




import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, logging, randn_tensor
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin


_lowerCamelCase							:						Optional[int]							     =  logging.get_logger(__name__)  # pylint: disable=invalid-name





@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerAncestralDiscrete
class 					lowerCamelCase_							(	lowerCAmelCase__							):




					'''simple docstring'''






					lowercase__   =			42
					lowercase__   =			None






def 		_lowercase		(     lowercase_						,							lowercase_=0.999						,							lowercase_="cosine"						,							):



		'''simple docstring'''







		if alpha_transform_type == "cosine":

				def alpha_bar_fn(lowercase_  ):
						return math.cos((t + 0.008) / 1.008 * math.pi / 2  ) ** 2

		elif alpha_transform_type == "exp":

				def alpha_bar_fn(lowercase_  ):
						return math.exp(t * -12.0  )

		else:
				raise ValueError(f'''Unsupported alpha_tranform_type: {alpha_transform_type}'''  )

		_snake_case     	=	[]
		for i in range(lowercase_  ):
				_snake_case     	=	i / num_diffusion_timesteps
				_snake_case     	=	(i + 1) / num_diffusion_timesteps
				betas.append(min(1 - alpha_bar_fn(lowercase_  ) / alpha_bar_fn(lowercase_  )						,							lowercase_  )  )
		return torch.tensor(lowercase_						,							dtype=torch.floataa  )





class 					lowerCamelCase_							(	lowerCAmelCase__,	lowerCAmelCase__							):




					'''simple docstring'''






					lowercase__   =			[e.name for e in KarrasDiffusionSchedulers]
					lowercase__   =			1






					@register_to_config
					def __init__(   self,  __UpperCamelCase = 1_000,  __UpperCamelCase = 0.00_01,  __UpperCamelCase = 0.02,  __UpperCamelCase = "linear",  __UpperCamelCase = None,  __UpperCamelCase = "epsilon",  __UpperCamelCase = "linspace",  __UpperCamelCase = 0,  ):


							if trained_betas is not None:
									_snake_case     	=	torch.tensor(_UpperCAmelCase,  dtype=torch.floataa			)
							elif beta_schedule == "linear":
									_snake_case     	=	torch.linspace(_UpperCAmelCase,  _UpperCAmelCase,  _UpperCAmelCase,  dtype=torch.floataa			)
							elif beta_schedule == "scaled_linear":
									# this schedule is very specific to the latent diffusion model.
									_snake_case     	=	(
									    torch.linspace(beta_start**0.5,  beta_end**0.5,  _UpperCAmelCase,  dtype=torch.floataa			) ** 2
									)
							elif beta_schedule == "squaredcos_cap_v2":
									# Glide cosine schedule
									_snake_case     	=	betas_for_alpha_bar(_UpperCAmelCase			)
							else:
									raise NotImplementedError(f'''{beta_schedule} does is not implemented for {self.__class__}'''			)

							_snake_case     	=	1.0 - self.betas
							_snake_case     	=	torch.cumprod(self.alphas,  dim=0			)

							_snake_case     	=	np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5			)
							_snake_case     	=	np.concatenate([sigmas[::-1], [0.0]]			).astype(np.floataa			)
							_snake_case     	=	torch.from_numpy(_UpperCAmelCase			)

							# setable values
							_snake_case     	=	None
							_snake_case     	=	np.linspace(0,  num_train_timesteps - 1,  _UpperCAmelCase,  dtype=_UpperCAmelCase			)[::-1].copy()
							_snake_case     	=	torch.from_numpy(_UpperCAmelCase			)
							_snake_case     	=	False






					@property
					def     __SCREAMING_SNAKE_CASE     (   self			):


							if self.config.timestep_spacing in ["linspace", "trailing"]:
									return self.sigmas.max()

							return (self.sigmas.max() ** 2 + 1) ** 0.5






					def     __SCREAMING_SNAKE_CASE     (   self,  __UpperCamelCase,  __UpperCamelCase			):


							if isinstance(_UpperCAmelCase,  torch.Tensor			):
									_snake_case     	=	timestep.to(self.timesteps.device			)
							_snake_case     	=	(self.timesteps == timestep).nonzero().item()
							_snake_case     	=	self.sigmas[step_index]
							_snake_case     	=	sample / ((sigma**2 + 1) ** 0.5)
							_snake_case     	=	True
							return sample






					def     __SCREAMING_SNAKE_CASE     (   self,  __UpperCamelCase,  __UpperCamelCase = None			):
							_snake_case     	=	num_inference_steps

							# "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
							if self.config.timestep_spacing == "linspace":
									_snake_case     	=	np.linspace(0,  self.config.num_train_timesteps - 1,  _UpperCAmelCase,  dtype=_UpperCAmelCase			)[
									    ::-1
									].copy()
							elif self.config.timestep_spacing == "leading":
									_snake_case     	=	self.config.num_train_timesteps // self.num_inference_steps
									# creates integer timesteps by multiplying by ratio
									# casting to int to avoid issues when num_inference_step is power of 3
									_snake_case     	=	(np.arange(0,  _UpperCAmelCase			) * step_ratio).round()[::-1].copy().astype(_UpperCAmelCase			)
									timesteps += self.config.steps_offset
							elif self.config.timestep_spacing == "trailing":
									_snake_case     	=	self.config.num_train_timesteps / self.num_inference_steps
									# creates integer timesteps by multiplying by ratio
									# casting to int to avoid issues when num_inference_step is power of 3
									_snake_case     	=	(np.arange(self.config.num_train_timesteps,  0,  -step_ratio			)).round().copy().astype(_UpperCAmelCase			)
									timesteps -= 1
							else:
									raise ValueError(
									    f'''{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.'''			)

							_snake_case     	=	np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5			)
							_snake_case     	=	np.interp(_UpperCAmelCase,  np.arange(0,  len(_UpperCAmelCase			)			),  _UpperCAmelCase			)
							_snake_case     	=	np.concatenate([sigmas, [0.0]]			).astype(np.floataa			)
							_snake_case     	=	torch.from_numpy(_UpperCAmelCase			).to(device=_UpperCAmelCase			)
							if str(_UpperCAmelCase			).startswith("mps"			):
									# mps does not support float64
									_snake_case     	=	torch.from_numpy(_UpperCAmelCase			).to(_UpperCAmelCase,  dtype=torch.floataa			)
							else:
									_snake_case     	=	torch.from_numpy(_UpperCAmelCase			).to(device=_UpperCAmelCase			)






					def     __SCREAMING_SNAKE_CASE     (   self,  __UpperCamelCase,  __UpperCamelCase,  __UpperCamelCase,  __UpperCamelCase = None,  __UpperCamelCase = True,  ):


							if (
							    isinstance(_UpperCAmelCase,  _UpperCAmelCase			)
							    or isinstance(_UpperCAmelCase,  torch.IntTensor			)
							    or isinstance(_UpperCAmelCase,  torch.LongTensor			)
							):
									raise ValueError(
									    (
									        "Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to"
									        " `EulerDiscreteScheduler.step()` is not supported. Make sure to pass"
									        " one of the `scheduler.timesteps` as a timestep."
									    ),  )

							if not self.is_scale_input_called:
									logger.warning(
									    "The `scale_model_input` function should be called before `step` to ensure correct denoising. "
									    "See `StableDiffusionPipeline` for a usage example."			)

							if isinstance(_UpperCAmelCase,  torch.Tensor			):
									_snake_case     	=	timestep.to(self.timesteps.device			)

							_snake_case     	=	(self.timesteps == timestep).nonzero().item()
							_snake_case     	=	self.sigmas[step_index]

							# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
							if self.config.prediction_type == "epsilon":
									_snake_case     	=	sample - sigma * model_output
							elif self.config.prediction_type == "v_prediction":
									# * c_out + input * c_skip
									_snake_case     	=	model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
							elif self.config.prediction_type == "sample":
									raise NotImplementedError("prediction_type not implemented yet: sample"			)
							else:
									raise ValueError(
									    f'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`'''			)

							_snake_case     	=	self.sigmas[step_index]
							_snake_case     	=	self.sigmas[step_index + 1]
							_snake_case     	=	(sigma_to**2 * (sigma_from**2 - sigma_to**2) / sigma_from**2) ** 0.5
							_snake_case     	=	(sigma_to**2 - sigma_up**2) ** 0.5

							# 2. Convert to an ODE derivative
							_snake_case     	=	(sample - pred_original_sample) / sigma

							_snake_case     	=	sigma_down - sigma

							_snake_case     	=	sample + derivative * dt

							_snake_case     	=	model_output.device
							_snake_case     	=	randn_tensor(model_output.shape,  dtype=model_output.dtype,  device=_UpperCAmelCase,  generator=_UpperCAmelCase			)

							_snake_case     	=	prev_sample + noise * sigma_up

							if not return_dict:
									return (prev_sample,)

							return EulerAncestralDiscreteSchedulerOutput(
							    prev_sample=_UpperCAmelCase,  pred_original_sample=_UpperCAmelCase			)






					def     __SCREAMING_SNAKE_CASE     (   self,  __UpperCamelCase,  __UpperCamelCase,  __UpperCamelCase,  ):
							_snake_case     	=	self.sigmas.to(device=original_samples.device,  dtype=original_samples.dtype			)
							if original_samples.device.type == "mps" and torch.is_floating_point(_UpperCAmelCase			):
									# mps does not support float64
									_snake_case     	=	self.timesteps.to(original_samples.device,  dtype=torch.floataa			)
									_snake_case     	=	timesteps.to(original_samples.device,  dtype=torch.floataa			)
							else:
									_snake_case     	=	self.timesteps.to(original_samples.device			)
									_snake_case     	=	timesteps.to(original_samples.device			)

							_snake_case     	=	[(schedule_timesteps == t).nonzero().item() for t in timesteps]

							_snake_case     	=	sigmas[step_indices].flatten()
							while len(sigma.shape			) < len(original_samples.shape			):
									_snake_case     	=	sigma.unsqueeze(-1			)

							_snake_case     	=	original_samples + noise * sigma
							return noisy_samples






					def __len__(   self			):
							return self.config.num_train_timesteps











'''simple docstring'''







import argparse

import requests
import torch
from PIL import Image

from transformers import (
    CLIPSegConfig,
    CLIPSegForImageSegmentation,
    CLIPSegProcessor,
    CLIPSegTextConfig,
    CLIPSegVisionConfig,
    CLIPTokenizer,
    ViTImageProcessor,
)






def 		_lowercase		(     lowercase_  ):



		'''simple docstring'''







		_snake_case     	=	CLIPSegTextConfig()
		_snake_case     	=	CLIPSegVisionConfig(patch_size=16  )

		_snake_case     	=	True if "refined" in model_name else False
		_snake_case     	=	16 if "rd16" in model_name else 64

		_snake_case     	=	CLIPSegConfig.from_text_vision_configs(
		    lowercase_						,							lowercase_						,							use_complex_transposed_convolution=lowercase_						,							reduce_dim=lowercase_						,							)
		return config






def 		_lowercase		(     lowercase_  ):



		'''simple docstring'''







		if "clip_model" in name:
				_snake_case     	=	name.replace("clip_model"						,							"clip"  )
		if "transformer" in name:
				if "visual" in name:
						_snake_case     	=	name.replace("visual.transformer"						,							"vision_model"  )
				else:
						_snake_case     	=	name.replace("transformer"						,							"text_model"  )
		if "resblocks" in name:
				_snake_case     	=	name.replace("resblocks"						,							"encoder.layers"  )
		if "ln_1" in name:
				_snake_case     	=	name.replace("ln_1"						,							"layer_norm1"  )
		if "ln_2" in name:
				_snake_case     	=	name.replace("ln_2"						,							"layer_norm2"  )
		if "c_fc" in name:
				_snake_case     	=	name.replace("c_fc"						,							"fc1"  )
		if "c_proj" in name:
				_snake_case     	=	name.replace("c_proj"						,							"fc2"  )
		if "attn" in name and "self" not in name:
				_snake_case     	=	name.replace("attn"						,							"self_attn"  )
		# text encoder
		if "token_embedding" in name:
				_snake_case     	=	name.replace("token_embedding"						,							"text_model.embeddings.token_embedding"  )
		if "positional_embedding" in name and "visual" not in name:
				_snake_case     	=	name.replace("positional_embedding"						,							"text_model.embeddings.position_embedding.weight"  )
		if "ln_final" in name:
				_snake_case     	=	name.replace("ln_final"						,							"text_model.final_layer_norm"  )
		# vision encoder
		if "visual.class_embedding" in name:
				_snake_case     	=	name.replace("visual.class_embedding"						,							"vision_model.embeddings.class_embedding"  )
		if "visual.conv1" in name:
				_snake_case     	=	name.replace("visual.conv1"						,							"vision_model.embeddings.patch_embedding"  )
		if "visual.positional_embedding" in name:
				_snake_case     	=	name.replace("visual.positional_embedding"						,							"vision_model.embeddings.position_embedding.weight"  )
		if "visual.ln_pre" in name:
				_snake_case     	=	name.replace("visual.ln_pre"						,							"vision_model.pre_layrnorm"  )
		if "visual.ln_post" in name:
				_snake_case     	=	name.replace("visual.ln_post"						,							"vision_model.post_layernorm"  )
		# projection layers
		if "visual.proj" in name:
				_snake_case     	=	name.replace("visual.proj"						,							"visual_projection.weight"  )
		if "text_projection" in name:
				_snake_case     	=	name.replace("text_projection"						,							"text_projection.weight"  )
		# decoder
		if "trans_conv" in name:
				_snake_case     	=	name.replace("trans_conv"						,							"transposed_convolution"  )
		if "film_mul" in name or "film_add" in name or "reduce" in name or "transposed_convolution" in name:
				_snake_case     	=	"decoder." + name
		if "blocks" in name:
				_snake_case     	=	name.replace("blocks"						,							"decoder.layers"  )
		if "linear1" in name:
				_snake_case     	=	name.replace("linear1"						,							"mlp.fc1"  )
		if "linear2" in name:
				_snake_case     	=	name.replace("linear2"						,							"mlp.fc2"  )
		if "norm1" in name and "layer_" not in name:
				_snake_case     	=	name.replace("norm1"						,							"layer_norm1"  )
		if "norm2" in name and "layer_" not in name:
				_snake_case     	=	name.replace("norm2"						,							"layer_norm2"  )

		return name






def 		_lowercase		(     lowercase_						,							lowercase_  ):



		'''simple docstring'''







		for key in orig_state_dict.copy().keys():
				_snake_case     	=	orig_state_dict.pop(lowercase_  )

				if key.startswith("clip_model"  ) and "attn.in_proj" in key:
						_snake_case     	=	key.split("."  )
						if "visual" in key:
								_snake_case     	=	int(key_split[4]  )
								_snake_case     	=	config.vision_config.hidden_size
								_snake_case     	=	"vision_model"
						else:
								_snake_case     	=	int(key_split[3]  )
								_snake_case     	=	config.text_config.hidden_size
								_snake_case     	=	"text_model"

						if "weight" in key:
								_snake_case     	=	val[:dim, :]
								_snake_case     	=	val[
								    dim : dim * 2, :
								]
								_snake_case     	=	val[-dim:, :]
						else:
								_snake_case     	=	val[:dim]
								_snake_case     	=	val[dim : dim * 2]
								_snake_case     	=	val[-dim:]
				elif "self_attn" in key and "out_proj" not in key:
						_snake_case     	=	key.split("."  )
						_snake_case     	=	int(key_split[1]  )
						_snake_case     	=	config.reduce_dim
						if "weight" in key:
								_snake_case     	=	val[:dim, :]
								_snake_case     	=	val[dim : dim * 2, :]
								_snake_case     	=	val[-dim:, :]
						else:
								_snake_case     	=	val[:dim]
								_snake_case     	=	val[dim : dim * 2]
								_snake_case     	=	val[-dim:]
				else:
						_snake_case     	=	rename_key(lowercase_  )
						if "visual_projection" in new_name or "text_projection" in new_name:
								_snake_case     	=	val.T
						_snake_case     	=	val

		return orig_state_dict






def 		_lowercase		(     ):



		'''simple docstring'''







		_snake_case     	=	"http://images.cocodataset.org/val2017/000000039769.jpg"
		_snake_case     	=	Image.open(requests.get(lowercase_						,							stream=lowercase_  ).raw  )
		return image






def 		_lowercase		(     lowercase_						,							lowercase_						,							lowercase_						,							lowercase_  ):



		'''simple docstring'''







		_snake_case     	=	get_clipseg_config(lowercase_  )
		_snake_case     	=	CLIPSegForImageSegmentation(lowercase_  )
		model.eval()

		_snake_case     	=	torch.load(lowercase_						,							map_location="cpu"  )

		# remove some keys
		for key in state_dict.copy().keys():
				if key.startswith("model"  ):
						state_dict.pop(lowercase_						,							lowercase_  )

    # rename some keys
		_snake_case     	=	convert_state_dict(lowercase_						,							lowercase_  )
		_snake_case	,   _snake_case     	=	model.load_state_dict(lowercase_						,							strict=lowercase_  )

		if missing_keys != ["clip.text_model.embeddings.position_ids", "clip.vision_model.embeddings.position_ids"]:
				raise ValueError("Missing keys that are not expected: {}".format(lowercase_  )  )
		if unexpected_keys != ["decoder.reduce.weight", "decoder.reduce.bias"]:
				raise ValueError(f'''Unexpected keys: {unexpected_keys}'''  )

		_snake_case     	=	ViTImageProcessor(size=352  )
		_snake_case     	=	CLIPTokenizer.from_pretrained("openai/clip-vit-base-patch32"  )
		_snake_case     	=	CLIPSegProcessor(image_processor=lowercase_						,							tokenizer=lowercase_  )

		_snake_case     	=	prepare_img()
		_snake_case     	=	["a glass", "something to fill", "wood", "a jar"]

		_snake_case     	=	processor(text=lowercase_						,							images=[image] * len(lowercase_  )						,							padding="max_length"						,							return_tensors="pt"  )

		with torch.no_grad():
				_snake_case     	=	model(**lowercase_  )

		# verify values
		_snake_case     	=	torch.tensor([0.1110, -0.1882, 0.1645]  )
		_snake_case     	=	torch.tensor([0.2692, -0.7197, -0.1328]  )
		if model_name == "clipseg-rd64-refined":
				_snake_case     	=	torch.tensor(
				    [[-10.0407, -9.9431, -10.2646], [-9.9751, -9.7064, -9.9586], [-9.6891, -9.5645, -9.9618]]  )
		elif model_name == "clipseg-rd64":
				_snake_case     	=	torch.tensor(
				    [[-7.2877, -7.2711, -7.2463], [-7.2652, -7.2780, -7.2520], [-7.2239, -7.2204, -7.2001]]  )
		elif model_name == "clipseg-rd16":
				_snake_case     	=	torch.tensor(
				    [[-6.3955, -6.4055, -6.4151], [-6.3911, -6.4033, -6.4100], [-6.3474, -6.3702, -6.3762]]  )
		else:
				raise ValueError(f'''Model name {model_name} not supported.'''  )

		assert torch.allclose(outputs.logits[0, :3, :3]						,							lowercase_						,							atol=1e-3  )
		assert torch.allclose(outputs.conditional_embeddings[0, :3]						,							lowercase_						,							atol=1e-3  )
		assert torch.allclose(outputs.pooled_output[0, :3]						,							lowercase_						,							atol=1e-3  )
		print("Looks ok!"  )

		if pytorch_dump_folder_path is not None:
				print(f'''Saving model and processor to {pytorch_dump_folder_path}'''  )
				model.save_pretrained(lowercase_  )
				processor.save_pretrained(lowercase_  )

		if push_to_hub:
				print(f'''Pushing model and processor for {model_name} to the hub'''  )
				model.push_to_hub(f'''CIDAS/{model_name}'''  )
				processor.push_to_hub(f'''CIDAS/{model_name}'''  )


if __name__ == "__main__":
						_lowerCamelCase							:						str							     =  argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    '--model_name',
						    default='clipseg-rd64',
						    type=str,
						    choices=['clipseg-rd16', 'clipseg-rd64', 'clipseg-rd64-refined'],
						    help=(
						        'Name of the model. Supported models are: clipseg-rd64, clipseg-rd16 and clipseg-rd64-refined (rd meaning'
						        ' reduce dimension)'
						    ),
						)
						parser.add_argument(
						    '--checkpoint_path',
						    default='/Users/nielsrogge/Documents/CLIPSeg/clip_plus_rd64-uni.pth',
						    type=str,
						    help=(
						        'Path to the original checkpoint. Note that the script assumes that the checkpoint includes both CLIP and'
						        ' the decoder weights.'
						    ),
						)
						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.'
						)

						_lowerCamelCase							:						Optional[int]							     =  parser.parse_args()
						convert_clipseg_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)





