





from __future__ import annotations

import math

import numpy as np
from numpy.linalg import norm







def        __lowerCamelCase (							__a       :np.ndarray   ,					__a       :np.ndarray   )		->    float:






		"""simple docstring"""

		return math.sqrt(sum(pow(a - b   ,					2   ) for a, b in zip(__a   ,					__a   )   )   )







def        __lowerCamelCase (							__a       :np.ndarray   ,					__a       :np.ndarray   )		->    list[list[list[float] | float]]:






		"""simple docstring"""

		if dataset.ndim != value_array.ndim:
				A__  				=       (
				    """Wrong input data's dimensions... """
				    F'dataset : {dataset.ndim}, value_array : {value_array.ndim}'
				)
				raise ValueError(__a   )

		try:
				if dataset.shape[1] != value_array.shape[1]:
						A__  				=       (
						    """Wrong input data's shape... """
						    F'dataset : {dataset.shape[1]}, value_array : {value_array.shape[1]}'
						)
						raise ValueError(__a   )
		except IndexError:
				if dataset.ndim != value_array.ndim:
						raise TypeError("""Wrong shape"""   )

		if dataset.dtype != value_array.dtype:
				A__  				=       (
				    """Input data have different datatype... """
				    F'dataset : {dataset.dtype}, value_array : {value_array.dtype}'
				)
				raise TypeError(__a   )

		A__  				=       []

		for value in value_array:
				A__  				=       euclidean(__a   ,					dataset[0]   )
				A__  				=       dataset[0].tolist()

				for dataset_value in dataset[1:]:
						A__  				=       euclidean(__a   ,					__a   )

						if dist > temp_dist:
								A__  				=       temp_dist
								A__  				=       dataset_value.tolist()

				answer.append([vector, dist]   )

		return answer







def        __lowerCamelCase (							__a       :np.ndarray   ,					__a       :np.ndarray   )		->    float:






		"""simple docstring"""

		return np.dot(__a   ,					__a   ) / (norm(__a   ) * norm(__a   ))


if __name__ == "__main__":
					import doctest

					doctest.testmod()










# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import torch

from accelerate import PartialState
from accelerate.utils.operations import broadcast, gather, gather_object, pad_across_processes, reduce







def        __lowerCamelCase (							__a       :List[str]   )		->    Tuple:






		"""simple docstring"""

		return (torch.arange(state.num_processes   ) + 1.0 + (state.num_processes * state.process_index)).to(state.device   )







def        __lowerCamelCase (							__a       :int   )		->    Optional[int]:






		"""simple docstring"""

		A__  				=       create_tensor(__a   )
		A__  				=       gather(__a   )
		assert gathered_tensor.tolist() == list(range(1   ,					state.num_processes**2 + 1   )   )







def        __lowerCamelCase (							__a       :Any   )		->    Any:






		"""simple docstring"""

		A__  				=       [state.process_index]
		A__  				=       gather_object(__a   )
		assert len(__a   ) == state.num_processes, F'{gathered_obj}, {len(__a   )} != {state.num_processes}'
		assert gathered_obj == list(range(state.num_processes   )   ), F'{gathered_obj} != {list(range(state.num_processes   )   )}'







def        __lowerCamelCase (							__a       :List[str]   )		->    List[str]:






		"""simple docstring"""

		A__  				=       create_tensor(__a   )
		A__  				=       broadcast(__a   )
		assert broadcasted_tensor.shape == torch.Size([state.num_processes]   )
		assert broadcasted_tensor.tolist() == list(range(1   ,					state.num_processes + 1   )   )







def        __lowerCamelCase (							__a       :Any   )		->    Any:






		"""simple docstring"""

		if state.is_main_process:
				A__  				=       torch.arange(state.num_processes + 1   ).to(state.device   )
		else:
				A__  				=       torch.arange(state.num_processes   ).to(state.device   )
		A__  				=       pad_across_processes(__a   )
		assert padded_tensor.shape == torch.Size([state.num_processes + 1]   )
		if not state.is_main_process:
				assert padded_tensor.tolist() == list(range(0   ,					state.num_processes   )   ) + [0]







def        __lowerCamelCase (							__a       :Union[str, Any]   )		->    str:






		"""simple docstring"""

		if state.num_processes != 2:
				return
		A__  				=       create_tensor(__a   )
		A__  				=       reduce(__a   ,					"""sum"""   )
		A__  				=       torch.tensor([4.0, 6]   ).to(state.device   )
		assert torch.allclose(__a   ,					__a   ), F'{reduced_tensor} != {truth_tensor}'







def        __lowerCamelCase (							__a       :List[Any]   )		->    List[str]:






		"""simple docstring"""

		if state.num_processes != 2:
				return
		A__  				=       create_tensor(__a   )
		A__  				=       reduce(__a   ,					"""mean"""   )
		A__  				=       torch.tensor([2.0, 3]   ).to(state.device   )
		assert torch.allclose(__a   ,					__a   ), F'{reduced_tensor} != {truth_tensor}'







def        __lowerCamelCase (							__a       :str   )		->    Optional[int]:






		"""simple docstring"""

		main()







def        __lowerCamelCase (							)		->    str:






		"""simple docstring"""

		A__  				=       PartialState()
		state.print(F'State: {state}'   )
		state.print("""testing gather"""   )
		test_gather(__a   )
		state.print("""testing gather_object"""   )
		test_gather_object(__a   )
		state.print("""testing broadcast"""   )
		test_broadcast(__a   )
		state.print("""testing pad_across_processes"""   )
		test_pad_across_processes(__a   )
		state.print("""testing reduce_sum"""   )
		test_reduce_sum(__a   )
		state.print("""testing reduce_mean"""   )
		test_reduce_mean(__a   )


if __name__ == "__main__":
					main()



