



'''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()
_lowerCAmelCase      						=	logging.get_logger(__name__)
def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase			):



    """simple docstring"""





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

    return new_state_dict
def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase							,    UpperCamelCase			):



    """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__ :  Optional[int]							      =					state_dict.pop(f"""glpn.encoder.block.{i}.{j}.attention.self.kv.weight"""			)
            lowerCAmelCase__ :  List[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
            lowerCAmelCase__ :  List[Any]							      =					kv_weight[
                : config.hidden_sizes[i], :
            ]
            lowerCAmelCase__ :  Optional[Any]							      =					kv_bias[: config.hidden_sizes[i]]
            lowerCAmelCase__ :  List[str]							      =					kv_weight[
                config.hidden_sizes[i] :, :
            ]
            lowerCAmelCase__ :  Union[str, Any]							      =					kv_bias[config.hidden_sizes[i] :]
def    _SCREAMING_SNAKE_CASE  (       ):



    """simple docstring"""





    lowerCAmelCase__ :  Tuple							      =					"""http://images.cocodataset.org/val2017/000000039769.jpg"""
    lowerCAmelCase__ :  str							      =					Image.open(requests.get(UpperCamelCase							,    stream=UpperCamelCase			).raw			)

    return image
@torch.no_grad()
def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase							,    UpperCamelCase							,    UpperCamelCase=False							,    UpperCamelCase=None			):



    """simple docstring"""





    lowerCAmelCase__ :  Tuple							      =					GLPNConfig(hidden_sizes=[64, 128, 320, 512]							,    decoder_hidden_size=64							,    depths=[3, 8, 27, 3]			)

    # load image processor (only resize + rescale)
    lowerCAmelCase__ :  str							      =					GLPNImageProcessor()

    # prepare image
    lowerCAmelCase__ :  Dict							      =					prepare_img()
    lowerCAmelCase__ :  Optional[Any]							      =					image_processor(images=UpperCamelCase							,    return_tensors="""pt"""			).pixel_values

    logger.info("""Converting model..."""			)

    # load original state dict
    lowerCAmelCase__ :  List[Any]							      =					torch.load(UpperCamelCase							,    map_location=torch.device("""cpu"""			)			)

    # rename keys
    lowerCAmelCase__ :  Any							      =					rename_keys(UpperCamelCase			)

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

    # create HuggingFace model and load state dict
    lowerCAmelCase__ :  List[Any]							      =					GLPNForDepthEstimation(UpperCamelCase			)
    model.load_state_dict(UpperCamelCase			)
    model.eval()

    # forward pass
    lowerCAmelCase__ :  int							      =					model(UpperCamelCase			)
    lowerCAmelCase__ :  List[Any]							      =					outputs.predicted_depth

    # verify output
    if model_name is not None:
        if "nyu" in model_name:
            lowerCAmelCase__ :  str							      =					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:
            lowerCAmelCase__ :  Optional[int]							      =					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}"""			)

        lowerCAmelCase__ :  Optional[int]							      =					torch.Size([1, 480, 640]			)

        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)







'''simple docstring'''





# coding=utf-8
# Copyright 2020 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

# this script dumps information about the environment

import os
import sys

import transformers


_lowerCAmelCase      						=	'''3'''

print('''Python version:''', sys.version)
print('''transformers version:''', transformers.__version__)

try:
 import torch

 print('''Torch version:''', torch.__version__)
 print('''Cuda available:''', torch.cuda.is_available())
 print('''Cuda version:''', torch.version.cuda)
 print('''CuDNN version:''', torch.backends.cudnn.version())
 print('''Number of GPUs available:''', torch.cuda.device_count())
 print('''NCCL version:''', torch.cuda.nccl.version())
except ImportError:
 print('''Torch version:''', None)

try:
 import deepspeed

 print('''DeepSpeed version:''', deepspeed.__version__)
except ImportError:
 print('''DeepSpeed version:''', None)

try:
 import tensorflow as tf

 print('''TensorFlow version:''', tf.__version__)
 print('''TF GPUs available:''', bool(tf.config.list_physical_devices('''GPU''')))
 print('''Number of TF GPUs available:''', len(tf.config.list_physical_devices('''GPU''')))
except ImportError:
 print('''TensorFlow version:''', None)


