



'''simple docstring'''






import argparse
import gc
import json
import os
import shutil
import warnings

import torch

from transformers import LlamaConfig, LlamaForCausalLM, LlamaTokenizer


try:
						from transformers import LlamaTokenizerFast
except ImportError as e:
						warnings.warn(e)
						warnings.warn(
						    "The converted tokenizer will be the `slow` tokenizer. To use the fast, update your `tokenizers` library and re-run the tokenizer conversion"
						)
						UpperCamelCase__:   str								=			None
UpperCamelCase__:   int								=			{
    "7B": 11008,
    "13B": 13824,
    "30B": 17920,
    "65B": 22016,
    "70B": 28672,
}
UpperCamelCase__:   List[Any]								=			{
    "7B": 1,
    "7Bf": 1,
    "13B": 2,
    "13Bf": 2,
    "30B": 4,
    "65B": 8,
    "70B": 8,
    "70Bf": 8,
}


def  snake_case_     (					_lowerCAmelCase  :						Tuple   ,      _lowerCAmelCase  :						Tuple=1   ,      _lowerCAmelCase  :						List[Any]=256  )      ->   Optional[Any]:
							return multiple_of * ((int(ffn_dim_multiplier * int(8 * n / 3  )  ) + multiple_of - 1) // multiple_of)


def  snake_case_     (					_lowerCAmelCase  :						List[str]  )      ->   str:
							with open(_lowerCAmelCase   ,      '''r'''  ) as f:
														return json.load(_lowerCAmelCase  )


def  snake_case_     (					_lowerCAmelCase  :						Union[str, Any]   ,      _lowerCAmelCase  :						Any  )      ->   Optional[Any]:
							with open(_lowerCAmelCase   ,      '''w'''  ) as f:
														json.dump(_lowerCAmelCase   ,      _lowerCAmelCase  )


def  snake_case_     (					_lowerCAmelCase  :						Dict   ,      _lowerCAmelCase  :						Tuple   ,      _lowerCAmelCase  :						Any   ,      _lowerCAmelCase  :						Any=True  )      ->   List[Any]:
							os.makedirs(_lowerCAmelCase   ,      exist_ok=_lowerCAmelCase  )
							UpperCAmelCase :       int 							=		os.path.join(_lowerCAmelCase   ,      '''tmp'''  )
							os.makedirs(_lowerCAmelCase   ,      exist_ok=_lowerCAmelCase  )

							UpperCAmelCase :       List[str] 							=		read_json(os.path.join(_lowerCAmelCase   ,      '''params.json'''  )  )
							UpperCAmelCase :       str 							=		NUM_SHARDS[model_size]
							UpperCAmelCase :       Any 							=		params['''n_layers''']
							UpperCAmelCase :       str 							=		params['''n_heads''']
							UpperCAmelCase :       Any 							=		n_heads // num_shards
							UpperCAmelCase :       List[str] 							=		params['''dim''']
							UpperCAmelCase :       Optional[Any] 							=		dim // n_heads
							UpperCAmelCase :       str 							=		1_0_0_0_0.0
							UpperCAmelCase :       Optional[int] 							=		1.0 / (base ** (torch.arange(0   ,      _lowerCAmelCase   ,      2  ).float() / dims_per_head))

							if "n_kv_heads" in params:
														UpperCAmelCase :       Tuple 							=		params['''n_kv_heads''']  # for GQA / MQA
														UpperCAmelCase :       Optional[int] 							=		n_heads_per_shard // num_key_value_heads
														UpperCAmelCase :       Optional[Any] 							=		dim // num_key_value_heads
							else:  # compatibility with other checkpoints
														UpperCAmelCase :       List[str] 							=		n_heads
														UpperCAmelCase :       Optional[int] 							=		n_heads_per_shard
														UpperCAmelCase :       List[str] 							=		dim

							# permute for sliced rotary
							def permute(_lowerCAmelCase  :						Tuple   ,      _lowerCAmelCase  :						Optional[Any]=n_heads   ,      _lowerCAmelCase  :						int=dim   ,      _lowerCAmelCase  :						Dict=dim  ):
														return w.view(_lowerCAmelCase   ,      dima // n_heads // 2   ,      2   ,      _lowerCAmelCase  ).transpose(1   ,      2  ).reshape(_lowerCAmelCase   ,      _lowerCAmelCase  )

							print(f"""Fetching all parameters from the checkpoint at {input_base_path}."""  )
							# Load weights
							if model_size == "7B":
														# Not sharded
														# (The sharded implementation would also work, but this is simpler.)
														UpperCAmelCase :       int 							=		torch.load(os.path.join(_lowerCAmelCase   ,      '''consolidated.00.pth'''  )   ,      map_location='''cpu'''  )
							else:
														# Sharded
														UpperCAmelCase :       Optional[Any] 							=		[
														    torch.load(os.path.join(_lowerCAmelCase   ,      f"""consolidated.{i:02d}.pth"""  )   ,      map_location='''cpu'''  )
														    for i in range(_lowerCAmelCase  )
														]
							UpperCAmelCase :       Any 							=		0
							UpperCAmelCase :       str 							=		{'''weight_map''': {}}
							for layer_i in range(_lowerCAmelCase  ):
														UpperCAmelCase :       Optional[Any] 							=		f"""pytorch_model-{layer_i + 1}-of-{n_layers + 1}.bin"""
														if model_size == "7B":
																					# Unsharded
																					UpperCAmelCase :       Optional[int] 							=		{
																					    f"""model.layers.{layer_i}.self_attn.q_proj.weight""": permute(
																					        loaded[f"""layers.{layer_i}.attention.wq.weight"""]  ),
																					    f"""model.layers.{layer_i}.self_attn.k_proj.weight""": permute(
																					        loaded[f"""layers.{layer_i}.attention.wk.weight"""]  ),
																					    f"""model.layers.{layer_i}.self_attn.v_proj.weight""": loaded[f"""layers.{layer_i}.attention.wv.weight"""],
																					    f"""model.layers.{layer_i}.self_attn.o_proj.weight""": loaded[f"""layers.{layer_i}.attention.wo.weight"""],
																					    f"""model.layers.{layer_i}.mlp.gate_proj.weight""": loaded[f"""layers.{layer_i}.feed_forward.w1.weight"""],
																					    f"""model.layers.{layer_i}.mlp.down_proj.weight""": loaded[f"""layers.{layer_i}.feed_forward.w2.weight"""],
																					    f"""model.layers.{layer_i}.mlp.up_proj.weight""": loaded[f"""layers.{layer_i}.feed_forward.w3.weight"""],
																					    f"""model.layers.{layer_i}.input_layernorm.weight""": loaded[f"""layers.{layer_i}.attention_norm.weight"""],
																					    f"""model.layers.{layer_i}.post_attention_layernorm.weight""": loaded[f"""layers.{layer_i}.ffn_norm.weight"""],
																					}
														else:
																					# Sharded
																					# Note that attention.w{q,k,v,o}, feed_fordward.w[1,2,3], attention_norm.weight and ffn_norm.weight share
																					# the same storage object, saving attention_norm and ffn_norm will save other weights too, which is
																					# redundant as other weights will be stitched from multiple shards. To avoid that, they are cloned.

																					UpperCAmelCase :       List[str] 							=		{
																					    f"""model.layers.{layer_i}.input_layernorm.weight""": loaded[0][
																					        f"""layers.{layer_i}.attention_norm.weight"""
																					    ].clone(),
																					    f"""model.layers.{layer_i}.post_attention_layernorm.weight""": loaded[0][
																					        f"""layers.{layer_i}.ffn_norm.weight"""
																					    ].clone(),
																					}
																					UpperCAmelCase :       Union[str, Any] 							=		permute(
																					    torch.cat(
																					        [
																					            loaded[i][f"""layers.{layer_i}.attention.wq.weight"""].view(_lowerCAmelCase   ,      _lowerCAmelCase   ,      _lowerCAmelCase  )
																					            for i in range(_lowerCAmelCase  )
																					        ]   ,      dim=0   ,      ).reshape(_lowerCAmelCase   ,      _lowerCAmelCase  )  )
																					UpperCAmelCase :       Optional[Any] 							=		permute(
																					    torch.cat(
																					        [
																					            loaded[i][f"""layers.{layer_i}.attention.wk.weight"""].view(
																					                _lowerCAmelCase   ,      _lowerCAmelCase   ,      _lowerCAmelCase  )
																					            for i in range(_lowerCAmelCase  )
																					        ]   ,      dim=0   ,      ).reshape(_lowerCAmelCase   ,      _lowerCAmelCase  )   ,      _lowerCAmelCase   ,      _lowerCAmelCase   ,      _lowerCAmelCase   ,      )
																					UpperCAmelCase :       str 							=		torch.cat(
																					    [
																					        loaded[i][f"""layers.{layer_i}.attention.wv.weight"""].view(
																					            _lowerCAmelCase   ,      _lowerCAmelCase   ,      _lowerCAmelCase  )
																					        for i in range(_lowerCAmelCase  )
																					    ]   ,      dim=0   ,      ).reshape(_lowerCAmelCase   ,      _lowerCAmelCase  )

																					UpperCAmelCase :       Optional[int] 							=		torch.cat(
																					    [loaded[i][f"""layers.{layer_i}.attention.wo.weight"""] for i in range(_lowerCAmelCase  )]   ,      dim=1  )
																					UpperCAmelCase :       Any 							=		torch.cat(
																					    [loaded[i][f"""layers.{layer_i}.feed_forward.w1.weight"""] for i in range(_lowerCAmelCase  )]   ,      dim=0  )
																					UpperCAmelCase :       str 							=		torch.cat(
																					    [loaded[i][f"""layers.{layer_i}.feed_forward.w2.weight"""] for i in range(_lowerCAmelCase  )]   ,      dim=1  )
																					UpperCAmelCase :       Tuple 							=		torch.cat(
																					    [loaded[i][f"""layers.{layer_i}.feed_forward.w3.weight"""] for i in range(_lowerCAmelCase  )]   ,      dim=0  )

														UpperCAmelCase :       Any 							=		inv_freq
														for k, v in state_dict.items():
																					UpperCAmelCase :       List[Any] 							=		filename
																					param_count += v.numel()
														torch.save(_lowerCAmelCase   ,      os.path.join(_lowerCAmelCase   ,      _lowerCAmelCase  )  )

							UpperCAmelCase :       Optional[int] 							=		f"""pytorch_model-{n_layers + 1}-of-{n_layers + 1}.bin"""
							if model_size == "7B":
														# Unsharded
														UpperCAmelCase :       str 							=		{
														    '''model.embed_tokens.weight''': loaded['''tok_embeddings.weight'''],
														    '''model.norm.weight''': loaded['''norm.weight'''],
														    '''lm_head.weight''': loaded['''output.weight'''],
														}
							else:
														UpperCAmelCase :       Any 							=		{
														    '''model.norm.weight''': loaded[0]['''norm.weight'''],
														    '''model.embed_tokens.weight''': torch.cat(
														        [loaded[i]['''tok_embeddings.weight'''] for i in range(_lowerCAmelCase  )]   ,      dim=1  ),
														    '''lm_head.weight''': torch.cat([loaded[i]['''output.weight'''] for i in range(_lowerCAmelCase  )]   ,      dim=0  ),
														}

							for k, v in state_dict.items():
														UpperCAmelCase :       Optional[int] 							=		filename
														param_count += v.numel()
							torch.save(_lowerCAmelCase   ,      os.path.join(_lowerCAmelCase   ,      _lowerCAmelCase  )  )

							# Write configs
							UpperCAmelCase :       Union[str, Any] 							=		{'''total_size''': param_count * 2}
							write_json(_lowerCAmelCase   ,      os.path.join(_lowerCAmelCase   ,      '''pytorch_model.bin.index.json'''  )  )
							UpperCAmelCase :       int 							=		params['''ffn_dim_multiplier'''] if '''ffn_dim_multiplier''' in params else 1
							UpperCAmelCase :       Tuple 							=		params['''multiple_of'''] if '''multiple_of''' in params else 256
							UpperCAmelCase :       Any 							=		LlamaConfig(
							    hidden_size=_lowerCAmelCase   ,      intermediate_size=compute_intermediate_size(_lowerCAmelCase   ,      _lowerCAmelCase   ,      _lowerCAmelCase  )   ,      num_attention_heads=params['''n_heads''']   ,      num_hidden_layers=params['''n_layers''']   ,      rms_norm_eps=params['''norm_eps''']   ,      num_key_value_heads=_lowerCAmelCase   ,      )
							config.save_pretrained(_lowerCAmelCase  )

							# Make space so we can load the model properly now.
							del state_dict
							del loaded
							gc.collect()

							print('''Loading the checkpoint in a Llama model.'''  )
							UpperCAmelCase :       Optional[int] 							=		LlamaForCausalLM.from_pretrained(_lowerCAmelCase   ,      torch_dtype=torch.floataa   ,      low_cpu_mem_usage=_lowerCAmelCase  )
							# Avoid saving this as part of the config.
							del model.config._name_or_path

							print('''Saving in the Transformers format.'''  )
							model.save_pretrained(_lowerCAmelCase   ,      safe_serialization=_lowerCAmelCase  )
							shutil.rmtree(_lowerCAmelCase  )


def  snake_case_     (					_lowerCAmelCase  :						int   ,      _lowerCAmelCase  :						Union[str, Any]  )      ->   List[str]:
							# Initialize the tokenizer based on the `spm` model
							UpperCAmelCase :       Dict 							=		LlamaTokenizer if LlamaTokenizerFast is None else LlamaTokenizerFast
							print(f"""Saving a {tokenizer_class.__name__} to {tokenizer_path}."""  )
							UpperCAmelCase :       List[Any] 							=		tokenizer_class(_lowerCAmelCase  )
							tokenizer.save_pretrained(_lowerCAmelCase  )


def  snake_case_     (					)      ->   List[Any]:
							UpperCAmelCase :       int 							=		argparse.ArgumentParser()
							parser.add_argument(
							    '''--input_dir'''   ,      help='''Location of LLaMA weights, which contains tokenizer.model and model folders'''   ,      )
							parser.add_argument(
							    '''--model_size'''   ,      choices=['''7B''', '''7Bf''', '''13B''', '''13Bf''', '''30B''', '''65B''', '''70B''', '''70Bf''', '''tokenizer_only''']   ,      )
							parser.add_argument(
							    '''--output_dir'''   ,      help='''Location to write HF model and tokenizer'''   ,      )
							parser.add_argument('''--safe_serialization'''   ,      type=_lowerCAmelCase   ,      help='''Whether or not to save using `safetensors`.'''  )
							UpperCAmelCase :       List[Any] 							=		parser.parse_args()
							if args.model_size != "tokenizer_only":
														write_model(
														    model_path=args.output_dir   ,      input_base_path=os.path.join(args.input_dir   ,      args.model_size  )   ,      model_size=args.model_size   ,      safe_serialization=args.safe_serialization   ,      )
							UpperCAmelCase :       Optional[int] 							=		os.path.join(args.input_dir   ,      '''tokenizer.model'''  )
							write_tokenizer(args.output_dir   ,      _lowerCAmelCase  )


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()
UpperCamelCase__:   Optional[int]								=			logging.get_logger(__name__)


def  snake_case_     (					_lowerCAmelCase  :						Optional[int]  )      ->   Optional[int]:
							UpperCAmelCase :       Tuple 							=		DPTConfig(embedding_type='''hybrid'''  )

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

							if "nyu" or "midas" in checkpoint_url:
														UpperCAmelCase :       Optional[Any] 							=		768
														UpperCAmelCase :       Tuple 							=		[1, 1, 1, 0.5]
														UpperCAmelCase :       int 							=		[256, 512, 768, 768]
														UpperCAmelCase :       Any 							=		150
														UpperCAmelCase :       Tuple 							=		16
														UpperCAmelCase :       Any 							=		(1, 384, 384)
														UpperCAmelCase :       Optional[Any] 							=		False
														UpperCAmelCase :       Tuple 							=		'''project'''

							if "ade" in checkpoint_url:
														UpperCAmelCase :       Any 							=		True
														UpperCAmelCase :       str 							=		768
														UpperCAmelCase :       Optional[int] 							=		[1, 1, 1, 0.5]
														UpperCAmelCase :       List[Any] 							=		150
														UpperCAmelCase :       List[Any] 							=		16
														UpperCAmelCase :       str 							=		'''huggingface/label-files'''
														UpperCAmelCase :       Tuple 							=		'''ade20k-id2label.json'''
														UpperCAmelCase :       Any 							=		json.load(open(cached_download(hf_hub_url(_lowerCAmelCase   ,      _lowerCAmelCase   ,      repo_type='''dataset'''  )  )   ,      '''r'''  )  )
														UpperCAmelCase :       Optional[Any] 							=		{int(_lowerCAmelCase  ): v for k, v in idalabel.items()}
														UpperCAmelCase :       List[Any] 							=		idalabel
														UpperCAmelCase :       Optional[int] 							=		{v: k for k, v in idalabel.items()}
														UpperCAmelCase :       Union[str, Any] 							=		[1, 150, 480, 480]

							return config, expected_shape


def  snake_case_     (					_lowerCAmelCase  :						Union[str, Any]  )      ->   int:
							UpperCAmelCase :       List[str] 							=		['''pretrained.model.head.weight''', '''pretrained.model.head.bias''']
							for k in ignore_keys:
														state_dict.pop(_lowerCAmelCase   ,      _lowerCAmelCase  )


def  snake_case_     (					_lowerCAmelCase  :						Tuple  )      ->   Any:
							if (
							    "pretrained.model" in name
							    and "cls_token" not in name
							    and "pos_embed" not in name
							    and "patch_embed" not in name
							):
														UpperCAmelCase :       Tuple 							=		name.replace('''pretrained.model'''   ,      '''dpt.encoder'''  )
							if "pretrained.model" in name:
														UpperCAmelCase :       Union[str, Any] 							=		name.replace('''pretrained.model'''   ,      '''dpt.embeddings'''  )
							if "patch_embed" in name:
														UpperCAmelCase :       int 							=		name.replace('''patch_embed'''   ,      ''''''  )
							if "pos_embed" in name:
														UpperCAmelCase :       Tuple 							=		name.replace('''pos_embed'''   ,      '''position_embeddings'''  )
							if "attn.proj" in name:
														UpperCAmelCase :       Any 							=		name.replace('''attn.proj'''   ,      '''attention.output.dense'''  )
							if "proj" in name and "project" not in name:
														UpperCAmelCase :       str 							=		name.replace('''proj'''   ,      '''projection'''  )
							if "blocks" in name:
														UpperCAmelCase :       Any 							=		name.replace('''blocks'''   ,      '''layer'''  )
							if "mlp.fc1" in name:
														UpperCAmelCase :       Optional[int] 							=		name.replace('''mlp.fc1'''   ,      '''intermediate.dense'''  )
							if "mlp.fc2" in name:
														UpperCAmelCase :       Optional[Any] 							=		name.replace('''mlp.fc2'''   ,      '''output.dense'''  )
							if "norm1" in name and "backbone" not in name:
														UpperCAmelCase :       Dict 							=		name.replace('''norm1'''   ,      '''layernorm_before'''  )
							if "norm2" in name and "backbone" not in name:
														UpperCAmelCase :       Tuple 							=		name.replace('''norm2'''   ,      '''layernorm_after'''  )
							if "scratch.output_conv" in name:
														UpperCAmelCase :       Tuple 							=		name.replace('''scratch.output_conv'''   ,      '''head'''  )
							if "scratch" in name:
														UpperCAmelCase :       str 							=		name.replace('''scratch'''   ,      '''neck'''  )
							if "layer1_rn" in name:
														UpperCAmelCase :       Dict 							=		name.replace('''layer1_rn'''   ,      '''convs.0'''  )
							if "layer2_rn" in name:
														UpperCAmelCase :       int 							=		name.replace('''layer2_rn'''   ,      '''convs.1'''  )
							if "layer3_rn" in name:
														UpperCAmelCase :       Tuple 							=		name.replace('''layer3_rn'''   ,      '''convs.2'''  )
							if "layer4_rn" in name:
														UpperCAmelCase :       int 							=		name.replace('''layer4_rn'''   ,      '''convs.3'''  )
							if "refinenet" in name:
														UpperCAmelCase :       List[str] 							=		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
														UpperCAmelCase :       str 							=		name.replace(f"""refinenet{layer_idx}"""   ,      f"""fusion_stage.layers.{abs(layer_idx-4  )}"""  )
							if "out_conv" in name:
														UpperCAmelCase :       List[str] 							=		name.replace('''out_conv'''   ,      '''projection'''  )
							if "resConfUnit1" in name:
														UpperCAmelCase :       Union[str, Any] 							=		name.replace('''resConfUnit1'''   ,      '''residual_layer1'''  )
							if "resConfUnit2" in name:
														UpperCAmelCase :       Any 							=		name.replace('''resConfUnit2'''   ,      '''residual_layer2'''  )
							if "conv1" in name:
														UpperCAmelCase :       Optional[int] 							=		name.replace('''conv1'''   ,      '''convolution1'''  )
							if "conv2" in name:
														UpperCAmelCase :       Tuple 							=		name.replace('''conv2'''   ,      '''convolution2'''  )
							# readout blocks
							if "pretrained.act_postprocess1.0.project.0" in name:
														UpperCAmelCase :       Dict 							=		name.replace('''pretrained.act_postprocess1.0.project.0'''   ,      '''neck.reassemble_stage.readout_projects.0.0'''  )
							if "pretrained.act_postprocess2.0.project.0" in name:
														UpperCAmelCase :       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:
														UpperCAmelCase :       Any 							=		name.replace('''pretrained.act_postprocess3.0.project.0'''   ,      '''neck.reassemble_stage.readout_projects.2.0'''  )
							if "pretrained.act_postprocess4.0.project.0" in name:
														UpperCAmelCase :       Optional[Any] 							=		name.replace('''pretrained.act_postprocess4.0.project.0'''   ,      '''neck.reassemble_stage.readout_projects.3.0'''  )

							# resize blocks
							if "pretrained.act_postprocess1.3" in name:
														UpperCAmelCase :       List[Any] 							=		name.replace('''pretrained.act_postprocess1.3'''   ,      '''neck.reassemble_stage.layers.0.projection'''  )
							if "pretrained.act_postprocess1.4" in name:
														UpperCAmelCase :       Any 							=		name.replace('''pretrained.act_postprocess1.4'''   ,      '''neck.reassemble_stage.layers.0.resize'''  )
							if "pretrained.act_postprocess2.3" in name:
														UpperCAmelCase :       Optional[int] 							=		name.replace('''pretrained.act_postprocess2.3'''   ,      '''neck.reassemble_stage.layers.1.projection'''  )
							if "pretrained.act_postprocess2.4" in name:
														UpperCAmelCase :       str 							=		name.replace('''pretrained.act_postprocess2.4'''   ,      '''neck.reassemble_stage.layers.1.resize'''  )
							if "pretrained.act_postprocess3.3" in name:
														UpperCAmelCase :       List[str] 							=		name.replace('''pretrained.act_postprocess3.3'''   ,      '''neck.reassemble_stage.layers.2.projection'''  )
							if "pretrained.act_postprocess4.3" in name:
														UpperCAmelCase :       Tuple 							=		name.replace('''pretrained.act_postprocess4.3'''   ,      '''neck.reassemble_stage.layers.3.projection'''  )
							if "pretrained.act_postprocess4.4" in name:
														UpperCAmelCase :       int 							=		name.replace('''pretrained.act_postprocess4.4'''   ,      '''neck.reassemble_stage.layers.3.resize'''  )
							if "pretrained" in name:
														UpperCAmelCase :       Optional[int] 							=		name.replace('''pretrained'''   ,      '''dpt'''  )
							if "bn" in name:
														UpperCAmelCase :       Dict 							=		name.replace('''bn'''   ,      '''batch_norm'''  )
							if "head" in name:
														UpperCAmelCase :       Any 							=		name.replace('''head'''   ,      '''head.head'''  )
							if "encoder.norm" in name:
														UpperCAmelCase :       Optional[int] 							=		name.replace('''encoder.norm'''   ,      '''layernorm'''  )
							if "auxlayer" in name:
														UpperCAmelCase :       Union[str, Any] 							=		name.replace('''auxlayer'''   ,      '''auxiliary_head.head'''  )
							if "backbone" in name:
														UpperCAmelCase :       List[Any] 							=		name.replace('''backbone'''   ,      '''backbone.bit.encoder'''  )

							if ".." in name:
														UpperCAmelCase :       Optional[int] 							=		name.replace('''..'''   ,      '''.'''  )

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


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


def  snake_case_     (					)      ->   List[str]:
							UpperCAmelCase :       Optional[int] 							=		'''http://images.cocodataset.org/val2017/000000039769.jpg'''
							UpperCAmelCase :       Optional[int] 							=		Image.open(requests.get(_lowerCAmelCase   ,      stream=_lowerCAmelCase  ).raw  )
							return im


@torch.no_grad()
def  snake_case_     (					_lowerCAmelCase  :						Optional[Any]   ,      _lowerCAmelCase  :						Union[str, Any]   ,      _lowerCAmelCase  :						str   ,      _lowerCAmelCase  :						Union[str, Any]   ,      _lowerCAmelCase  :						List[str]  )      ->   Any:
							UpperCAmelCase							,		UpperCAmelCase :       int 							=		get_dpt_config(_lowerCAmelCase  )
							# load original state_dict from URL
							# state_dict = torch.hub.load_state_dict_from_url(checkpoint_url, map_location="cpu")
							UpperCAmelCase :       List[Any] 							=		torch.load(_lowerCAmelCase   ,      map_location='''cpu'''  )
							# remove certain keys
							remove_ignore_keys_(_lowerCAmelCase  )
							# rename keys
							for key in state_dict.copy().keys():
														UpperCAmelCase :       Any 							=		state_dict.pop(_lowerCAmelCase  )
														UpperCAmelCase :       List[Any] 							=		val
							# read in qkv matrices
							read_in_q_k_v(_lowerCAmelCase   ,      _lowerCAmelCase  )

							# load HuggingFace model
							UpperCAmelCase :       Optional[Any] 							=		DPTForSemanticSegmentation(_lowerCAmelCase  ) if '''ade''' in checkpoint_url else DPTForDepthEstimation(_lowerCAmelCase  )
							model.load_state_dict(_lowerCAmelCase  )
							model.eval()

							# Check outputs on an image
							UpperCAmelCase :       int 							=		480 if '''ade''' in checkpoint_url else 384
							UpperCAmelCase :       List[Any] 							=		DPTImageProcessor(size=_lowerCAmelCase  )

							UpperCAmelCase :       Dict 							=		prepare_img()
							UpperCAmelCase :       Optional[int] 							=		image_processor(_lowerCAmelCase   ,      return_tensors='''pt'''  )

							# forward pass
							UpperCAmelCase :       Any 							=		model(**_lowerCAmelCase  ).logits if '''ade''' in checkpoint_url else model(**_lowerCAmelCase  ).predicted_depth

							if show_prediction:
														UpperCAmelCase :       Dict 							=		(
														    torch.nn.functional.interpolate(
														        outputs.unsqueeze(1  )   ,      size=(image.size[1], image.size[0])   ,      mode='''bicubic'''   ,      align_corners=_lowerCAmelCase   ,      )
														    .squeeze()
														    .cpu()
														    .numpy()
														)

														Image.fromarray((prediction / prediction.max()) * 255  ).show()

							if pytorch_dump_folder_path is not None:
														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:
														model.push_to_hub('''ybelkada/dpt-hybrid-midas'''  )
														image_processor.push_to_hub('''ybelkada/dpt-hybrid-midas'''  )


if __name__ == "__main__":
						UpperCamelCase__:   Dict								=			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",
						)

						UpperCamelCase__:   Tuple								=			parser.parse_args()
						convert_dpt_checkpoint(
						    args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name, args.show_prediction
						)





