


'''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 __UpperCamelCase   (			__lowerCamelCase		: List[Any]      ,  __lowerCamelCase		: Dict="shi-labs/oneformer_demo"				)      ->     int:







     '''simple docstring'''






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





class 						__SCREAMING_SNAKE_CASE					(      unittest.TestCase ):






     def __init__(			self     ,							__UpperCamelCase     ,							__UpperCamelCase=7     ,							__UpperCamelCase=3     ,							__UpperCamelCase=30     ,							__UpperCamelCase=400     ,							__UpperCamelCase=None     ,							__UpperCamelCase=True     ,							__UpperCamelCase=True     ,							__UpperCamelCase=[0.5, 0.5, 0.5]     ,							__UpperCamelCase=[0.5, 0.5, 0.5]     ,							__UpperCamelCase=10     ,							__UpperCamelCase=False     ,							__UpperCamelCase=255     ,							__UpperCamelCase="shi-labs/oneformer_demo"     ,							__UpperCamelCase="ade20k_panoptic.json"     ,							__UpperCamelCase=10     ,							)       ->    Optional[Any]:
          _a						=		parent
          _a						=		batch_size
          _a						=		num_channels
          _a						=		min_resolution
          _a						=		max_resolution
          _a						=		do_resize
          _a						=		{"shortest_edge": 32, "longest_edge": 1_333} if size is None else size
          _a						=		do_normalize
          _a						=		image_mean
          _a						=		image_std
          _a						=		class_info_file
          _a						=		prepare_metadata(__UpperCamelCase     ,							__UpperCamelCase		)
          _a						=		num_text
          _a						=		repo_path

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






     def  a_					(			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  a_					(			self     ,							__UpperCamelCase     ,							__UpperCamelCase=False		)       ->    Optional[Any]:
          if not batched:
               _a						=		image_inputs[0]
               if isinstance(__UpperCamelCase     ,							Image.Image		):
                    _a			,			_a						=		image.size
               else:
                    _a			,			_a						=		image.shape[1], image.shape[2]
               if w < h:
                    _a						=		int(self.size["shortest_edge"] * h / w		)
                    _a						=		self.size["shortest_edge"]
               elif w > h:
                    _a						=		self.size["shortest_edge"]
                    _a						=		int(self.size["shortest_edge"] * w / h		)
               else:
                    _a						=		self.size["shortest_edge"]
                    _a						=		self.size["shortest_edge"]

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

          return expected_height, expected_width






     def  a_					(			self		)       ->    List[Any]:
          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 						__SCREAMING_SNAKE_CASE					(      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  a_					(			self		)       ->    Tuple:
          _a						=		OneFormerImageProcessorTester(self		)






     @property
     def  a_					(			self		)       ->    List[str]:
          return self.image_processing_tester.prepare_image_processor_dict()






     def  a_					(			self		)       ->    Any:
          _a						=		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  a_					(			self		)       ->    Optional[Any]:
          pass






     def  a_					(			self		)       ->    int:
          # Initialize image_processor
          _a						=		self.image_processing_class(**self.image_processor_dict		)
          # create random PIL images
          _a						=		prepare_image_inputs(self.image_processing_tester     ,							equal_resolution=__UpperCamelCase		)
          for image in image_inputs:
               self.assertIsInstance(__UpperCamelCase     ,							Image.Image		)

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

          _a			,			_a						=		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
          _a			,			_a						=		self.image_processing_tester.get_expected_values(__UpperCamelCase     ,							batched=__UpperCamelCase		)

          _a						=		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  a_					(			self		)       ->    List[Any]:
          # Initialize image_processor
          _a						=		self.image_processing_class(**self.image_processor_dict		)
          # create random numpy tensors
          _a						=		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
          _a						=		image_processor(image_inputs[0]     ,							["semantic"]     ,							return_tensors="pt"		).pixel_values

          _a			,			_a						=		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
          _a			,			_a						=		self.image_processing_tester.get_expected_values(__UpperCamelCase     ,							batched=__UpperCamelCase		)

          _a						=		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  a_					(			self		)       ->    Union[str, Any]:
          # Initialize image_processor
          _a						=		self.image_processing_class(**self.image_processor_dict		)
          # create random PyTorch tensors
          _a						=		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
          _a						=		image_processor(image_inputs[0]     ,							["semantic"]     ,							return_tensors="pt"		).pixel_values

          _a			,			_a						=		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
          _a			,			_a						=		self.image_processing_tester.get_expected_values(__UpperCamelCase     ,							batched=__UpperCamelCase		)

          _a						=		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  a_					(			self     ,							__UpperCamelCase=False     ,							__UpperCamelCase=False     ,							__UpperCamelCase="np"		)       ->    Any:
          _a						=		self.image_processing_class(**self.image_processor_dict		)
          # prepare image and target
          _a						=		self.image_processing_tester.num_labels
          _a						=		None
          _a						=		None
          _a						=		prepare_image_inputs(self.image_processing_tester     ,							equal_resolution=__UpperCamelCase		)
          if with_segmentation_maps:
               _a						=		num_labels
               if is_instance_map:
                    _a						=		list(range(__UpperCamelCase		)		) * 2
                    _a						=		dict(enumerate(__UpperCamelCase		)		)
               _a						=		[
                   np.random.randint(0     ,							high * 2     ,							(img.size[1], img.size[0])		).astype(np.uinta		) for img in image_inputs
               ]
               if segmentation_type == "pil":
                    _a						=		[Image.fromarray(__UpperCamelCase		) for annotation in annotations]

          _a						=		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  a_					(			self		)       ->    Optional[int]:
          pass






     def  a_					(			self		)       ->    Tuple:
          def common(__UpperCamelCase=False     ,							__UpperCamelCase=None		):
               _a						=		self.comm_get_image_processor_inputs(
                   with_segmentation_maps=__UpperCamelCase     ,							is_instance_map=__UpperCamelCase     ,							segmentation_type=__UpperCamelCase		)

               _a						=		inputs["mask_labels"]
               _a						=		inputs["class_labels"]
               _a						=		inputs["pixel_values"]
               _a						=		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  a_					(			self		)       ->    List[str]:
          _a						=		np.zeros((20, 50)		)
          _a						=		1
          _a						=		1
          _a						=		1

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






     def  a_					(			self		)       ->    List[Any]:
          _a						=		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"     ,							)
          _a						=		self.image_processing_tester.get_fake_oneformer_outputs()

          _a						=		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,
              )     ,							)

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

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






     def  a_					(			self		)       ->    int:
          _a						=		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"     ,							)
          _a						=		self.image_processing_tester.get_fake_oneformer_outputs()
          _a						=		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  a_					(			self		)       ->    Optional[int]:
          _a						=		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"     ,							)
          _a						=		self.image_processing_tester.get_fake_oneformer_outputs()
          _a						=		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'''

from typing import TYPE_CHECKING

from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowercase__				     =		{
    "configuration_mctct": ["MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP", "MCTCTConfig"],
    "feature_extraction_mctct": ["MCTCTFeatureExtractor"],
    "processing_mctct": ["MCTCTProcessor"],
}


try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowercase__				     =		[
       "MCTCT_PRETRAINED_MODEL_ARCHIVE_LIST",
       "MCTCTForCTC",
       "MCTCTModel",
       "MCTCTPreTrainedModel",
   ]


if TYPE_CHECKING:
   from .configuration_mctct import MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP, MCTCTConfig
   from .feature_extraction_mctct import MCTCTFeatureExtractor
   from .processing_mctct import MCTCTProcessor

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_mctct import MCTCT_PRETRAINED_MODEL_ARCHIVE_LIST, MCTCTForCTC, MCTCTModel, MCTCTPreTrainedModel

else:
   import sys

   lowercase__				     =		_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

