



"""simple docstring"""







import argparse
import os

import gluonnlp as nlp
import mxnet as mx
import numpy as np
import torch
from gluonnlp.base import get_home_dir
from gluonnlp.model.bert import BERTEncoder
from gluonnlp.model.utils import _load_vocab
from gluonnlp.vocab import Vocab
from packaging import version
from torch import nn

from transformers import BertConfig, BertForMaskedLM, BertModel, RobertaTokenizer
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertSelfAttention,
    BertSelfOutput,
)
from transformers.utils import logging


if version.parse(nlp.__version__) != version.parse('0.8.3'):
							raise Exception('requires gluonnlp == 0.8.3')

if version.parse(mx.__version__) != version.parse('1.5.0'):
							raise Exception('requires mxnet == 1.5.0')

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

lowerCAmelCase_   						=   'The Nymphenburg Palace is a beautiful palace in Munich!'



def      __UpperCAmelCase      (    __lowerCamelCase						,     __lowerCamelCase	)							->      List[str]:
		lowercase__   :    Tuple				=			{
		    '''attention_cell''': '''multi_head''',
		    '''num_layers''': 4,
		    '''units''': 10_24,
		    '''hidden_size''': 7_68,
		    '''max_length''': 5_12,
		    '''num_heads''': 8,
		    '''scaled''': True,
		    '''dropout''': 0.1,
		    '''use_residual''': True,
		    '''embed_size''': 10_24,
		    '''embed_dropout''': 0.1,
		    '''word_embed''': None,
		    '''layer_norm_eps''': 1E-5,
		    '''token_type_vocab_size''': 2,
		}

		lowercase__   :    Optional[Any]				=			bort_4_8_768_1024_hparams

		# Let's construct the original Bort model here
		# Taken from official BERT implementation, see:
		# https://github.com/alexa/bort/blob/master/bort/bort.py
		lowercase__   :    Tuple				=			BERTEncoder(
		    attention_cell=predefined_args['''attention_cell''']						,     num_layers=predefined_args['''num_layers''']						,     units=predefined_args['''units''']						,     hidden_size=predefined_args['''hidden_size''']						,     max_length=predefined_args['''max_length''']						,     num_heads=predefined_args['''num_heads''']						,     scaled=predefined_args['''scaled''']						,     dropout=predefined_args['''dropout''']						,     output_attention=__lowerCamelCase						,     output_all_encodings=__lowerCamelCase						,     use_residual=predefined_args['''use_residual''']						,     activation=predefined_args.get('''activation'''						,     '''gelu'''	)						,     layer_norm_eps=predefined_args.get('''layer_norm_eps'''						,     __lowerCamelCase	)						,     )

		# Vocab information needs to be fetched first
		# It's the same as RoBERTa, so RobertaTokenizer can be used later
		lowercase__   :    List[str]				=			'''openwebtext_ccnews_stories_books_cased'''

		# Specify download folder to Gluonnlp's vocab
		lowercase__   :    Union[str, Any]				=			os.path.join(get_home_dir()						,     '''models'''	)
		lowercase__   :    List[str]				=			_load_vocab(__lowerCamelCase						,     __lowerCamelCase						,     __lowerCamelCase						,     cls=__lowerCamelCase	)

		lowercase__   :    str				=			nlp.model.BERTModel(
		    __lowerCamelCase						,     len(__lowerCamelCase	)						,     units=predefined_args['''units''']						,     embed_size=predefined_args['''embed_size''']						,     embed_dropout=predefined_args['''embed_dropout''']						,     word_embed=predefined_args['''word_embed''']						,     use_pooler=__lowerCamelCase						,     use_token_type_embed=__lowerCamelCase						,     token_type_vocab_size=predefined_args['''token_type_vocab_size''']						,     use_classifier=__lowerCamelCase						,     use_decoder=__lowerCamelCase						,     )

		original_bort.load_parameters(__lowerCamelCase						,     cast_dtype=__lowerCamelCase						,     ignore_extra=__lowerCamelCase	)
		lowercase__   :    List[Any]				=			original_bort._collect_params_with_prefix()

		# Build our config 🤗
		lowercase__   :    Optional[int]				=			{
		    '''architectures''': ['''BertForMaskedLM'''],
		    '''attention_probs_dropout_prob''': predefined_args['''dropout'''],
		    '''hidden_act''': '''gelu''',
		    '''hidden_dropout_prob''': predefined_args['''dropout'''],
		    '''hidden_size''': predefined_args['''embed_size'''],
		    '''initializer_range''': 0.0_2,
		    '''intermediate_size''': predefined_args['''hidden_size'''],
		    '''layer_norm_eps''': predefined_args['''layer_norm_eps'''],
		    '''max_position_embeddings''': predefined_args['''max_length'''],
		    '''model_type''': '''bort''',
		    '''num_attention_heads''': predefined_args['''num_heads'''],
		    '''num_hidden_layers''': predefined_args['''num_layers'''],
		    '''pad_token_id''': 1,  # 2 = BERT, 1 = RoBERTa
		    '''type_vocab_size''': 1,  # 2 = BERT, 1 = RoBERTa
		    '''vocab_size''': len(__lowerCamelCase	),
		}

		lowercase__   :    str				=			BertConfig.from_dict(__lowerCamelCase	)
		lowercase__   :    str				=			BertForMaskedLM(__lowerCamelCase	)
		hf_bort_model.eval()

		# Parameter mapping table (Gluonnlp to Transformers)
		# * denotes layer index
		#
		# | Gluon Parameter                                                | Transformers Parameter
		# | -------------------------------------------------------------- | ----------------------
		# | `encoder.layer_norm.beta`                                      | `bert.embeddings.LayerNorm.bias`
		# | `encoder.layer_norm.gamma`                                     | `bert.embeddings.LayerNorm.weight`
		# | `encoder.position_weight`                                      | `bert.embeddings.position_embeddings.weight`
		# | `word_embed.0.weight`                                          | `bert.embeddings.word_embeddings.weight`
		# | `encoder.transformer_cells.*.attention_cell.proj_key.bias`     | `bert.encoder.layer.*.attention.self.key.bias`
		# | `encoder.transformer_cells.*.attention_cell.proj_key.weight`   | `bert.encoder.layer.*.attention.self.key.weight`
		# | `encoder.transformer_cells.*.attention_cell.proj_query.bias`   | `bert.encoder.layer.*.attention.self.query.bias`
		# | `encoder.transformer_cells.*.attention_cell.proj_query.weight` | `bert.encoder.layer.*.attention.self.query.weight`
		# | `encoder.transformer_cells.*.attention_cell.proj_value.bias`   | `bert.encoder.layer.*.attention.self.value.bias`
		# | `encoder.transformer_cells.*.attention_cell.proj_value.weight` | `bert.encoder.layer.*.attention.self.value.weight`
		# | `encoder.transformer_cells.*.ffn.ffn_2.bias`                   | `bert.encoder.layer.*.attention.output.dense.bias`
		# | `encoder.transformer_cells.*.ffn.ffn_2.weight`                 | `bert.encoder.layer.*.attention.output.dense.weight`
		# | `encoder.transformer_cells.*.layer_norm.beta`                  | `bert.encoder.layer.*.attention.output.LayerNorm.bias`
		# | `encoder.transformer_cells.*.layer_norm.gamma`                 | `bert.encoder.layer.*.attention.output.LayerNorm.weight`
		# | `encoder.transformer_cells.*.ffn.ffn_1.bias`                   | `bert.encoder.layer.*.intermediate.dense.bias`
		# | `encoder.transformer_cells.*.ffn.ffn_1.weight`                 | `bert.encoder.layer.*.intermediate.dense.weight`
		# | `encoder.transformer_cells.*.ffn.layer_norm.beta`              | `bert.encoder.layer.*.output.LayerNorm.bias`
		# | `encoder.transformer_cells.*.ffn.layer_norm.gamma`             | `bert.encoder.layer.*.output.LayerNorm.weight`
		# | `encoder.transformer_cells.*.proj.bias`                        | `bert.encoder.layer.*.output.dense.bias`
		# | `encoder.transformer_cells.*.proj.weight`                      | `bert.encoder.layer.*.output.dense.weight`

		# Helper function to convert MXNET Arrays to PyTorch
		def to_torch(__lowerCamelCase	) -> nn.Parameter:
				return nn.Parameter(torch.FloatTensor(mx_array.data().asnumpy()	)	)

		# Check param shapes and map new HF param back
		def check_and_map_params(__lowerCamelCase						,     __lowerCamelCase	):
				lowercase__   :    str				=			hf_param.shape

				lowercase__   :    int				=			to_torch(params[gluon_param]	)
				lowercase__   :    Optional[Any]				=			gluon_param.shape

				assert (
				    shape_hf == shape_gluon
				), f"""The gluon parameter {gluon_param} has shape {shape_gluon}, but expects shape {shape_hf} for Transformers"""

				return gluon_param

		lowercase__   :    str				=			check_and_map_params(
		    hf_bort_model.bert.embeddings.word_embeddings.weight						,     '''word_embed.0.weight'''	)
		lowercase__   :    int				=			check_and_map_params(
		    hf_bort_model.bert.embeddings.position_embeddings.weight						,     '''encoder.position_weight'''	)
		lowercase__   :    int				=			check_and_map_params(
		    hf_bort_model.bert.embeddings.LayerNorm.bias						,     '''encoder.layer_norm.beta'''	)
		lowercase__   :    Optional[int]				=			check_and_map_params(
		    hf_bort_model.bert.embeddings.LayerNorm.weight						,     '''encoder.layer_norm.gamma'''	)

		# Inspired by RoBERTa conversion script, we just zero them out (Bort does not use them)
		lowercase__   :    int				=			torch.zeros_like(
		    hf_bort_model.bert.embeddings.token_type_embeddings.weight.data	)

		for i in range(hf_bort_config.num_hidden_layers	):
				lowercase__   :    BertLayer				=			hf_bort_model.bert.encoder.layer[i]

				# self attention
				lowercase__   :    BertSelfAttention				=			layer.attention.self

				lowercase__   :    Any				=			check_and_map_params(
				    self_attn.key.bias.data						,     f"""encoder.transformer_cells.{i}.attention_cell.proj_key.bias"""	)

				lowercase__   :    int				=			check_and_map_params(
				    self_attn.key.weight.data						,     f"""encoder.transformer_cells.{i}.attention_cell.proj_key.weight"""	)
				lowercase__   :    Optional[int]				=			check_and_map_params(
				    self_attn.query.bias.data						,     f"""encoder.transformer_cells.{i}.attention_cell.proj_query.bias"""	)
				lowercase__   :    int				=			check_and_map_params(
				    self_attn.query.weight.data						,     f"""encoder.transformer_cells.{i}.attention_cell.proj_query.weight"""	)
				lowercase__   :    Optional[int]				=			check_and_map_params(
				    self_attn.value.bias.data						,     f"""encoder.transformer_cells.{i}.attention_cell.proj_value.bias"""	)
				lowercase__   :    Optional[Any]				=			check_and_map_params(
				    self_attn.value.weight.data						,     f"""encoder.transformer_cells.{i}.attention_cell.proj_value.weight"""	)

				# self attention output
				lowercase__   :    BertSelfOutput				=			layer.attention.output

				lowercase__   :    Union[str, Any]				=			check_and_map_params(
				    self_output.dense.bias						,     f"""encoder.transformer_cells.{i}.proj.bias"""	)
				lowercase__   :    List[str]				=			check_and_map_params(
				    self_output.dense.weight						,     f"""encoder.transformer_cells.{i}.proj.weight"""	)
				lowercase__   :    List[str]				=			check_and_map_params(
				    self_output.LayerNorm.bias						,     f"""encoder.transformer_cells.{i}.layer_norm.beta"""	)
				lowercase__   :    Any				=			check_and_map_params(
				    self_output.LayerNorm.weight						,     f"""encoder.transformer_cells.{i}.layer_norm.gamma"""	)

				# intermediate
				lowercase__   :    BertIntermediate				=			layer.intermediate

				lowercase__   :    Dict				=			check_and_map_params(
				    intermediate.dense.bias						,     f"""encoder.transformer_cells.{i}.ffn.ffn_1.bias"""	)
				lowercase__   :    Union[str, Any]				=			check_and_map_params(
				    intermediate.dense.weight						,     f"""encoder.transformer_cells.{i}.ffn.ffn_1.weight"""	)

				# output
				lowercase__   :    BertOutput				=			layer.output

				lowercase__   :    str				=			check_and_map_params(
				    bert_output.dense.bias						,     f"""encoder.transformer_cells.{i}.ffn.ffn_2.bias"""	)
				lowercase__   :    Any				=			check_and_map_params(
				    bert_output.dense.weight						,     f"""encoder.transformer_cells.{i}.ffn.ffn_2.weight"""	)
				lowercase__   :    int				=			check_and_map_params(
				    bert_output.LayerNorm.bias						,     f"""encoder.transformer_cells.{i}.ffn.layer_norm.beta"""	)
				lowercase__   :    Any				=			check_and_map_params(
				    bert_output.LayerNorm.weight						,     f"""encoder.transformer_cells.{i}.ffn.layer_norm.gamma"""	)

		# Save space and energy 🎄
		hf_bort_model.half()

		# Compare output of both models
		lowercase__   :    str				=			RobertaTokenizer.from_pretrained('''roberta-base'''	)

		lowercase__   :    Tuple				=			tokenizer.encode_plus(__lowerCamelCase	)['''input_ids''']

		# Get gluon output
		lowercase__   :    List[str]				=			mx.nd.array([input_ids]	)
		lowercase__   :    Union[str, Any]				=			original_bort(inputs=__lowerCamelCase						,     token_types=[]	)

		# Get Transformer output (save and reload model again)
		hf_bort_model.save_pretrained(__lowerCamelCase	)
		lowercase__   :    List[str]				=			BertModel.from_pretrained(__lowerCamelCase	)
		hf_bort_model.eval()

		lowercase__   :    Tuple				=			tokenizer.encode_plus(__lowerCamelCase						,     return_tensors='''pt'''	)
		lowercase__   :    Dict				=			hf_bort_model(**__lowerCamelCase	)[0]

		lowercase__   :    List[str]				=			output_gluon[0].asnumpy()
		lowercase__   :    Optional[Any]				=			output_hf[0].detach().numpy()

		lowercase__   :    List[str]				=			np.max(np.abs(hf_layer - gluon_layer	)	).item()
		lowercase__   :    List[str]				=			np.allclose(__lowerCamelCase						,     __lowerCamelCase						,     atol=1E-3	)

		if success:
				print('''✔️ Both model do output the same tensors'''	)
		else:
				print('''❌ Both model do **NOT** output the same tensors'''	)
				print('''Absolute difference is:'''						,     __lowerCamelCase	)


if __name__ == "__main__":
							lowerCAmelCase_   						=   argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    '--bort_checkpoint_path', default=None, type=str, required=True, help='Path the official Bort params file.'
							)
							parser.add_argument(
							    '--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
							)
							lowerCAmelCase_   						=   parser.parse_args()
							convert_bort_checkpoint_to_pytorch(args.bort_checkpoint_path, args.pytorch_dump_folder_path)






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		:  Union[str, Any]	=   logging.get_logger(__name__)



def 					UpperCAmelCase__     (   lowerCamelCase      ):
  lowercase			:int							=  DPTConfig(embedding_type="hybrid"      )

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

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

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

  return config, expected_shape



def 					UpperCAmelCase__     (   lowerCamelCase      ):
  lowercase			:List[str]							=  ["pretrained.model.head.weight", "pretrained.model.head.bias"]
  for k in ignore_keys:
    state_dict.pop(lowerCamelCase,	lowerCamelCase      )



def 					UpperCAmelCase__     (   lowerCamelCase      ):
  if (
      "pretrained.model" in name
      and "cls_token" not in name
      and "pos_embed" not in name
      and "patch_embed" not in name
  ):
    lowercase			:Any							=  name.replace("pretrained.model",	"dpt.encoder"      )
  if "pretrained.model" in name:
    lowercase			:int							=  name.replace("pretrained.model",	"dpt.embeddings"      )
  if "patch_embed" in name:
    lowercase			:Any							=  name.replace("patch_embed",	""      )
  if "pos_embed" in name:
    lowercase			:Optional[Any]							=  name.replace("pos_embed",	"position_embeddings"      )
  if "attn.proj" in name:
    lowercase			:List[str]							=  name.replace("attn.proj",	"attention.output.dense"      )
  if "proj" in name and "project" not in name:
    lowercase			:str							=  name.replace("proj",	"projection"      )
  if "blocks" in name:
    lowercase			:List[Any]							=  name.replace("blocks",	"layer"      )
  if "mlp.fc1" in name:
    lowercase			:str							=  name.replace("mlp.fc1",	"intermediate.dense"      )
  if "mlp.fc2" in name:
    lowercase			:Any							=  name.replace("mlp.fc2",	"output.dense"      )
  if "norm1" in name and "backbone" not in name:
    lowercase			:Any							=  name.replace("norm1",	"layernorm_before"      )
  if "norm2" in name and "backbone" not in name:
    lowercase			:Any							=  name.replace("norm2",	"layernorm_after"      )
  if "scratch.output_conv" in name:
    lowercase			:Optional[Any]							=  name.replace("scratch.output_conv",	"head"      )
  if "scratch" in name:
    lowercase			:Union[str, Any]							=  name.replace("scratch",	"neck"      )
  if "layer1_rn" in name:
    lowercase			:str							=  name.replace("layer1_rn",	"convs.0"      )
  if "layer2_rn" in name:
    lowercase			:Dict							=  name.replace("layer2_rn",	"convs.1"      )
  if "layer3_rn" in name:
    lowercase			:Tuple							=  name.replace("layer3_rn",	"convs.2"      )
  if "layer4_rn" in name:
    lowercase			:Optional[int]							=  name.replace("layer4_rn",	"convs.3"      )
  if "refinenet" in name:
    lowercase			:Any							=  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
    lowercase			:Any							=  name.replace(F"refinenet{layer_idx}",	F"fusion_stage.layers.{abs(layer_idx-4      )}"      )
  if "out_conv" in name:
    lowercase			:str							=  name.replace("out_conv",	"projection"      )
  if "resConfUnit1" in name:
    lowercase			:Optional[Any]							=  name.replace("resConfUnit1",	"residual_layer1"      )
  if "resConfUnit2" in name:
    lowercase			:int							=  name.replace("resConfUnit2",	"residual_layer2"      )
  if "conv1" in name:
    lowercase			:Optional[int]							=  name.replace("conv1",	"convolution1"      )
  if "conv2" in name:
    lowercase			:List[str]							=  name.replace("conv2",	"convolution2"      )
  # readout blocks
  if "pretrained.act_postprocess1.0.project.0" in name:
    lowercase			:Tuple							=  name.replace("pretrained.act_postprocess1.0.project.0",	"neck.reassemble_stage.readout_projects.0.0"      )
  if "pretrained.act_postprocess2.0.project.0" in name:
    lowercase			:Dict							=  name.replace("pretrained.act_postprocess2.0.project.0",	"neck.reassemble_stage.readout_projects.1.0"      )
  if "pretrained.act_postprocess3.0.project.0" in name:
    lowercase			: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:
    lowercase			: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:
    lowercase			:Dict							=  name.replace("pretrained.act_postprocess1.3",	"neck.reassemble_stage.layers.0.projection"      )
  if "pretrained.act_postprocess1.4" in name:
    lowercase			:Any							=  name.replace("pretrained.act_postprocess1.4",	"neck.reassemble_stage.layers.0.resize"      )
  if "pretrained.act_postprocess2.3" in name:
    lowercase			:List[Any]							=  name.replace("pretrained.act_postprocess2.3",	"neck.reassemble_stage.layers.1.projection"      )
  if "pretrained.act_postprocess2.4" in name:
    lowercase			:str							=  name.replace("pretrained.act_postprocess2.4",	"neck.reassemble_stage.layers.1.resize"      )
  if "pretrained.act_postprocess3.3" in name:
    lowercase			:str							=  name.replace("pretrained.act_postprocess3.3",	"neck.reassemble_stage.layers.2.projection"      )
  if "pretrained.act_postprocess4.3" in name:
    lowercase			:List[Any]							=  name.replace("pretrained.act_postprocess4.3",	"neck.reassemble_stage.layers.3.projection"      )
  if "pretrained.act_postprocess4.4" in name:
    lowercase			:Any							=  name.replace("pretrained.act_postprocess4.4",	"neck.reassemble_stage.layers.3.resize"      )
  if "pretrained" in name:
    lowercase			:Any							=  name.replace("pretrained",	"dpt"      )
  if "bn" in name:
    lowercase			:Optional[int]							=  name.replace("bn",	"batch_norm"      )
  if "head" in name:
    lowercase			:Union[str, Any]							=  name.replace("head",	"head.head"      )
  if "encoder.norm" in name:
    lowercase			:Optional[Any]							=  name.replace("encoder.norm",	"layernorm"      )
  if "auxlayer" in name:
    lowercase			:str							=  name.replace("auxlayer",	"auxiliary_head.head"      )
  if "backbone" in name:
    lowercase			:List[str]							=  name.replace("backbone",	"backbone.bit.encoder"      )

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

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



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



def 					UpperCAmelCase__     (   ):
  lowercase			:List[str]							=  "http://images.cocodataset.org/val2017/000000039769.jpg"
  lowercase			:Any							=  Image.open(requests.get(lowerCamelCase,	stream=lowerCamelCase      ).raw      )
  return im



@torch.no_grad()
def 					UpperCAmelCase__     (   lowerCamelCase,	lowerCamelCase,	lowerCamelCase,	lowerCamelCase,	lowerCamelCase      ):
  lowercase		,			lowercase			:Optional[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")
  lowercase			:List[str]							=  torch.load(lowerCamelCase,	map_location="cpu"      )
  # remove certain keys
  remove_ignore_keys_(lowerCamelCase      )
  # rename keys
  for key in state_dict.copy().keys():
    lowercase			:int							=  state_dict.pop(lowerCamelCase      )
    lowercase			:Dict							=  val
  # read in qkv matrices
  read_in_q_k_v(lowerCamelCase,	lowerCamelCase      )

  # load HuggingFace model
  lowercase			:int							=  DPTForSemanticSegmentation(lowerCamelCase      ) if "ade" in checkpoint_url else DPTForDepthEstimation(lowerCamelCase      )
  model.load_state_dict(lowerCamelCase      )
  model.eval()

  # Check outputs on an image
  lowercase			:Union[str, Any]							=  480 if "ade" in checkpoint_url else 384
  lowercase			:Optional[int]							=  DPTImageProcessor(size=lowerCamelCase      )

  lowercase			:Dict							=  prepare_img()
  lowercase			:Optional[Any]							=  image_processor(lowerCamelCase,	return_tensors="pt"      )

  # forward pass
  lowercase			:Any							=  model(**lowerCamelCase      ).logits if "ade" in checkpoint_url else model(**lowerCamelCase      ).predicted_depth

  if show_prediction:
    lowercase			:List[Any]							=  (
        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		:  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",
     )

     _UpperCAmelCase		:  List[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
     )



