





'''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 typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType

class      snake_case     (    __lowerCamelCase       ):




 """simple docstring"""



 SCREAMING_SNAKE_CASE_	:					Any					=["image_processor", "tokenizer"]
 SCREAMING_SNAKE_CASE_	:					List[Any]					="BlipImageProcessor"
 SCREAMING_SNAKE_CASE_	:					Optional[int]					=("BertTokenizer", "BertTokenizerFast")






 def __init__(       self       :							Dict							,	__A       :							Optional[int]							,	__A       :							List[Any]	):
        __UpperCamelCase       =  False
        super().__init__(__A							,	__A	)
        __UpperCamelCase       =  self.image_processor






 def __call__(       self       :							List[Any]							,	__A       :							ImageInput = None							,	__A       :							Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None							,	__A       :							bool = True							,	__A       :							Union[bool, str, PaddingStrategy] = False							,	__A       :							Union[bool, str, TruncationStrategy] = None							,	__A       :							Optional[int] = None							,	__A       :							int = 0							,	__A       :							Optional[int] = None							,	__A       :							Optional[bool] = None							,	__A       :							bool = False							,	__A       :							bool = False							,	__A       :							bool = False							,	__A       :							bool = False							,	__A       :							bool = False							,	__A       :							bool = True							,	__A       :							Optional[Union[str, TensorType]] = None							,	**__A       :							List[Any]							,	):
        if images is None and text is None:
               raise ValueError('You have to specify either images or text.'	)

        # Get only text
        if images is None:
               __UpperCamelCase       =  self.tokenizer
               __UpperCamelCase       =  self.tokenizer(
                   text=__A							,	add_special_tokens=__A							,	padding=__A							,	truncation=__A							,	max_length=__A							,	stride=__A							,	pad_to_multiple_of=__A							,	return_attention_mask=__A							,	return_overflowing_tokens=__A							,	return_special_tokens_mask=__A							,	return_offsets_mapping=__A							,	return_token_type_ids=__A							,	return_length=__A							,	verbose=__A							,	return_tensors=__A							,	**__A							,	)
               return text_encoding

        # add pixel_values
        __UpperCamelCase       =  self.image_processor(__A							,	return_tensors=__A	)

        if text is not None:
               __UpperCamelCase       =  self.tokenizer(
                   text=__A							,	add_special_tokens=__A							,	padding=__A							,	truncation=__A							,	max_length=__A							,	stride=__A							,	pad_to_multiple_of=__A							,	return_attention_mask=__A							,	return_overflowing_tokens=__A							,	return_special_tokens_mask=__A							,	return_offsets_mapping=__A							,	return_token_type_ids=__A							,	return_length=__A							,	verbose=__A							,	return_tensors=__A							,	**__A							,	)
        else:
               __UpperCamelCase       =  None

        if text_encoding is not None:
               encoding_image_processor.update(__A	)

        return encoding_image_processor






 def        _lowerCamelCase					(       self       :							List[Any]							,	*__A       :							Dict							,	**__A       :							Optional[int]	):
        return self.tokenizer.batch_decode(*__A							,	**__A	)






 def        _lowerCamelCase					(       self       :							List[Any]							,	*__A       :							List[str]							,	**__A       :							Dict	):
        return self.tokenizer.decode(*__A							,	**__A	)






 @property
 def        _lowerCamelCase					(       self       :							Tuple	):
        __UpperCamelCase       =  self.tokenizer.model_input_names
        __UpperCamelCase       =  self.image_processor.model_input_names
        return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names	)	)
