



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 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase    ,		__lowerCAmelCase="shi-labs/oneformer_demo"							):






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




class 					_SCREAMING_SNAKE_CASE  (      unittest.TestCase ):
      def __init__(			self		,	lowerCamelCase		,	lowerCamelCase=7		,	lowerCamelCase=3		,	lowerCamelCase=30		,	lowerCamelCase=4_00		,	lowerCamelCase=None		,	lowerCamelCase=True		,	lowerCamelCase=True		,	lowerCamelCase=[0.5, 0.5, 0.5]		,	lowerCamelCase=[0.5, 0.5, 0.5]		,	lowerCamelCase=10		,	lowerCamelCase=False		,	lowerCamelCase=2_55		,	lowerCamelCase="shi-labs/oneformer_demo"		,	lowerCamelCase="ade20k_panoptic.json"		,	lowerCamelCase=10		,	):
          snake_case__						=				parent
          snake_case__						=				batch_size
          snake_case__						=				num_channels
          snake_case__						=				min_resolution
          snake_case__						=				max_resolution
          snake_case__						=				do_resize
          snake_case__						=				{"shortest_edge": 32, "longest_edge": 13_33} if size is None else size
          snake_case__						=				do_normalize
          snake_case__						=				image_mean
          snake_case__						=				image_std
          snake_case__						=				class_info_file
          snake_case__						=				prepare_metadata(lowerCamelCase		,	lowerCamelCase )
          snake_case__						=				num_text
          snake_case__						=				repo_path

          # for the post_process_functions
          snake_case__						=				2
          snake_case__						=				10
          snake_case__						=				10
          snake_case__						=				3
          snake_case__						=				4
          snake_case__						=				num_labels
          snake_case__						=				do_reduce_labels
          snake_case__						=				ignore_index
      def A_			(			self ):
          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		,	lowerCamelCase		,	lowerCamelCase=False ):
          if not batched:
              snake_case__						=				image_inputs[0]
              if isinstance(lowerCamelCase		,	Image.Image ):
                  snake_case__						,					snake_case__						=				image.size
              else:
                  snake_case__						,					snake_case__						=				image.shape[1], image.shape[2]
              if w < h:
                  snake_case__						=				int(self.size["shortest_edge"] * h / w )
                  snake_case__						=				self.size["shortest_edge"]
              elif w > h:
                  snake_case__						=				self.size["shortest_edge"]
                  snake_case__						=				int(self.size["shortest_edge"] * w / h )
              else:
                  snake_case__						=				self.size["shortest_edge"]
                  snake_case__						=				self.size["shortest_edge"]

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

          return expected_height, expected_width



      def A_			(			self ):
          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  (      lowercase_       ,					unittest.TestCase ):
      _A						:		Union[str, Any]    	=     OneFormerImageProcessor if (is_vision_available() and is_torch_available()) else None
      # only for test_image_processing_common.test_image_proc_to_json_string
      _A						:		Optional[int]    	=     image_processing_class
      def A_			(			self ):
          snake_case__						=				OneFormerImageProcessorTester(self )
      @property
      def A_			(			self ):
          return self.image_processing_tester.prepare_image_processor_dict()
      def A_			(			self ):
          snake_case__						=				self.image_processing_class(**self.image_processor_dict )
          self.assertTrue(hasattr(lowerCamelCase		,	"image_mean" ) )
          self.assertTrue(hasattr(lowerCamelCase		,	"image_std" ) )
          self.assertTrue(hasattr(lowerCamelCase		,	"do_normalize" ) )
          self.assertTrue(hasattr(lowerCamelCase		,	"do_resize" ) )
          self.assertTrue(hasattr(lowerCamelCase		,	"size" ) )
          self.assertTrue(hasattr(lowerCamelCase		,	"ignore_index" ) )
          self.assertTrue(hasattr(lowerCamelCase		,	"class_info_file" ) )
          self.assertTrue(hasattr(lowerCamelCase		,	"num_text" ) )
          self.assertTrue(hasattr(lowerCamelCase		,	"repo_path" ) )
          self.assertTrue(hasattr(lowerCamelCase		,	"metadata" ) )
          self.assertTrue(hasattr(lowerCamelCase		,	"do_reduce_labels" ) )
      def A_			(			self ):
          pass
      def A_			(			self ):
          # Initialize image_processor
          snake_case__						=				self.image_processing_class(**self.image_processor_dict )
          # create random PIL images
          snake_case__						=				prepare_image_inputs(self.image_processing_tester		,	equal_resolution=lowerCamelCase )
          for image in image_inputs:
              self.assertIsInstance(lowerCamelCase		,	Image.Image )

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

          snake_case__						,					snake_case__						=				self.image_processing_tester.get_expected_values(lowerCamelCase )

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

          # Test batched
          snake_case__						,					snake_case__						=				self.image_processing_tester.get_expected_values(lowerCamelCase		,	batched=lowerCamelCase )

          snake_case__						=				image_processor(
              lowerCamelCase		,	["semantic"] * len(lowerCamelCase )		,	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 ):
          # Initialize image_processor
          snake_case__						=				self.image_processing_class(**self.image_processor_dict )
          # create random numpy tensors
          snake_case__						=				prepare_image_inputs(self.image_processing_tester		,	equal_resolution=lowerCamelCase		,	numpify=lowerCamelCase )
          for image in image_inputs:
              self.assertIsInstance(lowerCamelCase		,	np.ndarray )

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

          snake_case__						,					snake_case__						=				self.image_processing_tester.get_expected_values(lowerCamelCase )

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

          # Test batched
          snake_case__						,					snake_case__						=				self.image_processing_tester.get_expected_values(lowerCamelCase		,	batched=lowerCamelCase )

          snake_case__						=				image_processor(
              lowerCamelCase		,	["semantic"] * len(lowerCamelCase )		,	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 ):
          # Initialize image_processor
          snake_case__						=				self.image_processing_class(**self.image_processor_dict )
          # create random PyTorch tensors
          snake_case__						=				prepare_image_inputs(self.image_processing_tester		,	equal_resolution=lowerCamelCase		,	torchify=lowerCamelCase )
          for image in image_inputs:
              self.assertIsInstance(lowerCamelCase		,	torch.Tensor )

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

          snake_case__						,					snake_case__						=				self.image_processing_tester.get_expected_values(lowerCamelCase )

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

          # Test batched
          snake_case__						,					snake_case__						=				self.image_processing_tester.get_expected_values(lowerCamelCase		,	batched=lowerCamelCase )

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

          snake_case__						=				image_processor(
              lowerCamelCase		,	["semantic"] * len(lowerCamelCase )		,	lowerCamelCase		,	return_tensors="pt"		,	instance_id_to_semantic_id=lowerCamelCase		,	pad_and_return_pixel_mask=lowerCamelCase		,	)

          return inputs
      def A_			(			self ):
          pass
      def A_			(			self ):
          def common(lowerCamelCase=False		,	lowerCamelCase=None ):
              snake_case__						=				self.comm_get_image_processor_inputs(
                  with_segmentation_maps=lowerCamelCase		,	is_instance_map=lowerCamelCase		,	segmentation_type=lowerCamelCase )

              snake_case__						=				inputs["mask_labels"]
              snake_case__						=				inputs["class_labels"]
              snake_case__						=				inputs["pixel_values"]
              snake_case__						=				inputs["text_inputs"]

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

          common()
          common(is_instance_map=lowerCamelCase )
          common(is_instance_map=lowerCamelCase		,	segmentation_type="pil" )
          common(is_instance_map=lowerCamelCase		,	segmentation_type="pil" )
      def A_			(			self ):
          snake_case__						=				np.zeros((20, 50) )
          snake_case__						=				1
          snake_case__						=				1
          snake_case__						=				1

          snake_case__						=				binary_mask_to_rle(lowerCamelCase )
          self.assertEqual(len(lowerCamelCase )		,	4 )
          self.assertEqual(rle[0]		,	21 )
          self.assertEqual(rle[1]		,	45 )
      def A_			(			self ):
          snake_case__						=				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"		,	)
          snake_case__						=				self.image_processing_tester.get_fake_oneformer_outputs()

          snake_case__						=				fature_extractor.post_process_semantic_segmentation(lowerCamelCase )

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

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

          self.assertEqual(segmentation[0].shape		,	target_sizes[0] )
      def A_			(			self ):
          snake_case__						=				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"		,	)
          snake_case__						=				self.image_processing_tester.get_fake_oneformer_outputs()
          snake_case__						=				image_processor.post_process_instance_segmentation(lowerCamelCase		,	threshold=0 )

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



      def A_			(			self ):
          snake_case__						=				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"		,	)
          snake_case__						=				self.image_processing_tester.get_fake_oneformer_outputs()
          snake_case__						=				image_processor.post_process_panoptic_segmentation(lowerCamelCase		,	threshold=0 )

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





import argparse
import json

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import AutoImageProcessor, SwinConfig, SwinForImageClassification





def 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase							):
    snake_case__						=				SwinConfig()
    snake_case__						=				swin_name.split("_"							)

    snake_case__						=				name_split[1]
    snake_case__						=				int(name_split[4]							)
    snake_case__						=				int(name_split[3][-1]							)

    if model_size == "tiny":
        snake_case__						=				96
        snake_case__						=				(2, 2, 6, 2)
        snake_case__						=				(3, 6, 12, 24)
    elif model_size == "small":
        snake_case__						=				96
        snake_case__						=				(2, 2, 18, 2)
        snake_case__						=				(3, 6, 12, 24)
    elif model_size == "base":
        snake_case__						=				128
        snake_case__						=				(2, 2, 18, 2)
        snake_case__						=				(4, 8, 16, 32)
    else:
        snake_case__						=				192
        snake_case__						=				(2, 2, 18, 2)
        snake_case__						=				(6, 12, 24, 48)

    if "in22k" in swin_name:
        snake_case__						=				21_841
    else:
        snake_case__						=				1_000
        snake_case__						=				"huggingface/label-files"
        snake_case__						=				"imagenet-1k-id2label.json"
        snake_case__						=				json.load(open(hf_hub_download(__lowerCAmelCase    ,		__lowerCAmelCase    ,		repo_type="dataset"							)    ,		"r"							)							)
        snake_case__						=				{int(__lowerCAmelCase							): v for k, v in idalabel.items()}
        snake_case__						=				idalabel
        snake_case__						=				{v: k for k, v in idalabel.items()}

    snake_case__						=				img_size
    snake_case__						=				num_classes
    snake_case__						=				embed_dim
    snake_case__						=				depths
    snake_case__						=				num_heads
    snake_case__						=				window_size

    return config





def 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase							):
    if "patch_embed.proj" in name:
        snake_case__						=				name.replace("patch_embed.proj"    ,		"embeddings.patch_embeddings.projection"							)
    if "patch_embed.norm" in name:
        snake_case__						=				name.replace("patch_embed.norm"    ,		"embeddings.norm"							)
    if "layers" in name:
        snake_case__						=				"encoder." + name
    if "attn.proj" in name:
        snake_case__						=				name.replace("attn.proj"    ,		"attention.output.dense"							)
    if "attn" in name:
        snake_case__						=				name.replace("attn"    ,		"attention.self"							)
    if "norm1" in name:
        snake_case__						=				name.replace("norm1"    ,		"layernorm_before"							)
    if "norm2" in name:
        snake_case__						=				name.replace("norm2"    ,		"layernorm_after"							)
    if "mlp.fc1" in name:
        snake_case__						=				name.replace("mlp.fc1"    ,		"intermediate.dense"							)
    if "mlp.fc2" in name:
        snake_case__						=				name.replace("mlp.fc2"    ,		"output.dense"							)

    if name == "norm.weight":
        snake_case__						=				"layernorm.weight"
    if name == "norm.bias":
        snake_case__						=				"layernorm.bias"

    if "head" in name:
        snake_case__						=				name.replace("head"    ,		"classifier"							)
    else:
        snake_case__						=				"swin." + name

    return name





def 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase    ,		__lowerCAmelCase							):
    for key in orig_state_dict.copy().keys():
        snake_case__						=				orig_state_dict.pop(__lowerCAmelCase							)

        if "mask" in key:
            continue
        elif "qkv" in key:
            snake_case__						=				key.split("."							)
            snake_case__						=				int(key_split[1]							)
            snake_case__						=				int(key_split[3]							)
            snake_case__						=				model.swin.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

            if "weight" in key:
                snake_case__						=				val[:dim, :]
                snake_case__						=				val[
                    dim : dim * 2, :
                ]
                snake_case__						=				val[-dim:, :]
            else:
                snake_case__						=				val[
                    :dim
                ]
                snake_case__						=				val[
                    dim : dim * 2
                ]
                snake_case__						=				val[
                    -dim:
                ]
        else:
            snake_case__						=				val

    return orig_state_dict





def 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase    ,		__lowerCAmelCase							):
    snake_case__						=				timm.create_model(__lowerCAmelCase    ,		pretrained=__lowerCAmelCase							)
    timm_model.eval()

    snake_case__						=				get_swin_config(__lowerCAmelCase							)
    snake_case__						=				SwinForImageClassification(__lowerCAmelCase							)
    model.eval()

    snake_case__						=				convert_state_dict(timm_model.state_dict()    ,		__lowerCAmelCase							)
    model.load_state_dict(__lowerCAmelCase							)

    snake_case__						=				"http://images.cocodataset.org/val2017/000000039769.jpg"

    snake_case__						=				AutoImageProcessor.from_pretrained("microsoft/{}".format(swin_name.replace("_"    ,		"-"							)							)							)
    snake_case__						=				Image.open(requests.get(__lowerCAmelCase    ,		stream=__lowerCAmelCase							).raw							)
    snake_case__						=				image_processor(images=__lowerCAmelCase    ,		return_tensors="pt"							)

    snake_case__						=				timm_model(inputs["pixel_values"]							)
    snake_case__						=				model(**__lowerCAmelCase							).logits

    assert torch.allclose(__lowerCAmelCase    ,		__lowerCAmelCase    ,		atol=1E-3							)

    print(F"""Saving model {swin_name} to {pytorch_dump_folder_path}"""							)
    model.save_pretrained(__lowerCAmelCase							)

    print(F"""Saving image processor to {pytorch_dump_folder_path}"""							)
    image_processor.save_pretrained(__lowerCAmelCase							)


if __name__ == "__main__":
     __magic_name__            =  argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         '''--swin_name''',
         default='''swin_tiny_patch4_window7_224''',
         type=str,
         help='''Name of the Swin timm model you\'d like to convert.''',
     )
     parser.add_argument(
         '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
     )

     __magic_name__            =  parser.parse_args()
     convert_swin_checkpoint(args.swin_name, args.pytorch_dump_folder_path)



