



'''simple docstring'''




from typing import List, Optional, Union

import numpy as np
import tensorflow as tf

from .utils import logging


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



def    __lowerCamelCase						(      _lowercase	)   ->    List[int]:
							if isinstance(_lowercase			,       np.ndarray	):
														return list(tensor.shape	)

							UpperCAmelCase  :	int							  =	tf.shape(_lowercase	)

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

							UpperCAmelCase  :	Optional[Any]							  =	tensor.shape.as_list()

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



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



def    __lowerCamelCase						(      _lowercase			,       _lowercase			,       _lowercase			,       _lowercase=1e-5			,       _lowercase=-1	)   ->    int:
							# 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(_lowercase			,       _lowercase	):
														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  :	Tuple							  =	tf.nn.moments(_lowercase			,       axes=[axis]			,       keepdims=_lowercase	)

							if axis != -1:
														# Reshape scale and weight to have the same rank as inputs, but with 1 dimensions
														# on every dimension except axis
														UpperCAmelCase  :	Union[str, Any]							  =	[1] * inputs.shape.rank
														UpperCAmelCase  :	List[Any]							  =	shape_list(_lowercase	)[axis]
														UpperCAmelCase  :	Dict							  =	tf.reshape(_lowercase			,       _lowercase	)
														UpperCAmelCase  :	int							  =	tf.reshape(_lowercase			,       _lowercase	)

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



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

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



def    __lowerCamelCase						(      _lowercase	)   ->    tf.Tensor:
							if not isinstance(_lowercase			,       tf.Tensor	):
														UpperCAmelCase  :	Optional[int]							  =	tf.convert_to_tensor(_lowercase	)  # Catches stray NumPy inputs
							if encoder_attention_mask.shape.rank == 3:
														UpperCAmelCase  :	Tuple							  =	encoder_attention_mask[:, None, :, :]
							if encoder_attention_mask.shape.rank == 2:
														UpperCAmelCase  :	Dict							  =	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  :	List[Any]							  =	(
							    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"	)   ->    None:
							tf.debugging.assert_less(
							    _lowercase			,       tf.cast(_lowercase			,       dtype=tensor.dtype	)			,       message=(
							        F'''The maximum value of {tensor_name} ({tf.math.reduce_max(_lowercase	)}) 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	)   ->    str:
							UpperCAmelCase  :	Optional[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.
							UpperCAmelCase  :	Optional[int]							  =	[x for x in data if len(_lowercase	) > 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  :	Dict							  =	np.asarray(_lowercase	)

							UpperCAmelCase  :	Union[str, Any]							  =	1
							UpperCAmelCase  :	Union[str, Any]							  =	np.array_split(_lowercase			,       _lowercase	)

							# 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  :	int							  =	np.array_split(_lowercase			,       _lowercase	)

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



def    __lowerCamelCase						(      _lowercase			,       _lowercase	)   ->    int:
							if name in group.attrs:
														UpperCAmelCase  :	List[str]							  =	[n.decode("""utf8"""	) if hasattr(_lowercase			,       """decode"""	) else n for n in group.attrs[name]]
							else:
														UpperCAmelCase  :	str							  =	[]
														UpperCAmelCase  :	int							  =	0
														while "%s%d" % (name, chunk_id) in group.attrs:
																					data.extend(
																					    [n.decode("""utf8"""	) if hasattr(_lowercase			,       """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(_lowercase			,       tf.Tensor	) and t.shape.rank == 1:
																					return tf.expand_dims(_lowercase			,       axis=-1	)
														return t

							return tf.nest.map_structure(_expand_single_ad_tensor			,       _lowercase	)






'''simple docstring'''




from __future__ import annotations

from collections.abc import Sequence
from typing import Literal



def    __lowerCamelCase						(      _lowercase			,       _lowercase	)   ->    str | Literal[False]:
							UpperCAmelCase  :	Optional[int]							  =	list(_lowercase	)
							UpperCAmelCase  :	Dict							  =	list(_lowercase	)
							UpperCAmelCase  :	str							  =	0
							for i in range(len(_lowercase	)	):
														if lista[i] != lista[i]:
																					count += 1
																					UpperCAmelCase  :	Optional[Any]							  =	"""_"""
							if count > 1:
														return False
							else:
														return "".join(_lowercase	)



def    __lowerCamelCase						(      _lowercase	)   ->    list[str]:
							UpperCAmelCase  :	List[str]							  =	[]
							while True:
														UpperCAmelCase  :	Optional[int]							  =	["""$"""] * len(_lowercase	)
														UpperCAmelCase  :	int							  =	[]
														for i in range(len(_lowercase	)	):
																					for j in range(i + 1			,       len(_lowercase	)	):
																												UpperCAmelCase  :	str							  =	compare_string(binary[i]			,       binary[j]	)
																												if k is False:
																																			UpperCAmelCase  :	Union[str, Any]							  =	"""*"""
																																			UpperCAmelCase  :	Optional[Any]							  =	"""*"""
																																			temp.append("""X"""	)
														for i in range(len(_lowercase	)	):
																					if checka[i] == "$":
																												pi.append(binary[i]	)
														if len(_lowercase	) == 0:
																					return pi
														UpperCAmelCase  :	List[Any]							  =	list(set(_lowercase	)	)



def    __lowerCamelCase						(      _lowercase			,       _lowercase	)   ->    list[str]:
							UpperCAmelCase  :	Dict							  =	[]
							for minterm in minterms:
														UpperCAmelCase  :	List[str]							  =	""""""
														for _ in range(_lowercase	):
																					UpperCAmelCase  :	Dict							  =	str(minterm % 2	) + string
																					minterm //= 2
														temp.append(_lowercase	)
							return temp



def    __lowerCamelCase						(      _lowercase			,       _lowercase			,       _lowercase	)   ->    bool:
							UpperCAmelCase  :	Optional[int]							  =	list(_lowercase	)
							UpperCAmelCase  :	Dict							  =	list(_lowercase	)
							UpperCAmelCase  :	Dict							  =	0
							for i in range(len(_lowercase	)	):
														if lista[i] != lista[i]:
																					count_n += 1
							return count_n == count



def    __lowerCamelCase						(      _lowercase			,       _lowercase	)   ->    list[str]:
							UpperCAmelCase  :	Tuple							  =	[]
							UpperCAmelCase  :	Optional[int]							  =	[0] * len(_lowercase	)
							for i in range(len(chart[0]	)	):
														UpperCAmelCase  :	Any							  =	0
														UpperCAmelCase  :	Optional[Any]							  =	-1
														for j in range(len(_lowercase	)	):
																					if chart[j][i] == 1:
																												count += 1
																												UpperCAmelCase  :	str							  =	j
														if count == 1:
																					UpperCAmelCase  :	Optional[int]							  =	1
							for i in range(len(_lowercase	)	):
														if select[i] == 1:
																					for j in range(len(chart[0]	)	):
																												if chart[i][j] == 1:
																																			for k in range(len(_lowercase	)	):
																																										UpperCAmelCase  :	List[str]							  =	0
																					temp.append(prime_implicants[i]	)
							while True:
														UpperCAmelCase  :	int							  =	0
														UpperCAmelCase  :	Tuple							  =	-1
														UpperCAmelCase  :	Union[str, Any]							  =	0
														for i in range(len(_lowercase	)	):
																					UpperCAmelCase  :	Optional[Any]							  =	chart[i].count(1	)
																					if count_n > max_n:
																												UpperCAmelCase  :	Union[str, Any]							  =	count_n
																												UpperCAmelCase  :	Optional[Any]							  =	i

														if max_n == 0:
																					return temp

														temp.append(prime_implicants[rem]	)

														for i in range(len(chart[0]	)	):
																					if chart[rem][i] == 1:
																												for j in range(len(_lowercase	)	):
																																			UpperCAmelCase  :	Optional[Any]							  =	0



def    __lowerCamelCase						(      _lowercase			,       _lowercase	)   ->    list[list[int]]:
							UpperCAmelCase  :	Optional[int]							  =	[[0 for x in range(len(_lowercase	)	)] for x in range(len(_lowercase	)	)]
							for i in range(len(_lowercase	)	):
														UpperCAmelCase  :	Tuple							  =	prime_implicants[i].count("""_"""	)
														for j in range(len(_lowercase	)	):
																					if is_for_table(prime_implicants[i]			,       binary[j]			,       _lowercase	):
																												UpperCAmelCase  :	List[Any]							  =	1

							return chart



def    __lowerCamelCase						(      )   ->    None:
							UpperCAmelCase  :	str							  =	int(input("""Enter the no. of variables\n"""	)	)
							UpperCAmelCase  :	List[Any]							  =	[
							    float(_lowercase	)
							    for x in input(
							        """Enter the decimal representation of Minterms 'Spaces Separated'\n"""	).split()
							]
							UpperCAmelCase  :	str							  =	decimal_to_binary(_lowercase			,       _lowercase	)

							UpperCAmelCase  :	Tuple							  =	check(_lowercase	)
							print("""Prime Implicants are:"""	)
							print(_lowercase	)
							UpperCAmelCase  :	Union[str, Any]							  =	prime_implicant_chart(_lowercase			,       _lowercase	)

							UpperCAmelCase  :	Tuple							  =	selection(_lowercase			,       _lowercase	)
							print("""Essential Prime Implicants are:"""	)
							print(_lowercase	)


if __name__ == "__main__":
							import doctest

							doctest.testmod()
							main()

