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 					A							(    unittest.TestCase					):


   def __init__(self					:						Tuple	,  __UpperCAmelCase					:						Union[str, Any]	,  __UpperCAmelCase					:						Union[str, Any]=7	,  __UpperCAmelCase					:						Dict=3	,  __UpperCAmelCase					:						Optional[Any]=1_8	,  __UpperCAmelCase					:						Optional[Any]=3_0	,  __UpperCAmelCase					:						Tuple=4_0_0	,  __UpperCAmelCase					:						Optional[int]=True	,  __UpperCAmelCase					:						int=None	,  __UpperCAmelCase					:						Union[str, Any]=True	,  __UpperCAmelCase					:						Optional[Any]=None	,  __UpperCAmelCase					:						str=True	,  __UpperCAmelCase					:						List[Any]=[0.5, 0.5, 0.5]	,  __UpperCAmelCase					:						Tuple=[0.5, 0.5, 0.5]	,  __UpperCAmelCase					:						Union[str, Any]=False	,  )						->    List[Any]:
         """simple docstring"""



         UpperCAmelCase__								=  size if size is not None else {"height": 2_0, "width": 2_0}
         UpperCAmelCase__								=  crop_size if crop_size is not None else {"height": 1_8, "width": 1_8}
         UpperCAmelCase__								=  parent
         UpperCAmelCase__								=  batch_size
         UpperCAmelCase__								=  num_channels
         UpperCAmelCase__								=  image_size
         UpperCAmelCase__								=  min_resolution
         UpperCAmelCase__								=  max_resolution
         UpperCAmelCase__								=  do_resize
         UpperCAmelCase__								=  size
         UpperCAmelCase__								=  do_center_crop
         UpperCAmelCase__								=  crop_size
         UpperCAmelCase__								=  do_normalize
         UpperCAmelCase__								=  image_mean
         UpperCAmelCase__								=  image_std
         UpperCAmelCase__								=  do_reduce_labels





   def       lowercase_				(self					:						Optional[Any]		)						->    List[Any]:
         """simple docstring"""



         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        lowerCAmelCase_			(					)					->    str:







      '''simple docstring'''


      UpperCAmelCase__								=  load_dataset("hf-internal-testing/fixtures_ade20k",  split="test"							)

      UpperCAmelCase__								=  Image.open(dataset[0]["file"]							)
      UpperCAmelCase__								=  Image.open(dataset[1]["file"]							)

      return image, map




def        lowerCAmelCase_			(					)					->    List[str]:







      '''simple docstring'''


      UpperCAmelCase__								=  load_dataset("hf-internal-testing/fixtures_ade20k",  split="test"							)

      UpperCAmelCase__								=  Image.open(ds[0]["file"]							)
      UpperCAmelCase__								=  Image.open(ds[1]["file"]							)
      UpperCAmelCase__								=  Image.open(ds[2]["file"]							)
      UpperCAmelCase__								=  Image.open(ds[3]["file"]							)

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







@require_torch
@require_vision
class 					A							(    __a		,    unittest.TestCase					):
   __UpperCAmelCase			:			Optional[Any]              =      BeitImageProcessor if is_vision_available() else None


   def       lowercase_				(self					:						Dict		)						->    List[str]:
         """simple docstring"""



         UpperCAmelCase__								=  BeitImageProcessingTester(self		)


   @property
   def       lowercase_				(self					:						Dict		)						->    Union[str, Any]:
         """simple docstring"""



         return self.image_processor_tester.prepare_image_processor_dict()


   def       lowercase_				(self					:						Optional[Any]		)						->    str:
         """simple docstring"""



         UpperCAmelCase__								=  self.image_processing_class(**self.image_processor_dict		)
         self.assertTrue(hasattr(a__	,  "do_resize"		)		)
         self.assertTrue(hasattr(a__	,  "size"		)		)
         self.assertTrue(hasattr(a__	,  "do_center_crop"		)		)
         self.assertTrue(hasattr(a__	,  "center_crop"		)		)
         self.assertTrue(hasattr(a__	,  "do_normalize"		)		)
         self.assertTrue(hasattr(a__	,  "image_mean"		)		)
         self.assertTrue(hasattr(a__	,  "image_std"		)		)


   def       lowercase_				(self					:						int		)						->    Any:
         """simple docstring"""



         UpperCAmelCase__								=  self.image_processing_class.from_dict(self.image_processor_dict		)
         self.assertEqual(image_processor.size	,  {"height": 2_0, "width": 2_0}		)
         self.assertEqual(image_processor.crop_size	,  {"height": 1_8, "width": 1_8}		)
         self.assertEqual(image_processor.do_reduce_labels	,  a__		)

         UpperCAmelCase__								=  self.image_processing_class.from_dict(
             self.image_processor_dict	,  size=4_2	,  crop_size=8_4	,  reduce_labels=a__		)
         self.assertEqual(image_processor.size	,  {"height": 4_2, "width": 4_2}		)
         self.assertEqual(image_processor.crop_size	,  {"height": 8_4, "width": 8_4}		)
         self.assertEqual(image_processor.do_reduce_labels	,  a__		)


   def       lowercase_				(self					:						Optional[Any]		)						->    Dict:
         """simple docstring"""



         pass


   def       lowercase_				(self					:						Dict		)						->    int:
         """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=a__		)
         for image in image_inputs:
               self.assertIsInstance(a__	,  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(a__	,  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       lowercase_				(self					:						Optional[int]		)						->    Optional[int]:
         """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=a__	,  numpify=a__		)
         for image in image_inputs:
               self.assertIsInstance(a__	,  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(a__	,  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       lowercase_				(self					:						List[str]		)						->    int:
         """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=a__	,  torchify=a__		)
         for image in image_inputs:
               self.assertIsInstance(a__	,  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(a__	,  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       lowercase_				(self					:						Union[str, Any]		)						->    int:
         """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=a__	,  torchify=a__		)
         UpperCAmelCase__								=  []
         for image in image_inputs:
               self.assertIsInstance(a__	,  torch.Tensor		)
               maps.append(torch.zeros(image.shape[-2:]		).long()		)

         # Test not batched input
         UpperCAmelCase__								=  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() <= 2_5_5		)

         # Test batched
         UpperCAmelCase__								=  image_processing(a__	,  a__	,  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() <= 2_5_5		)

         # Test not batched input (PIL images)
         UpperCAmelCase__		,				UpperCAmelCase__								=  prepare_semantic_single_inputs()

         UpperCAmelCase__								=  image_processing(a__	,  a__	,  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() <= 2_5_5		)

         # Test batched input (PIL images)
         UpperCAmelCase__		,				UpperCAmelCase__								=  prepare_semantic_batch_inputs()

         UpperCAmelCase__								=  image_processing(a__	,  a__	,  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() <= 2_5_5		)





   def       lowercase_				(self					:						Any		)						->    List[Any]:
         """simple docstring"""



         UpperCAmelCase__								=  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
         UpperCAmelCase__		,				UpperCAmelCase__								=  prepare_semantic_single_inputs()
         UpperCAmelCase__								=  image_processing(a__	,  a__	,  return_tensors="pt"		)
         self.assertTrue(encoding["labels"].min().item() >= 0		)
         self.assertTrue(encoding["labels"].max().item() <= 1_5_0		)

         UpperCAmelCase__								=  True
         UpperCAmelCase__								=  image_processing(a__	,  a__	,  return_tensors="pt"		)
         self.assertTrue(encoding["labels"].min().item() >= 0		)
         self.assertTrue(encoding["labels"].max().item() <= 2_5_5		)




# 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

from .config import config_command_parser
from .config_args import default_config_file, load_config_from_file  # noqa: F401
from .default import default_command_parser
from .update import update_command_parser




def        lowerCAmelCase_			(					__A=None							)					->    str:







      '''simple docstring'''


      UpperCAmelCase__								=  argparse.ArgumentParser(add_help=__A,  allow_abbrev=__A							)
      # The main config parser
      UpperCAmelCase__								=  config_command_parser(__A							)
      # The subparser to add commands to
      UpperCAmelCase__								=  config_parser.add_subparsers(title="subcommands",  dest="subcommand"							)

      # Then add other parsers with the parent parser
      default_command_parser(__A,  parents=[parent_parser]							)
      update_command_parser(__A,  parents=[parent_parser]							)

      return config_parser




def        lowerCAmelCase_			(					)					->    Any:







      '''simple docstring'''


      UpperCAmelCase__								=  get_config_parser()
      UpperCAmelCase__								=  config_parser.parse_args()

      if not hasattr(__A,  "func"							):
            config_parser.print_help()
            exit(1							)

      # Run
      args.func(__A							)


if __name__ == "__main__":
      main()



