




from typing import List, Optional, Union

import numpy as np

from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging


_snake_case            = logging.get_logger(__name__)







class 		lowercase							(     UpperCamelCase__	):
     _a											=		["input_values", "padding_mask"]

     def __init__(						self	,  _a = 1	,  _a = 2_4000	,  _a = 0.0	,  _a = None	,  _a = None	,  **_a	,  )		->							Optional[int]:
           super().__init__(feature_size=_a	,  sampling_rate=_a	,  padding_value=_a	,  **_a       )
           _A							:  List[Any]    =		chunk_length_s
           _A							:  Union[str, Any]    =		overlap

     @property
     def       a__			(						self       )		->							Optional[int]:
           if self.chunk_length_s is None:
                 return None
           else:
                 return int(self.chunk_length_s * self.sampling_rate       )

     @property
     def       a__			(						self       )		->							Optional[int]:
           if self.chunk_length_s is None or self.overlap is None:
                 return None
           else:
                 return max(1	,  int((1.0 - self.overlap) * self.chunk_length       )       )



     def __call__(						self	,  _a	,  _a = None	,  _a = False	,  _a = None	,  _a = None	,  _a = None	,  )		->							BatchFeature:
           if sampling_rate is not None:
                 if sampling_rate != self.sampling_rate:
                       raise ValueError(
                           F'''The model corresponding to this feature extractor: {self} was trained using a sampling rate of'''
                           F''' {self.sampling_rate}. Please make sure that the provided audio input was sampled with'''
                           F''' {self.sampling_rate} and not {sampling_rate}.'''       )
           else:
                 logger.warning(
                     """It is strongly recommended to pass the `sampling_rate` argument to this function. """
                     """Failing to do so can result in silent errors that might be hard to debug."""       )

           if padding and truncation:
                 raise ValueError("""Both padding and truncation were set. Make sure you only set one."""       )
           elif padding is None:
                 # by default let's pad the inputs
                 _A							:  Dict    =		True

           _A							:  Dict    =		bool(
               isinstance(_a	,  (list, tuple)       ) and (isinstance(raw_audio[0]	,  (np.ndarray, tuple, list)       ))       )

           if is_batched:
                 _A							:  Optional[int]    =		[np.asarray(_a	,  dtype=np.floataa       ).T for audio in raw_audio]
           elif not is_batched and not isinstance(_a	,  np.ndarray       ):
                 _A							:  List[str]    =		np.asarray(_a	,  dtype=np.floataa       )
           elif isinstance(_a	,  np.ndarray       ) and raw_audio.dtype is np.dtype(np.floataa       ):
                 _A							:  Union[str, Any]    =		raw_audio.astype(np.floataa       )

           # always return batch
           if not is_batched:
                 _A							:  int    =		[np.asarray(_a       ).T]

           # verify inputs are valid
           for idx, example in enumerate(_a       ):
                 if example.ndim > 2:
                       raise ValueError(F'''Expected input shape (channels, length) but got shape {example.shape}'''       )
                 if self.feature_size == 1 and example.ndim != 1:
                       raise ValueError(F'''Expected mono audio but example has {example.shape[-1]} channels'''       )
                 if self.feature_size == 2 and example.shape[-1] != 2:
                       raise ValueError(F'''Expected stereo audio but example has {example.shape[-1]} channels'''       )

           _A							:  Any    =		None
           _A							:  Optional[int]    =		BatchFeature({"""input_values""": raw_audio}       )
           if self.chunk_stride is not None and self.chunk_length is not None and max_length is None:
                 if truncation:
                       _A							:  Optional[int]    =		min(array.shape[0] for array in raw_audio       )
                       _A							:  str    =		int(np.floor(max_length / self.chunk_stride       )       )
                       _A							:  Union[str, Any]    =		(nb_step - 1) * self.chunk_stride + self.chunk_length
                 elif padding:
                       _A							:  Tuple    =		max(array.shape[0] for array in raw_audio       )
                       _A							:  Dict    =		int(np.ceil(max_length / self.chunk_stride       )       )
                       _A							:  Union[str, Any]    =		(nb_step - 1) * self.chunk_stride + self.chunk_length
                       _A							:  Tuple    =		"""max_length"""
                 else:
                       _A							:  Optional[int]    =		input_values

        # normal padding on batch
           if padded_inputs is None:
                 _A							:  Tuple    =		self.pad(
                     _a	,  max_length=_a	,  truncation=_a	,  padding=_a	,  return_attention_mask=_a	,  )
                 if padding:
                       _A							:  Dict    =		padded_inputs.pop("""attention_mask"""       )

           _A							:  Any    =		[]
           for example in padded_inputs.pop("""input_values"""       ):
                 if self.feature_size == 1:
                       _A							:  str    =		example[..., None]
                 input_values.append(example.T       )

           _A							:  List[str]    =		input_values
           if return_tensors is not None:
                 _A							:  List[str]    =		padded_inputs.convert_to_tensors(_a       )

           return padded_inputs







from __future__ import annotations







class 		lowercase							:

     def __init__(						self	,  _a = 0       )		->							str:
           _A							:  Any    =		key

     def       a__			(						self	,  _a	,  _a       )		->							list[str]:
           assert isinstance(_a	,  _a       ) and isinstance(_a	,  _a       )

           _A							:  Any    =		key or self.__key or 1

           # make sure key is an appropriate size
           key %= 255

           return [chr(ord(_a       ) ^ key       ) for ch in content]

     def       a__			(						self	,  _a	,  _a       )		->							list[str]:
           assert isinstance(_a	,  _a       ) and isinstance(_a	,  _a       )

           _A							:  List[Any]    =		key or self.__key or 1

           # make sure key is an appropriate size
           key %= 255

           return [chr(ord(_a       ) ^ key       ) for ch in content]

     def       a__			(						self	,  _a	,  _a = 0       )		->							str:
           assert isinstance(_a	,  _a       ) and isinstance(_a	,  _a       )

           _A							:  List[Any]    =		key or self.__key or 1

           # make sure key can be any size
           while key > 255:
                 key -= 255

           # This will be returned
           _A							:  List[str]    =		""""""

           for ch in content:
                 ans += chr(ord(_a       ) ^ key       )

           return ans

     def       a__			(						self	,  _a	,  _a = 0       )		->							str:
           assert isinstance(_a	,  _a       ) and isinstance(_a	,  _a       )

           _A							:  List[str]    =		key or self.__key or 1

           # make sure key can be any size
           while key > 255:
                 key -= 255

           # This will be returned
           _A							:  List[str]    =		""""""

           for ch in content:
                 ans += chr(ord(_a       ) ^ key       )

           return ans

     def       a__			(						self	,  _a	,  _a = 0       )		->							bool:
           assert isinstance(_a	,  _a       ) and isinstance(_a	,  _a       )

           try:
                 with open(_a       ) as fin, open("""encrypt.out"""	,  """w+"""       ) as fout:
                       # actual encrypt-process
                       for line in fin:
                             fout.write(self.encrypt_string(_a	,  _a       )       )

           except OSError:
                 return False

           return True



     def       a__			(						self	,  _a	,  _a       )		->							bool:
           assert isinstance(_a	,  _a       ) and isinstance(_a	,  _a       )

           try:
                 with open(_a       ) as fin, open("""decrypt.out"""	,  """w+"""       ) as fout:
                       # actual encrypt-process
                       for line in fin:
                             fout.write(self.decrypt_string(_a	,  _a       )       )

           except OSError:
                 return False

           return True


# Tests
# crypt = XORCipher()
# key = 67

# # test encrypt
# print(crypt.encrypt("hallo welt",key))
# # test decrypt
# print(crypt.decrypt(crypt.encrypt("hallo welt",key), key))

# # test encrypt_string
# print(crypt.encrypt_string("hallo welt",key))

# # test decrypt_string
# print(crypt.decrypt_string(crypt.encrypt_string("hallo welt",key),key))

# if (crypt.encrypt_file("test.txt",key)):
#       print("encrypt successful")
# else:
#       print("encrypt unsuccessful")

# if (crypt.decrypt_file("encrypt.out",key)):
#       print("decrypt successful")
# else:
#       print("decrypt unsuccessful")

