

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						:  Optional[int]									=			logging.get_logger(__name__)





def    __lowerCamelCase      (					UpperCamelCase__ ):







							'''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__  ,				UpperCamelCase__ ):







							'''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      (					):







							'''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__  ,				UpperCamelCase__  ,				UpperCamelCase__=False  ,				UpperCamelCase__=None ):







							'''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(
																					    [[4.41_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:
																					snake_case_     							=       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}''' )

														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						:  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.""",
					)
					_UpperCAmelCase						:  Any									=			parser.parse_args()
					convert_glpn_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)







from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding







class 					lowercase							(			lowercase_     ):
				__SCREAMING_SNAKE_CASE   :					Union[str, Any]			     =     ['''image_processor''', '''tokenizer''']
				__SCREAMING_SNAKE_CASE   :					Tuple			     =     '''AutoImageProcessor'''
				__SCREAMING_SNAKE_CASE   :					Dict			     =     '''AutoTokenizer'''




				def __init__(   self ,  snake_case ,  snake_case			):
											super().__init__(snake_case ,  snake_case			)
											snake_case_     							=       self.image_processor




				def __call__(   self ,  snake_case=None ,  snake_case=None ,  snake_case=None ,  **snake_case			):

											if text is None and images is None:
																		raise ValueError('You have to specify either text or images. Both cannot be none.'			)

											if text is not None:
																		snake_case_     							=       self.tokenizer(snake_case ,  return_tensors=snake_case ,  **snake_case			)

											if images is not None:
																		snake_case_     							=       self.image_processor(snake_case ,  return_tensors=snake_case ,  **snake_case			)

											if text is not None and images is not None:
																		snake_case_     							=       image_features.pixel_values
																		return encoding
											elif text is not None:
																		return encoding
											else:
																		return BatchEncoding(data=dict(**snake_case			) ,  tensor_type=snake_case			)




				def        a				(   self ,  *snake_case ,  **snake_case			):
											return self.tokenizer.batch_decode(*snake_case ,  **snake_case			)




				def        a				(   self ,  *snake_case ,  **snake_case			):
											return self.tokenizer.decode(*snake_case ,  **snake_case			)




				@property
				def        a				(   self			):
											return ["input_ids", "attention_mask", "pixel_values"]




