
"""simple docstring"""

from typing import List, Optional, Union

import numpy as np
import tensorflow as tf

from .utils import logging


SCREAMING_SNAKE_CASE_            =    logging.get_logger(__name__)




def      lowercase__    (       lowerCAmelCase					:       Union[tf.Tensor, np.ndarray]			)			->   List[int]:






    """simple docstring"""




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

    UpperCAmelCase          =       tf.shape(lowerCAmelCase			)

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

    UpperCAmelCase          =       tensor.shape.as_list()

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




def      lowercase__    (       lowerCAmelCase					:       tf.Tensor			,					lowerCAmelCase					:       Optional[int] = None			,					lowerCAmelCase					:       Optional[str] = None			)			->   tf.Tensor:






    """simple docstring"""




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




def      lowercase__    (       lowerCAmelCase					:       List[Any]			,					lowerCAmelCase					:       Optional[int]			,					lowerCAmelCase					:       List[str]			,					lowerCAmelCase					:       Union[str, Any]=1E-5			,					lowerCAmelCase					:       Union[str, Any]=-1			)			->   Dict:






    """simple docstring"""




    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
    UpperCAmelCase      ,				UpperCAmelCase          =       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
        UpperCAmelCase          =       [1] * inputs.shape.rank
        UpperCAmelCase          =       shape_list(lowerCAmelCase			)[axis]
        UpperCAmelCase          =       tf.reshape(lowerCAmelCase			,					lowerCAmelCase			)
        UpperCAmelCase          =       tf.reshape(lowerCAmelCase			,					lowerCAmelCase			)

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




def      lowercase__    (       lowerCAmelCase					:       int			,					lowerCAmelCase					:       int=0			,					lowerCAmelCase					:       List[str]=-1			)			->   Union[str, Any]:






    """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

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




def      lowercase__    (       lowerCAmelCase					:       tf.Tensor			)			->   tf.Tensor:






    """simple docstring"""




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

    return encoder_extended_attention_mask




def      lowercase__    (       lowerCAmelCase					:       tf.Tensor			,					lowerCAmelCase					:       int			,					lowerCAmelCase					:       str = "input_ids"			)			->   None:






    """simple docstring"""




    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      lowercase__    (       lowerCAmelCase					:       List[str]			,					lowerCAmelCase					:       Dict			,					lowerCAmelCase					:       Optional[Any]			)			->   int:






    """simple docstring"""




    UpperCAmelCase          =       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.
    UpperCAmelCase          =       [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}"			)

    UpperCAmelCase          =       np.asarray(lowerCAmelCase			)

    UpperCAmelCase          =       1
    UpperCAmelCase          =       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
        UpperCAmelCase          =       np.array_split(lowerCAmelCase			,					lowerCAmelCase			)

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




def      lowercase__    (       lowerCAmelCase					:       int			,					lowerCAmelCase					:       Any			)			->   List[str]:






    """simple docstring"""




    if name in group.attrs:
        UpperCAmelCase          =       [n.decode('utf8'			) if hasattr(lowerCAmelCase			,					'decode'			) else n for n in group.attrs[name]]
    else:
        UpperCAmelCase          =       []
        UpperCAmelCase          =       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      lowercase__    (       lowerCAmelCase					:       str			)			->   List[Any]:






    """simple docstring"""




    def _expand_single_ad_tensor(lowerCAmelCase					:       Any			):
        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			)





"""simple docstring"""

from math import ceil, sqrt




def      lowercase__    (       lowerCAmelCase					:       int = 1_000_000			)			->   int:






    """simple docstring"""




    UpperCAmelCase          =       0

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



