


from typing import List, Optional, Union

import numpy as np
import tensorflow as tf

from .utils import logging


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



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

    _lowercase			:				Optional[int] 						=						tf.shape(lowerCamelCase_					)

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

    _lowercase			:				List[Any] 						=						tensor.shape.as_list()

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



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



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

    # Compute layer normalization using the batch_normalization
    # function.
    _lowercase			:				Any 						=						tf.nn.batch_normalization(
        lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_				,					offset=lowerCamelCase_				,					scale=lowerCamelCase_				,					variance_epsilon=lowerCamelCase_				,					)
    return outputs



def 							UpperCamelCase_( lowerCamelCase_				,					lowerCamelCase_=0				,					lowerCamelCase_=-1					)       ->		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

    _lowercase			:				Union[str, Any] 						=						tf.shape(lowerCamelCase_					)
    _lowercase			:				str 						=						tf.math.reduce_prod(in_shape[start_dim : end_dim + 1]					)
    _lowercase			:				Optional[Any] 						=						tf.concat([in_shape[:start_dim], [flattened_dim], in_shape[end_dim + 1 :]]				,					axis=0					)
    return tf.reshape(lowerCamelCase_				,					lowerCamelCase_					)



def 							UpperCamelCase_( lowerCamelCase_					)       ->		tf.Tensor:
    if not isinstance(lowerCamelCase_				,					tf.Tensor					):
        _lowercase			:				Union[str, Any] 						=						tf.convert_to_tensor(lowerCamelCase_					)  # Catches stray NumPy inputs
    if encoder_attention_mask.shape.rank == 3:
        _lowercase			:				Optional[Any] 						=						encoder_attention_mask[:, None, :, :]
    if encoder_attention_mask.shape.rank == 2:
        _lowercase			:				Union[str, 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))
    _lowercase			:				Optional[int] 						=						(
        tf.cast(1				,					encoder_attention_mask.dtype					) - encoder_extended_attention_mask
    ) * encoder_extended_attention_mask.dtype.min

    return encoder_extended_attention_mask



def 							UpperCamelCase_( lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_ = "input_ids"					)       ->		None:
    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 							UpperCamelCase_( lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_					)       ->		int:
    _lowercase			:				Dict 						=						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.
    _lowercase			:				Dict 						=						[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}'''					)

    _lowercase			:				int 						=						np.asarray(lowerCamelCase_					)

    _lowercase			:				List[Any] 						=						1
    _lowercase			:				List[str] 						=						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
        _lowercase			:				Optional[Any] 						=						np.array_split(lowerCamelCase_				,					lowerCamelCase_					)

    if num_chunks > 1:
        for chunk_id, chunk_data in enumerate(lowerCamelCase_					):
            _lowercase			:				Union[str, Any] 						=						chunk_data
    else:
        _lowercase			:				Dict 						=						data



def 							UpperCamelCase_( lowerCamelCase_				,					lowerCamelCase_					)       ->		Optional[int]:
    if name in group.attrs:
        _lowercase			:				List[str] 						=						[n.decode('utf8'					) if hasattr(lowerCamelCase_				,					'decode'					) else n for n in group.attrs[name]]
    else:
        _lowercase			:				List[str] 						=						[]
        _lowercase			:				List[str] 						=						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 							UpperCamelCase_( lowerCamelCase_					)       ->		str:

    def _expand_single_ad_tensor(lowerCamelCase_					):
        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 __future__ import annotations



def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case							)		->    list[tuple[int, int]]:
		__lowercase						,      __lowercase											=     position
		__lowercase											=     [
		    (y + 1, x + 2),
		    (y - 1, x + 2),
		    (y + 1, x - 2),
		    (y - 1, x - 2),
		    (y + 2, x + 1),
		    (y + 2, x - 1),
		    (y - 2, x + 1),
		    (y - 2, x - 1),
		]
		__lowercase											=     []

		for position in positions:
				__lowercase						,      __lowercase											=     position
				if 0 <= y_test < n and 0 <= x_test < n:
						permissible_positions.append(snake_case							)

		return permissible_positions



def  SCREAMING_SNAKE_CASE				(      snake_case							)		->    bool:
		return not any(elem == 0 for row in board for elem in row							)



def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case						,   snake_case							)		->    bool:

		if is_complete(snake_case							):
				return True

		for position in get_valid_pos(snake_case						,   len(snake_case							)							):
				__lowercase						,      __lowercase											=     position

				if board[y][x] == 0:
						__lowercase											=     curr + 1
						if open_knight_tour_helper(snake_case						,   snake_case						,   curr + 1							):
								return True
						__lowercase											=     0

		return False



def  SCREAMING_SNAKE_CASE				(      snake_case							)		->    list[list[int]]:
		__lowercase											=     [[0 for i in range(snake_case							)] for j in range(snake_case							)]

		for i in range(snake_case							):
				for j in range(snake_case							):
						__lowercase											=     1
						if open_knight_tour_helper(snake_case						,   (i, j)						,   1							):
								return board
						__lowercase											=     0

		__lowercase											=     F"Open Kight Tour cannot be performed on a board of size {n}"
		raise ValueError(snake_case							)


if __name__ == "__main__":
		import doctest

		doctest.testmod()




