
"""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 				lowercase__(				unittest.TestCase   ):
	'''simple docstring'''

	def   __lowerCAmelCase (						self    :List[str]					)							->			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=lowerCamelCase_		, )
			assert hasattr(self		, '''env'''					)





	def   __lowerCAmelCase (						self    :Optional[Any]		, lowerCamelCase_    :Any=1					)							->			Optional[int]:







			'''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=lowerCamelCase_		, instance_type=self.instance_type		, debugger_hook_config=lowerCamelCase_		, 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    :str		, lowerCamelCase_    :int					)							->			Optional[Any]:







			'''simple docstring'''




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







	def   __lowerCAmelCase (						self    :str					)							->			Dict:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Tuple				= self.create_estimator()

			# run training
			estimator.fit()

			# result dataframe
			SCREAMING_SNAKE_CASE       :     Optional[int]				= TrainingJobAnalytics(estimator.latest_training_job.name					).dataframe()

			# extract kpis
			SCREAMING_SNAKE_CASE       :     int				= list(result_metrics_df[result_metrics_df.metric_name == '''eval_accuracy''']['''value''']					)
			SCREAMING_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
			SCREAMING_SNAKE_CASE       :     Any				= (
			    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}		, lowerCamelCase_					)







"""simple docstring"""






import os
from typing import Any, Callable, Dict, List, Optional, Tuple, Union

import torch
from torch import nn

from ...models.controlnet import ControlNetModel, ControlNetOutput
from ...models.modeling_utils import ModelMixin
from ...utils import logging


lowerCamelCase__ :       List[Any]						     =			logging.get_logger(__name__)




class 				lowercase__(				_UpperCAmelCase   ):
	'''simple docstring'''

	def __init__(						self    :List[str]		, lowerCamelCase_    :Union[List[ControlNetModel], Tuple[ControlNetModel]]					)							->			Union[str, Any]:







			'''simple docstring'''




			super().__init__()
			SCREAMING_SNAKE_CASE       :     Optional[Any]				= nn.ModuleList(lowerCamelCase_					)





	def   __lowerCAmelCase (						self    :Any		, lowerCamelCase_    :torch.FloatTensor		, lowerCamelCase_    :Union[torch.Tensor, float, int]		, lowerCamelCase_    :torch.Tensor		, lowerCamelCase_    :List[torch.tensor]		, lowerCamelCase_    :List[float]		, lowerCamelCase_    :Optional[torch.Tensor] = None		, lowerCamelCase_    :Optional[torch.Tensor] = None		, lowerCamelCase_    :Optional[torch.Tensor] = None		, lowerCamelCase_    :Optional[Dict[str, Any]] = None		, lowerCamelCase_    :bool = False		, lowerCamelCase_    :bool = True		, )							->			Union[ControlNetOutput, Tuple]:







			'''simple docstring'''




			for i, (image, scale, controlnet) in enumerate(zip(lowerCamelCase_		, lowerCamelCase_		, self.nets					)					):
					SCREAMING_SNAKE_CASE,					SCREAMING_SNAKE_CASE       :     List[Any]				= controlnet(
					    lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_		, )

					# merge samples
					if i == 0:
							SCREAMING_SNAKE_CASE,					SCREAMING_SNAKE_CASE       :     int				= down_samples, mid_sample
					else:
							SCREAMING_SNAKE_CASE       :     Optional[int]				= [
							    samples_prev + samples_curr
							    for samples_prev, samples_curr in zip(lowerCamelCase_		, lowerCamelCase_					)
							]
							mid_block_res_sample += mid_sample

			return down_block_res_samples, mid_block_res_sample





	def   __lowerCAmelCase (						self    :Any		, lowerCamelCase_    :Union[str, os.PathLike]		, lowerCamelCase_    :bool = True		, lowerCamelCase_    :Callable = None		, lowerCamelCase_    :bool = False		, lowerCamelCase_    :Optional[str] = None		, )							->			Optional[int]:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Optional[Any]				= 0
			SCREAMING_SNAKE_CASE       :     Any				= save_directory
			for controlnet in self.nets:
					controlnet.save_pretrained(
					    lowerCamelCase_		, is_main_process=lowerCamelCase_		, save_function=lowerCamelCase_		, safe_serialization=lowerCamelCase_		, variant=lowerCamelCase_		, )

					idx += 1
					SCREAMING_SNAKE_CASE       :     Union[str, Any]				= model_path_to_save + f"_{idx}"







	@classmethod
	def   __lowerCAmelCase (						cls    :Dict		, lowerCamelCase_    :Optional[Union[str, os.PathLike]]		, **lowerCamelCase_    :Tuple					)							->			List[Any]:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     str				= 0
			SCREAMING_SNAKE_CASE       :     Optional[int]				= []

			# load controlnet and append to list until no controlnet directory exists anymore
			# first controlnet has to be saved under `./mydirectory/controlnet` to be compliant with `DiffusionPipeline.from_prertained`
			# second, third, ... controlnets have to be saved under `./mydirectory/controlnet_1`, `./mydirectory/controlnet_2`, ...
			SCREAMING_SNAKE_CASE       :     Dict				= pretrained_model_path
			while os.path.isdir(lowerCamelCase_					):
					SCREAMING_SNAKE_CASE       :     Tuple				= ControlNetModel.from_pretrained(lowerCamelCase_		, **lowerCamelCase_					)
					controlnets.append(lowerCamelCase_					)

					idx += 1
					SCREAMING_SNAKE_CASE       :     Union[str, Any]				= pretrained_model_path + f"_{idx}"

			logger.info(f"{len(lowerCamelCase_					)} controlnets loaded from {pretrained_model_path}."					)

			if len(lowerCamelCase_					) == 0:
					raise ValueError(
					    f"No ControlNets found under {os.path.dirname(lowerCamelCase_					)}. Expected at least {pretrained_model_path + '_0'}."					)

			return cls(lowerCamelCase_					)





