



'''simple docstring'''
import argparse

import requests
import torch
from PIL import Image

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


def 	lowercase_						( lowerCAmelCase__						:	Optional[Any]			):

			"""simple docstring"""





			__UpperCAmelCase :   Optional[Any]    					=  CLIPSegTextConfig()
			__UpperCAmelCase :   Tuple    					=  CLIPSegVisionConfig(patch_size=16			)

			__UpperCAmelCase :   Union[str, Any]    					=  True if """refined""" in model_name else False
			__UpperCAmelCase :   Optional[Any]    					=  16 if """rd16""" in model_name else 64

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


def 	lowercase_						( lowerCAmelCase__						:	str			):

			"""simple docstring"""





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

			return name


def 	lowercase_						( lowerCAmelCase__						:	Tuple      ,				lowerCAmelCase__						:	Union[str, Any]			):

			"""simple docstring"""





			for key in orig_state_dict.copy().keys():
						__UpperCAmelCase :   str    					=  orig_state_dict.pop(lowerCAmelCase__			)

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

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

			return orig_state_dict


def 	lowercase_						( ):

			"""simple docstring"""





			__UpperCAmelCase :   Union[str, Any]    					=  """http://images.cocodataset.org/val2017/000000039769.jpg"""
			__UpperCAmelCase :   List[Any]    					=  Image.open(requests.get(lowerCAmelCase__      ,				stream=lowerCAmelCase__			).raw			)
			return image


def 	lowercase_						( lowerCAmelCase__						:	int      ,				lowerCAmelCase__						:	Tuple      ,				lowerCAmelCase__						:	Tuple      ,				lowerCAmelCase__						:	str			):

			"""simple docstring"""





			__UpperCAmelCase :   Dict    					=  get_clipseg_config(lowerCAmelCase__			)
			__UpperCAmelCase :   Optional[int]    					=  CLIPSegForImageSegmentation(lowerCAmelCase__			)
			model.eval()

			__UpperCAmelCase :   Dict    					=  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
			__UpperCAmelCase :   List[Any]    					=  convert_state_dict(lowerCAmelCase__      ,				lowerCAmelCase__			)
			__UpperCAmelCase					,	__UpperCAmelCase :   Optional[int]    					=  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}'			)

			__UpperCAmelCase :   Dict    					=  ViTImageProcessor(size=352			)
			__UpperCAmelCase :   Any    					=  CLIPTokenizer.from_pretrained("""openai/clip-vit-base-patch32"""			)
			__UpperCAmelCase :   List[Any]    					=  CLIPSegProcessor(image_processor=lowerCAmelCase__      ,				tokenizer=lowerCAmelCase__			)

			__UpperCAmelCase :   str    					=  prepare_img()
			__UpperCAmelCase :   Dict    					=  ["""a glass""", """something to fill""", """wood""", """a jar"""]

			__UpperCAmelCase :   Union[str, Any]    					=  processor(text=lowerCAmelCase__      ,				images=[image] * len(lowerCAmelCase__			)      ,				padding="""max_length"""      ,				return_tensors="""pt"""			)

			with torch.no_grad():
						__UpperCAmelCase :   List[Any]    					=  model(**lowerCAmelCase__			)

			# verify values
			__UpperCAmelCase :   Optional[Any]    					=  torch.tensor([0.1_110, -0.1_882, 0.1_645]			)
			__UpperCAmelCase :   Optional[Any]    					=  torch.tensor([0.2_692, -0.7_197, -0.1_328]			)
			if model_name == "clipseg-rd64-refined":
						__UpperCAmelCase :   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":
						__UpperCAmelCase :   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":
						__UpperCAmelCase :   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]      ,				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)









'''simple docstring'''
import inspect
import os
import unittest

import torch

import accelerate
from accelerate import debug_launcher
from accelerate.test_utils import (
    execute_subprocess_async,
    require_cpu,
    require_multi_gpu,
    require_single_gpu,
    test_sync,
)
from accelerate.utils import patch_environment



class _A	(     unittest.TestCase     ):





		def 				__A							(  self					)							->  Optional[Any]:

					'''simple docstring'''
					__UpperCAmelCase :   int    					=  inspect.getfile(accelerate.test_utils					)
					__UpperCAmelCase :   int    					=  os.path.sep.join(mod_file.split(os.path.sep					)[:-1] + ["""scripts""", """test_sync.py"""]					)





		@require_cpu
		def 				__A							(  self					)							->  Optional[Any]:

					'''simple docstring'''
					debug_launcher(test_sync.main			,							num_processes=1					)





		@require_cpu
		def 				__A							(  self					)							->  Optional[Any]:

					'''simple docstring'''
					debug_launcher(test_sync.main					)





		@require_single_gpu
		def 				__A							(  self					)							->  Tuple:

					'''simple docstring'''
					test_sync.main()





		@require_multi_gpu
		def 				__A							(  self					)							->  List[Any]:

					'''simple docstring'''
					print(f'Found {torch.cuda.device_count()} devices.'					)
					__UpperCAmelCase :   List[str]    					=  ["""torchrun""", f'--nproc_per_node={torch.cuda.device_count()}', self.test_file_path]
					with patch_environment(omp_num_threads=1					):
								execute_subprocess_async(__UpperCAmelCase			,							env=os.environ.copy()					)




