





"""simple docstring"""





import os
import tempfile
import unittest

import numpy as np

from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax, slow


if is_flax_available():
  import jax
  import jax.numpy as jnp
  from flax.jax_utils import replicate
  from flax.training.common_utils import shard

  from diffusers import FlaxDDIMScheduler, FlaxDiffusionPipeline, FlaxStableDiffusionPipeline






@require_flax
class    _lowerCAmelCase  (	unittest.TestCase			):
       """simple docstring"""




       def 	snake_case		(       self ):



         '''simple docstring'''

         with tempfile.TemporaryDirectory() as tmpdirname:
           # pipeline has Flax weights
           lowerCAmelCase__     :List[str]											=				FlaxDiffusionPipeline.from_pretrained(
               'hf-internal-testing/tiny-stable-diffusion-pipe'       ,					safety_checker=__UpperCAmelCase       ,					cache_dir=__UpperCAmelCase )

           lowerCAmelCase__     :int											=				[t[-1] for t in os.walk(os.path.join(__UpperCAmelCase       ,					os.listdir(__UpperCAmelCase )[0]       ,					'snapshots' ) )]
           lowerCAmelCase__     :Dict											=				[item for sublist in all_root_files for item in sublist]

           # None of the downloaded files should be a PyTorch file even if we have some here:
           # https://huggingface.co/hf-internal-testing/tiny-stable-diffusion-pipe/blob/main/unet/diffusion_pytorch_model.bin
           assert not any(f.endswith('.bin' ) for f in files )






@slow
@require_flax
class    _lowerCAmelCase  (	unittest.TestCase			):
       """simple docstring"""




       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__				,							lowerCAmelCase__     :int											=				FlaxStableDiffusionPipeline.from_pretrained(
             'hf-internal-testing/tiny-stable-diffusion-pipe'       ,					safety_checker=__UpperCAmelCase )

         lowerCAmelCase__     :int											=				(
             'A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'
             ' field, close up, split lighting, cinematic'
         )

         lowerCAmelCase__     :List[Any]											=				jax.random.PRNGKey(0 )
         lowerCAmelCase__     :List[Any]											=				4

         lowerCAmelCase__     :Optional[Any]											=				jax.device_count()
         lowerCAmelCase__     :Any											=				num_samples * [prompt]
         lowerCAmelCase__     :Optional[Any]											=				pipeline.prepare_inputs(__UpperCAmelCase )

         # shard inputs and rng
         lowerCAmelCase__     :Optional[Any]											=				replicate(__UpperCAmelCase )
         lowerCAmelCase__     :int											=				jax.random.split(__UpperCAmelCase       ,					__UpperCAmelCase )
         lowerCAmelCase__     :Dict											=				shard(__UpperCAmelCase )

         lowerCAmelCase__     :int											=				pipeline(__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					jit=__UpperCAmelCase ).images

         assert images.shape == (num_samples, 1, 6_4, 6_4, 3)
         if jax.device_count() == 8:
           assert np.abs(np.abs(images[0, 0, :2, :2, -2:]       ,					dtype=np.floataa ).sum() - 4.1_51_47_45 ) < 1E-3
           assert np.abs(np.abs(__UpperCAmelCase       ,					dtype=np.floataa ).sum() - 4_99_47.8_75 ) < 5E-1

         lowerCAmelCase__     :Union[str, Any]											=				pipeline.numpy_to_pil(np.asarray(images.reshape((num_samples,) + images.shape[-3:] ) ) )
         assert len(__UpperCAmelCase ) == num_samples






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__				,							lowerCAmelCase__     :List[Any]											=				FlaxStableDiffusionPipeline.from_pretrained(
             'CompVis/stable-diffusion-v1-4'       ,					revision='flax'       ,					safety_checker=__UpperCAmelCase )

         lowerCAmelCase__     :Tuple											=				(
             'A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'
             ' field, close up, split lighting, cinematic'
         )

         lowerCAmelCase__     :Optional[Any]											=				jax.random.PRNGKey(0 )
         lowerCAmelCase__     :Any											=				5_0

         lowerCAmelCase__     :Optional[int]											=				jax.device_count()
         lowerCAmelCase__     :Union[str, Any]											=				num_samples * [prompt]
         lowerCAmelCase__     :Dict											=				pipeline.prepare_inputs(__UpperCAmelCase )

         # shard inputs and rng
         lowerCAmelCase__     :Optional[Any]											=				replicate(__UpperCAmelCase )
         lowerCAmelCase__     :List[str]											=				jax.random.split(__UpperCAmelCase       ,					__UpperCAmelCase )
         lowerCAmelCase__     :str											=				shard(__UpperCAmelCase )

         lowerCAmelCase__     :int											=				pipeline(__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					jit=__UpperCAmelCase ).images

         assert images.shape == (num_samples, 1, 5_1_2, 5_1_2, 3)
         if jax.device_count() == 8:
           assert np.abs((np.abs(images[0, 0, :2, :2, -2:]       ,					dtype=np.floataa ).sum() - 0.05_65_24_01) ) < 1E-3
           assert np.abs((np.abs(__UpperCAmelCase       ,					dtype=np.floataa ).sum() - 2_38_38_08.2) ) < 5E-1






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__				,							lowerCAmelCase__     :Union[str, Any]											=				FlaxStableDiffusionPipeline.from_pretrained(
             'CompVis/stable-diffusion-v1-4'       ,					revision='bf16'       ,					dtype=jnp.bfloataa       ,					safety_checker=__UpperCAmelCase )

         lowerCAmelCase__     :Tuple											=				(
             'A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'
             ' field, close up, split lighting, cinematic'
         )

         lowerCAmelCase__     :Optional[Any]											=				jax.random.PRNGKey(0 )
         lowerCAmelCase__     :Tuple											=				5_0

         lowerCAmelCase__     :List[Any]											=				jax.device_count()
         lowerCAmelCase__     :Tuple											=				num_samples * [prompt]
         lowerCAmelCase__     :str											=				pipeline.prepare_inputs(__UpperCAmelCase )

         # shard inputs and rng
         lowerCAmelCase__     :List[Any]											=				replicate(__UpperCAmelCase )
         lowerCAmelCase__     :List[str]											=				jax.random.split(__UpperCAmelCase       ,					__UpperCAmelCase )
         lowerCAmelCase__     :Tuple											=				shard(__UpperCAmelCase )

         lowerCAmelCase__     :List[str]											=				pipeline(__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					jit=__UpperCAmelCase ).images

         assert images.shape == (num_samples, 1, 5_1_2, 5_1_2, 3)
         if jax.device_count() == 8:
           assert np.abs((np.abs(images[0, 0, :2, :2, -2:]       ,					dtype=np.floataa ).sum() - 0.04_00_39_06) ) < 1E-3
           assert np.abs((np.abs(__UpperCAmelCase       ,					dtype=np.floataa ).sum() - 2_37_35_16.75) ) < 5E-1






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__				,							lowerCAmelCase__     :Any											=				FlaxStableDiffusionPipeline.from_pretrained(
             'CompVis/stable-diffusion-v1-4'       ,					revision='bf16'       ,					dtype=jnp.bfloataa )

         lowerCAmelCase__     :Optional[Any]											=				(
             'A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'
             ' field, close up, split lighting, cinematic'
         )

         lowerCAmelCase__     :Optional[Any]											=				jax.random.PRNGKey(0 )
         lowerCAmelCase__     :Any											=				5_0

         lowerCAmelCase__     :Optional[int]											=				jax.device_count()
         lowerCAmelCase__     :Tuple											=				num_samples * [prompt]
         lowerCAmelCase__     :Any											=				pipeline.prepare_inputs(__UpperCAmelCase )

         # shard inputs and rng
         lowerCAmelCase__     :Any											=				replicate(__UpperCAmelCase )
         lowerCAmelCase__     :Any											=				jax.random.split(__UpperCAmelCase       ,					__UpperCAmelCase )
         lowerCAmelCase__     :int											=				shard(__UpperCAmelCase )

         lowerCAmelCase__     :Dict											=				pipeline(__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					jit=__UpperCAmelCase ).images

         assert images.shape == (num_samples, 1, 5_1_2, 5_1_2, 3)
         if jax.device_count() == 8:
           assert np.abs((np.abs(images[0, 0, :2, :2, -2:]       ,					dtype=np.floataa ).sum() - 0.04_00_39_06) ) < 1E-3
           assert np.abs((np.abs(__UpperCAmelCase       ,					dtype=np.floataa ).sum() - 2_37_35_16.75) ) < 5E-1






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :Tuple											=				FlaxDDIMScheduler(
             beta_start=0.0_00_85       ,					beta_end=0.0_12       ,					beta_schedule='scaled_linear'       ,					set_alpha_to_one=__UpperCAmelCase       ,					steps_offset=1       ,					)

         lowerCAmelCase__				,							lowerCAmelCase__     :Union[str, Any]											=				FlaxStableDiffusionPipeline.from_pretrained(
             'CompVis/stable-diffusion-v1-4'       ,					revision='bf16'       ,					dtype=jnp.bfloataa       ,					scheduler=__UpperCAmelCase       ,					safety_checker=__UpperCAmelCase       ,					)
         lowerCAmelCase__     :int											=				scheduler.create_state()

         lowerCAmelCase__     :Tuple											=				scheduler_state

         lowerCAmelCase__     :Tuple											=				(
             'A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'
             ' field, close up, split lighting, cinematic'
         )

         lowerCAmelCase__     :List[str]											=				jax.random.PRNGKey(0 )
         lowerCAmelCase__     :Union[str, Any]											=				5_0

         lowerCAmelCase__     :Any											=				jax.device_count()
         lowerCAmelCase__     :Tuple											=				num_samples * [prompt]
         lowerCAmelCase__     :List[str]											=				pipeline.prepare_inputs(__UpperCAmelCase )

         # shard inputs and rng
         lowerCAmelCase__     :Any											=				replicate(__UpperCAmelCase )
         lowerCAmelCase__     :Any											=				jax.random.split(__UpperCAmelCase       ,					__UpperCAmelCase )
         lowerCAmelCase__     :List[str]											=				shard(__UpperCAmelCase )

         lowerCAmelCase__     :Dict											=				pipeline(__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					jit=__UpperCAmelCase ).images

         assert images.shape == (num_samples, 1, 5_1_2, 5_1_2, 3)
         if jax.device_count() == 8:
           assert np.abs((np.abs(images[0, 0, :2, :2, -2:]       ,					dtype=np.floataa ).sum() - 0.0_45_04_39_45) ) < 1E-3
           assert np.abs((np.abs(__UpperCAmelCase       ,					dtype=np.floataa ).sum() - 2_34_76_93.5) ) < 5E-1






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :int											=				(
             'A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'
             ' field, close up, split lighting, cinematic'
         )

         lowerCAmelCase__     :List[str]											=				jax.device_count()
         lowerCAmelCase__     :str											=				num_samples * [prompt]
         lowerCAmelCase__     :str											=				jax.random.split(jax.random.PRNGKey(0 )       ,					__UpperCAmelCase )

         lowerCAmelCase__				,							lowerCAmelCase__     :Any											=				FlaxStableDiffusionPipeline.from_pretrained(
             'CompVis/stable-diffusion-v1-4'       ,					revision='bf16'       ,					dtype=jnp.bfloataa       ,					safety_checker=__UpperCAmelCase       ,					)

         lowerCAmelCase__     :int											=				replicate(__UpperCAmelCase )
         lowerCAmelCase__     :List[Any]											=				pipeline.prepare_inputs(__UpperCAmelCase )
         lowerCAmelCase__     :str											=				shard(__UpperCAmelCase )
         lowerCAmelCase__     :Dict											=				pipeline(__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					jit=__UpperCAmelCase ).images
         assert images.shape == (num_samples, 1, 5_1_2, 5_1_2, 3)
         lowerCAmelCase__     :Union[str, Any]											=				images[2, 0, 2_5_6, 1_0:1_7, 1]

         # With memory efficient attention
         lowerCAmelCase__				,							lowerCAmelCase__     :str											=				FlaxStableDiffusionPipeline.from_pretrained(
             'CompVis/stable-diffusion-v1-4'       ,					revision='bf16'       ,					dtype=jnp.bfloataa       ,					safety_checker=__UpperCAmelCase       ,					use_memory_efficient_attention=__UpperCAmelCase       ,					)

         lowerCAmelCase__     :List[str]											=				replicate(__UpperCAmelCase )
         lowerCAmelCase__     :Dict											=				pipeline.prepare_inputs(__UpperCAmelCase )
         lowerCAmelCase__     :int											=				shard(__UpperCAmelCase )
         lowerCAmelCase__     :int											=				pipeline(__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					jit=__UpperCAmelCase ).images
         assert images_eff.shape == (num_samples, 1, 5_1_2, 5_1_2, 3)
         lowerCAmelCase__     :Optional[Any]											=				images[2, 0, 2_5_6, 1_0:1_7, 1]

         # I checked the results visually and they are very similar. However, I saw that the max diff is `1` and the `sum`
         # over the 8 images is exactly `256`, which is very suspicious. Testing a random slice for now.
         assert abs(slice_eff - slice ).max() < 1E-2







"""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 					__A  (_SCREAMING_SNAKE_CASE     )   ->Any:





  """simple docstring"""


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

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

  lowerCAmelCase__     :List[str]											=				ConvNextConfig(
      depths=_SCREAMING_SNAKE_CASE	,  hidden_sizes=_SCREAMING_SNAKE_CASE	,  out_features=['stage1', 'stage2', 'stage3', 'stage4']     )
  lowerCAmelCase__     :Union[str, Any]											=				UperNetConfig(
      backbone_config=_SCREAMING_SNAKE_CASE	,  auxiliary_in_channels=_SCREAMING_SNAKE_CASE	,  num_labels=_SCREAMING_SNAKE_CASE	,  idalabel=_SCREAMING_SNAKE_CASE	,  labelaid=_SCREAMING_SNAKE_CASE	,  )

  return config
def 					__A  (_SCREAMING_SNAKE_CASE     )   ->str:





  """simple docstring"""


  lowerCAmelCase__     :str											=				[]

  # 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 					__A  (_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE     )   ->str:





  """simple docstring"""


  lowerCAmelCase__     :List[str]											=				dct.pop(_SCREAMING_SNAKE_CASE     )
  lowerCAmelCase__     :Tuple											=				val
def 					__A  (_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE     )   ->int:





  """simple docstring"""


  lowerCAmelCase__     :Dict											=				{
      '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__     :List[Any]											=				model_name_to_url[model_name]
  lowerCAmelCase__     :Optional[Any]											=				torch.hub.load_state_dict_from_url(_SCREAMING_SNAKE_CASE	,  map_location='cpu'     )['state_dict']

  lowerCAmelCase__     :List[Any]											=				get_upernet_config(_SCREAMING_SNAKE_CASE     )
  lowerCAmelCase__     :Dict											=				UperNetForSemanticSegmentation(_SCREAMING_SNAKE_CASE     )
  model.eval()

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

  # rename keys
  lowerCAmelCase__     :Optional[Any]											=				create_rename_keys(_SCREAMING_SNAKE_CASE     )
  for src, dest in rename_keys:
    rename_key(_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE     )

  model.load_state_dict(_SCREAMING_SNAKE_CASE     )

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

  lowerCAmelCase__     :Tuple											=				SegformerImageProcessor()
  lowerCAmelCase__     :List[Any]											=				processor(_SCREAMING_SNAKE_CASE	,  return_tensors='pt'     ).pixel_values

  with torch.no_grad():
    lowerCAmelCase__     :Optional[Any]											=				model(_SCREAMING_SNAKE_CASE     )

  if model_name == "upernet-convnext-tiny":
    lowerCAmelCase__     :Optional[Any]											=				torch.tensor(
        [[-8.8_1_1_0, -8.8_1_1_0, -8.6_5_2_1], [-8.8_1_1_0, -8.8_1_1_0, -8.6_5_2_1], [-8.7_7_4_6, -8.7_7_4_6, -8.6_1_3_0]]     )
  elif model_name == "upernet-convnext-small":
    lowerCAmelCase__     :Union[str, Any]											=				torch.tensor(
        [[-8.8_2_3_6, -8.8_2_3_6, -8.6_7_7_1], [-8.8_2_3_6, -8.8_2_3_6, -8.6_7_7_1], [-8.7_6_3_8, -8.7_6_3_8, -8.6_2_4_0]]     )
  elif model_name == "upernet-convnext-base":
    lowerCAmelCase__     :Dict											=				torch.tensor(
        [[-8.8_5_5_8, -8.8_5_5_8, -8.6_9_0_5], [-8.8_5_5_8, -8.8_5_5_8, -8.6_9_0_5], [-8.7_6_6_9, -8.7_6_6_9, -8.6_0_2_1]]     )
  elif model_name == "upernet-convnext-large":
    lowerCAmelCase__     :List[str]											=				torch.tensor(
        [[-8.6_6_6_0, -8.6_6_6_0, -8.6_2_1_0], [-8.6_6_6_0, -8.6_6_6_0, -8.6_2_1_0], [-8.6_3_1_0, -8.6_3_1_0, -8.5_9_6_4]]     )
  elif model_name == "upernet-convnext-xlarge":
    lowerCAmelCase__     :Optional[Any]											=				torch.tensor(
        [[-8.4_9_8_0, -8.4_9_8_0, -8.3_9_7_7], [-8.4_9_8_0, -8.4_9_8_0, -8.3_9_7_7], [-8.4_3_7_9, -8.4_3_7_9, -8.3_4_1_2]]     )
  print('Logits:'	,  outputs.logits[0, 0, :3, :3]     )
  assert torch.allclose(outputs.logits[0, 0, :3, :3]	,  _SCREAMING_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(_SCREAMING_SNAKE_CASE     )
    print(F"Saving processor to {pytorch_dump_folder_path}"     )
    processor.save_pretrained(_SCREAMING_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											=						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."""
  )

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