


'''simple docstring'''
import gc
import random
import unittest

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

from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableDiffusionUpscalePipeline, UNetaDConditionModel
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


enable_full_determinism()
class    lowercase	(							unittest.TestCase					):

		"""simple docstring"""

		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


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




		@property
		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							UpperCamelCase__   :str												= 1
							UpperCamelCase__   :Tuple												= 3
							UpperCamelCase__   :Union[str, Any]												= (32, 32)

							UpperCamelCase__   :Optional[int]												= floats_tensor((batch_size, num_channels) + sizes    ,   rng=random.Random(0							)							).to(UpperCamelCase_							)
							return image




		@property
		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							torch.manual_seed(0							)
							UpperCamelCase__   :Dict												= UNetaDConditionModel(
							    block_out_channels=(32, 32, 64)    ,   layers_per_block=2    ,   sample_size=32    ,   in_channels=7    ,   out_channels=4    ,   down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''', '''CrossAttnDownBlock2D''')    ,   up_block_types=('''CrossAttnUpBlock2D''', '''CrossAttnUpBlock2D''', '''UpBlock2D''')    ,   cross_attention_dim=32    ,   attention_head_dim=8    ,   use_linear_projection=UpperCamelCase_    ,   only_cross_attention=(True, True, False)    ,   num_class_embeds=100    ,   )
							return model




		@property
		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							torch.manual_seed(0							)
							UpperCamelCase__   :int												= AutoencoderKL(
							    block_out_channels=[32, 32, 64]    ,   in_channels=3    ,   out_channels=3    ,   down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''', '''DownEncoderBlock2D''']    ,   up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''', '''UpDecoderBlock2D''']    ,   latent_channels=4    ,   )
							return model




		@property
		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							torch.manual_seed(0							)
							UpperCamelCase__   :Any												= 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    ,   hidden_act='''gelu'''    ,   projection_dim=512    ,   )
							return CLIPTextModel(UpperCamelCase_							)




		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							UpperCamelCase__   :List[str]												= '''cpu'''  # ensure determinism for the device-dependent torch.Generator
							UpperCamelCase__   :str												= self.dummy_cond_unet_upscale
							UpperCamelCase__   :Tuple												= DDPMScheduler()
							UpperCamelCase__   :List[Any]												= DDIMScheduler(prediction_type='''v_prediction'''							)
							UpperCamelCase__   :Union[str, Any]												= self.dummy_vae
							UpperCamelCase__   :Any												= self.dummy_text_encoder
							UpperCamelCase__   :Any												= CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''							)

							UpperCamelCase__   :Optional[Any]												= self.dummy_image.cpu().permute(0    ,   2    ,   3    ,   1							)[0]
							UpperCamelCase__   :Optional[int]												= Image.fromarray(np.uinta(UpperCamelCase_							)							).convert('''RGB'''							).resize((64, 64)							)

							# make sure here that pndm scheduler skips prk
							UpperCamelCase__   :Dict												= StableDiffusionUpscalePipeline(
							    unet=UpperCamelCase_    ,   low_res_scheduler=UpperCamelCase_    ,   scheduler=UpperCamelCase_    ,   vae=UpperCamelCase_    ,   text_encoder=UpperCamelCase_    ,   tokenizer=UpperCamelCase_    ,   max_noise_level=350    ,   )
							UpperCamelCase__   :str												= sd_pipe.to(UpperCamelCase_							)
							sd_pipe.set_progress_bar_config(disable=UpperCamelCase_							)

							UpperCamelCase__   :Optional[Any]												= '''A painting of a squirrel eating a burger'''
							UpperCamelCase__   :Union[str, Any]												= torch.Generator(device=UpperCamelCase_							).manual_seed(0							)
							UpperCamelCase__   :Optional[int]												= sd_pipe(
							    [prompt]    ,   image=UpperCamelCase_    ,   generator=UpperCamelCase_    ,   guidance_scale=6.0    ,   noise_level=20    ,   num_inference_steps=2    ,   output_type='''np'''    ,   )

							UpperCamelCase__   :int												= output.images

							UpperCamelCase__   :Union[str, Any]												= torch.Generator(device=UpperCamelCase_							).manual_seed(0							)
							UpperCamelCase__   :str												= sd_pipe(
							    [prompt]    ,   image=UpperCamelCase_    ,   generator=UpperCamelCase_    ,   guidance_scale=6.0    ,   noise_level=20    ,   num_inference_steps=2    ,   output_type='''np'''    ,   return_dict=UpperCamelCase_    ,   )[0]

							UpperCamelCase__   :List[Any]												= image[0, -3:, -3:, -1]
							UpperCamelCase__   :Dict												= image_from_tuple[0, -3:, -3:, -1]

							UpperCamelCase__   :Tuple												= low_res_image.size[0] * 4
							assert image.shape == (1, expected_height_width, expected_height_width, 3)
							UpperCamelCase__   :Tuple												= np.array([0.3113, 0.3910, 0.4272, 0.4859, 0.5061, 0.4652, 0.5362, 0.5715, 0.5661]							)

							assert np.abs(image_slice.flatten() - expected_slice							).max() < 1e-2
							assert np.abs(image_from_tuple_slice.flatten() - expected_slice							).max() < 1e-2




		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							UpperCamelCase__   :Dict												= '''cpu'''  # ensure determinism for the device-dependent torch.Generator
							UpperCamelCase__   :List[str]												= self.dummy_cond_unet_upscale
							UpperCamelCase__   :Tuple												= DDPMScheduler()
							UpperCamelCase__   :Optional[Any]												= DDIMScheduler(prediction_type='''v_prediction'''							)
							UpperCamelCase__   :Dict												= self.dummy_vae
							UpperCamelCase__   :Any												= self.dummy_text_encoder
							UpperCamelCase__   :Optional[Any]												= CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''							)

							UpperCamelCase__   :List[str]												= self.dummy_image.cpu().permute(0    ,   2    ,   3    ,   1							)[0]
							UpperCamelCase__   :Optional[Any]												= Image.fromarray(np.uinta(UpperCamelCase_							)							).convert('''RGB'''							).resize((64, 64)							)

							# make sure here that pndm scheduler skips prk
							UpperCamelCase__   :Dict												= StableDiffusionUpscalePipeline(
							    unet=UpperCamelCase_    ,   low_res_scheduler=UpperCamelCase_    ,   scheduler=UpperCamelCase_    ,   vae=UpperCamelCase_    ,   text_encoder=UpperCamelCase_    ,   tokenizer=UpperCamelCase_    ,   max_noise_level=350    ,   )
							UpperCamelCase__   :int												= sd_pipe.to(UpperCamelCase_							)
							sd_pipe.set_progress_bar_config(disable=UpperCamelCase_							)

							UpperCamelCase__   :int												= '''A painting of a squirrel eating a burger'''
							UpperCamelCase__   :int												= sd_pipe(
							    2 * [prompt]    ,   image=2 * [low_res_image]    ,   guidance_scale=6.0    ,   noise_level=20    ,   num_inference_steps=2    ,   output_type='''np'''    ,   )
							UpperCamelCase__   :List[str]												= output.images
							assert image.shape[0] == 2

							UpperCamelCase__   :List[Any]												= torch.Generator(device=UpperCamelCase_							).manual_seed(0							)
							UpperCamelCase__   :Union[str, Any]												= sd_pipe(
							    [prompt]    ,   image=UpperCamelCase_    ,   generator=UpperCamelCase_    ,   num_images_per_prompt=2    ,   guidance_scale=6.0    ,   noise_level=20    ,   num_inference_steps=2    ,   output_type='''np'''    ,   )
							UpperCamelCase__   :List[Any]												= output.images
							assert image.shape[0] == 2




		@unittest.skipIf(torch_device != '''cuda'''    ,   '''This test requires a GPU'''							)
		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							UpperCamelCase__   :List[Any]												= self.dummy_cond_unet_upscale
							UpperCamelCase__   :List[Any]												= DDPMScheduler()
							UpperCamelCase__   :Optional[Any]												= DDIMScheduler(prediction_type='''v_prediction'''							)
							UpperCamelCase__   :Optional[int]												= self.dummy_vae
							UpperCamelCase__   :Union[str, Any]												= self.dummy_text_encoder
							UpperCamelCase__   :List[str]												= CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''							)

							UpperCamelCase__   :Union[str, Any]												= self.dummy_image.cpu().permute(0    ,   2    ,   3    ,   1							)[0]
							UpperCamelCase__   :Optional[Any]												= Image.fromarray(np.uinta(UpperCamelCase_							)							).convert('''RGB'''							).resize((64, 64)							)

							# put models in fp16, except vae as it overflows in fp16
							UpperCamelCase__   :Optional[int]												= unet.half()
							UpperCamelCase__   :List[str]												= text_encoder.half()

							# make sure here that pndm scheduler skips prk
							UpperCamelCase__   :Any												= StableDiffusionUpscalePipeline(
							    unet=UpperCamelCase_    ,   low_res_scheduler=UpperCamelCase_    ,   scheduler=UpperCamelCase_    ,   vae=UpperCamelCase_    ,   text_encoder=UpperCamelCase_    ,   tokenizer=UpperCamelCase_    ,   max_noise_level=350    ,   )
							UpperCamelCase__   :Any												= sd_pipe.to(UpperCamelCase_							)
							sd_pipe.set_progress_bar_config(disable=UpperCamelCase_							)

							UpperCamelCase__   :Any												= '''A painting of a squirrel eating a burger'''
							UpperCamelCase__   :Any												= torch.manual_seed(0							)
							UpperCamelCase__   :Dict												= sd_pipe(
							    [prompt]    ,   image=UpperCamelCase_    ,   generator=UpperCamelCase_    ,   num_inference_steps=2    ,   output_type='''np'''    ,   ).images

							UpperCamelCase__   :Tuple												= low_res_image.size[0] * 4
							assert image.shape == (1, expected_height_width, expected_height_width, 3)



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

		"""simple docstring"""

		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


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




		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							UpperCamelCase__   :Dict												= load_image(
							    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
							    '''/sd2-upscale/low_res_cat.png'''							)
							UpperCamelCase__   :Any												= load_numpy(
							    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale'''
							    '''/upsampled_cat.npy'''							)

							UpperCamelCase__   :List[Any]												= '''stabilityai/stable-diffusion-x4-upscaler'''
							UpperCamelCase__   :Any												= StableDiffusionUpscalePipeline.from_pretrained(UpperCamelCase_							)
							pipe.to(UpperCamelCase_							)
							pipe.set_progress_bar_config(disable=UpperCamelCase_							)
							pipe.enable_attention_slicing()

							UpperCamelCase__   :Optional[Any]												= '''a cat sitting on a park bench'''

							UpperCamelCase__   :Optional[int]												= torch.manual_seed(0							)
							UpperCamelCase__   :List[str]												= pipe(
							    prompt=UpperCamelCase_    ,   image=UpperCamelCase_    ,   generator=UpperCamelCase_    ,   output_type='''np'''    ,   )
							UpperCamelCase__   :Any												= output.images[0]

							assert image.shape == (512, 512, 3)
							assert np.abs(expected_image - image							).max() < 1e-3




		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							UpperCamelCase__   :List[Any]												= load_image(
							    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
							    '''/sd2-upscale/low_res_cat.png'''							)
							UpperCamelCase__   :Optional[Any]												= load_numpy(
							    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale'''
							    '''/upsampled_cat_fp16.npy'''							)

							UpperCamelCase__   :Tuple												= '''stabilityai/stable-diffusion-x4-upscaler'''
							UpperCamelCase__   :int												= StableDiffusionUpscalePipeline.from_pretrained(
							    UpperCamelCase_    ,   torch_dtype=torch.floataa    ,   )
							pipe.to(UpperCamelCase_							)
							pipe.set_progress_bar_config(disable=UpperCamelCase_							)
							pipe.enable_attention_slicing()

							UpperCamelCase__   :int												= '''a cat sitting on a park bench'''

							UpperCamelCase__   :int												= torch.manual_seed(0							)
							UpperCamelCase__   :int												= pipe(
							    prompt=UpperCamelCase_    ,   image=UpperCamelCase_    ,   generator=UpperCamelCase_    ,   output_type='''np'''    ,   )
							UpperCamelCase__   :Dict												= output.images[0]

							assert image.shape == (512, 512, 3)
							assert np.abs(expected_image - image							).max() < 5e-1




		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							torch.cuda.empty_cache()
							torch.cuda.reset_max_memory_allocated()
							torch.cuda.reset_peak_memory_stats()

							UpperCamelCase__   :Optional[int]												= load_image(
							    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
							    '''/sd2-upscale/low_res_cat.png'''							)

							UpperCamelCase__   :Union[str, Any]												= '''stabilityai/stable-diffusion-x4-upscaler'''
							UpperCamelCase__   :List[Any]												= StableDiffusionUpscalePipeline.from_pretrained(
							    UpperCamelCase_    ,   torch_dtype=torch.floataa    ,   )
							pipe.to(UpperCamelCase_							)
							pipe.set_progress_bar_config(disable=UpperCamelCase_							)
							pipe.enable_attention_slicing(1							)
							pipe.enable_sequential_cpu_offload()

							UpperCamelCase__   :List[Any]												= '''a cat sitting on a park bench'''

							UpperCamelCase__   :Any												= torch.manual_seed(0							)
							UpperCamelCase__   :Union[str, Any]												= pipe(
							    prompt=UpperCamelCase_    ,   image=UpperCamelCase_    ,   generator=UpperCamelCase_    ,   num_inference_steps=5    ,   output_type='''np'''    ,   )

							UpperCamelCase__   :Optional[Any]												= torch.cuda.max_memory_allocated()
							# make sure that less than 2.9 GB is allocated
							assert mem_bytes < 2.9 * 10**9




"""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 				lowerCamelCase  (			unittest.TestCase				):






					'''simple docstring'''





					def    lowerCAmelCase_				(       self:							Any						)   ->	str:
											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=snake_case						,     )
											assert hasattr(self						,     """env"""						)






					def    lowerCAmelCase_				(       self:							int						,     snake_case:							Dict						)   ->	List[Any]:
											# configuration for running training on smdistributed Model Parallel
											snake_case_ :Tuple						=			{
											    """enabled""": True,
											    """processes_per_host""": 8,
											}
											snake_case_ :List[Any]						=			{
											    """enabled""": True,
											    """parameters""": {
											        """microbatches""": 4,
											        """placement_strategy""": """spread""",
											        """pipeline""": """interleaved""",
											        """optimize""": """speed""",
											        """partitions""": 4,
											        """ddp""": True,
											    },
											}

											snake_case_ :Tuple						=			{"""smdistributed""": {"""modelparallel""": smp_options}, """mpi""": mpi_options}

											snake_case_ :Any						=			"""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=snake_case						,     instance_type=self.instance_type						,     debugger_hook_config=snake_case						,     hyperparameters={
											        **self.env.hyperparameters,
											        """model_name_or_path""": self.model_name_or_path,
											        """max_steps""": 500,
											    }						,     metric_definitions=self.env.metric_definitions						,     distribution=snake_case						,     py_version="""py36"""						,     )






					def    lowerCAmelCase_				(       self:							Any						,     snake_case:							Tuple						)   ->	List[str]:
											TrainingJobAnalytics(snake_case						).export_csv(f"""{self.env.test_path}/{job_name}_metrics.csv"""						)






					@parameterized.expand([(1,)]						)
					def    lowerCAmelCase_				(       self:							Dict						,     snake_case:							Dict						)   ->	List[Any]:
											# create estimator
											snake_case_ :List[Any]						=			self.create_estimator(snake_case						)

											# run training
											estimator.fit()

											# result dataframe
											snake_case_ :Any						=			TrainingJobAnalytics(estimator.latest_training_job.name						).dataframe()

											# extract kpis
											snake_case_ :Tuple						=			list(result_metrics_df[result_metrics_df.metric_name == """eval_accuracy"""]["""value"""]						)
											snake_case_ :Dict						=			list(result_metrics_df[result_metrics_df.metric_name == """eval_loss"""]["""value"""]						)
											# get train time from SageMaker job, this includes starting, preprocessing, stopping
											snake_case_ :int						=			(
											    Session().describe_training_job(estimator.latest_training_job.name						).get("""TrainingTimeInSeconds"""						,     999_999						)
											)

											# 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}						,     snake_case						)
