
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 						_a       :






  """simple docstring"""





  def __init__(     self		,		_UpperCAmelCase		,		_UpperCAmelCase=99		,		_UpperCAmelCase=13		,		_UpperCAmelCase=7		,		_UpperCAmelCase=9		,		_UpperCAmelCase=True		,		_UpperCAmelCase=True		,		_UpperCAmelCase=False		,		_UpperCAmelCase=32		,		_UpperCAmelCase=5		,		_UpperCAmelCase=4		,		_UpperCAmelCase=37		,		_UpperCAmelCase=8		,		_UpperCAmelCase=0.1		,		_UpperCAmelCase=0.0_0_2		,		_UpperCAmelCase=1		,		_UpperCAmelCase=0		,		_UpperCAmelCase=0		,		_UpperCAmelCase=None		,		_UpperCAmelCase=None		,		)			-> Union[str, Any]:
     UpperCamelCase_									=     parent
     UpperCamelCase_									=     batch_size
     UpperCamelCase_									=     encoder_seq_length
     UpperCamelCase_									=     decoder_seq_length
     # For common tests
     UpperCamelCase_									=     self.decoder_seq_length
     UpperCamelCase_									=     is_training
     UpperCamelCase_									=     use_attention_mask
     UpperCamelCase_									=     use_labels
     UpperCamelCase_									=     vocab_size
     UpperCamelCase_									=     hidden_size
     UpperCamelCase_									=     num_hidden_layers
     UpperCamelCase_									=     num_attention_heads
     UpperCamelCase_									=     d_ff
     UpperCamelCase_									=     relative_attention_num_buckets
     UpperCamelCase_									=     dropout_rate
     UpperCamelCase_									=     initializer_factor
     UpperCamelCase_									=     eos_token_id
     UpperCamelCase_									=     pad_token_id
     UpperCamelCase_									=     decoder_start_token_id
     UpperCamelCase_									=     None
     UpperCamelCase_									=     decoder_layers




  def    _UpperCAmelCase     (     self )			-> List[Any]:
     return TaConfig.from_pretrained('google/umt5-base' )




  def    _UpperCAmelCase     (     self		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase=None		,		_UpperCAmelCase=None		,		_UpperCAmelCase=None		,		_UpperCAmelCase=None		,		_UpperCAmelCase=None		,		)			-> Optional[Any]:
     if attention_mask is None:
        UpperCamelCase_									=     input_ids.ne(config.pad_token_id )
     if decoder_attention_mask is None:
        UpperCamelCase_									=     decoder_input_ids.ne(config.pad_token_id )
     if head_mask is None:
        UpperCamelCase_									=     torch.ones(config.num_hidden_layers		,		config.num_attention_heads		,		device=A_ )
     if decoder_head_mask is None:
        UpperCamelCase_									=     torch.ones(config.num_decoder_layers		,		config.num_attention_heads		,		device=A_ )
     if cross_attn_head_mask is None:
        UpperCamelCase_									=     torch.ones(
            config.num_decoder_layers		,		config.num_attention_heads		,		device=A_ )
     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 )			-> Tuple:
     UpperCamelCase_									=     ids_tensor([self.batch_size, self.encoder_seq_length]		,		self.vocab_size )
     UpperCamelCase_									=     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
     UpperCamelCase_									=     input_ids.clamp(self.pad_token_id + 1 )
     UpperCamelCase_									=     decoder_input_ids.clamp(self.pad_token_id + 1 )

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




  def    _UpperCAmelCase     (     self )			-> List[str]:
     UpperCamelCase_		,      UpperCamelCase_									=     self.prepare_config_and_inputs()
     return config, inputs_dict




  def    _UpperCAmelCase     (     self )			-> List[Any]:
     return TaConfig(
         vocab_size=166		,		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 )			-> Tuple:
     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		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		)			-> Any:
     UpperCamelCase_									=     UMTaModel(config=A_ )
     model.to(A_ )
     model.eval()
     UpperCamelCase_									=     model(
         input_ids=A_		,		decoder_input_ids=A_		,		attention_mask=A_		,		decoder_attention_mask=A_		,		)
     UpperCamelCase_									=     model(input_ids=A_		,		decoder_input_ids=A_ )
     UpperCamelCase_									=     result.last_hidden_state
     UpperCamelCase_									=     result.past_key_values
     UpperCamelCase_									=     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(A_ )		,		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		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		)			-> Tuple:
     UpperCamelCase_									=     UMTaModel(config=A_ ).get_decoder().to(A_ ).eval()
     # first forward pass
     UpperCamelCase_									=     model(A_		,		use_cache=A_ )
     UpperCamelCase_									=     model(A_ )
     UpperCamelCase_									=     model(A_		,		use_cache=A_ )

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

     UpperCamelCase_		,      UpperCamelCase_									=     outputs.to_tuple()

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

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

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

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

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




  def    _UpperCAmelCase     (     self		,		_UpperCAmelCase		,		_UpperCAmelCase		,		)			-> Optional[Any]:
     UpperCamelCase_									=     UMTaModel(config=A_ ).to(A_ ).half().eval()
     UpperCamelCase_									=     model(**A_ )['last_hidden_state']
     self.parent.assertFalse(torch.isnan(A_ ).any().item() )




@require_torch
class 						_a       (  _lowercase						,       _lowercase						,       _lowercase						,       unittest.TestCase    ):






  """simple docstring"""





  A_						=   (
      (UMTaModel, UMTaForConditionalGeneration, UMTaForQuestionAnswering) if is_torch_available() else ()
  )
  A_						=   (UMTaForConditionalGeneration,) if is_torch_available() else ()
  A_						=   (
      {
          '''conversational''': UMTaForConditionalGeneration,
          '''feature-extraction''': UMTaModel,
          '''summarization''': UMTaForConditionalGeneration,
          '''text2text-generation''': UMTaForConditionalGeneration,
          '''translation''': UMTaForConditionalGeneration,
          '''question-answering''': UMTaForQuestionAnswering,
      }
      if is_torch_available()
      else {}
  )
  A_						=   True
  A_						=   False
  A_						=   False
  A_						=   True
  A_						=   True
  # The small UMT5 model needs higher percentages for CPU/MP tests
  A_						=   [0.8, 0.9]




  def    _UpperCAmelCase     (     self )			-> Any:
     UpperCamelCase_									=     UMTaModelTester(self )




  @unittest.skip('Test has a segmentation fault on torch 1.8.0' )
  def    _UpperCAmelCase     (     self )			-> int:
     UpperCamelCase_									=     self.model_tester.prepare_config_and_inputs()
     UpperCamelCase_									=     UMTaModel(config_and_inputs[0] ).to(A_ )
     with tempfile.TemporaryDirectory() as tmpdirname:
        torch.onnx.export(
            A_		,		(config_and_inputs[1], config_and_inputs[3], config_and_inputs[2])		,		f"""{tmpdirname}/t5_test.onnx"""		,		export_params=A_		,		opset_version=9		,		input_names=['input_ids', 'decoder_input_ids']		,		)




  @unittest.skipIf(torch_device == 'cpu'		,		'Cant do half precision' )
  def    _UpperCAmelCase     (     self )			-> Union[str, Any]:
     UpperCamelCase_									=     self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model_fpaa_forward(*A_ )




  def    _UpperCAmelCase     (     self )			-> Optional[Any]:
     UpperCamelCase_									=     ['encoder_attentions', 'decoder_attentions', 'cross_attentions']
     UpperCamelCase_									=     self.model_tester.prepare_config_and_inputs()
     UpperCamelCase_									=     config_and_inputs[0]
     UpperCamelCase_									=     UMTaForConditionalGeneration(A_ ).eval()
     model.to(A_ )

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

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

        UpperCamelCase_									=     model.generate(
            config_and_inputs[1]['input_ids']		,		num_beams=1		,		max_length=3		,		output_attentions=A_		,		return_dict_in_generate=A_		,		**A_		,		)
        # We check the state of decoder_attentions and cross_attentions just from the last step
        UpperCamelCase_									=     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 )			-> str:
     pass




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






  """simple docstring"""





  @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 )			-> Dict:
     UpperCamelCase_									=     UMTaForConditionalGeneration.from_pretrained('google/umt5-small'		,		return_dict=A_ ).to(A_ )
     UpperCamelCase_									=     AutoTokenizer.from_pretrained('google/umt5-small'		,		use_fast=A_		,		legacy=A_ )
     UpperCamelCase_									=     [
         '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>.',
     ]
     UpperCamelCase_									=     tokenizer(A_		,		return_tensors='pt'		,		padding=A_ ).input_ids
     # fmt: off
     UpperCamelCase_									=     torch.tensor(
         [
             [ 38530, 210703, 256299, 1410, 256298, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
             [   826, 321, 671, 25922, 256299, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
             [  1460, 339, 312, 19014, 10620, 758, 256299, 2355,274, 1, 0, 0, 0, 0, 0, 0,0, 0],
             [   517, 256299, 14869, 281, 301, 256298, 275, 119983,1, 0, 0, 0, 0, 0, 0, 0,0, 0],
             [   320, 256299, 14869, 281, 2234, 289, 2275, 333,61391, 289, 256298, 543, 256297, 168714, 329, 256296,274, 1],
         ] )
     # fmt: on
     torch.testing.assert_allclose(A_		,		A_ )

     UpperCamelCase_									=     model.generate(input_ids.to(A_ ) )
     UpperCamelCase_									=     [
         '<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>',
     ]
     UpperCamelCase_									=     tokenizer.batch_decode(A_ )
     self.assertEqual(A_		,		A_ )













import inspect
import unittest

from transformers import DPTConfig
from transformers.file_utils import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
 import torch
 from torch import nn

 from transformers import MODEL_MAPPING, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTModel
 from transformers.models.dpt.modeling_dpt import DPT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
 from PIL import Image

 from transformers import DPTImageProcessor


class 						_a       :






  """simple docstring"""





  def __init__(     self		,		_UpperCAmelCase		,		_UpperCAmelCase=2		,		_UpperCAmelCase=32		,		_UpperCAmelCase=16		,		_UpperCAmelCase=3		,		_UpperCAmelCase=True		,		_UpperCAmelCase=True		,		_UpperCAmelCase=32		,		_UpperCAmelCase=4		,		_UpperCAmelCase=[0, 1, 2, 3]		,		_UpperCAmelCase=4		,		_UpperCAmelCase=37		,		_UpperCAmelCase="gelu"		,		_UpperCAmelCase=0.1		,		_UpperCAmelCase=0.1		,		_UpperCAmelCase=0.0_2		,		_UpperCAmelCase=3		,		_UpperCAmelCase=[1, 384, 24, 24]		,		_UpperCAmelCase=True		,		_UpperCAmelCase=None		,		)			-> Optional[Any]:
     UpperCamelCase_									=     parent
     UpperCamelCase_									=     batch_size
     UpperCamelCase_									=     image_size
     UpperCamelCase_									=     patch_size
     UpperCamelCase_									=     num_channels
     UpperCamelCase_									=     is_training
     UpperCamelCase_									=     use_labels
     UpperCamelCase_									=     hidden_size
     UpperCamelCase_									=     num_hidden_layers
     UpperCamelCase_									=     backbone_out_indices
     UpperCamelCase_									=     num_attention_heads
     UpperCamelCase_									=     intermediate_size
     UpperCamelCase_									=     hidden_act
     UpperCamelCase_									=     hidden_dropout_prob
     UpperCamelCase_									=     attention_probs_dropout_prob
     UpperCamelCase_									=     initializer_range
     UpperCamelCase_									=     num_labels
     UpperCamelCase_									=     backbone_featmap_shape
     UpperCamelCase_									=     scope
     UpperCamelCase_									=     is_hybrid
     # sequence length of DPT = num_patches + 1 (we add 1 for the [CLS] token)
     UpperCamelCase_									=     (image_size // patch_size) ** 2
     UpperCamelCase_									=     num_patches + 1




  def    _UpperCAmelCase     (     self )			-> Dict:
     UpperCamelCase_									=     floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

     UpperCamelCase_									=     None
     if self.use_labels:
        UpperCamelCase_									=     ids_tensor([self.batch_size, self.image_size, self.image_size]		,		self.num_labels )

     UpperCamelCase_									=     self.get_config()

     return config, pixel_values, labels




  def    _UpperCAmelCase     (     self )			-> Optional[int]:
     UpperCamelCase_									=     {
         'global_padding': 'same',
         'layer_type': 'bottleneck',
         'depths': [3, 4, 9],
         'out_features': ['stage1', 'stage2', 'stage3'],
         'embedding_dynamic_padding': True,
         'hidden_sizes': [96, 192, 384, 768],
         'num_groups': 2,
     }

     return DPTConfig(
         image_size=self.image_size		,		patch_size=self.patch_size		,		num_channels=self.num_channels		,		hidden_size=self.hidden_size		,		num_hidden_layers=self.num_hidden_layers		,		backbone_out_indices=self.backbone_out_indices		,		num_attention_heads=self.num_attention_heads		,		intermediate_size=self.intermediate_size		,		hidden_act=self.hidden_act		,		hidden_dropout_prob=self.hidden_dropout_prob		,		attention_probs_dropout_prob=self.attention_probs_dropout_prob		,		is_decoder=_UpperCAmelCase		,		initializer_range=self.initializer_range		,		is_hybrid=self.is_hybrid		,		backbone_config=_UpperCAmelCase		,		backbone_featmap_shape=self.backbone_featmap_shape		,		)




  def    _UpperCAmelCase     (     self		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase )			-> Optional[int]:
     UpperCamelCase_									=     DPTModel(config=_UpperCAmelCase )
     model.to(_UpperCAmelCase )
     model.eval()
     UpperCamelCase_									=     model(_UpperCAmelCase )
     self.parent.assertEqual(result.last_hidden_state.shape		,		(self.batch_size, self.seq_length, self.hidden_size) )




  def    _UpperCAmelCase     (     self		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase )			-> Union[str, Any]:
     UpperCamelCase_									=     self.num_labels
     UpperCamelCase_									=     DPTForDepthEstimation(_UpperCAmelCase )
     model.to(_UpperCAmelCase )
     model.eval()
     UpperCamelCase_									=     model(_UpperCAmelCase )
     self.parent.assertEqual(result.predicted_depth.shape		,		(self.batch_size, self.image_size, self.image_size) )




  def    _UpperCAmelCase     (     self		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase )			-> Optional[Any]:
     UpperCamelCase_									=     self.num_labels
     UpperCamelCase_									=     DPTForSemanticSegmentation(_UpperCAmelCase )
     model.to(_UpperCAmelCase )
     model.eval()
     UpperCamelCase_									=     model(_UpperCAmelCase		,		labels=_UpperCAmelCase )
     self.parent.assertEqual(
         result.logits.shape		,		(self.batch_size, self.num_labels, self.image_size, self.image_size) )




  def    _UpperCAmelCase     (     self )			-> str:
     UpperCamelCase_									=     self.prepare_config_and_inputs()
     UpperCamelCase_		,      UpperCamelCase_		,      UpperCamelCase_									=     config_and_inputs
     UpperCamelCase_									=     {'pixel_values': pixel_values}
     return config, inputs_dict




@require_torch
class 						_a       (  UpperCAmelCase__						,       UpperCAmelCase__						,       unittest.TestCase    ):






  """simple docstring"""





  A_						=   (DPTModel, DPTForDepthEstimation, DPTForSemanticSegmentation) if is_torch_available() else ()
  A_						=   (
      {
          """depth-estimation""": DPTForDepthEstimation,
          """feature-extraction""": DPTModel,
          """image-segmentation""": DPTForSemanticSegmentation,
      }
      if is_torch_available()
      else {}
  )

  A_						=   False
  A_						=   False
  A_						=   False




  def    _UpperCAmelCase     (     self )			-> List[str]:
     UpperCamelCase_									=     DPTModelTester(self )
     UpperCamelCase_									=     ConfigTester(self		,		config_class=_UpperCAmelCase		,		has_text_modality=_UpperCAmelCase		,		hidden_size=37 )




  def    _UpperCAmelCase     (     self )			-> Union[str, Any]:
     self.config_tester.run_common_tests()




  @unittest.skip(reason='DPT does not use inputs_embeds' )
  def    _UpperCAmelCase     (     self )			-> Optional[int]:
     pass




  def    _UpperCAmelCase     (     self )			-> Union[str, Any]:
     UpperCamelCase_		,      UpperCamelCase_									=     self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
        UpperCamelCase_									=     model_class(_UpperCAmelCase )
        self.assertIsInstance(model.get_input_embeddings()		,		(nn.Module) )
        UpperCamelCase_									=     model.get_output_embeddings()
        self.assertTrue(x is None or isinstance(_UpperCAmelCase		,		nn.Linear ) )




  def    _UpperCAmelCase     (     self )			-> Any:
     UpperCamelCase_		,      UpperCamelCase_									=     self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
        UpperCamelCase_									=     model_class(_UpperCAmelCase )
        UpperCamelCase_									=     inspect.signature(model.forward )
        # signature.parameters is an OrderedDict => so arg_names order is deterministic
        UpperCamelCase_									=     [*signature.parameters.keys()]

        UpperCamelCase_									=     ['pixel_values']
        self.assertListEqual(arg_names[:1]		,		_UpperCAmelCase )




  def    _UpperCAmelCase     (     self )			-> Optional[Any]:
     UpperCamelCase_									=     self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*_UpperCAmelCase )




  def    _UpperCAmelCase     (     self )			-> Dict:
     UpperCamelCase_									=     self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_depth_estimation(*_UpperCAmelCase )




  def    _UpperCAmelCase     (     self )			-> Tuple:
     UpperCamelCase_									=     self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_semantic_segmentation(*_UpperCAmelCase )




  def    _UpperCAmelCase     (     self )			-> str:
     for model_class in self.all_model_classes:
        if model_class.__name__ == "DPTForDepthEstimation":
           continue

        UpperCamelCase_		,      UpperCamelCase_									=     self.model_tester.prepare_config_and_inputs_for_common()
        UpperCamelCase_									=     True

        if model_class in get_values(_UpperCAmelCase ):
           continue

        UpperCamelCase_									=     model_class(_UpperCAmelCase )
        model.to(_UpperCAmelCase )
        model.train()
        UpperCamelCase_									=     self._prepare_for_class(_UpperCAmelCase		,		_UpperCAmelCase		,		return_labels=_UpperCAmelCase )
        UpperCamelCase_									=     model(**_UpperCAmelCase ).loss
        loss.backward()




  def    _UpperCAmelCase     (     self )			-> List[str]:
     for model_class in self.all_model_classes:
        if model_class.__name__ == "DPTForDepthEstimation":
           continue

        UpperCamelCase_		,      UpperCamelCase_									=     self.model_tester.prepare_config_and_inputs_for_common()
        UpperCamelCase_									=     False
        UpperCamelCase_									=     True

        if model_class in get_values(_UpperCAmelCase ) or not model_class.supports_gradient_checkpointing:
           continue
        UpperCamelCase_									=     model_class(_UpperCAmelCase )
        model.to(_UpperCAmelCase )
        model.gradient_checkpointing_enable()
        model.train()
        UpperCamelCase_									=     self._prepare_for_class(_UpperCAmelCase		,		_UpperCAmelCase		,		return_labels=_UpperCAmelCase )
        UpperCamelCase_									=     model(**_UpperCAmelCase ).loss
        loss.backward()




  def    _UpperCAmelCase     (     self )			-> Union[str, Any]:
     UpperCamelCase_		,      UpperCamelCase_									=     self.model_tester.prepare_config_and_inputs_for_common()

     UpperCamelCase_									=     _config_zero_init(_UpperCAmelCase )
     for model_class in self.all_model_classes:
        UpperCamelCase_									=     model_class(config=_UpperCAmelCase )
        # Skip the check for the backbone
        UpperCamelCase_									=     []
        for name, module in model.named_modules():
           if module.__class__.__name__ == "DPTViTHybridEmbeddings":
              UpperCamelCase_									=     [f"""{name}.{key}""" for key in module.state_dict().keys()]
              break

        for name, param in model.named_parameters():
           if param.requires_grad:
              if name in backbone_params:
                 continue
              self.assertIn(
                  ((param.data.mean() * 1e9).round() / 1e9).item()		,		[0.0, 1.0]		,		msg=f"""Parameter {name} of model {model_class} seems not properly initialized"""		,		)




  @unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.' )
  def    _UpperCAmelCase     (     self )			-> Any:
     pass




  @slow
  def    _UpperCAmelCase     (     self )			-> Optional[int]:
     for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[1:]:
        UpperCamelCase_									=     DPTModel.from_pretrained(_UpperCAmelCase )
        self.assertIsNotNone(_UpperCAmelCase )




  def    _UpperCAmelCase     (     self )			-> Optional[int]:
     # We do this test only for DPTForDepthEstimation since it is the only model that uses readout_type
     UpperCamelCase_		,      UpperCamelCase_									=     self.model_tester.prepare_config_and_inputs_for_common()
     UpperCamelCase_									=     'add'
     with self.assertRaises(_UpperCAmelCase ):
        UpperCamelCase_									=     DPTForDepthEstimation(_UpperCAmelCase )






def     _snake_case    ():
   UpperCamelCase_									=     Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png')
   return image


@require_torch
@require_vision
@slow
class 						_a       (  unittest.TestCase    ):






  """simple docstring"""





  def    _UpperCAmelCase     (     self )			-> Dict:
     UpperCamelCase_									=     DPTImageProcessor.from_pretrained('Intel/dpt-hybrid-midas' )
     UpperCamelCase_									=     DPTForDepthEstimation.from_pretrained('Intel/dpt-hybrid-midas' ).to(_UpperCAmelCase )

     UpperCamelCase_									=     prepare_img()
     UpperCamelCase_									=     image_processor(images=_UpperCAmelCase		,		return_tensors='pt' ).to(_UpperCAmelCase )

     # forward pass
     with torch.no_grad():
        UpperCamelCase_									=     model(**_UpperCAmelCase )
        UpperCamelCase_									=     outputs.predicted_depth

     # verify the predicted depth
     UpperCamelCase_									=     torch.Size((1, 384, 384) )
     self.assertEqual(predicted_depth.shape		,		_UpperCAmelCase )

     UpperCamelCase_									=     torch.tensor(
         [[[5.6_4_3_7, 5.6_1_4_6, 5.6_5_1_1], [5.4_3_7_1, 5.5_6_4_9, 5.5_9_5_8], [5.5_2_1_5, 5.5_1_8_4, 5.5_2_9_3]]] ).to(_UpperCAmelCase )

     self.assertTrue(torch.allclose(outputs.predicted_depth[:3, :3, :3] / 100		,		_UpperCAmelCase		,		atol=1e-4 ) )





