



'''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_						)    ->   Optional[Any]:
		_a  :   Tuple				 =							CLIPSegTextConfig()
		_a  :   Union[str, Any]				 =							CLIPSegVisionConfig(patch_size=16						)

		_a  :   Optional[Any]				 =							True if 'refined' in model_name else False
		_a  :   Optional[int]				 =							16 if 'rd16' in model_name else 64

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



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

		return name



def   __lowerCamelCase  (   lowerCAmelCase_		,    lowerCAmelCase_						)    ->   List[Any]:
		for key in orig_state_dict.copy().keys():
				_a  :   List[str]				 =							orig_state_dict.pop(lowerCAmelCase_						)

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

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

		return orig_state_dict



def   __lowerCamelCase  (   )    ->   Any:
		_a  :   Union[str, Any]				 =							'http://images.cocodataset.org/val2017/000000039769.jpg'
		_a  :   Optional[int]				 =							Image.open(requests.get(lowerCAmelCase_		,    stream=lowerCAmelCase_						).raw						)
		return image



def   __lowerCamelCase  (   lowerCAmelCase_		,    lowerCAmelCase_		,    lowerCAmelCase_		,    lowerCAmelCase_						)    ->   Union[str, Any]:
		_a  :   Union[str, Any]				 =							get_clipseg_config(lowerCAmelCase_						)
		_a  :   Tuple				 =							CLIPSegForImageSegmentation(lowerCAmelCase_						)
		model.eval()

		_a  :   Optional[Any]				 =							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
		_a  :   Optional[int]				 =							convert_state_dict(lowerCAmelCase_		,    lowerCAmelCase_						)
		_a ,      _a  :   Union[str, Any]				 =							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}"""						)

		_a  :   Optional[int]				 =							ViTImageProcessor(size=352						)
		_a  :   List[Any]				 =							CLIPTokenizer.from_pretrained('openai/clip-vit-base-patch32'						)
		_a  :   Dict				 =							CLIPSegProcessor(image_processor=lowerCAmelCase_		,    tokenizer=lowerCAmelCase_						)

		_a  :   Dict				 =							prepare_img()
		_a  :   List[str]				 =							['a glass', 'something to fill', 'wood', 'a jar']

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

		with torch.no_grad():
				_a  :   Any				 =							model(**lowerCAmelCase_						)

		# verify values
		_a  :   Dict				 =							torch.tensor([0.1_110, -0.1_882, 0.1_645]						)
		_a  :   Optional[Any]				 =							torch.tensor([0.2_692, -0.7_197, -0.1_328]						)
		if model_name == "clipseg-rd64-refined":
				_a  :   List[str]				 =							torch.tensor(
				    [[-10.0_407, -9.9_431, -10.2_646], [-9.9_751, -9.7_064, -9.9_586], [-9.6_891, -9.5_645, -9.9_618]]						)
		elif model_name == "clipseg-rd64":
				_a  :   Optional[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":
				_a  :   Any				 =							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]		,    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__":
							__lowerCAmelCase		    =     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.'''
							)

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









'''simple docstring'''

from transformers import SqueezeBertTokenizer, SqueezeBertTokenizerFast
from transformers.testing_utils import require_tokenizers, slow

from ..bert.test_tokenization_bert import BertTokenizationTest

@require_tokenizers
class 			__magic_name__		(					_UpperCamelCase     ):
							lowerCAmelCase :					int            =      SqueezeBertTokenizer
							lowerCAmelCase :					int            =      SqueezeBertTokenizerFast
							lowerCAmelCase :					List[Any]            =      True







							def 							__lowercase    (							self      : List[str]			,**_UpperCAmelCase      : Tuple	):
									return SqueezeBertTokenizerFast.from_pretrained(self.tmpdirname			,**_UpperCAmelCase	)







							@slow
							def 							__lowercase    (							self      : Tuple	):
									_a  :   Optional[int]				 =							SqueezeBertTokenizer.from_pretrained('squeezebert/squeezebert-mnli-headless'	)

									_a  :   int				 =							tokenizer.encode('sequence builders'			,add_special_tokens=_UpperCAmelCase	)
									_a  :   List[Any]				 =							tokenizer.encode('multi-sequence build'			,add_special_tokens=_UpperCAmelCase	)

									_a  :   Union[str, Any]				 =							tokenizer.build_inputs_with_special_tokens(_UpperCAmelCase	)
									_a  :   Dict				 =							tokenizer.build_inputs_with_special_tokens(_UpperCAmelCase			,_UpperCAmelCase	)

									assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
									assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [
									    tokenizer.sep_token_id
									]




