
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()
lowerCamelCase      =logging.get_logger(__name__)


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

      return new_state_dict


def 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__ ,		UpperCamelCase__		):
      # 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)
                  UpperCamelCase__    :   Dict					=				state_dict.pop(f'''glpn.encoder.block.{i}.{j}.attention.self.kv.weight'''		)
                  UpperCamelCase__    :   str					=				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
                  UpperCamelCase__    :   Any					=				kv_weight[
                      : config.hidden_sizes[i], :
                  ]
                  UpperCamelCase__    :   List[Any]					=				kv_bias[: config.hidden_sizes[i]]
                  UpperCamelCase__    :   Dict					=				kv_weight[
                      config.hidden_sizes[i] :, :
                  ]
                  UpperCamelCase__    :   Tuple					=				kv_bias[config.hidden_sizes[i] :]


def 							SCREAMING_SNAKE_CASE_      (			):
      UpperCamelCase__    :   List[str]					=				'''http://images.cocodataset.org/val2017/000000039769.jpg'''
      UpperCamelCase__    :   List[str]					=				Image.open(requests.get(UpperCamelCase__ ,		stream=UpperCamelCase__		).raw		)

      return image


@torch.no_grad()
def 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__=False ,		UpperCamelCase__=None		):
      UpperCamelCase__    :   List[str]					=				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)
      UpperCamelCase__    :   Optional[int]					=				GLPNImageProcessor()

      # prepare image
      UpperCamelCase__    :   Optional[Any]					=				prepare_img()
      UpperCamelCase__    :   int					=				image_processor(images=UpperCamelCase__ ,		return_tensors='''pt'''		).pixel_values

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

      # load original state dict
      UpperCamelCase__    :   Optional[Any]					=				torch.load(UpperCamelCase__ ,		map_location=torch.device('''cpu'''		)		)

      # rename keys
      UpperCamelCase__    :   str					=				rename_keys(UpperCamelCase__		)

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

      # create HuggingFace model and load state dict
      UpperCamelCase__    :   Optional[int]					=				GLPNForDepthEstimation(UpperCamelCase__		)
      model.load_state_dict(UpperCamelCase__		)
      model.eval()

      # forward pass
      UpperCamelCase__    :   Union[str, Any]					=				model(UpperCamelCase__		)
      UpperCamelCase__    :   Union[str, Any]					=				outputs.predicted_depth

      # verify output
      if model_name is not None:
            if "nyu" in model_name:
                  UpperCamelCase__    :   Optional[int]					=				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:
                  UpperCamelCase__    :   Any					=				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}'''		)

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




from io import BytesIO
from typing import List, Union

import requests

from ..utils import add_end_docstrings, is_decord_available, is_torch_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_decord_available():
    import numpy as np
    from decord import VideoReader


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

lowerCamelCase      =logging.get_logger(__name__)




@add_end_docstrings(UpperCamelCase_ )
class 				_lowerCamelCase (      UpperCamelCase_ ):





     """simple docstring"""







     def __init__(							self						,						*__SCREAMING_SNAKE_CASE						,						**__SCREAMING_SNAKE_CASE  )  ->   Dict:




           """simple docstring"""







           super().__init__(*__SCREAMING_SNAKE_CASE						,						**__SCREAMING_SNAKE_CASE  )
           requires_backends(self						,						'''decord'''  )
           self.check_model_type(__SCREAMING_SNAKE_CASE  )






     def    __SCREAMING_SNAKE_CASE			(							self						,						__SCREAMING_SNAKE_CASE=None						,						__SCREAMING_SNAKE_CASE=None						,						__SCREAMING_SNAKE_CASE=None  )  ->   Optional[Any]:




           """simple docstring"""







           UpperCamelCase__    :   List[str]					=				{}
           if frame_sampling_rate is not None:
                 UpperCamelCase__    :   Tuple					=				frame_sampling_rate
           if num_frames is not None:
                 UpperCamelCase__    :   str					=				num_frames

           UpperCamelCase__    :   List[str]					=				{}
           if top_k is not None:
                 UpperCamelCase__    :   List[Any]					=				top_k
           return preprocess_params, {}, postprocess_params






     def __call__(							self						,						__SCREAMING_SNAKE_CASE						,						**__SCREAMING_SNAKE_CASE  )  ->   Tuple:




           """simple docstring"""







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






     def    __SCREAMING_SNAKE_CASE			(							self						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE=None						,						__SCREAMING_SNAKE_CASE=1  )  ->   Optional[Any]:




           """simple docstring"""







           if num_frames is None:
                 UpperCamelCase__    :   Optional[Any]					=				self.model.config.num_frames

           if video.startswith('''http://'''  ) or video.startswith('''https://'''  ):
                 UpperCamelCase__    :   str					=				BytesIO(requests.get(__SCREAMING_SNAKE_CASE  ).content  )

           UpperCamelCase__    :   Tuple					=				VideoReader(__SCREAMING_SNAKE_CASE  )
           videoreader.seek(0  )

           UpperCamelCase__    :   Optional[int]					=				0
           UpperCamelCase__    :   str					=				num_frames * frame_sampling_rate - 1
           UpperCamelCase__    :   List[Any]					=				np.linspace(__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						num=__SCREAMING_SNAKE_CASE						,						dtype=np.intaa  )

           UpperCamelCase__    :   str					=				videoreader.get_batch(__SCREAMING_SNAKE_CASE  ).asnumpy()
           UpperCamelCase__    :   Tuple					=				list(__SCREAMING_SNAKE_CASE  )

           UpperCamelCase__    :   Dict					=				self.image_processor(__SCREAMING_SNAKE_CASE						,						return_tensors=self.framework  )
           return model_inputs






     def    __SCREAMING_SNAKE_CASE			(							self						,						__SCREAMING_SNAKE_CASE  )  ->   Union[str, Any]:




           """simple docstring"""







           UpperCamelCase__    :   Tuple					=				self.model(**__SCREAMING_SNAKE_CASE  )
           return model_outputs






     def    __SCREAMING_SNAKE_CASE			(							self						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE=5  )  ->   str:




           """simple docstring"""







           if top_k > self.model.config.num_labels:
                 UpperCamelCase__    :   Dict					=				self.model.config.num_labels

           if self.framework == "pt":
                 UpperCamelCase__    :   Any					=				model_outputs.logits.softmax(-1  )[0]
                 UpperCamelCase__				,UpperCamelCase__    :   List[str]					=				probs.topk(__SCREAMING_SNAKE_CASE  )
           else:
                 raise ValueError(F'''Unsupported framework: {self.framework}'''  )

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


