


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





def 	_lowerCAmelCase       (    _lowerCAmelCase )						->	Optional[int]:


      '''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(_lowerCamelCase )-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(_lowerCamelCase )-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(_lowerCamelCase )-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(_lowerCamelCase )-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       (    _lowerCAmelCase      ,	_lowerCAmelCase )						->	Dict:


      '''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       (    )						->	Optional[Any]:


      '''simple docstring'''






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

      return image





@torch.no_grad()
def 	_lowerCAmelCase       (    _lowerCAmelCase      ,	_lowerCAmelCase      ,	_lowerCAmelCase=False      ,	_lowerCAmelCase=None )						->	int:


      '''simple docstring'''






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

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

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

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

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

      # rename keys
      __snake_case				=		rename_keys(_lowerCamelCase )

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

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

      # forward pass
      __snake_case				=		model(_lowerCamelCase )
      __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, 480, 640] )

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


if __name__ == "__main__":
 A							:					Dict     						=     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.',
 )
 A							:					Any     						=     parser.parse_args()
 convert_glpn_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)









import os
import tempfile
import unittest
import uuid
from pathlib import Path

from transformers.testing_utils import get_tests_dir, require_soundfile, require_torch, require_vision
from transformers.tools.agent_types import AgentAudio, AgentImage, AgentText
from transformers.utils import is_soundfile_availble, is_torch_available, is_vision_available


if is_torch_available():
	import torch

if is_soundfile_availble():
	import soundfile as sf

if is_vision_available():
	from PIL import Image





def 	_lowerCAmelCase       (    _lowerCAmelCase="" )						->	str:


						'''simple docstring'''






						__snake_case				=		tempfile.mkdtemp()
						return os.path.join(_lowerCAmelCase      ,	str(uuid.uuida() ) + suffix )






@require_soundfile
@require_torch
class     UpperCamelCase(  unittest.TestCase   ):


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




											'''simple docstring'''

											__snake_case				=		torch.rand(1_2     ,    dtype=torch.floataa						) - 0.5
											__snake_case				=		AgentAudio(SCREAMING_SNAKE_CASE						)
											__snake_case				=		str(agent_type.to_string()						)

											# Ensure that the tensor and the agent_type's tensor are the same
											self.assertTrue(torch.allclose(SCREAMING_SNAKE_CASE     ,    agent_type.to_raw()     ,    atol=1e-4						)						)

											del agent_type

											# Ensure the path remains even after the object deletion
											self.assertTrue(os.path.exists(SCREAMING_SNAKE_CASE						)						)

											# Ensure that the file contains the same value as the original tensor
											__snake_case		,					__snake_case				=		sf.read(SCREAMING_SNAKE_CASE						)
											self.assertTrue(torch.allclose(SCREAMING_SNAKE_CASE     ,    torch.tensor(SCREAMING_SNAKE_CASE						)     ,    atol=1e-4						)						)






					def 							SCREAMING_SNAKE_CASE_  (						self	:		Dict						)			->	int:




											'''simple docstring'''

											__snake_case				=		torch.rand(1_2     ,    dtype=torch.floataa						) - 0.5
											__snake_case				=		get_new_path(suffix=".wav"						)
											sf.write(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE     ,    1_6_0_0_0						)

											__snake_case				=		AgentAudio(SCREAMING_SNAKE_CASE						)

											self.assertTrue(torch.allclose(SCREAMING_SNAKE_CASE     ,    agent_type.to_raw()     ,    atol=1e-4						)						)
											self.assertEqual(agent_type.to_string()     ,    SCREAMING_SNAKE_CASE						)






@require_vision
@require_torch
class     UpperCamelCase(  unittest.TestCase   ):


					def 							SCREAMING_SNAKE_CASE_  (						self	:		str						)			->	Optional[int]:




											'''simple docstring'''

											__snake_case				=		torch.randint(0     ,    2_5_6     ,    (6_4, 6_4, 3)						)
											__snake_case				=		AgentImage(SCREAMING_SNAKE_CASE						)
											__snake_case				=		str(agent_type.to_string()						)

											# Ensure that the tensor and the agent_type's tensor are the same
											self.assertTrue(torch.allclose(SCREAMING_SNAKE_CASE     ,    agent_type._tensor     ,    atol=1e-4						)						)

											self.assertIsInstance(agent_type.to_raw()     ,    Image.Image						)

											# Ensure the path remains even after the object deletion
											del agent_type
											self.assertTrue(os.path.exists(SCREAMING_SNAKE_CASE						)						)


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Tuple						)			->	Any:




											'''simple docstring'''

											__snake_case				=		Path(get_tests_dir("fixtures/tests_samples/COCO"						)						) / "000000039769.png"
											__snake_case				=		Image.open(SCREAMING_SNAKE_CASE						)
											__snake_case				=		AgentImage(SCREAMING_SNAKE_CASE						)

											self.assertTrue(path.samefile(agent_type.to_string()						)						)
											self.assertTrue(image == agent_type.to_raw()						)

											# Ensure the path remains even after the object deletion
											del agent_type
											self.assertTrue(os.path.exists(SCREAMING_SNAKE_CASE						)						)






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




											'''simple docstring'''

											__snake_case				=		Path(get_tests_dir("fixtures/tests_samples/COCO"						)						) / "000000039769.png"
											__snake_case				=		Image.open(SCREAMING_SNAKE_CASE						)
											__snake_case				=		AgentImage(SCREAMING_SNAKE_CASE						)

											self.assertFalse(path.samefile(agent_type.to_string()						)						)
											self.assertTrue(image == agent_type.to_raw()						)

											# Ensure the path remains even after the object deletion
											del agent_type
											self.assertTrue(os.path.exists(SCREAMING_SNAKE_CASE						)						)






class     UpperCamelCase(  unittest.TestCase   ):


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Any						)			->	Any:




											'''simple docstring'''

											__snake_case				=		"Hey!"
											__snake_case				=		AgentText(SCREAMING_SNAKE_CASE						)

											self.assertEqual(SCREAMING_SNAKE_CASE     ,    agent_type.to_string()						)
											self.assertEqual(SCREAMING_SNAKE_CASE     ,    agent_type.to_raw()						)
											self.assertEqual(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE						)





