'''simple docstring'''

from typing import Optional, Tuple, Union

import flax
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict

from ..configuration_utils import ConfigMixin, flax_register_to_config
from ..utils import BaseOutput
from .embeddings_flax import FlaxTimestepEmbedding, FlaxTimesteps
from .modeling_flax_utils import FlaxModelMixin
from .unet_ad_blocks_flax import (
    FlaxCrossAttnDownBlockaD,
    FlaxCrossAttnUpBlockaD,
    FlaxDownBlockaD,
    FlaxUNetMidBlockaDCrossAttn,
    FlaxUpBlockaD,
)







@flax.struct.dataclass
class 				_lowercase     (				UpperCAmelCase__			):


 '''simple docstring'''






 _SCREAMING_SNAKE_CASE     :       jnp.ndarray







@flax_register_to_config
class 				_lowercase     (				nn.Module  ,							UpperCAmelCase__  ,							UpperCAmelCase__			):


 '''simple docstring'''






 _SCREAMING_SNAKE_CASE     :       int      	=					32
 _SCREAMING_SNAKE_CASE     :       int      	=					4
 _SCREAMING_SNAKE_CASE     :       int      	=					4
 _SCREAMING_SNAKE_CASE     :       Tuple[str]      	=					(
     "CrossAttnDownBlock2D",
     "CrossAttnDownBlock2D",
     "CrossAttnDownBlock2D",
     "DownBlock2D",
 )
 _SCREAMING_SNAKE_CASE     :       Tuple[str]      	=					("UpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D")
 _SCREAMING_SNAKE_CASE     :       Union[bool, Tuple[bool]]      	=					False
 _SCREAMING_SNAKE_CASE     :       Tuple[int]      	=					(320, 640, 1280, 1280)
 _SCREAMING_SNAKE_CASE     :       int      	=					2
 _SCREAMING_SNAKE_CASE     :       Union[int, Tuple[int]]      	=					8
 _SCREAMING_SNAKE_CASE     :       Optional[Union[int, Tuple[int]]]      	=					None
 _SCREAMING_SNAKE_CASE     :       int      	=					1280
 _SCREAMING_SNAKE_CASE     :       float      	=					0.0
 _SCREAMING_SNAKE_CASE     :       bool      	=					False
 _SCREAMING_SNAKE_CASE     :       jnp.dtype      	=					jnp.floataa
 _SCREAMING_SNAKE_CASE     :       bool      	=					True
 _SCREAMING_SNAKE_CASE     :       int      	=					0
 _SCREAMING_SNAKE_CASE     :       bool      	=					False
 def a					(      self   :	Dict      ,       SCREAMING_SNAKE_CASE__   :	jax.random.KeyArray							)  ->    FrozenDict:
  # init input tensors
  __lowerCAmelCase											=						(1, self.in_channels, self.sample_size, self.sample_size)
  __lowerCAmelCase											=						jnp.zeros(SCREAMING_SNAKE_CASE__      ,       dtype=jnp.floataa							)
  __lowerCAmelCase											=						jnp.ones((1,)      ,       dtype=jnp.intaa							)
  __lowerCAmelCase											=						jnp.zeros((1, 1, self.cross_attention_dim)      ,       dtype=jnp.floataa							)

  __lowerCAmelCase							,       __lowerCAmelCase											=						jax.random.split(SCREAMING_SNAKE_CASE__							)
  __lowerCAmelCase											=						{"""params""": params_rng, """dropout""": dropout_rng}

  return self.init(SCREAMING_SNAKE_CASE__      ,       SCREAMING_SNAKE_CASE__      ,       SCREAMING_SNAKE_CASE__      ,       SCREAMING_SNAKE_CASE__							)["params"]
 def a					(      self   :	int							)  ->    List[str]:
  __lowerCAmelCase											=						self.block_out_channels
  __lowerCAmelCase											=						block_out_channels[0] * 4

  if self.num_attention_heads is not None:
   raise ValueError(
       """At the moment it is not possible to define the number of attention heads via `num_attention_heads` because of a naming issue as described in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131. Passing `num_attention_heads` will only be supported in diffusers v0.19."""							)

  # If `num_attention_heads` is not defined (which is the case for most models)
  # it will default to `attention_head_dim`. This looks weird upon first reading it and it is.
  # The reason for this behavior is to correct for incorrectly named variables that were introduced
  # when this library was created. The incorrect naming was only discovered much later in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131
  # Changing `attention_head_dim` to `num_attention_heads` for 40,000+ configurations is too backwards breaking
  # which is why we correct for the naming here.
  __lowerCAmelCase											=						self.num_attention_heads or self.attention_head_dim

  # input
  __lowerCAmelCase											=						nn.Conv(
      block_out_channels[0]      ,       kernel_size=(3, 3)      ,       strides=(1, 1)      ,       padding=((1, 1), (1, 1))      ,       dtype=self.dtype      ,       )

  # time
  __lowerCAmelCase											=						FlaxTimesteps(
      block_out_channels[0]      ,       flip_sin_to_cos=self.flip_sin_to_cos      ,       freq_shift=self.config.freq_shift							)
  __lowerCAmelCase											=						FlaxTimestepEmbedding(SCREAMING_SNAKE_CASE__      ,       dtype=self.dtype							)

  __lowerCAmelCase											=						self.only_cross_attention
  if isinstance(SCREAMING_SNAKE_CASE__      ,       SCREAMING_SNAKE_CASE__							):
   __lowerCAmelCase											=						(only_cross_attention,) * len(self.down_block_types							)

  if isinstance(SCREAMING_SNAKE_CASE__      ,       SCREAMING_SNAKE_CASE__							):
   __lowerCAmelCase											=						(num_attention_heads,) * len(self.down_block_types							)

  # down
  __lowerCAmelCase											=						[]
  __lowerCAmelCase											=						block_out_channels[0]
  for i, down_block_type in enumerate(self.down_block_types							):
   __lowerCAmelCase											=						output_channel
   __lowerCAmelCase											=						block_out_channels[i]
   __lowerCAmelCase											=						i == len(SCREAMING_SNAKE_CASE__							) - 1

   if down_block_type == "CrossAttnDownBlock2D":
    __lowerCAmelCase											=						FlaxCrossAttnDownBlockaD(
        in_channels=SCREAMING_SNAKE_CASE__      ,       out_channels=SCREAMING_SNAKE_CASE__      ,       dropout=self.dropout      ,       num_layers=self.layers_per_block      ,       num_attention_heads=num_attention_heads[i]      ,       add_downsample=not is_final_block      ,       use_linear_projection=self.use_linear_projection      ,       only_cross_attention=only_cross_attention[i]      ,       use_memory_efficient_attention=self.use_memory_efficient_attention      ,       dtype=self.dtype      ,       )
   else:
    __lowerCAmelCase											=						FlaxDownBlockaD(
        in_channels=SCREAMING_SNAKE_CASE__      ,       out_channels=SCREAMING_SNAKE_CASE__      ,       dropout=self.dropout      ,       num_layers=self.layers_per_block      ,       add_downsample=not is_final_block      ,       dtype=self.dtype      ,       )

   down_blocks.append(SCREAMING_SNAKE_CASE__							)
  __lowerCAmelCase											=						down_blocks

  # mid
  __lowerCAmelCase											=						FlaxUNetMidBlockaDCrossAttn(
      in_channels=block_out_channels[-1]      ,       dropout=self.dropout      ,       num_attention_heads=num_attention_heads[-1]      ,       use_linear_projection=self.use_linear_projection      ,       use_memory_efficient_attention=self.use_memory_efficient_attention      ,       dtype=self.dtype      ,       )

  # up
  __lowerCAmelCase											=						[]
  __lowerCAmelCase											=						list(reversed(SCREAMING_SNAKE_CASE__							)							)
  __lowerCAmelCase											=						list(reversed(SCREAMING_SNAKE_CASE__							)							)
  __lowerCAmelCase											=						list(reversed(SCREAMING_SNAKE_CASE__							)							)
  __lowerCAmelCase											=						reversed_block_out_channels[0]
  for i, up_block_type in enumerate(self.up_block_types							):
   __lowerCAmelCase											=						output_channel
   __lowerCAmelCase											=						reversed_block_out_channels[i]
   __lowerCAmelCase											=						reversed_block_out_channels[min(i + 1      ,       len(SCREAMING_SNAKE_CASE__							) - 1							)]

   __lowerCAmelCase											=						i == len(SCREAMING_SNAKE_CASE__							) - 1

   if up_block_type == "CrossAttnUpBlock2D":
    __lowerCAmelCase											=						FlaxCrossAttnUpBlockaD(
        in_channels=SCREAMING_SNAKE_CASE__      ,       out_channels=SCREAMING_SNAKE_CASE__      ,       prev_output_channel=SCREAMING_SNAKE_CASE__      ,       num_layers=self.layers_per_block + 1      ,       num_attention_heads=reversed_num_attention_heads[i]      ,       add_upsample=not is_final_block      ,       dropout=self.dropout      ,       use_linear_projection=self.use_linear_projection      ,       only_cross_attention=only_cross_attention[i]      ,       use_memory_efficient_attention=self.use_memory_efficient_attention      ,       dtype=self.dtype      ,       )
   else:
    __lowerCAmelCase											=						FlaxUpBlockaD(
        in_channels=SCREAMING_SNAKE_CASE__      ,       out_channels=SCREAMING_SNAKE_CASE__      ,       prev_output_channel=SCREAMING_SNAKE_CASE__      ,       num_layers=self.layers_per_block + 1      ,       add_upsample=not is_final_block      ,       dropout=self.dropout      ,       dtype=self.dtype      ,       )

   up_blocks.append(SCREAMING_SNAKE_CASE__							)
   __lowerCAmelCase											=						output_channel
  __lowerCAmelCase											=						up_blocks

  # out
  __lowerCAmelCase											=						nn.GroupNorm(num_groups=32      ,       epsilon=1e-5							)
  __lowerCAmelCase											=						nn.Conv(
      self.out_channels      ,       kernel_size=(3, 3)      ,       strides=(1, 1)      ,       padding=((1, 1), (1, 1))      ,       dtype=self.dtype      ,       )





 def __call__(      self   :	Dict      ,       SCREAMING_SNAKE_CASE__   :	int      ,       SCREAMING_SNAKE_CASE__   :	Tuple      ,       SCREAMING_SNAKE_CASE__   :	Union[str, Any]      ,       SCREAMING_SNAKE_CASE__   :	Optional[Any]=None      ,       SCREAMING_SNAKE_CASE__   :	List[str]=None      ,       SCREAMING_SNAKE_CASE__   :	bool = True      ,       SCREAMING_SNAKE_CASE__   :	bool = False      ,       )  ->    Union[FlaxUNetaDConditionOutput, Tuple]:
  # 1. time
  if not isinstance(SCREAMING_SNAKE_CASE__      ,       jnp.ndarray							):
   __lowerCAmelCase											=						jnp.array([timesteps]      ,       dtype=jnp.intaa							)
  elif isinstance(SCREAMING_SNAKE_CASE__      ,       jnp.ndarray							) and len(timesteps.shape							) == 0:
   __lowerCAmelCase											=						timesteps.astype(dtype=jnp.floataa							)
   __lowerCAmelCase											=						jnp.expand_dims(SCREAMING_SNAKE_CASE__      ,       0							)

  __lowerCAmelCase											=						self.time_proj(SCREAMING_SNAKE_CASE__							)
  __lowerCAmelCase											=						self.time_embedding(SCREAMING_SNAKE_CASE__							)

  # 2. pre-process
  __lowerCAmelCase											=						jnp.transpose(SCREAMING_SNAKE_CASE__      ,       (0, 2, 3, 1)							)
  __lowerCAmelCase											=						self.conv_in(SCREAMING_SNAKE_CASE__							)

  # 3. down
  __lowerCAmelCase											=						(sample,)
  for down_block in self.down_blocks:
   if isinstance(SCREAMING_SNAKE_CASE__      ,       SCREAMING_SNAKE_CASE__							):
    __lowerCAmelCase							,       __lowerCAmelCase											=						down_block(SCREAMING_SNAKE_CASE__      ,       SCREAMING_SNAKE_CASE__      ,       SCREAMING_SNAKE_CASE__      ,       deterministic=not train							)
   else:
    __lowerCAmelCase							,       __lowerCAmelCase											=						down_block(SCREAMING_SNAKE_CASE__      ,       SCREAMING_SNAKE_CASE__      ,       deterministic=not train							)
   down_block_res_samples += res_samples

  if down_block_additional_residuals is not None:
   __lowerCAmelCase											=						()

   for down_block_res_sample, down_block_additional_residual in zip(
       SCREAMING_SNAKE_CASE__      ,       SCREAMING_SNAKE_CASE__							):
    down_block_res_sample += down_block_additional_residual
    new_down_block_res_samples += (down_block_res_sample,)

   __lowerCAmelCase											=						new_down_block_res_samples

  # 4. mid
  __lowerCAmelCase											=						self.mid_block(SCREAMING_SNAKE_CASE__      ,       SCREAMING_SNAKE_CASE__      ,       SCREAMING_SNAKE_CASE__      ,       deterministic=not train							)

  if mid_block_additional_residual is not None:
   sample += mid_block_additional_residual

  # 5. up
  for up_block in self.up_blocks:
   __lowerCAmelCase											=						down_block_res_samples[-(self.layers_per_block + 1) :]
   __lowerCAmelCase											=						down_block_res_samples[: -(self.layers_per_block + 1)]
   if isinstance(SCREAMING_SNAKE_CASE__      ,       SCREAMING_SNAKE_CASE__							):
    __lowerCAmelCase											=						up_block(
        SCREAMING_SNAKE_CASE__      ,       temb=SCREAMING_SNAKE_CASE__      ,       encoder_hidden_states=SCREAMING_SNAKE_CASE__      ,       res_hidden_states_tuple=SCREAMING_SNAKE_CASE__      ,       deterministic=not train      ,       )
   else:
    __lowerCAmelCase											=						up_block(SCREAMING_SNAKE_CASE__      ,       temb=SCREAMING_SNAKE_CASE__      ,       res_hidden_states_tuple=SCREAMING_SNAKE_CASE__      ,       deterministic=not train							)

        # 6. post-process
  __lowerCAmelCase											=						self.conv_norm_out(SCREAMING_SNAKE_CASE__							)
  __lowerCAmelCase											=						nn.silu(SCREAMING_SNAKE_CASE__							)
  __lowerCAmelCase											=						self.conv_out(SCREAMING_SNAKE_CASE__							)
  __lowerCAmelCase											=						jnp.transpose(SCREAMING_SNAKE_CASE__      ,       (0, 3, 1, 2)							)

  if not return_dict:
   return (sample,)

  return FlaxUNetaDConditionOutput(sample=SCREAMING_SNAKE_CASE__							)







'''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      UpperCamelCase_						(					snake_case_       :       Optional[int]      )					->     Optional[Any]:






 '''simple docstring'''

 __lowerCAmelCase											=						3_84
 __lowerCAmelCase											=						7
 if "tiny" in model_name:
  __lowerCAmelCase											=						96
  __lowerCAmelCase											=						(2, 2, 6, 2)
  __lowerCAmelCase											=						(3, 6, 12, 24)
 elif "small" in model_name:
  __lowerCAmelCase											=						96
  __lowerCAmelCase											=						(2, 2, 18, 2)
  __lowerCAmelCase											=						(3, 6, 12, 24)
 elif "base" in model_name:
  __lowerCAmelCase											=						1_28
  __lowerCAmelCase											=						(2, 2, 18, 2)
  __lowerCAmelCase											=						(4, 8, 16, 32)
  __lowerCAmelCase											=						12
  __lowerCAmelCase											=						5_12
 elif "large" in model_name:
  __lowerCAmelCase											=						1_92
  __lowerCAmelCase											=						(2, 2, 18, 2)
  __lowerCAmelCase											=						(6, 12, 24, 48)
  __lowerCAmelCase											=						12
  __lowerCAmelCase											=						7_68

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

 __lowerCAmelCase											=						SwinConfig(
     embed_dim=snake_case_						,		depths=snake_case_						,		num_heads=snake_case_						,		window_size=snake_case_						,		out_features=["""stage1""", """stage2""", """stage3""", """stage4"""]						,		)
 __lowerCAmelCase											=						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_       :       Dict      )					->     int:






 '''simple docstring'''

 __lowerCAmelCase											=						[]

 # 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      UpperCamelCase_						(					snake_case_       :       int						,		snake_case_       :       List[str]						,		snake_case_       :       Any      )					->     Tuple:






 '''simple docstring'''

 __lowerCAmelCase											=						dct.pop(snake_case_      )
 __lowerCAmelCase											=						val







def      UpperCamelCase_						(					snake_case_       :       str						,		snake_case_       :       str      )					->     Optional[int]:






 '''simple docstring'''

 __lowerCAmelCase											=						[int(backbone_config.embed_dim * 2**i      ) for i in range(len(backbone_config.depths      )      )]
 for i in range(len(backbone_config.depths      )      ):
  __lowerCAmelCase											=						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)
   __lowerCAmelCase											=						state_dict.pop(f"""backbone.stages.{i}.blocks.{j}.attn.w_msa.qkv.weight"""      )
   __lowerCAmelCase											=						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
   __lowerCAmelCase											=						in_proj_weight[:dim, :]
   __lowerCAmelCase											=						in_proj_bias[: dim]
   __lowerCAmelCase											=						in_proj_weight[
       dim : dim * 2, :
   ]
   __lowerCAmelCase											=						in_proj_bias[
       dim : dim * 2
   ]
   __lowerCAmelCase											=						in_proj_weight[
       -dim :, :
   ]
   __lowerCAmelCase											=						in_proj_bias[-dim :]
   # fmt: on







def      UpperCamelCase_						(					snake_case_       :       str      )					->     int:






 '''simple docstring'''

 __lowerCAmelCase							,       __lowerCAmelCase											=						x.shape
 __lowerCAmelCase											=						x.reshape(snake_case_						,		4						,		in_channel // 4      )
 __lowerCAmelCase											=						x[:, [0, 2, 1, 3], :].transpose(1						,		2      ).reshape(snake_case_						,		snake_case_      )
 return x







def      UpperCamelCase_						(					snake_case_       :       List[str]      )					->     Union[str, Any]:






 '''simple docstring'''

 __lowerCAmelCase							,       __lowerCAmelCase											=						x.shape
 __lowerCAmelCase											=						x.reshape(snake_case_						,		in_channel // 4						,		4      )
 __lowerCAmelCase											=						x[:, :, [0, 2, 1, 3]].transpose(1						,		2      ).reshape(snake_case_						,		snake_case_      )

 return x







def      UpperCamelCase_						(					snake_case_       :       Tuple      )					->     int:






 '''simple docstring'''

 __lowerCAmelCase											=						x.shape[0]
 __lowerCAmelCase											=						x.reshape(4						,		in_channel // 4      )
 __lowerCAmelCase											=						x[[0, 2, 1, 3], :].transpose(0						,		1      ).reshape(snake_case_      )
 return x







def      UpperCamelCase_						(					snake_case_       :       Dict      )					->     List[str]:






 '''simple docstring'''

 __lowerCAmelCase											=						x.shape[0]
 __lowerCAmelCase											=						x.reshape(in_channel // 4						,		4      )
 __lowerCAmelCase											=						x[:, [0, 2, 1, 3]].transpose(0						,		1      ).reshape(snake_case_      )
 return x







def      UpperCamelCase_						(					snake_case_       :       Dict						,		snake_case_       :       Tuple						,		snake_case_       :       Dict      )					->     List[str]:






 '''simple docstring'''

 __lowerCAmelCase											=						{
     """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""",
 }
 __lowerCAmelCase											=						model_name_to_url[model_name]
 __lowerCAmelCase											=						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      )

 __lowerCAmelCase											=						get_upernet_config(snake_case_      )
 __lowerCAmelCase											=						UperNetForSemanticSegmentation(snake_case_      )
 model.eval()

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

 # rename keys
 __lowerCAmelCase											=						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:
    __lowerCAmelCase											=						reverse_correct_unfold_reduction_order(snake_case_      )
   if "norm" in key:
    __lowerCAmelCase											=						reverse_correct_unfold_norm_order(snake_case_      )

 model.load_state_dict(snake_case_      )

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

 __lowerCAmelCase											=						SegformerImageProcessor()
 __lowerCAmelCase											=						processor(snake_case_						,		return_tensors="""pt"""      ).pixel_values

 with torch.no_grad():
  __lowerCAmelCase											=						model(snake_case_      )
  __lowerCAmelCase											=						outputs.logits

 print(logits.shape      )
 print("""First values of logits:"""						,		logits[0, 0, :3, :3]      )
 # assert values
 if model_name == "upernet-swin-tiny":
  __lowerCAmelCase											=						torch.tensor(
      [[-7.5_9_5_8, -7.5_9_5_8, -7.4_3_0_2], [-7.5_9_5_8, -7.5_9_5_8, -7.4_3_0_2], [-7.4_7_9_7, -7.4_7_9_7, -7.3_0_6_8]]      )
 elif model_name == "upernet-swin-small":
  __lowerCAmelCase											=						torch.tensor(
      [[-7.1_9_2_1, -7.1_9_2_1, -6.9_5_3_2], [-7.1_9_2_1, -7.1_9_2_1, -6.9_5_3_2], [-7.0_9_0_8, -7.0_9_0_8, -6.8_5_3_4]]      )
 elif model_name == "upernet-swin-base":
  __lowerCAmelCase											=						torch.tensor(
      [[-6.5_8_5_1, -6.5_8_5_1, -6.4_3_3_0], [-6.5_8_5_1, -6.5_8_5_1, -6.4_3_3_0], [-6.4_7_6_3, -6.4_7_6_3, -6.3_2_5_4]]      )
 elif model_name == "upernet-swin-large":
  __lowerCAmelCase											=						torch.tensor(
      [[-7.5_2_9_7, -7.5_2_9_7, -7.3_8_0_2], [-7.5_2_9_7, -7.5_2_9_7, -7.3_8_0_2], [-7.4_0_4_4, -7.4_0_4_4, -7.2_5_8_6]]      )
 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__":
 _A			:      Dict					      =				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.'''
 )

 _A			:      int					      =				parser.parse_args()
 convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)






