'''simple docstring'''






import argparse

import torch
from torch import nn

from transformers import MBartConfig, MBartForConditionalGeneration






def  UpperCamelCase__					(					_lowercase    :				Union[str, Any]     )							->	List[Any]:
		__UpperCAmelCase:					int		= [
		    """encoder.version""",
		    """decoder.version""",
		    """model.encoder.version""",
		    """model.decoder.version""",
		    """_float_tensor""",
		    """decoder.output_projection.weight""",
		]
		for k in ignore_keys:
				state_dict.pop(__snake_case   ,    __snake_case     )






def  UpperCamelCase__					(					_lowercase    :				List[Any]     )							->	Any:
		__UpperCAmelCase, __UpperCAmelCase:					Optional[Any]		= emb.weight.shape
		__UpperCAmelCase:					Any		= nn.Linear(__snake_case   ,    __snake_case   ,    bias=__snake_case     )
		__UpperCAmelCase:					Optional[Any]		= emb.weight.data
		return lin_layer






def  UpperCamelCase__					(					_lowercase    :				Tuple   ,    _lowercase    :				Any="facebook/mbart-large-en-ro"   ,    _lowercase    :				Union[str, Any]=False   ,    _lowercase    :				Optional[Any]=False     )							->	List[Any]:
		__UpperCAmelCase:					Tuple		= torch.load(__snake_case   ,    map_location="""cpu"""     )["""model"""]
		remove_ignore_keys_(__snake_case     )
		__UpperCAmelCase:					Optional[int]		= state_dict["""encoder.embed_tokens.weight"""].shape[0]

		__UpperCAmelCase:					int		= MBartConfig.from_pretrained(__snake_case   ,    vocab_size=__snake_case     )
		if mbart_aa and finetuned:
				__UpperCAmelCase:					Dict		= """relu"""

		__UpperCAmelCase:					int		= state_dict["""decoder.embed_tokens.weight"""]
		__UpperCAmelCase:					int		= MBartForConditionalGeneration(__snake_case     )
		model.model.load_state_dict(__snake_case     )

		if finetuned:
				__UpperCAmelCase:					Union[str, Any]		= make_linear_from_emb(model.model.shared     )

		return model


if __name__ == "__main__":
				SCREAMING_SNAKE_CASE_	   =			argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    'fairseq_path', type=str, help='bart.large, bart.large.cnn or a path to a model.pt on local filesystem.'
				)
				parser.add_argument('pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
				parser.add_argument(
				    '--hf_config',
				    default='facebook/mbart-large-cc25',
				    type=str,
				    help='Which huggingface architecture to use: mbart-large',
				)
				parser.add_argument('--mbart_50', action='store_true', help='whether the model is mMART-50 checkpoint')
				parser.add_argument('--finetuned', action='store_true', help='whether the model is a fine-tuned checkpoint')
				SCREAMING_SNAKE_CASE_	   =			parser.parse_args()
				SCREAMING_SNAKE_CASE_	   =			convert_fairseq_mbart_checkpoint_from_disk(
				    args.fairseq_path, hf_config_path=args.hf_config, finetuned=args.finetuned, mbart_aa=args.mbart_aa
				)
				model.save_pretrained(args.pytorch_dump_folder_path)
'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


SCREAMING_SNAKE_CASE_	   =			{
    'configuration_electra': ['ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ElectraConfig', 'ElectraOnnxConfig'],
    'tokenization_electra': ['ElectraTokenizer'],
}

try:
    if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    SCREAMING_SNAKE_CASE_	   =			['ElectraTokenizerFast']

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    SCREAMING_SNAKE_CASE_	   =			[
        'ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST',
        'ElectraForCausalLM',
        'ElectraForMaskedLM',
        'ElectraForMultipleChoice',
        'ElectraForPreTraining',
        'ElectraForQuestionAnswering',
        'ElectraForSequenceClassification',
        'ElectraForTokenClassification',
        'ElectraModel',
        'ElectraPreTrainedModel',
        'load_tf_weights_in_electra',
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    SCREAMING_SNAKE_CASE_	   =			[
        'TF_ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST',
        'TFElectraForMaskedLM',
        'TFElectraForMultipleChoice',
        'TFElectraForPreTraining',
        'TFElectraForQuestionAnswering',
        'TFElectraForSequenceClassification',
        'TFElectraForTokenClassification',
        'TFElectraModel',
        'TFElectraPreTrainedModel',
    ]

try:
    if not is_flax_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    SCREAMING_SNAKE_CASE_	   =			[
        'FlaxElectraForCausalLM',
        'FlaxElectraForMaskedLM',
        'FlaxElectraForMultipleChoice',
        'FlaxElectraForPreTraining',
        'FlaxElectraForQuestionAnswering',
        'FlaxElectraForSequenceClassification',
        'FlaxElectraForTokenClassification',
        'FlaxElectraModel',
        'FlaxElectraPreTrainedModel',
    ]


if TYPE_CHECKING:
    from .configuration_electra import ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP, ElectraConfig, ElectraOnnxConfig
    from .tokenization_electra import ElectraTokenizer

    try:
        if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_electra_fast import ElectraTokenizerFast

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_electra import (
            ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST,
            ElectraForCausalLM,
            ElectraForMaskedLM,
            ElectraForMultipleChoice,
            ElectraForPreTraining,
            ElectraForQuestionAnswering,
            ElectraForSequenceClassification,
            ElectraForTokenClassification,
            ElectraModel,
            ElectraPreTrainedModel,
            load_tf_weights_in_electra,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_electra import (
            TF_ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFElectraForMaskedLM,
            TFElectraForMultipleChoice,
            TFElectraForPreTraining,
            TFElectraForQuestionAnswering,
            TFElectraForSequenceClassification,
            TFElectraForTokenClassification,
            TFElectraModel,
            TFElectraPreTrainedModel,
        )

    try:
        if not is_flax_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_flax_electra import (
            FlaxElectraForCausalLM,
            FlaxElectraForMaskedLM,
            FlaxElectraForMultipleChoice,
            FlaxElectraForPreTraining,
            FlaxElectraForQuestionAnswering,
            FlaxElectraForSequenceClassification,
            FlaxElectraForTokenClassification,
            FlaxElectraModel,
            FlaxElectraPreTrainedModel,
        )

else:
    import sys

    SCREAMING_SNAKE_CASE_	   =			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)