





"""simple docstring"""





import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
  from PIL import Image

  from transformers import CLIPSegProcessor, ViTImageProcessor






@require_vision
class    _lowerCAmelCase  (	unittest.TestCase			):
       """simple docstring"""




       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :Dict											=				tempfile.mkdtemp()

         # fmt: off
         lowerCAmelCase__     :List[str]											=				['l', 'o', 'w', 'e', 'r', 's', 't', 'i', 'd', 'n', 'lo', 'l</w>', 'w</w>', 'r</w>', 't</w>', 'low</w>', 'er</w>', 'lowest</w>', 'newer</w>', 'wider', '<unk>', '<|startoftext|>', '<|endoftext|>']
         # fmt: on
         lowerCAmelCase__     :Tuple											=				dict(zip(__UpperCAmelCase       ,					range(len(__UpperCAmelCase ) ) ) )
         lowerCAmelCase__     :int											=				['#version: 0.2', 'l o', 'lo w</w>', 'e r</w>', '']
         lowerCAmelCase__     :List[Any]											=				{'unk_token': '<unk>'}

         lowerCAmelCase__     :Tuple											=				os.path.join(self.tmpdirname       ,					VOCAB_FILES_NAMES['vocab_file'] )
         lowerCAmelCase__     :Dict											=				os.path.join(self.tmpdirname       ,					VOCAB_FILES_NAMES['merges_file'] )
         with open(self.vocab_file       ,					'w'       ,					encoding='utf-8' ) as fp:
           fp.write(json.dumps(__UpperCAmelCase ) + '\n' )
         with open(self.merges_file       ,					'w'       ,					encoding='utf-8' ) as fp:
           fp.write('\n'.join(__UpperCAmelCase ) )

         lowerCAmelCase__     :str											=				{
             'do_resize': True,
             'size': 2_0,
             'do_center_crop': True,
             'crop_size': 1_8,
             'do_normalize': True,
             'image_mean': [0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73],
             'image_std': [0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11],
         }
         lowerCAmelCase__     :Optional[Any]											=				os.path.join(self.tmpdirname       ,					__UpperCAmelCase )
         with open(self.image_processor_file       ,					'w'       ,					encoding='utf-8' ) as fp:
           json.dump(__UpperCAmelCase       ,					__UpperCAmelCase )






       def 	snake_case		(       self       ,					**__UpperCAmelCase ):



         '''simple docstring'''

         return CLIPTokenizer.from_pretrained(self.tmpdirname       ,					**__UpperCAmelCase )






       def 	snake_case		(       self       ,					**__UpperCAmelCase ):



         '''simple docstring'''

         return CLIPTokenizerFast.from_pretrained(self.tmpdirname       ,					**__UpperCAmelCase )






       def 	snake_case		(       self       ,					**__UpperCAmelCase ):



         '''simple docstring'''

         return ViTImageProcessor.from_pretrained(self.tmpdirname       ,					**__UpperCAmelCase )






       def 	snake_case		(       self ):



         '''simple docstring'''

         shutil.rmtree(self.tmpdirname )






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :Optional[Any]											=				[np.random.randint(2_5_5       ,					size=(3, 3_0, 4_0_0)       ,					dtype=np.uinta )]

         lowerCAmelCase__     :Optional[Any]											=				[Image.fromarray(np.moveaxis(__UpperCAmelCase       ,					0       ,					-1 ) ) for x in image_inputs]

         return image_inputs






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :Optional[Any]											=				self.get_tokenizer()
         lowerCAmelCase__     :str											=				self.get_rust_tokenizer()
         lowerCAmelCase__     :Optional[Any]											=				self.get_image_processor()

         lowerCAmelCase__     :Optional[Any]											=				CLIPSegProcessor(tokenizer=__UpperCAmelCase       ,					image_processor=__UpperCAmelCase )
         processor_slow.save_pretrained(self.tmpdirname )
         lowerCAmelCase__     :Tuple											=				CLIPSegProcessor.from_pretrained(self.tmpdirname       ,					use_fast=__UpperCAmelCase )

         lowerCAmelCase__     :int											=				CLIPSegProcessor(tokenizer=__UpperCAmelCase       ,					image_processor=__UpperCAmelCase )
         processor_fast.save_pretrained(self.tmpdirname )
         lowerCAmelCase__     :List[Any]											=				CLIPSegProcessor.from_pretrained(self.tmpdirname )

         self.assertEqual(processor_slow.tokenizer.get_vocab()       ,					tokenizer_slow.get_vocab() )
         self.assertEqual(processor_fast.tokenizer.get_vocab()       ,					tokenizer_fast.get_vocab() )
         self.assertEqual(tokenizer_slow.get_vocab()       ,					tokenizer_fast.get_vocab() )
         self.assertIsInstance(processor_slow.tokenizer       ,					__UpperCAmelCase )
         self.assertIsInstance(processor_fast.tokenizer       ,					__UpperCAmelCase )

         self.assertEqual(processor_slow.image_processor.to_json_string()       ,					image_processor.to_json_string() )
         self.assertEqual(processor_fast.image_processor.to_json_string()       ,					image_processor.to_json_string() )
         self.assertIsInstance(processor_slow.image_processor       ,					__UpperCAmelCase )
         self.assertIsInstance(processor_fast.image_processor       ,					__UpperCAmelCase )






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :Tuple											=				CLIPSegProcessor(tokenizer=self.get_tokenizer()       ,					image_processor=self.get_image_processor() )
         processor.save_pretrained(self.tmpdirname )

         lowerCAmelCase__     :str											=				self.get_tokenizer(bos_token='(BOS)'       ,					eos_token='(EOS)' )
         lowerCAmelCase__     :Optional[Any]											=				self.get_image_processor(do_normalize=__UpperCAmelCase       ,					padding_value=1.0 )

         lowerCAmelCase__     :str											=				CLIPSegProcessor.from_pretrained(
             self.tmpdirname       ,					bos_token='(BOS)'       ,					eos_token='(EOS)'       ,					do_normalize=__UpperCAmelCase       ,					padding_value=1.0 )

         self.assertEqual(processor.tokenizer.get_vocab()       ,					tokenizer_add_kwargs.get_vocab() )
         self.assertIsInstance(processor.tokenizer       ,					__UpperCAmelCase )

         self.assertEqual(processor.image_processor.to_json_string()       ,					image_processor_add_kwargs.to_json_string() )
         self.assertIsInstance(processor.image_processor       ,					__UpperCAmelCase )






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :List[str]											=				self.get_image_processor()
         lowerCAmelCase__     :Union[str, Any]											=				self.get_tokenizer()

         lowerCAmelCase__     :Tuple											=				CLIPSegProcessor(tokenizer=__UpperCAmelCase       ,					image_processor=__UpperCAmelCase )

         lowerCAmelCase__     :Any											=				self.prepare_image_inputs()

         lowerCAmelCase__     :int											=				image_processor(__UpperCAmelCase       ,					return_tensors='np' )
         lowerCAmelCase__     :Optional[Any]											=				processor(images=__UpperCAmelCase       ,					return_tensors='np' )

         for key in input_feat_extract.keys():
           self.assertAlmostEqual(input_feat_extract[key].sum()       ,					input_processor[key].sum()       ,					delta=1E-2 )






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :Union[str, Any]											=				self.get_image_processor()
         lowerCAmelCase__     :Dict											=				self.get_tokenizer()

         lowerCAmelCase__     :Tuple											=				CLIPSegProcessor(tokenizer=__UpperCAmelCase       ,					image_processor=__UpperCAmelCase )

         lowerCAmelCase__     :Any											=				'lower newer'

         lowerCAmelCase__     :Any											=				processor(text=__UpperCAmelCase )

         lowerCAmelCase__     :int											=				tokenizer(__UpperCAmelCase )

         for key in encoded_tok.keys():
           self.assertListEqual(encoded_tok[key]       ,					encoded_processor[key] )






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :Union[str, Any]											=				self.get_image_processor()
         lowerCAmelCase__     :int											=				self.get_tokenizer()

         lowerCAmelCase__     :Any											=				CLIPSegProcessor(tokenizer=__UpperCAmelCase       ,					image_processor=__UpperCAmelCase )

         lowerCAmelCase__     :Optional[Any]											=				'lower newer'
         lowerCAmelCase__     :Tuple											=				self.prepare_image_inputs()

         lowerCAmelCase__     :Any											=				processor(text=__UpperCAmelCase       ,					images=__UpperCAmelCase )

         self.assertListEqual(list(inputs.keys() )       ,					['input_ids', 'attention_mask', 'pixel_values'] )

         # test if it raises when no input is passed
         with pytest.raises(__UpperCAmelCase ):
           processor()






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :Optional[Any]											=				self.get_image_processor()
         lowerCAmelCase__     :Union[str, Any]											=				self.get_tokenizer()

         lowerCAmelCase__     :Any											=				CLIPSegProcessor(tokenizer=__UpperCAmelCase       ,					image_processor=__UpperCAmelCase )

         lowerCAmelCase__     :Optional[Any]											=				self.prepare_image_inputs()
         lowerCAmelCase__     :int											=				self.prepare_image_inputs()

         lowerCAmelCase__     :Dict											=				processor(images=__UpperCAmelCase       ,					visual_prompt=__UpperCAmelCase )

         self.assertListEqual(list(inputs.keys() )       ,					['pixel_values', 'conditional_pixel_values'] )

         # test if it raises when no input is passed
         with pytest.raises(__UpperCAmelCase ):
           processor()






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :Optional[Any]											=				self.get_image_processor()
         lowerCAmelCase__     :Any											=				self.get_tokenizer()

         lowerCAmelCase__     :Dict											=				CLIPSegProcessor(tokenizer=__UpperCAmelCase       ,					image_processor=__UpperCAmelCase )

         lowerCAmelCase__     :int											=				[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

         lowerCAmelCase__     :List[Any]											=				processor.batch_decode(__UpperCAmelCase )
         lowerCAmelCase__     :Optional[Any]											=				tokenizer.batch_decode(__UpperCAmelCase )

         self.assertListEqual(__UpperCAmelCase       ,					__UpperCAmelCase )


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

     return new_state_dict

def  __lowerCamelCase					( _lowercase						,     _lowercase			)       ->      Union[str, Any]:
     # 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)
               UpperCamelCase						    =					state_dict.pop(F'glpn.encoder.block.{i}.{j}.attention.self.kv.weight'			)
               UpperCamelCase						    =					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
               UpperCamelCase						    =					kv_weight[
                   : config.hidden_sizes[i], :
               ]
               UpperCamelCase						    =					kv_bias[: config.hidden_sizes[i]]
               UpperCamelCase						    =					kv_weight[
                   config.hidden_sizes[i] :, :
               ]
               UpperCamelCase						    =					kv_bias[config.hidden_sizes[i] :]

def  __lowerCamelCase					( )       ->      Any:
     UpperCamelCase						    =					'http://images.cocodataset.org/val2017/000000039769.jpg'
     UpperCamelCase						    =					Image.open(requests.get(_lowercase						,     stream=_lowercase			).raw			)

     return image

@torch.no_grad()
def  __lowerCamelCase					( _lowercase						,     _lowercase						,     _lowercase=False						,     _lowercase=None			)       ->      Tuple:
     UpperCamelCase						    =					GLPNConfig(hidden_sizes=[64, 128, 320, 512]						,     decoder_hidden_size=64						,     depths=[3, 8, 27, 3]			)

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

     # prepare image
     UpperCamelCase						    =					prepare_img()
     UpperCamelCase						    =					image_processor(images=_lowercase						,     return_tensors='pt'			).pixel_values

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

     # load original state dict
     UpperCamelCase						    =					torch.load(_lowercase						,     map_location=torch.device('cpu'			)			)

     # rename keys
     UpperCamelCase						    =					rename_keys(_lowercase			)

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

     # create HuggingFace model and load state dict
     UpperCamelCase						    =					GLPNForDepthEstimation(_lowercase			)
     model.load_state_dict(_lowercase			)
     model.eval()

     # forward pass
     UpperCamelCase						    =					model(_lowercase			)
     UpperCamelCase						    =					outputs.predicted_depth

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

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

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


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)


