

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  :Dict										=       logging.get_logger(__name__)

class    _a (       _a							):




      """simple docstring"""

      def __init__(					self				:							List[Any]		,       UpperCAmelCase				:							int		,       UpperCAmelCase				:							int		,       UpperCAmelCase				:							float		,       **UpperCAmelCase				:							Optional[int]     ):
            A_		   =							feature_size
            A_		   =							sampling_rate
            A_		   =							padding_value

            A_		   =							kwargs.pop("padding_side"		,       "right"     )
            A_		   =							kwargs.pop("return_attention_mask"		,       __UpperCAmelCase     )

            super().__init__(**__UpperCAmelCase     )





      def 						__A     (					self				:							Union[str, Any]		,       UpperCAmelCase				:							Union[
              BatchFeature,
              List[BatchFeature],
              Dict[str, BatchFeature],
              Dict[str, List[BatchFeature]],
              List[Dict[str, BatchFeature]],
          ]		,       UpperCAmelCase				:							Union[bool, str, PaddingStrategy] = True		,       UpperCAmelCase				:							Optional[int] = None		,       UpperCAmelCase				:							bool = False		,       UpperCAmelCase				:							Optional[int] = None		,       UpperCAmelCase				:							Optional[bool] = None		,       UpperCAmelCase				:							Optional[Union[str, TensorType]] = 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)     ):
                  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(__UpperCAmelCase     ) == 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(__UpperCAmelCase		,       (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(__UpperCAmelCase     ):
                        A_		   =							required_input[index][0]

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

                  # Convert padding_strategy in PaddingStrategy
            A_		   =							self._get_padding_strategies(padding=__UpperCAmelCase		,       max_length=__UpperCAmelCase     )

            A_		   =							processed_features[self.model_input_names[0]]

            A_		   =							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."     )

            A_		   =							[]
            for i in range(__UpperCAmelCase     ):
                  A_		   =							{k: v[i] for k, v in processed_features.items()}
                  # truncation
                  A_		   =							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
                  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(__UpperCAmelCase     ):
                  # padding
                  A_		   =							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:
                              A_		   =							[]
                        if value.dtype is np.dtype(np.floataa     ):
                              A_		   =							value.astype(np.floataa     )
                        batch_outputs[key].append(__UpperCAmelCase     )

            return BatchFeature(__UpperCAmelCase		,       tensor_type=__UpperCAmelCase     )





      def 						__A     (					self				:							List[str]		,       UpperCAmelCase				:							Union[Dict[str, np.ndarray], BatchFeature]		,       UpperCAmelCase				:							Optional[int] = None		,       UpperCAmelCase				:							PaddingStrategy = PaddingStrategy.DO_NOT_PAD		,       UpperCAmelCase				:							Optional[int] = None		,       UpperCAmelCase				:							Optional[bool] = None		,       ):
            A_		   =							processed_features[self.model_input_names[0]]

            if padding_strategy == PaddingStrategy.LONGEST:
                  A_		   =							len(__UpperCAmelCase     )

            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(__UpperCAmelCase     ) < max_length

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

            if needs_to_be_padded:
                  A_		   =							max_length - len(__UpperCAmelCase     )
                  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(
                            __UpperCAmelCase		,       __UpperCAmelCase		,       "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(
                            __UpperCAmelCase		,       __UpperCAmelCase		,       "constant"		,       constant_values=self.padding_value     )
                  else:
                        raise ValueError("Invalid padding strategy:" + str(self.padding_side     )     )

            return processed_features





      def 						__A     (					self				:							List[str]		,       UpperCAmelCase				:							Union[Dict[str, np.ndarray], BatchFeature]		,       UpperCAmelCase				:							Optional[int] = None		,       UpperCAmelCase				:							Optional[int] = None		,       UpperCAmelCase				:							Optional[bool] = 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."     )

            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(__UpperCAmelCase     ) > 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 						__A     (					self				:							Dict		,       UpperCAmelCase				:							Any=False		,       UpperCAmelCase				:							Optional[int]=None     ):

            # Get padding strategy
            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(__UpperCAmelCase		,       __UpperCAmelCase     ):
                        A_		   =							PaddingStrategy(__UpperCAmelCase     )
                  elif isinstance(__UpperCAmelCase		,       __UpperCAmelCase     ):
                        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"""







def      lowercase			(				A_       )->      bool:
  '''simple docstring'''







  if not all(x.isalpha() for x in string       ):
    raise ValueError("String must only contain alphabetic characters."       )

  a						:		Tuple      							=			sorted(string.lower()       )
  return len(A_       ) == len(set(A_       )       )


if __name__ == "__main__":
 __lowercase    					=      input("""Enter a string """).strip()

 __lowercase    					=      is_isogram(input_str)
 print(f'''{input_str} is {'an' if isogram else 'not an'} isogram.''')






