"""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


__lowerCamelCase							 =     logging.get_logger(__name__)
class 			UpperCamelCase__(	lowerCamelCase__       ):





						def __init__(				self						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase						,**__UpperCAmelCase   )    -> Union[str, Any]:
													A__		   =							feature_size
													A__		   =							sampling_rate
													A__		   =							padding_value

													A__		   =							kwargs.pop('padding_side'						,'right'   )
													A__		   =							kwargs.pop('return_attention_mask'						,lowerCAmelCase__   )

													super().__init__(**lowerCAmelCase__   )





						def  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase = True						,__UpperCAmelCase = None						,__UpperCAmelCase = False						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,)    -> Optional[int]:






													if isinstance(lowerCAmelCase__						,(list, tuple)   ) and isinstance(processed_features[0]						,(dict, BatchFeature)   ):
																				A__		   =							{
																				    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()   )}'''   )

													A__		   =							processed_features[self.model_input_names[0]]
													A__		   =							(
													    return_attention_mask if return_attention_mask is not None else self.return_attention_mask
													)

													if len(lowerCAmelCase__   ) == 0:
																				if return_attention_mask:
																											A__		   =							[]
																				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

													A__		   =							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.
																				A__		   =							0
																				while len(required_input[index]   ) == 0:
																											index += 1
																				if index < len(lowerCAmelCase__   ):
																											A__		   =							required_input[index][0]

													if return_tensors is None:
																				if is_tf_tensor(lowerCAmelCase__   ):
																											A__		   =							"""tf"""
																				elif is_torch_tensor(lowerCAmelCase__   ):
																											A__		   =							"""pt"""
																				elif isinstance(lowerCAmelCase__						,(int, float, list, tuple, np.ndarray)   ):
																											A__		   =							"""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)   ):
																											A__		   =							to_numpy(lowerCAmelCase__   )
																				else:
																											A__		   =							[to_numpy(lowerCAmelCase__   ) for v in value]

        # Convert padding_strategy in PaddingStrategy
													A__		   =							self._get_padding_strategies(padding=lowerCAmelCase__						,max_length=lowerCAmelCase__   )

													A__		   =							processed_features[self.model_input_names[0]]

													A__		   =							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.'   )

													A__		   =							[]
													for i in range(lowerCAmelCase__   ):
																				A__		   =							{k: v[i] for k, v in processed_features.items()}
																				# truncation
																				A__		   =							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
																				A__		   =							max(len(input_slice[self.model_input_names[0]]   ) for input_slice in truncated_inputs   )
																				A__		   =							PaddingStrategy.MAX_LENGTH

													A__		   =							{}
													for i in range(lowerCAmelCase__   ):
																				# padding
																				A__		   =							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:
																																		A__		   =							[]
																											if value.dtype is np.dtype(np.floataa   ):
																																		A__		   =							value.astype(np.floataa   )
																											batch_outputs[key].append(lowerCAmelCase__   )

													return BatchFeature(lowerCAmelCase__						,tensor_type=lowerCAmelCase__   )





						def  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase = None						,__UpperCAmelCase = PaddingStrategy.DO_NOT_PAD						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,)    -> str:
													A__		   =							processed_features[self.model_input_names[0]]

													if padding_strategy == PaddingStrategy.LONGEST:
																				A__		   =							len(lowerCAmelCase__   )

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

													A__		   =							padding_strategy != PaddingStrategy.DO_NOT_PAD and len(lowerCAmelCase__   ) < max_length

													if return_attention_mask and "attention_mask" not in processed_features:
																				A__		   =							np.ones(len(lowerCAmelCase__   )						,dtype=np.intaa   )

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

													return processed_features





						def  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,)    -> Optional[Any]:






													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.'   )

													A__		   =							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):
																				A__		   =							((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

													A__		   =							len(lowerCAmelCase__   ) > max_length

													if needs_to_be_truncated:
																				A__		   =							processed_features[self.model_input_names[0]][:max_length]
																				if "attention_mask" in processed_features:
																											A__		   =							processed_features["""attention_mask"""][:max_length]

													return processed_features





						def  snake_case__		(				self						,__UpperCAmelCase=False						,__UpperCAmelCase=None   )    -> List[str]:






													if padding is not False:
																				if padding is True:
																											A__		   =							PaddingStrategy.LONGEST  # Default to pad to the longest sequence in the batch
																				elif not isinstance(lowerCAmelCase__						,lowerCAmelCase__   ):
																											A__		   =							PaddingStrategy(lowerCAmelCase__   )
																				elif isinstance(lowerCAmelCase__						,lowerCAmelCase__   ):
																											A__		   =							padding
													else:
																				A__		   =							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 .dependency_versions_table import deps
from .utils.versions import require_version, require_version_core


# define which module versions we always want to check at run time
# (usually the ones defined in `install_requires` in setup.py)
#
# order specific notes:
# - tqdm must be checked before tokenizers

UpperCamelCase__       :	Dict    = [
    '''python''',
    '''tqdm''',
    '''regex''',
    '''requests''',
    '''packaging''',
    '''filelock''',
    '''numpy''',
    '''tokenizers''',
    '''huggingface-hub''',
    '''safetensors''',
    '''accelerate''',
    '''pyyaml''',
]

for pkg in pkgs_to_check_at_runtime:
						if pkg in deps:
												if pkg == "tokenizers":
																		# must be loaded here, or else tqdm check may fail
																		from .utils import is_tokenizers_available

																		if not is_tokenizers_available():
																								continue  # not required, check version only if installed
												elif pkg == "accelerate":
																		# must be loaded here, or else tqdm check may fail
																		from .utils import is_accelerate_available

																		# Maybe switch to is_torch_available in the future here so that Accelerate is hard dep of
																		# Transformers with PyTorch
																		if not is_accelerate_available():
																								continue  # not required, check version only if installed

												require_version_core(deps[pkg])
						else:
												raise ValueError(f"can't find {pkg} in {deps.keys()}, check dependency_versions_table.py")


def 				lowerCAmelCase_   (	_lowerCamelCase:   List[Any]       ,      _lowerCamelCase:   Optional[int]=None					):
		require_version(deps[pkg]       ,      _lowerCamelCase					)