



"""simple docstring"""







# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works
from ...utils import deprecate
from ..controlnet.multicontrolnet import MultiControlNetModel  # noqa: F401
from ..controlnet.pipeline_controlnet import StableDiffusionControlNetPipeline  # noqa: F401


deprecate(
    """stable diffusion controlnet""",
    """0.22.0""",
    """Importing `StableDiffusionControlNetPipeline` or `MultiControlNetModel` from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_controlnet is deprecated. Please import `from diffusers import StableDiffusionControlNetPipeline` instead.""",
    standard_warn=False,
    stacklevel=3,
)






import argparse
import json
import logging
import os
import shutil
import sys
import tempfile
import unittest
from unittest import mock

import torch
from accelerate.utils import write_basic_config

from transformers.testing_utils import TestCasePlus, get_gpu_count, run_command, slow, torch_device
from transformers.utils import is_apex_available


logging.basicConfig(level=logging.DEBUG)

lowerCamelCase__						=				logging.getLogger()


def 				__A()		->					str:

		"""simple docstring"""






		_UpperCamelCase         =						argparse.ArgumentParser()
		parser.add_argument("""-f"""			)
		_UpperCamelCase         =						parser.parse_args()
		return args.f


def 				__A(lowerCAmelCase			)		->					Any:

		"""simple docstring"""






		_UpperCamelCase         =						{}
		_UpperCamelCase         =						os.path.join(lowerCAmelCase    ,	"""all_results.json"""			)
		if os.path.exists(lowerCAmelCase			):
				with open(lowerCAmelCase    ,	"""r"""			) as f:
						_UpperCamelCase         =						json.load(lowerCAmelCase			)
		else:
				raise ValueError(F'can\'t find {path}'			)
		return results


def 				__A()		->					Tuple:

		"""simple docstring"""






		_UpperCamelCase         =						torch.cuda.is_available() and torch_device == """cuda"""
		return is_using_cuda and is_apex_available()


lowerCamelCase__						=				logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)







class 							lowerCAmelCase__   ( __lowercase						):

						@classmethod
						def 			A_					(      cls					)       ->   Tuple:


								'''simple docstring'''

								_UpperCamelCase         =						tempfile.mkdtemp()
								_UpperCamelCase         =						os.path.join(cls.tmpdir							,				"""default_config.yml"""					)
								write_basic_config(save_location=cls.configPath					)
								_UpperCamelCase         =						["""accelerate""", """launch""", """--config_file""", cls.configPath]

						@classmethod
						def 			A_					(      cls					)       ->   str:


								'''simple docstring'''

								shutil.rmtree(cls.tmpdir					)

						@mock.patch.dict(os.environ							,				{"""WANDB_MODE""": """offline"""}					)
						def 			A_					(      self					)       ->   Any:


								'''simple docstring'''

								_UpperCamelCase         =						self.get_auto_remove_tmp_dir()
								_UpperCamelCase         =						F'\n            {self.examples_dir}/pytorch/text-classification/run_glue_no_trainer.py\n            --model_name_or_path distilbert-base-uncased\n            --output_dir {tmp_dir}\n            --train_file ./tests/fixtures/tests_samples/MRPC/train.csv\n            --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --learning_rate=1e-4\n            --seed=42\n            --checkpointing_steps epoch\n            --with_tracking\n        '.split()

								if is_cuda_and_apex_available():
										testargs.append("""--fp16"""					)

								run_command(self._launch_args + testargs					)
								_UpperCamelCase         =						get_results(a					)
								self.assertGreaterEqual(result["""eval_accuracy"""]							,				0.75					)
								self.assertTrue(os.path.exists(os.path.join(a							,				"""epoch_0"""					)					)					)
								self.assertTrue(os.path.exists(os.path.join(a							,				"""glue_no_trainer"""					)					)					)

						@mock.patch.dict(os.environ							,				{"""WANDB_MODE""": """offline"""}					)
						def 			A_					(      self					)       ->   List[Any]:


								'''simple docstring'''

								_UpperCamelCase         =						self.get_auto_remove_tmp_dir()
								_UpperCamelCase         =						F'\n            {self.examples_dir}/pytorch/language-modeling/run_clm_no_trainer.py\n            --model_name_or_path distilgpt2\n            --train_file ./tests/fixtures/sample_text.txt\n            --validation_file ./tests/fixtures/sample_text.txt\n            --block_size 128\n            --per_device_train_batch_size 5\n            --per_device_eval_batch_size 5\n            --num_train_epochs 2\n            --output_dir {tmp_dir}\n            --checkpointing_steps epoch\n            --with_tracking\n        '.split()

								if torch.cuda.device_count() > 1:
										# Skipping because there are not enough batches to train the model + would need a drop_last to work.
										return

								run_command(self._launch_args + testargs					)
								_UpperCamelCase         =						get_results(a					)
								self.assertLess(result["""perplexity"""]							,				1_00					)
								self.assertTrue(os.path.exists(os.path.join(a							,				"""epoch_0"""					)					)					)
								self.assertTrue(os.path.exists(os.path.join(a							,				"""clm_no_trainer"""					)					)					)

						@mock.patch.dict(os.environ							,				{"""WANDB_MODE""": """offline"""}					)
						def 			A_					(      self					)       ->   Tuple:


								'''simple docstring'''

								_UpperCamelCase         =						self.get_auto_remove_tmp_dir()
								_UpperCamelCase         =						F'\n            {self.examples_dir}/pytorch/language-modeling/run_mlm_no_trainer.py\n            --model_name_or_path distilroberta-base\n            --train_file ./tests/fixtures/sample_text.txt\n            --validation_file ./tests/fixtures/sample_text.txt\n            --output_dir {tmp_dir}\n            --num_train_epochs=1\n            --checkpointing_steps epoch\n            --with_tracking\n        '.split()

								run_command(self._launch_args + testargs					)
								_UpperCamelCase         =						get_results(a					)
								self.assertLess(result["""perplexity"""]							,				42					)
								self.assertTrue(os.path.exists(os.path.join(a							,				"""epoch_0"""					)					)					)
								self.assertTrue(os.path.exists(os.path.join(a							,				"""mlm_no_trainer"""					)					)					)

						@mock.patch.dict(os.environ							,				{"""WANDB_MODE""": """offline"""}					)
						def 			A_					(      self					)       ->   Dict:


								'''simple docstring'''

								_UpperCamelCase         =						7 if get_gpu_count() > 1 else 2

								_UpperCamelCase         =						self.get_auto_remove_tmp_dir()
								_UpperCamelCase         =						F'\n            {self.examples_dir}/pytorch/token-classification/run_ner_no_trainer.py\n            --model_name_or_path bert-base-uncased\n            --train_file tests/fixtures/tests_samples/conll/sample.json\n            --validation_file tests/fixtures/tests_samples/conll/sample.json\n            --output_dir {tmp_dir}\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=2\n            --num_train_epochs={epochs}\n            --seed 7\n            --checkpointing_steps epoch\n            --with_tracking\n        '.split()

								run_command(self._launch_args + testargs					)
								_UpperCamelCase         =						get_results(a					)
								self.assertGreaterEqual(result["""eval_accuracy"""]							,				0.75					)
								self.assertLess(result["""train_loss"""]							,				0.5					)
								self.assertTrue(os.path.exists(os.path.join(a							,				"""epoch_0"""					)					)					)
								self.assertTrue(os.path.exists(os.path.join(a							,				"""ner_no_trainer"""					)					)					)

						@unittest.skip(reason="""Fix me @muellerzr"""					)
						@mock.patch.dict(os.environ							,				{"""WANDB_MODE""": """offline"""}					)
						def 			A_					(      self					)       ->   Dict:


								'''simple docstring'''

								_UpperCamelCase         =						self.get_auto_remove_tmp_dir()
								_UpperCamelCase         =						F'\n            {self.examples_dir}/pytorch/question-answering/run_qa_no_trainer.py\n            --model_name_or_path bert-base-uncased\n            --version_2_with_negative\n            --train_file tests/fixtures/tests_samples/SQUAD/sample.json\n            --validation_file tests/fixtures/tests_samples/SQUAD/sample.json\n            --output_dir {tmp_dir}\n            --seed=42\n            --max_train_steps=10\n            --num_warmup_steps=2\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --checkpointing_steps epoch\n            --with_tracking\n        '.split()

								run_command(self._launch_args + testargs					)
								_UpperCamelCase         =						get_results(a					)
								# Because we use --version_2_with_negative the testing script uses SQuAD v2 metrics.
								self.assertGreaterEqual(result["""eval_f1"""]							,				28					)
								self.assertGreaterEqual(result["""eval_exact"""]							,				28					)
								self.assertTrue(os.path.exists(os.path.join(a							,				"""epoch_0"""					)					)					)
								self.assertTrue(os.path.exists(os.path.join(a							,				"""qa_no_trainer"""					)					)					)

						@mock.patch.dict(os.environ							,				{"""WANDB_MODE""": """offline"""}					)
						def 			A_					(      self					)       ->   Dict:


								'''simple docstring'''

								_UpperCamelCase         =						self.get_auto_remove_tmp_dir()
								_UpperCamelCase         =						F'\n            {self.examples_dir}/pytorch/multiple-choice/run_swag_no_trainer.py\n            --model_name_or_path bert-base-uncased\n            --train_file tests/fixtures/tests_samples/swag/sample.json\n            --validation_file tests/fixtures/tests_samples/swag/sample.json\n            --output_dir {tmp_dir}\n            --max_train_steps=20\n            --num_warmup_steps=2\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --with_tracking\n        '.split()

								run_command(self._launch_args + testargs					)
								_UpperCamelCase         =						get_results(a					)
								self.assertGreaterEqual(result["""eval_accuracy"""]							,				0.8					)
								self.assertTrue(os.path.exists(os.path.join(a							,				"""swag_no_trainer"""					)					)					)

						@slow
						@mock.patch.dict(os.environ							,				{"""WANDB_MODE""": """offline"""}					)
						def 			A_					(      self					)       ->   Optional[Any]:


								'''simple docstring'''

								_UpperCamelCase         =						self.get_auto_remove_tmp_dir()
								_UpperCamelCase         =						F'\n            {self.examples_dir}/pytorch/summarization/run_summarization_no_trainer.py\n            --model_name_or_path t5-small\n            --train_file tests/fixtures/tests_samples/xsum/sample.json\n            --validation_file tests/fixtures/tests_samples/xsum/sample.json\n            --output_dir {tmp_dir}\n            --max_train_steps=50\n            --num_warmup_steps=8\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --checkpointing_steps epoch\n            --with_tracking\n        '.split()

								run_command(self._launch_args + testargs					)
								_UpperCamelCase         =						get_results(a					)
								self.assertGreaterEqual(result["""eval_rouge1"""]							,				10					)
								self.assertGreaterEqual(result["""eval_rouge2"""]							,				2					)
								self.assertGreaterEqual(result["""eval_rougeL"""]							,				7					)
								self.assertGreaterEqual(result["""eval_rougeLsum"""]							,				7					)
								self.assertTrue(os.path.exists(os.path.join(a							,				"""epoch_0"""					)					)					)
								self.assertTrue(os.path.exists(os.path.join(a							,				"""summarization_no_trainer"""					)					)					)

						@slow
						@mock.patch.dict(os.environ							,				{"""WANDB_MODE""": """offline"""}					)
						def 			A_					(      self					)       ->   Optional[Any]:


								'''simple docstring'''

								_UpperCamelCase         =						self.get_auto_remove_tmp_dir()
								_UpperCamelCase         =						F'\n            {self.examples_dir}/pytorch/translation/run_translation_no_trainer.py\n            --model_name_or_path sshleifer/student_marian_en_ro_6_1\n            --source_lang en\n            --target_lang ro\n            --train_file tests/fixtures/tests_samples/wmt16/sample.json\n            --validation_file tests/fixtures/tests_samples/wmt16/sample.json\n            --output_dir {tmp_dir}\n            --max_train_steps=50\n            --num_warmup_steps=8\n            --num_beams=6\n            --learning_rate=3e-3\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --source_lang en_XX\n            --target_lang ro_RO\n            --checkpointing_steps epoch\n            --with_tracking\n        '.split()

								run_command(self._launch_args + testargs					)
								_UpperCamelCase         =						get_results(a					)
								self.assertGreaterEqual(result["""eval_bleu"""]							,				30					)
								self.assertTrue(os.path.exists(os.path.join(a							,				"""epoch_0"""					)					)					)
								self.assertTrue(os.path.exists(os.path.join(a							,				"""translation_no_trainer"""					)					)					)

						@slow
						def 			A_					(      self					)       ->   Optional[Any]:


								'''simple docstring'''

								_UpperCamelCase         =						logging.StreamHandler(sys.stdout					)
								logger.addHandler(a					)

								_UpperCamelCase         =						self.get_auto_remove_tmp_dir()
								_UpperCamelCase         =						F'\n            {self.examples_dir}/pytorch/semantic-segmentation/run_semantic_segmentation_no_trainer.py\n            --dataset_name huggingface/semantic-segmentation-test-sample\n            --output_dir {tmp_dir}\n            --max_train_steps=10\n            --num_warmup_steps=2\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --checkpointing_steps epoch\n        '.split()

								run_command(self._launch_args + testargs					)
								_UpperCamelCase         =						get_results(a					)
								self.assertGreaterEqual(result["""eval_overall_accuracy"""]							,				0.10					)







						@mock.patch.dict(os.environ							,				{"""WANDB_MODE""": """offline"""}					)
						def 			A_					(      self					)       ->   List[str]:


								'''simple docstring'''

								_UpperCamelCase         =						self.get_auto_remove_tmp_dir()
								_UpperCamelCase         =						F'\n            {self.examples_dir}/pytorch/image-classification/run_image_classification_no_trainer.py\n            --model_name_or_path google/vit-base-patch16-224-in21k\n            --dataset_name hf-internal-testing/cats_vs_dogs_sample\n            --learning_rate 1e-4\n            --per_device_train_batch_size 2\n            --per_device_eval_batch_size 1\n            --max_train_steps 2\n            --train_val_split 0.1\n            --seed 42\n            --output_dir {tmp_dir}\n            --with_tracking\n            --checkpointing_steps 1\n        '.split()

								if is_cuda_and_apex_available():
										testargs.append("""--fp16"""					)

								run_command(self._launch_args + testargs					)
								_UpperCamelCase         =						get_results(a					)
								# The base model scores a 25%
								self.assertGreaterEqual(result["""eval_accuracy"""]							,				0.6					)
								self.assertTrue(os.path.exists(os.path.join(a							,				"""step_1"""					)					)					)
								self.assertTrue(os.path.exists(os.path.join(a							,				"""image_classification_no_trainer"""					)					)					)



