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

       return image






@torch.no_grad()
def 				__lowerCamelCase		(						lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__=False     ,  lowerCAmelCase__=None     ):
       lowerCAmelCase__					     = 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)
       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_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:
                     lowerCAmelCase__					     = 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}"""     )

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

              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__":
    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 typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


lowerCAmelCase__     						=			{
    'configuration_transfo_xl': ['TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TransfoXLConfig'],
    'tokenization_transfo_xl': ['TransfoXLCorpus', 'TransfoXLTokenizer'],
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCAmelCase__     						=			[
        'TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST',
        'AdaptiveEmbedding',
        'TransfoXLForSequenceClassification',
        'TransfoXLLMHeadModel',
        'TransfoXLModel',
        'TransfoXLPreTrainedModel',
        'load_tf_weights_in_transfo_xl',
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCAmelCase__     						=			[
        'TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST',
        'TFAdaptiveEmbedding',
        'TFTransfoXLForSequenceClassification',
        'TFTransfoXLLMHeadModel',
        'TFTransfoXLMainLayer',
        'TFTransfoXLModel',
        'TFTransfoXLPreTrainedModel',
    ]


if TYPE_CHECKING:
    from .configuration_transfo_xl import TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP, TransfoXLConfig
    from .tokenization_transfo_xl import TransfoXLCorpus, TransfoXLTokenizer

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_transfo_xl import (
            TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
            AdaptiveEmbedding,
            TransfoXLForSequenceClassification,
            TransfoXLLMHeadModel,
            TransfoXLModel,
            TransfoXLPreTrainedModel,
            load_tf_weights_in_transfo_xl,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_transfo_xl import (
            TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFAdaptiveEmbedding,
            TFTransfoXLForSequenceClassification,
            TFTransfoXLLMHeadModel,
            TFTransfoXLMainLayer,
            TFTransfoXLModel,
            TFTransfoXLPreTrainedModel,
        )

else:
    import sys

    lowerCAmelCase__     						=			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
