import gc
import random
import unittest

import numpy as np
import torch

from diffusers import (
    DDIMScheduler,
    KandinskyVaaControlnetPipeline,
    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 __snake_case				(						lowerCamelCase_   ,		unittest.TestCase		):
   lowerCAmelCase_														=							KandinskyVaaControlnetPipeline
   lowerCAmelCase_														=							["image_embeds", "negative_image_embeds", "hint"]
   lowerCAmelCase_														=							["image_embeds", "negative_image_embeds", "hint"]
   lowerCAmelCase_														=							[
       "generator",
       "height",
       "width",
       "latents",
       "guidance_scale",
       "num_inference_steps",
       "return_dict",
       "guidance_scale",
       "num_images_per_prompt",
       "output_type",
       "return_dict",
   ]
   lowerCAmelCase_														=							False




   @property
   def 	__a						(    self			:				Tuple      ):



     """simple docstring"""






     return 32




   @property
   def 	__a						(    self			:				Union[str, Any]      ):



     """simple docstring"""






     return 32




   @property
   def 	__a						(    self			:				Dict      ):



     """simple docstring"""






     return self.time_input_dim




   @property
   def 	__a						(    self			:				List[str]      ):



     """simple docstring"""






     return self.time_input_dim * 4




   @property
   def 	__a						(    self			:				Union[str, Any]      ):



     """simple docstring"""






     return 1_00




   @property
   def 	__a						(    self			:				int      ):



     """simple docstring"""






     torch.manual_seed(0      )

     SCREAMING_SNAKE_CASE__										=      {
         """in_channels""": 8,
         # Out channels is double in channels because predicts mean and variance
         """out_channels""": 8,
         """addition_embed_type""": """image_hint""",
         """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,
     }

     SCREAMING_SNAKE_CASE__										=      UNetaDConditionModel(**_lowercase      )
     return model




   @property
   def 	__a						(    self			:				List[str]      ):



     """simple docstring"""






     return {
         "block_out_channels": [32, 32, 64, 64],
         "down_block_types": [
             "DownEncoderBlock2D",
             "DownEncoderBlock2D",
             "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", "UpDecoderBlock2D", "UpDecoderBlock2D"],
         "vq_embed_dim": 4,
     }




   @property
   def 	__a						(    self			:				Optional[Any]      ):



     """simple docstring"""






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




   def 	__a						(    self			:				List[Any]      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      self.dummy_unet
     SCREAMING_SNAKE_CASE__										=      self.dummy_movq

     SCREAMING_SNAKE_CASE__										=      DDIMScheduler(
         num_train_timesteps=10_00       ,	beta_schedule="""linear"""       ,	beta_start=0.0_00_85       ,	beta_end=0.0_12       ,	clip_sample=_lowercase       ,	set_alpha_to_one=_lowercase       ,	steps_offset=1       ,	prediction_type="""epsilon"""       ,	thresholding=_lowercase       ,	)

     SCREAMING_SNAKE_CASE__										=      {
         """unet""": unet,
         """scheduler""": scheduler,
         """movq""": movq,
     }
     return components




   def 	__a						(    self			:				List[Any]       ,	_lowercase			:				Optional[Any]       ,	_lowercase			:				str=0      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      floats_tensor((1, self.text_embedder_hidden_size)       ,	rng=random.Random(_lowercase      )      ).to(_lowercase      )
     SCREAMING_SNAKE_CASE__										=      floats_tensor((1, self.text_embedder_hidden_size)       ,	rng=random.Random(seed + 1      )      ).to(
         _lowercase      )

     # create hint
     SCREAMING_SNAKE_CASE__										=      floats_tensor((1, 3, 64, 64)       ,	rng=random.Random(_lowercase      )      ).to(_lowercase      )

     if str(_lowercase      ).startswith("""mps"""      ):
       SCREAMING_SNAKE_CASE__										=      torch.manual_seed(_lowercase      )
     else:
       SCREAMING_SNAKE_CASE__										=      torch.Generator(device=_lowercase      ).manual_seed(_lowercase      )
     SCREAMING_SNAKE_CASE__										=      {
         """image_embeds""": image_embeds,
         """negative_image_embeds""": negative_image_embeds,
         """hint""": hint,
         """generator""": generator,
         """height""": 64,
         """width""": 64,
         """guidance_scale""": 4.0,
         """num_inference_steps""": 2,
         """output_type""": """np""",
     }
     return inputs




   def 	__a						(    self			:				int      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      """cpu"""

     SCREAMING_SNAKE_CASE__										=      self.get_dummy_components()

     SCREAMING_SNAKE_CASE__										=      self.pipeline_class(**_lowercase      )
     SCREAMING_SNAKE_CASE__										=      pipe.to(_lowercase      )

     pipe.set_progress_bar_config(disable=_lowercase      )

     SCREAMING_SNAKE_CASE__										=      pipe(**self.get_dummy_inputs(_lowercase      )      )
     SCREAMING_SNAKE_CASE__										=      output.images

     SCREAMING_SNAKE_CASE__										=      pipe(
         **self.get_dummy_inputs(_lowercase      )       ,	return_dict=_lowercase       ,	)[0]

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

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

     SCREAMING_SNAKE_CASE__										=      np.array(
         [0.6_95_98_26, 0.86_82_79, 0.7_55_80_92, 0.68_76_94_67, 0.85_80_58_04, 0.65_97_74_96, 0.44_88_53_02, 0.5_95_91_11, 0.4_25_15_95]      )

     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 __snake_case				(						unittest.TestCase		):




   def 	__a						(    self			:				int      ):



     """simple docstring"""






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




   def 	__a						(    self			:				str      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      load_numpy(
         """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
         """/kandinskyv22/kandinskyv22_controlnet_robotcat_fp16.npy"""      )

     SCREAMING_SNAKE_CASE__										=      load_image(
         """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
         """/kandinskyv22/hint_image_cat.png"""      )
     SCREAMING_SNAKE_CASE__										=      torch.from_numpy(np.array(_lowercase      )      ).float() / 2_55.0
     SCREAMING_SNAKE_CASE__										=      hint.permute(2       ,	0       ,	1      ).unsqueeze(0      )

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

     SCREAMING_SNAKE_CASE__										=      KandinskyVaaControlnetPipeline.from_pretrained(
         """kandinsky-community/kandinsky-2-2-controlnet-depth"""       ,	torch_dtype=torch.floataa      )
     SCREAMING_SNAKE_CASE__										=      pipeline.to(_lowercase      )
     pipeline.set_progress_bar_config(disable=_lowercase      )

     SCREAMING_SNAKE_CASE__										=      """A robot, 4k photo"""

     SCREAMING_SNAKE_CASE__										=      torch.Generator(device="""cuda"""      ).manual_seed(0      )
     SCREAMING_SNAKE_CASE__							,     SCREAMING_SNAKE_CASE__										=      pipe_prior(
         _lowercase       ,	generator=_lowercase       ,	num_inference_steps=5       ,	negative_prompt=""""""       ,	).to_tuple()

     SCREAMING_SNAKE_CASE__										=      torch.Generator(device="""cuda"""      ).manual_seed(0      )
     SCREAMING_SNAKE_CASE__										=      pipeline(
         image_embeds=_lowercase       ,	negative_image_embeds=_lowercase       ,	hint=_lowercase       ,	generator=_lowercase       ,	num_inference_steps=1_00       ,	output_type="""np"""       ,	)

     SCREAMING_SNAKE_CASE__										=      output.images[0]

     assert image.shape == (5_12, 5_12, 3)

     assert_mean_pixel_difference(_lowercase       ,	_lowercase      )

import unittest
from dataclasses import dataclass

import pytest

from accelerate.commands.config.config_args import SageMakerConfig
from accelerate.utils import ComputeEnvironment
from accelerate.utils.launch import _convert_nargs_to_dict







@dataclass
class __snake_case				(						lowerCamelCase_		):
   lowerCAmelCase_														=							ComputeEnvironment.AMAZON_SAGEMAKER
   lowerCAmelCase_														=							True
   lowerCAmelCase_														=							"ml.p3.2xlarge"
   lowerCAmelCase_														=							"accelerate_sagemaker_execution_role"
   lowerCAmelCase_														=							"hf-sm"
   lowerCAmelCase_														=							"us-east-1"
   lowerCAmelCase_														=							1
   lowerCAmelCase_														=							"accelerate-sagemaker-1"
   lowerCAmelCase_														=							"1.6"
   lowerCAmelCase_														=							"4.4"
   lowerCAmelCase_														=							"train.py"
   lowerCAmelCase_														=							[
       "--model_name_or_path",
       "bert",
       "--do_train",
       "False",
       "--epochs",
       "3",
       "--learning_rate",
       "5e-5",
       "--max_steps",
       "50.5",
   ]
   lowerCAmelCase_														=							[
       "--model_name_or_path",
       "bert",
       "--do_train",
       "--do_test",
       "False",
       "--do_predict",
       "--epochs",
       "3",
       "--learning_rate",
       "5e-5",
       "--max_steps",
       "50.5",
   ]







class __snake_case				(						unittest.TestCase		):




   def 	__a						(    self			:				List[Any]      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      _convert_nargs_to_dict(MockLaunchConfig.success_training_script_args      )
     assert isinstance(converted_args["""model_name_or_path"""]       ,	_lowercase      )
     assert isinstance(converted_args["""do_train"""]       ,	_lowercase      )
     assert isinstance(converted_args["""epochs"""]       ,	_lowercase      )
     assert isinstance(converted_args["""learning_rate"""]       ,	_lowercase      )
     assert isinstance(converted_args["""max_steps"""]       ,	_lowercase      )

     with pytest.raises(_lowercase      ):
       _convert_nargs_to_dict(MockLaunchConfig.fail_training_script_args      )
