





'''simple docstring'''






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   (__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE="shi-labs/oneformer_demo"   ):


  """simple docstring"""

  with open(hf_hub_download(__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      repo_type='''dataset'''   )  ,      '''r'''   ) as f:
    _UpperCamelCase       			=json.load(__SCREAMING_SNAKE_CASE   )
  _UpperCamelCase       			={}
  _UpperCamelCase       			=[]
  _UpperCamelCase       			=[]
  for key, info in class_info.items():
    _UpperCamelCase       			=info['''name''']
    class_names.append(info['''name''']   )
    if info["isthing"]:
      thing_ids.append(int(__SCREAMING_SNAKE_CASE   )   )
  _UpperCamelCase       			=thing_ids
  _UpperCamelCase       			=class_names
  return metadata







class 		UpperCAmelCase   (				unittest.TestCase):


  """simple docstring"""







  def __init__(				self    :					Tuple							,	UpperCamelCase__    :					List[Any]							,	UpperCamelCase__    :					Any=7							,	UpperCamelCase__    :					List[Any]=3							,	UpperCamelCase__    :					Union[str, Any]=30							,	UpperCamelCase__    :					List[str]=400							,	UpperCamelCase__    :					List[str]=None							,	UpperCamelCase__    :					List[Any]=True							,	UpperCamelCase__    :					Union[str, Any]=True							,	UpperCamelCase__    :					str=[0.5, 0.5, 0.5]							,	UpperCamelCase__    :					Optional[Any]=[0.5, 0.5, 0.5]							,	UpperCamelCase__    :					str=10							,	UpperCamelCase__    :					Optional[int]=False							,	UpperCamelCase__    :					Optional[Any]=255							,	UpperCamelCase__    :					Any="shi-labs/oneformer_demo"							,	UpperCamelCase__    :					Dict="ade20k_panoptic.json"							,	UpperCamelCase__    :					Dict=10							,	)     ->						Union[str, Any]:
    _UpperCamelCase       			=parent
    _UpperCamelCase       			=batch_size
    _UpperCamelCase       			=num_channels
    _UpperCamelCase       			=min_resolution
    _UpperCamelCase       			=max_resolution
    _UpperCamelCase       			=do_resize
    _UpperCamelCase       			={'''shortest_edge''': 32, '''longest_edge''': 1333} if size is None else size
    _UpperCamelCase       			=do_normalize
    _UpperCamelCase       			=image_mean
    _UpperCamelCase       			=image_std
    _UpperCamelCase       			=class_info_file
    _UpperCamelCase       			=prepare_metadata(UpperCamelCase__							,	UpperCamelCase__  )
    _UpperCamelCase       			=num_text
    _UpperCamelCase       			=repo_path

    # for the post_process_functions
    _UpperCamelCase       			=2
    _UpperCamelCase       			=10
    _UpperCamelCase       			=10
    _UpperCamelCase       			=3
    _UpperCamelCase       			=4
    _UpperCamelCase       			=num_labels
    _UpperCamelCase       			=do_reduce_labels
    _UpperCamelCase       			=ignore_index


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

    else:
      _UpperCamelCase       			=[]
      for image in image_inputs:
        _UpperCamelCase			,     _UpperCamelCase       			=self.get_expected_values([image]  )
        expected_values.append((expected_height, expected_width)  )
      _UpperCamelCase       			=max(UpperCamelCase__							,	key=lambda UpperCamelCase__  : item[0]  )[0]
      _UpperCamelCase       			=max(UpperCamelCase__							,	key=lambda UpperCamelCase__  : item[1]  )[1]

    return expected_height, expected_width




  def   UpperCamelCase__      (				self    :					Dict  )     ->						Optional[int]:
    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   (				lowercase_   ,     unittest.TestCase):


  """simple docstring"""







  lowerCAmelCase_					=				OneFormerImageProcessor if (is_vision_available() and is_torch_available()) else None
  # only for test_image_processing_common.test_image_proc_to_json_string
  lowerCAmelCase_					=				image_processing_class


  def   UpperCamelCase__      (				self    :					Tuple  )     ->						Tuple:
    _UpperCamelCase       			=OneFormerImageProcessorTester(self  )


  @property
  def   UpperCamelCase__      (				self    :					Dict  )     ->						Optional[int]:
    return self.image_processing_tester.prepare_image_processor_dict()


  def   UpperCamelCase__      (				self    :					Dict  )     ->						List[Any]:
    _UpperCamelCase       			=self.image_processing_class(**self.image_processor_dict  )
    self.assertTrue(hasattr(UpperCamelCase__							,	'''image_mean'''  )  )
    self.assertTrue(hasattr(UpperCamelCase__							,	'''image_std'''  )  )
    self.assertTrue(hasattr(UpperCamelCase__							,	'''do_normalize'''  )  )
    self.assertTrue(hasattr(UpperCamelCase__							,	'''do_resize'''  )  )
    self.assertTrue(hasattr(UpperCamelCase__							,	'''size'''  )  )
    self.assertTrue(hasattr(UpperCamelCase__							,	'''ignore_index'''  )  )
    self.assertTrue(hasattr(UpperCamelCase__							,	'''class_info_file'''  )  )
    self.assertTrue(hasattr(UpperCamelCase__							,	'''num_text'''  )  )
    self.assertTrue(hasattr(UpperCamelCase__							,	'''repo_path'''  )  )
    self.assertTrue(hasattr(UpperCamelCase__							,	'''metadata'''  )  )
    self.assertTrue(hasattr(UpperCamelCase__							,	'''do_reduce_labels'''  )  )


  def   UpperCamelCase__      (				self    :					str  )     ->						Union[str, Any]:
    pass


  def   UpperCamelCase__      (				self    :					Any  )     ->						Optional[Any]:
    # Initialize image_processor
    _UpperCamelCase       			=self.image_processing_class(**self.image_processor_dict  )
    # create random PIL images
    _UpperCamelCase       			=prepare_image_inputs(self.image_processing_tester							,	equal_resolution=UpperCamelCase__  )
    for image in image_inputs:
      self.assertIsInstance(UpperCamelCase__							,	Image.Image  )

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

    _UpperCamelCase			,     _UpperCamelCase       			=self.image_processing_tester.get_expected_values(UpperCamelCase__  )

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

    # Test batched
    _UpperCamelCase			,     _UpperCamelCase       			=self.image_processing_tester.get_expected_values(UpperCamelCase__							,	batched=UpperCamelCase__  )

    _UpperCamelCase       			=image_processor(
        UpperCamelCase__							,	['''semantic'''] * len(UpperCamelCase__  )							,	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   UpperCamelCase__      (				self    :					int  )     ->						int:
    # Initialize image_processor
    _UpperCamelCase       			=self.image_processing_class(**self.image_processor_dict  )
    # create random numpy tensors
    _UpperCamelCase       			=prepare_image_inputs(self.image_processing_tester							,	equal_resolution=UpperCamelCase__							,	numpify=UpperCamelCase__  )
    for image in image_inputs:
      self.assertIsInstance(UpperCamelCase__							,	np.ndarray  )

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

    _UpperCamelCase			,     _UpperCamelCase       			=self.image_processing_tester.get_expected_values(UpperCamelCase__  )

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

    # Test batched
    _UpperCamelCase			,     _UpperCamelCase       			=self.image_processing_tester.get_expected_values(UpperCamelCase__							,	batched=UpperCamelCase__  )

    _UpperCamelCase       			=image_processor(
        UpperCamelCase__							,	['''semantic'''] * len(UpperCamelCase__  )							,	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   UpperCamelCase__      (				self    :					Union[str, Any]  )     ->						Union[str, Any]:
    # Initialize image_processor
    _UpperCamelCase       			=self.image_processing_class(**self.image_processor_dict  )
    # create random PyTorch tensors
    _UpperCamelCase       			=prepare_image_inputs(self.image_processing_tester							,	equal_resolution=UpperCamelCase__							,	torchify=UpperCamelCase__  )
    for image in image_inputs:
      self.assertIsInstance(UpperCamelCase__							,	torch.Tensor  )

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

    _UpperCamelCase			,     _UpperCamelCase       			=self.image_processing_tester.get_expected_values(UpperCamelCase__  )

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

    # Test batched
    _UpperCamelCase			,     _UpperCamelCase       			=self.image_processing_tester.get_expected_values(UpperCamelCase__							,	batched=UpperCamelCase__  )

    _UpperCamelCase       			=image_processor(
        UpperCamelCase__							,	['''semantic'''] * len(UpperCamelCase__  )							,	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   UpperCamelCase__      (				self    :					Tuple							,	UpperCamelCase__    :					List[str]=False							,	UpperCamelCase__    :					Optional[Any]=False							,	UpperCamelCase__    :					List[Any]="np"  )     ->						Dict:
    _UpperCamelCase       			=self.image_processing_class(**self.image_processor_dict  )
    # prepare image and target
    _UpperCamelCase       			=self.image_processing_tester.num_labels
    _UpperCamelCase       			=None
    _UpperCamelCase       			=None
    _UpperCamelCase       			=prepare_image_inputs(self.image_processing_tester							,	equal_resolution=UpperCamelCase__  )
    if with_segmentation_maps:
      _UpperCamelCase       			=num_labels
      if is_instance_map:
        _UpperCamelCase       			=list(range(UpperCamelCase__  )  ) * 2
        _UpperCamelCase       			=dict(enumerate(UpperCamelCase__  )  )
      _UpperCamelCase       			=[
          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       			=[Image.fromarray(UpperCamelCase__  ) for annotation in annotations]

    _UpperCamelCase       			=image_processor(
        UpperCamelCase__							,	['''semantic'''] * len(UpperCamelCase__  )							,	UpperCamelCase__							,	return_tensors='''pt'''							,	instance_id_to_semantic_id=UpperCamelCase__							,	pad_and_return_pixel_mask=UpperCamelCase__							,	)

    return inputs


  def   UpperCamelCase__      (				self    :					Optional[int]  )     ->						str:
    pass


  def   UpperCamelCase__      (				self    :					Optional[Any]  )     ->						List[Any]:
    def common(UpperCamelCase__    :					Tuple=False							,	UpperCamelCase__    :					List[Any]=None  ):
      _UpperCamelCase       			=self.comm_get_image_processor_inputs(
          with_segmentation_maps=UpperCamelCase__							,	is_instance_map=UpperCamelCase__							,	segmentation_type=UpperCamelCase__  )

      _UpperCamelCase       			=inputs['''mask_labels''']
      _UpperCamelCase       			=inputs['''class_labels''']
      _UpperCamelCase       			=inputs['''pixel_values''']
      _UpperCamelCase       			=inputs['''text_inputs''']

      # check the batch_size
      for mask_label, class_label, text_input in zip(UpperCamelCase__							,	UpperCamelCase__							,	UpperCamelCase__  ):
        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(UpperCamelCase__  )							,	self.image_processing_tester.num_text  )

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


  def   UpperCamelCase__      (				self    :					Any  )     ->						Tuple:
    _UpperCamelCase       			=np.zeros((20, 50)  )
    _UpperCamelCase       			=1
    _UpperCamelCase       			=1
    _UpperCamelCase       			=1

    _UpperCamelCase       			=binary_mask_to_rle(UpperCamelCase__  )
    self.assertEqual(len(UpperCamelCase__  )							,	4  )
    self.assertEqual(rle[0]							,	21  )
    self.assertEqual(rle[1]							,	45  )


  def   UpperCamelCase__      (				self    :					Tuple  )     ->						List[Any]:
    _UpperCamelCase       			=self.image_processing_class(
        num_labels=self.image_processing_tester.num_classes							,	max_seq_length=77							,	task_seq_length=77							,	class_info_file='''ade20k_panoptic.json'''							,	num_text=self.image_processing_tester.num_text							,	repo_path='''shi-labs/oneformer_demo'''							,	)
    _UpperCamelCase       			=self.image_processing_tester.get_fake_oneformer_outputs()

    _UpperCamelCase       			=fature_extractor.post_process_semantic_segmentation(UpperCamelCase__  )

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

    _UpperCamelCase       			=[(1, 4) for i in range(self.image_processing_tester.batch_size  )]
    _UpperCamelCase       			=fature_extractor.post_process_semantic_segmentation(UpperCamelCase__							,	target_sizes=UpperCamelCase__  )

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


  def   UpperCamelCase__      (				self    :					int  )     ->						List[str]:
    _UpperCamelCase       			=self.image_processing_class(
        num_labels=self.image_processing_tester.num_classes							,	max_seq_length=77							,	task_seq_length=77							,	class_info_file='''ade20k_panoptic.json'''							,	num_text=self.image_processing_tester.num_text							,	repo_path='''shi-labs/oneformer_demo'''							,	)
    _UpperCamelCase       			=self.image_processing_tester.get_fake_oneformer_outputs()
    _UpperCamelCase       			=image_processor.post_process_instance_segmentation(UpperCamelCase__							,	threshold=0  )

    self.assertTrue(len(UpperCamelCase__  ) == 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''']  )							,	UpperCamelCase__  )
      self.assertEqual(
          el['''segmentation'''].shape							,	(self.image_processing_tester.height, self.image_processing_tester.width)  )




  def   UpperCamelCase__      (				self    :					int  )     ->						Tuple:
    _UpperCamelCase       			=self.image_processing_class(
        num_labels=self.image_processing_tester.num_classes							,	max_seq_length=77							,	task_seq_length=77							,	class_info_file='''ade20k_panoptic.json'''							,	num_text=self.image_processing_tester.num_text							,	repo_path='''shi-labs/oneformer_demo'''							,	)
    _UpperCamelCase       			=self.image_processing_tester.get_fake_oneformer_outputs()
    _UpperCamelCase       			=image_processor.post_process_panoptic_segmentation(UpperCamelCase__							,	threshold=0  )

    self.assertTrue(len(UpperCamelCase__  ) == 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''']  )							,	UpperCamelCase__  )
      self.assertEqual(
          el['''segmentation'''].shape							,	(self.image_processing_tester.height, self.image_processing_tester.width)  )













'''simple docstring'''






import importlib.util
import os
import platform
from argparse import ArgumentParser

import huggingface_hub

from .. import __version__ as version
from ..utils import (
    is_accelerate_available,
    is_flax_available,
    is_safetensors_available,
    is_tf_available,
    is_torch_available,
)
from . import BaseTransformersCLICommand


def 					_a   (__SCREAMING_SNAKE_CASE   ):


  """simple docstring"""

  return EnvironmentCommand()


def 					_a   (__SCREAMING_SNAKE_CASE   ):


  """simple docstring"""

  return EnvironmentCommand(args.accelerate_config_file   )







class 		UpperCAmelCase   (				lowercase_):


  """simple docstring"""







  @staticmethod
  def   UpperCamelCase__      (				UpperCamelCase__    :					ArgumentParser  )     ->						Union[str, Any]:
    _UpperCamelCase       			=parser.add_parser('''env'''  )
    download_parser.set_defaults(func=UpperCamelCase__  )
    download_parser.add_argument(
        '''--accelerate-config_file'''							,	default=UpperCamelCase__							,	help='''The accelerate config file to use for the default values in the launching script.'''							,	)
    download_parser.set_defaults(func=UpperCamelCase__  )


  def __init__(				self    :					Tuple							,	UpperCamelCase__    :					Dict							,	*UpperCamelCase__    :					str  )     ->						None:
    _UpperCamelCase       			=accelerate_config_file


  def   UpperCamelCase__      (				self    :					str  )     ->						int:
    _UpperCamelCase       			='''not installed'''
    if is_safetensors_available():
      import safetensors

      _UpperCamelCase       			=safetensors.__version__
    elif importlib.util.find_spec('''safetensors'''  ) is not None:
      import safetensors

      _UpperCamelCase       			=F'''{safetensors.__version__} but is ignored because of PyTorch version too old.'''

    _UpperCamelCase       			='''not installed'''
    _UpperCamelCase       			=_UpperCamelCase       			='''not found'''
    if is_accelerate_available():
      import accelerate
      from accelerate.commands.config import default_config_file, load_config_from_file

      _UpperCamelCase       			=accelerate.__version__
      # Get the default from the config file.
      if self._accelerate_config_file is not None or os.path.isfile(UpperCamelCase__  ):
        _UpperCamelCase       			=load_config_from_file(self._accelerate_config_file  ).to_dict()

      _UpperCamelCase       			=(
          '''\n'''.join([F'''\t- {prop}: {val}''' for prop, val in accelerate_config.items()]  )
          if isinstance(UpperCamelCase__							,	UpperCamelCase__  )
          else F'''\t{accelerate_config}'''
      )

    _UpperCamelCase       			='''not installed'''
    _UpperCamelCase       			='''NA'''
    if is_torch_available():
      import torch

      _UpperCamelCase       			=torch.__version__
      _UpperCamelCase       			=torch.cuda.is_available()

    _UpperCamelCase       			='''not installed'''
    _UpperCamelCase       			='''NA'''
    if is_tf_available():
      import tensorflow as tf

      _UpperCamelCase       			=tf.__version__
      try:
        # deprecated in v2.1
        _UpperCamelCase       			=tf.test.is_gpu_available()
      except AttributeError:
        # returns list of devices, convert to bool
        _UpperCamelCase       			=bool(tf.config.list_physical_devices('''GPU'''  )  )

    _UpperCamelCase       			='''not installed'''
    _UpperCamelCase       			='''not installed'''
    _UpperCamelCase       			='''not installed'''
    _UpperCamelCase       			='''NA'''
    if is_flax_available():
      import flax
      import jax
      import jaxlib

      _UpperCamelCase       			=flax.__version__
      _UpperCamelCase       			=jax.__version__
      _UpperCamelCase       			=jaxlib.__version__
      _UpperCamelCase       			=jax.lib.xla_bridge.get_backend().platform

    _UpperCamelCase       			={
        '''`transformers` version''': version,
        '''Platform''': platform.platform(),
        '''Python version''': platform.python_version(),
        '''Huggingface_hub version''': huggingface_hub.__version__,
        '''Safetensors version''': F'''{safetensors_version}''',
        '''Accelerate version''': F'''{accelerate_version}''',
        '''Accelerate config''': F'''{accelerate_config_str}''',
        '''PyTorch version (GPU?)''': F'''{pt_version} ({pt_cuda_available})''',
        '''Tensorflow version (GPU?)''': F'''{tf_version} ({tf_cuda_available})''',
        '''Flax version (CPU?/GPU?/TPU?)''': F'''{flax_version} ({jax_backend})''',
        '''Jax version''': F'''{jax_version}''',
        '''JaxLib version''': F'''{jaxlib_version}''',
        '''Using GPU in script?''': '''<fill in>''',
        '''Using distributed or parallel set-up in script?''': '''<fill in>''',
    }

    print('''\nCopy-and-paste the text below in your GitHub issue and FILL OUT the two last points.\n'''  )
    print(self.format_dict(UpperCamelCase__  )  )

    return info




  @staticmethod
  def   UpperCamelCase__      (				UpperCamelCase__    :					Optional[Any]  )     ->						Dict:
    return "\n".join([F'''- {prop}: {val}''' for prop, val in d.items()]  ) + "\n"






