




'''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     __lowercase  (_lowercase     )   ->     Tuple:


     """simple docstring"""
     __lowerCamelCase						:       List[str]				   =				384
     if "tiny" in model_name:
          __lowerCamelCase						:       Optional[Any]				   =				[3, 3, 9, 3]
          __lowerCamelCase						:       Optional[int]				   =				[96, 192, 384, 768]
     if "small" in model_name:
          __lowerCamelCase						:       List[Any]				   =				[3, 3, 27, 3]
          __lowerCamelCase						:       Optional[int]				   =				[96, 192, 384, 768]
     if "base" in model_name:
          __lowerCamelCase						:       int				   =				[3, 3, 27, 3]
          __lowerCamelCase						:       Optional[int]				   =				[128, 256, 512, 1_024]
          __lowerCamelCase						:       List[Any]				   =				512
     if "large" in model_name:
          __lowerCamelCase						:       List[Any]				   =				[3, 3, 27, 3]
          __lowerCamelCase						:       int				   =				[192, 384, 768, 1_536]
          __lowerCamelCase						:       int				   =				768
     if "xlarge" in model_name:
          __lowerCamelCase						:       List[Any]				   =				[3, 3, 27, 3]
          __lowerCamelCase						:       Dict				   =				[256, 512, 1_024, 2_048]
          __lowerCamelCase						:       Optional[Any]				   =				1_024

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

     __lowerCamelCase						:       str				   =				ConvNextConfig(
         depths=__A,						hidden_sizes=__A,						out_features=["""stage1""", """stage2""", """stage3""", """stage4"""]     )
     __lowerCamelCase						:       int				   =				UperNetConfig(
         backbone_config=__A,						auxiliary_in_channels=__A,						num_labels=__A,						idalabel=__A,						labelaid=__A,						)

     return config
def     __lowercase  (_lowercase     )   ->     Union[str, Any]:


     """simple docstring"""
     __lowerCamelCase						:       Dict				   =				[]

     # 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     __lowercase  (_lowercase,						_lowercase,						_lowercase     )   ->     Optional[Any]:


     """simple docstring"""
     __lowerCamelCase						:       Any				   =				dct.pop(__A     )
     __lowerCamelCase						:       Optional[int]				   =				val
def     __lowercase  (_lowercase,						_lowercase,						_lowercase     )   ->     List[Any]:


     """simple docstring"""
     __lowerCamelCase						:       Any				   =				{
         """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						:       Tuple				   =				model_name_to_url[model_name]
     __lowerCamelCase						:       Optional[int]				   =				torch.hub.load_state_dict_from_url(__A,						map_location="""cpu"""     )["""state_dict"""]

     __lowerCamelCase						:       Optional[int]				   =				get_upernet_config(__A     )
     __lowerCamelCase						:       Any				   =				UperNetForSemanticSegmentation(__A     )
     model.eval()

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

     # rename keys
     __lowerCamelCase						:       str				   =				create_rename_keys(__A     )
     for src, dest in rename_keys:
          rename_key(__A,						__A,						__A     )

     model.load_state_dict(__A     )

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

     __lowerCamelCase						:       Optional[int]				   =				SegformerImageProcessor()
     __lowerCamelCase						:       List[str]				   =				processor(__A,						return_tensors="""pt"""     ).pixel_values

     with torch.no_grad():
          __lowerCamelCase						:       Optional[Any]				   =				model(__A     )

     if model_name == "upernet-convnext-tiny":
          __lowerCamelCase						:       Optional[int]				   =				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":
          __lowerCamelCase						:       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":
          __lowerCamelCase						:       Dict				   =				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":
          __lowerCamelCase						:       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":
          __lowerCamelCase						:       Tuple				   =				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],						__A,						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(__A     )
          print(f"Saving processor to {pytorch_dump_folder_path}"     )
          processor.save_pretrained(__A     )

     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__						:str          =			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__						:Tuple          =			parser.parse_args()
   convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)











'''simple docstring'''

from typing import List, Optional, Tuple, Union

import torch

from ...utils import logging, randn_tensor
from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline


UpperCAmelCase__						:Dict          =			logging.get_logger(__name__)  # pylint: disable=invalid-name







class 	SCREAMING_SNAKE_CASE	(      lowerCAmelCase_  ):




       def __init__(					self      : int	,    A__      : List[Any]	,    A__      : Tuple					):


            """simple docstring"""







            super().__init__()
            self.register_modules(unet=A__	,    scheduler=A__					)




       @torch.no_grad()
       def __call__(					self      : List[str]	,    A__      : int = 1	,    A__      : int = 100	,    A__      : Optional[Union[torch.Generator, List[torch.Generator]]] = None	,    A__      : Optional[float] = None	,    A__      : bool = True	,    ):


            """simple docstring"""







            if audio_length_in_s is None:
                 __lowerCamelCase						:       Union[str, Any]				   =				self.unet.config.sample_size / self.unet.config.sample_rate

            __lowerCamelCase						:       int				   =				audio_length_in_s * self.unet.config.sample_rate

            __lowerCamelCase						:       List[Any]				   =				2 ** len(self.unet.up_blocks					)
            if sample_size < 3 * down_scale_factor:
                 raise ValueError(
                     f"{audio_length_in_s} is too small. Make sure it's bigger or equal to"
                     f" {3 * down_scale_factor / self.unet.config.sample_rate}."					)

            __lowerCamelCase						:       Any				   =				int(A__					)
            if sample_size % down_scale_factor != 0:
                 __lowerCamelCase						:       Optional[Any]				   =				(
                     (audio_length_in_s * self.unet.config.sample_rate) // down_scale_factor + 1
                 ) * down_scale_factor
                 logger.info(
                     f"{audio_length_in_s} is increased to {sample_size / self.unet.config.sample_rate} so that it can be handled"
                     f" by the model. It will be cut to {original_sample_size / self.unet.config.sample_rate} after the denoising"
                     """ process."""					)
            __lowerCamelCase						:       Dict				   =				int(A__					)

            __lowerCamelCase						:       Optional[int]				   =				next(iter(self.unet.parameters()					)					).dtype
            __lowerCamelCase						:       Optional[Any]				   =				(batch_size, self.unet.config.in_channels, sample_size)
            if isinstance(A__	,    A__					) and len(A__					) != batch_size:
                 raise ValueError(
                     f"You have passed a list of generators of length {len(A__					)}, but requested an effective batch"
                     f" size of {batch_size}. Make sure the batch size matches the length of the generators."					)

            __lowerCamelCase						:       int				   =				randn_tensor(A__	,    generator=A__	,    device=self.device	,    dtype=A__					)

            # set step values
            self.scheduler.set_timesteps(A__	,    device=audio.device					)
            __lowerCamelCase						:       List[Any]				   =				self.scheduler.timesteps.to(A__					)

            for t in self.progress_bar(self.scheduler.timesteps					):
                 # 1. predict noise model_output
                 __lowerCamelCase						:       List[str]				   =				self.unet(A__	,    A__					).sample

                 # 2. compute previous image: x_t -> t_t-1
                 __lowerCamelCase						:       Dict				   =				self.scheduler.step(A__	,    A__	,    A__					).prev_sample

            __lowerCamelCase						:       Optional[int]				   =				audio.clamp(-1	,    1					).float().cpu().numpy()

            __lowerCamelCase						:       int				   =				audio[:, :, :original_sample_size]

            if not return_dict:
                 return (audio,)

            return AudioPipelineOutput(audios=A__					)



