
from typing import List, Optional, Union

import numpy as np
import tensorflow as tf

from .utils import logging


lowercase__							:							Any									=			logging.get_logger(__name__)




def 	A_ (							snake_case							:   Union[tf.Tensor, np.ndarray] )      ->    Any:



						'''simple docstring'''

						if isinstance(snake_case ,      np.ndarray ):
												return list(tensor.shape )

						__UpperCamelCase       =			tf.shape(snake_case )

						if tensor.shape == tf.TensorShape(snake_case ):
												return dynamic

						__UpperCamelCase       =			tensor.shape.as_list()

						return [dynamic[i] if s is None else s for i, s in enumerate(snake_case )]




def 	A_ (							snake_case							:   tf.Tensor ,      snake_case							:   Optional[int] = None ,      snake_case							:   Optional[str] = None )      ->    Tuple:



						'''simple docstring'''

						return tf.nn.softmax(logits=logits + 1e-9 ,      axis=snake_case ,      name=snake_case )




def 	A_ (							snake_case							:   Optional[int] ,      snake_case							:   Union[str, Any] ,      snake_case							:   Dict ,      snake_case							:   int=1e-5 ,      snake_case							:   Dict=-1 )      ->    Dict:



						'''simple docstring'''

						if weight.shape.rank != 1 or bias.shape.rank != 1 or not isinstance(snake_case ,      snake_case ):
												raise NotImplementedError('''Only 1D weight and bias tensors are supported for now, with only a single axis.''' )

						# Get mean and variance on the axis to be normalized
						__UpperCamelCase  ,							__UpperCamelCase       =			tf.nn.moments(snake_case ,      axes=[axis] ,      keepdims=snake_case )

						if axis != -1:
												# Reshape scale and weight to have the same rank as inputs, but with 1 dimensions
												# on every dimension except axis
												__UpperCamelCase       =			[1] * inputs.shape.rank
												__UpperCamelCase       =			shape_list(snake_case )[axis]
												__UpperCamelCase       =			tf.reshape(snake_case ,      snake_case )
												__UpperCamelCase       =			tf.reshape(snake_case ,      snake_case )

						# Compute layer normalization using the batch_normalization
						# function.
						__UpperCamelCase       =			tf.nn.batch_normalization(
						    snake_case ,      snake_case ,      snake_case ,      offset=snake_case ,      scale=snake_case ,      variance_epsilon=snake_case ,      )
						return outputs




def 	A_ (							snake_case							:   Optional[int] ,      snake_case							:   Dict=0 ,      snake_case							:   List[Any]=-1 )      ->    Union[str, Any]:



						'''simple docstring'''

						if end_dim < 0:
												end_dim += input.shape.rank
						if start_dim < 0:
												start_dim += input.shape.rank

						if start_dim == end_dim:
												return input

						__UpperCamelCase       =			tf.shape(snake_case )
						__UpperCamelCase       =			tf.math.reduce_prod(in_shape[start_dim : end_dim + 1] )
						__UpperCamelCase       =			tf.concat([in_shape[:start_dim], [flattened_dim], in_shape[end_dim + 1 :]] ,      axis=0 )
						return tf.reshape(snake_case ,      snake_case )




def 	A_ (							snake_case							:   tf.Tensor )      ->    Optional[int]:



						'''simple docstring'''

						if not isinstance(snake_case ,      tf.Tensor ):
												__UpperCamelCase       =			tf.convert_to_tensor(snake_case )  # Catches stray NumPy inputs
						if encoder_attention_mask.shape.rank == 3:
												__UpperCamelCase       =			encoder_attention_mask[:, None, :, :]
						if encoder_attention_mask.shape.rank == 2:
												__UpperCamelCase       =			encoder_attention_mask[:, None, None, :]
						# T5 has a mask that can compare sequence ids, we can simulate this here with this transposition
						# Cf. https://github.com/tensorflow/mesh/blob/8d2465e9bc93129b913b5ccc6a59aa97abd96ec6/mesh_tensorflow
						# /transformer/transformer_layers.py#L270
						# encoder_extended_attention_mask = (encoder_extended_attention_mask ==
						# encoder_extended_attention_mask.transpose(-1, -2))
						__UpperCamelCase       =			(
						    tf.cast(1 ,      encoder_attention_mask.dtype ) - encoder_extended_attention_mask
						) * encoder_extended_attention_mask.dtype.min

						return encoder_extended_attention_mask




def 	A_ (							snake_case							:   tf.Tensor ,      snake_case							:   int ,      snake_case							:   str = "input_ids" )      ->    int:



						'''simple docstring'''

						tf.debugging.assert_less(
						    snake_case ,      tf.cast(snake_case ,      dtype=tensor.dtype ) ,      message=(
						        f"The maximum value of {tensor_name} ({tf.math.reduce_max(snake_case )}) must be smaller than the embedding "
						        f"layer\'s input dimension ({embed_dim}). The likely cause is some problem at tokenization time."
						    ) ,      )




def 	A_ (							snake_case							:   str ,      snake_case							:   Tuple ,      snake_case							:   Optional[int] )      ->    Tuple:



						'''simple docstring'''

						__UpperCamelCase       =			64512
						# Check that no item in `data` is larger than `HDF5_OBJECT_HEADER_LIMIT`
						# because in that case even chunking the array would not make the saving
						# possible.
						__UpperCamelCase       =			[x for x in data if len(snake_case ) > HDF5_OBJECT_HEADER_LIMIT]

						# Expecting this to never be true.
						if bad_attributes:
												raise RuntimeError(
												    '''The following attributes cannot be saved to HDF5 file because '''
												    f"they are larger than {HDF5_OBJECT_HEADER_LIMIT} "
												    f"bytes: {bad_attributes}" )

						__UpperCamelCase       =			np.asarray(snake_case )

						__UpperCamelCase       =			1
						__UpperCamelCase       =			np.array_split(snake_case ,      snake_case )

						# This will never loop forever thanks to the test above.
						while any(x.nbytes > HDF5_OBJECT_HEADER_LIMIT for x in chunked_data ):
												num_chunks += 1
												__UpperCamelCase       =			np.array_split(snake_case ,      snake_case )

						if num_chunks > 1:
												for chunk_id, chunk_data in enumerate(snake_case ):
																		__UpperCamelCase       =			chunk_data
						else:
												__UpperCamelCase       =			data




def 	A_ (							snake_case							:   List[Any] ,      snake_case							:   Optional[int] )      ->    List[str]:



						'''simple docstring'''

						if name in group.attrs:
												__UpperCamelCase       =			[n.decode('''utf8''' ) if hasattr(snake_case ,      '''decode''' ) else n for n in group.attrs[name]]
						else:
												__UpperCamelCase       =			[]
												__UpperCamelCase       =			0
												while "%s%d" % (name, chunk_id) in group.attrs:
																		data.extend(
																		    [n.decode('''utf8''' ) if hasattr(snake_case ,      '''decode''' ) else n for n in group.attrs['''%s%d''' % (name, chunk_id)]] )
																		chunk_id += 1
						return data




def 	A_ (							snake_case							:   Optional[int] )      ->    Optional[int]:



						'''simple docstring'''

						def _expand_single_ad_tensor(snake_case							:   Any ):
												if isinstance(snake_case ,      tf.Tensor ) and t.shape.rank == 1:
																		return tf.expand_dims(snake_case ,      axis=-1 )
												return t

						return tf.nest.map_structure(_expand_single_ad_tensor ,      snake_case )






from collections import Counter
from timeit import timeit




def 	A_ (							snake_case							:   str = "" ,      )      ->    bool:



						'''simple docstring'''

						return sum(c % 2 for c in Counter(input_str.replace(''' ''' ,      '''''' ).lower() ).values() ) < 2




def 	A_ (							snake_case							:   str = "" )      ->    bool:



						'''simple docstring'''

						if len(snake_case ) == 0:
												return True
						__UpperCamelCase       =			input_str.replace(''' ''' ,      '''''' ).lower()
						# character_freq_dict: Stores the frequency of every character in the input string
						__UpperCamelCase       =			{}

						for character in lower_case_input_str:
												__UpperCamelCase       =			character_freq_dict.get(snake_case ,      0 ) + 1
						__UpperCamelCase       =			0

						for character_count in character_freq_dict.values():
												if character_count % 2:
																		odd_char += 1
						if odd_char > 1:
												return False
						return True




def 	A_ (							snake_case							:   str = "" )      ->    None:



						'''simple docstring'''

						print('''\nFor string = ''' ,      snake_case ,      ''':''' )
						print(
						    '''> can_string_be_rearranged_as_palindrome_counter()''' ,      '''\tans =''' ,      can_string_be_rearranged_as_palindrome_counter(snake_case ) ,      '''\ttime =''' ,      timeit(
						        '''z.can_string_be_rearranged_as_palindrome_counter(z.check_str)''' ,      setup='''import __main__ as z''' ,      ) ,      '''seconds''' ,      )
						print(
						    '''> can_string_be_rearranged_as_palindrome()''' ,      '''\tans =''' ,      can_string_be_rearranged_as_palindrome(snake_case ) ,      '''\ttime =''' ,      timeit(
						        '''z.can_string_be_rearranged_as_palindrome(z.check_str)''' ,      setup='''import __main__ as z''' ,      ) ,      '''seconds''' ,      )


if __name__ == "__main__":
				lowercase__							:							Tuple									=			input(
				    "Enter string to determine if it can be rearranged as a palindrome or not: "
				).strip()
				benchmark(check_str)
				lowercase__							:							Dict									=			can_string_be_rearranged_as_palindrome_counter(check_str)
				print(F"{check_str} can {'' if status else 'not '}be rearranged as a palindrome")




