






"""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    			=       logging.get_logger(__name__)






def _lowerCAmelCase	(  __lowerCamelCase:Any   ):

						'''simple docstring'''






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

						return new_state_dict






def _lowerCAmelCase	(  __lowerCamelCase:Any ,						__lowerCamelCase: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)
																		__magic_name__            =     state_dict.pop(f'''glpn.encoder.block.{i}.{j}.attention.self.kv.weight'''   )
																		__magic_name__            =     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
																		__magic_name__            =     kv_weight[
																		    : config.hidden_sizes[i], :
																		]
																		__magic_name__            =     kv_bias[: config.hidden_sizes[i]]
																		__magic_name__            =     kv_weight[
																		    config.hidden_sizes[i] :, :
																		]
																		__magic_name__            =     kv_bias[config.hidden_sizes[i] :]






def _lowerCAmelCase	(  ):

						'''simple docstring'''






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

						return image






@torch.no_grad()
def _lowerCAmelCase	(  __lowerCamelCase:Tuple ,						__lowerCamelCase:Optional[int] ,						__lowerCamelCase:Dict=False ,						__lowerCamelCase:List[Any]=None   ):

						'''simple docstring'''






						__magic_name__            =     GLPNConfig(hidden_sizes=[6_4, 1_2_8, 3_2_0, 5_1_2] ,						decoder_hidden_size=6_4 ,						depths=[3, 8, 2_7, 3]   )

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

						# prepare image
						__magic_name__            =     prepare_img()
						__magic_name__            =     image_processor(images=_SCREAMING_SNAKE_CASE ,						return_tensors="pt"   ).pixel_values

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

						# load original state dict
						__magic_name__            =     torch.load(_SCREAMING_SNAKE_CASE ,						map_location=torch.device("cpu"   )   )

						# rename keys
						__magic_name__            =     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
						__magic_name__            =     GLPNForDepthEstimation(_SCREAMING_SNAKE_CASE   )
						model.load_state_dict(_SCREAMING_SNAKE_CASE   )
						model.eval()

						# forward pass
						__magic_name__            =     model(_SCREAMING_SNAKE_CASE   )
						__magic_name__            =     outputs.predicted_depth

						# verify output
						if model_name is not None:
												if "nyu" in model_name:
																		__magic_name__            =     torch.tensor(
																		    [[4.4_1_4_7, 4.0_8_7_3, 4.0_6_7_3], [3.7_8_9_0, 3.2_8_8_1, 3.1_5_2_5], [3.7_6_7_4, 3.5_4_2_3, 3.4_9_1_3]]   )
												elif "kitti" in model_name:
																		__magic_name__            =     torch.tensor(
																		    [[3.4_2_9_1, 2.7_8_6_5, 2.5_1_5_1], [3.2_8_4_1, 2.7_0_2_1, 2.3_5_0_2], [3.1_1_4_7, 2.4_6_2_5, 2.2_4_8_1]]   )
												else:
																		raise ValueError(f'''Unknown model name: {model_name}'''   )

												__magic_name__            =     torch.Size([1, 4_8_0, 6_4_0]   )

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











"""simple docstring"""

from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType

class 						A_							(      snake_case_  ):
				UpperCAmelCase__        =				['''image_processor''', '''tokenizer''']
				UpperCAmelCase__        =				'''BlipImageProcessor'''
				UpperCAmelCase__        =				'''AutoTokenizer'''







				def __init__(							self							:						Tuple						,    __lowerCamelCase							:						str						,    __lowerCamelCase							:						int	)				->					str:
										__magic_name__            =     False
										super().__init__(__lowerCamelCase						,    __lowerCamelCase	)
										__magic_name__            =     self.image_processor







				def __call__(							self							:						List[Any]						,    __lowerCamelCase							:						ImageInput = None						,    __lowerCamelCase							:						Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None						,    __lowerCamelCase							:						bool = True						,    __lowerCamelCase							:						Union[bool, str, PaddingStrategy] = False						,    __lowerCamelCase							:						Union[bool, str, TruncationStrategy] = None						,    __lowerCamelCase							:						Optional[int] = None						,    __lowerCamelCase							:						int = 0						,    __lowerCamelCase							:						Optional[int] = None						,    __lowerCamelCase							:						Optional[bool] = None						,    __lowerCamelCase							:						bool = False						,    __lowerCamelCase							:						bool = False						,    __lowerCamelCase							:						bool = False						,    __lowerCamelCase							:						bool = False						,    __lowerCamelCase							:						bool = False						,    __lowerCamelCase							:						bool = True						,    __lowerCamelCase							:						Optional[Union[str, TensorType]] = None						,    **__lowerCamelCase							:						Optional[int]						,    )				->					BatchEncoding:
										if images is None and text is None:
																raise ValueError("You have to specify either images or text."	)

										# Get only text
										if images is None:
																__magic_name__            =     self.tokenizer
																__magic_name__            =     self.tokenizer(
																    text=__lowerCamelCase						,    add_special_tokens=__lowerCamelCase						,    padding=__lowerCamelCase						,    truncation=__lowerCamelCase						,    max_length=__lowerCamelCase						,    stride=__lowerCamelCase						,    pad_to_multiple_of=__lowerCamelCase						,    return_attention_mask=__lowerCamelCase						,    return_overflowing_tokens=__lowerCamelCase						,    return_special_tokens_mask=__lowerCamelCase						,    return_offsets_mapping=__lowerCamelCase						,    return_token_type_ids=__lowerCamelCase						,    return_length=__lowerCamelCase						,    verbose=__lowerCamelCase						,    return_tensors=__lowerCamelCase						,    **__lowerCamelCase						,    )
																return text_encoding

										# add pixel_values
										__magic_name__            =     self.image_processor(__lowerCamelCase						,    return_tensors=__lowerCamelCase	)

										if text is not None:
																__magic_name__            =     self.tokenizer(
																    text=__lowerCamelCase						,    add_special_tokens=__lowerCamelCase						,    padding=__lowerCamelCase						,    truncation=__lowerCamelCase						,    max_length=__lowerCamelCase						,    stride=__lowerCamelCase						,    pad_to_multiple_of=__lowerCamelCase						,    return_attention_mask=__lowerCamelCase						,    return_overflowing_tokens=__lowerCamelCase						,    return_special_tokens_mask=__lowerCamelCase						,    return_offsets_mapping=__lowerCamelCase						,    return_token_type_ids=__lowerCamelCase						,    return_length=__lowerCamelCase						,    verbose=__lowerCamelCase						,    return_tensors=__lowerCamelCase						,    **__lowerCamelCase						,    )
										else:
																__magic_name__            =     None

										if text_encoding is not None:
																encoding_image_processor.update(__lowerCamelCase	)

										return encoding_image_processor







				def     _snake_case					(							self							:						Union[str, Any]						,    *__lowerCamelCase							:						Union[str, Any]						,    **__lowerCamelCase							:						Dict	)				->					Dict:
										return self.tokenizer.batch_decode(*__lowerCamelCase						,    **__lowerCamelCase	)







				def     _snake_case					(							self							:						str						,    *__lowerCamelCase							:						Optional[Any]						,    **__lowerCamelCase							:						Tuple	)				->					Optional[Any]:
										return self.tokenizer.decode(*__lowerCamelCase						,    **__lowerCamelCase	)







				@property
				# Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
				def     _snake_case					(							self							:						List[str]	)				->					Optional[Any]:
										__magic_name__            =     self.tokenizer.model_input_names
										__magic_name__            =     self.image_processor.model_input_names
										return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names	)	)



