

from argparse import ArgumentParser

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


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

def  lowerCamelCase__ (					_a):
	if not path:
		return "pipe"

	for ext in PipelineDataFormat.SUPPORTED_FORMATS:
		if path.endswith(_a):
			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__ (					_a):
	SCREAMING_SNAKE_CASE   :      str				     =       pipeline(
	    task=args.task							,			model=args.model if args.model else None							,			config=args.config							,			tokenizer=args.tokenizer							,			device=args.device							,			)
	SCREAMING_SNAKE_CASE   :      Tuple				     =       try_infer_format_from_ext(args.input) if args.format == "infer" else args.format
	SCREAMING_SNAKE_CASE   :      List[Any]				     =       PipelineDataFormat.from_str(
	    format=_a							,			output_path=args.output							,			input_path=args.input							,			column=args.column if args.column else nlp.default_input_names							,			overwrite=args.overwrite							,			)
	return RunCommand(_a							,			_a)






class 					_UpperCamelCase							(							__A   ):
			'''simple docstring'''







			def __init__(			self							: str      ,      a							: Pipeline      ,      a							: PipelineDataFormat	)							->       List[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       nlp
				SCREAMING_SNAKE_CASE   :      List[str]				     =       reader
			@staticmethod
			def 							__UpperCamelCase (			a							: ArgumentParser	)							->       int:

				"""simple docstring"""

				SCREAMING_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=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 							__UpperCamelCase (			self							: Any	)							->       str:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       self._nlp, []

				for entry in self._reader:
					SCREAMING_SNAKE_CASE   :      Any				     =       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:
					SCREAMING_SNAKE_CASE   :      List[Any]				     =       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	)


import functools
import operator

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


a_						     =    logging.get_logger(__name__)

a_						     =    {
    'microsoft/wavlm-base': 'https://huggingface.co/microsoft/wavlm-base/resolve/main/config.json',
    # See all WavLM models at https://huggingface.co/models?filter=wavlm
}






class _UpperCamelCase							(							__A   ):
			'''simple docstring'''







			lowerCamelCase__           ='wavlm'
			def __init__(			self							: Optional[int]      ,      a							: Optional[Any]=32      ,      a							: int=768      ,      a							: Tuple=12      ,      a							: List[str]=12      ,      a							: str=3072      ,      a							: Any="gelu"      ,      a							: Dict=0.1      ,      a							: int=0.1      ,      a							: str=0.1      ,      a							: Optional[Any]=0.0      ,      a							: Any=0.1      ,      a							: Any=0.1      ,      a							: List[str]=0.02      ,      a							: List[Any]=1e-5      ,      a							: Any="group"      ,      a							: Optional[int]="gelu"      ,      a							: List[str]=(512, 512, 512, 512, 512, 512, 512)      ,      a							: Any=(5, 2, 2, 2, 2, 2, 2)      ,      a							: Union[str, Any]=(10, 3, 3, 3, 3, 2, 2)      ,      a							: Optional[Any]=False      ,      a							: Dict=128      ,      a							: Optional[Any]=16      ,      a							: Optional[Any]=320      ,      a							: str=800      ,      a							: Optional[int]=False      ,      a							: Tuple=True      ,      a							: Optional[Any]=0.05      ,      a							: Any=10      ,      a							: Optional[int]=2      ,      a							: Dict=0.0      ,      a							: str=10      ,      a							: Tuple=320      ,      a							: Optional[int]=2      ,      a							: int=0.1      ,      a							: List[str]=100      ,      a							: Tuple=256      ,      a							: str=256      ,      a							: Tuple=0.1      ,      a							: str="mean"      ,      a							: int=False      ,      a							: int=False      ,      a							: Optional[Any]=256      ,      a							: Any=(512, 512, 512, 512, 1500)      ,      a							: Tuple=(5, 3, 3, 1, 1)      ,      a							: str=(1, 2, 3, 1, 1)      ,      a							: Optional[Any]=512      ,      a							: Optional[Any]=80      ,      a							: Tuple=0      ,      a							: Any=1      ,      a							: Optional[Any]=2      ,      a							: int=False      ,      a							: Dict=3      ,      a							: Any=2      ,      a							: List[Any]=3      ,      a							: int=None      ,      **a							: Any      ,      )							->       Dict:

				"""simple docstring"""

				super().__init__(**a      ,      pad_token_id=a      ,      bos_token_id=a      ,      eos_token_id=a	)
				SCREAMING_SNAKE_CASE   :      List[str]				     =       hidden_size
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       feat_extract_norm
				SCREAMING_SNAKE_CASE   :      Any				     =       feat_extract_activation
				SCREAMING_SNAKE_CASE   :      Any				     =       list(a	)
				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       list(a	)
				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       list(a	)
				SCREAMING_SNAKE_CASE   :      Any				     =       conv_bias
				SCREAMING_SNAKE_CASE   :      str				     =       num_buckets
				SCREAMING_SNAKE_CASE   :      str				     =       max_bucket_distance
				SCREAMING_SNAKE_CASE   :      List[str]				     =       num_conv_pos_embeddings
				SCREAMING_SNAKE_CASE   :      Any				     =       num_conv_pos_embedding_groups
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       len(self.conv_dim	)
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       num_hidden_layers
				SCREAMING_SNAKE_CASE   :      List[str]				     =       intermediate_size
				SCREAMING_SNAKE_CASE   :      Dict				     =       hidden_act
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       num_attention_heads
				SCREAMING_SNAKE_CASE   :      int				     =       hidden_dropout
				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       attention_dropout
				SCREAMING_SNAKE_CASE   :      List[str]				     =       activation_dropout
				SCREAMING_SNAKE_CASE   :      int				     =       feat_proj_dropout
				SCREAMING_SNAKE_CASE   :      Any				     =       final_dropout
				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       layerdrop
				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       layer_norm_eps
				SCREAMING_SNAKE_CASE   :      int				     =       initializer_range
				SCREAMING_SNAKE_CASE   :      Dict				     =       num_ctc_classes
				SCREAMING_SNAKE_CASE   :      Tuple				     =       vocab_size
				SCREAMING_SNAKE_CASE   :      int				     =       do_stable_layer_norm
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       use_weighted_layer_sum
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       classifier_proj_size

				if (
				    (len(self.conv_stride	) != self.num_feat_extract_layers)
				    or (len(self.conv_kernel	) != self.num_feat_extract_layers)
				    or (len(self.conv_dim	) != self.num_feat_extract_layers)
				):
					raise ValueError(
					    "Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =="
					    " `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ="
					    F" {len(self.conv_dim	)}`, `len(config.conv_stride) = {len(self.conv_stride	)}`,"
					    F" `len(config.conv_kernel) = {len(self.conv_kernel	)}`."	)

				# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
				SCREAMING_SNAKE_CASE   :      int				     =       apply_spec_augment
				SCREAMING_SNAKE_CASE   :      int				     =       mask_time_prob
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       mask_time_length
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       mask_time_min_masks
				SCREAMING_SNAKE_CASE   :      Tuple				     =       mask_feature_prob
				SCREAMING_SNAKE_CASE   :      List[str]				     =       mask_feature_length

				# parameters for pretraining with codevector quantized representations
				SCREAMING_SNAKE_CASE   :      str				     =       num_codevectors_per_group
				SCREAMING_SNAKE_CASE   :      Dict				     =       num_codevector_groups
				SCREAMING_SNAKE_CASE   :      Tuple				     =       contrastive_logits_temperature
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       num_negatives
				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       codevector_dim
				SCREAMING_SNAKE_CASE   :      int				     =       proj_codevector_dim
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       diversity_loss_weight

				# ctc loss
				SCREAMING_SNAKE_CASE   :      Any				     =       ctc_loss_reduction
				SCREAMING_SNAKE_CASE   :      Dict				     =       ctc_zero_infinity

				# adapter
				SCREAMING_SNAKE_CASE   :      Any				     =       add_adapter
				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       adapter_kernel_size
				SCREAMING_SNAKE_CASE   :      Any				     =       adapter_stride
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       num_adapter_layers
				SCREAMING_SNAKE_CASE   :      int				     =       output_hidden_size or hidden_size

				# SequenceClassification-specific parameter. Feel free to ignore for other classes.
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       classifier_proj_size

				# XVector-specific parameters. Feel free to ignore for other classes.
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       list(a	)
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       list(a	)
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       list(a	)
				SCREAMING_SNAKE_CASE   :      Tuple				     =       xvector_output_dim






			@property
			def 							__UpperCamelCase (			self							: int	)							->       Union[str, Any]:

				"""simple docstring"""

				return functools.reduce(operator.mul      ,      self.conv_stride      ,      1	)