





"""simple docstring"""




import unittest

import numpy as np
from datasets import load_dataset

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 BeitImageProcessor


class  __UpperCamelCase       (		unittest.TestCase		):






       def __init__(     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__=7   ,      lowerCAmelCase__=3   ,      lowerCAmelCase__=18   ,      lowerCAmelCase__=30   ,      lowerCAmelCase__=400   ,      lowerCAmelCase__=True   ,      lowerCAmelCase__=None   ,      lowerCAmelCase__=True   ,      lowerCAmelCase__=None   ,      lowerCAmelCase__=True   ,      lowerCAmelCase__=[0.5, 0.5, 0.5]   ,      lowerCAmelCase__=[0.5, 0.5, 0.5]   ,      lowerCAmelCase__=False   ,      )			->				Any:
        a   :	Union[str, Any]			=       size if size is not None else {'''height''': 20, '''width''': 20}
        a   :	Any			=       crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
        a   :	Any			=       parent
        a   :	List[Any]			=       batch_size
        a   :	Tuple			=       num_channels
        a   :	int			=       image_size
        a   :	List[Any]			=       min_resolution
        a   :	List[Any]			=       max_resolution
        a   :	List[Any]			=       do_resize
        a   :	int			=       size
        a   :	int			=       do_center_crop
        a   :	int			=       crop_size
        a   :	Tuple			=       do_normalize
        a   :	List[str]			=       image_mean
        a   :	Any			=       image_std
        a   :	Union[str, Any]			=       do_reduce_labels






       def 					__a     (     self   )			->				str:
        return {
            "do_resize": self.do_resize,
            "size": self.size,
            "do_center_crop": self.do_center_crop,
            "crop_size": self.crop_size,
            "do_normalize": self.do_normalize,
            "image_mean": self.image_mean,
            "image_std": self.image_std,
            "do_reduce_labels": self.do_reduce_labels,
        }






def    _SCREAMING_SNAKE_CASE	(			)				->Any:






 '''simple docstring'''






 a   :	int			=       load_dataset("hf-internal-testing/fixtures_ade20k"					, split="test"      )

 a   :	Any			=       Image.open(dataset[0]["file"]      )
 a   :	List[Any]			=       Image.open(dataset[1]["file"]      )

 return image, map






def    _SCREAMING_SNAKE_CASE	(			)				->Tuple:






 '''simple docstring'''






 a   :	Tuple			=       load_dataset("hf-internal-testing/fixtures_ade20k"					, split="test"      )

 a   :	List[str]			=       Image.open(ds[0]["file"]      )
 a   :	int			=       Image.open(ds[1]["file"]      )
 a   :	Any			=       Image.open(ds[2]["file"]      )
 a   :	Dict			=       Image.open(ds[3]["file"]      )

 return [imagea, imagea], [mapa, mapa]







@require_torch
@require_vision
class  __UpperCamelCase       (		SCREAMING_SNAKE_CASE_	, unittest.TestCase		):
       lowerCamelCase     :      List[str]         =BeitImageProcessor if is_vision_available() else None






       def 					__a     (     self   )			->				Optional[int]:
        a   :	str			=       BeitImageProcessingTester(self   )






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






       def 					__a     (     self   )			->				Tuple:
        a   :	str			=       self.image_processing_class(**self.image_processor_dict   )
        self.assertTrue(hasattr(UpperCamelCase__   ,      "do_resize"   )   )
        self.assertTrue(hasattr(UpperCamelCase__   ,      "size"   )   )
        self.assertTrue(hasattr(UpperCamelCase__   ,      "do_center_crop"   )   )
        self.assertTrue(hasattr(UpperCamelCase__   ,      "center_crop"   )   )
        self.assertTrue(hasattr(UpperCamelCase__   ,      "do_normalize"   )   )
        self.assertTrue(hasattr(UpperCamelCase__   ,      "image_mean"   )   )
        self.assertTrue(hasattr(UpperCamelCase__   ,      "image_std"   )   )






       def 					__a     (     self   )			->				str:
        a   :	Union[str, Any]			=       self.image_processing_class.from_dict(self.image_processor_dict   )
        self.assertEqual(image_processor.size   ,      {"height": 20, "width": 20}   )
        self.assertEqual(image_processor.crop_size   ,      {"height": 18, "width": 18}   )
        self.assertEqual(image_processor.do_reduce_labels   ,      UpperCamelCase__   )

        a   :	Optional[Any]			=       self.image_processing_class.from_dict(
            self.image_processor_dict   ,      size=42   ,      crop_size=84   ,      reduce_labels=UpperCamelCase__   )
        self.assertEqual(image_processor.size   ,      {"height": 42, "width": 42}   )
        self.assertEqual(image_processor.crop_size   ,      {"height": 84, "width": 84}   )
        self.assertEqual(image_processor.do_reduce_labels   ,      UpperCamelCase__   )






       def 					__a     (     self   )			->				Any:
        pass






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

        # Test not batched input
        a   :	Any			=       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
        a   :	List[Any]			=       image_processing(UpperCamelCase__   ,      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 					__a     (     self   )			->				Any:
        # Initialize image_processing
        a   :	str			=       self.image_processing_class(**self.image_processor_dict   )
        # create random numpy tensors
        a   :	Dict			=       prepare_image_inputs(self.image_processor_tester   ,      equal_resolution=UpperCamelCase__   ,      numpify=UpperCamelCase__   )
        for image in image_inputs:
         self.assertIsInstance(UpperCamelCase__   ,      np.ndarray   )

        # Test not batched input
        a   :	List[Any]			=       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
        a   :	Dict			=       image_processing(UpperCamelCase__   ,      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 					__a     (     self   )			->				Optional[Any]:
        # Initialize image_processing
        a   :	Optional[int]			=       self.image_processing_class(**self.image_processor_dict   )
        # create random PyTorch tensors
        a   :	Union[str, Any]			=       prepare_image_inputs(self.image_processor_tester   ,      equal_resolution=UpperCamelCase__   ,      torchify=UpperCamelCase__   )
        for image in image_inputs:
         self.assertIsInstance(UpperCamelCase__   ,      torch.Tensor   )

        # Test not batched input
        a   :	Tuple			=       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
        a   :	Tuple			=       image_processing(UpperCamelCase__   ,      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 					__a     (     self   )			->				Optional[int]:
        # Initialize image_processing
        a   :	List[str]			=       self.image_processing_class(**self.image_processor_dict   )
        # create random PyTorch tensors
        a   :	List[Any]			=       prepare_image_inputs(self.image_processor_tester   ,      equal_resolution=UpperCamelCase__   ,      torchify=UpperCamelCase__   )
        a   :	int			=       []
        for image in image_inputs:
         self.assertIsInstance(UpperCamelCase__   ,      torch.Tensor   )
         maps.append(torch.zeros(image.shape[-2:]   ).long()   )

        # Test not batched input
        a   :	Tuple			=       image_processing(image_inputs[0]   ,      maps[0]   ,      return_tensors="pt"   )
        self.assertEqual(
            encoding["pixel_values"].shape   ,      (
                1,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size["height"],
                self.image_processor_tester.crop_size["width"],
            )   ,      )
        self.assertEqual(
            encoding["labels"].shape   ,      (
                1,
                self.image_processor_tester.crop_size["height"],
                self.image_processor_tester.crop_size["width"],
            )   ,      )
        self.assertEqual(encoding["labels"].dtype   ,      torch.long   )
        self.assertTrue(encoding["labels"].min().item() >= 0   )
        self.assertTrue(encoding["labels"].max().item() <= 255   )

        # Test batched
        a   :	Any			=       image_processing(UpperCamelCase__   ,      UpperCamelCase__   ,      return_tensors="pt"   )
        self.assertEqual(
            encoding["pixel_values"].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"],
            )   ,      )
        self.assertEqual(
            encoding["labels"].shape   ,      (
                self.image_processor_tester.batch_size,
                self.image_processor_tester.crop_size["height"],
                self.image_processor_tester.crop_size["width"],
            )   ,      )
        self.assertEqual(encoding["labels"].dtype   ,      torch.long   )
        self.assertTrue(encoding["labels"].min().item() >= 0   )
        self.assertTrue(encoding["labels"].max().item() <= 255   )

        # Test not batched input (PIL images)
        a   :	Optional[Any]			=       prepare_semantic_single_inputs()

        a   :	Dict			=       image_processing(UpperCamelCase__   ,      UpperCamelCase__   ,      return_tensors="pt"   )
        self.assertEqual(
            encoding["pixel_values"].shape   ,      (
                1,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size["height"],
                self.image_processor_tester.crop_size["width"],
            )   ,      )
        self.assertEqual(
            encoding["labels"].shape   ,      (
                1,
                self.image_processor_tester.crop_size["height"],
                self.image_processor_tester.crop_size["width"],
            )   ,      )
        self.assertEqual(encoding["labels"].dtype   ,      torch.long   )
        self.assertTrue(encoding["labels"].min().item() >= 0   )
        self.assertTrue(encoding["labels"].max().item() <= 255   )

        # Test batched input (PIL images)
        a   :	List[str]			=       prepare_semantic_batch_inputs()

        a   :	List[str]			=       image_processing(UpperCamelCase__   ,      UpperCamelCase__   ,      return_tensors="pt"   )
        self.assertEqual(
            encoding["pixel_values"].shape   ,      (
                2,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size["height"],
                self.image_processor_tester.crop_size["width"],
            )   ,      )
        self.assertEqual(
            encoding["labels"].shape   ,      (
                2,
                self.image_processor_tester.crop_size["height"],
                self.image_processor_tester.crop_size["width"],
            )   ,      )
        self.assertEqual(encoding["labels"].dtype   ,      torch.long   )
        self.assertTrue(encoding["labels"].min().item() >= 0   )
        self.assertTrue(encoding["labels"].max().item() <= 255   )






       def 					__a     (     self   )			->				Any:
        # Initialize image_processing
        a   :	Optional[Any]			=       self.image_processing_class(**self.image_processor_dict   )

        # ADE20k has 150 classes, and the background is included, so labels should be between 0 and 150
        a   :	List[Any]			=       prepare_semantic_single_inputs()
        a   :	int			=       image_processing(UpperCamelCase__   ,      UpperCamelCase__   ,      return_tensors="pt"   )
        self.assertTrue(encoding["labels"].min().item() >= 0   )
        self.assertTrue(encoding["labels"].max().item() <= 150   )

        a   :	Optional[int]			=       True
        a   :	str			=       image_processing(UpperCamelCase__   ,      UpperCamelCase__   ,      return_tensors="pt"   )
        self.assertTrue(encoding["labels"].min().item() >= 0   )
        self.assertTrue(encoding["labels"].max().item() <= 255   )









"""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.

import argparse
import os

from accelerate.test_utils import execute_subprocess_async






def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					str=None      )				->Optional[Any]:






	'''simple docstring'''






	if subparsers is not None:
		a   :	Dict			=       subparsers.add_parser("test"      )
	else:
		a   :	Tuple			=       argparse.ArgumentParser("Accelerate test command"      )

	parser.add_argument(
	    "--config_file"					, default=_lowercase					, help=(
	        "The path to use to store the config file. Will default to a file named default_config.yaml in the cache "
	        "location, which is the content of the environment `HF_HOME` suffixed with 'accelerate', or if you don't have "
	        "such an environment variable, your cache directory ('~/.cache' or the content of `XDG_CACHE_HOME`) suffixed "
	        "with 'huggingface'."
	    )					, )

	if subparsers is not None:
		parser.set_defaults(func=_lowercase      )
	return parser






def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					str      )				->str:






	'''simple docstring'''






	a   :	List[Any]			=       os.path.sep.join(__file__.split(os.path.sep      )[:-2] + ["test_utils", "scripts", "test_script.py"]      )

	if args.config_file is None:
		a   :	int			=       script_name
	else:
		a   :	int			=       F"""--config_file={args.config_file} {script_name}"""

	a   :	Optional[int]			=       ["accelerate-launch"] + test_args.split()
	a   :	Optional[int]			=       execute_subprocess_async(_lowercase					, env=os.environ.copy()      )
	if result.returncode == 0:
		print("Test is a success! You are ready for your distributed training!"      )






def    _SCREAMING_SNAKE_CASE	(			)				->Tuple:






	'''simple docstring'''






	a   :	Any			=       test_command_parser()
	a   :	Union[str, Any]			=       parser.parse_args()
	test_command(_lowercase      )


if __name__ == "__main__":
		main()


