"""simple docstring"""







from argparse import ArgumentParser

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


_a  :  List[str]=				logging.get_logger(__name__)  # pylint: disable=invalid-name
def 						__UpperCAmelCase       (					UpperCAmelCase_					:       str						)							->    List[str]:

	'''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 						__UpperCAmelCase       (					UpperCAmelCase_					:       Tuple						)							->    str:

	'''simple docstring'''





	__snake_case    :							str   	=				pipeline(
	    task=args.task    ,							model=args.model if args.model else None    ,							config=args.config    ,							tokenizer=args.tokenizer    ,							device=args.device    ,							)
	__snake_case    :							Optional[Any]   	=				try_infer_format_from_ext(args.input						) if args.format == 'infer' else args.format
	__snake_case    :							List[str]   	=				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 	UpperCamelCase    (					lowercase		):



				def __init__(self       :   Optional[Any]			,						_A       :   Pipeline			,						_A       :   PipelineDataFormat)					->			List[Any]:
					__snake_case    :							Any   	=				nlp
					__snake_case    :							List[Any]   	=				reader



				@staticmethod
				def       _lowercase  (_A       :   ArgumentParser)					->			Union[str, Any]:
					__snake_case    :							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=_A			,						help='Path to the file to use for inference')
					run_parser.add_argument('--output'			,						type=_A			,						help='Path to the file that will be used post to write results.')
					run_parser.add_argument('--model'			,						type=_A			,						help='Name or path to the model to instantiate.')
					run_parser.add_argument('--config'			,						type=_A			,						help='Name or path to the model\'s config to instantiate.')
					run_parser.add_argument(
					    '--tokenizer'			,						type=_A			,						help='Name of the tokenizer to use. (default: same as the model name)')
					run_parser.add_argument(
					    '--column'			,						type=_A			,						help='Name of the column to use as input. (For multi columns input as QA use column1,columns2)'			,						)
					run_parser.add_argument(
					    '--format'			,						type=_A			,						default='infer'			,						choices=PipelineDataFormat.SUPPORTED_FORMATS			,						help='Input format to read from'			,						)
					run_parser.add_argument(
					    '--device'			,						type=_A			,						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=_A)






				def       _lowercase  (self       :   Union[str, Any])					->			Optional[Any]:
					__snake_case	,       __snake_case    :							int   	=				self._nlp, []

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

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






"""simple docstring"""







from arguments import InitializationArguments

from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, HfArgumentParser


# Configuration
_a  :  Tuple=				HfArgumentParser(InitializationArguments)
_a  :  str=				parser.parse_args()

# Load codeparrot tokenizer trained for Python code tokenization
_a  :  int=				AutoTokenizer.from_pretrained(args.tokenizer_name)

# Config: "scale_attn_by_layer_idx" and "reorder_and_upcast_attn" are Mistral stability tweaks
_a  :  Optional[Any]=				{
    "vocab_size": len(tokenizer),
    "scale_attn_by_inverse_layer_idx": True,
    "reorder_and_upcast_attn": True,
}

# Load model config (GPT-2 large in this case)
_a  :  Optional[int]=				AutoConfig.from_pretrained(args.config_name, **config_kwargs)

# Initialize new model with config
_a  :  str=				AutoModelForCausalLM.from_config(config)

# Save model to the hub
model.save_pretrained(args.model_name, push_to_hub=args.push_to_hub)





