

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  : List[str]      =	logging.get_logger(__name__)

def  A_	(				a	):

      """simple docstring"""



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

      return new_state_dict

def  A_	(				a       , a	):

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

def  A_	(				):

      """simple docstring"""



      SCREAMING_SNAKE_CASE_					:	int     							=  'http://images.cocodataset.org/val2017/000000039769.jpg'
      SCREAMING_SNAKE_CASE_					:	Tuple     							=  Image.open(requests.get(a       , stream=a	).raw	)

      return image

@torch.no_grad()
def  A_	(				a       , a       , a=False       , a=None	):

      """simple docstring"""



      SCREAMING_SNAKE_CASE_					:	Tuple     							=  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)
      SCREAMING_SNAKE_CASE_					:	Optional[Any]     							=  GLPNImageProcessor()

      # prepare image
      SCREAMING_SNAKE_CASE_					:	List[str]     							=  prepare_img()
      SCREAMING_SNAKE_CASE_					:	List[str]     							=  image_processor(images=a       , return_tensors='pt'	).pixel_values

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

      # load original state dict
      SCREAMING_SNAKE_CASE_					:	Optional[int]     							=  torch.load(a       , map_location=torch.device('cpu'	)	)

      # rename keys
      SCREAMING_SNAKE_CASE_					:	int     							=  rename_keys(a	)

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

      # create HuggingFace model and load state dict
      SCREAMING_SNAKE_CASE_					:	Optional[Any]     							=  GLPNForDepthEstimation(a	)
      model.load_state_dict(a	)
      model.eval()

      # forward pass
      SCREAMING_SNAKE_CASE_					:	int     							=  model(a	)
      SCREAMING_SNAKE_CASE_					:	int     							=  outputs.predicted_depth

      # verify output
      if model_name is not None:
            if "nyu" in model_name:
                  SCREAMING_SNAKE_CASE_					:	Tuple     							=  torch.tensor(
                      [[4.41_47, 4.08_73, 4.06_73], [3.78_90, 3.28_81, 3.15_25], [3.76_74, 3.54_23, 3.49_13]]	)
            elif "kitti" in model_name:
                  SCREAMING_SNAKE_CASE_					:	List[Any]     							=  torch.tensor(
                      [[3.42_91, 2.78_65, 2.51_51], [3.28_41, 2.70_21, 2.35_02], [3.11_47, 2.46_25, 2.24_81]]	)
            else:
                  raise ValueError(f"Unknown model name: {model_name}"	)

            SCREAMING_SNAKE_CASE_					:	Tuple     							=  torch.Size([1, 4_8_0, 6_4_0]	)

            assert predicted_depth.shape == expected_shape
            assert torch.allclose(predicted_depth[0, :3, :3]       , a       , 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(a       , a	)       , organization='nielsr'       , commit_message='Add model'       , use_temp_dir=a       , )
            image_processor.push_to_hub(
                repo_path_or_name=Path(a       , a	)       , organization='nielsr'       , commit_message='Add image processor'       , use_temp_dir=a       , )


if __name__ == "__main__":
    lowerCAmelCase  : List[str]      =	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  : Dict      =	parser.parse_args()
    convert_glpn_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)







import tempfile
import unittest

from make_student import create_student_by_copying_alternating_layers

from transformers import AutoConfig
from transformers.file_utils import cached_property
from transformers.testing_utils import require_torch


_lowercase:	str       			=		'''sshleifer/bart-tiny-random'''
_lowercase:	Union[str, Any]       			=		'''patrickvonplaten/t5-tiny-random'''



@require_torch
class        lowerCamelCase__				(   unittest.TestCase				):





			@cached_property
			def   SCREAMING_SNAKE_CASE__     (       self	:	Tuple						):
										return AutoConfig.from_pretrained(lowercase__						)





			def   SCREAMING_SNAKE_CASE__     (       self	:	int						):
										_lowerCAmelCase			,		*_lowerCAmelCase							=							create_student_by_copying_alternating_layers(lowercase__  ,  tempfile.mkdtemp()  ,  e=1  ,  d=1						)
										self.assertEqual(student.config.num_hidden_layers  ,  1						)





			def   SCREAMING_SNAKE_CASE__     (       self	:	Dict						):
										_lowerCAmelCase			,		*_lowerCAmelCase							=							create_student_by_copying_alternating_layers(lowercase__  ,  tempfile.mkdtemp()  ,  e=1  ,  d=lowercase__						)





			def   SCREAMING_SNAKE_CASE__     (       self	:	Union[str, Any]						):
										_lowerCAmelCase			,		*_lowerCAmelCase							=							create_student_by_copying_alternating_layers(lowercase__  ,  tempfile.mkdtemp()  ,  e=1  ,  d=lowercase__						)
										self.assertEqual(student.config.encoder_layers  ,  1						)
										self.assertEqual(student.config.decoder_layers  ,  self.teacher_config.encoder_layers						)





			def   SCREAMING_SNAKE_CASE__     (       self	:	str						):
										_lowerCAmelCase			,		*_lowerCAmelCase							=							create_student_by_copying_alternating_layers(lowercase__  ,  tempfile.mkdtemp()  ,  e=1  ,  d=1						)
										self.assertEqual(student.config.encoder_layers  ,  1						)
										self.assertEqual(student.config.decoder_layers  ,  1						)





			def   SCREAMING_SNAKE_CASE__     (       self	:	List[str]						):
										with self.assertRaises(lowercase__						):
																	create_student_by_copying_alternating_layers(lowercase__  ,  tempfile.mkdtemp()  ,  e=lowercase__  ,  d=lowercase__						)






