





"""simple docstring"""





import inspect
import unittest

import numpy as np

from transformers import ViTConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor


if is_flax_available():
  import jax

  from transformers.models.vit.modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel






class    _lowerCAmelCase  (	unittest.TestCase			):
       """simple docstring"""




       def __init__(       self       ,					__UpperCAmelCase       ,					__UpperCAmelCase=1_3       ,					__UpperCAmelCase=3_0       ,					__UpperCAmelCase=2       ,					__UpperCAmelCase=3       ,					__UpperCAmelCase=True       ,					__UpperCAmelCase=True       ,					__UpperCAmelCase=3_2       ,					__UpperCAmelCase=5       ,					__UpperCAmelCase=4       ,					__UpperCAmelCase=3_7       ,					__UpperCAmelCase="gelu"       ,					__UpperCAmelCase=0.1       ,					__UpperCAmelCase=0.1       ,					__UpperCAmelCase=1_0       ,					__UpperCAmelCase=0.02       ,					):



         '''simple docstring'''

         lowerCAmelCase__     :List[Any]											=				parent
         lowerCAmelCase__     :List[str]											=				batch_size
         lowerCAmelCase__     :str											=				image_size
         lowerCAmelCase__     :str											=				patch_size
         lowerCAmelCase__     :Optional[Any]											=				num_channels
         lowerCAmelCase__     :Optional[Any]											=				is_training
         lowerCAmelCase__     :int											=				use_labels
         lowerCAmelCase__     :Union[str, Any]											=				hidden_size
         lowerCAmelCase__     :Optional[int]											=				num_hidden_layers
         lowerCAmelCase__     :List[Any]											=				num_attention_heads
         lowerCAmelCase__     :Union[str, Any]											=				intermediate_size
         lowerCAmelCase__     :Any											=				hidden_act
         lowerCAmelCase__     :List[str]											=				hidden_dropout_prob
         lowerCAmelCase__     :str											=				attention_probs_dropout_prob
         lowerCAmelCase__     :str											=				type_sequence_label_size
         lowerCAmelCase__     :Optional[Any]											=				initializer_range

         # in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
         lowerCAmelCase__     :Optional[Any]											=				(image_size // patch_size) ** 2
         lowerCAmelCase__     :List[str]											=				num_patches + 1






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :Optional[Any]											=				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

         lowerCAmelCase__     :List[str]											=				ViTConfig(
             image_size=self.image_size       ,					patch_size=self.patch_size       ,					num_channels=self.num_channels       ,					hidden_size=self.hidden_size       ,					num_hidden_layers=self.num_hidden_layers       ,					num_attention_heads=self.num_attention_heads       ,					intermediate_size=self.intermediate_size       ,					hidden_act=self.hidden_act       ,					hidden_dropout_prob=self.hidden_dropout_prob       ,					attention_probs_dropout_prob=self.attention_probs_dropout_prob       ,					is_decoder=__UpperCAmelCase       ,					initializer_range=self.initializer_range       ,					)

         return config, pixel_values






       def 	snake_case		(       self       ,					__UpperCAmelCase       ,					__UpperCAmelCase ):



         '''simple docstring'''

         lowerCAmelCase__     :Dict											=				FlaxViTModel(config=__UpperCAmelCase )
         lowerCAmelCase__     :Dict											=				model(__UpperCAmelCase )
         # expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
         lowerCAmelCase__     :Tuple											=				(self.image_size, self.image_size)
         lowerCAmelCase__     :Optional[Any]											=				(self.patch_size, self.patch_size)
         lowerCAmelCase__     :Tuple											=				(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
         self.parent.assertEqual(result.last_hidden_state.shape       ,					(self.batch_size, num_patches + 1, self.hidden_size) )






       def 	snake_case		(       self       ,					__UpperCAmelCase       ,					__UpperCAmelCase ):



         '''simple docstring'''

         lowerCAmelCase__     :Tuple											=				self.type_sequence_label_size
         lowerCAmelCase__     :str											=				FlaxViTForImageClassification(config=__UpperCAmelCase )
         lowerCAmelCase__     :str											=				model(__UpperCAmelCase )
         self.parent.assertEqual(result.logits.shape       ,					(self.batch_size, self.type_sequence_label_size) )

         # test greyscale images
         lowerCAmelCase__     :Optional[int]											=				1
         lowerCAmelCase__     :Union[str, Any]											=				FlaxViTForImageClassification(__UpperCAmelCase )

         lowerCAmelCase__     :List[str]											=				floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
         lowerCAmelCase__     :Tuple											=				model(__UpperCAmelCase )






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :List[Any]											=				self.prepare_config_and_inputs()
         (
             (
             lowerCAmelCase__
         )				,							(
             lowerCAmelCase__
         )				,							
         )     :str											=				config_and_inputs
         lowerCAmelCase__     :Union[str, Any]											=				{'pixel_values': pixel_values}
         return config, inputs_dict






@require_flax
class    _lowerCAmelCase  (	a    ,  unittest.TestCase			):
       """simple docstring"""




       __magic_name__	:List[Any]						    =   (FlaxViTModel, FlaxViTForImageClassification) if is_flax_available() else ()






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :List[Any]											=				FlaxViTModelTester(self )
         lowerCAmelCase__     :Dict											=				ConfigTester(self       ,					config_class=__UpperCAmelCase       ,					has_text_modality=__UpperCAmelCase       ,					hidden_size=3_7 )






       def 	snake_case		(       self ):



         '''simple docstring'''

         self.config_tester.run_common_tests()






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :List[Any]											=				self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*__UpperCAmelCase )






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :str											=				self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_image_classification(*__UpperCAmelCase )






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__				,							lowerCAmelCase__     :Union[str, Any]											=				self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
           lowerCAmelCase__     :int											=				model_class(__UpperCAmelCase )
           lowerCAmelCase__     :Optional[int]											=				inspect.signature(model.__call__ )
           # signature.parameters is an OrderedDict => so arg_names order is deterministic
           lowerCAmelCase__     :Optional[Any]											=				[*signature.parameters.keys()]

           lowerCAmelCase__     :Any											=				['pixel_values']
           self.assertListEqual(arg_names[:1]       ,					__UpperCAmelCase )






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__				,							lowerCAmelCase__     :int											=				self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
           with self.subTest(model_class.__name__ ):
             lowerCAmelCase__     :Union[str, Any]											=				self._prepare_for_class(__UpperCAmelCase       ,					__UpperCAmelCase )
             lowerCAmelCase__     :Optional[int]											=				model_class(__UpperCAmelCase )

             @jax.jit
             def model_jitted(__UpperCAmelCase       ,					**__UpperCAmelCase ):
               return model(pixel_values=__UpperCAmelCase       ,					**__UpperCAmelCase )

             with self.subTest('JIT Enabled' ):
               lowerCAmelCase__     :List[Any]											=				model_jitted(**__UpperCAmelCase ).to_tuple()

             with self.subTest('JIT Disabled' ):
               with jax.disable_jit():
                 lowerCAmelCase__     :List[str]											=				model_jitted(**__UpperCAmelCase ).to_tuple()

             self.assertEqual(len(__UpperCAmelCase )       ,					len(__UpperCAmelCase ) )
             for jitted_output, output in zip(__UpperCAmelCase       ,					__UpperCAmelCase ):
               self.assertEqual(jitted_output.shape       ,					output.shape )






       @slow
       def 	snake_case		(       self ):



         '''simple docstring'''

         for model_class_name in self.all_model_classes:
           lowerCAmelCase__     :Optional[int]											=				model_class_name.from_pretrained('google/vit-base-patch16-224' )
           lowerCAmelCase__     :List[Any]											=				model(np.ones((1, 3, 2_2_4, 2_2_4) ) )
           self.assertIsNotNone(__UpperCAmelCase )







"""simple docstring"""





import argparse
import json

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

from transformers import ConvNextConfig, SegformerImageProcessor, UperNetConfig, UperNetForSemanticSegmentation
def 					__A  (_SCREAMING_SNAKE_CASE     )   ->Any:





  """simple docstring"""


  lowerCAmelCase__     :Union[str, Any]											=				384
  if "tiny" in model_name:
    lowerCAmelCase__     :List[Any]											=				[3, 3, 9, 3]
    lowerCAmelCase__     :Tuple											=				[96, 192, 384, 768]
  if "small" in model_name:
    lowerCAmelCase__     :Union[str, Any]											=				[3, 3, 27, 3]
    lowerCAmelCase__     :Any											=				[96, 192, 384, 768]
  if "base" in model_name:
    lowerCAmelCase__     :Dict											=				[3, 3, 27, 3]
    lowerCAmelCase__     :Any											=				[128, 256, 512, 1024]
    lowerCAmelCase__     :Union[str, Any]											=				512
  if "large" in model_name:
    lowerCAmelCase__     :int											=				[3, 3, 27, 3]
    lowerCAmelCase__     :Any											=				[192, 384, 768, 1536]
    lowerCAmelCase__     :Optional[Any]											=				768
  if "xlarge" in model_name:
    lowerCAmelCase__     :Optional[Any]											=				[3, 3, 27, 3]
    lowerCAmelCase__     :str											=				[256, 512, 1024, 2048]
    lowerCAmelCase__     :Union[str, Any]											=				1024

  # set label information
  lowerCAmelCase__     :Tuple											=				150
  lowerCAmelCase__     :List[Any]											=				'huggingface/label-files'
  lowerCAmelCase__     :Tuple											=				'ade20k-id2label.json'
  lowerCAmelCase__     :Tuple											=				json.load(open(hf_hub_download(_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  repo_type='dataset'     )	,  'r'     )     )
  lowerCAmelCase__     :Dict											=				{int(_SCREAMING_SNAKE_CASE     ): v for k, v in idalabel.items()}
  lowerCAmelCase__     :int											=				{v: k for k, v in idalabel.items()}

  lowerCAmelCase__     :List[str]											=				ConvNextConfig(
      depths=_SCREAMING_SNAKE_CASE	,  hidden_sizes=_SCREAMING_SNAKE_CASE	,  out_features=['stage1', 'stage2', 'stage3', 'stage4']     )
  lowerCAmelCase__     :Union[str, Any]											=				UperNetConfig(
      backbone_config=_SCREAMING_SNAKE_CASE	,  auxiliary_in_channels=_SCREAMING_SNAKE_CASE	,  num_labels=_SCREAMING_SNAKE_CASE	,  idalabel=_SCREAMING_SNAKE_CASE	,  labelaid=_SCREAMING_SNAKE_CASE	,  )

  return config
def 					__A  (_SCREAMING_SNAKE_CASE     )   ->str:





  """simple docstring"""


  lowerCAmelCase__     :str											=				[]

  # fmt: off
  # stem
  rename_keys.append(('backbone.downsample_layers.0.0.weight', 'backbone.embeddings.patch_embeddings.weight')     )
  rename_keys.append(('backbone.downsample_layers.0.0.bias', 'backbone.embeddings.patch_embeddings.bias')     )
  rename_keys.append(('backbone.downsample_layers.0.1.weight', 'backbone.embeddings.layernorm.weight')     )
  rename_keys.append(('backbone.downsample_layers.0.1.bias', 'backbone.embeddings.layernorm.bias')     )
  # stages
  for i in range(len(config.backbone_config.depths     )     ):
    for j in range(config.backbone_config.depths[i]     ):
      rename_keys.append((F"backbone.stages.{i}.{j}.gamma", F"backbone.encoder.stages.{i}.layers.{j}.layer_scale_parameter")     )
      rename_keys.append((F"backbone.stages.{i}.{j}.depthwise_conv.weight", F"backbone.encoder.stages.{i}.layers.{j}.dwconv.weight")     )
      rename_keys.append((F"backbone.stages.{i}.{j}.depthwise_conv.bias", F"backbone.encoder.stages.{i}.layers.{j}.dwconv.bias")     )
      rename_keys.append((F"backbone.stages.{i}.{j}.norm.weight", F"backbone.encoder.stages.{i}.layers.{j}.layernorm.weight")     )
      rename_keys.append((F"backbone.stages.{i}.{j}.norm.bias", F"backbone.encoder.stages.{i}.layers.{j}.layernorm.bias")     )
      rename_keys.append((F"backbone.stages.{i}.{j}.pointwise_conv1.weight", F"backbone.encoder.stages.{i}.layers.{j}.pwconv1.weight")     )
      rename_keys.append((F"backbone.stages.{i}.{j}.pointwise_conv1.bias", F"backbone.encoder.stages.{i}.layers.{j}.pwconv1.bias")     )
      rename_keys.append((F"backbone.stages.{i}.{j}.pointwise_conv2.weight", F"backbone.encoder.stages.{i}.layers.{j}.pwconv2.weight")     )
      rename_keys.append((F"backbone.stages.{i}.{j}.pointwise_conv2.bias", F"backbone.encoder.stages.{i}.layers.{j}.pwconv2.bias")     )
    if i > 0:
      rename_keys.append((F"backbone.downsample_layers.{i}.0.weight", F"backbone.encoder.stages.{i}.downsampling_layer.0.weight")     )
      rename_keys.append((F"backbone.downsample_layers.{i}.0.bias", F"backbone.encoder.stages.{i}.downsampling_layer.0.bias")     )
      rename_keys.append((F"backbone.downsample_layers.{i}.1.weight", F"backbone.encoder.stages.{i}.downsampling_layer.1.weight")     )
      rename_keys.append((F"backbone.downsample_layers.{i}.1.bias", F"backbone.encoder.stages.{i}.downsampling_layer.1.bias")     )

    rename_keys.append((F"backbone.norm{i}.weight", F"backbone.hidden_states_norms.stage{i+1}.weight")     )
    rename_keys.append((F"backbone.norm{i}.bias", F"backbone.hidden_states_norms.stage{i+1}.bias")     )

  # decode head
  rename_keys.extend(
      [
          ('decode_head.conv_seg.weight', 'decode_head.classifier.weight'),
          ('decode_head.conv_seg.bias', 'decode_head.classifier.bias'),
          ('auxiliary_head.conv_seg.weight', 'auxiliary_head.classifier.weight'),
          ('auxiliary_head.conv_seg.bias', 'auxiliary_head.classifier.bias'),
      ]     )
  # fmt: on

  return rename_keys
def 					__A  (_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE     )   ->str:





  """simple docstring"""


  lowerCAmelCase__     :List[str]											=				dct.pop(_SCREAMING_SNAKE_CASE     )
  lowerCAmelCase__     :Tuple											=				val
def 					__A  (_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE     )   ->int:





  """simple docstring"""


  lowerCAmelCase__     :Dict											=				{
      'upernet-convnext-tiny': 'https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_tiny_fp16_512x512_160k_ade20k/upernet_convnext_tiny_fp16_512x512_160k_ade20k_20220227_124553-cad485de.pth',
      'upernet-convnext-small': 'https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_small_fp16_512x512_160k_ade20k/upernet_convnext_small_fp16_512x512_160k_ade20k_20220227_131208-1b1e394f.pth',
      'upernet-convnext-base': 'https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_base_fp16_512x512_160k_ade20k/upernet_convnext_base_fp16_512x512_160k_ade20k_20220227_181227-02a24fc6.pth',
      'upernet-convnext-large': 'https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_large_fp16_640x640_160k_ade20k/upernet_convnext_large_fp16_640x640_160k_ade20k_20220226_040532-e57aa54d.pth',
      'upernet-convnext-xlarge': 'https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_xlarge_fp16_640x640_160k_ade20k/upernet_convnext_xlarge_fp16_640x640_160k_ade20k_20220226_080344-95fc38c2.pth',
  }
  lowerCAmelCase__     :List[Any]											=				model_name_to_url[model_name]
  lowerCAmelCase__     :Optional[Any]											=				torch.hub.load_state_dict_from_url(_SCREAMING_SNAKE_CASE	,  map_location='cpu'     )['state_dict']

  lowerCAmelCase__     :List[Any]											=				get_upernet_config(_SCREAMING_SNAKE_CASE     )
  lowerCAmelCase__     :Dict											=				UperNetForSemanticSegmentation(_SCREAMING_SNAKE_CASE     )
  model.eval()

  # replace "bn" => "batch_norm"
  for key in state_dict.copy().keys():
    lowerCAmelCase__     :Optional[int]											=				state_dict.pop(_SCREAMING_SNAKE_CASE     )
    if "bn" in key:
      lowerCAmelCase__     :Any											=				key.replace('bn'	,  'batch_norm'     )
    lowerCAmelCase__     :int											=				val

  # rename keys
  lowerCAmelCase__     :Optional[Any]											=				create_rename_keys(_SCREAMING_SNAKE_CASE     )
  for src, dest in rename_keys:
    rename_key(_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE     )

  model.load_state_dict(_SCREAMING_SNAKE_CASE     )

  # verify on image
  lowerCAmelCase__     :str											=				'https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg'
  lowerCAmelCase__     :Optional[Any]											=				Image.open(requests.get(_SCREAMING_SNAKE_CASE	,  stream=_SCREAMING_SNAKE_CASE     ).raw     ).convert('RGB'     )

  lowerCAmelCase__     :Tuple											=				SegformerImageProcessor()
  lowerCAmelCase__     :List[Any]											=				processor(_SCREAMING_SNAKE_CASE	,  return_tensors='pt'     ).pixel_values

  with torch.no_grad():
    lowerCAmelCase__     :Optional[Any]											=				model(_SCREAMING_SNAKE_CASE     )

  if model_name == "upernet-convnext-tiny":
    lowerCAmelCase__     :Optional[Any]											=				torch.tensor(
        [[-8.8_1_1_0, -8.8_1_1_0, -8.6_5_2_1], [-8.8_1_1_0, -8.8_1_1_0, -8.6_5_2_1], [-8.7_7_4_6, -8.7_7_4_6, -8.6_1_3_0]]     )
  elif model_name == "upernet-convnext-small":
    lowerCAmelCase__     :Union[str, Any]											=				torch.tensor(
        [[-8.8_2_3_6, -8.8_2_3_6, -8.6_7_7_1], [-8.8_2_3_6, -8.8_2_3_6, -8.6_7_7_1], [-8.7_6_3_8, -8.7_6_3_8, -8.6_2_4_0]]     )
  elif model_name == "upernet-convnext-base":
    lowerCAmelCase__     :Dict											=				torch.tensor(
        [[-8.8_5_5_8, -8.8_5_5_8, -8.6_9_0_5], [-8.8_5_5_8, -8.8_5_5_8, -8.6_9_0_5], [-8.7_6_6_9, -8.7_6_6_9, -8.6_0_2_1]]     )
  elif model_name == "upernet-convnext-large":
    lowerCAmelCase__     :List[str]											=				torch.tensor(
        [[-8.6_6_6_0, -8.6_6_6_0, -8.6_2_1_0], [-8.6_6_6_0, -8.6_6_6_0, -8.6_2_1_0], [-8.6_3_1_0, -8.6_3_1_0, -8.5_9_6_4]]     )
  elif model_name == "upernet-convnext-xlarge":
    lowerCAmelCase__     :Optional[Any]											=				torch.tensor(
        [[-8.4_9_8_0, -8.4_9_8_0, -8.3_9_7_7], [-8.4_9_8_0, -8.4_9_8_0, -8.3_9_7_7], [-8.4_3_7_9, -8.4_3_7_9, -8.3_4_1_2]]     )
  print('Logits:'	,  outputs.logits[0, 0, :3, :3]     )
  assert torch.allclose(outputs.logits[0, 0, :3, :3]	,  _SCREAMING_SNAKE_CASE	,  atol=1e-4     )
  print('Looks ok!'     )

  if pytorch_dump_folder_path is not None:
    print(F"Saving model {model_name} to {pytorch_dump_folder_path}"     )
    model.save_pretrained(_SCREAMING_SNAKE_CASE     )
    print(F"Saving processor to {pytorch_dump_folder_path}"     )
    processor.save_pretrained(_SCREAMING_SNAKE_CASE     )

  if push_to_hub:
    print(F"Pushing model and processor for {model_name} to hub"     )
    model.push_to_hub(F"openmmlab/{model_name}"     )
    processor.push_to_hub(F"openmmlab/{model_name}"     )


if __name__ == "__main__":
  __A											=						argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      """--model_name""",
      default="""upernet-convnext-tiny""",
      type=str,
      choices=[F'''upernet-convnext-{size}''' for size in ["""tiny""", """small""", """base""", """large""", """xlarge"""]],
      help="""Name of the ConvNext UperNet 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."""
  )
  parser.add_argument(
      """--push_to_hub""", action="""store_true""", help="""Whether or not to push the converted model to the 🤗 hub."""
  )

  __A											=						parser.parse_args()
  convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
