






from argparse import ArgumentParser

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


UpperCAmelCase_ :						List[Any]      =      logging.get_logger(__name__)  # pylint: disable=invalid-name

def    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  str					)		->			List[Any]:






	"""simple docstring"""



	if not path:
		return "pipe"

	for ext in PipelineDataFormat.SUPPORTED_FORMATS:
		if path.endswith(__magic_name__					):
			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    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  Any					)		->			Optional[int]:






	"""simple docstring"""



	UpperCamelCase     :List[str]          =	pipeline(
	    task=args.task	,			model=args.model if args.model else None	,			config=args.config	,			tokenizer=args.tokenizer	,			device=args.device	,			)
	UpperCamelCase     :Optional[int]          =	try_infer_format_from_ext(args.input					) if args.format == """infer""" else args.format
	UpperCamelCase     :Dict          =	PipelineDataFormat.from_str(
	    format=__magic_name__	,			output_path=args.output	,			input_path=args.input	,			column=args.column if args.column else nlp.default_input_names	,			overwrite=args.overwrite	,			)
	return RunCommand(__magic_name__	,			__magic_name__					)

class     _SCREAMING_SNAKE_CASE       (			_a      ):





	def __init__(		self				:					List[Any] ,  __lowerCamelCase				:					Pipeline ,  __lowerCamelCase				:					PipelineDataFormat	):
		UpperCamelCase     :str          =	nlp
		UpperCamelCase     :Union[str, Any]          =	reader





	@staticmethod
	def 						_A (		__lowerCamelCase				:					ArgumentParser	):
		UpperCamelCase     :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=__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				:					Optional[int]	):
		UpperCamelCase     ,      UpperCamelCase     :Union[str, Any]          =	self._nlp, []

		for entry in self._reader:
			UpperCamelCase     :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:
			UpperCamelCase     :List[Any]          =	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'''





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available


_UpperCamelCase											=						{'configuration_speech_encoder_decoder': ['SpeechEncoderDecoderConfig']}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCamelCase											=						['SpeechEncoderDecoderModel']

try:
   if not is_flax_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCamelCase											=						['FlaxSpeechEncoderDecoderModel']

if TYPE_CHECKING:
   from .configuration_speech_encoder_decoder import SpeechEncoderDecoderConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_speech_encoder_decoder import SpeechEncoderDecoderModel

   try:
      if not is_flax_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_flax_speech_encoder_decoder import FlaxSpeechEncoderDecoderModel

else:
   import sys

   _UpperCamelCase											=						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






