




"""simple docstring"""

from argparse import ArgumentParser

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


_lowerCamelCase  :			Union[str, Any]        =       logging.get_logger(__name__)  # pylint: disable=invalid-name

def 		lowercase_      (		_UpperCAmelCase	):




	"""simple docstring"""






	if not path:
		return "pipe"

	for ext in PipelineDataFormat.SUPPORTED_FORMATS:
		if path.endswith(_UpperCAmelCase	):
			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 		lowercase_      (		_UpperCAmelCase	):




	"""simple docstring"""






	A_  :				Optional[Any]	   =		pipeline(
	    task=args.task  ,      model=args.model if args.model else None  ,      config=args.config  ,      tokenizer=args.tokenizer  ,      device=args.device  ,      )
	A_  :				int	   =		try_infer_format_from_ext(args.input	) if args.format == '''infer''' else args.format
	A_  :				Any	   =		PipelineDataFormat.from_str(
	    format=_UpperCAmelCase  ,      output_path=args.output  ,      input_path=args.input  ,      column=args.column if args.column else nlp.default_input_names  ,      overwrite=args.overwrite  ,      )
	return RunCommand(_UpperCAmelCase  ,      _UpperCAmelCase	)

class        lowercase      (    __UpperCAmelCase):







				def __init__(							self   :					Union[str, Any]      ,     _lowerCamelCase   :					Pipeline      ,     _lowerCamelCase   :					PipelineDataFormat       ):



					"""simple docstring"""

					A_  :				Dict	   =		nlp
					A_  :				Dict	   =		reader







				@staticmethod
				def 					a_  (							_lowerCamelCase   :					ArgumentParser       ):



					"""simple docstring"""

					A_  :				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=_lowerCamelCase      ,     help='''Path to the file to use for inference'''       )
					run_parser.add_argument('''--output'''      ,     type=_lowerCamelCase      ,     help='''Path to the file that will be used post to write results.'''       )
					run_parser.add_argument('''--model'''      ,     type=_lowerCamelCase      ,     help='''Name or path to the model to instantiate.'''       )
					run_parser.add_argument('''--config'''      ,     type=_lowerCamelCase      ,     help='''Name or path to the model\'s config to instantiate.'''       )
					run_parser.add_argument(
					    '''--tokenizer'''      ,     type=_lowerCamelCase      ,     help='''Name of the tokenizer to use. (default: same as the model name)'''       )
					run_parser.add_argument(
					    '''--column'''      ,     type=_lowerCamelCase      ,     help='''Name of the column to use as input. (For multi columns input as QA use column1,columns2)'''      ,     )
					run_parser.add_argument(
					    '''--format'''      ,     type=_lowerCamelCase      ,     default='''infer'''      ,     choices=PipelineDataFormat.SUPPORTED_FORMATS      ,     help='''Input format to read from'''      ,     )
					run_parser.add_argument(
					    '''--device'''      ,     type=_lowerCamelCase      ,     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=_lowerCamelCase       )







				def 					a_  (							self   :					List[Any]       ):



					"""simple docstring"""

					A_  :				Any	   =		self._nlp, []

					for entry in self._reader:
						A_  :				Union[str, Any]	   =		nlp(**_lowerCamelCase       ) if self._reader.is_multi_columns else nlp(_lowerCamelCase       )
						if isinstance(_lowerCamelCase      ,     _lowerCamelCase       ):
							outputs.append(_lowerCamelCase       )
						else:
							outputs += output

        # Saving data
					if self._nlp.binary_output:
						A_  :				List[str]	   =		self._reader.save_binary(_lowerCamelCase       )
						logger.warning(F"""Current pipeline requires output to be in binary format, saving at {binary_path}"""       )
					else:
						self._reader.save(_lowerCamelCase       )












"""simple docstring"""

import argparse
from argparse import Namespace

import torch
from torch import nn

from transformers import XGLMConfig, XGLMForCausalLM

def 		lowercase_      (		_UpperCAmelCase	):




	"""simple docstring"""






	A_  :				Optional[Any]	   =		[
	    '''decoder.version''',
	    '''decoder.output_projection.weight''',
	    '''_float_tensor''',
	    '''decoder.embed_positions._float_tensor''',
	]
	for k in ignore_keys:
		state_dict.pop(_UpperCAmelCase  ,      _UpperCAmelCase	)

def 		lowercase_      (		_UpperCAmelCase	):




	"""simple docstring"""






	A_   , A_  :				List[Any]	   =		emb.weight.shape
	A_  :				List[Any]	   =		nn.Linear(_UpperCAmelCase  ,      _UpperCAmelCase  ,      bias=_UpperCAmelCase	)
	A_  :				Any	   =		emb.weight.data
	return lin_layer

def 		lowercase_      (		_UpperCAmelCase	):




	"""simple docstring"""






	A_  :				int	   =		torch.load(_UpperCAmelCase  ,      map_location='''cpu'''	)
	A_  :				Any	   =		Namespace(**checkpoint['''cfg''']['''model''']	)
	A_  :				List[str]	   =		checkpoint['''model''']
	remove_ignore_keys_(_UpperCAmelCase	)
	A_  :				Union[str, Any]	   =		state_dict['''decoder.embed_tokens.weight'''].shape[0]

	A_  :				List[str]	   =		{key.replace('''decoder'''  ,      '''model'''	): val for key, val in state_dict.items()}

	A_  :				int	   =		XGLMConfig(
	    vocab_size=_UpperCAmelCase  ,      max_position_embeddings=args.max_target_positions  ,      num_layers=args.decoder_layers  ,      attention_heads=args.decoder_attention_heads  ,      ffn_dim=args.decoder_ffn_embed_dim  ,      d_model=args.decoder_embed_dim  ,      layerdrop=args.decoder_layerdrop  ,      dropout=args.dropout  ,      attention_dropout=args.attention_dropout  ,      activation_dropout=args.activation_dropout  ,      activation_function='''gelu'''  ,      scale_embedding=not args.no_scale_embedding  ,      tie_word_embeddings=args.share_decoder_input_output_embed  ,      )

	A_  :				Any	   =		XGLMForCausalLM(_UpperCAmelCase	)
	A_  :				int	   =		model.load_state_dict(_UpperCAmelCase  ,      strict=_UpperCAmelCase	)
	print(_UpperCAmelCase	)
	A_  :				int	   =		make_linear_from_emb(model.model.embed_tokens	)

	return model


if __name__ == "__main__":
		_lowerCamelCase  :			Any        =       argparse.ArgumentParser()
		# Required parameters
		parser.add_argument('fairseq_path', type=str, help='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.')
		_lowerCamelCase  :			int        =       parser.parse_args()
		_lowerCamelCase  :			Optional[Any]        =       convert_fairseq_xglm_checkpoint_from_disk(args.fairseq_path)
		model.save_pretrained(args.pytorch_dump_folder_path)





