
'''simple docstring'''







from typing import List, Optional, Union

import numpy as np
import tensorflow as tf

from .utils import logging


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



def 			_a		(	_SCREAMING_SNAKE_CASE		:   Union[tf.Tensor, np.ndarray]  ):
						if isinstance(a_	,			np.ndarray  ):
												return list(tensor.shape  )

						_SCREAMING_SNAKE_CASE				= tf.shape(a_  )

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

						_SCREAMING_SNAKE_CASE				= tensor.shape.as_list()

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



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



def 			_a		(	_SCREAMING_SNAKE_CASE		:   Dict	,			_SCREAMING_SNAKE_CASE		:   Optional[int]	,			_SCREAMING_SNAKE_CASE		:   Union[str, Any]	,			_SCREAMING_SNAKE_CASE		:   str=1e-5	,			_SCREAMING_SNAKE_CASE		:   List[Any]=-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(a_	,			a_  ):
												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
						_SCREAMING_SNAKE_CASE				= tf.nn.moments(a_	,			axes=[axis]	,			keepdims=a_  )

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

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



def 			_a		(	_SCREAMING_SNAKE_CASE		:   Dict	,			_SCREAMING_SNAKE_CASE		:   List[str]=0	,			_SCREAMING_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

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



def 			_a		(	_SCREAMING_SNAKE_CASE		:   tf.Tensor  ):
						if not isinstance(a_	,			tf.Tensor  ):
												_SCREAMING_SNAKE_CASE				= tf.convert_to_tensor(a_  )  # Catches stray NumPy inputs
						if encoder_attention_mask.shape.rank == 3:
												_SCREAMING_SNAKE_CASE				= encoder_attention_mask[:, None, :, :]
						if encoder_attention_mask.shape.rank == 2:
												_SCREAMING_SNAKE_CASE				= 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))
						_SCREAMING_SNAKE_CASE				= (
						    tf.cast(1	,			encoder_attention_mask.dtype  ) - encoder_extended_attention_mask
						) * encoder_extended_attention_mask.dtype.min

						return encoder_extended_attention_mask



def 			_a		(	_SCREAMING_SNAKE_CASE		:   tf.Tensor	,			_SCREAMING_SNAKE_CASE		:   int	,			_SCREAMING_SNAKE_CASE		:   str = "input_ids"  ):
						tf.debugging.assert_less(
						    a_	,			tf.cast(a_	,			dtype=tensor.dtype  )	,			message=(
						        F'The maximum value of {tensor_name} ({tf.math.reduce_max(a_  )}) must be smaller than the embedding '
						        F'layer\'s input dimension ({embed_dim}). The likely cause is some problem at tokenization time.'
						    )	,			)



def 			_a		(	_SCREAMING_SNAKE_CASE		:   int	,			_SCREAMING_SNAKE_CASE		:   Dict	,			_SCREAMING_SNAKE_CASE		:   Any  ):
						_SCREAMING_SNAKE_CASE				= 6_4512
						# 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.
						_SCREAMING_SNAKE_CASE				= [x for x in data if len(a_  ) > 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}'  )

						_SCREAMING_SNAKE_CASE				= np.asarray(a_  )

						_SCREAMING_SNAKE_CASE				= 1
						_SCREAMING_SNAKE_CASE				= np.array_split(a_	,			a_  )

						# 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
												_SCREAMING_SNAKE_CASE				= np.array_split(a_	,			a_  )

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



def 			_a		(	_SCREAMING_SNAKE_CASE		:   str	,			_SCREAMING_SNAKE_CASE		:   List[Any]  ):
						if name in group.attrs:
												_SCREAMING_SNAKE_CASE				= [n.decode("utf8"  ) if hasattr(a_	,			"decode"  ) else n for n in group.attrs[name]]
						else:
												_SCREAMING_SNAKE_CASE				= []
												_SCREAMING_SNAKE_CASE				= 0
												while "%s%d" % (name, chunk_id) in group.attrs:
																		data.extend(
																		    [n.decode("utf8"  ) if hasattr(a_	,			"decode"  ) else n for n in group.attrs["%s%d" % (name, chunk_id)]]  )
																		chunk_id += 1
						return data




def 			_a		(	_SCREAMING_SNAKE_CASE		:   Optional[Any]  ):

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

						return tf.nest.map_structure(_expand_single_ad_tensor	,			a_  )

'''simple docstring'''







def 			_a		(	_SCREAMING_SNAKE_CASE		:   list[int]	,			_SCREAMING_SNAKE_CASE		:   list[int]  ):

						# Check if the input is valid
						if not len(_SCREAMING_SNAKE_CASE  ) == len(_SCREAMING_SNAKE_CASE  ) == 3:
												raise ValueError("Please enter a valid equation."  )
						if equationa[0] == equationa[1] == equationa[0] == equationa[1] == 0:
												raise ValueError("Both a & b of two equations can't be zero."  )

						# Extract the coefficients
						_SCREAMING_SNAKE_CASE,							_SCREAMING_SNAKE_CASE,							_SCREAMING_SNAKE_CASE				= equationa
						_SCREAMING_SNAKE_CASE,							_SCREAMING_SNAKE_CASE,							_SCREAMING_SNAKE_CASE				= equationa

						# Calculate the determinants of the matrices
						_SCREAMING_SNAKE_CASE				= aa * ba - aa * ba
						_SCREAMING_SNAKE_CASE				= ca * ba - ca * ba
						_SCREAMING_SNAKE_CASE				= aa * ca - aa * ca

						# Check if the system of linear equations has a solution (using Cramer's rule)
						if determinant == 0:
												if determinant_x == determinant_y == 0:
																		raise ValueError("Infinite solutions. (Consistent system)"  )
												else:
																		raise ValueError("No solution. (Inconsistent system)"  )
						else:
												if determinant_x == determinant_y == 0:
																		# Trivial solution (Inconsistent system)
																		return (0.0, 0.0)
												else:
																		_SCREAMING_SNAKE_CASE				= determinant_x / determinant
																		_SCREAMING_SNAKE_CASE				= determinant_y / determinant
																		# Non-Trivial Solution (Consistent system)
																		return (x, y)