
'''simple docstring'''



import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaImgaImgPipeline,
    KandinskyVaaPriorPipeline,
    UNetaDConditionModel,
    VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()




class     UpperCAmelCase_	(						_SCREAMING_SNAKE_CASE			,				unittest.TestCase					):






						'''simple docstring'''






						_lowercase						: List[Any]										=  KandinskyVaaImgaImgPipeline
						_lowercase						: List[Any]										=  ['''image_embeds''', '''negative_image_embeds''', '''image''']
						_lowercase						: str										=  [
						    '''image_embeds''',
						    '''negative_image_embeds''',
						    '''image''',
						]
						_lowercase						: int										=  [
						    '''generator''',
						    '''height''',
						    '''width''',
						    '''strength''',
						    '''guidance_scale''',
						    '''num_inference_steps''',
						    '''return_dict''',
						    '''guidance_scale''',
						    '''num_images_per_prompt''',
						    '''output_type''',
						    '''return_dict''',
						]
						_lowercase						: str										=  False




						@property
						def    _lowercase      (   self ):

												"""simple docstring"""



												return 32




						@property
						def    _lowercase      (   self ):

												"""simple docstring"""



												return 32




						@property
						def    _lowercase      (   self ):

												"""simple docstring"""



												return self.time_input_dim




						@property
						def    _lowercase      (   self ):

												"""simple docstring"""



												return self.time_input_dim * 4




						@property
						def    _lowercase      (   self ):

												"""simple docstring"""



												return 100




						@property
						def    _lowercase      (   self ):

												"""simple docstring"""



												torch.manual_seed(0 )

												_lowerCAmelCase    		=					{
												    """in_channels""": 4,
												    # Out channels is double in channels because predicts mean and variance
												    """out_channels""": 8,
												    """addition_embed_type""": """image""",
												    """down_block_types""": ("""ResnetDownsampleBlock2D""", """SimpleCrossAttnDownBlock2D"""),
												    """up_block_types""": ("""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""),
												    """mid_block_type""": """UNetMidBlock2DSimpleCrossAttn""",
												    """block_out_channels""": (self.block_out_channels_a, self.block_out_channels_a * 2),
												    """layers_per_block""": 1,
												    """encoder_hid_dim""": self.text_embedder_hidden_size,
												    """encoder_hid_dim_type""": """image_proj""",
												    """cross_attention_dim""": self.cross_attention_dim,
												    """attention_head_dim""": 4,
												    """resnet_time_scale_shift""": """scale_shift""",
												    """class_embed_type""": None,
												}

												_lowerCAmelCase    		=					UNetaDConditionModel(**_lowercase )
												return model




						@property
						def    _lowercase      (   self ):

												"""simple docstring"""



												return {
												    "block_out_channels": [32, 64],
												    "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
												    "in_channels": 3,
												    "latent_channels": 4,
												    "layers_per_block": 1,
												    "norm_num_groups": 8,
												    "norm_type": "spatial",
												    "num_vq_embeddings": 12,
												    "out_channels": 3,
												    "up_block_types": [
												        "AttnUpDecoderBlock2D",
												        "UpDecoderBlock2D",
												    ],
												    "vq_embed_dim": 4,
												}




						@property
						def    _lowercase      (   self ):

												"""simple docstring"""



												torch.manual_seed(0 )
												_lowerCAmelCase    		=					VQModel(**self.dummy_movq_kwargs )
												return model




						def    _lowercase      (   self ):

												"""simple docstring"""



												_lowerCAmelCase    		=					self.dummy_unet
												_lowerCAmelCase    		=					self.dummy_movq

												_lowerCAmelCase    		=					{
												    """num_train_timesteps""": 1_000,
												    """beta_schedule""": """linear""",
												    """beta_start""": 0.0_0085,
												    """beta_end""": 0.012,
												    """clip_sample""": False,
												    """set_alpha_to_one""": False,
												    """steps_offset""": 0,
												    """prediction_type""": """epsilon""",
												    """thresholding""": False,
												}

												_lowerCAmelCase    		=					DDIMScheduler(**_lowercase )

												_lowerCAmelCase    		=					{
												    """unet""": unet,
												    """scheduler""": scheduler,
												    """movq""": movq,
												}

												return components




						def    _lowercase      (   self       ,  _lowercase       ,  _lowercase=0 ):

												"""simple docstring"""



												_lowerCAmelCase    		=					floats_tensor((1, self.text_embedder_hidden_size)       ,  rng=random.Random(_lowercase ) ).to(_lowercase )
												_lowerCAmelCase    		=					floats_tensor((1, self.text_embedder_hidden_size)       ,  rng=random.Random(seed + 1 ) ).to(
												    _lowercase )
												# create init_image
												_lowerCAmelCase    		=					floats_tensor((1, 3, 64, 64)       ,  rng=random.Random(_lowercase ) ).to(_lowercase )
												_lowerCAmelCase    		=					image.cpu().permute(0       ,  2       ,  3       ,  1 )[0]
												_lowerCAmelCase    		=					Image.fromarray(np.uinta(_lowercase ) ).convert("""RGB""" ).resize((256, 256) )

												if str(_lowercase ).startswith("""mps""" ):
																		_lowerCAmelCase    		=					torch.manual_seed(_lowercase )
												else:
																		_lowerCAmelCase    		=					torch.Generator(device=_lowercase ).manual_seed(_lowercase )
												_lowerCAmelCase    		=					{
												    """image""": init_image,
												    """image_embeds""": image_embeds,
												    """negative_image_embeds""": negative_image_embeds,
												    """generator""": generator,
												    """height""": 64,
												    """width""": 64,
												    """num_inference_steps""": 10,
												    """guidance_scale""": 7.0,
												    """strength""": 0.2,
												    """output_type""": """np""",
												}
												return inputs




						def    _lowercase      (   self ):

												"""simple docstring"""



												_lowerCAmelCase    		=					"""cpu"""

												_lowerCAmelCase    		=					self.get_dummy_components()

												_lowerCAmelCase    		=					self.pipeline_class(**_lowercase )
												_lowerCAmelCase    		=					pipe.to(_lowercase )

												pipe.set_progress_bar_config(disable=_lowercase )

												_lowerCAmelCase    		=					pipe(**self.get_dummy_inputs(_lowercase ) )
												_lowerCAmelCase    		=					output.images

												_lowerCAmelCase    		=					pipe(
												    **self.get_dummy_inputs(_lowercase )       ,  return_dict=_lowercase       ,  )[0]

												_lowerCAmelCase    		=					image[0, -3:, -3:, -1]
												_lowerCAmelCase    		=					image_from_tuple[0, -3:, -3:, -1]

												assert image.shape == (1, 64, 64, 3)

												_lowerCAmelCase    		=					np.array(
												    [0.619_9778, 0.6398_4406, 0.4614_5785, 0.6294_4984, 0.562_2215, 0.4730_6132, 0.4744_1456, 0.460_7606, 0.4871_9263] )
												assert (
												    np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
												), F' expected_slice {expected_slice}, but got {image_slice.flatten()}'
												assert (
												    np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1e-2
												), F' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'






@slow
@require_torch_gpu
class     UpperCAmelCase_	(						unittest.TestCase					):






						'''simple docstring'''






						def    _lowercase      (   self ):

												"""simple docstring"""



												super().tearDown()
												gc.collect()
												torch.cuda.empty_cache()




						def    _lowercase      (   self ):

												"""simple docstring"""



												_lowerCAmelCase    		=					load_numpy(
												    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
												    """/kandinskyv22/kandinskyv22_img2img_frog.npy""" )

												_lowerCAmelCase    		=					load_image(
												    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/kandinsky/cat.png""" )
												_lowerCAmelCase    		=					"""A red cartoon frog, 4k"""

												_lowerCAmelCase    		=					KandinskyVaaPriorPipeline.from_pretrained(
												    """kandinsky-community/kandinsky-2-2-prior"""       ,  torch_dtype=torch.floataa )
												pipe_prior.to(_lowercase )

												_lowerCAmelCase    		=					KandinskyVaaImgaImgPipeline.from_pretrained(
												    """kandinsky-community/kandinsky-2-2-decoder"""       ,  torch_dtype=torch.floataa )
												_lowerCAmelCase    		=					pipeline.to(_lowercase )

												pipeline.set_progress_bar_config(disable=_lowercase )

												_lowerCAmelCase    		=					torch.Generator(device="""cpu""" ).manual_seed(0 )
												_lowerCAmelCase		,       _lowerCAmelCase    		=					pipe_prior(
												    _lowercase       ,  generator=_lowercase       ,  num_inference_steps=5       ,  negative_prompt=""""""       ,  ).to_tuple()

												_lowerCAmelCase    		=					pipeline(
												    image=_lowercase       ,  image_embeds=_lowercase       ,  negative_image_embeds=_lowercase       ,  generator=_lowercase       ,  num_inference_steps=100       ,  height=768       ,  width=768       ,  strength=0.2       ,  output_type="""np"""       ,  )

												_lowerCAmelCase    		=					output.images[0]

												assert image.shape == (768, 768, 3)

												assert_mean_pixel_difference(_lowercase       ,  _lowercase )


'''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 (__lowerCamelCase :List[Any]						):
						_lowerCAmelCase    		=					384
						if "tiny" in model_name:
												_lowerCAmelCase    		=					[3, 3, 9, 3]
												_lowerCAmelCase    		=					[96, 192, 384, 768]
						if "small" in model_name:
												_lowerCAmelCase    		=					[3, 3, 27, 3]
												_lowerCAmelCase    		=					[96, 192, 384, 768]
						if "base" in model_name:
												_lowerCAmelCase    		=					[3, 3, 27, 3]
												_lowerCAmelCase    		=					[128, 256, 512, 1024]
												_lowerCAmelCase    		=					512
						if "large" in model_name:
												_lowerCAmelCase    		=					[3, 3, 27, 3]
												_lowerCAmelCase    		=					[192, 384, 768, 1536]
												_lowerCAmelCase    		=					768
						if "xlarge" in model_name:
												_lowerCAmelCase    		=					[3, 3, 27, 3]
												_lowerCAmelCase    		=					[256, 512, 1024, 2048]
												_lowerCAmelCase    		=					1024

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

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

						return config


def A (__lowerCamelCase :Optional[Any]						):
						_lowerCAmelCase    		=					[]

						# 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 (__lowerCamelCase :Optional[Any]					,      __lowerCamelCase :Dict					,      __lowerCamelCase :Tuple						):
						_lowerCAmelCase    		=					dct.pop(__lowerCamelCase						)
						_lowerCAmelCase    		=					val


def A (__lowerCamelCase :Union[str, Any]					,      __lowerCamelCase :Optional[Any]					,      __lowerCamelCase :Any						):
						_lowerCAmelCase    		=					{
						    """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    		=					model_name_to_url[model_name]
						_lowerCAmelCase    		=					torch.hub.load_state_dict_from_url(__lowerCamelCase					,      map_location="""cpu"""						)["""state_dict"""]

						_lowerCAmelCase    		=					get_upernet_config(__lowerCamelCase						)
						_lowerCAmelCase    		=					UperNetForSemanticSegmentation(__lowerCamelCase						)
						model.eval()

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

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

						model.load_state_dict(__lowerCamelCase						)

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

						_lowerCAmelCase    		=					SegformerImageProcessor()
						_lowerCAmelCase    		=					processor(__lowerCamelCase					,      return_tensors="""pt"""						).pixel_values

						with torch.no_grad():
												_lowerCAmelCase    		=					model(__lowerCamelCase						)

						if model_name == "upernet-convnext-tiny":
												_lowerCAmelCase    		=					torch.tensor(
												    [[-8.8_110, -8.8_110, -8.6_521], [-8.8_110, -8.8_110, -8.6_521], [-8.7_746, -8.7_746, -8.6_130]]						)
						elif model_name == "upernet-convnext-small":
												_lowerCAmelCase    		=					torch.tensor(
												    [[-8.8_236, -8.8_236, -8.6_771], [-8.8_236, -8.8_236, -8.6_771], [-8.7_638, -8.7_638, -8.6_240]]						)
						elif model_name == "upernet-convnext-base":
												_lowerCAmelCase    		=					torch.tensor(
												    [[-8.8_558, -8.8_558, -8.6_905], [-8.8_558, -8.8_558, -8.6_905], [-8.7_669, -8.7_669, -8.6_021]]						)
						elif model_name == "upernet-convnext-large":
												_lowerCAmelCase    		=					torch.tensor(
												    [[-8.6_660, -8.6_660, -8.6_210], [-8.6_660, -8.6_660, -8.6_210], [-8.6_310, -8.6_310, -8.5_964]]						)
						elif model_name == "upernet-convnext-xlarge":
												_lowerCAmelCase    		=					torch.tensor(
												    [[-8.4_980, -8.4_980, -8.3_977], [-8.4_980, -8.4_980, -8.3_977], [-8.4_379, -8.4_379, -8.3_412]]						)
						print("""Logits:"""					,      outputs.logits[0, 0, :3, :3]						)
						assert torch.allclose(outputs.logits[0, 0, :3, :3]					,      __lowerCamelCase					,      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(__lowerCamelCase						)
												print(f'Saving processor to {pytorch_dump_folder_path}'						)
												processor.save_pretrained(__lowerCamelCase						)

						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__":
						_lowercase          =						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."""
						)

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