"""simple docstring"""






import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaImgaImgPipeline,
    KandinskyVaaPriorPipeline,
    UNetaDConditionModel,
    VQModel,
)
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 ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()





class 				_A		( lowerCAmelCase						,					unittest.TestCase			):
      snake_case__    :     str													=			KandinskyVaaImgaImgPipeline
      snake_case__    :     Tuple													=			['image_embeds', 'negative_image_embeds', 'image']
      snake_case__    :     Dict													=			[
          'image_embeds',
          'negative_image_embeds',
          'image',
      ]
      snake_case__    :     Dict													=			[
          'generator',
          'height',
          'width',
          'strength',
          'guidance_scale',
          'num_inference_steps',
          'return_dict',
          'guidance_scale',
          'num_images_per_prompt',
          'output_type',
          'return_dict',
      ]
      snake_case__    :     Optional[int]													=			False

      @property
      def        A__ (		self		):







            """simple docstring"""



            return 32

      @property
      def        A__ (		self		):







            """simple docstring"""



            return 32

      @property
      def        A__ (		self		):







            """simple docstring"""



            return self.time_input_dim

      @property
      def        A__ (		self		):







            """simple docstring"""



            return self.time_input_dim * 4

      @property
      def        A__ (		self		):







            """simple docstring"""



            return 100

      @property
      def        A__ (		self		):







            """simple docstring"""



            torch.manual_seed(0		)

            lowercase														=			{
                """in_channels""": 4,
                # Out channels is double in channels because predicts mean and variance
                """out_channels""": 8,
                """addition_embed_type""": """image""",
                """down_block_types""": ("""ResnetDownsampleBlock2D""", """SimpleCrossAttnDownBlock2D"""),
                """up_block_types""": ("""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""),
                """mid_block_type""": """UNetMidBlock2DSimpleCrossAttn""",
                """block_out_channels""": (self.block_out_channels_a, self.block_out_channels_a * 2),
                """layers_per_block""": 1,
                """encoder_hid_dim""": self.text_embedder_hidden_size,
                """encoder_hid_dim_type""": """image_proj""",
                """cross_attention_dim""": self.cross_attention_dim,
                """attention_head_dim""": 4,
                """resnet_time_scale_shift""": """scale_shift""",
                """class_embed_type""": None,
            }

            lowercase														=			UNetaDConditionModel(**__lowerCAmelCase		)
            return model

      @property
      def        A__ (		self		):







            """simple docstring"""



            return {
                "block_out_channels": [32, 64],
                "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
                "in_channels": 3,
                "latent_channels": 4,
                "layers_per_block": 1,
                "norm_num_groups": 8,
                "norm_type": "spatial",
                "num_vq_embeddings": 12,
                "out_channels": 3,
                "up_block_types": [
                    "AttnUpDecoderBlock2D",
                    "UpDecoderBlock2D",
                ],
                "vq_embed_dim": 4,
            }

      @property
      def        A__ (		self		):







            """simple docstring"""



            torch.manual_seed(0		)
            lowercase														=			VQModel(**self.dummy_movq_kwargs		)
            return model

      def        A__ (		self		):







            """simple docstring"""



            lowercase														=			self.dummy_unet
            lowercase														=			self.dummy_movq

            lowercase														=			{
                """num_train_timesteps""": 1000,
                """beta_schedule""": """linear""",
                """beta_start""": 0.0_0_0_8_5,
                """beta_end""": 0.0_1_2,
                """clip_sample""": False,
                """set_alpha_to_one""": False,
                """steps_offset""": 0,
                """prediction_type""": """epsilon""",
                """thresholding""": False,
            }

            lowercase														=			DDIMScheduler(**__lowerCAmelCase		)

            lowercase														=			{
                """unet""": unet,
                """scheduler""": scheduler,
                """movq""": movq,
            }

            return components

      def        A__ (		self	, __lowerCAmelCase	, __lowerCAmelCase=0		):







            """simple docstring"""



            lowercase														=			floats_tensor((1, self.text_embedder_hidden_size)	, rng=random.Random(__lowerCAmelCase		)		).to(__lowerCAmelCase		)
            lowercase														=			floats_tensor((1, self.text_embedder_hidden_size)	, rng=random.Random(seed + 1		)		).to(
                __lowerCAmelCase		)
            # create init_image
            lowercase														=			floats_tensor((1, 3, 64, 64)	, rng=random.Random(__lowerCAmelCase		)		).to(__lowerCAmelCase		)
            lowercase														=			image.cpu().permute(0	, 2	, 3	, 1		)[0]
            lowercase														=			Image.fromarray(np.uinta(__lowerCAmelCase		)		).convert("""RGB"""		).resize((256, 256)		)

            if str(__lowerCAmelCase		).startswith("""mps"""		):
                  lowercase														=			torch.manual_seed(__lowerCAmelCase		)
            else:
                  lowercase														=			torch.Generator(device=__lowerCAmelCase		).manual_seed(__lowerCAmelCase		)
            lowercase														=			{
                """image""": init_image,
                """image_embeds""": image_embeds,
                """negative_image_embeds""": negative_image_embeds,
                """generator""": generator,
                """height""": 64,
                """width""": 64,
                """num_inference_steps""": 10,
                """guidance_scale""": 7.0,
                """strength""": 0.2,
                """output_type""": """np""",
            }
            return inputs







      def        A__ (		self		):







            """simple docstring"""



            lowercase														=			"""cpu"""

            lowercase														=			self.get_dummy_components()

            lowercase														=			self.pipeline_class(**__lowerCAmelCase		)
            lowercase														=			pipe.to(__lowerCAmelCase		)

            pipe.set_progress_bar_config(disable=__lowerCAmelCase		)

            lowercase														=			pipe(**self.get_dummy_inputs(__lowerCAmelCase		)		)
            lowercase														=			output.images

            lowercase														=			pipe(
                **self.get_dummy_inputs(__lowerCAmelCase		)	, return_dict=__lowerCAmelCase	, )[0]

            lowercase														=			image[0, -3:, -3:, -1]
            lowercase														=			image_from_tuple[0, -3:, -3:, -1]

            assert image.shape == (1, 64, 64, 3)

            lowercase														=			np.array(
                [0.6_1_9_9_7_7_8, 0.6_3_9_8_4_4_0_6, 0.4_6_1_4_5_7_8_5, 0.6_2_9_4_4_9_8_4, 0.5_6_2_2_2_1_5, 0.4_7_3_0_6_1_3_2, 0.4_7_4_4_1_4_5_6, 0.4_6_0_7_6_0_6, 0.4_8_7_1_9_2_6_3]		)
            assert (
                np.abs(image_slice.flatten() - expected_slice		).max() < 1E-2
            ), f' expected_slice {expected_slice}, but got {image_slice.flatten()}'
            assert (
                np.abs(image_from_tuple_slice.flatten() - expected_slice		).max() < 1E-2
            ), f' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'






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

      def        A__ (		self		):







            """simple docstring"""



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







      def        A__ (		self		):







            """simple docstring"""



            lowercase														=			load_numpy(
                """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
                """/kandinskyv22/kandinskyv22_img2img_frog.npy"""		)

            lowercase														=			load_image(
                """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/kandinsky/cat.png"""		)
            lowercase														=			"""A red cartoon frog, 4k"""

            lowercase														=			KandinskyVaaPriorPipeline.from_pretrained(
                """kandinsky-community/kandinsky-2-2-prior"""	, torch_dtype=torch.floataa		)
            pipe_prior.to(__lowerCAmelCase		)

            lowercase														=			KandinskyVaaImgaImgPipeline.from_pretrained(
                """kandinsky-community/kandinsky-2-2-decoder"""	, torch_dtype=torch.floataa		)
            lowercase														=			pipeline.to(__lowerCAmelCase		)

            pipeline.set_progress_bar_config(disable=__lowerCAmelCase		)

            lowercase														=			torch.Generator(device="""cpu"""		).manual_seed(0		)
            lowercase							,							lowercase														=			pipe_prior(
                __lowerCAmelCase	, generator=__lowerCAmelCase	, num_inference_steps=5	, negative_prompt=""""""	, ).to_tuple()

            lowercase														=			pipeline(
                image=__lowerCAmelCase	, image_embeds=__lowerCAmelCase	, negative_image_embeds=__lowerCAmelCase	, generator=__lowerCAmelCase	, num_inference_steps=100	, height=768	, width=768	, strength=0.2	, output_type="""np"""	, )

            lowercase														=			output.images[0]

            assert image.shape == (768, 768, 3)

            assert_mean_pixel_difference(__lowerCAmelCase	, __lowerCAmelCase		)




"""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.

from argparse import ArgumentParser

from accelerate.commands.config import get_config_parser
from accelerate.commands.env import env_command_parser
from accelerate.commands.launch import launch_command_parser
from accelerate.commands.test import test_command_parser
from accelerate.commands.tpu import tpu_command_parser


def     UpperCAmelCase__ (       )				->					Tuple:
      '''simple docstring'''


      lowercase														=			ArgumentParser("""Accelerate CLI tool"""      ,	usage="""accelerate <command> [<args>]"""      ,	allow_abbrev=lowerCAmelCase__	)
      lowercase														=			parser.add_subparsers(help="""accelerate command helpers"""	)

      # Register commands
      get_config_parser(subparsers=lowerCAmelCase__	)
      env_command_parser(subparsers=lowerCAmelCase__	)
      launch_command_parser(subparsers=lowerCAmelCase__	)
      tpu_command_parser(subparsers=lowerCAmelCase__	)
      test_command_parser(subparsers=lowerCAmelCase__	)

      # Let's go
      lowercase														=			parser.parse_args()

      if not hasattr(lowerCAmelCase__      ,	"""func"""	):
            parser.print_help()
            exit(1	)

      # Run
      args.func(lowerCAmelCase__	)


if __name__ == "__main__":
       main()



