

'''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()
SCREAMING_SNAKE_CASE_     = logging.get_logger(__name__)

def 	lowerCamelCase__							(   a__)		->     Dict:



    """simple docstring"""







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

    return new_state_dict

def 	lowerCamelCase__							(   a__      ,   a__)		->     Union[str, Any]:



    """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			:			Tuple				       =		state_dict.pop(F"""glpn.encoder.block.{i}.{j}.attention.self.kv.weight""")
            _snake_case			:			Any				       =		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			:			List[str]				       =		kv_weight[
                : config.hidden_sizes[i], :
            ]
            _snake_case			:			Dict				       =		kv_bias[: config.hidden_sizes[i]]
            _snake_case			:			Optional[int]				       =		kv_weight[
                config.hidden_sizes[i] :, :
            ]
            _snake_case			:			Union[str, Any]				       =		kv_bias[config.hidden_sizes[i] :]

def 	lowerCamelCase__							(   )		->     Tuple:



    """simple docstring"""







    _snake_case			:			Dict				       =		'http://images.cocodataset.org/val2017/000000039769.jpg'
    _snake_case			:			int				       =		Image.open(requests.get(lowercase_      ,   stream=lowercase_).raw)

    return image

@torch.no_grad()
def 	lowerCamelCase__							(   a__      ,   a__      ,   a__=False      ,   a__=None)		->     List[str]:



    """simple docstring"""







    _snake_case			:			Optional[int]				       =		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)
    _snake_case			:			int				       =		GLPNImageProcessor()

    # prepare image
    _snake_case			:			Union[str, Any]				       =		prepare_img()
    _snake_case			:			Optional[Any]				       =		image_processor(images=lowercase_      ,   return_tensors='pt').pixel_values

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

    # load original state dict
    _snake_case			:			Dict				       =		torch.load(lowercase_      ,   map_location=torch.device('cpu'))

    # rename keys
    _snake_case			:			List[str]				       =		rename_keys(lowercase_)

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

    # create HuggingFace model and load state dict
    _snake_case			:			List[str]				       =		GLPNForDepthEstimation(lowercase_)
    model.load_state_dict(lowercase_)
    model.eval()

    # forward pass
    _snake_case			:			str				       =		model(lowercase_)
    _snake_case			:			Tuple				       =		outputs.predicted_depth

    # verify output
    if model_name is not None:
        if "nyu" in model_name:
            _snake_case			:			Dict				       =		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:
            _snake_case			:			Union[str, Any]				       =		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}""")

        _snake_case			:			int				       =		torch.Size([1, 4_8_0, 6_4_0])

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


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









from __future__ import annotations

import requests







def SCREAMING_SNAKE_CASE							(			lowercase_		)  ->			dict:



       """simple docstring"""

       A__ 					=       f"""https://hacker-news.firebaseio.com/v0/item/{story_id}.json?print=pretty"""
       return requests.get(lowercase_		).json()







def SCREAMING_SNAKE_CASE							(			lowercase_ = 10		)  ->			list[dict]:



       """simple docstring"""

       A__ 					=       '''https://hacker-news.firebaseio.com/v0/topstories.json?print=pretty'''
       A__ 					=       requests.get(lowercase_		).json()[:max_stories]
       return [get_hackernews_story(lowercase_		) for story_id in story_ids]







def SCREAMING_SNAKE_CASE							(			lowercase_ = 10		)  ->			str:



       """simple docstring"""

       A__ 					=       hackernews_top_stories(lowercase_		)
       return "\n".join('''* [{title}]({url})'''.format(**lowercase_		) for story in stories		)


if __name__ == "__main__":
  print(hackernews_top_stories_as_markdown())


