






# 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


_a     : int   	=      """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 					snake_case__				(				):
    lowerCAmelCase__ :Optional[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__ :List[Any]   				=					get_cluster_input()
    return config


def 					snake_case__				(				UpperCAmelCase				:	List[str]=None							):
    if subparsers is not None:
        lowerCAmelCase__ :Union[str, Any]   				=					subparsers.add_parser("config"     ,      description=UpperCamelCase_							)
    else:
        lowerCAmelCase__ :Union[str, Any]   				=					argparse.ArgumentParser("Accelerate config command"     ,      description=UpperCamelCase_							)

    parser.add_argument(
        "--config_file"     ,      default=UpperCamelCase_     ,      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=UpperCamelCase_							)
    return parser


def 					snake_case__				(				UpperCAmelCase				:	Optional[int]							):
    lowerCAmelCase__ :List[str]   				=					get_user_input()
    if args.config_file is not None:
        lowerCAmelCase__ :Optional[Any]   				=					args.config_file
    else:
        if not os.path.isdir(UpperCamelCase_							):
            os.makedirs(UpperCamelCase_							)
        lowerCAmelCase__ :Union[str, Any]   				=					default_yaml_config_file

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


def 					snake_case__				(				):
    lowerCAmelCase__ :str   				=					config_command_parser()
    lowerCAmelCase__ :List[str]   				=					parser.parse_args()
    config_command(UpperCamelCase_							)


if __name__ == "__main__":
 main()








import gc
import random
import unittest

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

import diffusers
from diffusers import (
    AutoencoderKL,
    EulerDiscreteScheduler,
    StableDiffusionLatentUpscalePipeline,
    StableDiffusionPipeline,
    UNetaDConditionModel,
)
from diffusers.schedulers import KarrasDiffusionSchedulers
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()



def 		lowerCAmelCase__     (       UpperCamelCase_					:							Union[str, Any]						)->	List[str]:
    A__					=       [tensor.shape for tensor in tensor_list]
    return all(shape == shapes[0] for shape in shapes[1:]						)



class      _UpperCAmelCase      (							A__      ,   A__      ,   A__      ,   unittest.TestCase  ):
     UpperCamelCase__		     =					StableDiffusionLatentUpscalePipeline
     UpperCamelCase__		     =					TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
         '''height''',
         '''width''',
         '''cross_attention_kwargs''',
         '''negative_prompt_embeds''',
         '''prompt_embeds''',
     }
     UpperCamelCase__		     =					PipelineTesterMixin.required_optional_params - {'''num_images_per_prompt'''}
     UpperCamelCase__		     =					TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
     UpperCamelCase__		     =					frozenset(
         []  )  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
     UpperCamelCase__		     =					frozenset([]  )

     UpperCamelCase__		     =					True




     @property
     def 		snake_case_		(					self):
         A__					=       1
         A__					=       4
         A__					=       (1_6, 1_6)

         A__					=       floats_tensor((batch_size, num_channels) + sizes			,    rng=random.Random(0)).to(a__)
         return image




     def 		snake_case_		(					self):
         torch.manual_seed(0)
         A__					=       UNetaDConditionModel(
             act_fn='''gelu'''			,    attention_head_dim=8			,    norm_num_groups=a__			,    block_out_channels=[3_2, 3_2, 6_4, 6_4]			,    time_cond_proj_dim=1_6_0			,    conv_in_kernel=1			,    conv_out_kernel=1			,    cross_attention_dim=3_2			,    down_block_types=(
                 '''KDownBlock2D''',
                 '''KCrossAttnDownBlock2D''',
                 '''KCrossAttnDownBlock2D''',
                 '''KCrossAttnDownBlock2D''',
             )			,    in_channels=8			,    mid_block_type=a__			,    only_cross_attention=a__			,    out_channels=5			,    resnet_time_scale_shift='''scale_shift'''			,    time_embedding_type='''fourier'''			,    timestep_post_act='''gelu'''			,    up_block_types=('''KCrossAttnUpBlock2D''', '''KCrossAttnUpBlock2D''', '''KCrossAttnUpBlock2D''', '''KUpBlock2D''')			,    )
         A__					=       AutoencoderKL(
             block_out_channels=[3_2, 3_2, 6_4, 6_4]			,    in_channels=3			,    out_channels=3			,    down_block_types=[
                 '''DownEncoderBlock2D''',
                 '''DownEncoderBlock2D''',
                 '''DownEncoderBlock2D''',
                 '''DownEncoderBlock2D''',
             ]			,    up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''', '''UpDecoderBlock2D''', '''UpDecoderBlock2D''']			,    latent_channels=4			,    )
         A__					=       EulerDiscreteScheduler(prediction_type='''sample''')
         A__					=       CLIPTextConfig(
             bos_token_id=0			,    eos_token_id=2			,    hidden_size=3_2			,    intermediate_size=3_7			,    layer_norm_eps=1e-05			,    num_attention_heads=4			,    num_hidden_layers=5			,    pad_token_id=1			,    vocab_size=1_0_0_0			,    hidden_act='''quick_gelu'''			,    projection_dim=5_1_2			,    )
         A__					=       CLIPTextModel(a__)
         A__					=       CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''')

         A__					=       {
             '''unet''': model.eval(),
             '''vae''': vae.eval(),
             '''scheduler''': scheduler,
             '''text_encoder''': text_encoder,
             '''tokenizer''': tokenizer,
         }

         return components




     def 		snake_case_		(					self			,    a__			,    a__=0):
         if str(a__).startswith('''mps'''):
             A__					=       torch.manual_seed(a__)
         else:
             A__					=       torch.Generator(device=a__).manual_seed(a__)
         A__					=       {
             '''prompt''': '''A painting of a squirrel eating a burger''',
             '''image''': self.dummy_image.cpu(),
             '''generator''': generator,
             '''num_inference_steps''': 2,
             '''output_type''': '''numpy''',
         }
         return inputs




     def 		snake_case_		(					self):
         A__					=       '''cpu'''

         A__					=       self.get_dummy_components()
         A__					=       self.pipeline_class(**a__)
         pipe.to(a__)
         pipe.set_progress_bar_config(disable=a__)

         A__					=       self.get_dummy_inputs(a__)
         A__					=       pipe(**a__).images
         A__					=       image[0, -3:, -3:, -1]

         self.assertEqual(image.shape			,    (1, 2_5_6, 2_5_6, 3))
         A__					=       np.array(
             [0.4_7_2_2_2_4_1_2, 0.4_1_9_2_1_6_3_3, 0.4_4_7_1_7_4_3_4, 0.4_6_8_7_4_1_9_2, 0.4_2_5_8_8_2_5_8, 0.4_6_1_5_0_7_2_6, 0.4_6_7_7_5_3_4, 0.4_5_5_8_3_8_3_2, 0.4_8_5_7_9_0_5_5])
         A__					=       np.abs(image_slice.flatten() - expected_slice).max()
         self.assertLessEqual(a__			,    1e-3)




     def 		snake_case_		(					self):
         super().test_attention_slicing_forward_pass(expected_max_diff=7e-3)




     def 		snake_case_		(					self):
         super().test_cpu_offload_forward_pass(expected_max_diff=3e-3)




     def 		snake_case_		(					self):
         super().test_dict_tuple_outputs_equivalent(expected_max_difference=3e-3)




     def 		snake_case_		(					self):
         super().test_inference_batch_single_identical(expected_max_diff=7e-3)




     def 		snake_case_		(					self):
         super().test_pt_np_pil_outputs_equivalent(expected_max_diff=3e-3)




     def 		snake_case_		(					self):
         super().test_save_load_local(expected_max_difference=3e-3)




     def 		snake_case_		(					self):
         super().test_save_load_optional_components(expected_max_difference=3e-3)




     def 		snake_case_		(					self):
         A__					=       [
             '''DDIMScheduler''',
             '''DDPMScheduler''',
             '''PNDMScheduler''',
             '''HeunDiscreteScheduler''',
             '''EulerAncestralDiscreteScheduler''',
             '''KDPM2DiscreteScheduler''',
             '''KDPM2AncestralDiscreteScheduler''',
             '''DPMSolverSDEScheduler''',
         ]
         A__					=       self.get_dummy_components()
         A__					=       self.pipeline_class(**a__)

         # make sure that PNDM does not need warm-up
         pipe.scheduler.register_to_config(skip_prk_steps=a__)

         pipe.to(a__)
         pipe.set_progress_bar_config(disable=a__)
         A__					=       self.get_dummy_inputs(a__)
         A__					=       2

         A__					=       []
         for scheduler_enum in KarrasDiffusionSchedulers:
             if scheduler_enum.name in skip_schedulers:
                 # no sigma schedulers are not supported
                 # no schedulers
                 continue

             A__					=       getattr(a__			,    scheduler_enum.name)
             A__					=       scheduler_cls.from_config(pipe.scheduler.config)
             A__					=       pipe(**a__)[0]
             outputs.append(a__)

         assert check_same_shape(a__)







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




     def 		snake_case_		(					self):
         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()




     def 		snake_case_		(					self):
         A__					=       torch.manual_seed(3_3)

         A__					=       StableDiffusionPipeline.from_pretrained('''CompVis/stable-diffusion-v1-4'''			,    torch_dtype=torch.floataa)
         pipe.to('''cuda''')

         A__					=       StableDiffusionLatentUpscalePipeline.from_pretrained(
             '''stabilityai/sd-x2-latent-upscaler'''			,    torch_dtype=torch.floataa)
         upscaler.to('''cuda''')

         A__					=       '''a photo of an astronaut high resolution, unreal engine, ultra realistic'''

         A__					=       pipe(a__			,    generator=a__			,    output_type='''latent''').images

         A__					=       upscaler(
             prompt=a__			,    image=a__			,    num_inference_steps=2_0			,    guidance_scale=0			,    generator=a__			,    output_type='''np'''			,    ).images[0]

         A__					=       load_numpy(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/astronaut_1024.npy''')
         assert np.abs((expected_image - image).mean()) < 5e-2




     def 		snake_case_		(					self):
         A__					=       torch.manual_seed(3_3)

         A__					=       StableDiffusionLatentUpscalePipeline.from_pretrained(
             '''stabilityai/sd-x2-latent-upscaler'''			,    torch_dtype=torch.floataa)
         upscaler.to('''cuda''')

         A__					=       '''the temple of fire by Ross Tran and Gerardo Dottori, oil on canvas'''

         A__					=       load_image(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/fire_temple_512.png''')

         A__					=       upscaler(
             prompt=a__			,    image=a__			,    num_inference_steps=2_0			,    guidance_scale=0			,    generator=a__			,    output_type='''np'''			,    ).images[0]

         A__					=       load_numpy(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/fire_temple_1024.npy''')
         assert np.abs((expected_image - image).max()) < 5e-2
