



'''simple docstring'''
from typing import Dict, List, Optional, Union

import numpy as np

from .feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from .utils import PaddingStrategy, TensorType, is_tf_tensor, is_torch_tensor, logging, to_numpy


snake_case_					:      Tuple    			=    logging.get_logger(__name__)






class 				lowercase__      (				lowercase  ):





	def __init__(					self   :							str					,lowerCamelCase__   :							int					,lowerCamelCase__   :							int					,lowerCamelCase__   :							float					,**lowerCamelCase__   :							Optional[Any]					):







							'''simple docstring'''
							_UpperCamelCase   :						List[str]												=  feature_size
							_UpperCamelCase   :						int												=  sampling_rate
							_UpperCamelCase   :						Tuple												=  padding_value

							_UpperCamelCase   :						Optional[int]												=  kwargs.pop('padding_side'					,'right'					)
							_UpperCamelCase   :						Tuple												=  kwargs.pop('return_attention_mask'					,lowerCamelCase__					)

							super().__init__(**lowerCamelCase__					)





	def       UpperCamelCase_    (					self   :							List[Any]					,lowerCamelCase__   :							Union[
	        BatchFeature,
	        List[BatchFeature],
	        Dict[str, BatchFeature],
	        Dict[str, List[BatchFeature]],
	        List[Dict[str, BatchFeature]],
	    ]					,lowerCamelCase__   :							Union[bool, str, PaddingStrategy] = True					,lowerCamelCase__   :							Optional[int] = None					,lowerCamelCase__   :							bool = False					,lowerCamelCase__   :							Optional[int] = None					,lowerCamelCase__   :							Optional[bool] = None					,lowerCamelCase__   :							Optional[Union[str, TensorType]] = None					,):







							'''simple docstring'''
							# If we have a list of dicts, let's convert it in a dict of lists
							# We do this to allow using this method as a collate_fn function in PyTorch Dataloader
							if isinstance(lowerCamelCase__					,(list, tuple)					) and isinstance(processed_features[0]					,(dict, BatchFeature)					):
													_UpperCamelCase   :						Optional[Any]												=  {
													    key: [example[key] for example in processed_features] for key in processed_features[0].keys()
													}

							# The model's main input name, usually `input_values`, has be passed for padding
							if self.model_input_names[0] not in processed_features:
													raise ValueError(
													    'You should supply an instance of `transformers.BatchFeature` or list of `transformers.BatchFeature`'
													    F' to this method that includes {self.model_input_names[0]}, but you provided'
													    F' {list(processed_features.keys()					)}'					)

							_UpperCamelCase   :						str												=  processed_features[self.model_input_names[0]]
							_UpperCamelCase   :						Union[str, Any]												=  (
							    return_attention_mask if return_attention_mask is not None else self.return_attention_mask
							)

							if len(lowerCamelCase__					) == 0:
													if return_attention_mask:
																			_UpperCamelCase   :						str												=  []
													return processed_features

							# If we have PyTorch/TF tensors or lists as inputs, we cast them as Numpy arrays
							# and rebuild them afterwards if no return_tensors is specified
							# Note that we lose the specific device the tensor may be on for PyTorch

							_UpperCamelCase   :						int												=  required_input[0]
							if isinstance(lowerCamelCase__					,(list, tuple)					):
													# first_element might be an empty list/tuple in some edge cases so we grab the first non empty element.
													_UpperCamelCase   :						Optional[int]												=  0
													while len(required_input[index]					) == 0:
																			index += 1
													if index < len(lowerCamelCase__					):
																			_UpperCamelCase   :						Optional[Any]												=  required_input[index][0]

							if return_tensors is None:
													if is_tf_tensor(lowerCamelCase__					):
																			_UpperCamelCase   :						List[Any]												=  'tf'
													elif is_torch_tensor(lowerCamelCase__					):
																			_UpperCamelCase   :						int												=  'pt'
													elif isinstance(lowerCamelCase__					,(int, float, list, tuple, np.ndarray)					):
																			_UpperCamelCase   :						Dict												=  'np'
													else:
																			raise ValueError(
																			    F'type of {first_element} unknown: {type(lowerCamelCase__					)}. '
																			    'Should be one of a python, numpy, pytorch or tensorflow object.'					)

							for key, value in processed_features.items():
													if isinstance(value[0]					,(int, float)					):
																			_UpperCamelCase   :						List[Any]												=  to_numpy(lowerCamelCase__					)
													else:
																			_UpperCamelCase   :						Tuple												=  [to_numpy(lowerCamelCase__					) for v in value]

        # Convert padding_strategy in PaddingStrategy
							_UpperCamelCase   :						Tuple												=  self._get_padding_strategies(padding=lowerCamelCase__					,max_length=lowerCamelCase__					)

							_UpperCamelCase   :						List[Any]												=  processed_features[self.model_input_names[0]]

							_UpperCamelCase   :						Any												=  len(lowerCamelCase__					)
							if not all(len(lowerCamelCase__					) == batch_size for v in processed_features.values()					):
													raise ValueError('Some items in the output dictionary have a different batch size than others.'					)

							_UpperCamelCase   :						str												=  []
							for i in range(lowerCamelCase__					):
													_UpperCamelCase   :						Union[str, Any]												=  {k: v[i] for k, v in processed_features.items()}
													# truncation
													_UpperCamelCase   :						int												=  self._truncate(
													    lowerCamelCase__					,max_length=lowerCamelCase__					,pad_to_multiple_of=lowerCamelCase__					,truncation=lowerCamelCase__					,)
													truncated_inputs.append(lowerCamelCase__					)

							if padding_strategy == PaddingStrategy.LONGEST:
													# make sure that `max_length` cannot be longer than the longest truncated length
													_UpperCamelCase   :						Union[str, Any]												=  max(len(input_slice[self.model_input_names[0]]					) for input_slice in truncated_inputs					)
													_UpperCamelCase   :						Union[str, Any]												=  PaddingStrategy.MAX_LENGTH

							_UpperCamelCase   :						str												=  {}
							for i in range(lowerCamelCase__					):
													# padding
													_UpperCamelCase   :						Any												=  self._pad(
													    truncated_inputs[i]					,max_length=lowerCamelCase__					,padding_strategy=lowerCamelCase__					,pad_to_multiple_of=lowerCamelCase__					,return_attention_mask=lowerCamelCase__					,)

													for key, value in outputs.items():
																			if key not in batch_outputs:
																									_UpperCamelCase   :						List[str]												=  []
																			if value.dtype is np.dtype(np.floataa					):
																									_UpperCamelCase   :						Optional[int]												=  value.astype(np.floataa					)
																			batch_outputs[key].append(lowerCamelCase__					)

							return BatchFeature(lowerCamelCase__					,tensor_type=lowerCamelCase__					)





	def       UpperCamelCase_    (					self   :							str					,lowerCamelCase__   :							Union[Dict[str, np.ndarray], BatchFeature]					,lowerCamelCase__   :							Optional[int] = None					,lowerCamelCase__   :							PaddingStrategy = PaddingStrategy.DO_NOT_PAD					,lowerCamelCase__   :							Optional[int] = None					,lowerCamelCase__   :							Optional[bool] = None					,):







							'''simple docstring'''
							_UpperCamelCase   :						Dict												=  processed_features[self.model_input_names[0]]

							if padding_strategy == PaddingStrategy.LONGEST:
													_UpperCamelCase   :						Tuple												=  len(lowerCamelCase__					)

							if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
													_UpperCamelCase   :						Tuple												=  ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

							_UpperCamelCase   :						List[Any]												=  padding_strategy != PaddingStrategy.DO_NOT_PAD and len(lowerCamelCase__					) < max_length

							if return_attention_mask and "attention_mask" not in processed_features:
													_UpperCamelCase   :						Tuple												=  np.ones(len(lowerCamelCase__					)					,dtype=np.intaa					)

							if needs_to_be_padded:
													_UpperCamelCase   :						Optional[int]												=  max_length - len(lowerCamelCase__					)
													if self.padding_side == "right":
																			if return_attention_mask:
																									_UpperCamelCase   :						str												=  np.pad(
																									    processed_features['attention_mask']					,(0, difference)					)
																			_UpperCamelCase   :						Optional[Any]												=  ((0, difference), (0, 0)) if self.feature_size > 1 else (0, difference)
																			_UpperCamelCase   :						Union[str, Any]												=  np.pad(
																			    lowerCamelCase__					,lowerCamelCase__					,'constant'					,constant_values=self.padding_value					)
													elif self.padding_side == "left":
																			if return_attention_mask:
																									_UpperCamelCase   :						Any												=  np.pad(
																									    processed_features['attention_mask']					,(difference, 0)					)
																			_UpperCamelCase   :						Dict												=  ((difference, 0), (0, 0)) if self.feature_size > 1 else (difference, 0)
																			_UpperCamelCase   :						str												=  np.pad(
																			    lowerCamelCase__					,lowerCamelCase__					,'constant'					,constant_values=self.padding_value					)
													else:
																			raise ValueError('Invalid padding strategy:' + str(self.padding_side					)					)

							return processed_features





	def       UpperCamelCase_    (					self   :							Any					,lowerCamelCase__   :							Union[Dict[str, np.ndarray], BatchFeature]					,lowerCamelCase__   :							Optional[int] = None					,lowerCamelCase__   :							Optional[int] = None					,lowerCamelCase__   :							Optional[bool] = None					,):







							'''simple docstring'''
							if not truncation:
													return processed_features
							elif truncation and max_length is None:
													raise ValueError('When setting ``truncation=True``, make sure that ``max_length`` is defined.'					)

							_UpperCamelCase   :						Any												=  processed_features[self.model_input_names[0]]

							# find `max_length` that fits `pad_to_multiple_of`
							if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
													_UpperCamelCase   :						Union[str, Any]												=  ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

							_UpperCamelCase   :						Dict												=  len(lowerCamelCase__					) > max_length

							if needs_to_be_truncated:
													_UpperCamelCase   :						Optional[int]												=  processed_features[self.model_input_names[0]][:max_length]
													if "attention_mask" in processed_features:
																			_UpperCamelCase   :						Optional[int]												=  processed_features['attention_mask'][:max_length]

							return processed_features





	def       UpperCamelCase_    (					self   :							str					,lowerCamelCase__   :							Any=False					,lowerCamelCase__   :							List[str]=None					):







							'''simple docstring'''

							# Get padding strategy
							if padding is not False:
													if padding is True:
																			_UpperCamelCase   :						List[Any]												=  PaddingStrategy.LONGEST  # Default to pad to the longest sequence in the batch
													elif not isinstance(lowerCamelCase__					,lowerCamelCase__					):
																			_UpperCamelCase   :						List[str]												=  PaddingStrategy(lowerCamelCase__					)
													elif isinstance(lowerCamelCase__					,lowerCamelCase__					):
																			_UpperCamelCase   :						int												=  padding
							else:
													_UpperCamelCase   :						Tuple												=  PaddingStrategy.DO_NOT_PAD

							# Set max length if needed
							if max_length is None:
													if padding_strategy == PaddingStrategy.MAX_LENGTH:
																			raise ValueError(
																			    F'When setting ``padding={PaddingStrategy.MAX_LENGTH}``, make sure that max_length is defined'					)

        # Test if we have a padding value
							if padding_strategy != PaddingStrategy.DO_NOT_PAD and (self.padding_value is None):
													raise ValueError(
													    'Asking to pad but the feature_extractor does not have a padding value. Please select a value to use'
													    ' as `padding_value`. For example: `feature_extractor.padding_value = 0.0`.'					)

							return padding_strategy







'''simple docstring'''
from pathlib import Path

import fire






def A__       (	UpperCAmelCase_    ,    UpperCAmelCase_    ,    UpperCAmelCase_				):
						_UpperCamelCase   :						int												=  Path(UpperCAmelCase_				)
						_UpperCamelCase   :						str												=  Path(UpperCAmelCase_				)
						dest_dir.mkdir(exist_ok=UpperCAmelCase_				)
						for path in src_dir.iterdir():
												_UpperCamelCase   :						int												=  [x.rstrip() for x in list(path.open().readlines()				)][:n]
												_UpperCamelCase   :						Any												=  dest_dir.joinpath(path.name				)
												print(UpperCAmelCase_				)
												dest_path.open('w'				).write('\n'.join(UpperCAmelCase_				)				)


if __name__ == "__main__":
					fire.Fire(minify)


