

from argparse import ArgumentParser

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


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






def 					lowerCamelCase_     (							UpperCAmelCase_							:     str	)						->				Union[str, Any]:




	'''simple docstring'''




	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 					lowerCamelCase_     (							UpperCAmelCase_							:     List[str]	)						->				Dict:




	'''simple docstring'''




	_UpperCamelCase   :		Dict			=						pipeline(
	    task=args.task							,       model=args.model if args.model else None							,       config=args.config							,       tokenizer=args.tokenizer							,       device=args.device							,       )
	_UpperCamelCase   :		List[str]			=						try_infer_format_from_ext(args.input	) if args.format == 'infer' else args.format
	_UpperCamelCase   :		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 	lowercase    (				_lowercase							):


		"""simple docstring"""




		def __init__(				self	,       __snake_case	,       __snake_case):
			_UpperCamelCase   :		List[Any]			=						nlp
			_UpperCamelCase   :		Dict			=						reader


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






		def 		A__					(				self):
			_UpperCamelCase	,		_UpperCamelCase   :		Union[str, Any]			=						self._nlp, []

			for entry in self._reader:
				_UpperCamelCase   :		Optional[int]			=						nlp(**__snake_case) if self._reader.is_multi_columns else nlp(__snake_case)
				if isinstance(__snake_case	,       __snake_case):
					outputs.append(__snake_case)
				else:
					outputs += output

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







from typing import TYPE_CHECKING

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


lowerCAmelCase__								=		{
    """configuration_canine""": ["""CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """CanineConfig"""],
    """tokenization_canine""": ["""CanineTokenizer"""],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCAmelCase__								=		[
							    """CANINE_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """CanineForMultipleChoice""",
							    """CanineForQuestionAnswering""",
							    """CanineForSequenceClassification""",
							    """CanineForTokenClassification""",
							    """CanineLayer""",
							    """CanineModel""",
							    """CaninePreTrainedModel""",
							    """load_tf_weights_in_canine""",
							]


if TYPE_CHECKING:
							from .configuration_canine import CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP, CanineConfig
							from .tokenization_canine import CanineTokenizer

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_canine import (
														    CANINE_PRETRAINED_MODEL_ARCHIVE_LIST,
														    CanineForMultipleChoice,
														    CanineForQuestionAnswering,
														    CanineForSequenceClassification,
														    CanineForTokenClassification,
														    CanineLayer,
														    CanineModel,
														    CaninePreTrainedModel,
														    load_tf_weights_in_canine,
														)


else:
							import sys

							lowerCAmelCase__								=		_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




