import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer

from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import load_numpy, slow
from diffusers.utils.testing_utils import require_torch_gpu, torch_device

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
class     lowerCAmelCase_     (       __lowercase,  unittest.TestCase ):
							UpperCAmelCase				   =     ShapEPipeline
							UpperCAmelCase				   =     ["prompt"]
							UpperCAmelCase				   =     ["prompt"]
							UpperCAmelCase				   =     [
							    "num_images_per_prompt",
							    "num_inference_steps",
							    "generator",
							    "latents",
							    "guidance_scale",
							    "frame_size",
							    "output_type",
							    "return_dict",
							]
							UpperCAmelCase				   =     False







							@property
							def  UpperCamelCase_						(   self      :     Union[str, Any]		):
												return 32







							@property
							def  UpperCamelCase_						(   self      :     int		):
												return 32







							@property
							def  UpperCamelCase_						(   self      :     List[str]		):
												return self.time_input_dim * 4







							@property
							def  UpperCamelCase_						(   self      :     Optional[Any]		):
												return 8







							@property
							def  UpperCamelCase_						(   self      :     int		):
												_UpperCamelCase			     =     CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''		)
												return tokenizer







							@property
							def  UpperCamelCase_						(   self      :     List[Any]		):
												torch.manual_seed(0		)
												_UpperCamelCase			     =     CLIPTextConfig(
												    bos_token_id=0 ,			eos_token_id=2 ,			hidden_size=self.text_embedder_hidden_size ,			projection_dim=self.text_embedder_hidden_size ,			intermediate_size=37 ,			layer_norm_eps=1e-05 ,			num_attention_heads=4 ,			num_hidden_layers=5 ,			pad_token_id=1 ,			vocab_size=1000 ,			)
												return CLIPTextModelWithProjection(_A		)







							@property
							def  UpperCamelCase_						(   self      :     int		):
												torch.manual_seed(0		)

												_UpperCamelCase			     =     {
												    '''num_attention_heads''': 2,
												    '''attention_head_dim''': 16,
												    '''embedding_dim''': self.time_input_dim,
												    '''num_embeddings''': 32,
												    '''embedding_proj_dim''': self.text_embedder_hidden_size,
												    '''time_embed_dim''': self.time_embed_dim,
												    '''num_layers''': 1,
												    '''clip_embed_dim''': self.time_input_dim * 2,
												    '''additional_embeddings''': 0,
												    '''time_embed_act_fn''': '''gelu''',
												    '''norm_in_type''': '''layer''',
												    '''encoder_hid_proj_type''': None,
												    '''added_emb_type''': None,
												}

												_UpperCamelCase			     =     PriorTransformer(**_A		)
												return model







							@property
							def  UpperCamelCase_						(   self      :     Union[str, Any]		):
												torch.manual_seed(0		)

												_UpperCamelCase			     =     {
												    '''param_shapes''': (
												        (self.renderer_dim, 93),
												        (self.renderer_dim, 8),
												        (self.renderer_dim, 8),
												        (self.renderer_dim, 8),
												    ),
												    '''d_latent''': self.time_input_dim,
												    '''d_hidden''': self.renderer_dim,
												    '''n_output''': 12,
												    '''background''': (
												        0.1,
												        0.1,
												        0.1,
												    ),
												}
												_UpperCamelCase			     =     ShapERenderer(**_A		)
												return model







							def  UpperCamelCase_						(   self      :     str		):
												_UpperCamelCase			     =     self.dummy_prior
												_UpperCamelCase			     =     self.dummy_text_encoder
												_UpperCamelCase			     =     self.dummy_tokenizer
												_UpperCamelCase			     =     self.dummy_renderer

												_UpperCamelCase			     =     HeunDiscreteScheduler(
												    beta_schedule='''exp''' ,			num_train_timesteps=1024 ,			prediction_type='''sample''' ,			use_karras_sigmas=_A ,			clip_sample=_A ,			clip_sample_range=1.0 ,			)
												_UpperCamelCase			     =     {
												    '''prior''': prior,
												    '''text_encoder''': text_encoder,
												    '''tokenizer''': tokenizer,
												    '''renderer''': renderer,
												    '''scheduler''': scheduler,
												}

												return components







							def  UpperCamelCase_						(   self      :     Tuple ,			_A      :     Tuple ,			_A      :     Optional[int]=0		):
												if str(_A		).startswith('''mps'''		):
																	_UpperCamelCase			     =     torch.manual_seed(_A		)
												else:
																	_UpperCamelCase			     =     torch.Generator(device=_A		).manual_seed(_A		)
												_UpperCamelCase			     =     {
												    '''prompt''': '''horse''',
												    '''generator''': generator,
												    '''num_inference_steps''': 1,
												    '''frame_size''': 32,
												    '''output_type''': '''np''',
												}
												return inputs







							def  UpperCamelCase_						(   self      :     Any		):
												_UpperCamelCase			     =     '''cpu'''

												_UpperCamelCase			     =     self.get_dummy_components()

												_UpperCamelCase			     =     self.pipeline_class(**_A		)
												_UpperCamelCase			     =     pipe.to(_A		)

												pipe.set_progress_bar_config(disable=_A		)

												_UpperCamelCase			     =     pipe(**self.get_dummy_inputs(_A		)		)
												_UpperCamelCase			     =     output.images[0]
												_UpperCamelCase			     =     image[0, -3:, -3:, -1]

												assert image.shape == (20, 32, 32, 3)

												_UpperCamelCase			     =     np.array(
												    [
												        0.0003_9216,
												        0.0003_9216,
												        0.0003_9216,
												        0.0003_9216,
												        0.0003_9216,
												        0.0003_9216,
												        0.0003_9216,
												        0.0003_9216,
												        0.0003_9216,
												    ]		)

												assert np.abs(image_slice.flatten() - expected_slice		).max() < 1e-2







							def  UpperCamelCase_						(   self      :     Any		):
												# NOTE: Larger batch sizes cause this test to timeout, only test on smaller batches
												self._test_inference_batch_consistent(batch_sizes=[1, 2]		)







							def  UpperCamelCase_						(   self      :     Any		):
												_UpperCamelCase			     =     torch_device == '''cpu'''
												_UpperCamelCase			     =     True

												self._test_inference_batch_single_identical(
												    batch_size=2 ,			test_max_difference=_A ,			relax_max_difference=_A ,			)







							def  UpperCamelCase_						(   self      :     Any		):
												_UpperCamelCase			     =     self.get_dummy_components()
												_UpperCamelCase			     =     self.pipeline_class(**_A		)
												_UpperCamelCase			     =     pipe.to(_A		)
												pipe.set_progress_bar_config(disable=_A		)

												_UpperCamelCase			     =     1
												_UpperCamelCase			     =     2

												_UpperCamelCase			     =     self.get_dummy_inputs(_A		)

												for key in inputs.keys():
																	if key in self.batch_params:
																						_UpperCamelCase			     =     batch_size * [inputs[key]]

												_UpperCamelCase			     =     pipe(**_A ,			num_images_per_prompt=_A		)[0]

												assert images.shape[0] == batch_size * num_images_per_prompt




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







							def  UpperCamelCase_						(   self      :     str		):
												# clean up the VRAM after each test
												super().tearDown()
												gc.collect()
												torch.cuda.empty_cache()







							def  UpperCamelCase_						(   self      :     List[str]		):
												_UpperCamelCase			     =     load_numpy(
												    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
												    '''/shap_e/test_shap_e_np_out.npy'''		)
												_UpperCamelCase			     =     ShapEPipeline.from_pretrained('''openai/shap-e'''		)
												_UpperCamelCase			     =     pipe.to(_A		)
												pipe.set_progress_bar_config(disable=_A		)

												_UpperCamelCase			     =     torch.Generator(device=_A		).manual_seed(0		)

												_UpperCamelCase			     =     pipe(
												    '''a shark''' ,			generator=_A ,			guidance_scale=15.0 ,			num_inference_steps=64 ,			frame_size=64 ,			output_type='''np''' ,			).images[0]

												assert images.shape == (20, 64, 64, 3)

												assert_mean_pixel_difference(_A ,			_A		)







# 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[int]         =			"Run commands across TPU VMs for initial setup before running `accelerate launch`."





def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE=None				)							-> str:




    """simple docstring"""




    if subparsers is not None:
        _A     =							subparsers.add_parser('tpu-config'							,     description=_description				)
    else:
        _A     =							argparse.ArgumentParser('Accelerate tpu-config command'							,     description=_description				)
    # Core arguments
    _A     =							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.'							,     )
    _A     =							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				)							-> int:




    """simple docstring"""




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

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

    # Then send it to gcloud
    # Eventually try to use google-api-core to do this instead of subprocess
    _A     =							['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(       )							-> Tuple:




    """simple docstring"""




    _A     =							tpu_command_parser()
    _A     =							parser.parse_args()

    tpu_command_launcher(_SCREAMING_SNAKE_CASE				)





