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_								=							logging.get_logger(__name__)

def 							UpperCamelCase				(    lowerCAmelCase_   )		->						Optional[int]:




       '''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(lowerCAmelCase_   )-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(lowerCAmelCase_   )-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(lowerCAmelCase_   )-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(lowerCAmelCase_   )-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 							UpperCamelCase				(    lowerCAmelCase_       ,					lowerCAmelCase_   )		->						List[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)
                     _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 							UpperCamelCase				(    )		->						Any:




       '''simple docstring'''






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

       return image

@torch.no_grad()
def 							UpperCamelCase				(    lowerCAmelCase_       ,					lowerCAmelCase_       ,					lowerCAmelCase_=False       ,					lowerCAmelCase_=None   )		->						List[Any]:




       '''simple docstring'''






       _A=	GLPNConfig(hidden_sizes=[64, 1_28, 3_20, 5_12]       ,					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=lowerCAmelCase_       ,					return_tensors='pt'   ).pixel_values

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

       # load original state dict
       _A=	torch.load(lowerCAmelCase_       ,					map_location=torch.device('cpu'   )   )

       # rename keys
       _A=	rename_keys(lowerCAmelCase_   )

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

       # create HuggingFace model and load state dict
       _A=	GLPNForDepthEstimation(lowerCAmelCase_   )
       model.load_state_dict(lowerCAmelCase_   )
       model.eval()

       # forward pass
       _A=	model(lowerCAmelCase_   )
       _A=	outputs.predicted_depth

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

              _A=	torch.Size([1, 4_80, 6_40]   )

              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__":
      UpperCAmelCase_								=							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_								=							parser.parse_args()
      convert_glpn_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)
import unittest

from transformers import AutoConfig, AutoTokenizer, BertConfig, TensorType, is_flax_available
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, require_flax, slow


if is_flax_available():
      import jax

      from transformers.models.auto.modeling_flax_auto import FlaxAutoModel
      from transformers.models.bert.modeling_flax_bert import FlaxBertModel
      from transformers.models.roberta.modeling_flax_roberta import FlaxRobertaModel





@require_flax
class      lowerCAmelCase						(					unittest.TestCase			):



     @slow
     def 			a__  (					self       ):
            for model_name in ["bert-base-cased", "bert-large-uncased"]:
                   with self.subTest(lowerCAmelCase__       ):
                          _A=	AutoConfig.from_pretrained(lowerCAmelCase__       )
                          self.assertIsNotNone(lowerCAmelCase__       )
                          self.assertIsInstance(lowerCAmelCase__				,      lowerCAmelCase__       )

                          _A=	FlaxAutoModel.from_pretrained(lowerCAmelCase__       )
                          self.assertIsNotNone(lowerCAmelCase__       )
                          self.assertIsInstance(lowerCAmelCase__				,      lowerCAmelCase__       )



     @slow
     def 			a__  (					self       ):
            for model_name in ["roberta-base", "roberta-large"]:
                   with self.subTest(lowerCAmelCase__       ):
                          _A=	AutoConfig.from_pretrained(lowerCAmelCase__       )
                          self.assertIsNotNone(lowerCAmelCase__       )
                          self.assertIsInstance(lowerCAmelCase__				,      lowerCAmelCase__       )

                          _A=	FlaxAutoModel.from_pretrained(lowerCAmelCase__       )
                          self.assertIsNotNone(lowerCAmelCase__       )
                          self.assertIsInstance(lowerCAmelCase__				,      lowerCAmelCase__       )



     @slow
     def 			a__  (					self       ):
            for model_name in ["bert-base-cased", "bert-large-uncased"]:
                   _A=	AutoTokenizer.from_pretrained(lowerCAmelCase__       )
                   _A=	FlaxBertModel.from_pretrained(lowerCAmelCase__       )
                   _A=	tokenizer('Do you support jax jitted function?'				,      return_tensors=TensorType.JAX       )

                   @jax.jit
                   def eval(**lowerCAmelCase__       ):
                          return model(**lowerCAmelCase__       )

                   eval(**lowerCAmelCase__       ).block_until_ready()



     @slow
     def 			a__  (					self       ):
            for model_name in ["roberta-base", "roberta-large"]:
                   _A=	AutoTokenizer.from_pretrained(lowerCAmelCase__       )
                   _A=	FlaxRobertaModel.from_pretrained(lowerCAmelCase__       )
                   _A=	tokenizer('Do you support jax jitted function?'				,      return_tensors=TensorType.JAX       )

                   @jax.jit
                   def eval(**lowerCAmelCase__       ):
                          return model(**lowerCAmelCase__       )

                   eval(**lowerCAmelCase__       ).block_until_ready()



     def 			a__  (					self       ):
            with self.assertRaisesRegex(
                lowerCAmelCase__				,      'bert-base is not a local folder and is not a valid model identifier'       ):
                   _A=	FlaxAutoModel.from_pretrained('bert-base'       )



     def 			a__  (					self       ):
            with self.assertRaisesRegex(
                lowerCAmelCase__				,      r'aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)'       ):
                   _A=	FlaxAutoModel.from_pretrained(lowerCAmelCase__				,      revision='aaaaaa'       )



     def 			a__  (					self       ):
            with self.assertRaisesRegex(
                lowerCAmelCase__				,      'hf-internal-testing/config-no-model does not appear to have a file named flax_model.msgpack'				,      ):
                   _A=	FlaxAutoModel.from_pretrained('hf-internal-testing/config-no-model'       )





     def 			a__  (					self       ):
            with self.assertRaisesRegex(lowerCAmelCase__				,      'Use `from_pt=True` to load this model'       ):
                   _A=	FlaxAutoModel.from_pretrained('hf-internal-testing/tiny-bert-pt-only'       )