



from argparse import ArgumentParser

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


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


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




							def __init__(       self     :       List[str]				,				A_     :       Pipeline				,				A_     :       PipelineDataFormat):
									lowerCAmelCase_			:     str									= nlp
									lowerCAmelCase_			:     Any									= reader




							@staticmethod
							def    UpperCAmelCase__			(       A_     :       ArgumentParser):
									lowerCAmelCase_			:     Union[str, 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    UpperCAmelCase__			(       self     :       Union[str, Any]):
									lowerCAmelCase_					,  lowerCAmelCase_			:     int									= self._nlp, []

									for entry in self._reader:
											lowerCAmelCase_			:     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:
											lowerCAmelCase_			:     Tuple									= 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_)










from ...configuration_utils import PretrainedConfig
from ...utils import logging


A__  :  List[str]								=					logging.get_logger(__name__)

A__  :  int								=					{
    '''weiweishi/roc-bert-base-zh''': '''https://huggingface.co/weiweishi/roc-bert-base-zh/resolve/main/config.json''',
}



class 				__snake_case						(     UpperCamelCase_						):
							_a			      = '''roc_bert'''




							def __init__(       self     :       List[Any]				,				A_     :       Optional[Any]=3_0_5_2_2				,				A_     :       List[str]=7_6_8				,				A_     :       Tuple=1_2				,				A_     :       List[str]=1_2				,				A_     :       List[str]=3_0_7_2				,				A_     :       Any="gelu"				,				A_     :       str=0.1				,				A_     :       int=0.1				,				A_     :       Optional[int]=5_1_2				,				A_     :       int=2				,				A_     :       List[str]=0.02				,				A_     :       Union[str, Any]=1e-12				,				A_     :       Union[str, Any]=True				,				A_     :       Tuple=0				,				A_     :       Union[str, Any]="absolute"				,				A_     :       Optional[Any]=None				,				A_     :       Any=True				,				A_     :       Optional[int]=True				,				A_     :       List[Any]=7_6_8				,				A_     :       str=9_1_0				,				A_     :       Dict=5_1_2				,				A_     :       Optional[int]=2_4_8_5_8				,				A_     :       Optional[Any]=True				,				**A_     :       Dict				,				):
									lowerCAmelCase_			:     List[str]									= vocab_size
									lowerCAmelCase_			:     Any									= max_position_embeddings
									lowerCAmelCase_			:     Dict									= hidden_size
									lowerCAmelCase_			:     Union[str, Any]									= num_hidden_layers
									lowerCAmelCase_			:     Tuple									= num_attention_heads
									lowerCAmelCase_			:     int									= intermediate_size
									lowerCAmelCase_			:     Tuple									= hidden_act
									lowerCAmelCase_			:     List[str]									= hidden_dropout_prob
									lowerCAmelCase_			:     Tuple									= attention_probs_dropout_prob
									lowerCAmelCase_			:     Any									= initializer_range
									lowerCAmelCase_			:     Union[str, Any]									= type_vocab_size
									lowerCAmelCase_			:     Union[str, Any]									= layer_norm_eps
									lowerCAmelCase_			:     Optional[int]									= use_cache
									lowerCAmelCase_			:     Tuple									= enable_pronunciation
									lowerCAmelCase_			:     Optional[Any]									= enable_shape
									lowerCAmelCase_			:     Union[str, Any]									= pronunciation_embed_dim
									lowerCAmelCase_			:     List[Any]									= pronunciation_vocab_size
									lowerCAmelCase_			:     Tuple									= shape_embed_dim
									lowerCAmelCase_			:     str									= shape_vocab_size
									lowerCAmelCase_			:     Optional[int]									= concat_input
									lowerCAmelCase_			:     Optional[Any]									= position_embedding_type
									lowerCAmelCase_			:     Optional[Any]									= classifier_dropout
									super().__init__(pad_token_id=A_				,				**A_)





