



import unittest

import numpy as np

from transformers.testing_utils import require_flax, require_tf, require_torch
from transformers.utils import (
    expand_dims,
    flatten_dict,
    is_flax_available,
    is_tf_available,
    is_torch_available,
    reshape,
    squeeze,
    transpose,
)


if is_flax_available():
	import jax.numpy as jnp

if is_tf_available():
	import tensorflow as tf

if is_torch_available():
	import torch





class 					__lowerCAmelCase (     unittest.TestCase       ):




				def 							snake_case   (							self		):



									"""simple docstring"""





									_lowerCAmelCase						=    {
									    """task_specific_params""": {
									        """summarization""": {"""length_penalty""": 1.0, """max_length""": 128, """min_length""": 12, """num_beams""": 4},
									        """summarization_cnn""": {"""length_penalty""": 2.0, """max_length""": 142, """min_length""": 56, """num_beams""": 4},
									        """summarization_xsum""": {"""length_penalty""": 1.0, """max_length""": 62, """min_length""": 11, """num_beams""": 6},
									    }
									}
									_lowerCAmelCase						=    {
									    """task_specific_params.summarization.length_penalty""": 1.0,
									    """task_specific_params.summarization.max_length""": 128,
									    """task_specific_params.summarization.min_length""": 12,
									    """task_specific_params.summarization.num_beams""": 4,
									    """task_specific_params.summarization_cnn.length_penalty""": 2.0,
									    """task_specific_params.summarization_cnn.max_length""": 142,
									    """task_specific_params.summarization_cnn.min_length""": 56,
									    """task_specific_params.summarization_cnn.num_beams""": 4,
									    """task_specific_params.summarization_xsum.length_penalty""": 1.0,
									    """task_specific_params.summarization_xsum.max_length""": 62,
									    """task_specific_params.summarization_xsum.min_length""": 11,
									    """task_specific_params.summarization_xsum.num_beams""": 6,
									}

									self.assertEqual(flatten_dict(_snake_case		)						,				_snake_case		)




				def 							snake_case   (							self		):



									"""simple docstring"""





									_lowerCAmelCase						=    np.random.randn(3						,				4		)
									self.assertTrue(np.allclose(transpose(_snake_case		)						,				x.transpose()		)		)

									_lowerCAmelCase						=    np.random.randn(3						,				4						,				5		)
									self.assertTrue(np.allclose(transpose(_snake_case						,				axes=(1, 2, 0)		)						,				x.transpose((1, 2, 0)		)		)		)




				@require_torch
				def 							snake_case   (							self		):



									"""simple docstring"""





									_lowerCAmelCase						=    np.random.randn(3						,				4		)
									_lowerCAmelCase						=    torch.tensor(_snake_case		)
									self.assertTrue(np.allclose(transpose(_snake_case		)						,				transpose(_snake_case		).numpy()		)		)

									_lowerCAmelCase						=    np.random.randn(3						,				4						,				5		)
									_lowerCAmelCase						=    torch.tensor(_snake_case		)
									self.assertTrue(np.allclose(transpose(_snake_case						,				axes=(1, 2, 0)		)						,				transpose(_snake_case						,				axes=(1, 2, 0)		).numpy()		)		)




				@require_tf
				def 							snake_case   (							self		):



									"""simple docstring"""





									_lowerCAmelCase						=    np.random.randn(3						,				4		)
									_lowerCAmelCase						=    tf.constant(_snake_case		)
									self.assertTrue(np.allclose(transpose(_snake_case		)						,				transpose(_snake_case		).numpy()		)		)

									_lowerCAmelCase						=    np.random.randn(3						,				4						,				5		)
									_lowerCAmelCase						=    tf.constant(_snake_case		)
									self.assertTrue(np.allclose(transpose(_snake_case						,				axes=(1, 2, 0)		)						,				transpose(_snake_case						,				axes=(1, 2, 0)		).numpy()		)		)




				@require_flax
				def 							snake_case   (							self		):



									"""simple docstring"""





									_lowerCAmelCase						=    np.random.randn(3						,				4		)
									_lowerCAmelCase						=    jnp.array(_snake_case		)
									self.assertTrue(np.allclose(transpose(_snake_case		)						,				np.asarray(transpose(_snake_case		)		)		)		)

									_lowerCAmelCase						=    np.random.randn(3						,				4						,				5		)
									_lowerCAmelCase						=    jnp.array(_snake_case		)
									self.assertTrue(np.allclose(transpose(_snake_case						,				axes=(1, 2, 0)		)						,				np.asarray(transpose(_snake_case						,				axes=(1, 2, 0)		)		)		)		)




				def 							snake_case   (							self		):



									"""simple docstring"""





									_lowerCAmelCase						=    np.random.randn(3						,				4		)
									self.assertTrue(np.allclose(reshape(_snake_case						,				(4, 3)		)						,				np.reshape(_snake_case						,				(4, 3)		)		)		)

									_lowerCAmelCase						=    np.random.randn(3						,				4						,				5		)
									self.assertTrue(np.allclose(reshape(_snake_case						,				(12, 5)		)						,				np.reshape(_snake_case						,				(12, 5)		)		)		)




				@require_torch
				def 							snake_case   (							self		):



									"""simple docstring"""





									_lowerCAmelCase						=    np.random.randn(3						,				4		)
									_lowerCAmelCase						=    torch.tensor(_snake_case		)
									self.assertTrue(np.allclose(reshape(_snake_case						,				(4, 3)		)						,				reshape(_snake_case						,				(4, 3)		).numpy()		)		)

									_lowerCAmelCase						=    np.random.randn(3						,				4						,				5		)
									_lowerCAmelCase						=    torch.tensor(_snake_case		)
									self.assertTrue(np.allclose(reshape(_snake_case						,				(12, 5)		)						,				reshape(_snake_case						,				(12, 5)		).numpy()		)		)




				@require_tf
				def 							snake_case   (							self		):



									"""simple docstring"""





									_lowerCAmelCase						=    np.random.randn(3						,				4		)
									_lowerCAmelCase						=    tf.constant(_snake_case		)
									self.assertTrue(np.allclose(reshape(_snake_case						,				(4, 3)		)						,				reshape(_snake_case						,				(4, 3)		).numpy()		)		)

									_lowerCAmelCase						=    np.random.randn(3						,				4						,				5		)
									_lowerCAmelCase						=    tf.constant(_snake_case		)
									self.assertTrue(np.allclose(reshape(_snake_case						,				(12, 5)		)						,				reshape(_snake_case						,				(12, 5)		).numpy()		)		)




				@require_flax
				def 							snake_case   (							self		):



									"""simple docstring"""





									_lowerCAmelCase						=    np.random.randn(3						,				4		)
									_lowerCAmelCase						=    jnp.array(_snake_case		)
									self.assertTrue(np.allclose(reshape(_snake_case						,				(4, 3)		)						,				np.asarray(reshape(_snake_case						,				(4, 3)		)		)		)		)

									_lowerCAmelCase						=    np.random.randn(3						,				4						,				5		)
									_lowerCAmelCase						=    jnp.array(_snake_case		)
									self.assertTrue(np.allclose(reshape(_snake_case						,				(12, 5)		)						,				np.asarray(reshape(_snake_case						,				(12, 5)		)		)		)		)




				def 							snake_case   (							self		):



									"""simple docstring"""





									_lowerCAmelCase						=    np.random.randn(1						,				3						,				4		)
									self.assertTrue(np.allclose(squeeze(_snake_case		)						,				np.squeeze(_snake_case		)		)		)

									_lowerCAmelCase						=    np.random.randn(1						,				4						,				1						,				5		)
									self.assertTrue(np.allclose(squeeze(_snake_case						,				axis=2		)						,				np.squeeze(_snake_case						,				axis=2		)		)		)




				@require_torch
				def 							snake_case   (							self		):



									"""simple docstring"""





									_lowerCAmelCase						=    np.random.randn(1						,				3						,				4		)
									_lowerCAmelCase						=    torch.tensor(_snake_case		)
									self.assertTrue(np.allclose(squeeze(_snake_case		)						,				squeeze(_snake_case		).numpy()		)		)

									_lowerCAmelCase						=    np.random.randn(1						,				4						,				1						,				5		)
									_lowerCAmelCase						=    torch.tensor(_snake_case		)
									self.assertTrue(np.allclose(squeeze(_snake_case						,				axis=2		)						,				squeeze(_snake_case						,				axis=2		).numpy()		)		)




				@require_tf
				def 							snake_case   (							self		):



									"""simple docstring"""





									_lowerCAmelCase						=    np.random.randn(1						,				3						,				4		)
									_lowerCAmelCase						=    tf.constant(_snake_case		)
									self.assertTrue(np.allclose(squeeze(_snake_case		)						,				squeeze(_snake_case		).numpy()		)		)

									_lowerCAmelCase						=    np.random.randn(1						,				4						,				1						,				5		)
									_lowerCAmelCase						=    tf.constant(_snake_case		)
									self.assertTrue(np.allclose(squeeze(_snake_case						,				axis=2		)						,				squeeze(_snake_case						,				axis=2		).numpy()		)		)




				@require_flax
				def 							snake_case   (							self		):



									"""simple docstring"""





									_lowerCAmelCase						=    np.random.randn(1						,				3						,				4		)
									_lowerCAmelCase						=    jnp.array(_snake_case		)
									self.assertTrue(np.allclose(squeeze(_snake_case		)						,				np.asarray(squeeze(_snake_case		)		)		)		)

									_lowerCAmelCase						=    np.random.randn(1						,				4						,				1						,				5		)
									_lowerCAmelCase						=    jnp.array(_snake_case		)
									self.assertTrue(np.allclose(squeeze(_snake_case						,				axis=2		)						,				np.asarray(squeeze(_snake_case						,				axis=2		)		)		)		)




				def 							snake_case   (							self		):



									"""simple docstring"""





									_lowerCAmelCase						=    np.random.randn(3						,				4		)
									self.assertTrue(np.allclose(expand_dims(_snake_case						,				axis=1		)						,				np.expand_dims(_snake_case						,				axis=1		)		)		)




				@require_torch
				def 							snake_case   (							self		):



									"""simple docstring"""





									_lowerCAmelCase						=    np.random.randn(3						,				4		)
									_lowerCAmelCase						=    torch.tensor(_snake_case		)
									self.assertTrue(np.allclose(expand_dims(_snake_case						,				axis=1		)						,				expand_dims(_snake_case						,				axis=1		).numpy()		)		)




				@require_tf
				def 							snake_case   (							self		):



									"""simple docstring"""





									_lowerCAmelCase						=    np.random.randn(3						,				4		)
									_lowerCAmelCase						=    tf.constant(_snake_case		)
									self.assertTrue(np.allclose(expand_dims(_snake_case						,				axis=1		)						,				expand_dims(_snake_case						,				axis=1		).numpy()		)		)




				@require_flax
				def 							snake_case   (							self		):



									"""simple docstring"""





									_lowerCAmelCase						=    np.random.randn(3						,				4		)
									_lowerCAmelCase						=    jnp.array(_snake_case		)
									self.assertTrue(np.allclose(expand_dims(_snake_case						,				axis=1		)						,				np.asarray(expand_dims(_snake_case						,				axis=1		)		)		)		)







'''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()
__snake_case    						=logging.get_logger(__name__)





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

   return new_state_dict





def 	a_					( lowerCamelCase	:     List[str]	, lowerCamelCase	:     str				):
   # 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     =			state_dict.pop(f'''glpn.encoder.block.{i}.{j}.attention.self.kv.weight'''				)
         lowerCAmelCase     =			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     =			kv_weight[
             : config.hidden_sizes[i], :
         ]
         lowerCAmelCase     =			kv_bias[: config.hidden_sizes[i]]
         lowerCAmelCase     =			kv_weight[
             config.hidden_sizes[i] :, :
         ]
         lowerCAmelCase     =			kv_bias[config.hidden_sizes[i] :]





def 	a_					( ):
   lowerCAmelCase     =			'http://images.cocodataset.org/val2017/000000039769.jpg'
   lowerCAmelCase     =			Image.open(requests.get(lowerCamelCase	, stream=lowerCamelCase				).raw				)

   return image





@torch.no_grad()
def 	a_					( lowerCamelCase	:     List[Any]	, lowerCamelCase	:     Tuple	, lowerCamelCase	:     Optional[Any]=False	, lowerCamelCase	:     List[str]=None				):
   lowerCAmelCase     =			GLPNConfig(hidden_sizes=[64, 128, 320, 512]	, decoder_hidden_size=64	, depths=[3, 8, 27, 3]				)

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

   # prepare image
   lowerCAmelCase     =			prepare_img()
   lowerCAmelCase     =			image_processor(images=lowerCamelCase	, return_tensors='pt'				).pixel_values

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

   # load original state dict
   lowerCAmelCase     =			torch.load(lowerCamelCase	, map_location=torch.device('cpu'				)				)

   # rename keys
   lowerCAmelCase     =			rename_keys(lowerCamelCase				)

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

   # create HuggingFace model and load state dict
   lowerCAmelCase     =			GLPNForDepthEstimation(lowerCamelCase				)
   model.load_state_dict(lowerCamelCase				)
   model.eval()

   # forward pass
   lowerCAmelCase     =			model(lowerCamelCase				)
   lowerCAmelCase     =			outputs.predicted_depth

   # verify output
   if model_name is not None:
      if "nyu" in model_name:
         lowerCAmelCase     =			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     =			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     =			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__":
       __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.""",
       )
       __snake_case    						=parser.parse_args()
       convert_glpn_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)





