



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

    return new_state_dict



def 		lowerCAmelCase__     (       UpperCamelCase_					:							Optional[Any]			,							UpperCamelCase_					:							int						)->	str:
    # 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)
            A__					=       state_dict.pop(f"glpn.encoder.block.{i}.{j}.attention.self.kv.weight"						)
            A__					=       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
            A__					=       kv_weight[
                : config.hidden_sizes[i], :
            ]
            A__					=       kv_bias[: config.hidden_sizes[i]]
            A__					=       kv_weight[
                config.hidden_sizes[i] :, :
            ]
            A__					=       kv_bias[config.hidden_sizes[i] :]



def 		lowerCAmelCase__     (       )->	str:
    A__					=       '''http://images.cocodataset.org/val2017/000000039769.jpg'''
    A__					=       Image.open(requests.get(UpperCamelCase_			,							stream=UpperCamelCase_						).raw						)

    return image



@torch.no_grad()
def 		lowerCAmelCase__     (       UpperCamelCase_					:							Union[str, Any]			,							UpperCamelCase_					:							Optional[int]			,							UpperCamelCase_					:							Any=False			,							UpperCamelCase_					:							int=None						)->	Dict:
    A__					=       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)
    A__					=       GLPNImageProcessor()

    # prepare image
    A__					=       prepare_img()
    A__					=       image_processor(images=UpperCamelCase_			,							return_tensors='''pt'''						).pixel_values

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

    # load original state dict
    A__					=       torch.load(UpperCamelCase_			,							map_location=torch.device('''cpu'''						)						)

    # rename keys
    A__					=       rename_keys(UpperCamelCase_						)

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

    # create HuggingFace model and load state dict
    A__					=       GLPNForDepthEstimation(UpperCamelCase_						)
    model.load_state_dict(UpperCamelCase_						)
    model.eval()

    # forward pass
    A__					=       model(UpperCamelCase_						)
    A__					=       outputs.predicted_depth

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

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

        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__":
   _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)





# Lint as: python3
# pylint: enable=line-too-long
# pylint: disable=g-import-not-at-top,g-bad-import-order,wrong-import-position

_lowercase    	=		"2.13.1"

import platform

import pyarrow
from packaging import version


if version.parse(platform.python_version()) < version.parse("3.7"):
   raise ImportWarning(
       "To use `datasets`, Python>=3.7 is required, and the current version of Python doesn't match this condition."
   )

if version.parse(pyarrow.__version__).major < 8:
   raise ImportWarning(
       "To use `datasets`, the module `pyarrow>=8.0.0` is required, and the current version of `pyarrow` doesn't match this condition.\n"
       "If you are running this in a Google Colab, you should probably just restart the runtime to use the right version of `pyarrow`."
   )

del platform
del pyarrow
del version

from .arrow_dataset import Dataset
from .arrow_reader import ReadInstruction
from .builder import ArrowBasedBuilder, BeamBasedBuilder, BuilderConfig, DatasetBuilder, GeneratorBasedBuilder
from .combine import concatenate_datasets, interleave_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .download import *
from .features import *
from .fingerprint import disable_caching, enable_caching, is_caching_enabled, set_caching_enabled
from .info import DatasetInfo, MetricInfo
from .inspect import (
    get_dataset_config_info,
    get_dataset_config_names,
    get_dataset_infos,
    get_dataset_split_names,
    inspect_dataset,
    inspect_metric,
    list_datasets,
    list_metrics,
)
from .iterable_dataset import IterableDataset
from .load import load_dataset, load_dataset_builder, load_from_disk, load_metric
from .metric import Metric
from .splits import (
    NamedSplit,
    NamedSplitAll,
    Split,
    SplitBase,
    SplitDict,
    SplitGenerator,
    SplitInfo,
    SubSplitInfo,
    percent,
)
from .tasks import *
from .utils import *
from .utils import logging


# deprecated modules
from datasets import arrow_dataset as _arrow_dataset  # isort:skip
from datasets import utils as _utils  # isort:skip
from datasets.utils import download_manager as _deprecated_download_manager  # isort:skip

_lowercase    	=		concatenate_datasets
_lowercase    	=		DownloadConfig
_lowercase    	=		DownloadManager
_lowercase    	=		DownloadMode
_lowercase    	=		DownloadConfig
_lowercase    	=		DownloadMode
_lowercase    	=		DownloadManager

del _arrow_dataset, _utils, _deprecated_download_manager
