"""simple docstring"""





import gc
import unittest

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

from diffusers import AltDiffusionPipeline, AutoencoderKL, DDIMScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.alt_diffusion.modeling_roberta_series import (
    RobertaSeriesConfig,
    RobertaSeriesModelWithTransformation,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()




class  _snake_case		(    __snake_case   ,       __snake_case   ,       __snake_case   ,       unittest.TestCase ):







				"""simple docstring"""






				a			=						AltDiffusionPipeline
				a			=						TEXT_TO_IMAGE_PARAMS
				a			=						TEXT_TO_IMAGE_BATCH_PARAMS
				a			=						TEXT_TO_IMAGE_IMAGE_PARAMS
				a			=						TEXT_TO_IMAGE_IMAGE_PARAMS





				def 		_lowerCAmelCase		(      self							:							int):



											"""simple docstring"""







											torch.manual_seed(0)
											_SCREAMING_SNAKE_CASE		:						int			  =    UNetaDConditionModel(
											    block_out_channels=(3_2, 6_4)      ,    layers_per_block=2      ,    sample_size=3_2      ,    in_channels=4      ,    out_channels=4      ,    down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")      ,    up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")      ,    cross_attention_dim=3_2      ,    )
											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    DDIMScheduler(
											    beta_start=0.00_085      ,    beta_end=0.012      ,    beta_schedule="""scaled_linear"""      ,    clip_sample=_A      ,    set_alpha_to_one=_A      ,    )
											torch.manual_seed(0)
											_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    AutoencoderKL(
											    block_out_channels=[3_2, 6_4]      ,    in_channels=3      ,    out_channels=3      ,    down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]      ,    up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]      ,    latent_channels=4      ,    )

											# TODO: address the non-deterministic text encoder (fails for save-load tests)
											# torch.manual_seed(0)
											# text_encoder_config = RobertaSeriesConfig(
											#     hidden_size=32,
											#     project_dim=32,
											#     intermediate_size=37,
											#     layer_norm_eps=1e-05,
											#     num_attention_heads=4,
											#     num_hidden_layers=5,
											#     vocab_size=5002,
											# )
											# text_encoder = RobertaSeriesModelWithTransformation(text_encoder_config)

											torch.manual_seed(0)
											_SCREAMING_SNAKE_CASE		:						List[Any]			  =    CLIPTextConfig(
											    bos_token_id=0      ,    eos_token_id=2      ,    hidden_size=3_2      ,    projection_dim=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=5_0_0_2      ,    )
											_SCREAMING_SNAKE_CASE		:						Dict			  =    CLIPTextModel(_A)

											_SCREAMING_SNAKE_CASE		:						Any			  =    XLMRobertaTokenizer.from_pretrained("""hf-internal-testing/tiny-xlm-roberta""")
											_SCREAMING_SNAKE_CASE		:						List[str]			  =    7_7

											_SCREAMING_SNAKE_CASE		:						List[Any]			  =    {
											    """unet""": unet,
											    """scheduler""": scheduler,
											    """vae""": vae,
											    """text_encoder""": text_encoder,
											    """tokenizer""": tokenizer,
											    """safety_checker""": None,
											    """feature_extractor""": None,
											}
											return components





				def 		_lowerCAmelCase		(      self							:							Tuple      ,    _A							:							List[str]      ,    _A							:							str=0):



											"""simple docstring"""







											if str(_A).startswith("""mps"""):
																		_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    torch.manual_seed(_A)
											else:
																		_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    torch.Generator(device=_A).manual_seed(_A)
											_SCREAMING_SNAKE_CASE		:						List[Any]			  =    {
											    """prompt""": """A painting of a squirrel eating a burger""",
											    """generator""": generator,
											    """num_inference_steps""": 2,
											    """guidance_scale""": 6.0,
											    """output_type""": """numpy""",
											}
											return inputs





				def 		_lowerCAmelCase		(      self							:							List[str]):



											"""simple docstring"""







											super().test_attention_slicing_forward_pass(expected_max_diff=3e-3)





				def 		_lowerCAmelCase		(      self							:							Optional[int]):



											"""simple docstring"""







											super().test_inference_batch_single_identical(expected_max_diff=3e-3)





				def 		_lowerCAmelCase		(      self							:							Any):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Tuple			  =    """cpu"""  # ensure determinism for the device-dependent torch.Generator

											_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    self.get_dummy_components()
											torch.manual_seed(0)
											_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    RobertaSeriesConfig(
											    hidden_size=3_2      ,    project_dim=3_2      ,    intermediate_size=3_7      ,    layer_norm_eps=1e-05      ,    num_attention_heads=4      ,    num_hidden_layers=5      ,    vocab_size=5_0_0_2      ,    )
											# TODO: remove after fixing the non-deterministic text encoder
											_SCREAMING_SNAKE_CASE		:						Tuple			  =    RobertaSeriesModelWithTransformation(_A)
											_SCREAMING_SNAKE_CASE		:						str			  =    text_encoder

											_SCREAMING_SNAKE_CASE		:						str			  =    AltDiffusionPipeline(**_A)
											_SCREAMING_SNAKE_CASE		:						str			  =    alt_pipe.to(_A)
											alt_pipe.set_progress_bar_config(disable=_A)

											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    self.get_dummy_inputs(_A)
											_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    """A photo of an astronaut"""
											_SCREAMING_SNAKE_CASE		:						List[str]			  =    alt_pipe(**_A)
											_SCREAMING_SNAKE_CASE		:						str			  =    output.images
											_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    image[0, -3:, -3:, -1]

											assert image.shape == (1, 6_4, 6_4, 3)
											_SCREAMING_SNAKE_CASE		:						Tuple			  =    np.array(
											    [0.5_748_162, 0.60_447_145, 0.48_821_217, 0.50_100_636, 0.5_431_185, 0.45_763_683, 0.49_657_696, 0.48_132_733, 0.47_573_093])

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





				def 		_lowerCAmelCase		(      self							:							Any):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						int			  =    """cpu"""  # ensure determinism for the device-dependent torch.Generator

											_SCREAMING_SNAKE_CASE		:						Tuple			  =    self.get_dummy_components()
											_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    PNDMScheduler(skip_prk_steps=_A)
											torch.manual_seed(0)
											_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    RobertaSeriesConfig(
											    hidden_size=3_2      ,    project_dim=3_2      ,    intermediate_size=3_7      ,    layer_norm_eps=1e-05      ,    num_attention_heads=4      ,    num_hidden_layers=5      ,    vocab_size=5_0_0_2      ,    )
											# TODO: remove after fixing the non-deterministic text encoder
											_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    RobertaSeriesModelWithTransformation(_A)
											_SCREAMING_SNAKE_CASE		:						Tuple			  =    text_encoder
											_SCREAMING_SNAKE_CASE		:						Dict			  =    AltDiffusionPipeline(**_A)
											_SCREAMING_SNAKE_CASE		:						str			  =    alt_pipe.to(_A)
											alt_pipe.set_progress_bar_config(disable=_A)

											_SCREAMING_SNAKE_CASE		:						Dict			  =    self.get_dummy_inputs(_A)
											_SCREAMING_SNAKE_CASE		:						List[str]			  =    alt_pipe(**_A)
											_SCREAMING_SNAKE_CASE		:						List[Any]			  =    output.images
											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    image[0, -3:, -3:, -1]

											assert image.shape == (1, 6_4, 6_4, 3)
											_SCREAMING_SNAKE_CASE		:						List[Any]			  =    np.array(
											    [0.51_605_093, 0.5_707_241, 0.47_365_507, 0.50_578_886, 0.5_633_877, 0.4_642_503, 0.5_182_081, 0.48_763_484, 0.49_084_237])

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




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







				"""simple docstring"""






				def 		_lowerCAmelCase		(      self							:							Any):



											"""simple docstring"""







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





				def 		_lowerCAmelCase		(      self							:							str):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    AltDiffusionPipeline.from_pretrained("""BAAI/AltDiffusion"""      ,    safety_checker=_A)
											_SCREAMING_SNAKE_CASE		:						List[str]			  =    alt_pipe.to(_A)
											alt_pipe.set_progress_bar_config(disable=_A)

											_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    """A painting of a squirrel eating a burger"""
											_SCREAMING_SNAKE_CASE		:						List[str]			  =    torch.manual_seed(0)
											_SCREAMING_SNAKE_CASE		:						Any			  =    alt_pipe([prompt]      ,    generator=_A      ,    guidance_scale=6.0      ,    num_inference_steps=2_0      ,    output_type="""np""")

											_SCREAMING_SNAKE_CASE		:						Dict			  =    output.images

											_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    image[0, -3:, -3:, -1]

											assert image.shape == (1, 5_1_2, 5_1_2, 3)
											_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    np.array([0.1_010, 0.0_800, 0.0_794, 0.0_885, 0.0_843, 0.0_762, 0.0_769, 0.0_729, 0.0_586])

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





				def 		_lowerCAmelCase		(      self							:							Optional[Any]):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Dict			  =    DDIMScheduler.from_pretrained("""BAAI/AltDiffusion"""      ,    subfolder="""scheduler""")

											_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    AltDiffusionPipeline.from_pretrained("""BAAI/AltDiffusion"""      ,    scheduler=_A      ,    safety_checker=_A)
											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    alt_pipe.to(_A)
											alt_pipe.set_progress_bar_config(disable=_A)

											_SCREAMING_SNAKE_CASE		:						Dict			  =    """A painting of a squirrel eating a burger"""
											_SCREAMING_SNAKE_CASE		:						List[Any]			  =    torch.manual_seed(0)

											_SCREAMING_SNAKE_CASE		:						str			  =    alt_pipe([prompt]      ,    generator=_A      ,    num_inference_steps=2      ,    output_type="""numpy""")
											_SCREAMING_SNAKE_CASE		:						List[Any]			  =    output.images

											_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    image[0, -3:, -3:, -1]

											assert image.shape == (1, 5_1_2, 5_1_2, 3)
											_SCREAMING_SNAKE_CASE		:						int			  =    np.array([0.4_019, 0.4_052, 0.3_810, 0.4_119, 0.3_916, 0.3_982, 0.4_651, 0.4_195, 0.5_323])

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



"""simple docstring"""





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

import pytest
from parameterized import 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.py",
            "model_name_or_path": "distilbert-base-cased",
            "instance_type": "ml.g4dn.xlarge",
            "results": {"train_runtime": 6_50, "eval_accuracy": 0.6, "eval_loss": 0.9},
        },
        {
            "framework": "tensorflow",
            "script": "run_tf.py",
            "model_name_or_path": "distilbert-base-cased",
            "instance_type": "ml.g4dn.xlarge",
            "results": {"train_runtime": 6_00, "eval_accuracy": 0.3, "eval_loss": 0.9},
        },
    ] )
class  _snake_case		(    unittest.TestCase ):







				"""simple docstring"""






				def 		_lowerCAmelCase		(      self							:							Optional[int]):



											"""simple docstring"""







											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 		_lowerCAmelCase		(      self							:							Union[str, Any]      ,    _A							:							str=1):



											"""simple docstring"""







											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}-single"""      ,    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}      ,    metric_definitions=self.env.metric_definitions      ,    py_version="""py36"""      ,    )





				def 		_lowerCAmelCase		(      self							:							Union[str, Any]      ,    _A							:							Union[str, Any]):



											"""simple docstring"""







											TrainingJobAnalytics(_A).export_csv(f"""{self.env.test_path}/{job_name}_metrics.csv""")





				def 		_lowerCAmelCase		(      self							:							Any):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						str			  =    self.create_estimator()

											# run training
											estimator.fit()

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

											# extract kpis
											_SCREAMING_SNAKE_CASE		:						Any			  =    list(result_metrics_df[result_metrics_df.metric_name == """eval_accuracy"""]["""value"""])
											_SCREAMING_SNAKE_CASE		:						Tuple			  =    list(result_metrics_df[result_metrics_df.metric_name == """eval_loss"""]["""value"""])
											# get train time from SageMaker job, this includes starting, preprocessing, stopping
											_SCREAMING_SNAKE_CASE		:						int			  =    (
											    Session().describe_training_job(estimator.latest_training_job.name).get("""TrainingTimeInSeconds"""      ,    9_9_9_9_9_9)
											)

											# 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)


