





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







class 	__lowerCAmelCase      (       unittest.TestCase	):





   def __init__(			self       ,      snake_case       ,      snake_case=7       ,      snake_case=3       ,      snake_case=30       ,      snake_case=400       ,      snake_case=None       ,      snake_case=True       ,      snake_case=True       ,      snake_case=[0.5, 0.5, 0.5]       ,      snake_case=[0.5, 0.5, 0.5]       ,      snake_case=10       ,      snake_case=False       ,      snake_case=255       ,      snake_case="shi-labs/oneformer_demo"       ,      snake_case="ade20k_panoptic.json"       ,      snake_case=10       ,      )					->	Optional[int]:
      """simple docstring"""





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

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





   def 						_snake_case			(			self   )					->	Optional[int]:
      """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 						_snake_case			(			self       ,      snake_case       ,      snake_case=False   )					->	List[Any]:
      """simple docstring"""





      if not batched:
         a__						:    Optional[int]   		=	image_inputs[0]
         if isinstance(snake_case       ,      Image.Image   ):
            a__		,  a__						:    str   		=	image.size
         else:
            a__		,  a__						:    str   		=	image.shape[1], image.shape[2]
         if w < h:
            a__						:    Optional[int]   		=	int(self.size["shortest_edge"] * h / w   )
            a__						:    Union[str, Any]   		=	self.size["shortest_edge"]
         elif w > h:
            a__						:    List[str]   		=	self.size["shortest_edge"]
            a__						:    Dict   		=	int(self.size["shortest_edge"] * w / h   )
         else:
            a__						:    int   		=	self.size["shortest_edge"]
            a__						:    Dict   		=	self.size["shortest_edge"]

      else:
         a__						:    Optional[Any]   		=	[]
         for image in image_inputs:
            a__		,  a__						:    Dict   		=	self.get_expected_values([image]   )
            expected_values.append((expected_height, expected_width)   )
         a__						:    Optional[Any]   		=	max(snake_case       ,      key=lambda snake_case   : item[0]   )[0]
         a__						:    Any   		=	max(snake_case       ,      key=lambda snake_case   : item[1]   )[1]

      return expected_height, expected_width





   def 						_snake_case			(			self   )					->	str:
      """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 	__lowerCAmelCase      (       _UpperCamelCase		,unittest.TestCase	):
   _UpperCamelCase			:    List[Any]  		=					OneFormerImageProcessor if (is_vision_available() and is_torch_available()) else None
   # only for test_image_processing_common.test_image_proc_to_json_string
   _UpperCamelCase			:    Any  		=					image_processing_class





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





      a__						:    Optional[int]   		=	OneFormerImageProcessorTester(self   )





   @property
   def 						_snake_case			(			self   )					->	Tuple:
      """simple docstring"""





      return self.image_processing_tester.prepare_image_processor_dict()





   def 						_snake_case			(			self   )					->	Union[str, Any]:
      """simple docstring"""





      a__						:    Union[str, Any]   		=	self.image_processing_class(**self.image_processor_dict   )
      self.assertTrue(hasattr(snake_case       ,      "image_mean"   )   )
      self.assertTrue(hasattr(snake_case       ,      "image_std"   )   )
      self.assertTrue(hasattr(snake_case       ,      "do_normalize"   )   )
      self.assertTrue(hasattr(snake_case       ,      "do_resize"   )   )
      self.assertTrue(hasattr(snake_case       ,      "size"   )   )
      self.assertTrue(hasattr(snake_case       ,      "ignore_index"   )   )
      self.assertTrue(hasattr(snake_case       ,      "class_info_file"   )   )
      self.assertTrue(hasattr(snake_case       ,      "num_text"   )   )
      self.assertTrue(hasattr(snake_case       ,      "repo_path"   )   )
      self.assertTrue(hasattr(snake_case       ,      "metadata"   )   )
      self.assertTrue(hasattr(snake_case       ,      "do_reduce_labels"   )   )





   def 						_snake_case			(			self   )					->	Optional[int]:
      """simple docstring"""





      pass





   def 						_snake_case			(			self   )					->	Optional[int]:
      """simple docstring"""





      a__						:    str   		=	self.image_processing_class(**self.image_processor_dict   )
      # create random PIL images
      a__						:    Optional[Any]   		=	prepare_image_inputs(self.image_processing_tester       ,      equal_resolution=snake_case   )
      for image in image_inputs:
         self.assertIsInstance(snake_case       ,      Image.Image   )

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

      a__		,  a__						:    List[str]   		=	self.image_processing_tester.get_expected_values(snake_case   )

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

      # Test batched
      a__		,  a__						:    int   		=	self.image_processing_tester.get_expected_values(snake_case       ,      batched=snake_case   )

      a__						:    List[str]   		=	image_processor(
          snake_case       ,      ["semantic"] * len(snake_case   )       ,      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 						_snake_case			(			self   )					->	Tuple:
      """simple docstring"""





      a__						:    List[str]   		=	self.image_processing_class(**self.image_processor_dict   )
      # create random numpy tensors
      a__						:    Union[str, Any]   		=	prepare_image_inputs(self.image_processing_tester       ,      equal_resolution=snake_case       ,      numpify=snake_case   )
      for image in image_inputs:
         self.assertIsInstance(snake_case       ,      np.ndarray   )

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

      a__		,  a__						:    List[Any]   		=	self.image_processing_tester.get_expected_values(snake_case   )

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

      # Test batched
      a__		,  a__						:    Union[str, Any]   		=	self.image_processing_tester.get_expected_values(snake_case       ,      batched=snake_case   )

      a__						:    str   		=	image_processor(
          snake_case       ,      ["semantic"] * len(snake_case   )       ,      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 						_snake_case			(			self   )					->	Optional[int]:
      """simple docstring"""





      a__						:    Dict   		=	self.image_processing_class(**self.image_processor_dict   )
      # create random PyTorch tensors
      a__						:    List[Any]   		=	prepare_image_inputs(self.image_processing_tester       ,      equal_resolution=snake_case       ,      torchify=snake_case   )
      for image in image_inputs:
         self.assertIsInstance(snake_case       ,      torch.Tensor   )

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

      a__		,  a__						:    Union[str, Any]   		=	self.image_processing_tester.get_expected_values(snake_case   )

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

      # Test batched
      a__		,  a__						:    List[str]   		=	self.image_processing_tester.get_expected_values(snake_case       ,      batched=snake_case   )

      a__						:    Tuple   		=	image_processor(
          snake_case       ,      ["semantic"] * len(snake_case   )       ,      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 						_snake_case			(			self       ,      snake_case=False       ,      snake_case=False       ,      snake_case="np"   )					->	Optional[Any]:
      """simple docstring"""





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

      a__						:    List[str]   		=	image_processor(
          snake_case       ,      ["semantic"] * len(snake_case   )       ,      snake_case       ,      return_tensors="pt"       ,      instance_id_to_semantic_id=snake_case       ,      pad_and_return_pixel_mask=snake_case       ,      )

      return inputs





   def 						_snake_case			(			self   )					->	Union[str, Any]:
      """simple docstring"""





      pass





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





      def common(snake_case=False       ,      snake_case=None   ):
         a__						:    Union[str, Any]   		=	self.comm_get_image_processor_inputs(
             with_segmentation_maps=snake_case       ,      is_instance_map=snake_case       ,      segmentation_type=snake_case   )

         a__						:    str   		=	inputs["mask_labels"]
         a__						:    str   		=	inputs["class_labels"]
         a__						:    Dict   		=	inputs["pixel_values"]
         a__						:    List[Any]   		=	inputs["text_inputs"]

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

      common()
      common(is_instance_map=snake_case   )
      common(is_instance_map=snake_case       ,      segmentation_type="pil"   )
      common(is_instance_map=snake_case       ,      segmentation_type="pil"   )





   def 						_snake_case			(			self   )					->	Dict:
      """simple docstring"""





      a__						:    str   		=	np.zeros((20, 50)   )
      a__						:    Optional[Any]   		=	1
      a__						:    Optional[Any]   		=	1
      a__						:    Tuple   		=	1

      a__						:    Optional[int]   		=	binary_mask_to_rle(snake_case   )
      self.assertEqual(len(snake_case   )       ,      4   )
      self.assertEqual(rle[0]       ,      21   )
      self.assertEqual(rle[1]       ,      45   )





   def 						_snake_case			(			self   )					->	Dict:
      """simple docstring"""





      a__						:    Optional[int]   		=	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__						:    Dict   		=	self.image_processing_tester.get_fake_oneformer_outputs()

      a__						:    Union[str, Any]   		=	fature_extractor.post_process_semantic_segmentation(snake_case   )

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

      a__						:    Tuple   		=	[(1, 4) for i in range(self.image_processing_tester.batch_size   )]
      a__						:    Optional[Any]   		=	fature_extractor.post_process_semantic_segmentation(snake_case       ,      target_sizes=snake_case   )

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





   def 						_snake_case			(			self   )					->	Union[str, Any]:
      """simple docstring"""





      a__						:    Optional[int]   		=	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__						:    int   		=	self.image_processing_tester.get_fake_oneformer_outputs()
      a__						:    int   		=	image_processor.post_process_instance_segmentation(snake_case       ,      threshold=0   )

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





   def 						_snake_case			(			self   )					->	Tuple:
      """simple docstring"""





      a__						:    int   		=	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__						:    Optional[int]   		=	self.image_processing_tester.get_fake_oneformer_outputs()
      a__						:    str   		=	image_processor.post_process_panoptic_segmentation(snake_case       ,      threshold=0   )

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








import argparse

import pytorch_lightning as pl
import torch
from torch import nn

from transformers import LongformerForQuestionAnswering, LongformerModel







class 	__lowerCAmelCase      (       pl.LightningModule	):





   def __init__(			self       ,      snake_case   )					->	Dict:
      """simple docstring"""





      super().__init__()
      a__						:    int   		=	model
      a__						:    Any   		=	2
      a__						:    List[str]   		=	nn.Linear(self.model.config.hidden_size       ,      self.num_labels   )





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





      pass







def    _A	(   lowerCamelCase				,			lowerCamelCase				,			lowerCamelCase ):
   # load longformer model from model identifier
   a__						:    List[str]   		=	LongformerModel.from_pretrained(lowerCamelCase )
   a__						:    Dict   		=	LightningModel(lowerCamelCase )

   a__						:    str   		=	torch.load(lowerCamelCase				,			map_location=torch.device("cpu" ) )
   lightning_model.load_state_dict(ckpt["state_dict"] )

   # init longformer question answering model
   a__						:    Dict   		=	LongformerForQuestionAnswering.from_pretrained(lowerCamelCase )

   # transfer weights
   longformer_for_qa.longformer.load_state_dict(lightning_model.model.state_dict() )
   longformer_for_qa.qa_outputs.load_state_dict(lightning_model.qa_outputs.state_dict() )
   longformer_for_qa.eval()

   # save model
   longformer_for_qa.save_pretrained(lowerCamelCase )

   print(F"""Conversion successful. Model saved under {pytorch_dump_folder_path}""" )


if __name__ == "__main__":
  SCREAMING_SNAKE_CASE__							:      Any         =    argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      """--longformer_model""",
      default=None,
      type=str,
      required=True,
      help="""model identifier of longformer. Should be either `longformer-base-4096` or `longformer-large-4096`.""",
  )
  parser.add_argument(
      """--longformer_question_answering_ckpt_path""",
      default=None,
      type=str,
      required=True,
      help="""Path the official PyTorch Lightning Checkpoint.""",
  )
  parser.add_argument(
      """--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
  )
  SCREAMING_SNAKE_CASE__							:      Optional[Any]         =    parser.parse_args()
  convert_longformer_qa_checkpoint_to_pytorch(
      args.longformer_model, args.longformer_question_answering_ckpt_path, args.pytorch_dump_folder_path
  )

