"""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    SCREAMING_SNAKE_CASE       (     snake_case):
     __snake_case				=							3_84
     if "tiny" in model_name:
          __snake_case				=							[3, 3, 9, 3]
          __snake_case				=							[96, 1_92, 3_84, 7_68]
     if "small" in model_name:
          __snake_case				=							[3, 3, 27, 3]
          __snake_case				=							[96, 1_92, 3_84, 7_68]
     if "base" in model_name:
          __snake_case				=							[3, 3, 27, 3]
          __snake_case				=							[1_28, 2_56, 5_12, 10_24]
          __snake_case				=							5_12
     if "large" in model_name:
          __snake_case				=							[3, 3, 27, 3]
          __snake_case				=							[1_92, 3_84, 7_68, 15_36]
          __snake_case				=							7_68
     if "xlarge" in model_name:
          __snake_case				=							[3, 3, 27, 3]
          __snake_case				=							[2_56, 5_12, 10_24, 20_48]
          __snake_case				=							10_24

     # set label information
     __snake_case				=							1_50
     __snake_case				=							'''huggingface/label-files'''
     __snake_case				=							'''ade20k-id2label.json'''
     __snake_case				=							json.load(open(hf_hub_download(snake_case,      snake_case,      repo_type='''dataset'''),      '''r'''))
     __snake_case				=							{int(snake_case): v for k, v in idalabel.items()}
     __snake_case				=							{v: k for k, v in idalabel.items()}

     __snake_case				=							ConvNextConfig(
         depths=snake_case,      hidden_sizes=snake_case,      out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4'''])
     __snake_case				=							UperNetConfig(
         backbone_config=snake_case,      auxiliary_in_channels=snake_case,      num_labels=snake_case,      idalabel=snake_case,      labelaid=snake_case,      )

     return config





def    SCREAMING_SNAKE_CASE       (     snake_case):
     __snake_case				=							[]

     # 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    SCREAMING_SNAKE_CASE       (     snake_case,      snake_case,      snake_case):
     __snake_case				=							dct.pop(snake_case)
     __snake_case				=							val





def    SCREAMING_SNAKE_CASE       (     snake_case,      snake_case,      snake_case):
     __snake_case				=							{
         '''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''',
     }
     __snake_case				=							model_name_to_url[model_name]
     __snake_case				=							torch.hub.load_state_dict_from_url(snake_case,      map_location='''cpu''')['''state_dict''']

     __snake_case				=							get_upernet_config(snake_case)
     __snake_case				=							UperNetForSemanticSegmentation(snake_case)
     model.eval()

     # replace "bn" => "batch_norm"
     for key in state_dict.copy().keys():
          __snake_case				=							state_dict.pop(snake_case)
          if "bn" in key:
               __snake_case				=							key.replace('''bn''',      '''batch_norm''')
          __snake_case				=							val

     # rename keys
     __snake_case				=							create_rename_keys(snake_case)
     for src, dest in rename_keys:
          rename_key(snake_case,      snake_case,      snake_case)

     model.load_state_dict(snake_case)

     # verify on image
     __snake_case				=							'''https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg'''
     __snake_case				=							Image.open(requests.get(snake_case,      stream=snake_case).raw).convert('''RGB''')

     __snake_case				=							SegformerImageProcessor()
     __snake_case				=							processor(snake_case,      return_tensors='''pt''').pixel_values

     with torch.no_grad():
          __snake_case				=							model(snake_case)

     if model_name == "upernet-convnext-tiny":
          __snake_case				=							torch.tensor(
              [[-8.8110, -8.8110, -8.6521], [-8.8110, -8.8110, -8.6521], [-8.7746, -8.7746, -8.6130]])
     elif model_name == "upernet-convnext-small":
          __snake_case				=							torch.tensor(
              [[-8.8236, -8.8236, -8.6771], [-8.8236, -8.8236, -8.6771], [-8.7638, -8.7638, -8.6240]])
     elif model_name == "upernet-convnext-base":
          __snake_case				=							torch.tensor(
              [[-8.8558, -8.8558, -8.6905], [-8.8558, -8.8558, -8.6905], [-8.7669, -8.7669, -8.6021]])
     elif model_name == "upernet-convnext-large":
          __snake_case				=							torch.tensor(
              [[-8.6660, -8.6660, -8.6210], [-8.6660, -8.6660, -8.6210], [-8.6310, -8.6310, -8.5964]])
     elif model_name == "upernet-convnext-xlarge":
          __snake_case				=							torch.tensor(
              [[-8.4980, -8.4980, -8.3977], [-8.4980, -8.4980, -8.3977], [-8.4379, -8.4379, -8.3412]])
     print('''Logits:''',      outputs.logits[0, 0, :3, :3])
     assert torch.allclose(outputs.logits[0, 0, :3, :3],      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(snake_case)
          print(f"Saving processor to {pytorch_dump_folder_path}")
          processor.save_pretrained(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__":
   __lowercase		:		Dict														=							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."
   )

   __lowercase		:		int														=							parser.parse_args()
   convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
"""simple docstring"""



import inspect
import unittest

import numpy as np

from transformers import BeitConfig
from transformers.testing_utils import require_flax, require_vision, slow
from transformers.utils import cached_property, is_flax_available, is_vision_available

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


if is_flax_available():
       import jax

       from transformers import FlaxBeitForImageClassification, FlaxBeitForMaskedImageModeling, FlaxBeitModel

if is_vision_available():
       from PIL import Image

       from transformers import BeitImageProcessor



class      _A	(						unittest.TestCase					):




    """simple docstring"""




    def __init__(		self							:     Union[str, Any]	,      A_							:     List[str]	,      A_							:     List[Any]=100	,      A_							:     Any=13	,      A_							:     Dict=30	,      A_							:     Optional[int]=2	,      A_							:     str=3	,      A_							:     Tuple=True	,      A_							:     str=True	,      A_							:     Union[str, Any]=32	,      A_							:     int=5	,      A_							:     List[Any]=4	,      A_							:     Optional[Any]=37	,      A_							:     Any="gelu"	,      A_							:     List[str]=0.1	,      A_							:     int=0.1	,      A_							:     Tuple=10	,      A_							:     int=0.02	,      A_							:     Tuple=3	,      )    ->       Optional[int]:
         __snake_case				=							parent
         __snake_case				=							vocab_size
         __snake_case				=							batch_size
         __snake_case				=							image_size
         __snake_case				=							patch_size
         __snake_case				=							num_channels
         __snake_case				=							is_training
         __snake_case				=							use_labels
         __snake_case				=							hidden_size
         __snake_case				=							num_hidden_layers
         __snake_case				=							num_attention_heads
         __snake_case				=							intermediate_size
         __snake_case				=							hidden_act
         __snake_case				=							hidden_dropout_prob
         __snake_case				=							attention_probs_dropout_prob
         __snake_case				=							type_sequence_label_size
         __snake_case				=							initializer_range

         # in BeiT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
         __snake_case				=							(image_size // patch_size) ** 2
         __snake_case				=							num_patches + 1
    def  lowercase					(		self							:     Optional[Any]    )    ->       List[str]:
         __snake_case				=							floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]    )

         __snake_case				=							None
         if self.use_labels:
              __snake_case				=							ids_tensor([self.batch_size]	,      self.type_sequence_label_size    )

         __snake_case				=							BeitConfig(
             vocab_size=self.vocab_size	,      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=A_	,      initializer_range=self.initializer_range	,      )

         return config, pixel_values, labels
    def  lowercase					(		self							:     Optional[int]	,      A_							:     str	,      A_							:     str	,      A_							:     List[str]    )    ->       List[Any]:
         __snake_case				=							FlaxBeitModel(config=A_    )
         __snake_case				=							model(A_    )
         self.parent.assertEqual(result.last_hidden_state.shape	,      (self.batch_size, self.seq_length, self.hidden_size)    )
    def  lowercase					(		self							:     List[str]	,      A_							:     Any	,      A_							:     Any	,      A_							:     str    )    ->       str:
         __snake_case				=							FlaxBeitForMaskedImageModeling(config=A_    )
         __snake_case				=							model(A_    )
         self.parent.assertEqual(result.logits.shape	,      (self.batch_size, self.seq_length - 1, self.vocab_size)    )
    def  lowercase					(		self							:     str	,      A_							:     List[Any]	,      A_							:     Optional[int]	,      A_							:     List[str]    )    ->       str:
         __snake_case				=							self.type_sequence_label_size
         __snake_case				=							FlaxBeitForImageClassification(config=A_    )
         __snake_case				=							model(A_    )
         self.parent.assertEqual(result.logits.shape	,      (self.batch_size, self.type_sequence_label_size)    )

         # test greyscale images
         __snake_case				=							1
         __snake_case				=							FlaxBeitForImageClassification(A_    )

         __snake_case				=							floats_tensor([self.batch_size, 1, self.image_size, self.image_size]    )
         __snake_case				=							model(A_    )







    def  lowercase					(		self							:     Dict    )    ->       List[str]:
         __snake_case				=							self.prepare_config_and_inputs()
         (
             (
             __snake_case
         )		,	(
             __snake_case
         )		,	(
             __snake_case
         )		,	
         )				=							config_and_inputs
         __snake_case				=							{'''pixel_values''': pixel_values}
         return config, inputs_dict






@require_flax
class      _A	(						_UpperCAmelCase							, unittest.TestCase					):




    """simple docstring"""




    UpperCamelCase_	:					Dict							=					(
        (FlaxBeitModel, FlaxBeitForImageClassification, FlaxBeitForMaskedImageModeling) if is_flax_available() else ()
    )
    def  lowercase					(		self							:     List[Any]    )    ->       None:
         __snake_case				=							FlaxBeitModelTester(self    )
         __snake_case				=							ConfigTester(self	,      config_class=A_	,      has_text_modality=A_	,      hidden_size=37    )
    def  lowercase					(		self							:     Tuple    )    ->       Union[str, Any]:
         self.config_tester.run_common_tests()
    def  lowercase					(		self							:     int    )    ->       Optional[Any]:
         __snake_case		,	__snake_case				=							self.model_tester.prepare_config_and_inputs_for_common()

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

              __snake_case				=							['''pixel_values''']
              self.assertListEqual(arg_names[:1]	,      A_    )
    def  lowercase					(		self							:     Union[str, Any]    )    ->       int:
         __snake_case		,	__snake_case				=							self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
              with self.subTest(model_class.__name__    ):
                   __snake_case				=							self._prepare_for_class(A_	,      A_    )
                   __snake_case				=							model_class(A_    )

                   @jax.jit
                   def model_jitted(A_							:     Union[str, Any]	,      **A_							:     Union[str, Any]    ):
                        return model(pixel_values=A_	,      **A_    )

                   with self.subTest('''JIT Enabled'''    ):
                        __snake_case				=							model_jitted(**A_    ).to_tuple()

                   with self.subTest('''JIT Disabled'''    ):
                        with jax.disable_jit():
                             __snake_case				=							model_jitted(**A_    ).to_tuple()

                   self.assertEqual(len(A_    )	,      len(A_    )    )
                   for jitted_output, output in zip(A_	,      A_    ):
                        self.assertEqual(jitted_output.shape	,      output.shape    )
    def  lowercase					(		self							:     str    )    ->       List[Any]:
         __snake_case				=							self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*A_    )
    def  lowercase					(		self							:     List[Any]    )    ->       Union[str, Any]:
         __snake_case				=							self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_masked_lm(*A_    )
    def  lowercase					(		self							:     str    )    ->       int:
         __snake_case				=							self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_image_classification(*A_    )







    @slow
    def  lowercase					(		self							:     int    )    ->       Optional[Any]:
         for model_class_name in self.all_model_classes:
              __snake_case				=							model_class_name.from_pretrained('''microsoft/beit-base-patch16-224'''    )
              __snake_case				=							model(np.ones((1, 3, 224, 224)    )    )
              self.assertIsNotNone(A_    )






def    SCREAMING_SNAKE_CASE       (     ):
     __snake_case				=							Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''')
     return image




@require_vision
@require_flax
class      _A	(						unittest.TestCase					):




    """simple docstring"""




    @cached_property
    def  lowercase					(		self							:     str    )    ->       str:
         return BeitImageProcessor.from_pretrained('''microsoft/beit-base-patch16-224'''    ) if is_vision_available() else None
    @slow
    def  lowercase					(		self							:     Union[str, Any]    )    ->       List[str]:
         __snake_case				=							FlaxBeitForMaskedImageModeling.from_pretrained('''microsoft/beit-base-patch16-224-pt22k'''    )

         __snake_case				=							self.default_image_processor
         __snake_case				=							prepare_img()
         __snake_case				=							image_processor(images=A_	,      return_tensors='''np'''    ).pixel_values

         # prepare bool_masked_pos
         __snake_case				=							np.ones((1, 196)	,      dtype=A_    )

         # forward pass
         __snake_case				=							model(pixel_values=A_	,      bool_masked_pos=A_    )
         __snake_case				=							outputs.logits

         # verify the logits
         __snake_case				=							(1, 196, 8_192)
         self.assertEqual(logits.shape	,      A_    )

         __snake_case				=							np.array(
             [[-3.24_37, 0.50_72, -13.91_74], [-3.24_56, 0.49_48, -13.94_01], [-3.20_33, 0.51_21, -13.85_50]]    )

         self.assertTrue(np.allclose(logits[bool_masked_pos][:3, :3]	,      A_	,      atol=1E-2    )    )
    @slow
    def  lowercase					(		self							:     List[Any]    )    ->       List[str]:
         __snake_case				=							FlaxBeitForImageClassification.from_pretrained('''microsoft/beit-base-patch16-224'''    )

         __snake_case				=							self.default_image_processor
         __snake_case				=							prepare_img()
         __snake_case				=							image_processor(images=A_	,      return_tensors='''np'''    )

         # forward pass
         __snake_case				=							model(**A_    )
         __snake_case				=							outputs.logits

         # verify the logits
         __snake_case				=							(1, 1_000)
         self.assertEqual(logits.shape	,      A_    )

         __snake_case				=							np.array([-1.23_85, -1.09_87, -1.01_08]    )

         self.assertTrue(np.allclose(logits[0, :3]	,      A_	,      atol=1E-4    )    )

         __snake_case				=							281
         self.assertEqual(logits.argmax(-1    ).item()	,      A_    )







    @slow
    def  lowercase					(		self							:     int    )    ->       str:
         __snake_case				=							FlaxBeitForImageClassification.from_pretrained('''microsoft/beit-large-patch16-224-pt22k-ft22k'''    )

         __snake_case				=							self.default_image_processor
         __snake_case				=							prepare_img()
         __snake_case				=							image_processor(images=A_	,      return_tensors='''np'''    )

         # forward pass
         __snake_case				=							model(**A_    )
         __snake_case				=							outputs.logits

         # verify the logits
         __snake_case				=							(1, 21_841)
         self.assertEqual(logits.shape	,      A_    )

         __snake_case				=							np.array([1.68_81, -0.27_87, 0.59_01]    )

         self.assertTrue(np.allclose(logits[0, :3]	,      A_	,      atol=1E-4    )    )

         __snake_case				=							2_396
         self.assertEqual(logits.argmax(-1    ).item()	,      A_    )