





from typing import List, Optional, Union

import numpy as np
import tensorflow as tf

from .utils import logging


_UpperCAmelCase							:			Optional[Any]        =							logging.get_logger(__name__)


def 			SCREAMING_SNAKE_CASE (			_UpperCAmelCase					)     ->	List[int]:
    if isinstance(_UpperCAmelCase		,      np.ndarray					):
        return list(tensor.shape					)

    lowerCamelCase__       : List[Any]   					=						tf.shape(_UpperCAmelCase					)

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

    lowerCamelCase__       : Tuple   					=						tensor.shape.as_list()

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


def 			SCREAMING_SNAKE_CASE (			_UpperCAmelCase		,      _UpperCAmelCase = None		,      _UpperCAmelCase = None					)     ->	tf.Tensor:
    return tf.nn.softmax(logits=logits + 1e-9		,      axis=_UpperCAmelCase		,      name=_UpperCAmelCase					)


def 			SCREAMING_SNAKE_CASE (			_UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase=1e-5		,      _UpperCAmelCase=-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(_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
    lowerCamelCase__					,				lowerCamelCase__       : Any   					=						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
        lowerCamelCase__       : List[str]   					=						[1] * inputs.shape.rank
        lowerCamelCase__       : Optional[Any]   					=						shape_list(_UpperCAmelCase					)[axis]
        lowerCamelCase__       : Any   					=						tf.reshape(_UpperCAmelCase		,      _UpperCAmelCase					)
        lowerCamelCase__       : int   					=						tf.reshape(_UpperCAmelCase		,      _UpperCAmelCase					)

    # Compute layer normalization using the batch_normalization
    # function.
    lowerCamelCase__       : List[Any]   					=						tf.nn.batch_normalization(
        _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      offset=_UpperCAmelCase		,      scale=_UpperCAmelCase		,      variance_epsilon=_UpperCAmelCase		,      )
    return outputs


def 			SCREAMING_SNAKE_CASE (			_UpperCAmelCase		,      _UpperCAmelCase=0		,      _UpperCAmelCase=-1					)     ->	Optional[int]:
    # 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__       : Any   					=						tf.shape(_UpperCAmelCase					)
    lowerCamelCase__       : List[str]   					=						tf.math.reduce_prod(in_shape[start_dim : end_dim + 1]					)
    lowerCamelCase__       : List[str]   					=						tf.concat([in_shape[:start_dim], [flattened_dim], in_shape[end_dim + 1 :]]		,      axis=0					)
    return tf.reshape(_UpperCAmelCase		,      _UpperCAmelCase					)


def 			SCREAMING_SNAKE_CASE (			_UpperCAmelCase					)     ->	tf.Tensor:
    if not isinstance(_UpperCAmelCase		,      tf.Tensor					):
        lowerCamelCase__       : Tuple   					=						tf.convert_to_tensor(_UpperCAmelCase					)  # Catches stray NumPy inputs
    if encoder_attention_mask.shape.rank == 3:
        lowerCamelCase__       : List[Any]   					=						encoder_attention_mask[:, None, :, :]
    if encoder_attention_mask.shape.rank == 2:
        lowerCamelCase__       : Dict   					=						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 			SCREAMING_SNAKE_CASE (			_UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase = "input_ids"					)     ->	None:
    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 			SCREAMING_SNAKE_CASE (			_UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase					)     ->	str:
    lowerCamelCase__       : Optional[Any]   					=						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.
    lowerCamelCase__       : str   					=						[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}"""					)

    lowerCamelCase__       : Any   					=						np.asarray(_UpperCAmelCase					)

    lowerCamelCase__       : Any   					=						1
    lowerCamelCase__       : Dict   					=						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
        lowerCamelCase__       : Union[str, Any]   					=						np.array_split(_UpperCAmelCase		,      _UpperCAmelCase					)

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


def 			SCREAMING_SNAKE_CASE (			_UpperCAmelCase		,      _UpperCAmelCase					)     ->	Any:
    if name in group.attrs:
        lowerCamelCase__       : Tuple   					=						[n.decode('utf8'					) if hasattr(_UpperCAmelCase		,      'decode'					) else n for n in group.attrs[name]]
    else:
        lowerCamelCase__       : Union[str, Any]   					=						[]
        lowerCamelCase__       : Optional[Any]   					=						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 			SCREAMING_SNAKE_CASE (			_UpperCAmelCase					)     ->	int:

    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					)












def 			SCREAMING_SNAKE_CASE (			_UpperCAmelCase		,      _UpperCAmelCase					)     ->	Union[str, Any]:
    lowerCamelCase__       : List[Any]   					=						[1]
    for i in range(2		,      _UpperCAmelCase					):
        factorials.append(factorials[-1] * i					)
    assert 0 <= k < factorials[-1] * n, "k out of bounds"

    lowerCamelCase__       : str   					=						[]
    lowerCamelCase__       : Optional[Any]   					=						list(range(_UpperCAmelCase					)					)

    # Find permutation
    while factorials:
        lowerCamelCase__       : List[str]   					=						factorials.pop()
        lowerCamelCase__					,				lowerCamelCase__       : Tuple   					=						divmod(_UpperCAmelCase		,      _UpperCAmelCase					)
        permutation.append(elements[number]					)
        elements.remove(elements[number]					)
    permutation.append(elements[0]					)

    return permutation


if __name__ == "__main__":
    import doctest

    doctest.testmod()





