
"""simple docstring"""







import importlib
import math
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Tuple, Union

import flax
import jax.numpy as jnp

from ..utils import BaseOutput


_SCREAMING_SNAKE_CASE	       =      """scheduler_config.json"""







class 							__magic_name__     (		lowercase__   ):
					_SCREAMING_SNAKE_CASE       :					List[str]     							=   1
					_SCREAMING_SNAKE_CASE       :					Tuple     							=   2
					_SCREAMING_SNAKE_CASE       :					List[Any]     							=   3
					_SCREAMING_SNAKE_CASE       :					Optional[int]     							=   4
					_SCREAMING_SNAKE_CASE       :					str     							=   5







@dataclass
class 							__magic_name__     (		lowercase__   ):
					_SCREAMING_SNAKE_CASE       :					jnp.ndarray







class 							__magic_name__     :
					_SCREAMING_SNAKE_CASE       :					Union[str, Any]     							=   SCHEDULER_CONFIG_NAME
					_SCREAMING_SNAKE_CASE       :					Optional[Any]     							=   ['dtype']
					_SCREAMING_SNAKE_CASE       :					int     							=   []
					_SCREAMING_SNAKE_CASE       :					Union[str, Any]     							=   True





					@classmethod
					def 							lowerCAmelCase      (				cls   :							Dict							,						snake_case_   :							Dict[str, Any] = None							,						snake_case_   :							Optional[str] = None							,						snake_case_   :							Dict=False							,						**snake_case_   :							Dict							,						):
												__snake_case			,   __snake_case     			=       cls.load_config(
												    pretrained_model_name_or_path=snake_case_							,						subfolder=snake_case_							,						return_unused_kwargs=snake_case_							,						**snake_case_							,						)
												__snake_case			,   __snake_case     			=       cls.from_config(snake_case_							,						return_unused_kwargs=snake_case_							,						**snake_case_    )

												if hasattr(snake_case_							,						"create_state"    ) and getattr(snake_case_							,						"has_state"							,						snake_case_    ):
																			__snake_case     			=       scheduler.create_state()

												if return_unused_kwargs:
																			return scheduler, state, unused_kwargs

												return scheduler, state





					def 							lowerCAmelCase      (				self   :							Dict							,						snake_case_   :							Union[str, os.PathLike]							,						snake_case_   :							bool = False							,						**snake_case_   :							List[str]    ):
												self.save_config(save_directory=snake_case_							,						push_to_hub=snake_case_							,						**snake_case_    )





					@property
					def 							lowerCAmelCase      (				self   :							Tuple    ):
												return self._get_compatibles()





					@classmethod
					def 							lowerCAmelCase      (				cls   :							Any    ):
												__snake_case     			=       list(set([cls.__name__] + cls._compatibles    )    )
												__snake_case     			=       importlib.import_module(__name__.split("."    )[0]    )
												__snake_case     			=       [
												    getattr(snake_case_							,						snake_case_    ) for c in compatible_classes_str if hasattr(snake_case_							,						snake_case_    )
												]
												return compatible_classes




def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE )  ->     jnp.ndarray:







							"""simple docstring"""


							assert len(SCREAMING_SNAKE_CASE ) >= x.ndim
							return jnp.broadcast_to(x.reshape(x.shape + (1,) * (len(SCREAMING_SNAKE_CASE ) - x.ndim) )							,    SCREAMING_SNAKE_CASE )
def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE=0.999							,    SCREAMING_SNAKE_CASE=jnp.floataa )  ->     jnp.ndarray:







							"""simple docstring"""


							def alpha_bar(SCREAMING_SNAKE_CASE ):
														return math.cos((time_step + 0.008) / 1.008 * math.pi / 2 ) ** 2

							__snake_case     			=       []
							for i in range(SCREAMING_SNAKE_CASE ):
														__snake_case     			=       i / num_diffusion_timesteps
														__snake_case     			=       (i + 1) / num_diffusion_timesteps
														betas.append(min(1 - alpha_bar(SCREAMING_SNAKE_CASE ) / alpha_bar(SCREAMING_SNAKE_CASE )							,    SCREAMING_SNAKE_CASE ) )
							return jnp.array(SCREAMING_SNAKE_CASE							,    dtype=SCREAMING_SNAKE_CASE )







@flax.struct.dataclass
class 							__magic_name__     :
					_SCREAMING_SNAKE_CASE       :					jnp.ndarray
					_SCREAMING_SNAKE_CASE       :					jnp.ndarray
					_SCREAMING_SNAKE_CASE       :					jnp.ndarray





					@classmethod
					def 							lowerCAmelCase      (				cls   :							Tuple							,						snake_case_   :							Any    ):
												__snake_case     			=       scheduler.config

												if config.trained_betas is not None:
																			__snake_case     			=       jnp.asarray(config.trained_betas							,						dtype=scheduler.dtype    )
												elif config.beta_schedule == "linear":
																			__snake_case     			=       jnp.linspace(config.beta_start							,						config.beta_end							,						config.num_train_timesteps							,						dtype=scheduler.dtype    )
												elif config.beta_schedule == "scaled_linear":
																			# this schedule is very specific to the latent diffusion model.
																			__snake_case     			=       (
																			    jnp.linspace(
																			        config.beta_start**0.5							,						config.beta_end**0.5							,						config.num_train_timesteps							,						dtype=scheduler.dtype    )
																			    ** 2
																			)
												elif config.beta_schedule == "squaredcos_cap_v2":
																			# Glide cosine schedule
																			__snake_case     			=       betas_for_alpha_bar(config.num_train_timesteps							,						dtype=scheduler.dtype    )
												else:
																			raise NotImplementedError(
																			    F'''beta_schedule {config.beta_schedule} is not implemented for scheduler {scheduler.__class__.__name__}'''    )

												__snake_case     			=       1.0 - betas

												__snake_case     			=       jnp.cumprod(snake_case_							,						axis=0    )

												return cls(
												    alphas=snake_case_							,						betas=snake_case_							,						alphas_cumprod=snake_case_							,						)




def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE )  ->     Any:







							"""simple docstring"""


							__snake_case     			=       state.alphas_cumprod

							__snake_case     			=       alphas_cumprod[timesteps] ** 0.5
							__snake_case     			=       sqrt_alpha_prod.flatten()
							__snake_case     			=       broadcast_to_shape_from_left(SCREAMING_SNAKE_CASE							,    original_samples.shape )

							__snake_case     			=       (1 - alphas_cumprod[timesteps]) ** 0.5
							__snake_case     			=       sqrt_one_minus_alpha_prod.flatten()
							__snake_case     			=       broadcast_to_shape_from_left(SCREAMING_SNAKE_CASE							,    original_samples.shape )

							return sqrt_alpha_prod, sqrt_one_minus_alpha_prod
def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE )  ->     str:







							"""simple docstring"""


							__snake_case			,   __snake_case     			=       get_sqrt_alpha_prod(SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE )
							__snake_case     			=       sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
							return noisy_samples



def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE )  ->     Optional[int]:







							"""simple docstring"""


							__snake_case			,   __snake_case     			=       get_sqrt_alpha_prod(SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE )
							__snake_case     			=       sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
							return velocity


"""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   __UpperCamelCase   (					SCREAMING_SNAKE_CASE )  ->     List[str]:







							"""simple docstring"""


							__snake_case     			=       3_84
							if "tiny" in model_name:
														__snake_case     			=       [3, 3, 9, 3]
														__snake_case     			=       [96, 1_92, 3_84, 7_68]
							if "small" in model_name:
														__snake_case     			=       [3, 3, 27, 3]
														__snake_case     			=       [96, 1_92, 3_84, 7_68]
							if "base" in model_name:
														__snake_case     			=       [3, 3, 27, 3]
														__snake_case     			=       [1_28, 2_56, 5_12, 10_24]
														__snake_case     			=       5_12
							if "large" in model_name:
														__snake_case     			=       [3, 3, 27, 3]
														__snake_case     			=       [1_92, 3_84, 7_68, 15_36]
														__snake_case     			=       7_68
							if "xlarge" in model_name:
														__snake_case     			=       [3, 3, 27, 3]
														__snake_case     			=       [2_56, 5_12, 10_24, 20_48]
														__snake_case     			=       10_24

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

							__snake_case     			=       ConvNextConfig(
							    depths=SCREAMING_SNAKE_CASE							,    hidden_sizes=SCREAMING_SNAKE_CASE							,    out_features=["stage1", "stage2", "stage3", "stage4"] )
							__snake_case     			=       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   __UpperCamelCase   (					SCREAMING_SNAKE_CASE )  ->     Optional[Any]:







							"""simple docstring"""


							__snake_case     			=       []

							# 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   __UpperCamelCase   (					SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE )  ->     str:







							"""simple docstring"""


							__snake_case     			=       dct.pop(SCREAMING_SNAKE_CASE )
							__snake_case     			=       val
def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE )  ->     Optional[int]:







							"""simple docstring"""


							__snake_case     			=       {
							    "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",
							}
							__snake_case     			=       model_name_to_url[model_name]
							__snake_case     			=       torch.hub.load_state_dict_from_url(SCREAMING_SNAKE_CASE							,    map_location="cpu" )["state_dict"]

							__snake_case     			=       get_upernet_config(SCREAMING_SNAKE_CASE )
							__snake_case     			=       UperNetForSemanticSegmentation(SCREAMING_SNAKE_CASE )
							model.eval()

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

							# rename keys
							__snake_case     			=       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
							__snake_case     			=       "https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg"
							__snake_case     			=       Image.open(requests.get(SCREAMING_SNAKE_CASE							,    stream=SCREAMING_SNAKE_CASE ).raw ).convert("RGB" )

							__snake_case     			=       SegformerImageProcessor()
							__snake_case     			=       processor(SCREAMING_SNAKE_CASE							,    return_tensors="pt" ).pixel_values

							with torch.no_grad():
														__snake_case     			=       model(SCREAMING_SNAKE_CASE )

							if model_name == "upernet-convnext-tiny":
														__snake_case     			=       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":
														__snake_case     			=       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":
														__snake_case     			=       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":
														__snake_case     			=       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":
														__snake_case     			=       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]							,    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__":
		_SCREAMING_SNAKE_CASE	       =      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."""
		)

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