

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			:					int						=			logging.get_logger(__name__)


__A			:					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'),
    ]
)

__A			:					Optional[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			:					Tuple						=			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			:					Dict						=			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			:					Optional[int]						=			OrderedDict(
    [
        ('vision-encoder-decoder', 'FlaxVisionEncoderDecoderModel'),
    ]
)

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

__A			:					Dict						=			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			:					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'),
    ]
)

__A			:					Any						=			OrderedDict(
    [
        ('bert', 'FlaxBertForNextSentencePrediction'),
    ]
)

__A			:					Optional[int]						=			OrderedDict(
    [
        ('speech-encoder-decoder', 'FlaxSpeechEncoderDecoderModel'),
        ('whisper', 'FlaxWhisperForConditionalGeneration'),
    ]
)

__A			:					List[str]						=			OrderedDict(
    [
        ('whisper', 'FlaxWhisperForAudioClassification'),
    ]
)

__A			:					Optional[int]						=			_LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_MAPPING_NAMES)
__A			:					str						=			_LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_PRETRAINING_MAPPING_NAMES)
__A			:					List[Any]						=			_LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_MASKED_LM_MAPPING_NAMES)
__A			:					Tuple						=			_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES
)
__A			:					Any						=			_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES
)
__A			:					str						=			_LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING_NAMES)
__A			:					Tuple						=			_LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_CAUSAL_LM_MAPPING_NAMES)
__A			:					Optional[int]						=			_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			:					Union[str, Any]						=			_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES
)
__A			:					List[Any]						=			_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES
)
__A			:					Optional[int]						=			_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			:					Tuple						=			_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES
)





class 			_SCREAMING_SNAKE_CASE (   _BaseAutoModelClass		):







   '''simple docstring'''




   lowerCamelCase__				=      FLAX_MODEL_MAPPING




__A			:					Optional[int]						=			auto_class_update(FlaxAutoModel)





class 			_SCREAMING_SNAKE_CASE (   _BaseAutoModelClass		):







   '''simple docstring'''




   lowerCamelCase__				=      FLAX_MODEL_FOR_PRETRAINING_MAPPING




__A			:					Optional[Any]						=			auto_class_update(FlaxAutoModelForPreTraining, head_doc='pretraining')





class 			_SCREAMING_SNAKE_CASE (   _BaseAutoModelClass		):







   '''simple docstring'''




   lowerCamelCase__				=      FLAX_MODEL_FOR_CAUSAL_LM_MAPPING




__A			:					Tuple						=			auto_class_update(FlaxAutoModelForCausalLM, head_doc='causal language modeling')





class 			_SCREAMING_SNAKE_CASE (   _BaseAutoModelClass		):







   '''simple docstring'''




   lowerCamelCase__				=      FLAX_MODEL_FOR_MASKED_LM_MAPPING




__A			:					List[Any]						=			auto_class_update(FlaxAutoModelForMaskedLM, head_doc='masked language modeling')





class 			_SCREAMING_SNAKE_CASE (   _BaseAutoModelClass		):







   '''simple docstring'''




   lowerCamelCase__				=      FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING




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





class 			_SCREAMING_SNAKE_CASE (   _BaseAutoModelClass		):







   '''simple docstring'''




   lowerCamelCase__				=      FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING




__A			:					Optional[int]						=			auto_class_update(
    FlaxAutoModelForSequenceClassification, head_doc='sequence classification'
)





class 			_SCREAMING_SNAKE_CASE (   _BaseAutoModelClass		):







   '''simple docstring'''




   lowerCamelCase__				=      FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING




__A			:					int						=			auto_class_update(FlaxAutoModelForQuestionAnswering, head_doc='question answering')





class 			_SCREAMING_SNAKE_CASE (   _BaseAutoModelClass		):







   '''simple docstring'''




   lowerCamelCase__				=      FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING




__A			:					List[Any]						=			auto_class_update(
    FlaxAutoModelForTokenClassification, head_doc='token classification'
)





class 			_SCREAMING_SNAKE_CASE (   _BaseAutoModelClass		):







   '''simple docstring'''




   lowerCamelCase__				=      FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING




__A			:					Any						=			auto_class_update(FlaxAutoModelForMultipleChoice, head_doc='multiple choice')





class 			_SCREAMING_SNAKE_CASE (   _BaseAutoModelClass		):







   '''simple docstring'''




   lowerCamelCase__				=      FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING




__A			:					Union[str, Any]						=			auto_class_update(
    FlaxAutoModelForNextSentencePrediction, head_doc='next sentence prediction'
)





class 			_SCREAMING_SNAKE_CASE (   _BaseAutoModelClass		):







   '''simple docstring'''




   lowerCamelCase__				=      FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING




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





class 			_SCREAMING_SNAKE_CASE (   _BaseAutoModelClass		):







   '''simple docstring'''




   lowerCamelCase__				=      FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING




__A			:					int						=			auto_class_update(FlaxAutoModelForVisionaSeq, head_doc='vision-to-text modeling')





class 			_SCREAMING_SNAKE_CASE (   _BaseAutoModelClass		):







   '''simple docstring'''




   lowerCamelCase__				=      FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING




__A			:					Optional[int]						=			auto_class_update(
    FlaxAutoModelForSpeechSeqaSeq, head_doc='sequence-to-sequence speech-to-text modeling'
)


from collections.abc import Callable

import numpy as np





def 	__a     (      A__			:  Callable			,  A__			:  float			,  A__			:  float			,  A__			:  float			,  A__			:  float       ):
    SCREAMING_SNAKE_CASE										=   int(np.ceil((x_end - xa) / step_size       )       )
    SCREAMING_SNAKE_CASE										=   np.zeros((n + 1,)       )
    SCREAMING_SNAKE_CASE										=   ya
    SCREAMING_SNAKE_CASE										=   xa

    for k in range(A__       ):
        SCREAMING_SNAKE_CASE										=   y[k] + step_size * ode_func(A__			,  y[k]       )
        SCREAMING_SNAKE_CASE										=   y[k] + (
            (step_size / 2) * (ode_func(A__			,  y[k]       ) + ode_func(x + step_size			,  A__       ))
        )
        x += step_size

    return y


if __name__ == "__main__":
    import doctest

    doctest.testmod()