






"""simple docstring"""





import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    EulerAncestralDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionInstructPixaPixPipeline,
    UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.utils import floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()
class    __lowerCamelCase    (  _a							,	_a							,	_a							,	unittest.TestCase  ):
   a      :				Tuple 						=StableDiffusionInstructPixaPixPipeline
   a      :				Optional[Any] 						=TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"""height""", """width""", """cross_attention_kwargs"""}
   a      :				Any 						=TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
   a      :				Dict 						=IMAGE_TO_IMAGE_IMAGE_PARAMS
   a      :				List[str] 						=IMAGE_TO_IMAGE_IMAGE_PARAMS

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Optional[Any]:
       torch.manual_seed(0      )
       UpperCamelCase__         =    UNetaDConditionModel(
           block_out_channels=(32, 64) ,				layers_per_block=2 ,				sample_size=32 ,				in_channels=8 ,				out_channels=4 ,				down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D') ,				up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D') ,				cross_attention_dim=32 ,				)
       UpperCamelCase__         =    PNDMScheduler(skip_prk_steps=snake_case_      )
       torch.manual_seed(0      )
       UpperCamelCase__         =    AutoencoderKL(
           block_out_channels=[32, 64] ,				in_channels=3 ,				out_channels=3 ,				down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D'] ,				up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D'] ,				latent_channels=4 ,				)
       torch.manual_seed(0      )
       UpperCamelCase__         =    CLIPTextConfig(
           bos_token_id=0 ,				eos_token_id=2 ,				hidden_size=32 ,				intermediate_size=37 ,				layer_norm_eps=1E-05 ,				num_attention_heads=4 ,				num_hidden_layers=5 ,				pad_token_id=1 ,				vocab_size=1000 ,				)
       UpperCamelCase__         =    CLIPTextModel(snake_case_      )
       UpperCamelCase__         =    CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'      )

       UpperCamelCase__         =    {
           'unet': unet,
           'scheduler': scheduler,
           'vae': vae,
           'text_encoder': text_encoder,
           'tokenizer': tokenizer,
           'safety_checker': None,
           'feature_extractor': None,
       }
       return components

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_ ,				snake_case_=0      )		->       Union[str, Any]:
       UpperCamelCase__         =    floats_tensor((1, 3, 32, 32) ,				rng=random.Random(snake_case_      )      ).to(snake_case_      )
       UpperCamelCase__         =    image.cpu().permute(0 ,				2 ,				3 ,				1      )[0]
       UpperCamelCase__         =    Image.fromarray(np.uinta(snake_case_      )      ).convert('RGB'      )
       if str(snake_case_      ).startswith('mps'      ):
           UpperCamelCase__         =    torch.manual_seed(snake_case_      )
       else:
           UpperCamelCase__         =    torch.Generator(device=snake_case_      ).manual_seed(snake_case_      )
       UpperCamelCase__         =    {
           'prompt': 'A painting of a squirrel eating a burger',
           'image': image,
           'generator': generator,
           'num_inference_steps': 2,
           'guidance_scale': 6.0,
           'image_guidance_scale': 1,
           'output_type': 'numpy',
       }
       return inputs

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Dict:
       UpperCamelCase__         =    'cpu'  # ensure determinism for the device-dependent torch.Generator
       UpperCamelCase__         =    self.get_dummy_components()
       UpperCamelCase__         =    StableDiffusionInstructPixaPixPipeline(**snake_case_      )
       UpperCamelCase__         =    sd_pipe.to(snake_case_      )
       sd_pipe.set_progress_bar_config(disable=snake_case_      )

       UpperCamelCase__         =    self.get_dummy_inputs(snake_case_      )
       UpperCamelCase__         =    sd_pipe(**snake_case_      ).images
       UpperCamelCase__         =    image[0, -3:, -3:, -1]
       assert image.shape == (1, 32, 32, 3)
       UpperCamelCase__         =    np.array([0.7_526, 0.3_750, 0.4_547, 0.6_117, 0.5_866, 0.5_016, 0.4_327, 0.5_642, 0.4_815]      )

       assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1E-3

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Union[str, Any]:
       UpperCamelCase__         =    'cpu'  # ensure determinism for the device-dependent torch.Generator
       UpperCamelCase__         =    self.get_dummy_components()
       UpperCamelCase__         =    StableDiffusionInstructPixaPixPipeline(**snake_case_      )
       UpperCamelCase__         =    sd_pipe.to(snake_case_      )
       sd_pipe.set_progress_bar_config(disable=snake_case_      )

       UpperCamelCase__         =    self.get_dummy_inputs(snake_case_      )
       UpperCamelCase__         =    'french fries'
       UpperCamelCase__         =    sd_pipe(**snake_case_ ,				negative_prompt=snake_case_      )
       UpperCamelCase__         =    output.images
       UpperCamelCase__         =    image[0, -3:, -3:, -1]

       assert image.shape == (1, 32, 32, 3)
       UpperCamelCase__         =    np.array([0.7_511, 0.3_642, 0.4_553, 0.6_236, 0.5_797, 0.5_013, 0.4_343, 0.5_611, 0.4_831]      )

       assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1E-3

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       int:
       UpperCamelCase__         =    'cpu'  # ensure determinism for the device-dependent torch.Generator
       UpperCamelCase__         =    self.get_dummy_components()
       UpperCamelCase__         =    StableDiffusionInstructPixaPixPipeline(**snake_case_      )
       UpperCamelCase__         =    sd_pipe.to(snake_case_      )
       sd_pipe.set_progress_bar_config(disable=snake_case_      )

       UpperCamelCase__         =    self.get_dummy_inputs(snake_case_      )
       UpperCamelCase__         =    [inputs['prompt']] * 2

       UpperCamelCase__         =    np.array(inputs['image']      ).astype(np.floataa      ) / 255.0
       UpperCamelCase__         =    torch.from_numpy(snake_case_      ).unsqueeze(0      ).to(snake_case_      )
       UpperCamelCase__         =    image / 2 + 0.5
       UpperCamelCase__         =    image.permute(0 ,				3 ,				1 ,				2      )
       UpperCamelCase__         =    image.repeat(2 ,				1 ,				1 ,				1      )

       UpperCamelCase__         =    sd_pipe(**snake_case_      ).images
       UpperCamelCase__         =    image[-1, -3:, -3:, -1]

       assert image.shape == (2, 32, 32, 3)
       UpperCamelCase__         =    np.array([0.5_812, 0.5_748, 0.5_222, 0.5_908, 0.5_695, 0.7_174, 0.6_804, 0.5_523, 0.5_579]      )

       assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1E-3

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       List[str]:
       UpperCamelCase__         =    'cpu'  # ensure determinism for the device-dependent torch.Generator
       UpperCamelCase__         =    self.get_dummy_components()
       UpperCamelCase__         =    EulerAncestralDiscreteScheduler(
           beta_start=0.00_085 ,				beta_end=0.012 ,				beta_schedule='scaled_linear'      )
       UpperCamelCase__         =    StableDiffusionInstructPixaPixPipeline(**snake_case_      )
       UpperCamelCase__         =    sd_pipe.to(snake_case_      )
       sd_pipe.set_progress_bar_config(disable=snake_case_      )

       UpperCamelCase__         =    self.get_dummy_inputs(snake_case_      )
       UpperCamelCase__         =    sd_pipe(**snake_case_      ).images
       UpperCamelCase__         =    image[0, -3:, -3:, -1]

       UpperCamelCase__         =    [round(snake_case_ ,				4      ) for x in image_slice.flatten().tolist()]
       print(','.join([str(snake_case_      ) for x in slice]      )      )

       assert image.shape == (1, 32, 32, 3)
       UpperCamelCase__         =    np.array([0.7_417, 0.3_842, 0.4_732, 0.5_776, 0.5_891, 0.5_139, 0.4_052, 0.5_673, 0.4_986]      )

       assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1E-3

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Union[str, Any]:
       super().test_inference_batch_single_identical(expected_max_diff=3E-3      )







   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Optional[Any]:
       UpperCamelCase__         =    self.get_dummy_components()
       UpperCamelCase__         =    StableDiffusionInstructPixaPixPipeline(**snake_case_      )
       UpperCamelCase__         =    VaeImageProcessor(do_resize=snake_case_ ,				do_normalize=snake_case_      )
       UpperCamelCase__         =    pipe.to(snake_case_      )
       pipe.set_progress_bar_config(disable=snake_case_      )

       UpperCamelCase__         =    pipe(**self.get_dummy_inputs_by_type(snake_case_ ,				input_image_type='pt'      )      )[0]

       UpperCamelCase__         =    components['vae']
       UpperCamelCase__         =    self.get_dummy_inputs_by_type(snake_case_ ,				input_image_type='pt'      )

       for image_param in self.image_latents_params:
           if image_param in inputs.keys():
               UpperCamelCase__         =    vae.encode(inputs[image_param]      ).latent_dist.mode()

       UpperCamelCase__         =    pipe(**snake_case_      )[0]

       UpperCamelCase__         =    np.abs(out - out_latents_inputs      ).max()
       self.assertLess(snake_case_ ,				1E-4 ,				'passing latents as image input generate different result from passing image'      )







@slow
@require_torch_gpu
class    __lowerCamelCase    (  unittest.TestCase  ):

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Optional[Any]:
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_=0      )		->       Tuple:
       UpperCamelCase__         =    torch.manual_seed(snake_case_      )
       UpperCamelCase__         =    load_image(
           'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_pix2pix/example.jpg'      )
       UpperCamelCase__         =    {
           'prompt': 'turn him into a cyborg',
           'image': image,
           'generator': generator,
           'num_inference_steps': 3,
           'guidance_scale': 7.5,
           'image_guidance_scale': 1.0,
           'output_type': 'numpy',
       }
       return inputs

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       str:
       UpperCamelCase__         =    StableDiffusionInstructPixaPixPipeline.from_pretrained(
           'timbrooks/instruct-pix2pix' ,				safety_checker=snake_case_      )
       pipe.to(snake_case_      )
       pipe.set_progress_bar_config(disable=snake_case_      )
       pipe.enable_attention_slicing()

       UpperCamelCase__         =    self.get_inputs()
       UpperCamelCase__         =    pipe(**snake_case_      ).images
       UpperCamelCase__         =    image[0, -3:, -3:, -1].flatten()

       assert image.shape == (1, 512, 512, 3)
       UpperCamelCase__         =    np.array([0.5_902, 0.6_015, 0.6_027, 0.5_983, 0.6_092, 0.6_061, 0.5_765, 0.5_785, 0.5_555]      )

       assert np.abs(expected_slice - image_slice      ).max() < 1E-3

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Optional[Any]:
       UpperCamelCase__         =    StableDiffusionInstructPixaPixPipeline.from_pretrained(
           'timbrooks/instruct-pix2pix' ,				safety_checker=snake_case_      )
       UpperCamelCase__         =    LMSDiscreteScheduler.from_config(pipe.scheduler.config      )
       pipe.to(snake_case_      )
       pipe.set_progress_bar_config(disable=snake_case_      )
       pipe.enable_attention_slicing()

       UpperCamelCase__         =    self.get_inputs()
       UpperCamelCase__         =    pipe(**snake_case_      ).images
       UpperCamelCase__         =    image[0, -3:, -3:, -1].flatten()

       assert image.shape == (1, 512, 512, 3)
       UpperCamelCase__         =    np.array([0.6_578, 0.6_817, 0.6_972, 0.6_761, 0.6_856, 0.6_916, 0.6_428, 0.6_516, 0.6_301]      )

       assert np.abs(expected_slice - image_slice      ).max() < 1E-3

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Optional[Any]:
       UpperCamelCase__         =    StableDiffusionInstructPixaPixPipeline.from_pretrained(
           'timbrooks/instruct-pix2pix' ,				safety_checker=snake_case_      )
       UpperCamelCase__         =    DDIMScheduler.from_config(pipe.scheduler.config      )
       pipe.to(snake_case_      )
       pipe.set_progress_bar_config(disable=snake_case_      )
       pipe.enable_attention_slicing()

       UpperCamelCase__         =    self.get_inputs()
       UpperCamelCase__         =    pipe(**snake_case_      ).images
       UpperCamelCase__         =    image[0, -3:, -3:, -1].flatten()

       assert image.shape == (1, 512, 512, 3)
       UpperCamelCase__         =    np.array([0.3_828, 0.3_834, 0.3_818, 0.3_792, 0.3_865, 0.3_752, 0.3_792, 0.3_847, 0.3_753]      )

       assert np.abs(expected_slice - image_slice      ).max() < 1E-3

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       List[str]:
       UpperCamelCase__         =    0

       def callback_fn(snake_case_ ,				snake_case_ ,				snake_case_      ) -> None:
           UpperCamelCase__         =    True
           nonlocal number_of_steps
           number_of_steps += 1
           if step == 1:
               UpperCamelCase__         =    latents.detach().cpu().numpy()
               assert latents.shape == (1, 4, 64, 64)
               UpperCamelCase__         =    latents[0, -3:, -3:, -1]
               UpperCamelCase__         =    np.array([-0.2_463, -0.4_644, -0.9_756, 1.5_176, 1.4_414, 0.7_866, 0.9_897, 0.8_521, 0.7_983]      )

               assert np.abs(latents_slice.flatten() - expected_slice      ).max() < 5E-2
           elif step == 2:
               UpperCamelCase__         =    latents.detach().cpu().numpy()
               assert latents.shape == (1, 4, 64, 64)
               UpperCamelCase__         =    latents[0, -3:, -3:, -1]
               UpperCamelCase__         =    np.array([-0.2_644, -0.4_626, -0.9_653, 1.5_176, 1.4_551, 0.7_686, 0.9_805, 0.8_452, 0.8_115]      )

               assert np.abs(latents_slice.flatten() - expected_slice      ).max() < 5E-2

       UpperCamelCase__         =    False

       UpperCamelCase__         =    StableDiffusionInstructPixaPixPipeline.from_pretrained(
           'timbrooks/instruct-pix2pix' ,				safety_checker=snake_case_ ,				torch_dtype=torch.floataa      )
       UpperCamelCase__         =    pipe.to(snake_case_      )
       pipe.set_progress_bar_config(disable=snake_case_      )
       pipe.enable_attention_slicing()

       UpperCamelCase__         =    self.get_inputs()
       pipe(**snake_case_ ,				callback=snake_case_ ,				callback_steps=1      )
       assert callback_fn.has_been_called
       assert number_of_steps == 3

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Optional[Any]:
       torch.cuda.empty_cache()
       torch.cuda.reset_max_memory_allocated()
       torch.cuda.reset_peak_memory_stats()

       UpperCamelCase__         =    StableDiffusionInstructPixaPixPipeline.from_pretrained(
           'timbrooks/instruct-pix2pix' ,				safety_checker=snake_case_ ,				torch_dtype=torch.floataa      )
       UpperCamelCase__         =    pipe.to(snake_case_      )
       pipe.set_progress_bar_config(disable=snake_case_      )
       pipe.enable_attention_slicing(1      )
       pipe.enable_sequential_cpu_offload()

       UpperCamelCase__         =    self.get_inputs()
       UpperCamelCase__         =    pipe(**snake_case_      )

       UpperCamelCase__         =    torch.cuda.max_memory_allocated()
       # make sure that less than 2.2 GB is allocated
       assert mem_bytes < 2.2 * 10**9







   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Dict:
       UpperCamelCase__         =    self.get_inputs()
       # resize to resolution that is divisible by 8 but not 16 or 32
       UpperCamelCase__         =    inputs['image'].resize((504, 504)      )

       UpperCamelCase__         =    'timbrooks/instruct-pix2pix'
       UpperCamelCase__         =    StableDiffusionInstructPixaPixPipeline.from_pretrained(
           snake_case_ ,				safety_checker=snake_case_ ,				)
       pipe.to(snake_case_      )
       pipe.set_progress_bar_config(disable=snake_case_      )
       pipe.enable_attention_slicing()

       UpperCamelCase__         =    pipe(**snake_case_      )
       UpperCamelCase__         =    output.images[0]

       UpperCamelCase__         =    image[255:258, 383:386, -1]

       assert image.shape == (504, 504, 3)
       UpperCamelCase__         =    np.array([0.2_726, 0.2_529, 0.2_664, 0.2_655, 0.2_641, 0.2_642, 0.2_591, 0.2_649, 0.2_590]      )

       assert np.abs(image_slice.flatten() - expected_slice      ).max() < 5E-3












"""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


A__					: Optional[Any]=	"""Run commands across TPU VMs for initial setup before running `accelerate launch`."""







def   lowerCAmelCase_( SCREAMING_SNAKE_CASE=None					)					->	Dict:
    """simple docstring"""

    if subparsers is not None:
        UpperCamelCase__         =    subparsers.add_parser('tpu-config' ,	description=_description					)
    else:
        UpperCamelCase__         =    argparse.ArgumentParser('Accelerate tpu-config command' ,	description=_description					)
    # Core arguments
    UpperCamelCase__         =    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.' ,	)
    UpperCamelCase__         =    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   lowerCAmelCase_( SCREAMING_SNAKE_CASE					)					->	Dict:
    """simple docstring"""

    UpperCamelCase__         =    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					):
        UpperCamelCase__         =    load_config_from_file(args.config_file					)
        if not args.command_file and defaults.command_file is not None and not args.command:
            UpperCamelCase__         =    defaults.command_file
        if not args.command and defaults.commands is not None:
            UpperCamelCase__         =    defaults.commands
        if not args.tpu_name:
            UpperCamelCase__         =    defaults.tpu_name
        if not args.tpu_zone:
            UpperCamelCase__         =    defaults.tpu_zone
    if args.accelerate_version == "dev":
        UpperCamelCase__         =    'git+https://github.com/huggingface/accelerate.git'
    elif args.accelerate_version == "latest":
        UpperCamelCase__         =    'accelerate -U'
    elif isinstance(parse(args.accelerate_version					) ,	SCREAMING_SNAKE_CASE					):
        UpperCamelCase__         =    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:
            UpperCamelCase__         =    [f.read().splitlines()]

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

    # Then send it to gcloud
    # Eventually try to use google-api-core to do this instead of subprocess
    UpperCamelCase__         =    ['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   lowerCAmelCase_( )					->	int:
    """simple docstring"""

    UpperCamelCase__         =    tpu_command_parser()
    UpperCamelCase__         =    parser.parse_args()

    tpu_command_launcher(SCREAMING_SNAKE_CASE					)




