





# Copyright 2021 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.

from pathlib import Path

import torch

from ...utils import is_npu_available, is_xpu_available
from .config_args import ClusterConfig, default_json_config_file
from .config_utils import SubcommandHelpFormatter


A    :					Optional[Any] =      '''Create a default config file for Accelerate with only a few flags set.'''







def    __lowerCamelCase					(							__a   :Optional[Any]="no"    ,     __a   :str = default_json_config_file    ,     __a   :bool = False    )		->				int:






		"""simple docstring"""

		A__        =   Path(__a    )
		path.parent.mkdir(parents=__a    ,     exist_ok=__a    )
		if path.exists():
				print(
				    F'Configuration already exists at {save_location}, will not override. Run `accelerate config` manually or pass a different `save_location`.'    )
				return False
		A__        =   mixed_precision.lower()
		if mixed_precision not in ["no", "fp16", "bf16", "fp8"]:
				raise ValueError(
				    F'`mixed_precision` should be one of \'no\', \'fp16\', \'bf16\', or \'fp8\'. Received {mixed_precision}'    )
		A__        =   {
		    """compute_environment""": """LOCAL_MACHINE""",
		    """mixed_precision""": mixed_precision,
		}
		if torch.cuda.is_available():
				A__        =   torch.cuda.device_count()
				A__        =   num_gpus
				A__        =   False
				if num_gpus > 1:
						A__        =   """MULTI_GPU"""
				else:
						A__        =   """NO"""
		elif is_xpu_available() and use_xpu:
				A__        =   torch.xpu.device_count()
				A__        =   num_xpus
				A__        =   False
				if num_xpus > 1:
						A__        =   """MULTI_XPU"""
				else:
						A__        =   """NO"""
		elif is_npu_available():
				A__        =   torch.npu.device_count()
				A__        =   num_npus
				A__        =   False
				if num_npus > 1:
						A__        =   """MULTI_NPU"""
				else:
						A__        =   """NO"""
		else:
				A__        =   0
				A__        =   True
				A__        =   1
				A__        =   """NO"""
		A__        =   ClusterConfig(**__a    )
		config.to_json_file(__a    )
		return path







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






		"""simple docstring"""

		A__        =   parser.add_parser("""default"""    ,     parents=__a    ,     help=__a    ,     formatter_class=__a    )
		parser.add_argument(
		    """--config_file"""    ,     default=__a    ,     help=(
		        """The path to use to store the config file. Will default to a file named default_config.yaml in the cache """
		        """location, which is the content of the environment `HF_HOME` suffixed with 'accelerate', or if you don't have """
		        """such an environment variable, your cache directory ('~/.cache' or the content of `XDG_CACHE_HOME`) suffixed """
		        """with 'huggingface'."""
		    )    ,     dest="""save_location"""    ,     )

		parser.add_argument(
		    """--mixed_precision"""    ,     choices=["""no""", """fp16""", """bf16"""]    ,     type=__a    ,     help="""Whether or not to use mixed precision training. """
		    """Choose between FP16 and BF16 (bfloat16) training. """
		    """BF16 training is only supported on Nvidia Ampere GPUs and PyTorch 1.10 or later."""    ,     default="""no"""    ,     )
		parser.set_defaults(func=__a    )
		return parser







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






		"""simple docstring"""

		A__        =   write_basic_config(args.mixed_precision    ,     args.save_location    )
		if config_file:
				print(F'accelerate configuration saved at {config_file}'    )










from __future__ import annotations

from PIL import Image

# Define glider example
A    :					Any =      [
    [0, 1, 0, 0, 0, 0, 0, 0],
    [0, 0, 1, 0, 0, 0, 0, 0],
    [1, 1, 1, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0],
]

# Define blinker example
A    :					Optional[Any] =      [[0, 1, 0], [0, 1, 0], [0, 1, 0]]







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






		"""simple docstring"""

		A__        =   []
		for i in range(len(__a    )    ):
				A__        =   []
				for j in range(len(cells[i]    )    ):
						# Get the number of live neighbours
						A__        =   0
						if i > 0 and j > 0:
								neighbour_count += cells[i - 1][j - 1]
						if i > 0:
								neighbour_count += cells[i - 1][j]
						if i > 0 and j < len(cells[i]    ) - 1:
								neighbour_count += cells[i - 1][j + 1]
						if j > 0:
								neighbour_count += cells[i][j - 1]
						if j < len(cells[i]    ) - 1:
								neighbour_count += cells[i][j + 1]
						if i < len(__a    ) - 1 and j > 0:
								neighbour_count += cells[i + 1][j - 1]
						if i < len(__a    ) - 1:
								neighbour_count += cells[i + 1][j]
						if i < len(__a    ) - 1 and j < len(cells[i]    ) - 1:
								neighbour_count += cells[i + 1][j + 1]

						# Rules of the game of life (excerpt from Wikipedia):
						# 1. Any live cell with two or three live neighbours survives.
						# 2. Any dead cell with three live neighbours becomes a live cell.
						# 3. All other live cells die in the next generation.
						#    Similarly, all other dead cells stay dead.
						A__        =   cells[i][j] == 1
						if (
						    (alive and 2 <= neighbour_count <= 3)
						    or not alive
						    and neighbour_count == 3
						):
								next_generation_row.append(1    )
						else:
								next_generation_row.append(0    )

				next_generation.append(__a    )
		return next_generation







def    __lowerCamelCase					(							__a   :list[list[int]]    ,     __a   :int    )		->				list[Image.Image]:






		"""simple docstring"""

		A__        =   []
		for _ in range(__a    ):
				# Create output image
				A__        =   Image.new("""RGB"""    ,     (len(cells[0]    ), len(__a    ))    )
				A__        =   img.load()

				# Save cells to image
				for x in range(len(__a    )    ):
						for y in range(len(cells[0]    )    ):
								A__        =   2_5_5 - cells[y][x] * 2_5_5
								A__        =   (colour, colour, colour)

        # Save image
				images.append(__a    )
				A__        =   new_generation(__a    )
		return images


if __name__ == "__main__":
					A    :					str =      generate_images(GLIDER, 1_6)
					images[0].save('''out.gif''', save_all=True, append_images=images[1:])



