




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)

UpperCamelCase__         =      logging.getLogger()







def      _a				(						):






					"""simple docstring"""




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







def      _a				(						SCREAMING_SNAKE_CASE_							:    Dict					):






					"""simple docstring"""




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







def      _a				(						):






					"""simple docstring"""




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


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


class       a__   ( lowerCAmelCase_				):






						@classmethod
						def     __SCREAMING_SNAKE_CASE(       cls      ):




											"""simple docstring"""



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






						@classmethod
						def     __SCREAMING_SNAKE_CASE(       cls      ):




											"""simple docstring"""



											shutil.rmtree(cls.tmpdir      )






						@mock.patch.dict(os.environ		,      {"WANDB_MODE": "offline"}      )
						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											__lowerCAmelCase           =	self.get_auto_remove_tmp_dir()
											__lowerCAmelCase           =	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      )
											__lowerCAmelCase           =	get_results(__SCREAMING_SNAKE_CASE      )
											self.assertGreaterEqual(result["eval_accuracy"]		,      0.75      )
											self.assertTrue(os.path.exists(os.path.join(__SCREAMING_SNAKE_CASE		,      "epoch_0"      )      )      )
											self.assertTrue(os.path.exists(os.path.join(__SCREAMING_SNAKE_CASE		,      "glue_no_trainer"      )      )      )






						@mock.patch.dict(os.environ		,      {"WANDB_MODE": "offline"}      )
						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											__lowerCAmelCase           =	self.get_auto_remove_tmp_dir()
											__lowerCAmelCase           =	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      )
											__lowerCAmelCase           =	get_results(__SCREAMING_SNAKE_CASE      )
											self.assertLess(result["perplexity"]		,      1_0_0      )
											self.assertTrue(os.path.exists(os.path.join(__SCREAMING_SNAKE_CASE		,      "epoch_0"      )      )      )
											self.assertTrue(os.path.exists(os.path.join(__SCREAMING_SNAKE_CASE		,      "clm_no_trainer"      )      )      )






						@mock.patch.dict(os.environ		,      {"WANDB_MODE": "offline"}      )
						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											__lowerCAmelCase           =	self.get_auto_remove_tmp_dir()
											__lowerCAmelCase           =	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      )
											__lowerCAmelCase           =	get_results(__SCREAMING_SNAKE_CASE      )
											self.assertLess(result["perplexity"]		,      4_2      )
											self.assertTrue(os.path.exists(os.path.join(__SCREAMING_SNAKE_CASE		,      "epoch_0"      )      )      )
											self.assertTrue(os.path.exists(os.path.join(__SCREAMING_SNAKE_CASE		,      "mlm_no_trainer"      )      )      )






						@mock.patch.dict(os.environ		,      {"WANDB_MODE": "offline"}      )
						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											__lowerCAmelCase           =	7 if get_gpu_count() > 1 else 2

											__lowerCAmelCase           =	self.get_auto_remove_tmp_dir()
											__lowerCAmelCase           =	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      )
											__lowerCAmelCase           =	get_results(__SCREAMING_SNAKE_CASE      )
											self.assertGreaterEqual(result["eval_accuracy"]		,      0.75      )
											self.assertLess(result["train_loss"]		,      0.5      )
											self.assertTrue(os.path.exists(os.path.join(__SCREAMING_SNAKE_CASE		,      "epoch_0"      )      )      )
											self.assertTrue(os.path.exists(os.path.join(__SCREAMING_SNAKE_CASE		,      "ner_no_trainer"      )      )      )






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




											"""simple docstring"""



											__lowerCAmelCase           =	self.get_auto_remove_tmp_dir()
											__lowerCAmelCase           =	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      )
											__lowerCAmelCase           =	get_results(__SCREAMING_SNAKE_CASE      )
											# Because we use --version_2_with_negative the testing script uses SQuAD v2 metrics.
											self.assertGreaterEqual(result["eval_f1"]		,      2_8      )
											self.assertGreaterEqual(result["eval_exact"]		,      2_8      )
											self.assertTrue(os.path.exists(os.path.join(__SCREAMING_SNAKE_CASE		,      "epoch_0"      )      )      )
											self.assertTrue(os.path.exists(os.path.join(__SCREAMING_SNAKE_CASE		,      "qa_no_trainer"      )      )      )






						@mock.patch.dict(os.environ		,      {"WANDB_MODE": "offline"}      )
						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											__lowerCAmelCase           =	self.get_auto_remove_tmp_dir()
											__lowerCAmelCase           =	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      )
											__lowerCAmelCase           =	get_results(__SCREAMING_SNAKE_CASE      )
											self.assertGreaterEqual(result["eval_accuracy"]		,      0.8      )
											self.assertTrue(os.path.exists(os.path.join(__SCREAMING_SNAKE_CASE		,      "swag_no_trainer"      )      )      )






						@slow
						@mock.patch.dict(os.environ		,      {"WANDB_MODE": "offline"}      )
						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											__lowerCAmelCase           =	self.get_auto_remove_tmp_dir()
											__lowerCAmelCase           =	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      )
											__lowerCAmelCase           =	get_results(__SCREAMING_SNAKE_CASE      )
											self.assertGreaterEqual(result["eval_rouge1"]		,      1_0      )
											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(__SCREAMING_SNAKE_CASE		,      "epoch_0"      )      )      )
											self.assertTrue(os.path.exists(os.path.join(__SCREAMING_SNAKE_CASE		,      "summarization_no_trainer"      )      )      )






						@slow
						@mock.patch.dict(os.environ		,      {"WANDB_MODE": "offline"}      )
						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											__lowerCAmelCase           =	self.get_auto_remove_tmp_dir()
											__lowerCAmelCase           =	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      )
											__lowerCAmelCase           =	get_results(__SCREAMING_SNAKE_CASE      )
											self.assertGreaterEqual(result["eval_bleu"]		,      3_0      )
											self.assertTrue(os.path.exists(os.path.join(__SCREAMING_SNAKE_CASE		,      "epoch_0"      )      )      )
											self.assertTrue(os.path.exists(os.path.join(__SCREAMING_SNAKE_CASE		,      "translation_no_trainer"      )      )      )






						@slow
						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											__lowerCAmelCase           =	logging.StreamHandler(sys.stdout      )
											logger.addHandler(__SCREAMING_SNAKE_CASE      )

											__lowerCAmelCase           =	self.get_auto_remove_tmp_dir()
											__lowerCAmelCase           =	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      )
											__lowerCAmelCase           =	get_results(__SCREAMING_SNAKE_CASE      )
											self.assertGreaterEqual(result["eval_overall_accuracy"]		,      0.10      )






						@mock.patch.dict(os.environ		,      {"WANDB_MODE": "offline"}      )
						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											__lowerCAmelCase           =	self.get_auto_remove_tmp_dir()
											__lowerCAmelCase           =	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      )
											__lowerCAmelCase           =	get_results(__SCREAMING_SNAKE_CASE      )
											# The base model scores a 25%
											self.assertGreaterEqual(result["eval_accuracy"]		,      0.6      )
											self.assertTrue(os.path.exists(os.path.join(__SCREAMING_SNAKE_CASE		,      "step_1"      )      )      )
											self.assertTrue(os.path.exists(os.path.join(__SCREAMING_SNAKE_CASE		,      "image_classification_no_trainer"      )      )      )










import argparse

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt


if __name__ == "__main__":
					UpperCamelCase__         =      argparse.ArgumentParser()

					parser.add_argument(
					    """--checkpoint_path""", default=None, type=str, required=True, help="""Path to the checkpoint to convert."""
					)
					parser.add_argument(
					    """--original_config_file""",
					    type=str,
					    required=True,
					    help="""The YAML config file corresponding to the original architecture.""",
					)
					parser.add_argument(
					    """--num_in_channels""",
					    default=None,
					    type=int,
					    help="""The number of input channels. If `None` number of input channels will be automatically inferred.""",
					)
					parser.add_argument(
					    """--image_size""",
					    default=512,
					    type=int,
					    help=(
					        """The image size that the model was trained on. Use 512 for Stable Diffusion v1.X and Stable Siffusion v2"""
					        """ Base. Use 768 for Stable Diffusion v2."""
					    ),
					)
					parser.add_argument(
					    """--extract_ema""",
					    action="""store_true""",
					    help=(
					        """Only relevant for checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights"""
					        """ or not. Defaults to `False`. Add `--extract_ema` to extract the EMA weights. EMA weights usually yield"""
					        """ higher quality images for inference. Non-EMA weights are usually better to continue fine-tuning."""
					    ),
					)
					parser.add_argument(
					    """--upcast_attention""",
					    action="""store_true""",
					    help=(
					        """Whether the attention computation should always be upcasted. This is necessary when running stable"""
					        """ diffusion 2.1."""
					    ),
					)
					parser.add_argument(
					    """--from_safetensors""",
					    action="""store_true""",
					    help="""If `--checkpoint_path` is in `safetensors` format, load checkpoint with safetensors instead of PyTorch.""",
					)
					parser.add_argument(
					    """--to_safetensors""",
					    action="""store_true""",
					    help="""Whether to store pipeline in safetensors format or not.""",
					)
					parser.add_argument("""--dump_path""", default=None, type=str, required=True, help="""Path to the output model.""")
					parser.add_argument("""--device""", type=str, help="""Device to use (e.g. cpu, cuda:0, cuda:1, etc.)""")







					def      _a				(						SCREAMING_SNAKE_CASE_							:    List[Any]					):
										if string == "True":
															return True
										elif string == "False":
															return False
										else:
															raise ValueError(F"""could not parse string as bool {string}"""					)

					parser.add_argument(
					    """--use_linear_projection""", help="""Override for use linear projection""", required=False, type=parse_bool
					)

					parser.add_argument("""--cross_attention_dim""", help="""Override for cross attention_dim""", required=False, type=int)

					UpperCamelCase__         =      parser.parse_args()

					UpperCamelCase__         =      download_controlnet_from_original_ckpt(
					    checkpoint_path=args.checkpoint_path,
					    original_config_file=args.original_config_file,
					    image_size=args.image_size,
					    extract_ema=args.extract_ema,
					    num_in_channels=args.num_in_channels,
					    upcast_attention=args.upcast_attention,
					    from_safetensors=args.from_safetensors,
					    device=args.device,
					    use_linear_projection=args.use_linear_projection,
					    cross_attention_dim=args.cross_attention_dim,
					)


					controlnet.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)




