAMING_SNAKE_CASE__     )

             # get the text config dict if we are loading from OwlViTConfig
             if config_dict.get('model_type'     ) == "owlvit":
                    a_					:		Union[str, Any]							=  config_dict['text_config']

             if "model_type" in config_dict and hasattr(cls     ,							'model_type'     ) and config_dict["model_type"] != cls.model_type:
                    logger.warning(
                        F"""You are using a model of type {config_dict["model_type"]} to instantiate a model of type """
                        F"""{cls.model_type}. This is not supported for all configurations of models and can yield errors."""     )

             return cls.from_dict(SCREAMING_SNAKE_CASE__     ,							**SCREAMING_SNAKE_CASE__     )
class  SCREAMING_SNAKE_CASE__  (				lowercase__			):
      snake_case__				:     Optional[Any]        =						'''owlvit_vision_model'''
      def __init__(       self				:       Tuple     ,							SCREAMING_SNAKE_CASE__				:       List[str]=7_6_8     ,							SCREAMING_SNAKE_CASE__				:       Dict=3_0_7_2     ,							SCREAMING_SNAKE_CASE__				:       int=1_2     ,							SCREAMING_SNAKE_CASE__				:       Optional[int]=1_2     ,							SCREAMING_SNAKE_CASE__				:       Optional[int]=3     ,							SCREAMING_SNAKE_CASE__				:       str=7_6_8     ,							SCREAMING_SNAKE_CASE__				:       List[str]=3_2     ,							SCREAMING_SNAKE_CASE__				:       Any="quick_gelu"     ,							SCREAMING_SNAKE_CASE__				:       Tuple=1E-5     ,							SCREAMING_SNAKE_CASE__				:       List[Any]=0.0     ,							SCREAMING_SNAKE_CASE__				:       int=0.02     ,							SCREAMING_SNAKE_CASE__				:       Tuple=1.0     ,							**SCREAMING_SNAKE_CASE__				:       List[Any]     ,							)		->			Optional[Any]:
             super().__init__(**SCREAMING_SNAKE_CASE__     )

             a_					:		List[Any]							=  hidden_size
             a_					:		Optional[Any]							=  intermediate_size
             a_					:		Optional[Any]							=  num_hidden_layers
             a_					:		Optional[Any]							=  num_attention_heads
             a_					:		List[str]							=  num_channels
             a_					:		Tuple							=  image_size
             a_					:		str							=  patch_size
             a_					:		str							=  hidden_act
             a_					:		Dict							=  layer_norm_eps
             a_					:		List[str]							=  attention_dropout
             a_					:		List[str]							=  initializer_range
             a_					:		List[str]							=  initializer_factor

      @classmethod
      def        SCREAMING_SNAKE_CASE							(       cls				:       Optional[Any]     ,							SCREAMING_SNAKE_CASE__				:       Union[str, os.PathLike]     ,							**SCREAMING_SNAKE_CASE__				:       Union[str, Any]     )		->			"PretrainedConfig":
             cls._set_token_in_kwargs(SCREAMING_SNAKE_CASE__     )

             a_							,		a_					:		Optional[Any]							=  cls.get_config_dict(SCREAMING_SNAKE_CASE__     ,							**SCREAMING_SNAKE_CASE__     )

             # get the vision config dict if we are loading from OwlViTConfig
             if config_dict.get('model_type'     ) == "owlvit":
                    a_					:		Any							=  config_dict['vision_config']

             if "model_type" in config_dict and hasattr(cls     ,							'model_type'     ) and config_dict["model_type"] != cls.model_type:
                    logger.warning(
                        F"""You are using a model of type {config_dict["model_type"]} to instantiate a model of type """
                        F"""{cls.model_type}. This is not supported for all configurations of models and can yield errors."""     )

             return cls.from_dict(SCREAMING_SNAKE_CASE__     ,							**SCREAMING_SNAKE_CASE__     )
class  SCREAMING_SNAKE_CASE__  (				lowercase__			):
      snake_case__				:     Optional[Any]        =						'''owlvit'''
      snake_case__				:     Dict        =						True
      def __init__(       self				:       Tuple     ,							SCREAMING_SNAKE_CASE__				:       Optional[int]=None     ,							SCREAMING_SNAKE_CASE__				:       List[str]=None     ,							SCREAMING_SNAKE_CASE__				:       Dict=5_1_2     ,							SCREAMING_SNAKE_CASE__				:       Dict=2.6592     ,							SCREAMING_SNAKE_CASE__				:       List[str]=True     ,							**SCREAMING_SNAKE_CASE__				:       List[str]     ,							)		->			Any:
             super().__init__(**SCREAMING_SNAKE_CASE__     )

             if text_config is None:
                    a_					:		Optional[int]							=  {}
                    logger.info('text_config is None. Initializing the OwlViTTextConfig with default values.'     )

             if vision_config is None:
                    a_					:		int							=  {}
                    logger.info('vision_config is None. initializing the OwlViTVisionConfig with default values.'     )

             a_					:		Union[str, Any]							=  OwlViTTextConfig(**SCREAMING_SNAKE_CASE__     )
             a_					:		Any							=  OwlViTVisionConfig(**SCREAMING_SNAKE_CASE__     )

             a_					:		str							=  projection_dim
             a_					:		Any							=  logit_scale_init_value
             a_					:		List[Any]							=  return_dict
             a_					:		Optional[int]							=  1.0
      @classmethod
      def        SCREAMING_SNAKE_CASE							(       cls				:       Tuple     ,							SCREAMING_SNAKE_CASE__				:       Union[str, os.PathLike]     ,							**SCREAMING_SNAKE_CASE__				:       List[str]     )		->			"PretrainedConfig":
             cls._set_token_in_kwargs(SCREAMING_SNAKE_CASE__     )

             a_							,		a_					:		Any							=  cls.get_config_dict(SCREAMING_SNAKE_CASE__     ,							**SCREAMING_SNAKE_CASE__     )

             if "model_type" in config_dict and hasattr(cls     ,							'model_type'     ) and config_dict["model_type"] != cls.model_type:
                    logger.warning(
                        F"""You are using a model of type {config_dict["model_type"]} to instantiate a model of type """
                        F"""{cls.model_type}. This is not supported for all configurations of models and can yield errors."""     )

             return cls.from_dict(SCREAMING_SNAKE_CASE__     ,							**SCREAMING_SNAKE_CASE__     )
      @classmethod
      def        SCREAMING_SNAKE_CASE							(       cls				:       Optional[Any]     ,							SCREAMING_SNAKE_CASE__				:       Dict     ,							SCREAMING_SNAKE_CASE__				:       Dict     ,							**SCREAMING_SNAKE_CASE__				:       List[Any]     )		->			List[Any]:
             a_					:		List[Any]							=  {}
             a_					:		List[str]							=  text_config
             a_					:		Any							=  vision_config

             return cls.from_dict(SCREAMING_SNAKE_CASE__     ,							**SCREAMING_SNAKE_CASE__     )

      def        SCREAMING_SNAKE_CASE							(       self				:       str     )		->			Tuple:
             a_					:		Optional[int]							=  copy.deepcopy(self.__dict__     )
             a_					:		Tuple							=  self.text_config.to_dict()
             a_					:		Optional[Any]							=  self.vision_config.to_dict()
             a_					:		Optional[Any]							=  self.__class__.model_type
             return output
class  SCREAMING_SNAKE_CASE__  (				lowercase__			):
      @property
      def        SCREAMING_SNAKE_CASE							(       self				:       Union[str, Any]     )		->			Mapping[str, Mapping[int, str]]:
             return OrderedDict(
                 [
                     ('input_ids', {0: 'batch', 1: 'sequence'}),
                     ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
                     ('attention_mask', {0: 'batch', 1: 'sequence'}),
                 ]     )
      @property
      def        SCREAMING_SNAKE_CASE							(       self				:       Any     )		->			Mapping[str, Mapping[int, str]]:
             return OrderedDict(
                 [
                     ('logits_per_image', {0: 'batch'}),
                     ('logits_per_text', {0: 'batch'}),
                     ('text_embeds', {0: 'batch'}),
                     ('image_embeds', {0: 'batch'}),
                 ]     )
      @property
      def        SCREAMING_SNAKE_CASE							(       self				:       List[str]     )		->			float:
             return 1E-4
      def        SCREAMING_SNAKE_CASE							(       self				:       List[Any]     ,							SCREAMING_SNAKE_CASE__				:       "ProcessorMixin"     ,							SCREAMING_SNAKE_CASE__				:       int = -1     ,							SCREAMING_SNAKE_CASE__				:       int = -1     ,							SCREAMING_SNAKE_CASE__				:       Optional["TensorType"] = None     ,							)		->			Mapping[str, Any]:
             a_					:		int							=  super().generate_dummy_inputs(
                 processor.tokenizer     ,							batch_size=SCREAMING_SNAKE_CASE__     ,							seq_length=SCREAMING_SNAKE_CASE__     ,							framework=SCREAMING_SNAKE_CASE__     )
             a_					:		List[Any]							=  super().generate_dummy_inputs(
                 processor.image_processor     ,							batch_size=SCREAMING_SNAKE_CASE__     ,							framework=SCREAMING_SNAKE_CASE__     )
             return {**text_input_dict, **image_input_dict}

      @property
      def        SCREAMING_SNAKE_CASE							(       self				:       Union[str, Any]     )		->			int:
             return 1_4






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_      : Union[str, Any]  					=       logging.get_logger(__name__)


UpperCAmelCase_      : List[str]  					=       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_      : str  					=       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_      : Dict  					=       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_      : Optional[Any]  					=       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_      : List[str]  					=       OrderedDict(
    [
        # Model for Image-classsification
        ('beit', 'FlaxBeitForImageClassification'),
        ('regnet', 'FlaxRegNetForImageClassification'),
        ('resnet', 'FlaxResNetForImageClassification'),
        ('vit', 'FlaxViTForImageClassification'),
    ]
)

UpperCAmelCase_      : int  					=       OrderedDict(
    [
        ('vision-encoder-decoder', 'FlaxVisionEncoderDecoderModel'),
    ]
)

UpperCAmelCase_      : List[str]  					=       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_      : List[str]  					=       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_      : List[str]  					=       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_      : Union[str, Any]  					=       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_      : Dict  					=       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_      : List[str]  					=       OrderedDict(
    [
        ('bert', 'FlaxBertForNextSentencePrediction'),
    ]
)

UpperCAmelCase_      : Dict  					=       OrderedDict(
    [
        ('speech-encoder-decoder', 'FlaxSpeechEncoderDecoderModel'),
        ('whisper', 'FlaxWhisperForConditionalGeneration'),
    ]
)

UpperCAmelCase_      : Union[str, Any]  					=       OrderedDict(
    [
        ('whisper', 'FlaxWhisperForAudioClassification'),
    ]
)

UpperCAmelCase_      : Any  					=       _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_MAPPING_NAMES)
UpperCAmelCase_      : int  					=       _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_PRETRAINING_MAPPING_NAMES)
UpperCAmelCase_      : Dict  					=       _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_MASKED_LM_MAPPING_NAMES)
UpperCAmelCase_      : List[Any]  					=       _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES
)
UpperCAmelCase_      : int  					=       _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES
)
UpperCAmelCase_      : Any  					=       _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING_NAMES)
UpperCAmelCase_      : int  					=       _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_CAUSAL_LM_MAPPING_NAMES)
UpperCAmelCase_      : Dict  					=       _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES
)
UpperCAmelCase_      : Optional[int]  					=       _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES
)
UpperCAmelCase_      : List[Any]  					=       _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES
)
UpperCAmelCase_      : int  					=       _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES
)
UpperCAmelCase_      : Tuple  					=       _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMES
)
UpperCAmelCase_      : Tuple  					=       _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES
)
UpperCAmelCase_      : Optional[Any]  					=       _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES
)
class  SCREAMING_SNAKE_CASE__  (				_BaseAutoModelClass			):
      snake_case__				:     List[Any]        =						FLAX_MODEL_MAPPING
UpperCAmelCase_      : Tuple  					=       auto_class_update(FlaxAutoModel)
class  SCREAMING_SNAKE_CASE__  (				_BaseAutoModelClass			):
      snake_case__				:     Any        =						FLAX_MODEL_FOR_PRETRAINING_MAPPING
UpperCAmelCase_      : Optional[Any]  					=       auto_class_update(FlaxAutoModelForPreTraining, head_doc='pretraining')
class  SCREAMING_SNAKE_CASE__  (				_BaseAutoModelClass			):
      snake_case__				:     List[Any]        =						FLAX_MODEL_FOR_CAUSAL_LM_MAPPING
UpperCAmelCase_      : Optional[Any]  					=       auto_class_update(FlaxAutoModelForCausalLM, head_doc='causal language modeling')
class  SCREAMING_SNAKE_CASE__  (				_BaseAutoModelClass			):
      snake_case__				:     Optional[Any]        =						FLAX_MODEL_FOR_MASKED_LM_MAPPING
UpperCAmelCase_      : Union[str, Any]  					=       auto_class_update(FlaxAutoModelForMaskedLM, head_doc='masked language modeling')
class  SCREAMING_SNAKE_CASE__  (				_BaseAutoModelClass			):
      snake_case__				:     Tuple        =						FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
UpperCAmelCase_      : Optional[int]  					=       auto_class_update(
    FlaxAutoModelForSeqaSeqLM, head_doc='sequence-to-sequence language modeling', checkpoint_for_example='t5-base'
)
class  SCREAMING_SNAKE_CASE__  (				_BaseAutoModelClass			):
      snake_case__				:     Tuple        =						FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
UpperCAmelCase_      : Optional[Any]  					=       auto_class_update(
    FlaxAutoModelForSequenceClassification, head_doc='sequence classification'
)
class  SCREAMING_SNAKE_CASE__  (				_BaseAutoModelClass			):
      snake_case__				:     Tuple        =						FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING
UpperCAmelCase_      : str  					=       auto_class_update(FlaxAutoModelForQuestionAnswering, head_doc='question answering')
class  SCREAMING_SNAKE_CASE__  (				_BaseAutoModelClass			):
      snake_case__				:     List[str]        =						FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING
UpperCAmelCase_      : Tuple  					=       auto_class_update(
    FlaxAutoModelForTokenClassification, head_doc='token classification'
)
class  SCREAMING_SNAKE_CASE__  (				_BaseAutoModelClass			):
      snake_case__				:     Dict        =						FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING
UpperCAmelCase_      : int  					=       auto_class_update(FlaxAutoModelForMultipleChoice, head_doc='multiple choice')
class  SCREAMING_SNAKE_CASE__  (				_BaseAutoModelClass			):
      snake_case__				:     Optional[int]        =						FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING
UpperCAmelCase_      : Dict  					=       auto_class_update(
    FlaxAutoModelForNextSentencePrediction, head_doc='next sentence prediction'
)
class  SCREAMING_SNAKE_CASE__  (				_BaseAutoModelClass			):
      snake_case__				:     Dict        =						FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
UpperCAmelCase_      : str  					=       auto_class_update(
    FlaxAutoModelForImageClassification, head_doc='image classification'
)
class  SCREAMING_SNAKE_CASE__  (				_BaseAutoModelClass			):
      snake_case__				:     Optional[Any]        =						FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING
UpperCAmelCase_      : Tuple  					=       auto_class_update(FlaxAutoModelForVisionaSeq, head_doc='vision-to-text modeling')
class  SCREAMING_SNAKE_CASE__  (				_BaseAutoModelClass			):
      snake_case__				:     Optional[int]        =						FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING
UpperCAmelCase_      : Union[str, Any]  					=       auto_class_update(
    FlaxAutoModelForSpeechSeqaSeq, head_doc='sequence-to-sequence speech-to-text modeling'
)


