


'''simple docstring'''



import logging

import torch
from accelerate import Accelerator
from arguments import EvaluationArguments
from datasets import load_dataset
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader

from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser, set_seed





class 							__UpperCamelCase				(		lowerCAmelCase_					):




	def __init__(   self						,				__a						,				__a						,				__a=1024						,				__a=1024						,				__a=3.6							):

						'''simple docstring'''





						__a			:       List[str]					    =  tokenizer
						__a			:       List[Any]					    =  tokenizer.bos_token_id
						__a			:       Tuple					    =  dataset
						__a			:       Optional[int]					    =  seq_length
						__a			:       Optional[int]					    =  seq_length * chars_per_token * num_of_sequences





	def __iter__(   self							):

						'''simple docstring'''





						__a			:       Optional[Any]					    =  iter(self.dataset							)
						__a			:       Optional[int]					    =  True
						while more_examples:
											__a    ,				__a			:       Optional[int]					    =  [], 0
											while True:
																if buffer_len >= self.input_characters:
																					break
																try:
																					buffer.append(next(__a							)['content']							)
																					buffer_len += len(buffer[-1]							)
																except StopIteration:
																					__a			:       str					    =  False
																					break
											__a			:       Optional[int]					    =  tokenizer(__a						,				truncation=__a							)['input_ids']
											__a			:       str					    =  []
											for tokenized_input in tokenized_inputs:
																all_token_ids.extend(tokenized_input + [self.concat_token_id]							)
											for i in range(0						,				len(__a							)						,				self.seq_length							):
																__a			:       int					    =  all_token_ids[i : i + self.seq_length]
																if len(__a							) == self.seq_length:
																					yield torch.tensor(__a							)






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   int   ):
					__a			:       List[Any]					    =  {'streaming': True}
					__a			:       int					    =  load_dataset(args.dataset_name						,      split='train'						,      **_SCREAMING_SNAKE_CASE   )
					__a			:       Union[str, Any]					    =  ConstantLengthDataset(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      seq_length=args.seq_length   )
					__a			:       Optional[Any]					    =  DataLoader(_SCREAMING_SNAKE_CASE						,      batch_size=args.batch_size   )
					return eval_dataloader






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   Optional[int]   ):
					model.eval()
					__a			:       Any					    =  []
					for step, batch in enumerate(_SCREAMING_SNAKE_CASE   ):
										with torch.no_grad():
															__a			:       str					    =  model(_SCREAMING_SNAKE_CASE						,      labels=_SCREAMING_SNAKE_CASE   )
										__a			:       int					    =  outputs.loss.repeat(args.batch_size   )
										losses.append(accelerator.gather(_SCREAMING_SNAKE_CASE   )   )

										if args.max_eval_steps > 0 and step >= args.max_eval_steps:
															break
					__a			:       Union[str, Any]					    =  torch.mean(torch.cat(_SCREAMING_SNAKE_CASE   )   )
					try:
										__a			:       int					    =  torch.exp(_SCREAMING_SNAKE_CASE   )
					except OverflowError:
										__a			:       str					    =  float('inf'   )
					return loss.item(), perplexity.item()


# Setup Accelerator
__lowercase	:						List[Any]       =	Accelerator()

# Parse configuration
__lowercase	:						Optional[int]       =	HfArgumentParser(EvaluationArguments)
__lowercase	:						int       =	parser.parse_args()
set_seed(args.seed)

# Logging
__lowercase	:						str       =	logging.getLogger(__name__)
logging.basicConfig(
    format='%(asctime)s - %(levelname)s - %(name)s - %(message)s', datefmt='%m/%d/%Y %H:%M:%S', level=logging.INFO
)

# Load model and tokenizer
__lowercase	:						Any       =	AutoModelForCausalLM.from_pretrained(args.model_ckpt)
__lowercase	:						Dict       =	AutoTokenizer.from_pretrained(args.model_ckpt)

# Load dataset and dataloader
__lowercase	:						List[str]       =	create_dataloader(args)

# Prepare everything with our `accelerator`.
__lowercase       ,      __lowercase	:						Any       =	accelerator.prepare(model, eval_dataloader)

# Evaluate and save the last checkpoint
logger.info('Evaluating and saving model after training')
__lowercase       ,      __lowercase	:						List[str]       =	evaluate(args)




logger.info(f'''loss/eval: {eval_loss}, perplexity: {perplexity}''')









'''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 		__magic_name__    (	__UpperCAmelCase	)		->					Optional[Any]:

					'''simple docstring'''

					snake_case_									=				DPTConfig()

					if "large" in checkpoint_url:
										snake_case_									=				1024
										snake_case_									=				4096
										snake_case_									=				24
										snake_case_									=				16
										snake_case_									=				[5, 11, 17, 23]
										snake_case_									=				[256, 512, 1024, 1024]
										snake_case_									=				(1, 384, 384)

					if "ade" in checkpoint_url:
										snake_case_									=				True

										snake_case_									=				150
										snake_case_									=				'''huggingface/label-files'''
										snake_case_									=				'''ade20k-id2label.json'''
										snake_case_									=				json.load(open(cached_download(hf_hub_url(__UpperCAmelCase,      __UpperCAmelCase,      repo_type='''dataset'''	)	),      '''r'''	)	)
										snake_case_									=				{int(__UpperCAmelCase	): v for k, v in idalabel.items()}
										snake_case_									=				idalabel
										snake_case_									=				{v: k for k, v in idalabel.items()}
										snake_case_									=				[1, 150, 480, 480]

					return config, expected_shape


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

					'''simple docstring'''

					snake_case_									=				['''pretrained.model.head.weight''', '''pretrained.model.head.bias''']
					for k in ignore_keys:
										state_dict.pop(__UpperCAmelCase,      __UpperCAmelCase	)


def 		__magic_name__    (	__UpperCAmelCase	)		->					List[Any]:

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

					return name


def 		__magic_name__    (	__UpperCAmelCase,      __UpperCAmelCase	)		->					Dict:

					'''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)
										snake_case_									=				state_dict.pop(F"dpt.encoder.layer.{i}.attn.qkv.weight"	)
										snake_case_									=				state_dict.pop(F"dpt.encoder.layer.{i}.attn.qkv.bias"	)
										# next, add query, keys and values (in that order) to the state dict
										snake_case_									=				in_proj_weight[: config.hidden_size, :]
										snake_case_									=				in_proj_bias[: config.hidden_size]
										snake_case_									=				in_proj_weight[
										    config.hidden_size : config.hidden_size * 2, :
										]
										snake_case_									=				in_proj_bias[
										    config.hidden_size : config.hidden_size * 2
										]
										snake_case_									=				in_proj_weight[
										    -config.hidden_size :, :
										]
										snake_case_									=				in_proj_bias[-config.hidden_size :]


def 		__magic_name__    (	)		->					Any:

					'''simple docstring'''

					snake_case_									=				'''http://images.cocodataset.org/val2017/000000039769.jpg'''
					snake_case_									=				Image.open(requests.get(__UpperCAmelCase,      stream=__UpperCAmelCase	).raw	)
					return im


@torch.no_grad()
def 		__magic_name__    (	__UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase	)		->					Union[str, Any]:

					'''simple docstring'''

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

					# load HuggingFace model
					snake_case_									=				DPTForSemanticSegmentation(__UpperCAmelCase	) if '''ade''' in checkpoint_url else DPTForDepthEstimation(__UpperCAmelCase	)
					model.load_state_dict(__UpperCAmelCase	)
					model.eval()

					# Check outputs on an image
					snake_case_									=				480 if '''ade''' in checkpoint_url else 384
					snake_case_									=				DPTImageProcessor(size=__UpperCAmelCase	)

					snake_case_									=				prepare_img()
					snake_case_									=				image_processor(__UpperCAmelCase,      return_tensors='''pt'''	)

					# forward pass
					snake_case_									=				model(**__UpperCAmelCase	).logits if '''ade''' in checkpoint_url else model(**__UpperCAmelCase	).predicted_depth

					# Assert logits
					snake_case_									=				torch.tensor([[6.3_1_9_9, 6.3_6_2_9, 6.4_1_4_8], [6.3_8_5_0, 6.3_6_1_5, 6.4_1_6_6], [6.3_5_1_9, 6.3_1_7_6, 6.3_5_7_5]]	)
					if "ade" in checkpoint_url:
										snake_case_									=				torch.tensor([[4.0_4_8_0, 4.2_4_2_0, 4.4_3_6_0], [4.3_1_2_4, 4.5_6_9_3, 4.8_2_6_1], [4.5_7_6_8, 4.8_9_6_5, 5.2_1_6_3]]	)
					assert outputs.shape == torch.Size(__UpperCAmelCase	)
					assert (
					    torch.allclose(outputs[0, 0, :3, :3],      __UpperCAmelCase,      atol=1e-4	)
					    if "ade" in checkpoint_url
					    else torch.allclose(outputs[0, :3, :3],      __UpperCAmelCase	)
					)

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

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


if __name__ == "__main__":
							a  :				Tuple									=  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  :				List[Any]									=  parser.parse_args()
							convert_dpt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)

