


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


SCREAMING_SNAKE_CASE       :		Optional[int]         =				logging.get_logger(__name__)






class      UpperCamelCase					(		lowercase__     ):


   '''simple docstring'''







   def __init__(     self   ,							UpperCamelCase_   ,							UpperCamelCase_   ,							UpperCamelCase_   ,							**UpperCamelCase_		):
        lowercase_       :int     =       feature_size
        lowercase_       :Optional[int]     =       sampling_rate
        lowercase_       :str     =       padding_value

        lowercase_       :Union[str, Any]     =       kwargs.pop('''padding_side'''   ,							'''right'''		)
        lowercase_       :Union[str, Any]     =       kwargs.pop('''return_attention_mask'''   ,							UpperCamelCase_		)

        super().__init__(**UpperCamelCase_		)
   def 		UpperCamelCase  (     self   ,							UpperCamelCase_   ,							UpperCamelCase_ = True   ,							UpperCamelCase_ = None   ,							UpperCamelCase_ = False   ,							UpperCamelCase_ = None   ,							UpperCamelCase_ = None   ,							UpperCamelCase_ = None   ,							):
        # 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(UpperCamelCase_   ,							(list, tuple)		) and isinstance(processed_features[0]   ,							(dict, BatchFeature)		):
             lowercase_       :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()		)}"		)

        lowercase_       :List[Any]     =       processed_features[self.model_input_names[0]]
        lowercase_       :Any     =       (
            return_attention_mask if return_attention_mask is not None else self.return_attention_mask
        )

        if len(UpperCamelCase_		) == 0:
             if return_attention_mask:
                  lowercase_       :Any     =       []
             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

        lowercase_       :List[str]     =       required_input[0]
        if isinstance(UpperCamelCase_   ,							(list, tuple)		):
             # first_element might be an empty list/tuple in some edge cases so we grab the first non empty element.
             lowercase_       :Union[str, Any]     =       0
             while len(required_input[index]		) == 0:
                  index += 1
             if index < len(UpperCamelCase_		):
                  lowercase_       :Dict     =       required_input[index][0]

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

        for key, value in processed_features.items():
             if isinstance(value[0]   ,							(int, float)		):
                  lowercase_       :int     =       to_numpy(UpperCamelCase_		)
             else:
                  lowercase_       :Tuple     =       [to_numpy(UpperCamelCase_		) for v in value]

        # Convert padding_strategy in PaddingStrategy
        lowercase_       :List[str]     =       self._get_padding_strategies(padding=UpperCamelCase_   ,							max_length=UpperCamelCase_		)

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

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

        lowercase_       :Dict     =       []
        for i in range(UpperCamelCase_		):
             lowercase_       :List[str]     =       {k: v[i] for k, v in processed_features.items()}
             # truncation
             lowercase_       :Dict     =       self._truncate(
                 UpperCamelCase_   ,							max_length=UpperCamelCase_   ,							pad_to_multiple_of=UpperCamelCase_   ,							truncation=UpperCamelCase_   ,							)
             truncated_inputs.append(UpperCamelCase_		)

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

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

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

        return BatchFeature(UpperCamelCase_   ,							tensor_type=UpperCamelCase_		)
   def 		UpperCamelCase  (     self   ,							UpperCamelCase_   ,							UpperCamelCase_ = None   ,							UpperCamelCase_ = PaddingStrategy.DO_NOT_PAD   ,							UpperCamelCase_ = None   ,							UpperCamelCase_ = None   ,							):
        lowercase_       :str     =       processed_features[self.model_input_names[0]]

        if padding_strategy == PaddingStrategy.LONGEST:
             lowercase_       :int     =       len(UpperCamelCase_		)

        if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
             lowercase_       :Optional[Any]     =       ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

        lowercase_       :Tuple     =       padding_strategy != PaddingStrategy.DO_NOT_PAD and len(UpperCamelCase_		) < max_length

        if return_attention_mask and "attention_mask" not in processed_features:
             lowercase_       :Dict     =       np.ones(len(UpperCamelCase_		)   ,							dtype=np.intaa		)

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

        return processed_features
   def 		UpperCamelCase  (     self   ,							UpperCamelCase_   ,							UpperCamelCase_ = None   ,							UpperCamelCase_ = None   ,							UpperCamelCase_ = None   ,							):
        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.'''		)

        lowercase_       :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):
             lowercase_       :Tuple     =       ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

        lowercase_       :List[str]     =       len(UpperCamelCase_		) > max_length

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

        return processed_features




   def 		UpperCamelCase  (     self   ,							UpperCamelCase_=False   ,							UpperCamelCase_=None		):

        # Get padding strategy
        if padding is not False:
             if padding is True:
                  lowercase_       :Optional[Any]     =       PaddingStrategy.LONGEST  # Default to pad to the longest sequence in the batch
             elif not isinstance(UpperCamelCase_   ,							UpperCamelCase_		):
                  lowercase_       :Union[str, Any]     =       PaddingStrategy(UpperCamelCase_		)
             elif isinstance(UpperCamelCase_   ,							UpperCamelCase_		):
                  lowercase_       :Dict     =       padding
        else:
             lowercase_       :Union[str, 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





def     UpperCamelCase    (      _a		)					->							int:
     '''simple docstring'''





     assert isinstance(_a       ,		_a		), f"The input value of [n={number}] is not an integer"

     if number == 1:
          return 2
     elif number < 1:
          lowercase_       :str     =       f"The input value of [n={number}] has to be > 0"
          raise ValueError(_a		)
     else:
          lowercase_       :List[str]     =       sylvester(number - 1		)
          lowercase_       :Union[str, Any]     =       num - 1
          lowercase_       :List[str]     =       num
          return lower * upper + 1


if __name__ == "__main__":
   print(f"The 8th number in Sylvester's sequence: {sylvester(8)}")

