
import argparse

import requests
import torch
from PIL import Image

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



def      _a     (   __UpperCamelCase       :  Tuple    )     ->   Optional[Any]:
			lowerCAmelCase__ :    Tuple				      =			CLIPSegTextConfig()
			lowerCAmelCase__ :    Optional[int]				      =			CLIPSegVisionConfig(patch_size=16    )

			lowerCAmelCase__ :    Any				      =			True if '''refined''' in model_name else False
			lowerCAmelCase__ :    Any				      =			16 if '''rd16''' in model_name else 64

			lowerCAmelCase__ :    str				      =			CLIPSegConfig.from_text_vision_configs(
			    _A	,_A	,use_complex_transposed_convolution=_A	,reduce_dim=_A	,)
			return config



def      _a     (   __UpperCamelCase       :  Any    )     ->   int:


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

			return name



def      _a     (   __UpperCamelCase       :  Dict	,__UpperCamelCase       :  Tuple    )     ->   Tuple:


			for key in orig_state_dict.copy().keys():
						lowerCAmelCase__ :    Union[str, Any]				      =			orig_state_dict.pop(_A    )

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

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

			return orig_state_dict



def      _a     (   )     ->   Dict:
			lowerCAmelCase__ :    Dict				      =			'''http://images.cocodataset.org/val2017/000000039769.jpg'''
			lowerCAmelCase__ :    int				      =			Image.open(requests.get(_A	,stream=_A    ).raw    )
			return image



def      _a     (   __UpperCamelCase       :  Optional[Any]	,__UpperCamelCase       :  Optional[Any]	,__UpperCamelCase       :  Optional[Any]	,__UpperCamelCase       :  Any    )     ->   List[str]:
			lowerCAmelCase__ :    List[str]				      =			get_clipseg_config(_A    )
			lowerCAmelCase__ :    Any				      =			CLIPSegForImageSegmentation(_A    )
			model.eval()

			lowerCAmelCase__ :    str				      =			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
			lowerCAmelCase__ :    Any				      =			convert_state_dict(_A	,_A    )
			lowerCAmelCase__      ,				lowerCAmelCase__ :    Union[str, Any]				      =			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}'''    )

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

			lowerCAmelCase__ :    Tuple				      =			prepare_img()
			lowerCAmelCase__ :    List[Any]				      =			['''a glass''', '''something to fill''', '''wood''', '''a jar''']

			lowerCAmelCase__ :    List[str]				      =			processor(text=_A	,images=[image] * len(_A    )	,padding='''max_length'''	,return_tensors='''pt'''    )

			with torch.no_grad():
						lowerCAmelCase__ :    Optional[int]				      =			model(**_A    )

			# verify values
			lowerCAmelCase__ :    Optional[int]				      =			torch.tensor([0.1_110, -0.1_882, 0.1_645]    )
			lowerCAmelCase__ :    Dict				      =			torch.tensor([0.2_692, -0.7_197, -0.1_328]    )
			if model_name == "clipseg-rd64-refined":
						lowerCAmelCase__ :    Dict				      =			torch.tensor(
						    [[-10.0407, -9.9_431, -10.2646], [-9.9_751, -9.7_064, -9.9_586], [-9.6_891, -9.5_645, -9.9_618]]    )
			elif model_name == "clipseg-rd64":
						lowerCAmelCase__ :    Union[str, Any]				      =			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":
						lowerCAmelCase__ :    Optional[int]				      =			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]	,_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__":
							A__       :   Optional[Any]   						=					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."""
							)

							A__       :   int   						=					parser.parse_args()
							convert_clipseg_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)





import unittest
from pathlib import Path
from tempfile import TemporaryDirectory
from unittest.mock import Mock, patch

import diffusers.utils.hub_utils





class 						lowercase       (  unittest.TestCase					):
					@patch('''diffusers.utils.hub_utils.get_full_repo_name'''    )
					def lowercase_			(		self ,	SCREAMING_SNAKE_CASE__    ):
								"""simple docstring"""







								lowerCAmelCase__ :    str				      =			'''full_repo_name'''
								with TemporaryDirectory() as tmpdir:
											# Dummy args values
											lowerCAmelCase__ :    Optional[int]				      =			Mock()
											lowerCAmelCase__ :    Optional[int]				      =			tmpdir
											lowerCAmelCase__ :    Optional[Any]				      =			0
											lowerCAmelCase__ :    Optional[int]				      =			'''hub_token'''
											lowerCAmelCase__ :    Tuple				      =			'''dataset_name'''
											lowerCAmelCase__ :    int				      =			0.01
											lowerCAmelCase__ :    Optional[int]				      =			100000
											lowerCAmelCase__ :    Any				      =			10000
											lowerCAmelCase__ :    Union[str, Any]				      =			0.01
											lowerCAmelCase__ :    int				      =			0.02
											lowerCAmelCase__ :    Union[str, Any]				      =			0.03
											lowerCAmelCase__ :    str				      =			0.0_005
											lowerCAmelCase__ :    Dict				      =			0.000_001
											lowerCAmelCase__ :    Tuple				      =			1
											lowerCAmelCase__ :    Optional[int]				      =			10
											lowerCAmelCase__ :    int				      =			0.001
											lowerCAmelCase__ :    Union[str, Any]				      =			0.1
											lowerCAmelCase__ :    Tuple				      =			0.2
											lowerCAmelCase__ :    Optional[int]				      =			True

											# Model card mush be rendered and saved
											diffusers.utils.hub_utils.create_model_card(SCREAMING_SNAKE_CASE__ ,	model_name='''model_name'''    )
											self.assertTrue((Path(SCREAMING_SNAKE_CASE__    ) / '''README.md''').is_file()    )



