
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 			__A( unittest.TestCase			):




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      str:


								'''simple docstring'''




								super().tearDown()
								gc.collect()




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Optional[Any]:


								'''simple docstring'''




								__a							,     __a										=    FlaxControlNetModel.from_pretrained(
								    '''lllyasviel/sd-controlnet-canny'''		,     from_pt=_snake_case		,     dtype=jnp.bfloataa				)
								__a							,     __a										=    FlaxStableDiffusionControlNetPipeline.from_pretrained(
								    '''runwayml/stable-diffusion-v1-5'''		,     controlnet=_snake_case		,     from_pt=_snake_case		,     dtype=jnp.bfloataa				)
								__a										=    controlnet_params

								__a										=    '''bird'''
								__a										=    jax.device_count()
								__a										=    pipe.prepare_text_inputs([prompts] * num_samples				)

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

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

								__a										=    replicate(_snake_case				)
								__a										=    shard(_snake_case				)
								__a										=    shard(_snake_case				)

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

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

								__a										=    jnp.asarray(jax.device_get(image_slice.flatten()				)				)
								__a										=    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 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Any:


								'''simple docstring'''




								__a							,     __a										=    FlaxControlNetModel.from_pretrained(
								    '''lllyasviel/sd-controlnet-openpose'''		,     from_pt=_snake_case		,     dtype=jnp.bfloataa				)
								__a							,     __a										=    FlaxStableDiffusionControlNetPipeline.from_pretrained(
								    '''runwayml/stable-diffusion-v1-5'''		,     controlnet=_snake_case		,     from_pt=_snake_case		,     dtype=jnp.bfloataa				)
								__a										=    controlnet_params

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

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

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

								__a										=    replicate(_snake_case				)
								__a										=    shard(_snake_case				)
								__a										=    shard(_snake_case				)

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

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

								__a										=    jnp.asarray(jax.device_get(image_slice.flatten()				)				)
								__a										=    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


'''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 		_A     (    A__=None      ):






 """simple docstring"""



 if subparsers is not None:
  __lowercase							  = subparsers.add_parser('''tpu-config'''     ,				description=_description      )
 else:
  __lowercase							  = argparse.ArgumentParser('''Accelerate tpu-config command'''     ,				description=_description      )
 # Core arguments
 __lowercase							  = parser.add_argument_group(
     '''Config Arguments'''     ,				'''Arguments that can be configured through `accelerate config`.'''      )
 config_args.add_argument(
     '''--config_file'''     ,				type=A__     ,				default=A__     ,				help='''Path to the config file to use for accelerate.'''     ,				)
 config_args.add_argument(
     '''--tpu_name'''     ,				default=A__     ,				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=A__     ,				help='''The zone of the TPU to use. If not specified, will use the zone specified in the config file.'''     ,				)
 __lowercase							  = 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=A__     ,				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=A__      )
 return parser


def 		_A     (    A__      ):






 """simple docstring"""



 __lowercase							  = None

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

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

 # Then send it to gcloud
 # Eventually try to use google-api-core to do this instead of subprocess
 __lowercase							  = ['''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(A__      )}"      )
  return
 subprocess.run(A__      )
 print('''Successfully setup pod.'''      )






def 		_A     (    ):






 """simple docstring"""



 __lowercase							  = tpu_command_parser()
 __lowercase							  = parser.parse_args()

 tpu_command_launcher(A__      )

