"""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()
SCREAMING_SNAKE_CASE__   =					logging.get_logger(__name__)





def 				lowerCAmelCase__      (     _UpperCamelCase    :			str   )   ->			Dict:





      """simple docstring"""






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

      return new_state_dict





def 				lowerCAmelCase__      (     _UpperCamelCase    :			Optional[Any]	,     _UpperCamelCase    :			str   )   ->			Any:





      """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)
                  snake_case        =     state_dict.pop(f"""glpn.encoder.block.{i}.{j}.attention.self.kv.weight"""   )
                  snake_case        =     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
                  snake_case        =     kv_weight[
                      : config.hidden_sizes[i], :
                  ]
                  snake_case        =     kv_bias[: config.hidden_sizes[i]]
                  snake_case        =     kv_weight[
                      config.hidden_sizes[i] :, :
                  ]
                  snake_case        =     kv_bias[config.hidden_sizes[i] :]





def 				lowerCAmelCase__      (     )   ->			Tuple:





      """simple docstring"""






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

      return image





@torch.no_grad()
def 				lowerCAmelCase__      (     _UpperCamelCase    :			List[str]	,     _UpperCamelCase    :			Union[str, Any]	,     _UpperCamelCase    :			Union[str, Any]=False	,     _UpperCamelCase    :			Any=None   )   ->			Tuple:





      """simple docstring"""






      snake_case        =     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)
      snake_case        =     GLPNImageProcessor()

      # prepare image
      snake_case        =     prepare_img()
      snake_case        =     image_processor(images=_UpperCamelCase	,     return_tensors='pt'   ).pixel_values

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

      # load original state dict
      snake_case        =     torch.load(_UpperCamelCase	,     map_location=torch.device('cpu'   )   )

      # rename keys
      snake_case        =     rename_keys(_UpperCamelCase   )

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

      # create HuggingFace model and load state dict
      snake_case        =     GLPNForDepthEstimation(_UpperCamelCase   )
      model.load_state_dict(_UpperCamelCase   )
      model.eval()

      # forward pass
      snake_case        =     model(_UpperCamelCase   )
      snake_case        =     outputs.predicted_depth

      # verify output
      if model_name is not None:
            if "nyu" in model_name:
                  snake_case        =     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:
                  snake_case        =     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}"""   )

            snake_case        =     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__":
     SCREAMING_SNAKE_CASE__   =					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.",
     )
     SCREAMING_SNAKE_CASE__   =					parser.parse_args()
     convert_glpn_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)



"""simple docstring"""
import os
import sys
import unittest


SCREAMING_SNAKE_CASE__   =					os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, "utils"))

import check_dummies  # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object, find_backend, read_init  # noqa: E402


# Align TRANSFORMERS_PATH in check_dummies with the current path
SCREAMING_SNAKE_CASE__   =					os.path.join(git_repo_path, "src", "transformers")

SCREAMING_SNAKE_CASE__   =					"\n{0} = None\n"

SCREAMING_SNAKE_CASE__   =					"\nclass {0}(metaclass=DummyObject):\n    _backends = {1}\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, {1})\n"


SCREAMING_SNAKE_CASE__   =					"\ndef {0}(*args, **kwargs):\n    requires_backends({0}, {1})\n"



class 					lowerCAmelCase_      (     unittest.TestCase   ):



				"""simple docstring"""

				def 						snake_case	(			self      ):






										"""simple docstring"""

										snake_case        =     find_backend('    _import_structure["models.albert"].append("AlbertTokenizerFast")'      )
										self.assertIsNone(lowerCAmelCase      )

										snake_case        =     find_backend('    if not is_tokenizers_available():'      )
										self.assertEqual(lowerCAmelCase						,      'tokenizers'      )

										snake_case        =     find_backend('    if not is_tensorflow_text_available():'      )
										self.assertEqual(lowerCAmelCase						,      'tensorflow_text'      )

										snake_case        =     find_backend('    if not (is_sentencepiece_available() and is_tokenizers_available()):'      )
										self.assertEqual(lowerCAmelCase						,      'sentencepiece_and_tokenizers'      )

										snake_case        =     find_backend(
										    '    if not (is_sentencepiece_available() and is_tensorflow_text_available()):'      )
										self.assertEqual(lowerCAmelCase						,      'sentencepiece_and_tensorflow_text'      )

										snake_case        =     find_backend(
										    '    if not (is_sentencepiece_available() and is_tokenizers_available() and is_vision_available()):'      )
										self.assertEqual(lowerCAmelCase						,      'sentencepiece_and_tokenizers_and_vision'      )

				def 						snake_case	(			self      ):






										"""simple docstring"""

										snake_case        =     read_init()
										# We don't assert on the exact list of keys to allow for smooth grow of backend-specific objects
										self.assertIn('torch'						,      lowerCAmelCase      )
										self.assertIn('tensorflow_text'						,      lowerCAmelCase      )
										self.assertIn('sentencepiece_and_tokenizers'						,      lowerCAmelCase      )

										# Likewise, we can't assert on the exact content of a key
										self.assertIn('BertModel'						,      objects['torch']      )
										self.assertIn('TFBertModel'						,      objects['tf']      )
										self.assertIn('FlaxBertModel'						,      objects['flax']      )
										self.assertIn('BertModel'						,      objects['torch']      )
										self.assertIn('TFBertTokenizer'						,      objects['tensorflow_text']      )
										self.assertIn('convert_slow_tokenizer'						,      objects['sentencepiece_and_tokenizers']      )

				def 						snake_case	(			self      ):






										"""simple docstring"""

										snake_case        =     create_dummy_object('CONSTANT'						,      '\'torch\''      )
										self.assertEqual(lowerCAmelCase						,      '\nCONSTANT = None\n'      )

										snake_case        =     create_dummy_object('function'						,      '\'torch\''      )
										self.assertEqual(
										    lowerCAmelCase						,      '\ndef function(*args, **kwargs):\n    requires_backends(function, \'torch\')\n'      )

										snake_case        =     '\nclass FakeClass(metaclass=DummyObject):\n    _backends = \'torch\'\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, \'torch\')\n'
										snake_case        =     create_dummy_object('FakeClass'						,      '\'torch\''      )
										self.assertEqual(lowerCAmelCase						,      lowerCAmelCase      )

				def 						snake_case	(			self      ):






										"""simple docstring"""

										snake_case        =     '# This file is autogenerated by the command `make fix-copies`, do not edit.\nfrom ..utils import DummyObject, requires_backends\n\n\nCONSTANT = None\n\n\ndef function(*args, **kwargs):\n    requires_backends(function, ["torch"])\n\n\nclass FakeClass(metaclass=DummyObject):\n    _backends = ["torch"]\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, ["torch"])\n'
										snake_case        =     create_dummy_files({'torch': ['CONSTANT', 'function', 'FakeClass']}      )
										self.assertEqual(dummy_files['torch']						,      lowerCAmelCase      )


