





"""simple docstring"""





import re
import time
from typing import Optional

import IPython.display as disp

from ..trainer_callback import TrainerCallback
from ..trainer_utils import IntervalStrategy, has_length


def 			UpperCAmelCase__							(  SCREAMING_SNAKE_CASE     :						Any			):







      '''simple docstring'''




      lowerCAmelCase  						=       int(SCREAMING_SNAKE_CASE			)
      lowerCAmelCase						,		lowerCAmelCase						,		lowerCAmelCase  						=       t // 36_00, (t // 60) % 60, t % 60
      return F'{h}:{m:02d}:{s:02d}' if h != 0 else F'{m:02d}:{s:02d}'


def 			UpperCAmelCase__							(  SCREAMING_SNAKE_CASE     :						List[Any]							,						SCREAMING_SNAKE_CASE     :						str							,						SCREAMING_SNAKE_CASE     :						Any							,						SCREAMING_SNAKE_CASE     :						Optional[Any]							,						SCREAMING_SNAKE_CASE     :						Union[str, Any]=3_00			):







      '''simple docstring'''




      return F'\n    <div>\n      {prefix}\n      <progress value=\'{value}\' max=\'{total}\' style=\'width:{width}px; height:20px; vertical-align: middle;\'></progress>\n      {label}\n    </div>\n    '


def 			UpperCAmelCase__							(  SCREAMING_SNAKE_CASE     :						Tuple			):







      '''simple docstring'''




      lowerCAmelCase  						=       """<table border=\"1\" class=\"dataframe\">\n"""
      html_code += """  <thead>\n <tr style="text-align: left;">\n"""
      for i in items[0]:
            html_code += F'      <th>{i}</th>\n'
      html_code += "    </tr>\n  </thead>\n  <tbody>\n"
      for line in items[1:]:
            html_code += "    <tr>\n"
            for elt in line:
                  lowerCAmelCase  						=       F'{elt:.6f}' if isinstance(SCREAMING_SNAKE_CASE							,						SCREAMING_SNAKE_CASE			) else str(SCREAMING_SNAKE_CASE			)
                  html_code += F'      <td>{elt}</td>\n'
            html_code += "    </tr>\n"
      html_code += "  </tbody>\n</table><p>"
      return html_code

class 		lowercase					:
      _SCREAMING_SNAKE_CASE					=      5
      _SCREAMING_SNAKE_CASE					=      0.2





      def __init__(       self      ,						lowercase      ,						lowercase = None      ,						lowercase = True      ,						lowercase = None      ,						lowercase = 300      ,						)		->     List[Any]:
            lowerCAmelCase  						=       total
            lowerCAmelCase  						=       """""" if prefix is None else prefix
            lowerCAmelCase  						=       leave
            lowerCAmelCase  						=       parent
            lowerCAmelCase  						=       width
            lowerCAmelCase  						=       None
            lowerCAmelCase  						=       None
            lowerCAmelCase  						=       None





      def     _snake_case			(       self      ,						lowercase      ,						lowercase = False      ,						lowercase = None )		->     Optional[int]:
            lowerCAmelCase  						=       value
            if comment is not None:
                  lowerCAmelCase  						=       comment
            if self.last_value is None:
                  lowerCAmelCase  						=       lowerCAmelCase  						=       time.time()
                  lowerCAmelCase  						=       lowerCAmelCase  						=       value
                  lowerCAmelCase  						=       lowerCAmelCase  						=       None
                  lowerCAmelCase  						=       self.warmup
                  lowerCAmelCase  						=       1
                  self.update_bar(lowercase )
            elif value <= self.last_value and not force_update:
                  return
            elif force_update or self.first_calls > 0 or value >= min(self.last_value + self.wait_for      ,						self.total ):
                  if self.first_calls > 0:
                        self.first_calls -= 1
                  lowerCAmelCase  						=       time.time()
                  lowerCAmelCase  						=       current_time - self.start_time
                  # We could have value = self.start_value if the update is called twixe with the same start value.
                  if value > self.start_value:
                        lowerCAmelCase  						=       self.elapsed_time / (value - self.start_value)
                  else:
                        lowerCAmelCase  						=       None
                  if value >= self.total:
                        lowerCAmelCase  						=       self.total
                        lowerCAmelCase  						=       None
                        if not self.leave:
                              self.close()
                  elif self.average_time_per_item is not None:
                        lowerCAmelCase  						=       self.average_time_per_item * (self.total - value)
                  self.update_bar(lowercase )
                  lowerCAmelCase  						=       value
                  lowerCAmelCase  						=       current_time
                  if self.average_time_per_item is None:
                        lowerCAmelCase  						=       1
                  else:
                        lowerCAmelCase  						=       max(int(self.update_every / self.average_time_per_item )      ,						1 )





      def     _snake_case			(       self      ,						lowercase      ,						lowercase=None )		->     Dict:
            lowerCAmelCase  						=       """ """ * (len(str(self.total ) ) - len(str(lowercase ) )) + str(lowercase )
            if self.elapsed_time is None:
                  lowerCAmelCase  						=       f'[{spaced_value}/{self.total} : < :'
            elif self.predicted_remaining is None:
                  lowerCAmelCase  						=       f'[{spaced_value}/{self.total} {format_time(self.elapsed_time )}'
            else:
                  lowerCAmelCase  						=       (
                      f'[{spaced_value}/{self.total} {format_time(self.elapsed_time )} <'
                      f' {format_time(self.predicted_remaining )}'
                  )
                  self.label += f', {1/self.average_time_per_item:.2f} it/s'
            self.label += "]" if self.comment is None or len(self.comment ) == 0 else f', {self.comment}]'
            self.display()





      def     _snake_case			(       self )		->     Optional[Any]:
            lowerCAmelCase  						=       html_progress_bar(self.value      ,						self.total      ,						self.prefix      ,						self.label      ,						self.width )
            if self.parent is not None:
                  # If this is a child bar, the parent will take care of the display.
                  self.parent.display()
                  return
            if self.output is None:
                  lowerCAmelCase  						=       disp.display(disp.HTML(self.html_code )      ,						display_id=lowercase )
            else:
                  self.output.update(disp.HTML(self.html_code ) )





      def     _snake_case			(       self )		->     Dict:
            if self.parent is None and self.output is not None:
                  self.output.update(disp.HTML("""""" ) )







class 		lowercase					(					_UpperCAmelCase					):





      def __init__(       self      ,						lowercase      ,						lowercase=None )		->     List[str]:
            super().__init__(lowercase )
            lowerCAmelCase  						=       None if column_names is None else [column_names]
            lowerCAmelCase  						=       None





      def     _snake_case			(       self )		->     Any:
            lowerCAmelCase  						=       html_progress_bar(self.value      ,						self.total      ,						self.prefix      ,						self.label      ,						self.width )
            if self.inner_table is not None:
                  self.html_code += text_to_html_table(self.inner_table )
            if self.child_bar is not None:
                  self.html_code += self.child_bar.html_code
            if self.output is None:
                  lowerCAmelCase  						=       disp.display(disp.HTML(self.html_code )      ,						display_id=lowercase )
            else:
                  self.output.update(disp.HTML(self.html_code ) )





      def     _snake_case			(       self      ,						lowercase )		->     int:
            if self.inner_table is None:
                  lowerCAmelCase  						=       [list(values.keys() ), list(values.values() )]
            else:
                  lowerCAmelCase  						=       self.inner_table[0]
                  if len(self.inner_table ) == 1:
                        # We give a chance to update the column names at the first iteration
                        for key in values.keys():
                              if key not in columns:
                                    columns.append(lowercase )
                        lowerCAmelCase  						=       columns
                  self.inner_table.append([values[c] for c in columns] )





      def     _snake_case			(       self      ,						lowercase      ,						lowercase=None      ,						lowercase=300 )		->     List[Any]:
            lowerCAmelCase  						=       NotebookProgressBar(lowercase      ,						prefix=lowercase      ,						parent=self      ,						width=lowercase )
            return self.child_bar





      def     _snake_case			(       self )		->     int:
            lowerCAmelCase  						=       None
            self.display()







class 		lowercase					(					_UpperCAmelCase					):





      def __init__(       self )		->     Union[str, Any]:
            lowerCAmelCase  						=       None
            lowerCAmelCase  						=       None
            lowerCAmelCase  						=       False





      def     _snake_case			(       self      ,						lowercase      ,						lowercase      ,						lowercase      ,						**lowercase )		->     Optional[Any]:
            lowerCAmelCase  						=       """Epoch""" if args.evaluation_strategy == IntervalStrategy.EPOCH else """Step"""
            lowerCAmelCase  						=       0
            lowerCAmelCase  						=       0
            lowerCAmelCase  						=       [self.first_column] + ["""Training Loss"""]
            if args.evaluation_strategy != IntervalStrategy.NO:
                  column_names.append("""Validation Loss""" )
            lowerCAmelCase  						=       NotebookTrainingTracker(state.max_steps      ,						lowercase )





      def     _snake_case			(       self      ,						lowercase      ,						lowercase      ,						lowercase      ,						**lowercase )		->     Dict:
            lowerCAmelCase  						=       int(state.epoch ) if int(state.epoch ) == state.epoch else f'{state.epoch:.2f}'
            self.training_tracker.update(
                state.global_step + 1      ,						comment=f'Epoch {epoch}/{state.num_train_epochs}'      ,						force_update=self._force_next_update      ,						)
            lowerCAmelCase  						=       False





      def     _snake_case			(       self      ,						lowercase      ,						lowercase      ,						lowercase      ,						lowercase=None      ,						**lowercase )		->     Dict:
            if not has_length(lowercase ):
                  return
            if self.prediction_bar is None:
                  if self.training_tracker is not None:
                        lowerCAmelCase  						=       self.training_tracker.add_child(len(lowercase ) )
                  else:
                        lowerCAmelCase  						=       NotebookProgressBar(len(lowercase ) )
                  self.prediction_bar.update(1 )
            else:
                  self.prediction_bar.update(self.prediction_bar.value + 1 )





      def     _snake_case			(       self      ,						lowercase      ,						lowercase      ,						lowercase      ,						**lowercase )		->     Dict:
            if self.prediction_bar is not None:
                  self.prediction_bar.close()
            lowerCAmelCase  						=       None





      def     _snake_case			(       self      ,						lowercase      ,						lowercase      ,						lowercase      ,						lowercase=None      ,						**lowercase )		->     List[Any]:
            # Only for when there is no evaluation
            if args.evaluation_strategy == IntervalStrategy.NO and "loss" in logs:
                  lowerCAmelCase  						=       {"""Training Loss""": logs["""loss"""]}
                  # First column is necessarily Step sine we're not in epoch eval strategy
                  lowerCAmelCase  						=       state.global_step
                  self.training_tracker.write_line(lowercase )





      def     _snake_case			(       self      ,						lowercase      ,						lowercase      ,						lowercase      ,						lowercase=None      ,						**lowercase )		->     List[str]:
            if self.training_tracker is not None:
                  lowerCAmelCase  						=       {"""Training Loss""": """No log""", """Validation Loss""": """No log"""}
                  for log in reversed(state.log_history ):
                        if "loss" in log:
                              lowerCAmelCase  						=       log["""loss"""]
                              break

                  if self.first_column == "Epoch":
                        lowerCAmelCase  						=       int(state.epoch )
                  else:
                        lowerCAmelCase  						=       state.global_step
                  lowerCAmelCase  						=       """eval"""
                  for k in metrics:
                        if k.endswith("""_loss""" ):
                              lowerCAmelCase  						=       re.sub(r"""\_loss$"""      ,						""""""      ,						lowercase )
                  lowerCAmelCase  						=       metrics.pop("""total_flos"""      ,						lowercase )
                  lowerCAmelCase  						=       metrics.pop("""epoch"""      ,						lowercase )
                  lowerCAmelCase  						=       metrics.pop(f'{metric_key_prefix}_runtime'      ,						lowercase )
                  lowerCAmelCase  						=       metrics.pop(f'{metric_key_prefix}_samples_per_second'      ,						lowercase )
                  lowerCAmelCase  						=       metrics.pop(f'{metric_key_prefix}_steps_per_second'      ,						lowercase )
                  lowerCAmelCase  						=       metrics.pop(f'{metric_key_prefix}_jit_compilation_time'      ,						lowercase )
                  for k, v in metrics.items():
                        if k == f'{metric_key_prefix}_loss':
                              lowerCAmelCase  						=       v
                        else:
                              lowerCAmelCase  						=       k.split("""_""" )
                              lowerCAmelCase  						=       """ """.join([part.capitalize() for part in splits[1:]] )
                              lowerCAmelCase  						=       v
                  self.training_tracker.write_line(lowercase )
                  self.training_tracker.remove_child()
                  lowerCAmelCase  						=       None
                  # Evaluation takes a long time so we should force the next update.
                  lowerCAmelCase  						=       True





      def     _snake_case			(       self      ,						lowercase      ,						lowercase      ,						lowercase      ,						**lowercase )		->     List[Any]:
            self.training_tracker.update(
                state.global_step      ,						comment=f'Epoch {int(state.epoch )}/{state.num_train_epochs}'      ,						force_update=lowercase )
            lowerCAmelCase  						=       None













"""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()
SCREAMING_SNAKE_CASE__					=   logging.get_logger(__name__)


def 			UpperCAmelCase__							(  SCREAMING_SNAKE_CASE     :						Dict			):







      '''simple docstring'''




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

      return new_state_dict


def 			UpperCAmelCase__							(  SCREAMING_SNAKE_CASE     :						List[Any]							,						SCREAMING_SNAKE_CASE     :						List[str]			):







      '''simple docstring'''




      for i in range(config.num_encoder_blocks			):
            for j in range(config.depths[i]			):
                  # read in weights + bias of keys and values (which is a single matrix in the original implementation)
                  lowerCAmelCase  						=       state_dict.pop(F'glpn.encoder.block.{i}.{j}.attention.self.kv.weight'			)
                  lowerCAmelCase  						=       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
                  lowerCAmelCase  						=       kv_weight[
                      : config.hidden_sizes[i], :
                  ]
                  lowerCAmelCase  						=       kv_bias[: config.hidden_sizes[i]]
                  lowerCAmelCase  						=       kv_weight[
                      config.hidden_sizes[i] :, :
                  ]
                  lowerCAmelCase  						=       kv_bias[config.hidden_sizes[i] :]


def 			UpperCAmelCase__							(  ):







      '''simple docstring'''




      lowerCAmelCase  						=       """http://images.cocodataset.org/val2017/000000039769.jpg"""
      lowerCAmelCase  						=       Image.open(requests.get(SCREAMING_SNAKE_CASE							,						stream=SCREAMING_SNAKE_CASE			).raw			)

      return image


@torch.no_grad()
def 			UpperCAmelCase__							(  SCREAMING_SNAKE_CASE     :						Tuple							,						SCREAMING_SNAKE_CASE     :						Optional[int]							,						SCREAMING_SNAKE_CASE     :						Union[str, Any]=False							,						SCREAMING_SNAKE_CASE     :						Union[str, Any]=None			):







      '''simple docstring'''




      lowerCAmelCase  						=       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)
      lowerCAmelCase  						=       GLPNImageProcessor()

      # prepare image
      lowerCAmelCase  						=       prepare_img()
      lowerCAmelCase  						=       image_processor(images=SCREAMING_SNAKE_CASE							,						return_tensors="""pt"""			).pixel_values

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

      # load original state dict
      lowerCAmelCase  						=       torch.load(SCREAMING_SNAKE_CASE							,						map_location=torch.device("""cpu"""			)			)

      # rename keys
      lowerCAmelCase  						=       rename_keys(SCREAMING_SNAKE_CASE			)

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

      # create HuggingFace model and load state dict
      lowerCAmelCase  						=       GLPNForDepthEstimation(SCREAMING_SNAKE_CASE			)
      model.load_state_dict(SCREAMING_SNAKE_CASE			)
      model.eval()

      # forward pass
      lowerCAmelCase  						=       model(SCREAMING_SNAKE_CASE			)
      lowerCAmelCase  						=       outputs.predicted_depth

      # verify output
      if model_name is not None:
            if "nyu" in model_name:
                  lowerCAmelCase  						=       torch.tensor(
                      [[4.41_47, 4.08_73, 4.06_73], [3.78_90, 3.28_81, 3.15_25], [3.76_74, 3.54_23, 3.49_13]]			)
            elif "kitti" in model_name:
                  lowerCAmelCase  						=       torch.tensor(
                      [[3.42_91, 2.78_65, 2.51_51], [3.28_41, 2.70_21, 2.35_02], [3.11_47, 2.46_25, 2.24_81]]			)
            else:
                  raise ValueError(F'Unknown model name: {model_name}'			)

            lowerCAmelCase  						=       torch.Size([1, 4_80, 6_40]			)

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


if __name__ == "__main__":
       SCREAMING_SNAKE_CASE__					=   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.",
       )
       SCREAMING_SNAKE_CASE__					=   parser.parse_args()
       convert_glpn_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)






