
from argparse import ArgumentParser

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


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






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE							):
	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							):
	lowercase       =    pipeline(
	    task=args.task ,							model=args.model if args.model else None ,							config=args.config ,							tokenizer=args.tokenizer ,							device=args.device ,							)
	lowercase       =    try_infer_format_from_ext(args.input							) if args.format == 'infer' else args.format
	lowercase       =    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        A_   (			__lowerCamelCase   ):




	'''simple docstring'''


	def __init__(       self		,    snake_case		,    snake_case       ):
		lowercase       =    nlp
		lowercase       =    reader





	@staticmethod
	def 	SCREAMING_SNAKE_CASE__						(       snake_case       ):
		lowercase       =    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 	SCREAMING_SNAKE_CASE__						(       self       ):
		lowercase  ,					lowercase       =    self._nlp, []

		for entry in self._reader:
			lowercase       =    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:
			lowercase       =    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       )



import warnings

from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase	      = logging.get_logger(__name__)

UpperCAmelCase	      = {
    '''xlnet-base-cased''': '''https://huggingface.co/xlnet-base-cased/resolve/main/config.json''',
    '''xlnet-large-cased''': '''https://huggingface.co/xlnet-large-cased/resolve/main/config.json''',
}







class        A_   (			__lowerCamelCase   ):




	'''simple docstring'''


	_UpperCamelCase     :     Tuple							 =       """xlnet"""
	_UpperCamelCase     :     Optional[Any]							 =       ["""mems"""]
	_UpperCamelCase     :     Tuple							 =       {
	    """n_token""": """vocab_size""",  # Backward compatibility
	    """hidden_size""": """d_model""",
	    """num_attention_heads""": """n_head""",
	    """num_hidden_layers""": """n_layer""",
	}





	def __init__(       self		,    snake_case=3_2000		,    snake_case=1024		,    snake_case=24		,    snake_case=16		,    snake_case=4096		,    snake_case="gelu"		,    snake_case=True		,    snake_case="bi"		,    snake_case=0.02		,    snake_case=1E-12		,    snake_case=0.1		,    snake_case=512		,    snake_case=None		,    snake_case=True		,    snake_case=False		,    snake_case=False		,    snake_case=-1		,    snake_case=False		,    snake_case="last"		,    snake_case=True		,    snake_case="tanh"		,    snake_case=0.1		,    snake_case=5		,    snake_case=5		,    snake_case=5		,    snake_case=1		,    snake_case=2		,    **snake_case		,    ):
		lowercase       =    vocab_size
		lowercase       =    d_model
		lowercase       =    n_layer
		lowercase       =    n_head
		if d_model % n_head != 0:
			raise ValueError(F'''\'d_model % n_head\' ({d_model % n_head}) should be equal to 0'''       )
		if "d_head" in kwargs:
			if kwargs["d_head"] != d_model // n_head:
				raise ValueError(
				    F'''`d_head` ({kwargs['d_head']}) should be equal to `d_model // n_head` ({d_model // n_head})'''       )
		lowercase       =    d_model // n_head
		lowercase       =    ff_activation
		lowercase       =    d_inner
		lowercase       =    untie_r
		lowercase       =    attn_type

		lowercase       =    initializer_range
		lowercase       =    layer_norm_eps

		lowercase       =    dropout
		lowercase       =    mem_len
		lowercase       =    reuse_len
		lowercase       =    bi_data
		lowercase       =    clamp_len
		lowercase       =    same_length

		lowercase       =    summary_type
		lowercase       =    summary_use_proj
		lowercase       =    summary_activation
		lowercase       =    summary_last_dropout
		lowercase       =    start_n_top
		lowercase       =    end_n_top

		lowercase       =    bos_token_id
		lowercase       =    pad_token_id
		lowercase       =    eos_token_id

		if "use_cache" in kwargs:
			warnings.warn(
			    'The `use_cache` argument is deprecated and will be removed in a future version, use `use_mems_eval`'
			    ' instead.'		,    snake_case		,    )
			lowercase       =    kwargs['use_cache']

		lowercase       =    use_mems_eval
		lowercase       =    use_mems_train
		super().__init__(pad_token_id=snake_case		,    bos_token_id=snake_case		,    eos_token_id=snake_case		,    **snake_case       )





	@property
	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		logger.info(F'''The model {self.model_type} is one of the few models that has no sequence length limit.'''       )
		return -1





	@max_position_embeddings.setter
	def 	SCREAMING_SNAKE_CASE__						(       self		,    snake_case       ):
		# Message copied from Transformer-XL documentation
		raise NotImplementedError(
		    F'''The model {self.model_type} is one of the few models that has no sequence length limit.'''       )

