






"""simple docstring"""



from typing import List, Optional, Union

import numpy as np
import tensorflow as tf

from .utils import logging


__A		:       Union[str, Any]  				=		logging.get_logger(__name__)



def 						lowercase    (				__snake_case				: Union[tf.Tensor, np.ndarray]	):
		if isinstance(__snake_case		,					np.ndarray	):
				return list(tensor.shape	)

		lowercase_ : Union[str, Any]							       =   tf.shape(__snake_case	)

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

		lowercase_ : Optional[int]							       =   tensor.shape.as_list()

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



def 						lowercase    (				__snake_case				: tf.Tensor		,					__snake_case				: Optional[int] = None		,					__snake_case				: Optional[str] = None	):
		return tf.nn.softmax(logits=logits + 1e-9		,					axis=__snake_case		,					name=__snake_case	)



def 						lowercase    (				__snake_case				: List[Any]		,					__snake_case				: Union[str, Any]		,					__snake_case				: List[str]		,					__snake_case				: Union[str, Any]=1e-5		,					__snake_case				: Optional[int]=-1	):
		# This is a very simplified functional layernorm, designed to duplicate
		# the functionality of PyTorch nn.functional.layer_norm when this is needed to port
		# models in Transformers.

		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
		lowercase_       ,					lowercase_ : Dict							       =   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
				lowercase_ : Optional[Any]							       =   [1] * inputs.shape.rank
				lowercase_ : List[str]							       =   shape_list(__snake_case	)[axis]
				lowercase_ : Any							       =   tf.reshape(__snake_case		,					__snake_case	)
				lowercase_ : Any							       =   tf.reshape(__snake_case		,					__snake_case	)

		# Compute layer normalization using the batch_normalization
		# function.
		lowercase_ : str							       =   tf.nn.batch_normalization(
		    __snake_case		,					__snake_case		,					__snake_case		,					offset=__snake_case		,					scale=__snake_case		,					variance_epsilon=__snake_case		,					)
		return outputs



def 						lowercase    (				__snake_case				: Any		,					__snake_case				: int=0		,					__snake_case				: Dict=-1	):
		# Replicates the behavior of torch.flatten in TF

		# If end_dim or start_dim is negative, count them from the end
		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

		lowercase_ : Tuple							       =   tf.shape(__snake_case	)
		lowercase_ : Optional[int]							       =   tf.math.reduce_prod(in_shape[start_dim : end_dim + 1]	)
		lowercase_ : Optional[Any]							       =   tf.concat([in_shape[:start_dim], [flattened_dim], in_shape[end_dim + 1 :]]		,					axis=0	)
		return tf.reshape(__snake_case		,					__snake_case	)



def 						lowercase    (				__snake_case				: tf.Tensor	):
		if not isinstance(__snake_case		,					tf.Tensor	):
				lowercase_ : Dict							       =   tf.convert_to_tensor(__snake_case	)  # Catches stray NumPy inputs
		if encoder_attention_mask.shape.rank == 3:
				lowercase_ : Union[str, Any]							       =   encoder_attention_mask[:, None, :, :]
		if encoder_attention_mask.shape.rank == 2:
				lowercase_ : List[Any]							       =   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))
		lowercase_ : int							       =   (
		    tf.cast(1		,					encoder_attention_mask.dtype	) - encoder_extended_attention_mask
		) * encoder_extended_attention_mask.dtype.min

		return encoder_extended_attention_mask



def 						lowercase    (				__snake_case				: tf.Tensor		,					__snake_case				: int		,					__snake_case				: str = "input_ids"	):
		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 						lowercase    (				__snake_case				: List[Any]		,					__snake_case				: Tuple		,					__snake_case				: Dict	):
		lowercase_ : int							       =   6_4_5_1_2
		# 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.
		lowercase_ : Optional[int]							       =   [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}'''	)

		lowercase_ : List[str]							       =   np.asarray(__snake_case	)

		lowercase_ : Union[str, Any]							       =   1
		lowercase_ : Dict							       =   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
				lowercase_ : List[Any]							       =   np.array_split(__snake_case		,					__snake_case	)

		if num_chunks > 1:
				for chunk_id, chunk_data in enumerate(__snake_case	):
						lowercase_ : Any							       =   chunk_data
		else:
				lowercase_ : Tuple							       =   data



def 						lowercase    (				__snake_case				: Any		,					__snake_case				: Union[str, Any]	):
		if name in group.attrs:
				lowercase_ : int							       =   [n.decode('''utf8'''	) if hasattr(__snake_case		,					'''decode'''	) else n for n in group.attrs[name]]
		else:
				lowercase_ : Any							       =   []
				lowercase_ : Union[str, Any]							       =   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 						lowercase    (				__snake_case				: Optional[Any]	):

		def _expand_single_ad_tensor(__snake_case				: Union[str, 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	)




'''simple docstring'''




import operator as op




def 							lowerCamelCase	(      UpperCAmelCase__       :					Optional[Any] )			->      int:
							lowercase_					:			Optional[Any]										=       []
							lowercase_					:			str										=       lambda UpperCAmelCase__      ,      UpperCAmelCase__ : int(x / y )  # noqa: E731 integer division operation
							lowercase_					:			Optional[Any]										=       {
							    """^""": op.pow,
							    """*""": op.mul,
							    """/""": div,
							    """+""": op.add,
							    """-""": op.sub,
							}  # operators & their respective operation

							# print table header
							print("""Symbol""".center(8 )      ,      """Action""".center(12 )      ,      """Stack"""      ,      sep=""" | """ )
							print("""-""" * (30 + len(UpperCAmelCase__ )) )

							for x in post_fix:
														if x.isdigit():  # if x in digit
																					stack.append(UpperCAmelCase__ )  # append x to stack
																					# output in tabular format
																					print(x.rjust(8 )      ,      ("""push(""" + x + """)""").ljust(12 )      ,      """,""".join(UpperCAmelCase__ )      ,      sep=""" | """ )
														else:
																					lowercase_					:			str										=       stack.pop()  # pop stack
																					# output in tabular format
																					print("""""".rjust(8 )      ,      ("""pop(""" + b + """)""").ljust(12 )      ,      """,""".join(UpperCAmelCase__ )      ,      sep=""" | """ )

																					lowercase_					:			Optional[int]										=       stack.pop()  # pop stack
																					# output in tabular format
																					print("""""".rjust(8 )      ,      ("""pop(""" + a + """)""").ljust(12 )      ,      """,""".join(UpperCAmelCase__ )      ,      sep=""" | """ )

																					stack.append(
																					    str(opr[x](int(UpperCAmelCase__ )      ,      int(UpperCAmelCase__ ) ) ) )  # evaluate the 2 values popped from stack & push result to stack
																					# output in tabular format
																					print(
																					    x.rjust(8 )      ,      ("""push(""" + a + x + b + """)""").ljust(12 )      ,      """,""".join(UpperCAmelCase__ )      ,      sep=""" | """      ,      )

							return int(stack[0] )


if __name__ == "__main__":
						_lowercase						: Tuple						=						input("\n\nEnter a Postfix Equation (space separated) = ").split(" ")
						print("\n\tResult = ", solve(Postfix))





