





import argparse

import requests
import torch
from PIL import Image

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





def   lowerCamelCase__   (   UpperCamelCase__	:				str  )	->       Tuple:



					'''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(
					    _UpperCAmelCase				,			_UpperCAmelCase				,			use_complex_transposed_convolution=_UpperCAmelCase				,			reduce_dim=_UpperCAmelCase				,			)
					return config





def   lowerCamelCase__   (   UpperCamelCase__	:				Any  )	->       int:



					'''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   lowerCamelCase__   (   UpperCamelCase__	:				Union[str, Any]				,			UpperCamelCase__	:				Dict  )	->       Optional[Any]:



					'''simple docstring'''




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

										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(_UpperCAmelCase  )
															if "visual_projection" in new_name or "text_projection" in new_name:
																				_snake_case													= val.T
															_snake_case													= val

					return orig_state_dict





def   lowerCamelCase__   (   )	->       Union[str, Any]:



					'''simple docstring'''




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





def   lowerCamelCase__   (   UpperCamelCase__	:				Union[str, Any]				,			UpperCamelCase__	:				List[Any]				,			UpperCamelCase__	:				Tuple				,			UpperCamelCase__	:				List[Any]  )	->       Any:



					'''simple docstring'''




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

					_snake_case													= 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
					_snake_case													= convert_state_dict(_UpperCAmelCase				,			_UpperCAmelCase  )
					_snake_case							,   _snake_case													= 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}'''  )

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

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

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

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

					# 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]				,			_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_												=   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)










"""simple docstring"""


import warnings

from ...utils import logging
from .image_processing_vilt import ViltImageProcessor


a__    :   str    						= logging.get_logger(__name__)



class    lowercase_ (   __lowerCAmelCase							):

       def __init__(	self      ,    *__SCREAMING_SNAKE_CASE      ,    **__SCREAMING_SNAKE_CASE   ):






        '''simple docstring'''

        warnings.warn(
            "The class ViltFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"
            " use ViltImageProcessor instead."      ,    __SCREAMING_SNAKE_CASE      ,    )
        super().__init__(*__SCREAMING_SNAKE_CASE      ,    **__SCREAMING_SNAKE_CASE   )

