

"""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 	lowerCAmelCase    (       __UpperCamelCase				,     __UpperCamelCase="shi-labs/oneformer_demo"   ):
 '''simple docstring'''





 with open(hf_hub_download(__UpperCamelCase				,     __UpperCamelCase				,     repo_type="""dataset"""   )				,     """r"""   ) as f:
  UpperCAmelCase__       :	Union[str, Any]        =       json.load(__UpperCamelCase   )
 UpperCAmelCase__       :	Tuple        =       {}
 UpperCAmelCase__       :	List[Any]        =       []
 UpperCAmelCase__       :	str        =       []
 for key, info in class_info.items():
  UpperCAmelCase__       :	Optional[int]        =       info["""name"""]
  class_names.append(info["""name"""]   )
  if info["isthing"]:
   thing_ids.append(int(__UpperCamelCase   )   )
 UpperCAmelCase__       :	Any        =       thing_ids
 UpperCAmelCase__       :	str        =       class_names
 return metadata







class   __lowercase      (     unittest.TestCase       ):

  def __init__(	self		:      List[Any]       ,A		:      Optional[int]       ,A		:      Optional[Any]=7       ,A		:      Any=3       ,A		:      Tuple=30       ,A		:      int=400       ,A		:      Tuple=None       ,A		:      Tuple=True       ,A		:      Tuple=True       ,A		:      Optional[int]=[0.5, 0.5, 0.5]       ,A		:      List[Any]=[0.5, 0.5, 0.5]       ,A		:      Dict=10       ,A		:      int=False       ,A		:      Optional[int]=255       ,A		:      Any="shi-labs/oneformer_demo"       ,A		:      Tuple="ade20k_panoptic.json"       ,A		:      Dict=10       ,):






   '''simple docstring'''
   UpperCAmelCase__       :	Optional[Any]        =       parent
   UpperCAmelCase__       :	List[str]        =       batch_size
   UpperCAmelCase__       :	Any        =       num_channels
   UpperCAmelCase__       :	List[str]        =       min_resolution
   UpperCAmelCase__       :	List[str]        =       max_resolution
   UpperCAmelCase__       :	Optional[Any]        =       do_resize
   UpperCAmelCase__       :	int        =       {"""shortest_edge""": 32, """longest_edge""": 1_333} if size is None else size
   UpperCAmelCase__       :	Dict        =       do_normalize
   UpperCAmelCase__       :	int        =       image_mean
   UpperCAmelCase__       :	Tuple        =       image_std
   UpperCAmelCase__       :	Optional[Any]        =       class_info_file
   UpperCAmelCase__       :	List[str]        =       prepare_metadata(A       ,A     )
   UpperCAmelCase__       :	Tuple        =       num_text
   UpperCAmelCase__       :	Dict        =       repo_path

   # for the post_process_functions
   UpperCAmelCase__       :	List[str]        =       2
   UpperCAmelCase__       :	Any        =       10
   UpperCAmelCase__       :	List[Any]        =       10
   UpperCAmelCase__       :	Optional[int]        =       3
   UpperCAmelCase__       :	Optional[int]        =       4
   UpperCAmelCase__       :	str        =       num_labels
   UpperCAmelCase__       :	List[str]        =       do_reduce_labels
   UpperCAmelCase__       :	int        =       ignore_index

  def  __lowercase							(	self		:      str     ):






   '''simple docstring'''
   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  __lowercase							(	self		:      Dict       ,A		:      List[str]       ,A		:      Dict=False     ):






   '''simple docstring'''
   if not batched:
    UpperCAmelCase__       :	Tuple        =       image_inputs[0]
    if isinstance(A       ,Image.Image     ):
     UpperCAmelCase__ ,   UpperCAmelCase__       :	Optional[int]        =       image.size
    else:
     UpperCAmelCase__ ,   UpperCAmelCase__       :	Union[str, Any]        =       image.shape[1], image.shape[2]
    if w < h:
     UpperCAmelCase__       :	Any        =       int(self.size["""shortest_edge"""] * h / w     )
     UpperCAmelCase__       :	int        =       self.size["""shortest_edge"""]
    elif w > h:
     UpperCAmelCase__       :	Any        =       self.size["""shortest_edge"""]
     UpperCAmelCase__       :	Any        =       int(self.size["""shortest_edge"""] * w / h     )
    else:
     UpperCAmelCase__       :	Optional[Any]        =       self.size["""shortest_edge"""]
     UpperCAmelCase__       :	str        =       self.size["""shortest_edge"""]

   else:
    UpperCAmelCase__       :	int        =       []
    for image in image_inputs:
     UpperCAmelCase__ ,   UpperCAmelCase__       :	Any        =       self.get_expected_values([image]     )
     expected_values.append((expected_height, expected_width)     )
    UpperCAmelCase__       :	str        =       max(A       ,key=lambda A     : item[0]     )[0]
    UpperCAmelCase__       :	List[Any]        =       max(A       ,key=lambda A     : item[1]     )[1]

   return expected_height, expected_width




  def  __lowercase							(	self		:      Union[str, Any]     ):






   '''simple docstring'''
   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   __lowercase      (     __lowerCamelCase						,      unittest.TestCase       ):
  snake_case_												=							OneFormerImageProcessor if (is_vision_available() and is_torch_available()) else None
  # only for test_image_processing_common.test_image_proc_to_json_string
  snake_case_												=							image_processing_class

  def  __lowercase							(	self		:      Any     ):






   '''simple docstring'''
   UpperCAmelCase__       :	Dict        =       OneFormerImageProcessorTester(self     )

  @property
  def  __lowercase							(	self		:      Tuple     ):






   '''simple docstring'''
   return self.image_processing_tester.prepare_image_processor_dict()

  def  __lowercase							(	self		:      List[Any]     ):






   '''simple docstring'''
   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  __lowercase							(	self		:      Dict     ):






   '''simple docstring'''
   pass

  def  __lowercase							(	self		:      List[str]     ):






   '''simple docstring'''
   # Initialize image_processor
   UpperCAmelCase__       :	Tuple        =       self.image_processing_class(**self.image_processor_dict     )
   # create random PIL images
   UpperCAmelCase__       :	List[str]        =       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__       :	Tuple        =       image_processor(image_inputs[0]       ,["""semantic"""]       ,return_tensors="""pt"""     ).pixel_values

   UpperCAmelCase__ ,   UpperCAmelCase__       :	Optional[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__ ,   UpperCAmelCase__       :	str        =       self.image_processing_tester.get_expected_values(A       ,batched=A     )

   UpperCAmelCase__       :	Tuple        =       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  __lowercase							(	self		:      List[Any]     ):






   '''simple docstring'''
   # Initialize image_processor
   UpperCAmelCase__       :	str        =       self.image_processing_class(**self.image_processor_dict     )
   # create random numpy tensors
   UpperCAmelCase__       :	Union[str, Any]        =       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__ ,   UpperCAmelCase__       :	Tuple        =       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__ ,   UpperCAmelCase__       :	Union[str, Any]        =       self.image_processing_tester.get_expected_values(A       ,batched=A     )

   UpperCAmelCase__       :	Tuple        =       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  __lowercase							(	self		:      int     ):






   '''simple docstring'''
   # Initialize image_processor
   UpperCAmelCase__       :	Union[str, Any]        =       self.image_processing_class(**self.image_processor_dict     )
   # create random PyTorch tensors
   UpperCAmelCase__       :	List[Any]        =       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__       :	str        =       image_processor(image_inputs[0]       ,["""semantic"""]       ,return_tensors="""pt"""     ).pixel_values

   UpperCAmelCase__ ,   UpperCAmelCase__       :	Union[str, 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__ ,   UpperCAmelCase__       :	Optional[Any]        =       self.image_processing_tester.get_expected_values(A       ,batched=A     )

   UpperCAmelCase__       :	Tuple        =       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  __lowercase							(	self		:      List[str]       ,A		:      Optional[Any]=False       ,A		:      List[Any]=False       ,A		:      str="np"     ):






   '''simple docstring'''
   UpperCAmelCase__       :	Union[str, Any]        =       self.image_processing_class(**self.image_processor_dict     )
   # prepare image and target
   UpperCAmelCase__       :	Any        =       self.image_processing_tester.num_labels
   UpperCAmelCase__       :	Optional[int]        =       None
   UpperCAmelCase__       :	int        =       None
   UpperCAmelCase__       :	Union[str, Any]        =       prepare_image_inputs(self.image_processing_tester       ,equal_resolution=A     )
   if with_segmentation_maps:
    UpperCAmelCase__       :	Optional[int]        =       num_labels
    if is_instance_map:
     UpperCAmelCase__       :	int        =       list(range(A     )     ) * 2
     UpperCAmelCase__       :	Dict        =       dict(enumerate(A     )     )
    UpperCAmelCase__       :	Union[str, 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__       :	Optional[Any]        =       [Image.fromarray(A     ) for annotation in annotations]

   UpperCAmelCase__       :	Optional[Any]        =       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  __lowercase							(	self		:      Tuple     ):






   '''simple docstring'''
   pass

  def  __lowercase							(	self		:      str     ):






   '''simple docstring'''
   def common(A		:      str=False       ,A		:      Tuple=None     ):
    UpperCAmelCase__       :	Tuple        =       self.comm_get_image_processor_inputs(
        with_segmentation_maps=A       ,is_instance_map=A       ,segmentation_type=A     )

    UpperCAmelCase__       :	int        =       inputs["""mask_labels"""]
    UpperCAmelCase__       :	Optional[int]        =       inputs["""class_labels"""]
    UpperCAmelCase__       :	str        =       inputs["""pixel_values"""]
    UpperCAmelCase__       :	int        =       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  __lowercase							(	self		:      Dict     ):






   '''simple docstring'''
   UpperCAmelCase__       :	str        =       np.zeros((20, 50)     )
   UpperCAmelCase__       :	List[Any]        =       1
   UpperCAmelCase__       :	List[Any]        =       1
   UpperCAmelCase__       :	Optional[int]        =       1

   UpperCAmelCase__       :	List[Any]        =       binary_mask_to_rle(A     )
   self.assertEqual(len(A     )       ,4     )
   self.assertEqual(rle[0]       ,21     )
   self.assertEqual(rle[1]       ,45     )

  def  __lowercase							(	self		:      str     ):






   '''simple docstring'''
   UpperCAmelCase__       :	Union[str, Any]        =       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__       :	List[Any]        =       self.image_processing_tester.get_fake_oneformer_outputs()

   UpperCAmelCase__       :	str        =       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__       :	Tuple        =       [(1, 4) for i in range(self.image_processing_tester.batch_size     )]
   UpperCAmelCase__       :	Any        =       fature_extractor.post_process_semantic_segmentation(A       ,target_sizes=A     )

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

  def  __lowercase							(	self		:      List[str]     ):






   '''simple docstring'''
   UpperCAmelCase__       :	List[str]        =       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__       :	Optional[int]        =       self.image_processing_tester.get_fake_oneformer_outputs()
   UpperCAmelCase__       :	Any        =       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  __lowercase							(	self		:      Optional[Any]     ):






   '''simple docstring'''
   UpperCAmelCase__       :	Union[str, Any]        =       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__       :	List[Any]        =       self.image_processing_tester.get_fake_oneformer_outputs()
   UpperCAmelCase__       :	str        =       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)     )








"""simple docstring"""




import logging
from dataclasses import dataclass, field
from typing import Optional

from seqaseq_trainer import arg_to_scheduler

from transformers import TrainingArguments


A_				:      Optional[Any]           =			logging.getLogger(__name__)





@dataclass
class  lowerCamelCase    (A__      ):
			lowerCamelCase__ :		Optional[float]       		=				field(
			    default=0.0      ,metadata={'help': 'The label smoothing epsilon to apply (if not zero).'}      )
			lowerCamelCase__ :		bool       		=				field(default=A__      ,metadata={'help': 'Whether to SortishSamler or not.'}      )
			lowerCamelCase__ :		bool       		=				field(
			    default=A__      ,metadata={'help': 'Whether to use generate to calculate generative metrics (ROUGE, BLEU).'}      )
			lowerCamelCase__ :		bool       		=				field(default=A__      ,metadata={'help': 'whether to use adafactor'}      )
			lowerCamelCase__ :		Optional[float]       		=				field(
			    default=A__      ,metadata={'help': 'Encoder layer dropout probability. Goes into model.config.'}      )
			lowerCamelCase__ :		Optional[float]       		=				field(
			    default=A__      ,metadata={'help': 'Decoder layer dropout probability. Goes into model.config.'}      )
			lowerCamelCase__ :		Optional[float]       		=				field(default=A__      ,metadata={'help': 'Dropout probability. Goes into model.config.'}      )
			lowerCamelCase__ :		Optional[float]       		=				field(
			    default=A__      ,metadata={'help': 'Attention dropout probability. Goes into model.config.'}      )
			lowerCamelCase__ :		Optional[str]       		=				field(
			    default='linear'      ,metadata={'help': f"Which lr scheduler to use. Selected in {sorted(arg_to_scheduler.keys()      )}"}      ,)


