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[str]       			=		logging.get_logger(__name__)






def _lowerCamelCase  (		snake_case  ):
							_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(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(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(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(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 _lowerCamelCase  (		snake_case     , snake_case  ):
							# 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)
																					_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 _lowerCamelCase  (		):
							_lowerCAmelCase							=							'http://images.cocodataset.org/val2017/000000039769.jpg'
							_lowerCAmelCase							=							Image.open(requests.get(snake_case     , stream=snake_case  ).raw  )

							return image






@torch.no_grad()
def _lowerCamelCase  (		snake_case     , snake_case     , snake_case=False     , snake_case=None  ):
							_lowerCAmelCase							=							GLPNConfig(hidden_sizes=[64, 128, 320, 512]     , 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=snake_case     , return_tensors='pt'  ).pixel_values

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

							# load original state dict
							_lowerCAmelCase							=							torch.load(snake_case     , map_location=torch.device('cpu'  )  )

							# rename keys
							_lowerCAmelCase							=							rename_keys(snake_case  )

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

							# create HuggingFace model and load state dict
							_lowerCAmelCase							=							GLPNForDepthEstimation(snake_case  )
							model.load_state_dict(snake_case  )
							model.eval()

							# forward pass
							_lowerCAmelCase							=							model(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, 480, 640]  )

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


if __name__ == "__main__":
				_lowercase:	List[str]       			=		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:	Dict       			=		parser.parse_args()
				convert_glpn_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)







from ...configuration_utils import PretrainedConfig
from ...utils import logging


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

_lowercase:	int       			=		{
    '''alibaba-damo/mgp-str-base''': '''https://huggingface.co/alibaba-damo/mgp-str-base/resolve/main/config.json''',
}



class        lowerCamelCase__				(   UpperCAmelCase				):
			UpperCamelCase__    							="mgp-str"





			def __init__(       self	:	int  ,  lowercase__	:	List[Any]=[32, 1_28]  ,  lowercase__	:	str=4  ,  lowercase__	:	List[str]=3  ,  lowercase__	:	Tuple=27  ,  lowercase__	:	Optional[Any]=38  ,  lowercase__	:	Optional[Any]=5_02_57  ,  lowercase__	:	Dict=3_05_22  ,  lowercase__	:	Dict=7_68  ,  lowercase__	:	Tuple=12  ,  lowercase__	:	int=12  ,  lowercase__	:	int=4.0  ,  lowercase__	:	Tuple=True  ,  lowercase__	:	List[Any]=False  ,  lowercase__	:	int=1e-5  ,  lowercase__	:	int=0.0  ,  lowercase__	:	Optional[Any]=0.0  ,  lowercase__	:	List[Any]=0.0  ,  lowercase__	:	Union[str, Any]=False  ,  lowercase__	:	str=0.0_2  ,  **lowercase__	:	Optional[int]  ,  ):
										super().__init__(**lowercase__						)

										_lowerCAmelCase							=							image_size
										_lowerCAmelCase							=							patch_size
										_lowerCAmelCase							=							num_channels
										_lowerCAmelCase							=							max_token_length
										_lowerCAmelCase							=							num_character_labels
										_lowerCAmelCase							=							num_bpe_labels
										_lowerCAmelCase							=							num_wordpiece_labels
										_lowerCAmelCase							=							hidden_size
										_lowerCAmelCase							=							num_hidden_layers
										_lowerCAmelCase							=							num_attention_heads
										_lowerCAmelCase							=							mlp_ratio
										_lowerCAmelCase							=							distilled
										_lowerCAmelCase							=							layer_norm_eps
										_lowerCAmelCase							=							drop_rate
										_lowerCAmelCase							=							qkv_bias
										_lowerCAmelCase							=							attn_drop_rate
										_lowerCAmelCase							=							drop_path_rate
										_lowerCAmelCase							=							output_aa_attentions
										_lowerCAmelCase							=							initializer_range






