




import argparse

import requests
import torch
from PIL import Image

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

def 						lowercase_ (						_UpperCAmelCase		):




	"""simple docstring"""






	A_					:			Union[str, Any]						=						CLIPSegTextConfig()
	A_					:			Optional[int]						=						CLIPSegVisionConfig(patch_size=16		)

	A_					:			Dict						=						True if '''refined''' in model_name else False
	A_					:			Union[str, Any]						=						16 if '''rd16''' in model_name else 64

	A_					:			List[str]						=						CLIPSegConfig.from_text_vision_configs(
	    _UpperCAmelCase					, _UpperCAmelCase					, use_complex_transposed_convolution=_UpperCAmelCase					, reduce_dim=_UpperCAmelCase					, )
	return config

def 						lowercase_ (						_UpperCAmelCase		):




	"""simple docstring"""






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

	return name

def 						lowercase_ (						_UpperCAmelCase					, _UpperCAmelCase		):




	"""simple docstring"""






	for key in orig_state_dict.copy().keys():
		A_					:			List[str]						=						orig_state_dict.pop(_UpperCAmelCase		)

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

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

	return orig_state_dict

def 						lowercase_ (						):




	"""simple docstring"""






	A_					:			Optional[int]						=						'''http://images.cocodataset.org/val2017/000000039769.jpg'''
	A_					:			Any						=						Image.open(requests.get(_UpperCAmelCase					, stream=_UpperCAmelCase		).raw		)
	return image

def 						lowercase_ (						_UpperCAmelCase					, _UpperCAmelCase					, _UpperCAmelCase					, _UpperCAmelCase		):




	"""simple docstring"""






	A_					:			List[Any]						=						get_clipseg_config(_UpperCAmelCase		)
	A_					:			Optional[Any]						=						CLIPSegForImageSegmentation(_UpperCAmelCase		)
	model.eval()

	A_					:			int						=						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_					:			Optional[Any]						=						convert_state_dict(_UpperCAmelCase					, _UpperCAmelCase		)
	A_					:			Union[str, Any]						=						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_					:			Optional[Any]						=						ViTImageProcessor(size=352		)
	A_					:			str						=						CLIPTokenizer.from_pretrained('''openai/clip-vit-base-patch32'''		)
	A_					:			Tuple						=						CLIPSegProcessor(image_processor=_UpperCAmelCase					, tokenizer=_UpperCAmelCase		)

	A_					:			Union[str, Any]						=						prepare_img()
	A_					:			Optional[int]						=						['''a glass''', '''something to fill''', '''wood''', '''a jar''']

	A_					:			List[Any]						=						processor(text=_UpperCAmelCase					, images=[image] * len(_UpperCAmelCase		)					, padding='''max_length'''					, return_tensors='''pt'''		)

	with torch.no_grad():
		A_					:			List[str]						=						model(**_UpperCAmelCase		)

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

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











"""simple docstring"""

import enum
import inspect
import warnings
from functools import wraps
from typing import Callable, Optional

from .logging import get_logger


_lowerCamelCase					:     Optional[int]				       =    set()
_lowerCamelCase					:     Union[str, Any]				       =    get_logger(__name__)

def 						lowercase_ (						_UpperCAmelCase = None		):




	"""simple docstring"""






	def decorator(_UpperCAmelCase		):
		global _emitted_deprecation_warnings

		if inspect.isclass(_UpperCAmelCase		):
			A_					:			Union[str, Any]						=						deprecated_class_or_function.__init__
			A_					:			int						=						deprecated_class_or_function.__name__
		else:
			A_					:			Union[str, Any]						=						deprecated_class_or_function
			A_					:			List[Any]						=						deprecated_function.__name__
			# Support deprecating __init__ class method: class name instead
			A_					:			List[Any]						=						name if name != '''__init__''' else deprecated_function.__qualname__.split('''.'''		)[-2]

		A_					:			str						=						(
		    f"""{name} is deprecated and will be removed in the next major version of datasets.""" + f""" {help_message}"""
		    if help_message
		    else ''''''
		)

		@wraps(_UpperCAmelCase		)
		def wrapper(*_UpperCAmelCase					, **_UpperCAmelCase		):
			A_					:			Any						=						hash(_UpperCAmelCase		)
			if func_hash not in _emitted_deprecation_warnings:
				warnings.warn(_UpperCAmelCase					, category=_UpperCAmelCase					, stacklevel=2		)
				_emitted_deprecation_warnings.add(_UpperCAmelCase		)
			return deprecated_function(*_UpperCAmelCase					, **_UpperCAmelCase		)

		A_					:			Any						=						'''deprecated'''

		if inspect.isclass(_UpperCAmelCase		):
			A_					:			List[Any]						=						wrapper
			return deprecated_class_or_function
		else:
			return wrapper

	return decorator

class     lowercase (      enum.EnumMeta):







				def __getattribute__(							cls				:   Any ,       _lowerCamelCase				:   List[Any]    ):



					"""simple docstring"""

					A_					:			str						=						super().__getattribute__(_lowerCamelCase    )
					if isinstance(_lowerCamelCase ,       enum.Enum    ) and obj._on_access:
						obj._on_access()
					return obj







				def __getitem__(							cls				:   Any ,       _lowerCamelCase				:   Union[str, Any]    ):



					"""simple docstring"""

					A_					:			Optional[Any]						=						super().__getitem__(_lowerCamelCase    )
					if member._on_access:
						member._on_access()
					return member







				def __call__(							cls				:   str ,       _lowerCamelCase				:   str ,       _lowerCamelCase				:   List[Any]=None ,       *, _lowerCamelCase				:   int=None ,       _lowerCamelCase				:   str=None ,       _lowerCamelCase				:   Dict=None ,       _lowerCamelCase				:   Tuple=1    ):



					"""simple docstring"""

					A_					:			Dict						=						super().__call__(_lowerCamelCase ,       _lowerCamelCase ,       module=_lowerCamelCase ,       qualname=_lowerCamelCase ,       type=_lowerCamelCase ,       start=_lowerCamelCase    )
					if isinstance(_lowerCamelCase ,       enum.Enum    ) and obj._on_access:
						obj._on_access()
					return obj

class     lowercase (      enum.Enum       ,		metaclass=__UpperCAmelCase):







				def __new__(							cls				:   Optional[Any] ,       _lowerCamelCase				:   int    ):



					"""simple docstring"""

					A_					:			Tuple						=						object.__new__(cls    )
					A_					:			Union[str, Any]						=						value
					A_					:			Union[str, Any]						=						member.deprecate
					return member







				@property
				def    a_					(							self				:   Tuple    ):



					"""simple docstring"""

					return ""







				def    a_					(							self				:   Any    ):



					"""simple docstring"""

					A_					:			Optional[int]						=						F""" {self.help_message}""" if self.help_message else ''''''
					warnings.warn(
					    F"""'{self.__objclass__.__name__}' is deprecated and will be removed in the next major version of datasets."""
					    + help_message ,       _lowerCamelCase ,       stacklevel=3 ,       )





