





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()
UpperCAmelCase_ 							=       logging.get_logger(__name__)





def   lowerCamelCase__       (       UpperCamelCase__     :    List[Any]  )     ->	int:



					'''simple docstring'''




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

					return new_state_dict





def   lowerCamelCase__       (       UpperCamelCase__     :    Optional[Any]    ,      UpperCamelCase__     :    Optional[Any]  )     ->	Optional[Any]:



					'''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)
															_snake_case													=			state_dict.pop(F'''glpn.encoder.block.{i}.{j}.attention.self.kv.weight'''  )
															_snake_case													=			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
															_snake_case													=			kv_weight[
															    : config.hidden_sizes[i], :
															]
															_snake_case													=			kv_bias[: config.hidden_sizes[i]]
															_snake_case													=			kv_weight[
															    config.hidden_sizes[i] :, :
															]
															_snake_case													=			kv_bias[config.hidden_sizes[i] :]





def   lowerCamelCase__       (       )     ->	int:



					'''simple docstring'''




					_snake_case													=			'http://images.cocodataset.org/val2017/000000039769.jpg'
					_snake_case													=			Image.open(requests.get(UpperCamelCase__    ,      stream=UpperCamelCase__  ).raw  )

					return image





@torch.no_grad()
def   lowerCamelCase__       (       UpperCamelCase__     :    List[str]    ,      UpperCamelCase__     :    Tuple    ,      UpperCamelCase__     :    int=False    ,      UpperCamelCase__     :    int=None  )     ->	Optional[int]:



					'''simple docstring'''




					_snake_case													=			GLPNConfig(hidden_sizes=[64, 128, 320, 512]    ,      decoder_hidden_size=64    ,      depths=[3, 8, 27, 3]  )

					# load image processor (only resize + rescale)
					_snake_case													=			GLPNImageProcessor()

					# prepare image
					_snake_case													=			prepare_img()
					_snake_case													=			image_processor(images=UpperCamelCase__    ,      return_tensors='pt'  ).pixel_values

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

					# load original state dict
					_snake_case													=			torch.load(UpperCamelCase__    ,      map_location=torch.device('cpu'  )  )

					# rename keys
					_snake_case													=			rename_keys(UpperCamelCase__  )

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

					# create HuggingFace model and load state dict
					_snake_case													=			GLPNForDepthEstimation(UpperCamelCase__  )
					model.load_state_dict(UpperCamelCase__  )
					model.eval()

					# forward pass
					_snake_case													=			model(UpperCamelCase__  )
					_snake_case													=			outputs.predicted_depth

					# verify output
					if model_name is not None:
										if "nyu" in model_name:
															_snake_case													=			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:
															_snake_case													=			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}'''  )

										_snake_case													=			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__":
					UpperCAmelCase_ 							=       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.""",
					)
					UpperCAmelCase_ 							=       parser.parse_args()
					convert_glpn_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)










import argparse

from torch import nn

# transformers_old should correspond to branch `save_old_prophetnet_model_structure` here
# original prophetnet_checkpoints are saved under `patrickvonplaten/..._old` respectively
from transformers_old.modeling_prophetnet import (
    ProphetNetForConditionalGeneration as ProphetNetForConditionalGenerationOld,
)
from transformers_old.modeling_xlm_prophetnet import (
    XLMProphetNetForConditionalGeneration as XLMProphetNetForConditionalGenerationOld,
)

from transformers import ProphetNetForConditionalGeneration, XLMProphetNetForConditionalGeneration, logging


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





def   lowerCamelCase__       (       UpperCamelCase__     :    str    ,      UpperCamelCase__     :    str  )     ->	Union[str, Any]:



					'''simple docstring'''




					if "xprophetnet" in prophetnet_checkpoint_path:
										_snake_case													=			XLMProphetNetForConditionalGenerationOld.from_pretrained(UpperCamelCase__  )
										_snake_case							,       _snake_case													=			XLMProphetNetForConditionalGeneration.from_pretrained(
										    UpperCamelCase__    ,      output_loading_info=UpperCamelCase__  )
					else:
										_snake_case													=			ProphetNetForConditionalGenerationOld.from_pretrained(UpperCamelCase__  )
										_snake_case							,       _snake_case													=			ProphetNetForConditionalGeneration.from_pretrained(
										    UpperCamelCase__    ,      output_loading_info=UpperCamelCase__  )

					_snake_case													=			['key_proj', 'value_proj', 'query_proj']

					_snake_case													=			{
					    'self_attn': 'ngram_self_attn',
					    'cross_attn': 'encoder_attn',
					    'cross_attn_layer_norm': 'encoder_attn_layer_norm',
					    'feed_forward_layer_norm': 'final_layer_norm',
					    'feed_forward': '',
					    'intermediate': 'fc1',
					    'output': 'fc2',
					    'key_proj': 'k_proj',
					    'query_proj': 'q_proj',
					    'value_proj': 'v_proj',
					    'word_embeddings': 'embed_tokens',
					    'embeddings_layer_norm': 'emb_layer_norm',
					    'relative_pos_embeddings': 'relative_linear',
					    'ngram_embeddings': 'ngram_input_embed',
					    'position_embeddings': 'embed_positions',
					}

					for key in loading_info["missing_keys"]:
										_snake_case													=			key.split('.'  )

										if attributes[0] == "lm_head":
															_snake_case													=			prophet
															_snake_case													=			prophet_old
										else:
															_snake_case													=			prophet.prophetnet
															_snake_case													=			prophet_old.model

										_snake_case													=			False
										for attribute in attributes:
															if attribute in mapping:
																				_snake_case													=			mapping[attribute]
																				if not hasattr(UpperCamelCase__    ,      UpperCamelCase__  ) and len(UpperCamelCase__  ) > 0:
																									_snake_case													=			attribute
															elif hasattr(UpperCamelCase__    ,      UpperCamelCase__  ):
																				_snake_case													=			attribute

															if attribute == "weight":
																				assert old_model.weight.shape == model.weight.shape, "Shapes have to match!"
																				_snake_case													=			old_model.weight
																				logger.info(F'''{attribute} is initialized.'''  )
																				_snake_case													=			True
																				break
															elif attribute == "bias":
																				assert old_model.bias.shape == model.bias.shape, "Shapes have to match!"
																				_snake_case													=			old_model.bias
																				logger.info(F'''{attribute} is initialized'''  )
																				_snake_case													=			True
																				break
															elif attribute in special_keys and hasattr(UpperCamelCase__    ,      'in_proj_weight'  ):
																				_snake_case													=			old_model.in_proj_weight.shape[0] // 3
																				_snake_case													=			getattr(UpperCamelCase__    ,      UpperCamelCase__  )
																				param.weight.shape == old_model.in_proj_weight[:embed_dim, :].shape, "Shapes have to match"
																				param.bias.shape == old_model.in_proj_bias[:embed_dim].shape, "Shapes have to match"
																				if attribute == "query_proj":
																									_snake_case													=			nn.Parameter(old_model.in_proj_weight[:embed_dim, :]  )
																									_snake_case													=			nn.Parameter(old_model.in_proj_bias[:embed_dim]  )

																				elif attribute == "key_proj":
																									_snake_case													=			nn.Parameter(old_model.in_proj_weight[embed_dim : 2 * embed_dim, :]  )
																									_snake_case													=			nn.Parameter(old_model.in_proj_bias[embed_dim : 2 * embed_dim]  )
																				elif attribute == "value_proj":
																									_snake_case													=			nn.Parameter(old_model.in_proj_weight[2 * embed_dim :, :]  )
																									_snake_case													=			nn.Parameter(old_model.in_proj_bias[2 * embed_dim :]  )
																				_snake_case													=			True
																				break
															elif attribute == "position_embeddings":
																				assert (
																				    model.position_embeddings.weight.shape[-1] == old_model.embed_positions.weight.shape[-1]
																				), "Hidden size has to match"
																				assert model.position_embeddings.weight.shape[0] == 512, "We want 512 position_embeddings."
																				_snake_case													=			nn.Parameter(old_model.embed_positions.weight[:512, :]  )
																				_snake_case													=			True
																				break

															if attribute.isdigit():
																				_snake_case													=			model[int(UpperCamelCase__  )]
																				_snake_case													=			old_model[int(UpperCamelCase__  )]
															else:
																				_snake_case													=			getattr(UpperCamelCase__    ,      UpperCamelCase__  )

																				if old_attribute == "":
																									_snake_case													=			old_model
																				else:
																									if not hasattr(UpperCamelCase__    ,      UpperCamelCase__  ):
																														raise ValueError(F'''{old_model} does not have {old_attribute}'''  )
																									_snake_case													=			getattr(UpperCamelCase__    ,      UpperCamelCase__  )

										if not is_key_init:
															raise ValueError(F'''{key} was not correctly initialized!'''  )

					print(F'''Saving model to {pytorch_dump_folder_path}'''  )
					prophet.save_pretrained(UpperCamelCase__  )


if __name__ == "__main__":
					UpperCAmelCase_ 							=       argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    """--prophetnet_checkpoint_path""", default=None, type=str, required=True, help="""Path the official PyTorch dump."""
					)
					parser.add_argument(
					    """--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
					)
					UpperCAmelCase_ 							=       parser.parse_args()
					convert_prophetnet_checkpoint_to_pytorch(args.prophetnet_checkpoint_path, args.pytorch_dump_folder_path)



