





'''simple docstring'''
from collections import OrderedDict

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


a__							:							Optional[Any] =logging.get_logger(__name__)


a__							:							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'''),
    ]
)

a__							:							Any =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'''),
    ]
)

a__							:							Union[str, Any] =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'''),
    ]
)

a__							:							Optional[int] =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'''),
    ]
)

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

a__							:							Any =OrderedDict(
    [
        ('''vision-encoder-decoder''', '''FlaxVisionEncoderDecoderModel'''),
    ]
)

a__							:							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'''),
    ]
)

a__							:							Optional[int] =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'''),
    ]
)

a__							:							Optional[int] =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'''),
    ]
)

a__							:							int =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'''),
    ]
)

a__							:							List[Any] =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'''),
    ]
)

a__							:							List[str] =OrderedDict(
    [
        ('''bert''', '''FlaxBertForNextSentencePrediction'''),
    ]
)

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

a__							:							int =OrderedDict(
    [
        ('''whisper''', '''FlaxWhisperForAudioClassification'''),
    ]
)

a__							:							Optional[int] =_LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_MAPPING_NAMES)
a__							:							Tuple =_LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_PRETRAINING_MAPPING_NAMES)
a__							:							str =_LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_MASKED_LM_MAPPING_NAMES)
a__							:							Optional[Any] =_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES
)
a__							:							Tuple =_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES
)
a__							:							Optional[int] =_LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING_NAMES)
a__							:							Union[str, Any] =_LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_CAUSAL_LM_MAPPING_NAMES)
a__							:							List[Any] =_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES
)
a__							:							List[str] =_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES
)
a__							:							Optional[int] =_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES
)
a__							:							Dict =_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES
)
a__							:							str =_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMES
)
a__							:							List[Any] =_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES
)
a__							:							Union[str, Any] =_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES
)

class      snake_case     (    _BaseAutoModelClass       ):




 """simple docstring"""



 SCREAMING_SNAKE_CASE_	:					List[str]					=FLAX_MODEL_MAPPING






a__							:							List[Any] =auto_class_update(FlaxAutoModel)

class      snake_case     (    _BaseAutoModelClass       ):




 """simple docstring"""



 SCREAMING_SNAKE_CASE_	:					List[str]					=FLAX_MODEL_FOR_PRETRAINING_MAPPING






a__							:							List[Any] =auto_class_update(FlaxAutoModelForPreTraining, head_doc='''pretraining''')

class      snake_case     (    _BaseAutoModelClass       ):




 """simple docstring"""



 SCREAMING_SNAKE_CASE_	:					str					=FLAX_MODEL_FOR_CAUSAL_LM_MAPPING






a__							:							Optional[int] =auto_class_update(FlaxAutoModelForCausalLM, head_doc='''causal language modeling''')

class      snake_case     (    _BaseAutoModelClass       ):




 """simple docstring"""



 SCREAMING_SNAKE_CASE_	:					Dict					=FLAX_MODEL_FOR_MASKED_LM_MAPPING






a__							:							Tuple =auto_class_update(FlaxAutoModelForMaskedLM, head_doc='''masked language modeling''')

class      snake_case     (    _BaseAutoModelClass       ):




 """simple docstring"""



 SCREAMING_SNAKE_CASE_	:					Dict					=FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING






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

class      snake_case     (    _BaseAutoModelClass       ):




 """simple docstring"""



 SCREAMING_SNAKE_CASE_	:					List[Any]					=FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING






a__							:							int =auto_class_update(
    FlaxAutoModelForSequenceClassification, head_doc='''sequence classification'''
)

class      snake_case     (    _BaseAutoModelClass       ):




 """simple docstring"""



 SCREAMING_SNAKE_CASE_	:					Any					=FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING






a__							:							Any =auto_class_update(FlaxAutoModelForQuestionAnswering, head_doc='''question answering''')

class      snake_case     (    _BaseAutoModelClass       ):




 """simple docstring"""



 SCREAMING_SNAKE_CASE_	:					Optional[int]					=FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING






a__							:							Optional[Any] =auto_class_update(
    FlaxAutoModelForTokenClassification, head_doc='''token classification'''
)

class      snake_case     (    _BaseAutoModelClass       ):




 """simple docstring"""



 SCREAMING_SNAKE_CASE_	:					str					=FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING






a__							:							Tuple =auto_class_update(FlaxAutoModelForMultipleChoice, head_doc='''multiple choice''')

class      snake_case     (    _BaseAutoModelClass       ):




 """simple docstring"""



 SCREAMING_SNAKE_CASE_	:					int					=FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING






a__							:							Optional[int] =auto_class_update(
    FlaxAutoModelForNextSentencePrediction, head_doc='''next sentence prediction'''
)

class      snake_case     (    _BaseAutoModelClass       ):




 """simple docstring"""



 SCREAMING_SNAKE_CASE_	:					Optional[int]					=FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING






a__							:							Optional[Any] =auto_class_update(
    FlaxAutoModelForImageClassification, head_doc='''image classification'''
)

class      snake_case     (    _BaseAutoModelClass       ):




 """simple docstring"""



 SCREAMING_SNAKE_CASE_	:					Optional[int]					=FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING






a__							:							Union[str, Any] =auto_class_update(FlaxAutoModelForVisionaSeq, head_doc='''vision-to-text modeling''')

class      snake_case     (    _BaseAutoModelClass       ):




 """simple docstring"""



 SCREAMING_SNAKE_CASE_	:					Union[str, Any]					=FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING






a__							:							Tuple =auto_class_update(
    FlaxAutoModelForSpeechSeqaSeq, head_doc='''sequence-to-sequence speech-to-text modeling'''
)



"""simple docstring"""


from collections import OrderedDict
from typing import Mapping

from packaging import version

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


__A						   =			logging.get_logger(__name__)

__A						   =			{
    "microsoft/beit-base-patch16-224-pt22k": (
        "https://huggingface.co/microsoft/beit-base-patch16-224-pt22k/resolve/main/config.json"
    ),
    # See all BEiT models at https://huggingface.co/models?filter=beit
}





class     snake_case	(				__snake_case  ):
				SCREAMING_SNAKE_CASE_   :      Tuple         =  """beit"""

				def __init__(	self							:				List[Any]     ,  UpperCamelCase__							:				List[str]=8_1_9_2     ,  UpperCamelCase__							:				Dict=7_6_8     ,  UpperCamelCase__							:				List[str]=1_2     ,  UpperCamelCase__							:				Union[str, Any]=1_2     ,  UpperCamelCase__							:				Dict=3_0_7_2     ,  UpperCamelCase__							:				Optional[int]="gelu"     ,  UpperCamelCase__							:				Tuple=0.0     ,  UpperCamelCase__							:				Tuple=0.0     ,  UpperCamelCase__							:				Union[str, Any]=0.02     ,  UpperCamelCase__							:				Optional[Any]=1e-12     ,  UpperCamelCase__							:				str=2_2_4     ,  UpperCamelCase__							:				str=1_6     ,  UpperCamelCase__							:				Union[str, Any]=3     ,  UpperCamelCase__							:				Optional[int]=False     ,  UpperCamelCase__							:				int=False     ,  UpperCamelCase__							:				Optional[Any]=False     ,  UpperCamelCase__							:				Optional[Any]=False     ,  UpperCamelCase__							:				Dict=0.1     ,  UpperCamelCase__							:				List[Any]=0.1     ,  UpperCamelCase__							:				Any=True     ,  UpperCamelCase__							:				Optional[Any]=[3, 5, 7, 1_1]     ,  UpperCamelCase__							:				Optional[Any]=[1, 2, 3, 6]     ,  UpperCamelCase__							:				Optional[Any]=True     ,  UpperCamelCase__							:				Tuple=0.4     ,  UpperCamelCase__							:				Optional[Any]=2_5_6     ,  UpperCamelCase__							:				Optional[int]=1     ,  UpperCamelCase__							:				Optional[int]=False     ,  UpperCamelCase__							:				Optional[Any]=2_5_5     ,  **UpperCamelCase__							:				Optional[int]     ,  )->   int:



						'''simple docstring'''


						super().__init__(**UpperCamelCase__)

						__lowerCAmelCase:       str          =			vocab_size
						__lowerCAmelCase:       List[Any]          =			hidden_size
						__lowerCAmelCase:       str          =			num_hidden_layers
						__lowerCAmelCase:       Tuple          =			num_attention_heads
						__lowerCAmelCase:       Union[str, Any]          =			intermediate_size
						__lowerCAmelCase:       List[Any]          =			hidden_act
						__lowerCAmelCase:       Optional[Any]          =			hidden_dropout_prob
						__lowerCAmelCase:       List[Any]          =			attention_probs_dropout_prob
						__lowerCAmelCase:       str          =			initializer_range
						__lowerCAmelCase:       Optional[Any]          =			layer_norm_eps

						__lowerCAmelCase:       Any          =			image_size
						__lowerCAmelCase:       Any          =			patch_size
						__lowerCAmelCase:       Union[str, Any]          =			num_channels
						__lowerCAmelCase:       Tuple          =			use_mask_token
						__lowerCAmelCase:       Optional[Any]          =			use_absolute_position_embeddings
						__lowerCAmelCase:       List[Any]          =			use_relative_position_bias
						__lowerCAmelCase:       Optional[Any]          =			use_shared_relative_position_bias
						__lowerCAmelCase:       List[str]          =			layer_scale_init_value
						__lowerCAmelCase:       str          =			drop_path_rate
						__lowerCAmelCase:       str          =			use_mean_pooling
						# decode head attributes (semantic segmentation)
						__lowerCAmelCase:       Optional[Any]          =			out_indices
						__lowerCAmelCase:       Union[str, Any]          =			pool_scales
						# auxiliary head attributes (semantic segmentation)
						__lowerCAmelCase:       List[str]          =			use_auxiliary_head
						__lowerCAmelCase:       Union[str, Any]          =			auxiliary_loss_weight
						__lowerCAmelCase:       Optional[int]          =			auxiliary_channels
						__lowerCAmelCase:       Dict          =			auxiliary_num_convs
						__lowerCAmelCase:       List[Any]          =			auxiliary_concat_input
						__lowerCAmelCase:       str          =			semantic_loss_ignore_index





class     snake_case	(				__snake_case  ):
				SCREAMING_SNAKE_CASE_   :      Optional[Any]         =  version.parse("""1.11"""  )

				@property
				def 							lowercase_     (	self							:				str)->   Mapping[str, Mapping[int, str]]:



						'''simple docstring'''


						return OrderedDict(
						    [
						        ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
						    ])




				@property
				def 							lowercase_     (	self							:				Any)->   float:



						'''simple docstring'''


						return 1e-4


