





import argparse
import logging
import sys
from unittest.mock import patch

import run_glue_deebert

from transformers.testing_utils import TestCasePlus, get_gpu_count, require_torch_non_multi_gpu, slow


logging.basicConfig(level=logging.DEBUG)

_UpperCAmelCase				:       int	   =				logging.getLogger()


def        SCREAMING_SNAKE_CASE      (       )			->     List[Any]:
    lowerCamelCase__    :      List[str]					 =		argparse.ArgumentParser()
    parser.add_argument('-f' )
    lowerCamelCase__    :      Tuple					 =		parser.parse_args()
    return args.f


class    lowerCAmelCase							(					__UpperCamelCase			):
 def      A_	(      self  :      Optional[int]	)			->	None:
     lowerCamelCase__    :      List[str]					 =		logging.StreamHandler(sys.stdout	)
     logger.addHandler(UpperCAmelCase	)
 def      A_	(      self  :      List[str]			,     UpperCAmelCase  :      Dict	)			->	List[str]:
     lowerCamelCase__    :      Any					 =		get_gpu_count()

     if n_gpu > 1:
         pass
         # XXX: doesn't quite work with n_gpu > 1 https://github.com/huggingface/transformers/issues/10560
         # script = f"{self.examples_dir_str}/research_projects/deebert/run_glue_deebert.py"
         # distributed_args = f"-m torch.distributed.launch --nproc_per_node={n_gpu} {script}".split()
         # cmd = [sys.executable] + distributed_args + args
         # execute_subprocess_async(cmd, env=self.get_env())
         # XXX: test the results - need to save them first into .json file
     else:
         args.insert(0			,     'run_glue_deebert.py'	)
         with patch.object(UpperCAmelCase			,     'argv'			,     UpperCAmelCase	):
             lowerCamelCase__    :      Optional[int]					 =		run_glue_deebert.main()
             for value in result.values():
                 self.assertGreaterEqual(UpperCAmelCase			,     0.6_6_6	)







 @slow
 @require_torch_non_multi_gpu
 def      A_	(      self  :      str	)			->	Optional[int]:
     lowerCamelCase__    :      Any					 =		'\n            --model_type roberta\n            --model_name_or_path roberta-base\n            --task_name MRPC\n            --do_train\n            --do_eval\n            --do_lower_case\n            --data_dir ./tests/fixtures/tests_samples/MRPC/\n            --max_seq_length 128\n            --per_gpu_eval_batch_size=1\n            --per_gpu_train_batch_size=8\n            --learning_rate 2e-4\n            --num_train_epochs 3\n            --overwrite_output_dir\n            --seed 42\n            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n            --plot_data_dir ./examples/deebert/results/\n            --save_steps 0\n            --overwrite_cache\n            --eval_after_first_stage\n            '.split()
     self.run_and_check(UpperCAmelCase	)

     lowerCamelCase__    :      Union[str, Any]					 =		'\n            --model_type roberta\n            --model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n            --task_name MRPC\n            --do_eval\n            --do_lower_case\n            --data_dir ./tests/fixtures/tests_samples/MRPC/\n            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n            --plot_data_dir ./examples/deebert/results/\n            --max_seq_length 128\n            --eval_each_highway\n            --eval_highway\n            --overwrite_cache\n            --per_gpu_eval_batch_size=1\n            '.split()
     self.run_and_check(UpperCAmelCase	)

     lowerCamelCase__    :      List[Any]					 =		'\n            --model_type roberta\n            --model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n            --task_name MRPC\n            --do_eval\n            --do_lower_case\n            --data_dir ./tests/fixtures/tests_samples/MRPC/\n            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n            --plot_data_dir ./examples/deebert/results/\n            --max_seq_length 128\n            --early_exit_entropy 0.1\n            --eval_highway\n            --overwrite_cache\n            --per_gpu_eval_batch_size=1\n            '.split()
     self.run_and_check(UpperCAmelCase	)












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				:       Dict	   =				logging.get_logger(__name__)


def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase )			->     List[str]:
    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__    :      List[str]					 =		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__    :      Tuple					 =		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__    :      Dict					 =		key[key.find('glpn.encoder.layer_norm' ) + len('glpn.encoder.layer_norm' )]
            lowerCamelCase__    :      str					 =		key.replace(F"""layer_norm{idx}"""						,	F"""layer_norm.{int(_UpperCAmelCase )-1}""" )
        if "layer_norm1" in key:
            lowerCamelCase__    :      Optional[int]					 =		key.replace('layer_norm1'						,	'layer_norm_1' )
        if "layer_norm2" in key:
            lowerCamelCase__    :      Optional[int]					 =		key.replace('layer_norm2'						,	'layer_norm_2' )
        if "block" in key:
            # replace for example block1 by block.0
            lowerCamelCase__    :      List[Any]					 =		key[key.find('block' ) + len('block' )]
            lowerCamelCase__    :      int					 =		key.replace(F"""block{idx}"""						,	F"""block.{int(_UpperCAmelCase )-1}""" )
        if "attn.q" in key:
            lowerCamelCase__    :      Union[str, 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__    :      Dict					 =		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__    :      Tuple					 =		key.replace('linear_fuse.conv'						,	'linear_fuse' )
            lowerCamelCase__    :      List[str]					 =		key.replace('linear_fuse.bn'						,	'batch_norm' )
        if "linear_c" in key:
            # replace for example linear_c4 by linear_c.3
            lowerCamelCase__    :      Optional[Any]					 =		key[key.find('linear_c' ) + len('linear_c' )]
            lowerCamelCase__    :      Dict					 =		key.replace(F"""linear_c{idx}"""						,	F"""linear_c.{int(_UpperCAmelCase )-1}""" )
        if "bot_conv" in key:
            lowerCamelCase__    :      str					 =		key.replace('bot_conv'						,	'0.convolution' )
        if "skip_conv1" in key:
            lowerCamelCase__    :      Union[str, Any]					 =		key.replace('skip_conv1'						,	'1.convolution' )
        if "skip_conv2" in key:
            lowerCamelCase__    :      List[Any]					 =		key.replace('skip_conv2'						,	'2.convolution' )
        if "fusion1" in key:
            lowerCamelCase__    :      Optional[int]					 =		key.replace('fusion1'						,	'1.fusion' )
        if "fusion2" in key:
            lowerCamelCase__    :      Union[str, Any]					 =		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__    :      Dict					 =		key.replace('module.last_layer_depth'						,	'head.head' )
        lowerCamelCase__    :      str					 =		value

    return new_state_dict


def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase						,	_UpperCAmelCase )			->     Optional[int]:
    # 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__    :      Any					 =		state_dict.pop(F"""glpn.encoder.block.{i}.{j}.attention.self.kv.weight""" )
            lowerCamelCase__    :      Optional[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__    :      Optional[int]					 =		kv_weight[
                : config.hidden_sizes[i], :
            ]
            lowerCamelCase__    :      Optional[int]					 =		kv_bias[: config.hidden_sizes[i]]
            lowerCamelCase__    :      Any					 =		kv_weight[
                config.hidden_sizes[i] :, :
            ]
            lowerCamelCase__    :      Dict					 =		kv_bias[config.hidden_sizes[i] :]


def        SCREAMING_SNAKE_CASE      (       )			->     str:
    lowerCamelCase__    :      List[str]					 =		'http://images.cocodataset.org/val2017/000000039769.jpg'
    lowerCamelCase__    :      Tuple					 =		Image.open(requests.get(_UpperCAmelCase						,	stream=_UpperCAmelCase ).raw )

    return image


@torch.no_grad()
def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase=False						,	_UpperCAmelCase=None )			->     Optional[int]:
    lowerCamelCase__    :      str					 =		GLPNConfig(hidden_sizes=[64, 128, 320, 512]						,	decoder_hidden_size=64						,	depths=[3, 8, 27, 3] )

    # load image processor (only resize + rescale)
    lowerCamelCase__    :      Union[str, Any]					 =		GLPNImageProcessor()

    # prepare image
    lowerCamelCase__    :      str					 =		prepare_img()
    lowerCamelCase__    :      Tuple					 =		image_processor(images=_UpperCAmelCase						,	return_tensors='pt' ).pixel_values

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

    # load original state dict
    lowerCamelCase__    :      Any					 =		torch.load(_UpperCAmelCase						,	map_location=torch.device('cpu' ) )

    # rename keys
    lowerCamelCase__    :      str					 =		rename_keys(_UpperCAmelCase )

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

    # create HuggingFace model and load state dict
    lowerCamelCase__    :      Dict					 =		GLPNForDepthEstimation(_UpperCAmelCase )
    model.load_state_dict(_UpperCAmelCase )
    model.eval()

    # forward pass
    lowerCamelCase__    :      List[str]					 =		model(_UpperCAmelCase )
    lowerCamelCase__    :      Tuple					 =		outputs.predicted_depth

    # verify output
    if model_name is not None:
        if "nyu" in model_name:
            lowerCamelCase__    :      List[Any]					 =		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__    :      List[str]					 =		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__    :      Tuple					 =		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__":
    _UpperCAmelCase				:       Tuple	   =				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				:       int	   =				parser.parse_args()
    convert_glpn_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)





