
import unittest

from transformers import SPIECE_UNDERLINE, ReformerTokenizer, ReformerTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


lowerCamelCase  =get_tests_dir("fixtures/test_sentencepiece.model")




@require_sentencepiece
@require_tokenizers
class 					_lowerCamelCase			(   UpperCamelCase_						,				unittest.TestCase			):





     """simple docstring"""







     SCREAMING_SNAKE_CASE_		 =	ReformerTokenizer
     SCREAMING_SNAKE_CASE_		 =	ReformerTokenizerFast
     SCREAMING_SNAKE_CASE_		 =	True
     SCREAMING_SNAKE_CASE_		 =	False
     SCREAMING_SNAKE_CASE_		 =	True






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> List[Any]:




           """simple docstring"""







           super().setUp()

           UpperCamelCase__     : Tuple											=					ReformerTokenizer(__SCREAMING_SNAKE_CASE    ,    keep_accents=__SCREAMING_SNAKE_CASE	)
           tokenizer.save_pretrained(self.tmpdirname	)






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> List[str]:




           """simple docstring"""







           UpperCamelCase__     : Union[str, Any]											=					'''<s>'''
           UpperCamelCase__     : Optional[int]											=					1

           self.assertEqual(self.get_tokenizer()._convert_token_to_id(__SCREAMING_SNAKE_CASE	)    ,    __SCREAMING_SNAKE_CASE	)
           self.assertEqual(self.get_tokenizer()._convert_id_to_token(__SCREAMING_SNAKE_CASE	)    ,    __SCREAMING_SNAKE_CASE	)






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> Dict:




           """simple docstring"""







           UpperCamelCase__     : Tuple											=					list(self.get_tokenizer().get_vocab().keys()	)

           self.assertEqual(vocab_keys[0]    ,    '''<unk>'''	)
           self.assertEqual(vocab_keys[1]    ,    '''<s>'''	)
           self.assertEqual(vocab_keys[-1]    ,    '''j'''	)
           self.assertEqual(len(__SCREAMING_SNAKE_CASE	)    ,    1_0_0_0	)






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> int:




           """simple docstring"""







           self.assertEqual(self.get_tokenizer().vocab_size    ,    1_0_0_0	)






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> str:




           """simple docstring"""







           if not self.test_rust_tokenizer:
                 return

           UpperCamelCase__     : Dict											=					self.get_tokenizer()
           UpperCamelCase__     : int											=					self.get_rust_tokenizer()

           UpperCamelCase__     : Optional[Any]											=					'''I was born in 92000, and this is falsé.'''

           UpperCamelCase__     : Union[str, Any]											=					tokenizer.tokenize(__SCREAMING_SNAKE_CASE	)
           UpperCamelCase__     : List[str]											=					rust_tokenizer.tokenize(__SCREAMING_SNAKE_CASE	)
           self.assertListEqual(__SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE	)

           UpperCamelCase__     : List[Any]											=					tokenizer.encode(__SCREAMING_SNAKE_CASE    ,    add_special_tokens=__SCREAMING_SNAKE_CASE	)
           UpperCamelCase__     : int											=					rust_tokenizer.encode(__SCREAMING_SNAKE_CASE    ,    add_special_tokens=__SCREAMING_SNAKE_CASE	)
           self.assertListEqual(__SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE	)

           UpperCamelCase__     : int											=					self.get_rust_tokenizer()
           UpperCamelCase__     : Union[str, Any]											=					tokenizer.encode(__SCREAMING_SNAKE_CASE	)
           UpperCamelCase__     : str											=					rust_tokenizer.encode(__SCREAMING_SNAKE_CASE	)
           self.assertListEqual(__SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE	)






     def  __SCREAMING_SNAKE_CASE      (				self    ,    __SCREAMING_SNAKE_CASE=1_5	)	-> int:




           """simple docstring"""







           for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                 with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''	):
                       UpperCamelCase__     : Dict											=					self.rust_tokenizer_class.from_pretrained(__SCREAMING_SNAKE_CASE    ,    **__SCREAMING_SNAKE_CASE	)

                       # Simple input
                       UpperCamelCase__     : Tuple											=					'''This is a simple input'''
                       UpperCamelCase__     : Union[str, Any]											=					['''This is a simple input 1''', '''This is a simple input 2''']
                       UpperCamelCase__     : List[str]											=					('''This is a simple input''', '''This is a pair''')
                       UpperCamelCase__     : Tuple											=					[
                           ('''This is a simple input 1''', '''This is a simple input 2'''),
                           ('''This is a simple pair 1''', '''This is a simple pair 2'''),
                       ]

                       # Simple input tests
                       self.assertRaises(__SCREAMING_SNAKE_CASE    ,    tokenizer_r.encode    ,    __SCREAMING_SNAKE_CASE    ,    max_length=__SCREAMING_SNAKE_CASE    ,    padding='''max_length'''	)

                       # Simple input
                       self.assertRaises(__SCREAMING_SNAKE_CASE    ,    tokenizer_r.encode_plus    ,    __SCREAMING_SNAKE_CASE    ,    max_length=__SCREAMING_SNAKE_CASE    ,    padding='''max_length'''	)

                       # Simple input
                       self.assertRaises(
                           __SCREAMING_SNAKE_CASE    ,    tokenizer_r.batch_encode_plus    ,    __SCREAMING_SNAKE_CASE    ,    max_length=__SCREAMING_SNAKE_CASE    ,    padding='''max_length'''    ,    )

                       # Pair input
                       self.assertRaises(__SCREAMING_SNAKE_CASE    ,    tokenizer_r.encode    ,    __SCREAMING_SNAKE_CASE    ,    max_length=__SCREAMING_SNAKE_CASE    ,    padding='''max_length'''	)

                       # Pair input
                       self.assertRaises(__SCREAMING_SNAKE_CASE    ,    tokenizer_r.encode_plus    ,    __SCREAMING_SNAKE_CASE    ,    max_length=__SCREAMING_SNAKE_CASE    ,    padding='''max_length'''	)

                       # Pair input
                       self.assertRaises(
                           __SCREAMING_SNAKE_CASE    ,    tokenizer_r.batch_encode_plus    ,    __SCREAMING_SNAKE_CASE    ,    max_length=__SCREAMING_SNAKE_CASE    ,    padding='''max_length'''    ,    )






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> int:




           """simple docstring"""







           pass






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> int:




           """simple docstring"""







           UpperCamelCase__     : List[Any]											=					ReformerTokenizer(__SCREAMING_SNAKE_CASE    ,    keep_accents=__SCREAMING_SNAKE_CASE	)

           UpperCamelCase__     : Union[str, Any]											=					tokenizer.tokenize('''This is a test'''	)
           self.assertListEqual(__SCREAMING_SNAKE_CASE    ,    ['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''']	)

           self.assertListEqual(
               tokenizer.convert_tokens_to_ids(__SCREAMING_SNAKE_CASE	)    ,    [2_8_5, 4_6, 1_0, 1_7_0, 3_8_2]    ,    )

           UpperCamelCase__     : Optional[Any]											=					tokenizer.tokenize('''I was born in 92000, and this is falsé.'''	)
           self.assertListEqual(
               __SCREAMING_SNAKE_CASE    ,    [
                   SPIECE_UNDERLINE + '''I''',
                   SPIECE_UNDERLINE + '''was''',
                   SPIECE_UNDERLINE + '''b''',
                   '''or''',
                   '''n''',
                   SPIECE_UNDERLINE + '''in''',
                   SPIECE_UNDERLINE + '''''',
                   '''9''',
                   '''2''',
                   '''0''',
                   '''0''',
                   '''0''',
                   ''',''',
                   SPIECE_UNDERLINE + '''and''',
                   SPIECE_UNDERLINE + '''this''',
                   SPIECE_UNDERLINE + '''is''',
                   SPIECE_UNDERLINE + '''f''',
                   '''al''',
                   '''s''',
                   '''é''',
                   '''.''',
               ]    ,    )
           UpperCamelCase__     : List[Any]											=					tokenizer.convert_tokens_to_ids(__SCREAMING_SNAKE_CASE	)
           self.assertListEqual(
               __SCREAMING_SNAKE_CASE    ,    [8, 2_1, 8_4, 5_5, 2_4, 1_9, 7, 0, 6_0_2, 3_4_7, 3_4_7, 3_4_7, 3, 1_2, 6_6, 4_6, 7_2, 8_0, 6, 0, 4]    ,    )

           UpperCamelCase__     : Tuple											=					tokenizer.convert_ids_to_tokens(__SCREAMING_SNAKE_CASE	)
           self.assertListEqual(
               __SCREAMING_SNAKE_CASE    ,    [
                   SPIECE_UNDERLINE + '''I''',
                   SPIECE_UNDERLINE + '''was''',
                   SPIECE_UNDERLINE + '''b''',
                   '''or''',
                   '''n''',
                   SPIECE_UNDERLINE + '''in''',
                   SPIECE_UNDERLINE + '''''',
                   '''<unk>''',
                   '''2''',
                   '''0''',
                   '''0''',
                   '''0''',
                   ''',''',
                   SPIECE_UNDERLINE + '''and''',
                   SPIECE_UNDERLINE + '''this''',
                   SPIECE_UNDERLINE + '''is''',
                   SPIECE_UNDERLINE + '''f''',
                   '''al''',
                   '''s''',
                   '''<unk>''',
                   '''.''',
               ]    ,    )






     @cached_property
     def  __SCREAMING_SNAKE_CASE      (				self	)	-> Dict:




           """simple docstring"""







           return ReformerTokenizer.from_pretrained('''google/reformer-crime-and-punishment'''	)






     @slow
     def  __SCREAMING_SNAKE_CASE      (				self	)	-> Optional[Any]:




           """simple docstring"""







           UpperCamelCase__     : Any											=					'''Hello World!'''
           UpperCamelCase__     : Tuple											=					[1_2_6, 3_2, 2_6_2, 1_5_2, 3_8, 7_2, 2_8_7]

           self.assertListEqual(__SCREAMING_SNAKE_CASE    ,    self.big_tokenizer.encode(__SCREAMING_SNAKE_CASE	)	)






     @slow
     def  __SCREAMING_SNAKE_CASE      (				self	)	-> List[str]:




           """simple docstring"""







           UpperCamelCase__     : int											=					(
               '''This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) " [ ] ! : - . Also we will'''
               ''' add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth'''
           )
           UpperCamelCase__     : int											=					[
               1_0_8,
               2_6_5,
               2_4,
               1_1_1,
               4,
               2_5_8,
               1_5_6,
               3_5,
               2_8,
               2_7_5,
               3,
               2_5_9,
               2_9_7,
               2_6_0,
               8_4,
               4,
               3_5,
               1_1_0,
               4_4,
               8,
               2_5_9,
               9_1,
               2_6_8,
               2_1,
               1_1,
               2_0_9,
               2_7_4,
               1_0_9,
               2_6_6,
               2_7_7,
               1_1_7,
               8_6,
               9_3,
               3_1_5,
               2_5_8,
               2_7_8,
               2_5_8,
               2_7_7,
               2_5_8,
               0,
               2_5_8,
               2_8_8,
               2_5_8,
               3_1_9,
               2_5_8,
               0,
               2_5_8,
               0,
               2_5_8,
               0,
               2_5_8,
               0,
               2_5_8,
               2_8_7,
               2_5_8,
               3_1_5,
               2_5_8,
               2_8_9,
               2_5_8,
               2_7_8,
               9_9,
               2_6_9,
               2_6_6,
               2_6_2,
               8,
               2_5_9,
               2_4_1,
               4,
               2_1_7,
               2_3_0,
               2_6_8,
               2_6_6,
               5_5,
               1_6_8,
               1_0_6,
               7_5,
               1_9_3,
               2_6_6,
               2_2_3,
               2_7,
               4_9,
               2_6,
               2_8_2,
               2_5,
               2_6_4,
               2_9_9,
               1_9,
               2_6,
               0,
               2_5_8,
               2_7_7,
               1_1_7,
               8_6,
               9_3,
               1_7_6,
               1_8_3,
               2_7_0,
               1_1,
               2_6_2,
               4_2,
               6_1,
               2_6_5,
           ]

           self.assertListEqual(__SCREAMING_SNAKE_CASE    ,    self.big_tokenizer.encode(__SCREAMING_SNAKE_CASE	)	)






     @require_torch
     @slow
     def  __SCREAMING_SNAKE_CASE      (				self	)	-> Union[str, Any]:




           """simple docstring"""







           import torch

           from transformers import ReformerConfig, ReformerModel

           # Build sequence
           UpperCamelCase__     : List[Any]											=					list(self.big_tokenizer.get_vocab().keys()	)[:1_0]
           UpperCamelCase__     : Tuple											=					''' '''.join(__SCREAMING_SNAKE_CASE	)
           UpperCamelCase__     : Dict											=					self.big_tokenizer.encode_plus(__SCREAMING_SNAKE_CASE    ,    return_tensors='''pt'''	)
           UpperCamelCase__     : List[Any]											=					self.big_tokenizer.batch_encode_plus([sequence, sequence]    ,    return_tensors='''pt'''	)

           UpperCamelCase__     : Any											=					ReformerConfig()
           # The input gets padded during training so adjust the axial position encodings from the pretrained model value of (512, 1024)
           UpperCamelCase__     : int											=					encoded_sequence['''input_ids'''].shape
           UpperCamelCase__     : List[Any]											=					ReformerModel(__SCREAMING_SNAKE_CASE	)

           # Reformer has config.vocab_size == tokenizer.vocab_size == len(tokenizer) - 1 = 320; len(tokenizer) is 321 (including a pad token with id 320)
           assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size

           with torch.no_grad():
                 model(**__SCREAMING_SNAKE_CASE	)
                 model(**__SCREAMING_SNAKE_CASE	)






     @slow
     def  __SCREAMING_SNAKE_CASE      (				self	)	-> Dict:




           """simple docstring"""







           UpperCamelCase__     : Union[str, Any]											=					{'''input_ids''': [[1_0_8, 2_6_5, 2_4, 1_1_1, 4, 2_5_8, 1_5_6, 7, 5_1, 2_7_9, 5_8, 7, 7_6, 2_5, 6_9, 2_7_8], [1_4_0, 2_4_3, 2_6_4, 1_3_4, 1_7, 2_6_7, 7_7, 2_6_3, 2_2, 2_6_2, 2_9_7, 2_5_8, 3_0_4, 1_7_7, 2_7_9, 2_6_6, 1_4, 8_9, 1_3, 3_5, 2_6_1, 2_9_9, 2_7_2, 1_3_7, 2_7_5, 2_7_8]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}  # noqa: E501
           # fmt: on

           # This tokenizer does not know some characters like ")".
           # That is the reason why we use very simple texts here.
           # Also see https://github.com/huggingface/transformers/pull/11737#issuecomment-850769064
           UpperCamelCase__     : List[str]											=					[
               '''This is a very simple sentence.''',
               '''The quick brown fox jumps over the lazy dog.''',
           ]

           self.tokenizer_integration_test_util(
               expected_encoding=__SCREAMING_SNAKE_CASE    ,    model_name='''google/reformer-crime-and-punishment'''    ,    revision='''0e6c3decb8211d49bf881013425dc8b0448b3f5a'''    ,    padding=__SCREAMING_SNAKE_CASE    ,    sequences=__SCREAMING_SNAKE_CASE    ,    )




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()
lowerCamelCase  =logging.get_logger(__name__)


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

      return new_state_dict


def 				SCREAMING_SNAKE_CASE_   (      UpperCamelCase__			,		UpperCamelCase__		):
      # 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__     : Optional[Any]											=					state_dict.pop(f'''glpn.encoder.block.{i}.{j}.attention.self.kv.weight'''		)
                  UpperCamelCase__     : 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
                  UpperCamelCase__     : Union[str, Any]											=					kv_weight[
                      : config.hidden_sizes[i], :
                  ]
                  UpperCamelCase__     : Tuple											=					kv_bias[: config.hidden_sizes[i]]
                  UpperCamelCase__     : Tuple											=					kv_weight[
                      config.hidden_sizes[i] :, :
                  ]
                  UpperCamelCase__     : Optional[Any]											=					kv_bias[config.hidden_sizes[i] :]


def 				SCREAMING_SNAKE_CASE_   (      ):
      UpperCamelCase__     : int											=					'''http://images.cocodataset.org/val2017/000000039769.jpg'''
      UpperCamelCase__     : Any											=					Image.open(requests.get(UpperCamelCase__			,		stream=UpperCamelCase__		).raw		)

      return image


@torch.no_grad()
def 				SCREAMING_SNAKE_CASE_   (      UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__=False			,		UpperCamelCase__=None		):
      UpperCamelCase__     : Any											=					GLPNConfig(hidden_sizes=[6_4, 1_2_8, 3_2_0, 5_1_2]			,		decoder_hidden_size=6_4			,		depths=[3, 8, 2_7, 3]		)

      # load image processor (only resize + rescale)
      UpperCamelCase__     : str											=					GLPNImageProcessor()

      # prepare image
      UpperCamelCase__     : List[str]											=					prepare_img()
      UpperCamelCase__     : List[str]											=					image_processor(images=UpperCamelCase__			,		return_tensors='''pt'''		).pixel_values

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

      # load original state dict
      UpperCamelCase__     : Tuple											=					torch.load(UpperCamelCase__			,		map_location=torch.device('''cpu'''		)		)

      # rename keys
      UpperCamelCase__     : Optional[Any]											=					rename_keys(UpperCamelCase__		)

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

      # create HuggingFace model and load state dict
      UpperCamelCase__     : str											=					GLPNForDepthEstimation(UpperCamelCase__		)
      model.load_state_dict(UpperCamelCase__		)
      model.eval()

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

      # verify output
      if model_name is not None:
            if "nyu" in model_name:
                  UpperCamelCase__     : Tuple											=					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__     : Union[str, Any]											=					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__     : Optional[Any]											=					torch.Size([1, 4_8_0, 6_4_0]		)

            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__":
    lowerCamelCase  =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.",
    )
    lowerCamelCase  =parser.parse_args()
    convert_glpn_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)


