


'''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()
_lowerCAmelCase  :		str		     =	logging.get_logger(__name__)

def 			__UpperCamelCase    (    _A    :				Tuple				)    ->      Optional[Any]:




	"""simple docstring"""





	lowerCAmelCase					:    List[Any]      							= DPTConfig(embedding_type='hybrid'				)

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

	if "nyu" or "midas" in checkpoint_url:
		lowerCAmelCase					:    int      							= 7_68
		lowerCAmelCase					:    Optional[int]      							= [1, 1, 1, 0.5]
		lowerCAmelCase					:    Any      							= [2_56, 5_12, 7_68, 7_68]
		lowerCAmelCase					:    Tuple      							= 1_50
		lowerCAmelCase					:    List[str]      							= 16
		lowerCAmelCase					:    Dict      							= (1, 3_84, 3_84)
		lowerCAmelCase					:    List[Any]      							= False
		lowerCAmelCase					:    Tuple      							= 'project'

	if "ade" in checkpoint_url:
		lowerCAmelCase					:    str      							= True
		lowerCAmelCase					:    Tuple      							= 7_68
		lowerCAmelCase					:    Optional[Any]      							= [1, 1, 1, 0.5]
		lowerCAmelCase					:    Optional[int]      							= 1_50
		lowerCAmelCase					:    Optional[Any]      							= 16
		lowerCAmelCase					:    List[Any]      							= 'huggingface/label-files'
		lowerCAmelCase					:    str      							= 'ade20k-id2label.json'
		lowerCAmelCase					:    str      							= json.load(open(cached_download(hf_hub_url(_A					,   _A					,   repo_type='dataset'				)				)					,   'r'				)				)
		lowerCAmelCase					:    Optional[Any]      							= {int(_A				): v for k, v in idalabel.items()}
		lowerCAmelCase					:    str      							= idalabel
		lowerCAmelCase					:    Dict      							= {v: k for k, v in idalabel.items()}
		lowerCAmelCase					:    Optional[int]      							= [1, 1_50, 4_80, 4_80]

	return config, expected_shape

def 			__UpperCamelCase    (    _A    :				int				)    ->      Union[str, Any]:




	"""simple docstring"""





	lowerCAmelCase					:    List[str]      							= ['pretrained.model.head.weight', 'pretrained.model.head.bias']
	for k in ignore_keys:
		state_dict.pop(_A					,   _A				)

def 			__UpperCamelCase    (    _A    :				Optional[Any]				)    ->      str:




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

	if ".." in name:
		lowerCAmelCase					:    List[Any]      							= name.replace('..'					,   '.'				)

	if "stem.conv" in name:
		lowerCAmelCase					:    List[str]      							= name.replace('stem.conv'					,   'bit.embedder.convolution'				)
	if "blocks" in name:
		lowerCAmelCase					:    Optional[int]      							= name.replace('blocks'					,   'layers'				)
	if "convolution" in name and "backbone" in name:
		lowerCAmelCase					:    Union[str, Any]      							= name.replace('convolution'					,   'conv'				)
	if "layer" in name and "backbone" in name:
		lowerCAmelCase					:    str      							= name.replace('layer'					,   'layers'				)
	if "backbone.bit.encoder.bit" in name:
		lowerCAmelCase					:    Any      							= name.replace('backbone.bit.encoder.bit'					,   'backbone.bit'				)
	if "embedder.conv" in name:
		lowerCAmelCase					:    Union[str, Any]      							= name.replace('embedder.conv'					,   'embedder.convolution'				)
	if "backbone.bit.encoder.stem.norm" in name:
		lowerCAmelCase					:    Optional[int]      							= name.replace('backbone.bit.encoder.stem.norm'					,   'backbone.bit.embedder.norm'				)
	return name

def 			__UpperCamelCase    (    _A    :				Dict					,   _A    :				str				)    ->      Union[str, Any]:




	"""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					:    Union[str, Any]      							= state_dict.pop(F"dpt.encoder.layer.{i}.attn.qkv.weight"				)
		lowerCAmelCase					:    Optional[Any]      							= 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					:    Dict      							= in_proj_weight[: config.hidden_size, :]
		lowerCAmelCase					:    Optional[int]      							= in_proj_bias[: config.hidden_size]
		lowerCAmelCase					:    Any      							= in_proj_weight[
		    config.hidden_size : config.hidden_size * 2, :
		]
		lowerCAmelCase					:    Optional[int]      							= in_proj_bias[
		    config.hidden_size : config.hidden_size * 2
		]
		lowerCAmelCase					:    Union[str, Any]      							= in_proj_weight[
		    -config.hidden_size :, :
		]
		lowerCAmelCase					:    str      							= in_proj_bias[-config.hidden_size :]

def 			__UpperCamelCase    (    )    ->      List[Any]:




	"""simple docstring"""





	lowerCAmelCase					:    int      							= 'http://images.cocodataset.org/val2017/000000039769.jpg'
	lowerCAmelCase					:    Dict      							= Image.open(requests.get(_A					,   stream=_A				).raw				)
	return im

@torch.no_grad()
def 			__UpperCamelCase    (    _A    :				Dict					,   _A    :				Union[str, Any]					,   _A    :				List[str]					,   _A    :				Optional[int]					,   _A    :				Optional[int]				)    ->      Union[str, Any]:




	"""simple docstring"""





	lowerCAmelCase					:    List[str]      							= get_dpt_config(_A				)
	# load original state_dict from URL
	# state_dict = torch.hub.load_state_dict_from_url(checkpoint_url, map_location="cpu")
	lowerCAmelCase					:    str      							= torch.load(_A					,   map_location='cpu'				)
	# remove certain keys
	remove_ignore_keys_(_A				)
	# rename keys
	for key in state_dict.copy().keys():
		lowerCAmelCase					:    str      							= state_dict.pop(_A				)
		lowerCAmelCase					:    Union[str, Any]      							= val
	# read in qkv matrices
	read_in_q_k_v(_A					,   _A				)

	# load HuggingFace model
	lowerCAmelCase					:    Any      							= DPTForSemanticSegmentation(_A				) if 'ade' in checkpoint_url else DPTForDepthEstimation(_A				)
	model.load_state_dict(_A				)
	model.eval()

	# Check outputs on an image
	lowerCAmelCase					:    List[str]      							= 4_80 if 'ade' in checkpoint_url else 3_84
	lowerCAmelCase					:    Any      							= DPTImageProcessor(size=_A				)

	lowerCAmelCase					:    Tuple      							= prepare_img()
	lowerCAmelCase					:    int      							= image_processor(_A					,   return_tensors='pt'				)

	# forward pass
	lowerCAmelCase					:    Optional[Any]      							= model(**_A				).logits if 'ade' in checkpoint_url else model(**_A				).predicted_depth

	if show_prediction:
		lowerCAmelCase					:    Union[str, Any]      							= (
		    torch.nn.functional.interpolate(
		        outputs.unsqueeze(1				)					,   size=(image.size[1], image.size[0])					,   mode='bicubic'					,   align_corners=_A					,   )
		    .squeeze()
		    .cpu()
		    .numpy()
		)

		Image.fromarray((prediction / prediction.max()) * 2_55				).show()

	if pytorch_dump_folder_path is not None:
		Path(_A				).mkdir(exist_ok=_A				)
		print(F"Saving model to {pytorch_dump_folder_path}"				)
		model.save_pretrained(_A				)
		print(F"Saving image processor to {pytorch_dump_folder_path}"				)
		image_processor.save_pretrained(_A				)

	if push_to_hub:
		model.push_to_hub('ybelkada/dpt-hybrid-midas'				)
		image_processor.push_to_hub('ybelkada/dpt-hybrid-midas'				)


if __name__ == "__main__":
					_lowerCAmelCase  :		str		     =	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=False,
					    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.',
					)
					parser.add_argument(
					    '--show_prediction',
					    action='store_true',
					)

					_lowerCAmelCase  :		Any		     =	parser.parse_args()
					convert_dpt_checkpoint(
					    args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name, args.show_prediction
					)




'''simple docstring'''




import argparse
import logging
import os

import datasets
import tensorflow as tf

from transformers import AutoTokenizer


_lowerCAmelCase  :		List[Any]		     =	logging.getLogger(__name__)

def 			__UpperCamelCase    (    )    ->      Any:




	"""simple docstring"""





	lowerCAmelCase					:    str      							= argparse.ArgumentParser(
	    description='Prepare TFRecord shards from pre-tokenized samples of the wikitext dataset.'				)
	parser.add_argument(
	    '--dataset_name'					,   type=_A					,   default='wikitext'					,   help='Name of the training. Explore datasets at: hf.co/datasets.'					,   )
	parser.add_argument(
	    '--dataset_config'					,   type=_A					,   default='wikitext-103-raw-v1'					,   help='Configuration name of the dataset.'				)
	parser.add_argument(
	    '--tokenizer_name_or_path'					,   type=_A					,   default='sayakpaul/unigram-tokenizer-wikitext'					,   help='Tokenizer identifier. Can be a local filepath or a Hub identifier.'					,   )
	parser.add_argument(
	    '--shard_size'					,   type=_A					,   default=10_00					,   help='Number of entries to go in a single shard.'					,   )
	parser.add_argument('--split'					,   type=_A					,   default='train'					,   choices=['train', 'test', 'validation']				)
	parser.add_argument(
	    '--limit'					,   default=_A					,   type=_A					,   help='Limit the number of shards (used for debugging).'					,   )
	parser.add_argument(
	    '--max_length'					,   type=_A					,   default=5_12					,   help='Maximum sequence length. For training on TPUs, it helps to have a maximum'
	    ' sequence length that is a multiple of 8.'					,   )
	parser.add_argument(
	    '--output_dir'					,   default='tf-tpu'					,   type=_A					,   help='Output directory where the TFRecord shards will be saved. If the'
	    ' path is appended with `gs://` (\'gs://tf-tpu\', for example) then the TFRecord'
	    ' shards will be directly saved to a Google Cloud Storage bucket.'					,   )

	lowerCAmelCase					:    Any      							= parser.parse_args()
	return args

def 			__UpperCamelCase    (    _A    :				Optional[int]				)    ->      int:




	"""simple docstring"""





	def fn(_A    :				Tuple				):
		return tokenizer(examples['text']				)

	return fn

def 			__UpperCamelCase    (    _A    :				int				)    ->      int:




	"""simple docstring"""





	lowerCAmelCase					:    Tuple      							= []
	for i in range(len(tokenized_data['input_ids']				)				):
		lowerCAmelCase					:    Optional[Any]      							= {
		    'input_ids': tf.train.Feature(intaa_list=tf.train.IntaaList(value=tokenized_data['input_ids'][i]				)				),
		    'attention_mask': tf.train.Feature(
		        intaa_list=tf.train.IntaaList(value=tokenized_data['attention_mask'][i]				)				),
		}
		lowerCAmelCase					:    Any      							= tf.train.Features(feature=_A				)
		lowerCAmelCase					:    List[str]      							= tf.train.Example(features=_A				)
		lowerCAmelCase					:    Tuple      							= example.SerializeToString()
		records.append(_A				)
	return records

def 			__UpperCamelCase    (    _A    :				int				)    ->      Union[str, Any]:




	"""simple docstring"""





	lowerCAmelCase					:    Union[str, Any]      							= datasets.load_dataset(args.dataset_name					,   args.dataset_config					,   split=args.split				)

	if args.limit is not None:
		lowerCAmelCase					:    Optional[Any]      							= min(len(_A				)					,   args.limit				)
		lowerCAmelCase					:    Dict      							= dataset.select(range(_A				)				)
		print(F"Limiting the dataset to {args.limit} entries."				)

	lowerCAmelCase					:    str      							= AutoTokenizer.from_pretrained(args.tokenizer_name_or_path				)

	# Handle output directory creation.
	# For serializing into a Google Cloud Storage Bucket, one needs to first
	# create a bucket.
	if "gs" not in args.output_dir:
		if not os.path.exists(args.output_dir				):
			os.makedirs(args.output_dir				)
		lowerCAmelCase					:    Any      							= os.path.join(args.output_dir					,   args.split				)
		if not os.path.exists(_A				):
			os.makedirs(_A				)
	else:
		lowerCAmelCase					:    List[Any]      							= os.path.join(args.output_dir					,   args.split				)

	# Tokenize the whole dataset at once.
	lowerCAmelCase					:    Any      							= tokenize_function(_A				)
	lowerCAmelCase					:    Optional[int]      							= dataset.map(_A					,   batched=_A					,   num_proc=4					,   remove_columns=['text']				)

	# We need to concatenate all our texts together, and then split the result
	# into chunks of a fixed size, which we will call block_size. To do this, we
	# will use the map method again, with the option batched=True. When we use batched=True,
	# the function we pass to map() will be passed multiple inputs at once, allowing us
	# to group them into more or fewer examples than we had in the input.
	# This allows us to create our new fixed-length samples. The advantage of this
	# method is that we don't lose a whole lot of content from the dataset compared to the
	# case where we simply tokenize with a pre-defined max_length.

	def group_texts(_A    :				str				):
		# Concatenate all texts.
		lowerCAmelCase					:    Optional[int]      							= {k: sum(examples[k]					,   []				) for k in examples.keys()}
		lowerCAmelCase					:    str      							= len(concatenated_examples[list(examples.keys()				)[0]]				)
		# We drop the small remainder, though you could add padding instead if the model supports it
		# In this, as in all things, we advise you to follow your heart 🫀
		lowerCAmelCase					:    List[Any]      							= (total_length // args.max_length) * args.max_length
		# Split by chunks of max_len.
		lowerCAmelCase					:    str      							= {
		    k: [t[i : i + args.max_length] for i in range(0					,   _A					,   args.max_length				)]
		    for k, t in concatenated_examples.items()
		}
		return result

	lowerCAmelCase					:    List[Any]      							= dataset_tokenized.map(_A					,   batched=_A					,   batch_size=10_00					,   num_proc=4				)

	lowerCAmelCase					:    Union[str, Any]      							= 0
	lowerCAmelCase					:    Tuple      							= 0
	for shard in range(0					,   len(_A				)					,   args.shard_size				):
		lowerCAmelCase					:    Optional[Any]      							= grouped_dataset[shard : shard + args.shard_size]
		lowerCAmelCase					:    List[str]      							= len(dataset_snapshot['input_ids']				)
		lowerCAmelCase					:    Union[str, Any]      							= os.path.join(_A					,   F"dataset-{shard_count}-{records_containing}.tfrecord"				)
		lowerCAmelCase					:    List[Any]      							= get_serialized_examples(_A				)

		with tf.io.TFRecordWriter(_A				) as out_file:
			for i in range(len(_A				)				):
				lowerCAmelCase					:    Union[str, Any]      							= serialized_examples[i]
				out_file.write(_A				)
			print('Wrote file {} containing {} records'.format(_A					,   _A				)				)

		shard_count += 1
		total_records += records_containing

	with open(F"split-{args.split}-records-count.txt"					,   'w'				) as f:
		print(F"Total {args.split} records: {total_records}"					,   file=_A				)


if __name__ == "__main__":
				_lowerCAmelCase  :		List[Any]		     =	parse_args()
				main(args)
