


"""simple docstring"""



from argparse import ArgumentParser

from ..pipelines import Pipeline, PipelineDataFormat, get_supported_tasks, pipeline
from ..utils import logging
from . import BaseTransformersCLICommand


UpperCAmelCase      		=			logging.get_logger(__name__)  # pylint: disable=invalid-name






def       lowerCamelCase					(a_						:str)     ->							Optional[int]:
       if not path:
              return "pipe"

       for ext in PipelineDataFormat.SUPPORTED_FORMATS:
              if path.endswith(a_):
                     return ext

       raise Exception(
           F"""Unable to determine file format from file extension {path}. """
           F"""Please provide the format through --format {PipelineDataFormat.SUPPORTED_FORMATS}""")






def       lowerCamelCase					(a_						:List[str])     ->							List[Any]:
       lowercase		:Optional[int]       							=				pipeline(
           task=args.task				,				model=args.model if args.model else None				,				config=args.config				,				tokenizer=args.tokenizer				,				device=args.device				,				)
       lowercase		:Any       							=				try_infer_format_from_ext(args.input) if args.format == '''infer''' else args.format
       lowercase		:str       							=				PipelineDataFormat.from_str(
           format=a_				,				output_path=args.output				,				input_path=args.input				,				column=args.column if args.column else nlp.default_input_names				,				overwrite=args.overwrite				,				)
       return RunCommand(a_				,				a_)




class 					__magic_name__		(			__UpperCAmelCase     ):






       def __init__(   self  :    Dict  ,     snake_case__  :    Pipeline  ,     snake_case__  :    PipelineDataFormat				):

              '''simple docstring'''






              lowercase		:Any       							=				nlp
              lowercase		:Optional[Any]       							=				reader






       @staticmethod
       def  __snake_case						(   snake_case__  :    ArgumentParser				):

              '''simple docstring'''






              lowercase		:Any       							=				parser.add_parser('''run'''  ,     help='''Run a pipeline through the CLI'''				)
              run_parser.add_argument('''--task'''  ,     choices=get_supported_tasks()  ,     help='''Task to run'''				)
              run_parser.add_argument('''--input'''  ,     type=snake_case__  ,     help='''Path to the file to use for inference'''				)
              run_parser.add_argument('''--output'''  ,     type=snake_case__  ,     help='''Path to the file that will be used post to write results.'''				)
              run_parser.add_argument('''--model'''  ,     type=snake_case__  ,     help='''Name or path to the model to instantiate.'''				)
              run_parser.add_argument('''--config'''  ,     type=snake_case__  ,     help='''Name or path to the model\'s config to instantiate.'''				)
              run_parser.add_argument(
                  '''--tokenizer'''  ,     type=snake_case__  ,     help='''Name of the tokenizer to use. (default: same as the model name)'''				)
              run_parser.add_argument(
                  '''--column'''  ,     type=snake_case__  ,     help='''Name of the column to use as input. (For multi columns input as QA use column1,columns2)'''  ,     )
              run_parser.add_argument(
                  '''--format'''  ,     type=snake_case__  ,     default='''infer'''  ,     choices=PipelineDataFormat.SUPPORTED_FORMATS  ,     help='''Input format to read from'''  ,     )
              run_parser.add_argument(
                  '''--device'''  ,     type=snake_case__  ,     default=-1  ,     help='''Indicate the device to run onto, -1 indicates CPU, >= 0 indicates GPU (default: -1)'''  ,     )
              run_parser.add_argument('''--overwrite'''  ,     action='''store_true'''  ,     help='''Allow overwriting the output file.'''				)
              run_parser.set_defaults(func=snake_case__				)







       def  __snake_case						(   self  :    Optional[Any]				):

              '''simple docstring'''






              lowercase							,  lowercase		:Union[str, Any]       							=				self._nlp, []

              for entry in self._reader:
                     lowercase		:Optional[Any]       							=				nlp(**snake_case__				) if self._reader.is_multi_columns else nlp(snake_case__				)
                     if isinstance(snake_case__  ,     snake_case__				):
                            outputs.append(snake_case__				)
                     else:
                            outputs += output

        # Saving data
              if self._nlp.binary_output:
                     lowercase		:str       							=				self._reader.save_binary(snake_case__				)
                     logger.warning(f"""Current pipeline requires output to be in binary format, saving at {binary_path}"""				)
              else:
                     self._reader.save(snake_case__				)





"""simple docstring"""



import argparse

import torch
from torch import nn

from transformers import MBartConfig, MBartForConditionalGeneration






def       lowerCamelCase					(a_						:Dict)     ->							Dict:
       lowercase		:Tuple       							=				[
           '''encoder.version''',
           '''decoder.version''',
           '''model.encoder.version''',
           '''model.decoder.version''',
           '''_float_tensor''',
           '''decoder.output_projection.weight''',
       ]
       for k in ignore_keys:
              state_dict.pop(a_				,				a_)






def       lowerCamelCase					(a_						:Union[str, Any])     ->							str:
       lowercase							,  lowercase		:Tuple       							=				emb.weight.shape
       lowercase		:List[str]       							=				nn.Linear(a_				,				a_				,				bias=a_)
       lowercase		:List[str]       							=				emb.weight.data
       return lin_layer






def       lowerCamelCase					(a_						:int				,				a_						:Union[str, Any]="facebook/mbart-large-en-ro"				,				a_						:Union[str, Any]=False				,				a_						:List[Any]=False)     ->							List[Any]:
       lowercase		:List[Any]       							=				torch.load(a_				,				map_location='''cpu''')['''model''']
       remove_ignore_keys_(a_)
       lowercase		:Dict       							=				state_dict['''encoder.embed_tokens.weight'''].shape[0]

       lowercase		:Tuple       							=				MBartConfig.from_pretrained(a_				,				vocab_size=a_)
       if mbart_aa and finetuned:
              lowercase		:List[Any]       							=				'''relu'''

       lowercase		:Optional[int]       							=				state_dict['''decoder.embed_tokens.weight''']
       lowercase		:Union[str, Any]       							=				MBartForConditionalGeneration(a_)
       model.model.load_state_dict(a_)

       if finetuned:
              lowercase		:Dict       							=				make_linear_from_emb(model.model.shared)

       return model


if __name__ == "__main__":
 UpperCAmelCase      		=			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''')
 UpperCAmelCase      		=			parser.parse_args()
 UpperCAmelCase      		=			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)

