



'''simple docstring'''






import argparse

import torch
from torch import nn

from transformers import PLBartConfig, PLBartForConditionalGeneration, PLBartForSequenceClassification




def        snake_case_   (	lowerCAmelCase_						)->						Dict:





       '''simple docstring'''


       _UpperCAmelCase    :		List[Any]     	=					[
           """encoder.version""",
           """decoder.version""",
           """model.encoder.version""",
           """model.decoder.version""",
           """_float_tensor""",
           """decoder.output_projection.weight""",
       ]
       for k in ignore_keys:
              state_dict.pop(lowerCamelCase_			,  lowerCamelCase_						)




def        snake_case_   (	lowerCAmelCase_						)->						Any:





       '''simple docstring'''


       _UpperCAmelCase    :		Any     	=					emb.weight.shape
       _UpperCAmelCase    :		Dict     	=					nn.Linear(lowerCamelCase_			,  lowerCamelCase_			,  bias=lowerCamelCase_						)
       _UpperCAmelCase    :		Any     	=					emb.weight.data
       return lin_layer




def        snake_case_   (	lowerCAmelCase_			,  lowerCAmelCase_="uclanlp/plbart-base"			,  lowerCAmelCase_=False			,  lowerCAmelCase_=False						)->						Dict:





       '''simple docstring'''


       _UpperCAmelCase    :		Tuple     	=					torch.load(lowerCamelCase_			,  map_location="""cpu"""						)["""model"""]
       remove_ignore_keys_(lowerCamelCase_						)
       _UpperCAmelCase    :		str     	=					state_dict["""encoder.embed_tokens.weight"""].shape[0]

       _UpperCAmelCase    :		List[Any]     	=					PLBartConfig.from_pretrained(lowerCamelCase_			,  vocab_size=lowerCamelCase_						)

       _UpperCAmelCase    :		Union[str, Any]     	=					state_dict["""decoder.embed_tokens.weight"""]
       if not classification:
              _UpperCAmelCase    :		Any     	=					PLBartForConditionalGeneration(lowerCamelCase_						)
              model.model.load_state_dict(lowerCamelCase_						)
              if finetuned:
                     _UpperCAmelCase    :		List[Any]     	=					make_linear_from_emb(model.model.shared						)

       else:
              _UpperCAmelCase    :		Optional[int]     	=					{}
              for key, value in state_dict.copy().items():
                     if key.startswith("""classification_heads.sentence_classification_head"""						):
                            _UpperCAmelCase    :		List[Any]     	=					value
                            state_dict.pop(lowerCamelCase_						)
              _UpperCAmelCase    :		int     	=					PLBartForSequenceClassification(lowerCamelCase_						)
              model.model.load_state_dict(lowerCamelCase_						)
              model.classification_head.load_state_dict(lowerCamelCase_						)

       return model


if __name__ == "__main__":
   A_				:					Dict							=      argparse.ArgumentParser()
   # Required parameters
   parser.add_argument("""fairseq_path""", type=str, help="""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="""uclanlp/plbart-base""",
       type=str,
       help="""Which huggingface architecture to use: plbart-base""",
   )
   parser.add_argument("""--finetuned""", action="""store_true""", help="""whether the model is a fine-tuned checkpoint""")
   parser.add_argument(
       """--classification""", action="""store_true""", help="""whether the model is a classification checkpoint"""
   )
   A_				:					int							=      parser.parse_args()
   A_				:					Union[str, Any]							=      convert_fairseq_plbart_checkpoint_from_disk(
       args.fairseq_path,
       hf_config_path=args.hf_config,
       finetuned=args.finetuned,
       classification=args.classification,
   )
   model.save_pretrained(args.pytorch_dump_folder_path)





"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


lowercase		:    Dict			=       {
    'configuration_mobilenet_v1': [
        'MOBILENET_V1_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'MobileNetV1Config',
        'MobileNetV1OnnxConfig',
    ],
}

try:
			if not is_vision_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowercase		:    Optional[int]			=       ['MobileNetV1FeatureExtractor']
			lowercase		:    Union[str, Any]			=       ['MobileNetV1ImageProcessor']

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowercase		:    Tuple			=       [
			    'MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST',
			    'MobileNetV1ForImageClassification',
			    'MobileNetV1Model',
			    'MobileNetV1PreTrainedModel',
			    'load_tf_weights_in_mobilenet_v1',
			]


if TYPE_CHECKING:
			from .configuration_mobilenet_va import (
			    MOBILENET_V1_PRETRAINED_CONFIG_ARCHIVE_MAP,
			    MobileNetVaConfig,
			    MobileNetVaOnnxConfig,
			)

			try:
						if not is_vision_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .feature_extraction_mobilenet_va import MobileNetVaFeatureExtractor
						from .image_processing_mobilenet_va import MobileNetVaImageProcessor

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_mobilenet_va import (
						    MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST,
						    MobileNetVaForImageClassification,
						    MobileNetVaModel,
						    MobileNetVaPreTrainedModel,
						    load_tf_weights_in_mobilenet_va,
						)


else:
			import sys

			lowercase		:    Union[str, Any]			=       _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
