



"""simple docstring"""






from typing import List, Optional, Union

import numpy as np
import tensorflow as tf

from .utils import logging


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


def 			_lowercase				(  __snake_case	)						->   Dict:
			if isinstance(__snake_case					,np.ndarray	):
						return list(tensor.shape	)

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

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

			__lowerCAmelCase      :  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					,__snake_case = None					,__snake_case = None	)						->   Dict:
			return tf.nn.softmax(logits=logits + 1e-9					,axis=__snake_case					,name=__snake_case	)


def 			_lowercase				(  __snake_case					,__snake_case					,__snake_case					,__snake_case=1e-5					,__snake_case=-1	)						->   List[str]:
			# 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
			__lowerCAmelCase      :  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
						__lowerCAmelCase      :  Optional[Any]												=       [1] * inputs.shape.rank
						__lowerCAmelCase      :  List[str]												=       shape_list(__snake_case	)[axis]
						__lowerCAmelCase      :  Any												=       tf.reshape(__snake_case					,__snake_case	)
						__lowerCAmelCase      :  Any												=       tf.reshape(__snake_case					,__snake_case	)

			# Compute layer normalization using the batch_normalization
			# function.
			__lowerCAmelCase      :  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					,__snake_case=0					,__snake_case=-1	)						->   List[str]:
			# 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

			__lowerCAmelCase      :  Tuple												=       tf.shape(__snake_case	)
			__lowerCAmelCase      :  Optional[int]												=       tf.math.reduce_prod(in_shape[start_dim : end_dim + 1]	)
			__lowerCAmelCase      :  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	)						->   str:
			if not isinstance(__snake_case					,tf.Tensor	):
						__lowerCAmelCase      :  Dict												=       tf.convert_to_tensor(__snake_case	)  # Catches stray NumPy inputs
			if encoder_attention_mask.shape.rank == 3:
						__lowerCAmelCase      :  Union[str, Any]												=       encoder_attention_mask[:, None, :, :]
			if encoder_attention_mask.shape.rank == 2:
						__lowerCAmelCase      :  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))
			__lowerCAmelCase      :  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					,__snake_case					,__snake_case = "input_ids"	)						->   Any:
			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					,__snake_case					,__snake_case	)						->   Optional[Any]:
			__lowerCAmelCase      :  int												=       64_512
			# 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.
			__lowerCAmelCase      :  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}"""	)

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

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

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


def 			_lowercase				(  __snake_case					,__snake_case	)						->   Dict:
			if name in group.attrs:
						__lowerCAmelCase      :  int												=       [n.decode("utf8"	) if hasattr(__snake_case					,"decode"	) else n for n in group.attrs[name]]
			else:
						__lowerCAmelCase      :  Any												=       []
						__lowerCAmelCase      :  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	)						->   List[Any]:

			def _expand_single_ad_tensor(__snake_case	):
						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"""



from __future__ import annotations

__A		:       List[Any]  				=		[-10, -5, 0, 5, 5.1, 11, 13, 21, 3, 4, -21, -10, -5, -1, 0]
__A		:       str  				=		[-5, 0, 5, 5.1, 11, 13, 21, -1, 4, -1, -10, -5, -1, 0, -1]



def 						lowercase    (				__snake_case				: list[float]	):
		lowercase_ : List[str]							       =   []
		lowercase_ : List[Any]							       =   len(__snake_case	)

		for i in range(__snake_case	):
				lowercase_ : float							       =   -1
				for j in range(i + 1		,					__snake_case	):
						if arr[i] < arr[j]:
								lowercase_ : List[str]							       =   arr[j]
								break
				result.append(__snake_case	)
		return result



def 						lowercase    (				__snake_case				: list[float]	):
		lowercase_ : List[str]							       =   []
		for i, outer in enumerate(__snake_case	):
				lowercase_ : float							       =   -1
				for inner in arr[i + 1 :]:
						if outer < inner:
								lowercase_ : List[Any]							       =   inner
								break
				result.append(__snake_case	)
		return result



def 						lowercase    (				__snake_case				: list[float]	):
		lowercase_ : List[str]							       =   len(__snake_case	)
		lowercase_ : list[float]							       =   []
		lowercase_ : list[float]							       =   [-1] * arr_size

		for index in reversed(range(__snake_case	)	):
				if stack:
						while stack[-1] <= arr[index]:
								stack.pop()
								if not stack:
										break
				if stack:
						lowercase_ : Optional[Any]							       =   stack[-1]
				stack.append(arr[index]	)
		return result


if __name__ == "__main__":
						from doctest import testmod
						from timeit import timeit

						testmod()
						print(next_greatest_element_slow(arr))
						print(next_greatest_element_fast(arr))
						print(next_greatest_element(arr))

						__A		:       int  				=		(
						    '''from __main__ import arr, next_greatest_element_slow, '''
						    '''next_greatest_element_fast, next_greatest_element'''
						)
						print(
						    '''next_greatest_element_slow():''',
						    timeit('''next_greatest_element_slow(arr)''', setup=setup),
						)
						print(
						    '''next_greatest_element_fast():''',
						    timeit('''next_greatest_element_fast(arr)''', setup=setup),
						)
						print(
						    '''     next_greatest_element():''',
						    timeit('''next_greatest_element(arr)''', setup=setup),
						)



