


"""simple docstring"""





import unittest

from transformers import is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_vision_available():
 from PIL import Image
else:



 class 						snake_case_:






      @staticmethod
      def    lowerCamelCase__   (		*UpperCamelCase_     :    Tuple    , **UpperCamelCase_     :    List[str] ):
            pass



@is_pipeline_test
@require_vision
class 						snake_case_(				unittest.TestCase	):






     @require_torch
     def    lowerCamelCase__   (		self     :    Dict ):
           lowerCAmelCase    :		Optional[Any]				=      pipeline(
               model='''hf-internal-testing/tiny-random-clip-zero-shot-image-classification'''    , )
           lowerCAmelCase    :		int				=      Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
           lowerCAmelCase    :		Optional[Any]				=      image_classifier(UpperCamelCase_    , candidate_labels=['''a''', '''b''', '''c'''] )

           # The floating scores are so close, we enter floating error approximation and the order is not guaranteed across
           # python and torch versions.
           self.assertIn(
               nested_simplify(UpperCamelCase_ )    , [
                   [{'''score''': 0.333, '''label''': '''a'''}, {'''score''': 0.333, '''label''': '''b'''}, {'''score''': 0.333, '''label''': '''c'''}],
                   [{'''score''': 0.333, '''label''': '''a'''}, {'''score''': 0.333, '''label''': '''c'''}, {'''score''': 0.333, '''label''': '''b'''}],
               ]    , )

           lowerCAmelCase    :		Union[str, Any]				=      image_classifier([image] * 5    , candidate_labels=['''A''', '''B''', '''C''']    , batch_size=2 )
           self.assertEqual(
               nested_simplify(UpperCamelCase_ )    , [
                   [
                       {'''score''': 0.333, '''label''': ANY(UpperCamelCase_ )},
                       {'''score''': 0.333, '''label''': ANY(UpperCamelCase_ )},
                       {'''score''': 0.333, '''label''': ANY(UpperCamelCase_ )},
                   ],
                   [
                       {'''score''': 0.333, '''label''': ANY(UpperCamelCase_ )},
                       {'''score''': 0.333, '''label''': ANY(UpperCamelCase_ )},
                       {'''score''': 0.333, '''label''': ANY(UpperCamelCase_ )},
                   ],
                   [
                       {'''score''': 0.333, '''label''': ANY(UpperCamelCase_ )},
                       {'''score''': 0.333, '''label''': ANY(UpperCamelCase_ )},
                       {'''score''': 0.333, '''label''': ANY(UpperCamelCase_ )},
                   ],
                   [
                       {'''score''': 0.333, '''label''': ANY(UpperCamelCase_ )},
                       {'''score''': 0.333, '''label''': ANY(UpperCamelCase_ )},
                       {'''score''': 0.333, '''label''': ANY(UpperCamelCase_ )},
                   ],
                   [
                       {'''score''': 0.333, '''label''': ANY(UpperCamelCase_ )},
                       {'''score''': 0.333, '''label''': ANY(UpperCamelCase_ )},
                       {'''score''': 0.333, '''label''': ANY(UpperCamelCase_ )},
                   ],
               ]    , )






     @require_tf
     def    lowerCamelCase__   (		self     :    str ):
           lowerCAmelCase    :		Union[str, Any]				=      pipeline(
               model='''hf-internal-testing/tiny-random-clip-zero-shot-image-classification'''    , framework='''tf''' )
           lowerCAmelCase    :		Optional[Any]				=      Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
           lowerCAmelCase    :		List[str]				=      image_classifier(UpperCamelCase_    , candidate_labels=['''a''', '''b''', '''c'''] )

           self.assertEqual(
               nested_simplify(UpperCamelCase_ )    , [{'''score''': 0.333, '''label''': '''a'''}, {'''score''': 0.333, '''label''': '''b'''}, {'''score''': 0.333, '''label''': '''c'''}]    , )

           lowerCAmelCase    :		List[str]				=      image_classifier([image] * 5    , candidate_labels=['''A''', '''B''', '''C''']    , batch_size=2 )
           self.assertEqual(
               nested_simplify(UpperCamelCase_ )    , [
                   [
                       {'''score''': 0.333, '''label''': ANY(UpperCamelCase_ )},
                       {'''score''': 0.333, '''label''': ANY(UpperCamelCase_ )},
                       {'''score''': 0.333, '''label''': ANY(UpperCamelCase_ )},
                   ],
                   [
                       {'''score''': 0.333, '''label''': ANY(UpperCamelCase_ )},
                       {'''score''': 0.333, '''label''': ANY(UpperCamelCase_ )},
                       {'''score''': 0.333, '''label''': ANY(UpperCamelCase_ )},
                   ],
                   [
                       {'''score''': 0.333, '''label''': ANY(UpperCamelCase_ )},
                       {'''score''': 0.333, '''label''': ANY(UpperCamelCase_ )},
                       {'''score''': 0.333, '''label''': ANY(UpperCamelCase_ )},
                   ],
                   [
                       {'''score''': 0.333, '''label''': ANY(UpperCamelCase_ )},
                       {'''score''': 0.333, '''label''': ANY(UpperCamelCase_ )},
                       {'''score''': 0.333, '''label''': ANY(UpperCamelCase_ )},
                   ],
                   [
                       {'''score''': 0.333, '''label''': ANY(UpperCamelCase_ )},
                       {'''score''': 0.333, '''label''': ANY(UpperCamelCase_ )},
                       {'''score''': 0.333, '''label''': ANY(UpperCamelCase_ )},
                   ],
               ]    , )






     @slow
     @require_torch
     def    lowerCamelCase__   (		self     :    Optional[Any] ):
           lowerCAmelCase    :		Any				=      pipeline(
               task='''zero-shot-image-classification'''    , model='''openai/clip-vit-base-patch32'''    , )
           # This is an image of 2 cats with remotes and no planes
           lowerCAmelCase    :		Tuple				=      Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
           lowerCAmelCase    :		Tuple				=      image_classifier(UpperCamelCase_    , candidate_labels=['''cat''', '''plane''', '''remote'''] )

           self.assertEqual(
               nested_simplify(UpperCamelCase_ )    , [
                   {'''score''': 0.511, '''label''': '''remote'''},
                   {'''score''': 0.485, '''label''': '''cat'''},
                   {'''score''': 0.004, '''label''': '''plane'''},
               ]    , )

           lowerCAmelCase    :		Any				=      image_classifier([image] * 5    , candidate_labels=['''cat''', '''plane''', '''remote''']    , batch_size=2 )
           self.assertEqual(
               nested_simplify(UpperCamelCase_ )    , [
                   [
                       {'''score''': 0.511, '''label''': '''remote'''},
                       {'''score''': 0.485, '''label''': '''cat'''},
                       {'''score''': 0.004, '''label''': '''plane'''},
                   ],
               ]
               * 5    , )






     @slow
     @require_tf
     def    lowerCamelCase__   (		self     :    Union[str, Any] ):
           lowerCAmelCase    :		Any				=      pipeline(
               task='''zero-shot-image-classification'''    , model='''openai/clip-vit-base-patch32'''    , framework='''tf''' )
           # This is an image of 2 cats with remotes and no planes
           lowerCAmelCase    :		int				=      Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
           lowerCAmelCase    :		Tuple				=      image_classifier(UpperCamelCase_    , candidate_labels=['''cat''', '''plane''', '''remote'''] )
           self.assertEqual(
               nested_simplify(UpperCamelCase_ )    , [
                   {'''score''': 0.511, '''label''': '''remote'''},
                   {'''score''': 0.485, '''label''': '''cat'''},
                   {'''score''': 0.004, '''label''': '''plane'''},
               ]    , )

           lowerCAmelCase    :		Dict				=      image_classifier([image] * 5    , candidate_labels=['''cat''', '''plane''', '''remote''']    , batch_size=2 )
           self.assertEqual(
               nested_simplify(UpperCamelCase_ )    , [
                   [
                       {'''score''': 0.511, '''label''': '''remote'''},
                       {'''score''': 0.485, '''label''': '''cat'''},
                       {'''score''': 0.004, '''label''': '''plane'''},
                   ],
               ]
               * 5    , )






"""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__						:   int			 = logging.get_logger(__name__)


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

      return new_state_dict


def    _snake_case  (  _snake_case :	Optional[Any]       ,      _snake_case :	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    :		int				=      state_dict.pop(f'''glpn.encoder.block.{i}.{j}.attention.self.kv.weight'''	)
                  lowerCAmelCase    :		Optional[int]				=      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    :		str				=      kv_weight[
                      : config.hidden_sizes[i], :
                  ]
                  lowerCAmelCase    :		Union[str, Any]				=      kv_bias[: config.hidden_sizes[i]]
                  lowerCAmelCase    :		Dict				=      kv_weight[
                      config.hidden_sizes[i] :, :
                  ]
                  lowerCAmelCase    :		List[str]				=      kv_bias[config.hidden_sizes[i] :]


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

      return image


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

      # load image processor (only resize + rescale)
      lowerCAmelCase    :		Union[str, Any]				=      GLPNImageProcessor()

      # prepare image
      lowerCAmelCase    :		Tuple				=      prepare_img()
      lowerCAmelCase    :		Dict				=      image_processor(images=_snake_case       ,      return_tensors='''pt'''	).pixel_values

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

      # load original state dict
      lowerCAmelCase    :		List[str]				=      torch.load(_snake_case       ,      map_location=torch.device('''cpu'''	)	)

      # rename keys
      lowerCAmelCase    :		Tuple				=      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
      lowerCAmelCase    :		str				=      GLPNForDepthEstimation(_snake_case	)
      model.load_state_dict(_snake_case	)
      model.eval()

      # forward pass
      lowerCAmelCase    :		Union[str, Any]				=      model(_snake_case	)
      lowerCAmelCase    :		int				=      outputs.predicted_depth

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

            lowerCAmelCase    :		List[Any]				=      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__":
 snake_case__						:   Tuple			 = 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__						:   List[str]			 = parser.parse_args()
 convert_glpn_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)


