'''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()
_lowercase						: List[Any]						=						logging.get_logger(__name__)




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

							return new_state_dict




def 							lowerCamelCase	(      UpperCAmelCase__       :					int      ,      UpperCAmelCase__       :					int )			->      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)
																					lowercase_					:			List[Any]										=       state_dict.pop(F'''glpn.encoder.block.{i}.{j}.attention.self.kv.weight''' )
																					lowercase_					:			Dict										=       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
																					lowercase_					:			Optional[Any]										=       kv_weight[
																					    : config.hidden_sizes[i], :
																					]
																					lowercase_					:			Tuple										=       kv_bias[: config.hidden_sizes[i]]
																					lowercase_					:			List[Any]										=       kv_weight[
																					    config.hidden_sizes[i] :, :
																					]
																					lowercase_					:			Optional[Any]										=       kv_bias[config.hidden_sizes[i] :]




def 							lowerCamelCase	(      )			->      Optional[Any]:
							lowercase_					:			str										=       """http://images.cocodataset.org/val2017/000000039769.jpg"""
							lowercase_					:			Dict										=       Image.open(requests.get(UpperCAmelCase__      ,      stream=UpperCAmelCase__ ).raw )

							return image




@torch.no_grad()
def 							lowerCamelCase	(      UpperCAmelCase__       :					List[str]      ,      UpperCAmelCase__       :					Union[str, Any]      ,      UpperCAmelCase__       :					Optional[int]=False      ,      UpperCAmelCase__       :					str=None )			->      int:
							lowercase_					:			Dict										=       GLPNConfig(hidden_sizes=[64, 128, 320, 512]      ,      decoder_hidden_size=64      ,      depths=[3, 8, 27, 3] )

							# load image processor (only resize + rescale)
							lowercase_					:			Tuple										=       GLPNImageProcessor()

							# prepare image
							lowercase_					:			List[str]										=       prepare_img()
							lowercase_					:			Any										=       image_processor(images=UpperCAmelCase__      ,      return_tensors="""pt""" ).pixel_values

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

							# load original state dict
							lowercase_					:			Any										=       torch.load(UpperCAmelCase__      ,      map_location=torch.device("""cpu""" ) )

							# rename keys
							lowercase_					:			List[Any]										=       rename_keys(UpperCAmelCase__ )

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

							# create HuggingFace model and load state dict
							lowercase_					:			int										=       GLPNForDepthEstimation(UpperCAmelCase__ )
							model.load_state_dict(UpperCAmelCase__ )
							model.eval()

							# forward pass
							lowercase_					:			int										=       model(UpperCAmelCase__ )
							lowercase_					:			List[str]										=       outputs.predicted_depth

							# verify output
							if model_name is not None:
														if "nyu" in model_name:
																					lowercase_					:			List[str]										=       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:
																					lowercase_					:			List[str]										=       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}''' )

														lowercase_					:			Optional[Any]										=       torch.Size([1, 480, 640] )

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


if __name__ == "__main__":
						_lowercase						: Optional[Any]						=						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.",
						)
						_lowercase						: int						=						parser.parse_args()
						convert_glpn_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)






'''simple docstring'''




from itertools import product




def 							lowerCamelCase	(      UpperCAmelCase__       :					int      ,      UpperCAmelCase__       :					int )			->      list[int]:
							lowercase_					:			List[Any]										=       sides_number
							lowercase_					:			Dict										=       max_face_number * dice_number
							lowercase_					:			List[str]										=       [0] * (max_total + 1)

							lowercase_					:			Union[str, Any]										=       1
							lowercase_					:			Dict										=       range(UpperCAmelCase__      ,      max_face_number + 1 )
							for dice_numbers in product(UpperCAmelCase__      ,      repeat=UpperCAmelCase__ ):
														lowercase_					:			Any										=       sum(UpperCAmelCase__ )
														totals_frequencies[total] += 1

							return totals_frequencies




def 							lowerCamelCase	(      )			->      float:
							lowercase_					:			Optional[Any]										=       total_frequency_distribution(
							    sides_number=4      ,      dice_number=9 )
							lowercase_					:			List[str]										=       total_frequency_distribution(
							    sides_number=6      ,      dice_number=6 )

							lowercase_					:			Union[str, Any]										=       0
							lowercase_					:			Tuple										=       9
							lowercase_					:			Optional[int]										=       4 * 9
							lowercase_					:			List[Any]										=       6
							for peter_total in range(UpperCAmelCase__      ,      max_peter_total + 1 ):
														peter_wins_count += peter_totals_frequencies[peter_total] * sum(
														    colin_totals_frequencies[min_colin_total:peter_total] )

							lowercase_					:			str										=       (4**9) * (6**6)
							lowercase_					:			List[Any]										=       peter_wins_count / total_games_number

							lowercase_					:			Dict										=       round(UpperCAmelCase__      ,      ndigits=7 )

							return rounded_peter_win_probability


if __name__ == "__main__":
						print(f"""{solution() = }""")





