
import argparse

import requests
import torch
from PIL import Image

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

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

				"""simple docstring"""







				A__	:						Tuple								=				CLIPSegTextConfig()
				A__	:						List[Any]								=				CLIPSegVisionConfig(patch_size=16							)

				A__	:						Dict								=				True if """refined""" in model_name else False
				A__	:						Tuple								=				16 if """rd16""" in model_name else 64

				A__	:						Optional[int]								=				CLIPSegConfig.from_text_vision_configs(
				    __UpperCamelCase				,    __UpperCamelCase				,    use_complex_transposed_convolution=__UpperCamelCase				,    reduce_dim=__UpperCamelCase				,    )
				return config

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

				"""simple docstring"""







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

				return name

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

				"""simple docstring"""







				for key in orig_state_dict.copy().keys():
								A__	:						Dict								=				orig_state_dict.pop(__UpperCamelCase							)

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

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

				return orig_state_dict

def 					SCREAMING_SNAKE_CASE  (   )			->							List[str]:

				"""simple docstring"""







				A__	:						List[Any]								=				"""http://images.cocodataset.org/val2017/000000039769.jpg"""
				A__	:						Tuple								=				Image.open(requests.get(__UpperCamelCase				,    stream=__UpperCamelCase							).raw							)
				return image

def 					SCREAMING_SNAKE_CASE  (   __UpperCamelCase			:    Dict				,    __UpperCamelCase			:    List[str]				,    __UpperCamelCase			:    Any				,    __UpperCamelCase			:    Optional[int]							)			->							Dict:

				"""simple docstring"""







				A__	:						Optional[int]								=				get_clipseg_config(__UpperCamelCase							)
				A__	:						int								=				CLIPSegForImageSegmentation(__UpperCamelCase							)
				model.eval()

				A__	:						Optional[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
				A__	:						Tuple								=				convert_state_dict(__UpperCamelCase				,    __UpperCamelCase							)
				A__	:						str								=				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}"							)

				A__	:						Dict								=				ViTImageProcessor(size=3_52							)
				A__	:						Optional[int]								=				CLIPTokenizer.from_pretrained('''openai/clip-vit-base-patch32'''							)
				A__	:						List[Any]								=				CLIPSegProcessor(image_processor=__UpperCamelCase				,    tokenizer=__UpperCamelCase							)

				A__	:						str								=				prepare_img()
				A__	:						Optional[int]								=				["""a glass""", """something to fill""", """wood""", """a jar"""]

				A__	:						Union[str, Any]								=				processor(text=__UpperCamelCase				,    images=[image] * len(__UpperCamelCase							)				,    padding='''max_length'''				,    return_tensors='''pt'''							)

				with torch.no_grad():
								A__	:						Tuple								=				model(**__UpperCamelCase							)

				# verify values
				A__	:						List[Any]								=				torch.tensor([0.1_1_1_0, -0.1_8_8_2, 0.1_6_4_5]							)
				A__	:						Dict								=				torch.tensor([0.2_6_9_2, -0.7_1_9_7, -0.1_3_2_8]							)
				if model_name == "clipseg-rd64-refined":
								A__	:						Any								=				torch.tensor(
								    [[-10.04_07, -9.9_4_3_1, -10.26_46], [-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":
								A__	:						Any								=				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":
								A__	:						int								=				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]				,    __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__":
					_SCREAMING_SNAKE_CASE     :   int							 =      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.'
					)

					_SCREAMING_SNAKE_CASE     :   str							 =      parser.parse_args()
					convert_clipseg_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)







def    a__    (	A       ):
   return int(A       ) if x - int(A       ) >= 0 else int(A       ) - 1


if __name__ == "__main__":
 import doctest

 doctest.testmod()


