


import argparse
from collections import OrderedDict
from pathlib import Path

import requests
import torch
from PIL import Image

from transformers import GLPNConfig, GLPNForDepthEstimation, GLPNImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
lowerCAmelCase       		=    logging.get_logger(__name__)




def     __SCREAMING_SNAKE_CASE							(       lowercase_      )		->     Optional[Any]:




						'''simple docstring'''







						__UpperCAmelCase			:							int	    =	OrderedDict()
						for key, value in state_dict.items():
												if key.startswith('''module.encoder'''      ):
																		__UpperCAmelCase			:							Optional[Any]	    =	key.replace('''module.encoder'''      ,       '''glpn.encoder'''      )
												if key.startswith('''module.decoder'''      ):
																		__UpperCAmelCase			:							str	    =	key.replace('''module.decoder'''      ,       '''decoder.stages'''      )
												if "patch_embed" in key:
																		# replace for example patch_embed1 by patch_embeddings.0
																		__UpperCAmelCase			:							int	    =	key[key.find('''patch_embed'''      ) + len('''patch_embed'''      )]
																		__UpperCAmelCase			:							List[Any]	    =	key.replace(f"patch_embed{idx}"      ,       f"patch_embeddings.{int(lowercase_      )-1}"      )
												if "norm" in key:
																		__UpperCAmelCase			:							int	    =	key.replace('''norm'''      ,       '''layer_norm'''      )
												if "glpn.encoder.layer_norm" in key:
																		# replace for example layer_norm1 by layer_norm.0
																		__UpperCAmelCase			:							Optional[Any]	    =	key[key.find('''glpn.encoder.layer_norm'''      ) + len('''glpn.encoder.layer_norm'''      )]
																		__UpperCAmelCase			:							List[str]	    =	key.replace(f"layer_norm{idx}"      ,       f"layer_norm.{int(lowercase_      )-1}"      )
												if "layer_norm1" in key:
																		__UpperCAmelCase			:							Union[str, Any]	    =	key.replace('''layer_norm1'''      ,       '''layer_norm_1'''      )
												if "layer_norm2" in key:
																		__UpperCAmelCase			:							str	    =	key.replace('''layer_norm2'''      ,       '''layer_norm_2'''      )
												if "block" in key:
																		# replace for example block1 by block.0
																		__UpperCAmelCase			:							Any	    =	key[key.find('''block'''      ) + len('''block'''      )]
																		__UpperCAmelCase			:							int	    =	key.replace(f"block{idx}"      ,       f"block.{int(lowercase_      )-1}"      )
												if "attn.q" in key:
																		__UpperCAmelCase			:							Union[str, Any]	    =	key.replace('''attn.q'''      ,       '''attention.self.query'''      )
												if "attn.proj" in key:
																		__UpperCAmelCase			:							int	    =	key.replace('''attn.proj'''      ,       '''attention.output.dense'''      )
												if "attn" in key:
																		__UpperCAmelCase			:							Optional[int]	    =	key.replace('''attn'''      ,       '''attention.self'''      )
												if "fc1" in key:
																		__UpperCAmelCase			:							int	    =	key.replace('''fc1'''      ,       '''dense1'''      )
												if "fc2" in key:
																		__UpperCAmelCase			:							List[str]	    =	key.replace('''fc2'''      ,       '''dense2'''      )
												if "linear_pred" in key:
																		__UpperCAmelCase			:							int	    =	key.replace('''linear_pred'''      ,       '''classifier'''      )
												if "linear_fuse" in key:
																		__UpperCAmelCase			:							Optional[Any]	    =	key.replace('''linear_fuse.conv'''      ,       '''linear_fuse'''      )
																		__UpperCAmelCase			:							str	    =	key.replace('''linear_fuse.bn'''      ,       '''batch_norm'''      )
												if "linear_c" in key:
																		# replace for example linear_c4 by linear_c.3
																		__UpperCAmelCase			:							str	    =	key[key.find('''linear_c'''      ) + len('''linear_c'''      )]
																		__UpperCAmelCase			:							Dict	    =	key.replace(f"linear_c{idx}"      ,       f"linear_c.{int(lowercase_      )-1}"      )
												if "bot_conv" in key:
																		__UpperCAmelCase			:							Optional[int]	    =	key.replace('''bot_conv'''      ,       '''0.convolution'''      )
												if "skip_conv1" in key:
																		__UpperCAmelCase			:							List[str]	    =	key.replace('''skip_conv1'''      ,       '''1.convolution'''      )
												if "skip_conv2" in key:
																		__UpperCAmelCase			:							int	    =	key.replace('''skip_conv2'''      ,       '''2.convolution'''      )
												if "fusion1" in key:
																		__UpperCAmelCase			:							Dict	    =	key.replace('''fusion1'''      ,       '''1.fusion'''      )
												if "fusion2" in key:
																		__UpperCAmelCase			:							str	    =	key.replace('''fusion2'''      ,       '''2.fusion'''      )
												if "fusion3" in key:
																		__UpperCAmelCase			:							Any	    =	key.replace('''fusion3'''      ,       '''3.fusion'''      )
												if "fusion" in key and "conv" in key:
																		__UpperCAmelCase			:							Dict	    =	key.replace('''conv'''      ,       '''convolutional_layer'''      )
												if key.startswith('''module.last_layer_depth'''      ):
																		__UpperCAmelCase			:							Optional[int]	    =	key.replace('''module.last_layer_depth'''      ,       '''head.head'''      )
												__UpperCAmelCase			:							Dict	    =	value

						return new_state_dict




def     __SCREAMING_SNAKE_CASE							(       lowercase_      ,       lowercase_      )		->     Union[str, Any]:




						'''simple docstring'''







						for i in range(config.num_encoder_blocks      ):
												for j in range(config.depths[i]      ):
																		# read in weights + bias of keys and values (which is a single matrix in the original implementation)
																		__UpperCAmelCase			:							Optional[Any]	    =	state_dict.pop(f"glpn.encoder.block.{i}.{j}.attention.self.kv.weight"      )
																		__UpperCAmelCase			:							Optional[int]	    =	state_dict.pop(f"glpn.encoder.block.{i}.{j}.attention.self.kv.bias"      )
																		# next, add keys and values (in that order) to the state dict
																		__UpperCAmelCase			:							str	    =	kv_weight[
																		    : config.hidden_sizes[i], :
																		]
																		__UpperCAmelCase			:							Tuple	    =	kv_bias[: config.hidden_sizes[i]]
																		__UpperCAmelCase			:							Tuple	    =	kv_weight[
																		    config.hidden_sizes[i] :, :
																		]
																		__UpperCAmelCase			:							Optional[Any]	    =	kv_bias[config.hidden_sizes[i] :]




def     __SCREAMING_SNAKE_CASE							(       )		->     Union[str, Any]:




						'''simple docstring'''







						__UpperCAmelCase			:							Tuple	    =	'''http://images.cocodataset.org/val2017/000000039769.jpg'''
						__UpperCAmelCase			:							Optional[Any]	    =	Image.open(requests.get(lowercase_      ,       stream=lowercase_      ).raw      )

						return image




@torch.no_grad()
def     __SCREAMING_SNAKE_CASE							(       lowercase_      ,       lowercase_      ,       lowercase_=False      ,       lowercase_=None      )		->     Tuple:




						'''simple docstring'''







						__UpperCAmelCase			:							str	    =	GLPNConfig(hidden_sizes=[64, 128, 320, 512]      ,       decoder_hidden_size=64      ,       depths=[3, 8, 27, 3]      )

						# load image processor (only resize + rescale)
						__UpperCAmelCase			:							Any	    =	GLPNImageProcessor()

						# prepare image
						__UpperCAmelCase			:							Any	    =	prepare_img()
						__UpperCAmelCase			:							Any	    =	image_processor(images=lowercase_      ,       return_tensors='''pt'''      ).pixel_values

						logger.info('''Converting model...'''      )

						# load original state dict
						__UpperCAmelCase			:							List[Any]	    =	torch.load(lowercase_      ,       map_location=torch.device('''cpu'''      )      )

						# rename keys
						__UpperCAmelCase			:							str	    =	rename_keys(lowercase_      )

						# key and value matrices need special treatment
						read_in_k_v(lowercase_      ,       lowercase_      )

						# create HuggingFace model and load state dict
						__UpperCAmelCase			:							Union[str, Any]	    =	GLPNForDepthEstimation(lowercase_      )
						model.load_state_dict(lowercase_      )
						model.eval()

						# forward pass
						__UpperCAmelCase			:							str	    =	model(lowercase_      )
						__UpperCAmelCase			:							Any	    =	outputs.predicted_depth

						# verify output
						if model_name is not None:
												if "nyu" in model_name:
																		__UpperCAmelCase			:							Optional[int]	    =	torch.tensor(
																		    [[4.4_1_4_7, 4.0_8_7_3, 4.0_6_7_3], [3.7_8_9_0, 3.2_8_8_1, 3.1_5_2_5], [3.7_6_7_4, 3.5_4_2_3, 3.4_9_1_3]]      )
												elif "kitti" in model_name:
																		__UpperCAmelCase			:							Tuple	    =	torch.tensor(
																		    [[3.4_2_9_1, 2.7_8_6_5, 2.5_1_5_1], [3.2_8_4_1, 2.7_0_2_1, 2.3_5_0_2], [3.1_1_4_7, 2.4_6_2_5, 2.2_4_8_1]]      )
												else:
																		raise ValueError(f"Unknown model name: {model_name}"      )

												__UpperCAmelCase			:							List[Any]	    =	torch.Size([1, 480, 640]      )

												assert predicted_depth.shape == expected_shape
												assert torch.allclose(predicted_depth[0, :3, :3]      ,       lowercase_      ,       atol=1e-4      )
												print('''Looks ok!'''      )

						# finally, push to hub if required
						if push_to_hub:
												logger.info('''Pushing model and image processor to the hub...'''      )
												model.push_to_hub(
												    repo_path_or_name=Path(lowercase_      ,       lowercase_      )      ,       organization='''nielsr'''      ,       commit_message='''Add model'''      ,       use_temp_dir=lowercase_      ,       )
												image_processor.push_to_hub(
												    repo_path_or_name=Path(lowercase_      ,       lowercase_      )      ,       organization='''nielsr'''      ,       commit_message='''Add image processor'''      ,       use_temp_dir=lowercase_      ,       )


if __name__ == "__main__":
				lowerCAmelCase       		=    argparse.ArgumentParser()

				parser.add_argument(
				    """--checkpoint_path""",
				    default=None,
				    type=str,
				    help="""Path to the original PyTorch checkpoint (.pth file).""",
				)
				parser.add_argument(
				    """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the folder to output PyTorch model."""
				)
				parser.add_argument(
				    """--push_to_hub""", action="""store_true""", help="""Whether to upload the model to the HuggingFace hub."""
				)
				parser.add_argument(
				    """--model_name""",
				    default="""glpn-kitti""",
				    type=str,
				    help="""Name of the model in case you're pushing to the hub.""",
				)
				lowerCAmelCase       		=    parser.parse_args()
				convert_glpn_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)





import argparse
import json
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
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler


lowerCAmelCase       		=    16
lowerCAmelCase       		=    32




def     __SCREAMING_SNAKE_CASE							(       lowercase_      ,       lowercase_ = 16      ,       lowercase_ = "bert-base-cased"      )		->     Union[str, Any]:




						'''simple docstring'''







						__UpperCAmelCase			:							List[str]	    =	AutoTokenizer.from_pretrained(lowercase_      )
						__UpperCAmelCase			:							int	    =	load_dataset('''glue'''      ,       '''mrpc'''      )

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

						# Apply the method we just defined to all the examples in all the splits of the dataset
						__UpperCAmelCase			:							Dict	    =	datasets.map(
						    lowercase_      ,       batched=lowercase_      ,       remove_columns=['''idx''', '''sentence1''', '''sentence2''']      ,       load_from_cache_file=lowercase_      )

						# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
						# transformers library
						__UpperCAmelCase			:							List[str]	    =	tokenized_datasets.rename_column('''label'''      ,       '''labels'''      )

						def collate_fn(lowercase_      ):
												# On TPU it's best to pad everything to the same length or training will be very slow.
												if accelerator.distributed_type == DistributedType.TPU:
																		return tokenizer.pad(lowercase_      ,       padding='''max_length'''      ,       max_length=128      ,       return_tensors='''pt'''      )
												return tokenizer.pad(lowercase_      ,       padding='''longest'''      ,       return_tensors='''pt'''      )

						# Instantiate dataloaders.
						__UpperCAmelCase			:							Optional[Any]	    =	DataLoader(
						    tokenized_datasets['''train''']      ,       shuffle=lowercase_      ,       collate_fn=lowercase_      ,       batch_size=lowercase_      )
						__UpperCAmelCase			:							Any	    =	DataLoader(
						    tokenized_datasets['''validation''']      ,       shuffle=lowercase_      ,       collate_fn=lowercase_      ,       batch_size=lowercase_      )

						return train_dataloader, eval_dataloader




def     __SCREAMING_SNAKE_CASE							(       lowercase_      ,       lowercase_      ,       lowercase_      ,       lowercase_      )		->     str:




						'''simple docstring'''







						model.eval()
						__UpperCAmelCase			:							Optional[int]	    =	0
						for step, batch in enumerate(lowercase_      ):
												# We could avoid this line since we set the accelerator with `device_placement=True`.
												batch.to(accelerator.device      )
												with torch.no_grad():
																		__UpperCAmelCase			:							Any	    =	model(**lowercase_      )
												__UpperCAmelCase			:							Optional[int]	    =	outputs.logits.argmax(dim=-1      )
												# It is slightly faster to call this once, than multiple times
												__UpperCAmelCase    ,      __UpperCAmelCase			:							Union[str, Any]	    =	accelerator.gather(
												    (predictions, batch['''labels'''])      )  # If we are in a multiprocess environment, the last batch has duplicates
												if accelerator.use_distributed:
																		if step == len(lowercase_      ) - 1:
																								__UpperCAmelCase			:							Optional[int]	    =	predictions[: len(eval_dataloader.dataset      ) - samples_seen]
																								__UpperCAmelCase			:							List[Any]	    =	references[: len(eval_dataloader.dataset      ) - samples_seen]
																		else:
																								samples_seen += references.shape[0]
												metric.add_batch(
												    predictions=lowercase_      ,       references=lowercase_      ,       )

						__UpperCAmelCase			:							Union[str, Any]	    =	metric.compute()
						return eval_metric["accuracy"]




def     __SCREAMING_SNAKE_CASE							(       lowercase_      ,       lowercase_      )		->     Dict:




						'''simple docstring'''







						__UpperCAmelCase			:							List[Any]	    =	Accelerator()

						# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
						__UpperCAmelCase			:							Optional[Any]	    =	config['''lr''']
						__UpperCAmelCase			:							List[Any]	    =	int(config['''num_epochs''']      )
						__UpperCAmelCase			:							Dict	    =	int(config['''seed''']      )
						__UpperCAmelCase			:							Tuple	    =	int(config['''batch_size''']      )
						__UpperCAmelCase			:							Any	    =	args.model_name_or_path

						set_seed(lowercase_      )
						__UpperCAmelCase    ,      __UpperCAmelCase			:							Tuple	    =	get_dataloaders(lowercase_      ,       lowercase_      ,       lowercase_      )

						# Instantiate the model (we build the model here so that the seed also control new weights initialization)
						__UpperCAmelCase			:							List[str]	    =	AutoModelForSequenceClassification.from_pretrained(lowercase_      ,       return_dict=lowercase_      )

						# Instantiate optimizer
						__UpperCAmelCase			:							str	    =	(
						    AdamW
						    if accelerator.state.deepspeed_plugin is None
						    or '''optimizer''' not in accelerator.state.deepspeed_plugin.deepspeed_config
						    else DummyOptim
						)
						__UpperCAmelCase			:							int	    =	optimizer_cls(params=model.parameters()      ,       lr=lowercase_      )

						if accelerator.state.deepspeed_plugin is not None:
												__UpperCAmelCase			:							int	    =	accelerator.state.deepspeed_plugin.deepspeed_config[
												    '''gradient_accumulation_steps'''
												]
						else:
												__UpperCAmelCase			:							List[Any]	    =	1
						__UpperCAmelCase			:							str	    =	(len(lowercase_      ) * num_epochs) // gradient_accumulation_steps

						# Instantiate scheduler
						if (
						    accelerator.state.deepspeed_plugin is None
						    or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
						):
												__UpperCAmelCase			:							List[str]	    =	get_linear_schedule_with_warmup(
												    optimizer=lowercase_      ,       num_warmup_steps=0      ,       num_training_steps=lowercase_      ,       )
						else:
												__UpperCAmelCase			:							Union[str, Any]	    =	DummyScheduler(lowercase_      ,       total_num_steps=lowercase_      ,       warmup_num_steps=0      )

						# 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			:							int	    =	accelerator.prepare(
						    lowercase_      ,       lowercase_      ,       lowercase_      ,       lowercase_      ,       lowercase_      )

						# We need to keep track of how many total steps we have iterated over
						__UpperCAmelCase			:							Tuple	    =	0
						# We also need to keep track of the stating epoch so files are named properly
						__UpperCAmelCase			:							Any	    =	0
						__UpperCAmelCase			:							Tuple	    =	evaluate.load('''glue'''      ,       '''mrpc'''      )
						__UpperCAmelCase			:							Any	    =	num_epochs

						if args.partial_train_epoch is not None:
												__UpperCAmelCase			:							Any	    =	args.partial_train_epoch

						if args.resume_from_checkpoint:
												accelerator.load_state(args.resume_from_checkpoint      )
												__UpperCAmelCase			:							List[Any]	    =	args.resume_from_checkpoint.split('''epoch_'''      )[1]
												__UpperCAmelCase			:							Union[str, Any]	    =	''''''
												for char in epoch_string:
																		if char.isdigit():
																								state_epoch_num += char
																		else:
																								break
												__UpperCAmelCase			:							Tuple	    =	int(lowercase_      ) + 1
												__UpperCAmelCase			:							Optional[int]	    =	evaluation_loop(lowercase_      ,       lowercase_      ,       lowercase_      ,       lowercase_      )
												accelerator.print('''resumed checkpoint performance:'''      ,       lowercase_      )
												accelerator.print('''resumed checkpoint\'s scheduler\'s lr:'''      ,       lr_scheduler.get_lr()[0]      )
												accelerator.print('''resumed optimizers\'s lr:'''      ,       optimizer.param_groups[0]['''lr''']      )
												with open(os.path.join(args.output_dir      ,       f"state_{starting_epoch-1}.json"      )      ,       '''r'''      ) as f:
																		__UpperCAmelCase			:							str	    =	json.load(lowercase_      )
																		assert resumed_state["accuracy"] == accuracy, "Accuracy mismatch, loading from checkpoint failed"
																		assert (
																		    resumed_state["lr"] == lr_scheduler.get_lr()[0]
																		), "Scheduler learning rate mismatch, loading from checkpoint failed"
																		assert (
																		    resumed_state["optimizer_lr"] == optimizer.param_groups[0]["lr"]
																		), "Optimizer learning rate mismatch, loading from checkpoint failed"
																		assert resumed_state["epoch"] == starting_epoch - 1, "Epoch mismatch, loading from checkpoint failed"
																		return

    # Now we train the model
						__UpperCAmelCase			:							Tuple	    =	{}
						for epoch in range(lowercase_      ,       lowercase_      ):
												model.train()
												for step, batch in enumerate(lowercase_      ):
																		__UpperCAmelCase			:							Dict	    =	model(**lowercase_      )
																		__UpperCAmelCase			:							Union[str, Any]	    =	outputs.loss
																		__UpperCAmelCase			:							Tuple	    =	loss / gradient_accumulation_steps
																		accelerator.backward(lowercase_      )
																		if step % gradient_accumulation_steps == 0:
																								optimizer.step()
																								lr_scheduler.step()
																								optimizer.zero_grad()

																		overall_step += 1
												__UpperCAmelCase			:							Optional[Any]	    =	f"epoch_{epoch}"
												__UpperCAmelCase			:							Optional[Any]	    =	os.path.join(args.output_dir      ,       lowercase_      )
												accelerator.save_state(lowercase_      )
												__UpperCAmelCase			:							Any	    =	evaluation_loop(lowercase_      ,       lowercase_      ,       lowercase_      ,       lowercase_      )
												__UpperCAmelCase			:							List[str]	    =	accuracy
												__UpperCAmelCase			:							Dict	    =	lr_scheduler.get_lr()[0]
												__UpperCAmelCase			:							List[str]	    =	optimizer.param_groups[0]['''lr''']
												__UpperCAmelCase			:							List[Any]	    =	epoch
												__UpperCAmelCase			:							Any	    =	overall_step
												accelerator.print(f"epoch {epoch}:"      ,       lowercase_      )

												accelerator.wait_for_everyone()
												if accelerator.is_main_process:
																		with open(os.path.join(args.output_dir      ,       f"state_{epoch}.json"      )      ,       '''w'''      ) as f:
																								json.dump(lowercase_      ,       lowercase_      )




def     __SCREAMING_SNAKE_CASE							(       )		->     List[Any]:




						'''simple docstring'''







						__UpperCAmelCase			:							Any	    =	argparse.ArgumentParser(description='''Simple example of training script tracking peak GPU memory usage.'''      )
						parser.add_argument(
						    '''--model_name_or_path'''      ,       type=lowercase_      ,       default='''bert-base-cased'''      ,       help='''Path to pretrained model or model identifier from huggingface.co/models.'''      ,       required=lowercase_      ,       )
						parser.add_argument(
						    '''--output_dir'''      ,       type=lowercase_      ,       default='''.'''      ,       help='''Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'''      ,       )
						parser.add_argument(
						    '''--resume_from_checkpoint'''      ,       type=lowercase_      ,       default=lowercase_      ,       help='''If the training should continue from a checkpoint folder.'''      ,       )
						parser.add_argument(
						    '''--partial_train_epoch'''      ,       type=lowercase_      ,       default=lowercase_      ,       help='''If passed, the training will stop after this number of epochs.'''      ,       )
						parser.add_argument(
						    '''--num_epochs'''      ,       type=lowercase_      ,       default=2      ,       help='''Number of train epochs.'''      ,       )
						__UpperCAmelCase			:							Optional[int]	    =	parser.parse_args()
						__UpperCAmelCase			:							str	    =	{'''lr''': 2e-5, '''num_epochs''': args.num_epochs, '''seed''': 42, '''batch_size''': 16}

						training_function(lowercase_      ,       lowercase_      )


if __name__ == "__main__":
				main()

