

'''simple docstring'''

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_     =      logging.get_logger(__name__)  # pylint: disable=invalid-name







@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerAncestralDiscrete
class lowerCamelCase   (    __lowerCAmelCase				):
				snake_case_							     = 42
				snake_case_							     = None

def      __magic_name__							(			A       , A=0.999       , A="cosine"       , )      ->		Optional[int]:
	if alpha_transform_type == "cosine":

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

	elif alpha_transform_type == "exp":

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

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

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







class lowerCamelCase   (    __lowerCAmelCase				,				__lowerCAmelCase				):
				snake_case_							     = [e.name for e in KarrasDiffusionSchedulers]
				snake_case_							     = 1

				@register_to_config
				def __init__(       self,						lowercase_ = 1000,						lowercase_ = 0.0_001,						lowercase_ = 0.02,						lowercase_ = "linear",						lowercase_ = None,						lowercase_ = "epsilon",						lowercase_ = "linspace",						lowercase_ = 0,						)    ->							List[str]:
					if trained_betas is not None:
						snake_case							=					torch.tensor(lowercase_,						dtype=torch.floataa )
					elif beta_schedule == "linear":
						snake_case							=					torch.linspace(lowercase_,						lowercase_,						lowercase_,						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,						lowercase_,						dtype=torch.floataa ) ** 2
						)
					elif beta_schedule == "squaredcos_cap_v2":
						# Glide cosine schedule
						snake_case							=					betas_for_alpha_bar(lowercase_ )
					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(lowercase_ )

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

				@property
				def 					_lowerCamelCase    (       self )    ->							Any:
					# standard deviation of the initial noise distribution
					if self.config.timestep_spacing in ["linspace", "trailing"]:
						return self.sigmas.max()

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

				def 					_lowerCamelCase    (       self,						lowercase_,						lowercase_ )    ->							torch.FloatTensor:
					if isinstance(lowercase_,						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 					_lowerCamelCase    (       self,						lowercase_,						lowercase_ = None )    ->							List[Any]:
					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,						lowercase_,						dtype=lowercase_ )[
						    ::-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,						lowercase_ ) * step_ratio).round()[::-1].copy().astype(lowercase_ )
						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(lowercase_ )
						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(lowercase_,						np.arange(0,						len(lowercase_ ) ),						lowercase_ )
					snake_case							=					np.concatenate([sigmas, [0.0]] ).astype(np.floataa )
					snake_case							=					torch.from_numpy(lowercase_ ).to(device=lowercase_ )
					if str(lowercase_ ).startswith('mps' ):
						# mps does not support float64
						snake_case							=					torch.from_numpy(lowercase_ ).to(lowercase_,						dtype=torch.floataa )
					else:
						snake_case							=					torch.from_numpy(lowercase_ ).to(device=lowercase_ )

				def 					_lowerCamelCase    (       self,						lowercase_,						lowercase_,						lowercase_,						lowercase_ = None,						lowercase_ = True,						)    ->							Union[EulerAncestralDiscreteSchedulerOutput, Tuple]:

					if (
					    isinstance(lowercase_,						lowercase_ )
					    or isinstance(lowercase_,						torch.IntTensor )
					    or isinstance(lowercase_,						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(lowercase_,						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=lowercase_,						generator=lowercase_ )

					snake_case							=					prev_sample + noise * sigma_up

					if not return_dict:
						return (prev_sample,)

					return EulerAncestralDiscreteSchedulerOutput(
					    prev_sample=lowercase_,						pred_original_sample=lowercase_ )

				def 					_lowerCamelCase    (       self,						lowercase_,						lowercase_,						lowercase_,						)    ->							torch.FloatTensor:
					# Make sure sigmas and timesteps have the same device and dtype as original_samples
					snake_case							=					self.sigmas.to(device=original_samples.device,						dtype=original_samples.dtype )
					if original_samples.device.type == "mps" and torch.is_floating_point(lowercase_ ):
						# 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 )    ->							List[str]:
					return self.config.num_train_timesteps







import argparse

import requests
import torch
from PIL import Image

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


def  A__ (       __lowerCamelCase						):
   SCREAMING_SNAKE_CASE_	=      CLIPSegTextConfig()
   SCREAMING_SNAKE_CASE_	=      CLIPSegVisionConfig(patch_size=16						)

   SCREAMING_SNAKE_CASE_	=      True if '''refined''' in model_name else False
   SCREAMING_SNAKE_CASE_	=      16 if '''rd16''' in model_name else 64

   SCREAMING_SNAKE_CASE_	=      CLIPSegConfig.from_text_vision_configs(
       __lowerCamelCase,  __lowerCamelCase,  use_complex_transposed_convolution=__lowerCamelCase,  reduce_dim=__lowerCamelCase,  )
   return config


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

   return name


def  A__ (       __lowerCamelCase,  __lowerCamelCase						):
   for key in orig_state_dict.copy().keys():
      SCREAMING_SNAKE_CASE_	=      orig_state_dict.pop(__lowerCamelCase						)

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

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

   return orig_state_dict


def  A__ (       ):
   SCREAMING_SNAKE_CASE_	=      '''http://images.cocodataset.org/val2017/000000039769.jpg'''
   SCREAMING_SNAKE_CASE_	=      Image.open(requests.get(__lowerCamelCase,  stream=__lowerCamelCase						).raw						)
   return image


def  A__ (       __lowerCamelCase,  __lowerCamelCase,  __lowerCamelCase,  __lowerCamelCase						):
   SCREAMING_SNAKE_CASE_	=      get_clipseg_config(__lowerCamelCase						)
   SCREAMING_SNAKE_CASE_	=      CLIPSegForImageSegmentation(__lowerCamelCase						)
   model.eval()

   SCREAMING_SNAKE_CASE_	=      torch.load(__lowerCamelCase,  map_location='''cpu'''						)

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

    # rename some keys
   SCREAMING_SNAKE_CASE_	=      convert_state_dict(__lowerCamelCase,  __lowerCamelCase						)
   SCREAMING_SNAKE_CASE_	, SCREAMING_SNAKE_CASE_	=      model.load_state_dict(__lowerCamelCase,  strict=__lowerCamelCase						)

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

   SCREAMING_SNAKE_CASE_	=      ViTImageProcessor(size=3_52						)
   SCREAMING_SNAKE_CASE_	=      CLIPTokenizer.from_pretrained('''openai/clip-vit-base-patch32'''						)
   SCREAMING_SNAKE_CASE_	=      CLIPSegProcessor(image_processor=__lowerCamelCase,  tokenizer=__lowerCamelCase						)

   SCREAMING_SNAKE_CASE_	=      prepare_img()
   SCREAMING_SNAKE_CASE_	=      ['''a glass''', '''something to fill''', '''wood''', '''a jar''']

   SCREAMING_SNAKE_CASE_	=      processor(text=__lowerCamelCase,  images=[image] * len(__lowerCamelCase						),  padding='''max_length''',  return_tensors='''pt'''						)

   with torch.no_grad():
      SCREAMING_SNAKE_CASE_	=      model(**__lowerCamelCase						)

   # verify values
   SCREAMING_SNAKE_CASE_	=      torch.tensor([0.11_10, -0.18_82, 0.16_45]						)
   SCREAMING_SNAKE_CASE_	=      torch.tensor([0.26_92, -0.71_97, -0.13_28]						)
   if model_name == "clipseg-rd64-refined":
      SCREAMING_SNAKE_CASE_	=      torch.tensor(
          [[-10.04_07, -9.94_31, -10.26_46], [-9.97_51, -9.70_64, -9.95_86], [-9.68_91, -9.56_45, -9.96_18]]						)
   elif model_name == "clipseg-rd64":
      SCREAMING_SNAKE_CASE_	=      torch.tensor(
          [[-7.28_77, -7.27_11, -7.24_63], [-7.26_52, -7.27_80, -7.25_20], [-7.22_39, -7.22_04, -7.20_01]]						)
   elif model_name == "clipseg-rd16":
      SCREAMING_SNAKE_CASE_	=      torch.tensor(
          [[-6.39_55, -6.40_55, -6.41_51], [-6.39_11, -6.40_33, -6.41_00], [-6.34_74, -6.37_02, -6.37_62]]						)
   else:
      raise ValueError(F'''Model name {model_name} not supported.'''						)

   assert torch.allclose(outputs.logits[0, :3, :3],  __lowerCamelCase,  atol=1E-3						)
   assert torch.allclose(outputs.conditional_embeddings[0, :3],  __lowerCamelCase,  atol=1E-3						)
   assert torch.allclose(outputs.pooled_output[0, :3],  __lowerCamelCase,  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(__lowerCamelCase						)
      processor.save_pretrained(__lowerCamelCase						)

   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__":
   __UpperCAmelCase   						=     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."
   )

   __UpperCAmelCase   						=     parser.parse_args()
   convert_clipseg_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)


