
from argparse import ArgumentParser

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


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


					'''simple docstring'''

					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      UpperCAmelCase__				(							__magic_name__    :  str	):


					'''simple docstring'''

					lowerCAmelCase		:       int							=  pipeline(
					    task=args.task				,	model=args.model if args.model else None				,	config=args.config				,	tokenizer=args.tokenizer				,	device=args.device				,	)
					lowerCAmelCase		:       Optional[Any]							=  try_infer_format_from_ext(args.input	) if args.format == "infer" else args.format
					lowerCAmelCase		:       Optional[int]							=  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 		__magic_name__ (    a_     ):





		def __init__(				self     :						Union[str, Any]    ,	lowerCamelCase__     :						Any    ,	lowerCamelCase__     :						Optional[int]					):
							lowerCAmelCase		:       List[str]							=  nlp
							lowerCAmelCase		:       Optional[Any]							=  reader





		@staticmethod
		def    _A				(				lowerCamelCase__     :						Optional[Any]					):
							lowerCAmelCase		:       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    _A				(				self     :						Union[str, Any]					):
							lowerCAmelCase		:       int							=  self._nlp, []

							for entry in self._reader:
												lowerCAmelCase		:       str							=  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		:       Dict							=  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 typing import TYPE_CHECKING

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


_A     :			Optional[Any]  						=			{
    """configuration_bridgetower""": [
        """BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """BridgeTowerConfig""",
        """BridgeTowerTextConfig""",
        """BridgeTowerVisionConfig""",
    ],
    """processing_bridgetower""": ["""BridgeTowerProcessor"""],
}

try:
   if not is_vision_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A     :			Any  						=			["""BridgeTowerImageProcessor"""]

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A     :			List[Any]  						=			[
       """BRIDGETOWER_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """BridgeTowerForContrastiveLearning""",
       """BridgeTowerForImageAndTextRetrieval""",
       """BridgeTowerForMaskedLM""",
       """BridgeTowerModel""",
       """BridgeTowerPreTrainedModel""",
   ]


if TYPE_CHECKING:
   from .configuration_bridgetower import (
       BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP,
       BridgeTowerConfig,
       BridgeTowerTextConfig,
       BridgeTowerVisionConfig,
   )
   from .processing_bridgetower import BridgeTowerProcessor

   try:
      if not is_vision_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .image_processing_bridgetower import BridgeTowerImageProcessor

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_bridgetower import (
          BRIDGETOWER_PRETRAINED_MODEL_ARCHIVE_LIST,
          BridgeTowerForContrastiveLearning,
          BridgeTowerForImageAndTextRetrieval,
          BridgeTowerForMaskedLM,
          BridgeTowerModel,
          BridgeTowerPreTrainedModel,
      )


else:
   import sys

   _A     :			Any  						=			_LazyModule(__name__, globals()["""__file__"""], _import_structure)


