



import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
	import torch

if is_vision_available():
	from PIL import Image

	from transformers import PoolFormerImageProcessor


class 					_lowercase      (unittest.TestCase					):
				'''simple docstring'''







				def __init__(       self   ,       snake_case__   ,       snake_case__=7   ,       snake_case__=3   ,       snake_case__=30   ,       snake_case__=400   ,       snake_case__=True   ,       snake_case__=None   ,       snake_case__=0.9   ,       snake_case__=None   ,       snake_case__=True   ,       snake_case__=[0.5, 0.5, 0.5]   ,       snake_case__=[0.5, 0.5, 0.5]   ,       ):

									'''simple docstring'''


									UpperCamelCase_						     =      size if size is not None else {"shortest_edge": 30}
									UpperCamelCase_						     =      crop_size if crop_size is not None else {"height": 30, "width": 30}
									UpperCamelCase_						     =      parent
									UpperCamelCase_						     =      batch_size
									UpperCamelCase_						     =      num_channels
									UpperCamelCase_						     =      min_resolution
									UpperCamelCase_						     =      max_resolution
									UpperCamelCase_						     =      do_resize_and_center_crop
									UpperCamelCase_						     =      size
									UpperCamelCase_						     =      crop_pct
									UpperCamelCase_						     =      crop_size
									UpperCamelCase_						     =      do_normalize
									UpperCamelCase_						     =      image_mean
									UpperCamelCase_						     =      image_std

				def 					_lowerCamelCase    (       self			):

									'''simple docstring'''


									return {
									    "size": self.size,
									    "do_resize_and_center_crop": self.do_resize_and_center_crop,
									    "crop_pct": self.crop_pct,
									    "crop_size": self.crop_size,
									    "do_normalize": self.do_normalize,
									    "image_mean": self.image_mean,
									    "image_std": self.image_std,
									}




@require_torch
@require_vision
class 					_lowercase      (__lowercase					,      unittest.TestCase					):
				'''simple docstring'''







				lowercase__        =  PoolFormerImageProcessor if is_vision_available() else None

				def 					_lowerCamelCase    (       self			):

									'''simple docstring'''


									UpperCamelCase_						     =      PoolFormerImageProcessingTester(self			)

				@property
				def 					_lowerCamelCase    (       self			):

									'''simple docstring'''


									return self.image_processor_tester.prepare_image_processor_dict()

				def 					_lowerCamelCase    (       self			):

									'''simple docstring'''


									UpperCamelCase_						     =      self.image_processing_class(**self.image_processor_dict			)
									self.assertTrue(hasattr(snake_case__   ,       "do_resize_and_center_crop"			)			)
									self.assertTrue(hasattr(snake_case__   ,       "size"			)			)
									self.assertTrue(hasattr(snake_case__   ,       "crop_pct"			)			)
									self.assertTrue(hasattr(snake_case__   ,       "do_normalize"			)			)
									self.assertTrue(hasattr(snake_case__   ,       "image_mean"			)			)
									self.assertTrue(hasattr(snake_case__   ,       "image_std"			)			)

				def 					_lowerCamelCase    (       self			):

									'''simple docstring'''


									UpperCamelCase_						     =      self.image_processing_class.from_dict(self.image_processor_dict			)
									self.assertEqual(image_processor.size   ,       {"shortest_edge": 30}			)
									self.assertEqual(image_processor.crop_size   ,       {"height": 30, "width": 30}			)

									UpperCamelCase_						     =      self.image_processing_class.from_dict(self.image_processor_dict   ,       size=42   ,       crop_size=84			)
									self.assertEqual(image_processor.size   ,       {"shortest_edge": 42}			)
									self.assertEqual(image_processor.crop_size   ,       {"height": 84, "width": 84}			)

				def 					_lowerCamelCase    (       self			):

									'''simple docstring'''


									pass

				def 					_lowerCamelCase    (       self			):

									'''simple docstring'''


									UpperCamelCase_						     =      self.image_processing_class(**self.image_processor_dict			)
									# create random PIL images
									UpperCamelCase_						     =      prepare_image_inputs(self.image_processor_tester   ,       equal_resolution=snake_case__			)
									for image in image_inputs:
														self.assertIsInstance(snake_case__   ,       Image.Image			)

									# Test not batched input
									UpperCamelCase_						     =      image_processing(image_inputs[0]   ,       return_tensors="pt"			).pixel_values

									self.assertEqual(
									    encoded_images.shape   ,       (
									        1,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size["height"],
									        self.image_processor_tester.crop_size["width"],
									    )   ,       )

									# Test batched
									UpperCamelCase_						     =      image_processing(snake_case__   ,       return_tensors="pt"			).pixel_values
									self.assertEqual(
									    encoded_images.shape   ,       (
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size["height"],
									        self.image_processor_tester.crop_size["width"],
									    )   ,       )

				def 					_lowerCamelCase    (       self			):

									'''simple docstring'''


									UpperCamelCase_						     =      self.image_processing_class(**self.image_processor_dict			)
									# create random numpy tensors
									UpperCamelCase_						     =      prepare_image_inputs(self.image_processor_tester   ,       equal_resolution=snake_case__   ,       numpify=snake_case__			)
									for image in image_inputs:
														self.assertIsInstance(snake_case__   ,       np.ndarray			)

									# Test not batched input
									UpperCamelCase_						     =      image_processing(image_inputs[0]   ,       return_tensors="pt"			).pixel_values
									self.assertEqual(
									    encoded_images.shape   ,       (
									        1,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size["height"],
									        self.image_processor_tester.crop_size["width"],
									    )   ,       )

									# Test batched
									UpperCamelCase_						     =      image_processing(snake_case__   ,       return_tensors="pt"			).pixel_values
									self.assertEqual(
									    encoded_images.shape   ,       (
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size["height"],
									        self.image_processor_tester.crop_size["width"],
									    )   ,       )

				def 					_lowerCamelCase    (       self			):

									'''simple docstring'''


									UpperCamelCase_						     =      self.image_processing_class(**self.image_processor_dict			)
									# create random PyTorch tensors
									UpperCamelCase_						     =      prepare_image_inputs(self.image_processor_tester   ,       equal_resolution=snake_case__   ,       torchify=snake_case__			)
									for image in image_inputs:
														self.assertIsInstance(snake_case__   ,       torch.Tensor			)

									# Test not batched input
									UpperCamelCase_						     =      image_processing(image_inputs[0]   ,       return_tensors="pt"			).pixel_values
									self.assertEqual(
									    encoded_images.shape   ,       (
									        1,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size["height"],
									        self.image_processor_tester.crop_size["width"],
									    )   ,       )

									# Test batched
									UpperCamelCase_						     =      image_processing(snake_case__   ,       return_tensors="pt"			).pixel_values
									self.assertEqual(
									    encoded_images.shape   ,       (
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size["height"],
									        self.image_processor_tester.crop_size["width"],
									    )   ,       )


import unittest
from dataclasses import dataclass

import pytest

from accelerate.commands.config.config_args import SageMakerConfig
from accelerate.utils import ComputeEnvironment
from accelerate.utils.launch import _convert_nargs_to_dict




@dataclass
class  lowerCamelCase__							(				__lowercase):




	'''simple docstring'''




	_A								=     ComputeEnvironment.AMAZON_SAGEMAKER
	_A								=     True
	_A								=     'ml.p3.2xlarge'
	_A								=     'accelerate_sagemaker_execution_role'
	_A								=     'hf-sm'
	_A								=     'us-east-1'
	_A								=     1
	_A								=     'accelerate-sagemaker-1'
	_A								=     '1.6'
	_A								=     '4.4'
	_A								=     'train.py'
	_A								=     [
	    '--model_name_or_path',
	    'bert',
	    '--do_train',
	    'False',
	    '--epochs',
	    '3',
	    '--learning_rate',
	    '5e-5',
	    '--max_steps',
	    '50.5',
	]
	_A								=     [
	    '--model_name_or_path',
	    'bert',
	    '--do_train',
	    '--do_test',
	    'False',
	    '--do_predict',
	    '--epochs',
	    '3',
	    '--learning_rate',
	    '5e-5',
	    '--max_steps',
	    '50.5',
	]




class  lowerCamelCase__							(				unittest.TestCase):




	'''simple docstring'''




	def    _lowerCamelCase				(					self		:Any						)		-> Optional[int]:
								# If no defaults are changed, `to_kwargs` returns an empty dict.
								__UpperCamelCase	:   Optional[int]			   =					_convert_nargs_to_dict(MockLaunchConfig.success_training_script_args						)
								assert isinstance(converted_args["model_name_or_path"]     ,   a						)
								assert isinstance(converted_args["do_train"]     ,   a						)
								assert isinstance(converted_args["epochs"]     ,   a						)
								assert isinstance(converted_args["learning_rate"]     ,   a						)
								assert isinstance(converted_args["max_steps"]     ,   a						)

								with pytest.raises(a						):
															_convert_nargs_to_dict(MockLaunchConfig.fail_training_script_args						)