

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		:       Dict						    =	logging.get_logger(__name__)


_UpperCAmelCase		:       Optional[int]						    =	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		:       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"""),
    ]
)

_UpperCAmelCase		:       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"""),
    ]
)

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

_UpperCAmelCase		:       List[str]						    =	OrderedDict(
    [
        ("""vision-encoder-decoder""", """FlaxVisionEncoderDecoderModel"""),
    ]
)

_UpperCAmelCase		:       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"""),
    ]
)

_UpperCAmelCase		:       Optional[Any]						    =	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		:       Union[str, Any]						    =	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		:       Optional[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"""),
    ]
)

_UpperCAmelCase		:       Union[str, Any]						    =	OrderedDict(
    [
        ("""bert""", """FlaxBertForNextSentencePrediction"""),
    ]
)

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

_UpperCAmelCase		:       List[str]						    =	OrderedDict(
    [
        ("""whisper""", """FlaxWhisperForAudioClassification"""),
    ]
)

_UpperCAmelCase		:       List[Any]						    =	_LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_MAPPING_NAMES)
_UpperCAmelCase		:       Tuple						    =	_LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_PRETRAINING_MAPPING_NAMES)
_UpperCAmelCase		:       Optional[int]						    =	_LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_MASKED_LM_MAPPING_NAMES)
_UpperCAmelCase		:       List[str]						    =	_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES
)
_UpperCAmelCase		:       str						    =	_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES
)
_UpperCAmelCase		:       Tuple						    =	_LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING_NAMES)
_UpperCAmelCase		:       List[str]						    =	_LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_CAUSAL_LM_MAPPING_NAMES)
_UpperCAmelCase		:       Union[str, Any]						    =	_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES
)
_UpperCAmelCase		:       List[Any]						    =	_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES
)
_UpperCAmelCase		:       Tuple						    =	_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES
)
_UpperCAmelCase		:       str						    =	_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES
)
_UpperCAmelCase		:       int						    =	_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 			lowercase    (	_BaseAutoModelClass      ):
				__SCREAMING_SNAKE_CASE :			Any	      =      FLAX_MODEL_MAPPING



_UpperCAmelCase		:       Any						    =	auto_class_update(FlaxAutoModel)







class 			lowercase    (	_BaseAutoModelClass      ):
				__SCREAMING_SNAKE_CASE :			List[str]	      =      FLAX_MODEL_FOR_PRETRAINING_MAPPING



_UpperCAmelCase		:       Dict						    =	auto_class_update(FlaxAutoModelForPreTraining, head_doc="""pretraining""")







class 			lowercase    (	_BaseAutoModelClass      ):
				__SCREAMING_SNAKE_CASE :			Dict	      =      FLAX_MODEL_FOR_CAUSAL_LM_MAPPING



_UpperCAmelCase		:       Any						    =	auto_class_update(FlaxAutoModelForCausalLM, head_doc="""causal language modeling""")







class 			lowercase    (	_BaseAutoModelClass      ):
				__SCREAMING_SNAKE_CASE :			Any	      =      FLAX_MODEL_FOR_MASKED_LM_MAPPING



_UpperCAmelCase		:       Optional[Any]						    =	auto_class_update(FlaxAutoModelForMaskedLM, head_doc="""masked language modeling""")







class 			lowercase    (	_BaseAutoModelClass      ):
				__SCREAMING_SNAKE_CASE :			List[str]	      =      FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING



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







class 			lowercase    (	_BaseAutoModelClass      ):
				__SCREAMING_SNAKE_CASE :			Optional[int]	      =      FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING



_UpperCAmelCase		:       Any						    =	auto_class_update(
    FlaxAutoModelForSequenceClassification, head_doc="""sequence classification"""
)







class 			lowercase    (	_BaseAutoModelClass      ):
				__SCREAMING_SNAKE_CASE :			Dict	      =      FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING



_UpperCAmelCase		:       int						    =	auto_class_update(FlaxAutoModelForQuestionAnswering, head_doc="""question answering""")







class 			lowercase    (	_BaseAutoModelClass      ):
				__SCREAMING_SNAKE_CASE :			Tuple	      =      FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING



_UpperCAmelCase		:       int						    =	auto_class_update(
    FlaxAutoModelForTokenClassification, head_doc="""token classification"""
)







class 			lowercase    (	_BaseAutoModelClass      ):
				__SCREAMING_SNAKE_CASE :			List[str]	      =      FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING



_UpperCAmelCase		:       str						    =	auto_class_update(FlaxAutoModelForMultipleChoice, head_doc="""multiple choice""")







class 			lowercase    (	_BaseAutoModelClass      ):
				__SCREAMING_SNAKE_CASE :			str	      =      FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING



_UpperCAmelCase		:       str						    =	auto_class_update(
    FlaxAutoModelForNextSentencePrediction, head_doc="""next sentence prediction"""
)







class 			lowercase    (	_BaseAutoModelClass      ):
				__SCREAMING_SNAKE_CASE :			Dict	      =      FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING



_UpperCAmelCase		:       int						    =	auto_class_update(
    FlaxAutoModelForImageClassification, head_doc="""image classification"""
)







class 			lowercase    (	_BaseAutoModelClass      ):
				__SCREAMING_SNAKE_CASE :			Tuple	      =      FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING



_UpperCAmelCase		:       int						    =	auto_class_update(FlaxAutoModelForVisionaSeq, head_doc="""vision-to-text modeling""")







class 			lowercase    (	_BaseAutoModelClass      ):
				__SCREAMING_SNAKE_CASE :			List[Any]	      =      FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING



_UpperCAmelCase		:       Dict						    =	auto_class_update(
    FlaxAutoModelForSpeechSeqaSeq, head_doc="""sequence-to-sequence speech-to-text modeling"""
)








import argparse

from transformers import BigBirdConfig, BigBirdForPreTraining, BigBirdForQuestionAnswering, load_tf_weights_in_big_bird
from transformers.utils import logging


logging.set_verbosity_info()





def  __lowerCamelCase     (			UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__				):







							'''simple docstring'''






							snake_case_          =   BigBirdConfig.from_json_file(UpperCamelCase__				)
							print(F'''Building PyTorch model from configuration: {config}'''				)

							if is_trivia_qa:
														snake_case_          =   BigBirdForQuestionAnswering(UpperCamelCase__				)
							else:
														snake_case_          =   BigBirdForPreTraining(UpperCamelCase__				)

							# Load weights from tf checkpoint
							load_tf_weights_in_big_bird(UpperCamelCase__       ,							UpperCamelCase__       ,							is_trivia_qa=UpperCamelCase__				)

							# Save pytorch-model
							print(F'''Save PyTorch model to {pytorch_dump_path}'''				)
							model.save_pretrained(UpperCamelCase__				)


if __name__ == "__main__":
					_UpperCAmelCase		:       Dict						    =	argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    """--tf_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
					)
					parser.add_argument(
					    """--big_bird_config_file""",
					    default=None,
					    type=str,
					    required=True,
					    help=(
					        """The config json file corresponding to the pre-trained BERT model. \n"""
					        """This specifies the model architecture."""
					    ),
					)
					parser.add_argument(
					    """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
					)
					parser.add_argument(
					    """--is_trivia_qa""", action="""store_true""", help="""Whether to convert a model with a trivia_qa head."""
					)
					_UpperCAmelCase		:       str						    =	parser.parse_args()
					convert_tf_checkpoint_to_pytorch(
					    args.tf_checkpoint_path, args.big_bird_config_file, args.pytorch_dump_path, args.is_trivia_qa
					)




