

"""simple docstring"""






# 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.

import argparse
import os

from accelerate.utils import ComputeEnvironment

from .cluster import get_cluster_input
from .config_args import cache_dir, default_config_file, default_yaml_config_file, load_config_from_file  # noqa: F401
from .config_utils import _ask_field, _ask_options, _convert_compute_environment  # noqa: F401
from .sagemaker import get_sagemaker_input


_lowerCAmelCase						=		"""Launches a series of prompts to create and save a `default_config.yaml` configuration file for your training system. Should always be ran first on your machine"""

def 		lowerCamelCase__		(					):

    '''simple docstring'''



    _lowerCAmelCase       :   int  =    _ask_options(
        'In which compute environment are you running?' ,   ['This machine', 'AWS (Amazon SageMaker)'] ,   _convert_compute_environment ,   )
    if compute_environment == ComputeEnvironment.AMAZON_SAGEMAKER:
        _lowerCAmelCase       :   Any  =    get_sagemaker_input()
    else:
        _lowerCAmelCase       :   Optional[Any]  =    get_cluster_input()
    return config

def 		lowerCamelCase__		(					_lowerCamelCase=None   ):

    '''simple docstring'''



    if subparsers is not None:
        _lowerCAmelCase       :   List[Any]  =    subparsers.add_parser('config' ,   description=_lowerCamelCase   )
    else:
        _lowerCAmelCase       :   Union[str, Any]  =    argparse.ArgumentParser('Accelerate config command' ,   description=_lowerCamelCase   )

    parser.add_argument(
        '--config_file' ,   default=_lowerCamelCase ,   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\'.'
        ) ,   )

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

def 		lowerCamelCase__		(					_lowerCamelCase   ):

    '''simple docstring'''



    _lowerCAmelCase       :   int  =    get_user_input()
    if args.config_file is not None:
        _lowerCAmelCase       :   str  =    args.config_file
    else:
        if not os.path.isdir(_lowerCamelCase   ):
            os.makedirs(_lowerCamelCase   )
        _lowerCAmelCase       :   int  =    default_yaml_config_file

    if config_file.endswith('.json'   ):
        config.to_json_file(_lowerCamelCase   )
    else:
        config.to_yaml_file(_lowerCamelCase   )
    print(f"""accelerate configuration saved at {config_file}"""   )

def 		lowerCamelCase__		(					):

    '''simple docstring'''



    _lowerCAmelCase       :   Optional[int]  =    config_command_parser()
    _lowerCAmelCase       :   Dict  =    parser.parse_args()
    config_command(_lowerCamelCase   )


if __name__ == "__main__":
 main()








"""simple docstring"""






import gc
import unittest

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

from diffusers import AutoencoderKL, DDIMScheduler, LDMTextToImagePipeline, UNetaDConditionModel
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    load_numpy,
    nightly,
    require_torch_gpu,
    slow,
    torch_device,
)

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()







class 	__UpperCamelCase    (    a__							,							unittest.TestCase						):
  _UpperCAmelCase          =		LDMTextToImagePipeline
  _UpperCAmelCase          =		TEXT_TO_IMAGE_PARAMS - {
      "negative_prompt",
      "negative_prompt_embeds",
      "cross_attention_kwargs",
      "prompt_embeds",
  }
  _UpperCAmelCase          =		PipelineTesterMixin.required_optional_params - {
      "num_images_per_prompt",
      "callback",
      "callback_steps",
  }
  _UpperCAmelCase          =		TEXT_TO_IMAGE_BATCH_PARAMS
  _UpperCAmelCase          =		False

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      torch.manual_seed(0      )
      _lowerCAmelCase       :   Optional[int]  =    UNetaDConditionModel(
          block_out_channels=(32, 64)    ,layers_per_block=2    ,sample_size=32    ,in_channels=4    ,out_channels=4    ,down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')    ,up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')    ,cross_attention_dim=32    ,)
      _lowerCAmelCase       :   Union[str, Any]  =    DDIMScheduler(
          beta_start=0.0_0_0_8_5    ,beta_end=0.0_1_2    ,beta_schedule='scaled_linear'    ,clip_sample=_A    ,set_alpha_to_one=_A    ,)
      torch.manual_seed(0      )
      _lowerCAmelCase       :   Union[str, Any]  =    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      )
      _lowerCAmelCase       :   Dict  =    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    ,)
      _lowerCAmelCase       :   Tuple  =    CLIPTextModel(_A      )
      _lowerCAmelCase       :   Any  =    CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'      )

      _lowerCAmelCase       :   List[str]  =    {
          'unet': unet,
          'scheduler': scheduler,
          'vqvae': vae,
          'bert': text_encoder,
          'tokenizer': tokenizer,
      }
      return components

  def __lowerCamelCase		(		self    ,_A    ,_A=0      ):
      '''simple docstring'''

      if str(_A      ).startswith('mps'      ):
          _lowerCAmelCase       :   int  =    torch.manual_seed(_A      )
      else:
          _lowerCAmelCase       :   Optional[Any]  =    torch.Generator(device=_A      ).manual_seed(_A      )
      _lowerCAmelCase       :   List[Any]  =    {
          'prompt': 'A painting of a squirrel eating a burger',
          'generator': generator,
          'num_inference_steps': 2,
          'guidance_scale': 6.0,
          'output_type': 'numpy',
      }
      return inputs







  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Optional[int]  =    'cpu'  # ensure determinism for the device-dependent torch.Generator

      _lowerCAmelCase       :   int  =    self.get_dummy_components()
      _lowerCAmelCase       :   str  =    LDMTextToImagePipeline(**_A      )
      pipe.to(_A      )
      pipe.set_progress_bar_config(disable=_A      )

      _lowerCAmelCase       :   List[Any]  =    self.get_dummy_inputs(_A      )
      _lowerCAmelCase       :   Any  =    pipe(**_A      ).images
      _lowerCAmelCase       :   Optional[int]  =    image[0, -3:, -3:, -1]

      assert image.shape == (1, 16, 16, 3)
      _lowerCAmelCase       :   Tuple  =    np.array([0.6_1_0_1, 0.6_1_5_6, 0.5_6_2_2, 0.4_8_9_5, 0.6_6_6_1, 0.3_8_0_4, 0.5_7_4_8, 0.6_1_3_6, 0.5_0_1_4]      )

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







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

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      super().tearDown()
      gc.collect()
      torch.cuda.empty_cache()

  def __lowerCamelCase		(		self    ,_A    ,_A=torch.floataa    ,_A=0      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Optional[Any]  =    torch.manual_seed(_A      )
      _lowerCAmelCase       :   Union[str, Any]  =    np.random.RandomState(_A      ).standard_normal((1, 4, 32, 32)      )
      _lowerCAmelCase       :   Optional[Any]  =    torch.from_numpy(_A      ).to(device=_A    ,dtype=_A      )
      _lowerCAmelCase       :   List[str]  =    {
          'prompt': 'A painting of a squirrel eating a burger',
          'latents': latents,
          'generator': generator,
          'num_inference_steps': 3,
          'guidance_scale': 6.0,
          'output_type': 'numpy',
      }
      return inputs







  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      _lowerCAmelCase       :   List[str]  =    LDMTextToImagePipeline.from_pretrained('CompVis/ldm-text2im-large-256'      ).to(_A      )
      pipe.set_progress_bar_config(disable=_A      )

      _lowerCAmelCase       :   Optional[Any]  =    self.get_inputs(_A      )
      _lowerCAmelCase       :   List[Any]  =    pipe(**_A      ).images
      _lowerCAmelCase       :   Dict  =    image[0, -3:, -3:, -1].flatten()

      assert image.shape == (1, 256, 256, 3)
      _lowerCAmelCase       :   str  =    np.array([0.5_1_8_2_5, 0.5_2_8_5_0, 0.5_2_5_4_3, 0.5_4_2_5_8, 0.5_2_3_0_4, 0.5_2_5_6_9, 0.5_4_3_6_3, 0.5_5_2_7_6, 0.5_6_8_7_8]      )
      _lowerCAmelCase       :   Dict  =    np.abs(expected_slice - image_slice      ).max()
      assert max_diff < 1E-3







@nightly
@require_torch_gpu
class 	__UpperCamelCase    (    unittest.TestCase						):

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      super().tearDown()
      gc.collect()
      torch.cuda.empty_cache()

  def __lowerCamelCase		(		self    ,_A    ,_A=torch.floataa    ,_A=0      ):
      '''simple docstring'''

      _lowerCAmelCase       :   List[str]  =    torch.manual_seed(_A      )
      _lowerCAmelCase       :   Optional[int]  =    np.random.RandomState(_A      ).standard_normal((1, 4, 32, 32)      )
      _lowerCAmelCase       :   List[Any]  =    torch.from_numpy(_A      ).to(device=_A    ,dtype=_A      )
      _lowerCAmelCase       :   int  =    {
          'prompt': 'A painting of a squirrel eating a burger',
          'latents': latents,
          'generator': generator,
          'num_inference_steps': 50,
          'guidance_scale': 6.0,
          'output_type': 'numpy',
      }
      return inputs







  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      _lowerCAmelCase       :   List[str]  =    LDMTextToImagePipeline.from_pretrained('CompVis/ldm-text2im-large-256'      ).to(_A      )
      pipe.set_progress_bar_config(disable=_A      )

      _lowerCAmelCase       :   str  =    self.get_inputs(_A      )
      _lowerCAmelCase       :   Union[str, Any]  =    pipe(**_A      ).images[0]

      _lowerCAmelCase       :   int  =    load_numpy(
          'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/ldm_text2img/ldm_large_256_ddim.npy'      )
      _lowerCAmelCase       :   List[str]  =    np.abs(expected_image - image      ).max()
      assert max_diff < 1E-3





