
from argparse import ArgumentParser

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


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




def 	A_ (							snake_case							:   str )      ->    Optional[int]:

						if not path:
												return "pipe"

						for ext in PipelineDataFormat.SUPPORTED_FORMATS:
												if path.endswith(snake_case ):
																		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 	A_ (							snake_case							:   List[Any] )      ->    int:
						__UpperCamelCase       =			pipeline(
						    task=args.task ,      model=args.model if args.model else None ,      config=args.config ,      tokenizer=args.tokenizer ,      device=args.device ,      )
						__UpperCamelCase       =			try_infer_format_from_ext(args.input ) if args.format == '''infer''' else args.format
						__UpperCamelCase       =			PipelineDataFormat.from_str(
						    format=snake_case ,      output_path=args.output ,      input_path=args.input ,      column=args.column if args.column else nlp.default_input_names ,      overwrite=args.overwrite ,      )
						return RunCommand(snake_case ,      snake_case )






class        SCREAMING_SNAKE_CASE__    (							SCREAMING_SNAKE_CASE_    ):

							"""simple docstring"""






							def __init__(  self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_			)->   List[str]:





													'''simple docstring'''






													__UpperCamelCase       =			nlp
													__UpperCamelCase       =			reader



							@staticmethod
							def     A__		(  SCREAMING_SNAKE_CASE_			)->   int:





													'''simple docstring'''






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



							def     A__		(  self			)->   Union[str, Any]:





													'''simple docstring'''






													__UpperCamelCase  ,							__UpperCamelCase       =			self._nlp, []

													for entry in self._reader:
																			__UpperCamelCase       =			nlp(**SCREAMING_SNAKE_CASE_			) if self._reader.is_multi_columns else nlp(SCREAMING_SNAKE_CASE_			)
																			if isinstance(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_			):
																									outputs.append(SCREAMING_SNAKE_CASE_			)
																			else:
																									outputs += output

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






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


lowercase__							:							Tuple									=			logging.get_logger(__name__)

lowercase__							:							Union[str, Any]									=			{
    "speechbrain/m-ctc-t-large": "https://huggingface.co/speechbrain/m-ctc-t-large/resolve/main/config.json",
    # See all M-CTC-T models at https://huggingface.co/models?filter=mctct
}






class        SCREAMING_SNAKE_CASE__    (							SCREAMING_SNAKE_CASE_    ):

							"""simple docstring"""






							_snake_case     		=  'mctct'



							def __init__(  self     ,	SCREAMING_SNAKE_CASE_=8065     ,	SCREAMING_SNAKE_CASE_=1536     ,	SCREAMING_SNAKE_CASE_=36     ,	SCREAMING_SNAKE_CASE_=6144     ,	SCREAMING_SNAKE_CASE_=4     ,	SCREAMING_SNAKE_CASE_=384     ,	SCREAMING_SNAKE_CASE_=920     ,	SCREAMING_SNAKE_CASE_=1E-5     ,	SCREAMING_SNAKE_CASE_=0.3     ,	SCREAMING_SNAKE_CASE_="relu"     ,	SCREAMING_SNAKE_CASE_=0.0_2     ,	SCREAMING_SNAKE_CASE_=0.3     ,	SCREAMING_SNAKE_CASE_=0.3     ,	SCREAMING_SNAKE_CASE_=1     ,	SCREAMING_SNAKE_CASE_=0     ,	SCREAMING_SNAKE_CASE_=2     ,	SCREAMING_SNAKE_CASE_=1     ,	SCREAMING_SNAKE_CASE_=0.3     ,	SCREAMING_SNAKE_CASE_=1     ,	SCREAMING_SNAKE_CASE_=(7,)     ,	SCREAMING_SNAKE_CASE_=(3,)     ,	SCREAMING_SNAKE_CASE_=80     ,	SCREAMING_SNAKE_CASE_=1     ,	SCREAMING_SNAKE_CASE_=None     ,	SCREAMING_SNAKE_CASE_="sum"     ,	SCREAMING_SNAKE_CASE_=False     ,	**SCREAMING_SNAKE_CASE_     ,	)->   Tuple:





													'''simple docstring'''






													super().__init__(**SCREAMING_SNAKE_CASE_     ,	pad_token_id=SCREAMING_SNAKE_CASE_     ,	bos_token_id=SCREAMING_SNAKE_CASE_     ,	eos_token_id=SCREAMING_SNAKE_CASE_			)
													__UpperCamelCase       =			vocab_size
													__UpperCamelCase       =			hidden_size
													__UpperCamelCase       =			num_hidden_layers
													__UpperCamelCase       =			intermediate_size
													__UpperCamelCase       =			num_attention_heads
													__UpperCamelCase       =			attention_head_dim
													__UpperCamelCase       =			max_position_embeddings
													__UpperCamelCase       =			layer_norm_eps
													__UpperCamelCase       =			layerdrop
													__UpperCamelCase       =			hidden_act
													__UpperCamelCase       =			initializer_range
													__UpperCamelCase       =			hidden_dropout_prob
													__UpperCamelCase       =			attention_probs_dropout_prob
													__UpperCamelCase       =			pad_token_id
													__UpperCamelCase       =			bos_token_id
													__UpperCamelCase       =			eos_token_id
													__UpperCamelCase       =			conv_glu_dim
													__UpperCamelCase       =			conv_dropout
													__UpperCamelCase       =			num_conv_layers
													__UpperCamelCase       =			input_feat_per_channel
													__UpperCamelCase       =			input_channels
													__UpperCamelCase       =			conv_channels
													__UpperCamelCase       =			ctc_loss_reduction
													__UpperCamelCase       =			ctc_zero_infinity

													# prevents config testing fail with exporting to json
													__UpperCamelCase       =			list(SCREAMING_SNAKE_CASE_			)
													__UpperCamelCase       =			list(SCREAMING_SNAKE_CASE_			)

													if len(self.conv_kernel			) != self.num_conv_layers:
																			raise ValueError(
																			    '''Configuration for convolutional module is incorrect. '''
																			    '''It is required that `len(config.conv_kernel)` == `config.num_conv_layers` '''
																			    F"but is `len(config.conv_kernel) = {len(self.conv_kernel			)}`, "
																			    F"`config.num_conv_layers = {self.num_conv_layers}`."			)




