
"""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()
UpperCamelCase_					:						int				= logging.get_logger(__name__)
def 							A_     (__a				):

     '''simple docstring'''





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

     '''simple docstring'''





     A_				 =						"http://images.cocodataset.org/val2017/000000039769.jpg"
     A_				 =						Image.open(requests.get(__snake_case				,   stream=__snake_case				).raw				)

     return image
@torch.no_grad()
def 							A_     (__a				,   __a				,   __a=False				,   __a=None				):

     '''simple docstring'''





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

     # load image processor (only resize + rescale)
     A_				 =						GLPNImageProcessor()

     # prepare image
     A_				 =						prepare_img()
     A_				 =						image_processor(images=__snake_case				,   return_tensors="pt"				).pixel_values

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

     # load original state dict
     A_				 =						torch.load(__snake_case				,   map_location=torch.device("cpu"				)				)

     # rename keys
     A_				 =						rename_keys(__snake_case				)

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

     # create HuggingFace model and load state dict
     A_				 =						GLPNForDepthEstimation(__snake_case				)
     model.load_state_dict(__snake_case				)
     model.eval()

     # forward pass
     A_				 =						model(__snake_case				)
     A_				 =						outputs.predicted_depth

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

          A_				 =						torch.Size([1, 480, 640]				)

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


if __name__ == "__main__":
  UpperCamelCase_					:						Optional[Any]				= 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_					:						str				= parser.parse_args()
  convert_glpn_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)






"""simple docstring"""





import unittest

from transformers import load_tool

from .test_tools_common import ToolTesterMixin


UpperCamelCase_					:						int				= '''
Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf originally as a company that developed a chatbot app targeted at teenagers.[2] After open-sourcing the model behind the chatbot, the company pivoted to focus on being a platform for machine learning.

In March 2021, Hugging Face raised $40 million in a Series B funding round.[3]

On April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model.[4] In 2022, the workshop concluded with the announcement of BLOOM, a multilingual large language model with 176 billion parameters.[5]
'''



class __lowerCAmelCase				(					unittest.TestCase     ,				_lowercase      ):




			"""simple docstring"""







			def      lowerCamelCase__     (				self						:					str	)      -> Dict:







								"""simple docstring"""
								A_				 =						load_tool("text-question-answering"	)
								self.tool.setup()
								A_				 =						load_tool("text-question-answering"       ,   remote=_snake_case	)




			def      lowerCamelCase__     (				self						:					Union[str, Any]	)      -> Any:







								"""simple docstring"""
								A_				 =						self.tool(_snake_case       ,   "What did Hugging Face do in April 2021?"	)
								self.assertEqual(_snake_case       ,   "launched the BigScience Research Workshop"	)




			def      lowerCamelCase__     (				self						:					Union[str, Any]	)      -> int:







								"""simple docstring"""
								A_				 =						self.remote_tool(_snake_case       ,   "What did Hugging Face do in April 2021?"	)
								self.assertEqual(_snake_case       ,   "launched the BigScience Research Workshop"	)




			def      lowerCamelCase__     (				self						:					Optional[Any]	)      -> Optional[Any]:







								"""simple docstring"""
								A_				 =						self.tool(text=_snake_case       ,   question="What did Hugging Face do in April 2021?"	)
								self.assertEqual(_snake_case       ,   "launched the BigScience Research Workshop"	)






			def      lowerCamelCase__     (				self						:					str	)      -> Optional[Any]:







								"""simple docstring"""
								A_				 =						self.remote_tool(text=_snake_case       ,   question="What did Hugging Face do in April 2021?"	)
								self.assertEqual(_snake_case       ,   "launched the BigScience Research Workshop"	)




