





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:	Union[str, Any] = logging.getLogger()


def       SCREAMING_SNAKE_CASE__  (       )   ->							Union[str, Any]:




		'''simple docstring'''
		lowercase__					:      Optional[int]      =		argparse.ArgumentParser()
		parser.add_argument('-f'			)
		lowercase__					:      int      =		parser.parse_args()
		return args.f


def       SCREAMING_SNAKE_CASE__  (       _lowercase     : List[str]			)   ->							Optional[Any]:




		'''simple docstring'''
		lowercase__					:      Union[str, Any]      =		{}
		lowercase__					:      Dict      =		os.path.join(_lowercase   ,    'all_results.json'			)
		if os.path.exists(_lowercase			):
				with open(_lowercase   ,    'r'			) as f:
						lowercase__					:      Union[str, Any]      =		json.load(_lowercase			)
		else:
				raise ValueError(f"""can't find {path}"""			)
		return results


def       SCREAMING_SNAKE_CASE__  (       )   ->							Tuple:




		'''simple docstring'''
		lowercase__					:      Optional[Any]      =		torch.cuda.is_available() and torch_device == 'cuda'
		return is_using_cuda and is_apex_available()


__UpperCamelCase:	List[Any] = logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)





class       __lowerCAmelCase					(       _UpperCamelCase      ):


	'''simple docstring'''





	@classmethod
	def    snake_case__(						cls:						str			):
			# Write Accelerate config, will pick up on CPU, GPU, and multi-GPU
			lowercase__					:      Optional[int]      =		tempfile.mkdtemp()
			lowercase__					:      List[Any]      =		os.path.join(cls.tmpdir,					'default_config.yml'			)
			write_basic_config(save_location=cls.configPath			)
			lowercase__					:      Optional[int]      =		['accelerate', 'launch', '--config_file', cls.configPath]

	@classmethod
	def    snake_case__(						cls:						Any			):
			shutil.rmtree(cls.tmpdir			)

	@mock.patch.dict(os.environ,					{'WANDB_MODE': 'offline'}			)
	def    snake_case__(						self:						Dict			):
			lowercase__					:      Optional[int]      =		self.get_auto_remove_tmp_dir()
			lowercase__					:      List[str]      =		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			)
			lowercase__					:      int      =		get_results(lowerCamelCase_			)
			self.assertGreaterEqual(result['eval_accuracy'],					0.7_5			)
			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    snake_case__(						self:						Optional[int]			):
			lowercase__					:      str      =		self.get_auto_remove_tmp_dir()
			lowercase__					:      Union[str, Any]      =		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			)
			lowercase__					:      int      =		get_results(lowerCamelCase_			)
			self.assertLess(result['perplexity'],					100			)
			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    snake_case__(						self:						str			):
			lowercase__					:      Tuple      =		self.get_auto_remove_tmp_dir()
			lowercase__					:      Dict      =		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			)
			lowercase__					:      Union[str, Any]      =		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    snake_case__(						self:						Optional[int]			):
			# with so little data distributed training needs more epochs to get the score on par with 0/1 gpu
			lowercase__					:      str      =		7 if get_gpu_count() > 1 else 2

			lowercase__					:      Dict      =		self.get_auto_remove_tmp_dir()
			lowercase__					:      Tuple      =		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			)
			lowercase__					:      Union[str, Any]      =		get_results(lowerCamelCase_			)
			self.assertGreaterEqual(result['eval_accuracy'],					0.7_5			)
			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    snake_case__(						self:						Optional[int]			):
			lowercase__					:      Optional[Any]      =		self.get_auto_remove_tmp_dir()
			lowercase__					:      Tuple      =		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			)
			lowercase__					:      int      =		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    snake_case__(						self:						Tuple			):
			lowercase__					:      Any      =		self.get_auto_remove_tmp_dir()
			lowercase__					:      int      =		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			)
			lowercase__					:      str      =		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    snake_case__(						self:						Union[str, Any]			):
			lowercase__					:      int      =		self.get_auto_remove_tmp_dir()
			lowercase__					:      int      =		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			)
			lowercase__					:      List[Any]      =		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    snake_case__(						self:						int			):
			lowercase__					:      Any      =		self.get_auto_remove_tmp_dir()
			lowercase__					:      int      =		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			)
			lowercase__					:      Dict      =		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    snake_case__(						self:						str			):
			lowercase__					:      Any      =		logging.StreamHandler(sys.stdout			)
			logger.addHandler(lowerCamelCase_			)

			lowercase__					:      Tuple      =		self.get_auto_remove_tmp_dir()
			lowercase__					:      List[str]      =		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			)
			lowercase__					:      str      =		get_results(lowerCamelCase_			)
			self.assertGreaterEqual(result['eval_overall_accuracy'],					0.1_0			)





	@mock.patch.dict(os.environ,					{'WANDB_MODE': 'offline'}			)
	def    snake_case__(						self:						str			):
			lowercase__					:      int      =		self.get_auto_remove_tmp_dir()
			lowercase__					:      Union[str, Any]      =		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			)
			lowercase__					:      Dict      =		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'			)			)			)









import argparse
import os
import re


__UpperCamelCase:	Any = """src/transformers/models/auto"""


# re pattern that matches mapping introductions:
#    SUPER_MODEL_MAPPING_NAMES = OrderedDict or SUPER_MODEL_MAPPING = OrderedDict
__UpperCamelCase:	Dict = re.compile(r"""[A-Z_]+_MAPPING(\s+|_[A-Z_]+\s+)=\s+OrderedDict""")
# re pattern that matches identifiers in mappings
__UpperCamelCase:	List[str] = re.compile(r"""\s*\(\s*\"(\S[^\"]+)\"""")


def       SCREAMING_SNAKE_CASE__  (       _lowercase     : int   ,    _lowercase     : bool = False			)   ->							List[str]:




		'''simple docstring'''
		with open(_lowercase   ,    'r'   ,    encoding='utf-8'			) as f:
				lowercase__					:      Union[str, Any]      =		f.read()

		lowercase__					:      Optional[Any]      =		content.split('\n'			)
		lowercase__					:      Optional[int]      =		[]
		lowercase__					:      int      =		0
		while line_idx < len(_lowercase			):
				if _re_intro_mapping.search(lines[line_idx]			) is not None:
						lowercase__					:      Tuple      =		len(re.search(r'^(\s*)\S'   ,    lines[line_idx]			).groups()[0]			) + 8
						# Start of a new mapping!
						while not lines[line_idx].startswith(' ' * indent + '('			):
								new_lines.append(lines[line_idx]			)
								line_idx += 1

						lowercase__					:      Tuple      =		[]
						while lines[line_idx].strip() != "]":
								# Blocks either fit in one line or not
								if lines[line_idx].strip() == "(":
										lowercase__					:      Any      =		line_idx
										while not lines[line_idx].startswith(' ' * indent + ')'			):
												line_idx += 1
										blocks.append('\n'.join(lines[start_idx : line_idx + 1]			)			)
								else:
										blocks.append(lines[line_idx]			)
								line_idx += 1

						# Sort blocks by their identifiers
						lowercase__					:      List[str]      =		sorted(_lowercase   ,    key=lambda _lowercase			: _re_identifier.search(_lowercase			).groups()[0]			)
						new_lines += blocks
				else:
						new_lines.append(lines[line_idx]			)
						line_idx += 1

		if overwrite:
				with open(_lowercase   ,    'w'   ,    encoding='utf-8'			) as f:
						f.write('\n'.join(_lowercase			)			)
		elif "\n".join(_lowercase			) != content:
				return True


def       SCREAMING_SNAKE_CASE__  (       _lowercase     : bool = False			)   ->							List[Any]:




		'''simple docstring'''
		lowercase__					:      List[Any]      =		[os.path.join(_lowercase   ,    _lowercase			) for f in os.listdir(_lowercase			) if f.endswith('.py'			)]
		lowercase__					:      str      =		[sort_auto_mapping(_lowercase   ,    overwrite=_lowercase			) for fname in fnames]

		if not overwrite and any(_lowercase			):
				lowercase__					:      List[Any]      =		[f for f, d in zip(_lowercase   ,    _lowercase			) if d]
				raise ValueError(
				    f"""The following files have auto mappings that need sorting: {", ".join(_lowercase			)}. Run `make style` to fix"""
				    ' this.'			)


if __name__ == "__main__":
			__UpperCamelCase:	Tuple = argparse.ArgumentParser()
			parser.add_argument("""--check_only""", action="""store_true""", help="""Whether to only check or fix style.""")
			__UpperCamelCase:	List[Any] = parser.parse_args()

			sort_all_auto_mappings(not args.check_only)


