






import argparse

import torch

# Step 1. clone https://github.com/microsoft/unilm
# Step 2. git checkout to https://github.com/microsoft/unilm/commit/b94ec76c36f02fb2b0bf0dcb0b8554a2185173cd
# Step 3. cd unilm
# Step 4. ln -s $(realpath wavlm/modules.py) ./  # create simlink
# import classes
from unilm.wavlm.WavLM import WavLM as WavLMOrig
from unilm.wavlm.WavLM import WavLMConfig as WavLMConfigOrig

from transformers import WavLMConfig, WavLMModel, logging


logging.set_verbosity_info()
a		:       List[str]			     = logging.get_logger(__name__)

a		:       str			     = {
    'post_extract_proj': 'feature_projection.projection',
    'encoder.pos_conv.0': 'encoder.pos_conv_embed.conv',
    'self_attn.k_proj': 'encoder.layers.*.attention.k_proj',
    'self_attn.v_proj': 'encoder.layers.*.attention.v_proj',
    'self_attn.q_proj': 'encoder.layers.*.attention.q_proj',
    'self_attn.out_proj': 'encoder.layers.*.attention.out_proj',
    'self_attn.grep_linear': 'encoder.layers.*.attention.gru_rel_pos_linear',
    'self_attn.relative_attention_bias': 'encoder.layers.*.attention.rel_attn_embed',
    'self_attn.grep_a': 'encoder.layers.*.attention.gru_rel_pos_const',
    'self_attn_layer_norm': 'encoder.layers.*.layer_norm',
    'fc1': 'encoder.layers.*.feed_forward.intermediate_dense',
    'fc2': 'encoder.layers.*.feed_forward.output_dense',
    'final_layer_norm': 'encoder.layers.*.final_layer_norm',
    'encoder.layer_norm': 'encoder.layer_norm',
    'w2v_model.layer_norm': 'feature_projection.layer_norm',
    'quantizer.weight_proj': 'quantizer.weight_proj',
    'quantizer.vars': 'quantizer.codevectors',
    'project_q': 'project_q',
    'final_proj': 'project_hid',
    'w2v_encoder.proj': 'ctc_proj',
    'mask_emb': 'masked_spec_embed',
}
a		:       Union[str, Any]			     = [
    'ctc_proj',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
]






def 				lowerCamelCase__      (    __lowerCamelCase			:     Optional[int]  ,		__lowerCamelCase			:     Any  ,		__lowerCamelCase			:     List[str]  ,		__lowerCamelCase			:     int  ,		__lowerCamelCase			:     Union[str, Any]		):
  for attribute in key.split("""."""		):
    __UpperCAmelCase							:	Dict           =  getattr(_snake_case  ,		_snake_case		)

  if weight_type is not None:
    __UpperCAmelCase							:	int           =  getattr(_snake_case  ,		_snake_case		).shape
  else:
    __UpperCAmelCase							:	Any           =  hf_pointer.shape

  assert hf_shape == value.shape, (
      f"""Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be"""
      f""" {value.shape} for {full_name}"""
  )

  if weight_type == "weight":
    __UpperCAmelCase							:	Optional[Any]           =  value
  elif weight_type == "weight_g":
    __UpperCAmelCase							:	Union[str, Any]           =  value
  elif weight_type == "weight_v":
    __UpperCAmelCase							:	List[Any]           =  value
  elif weight_type == "bias":
    __UpperCAmelCase							:	Dict           =  value
  else:
    __UpperCAmelCase							:	int           =  value

  logger.info(f"""{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}."""		)






def 				lowerCamelCase__      (    __lowerCamelCase			:     Dict  ,		__lowerCamelCase			:     Optional[int]		):
  __UpperCAmelCase							:	int           =  []
  __UpperCAmelCase							:	List[str]           =  fairseq_model.state_dict()

  __UpperCAmelCase							:	str           =  hf_model.feature_extractor

  for name, value in fairseq_dict.items():
    __UpperCAmelCase							:	Tuple           =  False
    if "conv_layers" in name:
      load_conv_layer(
          _snake_case  ,		_snake_case  ,		_snake_case  ,		_snake_case  ,		hf_model.config.feat_extract_norm == """group"""  ,		)
      __UpperCAmelCase							:	str           =  True
    else:
      for key, mapped_key in MAPPING.items():
        if key in name or key.split("""w2v_model."""		)[-1] == name.split("""."""		)[0]:
          __UpperCAmelCase							:	int           =  True
          if "*" in mapped_key:
            __UpperCAmelCase							:	Dict           =  name.split(_snake_case		)[0].split("""."""		)[-2]
            __UpperCAmelCase							:	Union[str, Any]           =  mapped_key.replace("""*"""  ,		_snake_case		)
          if "weight_g" in name:
            __UpperCAmelCase							:	Dict           =  """weight_g"""
          elif "weight_v" in name:
            __UpperCAmelCase							:	int           =  """weight_v"""
          elif "bias" in name and "relative_attention_bias" not in name:
            __UpperCAmelCase							:	str           =  """bias"""
          elif "weight" in name:
            # TODO: don't match quantizer.weight_proj
            __UpperCAmelCase							:	Any           =  """weight"""
          else:
            __UpperCAmelCase							:	List[str]           =  None

          set_recursively(_snake_case  ,		_snake_case  ,		_snake_case  ,		_snake_case  ,		_snake_case		)
        continue
    if not is_used:
      unused_weights.append(_snake_case		)

  logger.warning(f"""Unused weights: {unused_weights}"""		)






def 				lowerCamelCase__      (    __lowerCamelCase			:     List[str]  ,		__lowerCamelCase			:     Dict  ,		__lowerCamelCase			:     List[str]  ,		__lowerCamelCase			:     List[Any]  ,		__lowerCamelCase			:     Any		):
  __UpperCAmelCase							:	List[str]           =  full_name.split("""conv_layers."""		)[-1]
  __UpperCAmelCase							:	List[str]           =  name.split("""."""		)
  __UpperCAmelCase							:	Optional[int]           =  int(items[0]		)
  __UpperCAmelCase							:	Union[str, Any]           =  int(items[1]		)

  if type_id == 0:
    if "bias" in name:
      assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
          f"""{full_name} has size {value.shape}, but"""
          f""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."""
      )
      __UpperCAmelCase							:	Optional[Any]           =  value
      logger.info(f"""Feat extract conv layer {layer_id} was initialized from {full_name}."""		)
    elif "weight" in name:
      assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
          f"""{full_name} has size {value.shape}, but"""
          f""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."""
      )
      __UpperCAmelCase							:	Optional[Any]           =  value
      logger.info(f"""Feat extract conv layer {layer_id} was initialized from {full_name}."""		)
  elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
    if "bias" in name:
      assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
          f"""{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was"""
          " found."
      )
      __UpperCAmelCase							:	Optional[int]           =  value
      logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""		)
    elif "weight" in name:
      assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
          f"""{full_name} has size {value.shape}, but"""
          f""" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found."""
      )
      __UpperCAmelCase							:	int           =  value
      logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""		)
  else:
    unused_weights.append(_snake_case		)






@torch.no_grad()
def 				lowerCamelCase__      (    __lowerCamelCase			:     Dict  ,		__lowerCamelCase			:     Tuple  ,		__lowerCamelCase			:     Optional[Any]=None		):
  # load the pre-trained checkpoints
  __UpperCAmelCase							:	Any           =  torch.load(_snake_case		)
  __UpperCAmelCase							:	List[str]           =  WavLMConfigOrig(checkpoint["""cfg"""]		)
  __UpperCAmelCase							:	Optional[Any]           =  WavLMOrig(_snake_case		)
  model.load_state_dict(checkpoint["""model"""]		)
  model.eval()

  if config_path is not None:
    __UpperCAmelCase							:	str           =  WavLMConfig.from_pretrained(_snake_case		)
  else:
    __UpperCAmelCase							:	Any           =  WavLMConfig()

  __UpperCAmelCase							:	Tuple           =  WavLMModel(_snake_case		)

  recursively_load_weights(_snake_case  ,		_snake_case		)

  hf_wavlm.save_pretrained(_snake_case		)


if __name__ == "__main__":
       a		:       Optional[int]			     = argparse.ArgumentParser()
       parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
       parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to fairseq checkpoint")
       parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
       a		:       List[str]			     = parser.parse_args()
       convert_wavlm_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)




"""simple docstring"""




import argparse
import json

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

from transformers import SegformerImageProcessor, SwinConfig, UperNetConfig, UperNetForSemanticSegmentation

def lowercase_  (   _snake_case						):
							SCREAMING_SNAKE_CASE__						:       List[Any]           =   384
							SCREAMING_SNAKE_CASE__						:       Tuple           =   7
							if "tiny" in model_name:
														SCREAMING_SNAKE_CASE__						:       int           =   96
														SCREAMING_SNAKE_CASE__						:       str           =   (2, 2, 6, 2)
														SCREAMING_SNAKE_CASE__						:       List[Any]           =   (3, 6, 12, 24)
							elif "small" in model_name:
														SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   96
														SCREAMING_SNAKE_CASE__						:       Any           =   (2, 2, 18, 2)
														SCREAMING_SNAKE_CASE__						:       Tuple           =   (3, 6, 12, 24)
							elif "base" in model_name:
														SCREAMING_SNAKE_CASE__						:       Tuple           =   128
														SCREAMING_SNAKE_CASE__						:       List[Any]           =   (2, 2, 18, 2)
														SCREAMING_SNAKE_CASE__						:       int           =   (4, 8, 16, 32)
														SCREAMING_SNAKE_CASE__						:       Optional[int]           =   12
														SCREAMING_SNAKE_CASE__						:       Optional[int]           =   512
							elif "large" in model_name:
														SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   192
														SCREAMING_SNAKE_CASE__						:       int           =   (2, 2, 18, 2)
														SCREAMING_SNAKE_CASE__						:       int           =   (6, 12, 24, 48)
														SCREAMING_SNAKE_CASE__						:       List[Any]           =   12
														SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   768

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

							SCREAMING_SNAKE_CASE__						:       str           =   SwinConfig(
							    embed_dim=_snake_case   ,depths=_snake_case   ,num_heads=_snake_case   ,window_size=_snake_case   ,out_features=["""stage1""", """stage2""", """stage3""", """stage4"""]   ,)
							SCREAMING_SNAKE_CASE__						:       int           =   UperNetConfig(
							    backbone_config=_snake_case   ,auxiliary_in_channels=_snake_case   ,num_labels=_snake_case   ,idalabel=_snake_case   ,labelaid=_snake_case   ,)

							return config

def lowercase_  (   _snake_case						):
							SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   []

							# fmt: off
							# stem
							rename_keys.append(("""backbone.patch_embed.projection.weight""", """backbone.embeddings.patch_embeddings.projection.weight""")						)
							rename_keys.append(("""backbone.patch_embed.projection.bias""", """backbone.embeddings.patch_embeddings.projection.bias""")						)
							rename_keys.append(("""backbone.patch_embed.norm.weight""", """backbone.embeddings.norm.weight""")						)
							rename_keys.append(("""backbone.patch_embed.norm.bias""", """backbone.embeddings.norm.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}.blocks.{j}.norm1.weight''', f'''backbone.encoder.layers.{i}.blocks.{j}.layernorm_before.weight''')						)
																					rename_keys.append((f'''backbone.stages.{i}.blocks.{j}.norm1.bias''', f'''backbone.encoder.layers.{i}.blocks.{j}.layernorm_before.bias''')						)
																					rename_keys.append((f'''backbone.stages.{i}.blocks.{j}.attn.w_msa.relative_position_bias_table''', f'''backbone.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table''')						)
																					rename_keys.append((f'''backbone.stages.{i}.blocks.{j}.attn.w_msa.relative_position_index''', f'''backbone.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index''')						)
																					rename_keys.append((f'''backbone.stages.{i}.blocks.{j}.attn.w_msa.proj.weight''', f'''backbone.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight''')						)
																					rename_keys.append((f'''backbone.stages.{i}.blocks.{j}.attn.w_msa.proj.bias''', f'''backbone.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias''')						)
																					rename_keys.append((f'''backbone.stages.{i}.blocks.{j}.norm2.weight''', f'''backbone.encoder.layers.{i}.blocks.{j}.layernorm_after.weight''')						)
																					rename_keys.append((f'''backbone.stages.{i}.blocks.{j}.norm2.bias''', f'''backbone.encoder.layers.{i}.blocks.{j}.layernorm_after.bias''')						)
																					rename_keys.append((f'''backbone.stages.{i}.blocks.{j}.ffn.layers.0.0.weight''', f'''backbone.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight''')						)
																					rename_keys.append((f'''backbone.stages.{i}.blocks.{j}.ffn.layers.0.0.bias''', f'''backbone.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias''')						)
																					rename_keys.append((f'''backbone.stages.{i}.blocks.{j}.ffn.layers.1.weight''', f'''backbone.encoder.layers.{i}.blocks.{j}.output.dense.weight''')						)
																					rename_keys.append((f'''backbone.stages.{i}.blocks.{j}.ffn.layers.1.bias''', f'''backbone.encoder.layers.{i}.blocks.{j}.output.dense.bias''')						)

														if i < 3:
																					rename_keys.append((f'''backbone.stages.{i}.downsample.reduction.weight''', f'''backbone.encoder.layers.{i}.downsample.reduction.weight''')						)
																					rename_keys.append((f'''backbone.stages.{i}.downsample.norm.weight''', f'''backbone.encoder.layers.{i}.downsample.norm.weight''')						)
																					rename_keys.append((f'''backbone.stages.{i}.downsample.norm.bias''', f'''backbone.encoder.layers.{i}.downsample.norm.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 lowercase_  (   _snake_case   ,_snake_case   ,_snake_case						):
							SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   dct.pop(_snake_case						)
							SCREAMING_SNAKE_CASE__						:       Tuple           =   val

def lowercase_  (   _snake_case   ,_snake_case						):
							SCREAMING_SNAKE_CASE__						:       int           =   [int(backbone_config.embed_dim * 2**i						) for i in range(len(backbone_config.depths						)						)]
							for i in range(len(backbone_config.depths						)						):
														SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   num_features[i]
														for j in range(backbone_config.depths[i]						):
																					# fmt: off
																					# read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
																					SCREAMING_SNAKE_CASE__						:       List[Any]           =   state_dict.pop(f'''backbone.stages.{i}.blocks.{j}.attn.w_msa.qkv.weight'''						)
																					SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   state_dict.pop(f'''backbone.stages.{i}.blocks.{j}.attn.w_msa.qkv.bias'''						)
																					# next, add query, keys and values (in that order) to the state dict
																					SCREAMING_SNAKE_CASE__						:       Tuple           =   in_proj_weight[:dim, :]
																					SCREAMING_SNAKE_CASE__						:       List[Any]           =   in_proj_bias[: dim]
																					SCREAMING_SNAKE_CASE__						:       Optional[int]           =   in_proj_weight[
																					    dim : dim * 2, :
																					]
																					SCREAMING_SNAKE_CASE__						:       List[Any]           =   in_proj_bias[
																					    dim : dim * 2
																					]
																					SCREAMING_SNAKE_CASE__						:       Tuple           =   in_proj_weight[
																					    -dim :, :
																					]
																					SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   in_proj_bias[-dim :]
																					# fmt: on

def lowercase_  (   _snake_case						):
							SCREAMING_SNAKE_CASE__      ,      SCREAMING_SNAKE_CASE__						:       List[Any]           =   x.shape
							SCREAMING_SNAKE_CASE__						:       List[Any]           =   x.reshape(_snake_case   ,4   ,in_channel // 4						)
							SCREAMING_SNAKE_CASE__						:       Dict           =   x[:, [0, 2, 1, 3], :].transpose(1   ,2						).reshape(_snake_case   ,_snake_case						)
							return x

def lowercase_  (   _snake_case						):
							SCREAMING_SNAKE_CASE__      ,      SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   x.shape
							SCREAMING_SNAKE_CASE__						:       Any           =   x.reshape(_snake_case   ,in_channel // 4   ,4						)
							SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   x[:, :, [0, 2, 1, 3]].transpose(1   ,2						).reshape(_snake_case   ,_snake_case						)

							return x

def lowercase_  (   _snake_case						):
							SCREAMING_SNAKE_CASE__						:       Tuple           =   x.shape[0]
							SCREAMING_SNAKE_CASE__						:       List[str]           =   x.reshape(4   ,in_channel // 4						)
							SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   x[[0, 2, 1, 3], :].transpose(0   ,1						).reshape(_snake_case						)
							return x

def lowercase_  (   _snake_case						):
							SCREAMING_SNAKE_CASE__						:       int           =   x.shape[0]
							SCREAMING_SNAKE_CASE__						:       List[str]           =   x.reshape(in_channel // 4   ,4						)
							SCREAMING_SNAKE_CASE__						:       Tuple           =   x[:, [0, 2, 1, 3]].transpose(0   ,1						).reshape(_snake_case						)
							return x

def lowercase_  (   _snake_case   ,_snake_case   ,_snake_case						):
							SCREAMING_SNAKE_CASE__						:       List[Any]           =   {
							    """upernet-swin-tiny""": """https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K/upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K_20210531_112542-e380ad3e.pth""",
							    """upernet-swin-small""": """https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_small_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K/upernet_swin_small_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K_20210526_192015-ee2fff1c.pth""",
							    """upernet-swin-base""": """https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_22K/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_22K_20210531_125459-429057bf.pth""",
							    """upernet-swin-large""": """https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_large_patch4_window12_512x512_pretrain_384x384_22K_160k_ade20k/upernet_swin_large_patch4_window12_512x512_pretrain_384x384_22K_160k_ade20k_20220318_091743-9ba68901.pth""",
							}
							SCREAMING_SNAKE_CASE__						:       Optional[int]           =   model_name_to_url[model_name]
							SCREAMING_SNAKE_CASE__						:       Optional[int]           =   torch.hub.load_state_dict_from_url(_snake_case   ,map_location="""cpu"""   ,file_name=_snake_case						)[
							    """state_dict"""
							]

							for name, param in state_dict.items():
														print(_snake_case   ,param.shape						)

							SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   get_upernet_config(_snake_case						)
							SCREAMING_SNAKE_CASE__						:       List[str]           =   UperNetForSemanticSegmentation(_snake_case						)
							model.eval()

							# replace "bn" => "batch_norm"
							for key in state_dict.copy().keys():
														SCREAMING_SNAKE_CASE__						:       Optional[int]           =   state_dict.pop(_snake_case						)
														if "bn" in key:
																					SCREAMING_SNAKE_CASE__						:       Optional[int]           =   key.replace("""bn"""   ,"""batch_norm"""						)
														SCREAMING_SNAKE_CASE__						:       Dict           =   val

							# rename keys
							SCREAMING_SNAKE_CASE__						:       str           =   create_rename_keys(_snake_case						)
							for src, dest in rename_keys:
														rename_key(_snake_case   ,_snake_case   ,_snake_case						)
							read_in_q_k_v(_snake_case   ,config.backbone_config						)

							# fix downsample parameters
							for key, value in state_dict.items():
														if "downsample" in key:
																					if "reduction" in key:
																												SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   reverse_correct_unfold_reduction_order(_snake_case						)
																					if "norm" in key:
																												SCREAMING_SNAKE_CASE__						:       Tuple           =   reverse_correct_unfold_norm_order(_snake_case						)

							model.load_state_dict(_snake_case						)

							# verify on image
							SCREAMING_SNAKE_CASE__						:       List[str]           =   """https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg"""
							SCREAMING_SNAKE_CASE__						:       str           =   Image.open(requests.get(_snake_case   ,stream=_snake_case						).raw						).convert("""RGB"""						)

							SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   SegformerImageProcessor()
							SCREAMING_SNAKE_CASE__						:       Optional[int]           =   processor(_snake_case   ,return_tensors="""pt"""						).pixel_values

							with torch.no_grad():
														SCREAMING_SNAKE_CASE__						:       Tuple           =   model(_snake_case						)
														SCREAMING_SNAKE_CASE__						:       List[Any]           =   outputs.logits

							print(logits.shape						)
							print("""First values of logits:"""   ,logits[0, 0, :3, :3]						)
							# assert values
							if model_name == "upernet-swin-tiny":
														SCREAMING_SNAKE_CASE__						:       Tuple           =   torch.tensor(
														    [[-7.5958, -7.5958, -7.4302], [-7.5958, -7.5958, -7.4302], [-7.4797, -7.4797, -7.3068]]						)
							elif model_name == "upernet-swin-small":
														SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   torch.tensor(
														    [[-7.1921, -7.1921, -6.9532], [-7.1921, -7.1921, -6.9532], [-7.0908, -7.0908, -6.8534]]						)
							elif model_name == "upernet-swin-base":
														SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   torch.tensor(
														    [[-6.5851, -6.5851, -6.4330], [-6.5851, -6.5851, -6.4330], [-6.4763, -6.4763, -6.3254]]						)
							elif model_name == "upernet-swin-large":
														SCREAMING_SNAKE_CASE__						:       Dict           =   torch.tensor(
														    [[-7.5297, -7.5297, -7.3802], [-7.5297, -7.5297, -7.3802], [-7.4044, -7.4044, -7.2586]]						)
							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__      :    List[Any]    							= argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    '--model_name',
					    default='upernet-swin-tiny',
					    type=str,
					    choices=[f"""upernet-swin-{size}""" for size in ['tiny', 'small', 'base', 'large']],
					    help='Name of the Swin + 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__      :    List[str]    							= parser.parse_args()
					convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)




