





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







def 					_a						(       _lowerCamelCase ) ->				Tuple:



			"""simple docstring"""


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

			return new_state_dict







def 					_a						(       _lowerCamelCase    ,	_lowerCamelCase ) ->				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)
									__snake_case					:      Optional[int]							      =     state_dict.pop(F'''glpn.encoder.block.{i}.{j}.attention.self.kv.weight''' )
									__snake_case					:      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
									__snake_case					:      str							      =     kv_weight[
									    : config.hidden_sizes[i], :
									]
									__snake_case					:      Dict							      =     kv_bias[: config.hidden_sizes[i]]
									__snake_case					:      List[Any]							      =     kv_weight[
									    config.hidden_sizes[i] :, :
									]
									__snake_case					:      List[str]							      =     kv_bias[config.hidden_sizes[i] :]







def 					_a						(       ) ->				Union[str, Any]:



			"""simple docstring"""


			__snake_case					:      Optional[int]							      =     """http://images.cocodataset.org/val2017/000000039769.jpg"""
			__snake_case					:      str							      =     Image.open(requests.get(_lowerCamelCase    ,	stream=_lowerCamelCase ).raw )

			return image







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



			"""simple docstring"""


			__snake_case					:      Optional[Any]							      =     GLPNConfig(hidden_sizes=[64, 128, 320, 512]    ,	decoder_hidden_size=64    ,	depths=[3, 8, 27, 3] )

			# load image processor (only resize + rescale)
			__snake_case					:      List[Any]							      =     GLPNImageProcessor()

			# prepare image
			__snake_case					:      str							      =     prepare_img()
			__snake_case					:      str							      =     image_processor(images=_lowerCamelCase    ,	return_tensors="""pt""" ).pixel_values

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

			# load original state dict
			__snake_case					:      Union[str, Any]							      =     torch.load(_lowerCamelCase    ,	map_location=torch.device("""cpu""" ) )

			# rename keys
			__snake_case					:      Any							      =     rename_keys(_lowerCamelCase )

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

			# create HuggingFace model and load state dict
			__snake_case					:      Any							      =     GLPNForDepthEstimation(_lowerCamelCase )
			model.load_state_dict(_lowerCamelCase )
			model.eval()

			# forward pass
			__snake_case					:      Optional[Any]							      =     model(_lowerCamelCase )
			__snake_case					:      Any							      =     outputs.predicted_depth

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

						__snake_case					:      List[Any]							      =     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__":
	__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)









'''simple docstring'''






import os
import unittest

from transformers import BatchEncoding
from transformers.models.bert.tokenization_bert import (
    BasicTokenizer,
    WordpieceTokenizer,
    _is_control,
    _is_punctuation,
    _is_whitespace,
)
from transformers.models.prophetnet.tokenization_prophetnet import VOCAB_FILES_NAMES, ProphetNetTokenizer
from transformers.testing_utils import require_torch, slow

from ...test_tokenization_common import TokenizerTesterMixin




class 					UpperCAmelCase	(     snake_case_				,						unittest.TestCase  ):
      _lowercase:     Optional[int]			=  ProphetNetTokenizer
      _lowercase:     Union[str, Any]			=  False




      def 							lowercase__       (     self     :					Dict    )							-> Optional[int]:
            super().setUp()

            _lowerCAmelCase					=				[
                """[UNK]""",
                """[CLS]""",
                """[SEP]""",
                """[PAD]""",
                """[MASK]""",
                """want""",
                """##want""",
                """##ed""",
                """wa""",
                """un""",
                """runn""",
                """##ing""",
                """,""",
                """low""",
                """lowest""",
            ]
            _lowerCAmelCase					=				os.path.join(self.tmpdirname     ,     VOCAB_FILES_NAMES["""vocab_file"""]    )
            with open(self.vocab_file     ,     """w"""     ,     encoding="""utf-8"""    ) as vocab_writer:
                  vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens]    )    )




      def 							lowercase__       (     self     :					Tuple     ,     __snake_case     :					str    )							-> List[str]:
            _lowerCAmelCase					=				"""UNwant\u00E9d,running"""
            _lowerCAmelCase					=				"""unwanted, running"""
            return input_text, output_text




      def 							lowercase__       (     self     :					Union[str, Any]    )							-> Optional[int]:
            _lowerCAmelCase					=				self.tokenizer_class(self.vocab_file    )

            _lowerCAmelCase					=				tokenizer.tokenize("""UNwant\u00E9d,running"""    )
            self.assertListEqual(__snake_case     ,     ["""un""", """##want""", """##ed""", """,""", """runn""", """##ing"""]    )
            self.assertListEqual(tokenizer.convert_tokens_to_ids(__snake_case    )     ,     [9, 6, 7, 12, 10, 11]    )




      def 							lowercase__       (     self     :					List[Any]    )							-> List[str]:
            _lowerCAmelCase					=				BasicTokenizer()

            self.assertListEqual(tokenizer.tokenize("""ah\u535A\u63A8zz"""    )     ,     ["""ah""", """\u535A""", """\u63A8""", """zz"""]    )




      def 							lowercase__       (     self     :					Optional[Any]    )							-> List[Any]:
            _lowerCAmelCase					=				BasicTokenizer(do_lower_case=__snake_case    )

            self.assertListEqual(
                tokenizer.tokenize(""" \tHeLLo!how  \n Are yoU?  """    )     ,     ["""hello""", """!""", """how""", """are""", """you""", """?"""]    )
            self.assertListEqual(tokenizer.tokenize("""H\u00E9llo"""    )     ,     ["""hello"""]    )




      def 							lowercase__       (     self     :					Dict    )							-> int:
            _lowerCAmelCase					=				BasicTokenizer(do_lower_case=__snake_case     ,     strip_accents=__snake_case    )

            self.assertListEqual(
                tokenizer.tokenize(""" \tHäLLo!how  \n Are yoU?  """    )     ,     ["""hällo""", """!""", """how""", """are""", """you""", """?"""]    )
            self.assertListEqual(tokenizer.tokenize("""H\u00E9llo"""    )     ,     ["""h\u00E9llo"""]    )




      def 							lowercase__       (     self     :					Dict    )							-> int:
            _lowerCAmelCase					=				BasicTokenizer(do_lower_case=__snake_case     ,     strip_accents=__snake_case    )

            self.assertListEqual(
                tokenizer.tokenize(""" \tHäLLo!how  \n Are yoU?  """    )     ,     ["""hallo""", """!""", """how""", """are""", """you""", """?"""]    )
            self.assertListEqual(tokenizer.tokenize("""H\u00E9llo"""    )     ,     ["""hello"""]    )




      def 							lowercase__       (     self     :					Dict    )							-> Optional[Any]:
            _lowerCAmelCase					=				BasicTokenizer(do_lower_case=__snake_case    )

            self.assertListEqual(
                tokenizer.tokenize(""" \tHäLLo!how  \n Are yoU?  """    )     ,     ["""hallo""", """!""", """how""", """are""", """you""", """?"""]    )
            self.assertListEqual(tokenizer.tokenize("""H\u00E9llo"""    )     ,     ["""hello"""]    )




      def 							lowercase__       (     self     :					str    )							-> List[str]:
            _lowerCAmelCase					=				BasicTokenizer(do_lower_case=__snake_case    )

            self.assertListEqual(
                tokenizer.tokenize(""" \tHeLLo!how  \n Are yoU?  """    )     ,     ["""HeLLo""", """!""", """how""", """Are""", """yoU""", """?"""]    )




      def 							lowercase__       (     self     :					Union[str, Any]    )							-> str:
            _lowerCAmelCase					=				BasicTokenizer(do_lower_case=__snake_case     ,     strip_accents=__snake_case    )

            self.assertListEqual(
                tokenizer.tokenize(""" \tHäLLo!how  \n Are yoU?  """    )     ,     ["""HäLLo""", """!""", """how""", """Are""", """yoU""", """?"""]    )




      def 							lowercase__       (     self     :					Tuple    )							-> str:
            _lowerCAmelCase					=				BasicTokenizer(do_lower_case=__snake_case     ,     strip_accents=__snake_case    )

            self.assertListEqual(
                tokenizer.tokenize(""" \tHäLLo!how  \n Are yoU?  """    )     ,     ["""HaLLo""", """!""", """how""", """Are""", """yoU""", """?"""]    )




      def 							lowercase__       (     self     :					Dict    )							-> Optional[int]:
            _lowerCAmelCase					=				BasicTokenizer(do_lower_case=__snake_case     ,     never_split=["""[UNK]"""]    )

            self.assertListEqual(
                tokenizer.tokenize(""" \tHeLLo!how  \n Are yoU? [UNK]"""    )     ,     ["""HeLLo""", """!""", """how""", """Are""", """yoU""", """?""", """[UNK]"""]    )




      def 							lowercase__       (     self     :					Any    )							-> Dict:
            _lowerCAmelCase					=				["""[UNK]""", """[CLS]""", """[SEP]""", """want""", """##want""", """##ed""", """wa""", """un""", """runn""", """##ing"""]

            _lowerCAmelCase					=				{}
            for i, token in enumerate(__snake_case    ):
                  _lowerCAmelCase					=				i
            _lowerCAmelCase					=				WordpieceTokenizer(vocab=__snake_case     ,     unk_token="""[UNK]"""    )

            self.assertListEqual(tokenizer.tokenize(""""""    )     ,     []    )

            self.assertListEqual(tokenizer.tokenize("""unwanted running"""    )     ,     ["""un""", """##want""", """##ed""", """runn""", """##ing"""]    )

            self.assertListEqual(tokenizer.tokenize("""unwantedX running"""    )     ,     ["""[UNK]""", """runn""", """##ing"""]    )




      @require_torch
      def 							lowercase__       (     self     :					Optional[int]    )							-> Optional[Any]:
            _lowerCAmelCase					=				self.tokenizer_class.from_pretrained("""microsoft/prophetnet-large-uncased"""    )

            _lowerCAmelCase					=				["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
            _lowerCAmelCase					=				[10_37, 21_46, 2_04_23, 20_05, 76_80, 78_49, 39_89, 10_12, 1_02]
            _lowerCAmelCase					=				tokenizer(__snake_case     ,     padding=__snake_case     ,     return_tensors="""pt"""    )
            self.assertIsInstance(__snake_case     ,     __snake_case    )
            _lowerCAmelCase					=				list(batch.input_ids.numpy()[0]    )
            self.assertListEqual(__snake_case     ,     __snake_case    )

            self.assertEqual((2, 9)     ,     batch.input_ids.shape    )
            self.assertEqual((2, 9)     ,     batch.attention_mask.shape    )




      def 							lowercase__       (     self     :					List[Any]    )							-> Any:
            self.assertTrue(_is_whitespace(""" """    )    )
            self.assertTrue(_is_whitespace("""\t"""    )    )
            self.assertTrue(_is_whitespace("""\r"""    )    )
            self.assertTrue(_is_whitespace("""\n"""    )    )
            self.assertTrue(_is_whitespace("""\u00A0"""    )    )

            self.assertFalse(_is_whitespace("""A"""    )    )
            self.assertFalse(_is_whitespace("""-"""    )    )




      def 							lowercase__       (     self     :					str    )							-> Union[str, Any]:
            self.assertTrue(_is_control("""\u0005"""    )    )

            self.assertFalse(_is_control("""A"""    )    )
            self.assertFalse(_is_control(""" """    )    )
            self.assertFalse(_is_control("""\t"""    )    )
            self.assertFalse(_is_control("""\r"""    )    )




      def 							lowercase__       (     self     :					int    )							-> int:
            self.assertTrue(_is_punctuation("""-"""    )    )
            self.assertTrue(_is_punctuation("""$"""    )    )
            self.assertTrue(_is_punctuation("""`"""    )    )
            self.assertTrue(_is_punctuation("""."""    )    )

            self.assertFalse(_is_punctuation("""A"""    )    )
            self.assertFalse(_is_punctuation(""" """    )    )




      @slow
      def 							lowercase__       (     self     :					int    )							-> Optional[int]:
            _lowerCAmelCase					=				self.tokenizer_class.from_pretrained("""microsoft/prophetnet-large-uncased"""    )

            _lowerCAmelCase					=				tokenizer.encode("""sequence builders"""     ,     add_special_tokens=__snake_case    )
            _lowerCAmelCase					=				tokenizer.encode("""multi-sequence build"""     ,     add_special_tokens=__snake_case    )

            _lowerCAmelCase					=				tokenizer.build_inputs_with_special_tokens(__snake_case    )
            _lowerCAmelCase					=				tokenizer.build_inputs_with_special_tokens(__snake_case     ,     __snake_case    )

            assert encoded_sentence == text + [1_02]
            assert encoded_pair == text + [1_02] + text_a + [1_02]






