import sys
from typing import Tuple

import numpy as np
import torch
from PIL import Image
from torch import nn

from transformers.image_utils import PILImageResampling
from utils import img_tensorize






class 						A   :





							def __init__(      self				:							List[Any]     ,		__a				:							int     ,		__a				:							Optional[Any]=sys.maxsize	)							->	Union[str, Any]:
								__UpperCAmelCase							=			'''bilinear'''
								__UpperCAmelCase							=			max_size
								__UpperCAmelCase							=			short_edge_length






							def __call__(      self				:							List[Any]     ,		__a				:							Optional[Any]	)							->	int:
								__UpperCAmelCase							=			[]
								for img in imgs:
									__UpperCAmelCase				,			__UpperCAmelCase							=			img.shape[:2]
									# later: provide list and randomly choose index for resize
									__UpperCAmelCase							=			np.random.randint(self.short_edge_length[0]     ,		self.short_edge_length[1] + 1	)
									if size == 0:
										return img
									__UpperCAmelCase							=			size * 1.0 / min(__a     ,		__a	)
									if h < w:
										__UpperCAmelCase				,			__UpperCAmelCase							=			size, scale * w
									else:
										__UpperCAmelCase				,			__UpperCAmelCase							=			scale * h, size
									if max(__a     ,		__a	) > self.max_size:
										__UpperCAmelCase							=			self.max_size * 1.0 / max(__a     ,		__a	)
										__UpperCAmelCase							=			newh * scale
										__UpperCAmelCase							=			neww * scale
									__UpperCAmelCase							=			int(neww + 0.5	)
									__UpperCAmelCase							=			int(newh + 0.5	)

									if img.dtype == np.uinta:
										__UpperCAmelCase							=			Image.fromarray(__a	)
										__UpperCAmelCase							=			pil_image.resize((neww, newh)     ,		PILImageResampling.BILINEAR	)
										__UpperCAmelCase							=			np.asarray(__a	)
									else:
										__UpperCAmelCase							=			img.permute(2     ,		0     ,		1	).unsqueeze(0	)  # 3, 0, 1)  # hw(c) -> nchw
										__UpperCAmelCase							=			nn.functional.interpolate(
										    __a     ,		(newh, neww)     ,		mode=self.interp_method     ,		align_corners=__a	).squeeze(0	)
									img_augs.append(__a	)

								return img_augs






class 						A   :





							def __init__(      self				:							Union[str, Any]     ,		__a				:							Any	)							->	Optional[int]:
								__UpperCAmelCase							=			ResizeShortestEdge([cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST]     ,		cfg.INPUT.MAX_SIZE_TEST	)
								__UpperCAmelCase							=			cfg.INPUT.FORMAT
								__UpperCAmelCase							=			cfg.SIZE_DIVISIBILITY
								__UpperCAmelCase							=			cfg.PAD_VALUE
								__UpperCAmelCase							=			cfg.INPUT.MAX_SIZE_TEST
								__UpperCAmelCase							=			cfg.MODEL.DEVICE
								__UpperCAmelCase							=			torch.tensor(cfg.MODEL.PIXEL_STD	).to(self.device	).view(len(cfg.MODEL.PIXEL_STD	)     ,		1     ,		1	)
								__UpperCAmelCase							=			torch.tensor(cfg.MODEL.PIXEL_MEAN	).to(self.device	).view(len(cfg.MODEL.PIXEL_STD	)     ,		1     ,		1	)
								__UpperCAmelCase							=			lambda __a	: (x - self.pixel_mean) / self.pixel_std





							def       snake_case__						(      self				:							Optional[Any]     ,		__a				:							Optional[Any]	)							->	str:
								__UpperCAmelCase							=			tuple(max(__a	) for s in zip(*[img.shape for img in images]	)	)
								__UpperCAmelCase							=			[im.shape[-2:] for im in images]
								__UpperCAmelCase							=			[
								    nn.functional.pad(
								        __a     ,		[0, max_size[-1] - size[1], 0, max_size[-2] - size[0]]     ,		value=self.pad_value     ,		)
								    for size, im in zip(__a     ,		__a	)
								]

								return torch.stack(__a	), torch.tensor(__a	)






							def __call__(      self				:							Tuple     ,		__a				:							Optional[int]     ,		__a				:							Union[str, Any]=False	)							->	Union[str, Any]:
								with torch.no_grad():
									if not isinstance(__a     ,		__a	):
										__UpperCAmelCase							=			[images]
									if single_image:
										assert len(__a	) == 1
									for i in range(len(__a	)	):
										if isinstance(images[i]     ,		torch.Tensor	):
											images.insert(__a     ,		images.pop(__a	).to(self.device	).float()	)
										elif not isinstance(images[i]     ,		torch.Tensor	):
											images.insert(
											    __a     ,		torch.as_tensor(img_tensorize(images.pop(__a	)     ,		input_format=self.input_format	)	)
											    .to(self.device	)
											    .float()     ,		)
            # resize smallest edge
									__UpperCAmelCase							=			torch.tensor([im.shape[:2] for im in images]	)
									__UpperCAmelCase							=			self.aug(__a	)
									# transpose images and convert to torch tensors
									# images = [torch.as_tensor(i.astype("float32")).permute(2, 0, 1).to(self.device) for i in images]
									# now normalize before pad to avoid useless arithmetic
									__UpperCAmelCase							=			[self.normalizer(__a	) for x in images]
									# now pad them to do the following operations
									__UpperCAmelCase				,			__UpperCAmelCase							=			self.pad(__a	)
									# Normalize

									if self.size_divisibility > 0:
										raise NotImplementedError()
									# pad
									__UpperCAmelCase							=			torch.true_divide(__a     ,		__a	)
									if single_image:
										return images[0], sizes[0], scales_yx[0]
									else:
										return images, sizes, scales_yx





def       lowerCAmelCase					(		UpperCamelCase__				:   Optional[Any]    ,					UpperCamelCase__				:   Any       ):


	"""simple docstring"""
	boxes[:, 0::2] *= scale_yx[:, 1]
	boxes[:, 1::2] *= scale_yx[:, 0]
	return boxes





def       lowerCAmelCase					(		UpperCamelCase__				:   Union[str, Any]    ,					UpperCamelCase__				:   Tuple[int, int]       ):


	"""simple docstring"""
	assert torch.isfinite(UpperCamelCase__       ).all(), "Box tensor contains infinite or NaN!"
	__UpperCAmelCase				,			__UpperCAmelCase							=			box_size
	tensor[:, 0].clamp_(min=0    ,					max=UpperCamelCase__       )
	tensor[:, 1].clamp_(min=0    ,					max=UpperCamelCase__       )
	tensor[:, 2].clamp_(min=0    ,					max=UpperCamelCase__       )
	tensor[:, 3].clamp_(min=0    ,					max=UpperCamelCase__       )

'''simple docstring'''


import ast
import os
import re
import shutil
import tempfile
import unittest
from unittest import mock

import torch

from accelerate.test_utils.examples import compare_against_test
from accelerate.test_utils.testing import TempDirTestCase, require_trackers, run_command, slow
from accelerate.utils import write_basic_config


# DataLoaders built from `test_samples/MRPC` for quick testing
# Should mock `{script_name}.get_dataloaders` via:
# @mock.patch("{script_name}.get_dataloaders", mocked_dataloaders)

__lowerCAmelCase	:       Optional[int]						       =	[
    "cross_validation.py",
    "gradient_accumulation.py",
    "local_sgd.py",
    "multi_process_metrics.py",
    "memory.py",
    "automatic_gradient_accumulation.py",
    "fsdp_with_peak_mem_tracking.py",
    "deepspeed_with_config_support.py",
    "megatron_lm_gpt_pretraining.py",
]






class 						A   (   unittest.TestCase   ):





							def       snake_case__						(      self				:							Any     ,		__a				:							str     ,		__a				:							bool     ,		__a				:							str = None     ,		__a				:							list = None	)							->	Tuple:
								__UpperCAmelCase							=			None
								__UpperCAmelCase							=			os.path.abspath(os.path.join('''examples'''     ,		'''by_feature'''	)	)
								__UpperCAmelCase							=			os.path.abspath('''examples'''	)
								for item in os.listdir(__a	):
									if item not in EXCLUDE_EXAMPLES:
										__UpperCAmelCase							=			os.path.join(__a     ,		__a	)
										if os.path.isfile(__a	) and ".py" in item_path:
											with self.subTest(
											    tested_script=__a     ,		feature_script=__a     ,		tested_section='''main()''' if parser_only else '''training_function()'''     ,		):
												__UpperCAmelCase							=			compare_against_test(
												    os.path.join(__a     ,		__a	)     ,		__a     ,		__a     ,		__a	)
												__UpperCAmelCase							=			'''\n'''.join(__a	)
												if special_strings is not None:
													for string in special_strings:
														__UpperCAmelCase							=			diff.replace(__a     ,		''''''	)
												self.assertEqual(__a     ,		''''''	)





							def       snake_case__						(      self				:							Optional[Any]	)							->	str:
								self.one_complete_example('''complete_nlp_example.py'''     ,		__a	)
								self.one_complete_example('''complete_nlp_example.py'''     ,		__a	)






							def       snake_case__						(      self				:							List[str]	)							->	Tuple:
								__UpperCAmelCase							=			os.path.abspath(os.path.join('''examples'''     ,		'''cv_example.py'''	)	)
								__UpperCAmelCase							=			[
								    ''' ''' * 1_6 + '''{\n\n''',
								    ''' ''' * 2_0 + '''"accuracy": eval_metric["accuracy"],\n\n''',
								    ''' ''' * 2_0 + '''"f1": eval_metric["f1"],\n\n''',
								    ''' ''' * 2_0 + '''"train_loss": total_loss.item() / len(train_dataloader),\n\n''',
								    ''' ''' * 2_0 + '''"epoch": epoch,\n\n''',
								    ''' ''' * 1_6 + '''},\n\n''',
								    ''' ''' * 1_6 + '''step=epoch,\n''',
								    ''' ''' * 1_2,
								    ''' ''' * 8 + '''for step, batch in enumerate(active_dataloader):\n''',
								]
								self.one_complete_example('''complete_cv_example.py'''     ,		__a     ,		__a     ,		__a	)
								self.one_complete_example('''complete_cv_example.py'''     ,		__a     ,		__a     ,		__a	)






@mock.patch.dict(os.environ       ,	{'''TESTING_MOCKED_DATALOADERS''': '''1'''}   )
class 						A   (   UpperCAmelCase   ):
							a_        =      False





							@classmethod
							def       snake_case__						(      cls				:							Tuple	)							->	str:
								super().setUpClass()
								__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				:							Dict	)							->	int:
								super().tearDownClass()
								shutil.rmtree(cls._tmpdir	)





							def       snake_case__						(      self				:							Tuple	)							->	Dict:
								__UpperCAmelCase							=			f"""
        examples/by_feature/checkpointing.py
        --checkpointing_steps epoch
        --output_dir {self.tmpdir}
        """.split()
								run_command(self._launch_args + testargs	)
								self.assertTrue(os.path.exists(os.path.join(self.tmpdir     ,		'''epoch_0'''	)	)	)





							def       snake_case__						(      self				:							str	)							->	int:
								__UpperCAmelCase							=			f"""
        examples/by_feature/checkpointing.py
        --checkpointing_steps 1
        --output_dir {self.tmpdir}
        """.split()
								__UpperCAmelCase							=			run_command(self._launch_args + testargs	)
								self.assertTrue(os.path.exists(os.path.join(self.tmpdir     ,		'''step_2'''	)	)	)





							def       snake_case__						(      self				:							Any	)							->	Any:
								__UpperCAmelCase							=			f"""
        examples/by_feature/checkpointing.py
        --resume_from_checkpoint {os.path.join(self.tmpdir     ,		'epoch_0'	)}
        """.split()
								__UpperCAmelCase							=			run_command(self._launch_args + testargs     ,		return_stdout=__a	)
								self.assertNotIn('''epoch 0:'''     ,		__a	)
								self.assertIn('''epoch 1:'''     ,		__a	)





							def       snake_case__						(      self				:							Tuple	)							->	Optional[int]:
								__UpperCAmelCase							=			f"""
        examples/by_feature/checkpointing.py
        --resume_from_checkpoint {os.path.join(self.tmpdir     ,		'step_2'	)}
        """.split()
								__UpperCAmelCase							=			run_command(self._launch_args + testargs     ,		return_stdout=__a	)
								if torch.cuda.is_available():
									__UpperCAmelCase							=			torch.cuda.device_count()
								else:
									__UpperCAmelCase							=			1
								if num_processes > 1:
									self.assertNotIn('''epoch 0:'''     ,		__a	)
									self.assertIn('''epoch 1:'''     ,		__a	)
								else:
									self.assertIn('''epoch 0:'''     ,		__a	)
									self.assertIn('''epoch 1:'''     ,		__a	)





							@slow
							def       snake_case__						(      self				:							Any	)							->	Optional[Any]:
								__UpperCAmelCase							=			'''
        examples/by_feature/cross_validation.py
        --num_folds 2
        '''.split()
								with mock.patch.dict(os.environ     ,		{'''TESTING_MOCKED_DATALOADERS''': '''0'''}	):
									__UpperCAmelCase							=			run_command(self._launch_args + testargs     ,		return_stdout=__a	)
									__UpperCAmelCase							=			re.findall('''({.+})'''     ,		__a	)
									__UpperCAmelCase							=			[r for r in results if '''accuracy''' in r][-1]
									__UpperCAmelCase							=			ast.literal_eval(__a	)
									self.assertGreaterEqual(results['''accuracy''']     ,		0.7_5	)





							def       snake_case__						(      self				:							Dict	)							->	int:
								__UpperCAmelCase							=			['''examples/by_feature/multi_process_metrics.py''']
								run_command(self._launch_args + testargs	)





							@require_trackers
							@mock.patch.dict(os.environ     ,		{'''WANDB_MODE''': '''offline'''}	)
							def       snake_case__						(      self				:							Optional[Any]	)							->	Optional[int]:
								with tempfile.TemporaryDirectory() as tmpdir:
									__UpperCAmelCase							=			f"""
            examples/by_feature/tracking.py
            --with_tracking
            --project_dir {tmpdir}
            """.split()
									run_command(self._launch_args + testargs	)
									self.assertTrue(os.path.exists(os.path.join(__a     ,		'''tracking'''	)	)	)





							def       snake_case__						(      self				:							Optional[int]	)							->	List[Any]:
								__UpperCAmelCase							=			['''examples/by_feature/gradient_accumulation.py''']
								run_command(self._launch_args + testargs	)






							def       snake_case__						(      self				:							Tuple	)							->	Optional[Any]:
								__UpperCAmelCase							=			['''examples/by_feature/local_sgd.py''']
								run_command(self._launch_args + testargs	)
