



'''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
# and perform 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)
#
# 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							   =				16
a							   =				32







def a_     (    __UpperCAmelCase				,							__UpperCAmelCase = 16					)				->					int:





			"""simple docstring"""







			snake_case:    int      							=AutoTokenizer.from_pretrained('bert-base-cased'					)
			snake_case:    str      							=load_dataset('glue'				,							'mrpc'					)

			def tokenize_function(__UpperCAmelCase					):
						# max_length=None => use the model max length (it's actually the default)
						snake_case:    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():
						snake_case:    List[str]      							=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
			snake_case:    Tuple      							=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.
						snake_case:    List[str]      							=1_28 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:    Tuple      							=16
						elif accelerator.mixed_precision != "no":
									snake_case:    Tuple      							=8
						else:
									snake_case:    Dict      							=None

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

			# Instantiate dataloaders.
			snake_case:    Dict      							=DataLoader(
			    tokenized_datasets['train']				,							shuffle=__UpperCAmelCase				,							collate_fn=__UpperCAmelCase				,							batch_size=__UpperCAmelCase					)
			snake_case:    Optional[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							   =				mocked_dataloaders  # noqa: F811







def a_     (    __UpperCAmelCase				,							__UpperCAmelCase					)				->					str:





			"""simple docstring"""







			if os.environ.get('TESTING_MOCKED_DATALOADERS'				,							__UpperCAmelCase					) == "1":
						snake_case:    str      							=2
			# New Code #
			snake_case:    Union[str, Any]      							=int(args.gradient_accumulation_steps					)
			# Initialize accelerator
			snake_case:    List[Any]      							=Accelerator(
			    cpu=args.cpu				,							mixed_precision=args.mixed_precision				,							gradient_accumulation_steps=__UpperCAmelCase					)
			if accelerator.distributed_type == DistributedType.TPU and gradient_accumulation_steps > 1:
						raise NotImplementedError(
						    'Gradient accumulation on TPUs is currently not supported. Pass `gradient_accumulation_steps=1`'					)
			# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
			snake_case:    Union[str, Any]      							=config['lr']
			snake_case:    List[str]      							=int(config['num_epochs']					)
			snake_case:    List[Any]      							=int(config['seed']					)
			snake_case:    Optional[Any]      							=int(config['batch_size']					)

			snake_case:    Union[str, Any]      							=evaluate.load('glue'				,							'mrpc'					)

			set_seed(__UpperCAmelCase					)
			snake_case							,  snake_case:    Any      							=get_dataloaders(__UpperCAmelCase				,							__UpperCAmelCase					)
			# Instantiate the model (we build the model here so that the seed also control new weights initialization)
			snake_case:    str      							=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).
			snake_case:    Optional[int]      							=model.to(accelerator.device					)

			# Instantiate optimizer
			snake_case:    Tuple      							=AdamW(params=model.parameters()				,							lr=__UpperCAmelCase					)

			# Instantiate scheduler
			snake_case:    int      							=get_linear_schedule_with_warmup(
			    optimizer=__UpperCAmelCase				,							num_warmup_steps=1_00				,							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.
			snake_case							,  snake_case							,  snake_case							,  snake_case							,  snake_case:    Optional[int]      							=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					)
									# New code #
									# We use the new `accumulate` context manager to perform gradient accumulation
									# We also currently do not support TPUs nor advise it as bugs were found on the XLA side when running our tests.
									with accelerator.accumulate(__UpperCAmelCase					):
												snake_case:    Any      							=model(**__UpperCAmelCase					)
												snake_case:    str      							=output.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():
												snake_case:    List[Any]      							=model(**__UpperCAmelCase					)
									snake_case:    Dict      							=outputs.logits.argmax(dim=-1					)
									snake_case							,  snake_case:    Any      							=accelerator.gather_for_metrics((predictions, batch['labels'])					)
									metric.add_batch(
									    predictions=__UpperCAmelCase				,							references=__UpperCAmelCase				,							)

						snake_case:    str      							=metric.compute()
						# Use accelerator.print to print only on the main process.
						accelerator.print(f'''epoch {epoch}:'''				,							__UpperCAmelCase					)







def a_     (    )				->					Optional[int]:





			"""simple docstring"""







			snake_case:    List[Any]      							=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.'				,							)
			# New Code #
			parser.add_argument(
			    '--gradient_accumulation_steps'				,							type=__UpperCAmelCase				,							default=1				,							help='The number of minibatches to be ran before gradients are accumulated.'				,							)
			parser.add_argument('--cpu'				,							action='store_true'				,							help='If passed, will train on the CPU.'					)
			snake_case:    List[Any]      							=parser.parse_args()
			snake_case:    Dict      							={'lr': 2e-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
			training_function(__UpperCAmelCase				,							__UpperCAmelCase					)


if __name__ == "__main__":
	main()











'''simple docstring'''






import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image

from transformers import DPTConfig, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
A__							:  Any    =logging.get_logger(__name__)
def   UpperCamelCase__ (						lowerCAmelCase    ):





      """simple docstring"""


      _lowerCAmelCase					=				DPTConfig()

      if "large" in checkpoint_url:
            _lowerCAmelCase					=				10_24
            _lowerCAmelCase					=				40_96
            _lowerCAmelCase					=				24
            _lowerCAmelCase					=				16
            _lowerCAmelCase					=				[5, 11, 17, 23]
            _lowerCAmelCase					=				[2_56, 5_12, 10_24, 10_24]
            _lowerCAmelCase					=				(1, 3_84, 3_84)

      if "ade" in checkpoint_url:
            _lowerCAmelCase					=				True

            _lowerCAmelCase					=				1_50
            _lowerCAmelCase					=				"""huggingface/label-files"""
            _lowerCAmelCase					=				"""ade20k-id2label.json"""
            _lowerCAmelCase					=				json.load(open(cached_download(hf_hub_url(lowerCAmelCase				,      lowerCAmelCase				,      repo_type="""dataset"""    )    )				,      """r"""    )    )
            _lowerCAmelCase					=				{int(lowerCAmelCase    ): v for k, v in idalabel.items()}
            _lowerCAmelCase					=				idalabel
            _lowerCAmelCase					=				{v: k for k, v in idalabel.items()}
            _lowerCAmelCase					=				[1, 1_50, 4_80, 4_80]

      return config, expected_shape
def   UpperCamelCase__ (						lowerCAmelCase    ):





      """simple docstring"""


      _lowerCAmelCase					=				["""pretrained.model.head.weight""", """pretrained.model.head.bias"""]
      for k in ignore_keys:
            state_dict.pop(lowerCAmelCase				,      lowerCAmelCase    )
def   UpperCamelCase__ (						lowerCAmelCase    ):





      """simple docstring"""


      if (
          "pretrained.model" in name
          and "cls_token" not in name
          and "pos_embed" not in name
          and "patch_embed" not in name
      ):
            _lowerCAmelCase					=				name.replace("""pretrained.model"""				,      """dpt.encoder"""    )
      if "pretrained.model" in name:
            _lowerCAmelCase					=				name.replace("""pretrained.model"""				,      """dpt.embeddings"""    )
      if "patch_embed" in name:
            _lowerCAmelCase					=				name.replace("""patch_embed"""				,      """patch_embeddings"""    )
      if "pos_embed" in name:
            _lowerCAmelCase					=				name.replace("""pos_embed"""				,      """position_embeddings"""    )
      if "attn.proj" in name:
            _lowerCAmelCase					=				name.replace("""attn.proj"""				,      """attention.output.dense"""    )
      if "proj" in name and "project" not in name:
            _lowerCAmelCase					=				name.replace("""proj"""				,      """projection"""    )
      if "blocks" in name:
            _lowerCAmelCase					=				name.replace("""blocks"""				,      """layer"""    )
      if "mlp.fc1" in name:
            _lowerCAmelCase					=				name.replace("""mlp.fc1"""				,      """intermediate.dense"""    )
      if "mlp.fc2" in name:
            _lowerCAmelCase					=				name.replace("""mlp.fc2"""				,      """output.dense"""    )
      if "norm1" in name:
            _lowerCAmelCase					=				name.replace("""norm1"""				,      """layernorm_before"""    )
      if "norm2" in name:
            _lowerCAmelCase					=				name.replace("""norm2"""				,      """layernorm_after"""    )
      if "scratch.output_conv" in name:
            _lowerCAmelCase					=				name.replace("""scratch.output_conv"""				,      """head"""    )
      if "scratch" in name:
            _lowerCAmelCase					=				name.replace("""scratch"""				,      """neck"""    )
      if "layer1_rn" in name:
            _lowerCAmelCase					=				name.replace("""layer1_rn"""				,      """convs.0"""    )
      if "layer2_rn" in name:
            _lowerCAmelCase					=				name.replace("""layer2_rn"""				,      """convs.1"""    )
      if "layer3_rn" in name:
            _lowerCAmelCase					=				name.replace("""layer3_rn"""				,      """convs.2"""    )
      if "layer4_rn" in name:
            _lowerCAmelCase					=				name.replace("""layer4_rn"""				,      """convs.3"""    )
      if "refinenet" in name:
            _lowerCAmelCase					=				int(name[len("""neck.refinenet"""    ) : len("""neck.refinenet"""    ) + 1]    )
            # tricky here: we need to map 4 to 0, 3 to 1, 2 to 2 and 1 to 3
            _lowerCAmelCase					=				name.replace(f"refinenet{layer_idx}"				,      f"fusion_stage.layers.{abs(layer_idx-4    )}"    )
      if "out_conv" in name:
            _lowerCAmelCase					=				name.replace("""out_conv"""				,      """projection"""    )
      if "resConfUnit1" in name:
            _lowerCAmelCase					=				name.replace("""resConfUnit1"""				,      """residual_layer1"""    )
      if "resConfUnit2" in name:
            _lowerCAmelCase					=				name.replace("""resConfUnit2"""				,      """residual_layer2"""    )
      if "conv1" in name:
            _lowerCAmelCase					=				name.replace("""conv1"""				,      """convolution1"""    )
      if "conv2" in name:
            _lowerCAmelCase					=				name.replace("""conv2"""				,      """convolution2"""    )
      # readout blocks
      if "pretrained.act_postprocess1.0.project.0" in name:
            _lowerCAmelCase					=				name.replace("""pretrained.act_postprocess1.0.project.0"""				,      """neck.reassemble_stage.readout_projects.0.0"""    )
      if "pretrained.act_postprocess2.0.project.0" in name:
            _lowerCAmelCase					=				name.replace("""pretrained.act_postprocess2.0.project.0"""				,      """neck.reassemble_stage.readout_projects.1.0"""    )
      if "pretrained.act_postprocess3.0.project.0" in name:
            _lowerCAmelCase					=				name.replace("""pretrained.act_postprocess3.0.project.0"""				,      """neck.reassemble_stage.readout_projects.2.0"""    )
      if "pretrained.act_postprocess4.0.project.0" in name:
            _lowerCAmelCase					=				name.replace("""pretrained.act_postprocess4.0.project.0"""				,      """neck.reassemble_stage.readout_projects.3.0"""    )
      # resize blocks
      if "pretrained.act_postprocess1.3" in name:
            _lowerCAmelCase					=				name.replace("""pretrained.act_postprocess1.3"""				,      """neck.reassemble_stage.layers.0.projection"""    )
      if "pretrained.act_postprocess1.4" in name:
            _lowerCAmelCase					=				name.replace("""pretrained.act_postprocess1.4"""				,      """neck.reassemble_stage.layers.0.resize"""    )
      if "pretrained.act_postprocess2.3" in name:
            _lowerCAmelCase					=				name.replace("""pretrained.act_postprocess2.3"""				,      """neck.reassemble_stage.layers.1.projection"""    )
      if "pretrained.act_postprocess2.4" in name:
            _lowerCAmelCase					=				name.replace("""pretrained.act_postprocess2.4"""				,      """neck.reassemble_stage.layers.1.resize"""    )
      if "pretrained.act_postprocess3.3" in name:
            _lowerCAmelCase					=				name.replace("""pretrained.act_postprocess3.3"""				,      """neck.reassemble_stage.layers.2.projection"""    )
      if "pretrained.act_postprocess4.3" in name:
            _lowerCAmelCase					=				name.replace("""pretrained.act_postprocess4.3"""				,      """neck.reassemble_stage.layers.3.projection"""    )
      if "pretrained.act_postprocess4.4" in name:
            _lowerCAmelCase					=				name.replace("""pretrained.act_postprocess4.4"""				,      """neck.reassemble_stage.layers.3.resize"""    )
      if "pretrained" in name:
            _lowerCAmelCase					=				name.replace("""pretrained"""				,      """dpt"""    )
      if "bn" in name:
            _lowerCAmelCase					=				name.replace("""bn"""				,      """batch_norm"""    )
      if "head" in name:
            _lowerCAmelCase					=				name.replace("""head"""				,      """head.head"""    )
      if "encoder.norm" in name:
            _lowerCAmelCase					=				name.replace("""encoder.norm"""				,      """layernorm"""    )
      if "auxlayer" in name:
            _lowerCAmelCase					=				name.replace("""auxlayer"""				,      """auxiliary_head.head"""    )

      return name
def   UpperCamelCase__ (						lowerCAmelCase				,      lowerCAmelCase    ):





      """simple docstring"""


      for i in range(config.num_hidden_layers    ):
            # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
            _lowerCAmelCase					=				state_dict.pop(f"dpt.encoder.layer.{i}.attn.qkv.weight"    )
            _lowerCAmelCase					=				state_dict.pop(f"dpt.encoder.layer.{i}.attn.qkv.bias"    )
            # next, add query, keys and values (in that order) to the state dict
            _lowerCAmelCase					=				in_proj_weight[: config.hidden_size, :]
            _lowerCAmelCase					=				in_proj_bias[: config.hidden_size]
            _lowerCAmelCase					=				in_proj_weight[
                config.hidden_size : config.hidden_size * 2, :
            ]
            _lowerCAmelCase					=				in_proj_bias[
                config.hidden_size : config.hidden_size * 2
            ]
            _lowerCAmelCase					=				in_proj_weight[
                -config.hidden_size :, :
            ]
            _lowerCAmelCase					=				in_proj_bias[-config.hidden_size :]
def   UpperCamelCase__ (						):





      """simple docstring"""


      _lowerCAmelCase					=				"""http://images.cocodataset.org/val2017/000000039769.jpg"""
      _lowerCAmelCase					=				Image.open(requests.get(lowerCAmelCase				,      stream=lowerCAmelCase    ).raw    )
      return im
@torch.no_grad()
def   UpperCamelCase__ (						lowerCAmelCase				,      lowerCAmelCase				,      lowerCAmelCase				,      lowerCAmelCase    ):





      """simple docstring"""


      _lowerCAmelCase	,				_lowerCAmelCase					=				get_dpt_config(lowerCAmelCase    )
      # load original state_dict from URL
      _lowerCAmelCase					=				torch.hub.load_state_dict_from_url(lowerCAmelCase				,      map_location="""cpu"""    )
      # remove certain keys
      remove_ignore_keys_(lowerCAmelCase    )
      # rename keys
      for key in state_dict.copy().keys():
            _lowerCAmelCase					=				state_dict.pop(lowerCAmelCase    )
            _lowerCAmelCase					=				val
      # read in qkv matrices
      read_in_q_k_v(lowerCAmelCase				,      lowerCAmelCase    )

      # load HuggingFace model
      _lowerCAmelCase					=				DPTForSemanticSegmentation(lowerCAmelCase    ) if """ade""" in checkpoint_url else DPTForDepthEstimation(lowerCAmelCase    )
      model.load_state_dict(lowerCAmelCase    )
      model.eval()

      # Check outputs on an image
      _lowerCAmelCase					=				4_80 if """ade""" in checkpoint_url else 3_84
      _lowerCAmelCase					=				DPTImageProcessor(size=lowerCAmelCase    )

      _lowerCAmelCase					=				prepare_img()
      _lowerCAmelCase					=				image_processor(lowerCAmelCase				,      return_tensors="""pt"""    )

      # forward pass
      _lowerCAmelCase					=				model(**lowerCAmelCase    ).logits if """ade""" in checkpoint_url else model(**lowerCAmelCase    ).predicted_depth

      # Assert logits
      _lowerCAmelCase					=				torch.tensor([[6.3_199, 6.3_629, 6.4_148], [6.3_850, 6.3_615, 6.4_166], [6.3_519, 6.3_176, 6.3_575]]    )
      if "ade" in checkpoint_url:
            _lowerCAmelCase					=				torch.tensor([[4.0_480, 4.2_420, 4.4_360], [4.3_124, 4.5_693, 4.8_261], [4.5_768, 4.8_965, 5.2_163]]    )
      assert outputs.shape == torch.Size(lowerCAmelCase    )
      assert (
          torch.allclose(outputs[0, 0, :3, :3]				,      lowerCAmelCase				,      atol=1e-4    )
          if "ade" in checkpoint_url
          else torch.allclose(outputs[0, :3, :3]				,      lowerCAmelCase    )
      )

      Path(lowerCAmelCase    ).mkdir(exist_ok=lowerCAmelCase    )
      print(f"Saving model to {pytorch_dump_folder_path}"    )
      model.save_pretrained(lowerCAmelCase    )
      print(f"Saving image processor to {pytorch_dump_folder_path}"    )
      image_processor.save_pretrained(lowerCAmelCase    )

      if push_to_hub:
            print("""Pushing model to hub..."""    )
            model.push_to_hub(
                repo_path_or_name=Path(lowerCAmelCase				,      lowerCAmelCase    )				,      organization="""nielsr"""				,      commit_message="""Add model"""				,      use_temp_dir=lowerCAmelCase				,      )
            image_processor.push_to_hub(
                repo_path_or_name=Path(lowerCAmelCase				,      lowerCAmelCase    )				,      organization="""nielsr"""				,      commit_message="""Add image processor"""				,      use_temp_dir=lowerCAmelCase				,      )


if __name__ == "__main__":
      A__							:  Optional[int]    =argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          '''--checkpoint_url''',
          default='''https://github.com/intel-isl/DPT/releases/download/1_0/dpt_large-midas-2f21e586.pt''',
          type=str,
          help='''URL of the original DPT checkpoint you\'d like to convert.''',
      )
      parser.add_argument(
          '''--pytorch_dump_folder_path''',
          default=None,
          type=str,
          required=True,
          help='''Path to the output PyTorch model directory.''',
      )
      parser.add_argument(
          '''--push_to_hub''',
          action='''store_true''',
      )
      parser.add_argument(
          '''--model_name''',
          default='''dpt-large''',
          type=str,
          help='''Name of the model, in case you\'re pushing to the hub.''',
      )

      A__							:  Optional[int]    =parser.parse_args()
      convert_dpt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)






