
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   =			logging.get_logger(__name__)


_A   =			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   =			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   =			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   =			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   =			OrderedDict(
    [
        # Model for Image-classsification
        ("beit", "FlaxBeitForImageClassification"),
        ("regnet", "FlaxRegNetForImageClassification"),
        ("resnet", "FlaxResNetForImageClassification"),
        ("vit", "FlaxViTForImageClassification"),
    ]
)

_A   =			OrderedDict(
    [
        ("vision-encoder-decoder", "FlaxVisionEncoderDecoderModel"),
    ]
)

_A   =			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   =			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   =			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   =			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   =			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   =			OrderedDict(
    [
        ("bert", "FlaxBertForNextSentencePrediction"),
    ]
)

_A   =			OrderedDict(
    [
        ("speech-encoder-decoder", "FlaxSpeechEncoderDecoderModel"),
        ("whisper", "FlaxWhisperForConditionalGeneration"),
    ]
)

_A   =			OrderedDict(
    [
        ("whisper", "FlaxWhisperForAudioClassification"),
    ]
)

_A   =			_LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_MAPPING_NAMES)
_A   =			_LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_PRETRAINING_MAPPING_NAMES)
_A   =			_LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_MASKED_LM_MAPPING_NAMES)
_A   =			_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES
)
_A   =			_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES
)
_A   =			_LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING_NAMES)
_A   =			_LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_CAUSAL_LM_MAPPING_NAMES)
_A   =			_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES
)
_A   =			_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES
)
_A   =			_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES
)
_A   =			_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES
)
_A   =			_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMES
)
_A   =			_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES
)
_A   =			_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES
)







class 					lowerCamelCase		(  _BaseAutoModelClass  ):
     UpperCAmelCase__    :							int									=		FLAX_MODEL_MAPPING


_A   =			auto_class_update(FlaxAutoModel)







class 					lowerCamelCase		(  _BaseAutoModelClass  ):
     UpperCAmelCase__    :							Optional[Any]									=		FLAX_MODEL_FOR_PRETRAINING_MAPPING


_A   =			auto_class_update(FlaxAutoModelForPreTraining, head_doc="pretraining")







class 					lowerCamelCase		(  _BaseAutoModelClass  ):
     UpperCAmelCase__    :							Any									=		FLAX_MODEL_FOR_CAUSAL_LM_MAPPING


_A   =			auto_class_update(FlaxAutoModelForCausalLM, head_doc="causal language modeling")







class 					lowerCamelCase		(  _BaseAutoModelClass  ):
     UpperCAmelCase__    :							Any									=		FLAX_MODEL_FOR_MASKED_LM_MAPPING


_A   =			auto_class_update(FlaxAutoModelForMaskedLM, head_doc="masked language modeling")







class 					lowerCamelCase		(  _BaseAutoModelClass  ):
     UpperCAmelCase__    :							List[str]									=		FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING


_A   =			auto_class_update(
    FlaxAutoModelForSeqaSeqLM, head_doc="sequence-to-sequence language modeling", checkpoint_for_example="t5-base"
)







class 					lowerCamelCase		(  _BaseAutoModelClass  ):
     UpperCAmelCase__    :							List[str]									=		FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING


_A   =			auto_class_update(
    FlaxAutoModelForSequenceClassification, head_doc="sequence classification"
)







class 					lowerCamelCase		(  _BaseAutoModelClass  ):
     UpperCAmelCase__    :							List[str]									=		FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING


_A   =			auto_class_update(FlaxAutoModelForQuestionAnswering, head_doc="question answering")







class 					lowerCamelCase		(  _BaseAutoModelClass  ):
     UpperCAmelCase__    :							Dict									=		FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING


_A   =			auto_class_update(
    FlaxAutoModelForTokenClassification, head_doc="token classification"
)







class 					lowerCamelCase		(  _BaseAutoModelClass  ):
     UpperCAmelCase__    :							Optional[int]									=		FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING


_A   =			auto_class_update(FlaxAutoModelForMultipleChoice, head_doc="multiple choice")







class 					lowerCamelCase		(  _BaseAutoModelClass  ):
     UpperCAmelCase__    :							Any									=		FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING


_A   =			auto_class_update(
    FlaxAutoModelForNextSentencePrediction, head_doc="next sentence prediction"
)







class 					lowerCamelCase		(  _BaseAutoModelClass  ):
     UpperCAmelCase__    :							Dict									=		FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING


_A   =			auto_class_update(
    FlaxAutoModelForImageClassification, head_doc="image classification"
)







class 					lowerCamelCase		(  _BaseAutoModelClass  ):
     UpperCAmelCase__    :							Dict									=		FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING


_A   =			auto_class_update(FlaxAutoModelForVisionaSeq, head_doc="vision-to-text modeling")







class 					lowerCamelCase		(  _BaseAutoModelClass  ):
     UpperCAmelCase__    :							Union[str, Any]									=		FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING


_A   =			auto_class_update(
    FlaxAutoModelForSpeechSeqaSeq, head_doc="sequence-to-sequence speech-to-text modeling"
)



import argparse

import torch
from huggingface_hub import hf_hub_download

from transformers import AutoTokenizer, RobertaPreLayerNormConfig, RobertaPreLayerNormForMaskedLM
from transformers.utils import logging


logging.set_verbosity_info()
_A   =			logging.get_logger(__name__)



def 						lowercase_      (       A__			,		A__		)							->							int:
    """simple docstring"""

    snake_case								= RobertaPreLayerNormConfig.from_pretrained(
        A__			,		architectures=["RobertaPreLayerNormForMaskedLM"]		)

    # convert state_dict
    snake_case								= torch.load(hf_hub_download(repo_id=A__			,		filename="pytorch_model.bin"		)		)
    snake_case								= {}
    for tensor_key, tensor_value in original_state_dict.items():
        # The transformer implementation gives the model a unique name, rather than overwiriting 'roberta'
        if tensor_key.startswith("roberta."		):
            snake_case								= "roberta_prelayernorm." + tensor_key[len("roberta."		) :]

        # The original implementation contains weights which are not used, remove them from the state_dict
        if tensor_key.endswith(".self.LayerNorm.weight"		) or tensor_key.endswith(".self.LayerNorm.bias"		):
            continue

        snake_case								= tensor_value

    snake_case								= RobertaPreLayerNormForMaskedLM.from_pretrained(
        pretrained_model_name_or_path=A__			,		config=A__			,		state_dict=A__		)
    model.save_pretrained(A__		)

    # convert tokenizer
    snake_case								= AutoTokenizer.from_pretrained(A__		)
    tokenizer.save_pretrained(A__		)


if __name__ == "__main__":
   _A   =			argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       "--checkpoint-repo",
       default=None,
       type=str,
       required=True,
       help="Path the official PyTorch dump, e.g. 'andreasmadsen/efficient_mlm_m0.40'.",
   )
   parser.add_argument(
       "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
   )
   _A   =			parser.parse_args()
   convert_roberta_prelayernorm_checkpoint_to_pytorch(args.checkpoint_repo, args.pytorch_dump_folder_path)

