





"""simple docstring"""




import unittest

import numpy as np
import requests

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

		from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
		a						: Optional[Any]     	=			False

if is_vision_available():
		from PIL import Image

		from transformers import PixaStructImageProcessor


class        __UpperCamelCase				(						unittest.TestCase						):






							def __init__(   self      ,					lowerCAmelCase__      ,					lowerCAmelCase__=7      ,					lowerCAmelCase__=3      ,					lowerCAmelCase__=18      ,					lowerCAmelCase__=30      ,					lowerCAmelCase__=400      ,					lowerCAmelCase__=None      ,					lowerCAmelCase__=True      ,					lowerCAmelCase__=True      ,					lowerCAmelCase__=None      ,					)	-> Tuple:
								a      :		Tuple	=				size if size is not None else {"height": 20, "width": 20}
								a      :		List[Any]	=				parent
								a      :		Optional[int]	=				batch_size
								a      :		List[Any]	=				num_channels
								a      :		Any	=				image_size
								a      :		Optional[int]	=				min_resolution
								a      :		Tuple	=				max_resolution
								a      :		Optional[int]	=				size
								a      :		List[str]	=				do_normalize
								a      :		List[str]	=				do_convert_rgb
								a      :		Optional[Any]	=				[512, 1024, 2048, 4096]
								a      :		List[str]	=				patch_size if patch_size is not None else {"height": 16, "width": 16}






							def     __a   (   self      )	-> str:
								return {"do_normalize": self.do_normalize, "do_convert_rgb": self.do_convert_rgb}






							def     __a   (   self      )	-> Dict:
								a      :		Union[str, Any]	=				"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg"
								a      :		str	=				Image.open(requests.get(lowerCAmelCase__      ,					stream=lowerCAmelCase__      ).raw      ).convert("RGB"      )
								return raw_image


@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11		,       reason="""`Pix2StructImageProcessor` requires `torch>=1.11.0`."""		,       )
@require_torch
@require_vision
class        __UpperCamelCase				(						a__		,       unittest.TestCase						):
							lowerCamelCase   :					Union[str, Any]      					=PixaStructImageProcessor if is_vision_available() else None






							def     __a   (   self      )	-> Union[str, Any]:
								a      :		List[Any]	=				PixaStructImageProcessingTester(self      )






							@property
							def     __a   (   self      )	-> List[str]:
								return self.image_processor_tester.prepare_image_processor_dict()






							def     __a   (   self      )	-> Dict:
								a      :		Optional[int]	=				self.image_processing_class(**self.image_processor_dict      )
								self.assertTrue(hasattr(lowerCAmelCase__      ,					"do_normalize"      )      )
								self.assertTrue(hasattr(lowerCAmelCase__      ,					"do_convert_rgb"      )      )






							def     __a   (   self      )	-> str:
								a      :		Any	=				self.image_processor_tester.prepare_dummy_image()

								a      :		Optional[int]	=				self.image_processing_class(**self.image_processor_dict      )
								a      :		Union[str, Any]	=				2048

								a      :		List[Any]	=				image_processor(lowerCAmelCase__      ,					return_tensors="pt"      ,					max_patches=lowerCAmelCase__      )
								self.assertTrue(torch.allclose(inputs.flattened_patches.mean()      ,					torch.tensor(0.0_606      )      ,					atol=1E-3      ,					rtol=1E-3      )      )






							def     __a   (   self      )	-> List[Any]:
								# Initialize image_processor
								a      :		str	=				self.image_processing_class(**self.image_processor_dict      )
								# create random PIL images
								a      :		Optional[int]	=				prepare_image_inputs(self.image_processor_tester      ,					equal_resolution=lowerCAmelCase__      )
								for image in image_inputs:
									self.assertIsInstance(lowerCAmelCase__      ,					Image.Image      )

								# Test not batched input
								a      :		int	=				(
								    (self.image_processor_tester.patch_size["height"] * self.image_processor_tester.patch_size["width"])
								    * self.image_processor_tester.num_channels
								) + 2

								for max_patch in self.image_processor_tester.max_patches:
									# Test not batched input
									a      :		List[Any]	=				image_processor(
									    image_inputs[0]      ,					return_tensors="pt"      ,					max_patches=lowerCAmelCase__      ).flattened_patches
									self.assertEqual(
									    encoded_images.shape      ,					(1, max_patch, expected_hidden_dim)      ,					)

									# Test batched
									a      :		Optional[int]	=				image_processor(
									    lowerCAmelCase__      ,					return_tensors="pt"      ,					max_patches=lowerCAmelCase__      ).flattened_patches
									self.assertEqual(
									    encoded_images.shape      ,					(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)      ,					)






							def     __a   (   self      )	-> Dict:
								# Initialize image_processor
								a      :		int	=				self.image_processing_class(**self.image_processor_dict      )
								# create random PIL images
								a      :		Tuple	=				prepare_image_inputs(self.image_processor_tester      ,					equal_resolution=lowerCAmelCase__      )
								for image in image_inputs:
									self.assertIsInstance(lowerCAmelCase__      ,					Image.Image      )

								# Test not batched input
								a      :		List[Any]	=				(
								    (self.image_processor_tester.patch_size["height"] * self.image_processor_tester.patch_size["width"])
								    * self.image_processor_tester.num_channels
								) + 2

								a      :		int	=				True

								for max_patch in self.image_processor_tester.max_patches:
									# Test not batched input
									with self.assertRaises(lowerCAmelCase__      ):
										a      :		List[str]	=				image_processor(
										    image_inputs[0]      ,					return_tensors="pt"      ,					max_patches=lowerCAmelCase__      ).flattened_patches

									a      :		int	=				"Hello"

									a      :		Optional[Any]	=				image_processor(
									    image_inputs[0]      ,					return_tensors="pt"      ,					max_patches=lowerCAmelCase__      ,					header_text=lowerCAmelCase__      ).flattened_patches
									self.assertEqual(
									    encoded_images.shape      ,					(1, max_patch, expected_hidden_dim)      ,					)

									# Test batched
									a      :		Union[str, Any]	=				image_processor(
									    lowerCAmelCase__      ,					return_tensors="pt"      ,					max_patches=lowerCAmelCase__      ,					header_text=lowerCAmelCase__      ).flattened_patches
									self.assertEqual(
									    encoded_images.shape      ,					(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)      ,					)






							def     __a   (   self      )	-> List[str]:
								# Initialize image_processor
								a      :		str	=				self.image_processing_class(**self.image_processor_dict      )
								# create random numpy tensors
								a      :		Optional[int]	=				prepare_image_inputs(self.image_processor_tester      ,					equal_resolution=lowerCAmelCase__      ,					numpify=lowerCAmelCase__      )
								for image in image_inputs:
									self.assertIsInstance(lowerCAmelCase__      ,					np.ndarray      )

								a      :		Optional[int]	=				(
								    (self.image_processor_tester.patch_size["height"] * self.image_processor_tester.patch_size["width"])
								    * self.image_processor_tester.num_channels
								) + 2

								for max_patch in self.image_processor_tester.max_patches:
									# Test not batched input
									a      :		Any	=				image_processor(
									    image_inputs[0]      ,					return_tensors="pt"      ,					max_patches=lowerCAmelCase__      ).flattened_patches
									self.assertEqual(
									    encoded_images.shape      ,					(1, max_patch, expected_hidden_dim)      ,					)

									# Test batched
									a      :		Union[str, Any]	=				image_processor(
									    lowerCAmelCase__      ,					return_tensors="pt"      ,					max_patches=lowerCAmelCase__      ).flattened_patches
									self.assertEqual(
									    encoded_images.shape      ,					(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)      ,					)






							def     __a   (   self      )	-> Any:
								# Initialize image_processor
								a      :		Tuple	=				self.image_processing_class(**self.image_processor_dict      )
								# create random PyTorch tensors
								a      :		Dict	=				prepare_image_inputs(self.image_processor_tester      ,					equal_resolution=lowerCAmelCase__      ,					torchify=lowerCAmelCase__      )
								for image in image_inputs:
									self.assertIsInstance(lowerCAmelCase__      ,					torch.Tensor      )

								# Test not batched input
								a      :		List[Any]	=				(
								    (self.image_processor_tester.patch_size["height"] * self.image_processor_tester.patch_size["width"])
								    * self.image_processor_tester.num_channels
								) + 2

								for max_patch in self.image_processor_tester.max_patches:
									# Test not batched input
									a      :		Union[str, Any]	=				image_processor(
									    image_inputs[0]      ,					return_tensors="pt"      ,					max_patches=lowerCAmelCase__      ).flattened_patches
									self.assertEqual(
									    encoded_images.shape      ,					(1, max_patch, expected_hidden_dim)      ,					)

									# Test batched
									a      :		Optional[Any]	=				image_processor(
									    lowerCAmelCase__      ,					return_tensors="pt"      ,					max_patches=lowerCAmelCase__      ).flattened_patches
									self.assertEqual(
									    encoded_images.shape      ,					(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)      ,					)


@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11		,       reason="""`Pix2StructImageProcessor` requires `torch>=1.11.0`."""		,       )
@require_torch
@require_vision
class        __UpperCamelCase				(						a__		,       unittest.TestCase						):
							lowerCamelCase   :					Optional[Any]      					=PixaStructImageProcessor if is_vision_available() else None






							def     __a   (   self      )	-> Optional[int]:
								a      :		List[Any]	=				PixaStructImageProcessingTester(self      ,					num_channels=4      )
								a      :		int	=				3






							@property
							def     __a   (   self      )	-> Optional[Any]:
								return self.image_processor_tester.prepare_image_processor_dict()






							def     __a   (   self      )	-> Dict:
								a      :		Optional[int]	=				self.image_processing_class(**self.image_processor_dict      )
								self.assertTrue(hasattr(lowerCAmelCase__      ,					"do_normalize"      )      )
								self.assertTrue(hasattr(lowerCAmelCase__      ,					"do_convert_rgb"      )      )






							def     __a   (   self      )	-> Dict:
								# Initialize image_processor
								a      :		Union[str, Any]	=				self.image_processing_class(**self.image_processor_dict      )
								# create random PIL images
								a      :		str	=				prepare_image_inputs(self.image_processor_tester      ,					equal_resolution=lowerCAmelCase__      )
								for image in image_inputs:
									self.assertIsInstance(lowerCAmelCase__      ,					Image.Image      )

								# Test not batched input
								a      :		List[Any]	=				(
								    (self.image_processor_tester.patch_size["height"] * self.image_processor_tester.patch_size["width"])
								    * (self.image_processor_tester.num_channels - 1)
								) + 2

								for max_patch in self.image_processor_tester.max_patches:
									# Test not batched input
									a      :		Tuple	=				image_processor(
									    image_inputs[0]      ,					return_tensors="pt"      ,					max_patches=lowerCAmelCase__      ).flattened_patches
									self.assertEqual(
									    encoded_images.shape      ,					(1, max_patch, expected_hidden_dim)      ,					)

									# Test batched
									a      :		Optional[Any]	=				image_processor(
									    lowerCAmelCase__      ,					return_tensors="pt"      ,					max_patches=lowerCAmelCase__      ).flattened_patches
									self.assertEqual(
									    encoded_images.shape      ,					(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)      ,					)









"""simple docstring"""




# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from argparse import ArgumentParser

from accelerate.commands.config import get_config_parser
from accelerate.commands.env import env_command_parser
from accelerate.commands.launch import launch_command_parser
from accelerate.commands.test import test_command_parser
from accelerate.commands.tpu import tpu_command_parser






def       _SCREAMING_SNAKE_CASE		(	) ->Dict:






	'''simple docstring'''






	a      :		Any	=				ArgumentParser("Accelerate CLI tool"    ,       usage="accelerate <command> [<args>]"    ,       allow_abbrev=_lowercase					)
	a      :		List[Any]	=				parser.add_subparsers(help="accelerate command helpers"					)

	# Register commands
	get_config_parser(subparsers=_lowercase					)
	env_command_parser(subparsers=_lowercase					)
	launch_command_parser(subparsers=_lowercase					)
	tpu_command_parser(subparsers=_lowercase					)
	test_command_parser(subparsers=_lowercase					)

	# Let's go
	a      :		List[Any]	=				parser.parse_args()

	if not hasattr(_lowercase    ,       "func"					):
		parser.print_help()
		exit(1					)

	# Run
	args.func(_lowercase					)


if __name__ == "__main__":
		main()


