





import argparse
import re

import numpy as np
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    SamConfig,
    SamImageProcessor,
    SamModel,
    SamProcessor,
    SamVisionConfig,
)


__lowercase	: Dict       ={
    """iou_prediction_head.layers.0""": """iou_prediction_head.proj_in""",
    """iou_prediction_head.layers.1""": """iou_prediction_head.layers.0""",
    """iou_prediction_head.layers.2""": """iou_prediction_head.proj_out""",
    """mask_decoder.output_upscaling.0""": """mask_decoder.upscale_conv1""",
    """mask_decoder.output_upscaling.1""": """mask_decoder.upscale_layer_norm""",
    """mask_decoder.output_upscaling.3""": """mask_decoder.upscale_conv2""",
    """mask_downscaling.0""": """mask_embed.conv1""",
    """mask_downscaling.1""": """mask_embed.layer_norm1""",
    """mask_downscaling.3""": """mask_embed.conv2""",
    """mask_downscaling.4""": """mask_embed.layer_norm2""",
    """mask_downscaling.6""": """mask_embed.conv3""",
    """point_embeddings""": """point_embed""",
    """pe_layer.positional_encoding_gaussian_matrix""": """shared_embedding.positional_embedding""",
    """image_encoder""": """vision_encoder""",
    """neck.0""": """neck.conv1""",
    """neck.1""": """neck.layer_norm1""",
    """neck.2""": """neck.conv2""",
    """neck.3""": """neck.layer_norm2""",
    """patch_embed.proj""": """patch_embed.projection""",
    """.norm""": """.layer_norm""",
    """blocks""": """layers""",
}




def      a__			(       lowercase__	):






   '''simple docstring'''






   UpperCAmelCase_           ={}
   state_dict.pop("pixel_mean"	,     lowercase__	)
   state_dict.pop("pixel_std"	,     lowercase__	)

   UpperCAmelCase_           =R".*.output_hypernetworks_mlps.(\d+).layers.(\d+).*"

   for key, value in state_dict.items():
      for key_to_modify, new_key in KEYS_TO_MODIFY_MAPPING.items():
         if key_to_modify in key:
            UpperCAmelCase_           =key.replace(lowercase__	,     lowercase__	)

      if re.match(lowercase__	,     lowercase__	):
         UpperCAmelCase_           =int(re.match(lowercase__	,     lowercase__	).group(2	)	)
         if layer_nb == 0:
            UpperCAmelCase_           =key.replace("layers.0"	,     "proj_in"	)
         elif layer_nb == 1:
            UpperCAmelCase_           =key.replace("layers.1"	,     "layers.0"	)
         elif layer_nb == 2:
            UpperCAmelCase_           =key.replace("layers.2"	,     "proj_out"	)

      UpperCAmelCase_           =value

   UpperCAmelCase_           =model_state_dict[
       "prompt_encoder.shared_embedding.positional_embedding"
   ]

   return model_state_dict




def      a__			(       lowercase__	,     lowercase__	,     lowercase__	,     lowercase__="ybelkada/segment-anything"	):






   '''simple docstring'''






   UpperCAmelCase_           =hf_hub_download(lowercase__	,     F'checkpoints/{model_name}.pth'	)

   if "sam_vit_b" in model_name:
      UpperCAmelCase_           =SamConfig()
   elif "sam_vit_l" in model_name:
      UpperCAmelCase_           =SamVisionConfig(
          hidden_size=1_0_2_4	,     num_hidden_layers=2_4	,     num_attention_heads=1_6	,     global_attn_indexes=[5, 1_1, 1_7, 2_3]	,     )

      UpperCAmelCase_           =SamConfig(
          vision_config=lowercase__	,     )
   elif "sam_vit_h" in model_name:
      UpperCAmelCase_           =SamVisionConfig(
          hidden_size=1_2_8_0	,     num_hidden_layers=3_2	,     num_attention_heads=1_6	,     global_attn_indexes=[7, 1_5, 2_3, 3_1]	,     )

      UpperCAmelCase_           =SamConfig(
          vision_config=lowercase__	,     )

   UpperCAmelCase_           =torch.load(lowercase__	,     map_location="cpu"	)
   UpperCAmelCase_           =replace_keys(lowercase__	)

   UpperCAmelCase_           =SamImageProcessor()

   UpperCAmelCase_           =SamProcessor(image_processor=lowercase__	)
   UpperCAmelCase_           =SamModel(lowercase__	)

   hf_model.load_state_dict(lowercase__	)
   UpperCAmelCase_           =hf_model.to("cuda"	)

   UpperCAmelCase_           ="https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
   UpperCAmelCase_           =Image.open(requests.get(lowercase__	,     stream=lowercase__	).raw	).convert("RGB"	)

   UpperCAmelCase_           =[[[4_0_0, 6_5_0]]]
   UpperCAmelCase_           =[[1]]

   UpperCAmelCase_           =processor(images=np.array(lowercase__	)	,     return_tensors="pt"	).to("cuda"	)

   with torch.no_grad():
      UpperCAmelCase_           =hf_model(**lowercase__	)
   UpperCAmelCase_           =output.iou_scores.squeeze()

   if model_name == "sam_vit_h_4b8939":
      assert scores[-1].item() == 0.579_8902_5115_9668

      UpperCAmelCase_           =processor(
          images=np.array(lowercase__	)	,     input_points=lowercase__	,     input_labels=lowercase__	,     return_tensors="pt"	).to("cuda"	)

      with torch.no_grad():
         UpperCAmelCase_           =hf_model(**lowercase__	)
      UpperCAmelCase_           =output.iou_scores.squeeze()

      assert scores[-1].item() == 0.9712_6030_9219_3604

      UpperCAmelCase_           =((7_5, 2_7_5, 1_7_2_5, 8_5_0),)

      UpperCAmelCase_           =processor(images=np.array(lowercase__	)	,     input_boxes=lowercase__	,     return_tensors="pt"	).to("cuda"	)

      with torch.no_grad():
         UpperCAmelCase_           =hf_model(**lowercase__	)
      UpperCAmelCase_           =output.iou_scores.squeeze()

      assert scores[-1].item() == 0.8686_0156_0592_6514

      # Test with 2 points and 1 image.
      UpperCAmelCase_           =[[[4_0_0, 6_5_0], [8_0_0, 6_5_0]]]
      UpperCAmelCase_           =[[1, 1]]

      UpperCAmelCase_           =processor(
          images=np.array(lowercase__	)	,     input_points=lowercase__	,     input_labels=lowercase__	,     return_tensors="pt"	).to("cuda"	)

      with torch.no_grad():
         UpperCAmelCase_           =hf_model(**lowercase__	)
      UpperCAmelCase_           =output.iou_scores.squeeze()

      assert scores[-1].item() == 0.9936_0477_9243_4692


if __name__ == "__main__":
     __lowercase	: Optional[Any]       =argparse.ArgumentParser()
     __lowercase	: List[Any]       =["""sam_vit_b_01ec64""", """sam_vit_h_4b8939""", """sam_vit_l_0b3195"""]
     parser.add_argument(
         """--model_name""",
         default="""sam_vit_h_4b8939""",
         choices=choices,
         type=str,
         help="""Path to hf config.json of model to convert""",
     )
     parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
     parser.add_argument(
         """--push_to_hub""",
         action="""store_true""",
         help="""Whether to push the model and processor to the hub after converting""",
     )
     parser.add_argument(
         """--model_hub_id""",
         default="""ybelkada/segment-anything""",
         choices=choices,
         type=str,
         help="""Path to hf config.json of model to convert""",
     )

     __lowercase	: List[Any]       =parser.parse_args()

     convert_sam_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub, args.model_hub_id)






"""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, DistributedType
from accelerate.utils import find_executable_batch_size


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to ensure out-of-memory errors never
# interrupt training, and builds off the `nlp_example.py` script.
#
# 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
#
########################################################################


A__ :       List[Any]				= 1_6
A__ :       Union[str, Any]				= 3_2




def 			_lowerCAmelCase		(		_UpperCamelCase			,					_UpperCamelCase = 16    ):






  """simple docstring"""



  _lowercase:      Any   			=  AutoTokenizer.from_pretrained('''bert-base-cased'''    )
  _lowercase:      List[str]   			=  load_dataset('''glue'''			,					'''mrpc'''    )

  def tokenize_function(_UpperCamelCase    ):
    # max_length=None => use the model max length (it's actually the default)
    _lowercase:      Union[str, Any]   			=  tokenizer(examples['''sentence1''']			,					examples['''sentence2''']			,					truncation=_UpperCamelCase			,					max_length=_UpperCamelCase    )
    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():
    _lowercase:      Tuple   			=  datasets.map(
        _UpperCamelCase			,					batched=_UpperCamelCase			,					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
  _lowercase:      Optional[Any]   			=  tokenized_datasets.rename_column('''label'''			,					'''labels'''    )

  def collate_fn(_UpperCamelCase    ):
    # On TPU it's best to pad everything to the same length or training will be very slow.
    _lowercase:      Union[str, Any]   			=  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":
      _lowercase:      int   			=  16
    elif accelerator.mixed_precision != "no":
      _lowercase:      Tuple   			=  8
    else:
      _lowercase:      str   			=  None

    return tokenizer.pad(
        _UpperCamelCase			,					padding='''longest'''			,					max_length=_UpperCamelCase			,					pad_to_multiple_of=_UpperCamelCase			,					return_tensors='''pt'''			,					)

  # Instantiate dataloaders.
  _lowercase:      List[Any]   			=  DataLoader(
      tokenized_datasets['''train''']			,					shuffle=_UpperCamelCase			,					collate_fn=_UpperCamelCase			,					batch_size=_UpperCamelCase    )
  _lowercase:      int   			=  DataLoader(
      tokenized_datasets['''validation''']			,					shuffle=_UpperCamelCase			,					collate_fn=_UpperCamelCase			,					batch_size=_UpperCamelCase    )

  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

  A__ :       List[Any]				= mocked_dataloaders  # noqa: F811




def 			_lowerCAmelCase		(		_UpperCamelCase			,					_UpperCamelCase    ):






  """simple docstring"""



  if os.environ.get('''TESTING_MOCKED_DATALOADERS'''			,					_UpperCamelCase    ) == "1":
    _lowercase:      Tuple   			=  2
  # Initialize accelerator
  _lowercase:      Optional[Any]   			=  Accelerator(cpu=args.cpu			,					mixed_precision=args.mixed_precision    )
  # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
  _lowercase:      Optional[Any]   			=  config['''lr''']
  _lowercase:      Tuple   			=  int(config['''num_epochs''']    )
  _lowercase:      Any   			=  int(config['''seed''']    )
  _lowercase:      List[Any]   			=  int(config['''batch_size''']    )

  _lowercase:      Optional[int]   			=  evaluate.load('''glue'''			,					'''mrpc'''    )

  # New Code #
  # We now can define an inner training loop function. It should take a batch size as the only parameter,
  # and build the dataloaders in there.
  # It also gets our decorator
  @find_executable_batch_size(starting_batch_size=_UpperCamelCase    )
  def inner_training_loop(_UpperCamelCase    ):
    # And now just move everything below under this function
    # We need to bring in the Accelerator object from earlier
    nonlocal accelerator
    # And reset all of its attributes that could hold onto any memory:
    accelerator.free_memory()

    # Then we can declare the model, optimizer, and everything else:
    set_seed(_UpperCamelCase    )

    # Instantiate the model (we build the model here so that the seed also control new weights initialization)
    _lowercase:      Optional[int]   			=  AutoModelForSequenceClassification.from_pretrained('''bert-base-cased'''			,					return_dict=_UpperCamelCase    )

    # 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).
    _lowercase:      List[Any]   			=  model.to(accelerator.device    )

    # Instantiate optimizer
    _lowercase:      List[str]   			=  AdamW(params=model.parameters()			,					lr=_UpperCamelCase    )
    _lowercase			,					_lowercase:      Tuple   			=  get_dataloaders(_UpperCamelCase			,					_UpperCamelCase    )

    # Instantiate scheduler
    _lowercase:      str   			=  get_linear_schedule_with_warmup(
        optimizer=_UpperCamelCase			,					num_warmup_steps=100			,					num_training_steps=(len(_UpperCamelCase    ) * 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.
    _lowercase			,					_lowercase			,					_lowercase			,					_lowercase			,					_lowercase:      Union[str, Any]   			=  accelerator.prepare(
        _UpperCamelCase			,					_UpperCamelCase			,					_UpperCamelCase			,					_UpperCamelCase			,					_UpperCamelCase    )

    # Now we train the model
    for epoch in range(_UpperCamelCase    ):
      model.train()
      for step, batch in enumerate(_UpperCamelCase    ):
        # We could avoid this line since we set the accelerator with `device_placement=True`.
        batch.to(accelerator.device    )
        _lowercase:      Tuple   			=  model(**_UpperCamelCase    )
        _lowercase:      Union[str, Any]   			=  outputs.loss
        accelerator.backward(_UpperCamelCase    )
        optimizer.step()
        lr_scheduler.step()
        optimizer.zero_grad()

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

      _lowercase:      int   			=  metric.compute()
      # Use accelerator.print to print only on the main process.
      accelerator.print(f'''epoch {epoch}:'''			,					_UpperCamelCase    )

    # 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 			_lowerCAmelCase		(		):






  """simple docstring"""



  _lowercase:      Dict   			=  argparse.ArgumentParser(description='''Simple example of training script.'''    )
  parser.add_argument(
      '''--mixed_precision'''			,					type=_UpperCamelCase			,					default=_UpperCamelCase			,					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.'''    )
  _lowercase:      Tuple   			=  parser.parse_args()
  _lowercase:      str   			=  {'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
  training_function(_UpperCamelCase			,					_UpperCamelCase    )


if __name__ == "__main__":
  main()
