

'''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()
_UpperCamelCase						:  Union[str, Any]							       =				logging.get_logger(__name__)


def       snake_case   (	snake_case      : List[Any]			) ->  Optional[int]:


    """simple docstring"""


    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(snake_case			)-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(snake_case			)-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(snake_case			)-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(snake_case			)-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       snake_case   (	snake_case      : Any					,		snake_case      : List[Any]			) ->  List[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)
            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       snake_case   (	) ->  List[str]:


    """simple docstring"""


    lowerCAmelCase 						=							'http://images.cocodataset.org/val2017/000000039769.jpg'
    lowerCAmelCase 						=							Image.open(requests.get(snake_case					,		stream=snake_case			).raw			)

    return image


@torch.no_grad()
def       snake_case   (	snake_case      : Optional[int]					,		snake_case      : Optional[Any]					,		snake_case      : Union[str, Any]=False					,		snake_case      : str=None			) ->  List[Any]:


    """simple docstring"""


    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=snake_case					,		return_tensors='pt'			).pixel_values

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

    # load original state dict
    lowerCAmelCase 						=							torch.load(snake_case					,		map_location=torch.device('cpu'			)			)

    # rename keys
    lowerCAmelCase 						=							rename_keys(snake_case			)

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

    # create HuggingFace model and load state dict
    lowerCAmelCase 						=							GLPNForDepthEstimation(snake_case			)
    model.load_state_dict(snake_case			)
    model.eval()

    # forward pass
    lowerCAmelCase 						=							model(snake_case			)
    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]					,		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(snake_case					,		snake_case			)					,		organization='nielsr'					,		commit_message='Add model'					,		use_temp_dir=snake_case					,		)
        image_processor.push_to_hub(
            repo_path_or_name=Path(snake_case					,		snake_case			)					,		organization='nielsr'					,		commit_message='Add image processor'					,		use_temp_dir=snake_case					,		)


if __name__ == "__main__":
     _UpperCamelCase						:  int							       =				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						:  Tuple							       =				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, Union

from ..utils import (
    add_end_docstrings,
    is_tf_available,
    is_torch_available,
    is_vision_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
     from PIL import Image

     from ..image_utils import load_image

if is_tf_available():
     import tensorflow as tf

     from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
     from ..tf_utils import stable_softmax

if is_torch_available():
     from ..models.auto.modeling_auto import MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING

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



@add_end_docstrings(a_			)
class 				_snake_case					(   a_			):

  def __init__(				self      , *_SCREAMING_SNAKE_CASE      , **_SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      super().__init__(*_SCREAMING_SNAKE_CASE      , **_SCREAMING_SNAKE_CASE	)
      requires_backends(self      , 'vision'	)
      self.check_model_type(
          TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
          if self.framework == 'tf'
          else MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING	)

  def   _SCREAMING_SNAKE_CASE				(				self      , _SCREAMING_SNAKE_CASE=None	):





      '''simple docstring'''

      lowerCAmelCase 						=							{}
      if top_k is not None:
          lowerCAmelCase 						=							top_k
      return {}, {}, postprocess_params

  def __call__(				self      , _SCREAMING_SNAKE_CASE      , **_SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      return super().__call__(_SCREAMING_SNAKE_CASE      , **_SCREAMING_SNAKE_CASE	)

  def   _SCREAMING_SNAKE_CASE				(				self      , _SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      lowerCAmelCase 						=							load_image(_SCREAMING_SNAKE_CASE	)
      lowerCAmelCase 						=							self.image_processor(images=_SCREAMING_SNAKE_CASE      , return_tensors=self.framework	)
      return model_inputs

  def   _SCREAMING_SNAKE_CASE				(				self      , _SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      lowerCAmelCase 						=							self.model(**_SCREAMING_SNAKE_CASE	)
      return model_outputs


  def   _SCREAMING_SNAKE_CASE				(				self      , _SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE=5	):





      '''simple docstring'''

      if top_k > self.model.config.num_labels:
          lowerCAmelCase 						=							self.model.config.num_labels

      if self.framework == "pt":
          lowerCAmelCase 						=							model_outputs.logits.softmax(-1	)[0]
          lowerCAmelCase						,   lowerCAmelCase 						=							probs.topk(_SCREAMING_SNAKE_CASE	)
      elif self.framework == "tf":
          lowerCAmelCase 						=							stable_softmax(model_outputs.logits      , axis=-1	)[0]
          lowerCAmelCase 						=							tf.math.top_k(_SCREAMING_SNAKE_CASE      , k=_SCREAMING_SNAKE_CASE	)
          lowerCAmelCase						,   lowerCAmelCase 						=							topk.values.numpy(), topk.indices.numpy()
      else:
          raise ValueError(F'Unsupported framework: {self.framework}'	)

      lowerCAmelCase 						=							scores.tolist()
      lowerCAmelCase 						=							ids.tolist()
      return [{"score": score, "label": self.model.config.idalabel[_id]} for score, _id in zip(_SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE	)]





