





"""simple docstring"""





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__					(					):




		"""simple docstring"""


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


def       a__					(					_SCREAMING_SNAKE_CASE    ):




		"""simple docstring"""


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


def       a__					(					):




		"""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     snake_case_   (cls		) ->   Union[str, Any]:
								# Write Accelerate config, will pick up on CPU, GPU, and multi-GPU
								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     snake_case_   (cls		) ->   int:
								shutil.rmtree(cls.tmpdir		)


						@mock.patch.dict(os.environ   ,							{"WANDB_MODE": "offline"}		)
						def     snake_case_   (self		) ->   int:
								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     snake_case_   (self		) ->   Tuple:
								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     snake_case_   (self		) ->   Optional[Any]:
								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     snake_case_   (self		) ->   Tuple:
								# with so little data distributed training needs more epochs to get the score on par with 0/1 gpu
								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     snake_case_   (self		) ->   Optional[int]:
								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     snake_case_   (self		) ->   Dict:
								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     snake_case_   (self		) ->   Any:
								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     snake_case_   (self		) ->   Any:
								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     snake_case_   (self		) ->   List[str]:
								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     snake_case_   (self		) ->   int:
								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"		)		)		)













"""simple docstring"""





lowerCAmelCase__     			=			'''
# Transformers installation
! pip install transformers datasets
# To install from source instead of the last release, comment the command above and uncomment the following one.
# ! pip install git+https://github.com/huggingface/transformers.git
'''

lowerCAmelCase__     			=			[{'''type''': '''code''', '''content''': INSTALL_CONTENT}]
lowerCAmelCase__     			=			{
    '''{processor_class}''': '''FakeProcessorClass''',
    '''{model_class}''': '''FakeModelClass''',
    '''{object_class}''': '''FakeObjectClass''',
}






