



import argparse

import requests
import torch
from PIL import Image

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


def 	_A	(   _UpperCamelCase			):
				_UpperCAmelCase						:  List[str]     					=	CLIPSegTextConfig()
				_UpperCAmelCase						:  Union[str, Any]     					=	CLIPSegVisionConfig(patch_size=16			)

				_UpperCAmelCase						:  Optional[int]     					=	True if '''refined''' in model_name else False
				_UpperCAmelCase						:  Union[str, Any]     					=	16 if '''rd16''' in model_name else 64

				_UpperCAmelCase						:  Union[str, Any]     					=	CLIPSegConfig.from_text_vision_configs(
				    _UpperCamelCase      ,					_UpperCamelCase      ,					use_complex_transposed_convolution=_UpperCamelCase      ,					reduce_dim=_UpperCamelCase      ,					)
				return config


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

				return name


def 	_A	(   _UpperCamelCase      ,					_UpperCamelCase			):
				for key in orig_state_dict.copy().keys():
								_UpperCAmelCase						:  str     					=	orig_state_dict.pop(_UpperCamelCase			)

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

												if "weight" in key:
																_UpperCAmelCase						:  Union[str, Any]     					=	val[:dim, :]
																_UpperCAmelCase						:  Optional[Any]     					=	val[
																    dim : dim * 2, :
																]
																_UpperCAmelCase						:  Union[str, Any]     					=	val[-dim:, :]
												else:
																_UpperCAmelCase						:  List[str]     					=	val[:dim]
																_UpperCAmelCase						:  List[Any]     					=	val[dim : dim * 2]
																_UpperCAmelCase						:  List[Any]     					=	val[-dim:]
								elif "self_attn" in key and "out_proj" not in key:
												_UpperCAmelCase						:  Optional[int]     					=	key.split('''.'''			)
												_UpperCAmelCase						:  Any     					=	int(key_split[1]			)
												_UpperCAmelCase						:  Any     					=	config.reduce_dim
												if "weight" in key:
																_UpperCAmelCase						:  Union[str, Any]     					=	val[:dim, :]
																_UpperCAmelCase						:  Union[str, Any]     					=	val[dim : dim * 2, :]
																_UpperCAmelCase						:  List[Any]     					=	val[-dim:, :]
												else:
																_UpperCAmelCase						:  Any     					=	val[:dim]
																_UpperCAmelCase						:  Any     					=	val[dim : dim * 2]
																_UpperCAmelCase						:  Dict     					=	val[-dim:]
								else:
												_UpperCAmelCase						:  Tuple     					=	rename_key(_UpperCamelCase			)
												if "visual_projection" in new_name or "text_projection" in new_name:
																_UpperCAmelCase						:  str     					=	val.T
												_UpperCAmelCase						:  Tuple     					=	val

				return orig_state_dict


def 	_A	(   ):
				_UpperCAmelCase						:  int     					=	'''http://images.cocodataset.org/val2017/000000039769.jpg'''
				_UpperCAmelCase						:  str     					=	Image.open(requests.get(_UpperCamelCase      ,					stream=_UpperCamelCase			).raw			)
				return image


def 	_A	(   _UpperCamelCase      ,					_UpperCamelCase      ,					_UpperCamelCase      ,					_UpperCamelCase			):
				_UpperCAmelCase						:  Union[str, Any]     					=	get_clipseg_config(_UpperCamelCase			)
				_UpperCAmelCase						:  List[str]     					=	CLIPSegForImageSegmentation(_UpperCamelCase			)
				model.eval()

				_UpperCAmelCase						:  List[Any]     					=	torch.load(_UpperCamelCase      ,					map_location='''cpu'''			)

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

    # rename some keys
				_UpperCAmelCase						:  List[Any]     					=	convert_state_dict(_UpperCamelCase      ,					_UpperCamelCase			)
				_UpperCAmelCase					, _UpperCAmelCase						:  int     					=	model.load_state_dict(_UpperCamelCase      ,					strict=_UpperCamelCase			)

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

				_UpperCAmelCase						:  List[Any]     					=	ViTImageProcessor(size=352			)
				_UpperCAmelCase						:  List[str]     					=	CLIPTokenizer.from_pretrained('''openai/clip-vit-base-patch32'''			)
				_UpperCAmelCase						:  Any     					=	CLIPSegProcessor(image_processor=_UpperCamelCase      ,					tokenizer=_UpperCamelCase			)

				_UpperCAmelCase						:  int     					=	prepare_img()
				_UpperCAmelCase						:  List[str]     					=	['''a glass''', '''something to fill''', '''wood''', '''a jar''']

				_UpperCAmelCase						:  int     					=	processor(text=_UpperCamelCase      ,					images=[image] * len(_UpperCamelCase			)      ,					padding='''max_length'''      ,					return_tensors='''pt'''			)

				with torch.no_grad():
								_UpperCAmelCase						:  str     					=	model(**_UpperCamelCase			)

				# verify values
				_UpperCAmelCase						:  List[Any]     					=	torch.tensor([0.1_110, -0.1_882, 0.1_645]			)
				_UpperCAmelCase						:  Optional[Any]     					=	torch.tensor([0.2_692, -0.7_197, -0.1_328]			)
				if model_name == "clipseg-rd64-refined":
								_UpperCAmelCase						:  Optional[int]     					=	torch.tensor(
								    [[-10.0_407, -9.9_431, -10.2_646], [-9.9_751, -9.7_064, -9.9_586], [-9.6_891, -9.5_645, -9.9_618]]			)
				elif model_name == "clipseg-rd64":
								_UpperCAmelCase						:  str     					=	torch.tensor(
								    [[-7.2_877, -7.2_711, -7.2_463], [-7.2_652, -7.2_780, -7.2_520], [-7.2_239, -7.2_204, -7.2_001]]			)
				elif model_name == "clipseg-rd16":
								_UpperCAmelCase						:  List[Any]     					=	torch.tensor(
								    [[-6.3_955, -6.4_055, -6.4_151], [-6.3_911, -6.4_033, -6.4_100], [-6.3_474, -6.3_702, -6.3_762]]			)
				else:
								raise ValueError(F'''Model name {model_name} not supported.'''			)

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

				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__			:   Tuple      			=    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__			:   Tuple      			=    parser.parse_args()
					convert_clipseg_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)





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

import flax
import jax.numpy as jnp
from scipy import integrate

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
    CommonSchedulerState,
    FlaxKarrasDiffusionSchedulers,
    FlaxSchedulerMixin,
    FlaxSchedulerOutput,
    broadcast_to_shape_from_left,
)





@flax.struct.dataclass
class 							lowerCAmelCase_		:
		SCREAMING_SNAKE_CASE_       :			CommonSchedulerState

		# setable values
		SCREAMING_SNAKE_CASE_       :			jnp.ndarray
		SCREAMING_SNAKE_CASE_       :			jnp.ndarray
		SCREAMING_SNAKE_CASE_       :			jnp.ndarray
		SCREAMING_SNAKE_CASE_       :			Optional[int]					       =	None

		# running values
		SCREAMING_SNAKE_CASE_       :			Optional[jnp.ndarray]					       =	None







		@classmethod
		def 					a_  (    cls  :   List[Any]       ,		UpperCAmelCase_  :   CommonSchedulerState       ,		UpperCAmelCase_  :   jnp.ndarray       ,		UpperCAmelCase_  :   jnp.ndarray       ,		UpperCAmelCase_  :   jnp.ndarray					)       ->		Optional[Any]:


						'''simple docstring'''







						return cls(common=UpperCAmelCase_       ,		init_noise_sigma=UpperCAmelCase_       ,		timesteps=UpperCAmelCase_       ,		sigmas=UpperCAmelCase_					)





@dataclass
class 							lowerCAmelCase_		(		lowercase_     ):
		SCREAMING_SNAKE_CASE_       :			LMSDiscreteSchedulerState





class 							lowerCAmelCase_		(		lowercase_							,		lowercase_     ):
		SCREAMING_SNAKE_CASE_       :			Union[str, Any]					       =	[e.name for e in FlaxKarrasDiffusionSchedulers]

		SCREAMING_SNAKE_CASE_       :			jnp.dtype







		@property
		def 					a_  (    self  :   Union[str, Any]					)       ->		List[Any]:


						'''simple docstring'''







						return True







		@register_to_config
		def __init__(    self  :   Optional[Any]       ,		UpperCAmelCase_  :   int = 1000       ,		UpperCAmelCase_  :   float = 0.0_001       ,		UpperCAmelCase_  :   float = 0.02       ,		UpperCAmelCase_  :   str = "linear"       ,		UpperCAmelCase_  :   Optional[jnp.ndarray] = None       ,		UpperCAmelCase_  :   str = "epsilon"       ,		UpperCAmelCase_  :   jnp.dtype = jnp.floataa       ,		)       ->		List[str]:


						'''simple docstring'''







						_UpperCAmelCase						:  str     					=	dtype







		def 					a_  (    self  :   Tuple       ,		UpperCAmelCase_  :   Optional[CommonSchedulerState] = None					)       ->		LMSDiscreteSchedulerState:


						'''simple docstring'''







						if common is None:
										_UpperCAmelCase						:  int     					=	CommonSchedulerState.create(self					)

						_UpperCAmelCase						:  Any     					=	jnp.arange(0       ,		self.config.num_train_timesteps					).round()[::-1]
						_UpperCAmelCase						:  Any     					=	((1 - common.alphas_cumprod) / common.alphas_cumprod) ** 0.5

						# standard deviation of the initial noise distribution
						_UpperCAmelCase						:  Dict     					=	sigmas.max()

						return LMSDiscreteSchedulerState.create(
						    common=UpperCAmelCase_       ,		init_noise_sigma=UpperCAmelCase_       ,		timesteps=UpperCAmelCase_       ,		sigmas=UpperCAmelCase_       ,		)







		def 					a_  (    self  :   Optional[Any]       ,		UpperCAmelCase_  :   LMSDiscreteSchedulerState       ,		UpperCAmelCase_  :   jnp.ndarray       ,		UpperCAmelCase_  :   int					)       ->		jnp.ndarray:


						'''simple docstring'''







						((_UpperCAmelCase)					, )						:  Optional[Any]     					=	jnp.where(state.timesteps == timestep       ,		size=1					)
						_UpperCAmelCase						:  List[str]     					=	step_index[0]

						_UpperCAmelCase						:  List[Any]     					=	state.sigmas[step_index]
						_UpperCAmelCase						:  Union[str, Any]     					=	sample / ((sigma**2 + 1) ** 0.5)
						return sample







		def 					a_  (    self  :   List[Any]       ,		UpperCAmelCase_  :   LMSDiscreteSchedulerState       ,		UpperCAmelCase_  :   int       ,		UpperCAmelCase_  :   str       ,		UpperCAmelCase_  :   Union[str, Any]					)       ->		Union[str, Any]:


						'''simple docstring'''







						def lms_derivative(UpperCAmelCase_  :   Optional[int]					):
										_UpperCAmelCase						:  Optional[Any]     					=	1.0
										for k in range(UpperCAmelCase_					):
														if current_order == k:
																		continue
														prod *= (tau - state.sigmas[t - k]) / (state.sigmas[t - current_order] - state.sigmas[t - k])
										return prod

						_UpperCAmelCase						:  Optional[Any]     					=	integrate.quad(UpperCAmelCase_       ,		state.sigmas[t]       ,		state.sigmas[t + 1]       ,		epsrel=1E-4					)[0]

						return integrated_coeff







		def 					a_  (    self  :   Dict       ,		UpperCAmelCase_  :   LMSDiscreteSchedulerState       ,		UpperCAmelCase_  :   int       ,		UpperCAmelCase_  :   Tuple = ()					)       ->		LMSDiscreteSchedulerState:


						'''simple docstring'''







						_UpperCAmelCase						:  List[str]     					=	jnp.linspace(self.config.num_train_timesteps - 1       ,		0       ,		UpperCAmelCase_       ,		dtype=self.dtype					)

						_UpperCAmelCase						:  Any     					=	jnp.floor(UpperCAmelCase_					).astype(jnp.intaa					)
						_UpperCAmelCase						:  Optional[Any]     					=	jnp.ceil(UpperCAmelCase_					).astype(jnp.intaa					)

						_UpperCAmelCase						:  str     					=	jnp.mod(UpperCAmelCase_       ,		1.0					)

						_UpperCAmelCase						:  Optional[Any]     					=	((1 - state.common.alphas_cumprod) / state.common.alphas_cumprod) ** 0.5
						_UpperCAmelCase						:  Any     					=	(1 - frac) * sigmas[low_idx] + frac * sigmas[high_idx]
						_UpperCAmelCase						:  Tuple     					=	jnp.concatenate([sigmas, jnp.array([0.0]       ,		dtype=self.dtype					)]					)

						_UpperCAmelCase						:  int     					=	timesteps.astype(jnp.intaa					)

						# initial running values
						_UpperCAmelCase						:  List[str]     					=	jnp.zeros((0,) + shape       ,		dtype=self.dtype					)

						return state.replace(
						    timesteps=UpperCAmelCase_       ,		sigmas=UpperCAmelCase_       ,		num_inference_steps=UpperCAmelCase_       ,		derivatives=UpperCAmelCase_       ,		)







		def 					a_  (    self  :   Tuple       ,		UpperCAmelCase_  :   LMSDiscreteSchedulerState       ,		UpperCAmelCase_  :   jnp.ndarray       ,		UpperCAmelCase_  :   int       ,		UpperCAmelCase_  :   jnp.ndarray       ,		UpperCAmelCase_  :   int = 4       ,		UpperCAmelCase_  :   bool = True       ,		)       ->		Union[FlaxLMSSchedulerOutput, Tuple]:


						'''simple docstring'''







						if state.num_inference_steps is None:
										raise ValueError(
										    '''Number of inference steps is \'None\', you need to run \'set_timesteps\' after creating the scheduler'''					)

						_UpperCAmelCase						:  List[Any]     					=	state.sigmas[timestep]

						# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
						if self.config.prediction_type == "epsilon":
										_UpperCAmelCase						:  Optional[int]     					=	sample - sigma * model_output
						elif self.config.prediction_type == "v_prediction":
										# * c_out + input * c_skip
										_UpperCAmelCase						:  Tuple     					=	model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
						else:
										raise ValueError(
										    F'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`'''					)

						# 2. Convert to an ODE derivative
						_UpperCAmelCase						:  Tuple     					=	(sample - pred_original_sample) / sigma
						_UpperCAmelCase						:  Tuple     					=	state.replace(derivatives=jnp.append(state.derivatives       ,		UpperCAmelCase_					)					)
						if len(state.derivatives					) > order:
										_UpperCAmelCase						:  Union[str, Any]     					=	state.replace(derivatives=jnp.delete(state.derivatives       ,		0					)					)

						# 3. Compute linear multistep coefficients
						_UpperCAmelCase						:  int     					=	min(timestep + 1       ,		UpperCAmelCase_					)
						_UpperCAmelCase						:  Optional[int]     					=	[self.get_lms_coefficient(UpperCAmelCase_       ,		UpperCAmelCase_       ,		UpperCAmelCase_       ,		UpperCAmelCase_					) for curr_order in range(UpperCAmelCase_					)]

						# 4. Compute previous sample based on the derivatives path
						_UpperCAmelCase						:  int     					=	sample + sum(
						    coeff * derivative for coeff, derivative in zip(UpperCAmelCase_       ,		reversed(state.derivatives					)					)					)

						if not return_dict:
										return (prev_sample, state)

						return FlaxLMSSchedulerOutput(prev_sample=UpperCAmelCase_       ,		state=UpperCAmelCase_					)







		def 					a_  (    self  :   Union[str, Any]       ,		UpperCAmelCase_  :   LMSDiscreteSchedulerState       ,		UpperCAmelCase_  :   jnp.ndarray       ,		UpperCAmelCase_  :   jnp.ndarray       ,		UpperCAmelCase_  :   jnp.ndarray       ,		)       ->		jnp.ndarray:


						'''simple docstring'''







						_UpperCAmelCase						:  Union[str, Any]     					=	state.sigmas[timesteps].flatten()
						_UpperCAmelCase						:  Union[str, Any]     					=	broadcast_to_shape_from_left(UpperCAmelCase_       ,		noise.shape					)

						_UpperCAmelCase						:  Any     					=	original_samples + noise * sigma

						return noisy_samples







		def __len__(    self  :   Dict					)       ->		Tuple:


						'''simple docstring'''







						return self.config.num_train_timesteps
