

import json
import unittest

import numpy as np
from huggingface_hub import hf_hub_download

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 transformers import OneFormerImageProcessor
          from transformers.models.oneformer.image_processing_oneformer import binary_mask_to_rle
          from transformers.models.oneformer.modeling_oneformer import OneFormerForUniversalSegmentationOutput

if is_vision_available():
     from PIL import Image

def       a__					(		UpperCAmelCase							:      Optional[Any]    ,			UpperCAmelCase							:      Optional[int]="shi-labs/oneformer_demo"      )  ->						int:
  with open(hf_hub_download(UpperCAmelCase    ,			UpperCAmelCase    ,			repo_type='''dataset'''      )    ,			'''r'''      ) as f:
    UpperCAmelCase	:							Any						=			json.load(UpperCAmelCase      )
  UpperCAmelCase	:							List[Any]						=			{}
  UpperCAmelCase	:							Any						=			[]
  UpperCAmelCase	:							List[Any]						=			[]
  for key, info in class_info.items():
    UpperCAmelCase	:							Tuple						=			info['''name''']
    class_names.append(info['''name''']      )
    if info["isthing"]:
      thing_ids.append(int(UpperCAmelCase      )      )
  UpperCAmelCase	:							Optional[Any]						=			thing_ids
  UpperCAmelCase	:							Optional[Any]						=			class_names
  return metadata





class  __UpperCAmelCase					(	unittest.TestCase						):



     def __init__(							self							:					Optional[int],		__A							:					int,		__A							:					str=7,		__A							:					Tuple=3,		__A							:					Tuple=3_0,		__A							:					Optional[Any]=4_0_0,		__A							:					Tuple=None,		__A							:					Union[str, Any]=True,		__A							:					Union[str, Any]=True,		__A							:					Optional[int]=[0.5, 0.5, 0.5],		__A							:					Any=[0.5, 0.5, 0.5],		__A							:					str=1_0,		__A							:					Optional[Any]=False,		__A							:					Tuple=2_5_5,		__A							:					Any="shi-labs/oneformer_demo",		__A							:					int="ade20k_panoptic.json",		__A							:					Union[str, Any]=1_0,		):
       UpperCAmelCase	:							List[str]						=			parent
       UpperCAmelCase	:							str						=			batch_size
       UpperCAmelCase	:							Any						=			num_channels
       UpperCAmelCase	:							List[Any]						=			min_resolution
       UpperCAmelCase	:							Optional[Any]						=			max_resolution
       UpperCAmelCase	:							Optional[Any]						=			do_resize
       UpperCAmelCase	:							Optional[int]						=			{'''shortest_edge''': 3_2, '''longest_edge''': 1_3_3_3} if size is None else size
       UpperCAmelCase	:							Union[str, Any]						=			do_normalize
       UpperCAmelCase	:							List[Any]						=			image_mean
       UpperCAmelCase	:							List[Any]						=			image_std
       UpperCAmelCase	:							List[Any]						=			class_info_file
       UpperCAmelCase	:							Any						=			prepare_metadata(__A,		__A						)
       UpperCAmelCase	:							Dict						=			num_text
       UpperCAmelCase	:							int						=			repo_path

       # for the post_process_functions
       UpperCAmelCase	:							Dict						=			2
       UpperCAmelCase	:							List[Any]						=			1_0
       UpperCAmelCase	:							Optional[int]						=			1_0
       UpperCAmelCase	:							Optional[Any]						=			3
       UpperCAmelCase	:							Optional[Any]						=			4
       UpperCAmelCase	:							int						=			num_labels
       UpperCAmelCase	:							Optional[Any]						=			do_reduce_labels
       UpperCAmelCase	:							Union[str, Any]						=			ignore_index



     def 	__magic_name__   (							self							:					List[Any]						):
       return {
           "do_resize": self.do_resize,
           "size": self.size,
           "do_normalize": self.do_normalize,
           "image_mean": self.image_mean,
           "image_std": self.image_std,
           "num_labels": self.num_labels,
           "do_reduce_labels": self.do_reduce_labels,
           "ignore_index": self.ignore_index,
           "class_info_file": self.class_info_file,
           "metadata": self.metadata,
           "num_text": self.num_text,
       }



     def 	__magic_name__   (							self							:					int,		__A							:					Optional[Any],		__A							:					str=False						):
       if not batched:
         UpperCAmelCase	:							int						=			image_inputs[0]
         if isinstance(__A,		Image.Image						):
           UpperCAmelCase	:							Tuple						=			image.size
         else:
           UpperCAmelCase	:							int						=			image.shape[1], image.shape[2]
         if w < h:
           UpperCAmelCase	:							Tuple						=			int(self.size['''shortest_edge'''] * h / w						)
           UpperCAmelCase	:							int						=			self.size['''shortest_edge''']
         elif w > h:
           UpperCAmelCase	:							Optional[int]						=			self.size['''shortest_edge''']
           UpperCAmelCase	:							Union[str, Any]						=			int(self.size['''shortest_edge'''] * w / h						)
         else:
           UpperCAmelCase	:							List[str]						=			self.size['''shortest_edge''']
           UpperCAmelCase	:							Any						=			self.size['''shortest_edge''']

       else:
         UpperCAmelCase	:							Tuple						=			[]
         for image in image_inputs:
           UpperCAmelCase	:							str						=			self.get_expected_values([image]						)
           expected_values.append((expected_height, expected_width)						)
         UpperCAmelCase	:							Dict						=			max(__A,		key=lambda __A						: item[0]						)[0]
         UpperCAmelCase	:							Tuple						=			max(__A,		key=lambda __A						: item[1]						)[1]

       return expected_height, expected_width





     def 	__magic_name__   (							self							:					str						):
       return OneFormerForUniversalSegmentationOutput(
           # +1 for null class
           class_queries_logits=torch.randn((self.batch_size, self.num_queries, self.num_classes + 1)						),		masks_queries_logits=torch.randn((self.batch_size, self.num_queries, self.height, self.width)						),		)






@require_torch
@require_vision
class  __UpperCAmelCase					(	lowerCamelCase__      ,  unittest.TestCase						):
     UpperCamelCase       					=					OneFormerImageProcessor if (is_vision_available() and is_torch_available()) else None
     # only for test_image_processing_common.test_image_proc_to_json_string
     UpperCamelCase       					=					image_processing_class



     def 	__magic_name__   (							self							:					Dict						):
       UpperCAmelCase	:							List[Any]						=			OneFormerImageProcessorTester(self						)



     @property
     def 	__magic_name__   (							self							:					Any						):
       return self.image_processing_tester.prepare_image_processor_dict()



     def 	__magic_name__   (							self							:					str						):
       UpperCAmelCase	:							int						=			self.image_processing_class(**self.image_processor_dict						)
       self.assertTrue(hasattr(__A,		'''image_mean'''						)						)
       self.assertTrue(hasattr(__A,		'''image_std'''						)						)
       self.assertTrue(hasattr(__A,		'''do_normalize'''						)						)
       self.assertTrue(hasattr(__A,		'''do_resize'''						)						)
       self.assertTrue(hasattr(__A,		'''size'''						)						)
       self.assertTrue(hasattr(__A,		'''ignore_index'''						)						)
       self.assertTrue(hasattr(__A,		'''class_info_file'''						)						)
       self.assertTrue(hasattr(__A,		'''num_text'''						)						)
       self.assertTrue(hasattr(__A,		'''repo_path'''						)						)
       self.assertTrue(hasattr(__A,		'''metadata'''						)						)
       self.assertTrue(hasattr(__A,		'''do_reduce_labels'''						)						)



     def 	__magic_name__   (							self							:					List[Any]						):
       pass



     def 	__magic_name__   (							self							:					Optional[Any]						):
       # Initialize image_processor
       UpperCAmelCase	:							Union[str, Any]						=			self.image_processing_class(**self.image_processor_dict						)
       # create random PIL images
       UpperCAmelCase	:							Dict						=			prepare_image_inputs(self.image_processing_tester,		equal_resolution=__A						)
       for image in image_inputs:
         self.assertIsInstance(__A,		Image.Image						)

       # Test not batched input
       UpperCAmelCase	:							List[str]						=			image_processor(image_inputs[0],		['''semantic'''],		return_tensors='''pt'''						).pixel_values

       UpperCAmelCase	:							int						=			self.image_processing_tester.get_expected_values(__A						)

       self.assertEqual(
           encoded_images.shape,		(1, self.image_processing_tester.num_channels, expected_height, expected_width),		)

       # Test batched
       UpperCAmelCase	:							Tuple						=			self.image_processing_tester.get_expected_values(__A,		batched=__A						)

       UpperCAmelCase	:							List[Any]						=			image_processor(
           __A,		['''semantic'''] * len(__A						),		return_tensors='''pt'''						).pixel_values
       self.assertEqual(
           encoded_images.shape,		(
               self.image_processing_tester.batch_size,
               self.image_processing_tester.num_channels,
               expected_height,
               expected_width,
           ),		)



     def 	__magic_name__   (							self							:					List[str]						):
       # Initialize image_processor
       UpperCAmelCase	:							Any						=			self.image_processing_class(**self.image_processor_dict						)
       # create random numpy tensors
       UpperCAmelCase	:							List[str]						=			prepare_image_inputs(self.image_processing_tester,		equal_resolution=__A,		numpify=__A						)
       for image in image_inputs:
         self.assertIsInstance(__A,		np.ndarray						)

       # Test not batched input
       UpperCAmelCase	:							List[str]						=			image_processor(image_inputs[0],		['''semantic'''],		return_tensors='''pt'''						).pixel_values

       UpperCAmelCase	:							Any						=			self.image_processing_tester.get_expected_values(__A						)

       self.assertEqual(
           encoded_images.shape,		(1, self.image_processing_tester.num_channels, expected_height, expected_width),		)

       # Test batched
       UpperCAmelCase	:							List[Any]						=			self.image_processing_tester.get_expected_values(__A,		batched=__A						)

       UpperCAmelCase	:							str						=			image_processor(
           __A,		['''semantic'''] * len(__A						),		return_tensors='''pt'''						).pixel_values
       self.assertEqual(
           encoded_images.shape,		(
               self.image_processing_tester.batch_size,
               self.image_processing_tester.num_channels,
               expected_height,
               expected_width,
           ),		)



     def 	__magic_name__   (							self							:					Any						):
       # Initialize image_processor
       UpperCAmelCase	:							Optional[int]						=			self.image_processing_class(**self.image_processor_dict						)
       # create random PyTorch tensors
       UpperCAmelCase	:							Dict						=			prepare_image_inputs(self.image_processing_tester,		equal_resolution=__A,		torchify=__A						)
       for image in image_inputs:
         self.assertIsInstance(__A,		torch.Tensor						)

       # Test not batched input
       UpperCAmelCase	:							Dict						=			image_processor(image_inputs[0],		['''semantic'''],		return_tensors='''pt'''						).pixel_values

       UpperCAmelCase	:							int						=			self.image_processing_tester.get_expected_values(__A						)

       self.assertEqual(
           encoded_images.shape,		(1, self.image_processing_tester.num_channels, expected_height, expected_width),		)

       # Test batched
       UpperCAmelCase	:							Dict						=			self.image_processing_tester.get_expected_values(__A,		batched=__A						)

       UpperCAmelCase	:							Dict						=			image_processor(
           __A,		['''semantic'''] * len(__A						),		return_tensors='''pt'''						).pixel_values
       self.assertEqual(
           encoded_images.shape,		(
               self.image_processing_tester.batch_size,
               self.image_processing_tester.num_channels,
               expected_height,
               expected_width,
           ),		)



     def 	__magic_name__   (							self							:					Any,		__A							:					str=False,		__A							:					Union[str, Any]=False,		__A							:					Union[str, Any]="np"						):
       UpperCAmelCase	:							List[str]						=			self.image_processing_class(**self.image_processor_dict						)
       # prepare image and target
       UpperCAmelCase	:							Tuple						=			self.image_processing_tester.num_labels
       UpperCAmelCase	:							List[Any]						=			None
       UpperCAmelCase	:							Any						=			None
       UpperCAmelCase	:							int						=			prepare_image_inputs(self.image_processing_tester,		equal_resolution=__A						)
       if with_segmentation_maps:
         UpperCAmelCase	:							Any						=			num_labels
         if is_instance_map:
           UpperCAmelCase	:							Any						=			list(range(__A						)						) * 2
           UpperCAmelCase	:							Any						=			dict(enumerate(__A						)						)
         UpperCAmelCase	:							Optional[Any]						=			[
             np.random.randint(0,		high * 2,		(img.size[1], img.size[0])						).astype(np.uinta						) for img in image_inputs
         ]
         if segmentation_type == "pil":
           UpperCAmelCase	:							Tuple						=			[Image.fromarray(__A						) for annotation in annotations]

       UpperCAmelCase	:							Tuple						=			image_processor(
           __A,		['''semantic'''] * len(__A						),		__A,		return_tensors='''pt''',		instance_id_to_semantic_id=__A,		pad_and_return_pixel_mask=__A,		)

       return inputs



     def 	__magic_name__   (							self							:					Tuple						):
       pass



     def 	__magic_name__   (							self							:					Optional[Any]						):
       def common(__A							:					Any=False,		__A							:					int=None						):
         UpperCAmelCase	:							List[str]						=			self.comm_get_image_processor_inputs(
             with_segmentation_maps=__A,		is_instance_map=__A,		segmentation_type=__A						)

         UpperCAmelCase	:							List[Any]						=			inputs['''mask_labels''']
         UpperCAmelCase	:							Tuple						=			inputs['''class_labels''']
         UpperCAmelCase	:							Any						=			inputs['''pixel_values''']
         UpperCAmelCase	:							Optional[Any]						=			inputs['''text_inputs''']

         # check the batch_size
         for mask_label, class_label, text_input in zip(__A,		__A,		__A						):
           self.assertEqual(mask_label.shape[0],		class_label.shape[0]						)
           # this ensure padding has happened
           self.assertEqual(mask_label.shape[1:],		pixel_values.shape[2:]						)
           self.assertEqual(len(__A						),		self.image_processing_tester.num_text						)

       common()
       common(is_instance_map=__A						)
       common(is_instance_map=__A,		segmentation_type='''pil'''						)
       common(is_instance_map=__A,		segmentation_type='''pil'''						)



     def 	__magic_name__   (							self							:					int						):
       UpperCAmelCase	:							int						=			np.zeros((2_0, 5_0)						)
       UpperCAmelCase	:							Optional[int]						=			1
       UpperCAmelCase	:							List[str]						=			1
       UpperCAmelCase	:							str						=			1

       UpperCAmelCase	:							List[Any]						=			binary_mask_to_rle(__A						)
       self.assertEqual(len(__A						),		4						)
       self.assertEqual(rle[0],		2_1						)
       self.assertEqual(rle[1],		4_5						)



     def 	__magic_name__   (							self							:					List[Any]						):
       UpperCAmelCase	:							Tuple						=			self.image_processing_class(
           num_labels=self.image_processing_tester.num_classes,		max_seq_length=7_7,		task_seq_length=7_7,		class_info_file='''ade20k_panoptic.json''',		num_text=self.image_processing_tester.num_text,		repo_path='''shi-labs/oneformer_demo''',		)
       UpperCAmelCase	:							str						=			self.image_processing_tester.get_fake_oneformer_outputs()

       UpperCAmelCase	:							Optional[int]						=			fature_extractor.post_process_semantic_segmentation(__A						)

       self.assertEqual(len(__A						),		self.image_processing_tester.batch_size						)
       self.assertEqual(
           segmentation[0].shape,		(
               self.image_processing_tester.height,
               self.image_processing_tester.width,
           ),		)

       UpperCAmelCase	:							Union[str, Any]						=			[(1, 4) for i in range(self.image_processing_tester.batch_size						)]
       UpperCAmelCase	:							int						=			fature_extractor.post_process_semantic_segmentation(__A,		target_sizes=__A						)

       self.assertEqual(segmentation[0].shape,		target_sizes[0]						)



     def 	__magic_name__   (							self							:					Any						):
       UpperCAmelCase	:							List[Any]						=			self.image_processing_class(
           num_labels=self.image_processing_tester.num_classes,		max_seq_length=7_7,		task_seq_length=7_7,		class_info_file='''ade20k_panoptic.json''',		num_text=self.image_processing_tester.num_text,		repo_path='''shi-labs/oneformer_demo''',		)
       UpperCAmelCase	:							Optional[int]						=			self.image_processing_tester.get_fake_oneformer_outputs()
       UpperCAmelCase	:							Tuple						=			image_processor.post_process_instance_segmentation(__A,		threshold=0						)

       self.assertTrue(len(__A						) == self.image_processing_tester.batch_size						)
       for el in segmentation:
         self.assertTrue('''segmentation''' in el						)
         self.assertTrue('''segments_info''' in el						)
         self.assertEqual(type(el['''segments_info''']						),		__A						)
         self.assertEqual(
             el['''segmentation'''].shape,		(self.image_processing_tester.height, self.image_processing_tester.width)						)





     def 	__magic_name__   (							self							:					Any						):
       UpperCAmelCase	:							Tuple						=			self.image_processing_class(
           num_labels=self.image_processing_tester.num_classes,		max_seq_length=7_7,		task_seq_length=7_7,		class_info_file='''ade20k_panoptic.json''',		num_text=self.image_processing_tester.num_text,		repo_path='''shi-labs/oneformer_demo''',		)
       UpperCAmelCase	:							Dict						=			self.image_processing_tester.get_fake_oneformer_outputs()
       UpperCAmelCase	:							Optional[Any]						=			image_processor.post_process_panoptic_segmentation(__A,		threshold=0						)

       self.assertTrue(len(__A						) == self.image_processing_tester.batch_size						)
       for el in segmentation:
         self.assertTrue('''segmentation''' in el						)
         self.assertTrue('''segments_info''' in el						)
         self.assertEqual(type(el['''segments_info''']						),		__A						)
         self.assertEqual(
             el['''segmentation'''].shape,		(self.image_processing_tester.height, self.image_processing_tester.width)						)








# 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.utils import ComputeEnvironment

from .cluster import get_cluster_input
from .config_args import cache_dir, default_config_file, default_yaml_config_file, load_config_from_file  # noqa: F401
from .config_utils import _ask_field, _ask_options, _convert_compute_environment  # noqa: F401
from .sagemaker import get_sagemaker_input


_lowerCamelCase    :       Optional[Any]        =							"Launches a series of prompts to create and save a `default_config.yaml` configuration file for your training system. Should always be ran first on your machine"

def       a__					(		)  ->						List[Any]:
  UpperCAmelCase	:							Dict						=			_ask_options(
      '''In which compute environment are you running?'''    ,			['''This machine''', '''AWS (Amazon SageMaker)''']    ,			_convert_compute_environment    ,			)
  if compute_environment == ComputeEnvironment.AMAZON_SAGEMAKER:
    UpperCAmelCase	:							List[Any]						=			get_sagemaker_input()
  else:
    UpperCAmelCase	:							Optional[Any]						=			get_cluster_input()
  return config

def       a__					(		UpperCAmelCase							:      Union[str, Any]=None      )  ->						List[Any]:
  if subparsers is not None:
    UpperCAmelCase	:							Optional[Any]						=			subparsers.add_parser('''config'''    ,			description=UpperCAmelCase      )
  else:
    UpperCAmelCase	:							List[str]						=			argparse.ArgumentParser('''Accelerate config command'''    ,			description=UpperCAmelCase      )

  parser.add_argument(
      '''--config_file'''    ,			default=UpperCAmelCase    ,			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=UpperCAmelCase      )
  return parser

def       a__					(		UpperCAmelCase							:      List[Any]      )  ->						Optional[int]:
  UpperCAmelCase	:							str						=			get_user_input()
  if args.config_file is not None:
    UpperCAmelCase	:							Any						=			args.config_file
  else:
    if not os.path.isdir(UpperCAmelCase      ):
      os.makedirs(UpperCAmelCase      )
    UpperCAmelCase	:							List[str]						=			default_yaml_config_file

  if config_file.endswith('''.json'''      ):
    config.to_json_file(UpperCAmelCase      )
  else:
    config.to_yaml_file(UpperCAmelCase      )
  print(f'''accelerate configuration saved at {config_file}'''      )

def       a__					(		)  ->						Dict:
  UpperCAmelCase	:							str						=			config_command_parser()
  UpperCAmelCase	:							str						=			parser.parse_args()
  config_command(UpperCAmelCase      )


if __name__ == "__main__":
     main()





