



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

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

   UpperCamelCase   :  Optional[Any]						     =     ConvNextConfig(
       depths=snake_case__	,					hidden_sizes=snake_case__	,					out_features=['stage1', 'stage2', 'stage3', 'stage4']     )
   UpperCamelCase   :  List[Any]						     =     UperNetConfig(
       backbone_config=snake_case__	,					auxiliary_in_channels=snake_case__	,					num_labels=snake_case__	,					idalabel=snake_case__	,					labelaid=snake_case__	,					)

   return config
def 		UpperCamelCase     (       snake_case__	:   List[str]     )     ->					List[Any]:
   UpperCamelCase   :  Optional[int]						     =     []

   # 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 		UpperCamelCase     (       snake_case__	:   Dict	,					snake_case__	:   str	,					snake_case__	:   Dict     )     ->					Optional[Any]:
   UpperCamelCase   :  List[str]						     =     dct.pop(snake_case__     )
   UpperCamelCase   :  Optional[Any]						     =     val
def 		UpperCamelCase     (       snake_case__	:   Optional[Any]	,					snake_case__	:   Tuple	,					snake_case__	:   Optional[int]     )     ->					Any:
   UpperCamelCase   :  Optional[int]						     =     {
       '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',
   }
   UpperCamelCase   :  Optional[Any]						     =     model_name_to_url[model_name]
   UpperCamelCase   :  Dict						     =     torch.hub.load_state_dict_from_url(snake_case__	,					map_location='cpu'     )['state_dict']

   UpperCamelCase   :  List[str]						     =     get_upernet_config(snake_case__     )
   UpperCamelCase   :  int						     =     UperNetForSemanticSegmentation(snake_case__     )
   model.eval()

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

   # rename keys
   UpperCamelCase   :  Optional[Any]						     =     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
   UpperCamelCase   :  int						     =     'https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg'
   UpperCamelCase   :  Any						     =     Image.open(requests.get(snake_case__	,					stream=snake_case__     ).raw     ).convert('RGB'     )

   UpperCamelCase   :  Tuple						     =     SegformerImageProcessor()
   UpperCamelCase   :  Any						     =     processor(snake_case__	,					return_tensors='pt'     ).pixel_values

   with torch.no_grad():
      UpperCamelCase   :  str						     =     model(snake_case__     )

   if model_name == "upernet-convnext-tiny":
      UpperCamelCase   :  List[str]						     =     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":
      UpperCamelCase   :  List[Any]						     =     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":
      UpperCamelCase   :  Tuple						     =     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":
      UpperCamelCase   :  Optional[int]						     =     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":
      UpperCamelCase   :  List[Any]						     =     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__":
    __UpperCAmelCase	       =	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.'''
    )

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








import unittest

from transformers import (
    MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
    TextClassificationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_torch, slow

from .test_pipelines_common import ANY


# These 2 model types require different inputs than those of the usual text models.
__UpperCAmelCase	       =	{'''LayoutLMv2Config''', '''LayoutLMv3Config'''}



@is_pipeline_test
class     lowerCAmelCase_		(  unittest.TestCase							):
       UpperCAmelCase__    : Dict       					=  MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
       UpperCAmelCase__    : List[str]       					=  TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

       if model_mapping is not None:
              UpperCAmelCase__    : Optional[Any]       					=  {config: model for config, model in model_mapping.items() if config.__name__ not in _TO_SKIP}
       if tf_model_mapping is not None:
              UpperCAmelCase__    : Dict       					=  {
                  config: model for config, model in tf_model_mapping.items() if config.__name__ not in _TO_SKIP
              }
       @require_torch
       def  snake_case_	(      self	)					->		int:
          UpperCamelCase   :  List[str]						     =     pipeline(
              task='text-classification',      model='hf-internal-testing/tiny-random-distilbert',      framework='pt'	)

          UpperCamelCase   :  str						     =     text_classifier('This is great !'	)
          self.assertEqual(nested_simplify(SCREAMING_SNAKE_CASE_	),      [{'label': 'LABEL_0', 'score': 0.5_04}]	)

          UpperCamelCase   :  Dict						     =     text_classifier('This is great !',      top_k=2	)
          self.assertEqual(
              nested_simplify(SCREAMING_SNAKE_CASE_	),      [{'label': 'LABEL_0', 'score': 0.5_04}, {'label': 'LABEL_1', 'score': 0.4_96}]	)

          UpperCamelCase   :  List[Any]						     =     text_classifier(['This is great !', 'This is bad'],      top_k=2	)
          self.assertEqual(
              nested_simplify(SCREAMING_SNAKE_CASE_	),      [
                  [{'label': 'LABEL_0', 'score': 0.5_04}, {'label': 'LABEL_1', 'score': 0.4_96}],
                  [{'label': 'LABEL_0', 'score': 0.5_04}, {'label': 'LABEL_1', 'score': 0.4_96}],
              ],      )

          UpperCamelCase   :  List[Any]						     =     text_classifier('This is great !',      top_k=1	)
          self.assertEqual(nested_simplify(SCREAMING_SNAKE_CASE_	),      [{'label': 'LABEL_0', 'score': 0.5_04}]	)

          # Legacy behavior
          UpperCamelCase   :  str						     =     text_classifier('This is great !',      return_all_scores=SCREAMING_SNAKE_CASE_	)
          self.assertEqual(nested_simplify(SCREAMING_SNAKE_CASE_	),      [{'label': 'LABEL_0', 'score': 0.5_04}]	)

          UpperCamelCase   :  Tuple						     =     text_classifier('This is great !',      return_all_scores=SCREAMING_SNAKE_CASE_	)
          self.assertEqual(
              nested_simplify(SCREAMING_SNAKE_CASE_	),      [[{'label': 'LABEL_0', 'score': 0.5_04}, {'label': 'LABEL_1', 'score': 0.4_96}]]	)

          UpperCamelCase   :  List[Any]						     =     text_classifier(['This is great !', 'Something else'],      return_all_scores=SCREAMING_SNAKE_CASE_	)
          self.assertEqual(
              nested_simplify(SCREAMING_SNAKE_CASE_	),      [
                  [{'label': 'LABEL_0', 'score': 0.5_04}, {'label': 'LABEL_1', 'score': 0.4_96}],
                  [{'label': 'LABEL_0', 'score': 0.5_04}, {'label': 'LABEL_1', 'score': 0.4_96}],
              ],      )

          UpperCamelCase   :  Optional[int]						     =     text_classifier(['This is great !', 'Something else'],      return_all_scores=SCREAMING_SNAKE_CASE_	)
          self.assertEqual(
              nested_simplify(SCREAMING_SNAKE_CASE_	),      [
                  {'label': 'LABEL_0', 'score': 0.5_04},
                  {'label': 'LABEL_0', 'score': 0.5_04},
              ],      )
       @require_torch
       def  snake_case_	(      self	)					->		Optional[Any]:
          import torch

          UpperCamelCase   :  str						     =     pipeline(
              task='text-classification',      model='hf-internal-testing/tiny-random-distilbert',      framework='pt',      device=torch.device('cpu'	),      )

          UpperCamelCase   :  List[Any]						     =     text_classifier('This is great !'	)
          self.assertEqual(nested_simplify(SCREAMING_SNAKE_CASE_	),      [{'label': 'LABEL_0', 'score': 0.5_04}]	)
       @require_tf
       def  snake_case_	(      self	)					->		Dict:
          UpperCamelCase   :  List[Any]						     =     pipeline(
              task='text-classification',      model='hf-internal-testing/tiny-random-distilbert',      framework='tf'	)

          UpperCamelCase   :  str						     =     text_classifier('This is great !'	)
          self.assertEqual(nested_simplify(SCREAMING_SNAKE_CASE_	),      [{'label': 'LABEL_0', 'score': 0.5_04}]	)
       @slow
       @require_torch
       def  snake_case_	(      self	)					->		int:
          UpperCamelCase   :  str						     =     pipeline('text-classification'	)

          UpperCamelCase   :  str						     =     text_classifier('This is great !'	)
          self.assertEqual(nested_simplify(SCREAMING_SNAKE_CASE_	),      [{'label': 'POSITIVE', 'score': 1.0}]	)
          UpperCamelCase   :  List[str]						     =     text_classifier('This is bad !'	)
          self.assertEqual(nested_simplify(SCREAMING_SNAKE_CASE_	),      [{'label': 'NEGATIVE', 'score': 1.0}]	)
          UpperCamelCase   :  int						     =     text_classifier('Birds are a type of animal'	)
          self.assertEqual(nested_simplify(SCREAMING_SNAKE_CASE_	),      [{'label': 'POSITIVE', 'score': 0.9_88}]	)
       @slow
       @require_tf
       def  snake_case_	(      self	)					->		Tuple:
          UpperCamelCase   :  int						     =     pipeline('text-classification',      framework='tf'	)

          UpperCamelCase   :  Tuple						     =     text_classifier('This is great !'	)
          self.assertEqual(nested_simplify(SCREAMING_SNAKE_CASE_	),      [{'label': 'POSITIVE', 'score': 1.0}]	)
          UpperCamelCase   :  Union[str, Any]						     =     text_classifier('This is bad !'	)
          self.assertEqual(nested_simplify(SCREAMING_SNAKE_CASE_	),      [{'label': 'NEGATIVE', 'score': 1.0}]	)
          UpperCamelCase   :  int						     =     text_classifier('Birds are a type of animal'	)
          self.assertEqual(nested_simplify(SCREAMING_SNAKE_CASE_	),      [{'label': 'POSITIVE', 'score': 0.9_88}]	)
       def  snake_case_	(      self,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_	)					->		Union[str, Any]:
          UpperCamelCase   :  str						     =     TextClassificationPipeline(model=SCREAMING_SNAKE_CASE_,      tokenizer=SCREAMING_SNAKE_CASE_	)
          return text_classifier, ["HuggingFace is in", "This is another test"]






       def  snake_case_	(      self,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_	)					->		Any:
          UpperCamelCase   :  List[str]						     =     text_classifier.model
          # Small inputs because BartTokenizer tiny has maximum position embeddings = 22
          UpperCamelCase   :  List[str]						     =     'HuggingFace is in'
          UpperCamelCase   :  Tuple						     =     text_classifier(SCREAMING_SNAKE_CASE_	)

          self.assertEqual(nested_simplify(SCREAMING_SNAKE_CASE_	),      [{'label': ANY(SCREAMING_SNAKE_CASE_	), 'score': ANY(SCREAMING_SNAKE_CASE_	)}]	)
          self.assertTrue(outputs[0]['label'] in model.config.idalabel.values()	)

          UpperCamelCase   :  List[Any]						     =     ['HuggingFace is in ', 'Paris is in France']
          UpperCamelCase   :  List[str]						     =     text_classifier(SCREAMING_SNAKE_CASE_	)
          self.assertEqual(
              nested_simplify(SCREAMING_SNAKE_CASE_	),      [{'label': ANY(SCREAMING_SNAKE_CASE_	), 'score': ANY(SCREAMING_SNAKE_CASE_	)}, {'label': ANY(SCREAMING_SNAKE_CASE_	), 'score': ANY(SCREAMING_SNAKE_CASE_	)}],      )
          self.assertTrue(outputs[0]['label'] in model.config.idalabel.values()	)
          self.assertTrue(outputs[1]['label'] in model.config.idalabel.values()	)

          # Forcing to get all results with `top_k=None`
          # This is NOT the legacy format
          UpperCamelCase   :  Optional[int]						     =     text_classifier(SCREAMING_SNAKE_CASE_,      top_k=SCREAMING_SNAKE_CASE_	)
          UpperCamelCase   :  str						     =     len(model.config.idalabel.values()	)
          self.assertEqual(
              nested_simplify(SCREAMING_SNAKE_CASE_	),      [[{'label': ANY(SCREAMING_SNAKE_CASE_	), 'score': ANY(SCREAMING_SNAKE_CASE_	)}] * N, [{'label': ANY(SCREAMING_SNAKE_CASE_	), 'score': ANY(SCREAMING_SNAKE_CASE_	)}] * N],      )

          UpperCamelCase   :  str						     =     {'text': 'HuggingFace is in ', 'text_pair': 'Paris is in France'}
          UpperCamelCase   :  Union[str, Any]						     =     text_classifier(SCREAMING_SNAKE_CASE_	)
          self.assertEqual(
              nested_simplify(SCREAMING_SNAKE_CASE_	),      {'label': ANY(SCREAMING_SNAKE_CASE_	), 'score': ANY(SCREAMING_SNAKE_CASE_	)},      )
          self.assertTrue(outputs['label'] in model.config.idalabel.values()	)

          # This might be used a text pair, but tokenizer + pipe interaction
          # makes it hard to understand that it's not using the pair properly
          # https://github.com/huggingface/transformers/issues/17305
          # We disabled this usage instead as it was outputting wrong outputs.
          UpperCamelCase   :  List[str]						     =     [['HuggingFace is in ', 'Paris is in France']]
          with self.assertRaises(SCREAMING_SNAKE_CASE_	):
             text_classifier(SCREAMING_SNAKE_CASE_	)

          # This used to be valid for doing text pairs
          # We're keeping it working because of backward compatibility
          UpperCamelCase   :  List[str]						     =     text_classifier([[['HuggingFace is in ', 'Paris is in France']]]	)
          self.assertEqual(
              nested_simplify(SCREAMING_SNAKE_CASE_	),      [{'label': ANY(SCREAMING_SNAKE_CASE_	), 'score': ANY(SCREAMING_SNAKE_CASE_	)}],      )
          self.assertTrue(outputs[0]['label'] in model.config.idalabel.values()	)



