




import argparse

import requests
import torch
from PIL import Image

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


def 						lowerCamelCase__   (		_A							):







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


def 						lowerCamelCase__   (		_A							):







	'''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__   (		_A					,     _A							):







	'''simple docstring'''





	for key in orig_state_dict.copy().keys():
		snake_case_		   =					orig_state_dict.pop(_A							)

		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(_A							)
			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__   (		):







	'''simple docstring'''





	snake_case_		   =					"http://images.cocodataset.org/val2017/000000039769.jpg"
	snake_case_		   =					Image.open(requests.get(_A					,     stream=_A							).raw							)
	return image


def 						lowerCamelCase__   (		_A					,     _A					,     _A					,     _A							):







	'''simple docstring'''





	snake_case_		   =					get_clipseg_config(_A							)
	snake_case_		   =					CLIPSegForImageSegmentation(_A							)
	model.eval()

	snake_case_		   =					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
	snake_case_		   =					convert_state_dict(_A					,     _A							)
	snake_case_   ,				snake_case_		   =					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}"							)

	snake_case_		   =					ViTImageProcessor(size=352							)
	snake_case_		   =					CLIPTokenizer.from_pretrained("openai/clip-vit-base-patch32"							)
	snake_case_		   =					CLIPSegProcessor(image_processor=_A					,     tokenizer=_A							)

	snake_case_		   =					prepare_img()
	snake_case_		   =					["a glass", "something to fill", "wood", "a jar"]

	snake_case_		   =					processor(text=_A					,     images=[image] * len(_A							)					,     padding="max_length"					,     return_tensors="pt"							)

	with torch.no_grad():
		snake_case_		   =					model(**_A							)

	# verify values
	snake_case_		   =					torch.tensor([0.11_10, -0.18_82, 0.16_45]							)
	snake_case_		   =					torch.tensor([0.26_92, -0.71_97, -0.13_28]							)
	if model_name == "clipseg-rd64-refined":
		snake_case_		   =					torch.tensor(
		    [[-10.04_07, -9.94_31, -10.26_46], [-9.97_51, -9.70_64, -9.95_86], [-9.68_91, -9.56_45, -9.96_18]]							)
	elif model_name == "clipseg-rd64":
		snake_case_		   =					torch.tensor(
		    [[-7.28_77, -7.27_11, -7.24_63], [-7.26_52, -7.27_80, -7.25_20], [-7.22_39, -7.22_04, -7.20_01]]							)
	elif model_name == "clipseg-rd16":
		snake_case_		   =					torch.tensor(
		    [[-6.39_55, -6.40_55, -6.41_51], [-6.39_11, -6.40_33, -6.41_00], [-6.34_74, -6.37_02, -6.37_62]]							)
	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__":
		lowercase__    :					Dict					=						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."
		)

		lowercase__    :					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 functools import partial

import torch

from accelerate import Accelerator, debug_launcher
from accelerate.state import AcceleratorState, GradientState
from accelerate.test_utils import require_cpu, require_huggingface_suite
from accelerate.utils import GradientAccumulationPlugin


def 						lowerCamelCase__   (		_A=2					,     _A=True					,     _A=False							):







	'''simple docstring'''





	snake_case_		   =					Accelerator(step_scheduler_with_optimizer=_A					,     split_batches=_A							)
	snake_case_		   =					torch.nn.Linear(2					,     4							)
	snake_case_		   =					torch.optim.AdamW(model.parameters()					,     lr=1.0							)
	snake_case_		   =					torch.optim.lr_scheduler.OneCycleLR(_A					,     max_lr=0.01					,     steps_per_epoch=2					,     epochs=1							)
	snake_case_   ,				snake_case_   ,				snake_case_		   =					accelerator.prepare(_A					,     _A					,     _A							)

	# Optimizer has stepped
	scheduler.step()
	if step_scheduler_with_optimizer or (num_processes == 1):
		assert (
		    scheduler.scheduler.last_epoch == num_processes
		), f"Last Epoch ({scheduler.scheduler.last_epoch}) != Num Processes ({num_processes})"
	else:
		assert (
		    scheduler.scheduler.last_epoch != num_processes
		), f"Last Epoch ({scheduler.scheduler.last_epoch}) == Num Processes ({num_processes})"


def 						lowerCamelCase__   (		_A=2					,     _A=True					,     _A=False							):







	'''simple docstring'''





	snake_case_		   =					Accelerator(step_scheduler_with_optimizer=_A					,     split_batches=_A							)
	snake_case_		   =					torch.nn.Linear(2					,     4							)
	snake_case_		   =					torch.optim.AdamW(model.parameters()					,     lr=1.0							)
	snake_case_		   =					torch.optim.lr_scheduler.LambdaLR(_A					,     lr_lambda=lambda _A							: 1 - n / 10							)
	snake_case_   ,				snake_case_   ,				snake_case_		   =					accelerator.prepare(_A					,     _A					,     _A							)

	# Optimizer has stepped
	snake_case_		   =					False
	scheduler.step()
	snake_case_		   =					1 - (num_processes if (step_scheduler_with_optimizer and not split_batches) else 1) / 10
	assert (
	    scheduler.get_last_lr()[0] == expected_lr
	), f"Wrong lr found at first step, expected {expected_lr}, got {scheduler.get_last_lr()[0]}"

	# Optimizer has not stepped
	snake_case_		   =					True
	scheduler.step()
	if not step_scheduler_with_optimizer:
		snake_case_		   =					1 - 2 / 10
	assert (
	    scheduler.get_last_lr()[0] == expected_lr
	), f"Wrong lr found at second step, expected {expected_lr}, got {scheduler.get_last_lr()[0]}"


def 						lowerCamelCase__   (		_A = 2							):







	'''simple docstring'''





	from transformers import get_linear_schedule_with_warmup

	snake_case_		   =					[1, 2, 4]
	for num_steps in steps:
		snake_case_		   =					GradientAccumulationPlugin(num_steps=_A					,     adjust_scheduler=num_steps > 1							)
		snake_case_		   =					Accelerator(gradient_accumulation_plugin=_A							)
		snake_case_		   =					torch.nn.Linear(2					,     4							)
		snake_case_		   =					torch.optim.AdamW(model.parameters()					,     lr=10.0							)
		snake_case_		   =					get_linear_schedule_with_warmup(optimizer=_A					,     num_warmup_steps=0					,     num_training_steps=20							)

		snake_case_   ,				snake_case_   ,				snake_case_		   =					accelerator.prepare(_A					,     _A					,     _A							)

		for i in range(10 * num_steps							):
			with accelerator.accumulate(_A							):
				optimizer.step()
				scheduler.step()

			if i == (10 * num_steps - 2):
				assert (
				    scheduler.get_last_lr()[0] != 0
				), f"Wrong lr found at second-to-last step, expected non-zero, got {scheduler.get_last_lr()[0]}. num_steps: {num_steps}"
		assert (
		    scheduler.get_last_lr()[0] == 0
		), f"Wrong lr found at last step, expected 0, got {scheduler.get_last_lr()[0]}"
		GradientState._reset_state()






@require_cpu
class 		UpperCAmelCase       (    unittest.TestCase      ):







						'''simple docstring'''





						def 	snake_case__						(						self							:					Tuple						):






							"""simple docstring"""






							debug_launcher(partial(__lowercase	,     num_processes=1						)	,     num_processes=1						)
							debug_launcher(partial(__lowercase	,     num_processes=1	,     split_batches=__lowercase						)	,     num_processes=1						)






						def 	snake_case__						(						self							:					int						):






							"""simple docstring"""






							debug_launcher(partial(__lowercase	,     num_processes=1						)	,     num_processes=1						)
							debug_launcher(partial(__lowercase	,     num_processes=1	,     split_batches=__lowercase						)	,     num_processes=1						)






						def 	snake_case__						(						self							:					Dict						):






							"""simple docstring"""






							debug_launcher(partial(__lowercase	,     num_processes=1	,     step_scheduler_with_optimizer=__lowercase						)	,     num_processes=1						)






						def 	snake_case__						(						self							:					Union[str, Any]						):






							"""simple docstring"""






							debug_launcher(partial(__lowercase	,     num_processes=1	,     step_scheduler_with_optimizer=__lowercase						)	,     num_processes=1						)






						def 	snake_case__						(						self							:					List[Any]						):






							"""simple docstring"""






							AcceleratorState._reset_state(__lowercase						)
							debug_launcher(__lowercase						)
							debug_launcher(partial(__lowercase	,     num_processes=1	,     split_batches=__lowercase						)	,     num_processes=1						)






						def 	snake_case__						(						self							:					Dict						):






							"""simple docstring"""






							AcceleratorState._reset_state(__lowercase						)
							debug_launcher(__lowercase						)
							debug_launcher(partial(__lowercase	,     num_processes=1	,     split_batches=__lowercase						)	,     num_processes=1						)






						def 	snake_case__						(						self							:					List[str]						):






							"""simple docstring"""






							AcceleratorState._reset_state(__lowercase						)
							debug_launcher(partial(__lowercase	,     step_scheduler_with_optimizer=__lowercase						)						)






						def 	snake_case__						(						self							:					Optional[Any]						):






							"""simple docstring"""






							AcceleratorState._reset_state(__lowercase						)
							debug_launcher(partial(__lowercase	,     step_scheduler_with_optimizer=__lowercase						)						)






						@require_huggingface_suite
						def 	snake_case__						(						self							:					Optional[Any]						):






							"""simple docstring"""






							AcceleratorState._reset_state(__lowercase						)
							debug_launcher(partial(__lowercase	,     num_processes=1						)						)
							debug_launcher(__lowercase						)
