
"""simple docstring"""






import argparse

import requests
import torch
from PIL import Image

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






def 		_snake_case			(      lowercase__						:    Tuple						)    ->							Dict:


			'''simple docstring'''





			lowerCAmelCase_       :Any     	=    CLIPSegTextConfig()
			lowerCAmelCase_       :Dict     	=    CLIPSegVisionConfig(patch_size=1_6						)

			lowerCAmelCase_       :Optional[Any]     	=    True if """refined""" in model_name else False
			lowerCAmelCase_       :Optional[Any]     	=    1_6 if """rd16""" in model_name else 6_4

			lowerCAmelCase_       :List[Any]     	=    CLIPSegConfig.from_text_vision_configs(
			    __lowerCamelCase ,	__lowerCamelCase ,	use_complex_transposed_convolution=__lowerCamelCase ,	reduce_dim=__lowerCamelCase ,	)
			return config






def 		_snake_case			(      lowercase__						:    Any						)    ->							str:


			'''simple docstring'''





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

			return name






def 		_snake_case			(      lowercase__						:    List[str] ,	lowercase__						:    Dict						)    ->							str:


			'''simple docstring'''





			for key in orig_state_dict.copy().keys():
						lowerCAmelCase_       :Dict     	=    orig_state_dict.pop(__lowerCamelCase						)

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

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

			return orig_state_dict






def 		_snake_case			(      )    ->							Optional[int]:


			'''simple docstring'''





			lowerCAmelCase_       :int     	=    """http://images.cocodataset.org/val2017/000000039769.jpg"""
			lowerCAmelCase_       :Any     	=    Image.open(requests.get(__lowerCamelCase ,	stream=__lowerCamelCase						).raw						)
			return image






def 		_snake_case			(      lowercase__						:    str ,	lowercase__						:    Tuple ,	lowercase__						:    Tuple ,	lowercase__						:    int						)    ->							Optional[Any]:


			'''simple docstring'''





			lowerCAmelCase_       :List[str]     	=    get_clipseg_config(__lowerCamelCase						)
			lowerCAmelCase_       :int     	=    CLIPSegForImageSegmentation(__lowerCamelCase						)
			model.eval()

			lowerCAmelCase_       :List[str]     	=    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
			lowerCAmelCase_       :Optional[Any]     	=    convert_state_dict(__lowerCamelCase ,	__lowerCamelCase						)
			lowerCAmelCase_	,			lowerCAmelCase_       :str     	=    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}"""						)

			lowerCAmelCase_       :Any     	=    ViTImageProcessor(size=3_5_2						)
			lowerCAmelCase_       :str     	=    CLIPTokenizer.from_pretrained("""openai/clip-vit-base-patch32"""						)
			lowerCAmelCase_       :int     	=    CLIPSegProcessor(image_processor=__lowerCamelCase ,	tokenizer=__lowerCamelCase						)

			lowerCAmelCase_       :Union[str, Any]     	=    prepare_img()
			lowerCAmelCase_       :Union[str, Any]     	=    ["""a glass""", """something to fill""", """wood""", """a jar"""]

			lowerCAmelCase_       :str     	=    processor(text=__lowerCamelCase ,	images=[image] * len(__lowerCamelCase						) ,	padding="""max_length""" ,	return_tensors="""pt"""						)

			with torch.no_grad():
						lowerCAmelCase_       :Optional[Any]     	=    model(**__lowerCamelCase						)

			# verify values
			lowerCAmelCase_       :str     	=    torch.tensor([0.1110, -0.1882, 0.1645]						)
			lowerCAmelCase_       :Any     	=    torch.tensor([0.2692, -0.7197, -0.1328]						)
			if model_name == "clipseg-rd64-refined":
						lowerCAmelCase_       :Optional[Any]     	=    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":
						lowerCAmelCase_       :List[str]     	=    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":
						lowerCAmelCase_       :str     	=    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] ,	__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)








import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig


__UpperCAmelCase   						=     logging.get_logger(__name__)





class 				UpperCamelCase__		(	__SCREAMING_SNAKE_CASE	):






       """simple docstring"""


       UpperCAmelCase_				       ="speech-encoder-decoder"
       UpperCAmelCase_				       =True



       def __init__(							self ,     **_A	)				->				Dict:
          super().__init__(**_A	)
          if "encoder" not in kwargs or "decoder" not in kwargs:
             raise ValueError(
                 F'''A configuraton of type {self.model_type} cannot be instantiated because not both `encoder` and'''
                 F''' `decoder` sub-configurations are passed, but only {kwargs}'''	)

          SCREAMING_SNAKE_CASE_	=      kwargs.pop('''encoder'''	)
          SCREAMING_SNAKE_CASE_	=      encoder_config.pop('''model_type'''	)
          SCREAMING_SNAKE_CASE_	=      kwargs.pop('''decoder'''	)
          SCREAMING_SNAKE_CASE_	=      decoder_config.pop('''model_type'''	)

          SCREAMING_SNAKE_CASE_	=      AutoConfig.for_model(_A ,     **_A	)
          SCREAMING_SNAKE_CASE_	=      AutoConfig.for_model(_A ,     **_A	)
          SCREAMING_SNAKE_CASE_	=      True



       @classmethod
       def     _UpperCamelCase	(							cls ,     _A ,     _A ,     **_A	)				->				PretrainedConfig:
          logger.info('''Setting `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config'''	)
          SCREAMING_SNAKE_CASE_	=      True
          SCREAMING_SNAKE_CASE_	=      True

          return cls(encoder=encoder_config.to_dict() ,     decoder=decoder_config.to_dict() ,     **_A	)




       def     _UpperCamelCase	(							self	)				->				Union[str, Any]:
          SCREAMING_SNAKE_CASE_	=      copy.deepcopy(self.__dict__	)
          SCREAMING_SNAKE_CASE_	=      self.encoder.to_dict()
          SCREAMING_SNAKE_CASE_	=      self.decoder.to_dict()
          SCREAMING_SNAKE_CASE_	=      self.__class__.model_type
          return output


