





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

      return new_state_dict



def 					__lowerCamelCase       (							a_					:		Tuple			,       a_					:		Optional[Any]							)      ->				List[Any]:
      # for each of the encoder blocks:
      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)
                  __SCREAMING_SNAKE_CASE       :Optional[int]		    =  state_dict.pop(f'''glpn.encoder.block.{i}.{j}.attention.self.kv.weight'''							)
                  __SCREAMING_SNAKE_CASE       :Tuple		    =  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
                  __SCREAMING_SNAKE_CASE       :List[Any]		    =  kv_weight[
                      : config.hidden_sizes[i], :
                  ]
                  __SCREAMING_SNAKE_CASE       :Optional[int]		    =  kv_bias[: config.hidden_sizes[i]]
                  __SCREAMING_SNAKE_CASE       :Any		    =  kv_weight[
                      config.hidden_sizes[i] :, :
                  ]
                  __SCREAMING_SNAKE_CASE       :List[str]		    =  kv_bias[config.hidden_sizes[i] :]



def 					__lowerCamelCase       (							)      ->				List[Any]:
      __SCREAMING_SNAKE_CASE       :Tuple		    =  '''http://images.cocodataset.org/val2017/000000039769.jpg'''
      __SCREAMING_SNAKE_CASE       :Tuple		    =  Image.open(requests.get(a_			,       stream=a_							).raw							)

      return image



@torch.no_grad()
def 					__lowerCamelCase       (							a_					:		List[str]			,       a_					:		List[Any]			,       a_					:		List[str]=False			,       a_					:		Optional[Any]=None							)      ->				Optional[int]:
      __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  GLPNConfig(hidden_sizes=[64, 1_28, 3_20, 5_12]			,       decoder_hidden_size=64			,       depths=[3, 8, 27, 3]							)

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

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

      logger.info('''Converting model...'''							)

      # load original state dict
      __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  torch.load(a_			,       map_location=torch.device('''cpu'''							)							)

      # rename keys
      __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  rename_keys(a_							)

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

      # create HuggingFace model and load state dict
      __SCREAMING_SNAKE_CASE       :Dict		    =  GLPNForDepthEstimation(a_							)
      model.load_state_dict(a_							)
      model.eval()

      # forward pass
      __SCREAMING_SNAKE_CASE       :List[str]		    =  model(a_							)
      __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  outputs.predicted_depth

      # verify output
      if model_name is not None:
            if "nyu" in model_name:
                  __SCREAMING_SNAKE_CASE       :Any		    =  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:
                  __SCREAMING_SNAKE_CASE       :Optional[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}'''							)

            __SCREAMING_SNAKE_CASE       :List[Any]		    =  torch.Size([1, 4_80, 6_40]							)

            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)






"""simple docstring"""
import baseaa
import io
import json
import os
from copy import deepcopy

from ..optimizer import AcceleratedOptimizer
from ..scheduler import AcceleratedScheduler






class  _SCREAMING_SNAKE_CASE:




       def __init__(						self		,SCREAMING_SNAKE_CASE__							)      ->				int:





             """simple docstring"""






             if isinstance(SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__							):
                   # Don't modify user's data should they want to reuse it (e.g. in tests), because once we
                   # modified it, it will not be accepted here again, since `auto` values would have been overridden
                   __SCREAMING_SNAKE_CASE       :Optional[int]		    =  deepcopy(SCREAMING_SNAKE_CASE__							)
             elif os.path.exists(SCREAMING_SNAKE_CASE__							):
                   with io.open(SCREAMING_SNAKE_CASE__		,'''r'''		,encoding='''utf-8'''							) as f:
                         __SCREAMING_SNAKE_CASE       :Dict		    =  json.load(SCREAMING_SNAKE_CASE__							)
             else:
                   try:
                         __SCREAMING_SNAKE_CASE       :str		    =  baseaa.urlsafe_baadecode(SCREAMING_SNAKE_CASE__							).decode('''utf-8'''							)
                         __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  json.loads(SCREAMING_SNAKE_CASE__							)
                   except (UnicodeDecodeError, AttributeError, ValueError):
                         raise ValueError(
                             f'''Expected a string path to an existing deepspeed config, or a dictionary, or a base64 encoded string. Received: {config_file_or_dict}'''							)

             __SCREAMING_SNAKE_CASE       :Optional[int]		    =  config

             self.set_stage_and_offload()




       def    _UpperCamelCase					(						self							)      ->				Any:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  self.get_value('''zero_optimization.stage'''		,-1							)

             # offload
             __SCREAMING_SNAKE_CASE       :Any		    =  False
             if self.is_zeroa() or self.is_zeroa():
                   __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  set(['''cpu''', '''nvme''']							)
                   __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  set(
                       [
                           self.get_value('''zero_optimization.offload_optimizer.device'''							),
                           self.get_value('''zero_optimization.offload_param.device'''							),
                       ]							)
                   if len(offload_devices & offload_devices_valid							) > 0:
                         __SCREAMING_SNAKE_CASE       :int		    =  True




       def    _UpperCamelCase					(						self		,SCREAMING_SNAKE_CASE__							)      ->				Tuple:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :Any		    =  self.config

             # find the config node of interest if it exists
             __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  ds_key_long.split('''.'''							)
             __SCREAMING_SNAKE_CASE       :List[str]		    =  nodes.pop()
             for node in nodes:
                   __SCREAMING_SNAKE_CASE       :Dict		    =  config.get(SCREAMING_SNAKE_CASE__							)
                   if config is None:
                         return None, ds_key

             return config, ds_key




       def    _UpperCamelCase					(						self		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__=None							)      ->				Tuple:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE       :Optional[Any]		    =  self.find_config_node(SCREAMING_SNAKE_CASE__							)
             if config is None:
                   return default
             return config.get(SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__							)




       def    _UpperCamelCase					(						self		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__=False							)      ->				int:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :Dict		    =  self.config

             # find the config node of interest if it exists
             __SCREAMING_SNAKE_CASE       :str		    =  ds_key_long.split('''.'''							)
             for node in nodes:
                   __SCREAMING_SNAKE_CASE       :Any		    =  config
                   __SCREAMING_SNAKE_CASE       :int		    =  config.get(SCREAMING_SNAKE_CASE__							)
                   if config is None:
                         if must_exist:
                               raise ValueError(f'''Can\'t find {ds_key_long} entry in the config: {self.config}'''							)
                         else:
                               return

        # if found remove it
             if parent_config is not None:
                   parent_config.pop(SCREAMING_SNAKE_CASE__							)




       def    _UpperCamelCase					(						self		,SCREAMING_SNAKE_CASE__							)      ->				Optional[int]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :int		    =  self.get_value(SCREAMING_SNAKE_CASE__							)
             return False if value is None else bool(SCREAMING_SNAKE_CASE__							)




       def    _UpperCamelCase					(						self		,SCREAMING_SNAKE_CASE__							)      ->				Optional[Any]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :str		    =  self.get_value(SCREAMING_SNAKE_CASE__							)
             return False if value is None else not bool(SCREAMING_SNAKE_CASE__							)




       def    _UpperCamelCase					(						self							)      ->				List[str]:





             """simple docstring"""






             return self._stage == 2




       def    _UpperCamelCase					(						self							)      ->				List[Any]:





             """simple docstring"""






             return self._stage == 3




       def    _UpperCamelCase					(						self							)      ->				Union[str, Any]:





             """simple docstring"""






             return self._offload






class  _SCREAMING_SNAKE_CASE:




       def __init__(						self		,SCREAMING_SNAKE_CASE__							)      ->				Optional[Any]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :Optional[int]		    =  engine




       def    _UpperCamelCase					(						self		,SCREAMING_SNAKE_CASE__		,**SCREAMING_SNAKE_CASE__							)      ->				List[Any]:





             """simple docstring"""






             self.engine.backward(SCREAMING_SNAKE_CASE__		,**SCREAMING_SNAKE_CASE__							)

             # Deepspeed's `engine.step` performs the following operations:
             # - gradient accumulation check
             # - gradient clipping
             # - optimizer step
             # - zero grad
             # - checking overflow
             # - lr_scheduler step (only if engine.lr_scheduler is not None)
             self.engine.step()
             # and this plugin overrides the above calls with no-ops when Accelerate runs under
             # Deepspeed, but allows normal functionality for non-Deepspeed cases thus enabling a simple
             # training loop that works transparently under many training regimes.






class  _SCREAMING_SNAKE_CASE(       A  ):




       def __init__(						self		,SCREAMING_SNAKE_CASE__							)      ->				str:





             """simple docstring"""






             super().__init__(SCREAMING_SNAKE_CASE__		,device_placement=SCREAMING_SNAKE_CASE__		,scaler=SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE       :int		    =  hasattr(self.optimizer		,'''overflow'''							)




       def    _UpperCamelCase					(						self		,SCREAMING_SNAKE_CASE__=None							)      ->				Union[str, Any]:





             """simple docstring"""






             pass  # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed




       def    _UpperCamelCase					(						self							)      ->				Union[str, Any]:





             """simple docstring"""






             pass  # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed




       @property
       def    _UpperCamelCase					(						self							)      ->				Dict:





             """simple docstring"""






             if self.__has_overflow__:
                   return self.optimizer.overflow
             return False






class  _SCREAMING_SNAKE_CASE(       A  ):




       def __init__(						self		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__							)      ->				Optional[int]:





             """simple docstring"""






             super().__init__(SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__							)




       def    _UpperCamelCase					(						self							)      ->				Optional[int]:





             """simple docstring"""






             pass  # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed






class  _SCREAMING_SNAKE_CASE:




       def __init__(						self		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__=0.0_0_1		,SCREAMING_SNAKE_CASE__=0		,**SCREAMING_SNAKE_CASE__							)      ->				List[str]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  params
             __SCREAMING_SNAKE_CASE       :str		    =  lr
             __SCREAMING_SNAKE_CASE       :str		    =  weight_decay
             __SCREAMING_SNAKE_CASE       :int		    =  kwargs






class  _SCREAMING_SNAKE_CASE:




       def __init__(						self		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__=None		,SCREAMING_SNAKE_CASE__=0		,**SCREAMING_SNAKE_CASE__							)      ->				str:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :Dict		    =  optimizer
             __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  total_num_steps
             __SCREAMING_SNAKE_CASE       :Any		    =  warmup_num_steps
             __SCREAMING_SNAKE_CASE       :int		    =  kwargs