




"""simple docstring"""

from argparse import ArgumentParser

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


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

def 		lowercase_      (		_UpperCAmelCase	):




	"""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 		lowercase_      (		_UpperCAmelCase	):




	"""simple docstring"""






	A_  :				Optional[Any]	   =		pipeline(
	    task=args.task  ,      model=args.model if args.model else None  ,      config=args.config  ,      tokenizer=args.tokenizer  ,      device=args.device  ,      )
	A_  :				int	   =		try_infer_format_from_ext(args.input	) if args.format == '''infer''' else args.format
	A_  :				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      (    __UpperCAmelCase):







				def __init__(							self   :					Union[str, Any]      ,     _lowerCamelCase   :					Pipeline      ,     _lowerCamelCase   :					PipelineDataFormat       ):



					"""simple docstring"""

					A_  :				Dict	   =		nlp
					A_  :				Dict	   =		reader







				@staticmethod
				def 					a_  (							_lowerCamelCase   :					ArgumentParser       ):



					"""simple docstring"""

					A_  :				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   :					List[Any]       ):



					"""simple docstring"""

					A_   , A_  :				Any	   =		self._nlp, []

					for entry in self._reader:
						A_  :				Union[str, 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:
						A_  :				List[str]	   =		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 collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_lowerCamelCase  :			List[Any]        =       logging.get_logger(__name__)

_lowerCamelCase  :			Tuple        =       {
    'camembert-base': 'https://huggingface.co/camembert-base/resolve/main/config.json',
    'umberto-commoncrawl-cased-v1': (
        'https://huggingface.co/Musixmatch/umberto-commoncrawl-cased-v1/resolve/main/config.json'
    ),
    'umberto-wikipedia-uncased-v1': (
        'https://huggingface.co/Musixmatch/umberto-wikipedia-uncased-v1/resolve/main/config.json'
    ),
}

class        lowercase      (    __UpperCAmelCase):
				__lowerCAmelCase    :    Optional[Any]         =   """camembert"""







				def __init__(							self   :					List[Any]      ,     _lowerCamelCase   :					Optional[Any]=3_05_22      ,     _lowerCamelCase   :					List[str]=7_68      ,     _lowerCamelCase   :					Optional[Any]=12      ,     _lowerCamelCase   :					List[str]=12      ,     _lowerCamelCase   :					Any=30_72      ,     _lowerCamelCase   :					List[str]="gelu"      ,     _lowerCamelCase   :					Optional[Any]=0.1      ,     _lowerCamelCase   :					Optional[int]=0.1      ,     _lowerCamelCase   :					Union[str, Any]=5_12      ,     _lowerCamelCase   :					str=2      ,     _lowerCamelCase   :					Optional[Any]=0.02      ,     _lowerCamelCase   :					int=1E-12      ,     _lowerCamelCase   :					str=1      ,     _lowerCamelCase   :					Union[str, Any]=0      ,     _lowerCamelCase   :					Optional[int]=2      ,     _lowerCamelCase   :					List[str]="absolute"      ,     _lowerCamelCase   :					str=True      ,     _lowerCamelCase   :					List[Any]=None      ,     **_lowerCamelCase   :					Union[str, Any]      ,     ):



					"""simple docstring"""

					super().__init__(pad_token_id=_lowerCamelCase      ,     bos_token_id=_lowerCamelCase      ,     eos_token_id=_lowerCamelCase      ,     **_lowerCamelCase       )

					A_  :				Any	   =		vocab_size
					A_  :				Optional[Any]	   =		hidden_size
					A_  :				Any	   =		num_hidden_layers
					A_  :				List[Any]	   =		num_attention_heads
					A_  :				List[Any]	   =		hidden_act
					A_  :				Optional[int]	   =		intermediate_size
					A_  :				Tuple	   =		hidden_dropout_prob
					A_  :				Union[str, Any]	   =		attention_probs_dropout_prob
					A_  :				int	   =		max_position_embeddings
					A_  :				Tuple	   =		type_vocab_size
					A_  :				Dict	   =		initializer_range
					A_  :				Tuple	   =		layer_norm_eps
					A_  :				List[Any]	   =		position_embedding_type
					A_  :				Any	   =		use_cache
					A_  :				Dict	   =		classifier_dropout

class        lowercase      (    __UpperCAmelCase):







				@property
				def 					a_  (							self   :					Optional[int]       ):



					"""simple docstring"""

					if self.task == "multiple-choice":
						A_  :				Union[str, Any]	   =		{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
					else:
						A_  :				Dict	   =		{0: '''batch''', 1: '''sequence'''}
					return OrderedDict(
					    [
					        ('''input_ids''', dynamic_axis),
					        ('''attention_mask''', dynamic_axis),
					    ]       )





