


'''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)

__UpperCAmelCase       							=				logging.getLogger()


def    _snake_case  (	)		->       int:
							lowerCAmelCase__			  =      argparse.ArgumentParser()
							parser.add_argument('''-f'''			)
							lowerCAmelCase__			  =      parser.parse_args()
							return args.f


def    _snake_case  (	A			)		->       Tuple:
							lowerCAmelCase__			  =      {}
							lowerCAmelCase__			  =      os.path.join(A					,       '''all_results.json'''			)
							if os.path.exists(A			):
														with open(A					,       '''r'''			) as f:
																					lowerCAmelCase__			  =      json.load(A			)
							else:
														raise ValueError(F"""can't find {path}"""			)
							return results


def    _snake_case  (	)		->       Any:
							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__							(				a__    ):





							'''simple docstring'''






							@classmethod
							def     __SCREAMING_SNAKE_CASE				(						cls  )			->				Any:
														# Write Accelerate config, will pick up on CPU, GPU, and multi-GPU
														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  )			->				Tuple:
														shutil.rmtree(cls.tmpdir  )







							@mock.patch.dict(os.environ   ,       {'''WANDB_MODE''': '''offline'''}  )
							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Tuple:
														lowerCAmelCase__			  =      self.get_auto_remove_tmp_dir()
														lowerCAmelCase__			  =      F"""
            {self.examples_dir}/pytorch/text-classification/run_glue_no_trainer.py
            --model_name_or_path distilbert-base-uncased
            --output_dir {tmp_dir}
            --train_file ./tests/fixtures/tests_samples/MRPC/train.csv
            --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --learning_rate=1e-4
            --seed=42
            --checkpointing_steps epoch
            --with_tracking
        """.split()

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

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







							@mock.patch.dict(os.environ   ,       {'''WANDB_MODE''': '''offline'''}  )
							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Tuple:
														lowerCAmelCase__			  =      self.get_auto_remove_tmp_dir()
														lowerCAmelCase__			  =      F"""
            {self.examples_dir}/pytorch/language-modeling/run_clm_no_trainer.py
            --model_name_or_path distilgpt2
            --train_file ./tests/fixtures/sample_text.txt
            --validation_file ./tests/fixtures/sample_text.txt
            --block_size 128
            --per_device_train_batch_size 5
            --per_device_eval_batch_size 5
            --num_train_epochs 2
            --output_dir {tmp_dir}
            --checkpointing_steps epoch
            --with_tracking
        """.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(lowerCamelCase_  )
														self.assertLess(result['''perplexity''']   ,       1_00  )
														self.assertTrue(os.path.exists(os.path.join(lowerCamelCase_   ,       '''epoch_0'''  )  )  )
														self.assertTrue(os.path.exists(os.path.join(lowerCamelCase_   ,       '''clm_no_trainer'''  )  )  )







							@mock.patch.dict(os.environ   ,       {'''WANDB_MODE''': '''offline'''}  )
							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Any:
														lowerCAmelCase__			  =      self.get_auto_remove_tmp_dir()
														lowerCAmelCase__			  =      F"""
            {self.examples_dir}/pytorch/language-modeling/run_mlm_no_trainer.py
            --model_name_or_path distilroberta-base
            --train_file ./tests/fixtures/sample_text.txt
            --validation_file ./tests/fixtures/sample_text.txt
            --output_dir {tmp_dir}
            --num_train_epochs=1
            --checkpointing_steps epoch
            --with_tracking
        """.split()

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







							@mock.patch.dict(os.environ   ,       {'''WANDB_MODE''': '''offline'''}  )
							def     __SCREAMING_SNAKE_CASE				(						self  )			->				List[str]:
														# with so little data distributed training needs more epochs to get the score on par with 0/1 gpu
														lowerCAmelCase__			  =      7 if get_gpu_count() > 1 else 2

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

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







							@unittest.skip(reason='''Fix me @muellerzr'''  )
							@mock.patch.dict(os.environ   ,       {'''WANDB_MODE''': '''offline'''}  )
							def     __SCREAMING_SNAKE_CASE				(						self  )			->				List[Any]:
														lowerCAmelCase__			  =      self.get_auto_remove_tmp_dir()
														lowerCAmelCase__			  =      F"""
            {self.examples_dir}/pytorch/question-answering/run_qa_no_trainer.py
            --model_name_or_path bert-base-uncased
            --version_2_with_negative
            --train_file tests/fixtures/tests_samples/SQUAD/sample.json
            --validation_file tests/fixtures/tests_samples/SQUAD/sample.json
            --output_dir {tmp_dir}
            --seed=42
            --max_train_steps=10
            --num_warmup_steps=2
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --checkpointing_steps epoch
            --with_tracking
        """.split()

														run_command(self._launch_args + testargs  )
														lowerCAmelCase__			  =      get_results(lowerCamelCase_  )
														# 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(lowerCamelCase_   ,       '''epoch_0'''  )  )  )
														self.assertTrue(os.path.exists(os.path.join(lowerCamelCase_   ,       '''qa_no_trainer'''  )  )  )







							@mock.patch.dict(os.environ   ,       {'''WANDB_MODE''': '''offline'''}  )
							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Optional[int]:
														lowerCAmelCase__			  =      self.get_auto_remove_tmp_dir()
														lowerCAmelCase__			  =      F"""
            {self.examples_dir}/pytorch/multiple-choice/run_swag_no_trainer.py
            --model_name_or_path bert-base-uncased
            --train_file tests/fixtures/tests_samples/swag/sample.json
            --validation_file tests/fixtures/tests_samples/swag/sample.json
            --output_dir {tmp_dir}
            --max_train_steps=20
            --num_warmup_steps=2
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --with_tracking
        """.split()

														run_command(self._launch_args + testargs  )
														lowerCAmelCase__			  =      get_results(lowerCamelCase_  )
														self.assertGreaterEqual(result['''eval_accuracy''']   ,       0.8  )
														self.assertTrue(os.path.exists(os.path.join(lowerCamelCase_   ,       '''swag_no_trainer'''  )  )  )







							@slow
							@mock.patch.dict(os.environ   ,       {'''WANDB_MODE''': '''offline'''}  )
							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Optional[Any]:
														lowerCAmelCase__			  =      self.get_auto_remove_tmp_dir()
														lowerCAmelCase__			  =      F"""
            {self.examples_dir}/pytorch/summarization/run_summarization_no_trainer.py
            --model_name_or_path t5-small
            --train_file tests/fixtures/tests_samples/xsum/sample.json
            --validation_file tests/fixtures/tests_samples/xsum/sample.json
            --output_dir {tmp_dir}
            --max_train_steps=50
            --num_warmup_steps=8
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --checkpointing_steps epoch
            --with_tracking
        """.split()

														run_command(self._launch_args + testargs  )
														lowerCAmelCase__			  =      get_results(lowerCamelCase_  )
														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(lowerCamelCase_   ,       '''epoch_0'''  )  )  )
														self.assertTrue(os.path.exists(os.path.join(lowerCamelCase_   ,       '''summarization_no_trainer'''  )  )  )







							@slow
							@mock.patch.dict(os.environ   ,       {'''WANDB_MODE''': '''offline'''}  )
							def     __SCREAMING_SNAKE_CASE				(						self  )			->				int:
														lowerCAmelCase__			  =      self.get_auto_remove_tmp_dir()
														lowerCAmelCase__			  =      F"""
            {self.examples_dir}/pytorch/translation/run_translation_no_trainer.py
            --model_name_or_path sshleifer/student_marian_en_ro_6_1
            --source_lang en
            --target_lang ro
            --train_file tests/fixtures/tests_samples/wmt16/sample.json
            --validation_file tests/fixtures/tests_samples/wmt16/sample.json
            --output_dir {tmp_dir}
            --max_train_steps=50
            --num_warmup_steps=8
            --num_beams=6
            --learning_rate=3e-3
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --source_lang en_XX
            --target_lang ro_RO
            --checkpointing_steps epoch
            --with_tracking
        """.split()

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







							@slow
							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Union[str, Any]:
														lowerCAmelCase__			  =      logging.StreamHandler(sys.stdout  )
														logger.addHandler(lowerCamelCase_  )

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

														run_command(self._launch_args + testargs  )
														lowerCAmelCase__			  =      get_results(lowerCamelCase_  )
														self.assertGreaterEqual(result['''eval_overall_accuracy''']   ,       0.10  )







							@mock.patch.dict(os.environ   ,       {'''WANDB_MODE''': '''offline'''}  )
							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Union[str, Any]:
														lowerCAmelCase__			  =      self.get_auto_remove_tmp_dir()
														lowerCAmelCase__			  =      F"""
            {self.examples_dir}/pytorch/image-classification/run_image_classification_no_trainer.py
            --model_name_or_path google/vit-base-patch16-224-in21k
            --dataset_name hf-internal-testing/cats_vs_dogs_sample
            --learning_rate 1e-4
            --per_device_train_batch_size 2
            --per_device_eval_batch_size 1
            --max_train_steps 2
            --train_val_split 0.1
            --seed 42
            --output_dir {tmp_dir}
            --with_tracking
            --checkpointing_steps 1
        """.split()

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

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



'''simple docstring'''






import sys

from .dependency_versions_table import deps
from .utils.versions import require_version, require_version_core


# define which module versions we always want to check at run time
# (usually the ones defined in `install_requires` in setup.py)
#
# order specific notes:
# - tqdm must be checked before tokenizers

__UpperCAmelCase       							=				'''python tqdm regex requests packaging filelock numpy tokenizers'''.split()
if sys.version_info < (3, 7):
		pkgs_to_check_at_runtime.append('''dataclasses''')
if sys.version_info < (3, 8):
		pkgs_to_check_at_runtime.append('''importlib_metadata''')

for pkg in pkgs_to_check_at_runtime:
		if pkg in deps:
				if pkg == "tokenizers":
						# must be loaded here, or else tqdm check may fail
						from .utils import is_tokenizers_available

						if not is_tokenizers_available():
								continue  # not required, check version only if installed

				require_version_core(deps[pkg])
		else:
				raise ValueError(f"""can't find {pkg} in {deps.keys()}, check dependency_versions_table.py""")


def    _snake_case  (	A					,       A=None			)		->       Optional[Any]:
							require_version(deps[pkg]					,       A			)