




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

      return new_state_dict







def 				lowerCAmelCase_	(     snake_case_,snake_case_     ):
      # 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)
                  _A							:     Any			=						state_dict.pop(f'''glpn.encoder.block.{i}.{j}.attention.self.kv.weight'''     )
                  _A							:     Optional[Any]			=						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							:     Optional[int]			=						kv_weight[
                      : config.hidden_sizes[i], :
                  ]
                  _A							:     Optional[int]			=						kv_bias[: config.hidden_sizes[i]]
                  _A							:     Any			=						kv_weight[
                      config.hidden_sizes[i] :, :
                  ]
                  _A							:     Dict			=						kv_bias[config.hidden_sizes[i] :]







def 				lowerCAmelCase_	(     ):
      _A							:     List[str]			=						'http://images.cocodataset.org/val2017/000000039769.jpg'
      _A							:     Tuple			=						Image.open(requests.get(_UpperCAmelCase,stream=_UpperCAmelCase     ).raw     )

      return image







@torch.no_grad()
def 				lowerCAmelCase_	(     snake_case_,snake_case_,snake_case_=False,snake_case_=None     ):
      _A							:     str			=						GLPNConfig(hidden_sizes=[64, 128, 320, 512],decoder_hidden_size=64,depths=[3, 8, 27, 3]     )

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

      # prepare image
      _A							:     str			=						prepare_img()
      _A							:     Tuple			=						image_processor(images=_UpperCAmelCase,return_tensors="""pt"""     ).pixel_values

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

      # load original state dict
      _A							:     Any			=						torch.load(_UpperCAmelCase,map_location=torch.device("""cpu"""     )     )

      # rename keys
      _A							:     str			=						rename_keys(_UpperCAmelCase     )

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

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

      # forward pass
      _A							:     List[str]			=						model(_UpperCAmelCase     )
      _A							:     Tuple			=						outputs.predicted_depth

      # verify output
      if model_name is not None:
            if "nyu" in model_name:
                  _A							:     List[Any]			=						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:
                  _A							:     List[str]			=						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}'''     )

            _A							:     Tuple			=						torch.Size([1, 480, 640]     )

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


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)








import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch

import numpy as np
import pytest

from datasets.arrow_dataset import Dataset
from datasets.search import ElasticSearchIndex, FaissIndex, MissingIndex

from .utils import require_elasticsearch, require_faiss


_UpperCAmelCase				:       str	   =				pytest.mark.integration

@require_faiss
class    lowerCAmelCase							(					__UpperCamelCase			):
 def      A_	(      self  :      List[Any]	)			->	Union[str, Any]:
     lowerCamelCase__    :      List[Any]					 =		Dataset.from_dict({'filename': ['my_name-train' + '_' + str(UpperCAmelCase	) for x in np.arange(30	).tolist()]}	)
     return dset
 def      A_	(      self  :      Optional[Any]	)			->	Optional[int]:
     import faiss

     lowerCamelCase__    :      Dataset					 =		self._create_dummy_dataset()
     lowerCamelCase__    :      List[Any]					 =		dset.map(
         lambda UpperCAmelCase			,     UpperCAmelCase	: {"vecs": i * np.ones(5			,     dtype=np.floataa	)}			,     with_indices=UpperCAmelCase			,     keep_in_memory=UpperCAmelCase	)
     lowerCamelCase__    :      Tuple					 =		dset.add_faiss_index('vecs'			,     batch_size=100			,     metric_type=faiss.METRIC_INNER_PRODUCT	)
     lowerCamelCase__ ,							lowerCamelCase__    :      str					 =		dset.get_nearest_examples('vecs'			,     np.ones(5			,     dtype=np.floataa	)	)
     self.assertEqual(examples['filename'][0]			,     'my_name-train_29'	)
     dset.drop_index('vecs'	)
 def      A_	(      self  :      Union[str, Any]	)			->	int:
     import faiss

     lowerCamelCase__    :      Dataset					 =		self._create_dummy_dataset()
     dset.add_faiss_index_from_external_arrays(
         external_arrays=np.ones((30, 5)	) * np.arange(30	).reshape(-1			,     1	)			,     index_name='vecs'			,     batch_size=100			,     metric_type=faiss.METRIC_INNER_PRODUCT			,     )
     lowerCamelCase__ ,							lowerCamelCase__    :      Optional[Any]					 =		dset.get_nearest_examples('vecs'			,     np.ones(5			,     dtype=np.floataa	)	)
     self.assertEqual(examples['filename'][0]			,     'my_name-train_29'	)
 def      A_	(      self  :      List[str]	)			->	Tuple:
     import faiss

     lowerCamelCase__    :      Dataset					 =		self._create_dummy_dataset()
     dset.add_faiss_index_from_external_arrays(
         external_arrays=np.ones((30, 5)	) * np.arange(30	).reshape(-1			,     1	)			,     index_name='vecs'			,     metric_type=faiss.METRIC_INNER_PRODUCT			,     )

     # Setting delete=False and unlinking manually is not pretty... but it is required on Windows to
     # ensure somewhat stable behaviour. If we don't, we get PermissionErrors. This is an age-old issue.
     # see https://bugs.python.org/issue14243 and
     # https://stackoverflow.com/questions/23212435/permission-denied-to-write-to-my-temporary-file/23212515
     with tempfile.NamedTemporaryFile(delete=UpperCAmelCase	) as tmp_file:
         dset.save_faiss_index('vecs'			,     tmp_file.name	)
         dset.load_faiss_index('vecs2'			,     tmp_file.name	)
     os.unlink(tmp_file.name	)

     lowerCamelCase__ ,							lowerCamelCase__    :      str					 =		dset.get_nearest_examples('vecs2'			,     np.ones(5			,     dtype=np.floataa	)	)
     self.assertEqual(examples['filename'][0]			,     'my_name-train_29'	)
 def      A_	(      self  :      Any	)			->	Optional[Any]:
     lowerCamelCase__    :      Dataset					 =		self._create_dummy_dataset()
     dset.add_faiss_index_from_external_arrays(
         external_arrays=np.ones((30, 5)	) * np.arange(30	).reshape(-1			,     1	)			,     index_name='vecs'	)
     dset.drop_index('vecs'	)
     self.assertRaises(UpperCAmelCase			,     partial(dset.get_nearest_examples			,     'vecs2'			,     np.ones(5			,     dtype=np.floataa	)	)	)







 def      A_	(      self  :      Dict	)			->	Dict:
     from elasticsearch import Elasticsearch

     lowerCamelCase__    :      Dataset					 =		self._create_dummy_dataset()
     with patch('elasticsearch.Elasticsearch.search'	) as mocked_search, patch(
         'elasticsearch.client.IndicesClient.create'	) as mocked_index_create, patch('elasticsearch.helpers.streaming_bulk'	) as mocked_bulk:
         lowerCamelCase__    :      List[Any]					 =		{'acknowledged': True}
         mocked_bulk.return_value([(True, None)] * 30	)
         lowerCamelCase__    :      int					 =		{'hits': {'hits': [{'_score': 1, '_id': 29}]}}
         lowerCamelCase__    :      List[str]					 =		Elasticsearch()

         dset.add_elasticsearch_index('filename'			,     es_client=UpperCAmelCase	)
         lowerCamelCase__ ,							lowerCamelCase__    :      Dict					 =		dset.get_nearest_examples('filename'			,     'my_name-train_29'	)
         self.assertEqual(examples['filename'][0]			,     'my_name-train_29'	)


@require_faiss
class    lowerCAmelCase							(					__UpperCamelCase			):
 def      A_	(      self  :      Any	)			->	Dict:
     import faiss

     lowerCamelCase__    :      Tuple					 =		FaissIndex(metric_type=faiss.METRIC_INNER_PRODUCT	)

     # add vectors
     index.add_vectors(np.eye(5			,     dtype=np.floataa	)	)
     self.assertIsNotNone(index.faiss_index	)
     self.assertEqual(index.faiss_index.ntotal			,     5	)
     index.add_vectors(np.zeros((5, 5)			,     dtype=np.floataa	)	)
     self.assertEqual(index.faiss_index.ntotal			,     10	)

     # single query
     lowerCamelCase__    :      int					 =		np.zeros(5			,     dtype=np.floataa	)
     lowerCamelCase__    :      Any					 =		1
     lowerCamelCase__ ,							lowerCamelCase__    :      Union[str, Any]					 =		index.search(UpperCAmelCase	)
     self.assertRaises(UpperCAmelCase			,     index.search			,     query.reshape(-1			,     1	)	)
     self.assertGreater(scores[0]			,     0	)
     self.assertEqual(indices[0]			,     1	)

     # batched queries
     lowerCamelCase__    :      str					 =		np.eye(5			,     dtype=np.floataa	)[::-1]
     lowerCamelCase__ ,							lowerCamelCase__    :      Dict					 =		index.search_batch(UpperCAmelCase	)
     self.assertRaises(UpperCAmelCase			,     index.search_batch			,     queries[0]	)
     lowerCamelCase__    :      str					 =		[scores[0] for scores in total_scores]
     lowerCamelCase__    :      List[str]					 =		[indices[0] for indices in total_indices]
     self.assertGreater(np.min(UpperCAmelCase	)			,     0	)
     self.assertListEqual([4, 3, 2, 1, 0]			,     UpperCAmelCase	)
 def      A_	(      self  :      List[Any]	)			->	List[Any]:
     import faiss

     lowerCamelCase__    :      Any					 =		FaissIndex(string_factory='Flat'	)
     index.add_vectors(np.eye(5			,     dtype=np.floataa	)	)
     self.assertIsInstance(index.faiss_index			,     faiss.IndexFlat	)
     lowerCamelCase__    :      Tuple					 =		FaissIndex(string_factory='LSH'	)
     index.add_vectors(np.eye(5			,     dtype=np.floataa	)	)
     self.assertIsInstance(index.faiss_index			,     faiss.IndexLSH	)
     with self.assertRaises(UpperCAmelCase	):
         lowerCamelCase__    :      List[str]					 =		FaissIndex(string_factory='Flat'			,     custom_index=faiss.IndexFlat(5	)	)
 def      A_	(      self  :      List[str]	)			->	Optional[int]:
     import faiss

     lowerCamelCase__    :      Optional[Any]					 =		faiss.IndexFlat(5	)
     lowerCamelCase__    :      int					 =		FaissIndex(custom_index=UpperCAmelCase	)
     index.add_vectors(np.eye(5			,     dtype=np.floataa	)	)
     self.assertIsInstance(index.faiss_index			,     faiss.IndexFlat	)







 def      A_	(      self  :      Any	)			->	Optional[int]:
     import faiss

     lowerCamelCase__    :      Any					 =		FaissIndex(metric_type=faiss.METRIC_INNER_PRODUCT	)
     index.add_vectors(np.eye(5			,     dtype=np.floataa	)	)

     # Setting delete=False and unlinking manually is not pretty... but it is required on Windows to
     # ensure somewhat stable behaviour. If we don't, we get PermissionErrors. This is an age-old issue.
     # see https://bugs.python.org/issue14243 and
     # https://stackoverflow.com/questions/23212435/permission-denied-to-write-to-my-temporary-file/23212515
     with tempfile.NamedTemporaryFile(delete=UpperCAmelCase	) as tmp_file:
         index.save(tmp_file.name	)
         lowerCamelCase__    :      List[Any]					 =		FaissIndex.load(tmp_file.name	)
     os.unlink(tmp_file.name	)

     lowerCamelCase__    :      List[str]					 =		np.zeros(5			,     dtype=np.floataa	)
     lowerCamelCase__    :      Tuple					 =		1
     lowerCamelCase__ ,							lowerCamelCase__    :      str					 =		index.search(UpperCAmelCase	)
     self.assertGreater(scores[0]			,     0	)
     self.assertEqual(indices[0]			,     1	)


@require_faiss
def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase )			->     Any:
    import faiss

    lowerCamelCase__    :      Optional[Any]					 =		FaissIndex(metric_type=faiss.METRIC_INNER_PRODUCT )
    index.add_vectors(np.eye(5						,	dtype=np.floataa ) )

    lowerCamelCase__    :      Optional[int]					 =		'index.faiss'
    lowerCamelCase__    :      Optional[Any]					 =		F"""mock://{index_name}"""
    index.save(_UpperCAmelCase						,	storage_options=mockfs.storage_options )
    lowerCamelCase__    :      Tuple					 =		FaissIndex.load(_UpperCAmelCase						,	storage_options=mockfs.storage_options )

    lowerCamelCase__    :      Optional[int]					 =		np.zeros(5						,	dtype=np.floataa )
    lowerCamelCase__    :      Dict					 =		1
    lowerCamelCase__ ,							lowerCamelCase__    :      str					 =		index.search(_UpperCAmelCase )
    assert scores[0] > 0
    assert indices[0] == 1


@require_elasticsearch
class    lowerCAmelCase							(					__UpperCamelCase			):
 def      A_	(      self  :      Dict	)			->	List[Any]:
     from elasticsearch import Elasticsearch

     with patch('elasticsearch.Elasticsearch.search'	) as mocked_search, patch(
         'elasticsearch.client.IndicesClient.create'	) as mocked_index_create, patch('elasticsearch.helpers.streaming_bulk'	) as mocked_bulk:
         lowerCamelCase__    :      Any					 =		Elasticsearch()
         lowerCamelCase__    :      Tuple					 =		{'acknowledged': True}
         lowerCamelCase__    :      Tuple					 =		ElasticSearchIndex(es_client=UpperCAmelCase	)
         mocked_bulk.return_value([(True, None)] * 3	)
         index.add_documents(['foo', 'bar', 'foobar']	)

         # single query
         lowerCamelCase__    :      Optional[int]					 =		'foo'
         lowerCamelCase__    :      str					 =		{'hits': {'hits': [{'_score': 1, '_id': 0}]}}
         lowerCamelCase__ ,							lowerCamelCase__    :      Optional[Any]					 =		index.search(UpperCAmelCase	)
         self.assertEqual(scores[0]			,     1	)
         self.assertEqual(indices[0]			,     0	)

         # single query with timeout
         lowerCamelCase__    :      Any					 =		'foo'
         lowerCamelCase__    :      List[str]					 =		{'hits': {'hits': [{'_score': 1, '_id': 0}]}}
         lowerCamelCase__ ,							lowerCamelCase__    :      Tuple					 =		index.search(UpperCAmelCase			,     request_timeout=30	)
         self.assertEqual(scores[0]			,     1	)
         self.assertEqual(indices[0]			,     0	)

         # batched queries
         lowerCamelCase__    :      List[str]					 =		['foo', 'bar', 'foobar']
         lowerCamelCase__    :      str					 =		{'hits': {'hits': [{'_score': 1, '_id': 1}]}}
         lowerCamelCase__ ,							lowerCamelCase__    :      str					 =		index.search_batch(UpperCAmelCase	)
         lowerCamelCase__    :      List[str]					 =		[scores[0] for scores in total_scores]
         lowerCamelCase__    :      List[Any]					 =		[indices[0] for indices in total_indices]
         self.assertGreater(np.min(UpperCAmelCase	)			,     0	)
         self.assertListEqual([1, 1, 1]			,     UpperCAmelCase	)

         # batched queries with timeout
         lowerCamelCase__    :      str					 =		['foo', 'bar', 'foobar']
         lowerCamelCase__    :      List[Any]					 =		{'hits': {'hits': [{'_score': 1, '_id': 1}]}}
         lowerCamelCase__ ,							lowerCamelCase__    :      Optional[Any]					 =		index.search_batch(UpperCAmelCase			,     request_timeout=30	)
         lowerCamelCase__    :      Optional[Any]					 =		[scores[0] for scores in total_scores]
         lowerCamelCase__    :      Dict					 =		[indices[0] for indices in total_indices]
         self.assertGreater(np.min(UpperCAmelCase	)			,     0	)
         self.assertListEqual([1, 1, 1]			,     UpperCAmelCase	)





