
'''simple docstring'''
from argparse import ArgumentParser

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


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




def    _UpperCamelCase						(       SCREAMING_SNAKE_CASE__			)  ->							str:


		'''simple docstring'''


		if not path:
				return "pipe"

		for ext in PipelineDataFormat.SUPPORTED_FORMATS:
				if path.endswith(SCREAMING_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						(       SCREAMING_SNAKE_CASE__			)  ->							Optional[int]:


		'''simple docstring'''


		snake_case  :       Optional[Any]     			=						pipeline(
		    task=args.task ,       model=args.model if args.model else None ,       config=args.config ,       tokenizer=args.tokenizer ,       device=args.device ,       )
		snake_case  :       Dict     			=						try_infer_format_from_ext(args.input			) if args.format == '''infer''' else args.format
		snake_case  :       Optional[int]     			=						PipelineDataFormat.from_str(
		    format=SCREAMING_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(SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__			)






class 	snake_case__       (      __SCREAMING_SNAKE_CASE					):







		"""simple docstring"""

		def __init__(      self				:			Any  ,	UpperCamelCase__				:			Pipeline  ,	UpperCamelCase__				:			PipelineDataFormat      )						->			Optional[Any]:






				"""simple docstring"""







				snake_case  :       List[str]     			=						nlp
				snake_case  :       Optional[int]     			=						reader



		@staticmethod
		def       lowerCAmelCase				(      UpperCamelCase__				:			ArgumentParser      )						->			Any:






				"""simple docstring"""







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



		def       lowerCAmelCase				(      self				:			str      )						->			Dict:






				"""simple docstring"""







				snake_case			,snake_case  :       Dict     			=						self._nlp, []

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

        # Saving data
				if self._nlp.binary_output:
						snake_case  :       Dict     			=						self._reader.save_binary(UpperCamelCase__      )
						logger.warning(f'Current pipeline requires output to be in binary format, saving at {binary_path}'      )
				else:
						self._reader.save(UpperCamelCase__      )




'''simple docstring'''
from typing import TYPE_CHECKING

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


lowercase__						       =      {
    "configuration_poolformer": [
        "POOLFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "PoolFormerConfig",
        "PoolFormerOnnxConfig",
    ]
}

try:
						if not is_vision_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowercase__						       =      ["PoolFormerFeatureExtractor"]
						lowercase__						       =      ["PoolFormerImageProcessor"]

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowercase__						       =      [
						    "POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "PoolFormerForImageClassification",
						    "PoolFormerModel",
						    "PoolFormerPreTrainedModel",
						]


if TYPE_CHECKING:
						from .configuration_poolformer import (
						    POOLFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
						    PoolFormerConfig,
						    PoolFormerOnnxConfig,
						)

						try:
												if not is_vision_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .feature_extraction_poolformer import PoolFormerFeatureExtractor
												from .image_processing_poolformer import PoolFormerImageProcessor

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_poolformer import (
												    POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
												    PoolFormerForImageClassification,
												    PoolFormerModel,
												    PoolFormerPreTrainedModel,
												)


else:
						import sys

						lowercase__						       =      _LazyModule(__name__, globals()["__file__"], _import_structure)


