



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  __lowerCamelCase (       lowerCamelCase__						):

				"""simple docstring"""


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

				return new_state_dict


def  __lowerCamelCase (       lowerCamelCase__					,			lowerCamelCase__						):

				"""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)
												lowercase__					:   Union[str, Any]													=					state_dict.pop(F"""glpn.encoder.block.{i}.{j}.attention.self.kv.weight"""						)
												lowercase__					:   Any													=					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
												lowercase__					:   Optional[Any]													=					kv_weight[
												    : config.hidden_sizes[i], :
												]
												lowercase__					:   int													=					kv_bias[: config.hidden_sizes[i]]
												lowercase__					:   int													=					kv_weight[
												    config.hidden_sizes[i] :, :
												]
												lowercase__					:   int													=					kv_bias[config.hidden_sizes[i] :]


def  __lowerCamelCase (       ):

				"""simple docstring"""


				lowercase__					:   List[str]													=					"http://images.cocodataset.org/val2017/000000039769.jpg"
				lowercase__					:   Dict													=					Image.open(requests.get(a_					,			stream=a_						).raw						)

				return image


@torch.no_grad()
def  __lowerCamelCase (       lowerCamelCase__					,			lowerCamelCase__					,			lowerCamelCase__=False					,			lowerCamelCase__=None						):

				"""simple docstring"""


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

				# load image processor (only resize + rescale)
				lowercase__					:   List[str]													=					GLPNImageProcessor()

				# prepare image
				lowercase__					:   Dict													=					prepare_img()
				lowercase__					:   Optional[Any]													=					image_processor(images=a_					,			return_tensors="pt"						).pixel_values

				logger.info("Converting model..."						)

				# load original state dict
				lowercase__					:   Tuple													=					torch.load(a_					,			map_location=torch.device("cpu"						)						)

				# rename keys
				lowercase__					:   int													=					rename_keys(a_						)

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

				# create HuggingFace model and load state dict
				lowercase__					:   Optional[int]													=					GLPNForDepthEstimation(a_						)
				model.load_state_dict(a_						)
				model.eval()

				# forward pass
				lowercase__					:   int													=					model(a_						)
				lowercase__					:   Any													=					outputs.predicted_depth

				# verify output
				if model_name is not None:
								if "nyu" in model_name:
												lowercase__					:   Tuple													=					torch.tensor(
												    [[4.4147, 4.0873, 4.0673], [3.7890, 3.2881, 3.1525], [3.7674, 3.5423, 3.4913]]						)
								elif "kitti" in model_name:
												lowercase__					:   int													=					torch.tensor(
												    [[3.4291, 2.7865, 2.5151], [3.2841, 2.7021, 2.3502], [3.1147, 2.4625, 2.2481]]						)
								else:
												raise ValueError(F"""Unknown model name: {model_name}"""						)

								lowercase__					:   int													=					torch.Size([1, 480, 640]						)

								assert predicted_depth.shape == expected_shape
								assert torch.allclose(predicted_depth[0, :3, :3]					,			a_					,			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(a_					,			a_						)					,			organization="nielsr"					,			commit_message="Add model"					,			use_temp_dir=a_					,			)
								image_processor.push_to_hub(
								    repo_path_or_name=Path(a_					,			a_						)					,			organization="nielsr"					,			commit_message="Add image processor"					,			use_temp_dir=a_					,			)


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 multiprocessing
import time

from arguments import PretokenizationArguments
from datasets import load_dataset

from transformers import AutoTokenizer, HfArgumentParser


def  __lowerCamelCase (       lowerCamelCase__						):

				"""simple docstring"""


				lowercase__					:   Optional[Any]													=					{}
				lowercase__					:   Tuple													=					tokenizer(example["content"]					,			truncation=lowerCamelCase__						)["input_ids"]
				lowercase__					:   Optional[int]													=					len(example["content"]						) / len(output["input_ids"]						)
				return output


lowerCAmelCase__					  =  HfArgumentParser(PretokenizationArguments)
lowerCAmelCase__					  =  parser.parse_args()
if args.num_workers is None:
				lowerCAmelCase__					  =  multiprocessing.cpu_count()
lowerCAmelCase__					  =  AutoTokenizer.from_pretrained(args.tokenizer_dir)

lowerCAmelCase__					  =  time.time()
lowerCAmelCase__					  =  load_dataset(args.dataset_name, split='''train''')
print(f'''Dataset loaded in {time.time()-t_start:.2f}s''')

lowerCAmelCase__					  =  time.time()
lowerCAmelCase__					  =  ds.map(
    tokenize,
    num_proc=args.num_workers,
    remove_columns=[
        '''repo_name''',
        '''path''',
        '''copies''',
        '''size''',
        '''content''',
        '''license''',
        '''hash''',
        '''line_mean''',
        '''line_max''',
        '''alpha_frac''',
        '''autogenerated''',
    ],
)
print(f'''Dataset tokenized in {time.time()-t_start:.2f}s''')

lowerCAmelCase__					  =  time.time()
ds.push_to_hub(args.tokenized_data_repo)
print(f'''Data pushed to the hub in {time.time()-t_start:.2f}s''')




