
"""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()
A_    							=					logging.get_logger(__name__)






def  lowercase						(	lowerCAmelCase__						):
			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(lowerCAmelCase__						)-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(lowerCAmelCase__						)-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(lowerCAmelCase__						)-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(lowerCAmelCase__						)-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  lowercase						(	lowerCAmelCase__	,lowerCAmelCase__						):
			# 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  lowercase						(	):
			lowerCamelCase_					=  '''http://images.cocodataset.org/val2017/000000039769.jpg'''
			lowerCamelCase_					=  Image.open(requests.get(lowerCAmelCase__	,stream=lowerCAmelCase__						).raw						)

			return image






@torch.no_grad()
def  lowercase						(	lowerCAmelCase__	,lowerCAmelCase__	,lowerCAmelCase__=False	,lowerCAmelCase__=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=lowerCAmelCase__	,return_tensors='''pt'''						).pixel_values

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

			# load original state dict
			lowerCamelCase_					=  torch.load(lowerCAmelCase__	,map_location=torch.device('''cpu'''						)						)

			# rename keys
			lowerCamelCase_					=  rename_keys(lowerCAmelCase__						)

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

			# create HuggingFace model and load state dict
			lowerCamelCase_					=  GLPNForDepthEstimation(lowerCAmelCase__						)
			model.load_state_dict(lowerCAmelCase__						)
			model.eval()

			# forward pass
			lowerCamelCase_					=  model(lowerCAmelCase__						)
			lowerCamelCase_					=  outputs.predicted_depth

			# verify output
			if model_name is not None:
						if "nyu" in model_name:
									lowerCamelCase_					=  torch.tensor(
									    [[4.4_147, 4.0_873, 4.0_673], [3.7_890, 3.2_881, 3.1_525], [3.7_674, 3.5_423, 3.4_913]]						)
						elif "kitti" in model_name:
									lowerCamelCase_					=  torch.tensor(
									    [[3.4_291, 2.7_865, 2.5_151], [3.2_841, 2.7_021, 2.3_502], [3.1_147, 2.4_625, 2.2_481]]						)
						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]	,lowerCAmelCase__	,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(lowerCAmelCase__	,lowerCAmelCase__						)	,organization='''nielsr'''	,commit_message='''Add model'''	,use_temp_dir=lowerCAmelCase__	,)
						image_processor.push_to_hub(
						    repo_path_or_name=Path(lowerCAmelCase__	,lowerCAmelCase__						)	,organization='''nielsr'''	,commit_message='''Add image processor'''	,use_temp_dir=lowerCAmelCase__	,)


if __name__ == "__main__":
						A_    							=					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.""",
						)
						A_    							=					parser.parse_args()
						convert_glpn_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)





"""simple docstring"""


from dataclasses import dataclass, field
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union

import pyarrow as pa


if TYPE_CHECKING:
						from .features import FeatureType




@dataclass
class  __lowerCamelCase  :
				a__:      List[str]
				a__:      Optional[str]          =    None
				# Automatically constructed
				a__:      ClassVar[str]          =    "dict"
				a__:      ClassVar[Any]          =    None
				a__:      str          =    field(default='Translation'						,	init=lowerCAmelCase						,	repr=lowerCAmelCase			)




				def __call__(				self   ):
							return pa.struct({lang: pa.string() for lang in sorted(self.languages   )}   )





				def        UpperCAmelCase__						(				self   ):
							from .features import Value

							return {k: Value('''string'''   ) for k in sorted(self.languages   )}




@dataclass
class  __lowerCamelCase  :
				a__:      Optional[List]          =    None
				a__:      Optional[int]          =    None
				a__:      Optional[str]          =    None
				# Automatically constructed
				a__:      ClassVar[str]          =    "dict"
				a__:      ClassVar[Any]          =    None
				a__:      str          =    field(default='TranslationVariableLanguages'						,	init=lowerCAmelCase						,	repr=lowerCAmelCase			)




				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  sorted(set(self.languages   )   ) if self.languages else None
							lowerCamelCase_					=  len(self.languages   ) if self.languages else None




				def __call__(				self   ):
							return pa.struct({'''language''': pa.list_(pa.string()   ), '''translation''': pa.list_(pa.string()   )}   )




				def        UpperCAmelCase__						(				self							,						UpperCAmelCase   ):
							lowerCamelCase_					=  set(self.languages   )
							if self.languages and set(UpperCAmelCase   ) - lang_set:
										raise ValueError(
										    f"Some languages in example ({', '.join(sorted(set(UpperCAmelCase   ) - lang_set   )   )}) are not in valid set ({', '.join(UpperCAmelCase   )})."   )

							# Convert dictionary into tuples, splitting out cases where there are
							# multiple translations for a single language.
							lowerCamelCase_					=  []
							for lang, text in translation_dict.items():
										if isinstance(UpperCAmelCase							,						UpperCAmelCase   ):
													translation_tuples.append((lang, text)   )
										else:
													translation_tuples.extend([(lang, el) for el in text]   )

        # Ensure translations are in ascending order by language code.
							lowerCamelCase_		, lowerCamelCase_					=  zip(*sorted(UpperCAmelCase   )   )

							return {"language": languages, "translation": translations}





				def        UpperCAmelCase__						(				self   ):
							from .features import Sequence, Value

							return {
							    "language": Sequence(Value('''string'''   )   ),
							    "translation": Sequence(Value('''string'''   )   ),
							}



