






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


A_       :int       =						logging.get_logger(__name__)




class 				__A     (			a ):





				"""simple docstring"""







				def __init__( self							,  lowerCamelCase__							,  lowerCamelCase__							,  lowerCamelCase__							,  **lowerCamelCase__    ):




									"""simple docstring"""

									__UpperCamelCase      :					Optional[Any]       		=feature_size
									__UpperCamelCase      :					Tuple       		=sampling_rate
									__UpperCamelCase      :					List[Any]       		=padding_value

									__UpperCamelCase      :					List[Any]       		=kwargs.pop('padding_side'							,  'right'    )
									__UpperCamelCase      :					Optional[Any]       		=kwargs.pop('return_attention_mask'							,  lowerCamelCase__    )

									super().__init__(**lowerCamelCase__    )


				def       __lowercase							( self							,  lowerCamelCase__							,  lowerCamelCase__ = True							,  lowerCamelCase__ = None							,  lowerCamelCase__ = False							,  lowerCamelCase__ = None							,  lowerCamelCase__ = None							,  lowerCamelCase__ = None							,  ):




									"""simple docstring"""

									if isinstance(lowerCamelCase__							,  (list, tuple)    ) and isinstance(processed_features[0]							,  (dict, BatchFeature)    ):
														__UpperCamelCase      :					List[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      :					Any       		=processed_features[self.model_input_names[0]]
									__UpperCamelCase      :					Optional[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      :					Dict       		=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      :					int       		=0
														while len(required_input[index]    ) == 0:
																			index += 1
														if index < len(lowerCamelCase__    ):
																			__UpperCamelCase      :					Any       		=required_input[index][0]

									if return_tensors is None:
														if is_tf_tensor(lowerCamelCase__    ):
																			__UpperCamelCase      :					str       		='tf'
														elif is_torch_tensor(lowerCamelCase__    ):
																			__UpperCamelCase      :					Union[str, Any]       		='pt'
														elif isinstance(lowerCamelCase__							,  (int, float, list, tuple, np.ndarray)    ):
																			__UpperCamelCase      :					List[str]       		='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      :					Dict       		=to_numpy(lowerCamelCase__    )
														else:
																			__UpperCamelCase      :					List[Any]       		=[to_numpy(lowerCamelCase__    ) for v in value]

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

									__UpperCamelCase      :					int       		=processed_features[self.model_input_names[0]]

									__UpperCamelCase      :					int       		=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      :					Union[str, Any]       		=[]
									for i in range(lowerCamelCase__    ):
														__UpperCamelCase      :					Tuple       		={k: v[i] for k, v in processed_features.items()}
														# truncation
														__UpperCamelCase      :					Tuple       		=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      :					Tuple       		=max(len(input_slice[self.model_input_names[0]]    ) for input_slice in truncated_inputs    )
														__UpperCamelCase      :					List[Any]       		=PaddingStrategy.MAX_LENGTH

									__UpperCamelCase      :					Any       		={}
									for i in range(lowerCamelCase__    ):
														# padding
														__UpperCamelCase      :					Union[str, 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      :					str       		=[]
																			if value.dtype is np.dtype(np.floataa    ):
																								__UpperCamelCase      :					Tuple       		=value.astype(np.floataa    )
																			batch_outputs[key].append(lowerCamelCase__    )

									return BatchFeature(lowerCamelCase__							,  tensor_type=lowerCamelCase__    )


				def       __lowercase							( self							,  lowerCamelCase__							,  lowerCamelCase__ = None							,  lowerCamelCase__ = PaddingStrategy.DO_NOT_PAD							,  lowerCamelCase__ = None							,  lowerCamelCase__ = None							,  ):




									"""simple docstring"""

									__UpperCamelCase      :					Dict       		=processed_features[self.model_input_names[0]]

									if padding_strategy == PaddingStrategy.LONGEST:
														__UpperCamelCase      :					List[str]       		=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      :					List[Any]       		=((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

									__UpperCamelCase      :					List[str]       		=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      :					Any       		=max_length - len(lowerCamelCase__    )
														if self.padding_side == "right":
																			if return_attention_mask:
																								__UpperCamelCase      :					Optional[Any]       		=np.pad(
																								    processed_features['attention_mask']							,  (0, difference)    )
																			__UpperCamelCase      :					List[str]       		=((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      :					int       		=((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       __lowercase							( self							,  lowerCamelCase__							,  lowerCamelCase__ = None							,  lowerCamelCase__ = None							,  lowerCamelCase__ = 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      :					str       		=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      :					Optional[int]       		=((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

									__UpperCamelCase      :					Union[str, Any]       		=len(lowerCamelCase__    ) > max_length

									if needs_to_be_truncated:
														__UpperCamelCase      :					Dict       		=processed_features[self.model_input_names[0]][:max_length]
														if "attention_mask" in processed_features:
																			__UpperCamelCase      :					Any       		=processed_features['attention_mask'][:max_length]

									return processed_features







				def       __lowercase							( self							,  lowerCamelCase__=False							,  lowerCamelCase__=None    ):




									"""simple docstring"""

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







class 			a__    (					snake_case__      ):
						pass






class 			a__    (					snake_case__      ):
						pass






class 			a__    :






						def __init__(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       [
											    [],
											    [],
											    [],
											]






						def 				__SCREAMING_SNAKE_CASE(   self		,			_A		,			_A			):




											"""simple docstring"""



											try:
																if len(self.queues[priority]			) >= 1_0_0:
																					raise OverflowError("Maximum queue size is 100"			)
																self.queues[priority].append(_A			)
											except IndexError:
																raise ValueError("Valid priorities are 0, 1, and 2"			)






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											for queue in self.queues:
																if queue:
																					return queue.pop(0			)
											raise UnderFlowError("All queues are empty"			)






						def __str__(   self			):




											"""simple docstring"""



											return "\n".join(f"""Priority {i}: {q}""" for i, q in enumerate(self.queues			)			)






class 			a__    :






						def __init__(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       []






						def 				__SCREAMING_SNAKE_CASE(   self		,			_A			):




											"""simple docstring"""



											if len(self.queue			) == 1_0_0:
																raise OverFlowError("Maximum queue size is 100"			)
											self.queue.append(_A			)






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											if not self.queue:
																raise UnderFlowError("The queue is empty"			)
											else:
																__lowerCAmelCase				   =       min(self.queue			)
																self.queue.remove(_A			)
																return data






						def __str__(   self			):




											"""simple docstring"""



											return str(self.queue			)







def 	_a      (						):
					__lowerCAmelCase				   =       FixedPriorityQueue()
					fpq.enqueue(0      ,      10     )
					fpq.enqueue(1      ,      70     )
					fpq.enqueue(0      ,      1_00     )
					fpq.enqueue(2      ,      1     )
					fpq.enqueue(2      ,      5     )
					fpq.enqueue(1      ,      7     )
					fpq.enqueue(2      ,      4     )
					fpq.enqueue(1      ,      64     )
					fpq.enqueue(0      ,      1_28     )
					print(SCREAMING_SNAKE_CASE_     )
					print(fpq.dequeue()     )
					print(fpq.dequeue()     )
					print(fpq.dequeue()     )
					print(fpq.dequeue()     )
					print(fpq.dequeue()     )
					print(SCREAMING_SNAKE_CASE_     )
					print(fpq.dequeue()     )
					print(fpq.dequeue()     )
					print(fpq.dequeue()     )
					print(fpq.dequeue()     )
					print(fpq.dequeue()     )







def 	_a      (						):
					__lowerCAmelCase				   =       ElementPriorityQueue()
					epq.enqueue(10     )
					epq.enqueue(70     )
					epq.enqueue(1_00     )
					epq.enqueue(1     )
					epq.enqueue(5     )
					epq.enqueue(7     )
					epq.enqueue(4     )
					epq.enqueue(64     )
					epq.enqueue(1_28     )
					print(SCREAMING_SNAKE_CASE_     )
					print(epq.dequeue()     )
					print(epq.dequeue()     )
					print(epq.dequeue()     )
					print(epq.dequeue()     )
					print(epq.dequeue()     )
					print(SCREAMING_SNAKE_CASE_     )
					print(epq.dequeue()     )
					print(epq.dequeue()     )
					print(epq.dequeue()     )
					print(epq.dequeue()     )
					print(epq.dequeue()     )


if __name__ == "__main__":
					fixed_priority_queue()
					element_priority_queue()




