




from typing import List, Optional, Union

import numpy as np
import tensorflow as tf

from .utils import logging


UpperCamelCase__					    =    logging.get_logger(__name__)







def 			_a   (		SCREAMING_SNAKE_CASE_							:       Union[str, Any]  ):


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

					__lowerCAmelCase            = tf.shape(lowerCamelCase__  )

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

					__lowerCAmelCase            = tensor.shape.as_list()

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







def 			_a   (		SCREAMING_SNAKE_CASE_							:       Union[str, Any]   ,   SCREAMING_SNAKE_CASE_							:       List[Any] = None   ,   SCREAMING_SNAKE_CASE_							:       str = None  ):
					return tf.nn.softmax(logits=logits + 1E-9   ,   axis=lowerCamelCase__   ,   name=lowerCamelCase__  )







def 			_a   (		SCREAMING_SNAKE_CASE_							:       Tuple   ,   SCREAMING_SNAKE_CASE_							:       str   ,   SCREAMING_SNAKE_CASE_							:       Optional[int]   ,   SCREAMING_SNAKE_CASE_							:       Any=1E-5   ,   SCREAMING_SNAKE_CASE_							:       Dict=-1  ):


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

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

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







def 			_a   (		SCREAMING_SNAKE_CASE_							:       Dict   ,   SCREAMING_SNAKE_CASE_							:       int=0   ,   SCREAMING_SNAKE_CASE_							:       int=-1  ):


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







def 			_a   (		SCREAMING_SNAKE_CASE_							:       Dict  ):


					if not isinstance(lowerCamelCase__   ,   tf.Tensor  ):
										__lowerCAmelCase            = tf.convert_to_tensor(lowerCamelCase__  )  # Catches stray NumPy inputs
					if encoder_attention_mask.shape.rank == 3:
										__lowerCAmelCase            = encoder_attention_mask[:, None, :, :]
					if encoder_attention_mask.shape.rank == 2:
										__lowerCAmelCase            = 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            = (
					    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_							:       List[Any]   ,   SCREAMING_SNAKE_CASE_							:       Optional[Any]   ,   SCREAMING_SNAKE_CASE_							:       Optional[int] = "input_ids"  ):
					tf.debugging.assert_less(
					    lowerCamelCase__   ,   tf.cast(lowerCamelCase__   ,   dtype=tensor.dtype  )   ,   message=(
					        F"""The maximum value of {tensor_name} ({tf.math.reduce_max(lowerCamelCase__  )}) 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_							:       Optional[Any]   ,   SCREAMING_SNAKE_CASE_							:       List[str]   ,   SCREAMING_SNAKE_CASE_							:       Optional[int]  ):
					__lowerCAmelCase            = 6_45_12
					# 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            = [x for x in data if len(lowerCamelCase__  ) > 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            = np.asarray(lowerCamelCase__  )

					__lowerCAmelCase            = 1
					__lowerCAmelCase            = np.array_split(lowerCamelCase__   ,   lowerCamelCase__  )

					# 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            = np.array_split(lowerCamelCase__   ,   lowerCamelCase__  )

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







def 			_a   (		SCREAMING_SNAKE_CASE_							:       str   ,   SCREAMING_SNAKE_CASE_							:       List[Any]  ):


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







def 			_a   (		SCREAMING_SNAKE_CASE_							:       Tuple  ):


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

					return tf.nest.map_structure(_expand_single_ad_tensor   ,   lowerCamelCase__  )










from math import ceil, sqrt







def 			_a   (		SCREAMING_SNAKE_CASE_							:       int = 1_00_00_00  ):
					__lowerCAmelCase            = 0

					for outer_width in range(3   ,   (limit // 4) + 2  ):
										if outer_width**2 > limit:
															__lowerCAmelCase            = max(ceil(sqrt(outer_width**2 - limit  )  )   ,   1  )
										else:
															__lowerCAmelCase            = 1
										if (outer_width - hole_width_lower_bound) % 2:
															hole_width_lower_bound += 1

										answer += (outer_width - hole_width_lower_bound - 2) // 2 + 1

					return answer


if __name__ == "__main__":
					print(f'''{solution() = }''')




