



from typing import List, Optional, Union

import numpy as np
import tensorflow as tf

from .utils import logging


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



def    __lowerCamelCase						(      _lowercase	)   ->    Tuple:





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

							UpperCAmelCase  :	Optional[int]							  =	tf.shape(SCREAMING_SNAKE_CASE__	)

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

							UpperCAmelCase  :	List[str]							  =	tensor.shape.as_list()

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



def    __lowerCamelCase						(      _lowercase			,       _lowercase = None			,       _lowercase = None	)   ->    str:
							return tf.nn.softmax(logits=logits + 1e-9			,       axis=SCREAMING_SNAKE_CASE__			,       name=SCREAMING_SNAKE_CASE__	)



def    __lowerCamelCase						(      _lowercase			,       _lowercase			,       _lowercase			,       _lowercase=1e-5			,       _lowercase=-1	)   ->    Optional[int]:





							if weight.shape.rank != 1 or bias.shape.rank != 1 or not isinstance(SCREAMING_SNAKE_CASE__			,       SCREAMING_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  :	str							  =	tf.nn.moments(SCREAMING_SNAKE_CASE__			,       axes=[axis]			,       keepdims=SCREAMING_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  :	Optional[Any]							  =	[1] * inputs.shape.rank
														UpperCAmelCase  :	Any							  =	shape_list(SCREAMING_SNAKE_CASE__	)[axis]
														UpperCAmelCase  :	List[str]							  =	tf.reshape(SCREAMING_SNAKE_CASE__			,       SCREAMING_SNAKE_CASE__	)
														UpperCAmelCase  :	List[Any]							  =	tf.reshape(SCREAMING_SNAKE_CASE__			,       SCREAMING_SNAKE_CASE__	)

							# Compute layer normalization using the batch_normalization
							# function.
							UpperCAmelCase  :	List[Any]							  =	tf.nn.batch_normalization(
							    SCREAMING_SNAKE_CASE__			,       SCREAMING_SNAKE_CASE__			,       SCREAMING_SNAKE_CASE__			,       offset=SCREAMING_SNAKE_CASE__			,       scale=SCREAMING_SNAKE_CASE__			,       variance_epsilon=SCREAMING_SNAKE_CASE__			,       )
							return outputs



def    __lowerCamelCase						(      _lowercase			,       _lowercase=0			,       _lowercase=-1	)   ->    int:





							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  :	int							  =	tf.shape(SCREAMING_SNAKE_CASE__	)
							UpperCAmelCase  :	str							  =	tf.math.reduce_prod(in_shape[start_dim : end_dim + 1]	)
							UpperCAmelCase  :	List[str]							  =	tf.concat([in_shape[:start_dim], [flattened_dim], in_shape[end_dim + 1 :]]			,       axis=0	)
							return tf.reshape(SCREAMING_SNAKE_CASE__			,       SCREAMING_SNAKE_CASE__	)



def    __lowerCamelCase						(      _lowercase	)   ->    str:





							if not isinstance(SCREAMING_SNAKE_CASE__			,       tf.Tensor	):
														UpperCAmelCase  :	Optional[Any]							  =	tf.convert_to_tensor(SCREAMING_SNAKE_CASE__	)  # Catches stray NumPy inputs
							if encoder_attention_mask.shape.rank == 3:
														UpperCAmelCase  :	Optional[int]							  =	encoder_attention_mask[:, None, :, :]
							if encoder_attention_mask.shape.rank == 2:
														UpperCAmelCase  :	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))
							UpperCAmelCase  :	int							  =	(
							    tf.cast(1			,       encoder_attention_mask.dtype	) - encoder_extended_attention_mask
							) * encoder_extended_attention_mask.dtype.min

							return encoder_extended_attention_mask



def    __lowerCamelCase						(      _lowercase			,       _lowercase			,       _lowercase = "input_ids"	)   ->    Union[str, Any]:
							tf.debugging.assert_less(
							    SCREAMING_SNAKE_CASE__			,       tf.cast(SCREAMING_SNAKE_CASE__			,       dtype=tensor.dtype	)			,       message=(
							        F'''The maximum value of {tensor_name} ({tf.math.reduce_max(SCREAMING_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    __lowerCamelCase						(      _lowercase			,       _lowercase			,       _lowercase	)   ->    Tuple:
							UpperCAmelCase  :	Any							  =	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.
							UpperCAmelCase  :	Optional[int]							  =	[x for x in data if len(SCREAMING_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  :	str							  =	np.asarray(SCREAMING_SNAKE_CASE__	)

							UpperCAmelCase  :	Union[str, Any]							  =	1
							UpperCAmelCase  :	Optional[Any]							  =	np.array_split(SCREAMING_SNAKE_CASE__			,       SCREAMING_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  :	Any							  =	np.array_split(SCREAMING_SNAKE_CASE__			,       SCREAMING_SNAKE_CASE__	)

							if num_chunks > 1:
														for chunk_id, chunk_data in enumerate(SCREAMING_SNAKE_CASE__	):
																					UpperCAmelCase  :	List[Any]							  =	chunk_data
							else:
														UpperCAmelCase  :	Union[str, Any]							  =	data



def    __lowerCamelCase						(      _lowercase			,       _lowercase	)   ->    Tuple:





							if name in group.attrs:
														UpperCAmelCase  :	Tuple							  =	[n.decode("""utf8"""	) if hasattr(SCREAMING_SNAKE_CASE__			,       """decode"""	) else n for n in group.attrs[name]]
							else:
														UpperCAmelCase  :	Tuple							  =	[]
														UpperCAmelCase  :	Optional[Any]							  =	0
														while "%s%d" % (name, chunk_id) in group.attrs:
																					data.extend(
																					    [n.decode("""utf8"""	) if hasattr(SCREAMING_SNAKE_CASE__			,       """decode"""	) else n for n in group.attrs["""%s%d""" % (name, chunk_id)]]	)
																					chunk_id += 1
							return data



def    __lowerCamelCase						(      _lowercase	)   ->    Tuple:





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

							return tf.nest.map_structure(_expand_single_ad_tensor			,       SCREAMING_SNAKE_CASE__	)






'''simple docstring'''




a     :    List[Any]      						=					"""Alexander Joslin"""

import operator as op

from .stack import Stack



def    __lowerCamelCase						(      _lowercase	)   ->    int:
							UpperCAmelCase  :	Dict							  =	{"""*""": op.mul, """/""": op.truediv, """+""": op.add, """-""": op.sub}

							UpperCAmelCase  :	Stack[int]							  =	Stack()
							UpperCAmelCase  :	Stack[str]							  =	Stack()

							for i in equation:
														if i.isdigit():
																					# RULE 1
																					operand_stack.push(int(_lowercase	)	)
														elif i in operators:
																					# RULE 2
																					operator_stack.push(_lowercase	)
														elif i == ")":
																					# RULE 4
																					UpperCAmelCase  :	List[Any]							  =	operator_stack.peek()
																					operator_stack.pop()
																					UpperCAmelCase  :	str							  =	operand_stack.peek()
																					operand_stack.pop()
																					UpperCAmelCase  :	str							  =	operand_stack.peek()
																					operand_stack.pop()

																					UpperCAmelCase  :	List[Any]							  =	operators[opr](_lowercase			,       _lowercase	)
																					operand_stack.push(_lowercase	)

    # RULE 5
							return operand_stack.peek()


if __name__ == "__main__":
							a     :    Tuple      						=					"""(5 + ((4 * 2) * (2 + 3)))"""
							# answer = 45
							print(F'''{equation} = {dijkstras_two_stack_algorithm(equation)}''')

