





import tempfile
import unittest

from transformers import TaConfig, is_torch_available
from transformers.testing_utils import (
    require_sentencepiece,
    require_tokenizers,
    require_torch,
    slow,
    torch_device,
)

from ...generation.test_utils import GenerationTesterMixin
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
      import torch

      from transformers import AutoTokenizer, UMTaForConditionalGeneration, UMTaForQuestionAnswering, UMTaModel





class UpperCAmelCase_				:


  def __init__(							self ,__snake_case ,__snake_case=9_9 ,__snake_case=1_3 ,__snake_case=7 ,__snake_case=9 ,__snake_case=True ,__snake_case=True ,__snake_case=False ,__snake_case=3_2 ,__snake_case=5 ,__snake_case=4 ,__snake_case=3_7 ,__snake_case=8 ,__snake_case=0.1 ,__snake_case=0.002 ,__snake_case=1 ,__snake_case=0 ,__snake_case=0 ,__snake_case=None ,__snake_case=None ,):



       """simple docstring"""





       A_          =       parent
       A_          =       batch_size
       A_          =       encoder_seq_length
       A_          =       decoder_seq_length
       # For common tests
       A_          =       self.decoder_seq_length
       A_          =       is_training
       A_          =       use_attention_mask
       A_          =       use_labels
       A_          =       vocab_size
       A_          =       hidden_size
       A_          =       num_hidden_layers
       A_          =       num_attention_heads
       A_          =       d_ff
       A_          =       relative_attention_num_buckets
       A_          =       dropout_rate
       A_          =       initializer_factor
       A_          =       eos_token_id
       A_          =       pad_token_id
       A_          =       decoder_start_token_id
       A_          =       None
       A_          =       decoder_layers


  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       return TaConfig.from_pretrained('''google/umt5-base''' )


  def 						__UpperCAmelCase     (							self ,__snake_case ,__snake_case ,__snake_case ,__snake_case=None ,__snake_case=None ,__snake_case=None ,__snake_case=None ,__snake_case=None ,):



       """simple docstring"""





       if attention_mask is None:
            A_          =       input_ids.ne(config.pad_token_id )
       if decoder_attention_mask is None:
            A_          =       decoder_input_ids.ne(config.pad_token_id )
       if head_mask is None:
            A_          =       torch.ones(config.num_hidden_layers ,config.num_attention_heads ,device=__snake_case )
       if decoder_head_mask is None:
            A_          =       torch.ones(config.num_decoder_layers ,config.num_attention_heads ,device=__snake_case )
       if cross_attn_head_mask is None:
            A_          =       torch.ones(
                config.num_decoder_layers ,config.num_attention_heads ,device=__snake_case )
       return {
           "input_ids": input_ids,
           "decoder_input_ids": decoder_input_ids,
           "attention_mask": attention_mask,
           "decoder_attention_mask": decoder_attention_mask,
           "head_mask": head_mask,
           "decoder_head_mask": decoder_head_mask,
           "cross_attn_head_mask": cross_attn_head_mask,
       }


  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       A_          =       ids_tensor([self.batch_size, self.encoder_seq_length] ,self.vocab_size )
       A_          =       ids_tensor([self.batch_size, self.decoder_seq_length] ,self.vocab_size )

       # we need to clamp the input ids here to avoid having pad token in between
       # this is because for NllbMoe the position_ids are prepared such that
       # all pad tokens have pos id = 2 and rest are between 2..seq_length
       # and the seq_length here is seq_length - num_pad_tokens
       # but when using past, there is no way of knowing if the past input ids had
       # pad tokens in them, which results in incorrect seq_lenth and which in turn results in
       # position_ids being off by num_pad_tokens in past input
       A_          =       input_ids.clamp(self.pad_token_id + 1 )
       A_          =       decoder_input_ids.clamp(self.pad_token_id + 1 )

       A_          =       self.get_config()
       A_          =       config.num_attention_heads
       A_          =       self.prepare_inputs_dict(__snake_case ,__snake_case ,__snake_case )
       return config, input_dict


  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       A_   ,    A_          =       self.prepare_config_and_inputs()
       return config, inputs_dict


  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       return TaConfig(
           vocab_size=1_6_6 ,d_model=self.hidden_size ,d_ff=self.d_ff ,d_kv=self.hidden_size // self.num_attention_heads ,num_layers=self.num_hidden_layers ,num_decoder_layers=self.decoder_layers ,num_heads=self.num_attention_heads ,relative_attention_num_buckets=self.relative_attention_num_buckets ,dropout_rate=self.dropout_rate ,initializer_factor=self.initializer_factor ,eos_token_id=self.eos_token_id ,bos_token_id=self.pad_token_id ,pad_token_id=self.pad_token_id ,decoder_start_token_id=self.decoder_start_token_id ,)


  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       return TaConfig(
           vocab_size=self.vocab_size ,d_model=self.hidden_size ,d_ff=self.d_ff ,d_kv=self.hidden_size // self.num_attention_heads ,num_layers=self.num_hidden_layers ,num_decoder_layers=self.decoder_layers ,num_heads=self.num_attention_heads ,relative_attention_num_buckets=self.relative_attention_num_buckets ,dropout_rate=self.dropout_rate ,initializer_factor=self.initializer_factor ,eos_token_id=self.eos_token_id ,bos_token_id=self.pad_token_id ,pad_token_id=self.pad_token_id ,decoder_start_token_id=self.decoder_start_token_id ,)


  def 						__UpperCAmelCase     (							self ,__snake_case ,__snake_case ,__snake_case ,__snake_case ,__snake_case ,__snake_case ,):



       """simple docstring"""





       A_          =       UMTaModel(config=__snake_case )
       model.to(__snake_case )
       model.eval()
       A_          =       model(
           input_ids=__snake_case ,decoder_input_ids=__snake_case ,attention_mask=__snake_case ,decoder_attention_mask=__snake_case ,)
       A_          =       model(input_ids=__snake_case ,decoder_input_ids=__snake_case )
       A_          =       result.last_hidden_state
       A_          =       result.past_key_values
       A_          =       result.encoder_last_hidden_state

       self.parent.assertEqual(encoder_output.size() ,(self.batch_size, self.encoder_seq_length, self.hidden_size) )
       self.parent.assertEqual(decoder_output.size() ,(self.batch_size, self.decoder_seq_length, self.hidden_size) )
       # There should be `num_layers` key value embeddings stored in decoder_past
       self.parent.assertEqual(len(__snake_case ) ,config.num_layers )
       # There should be a self attn key, a self attn value, a cross attn key and a cross attn value stored in each decoder_past tuple
       self.parent.assertEqual(len(decoder_past[0] ) ,4 )


  def 						__UpperCAmelCase     (							self ,__snake_case ,__snake_case ,__snake_case ,__snake_case ,__snake_case ,__snake_case ,):



       """simple docstring"""





       A_          =       UMTaModel(config=__snake_case ).get_decoder().to(__snake_case ).eval()
       # first forward pass
       A_          =       model(__snake_case ,use_cache=__snake_case )
       A_          =       model(__snake_case )
       A_          =       model(__snake_case ,use_cache=__snake_case )

       self.parent.assertTrue(len(__snake_case ) == len(__snake_case ) )
       self.parent.assertTrue(len(__snake_case ) == len(__snake_case ) + 1 )

       A_   ,    A_          =       outputs.to_tuple()

       # create hypothetical next token and extent to next_input_ids
       A_          =       ids_tensor((self.batch_size, 1) ,config.vocab_size )

       # append to next input_ids and
       A_          =       torch.cat([input_ids, next_tokens] ,dim=-1 )

       A_          =       model(__snake_case )['''last_hidden_state''']
       A_          =       model(__snake_case ,past_key_values=__snake_case )['''last_hidden_state''']

       # select random slice
       A_          =       ids_tensor((1,) ,output_from_past.shape[-1] ).item()
       A_          =       output_from_no_past[:, -1, random_slice_idx].detach()
       A_          =       output_from_past[:, 0, random_slice_idx].detach()

       # test that outputs are equal for slice
       self.parent.assertTrue(torch.allclose(__snake_case ,__snake_case ,atol=1E-3 ) )




  def 						__UpperCAmelCase     (							self ,__snake_case ,__snake_case ,):



       """simple docstring"""





       A_          =       UMTaModel(config=__snake_case ).to(__snake_case ).half().eval()
       A_          =       model(**__snake_case )['''last_hidden_state''']
       self.parent.assertFalse(torch.isnan(__snake_case ).any().item() )





@require_torch
class UpperCAmelCase_				( _SCREAMING_SNAKE_CASE     ,		_SCREAMING_SNAKE_CASE     ,		_SCREAMING_SNAKE_CASE     ,		unittest.TestCase ):
  __lowerCAmelCase   :						int							=   (
      (UMTaModel, UMTaForConditionalGeneration, UMTaForQuestionAnswering) if is_torch_available() else ()
  )
  __lowerCAmelCase   :						Optional[int]							=   (UMTaForConditionalGeneration,) if is_torch_available() else ()
  __lowerCAmelCase   :						Optional[int]							=   (
      {
          """conversational""": UMTaForConditionalGeneration,
          """feature-extraction""": UMTaModel,
          """summarization""": UMTaForConditionalGeneration,
          """text2text-generation""": UMTaForConditionalGeneration,
          """translation""": UMTaForConditionalGeneration,
          """question-answering""": UMTaForQuestionAnswering,
      }
      if is_torch_available()
      else {}
  )
  __lowerCAmelCase   :						List[str]							=   True
  __lowerCAmelCase   :						Tuple							=   False
  __lowerCAmelCase   :						List[str]							=   False
  __lowerCAmelCase   :						List[Any]							=   True
  __lowerCAmelCase   :						List[Any]							=   True
  # The small UMT5 model needs higher percentages for CPU/MP tests
  __lowerCAmelCase   :						List[str]							=   [0.8, 0.9]


  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       A_          =       UMTaModelTester(self )


  @unittest.skip('''Test has a segmentation fault on torch 1.8.0''' )
  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       A_          =       self.model_tester.prepare_config_and_inputs()
       A_          =       UMTaModel(config_and_inputs[0] ).to(__snake_case )
       with tempfile.TemporaryDirectory() as tmpdirname:
            torch.onnx.export(
                __snake_case ,(config_and_inputs[1], config_and_inputs[3], config_and_inputs[2]) ,f'{tmpdirname}/t5_test.onnx' ,export_params=__snake_case ,opset_version=9 ,input_names=['''input_ids''', '''decoder_input_ids'''] ,)


  @unittest.skipIf(torch_device == '''cpu''' ,'''Cant do half precision''' )
  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       A_          =       self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model_fpaa_forward(*__snake_case )


  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       A_          =       ['''encoder_attentions''', '''decoder_attentions''', '''cross_attentions''']
       A_          =       self.model_tester.prepare_config_and_inputs()
       A_          =       config_and_inputs[0]
       A_          =       UMTaForConditionalGeneration(__snake_case ).eval()
       model.to(__snake_case )

       A_          =       {
           '''head_mask''': torch.zeros(config.num_layers ,config.num_heads ,device=__snake_case ),
           '''decoder_head_mask''': torch.zeros(config.num_decoder_layers ,config.num_heads ,device=__snake_case ),
           '''cross_attn_head_mask''': torch.zeros(config.num_decoder_layers ,config.num_heads ,device=__snake_case ),
       }

       for attn_name, (name, mask) in zip(__snake_case ,head_masking.items() ):
            A_          =       {name: mask}
            # Explicitly pass decoder_head_mask as it is required from T5 model when head_mask specified
            if name == "head_mask":
                 A_          =       torch.ones(
                     config.num_decoder_layers ,config.num_heads ,device=__snake_case )

            A_          =       model.generate(
                config_and_inputs[1]['''input_ids'''] ,num_beams=1 ,max_length=3 ,output_attentions=__snake_case ,return_dict_in_generate=__snake_case ,**__snake_case ,)
            # We check the state of decoder_attentions and cross_attentions just from the last step
            A_          =       out[attn_name] if attn_name == attention_names[0] else out[attn_name][-1]
            self.assertEqual(sum([w.sum().item() for w in attn_weights] ) ,0.0 )




  @unittest.skip('''Does not work on the tiny model as we keep hitting edge cases.''' )
  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       pass





@require_torch
@require_sentencepiece
@require_tokenizers
class UpperCAmelCase_				( unittest.TestCase ):


  @slow
  @unittest.skip(
      '''Unless we stop stripping left and right by default for all special tokens, the expected ids obtained here will not match the original ones. Wait for https://github.com/huggingface/transformers/pull/23909 to be merged''' )
  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       A_          =       UMTaForConditionalGeneration.from_pretrained('''google/umt5-small''' ,return_dict=__snake_case ).to(__snake_case )
       A_          =       AutoTokenizer.from_pretrained('''google/umt5-small''' ,use_fast=__snake_case ,legacy=__snake_case )
       A_          =       [
           '''Bonjour monsieur <extra_id_0> bien <extra_id_1>.''',
           '''No se como puedo <extra_id_0>.''',
           '''This is the reason why we <extra_id_0> them.''',
           '''The <extra_id_0> walks in <extra_id_1>, seats''',
           '''A <extra_id_0> walks into a bar and orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>.''',
       ]
       A_          =       tokenizer(__snake_case ,return_tensors='''pt''' ,padding=__snake_case ).input_ids
       # fmt: off
       A_          =       torch.tensor(
           [
               [ 3_8_5_3_0, 2_1_0_7_0_3, 2_5_6_2_9_9, 1_4_1_0, 2_5_6_2_9_8, 2_7_4, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
               [   8_2_6, 3_2_1, 6_7_1, 2_5_9_2_2, 2_5_6_2_9_9, 2_7_4, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
               [  1_4_6_0, 3_3_9, 3_1_2, 1_9_0_1_4, 1_0_6_2_0, 7_5_8, 2_5_6_2_9_9, 2_3_5_5,2_7_4, 1, 0, 0, 0, 0, 0, 0,0, 0],
               [   5_1_7, 2_5_6_2_9_9, 1_4_8_6_9, 2_8_1, 3_0_1, 2_5_6_2_9_8, 2_7_5, 1_1_9_9_8_3,1, 0, 0, 0, 0, 0, 0, 0,0, 0],
               [   3_2_0, 2_5_6_2_9_9, 1_4_8_6_9, 2_8_1, 2_2_3_4, 2_8_9, 2_2_7_5, 3_3_3,6_1_3_9_1, 2_8_9, 2_5_6_2_9_8, 5_4_3, 2_5_6_2_9_7, 1_6_8_7_1_4, 3_2_9, 2_5_6_2_9_6,2_7_4, 1],
           ] )
       # fmt: on
       torch.testing.assert_allclose(__snake_case ,__snake_case )

       A_          =       model.generate(input_ids.to(__snake_case ) )
       A_          =       [
           '''<pad><extra_id_0> et<extra_id_1> [eod] <extra_id_2><extra_id_55>.. [eod] 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 <extra_id_56>ajšietosto<extra_id_56>lleux<extra_id_19><extra_id_6>ajšie</s>''',
           '''<pad><extra_id_0>.<extra_id_1>.,<0x0A>...spech <0x0A><extra_id_20> <extra_id_21></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>''',
           '''<pad><extra_id_0> are not going to be a part of the world. We are not going to be a part of<extra_id_1> and<extra_id_2><0x0A><extra_id_48>.<extra_id_48></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>''',
           '''<pad><extra_id_0> door<extra_id_1>, the door<extra_id_2> 피해[/</s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>''',
           '''<pad><extra_id_0>nyone who<extra_id_1> drink<extra_id_2> a<extra_id_3> alcohol<extra_id_4> A<extra_id_5> A. This<extra_id_6> I<extra_id_7><extra_id_52><extra_id_53></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>''',
       ]
       A_          =       tokenizer.batch_decode(__snake_case )
       self.assertEqual(__snake_case ,__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()
a__      :     Any         =		logging.get_logger(__name__)

def      UpperCAmelCase_    (    _UpperCAmelCase							:List[Any]				)	->				Optional[int]:
     '''simple docstring'''


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

     return new_state_dict

def      UpperCAmelCase_    (    _UpperCAmelCase							:List[Any]  ,						_UpperCAmelCase							:Union[str, Any]				)	->				Tuple:
     '''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)
               A_          =       state_dict.pop(f'glpn.encoder.block.{i}.{j}.attention.self.kv.weight'				)
               A_          =       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_          =       kv_weight[
                   : config.hidden_sizes[i], :
               ]
               A_          =       kv_bias[: config.hidden_sizes[i]]
               A_          =       kv_weight[
                   config.hidden_sizes[i] :, :
               ]
               A_          =       kv_bias[config.hidden_sizes[i] :]

def      UpperCAmelCase_    (    )	->				str:
     '''simple docstring'''


     A_          =       '''http://images.cocodataset.org/val2017/000000039769.jpg'''
     A_          =       Image.open(requests.get(_UpperCAmelCase  ,						stream=_UpperCAmelCase				).raw				)

     return image

@torch.no_grad()
def      UpperCAmelCase_    (    _UpperCAmelCase							:Tuple  ,						_UpperCAmelCase							:List[Any]  ,						_UpperCAmelCase							:Union[str, Any]=False  ,						_UpperCAmelCase							:Any=None				)	->				Optional[int]:
     '''simple docstring'''


     A_          =       GLPNConfig(hidden_sizes=[64, 128, 320, 512]  ,						decoder_hidden_size=64  ,						depths=[3, 8, 27, 3]				)

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

     # prepare image
     A_          =       prepare_img()
     A_          =       image_processor(images=_UpperCAmelCase  ,						return_tensors='''pt'''				).pixel_values

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

     # load original state dict
     A_          =       torch.load(_UpperCAmelCase  ,						map_location=torch.device('''cpu'''				)				)

     # rename keys
     A_          =       rename_keys(_UpperCAmelCase				)

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

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

     # forward pass
     A_          =       model(_UpperCAmelCase				)
     A_          =       outputs.predicted_depth

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

          A_          =       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__":
      a__      :     Dict         =		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.',
      )
      a__      :     int         =		parser.parse_args()
      convert_glpn_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)



