
from typing import List, Optional, Union

import numpy as np
import tensorflow as tf

from .utils import logging


lowerCAmelCase__		:					List[str]							=logging.get_logger(__name__)






def      __lowercase       (			a__       )    ->			List[int]:
							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      __lowercase       (			a__							,       a__ = None							,       a__ = None       )    ->			tf.Tensor:
							return tf.nn.softmax(logits=logits + 1E-9							,       axis=a__							,       name=a__       )






def      __lowercase       (			a__							,       a__							,       a__							,       a__=1E-5							,       a__=-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(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							,	__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      __lowercase       (			a__							,       a__=0							,       a__=-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

							__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      __lowercase       (			a__       )    ->			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      __lowercase       (			a__							,       a__							,       a__ = "input_ids"       )    ->			None:
							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      __lowercase       (			a__							,       a__							,       a__       )    ->			int:
							__SCREAMING_SNAKE_CASE									= 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.
							__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      __lowercase       (			a__							,       a__       )    ->			str:
							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      __lowercase       (			a__       )    ->			List[str]:

							def _expand_single_ad_tensor(a__       ):
														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__       )











from __future__ import annotations

from collections.abc import Generator






def      __lowercase       (			)    ->			Generator[int, None, None]:
							__SCREAMING_SNAKE_CASE									= {}
							__SCREAMING_SNAKE_CASE									= 2
							while True:
														__SCREAMING_SNAKE_CASE									= factor_map.pop(a__							,       a__       )
														if factor:
																					__SCREAMING_SNAKE_CASE									= factor + prime
																					while x in factor_map:
																												x += factor
																					__SCREAMING_SNAKE_CASE									= factor
														else:
																					__SCREAMING_SNAKE_CASE									= prime
																					yield prime
														prime += 1






def      __lowercase       (			a__ = 1E10       )    ->			int:
							__SCREAMING_SNAKE_CASE									= sieve()

							__SCREAMING_SNAKE_CASE									= 1
							while True:
														__SCREAMING_SNAKE_CASE									= next(a__       )
														if (2 * prime * n) > limit:
																					return n
														# Ignore the next prime as the reminder will be 2.
														next(a__       )
														n += 2


if __name__ == "__main__":
			print(solution())






