


import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaInpaintPipeline,
    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 						_UpperCamelCase						(     lowerCAmelCase__ ,unittest.TestCase	):
							'''simple docstring'''







							__UpperCAmelCase		:					Optional[Any]          =KandinskyVaaInpaintPipeline
							__UpperCAmelCase		:					str          =["""image_embeds""", """negative_image_embeds""", """image""", """mask_image"""]
							__UpperCAmelCase		:					List[Any]          =[
							    """image_embeds""",
							    """negative_image_embeds""",
							    """image""",
							    """mask_image""",
							]
							__UpperCAmelCase		:					Union[str, Any]          =[
							    """generator""",
							    """height""",
							    """width""",
							    """latents""",
							    """guidance_scale""",
							    """num_inference_steps""",
							    """return_dict""",
							    """guidance_scale""",
							    """num_images_per_prompt""",
							    """output_type""",
							    """return_dict""",
							]
							__UpperCAmelCase		:					Tuple          =False

							@property
							def     snake_case       (  self			):
										return 32

							@property
							def     snake_case       (  self			):
										return 32

							@property
							def     snake_case       (  self			):
										return self.time_input_dim

							@property
							def     snake_case       (  self			):
										return self.time_input_dim * 4

							@property
							def     snake_case       (  self			):
										return 1_00

							@property
							def     snake_case       (  self			):
										torch.manual_seed(0			)

										__lowerCAmelCase       					=    {
										    "in_channels": 9,
										    # 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,
										}

										__lowerCAmelCase       					=    UNetaDConditionModel(**_UpperCAmelCase			)
										return model

							@property
							def     snake_case       (  self			):
										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     snake_case       (  self			):
										torch.manual_seed(0			)
										__lowerCAmelCase       					=    VQModel(**self.dummy_movq_kwargs			)
										return model

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    self.dummy_unet
										__lowerCAmelCase       					=    self.dummy_movq

										__lowerCAmelCase       					=    DDIMScheduler(
										    num_train_timesteps=10_00					,     beta_schedule="linear"					,     beta_start=0.0_0_0_8_5					,     beta_end=0.0_1_2					,     clip_sample=_UpperCAmelCase					,     set_alpha_to_one=_UpperCAmelCase					,     steps_offset=1					,     prediction_type="epsilon"					,     thresholding=_UpperCAmelCase					,     )

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

										return components

							def     snake_case       (  self					,     __a					,     __a=0			):
										__lowerCAmelCase       					=    floats_tensor((1, self.text_embedder_hidden_size)					,     rng=random.Random(_UpperCAmelCase			)			).to(_UpperCAmelCase			)
										__lowerCAmelCase       					=    floats_tensor((1, self.text_embedder_hidden_size)					,     rng=random.Random(seed + 1			)			).to(
										    _UpperCAmelCase			)
										# create init_image
										__lowerCAmelCase       					=    floats_tensor((1, 3, 64, 64)					,     rng=random.Random(_UpperCAmelCase			)			).to(_UpperCAmelCase			)
										__lowerCAmelCase       					=    image.cpu().permute(0					,     2					,     3					,     1			)[0]
										__lowerCAmelCase       					=    Image.fromarray(np.uinta(_UpperCAmelCase			)			).convert("RGB"			).resize((2_56, 2_56)			)
										# create mask
										__lowerCAmelCase       					=    np.ones((64, 64)					,     dtype=np.floataa			)
										__lowerCAmelCase       					=    0

										if str(_UpperCAmelCase			).startswith("mps"			):
													__lowerCAmelCase       					=    torch.manual_seed(_UpperCAmelCase			)
										else:
													__lowerCAmelCase       					=    torch.Generator(device=_UpperCAmelCase			).manual_seed(_UpperCAmelCase			)
										__lowerCAmelCase       					=    {
										    "image": init_image,
										    "mask_image": mask,
										    "image_embeds": image_embeds,
										    "negative_image_embeds": negative_image_embeds,
										    "generator": generator,
										    "height": 64,
										    "width": 64,
										    "num_inference_steps": 2,
										    "guidance_scale": 4.0,
										    "output_type": "np",
										}
										return inputs

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    "cpu"

										__lowerCAmelCase       					=    self.get_dummy_components()

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

										pipe.set_progress_bar_config(disable=_UpperCAmelCase			)

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

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

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

										print(f"image.shape {image.shape}"			)

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

										__lowerCAmelCase       					=    np.array(
										    [0.5_0_7_7_5_9_0_3, 0.4_9_5_2_7_1_9_5, 0.4_8_8_2_4_5_4_3, 0.5_0_1_9_2_2_3_7, 0.4_8_6_4_4_9_0_6, 0.4_9_3_7_3_8_1_4, 0.4_7_8_0_5_9_8, 0.4_7_2_3_4_8_2_7, 0.4_8_3_2_7_8_4_8]			)

										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()}"



							def     snake_case       (  self			):
										super().test_inference_batch_single_identical(expected_max_diff=3e-3			)
@slow
@require_torch_gpu
class 						_UpperCamelCase						(     unittest.TestCase	):
							'''simple docstring'''







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



							def     snake_case       (  self			):
										__lowerCAmelCase       					=    load_numpy(
										    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
										    "/kandinskyv22/kandinskyv22_inpaint_cat_with_hat_fp16.npy"			)

										__lowerCAmelCase       					=    load_image(
										    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/kandinsky/cat.png"			)
										__lowerCAmelCase       					=    np.ones((7_68, 7_68)					,     dtype=np.floataa			)
										__lowerCAmelCase       					=    0

										__lowerCAmelCase       					=    "a hat"

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

										__lowerCAmelCase       					=    KandinskyVaaInpaintPipeline.from_pretrained(
										    "kandinsky-community/kandinsky-2-2-decoder-inpaint"					,     torch_dtype=torch.floataa			)
										__lowerCAmelCase       					=    pipeline.to(_UpperCAmelCase			)
										pipeline.set_progress_bar_config(disable=_UpperCAmelCase			)

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

										__lowerCAmelCase       					=    pipeline(
										    image=_UpperCAmelCase					,     mask_image=_UpperCAmelCase					,     image_embeds=_UpperCAmelCase					,     negative_image_embeds=_UpperCAmelCase					,     generator=_UpperCAmelCase					,     num_inference_steps=1_00					,     height=7_68					,     width=7_68					,     output_type="np"					,     )

										__lowerCAmelCase       					=    output.images[0]

										assert image.shape == (7_68, 7_68, 3)

										assert_mean_pixel_difference(_UpperCAmelCase					,     _UpperCAmelCase			)








"""simple docstring"""


import json
import os
import subprocess
import unittest
from ast import literal_eval

import pytest
from parameterized import parameterized, parameterized_class

from . import is_sagemaker_available


if is_sagemaker_available():
	from sagemaker import Session, TrainingJobAnalytics
	from sagemaker.huggingface import HuggingFace
@pytest.mark.skipif(
    literal_eval(os.getenv("""TEST_SAGEMAKER""" ,"""False"""	)	) is not True ,reason="""Skipping test because should only be run when releasing minor transformers version""" ,)
@pytest.mark.usefixtures("""sm_env"""	)
@parameterized_class(
    [
        {
            """framework""": """pytorch""",
            """script""": """run_glue_model_parallelism.py""",
            """model_name_or_path""": """roberta-large""",
            """instance_type""": """ml.p3dn.24xlarge""",
            """results""": {"""train_runtime""": 1_6_0_0, """eval_accuracy""": 0.3, """eval_loss""": 1.2},
        },
        {
            """framework""": """pytorch""",
            """script""": """run_glue.py""",
            """model_name_or_path""": """roberta-large""",
            """instance_type""": """ml.p3dn.24xlarge""",
            """results""": {"""train_runtime""": 1_6_0_0, """eval_accuracy""": 0.3, """eval_loss""": 1.2},
        },
    ]	)
class 						_UpperCamelCase						(     unittest.TestCase	):
							'''simple docstring'''







							def     snake_case       (  self			):
										if self.framework == "pytorch":
													subprocess.run(
													    f"cp ./examples/pytorch/text-classification/run_glue.py {self.env.test_path}/run_glue.py".split()					,     encoding="utf-8"					,     check=__a					,     )
										assert hasattr(self					,     "env"			)

							def     snake_case       (  self					,     __a			):
										# configuration for running training on smdistributed Model Parallel
										__lowerCAmelCase       					=    {
										    "enabled": True,
										    "processes_per_host": 8,
										}
										__lowerCAmelCase       					=    {
										    "enabled": True,
										    "parameters": {
										        "microbatches": 4,
										        "placement_strategy": "spread",
										        "pipeline": "interleaved",
										        "optimize": "speed",
										        "partitions": 4,
										        "ddp": True,
										    },
										}

										__lowerCAmelCase       					=    {"smdistributed": {"modelparallel": smp_options}, "mpi": mpi_options}

										__lowerCAmelCase       					=    "trainer" if self.script == "run_glue.py" else "smtrainer"
										# creates estimator
										return HuggingFace(
										    entry_point=self.script					,     source_dir=self.env.test_path					,     role=self.env.role					,     image_uri=self.env.image_uri					,     base_job_name=f"{self.env.base_job_name}-{instance_count}-smp-{name_extension}"					,     instance_count=__a					,     instance_type=self.instance_type					,     debugger_hook_config=__a					,     hyperparameters={
										        **self.env.hyperparameters,
										        "model_name_or_path": self.model_name_or_path,
										        "max_steps": 5_00,
										    }					,     metric_definitions=self.env.metric_definitions					,     distribution=__a					,     py_version="py36"					,     )

							def     snake_case       (  self					,     __a			):
										TrainingJobAnalytics(__a			).export_csv(f"{self.env.test_path}/{job_name}_metrics.csv"			)



							@parameterized.expand([(1,)]			)
							def     snake_case       (  self					,     __a			):
										# create estimator
										__lowerCAmelCase       					=    self.create_estimator(__a			)

										# run training
										estimator.fit()

										# result dataframe
										__lowerCAmelCase       					=    TrainingJobAnalytics(estimator.latest_training_job.name			).dataframe()

										# extract kpis
										__lowerCAmelCase       					=    list(result_metrics_df[result_metrics_df.metric_name == "eval_accuracy"]["value"]			)
										__lowerCAmelCase       					=    list(result_metrics_df[result_metrics_df.metric_name == "eval_loss"]["value"]			)
										# get train time from SageMaker job, this includes starting, preprocessing, stopping
										__lowerCAmelCase       					=    (
										    Session().describe_training_job(estimator.latest_training_job.name			).get("TrainingTimeInSeconds"					,     99_99_99			)
										)

										# assert kpis
										assert train_runtime <= self.results["train_runtime"]
										assert all(t >= self.results["eval_accuracy"] for t in eval_accuracy			)
										assert all(t <= self.results["eval_loss"] for t in eval_loss			)

										# dump tests result into json file to share in PR
										with open(f"{estimator.latest_training_job.name}.json"					,     "w"			) as outfile:
													json.dump({"train_time": train_runtime, "eval_accuracy": eval_accuracy, "eval_loss": eval_loss}					,     __a			)




