



"""simple docstring"""






import argparse

import requests
import torch
from PIL import Image

from transformers import (
    CLIPSegConfig,
    CLIPSegForImageSegmentation,
    CLIPSegProcessor,
    CLIPSegTextConfig,
    CLIPSegVisionConfig,
    CLIPTokenizer,
    ViTImageProcessor,
)
def      A      (			_A							):




					"""simple docstring"""
					snake_case_						:Tuple								=						CLIPSegTextConfig()
					snake_case_						:Union[str, Any]								=						CLIPSegVisionConfig(patch_size=16							)

					snake_case_						:int								=						True if "refined" in model_name else False
					snake_case_						:Dict								=						16 if "rd16" in model_name else 64

					snake_case_						:Any								=						CLIPSegConfig.from_text_vision_configs(
					    _A,     _A,     use_complex_transposed_convolution=_A,     reduce_dim=_A,     )
					return config
def      A      (			_A							):




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

					return name
def      A      (			_A,     _A							):




					"""simple docstring"""
					for key in orig_state_dict.copy().keys():
										snake_case_						:List[Any]								=						orig_state_dict.pop(_A							)

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

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

					return orig_state_dict
def      A      (			):




					"""simple docstring"""
					snake_case_						:Optional[Any]								=						"http://images.cocodataset.org/val2017/000000039769.jpg"
					snake_case_						:Optional[Any]								=						Image.open(requests.get(_A,     stream=_A							).raw							)
					return image
def      A      (			_A,     _A,     _A,     _A							):




					"""simple docstring"""
					snake_case_						:Tuple								=						get_clipseg_config(_A							)
					snake_case_						:Optional[Any]								=						CLIPSegForImageSegmentation(_A							)
					model.eval()

					snake_case_						:Optional[Any]								=						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_						:Dict								=						convert_state_dict(_A,     _A							)
					snake_case_	,  snake_case_						:Optional[int]								=						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_						:Dict								=						ViTImageProcessor(size=352							)
					snake_case_						:Any								=						CLIPTokenizer.from_pretrained("openai/clip-vit-base-patch32"							)
					snake_case_						:Optional[int]								=						CLIPSegProcessor(image_processor=_A,     tokenizer=_A							)

					snake_case_						:Union[str, Any]								=						prepare_img()
					snake_case_						:List[str]								=						["a glass", "something to fill", "wood", "a jar"]

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

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

					# verify values
					snake_case_						:str								=						torch.tensor([0.1_110, -0.1_882, 0.1_645]							)
					snake_case_						:Optional[int]								=						torch.tensor([0.2_692, -0.7_197, -0.1_328]							)
					if model_name == "clipseg-rd64-refined":
										snake_case_						:Tuple								=						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":
										snake_case_						: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":
										snake_case_						: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],     _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__":
				__UpperCAmelCase      :      List[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.'
				)

				__UpperCAmelCase      :      List[str]   					=					parser.parse_args()
				convert_clipseg_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)






"""simple docstring"""






import argparse
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType


########################################################################
# This is a fully working simple example to use Accelerate
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# This example also demonstrates the checkpointing and sharding capabilities
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


__UpperCAmelCase      :      Dict   					=					16
__UpperCAmelCase      :      List[Any]   					=					32
def      A      (			_A,     _A							):




					"""simple docstring"""
					# Initialize accelerator
					if args.with_tracking:
										snake_case_						:Union[str, Any]								=						Accelerator(
										    cpu=args.cpu,     mixed_precision=args.mixed_precision,     log_with="all",     project_dir=args.project_dir							)
					else:
										snake_case_						:Tuple								=						Accelerator(cpu=args.cpu,     mixed_precision=args.mixed_precision							)

					if hasattr(args.checkpointing_steps,     "isdigit"							):
										if args.checkpointing_steps == "epoch":
															snake_case_						:Tuple								=						args.checkpointing_steps
										elif args.checkpointing_steps.isdigit():
															snake_case_						:int								=						int(args.checkpointing_steps							)
										else:
															raise ValueError(
															    F'''Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.'''							)
					else:
										snake_case_						:Tuple								=						None
					# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
					snake_case_						:Union[str, Any]								=						config["lr"]
					snake_case_						:Any								=						int(config["num_epochs"]							)
					snake_case_						:Optional[int]								=						int(config["seed"]							)
					snake_case_						:List[Any]								=						int(config["batch_size"]							)

					# We need to initialize the trackers we use, and also store our configuration
					if args.with_tracking:
										snake_case_						:Union[str, Any]								=						os.path.split(_A							)[-1].split("."							)[0]
										accelerator.init_trackers(_A,     _A							)

					snake_case_						:Any								=						AutoTokenizer.from_pretrained("bert-base-cased"							)
					snake_case_						:Any								=						load_dataset("glue",     "mrpc"							)
					snake_case_						:Optional[Any]								=						evaluate.load("glue",     "mrpc"							)

					def tokenize_function(_A							):
										# max_length=None => use the model max length (it's actually the default)
										snake_case_						:Any								=						tokenizer(examples["sentence1"],     examples["sentence2"],     truncation=_A,     max_length=_A							)
										return outputs

					# Apply the method we just defined to all the examples in all the splits of the dataset
					# starting with the main process first:
					with accelerator.main_process_first():
										snake_case_						:int								=						datasets.map(
										    _A,     batched=_A,     remove_columns=["idx", "sentence1", "sentence2"],     )

					# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
					# transformers library
					snake_case_						:Any								=						tokenized_datasets.rename_column("label",     "labels"							)

					# If the batch size is too big we use gradient accumulation
					snake_case_						:List[Any]								=						1
					if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
										snake_case_						:Optional[int]								=						batch_size // MAX_GPU_BATCH_SIZE
										snake_case_						:List[str]								=						MAX_GPU_BATCH_SIZE

					def collate_fn(_A							):
										# On TPU it's best to pad everything to the same length or training will be very slow.
										snake_case_						:Optional[int]								=						128 if accelerator.distributed_type == DistributedType.TPU else None
										# When using mixed precision we want round multiples of 8/16
										if accelerator.mixed_precision == "fp8":
															snake_case_						:Union[str, Any]								=						16
										elif accelerator.mixed_precision != "no":
															snake_case_						:List[Any]								=						8
										else:
															snake_case_						:Any								=						None

										return tokenizer.pad(
										    _A,     padding="longest",     max_length=_A,     pad_to_multiple_of=_A,     return_tensors="pt",     )

					# Instantiate dataloaders.
					snake_case_						:str								=						DataLoader(
					    tokenized_datasets["train"],     shuffle=_A,     collate_fn=_A,     batch_size=_A							)
					snake_case_						:Any								=						DataLoader(
					    tokenized_datasets["validation"],     shuffle=_A,     collate_fn=_A,     batch_size=_A							)

					set_seed(_A							)

					# Instantiate the model (we build the model here so that the seed also control new weights initialization)
					snake_case_						:Optional[Any]								=						AutoModelForSequenceClassification.from_pretrained("bert-base-cased",     return_dict=_A							)

					# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
					# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
					# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
					snake_case_						:str								=						model.to(accelerator.device							)

					# Instantiate optimizer
					snake_case_						:List[Any]								=						AdamW(params=model.parameters(),     lr=_A							)

					# Instantiate scheduler
					snake_case_						:Tuple								=						get_linear_schedule_with_warmup(
					    optimizer=_A,     num_warmup_steps=100,     num_training_steps=(len(_A							) * num_epochs) // gradient_accumulation_steps,     )

					# Prepare everything
					# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
					# prepare method.
					snake_case_	,  snake_case_	,  snake_case_	,  snake_case_	,  snake_case_						:Optional[Any]								=						accelerator.prepare(
					    _A,     _A,     _A,     _A,     _A							)

					# We need to keep track of how many total steps we have iterated over
					snake_case_						:Optional[int]								=						0
					# We also need to keep track of the stating epoch so files are named properly
					snake_case_						:int								=						0

					# Potentially load in the weights and states from a previous save
					if args.resume_from_checkpoint:
										if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
															accelerator.print(F'''Resumed from checkpoint: {args.resume_from_checkpoint}'''							)
															accelerator.load_state(args.resume_from_checkpoint							)
															snake_case_						:str								=						os.path.basename(args.resume_from_checkpoint							)
										else:
															# Get the most recent checkpoint
															snake_case_						:int								=						[f.name for f in os.scandir(os.getcwd()							) if f.is_dir()]
															dirs.sort(key=os.path.getctime							)
															snake_case_						:Tuple								=						dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
										# Extract `epoch_{i}` or `step_{i}`
										snake_case_						:Optional[int]								=						os.path.splitext(_A							)[0]

										if "epoch" in training_difference:
															snake_case_						:Union[str, Any]								=						int(training_difference.replace("epoch_",     ""							)							) + 1
															snake_case_						:List[str]								=						None
										else:
															snake_case_						:Tuple								=						int(training_difference.replace("step_",     ""							)							)
															snake_case_						:Optional[int]								=						resume_step // len(_A							)
															resume_step -= starting_epoch * len(_A							)

    # Now we train the model
					for epoch in range(_A,     _A							):
										model.train()
										if args.with_tracking:
															snake_case_						:Optional[Any]								=						0
										if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
															# We need to skip steps until we reach the resumed step
															snake_case_						:Dict								=						accelerator.skip_first_batches(_A,     _A							)
															overall_step += resume_step
										else:
															# After the first iteration though, we need to go back to the original dataloader
															snake_case_						:Optional[Any]								=						train_dataloader
										for step, batch in enumerate(_A							):
															# We could avoid this line since we set the accelerator with `device_placement=True`.
															batch.to(accelerator.device							)
															snake_case_						:Union[str, Any]								=						model(**_A							)
															snake_case_						:str								=						outputs.loss
															snake_case_						:int								=						loss / gradient_accumulation_steps
															# We keep track of the loss at each epoch
															if args.with_tracking:
																				total_loss += loss.detach().float()
															accelerator.backward(_A							)
															if step % gradient_accumulation_steps == 0:
																				optimizer.step()
																				lr_scheduler.step()
																				optimizer.zero_grad()

															overall_step += 1

															if isinstance(_A,     _A							):
																				snake_case_						:List[str]								=						F'''step_{overall_step}'''
																				if overall_step % checkpointing_steps == 0:
																									if args.output_dir is not None:
																														snake_case_						:Tuple								=						os.path.join(args.output_dir,     _A							)
																									accelerator.save_state(_A							)

										model.eval()
										for step, batch in enumerate(_A							):
															# We could avoid this line since we set the accelerator with `device_placement=True`.
															batch.to(accelerator.device							)
															with torch.no_grad():
																				snake_case_						:str								=						model(**_A							)
															snake_case_						:str								=						outputs.logits.argmax(dim=-1							)
															snake_case_	,  snake_case_						:List[str]								=						accelerator.gather_for_metrics((predictions, batch["labels"])							)
															metric.add_batch(
															    predictions=_A,     references=_A,     )

										snake_case_						:int								=						metric.compute()
										# Use accelerator.print to print only on the main process.
										accelerator.print(F'''epoch {epoch}:''',     _A							)
										if args.with_tracking:
															accelerator.log(
															    {
															        "accuracy": eval_metric["accuracy"],
															        "f1": eval_metric["f1"],
															        "train_loss": total_loss.item() / len(_A							),
															        "epoch": epoch,
															    },     step=_A,     )

										if checkpointing_steps == "epoch":
															snake_case_						:str								=						F'''epoch_{epoch}'''
															if args.output_dir is not None:
																				snake_case_						:Any								=						os.path.join(args.output_dir,     _A							)
															accelerator.save_state(_A							)

					if args.with_tracking:
										accelerator.end_training()
def      A      (			):




					"""simple docstring"""
					snake_case_						:str								=						argparse.ArgumentParser(description="Simple example of training script."							)
					parser.add_argument(
					    "--mixed_precision",     type=_A,     default=_A,     choices=["no", "fp16", "bf16", "fp8"],     help="Whether to use mixed precision. Choose"
					    "between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."
					    "and an Nvidia Ampere GPU.",     )
					parser.add_argument("--cpu",     action="store_true",     help="If passed, will train on the CPU."							)
					parser.add_argument(
					    "--checkpointing_steps",     type=_A,     default=_A,     help="Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch.",     )
					parser.add_argument(
					    "--resume_from_checkpoint",     type=_A,     default=_A,     help="If the training should continue from a checkpoint folder.",     )
					parser.add_argument(
					    "--with_tracking",     action="store_true",     help="Whether to load in all available experiment trackers from the environment and use them for logging.",     )
					parser.add_argument(
					    "--output_dir",     type=_A,     default=".",     help="Optional save directory where all checkpoint folders will be stored. Default is the current working directory.",     )
					parser.add_argument(
					    "--project_dir",     type=_A,     default="logs",     help="Location on where to store experiment tracking logs` and relevent project information",     )
					snake_case_						:Optional[int]								=						parser.parse_args()
					snake_case_						:Optional[Any]								=						{"lr": 2e-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
					training_function(_A,     _A							)


if __name__ == "__main__":
				main()

