
'''simple docstring'''
# Copyright 2022 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 argparse
import os
import subprocess

from packaging.version import Version, parse

from accelerate.commands.config.config_args import default_config_file, load_config_from_file


__lowerCAmelCase												=	'Run commands across TPU VMs for initial setup before running `accelerate launch`.'






def  __SCREAMING_SNAKE_CASE				(  _SCREAMING_SNAKE_CASE=None				):
							if subparsers is not None:
														_snake_case       =  subparsers.add_parser("""tpu-config"""							, description=_description				)
							else:
														_snake_case       =  argparse.ArgumentParser("""Accelerate tpu-config command"""							, description=_description				)
							# Core arguments
							_snake_case       =  parser.add_argument_group(
							    """Config Arguments"""							, """Arguments that can be configured through `accelerate config`."""				)
							config_args.add_argument(
							    """--config_file"""							, type=_SCREAMING_SNAKE_CASE							, default=_SCREAMING_SNAKE_CASE							, help="""Path to the config file to use for accelerate."""							, )
							config_args.add_argument(
							    """--tpu_name"""							, default=_SCREAMING_SNAKE_CASE							, help="""The name of the TPU to use. If not specified, will use the TPU specified in the config file."""							, )
							config_args.add_argument(
							    """--tpu_zone"""							, default=_SCREAMING_SNAKE_CASE							, help="""The zone of the TPU to use. If not specified, will use the zone specified in the config file."""							, )
							_snake_case       =  parser.add_argument_group("""TPU Arguments"""							, """Arguments for options ran inside the TPU."""				)
							pod_args.add_argument(
							    """--use_alpha"""							, action="""store_true"""							, help="""Whether to use `gcloud alpha` when running the TPU training script instead of `gcloud`."""							, )
							pod_args.add_argument(
							    """--command_file"""							, default=_SCREAMING_SNAKE_CASE							, help="""The path to the file containing the commands to run on the pod on startup."""							, )
							pod_args.add_argument(
							    """--command"""							, action="""append"""							, nargs="""+"""							, help="""A command to run on the pod. Can be passed multiple times."""							, )
							pod_args.add_argument(
							    """--install_accelerate"""							, action="""store_true"""							, help="""Whether to install accelerate on the pod. Defaults to False."""							, )
							pod_args.add_argument(
							    """--accelerate_version"""							, default="""latest"""							, help="""The version of accelerate to install on the pod. If not specified, will use the latest pypi version. Specify 'dev' to install from GitHub."""							, )
							pod_args.add_argument(
							    """--debug"""							, action="""store_true"""							, help="""If set, will print the command that would be run instead of running it."""				)

							if subparsers is not None:
														parser.set_defaults(func=_SCREAMING_SNAKE_CASE				)
							return parser






def  __SCREAMING_SNAKE_CASE				(  _SCREAMING_SNAKE_CASE				):
							_snake_case       =  None

							# Get the default from the config file if it exists.
							if args.config_file is not None or os.path.isfile(_SCREAMING_SNAKE_CASE				):
														_snake_case       =  load_config_from_file(args.config_file				)
														if not args.command_file and defaults.command_file is not None and not args.command:
																					_snake_case       =  defaults.command_file
														if not args.command and defaults.commands is not None:
																					_snake_case       =  defaults.commands
														if not args.tpu_name:
																					_snake_case       =  defaults.tpu_name
														if not args.tpu_zone:
																					_snake_case       =  defaults.tpu_zone
							if args.accelerate_version == "dev":
														_snake_case       =  """git+https://github.com/huggingface/accelerate.git"""
							elif args.accelerate_version == "latest":
														_snake_case       =  """accelerate -U"""
							elif isinstance(parse(args.accelerate_version				)							, _SCREAMING_SNAKE_CASE				):
														_snake_case       =  f"""accelerate=={args.accelerate_version}"""

							if not args.command_file and not args.command:
														raise ValueError("""You must specify either a command file or a command to run on the pod."""				)

							if args.command_file:
														with open(args.command_file							, """r"""				) as f:
																					_snake_case       =  [f.read().splitlines()]

    # To turn list of lists into list of strings
							if isinstance(args.command[0]							, _SCREAMING_SNAKE_CASE				):
														_snake_case       =  [line for cmd in args.command for line in cmd]
							# Default to the shared folder and install accelerate
							_snake_case       =  ["""cd /usr/share"""]
							if args.install_accelerate:
														new_cmd += [f"""pip install {args.accelerate_version}"""]
							new_cmd += args.command
							_snake_case       =  """; """.join(_SCREAMING_SNAKE_CASE				)

							# Then send it to gcloud
							# Eventually try to use google-api-core to do this instead of subprocess
							_snake_case       =  ["""gcloud"""]
							if args.use_alpha:
														cmd += ["alpha"]
							cmd += [
							    "compute",
							    "tpus",
							    "tpu-vm",
							    "ssh",
							    args.tpu_name,
							    "--zone",
							    args.tpu_zone,
							    "--command",
							    args.command,
							    "--worker",
							    "all",
							]
							if args.debug:
														print(f"""Running {" ".join(_SCREAMING_SNAKE_CASE				)}"""				)
														return
							subprocess.run(_SCREAMING_SNAKE_CASE				)
							print("""Successfully setup pod."""				)






def  __SCREAMING_SNAKE_CASE				(  ):
							_snake_case       =  tpu_command_parser()
							_snake_case       =  parser.parse_args()

							tpu_command_launcher(_SCREAMING_SNAKE_CASE				)

'''simple docstring'''
import gc
import unittest

from diffusers import FlaxControlNetModel, FlaxStableDiffusionControlNetPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax


if is_flax_available():
				import jax
				import jax.numpy as jnp
				from flax.jax_utils import replicate
				from flax.training.common_utils import shard


@slow
@require_flax
class       _lowerCAmelCase   (   unittest.TestCase				):







							'''simple docstring'''

							def 	lowercase							(self  )					->       Optional[int]:
														# clean up the VRAM after each test
														super().tearDown()
														gc.collect()

							def 	lowercase							(self  )					->       Dict:
														_snake_case,							_snake_case       =  FlaxControlNetModel.from_pretrained(
														    """lllyasviel/sd-controlnet-canny"""  ,  from_pt=UpperCAmelCase  ,  dtype=jnp.bfloataa  )
														_snake_case,							_snake_case       =  FlaxStableDiffusionControlNetPipeline.from_pretrained(
														    """runwayml/stable-diffusion-v1-5"""  ,  controlnet=UpperCAmelCase  ,  from_pt=UpperCAmelCase  ,  dtype=jnp.bfloataa  )
														_snake_case       =  controlnet_params

														_snake_case       =  """bird"""
														_snake_case       =  jax.device_count()
														_snake_case       =  pipe.prepare_text_inputs([prompts] * num_samples  )

														_snake_case       =  load_image(
														    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png"""  )
														_snake_case       =  pipe.prepare_image_inputs([canny_image] * num_samples  )

														_snake_case       =  jax.random.PRNGKey(0  )
														_snake_case       =  jax.random.split(UpperCAmelCase  ,  jax.device_count()  )

														_snake_case       =  replicate(UpperCAmelCase  )
														_snake_case       =  shard(UpperCAmelCase  )
														_snake_case       =  shard(UpperCAmelCase  )

														_snake_case       =  pipe(
														    prompt_ids=UpperCAmelCase  ,  image=UpperCAmelCase  ,  params=UpperCAmelCase  ,  prng_seed=UpperCAmelCase  ,  num_inference_steps=50  ,  jit=UpperCAmelCase  ,  ).images
														assert images.shape == (jax.device_count(), 1, 768, 512, 3)

														_snake_case       =  images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]  )
														_snake_case       =  images[0, 253:256, 253:256, -1]

														_snake_case       =  jnp.asarray(jax.device_get(image_slice.flatten()  )  )
														_snake_case       =  jnp.array(
														    [0.16_7969, 0.11_6699, 0.08_1543, 0.15_4297, 0.13_2812, 0.10_8887, 0.16_9922, 0.16_9922, 0.20_5078]  )
														print(f"""output_slice: {output_slice}"""  )
														assert jnp.abs(output_slice - expected_slice  ).max() < 1e-2





							def 	lowercase							(self  )					->       Optional[int]:
														_snake_case,							_snake_case       =  FlaxControlNetModel.from_pretrained(
														    """lllyasviel/sd-controlnet-openpose"""  ,  from_pt=UpperCAmelCase  ,  dtype=jnp.bfloataa  )
														_snake_case,							_snake_case       =  FlaxStableDiffusionControlNetPipeline.from_pretrained(
														    """runwayml/stable-diffusion-v1-5"""  ,  controlnet=UpperCAmelCase  ,  from_pt=UpperCAmelCase  ,  dtype=jnp.bfloataa  )
														_snake_case       =  controlnet_params

														_snake_case       =  """Chef in the kitchen"""
														_snake_case       =  jax.device_count()
														_snake_case       =  pipe.prepare_text_inputs([prompts] * num_samples  )

														_snake_case       =  load_image(
														    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/pose.png"""  )
														_snake_case       =  pipe.prepare_image_inputs([pose_image] * num_samples  )

														_snake_case       =  jax.random.PRNGKey(0  )
														_snake_case       =  jax.random.split(UpperCAmelCase  ,  jax.device_count()  )

														_snake_case       =  replicate(UpperCAmelCase  )
														_snake_case       =  shard(UpperCAmelCase  )
														_snake_case       =  shard(UpperCAmelCase  )

														_snake_case       =  pipe(
														    prompt_ids=UpperCAmelCase  ,  image=UpperCAmelCase  ,  params=UpperCAmelCase  ,  prng_seed=UpperCAmelCase  ,  num_inference_steps=50  ,  jit=UpperCAmelCase  ,  ).images
														assert images.shape == (jax.device_count(), 1, 768, 512, 3)

														_snake_case       =  images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]  )
														_snake_case       =  images[0, 253:256, 253:256, -1]

														_snake_case       =  jnp.asarray(jax.device_get(image_slice.flatten()  )  )
														_snake_case       =  jnp.array(
														    [[0.27_1484, 0.26_1719, 0.27_5391, 0.27_7344, 0.27_9297, 0.29_1016, 0.29_4922, 0.30_2734, 0.30_2734]]  )
														print(f"""output_slice: {output_slice}"""  )
														assert jnp.abs(output_slice - expected_slice  ).max() < 1e-2