



'''simple docstring'''







import argparse

import requests
import torch
from PIL import Image

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





def     snake_case__			(lowerCAmelCase_						):
    UpperCamelCase__	     =      CLIPSegTextConfig()
    UpperCamelCase__	     =      CLIPSegVisionConfig(patch_size=16						)

    UpperCamelCase__	     =      True if "refined" in model_name else False
    UpperCamelCase__	     =      16 if "rd16" in model_name else 64

    UpperCamelCase__	     =      CLIPSegConfig.from_text_vision_configs(
        lowerCAmelCase_       ,	lowerCAmelCase_       ,	use_complex_transposed_convolution=lowerCAmelCase_       ,	reduce_dim=lowerCAmelCase_       ,	)
    return config





def     snake_case__			(lowerCAmelCase_						):
    # update prefixes
    if "clip_model" in name:
        UpperCamelCase__	     =      name.replace("clip_model"       ,	"clip"						)
    if "transformer" in name:
        if "visual" in name:
            UpperCamelCase__	     =      name.replace("visual.transformer"       ,	"vision_model"						)
        else:
            UpperCamelCase__	     =      name.replace("transformer"       ,	"text_model"						)
    if "resblocks" in name:
        UpperCamelCase__	     =      name.replace("resblocks"       ,	"encoder.layers"						)
    if "ln_1" in name:
        UpperCamelCase__	     =      name.replace("ln_1"       ,	"layer_norm1"						)
    if "ln_2" in name:
        UpperCamelCase__	     =      name.replace("ln_2"       ,	"layer_norm2"						)
    if "c_fc" in name:
        UpperCamelCase__	     =      name.replace("c_fc"       ,	"fc1"						)
    if "c_proj" in name:
        UpperCamelCase__	     =      name.replace("c_proj"       ,	"fc2"						)
    if "attn" in name and "self" not in name:
        UpperCamelCase__	     =      name.replace("attn"       ,	"self_attn"						)
    # text encoder
    if "token_embedding" in name:
        UpperCamelCase__	     =      name.replace("token_embedding"       ,	"text_model.embeddings.token_embedding"						)
    if "positional_embedding" in name and "visual" not in name:
        UpperCamelCase__	     =      name.replace("positional_embedding"       ,	"text_model.embeddings.position_embedding.weight"						)
    if "ln_final" in name:
        UpperCamelCase__	     =      name.replace("ln_final"       ,	"text_model.final_layer_norm"						)
    # vision encoder
    if "visual.class_embedding" in name:
        UpperCamelCase__	     =      name.replace("visual.class_embedding"       ,	"vision_model.embeddings.class_embedding"						)
    if "visual.conv1" in name:
        UpperCamelCase__	     =      name.replace("visual.conv1"       ,	"vision_model.embeddings.patch_embedding"						)
    if "visual.positional_embedding" in name:
        UpperCamelCase__	     =      name.replace("visual.positional_embedding"       ,	"vision_model.embeddings.position_embedding.weight"						)
    if "visual.ln_pre" in name:
        UpperCamelCase__	     =      name.replace("visual.ln_pre"       ,	"vision_model.pre_layrnorm"						)
    if "visual.ln_post" in name:
        UpperCamelCase__	     =      name.replace("visual.ln_post"       ,	"vision_model.post_layernorm"						)
    # projection layers
    if "visual.proj" in name:
        UpperCamelCase__	     =      name.replace("visual.proj"       ,	"visual_projection.weight"						)
    if "text_projection" in name:
        UpperCamelCase__	     =      name.replace("text_projection"       ,	"text_projection.weight"						)
    # decoder
    if "trans_conv" in name:
        UpperCamelCase__	     =      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__	     =      "decoder." + name
    if "blocks" in name:
        UpperCamelCase__	     =      name.replace("blocks"       ,	"decoder.layers"						)
    if "linear1" in name:
        UpperCamelCase__	     =      name.replace("linear1"       ,	"mlp.fc1"						)
    if "linear2" in name:
        UpperCamelCase__	     =      name.replace("linear2"       ,	"mlp.fc2"						)
    if "norm1" in name and "layer_" not in name:
        UpperCamelCase__	     =      name.replace("norm1"       ,	"layer_norm1"						)
    if "norm2" in name and "layer_" not in name:
        UpperCamelCase__	     =      name.replace("norm2"       ,	"layer_norm2"						)

    return name





def     snake_case__			(lowerCAmelCase_       ,	lowerCAmelCase_						):
    for key in orig_state_dict.copy().keys():
        UpperCamelCase__	     =      orig_state_dict.pop(lowerCAmelCase_						)

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

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

    return orig_state_dict





def     snake_case__			():
    UpperCamelCase__	     =      "http://images.cocodataset.org/val2017/000000039769.jpg"
    UpperCamelCase__	     =      Image.open(requests.get(lowerCAmelCase_       ,	stream=lowerCAmelCase_						).raw						)
    return image





def     snake_case__			(lowerCAmelCase_       ,	lowerCAmelCase_       ,	lowerCAmelCase_       ,	lowerCAmelCase_						):
    UpperCamelCase__	     =      get_clipseg_config(lowerCAmelCase_						)
    UpperCamelCase__	     =      CLIPSegForImageSegmentation(lowerCAmelCase_						)
    model.eval()

    UpperCamelCase__	     =      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__	     =      convert_state_dict(lowerCAmelCase_       ,	lowerCAmelCase_						)
    UpperCamelCase__     ,	UpperCamelCase__	     =      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__	     =      ViTImageProcessor(size=3_52						)
    UpperCamelCase__	     =      CLIPTokenizer.from_pretrained("openai/clip-vit-base-patch32"						)
    UpperCamelCase__	     =      CLIPSegProcessor(image_processor=lowerCAmelCase_       ,	tokenizer=lowerCAmelCase_						)

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

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

    with torch.no_grad():
        UpperCamelCase__	     =      model(**lowerCAmelCase_						)

    # verify values
    UpperCamelCase__	     =      torch.tensor([0.11_10, -0.18_82, 0.16_45]						)
    UpperCamelCase__	     =      torch.tensor([0.26_92, -0.71_97, -0.13_28]						)
    if model_name == "clipseg-rd64-refined":
        UpperCamelCase__	     =      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":
        UpperCamelCase__	     =      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":
        UpperCamelCase__	     =      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]       ,	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__":
      lowercase__  :	Union[str, 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.'
      )

      lowercase__  :	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

# New Code #
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
from accelerate.utils import find_executable_batch_size


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to combine both the gradient accumulation
# and automatic batch size finder utilities of Accelerate to perfrom
# automatic gradient accumulation
#
# 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)
#
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# 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
#
########################################################################

lowercase__  :	Optional[Any]            =      32





def     snake_case__			(lowerCAmelCase_       ,	lowerCAmelCase_ = 16						):
    UpperCamelCase__	     =      AutoTokenizer.from_pretrained("bert-base-cased"						)
    UpperCamelCase__	     =      load_dataset("glue"       ,	"mrpc"						)

    def tokenize_function(lowerCAmelCase_						):
        # max_length=None => use the model max length (it's actually the default)
        UpperCamelCase__	     =      tokenizer(examples["sentence1"]       ,	examples["sentence2"]       ,	truncation=lowerCAmelCase_       ,	max_length=lowerCAmelCase_						)
        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():
        UpperCamelCase__	     =      datasets.map(
            lowerCAmelCase_       ,	batched=lowerCAmelCase_       ,	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
    UpperCamelCase__	     =      tokenized_datasets.rename_column("label"       ,	"labels"						)

    def collate_fn(lowerCAmelCase_						):
        # When using mixed precision we want round multiples of 8/16
        if accelerator.mixed_precision == "fp8":
            UpperCamelCase__	     =      16
        elif accelerator.mixed_precision != "no":
            UpperCamelCase__	     =      8
        else:
            UpperCamelCase__	     =      None

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

    # Instantiate dataloaders.
    UpperCamelCase__	     =      DataLoader(
        tokenized_datasets["train"]       ,	shuffle=lowerCAmelCase_       ,	collate_fn=lowerCAmelCase_       ,	batch_size=lowerCAmelCase_						)
    UpperCamelCase__	     =      DataLoader(
        tokenized_datasets["validation"]       ,	shuffle=lowerCAmelCase_       ,	collate_fn=lowerCAmelCase_       ,	batch_size=lowerCAmelCase_						)

    return train_dataloader, eval_dataloader


# For testing only
if os.environ.get('TESTING_MOCKED_DATALOADERS', None) == "1":
      from accelerate.test_utils.training import mocked_dataloaders

      lowercase__  :	Dict            =      mocked_dataloaders  # noqa: F811





def     snake_case__			(lowerCAmelCase_       ,	lowerCAmelCase_						):
    # For testing only
    if os.environ.get("TESTING_MOCKED_DATALOADERS"       ,	lowerCAmelCase_						) == "1":
        UpperCamelCase__	     =      2
    # Initialize accelerator
    UpperCamelCase__	     =      Accelerator(cpu=args.cpu       ,	mixed_precision=args.mixed_precision						)
    # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
    UpperCamelCase__	     =      config["lr"]
    UpperCamelCase__	     =      int(config["num_epochs"]						)
    UpperCamelCase__	     =      int(config["seed"]						)
    UpperCamelCase__	     =      int(config["batch_size"]						)

    UpperCamelCase__	     =      evaluate.load("glue"       ,	"mrpc"						)

    # New Code #
    # We use the `find_executable_batch_size` decorator, passing in the desired observed batch size
    # to train on. If a CUDA OOM error occurs, it will retry this loop cutting the batch size in
    # half each time. From this, we can calculate the number of gradient accumulation steps needed
    # and modify the Accelerator object as a result
    @find_executable_batch_size(starting_batch_size=int(lowerCAmelCase_						)						)
    def inner_training_loop(lowerCAmelCase_						):
        # Since we need to modify the outside accelerator object, we need to bring it
        # to the local scope
        nonlocal accelerator

        # We can calculate the number of gradient accumulation steps based on the current
        # batch size vs the starting batch size
        UpperCamelCase__	     =      observed_batch_size // batch_size

        # And then set it in the Accelerator directly:
        UpperCamelCase__	     =      num_gradient_accumulation_steps

        # Next we need to free all of the stored model references in the Accelerator each time
        accelerator.free_memory()

        # And set the seed so our results are reproducable each reset
        set_seed(lowerCAmelCase_						)

        # Instantiate the model (we build the model here so that the seed also control new weights initialization)
        UpperCamelCase__	     =      AutoModelForSequenceClassification.from_pretrained("bert-base-cased"       ,	return_dict=lowerCAmelCase_						)

        # 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).
        UpperCamelCase__	     =      model.to(accelerator.device						)

        # Instantiate optimizer
        UpperCamelCase__	     =      AdamW(params=model.parameters()       ,	lr=lowerCAmelCase_						)
        UpperCamelCase__     ,	UpperCamelCase__	     =      get_dataloaders(lowerCAmelCase_       ,	lowerCAmelCase_						)

        # Instantiate scheduler
        UpperCamelCase__	     =      get_linear_schedule_with_warmup(
            optimizer=lowerCAmelCase_       ,	num_warmup_steps=1_00       ,	num_training_steps=(len(lowerCAmelCase_						) * num_epochs)       ,	)

        # 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.
        UpperCamelCase__     ,	UpperCamelCase__     ,	UpperCamelCase__     ,	UpperCamelCase__     ,	UpperCamelCase__	     =      accelerator.prepare(
            lowerCAmelCase_       ,	lowerCAmelCase_       ,	lowerCAmelCase_       ,	lowerCAmelCase_       ,	lowerCAmelCase_						)

        # Now we train the model
        for epoch in range(lowerCAmelCase_						):
            model.train()
            for step, batch in enumerate(lowerCAmelCase_						):
                # And perform gradient accumulation
                with accelerator.accumulate(lowerCAmelCase_						):
                    # We could avoid this line since we set the accelerator with `device_placement=True`.
                    batch.to(accelerator.device						)
                    UpperCamelCase__	     =      model(**lowerCAmelCase_						)
                    UpperCamelCase__	     =      outputs.loss
                    accelerator.backward(lowerCAmelCase_						)
                    optimizer.step()
                    lr_scheduler.step()
                    optimizer.zero_grad()

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

            UpperCamelCase__	     =      metric.compute()
            # Use accelerator.print to print only on the main process.
            accelerator.print(F'epoch {epoch}:'       ,	lowerCAmelCase_						)

    # New Code #
    # And call it at the end with no arguments
    # Note: You could also refactor this outside of your training loop function
    inner_training_loop()





def     snake_case__			():
    UpperCamelCase__	     =      argparse.ArgumentParser(description="Simple example of training script."						)
    parser.add_argument(
        "--mixed_precision"       ,	type=lowerCAmelCase_       ,	default=lowerCAmelCase_       ,	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."						)
    UpperCamelCase__	     =      parser.parse_args()
    # New Code #
    # We modify the starting batch size to be an observed batch size of 256, to guarentee an initial CUDA OOM
    UpperCamelCase__	     =      {"lr": 2e-5, "num_epochs": 3, "seed": 42, "batch_size": 2_56}
    training_function(lowerCAmelCase_       ,	lowerCAmelCase_						)


if __name__ == "__main__":
      main()




