




'''simple docstring'''

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 		__magic_name__    (	__UpperCAmelCase	)		->					List[int]:

					'''simple docstring'''

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

					snake_case_									=				tf.shape(__UpperCAmelCase	)

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

					snake_case_									=				tensor.shape.as_list()

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


def 		__magic_name__    (	__UpperCAmelCase,      __UpperCAmelCase = None,      __UpperCAmelCase = None	)		->					tf.Tensor:

					'''simple docstring'''

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


def 		__magic_name__    (	__UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase=1e-5,      __UpperCAmelCase=-1	)		->					Any:

					'''simple docstring'''

					if weight.shape.rank != 1 or bias.shape.rank != 1 or not isinstance(__UpperCAmelCase,      __UpperCAmelCase	):
										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
					snake_case_			,snake_case_									=				tf.nn.moments(__UpperCAmelCase,      axes=[axis],      keepdims=__UpperCAmelCase	)

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

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


def 		__magic_name__    (	__UpperCAmelCase,      __UpperCAmelCase=0,      __UpperCAmelCase=-1	)		->					Optional[int]:

					'''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

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


def 		__magic_name__    (	__UpperCAmelCase	)		->					tf.Tensor:

					'''simple docstring'''

					if not isinstance(__UpperCAmelCase,      tf.Tensor	):
										snake_case_									=				tf.convert_to_tensor(__UpperCAmelCase	)  # Catches stray NumPy inputs
					if encoder_attention_mask.shape.rank == 3:
										snake_case_									=				encoder_attention_mask[:, None, :, :]
					if encoder_attention_mask.shape.rank == 2:
										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))
					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 		__magic_name__    (	__UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase = "input_ids"	)		->					None:

					'''simple docstring'''

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


def 		__magic_name__    (	__UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase	)		->					Union[str, Any]:

					'''simple docstring'''

					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.
					snake_case_									=				[x for x in data if len(__UpperCAmelCase	) > 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}"	)

					snake_case_									=				np.asarray(__UpperCAmelCase	)

					snake_case_									=				1
					snake_case_									=				np.array_split(__UpperCAmelCase,      __UpperCAmelCase	)

					# 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
										snake_case_									=				np.array_split(__UpperCAmelCase,      __UpperCAmelCase	)

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


def 		__magic_name__    (	__UpperCAmelCase,      __UpperCAmelCase	)		->					Tuple:

					'''simple docstring'''

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


def 		__magic_name__    (	__UpperCAmelCase	)		->					List[Any]:

					'''simple docstring'''

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

					return tf.nest.map_structure(_expand_single_ad_tensor,      __UpperCAmelCase	)







'''simple docstring'''

import math
from collections.abc import Callable


def 		__magic_name__    (	__UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase	)		->					float:

					'''simple docstring'''

					snake_case_									=				xa
					snake_case_									=				xa
					while True:
										if x_n == x_na or function(__UpperCAmelCase	) == function(__UpperCAmelCase	):
															raise ZeroDivisionError('''float division by zero, could not find root'''	)
										snake_case_									=				x_na - (
										    function(__UpperCAmelCase	) / ((function(__UpperCAmelCase	) - function(__UpperCAmelCase	)) / (x_na - x_n))
										)
										if abs(x_na - x_na	) < 10**-5:
															return x_na
										snake_case_									=				x_na
										snake_case_									=				x_na


def 		__magic_name__    (	__UpperCAmelCase	)		->					float:

					'''simple docstring'''

					return math.pow(__UpperCAmelCase,      3	) - (2 * x) - 5


if __name__ == "__main__":
							print(intersection(f, 3, 3.5))

