






"""simple docstring"""

import argparse

import requests
import torch
from PIL import Image

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






def _lowerCAmelCase						(	__lowerCamelCase:Dict ):

						'''simple docstring'''






						__magic_name__      =  CLIPSegTextConfig()
						__magic_name__      =  CLIPSegVisionConfig(patch_size=1_6 )

						__magic_name__      =  True if "refined" in model_name else False
						__magic_name__      =  1_6 if "rd16" in model_name else 6_4

						__magic_name__      =  CLIPSegConfig.from_text_vision_configs(
						    __A			,    __A			,    use_complex_transposed_convolution=__A			,    reduce_dim=__A			,    )
						return config






def _lowerCAmelCase						(	__lowerCamelCase:Union[str, Any] ):

						'''simple docstring'''






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

						return name






def _lowerCAmelCase						(	__lowerCamelCase:Optional[int]			,    __lowerCamelCase:Union[str, Any] ):

						'''simple docstring'''






						for key in orig_state_dict.copy().keys():
												__magic_name__      =  orig_state_dict.pop(__A )

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

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

						return orig_state_dict






def _lowerCAmelCase						(	):

						'''simple docstring'''






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






def _lowerCAmelCase						(	__lowerCamelCase:Tuple			,    __lowerCamelCase:Union[str, Any]			,    __lowerCamelCase:List[Any]			,    __lowerCamelCase:Dict ):

						'''simple docstring'''






						__magic_name__      =  get_clipseg_config(__A )
						__magic_name__      =  CLIPSegForImageSegmentation(__A )
						model.eval()

						__magic_name__      =  torch.load(__A			,    map_location="cpu" )

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

												# rename some keys
						__magic_name__      =  convert_state_dict(__A			,    __A )
						__magic_name__   ,     __magic_name__      =  model.load_state_dict(__A			,    strict=__A )

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

						__magic_name__      =  ViTImageProcessor(size=3_5_2 )
						__magic_name__      =  CLIPTokenizer.from_pretrained("openai/clip-vit-base-patch32" )
						__magic_name__      =  CLIPSegProcessor(image_processor=__A			,    tokenizer=__A )

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

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

						with torch.no_grad():
												__magic_name__      =  model(**__A )

						# verify values
						__magic_name__      =  torch.tensor([0.1_1_1_0, -0.1_8_8_2, 0.1_6_4_5] )
						__magic_name__      =  torch.tensor([0.2_6_9_2, -0.7_1_9_7, -0.1_3_2_8] )
						if model_name == "clipseg-rd64-refined":
												__magic_name__      =  torch.tensor(
												    [[-1_0.0_4_0_7, -9.9_4_3_1, -1_0.2_6_4_6], [-9.9_7_5_1, -9.7_0_6_4, -9.9_5_8_6], [-9.6_8_9_1, -9.5_6_4_5, -9.9_6_1_8]] )
						elif model_name == "clipseg-rd64":
												__magic_name__      =  torch.tensor(
												    [[-7.2_8_7_7, -7.2_7_1_1, -7.2_4_6_3], [-7.2_6_5_2, -7.2_7_8_0, -7.2_5_2_0], [-7.2_2_3_9, -7.2_2_0_4, -7.2_0_0_1]] )
						elif model_name == "clipseg-rd16":
												__magic_name__      =  torch.tensor(
												    [[-6.3_9_5_5, -6.4_0_5_5, -6.4_1_5_1], [-6.3_9_1_1, -6.4_0_3_3, -6.4_1_0_0], [-6.3_4_7_4, -6.3_7_0_2, -6.3_7_6_2]] )
						else:
												raise ValueError(f'''Model name {model_name} not supported.''' )

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

						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__":
			lowercase           =							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.'''
			)

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






from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


lowerCAmelCase__							=			logging.get_logger(__name__)


if is_vision_available():
      import PIL






class     __magic_name__     (							_snake_case     ):
 UpperCAmelCase				     =    ["""pixel_values"""]


 def __init__(    self		:			str      ,    lowerCAmelCase__		:			bool = True      ,    lowerCAmelCase__		:			Dict[str, int] = None      ,    lowerCAmelCase__		:			PILImageResampling = PILImageResampling.BICUBIC      ,    lowerCAmelCase__		:			bool = True      ,    lowerCAmelCase__		:			Dict[str, int] = None      ,    lowerCAmelCase__		:			bool = True      ,    lowerCAmelCase__		:			Union[int, float] = 1 / 2_5_5      ,    lowerCAmelCase__		:			bool = True      ,    lowerCAmelCase__		:			Optional[Union[float, List[float]]] = None      ,    lowerCAmelCase__		:			Optional[Union[float, List[float]]] = None      ,    lowerCAmelCase__		:			bool = True      ,    **lowerCAmelCase__		:			str      ,    )     ->   None:
   super().__init__(**lowerCAmelCase__     )
   UpperCAmelCase          =	size if size is not None else {"shortest_edge": 2_2_4}
   UpperCAmelCase          =	get_size_dict(lowerCAmelCase__      ,    default_to_square=lowerCAmelCase__     )
   UpperCAmelCase          =	crop_size if crop_size is not None else {"height": 2_2_4, "width": 2_2_4}
   UpperCAmelCase          =	get_size_dict(lowerCAmelCase__     )

   UpperCAmelCase          =	do_resize
   UpperCAmelCase          =	size
   UpperCAmelCase          =	resample
   UpperCAmelCase          =	do_center_crop
   UpperCAmelCase          =	crop_size
   UpperCAmelCase          =	do_rescale
   UpperCAmelCase          =	rescale_factor
   UpperCAmelCase          =	do_normalize
   UpperCAmelCase          =	image_mean if image_mean is not None else OPENAI_CLIP_MEAN
   UpperCAmelCase          =	image_std if image_std is not None else OPENAI_CLIP_STD
   UpperCAmelCase          =	do_convert_rgb


 def _UpperCamelCase						(    self		:			Optional[Any]      ,    lowerCAmelCase__		:			np.ndarray      ,    lowerCAmelCase__		:			Dict[str, int]      ,    lowerCAmelCase__		:			PILImageResampling = PILImageResampling.BICUBIC      ,    lowerCAmelCase__		:			Optional[Union[str, ChannelDimension]] = None      ,    **lowerCAmelCase__		:			Optional[int]      ,    )     ->   np.ndarray:
   UpperCAmelCase          =	get_size_dict(lowerCAmelCase__      ,    default_to_square=lowerCAmelCase__     )
   UpperCAmelCase          =	get_resize_output_image_size(
       lowerCAmelCase__      ,    size=(size["height"], size["width"])      ,    default_to_square=lowerCAmelCase__     )
   return resize(lowerCAmelCase__      ,    size=lowerCAmelCase__      ,    resample=lowerCAmelCase__      ,    data_format=lowerCAmelCase__      ,    **lowerCAmelCase__     )


 def _UpperCamelCase						(    self		:			List[Any]      ,    lowerCAmelCase__		:			np.ndarray      ,    lowerCAmelCase__		:			Dict[str, int]      ,    lowerCAmelCase__		:			Optional[Union[str, ChannelDimension]] = None      ,    **lowerCAmelCase__		:			List[Any]      ,    )     ->   np.ndarray:
   UpperCAmelCase          =	get_size_dict(lowerCAmelCase__     )
   return center_crop(lowerCAmelCase__      ,    size=(size["height"], size["width"])      ,    data_format=lowerCAmelCase__      ,    **lowerCAmelCase__     )


 def _UpperCamelCase						(    self		:			Any      ,    lowerCAmelCase__		:			np.ndarray      ,    lowerCAmelCase__		:			Union[int, float]      ,    lowerCAmelCase__		:			Optional[Union[str, ChannelDimension]] = None      ,    **lowerCAmelCase__		:			List[Any]      ,    )     ->   Optional[int]:
   return rescale(lowerCAmelCase__      ,    scale=lowerCAmelCase__      ,    data_format=lowerCAmelCase__      ,    **lowerCAmelCase__     )


 def _UpperCamelCase						(    self		:			Union[str, Any]      ,    lowerCAmelCase__		:			np.ndarray      ,    lowerCAmelCase__		:			Union[float, List[float]]      ,    lowerCAmelCase__		:			Union[float, List[float]]      ,    lowerCAmelCase__		:			Optional[Union[str, ChannelDimension]] = None      ,    **lowerCAmelCase__		:			List[str]      ,    )     ->   np.ndarray:
   return normalize(lowerCAmelCase__      ,    mean=lowerCAmelCase__      ,    std=lowerCAmelCase__      ,    data_format=lowerCAmelCase__      ,    **lowerCAmelCase__     )


 def _UpperCamelCase						(    self		:			List[str]      ,    lowerCAmelCase__		:			ImageInput      ,    lowerCAmelCase__		:			bool = None      ,    lowerCAmelCase__		:			Dict[str, int] = None      ,    lowerCAmelCase__		:			PILImageResampling = None      ,    lowerCAmelCase__		:			bool = None      ,    lowerCAmelCase__		:			int = None      ,    lowerCAmelCase__		:			bool = None      ,    lowerCAmelCase__		:			float = None      ,    lowerCAmelCase__		:			bool = None      ,    lowerCAmelCase__		:			Optional[Union[float, List[float]]] = None      ,    lowerCAmelCase__		:			Optional[Union[float, List[float]]] = None      ,    lowerCAmelCase__		:			bool = None      ,    lowerCAmelCase__		:			Optional[Union[str, TensorType]] = None      ,    lowerCAmelCase__		:			Optional[ChannelDimension] = ChannelDimension.FIRST      ,    **lowerCAmelCase__		:			int      ,    )     ->   PIL.Image.Image:
   UpperCAmelCase          =	do_resize if do_resize is not None else self.do_resize
   UpperCAmelCase          =	size if size is not None else self.size
   UpperCAmelCase          =	get_size_dict(lowerCAmelCase__      ,    default_to_square=lowerCAmelCase__     )
   UpperCAmelCase          =	resample if resample is not None else self.resample
   UpperCAmelCase          =	do_center_crop if do_center_crop is not None else self.do_center_crop
   UpperCAmelCase          =	crop_size if crop_size is not None else self.crop_size
   UpperCAmelCase          =	get_size_dict(lowerCAmelCase__     )
   UpperCAmelCase          =	do_rescale if do_rescale is not None else self.do_rescale
   UpperCAmelCase          =	rescale_factor if rescale_factor is not None else self.rescale_factor
   UpperCAmelCase          =	do_normalize if do_normalize is not None else self.do_normalize
   UpperCAmelCase          =	image_mean if image_mean is not None else self.image_mean
   UpperCAmelCase          =	image_std if image_std is not None else self.image_std
   UpperCAmelCase          =	do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

   UpperCAmelCase          =	make_list_of_images(lowerCAmelCase__     )

   if not valid_images(lowerCAmelCase__     ):
     raise ValueError(
         "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
         "torch.Tensor, tf.Tensor or jax.ndarray."     )

   if do_resize and size is None:
     raise ValueError("Size must be specified if do_resize is True."     )

   if do_center_crop and crop_size is None:
     raise ValueError("Crop size must be specified if do_center_crop is True."     )

   if do_rescale and rescale_factor is None:
     raise ValueError("Rescale factor must be specified if do_rescale is True."     )

   if do_normalize and (image_mean is None or image_std is None):
     raise ValueError("Image mean and std must be specified if do_normalize is True."     )

   # PIL RGBA images are converted to RGB
   if do_convert_rgb:
     UpperCAmelCase          =	[convert_to_rgb(lowerCAmelCase__     ) for image in images]

   # All transformations expect numpy arrays.
   UpperCAmelCase          =	[to_numpy_array(lowerCAmelCase__     ) for image in images]

   if do_resize:
     UpperCAmelCase          =	[self.resize(image=lowerCAmelCase__      ,    size=lowerCAmelCase__      ,    resample=lowerCAmelCase__     ) for image in images]

   if do_center_crop:
     UpperCAmelCase          =	[self.center_crop(image=lowerCAmelCase__      ,    size=lowerCAmelCase__     ) for image in images]

   if do_rescale:
     UpperCAmelCase          =	[self.rescale(image=lowerCAmelCase__      ,    scale=lowerCAmelCase__     ) for image in images]

   if do_normalize:
     UpperCAmelCase          =	[self.normalize(image=lowerCAmelCase__      ,    mean=lowerCAmelCase__      ,    std=lowerCAmelCase__     ) for image in images]

   UpperCAmelCase          =	[to_channel_dimension_format(lowerCAmelCase__      ,    lowerCAmelCase__     ) for image in images]

   UpperCAmelCase          =	{"pixel_values": images}
   return BatchFeature(data=lowerCAmelCase__      ,    tensor_type=lowerCAmelCase__     )



