
import inspect
import unittest

from transformers 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, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
  import torch

  from transformers import MODEL_MAPPING, GLPNConfig, GLPNForDepthEstimation, GLPNModel
  from transformers.models.glpn.modeling_glpn import GLPN_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
  from PIL import Image

  from transformers import GLPNImageProcessor

class 		__magic_name__				( __A					):


  """simple docstring"""





  def     _UpperCAmelCase  (					self		):
    _lowerCamelCase      =						self.config_class(**self.inputs_dict		)
    self.parent.assertTrue(hasattr(_lowerCamelCase	,		'''hidden_sizes'''		)		)
    self.parent.assertTrue(hasattr(_lowerCamelCase	,		'''num_attention_heads'''		)		)
    self.parent.assertTrue(hasattr(_lowerCamelCase	,		'''num_encoder_blocks'''		)		)







class 		__magic_name__				:


  """simple docstring"""





  def __init__(					self	,		a__	,		a__=13	,		a__=64	,		a__=3	,		a__=4	,		a__=[2, 2, 2, 2]	,		a__=[8, 4, 2, 1]	,		a__=[16, 32, 64, 1_28]	,		a__=[1, 4, 8, 16]	,		a__=[1, 2, 4, 8]	,		a__=True	,		a__=True	,		a__="gelu"	,		a__=0.1	,		a__=0.1	,		a__=0.02	,		a__=16	,		a__=3	,		a__=None	,		):
    _lowerCamelCase      =						parent
    _lowerCamelCase      =						batch_size
    _lowerCamelCase      =						image_size
    _lowerCamelCase      =						num_channels
    _lowerCamelCase      =						num_encoder_blocks
    _lowerCamelCase      =						sr_ratios
    _lowerCamelCase      =						depths
    _lowerCamelCase      =						hidden_sizes
    _lowerCamelCase      =						downsampling_rates
    _lowerCamelCase      =						num_attention_heads
    _lowerCamelCase      =						is_training
    _lowerCamelCase      =						use_labels
    _lowerCamelCase      =						hidden_act
    _lowerCamelCase      =						hidden_dropout_prob
    _lowerCamelCase      =						attention_probs_dropout_prob
    _lowerCamelCase      =						initializer_range
    _lowerCamelCase      =						decoder_hidden_size
    _lowerCamelCase      =						num_labels
    _lowerCamelCase      =						scope


  def     _UpperCAmelCase  (					self		):
    _lowerCamelCase      =						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

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

    _lowerCamelCase      =						self.get_config()
    return config, pixel_values, labels


  def     _UpperCAmelCase  (					self		):
    return GLPNConfig(
        image_size=self.image_size	,		num_channels=self.num_channels	,		num_encoder_blocks=self.num_encoder_blocks	,		depths=self.depths	,		hidden_sizes=self.hidden_sizes	,		num_attention_heads=self.num_attention_heads	,		hidden_act=self.hidden_act	,		hidden_dropout_prob=self.hidden_dropout_prob	,		attention_probs_dropout_prob=self.attention_probs_dropout_prob	,		initializer_range=self.initializer_range	,		decoder_hidden_size=self.decoder_hidden_size	,		)


  def     _UpperCAmelCase  (					self	,		a__	,		a__	,		a__		):
    _lowerCamelCase      =						GLPNModel(config=_lowerCamelCase		)
    model.to(_lowerCamelCase		)
    model.eval()
    _lowerCamelCase      =						model(_lowerCamelCase		)
    _lowerCamelCase      =						self.image_size // (self.downsampling_rates[-1] * 2)
    self.parent.assertEqual(
        result.last_hidden_state.shape	,		(self.batch_size, self.hidden_sizes[-1], expected_height, expected_width)		)


  def     _UpperCAmelCase  (					self	,		a__	,		a__	,		a__		):
    _lowerCamelCase      =						self.num_labels
    _lowerCamelCase      =						GLPNForDepthEstimation(_lowerCamelCase		)
    model.to(_lowerCamelCase		)
    model.eval()
    _lowerCamelCase      =						model(_lowerCamelCase		)
    self.parent.assertEqual(result.predicted_depth.shape	,		(self.batch_size, self.image_size, self.image_size)		)
    _lowerCamelCase      =						model(_lowerCamelCase	,		labels=_lowerCamelCase		)
    self.parent.assertEqual(result.predicted_depth.shape	,		(self.batch_size, self.image_size, self.image_size)		)


  def     _UpperCAmelCase  (					self		):
    _lowerCamelCase      =						self.prepare_config_and_inputs()
    _lowerCamelCase      =						config_and_inputs
    _lowerCamelCase      =						{"""pixel_values""": pixel_values}
    return config, inputs_dict







@require_torch
class 		__magic_name__				( __A						,__A						,unittest.TestCase					):


  """simple docstring"""





  _UpperCamelCase						 = (GLPNModel, GLPNForDepthEstimation) if is_torch_available() else ()
  _UpperCamelCase						 = (
      {"depth-estimation": GLPNForDepthEstimation, "feature-extraction": GLPNModel} if is_torch_available() else {}
  )

  _UpperCamelCase						 = False
  _UpperCamelCase						 = False
  _UpperCamelCase						 = False


  def     _UpperCAmelCase  (					self		):
    _lowerCamelCase      =						GLPNModelTester(self		)
    _lowerCamelCase      =						GLPNConfigTester(self	,		config_class=_lowerCamelCase		)


  def     _UpperCAmelCase  (					self		):
    self.config_tester.run_common_tests()


  def     _UpperCAmelCase  (					self		):
    _lowerCamelCase      =						self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_model(*_lowerCamelCase		)


  def     _UpperCAmelCase  (					self		):
    _lowerCamelCase      =						self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_depth_estimation(*_lowerCamelCase		)


  @unittest.skip('''GLPN does not use inputs_embeds'''		)
  def     _UpperCAmelCase  (					self		):
    pass


  @unittest.skip('''GLPN does not have get_input_embeddings method and get_output_embeddings methods'''		)
  def     _UpperCAmelCase  (					self		):
    pass


  def     _UpperCAmelCase  (					self		):
    _lowerCamelCase      =						self.model_tester.prepare_config_and_inputs_for_common()

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

      _lowerCamelCase      =						["""pixel_values"""]
      self.assertListEqual(arg_names[:1]	,		_lowerCamelCase		)


  def     _UpperCAmelCase  (					self		):
    _lowerCamelCase      =						self.model_tester.prepare_config_and_inputs_for_common()
    _lowerCamelCase      =						True

    for model_class in self.all_model_classes:
      _lowerCamelCase      =						True
      _lowerCamelCase      =						False
      _lowerCamelCase      =						True
      _lowerCamelCase      =						model_class(_lowerCamelCase		)
      model.to(_lowerCamelCase		)
      model.eval()
      with torch.no_grad():
        _lowerCamelCase      =						model(**self._prepare_for_class(_lowerCamelCase	,		_lowerCamelCase		)		)
      _lowerCamelCase      =						outputs.attentions

      _lowerCamelCase      =						sum(self.model_tester.depths		)
      self.assertEqual(len(_lowerCamelCase		)	,		_lowerCamelCase		)

      # check that output_attentions also work using config
      del inputs_dict["output_attentions"]
      _lowerCamelCase      =						True
      _lowerCamelCase      =						model_class(_lowerCamelCase		)
      model.to(_lowerCamelCase		)
      model.eval()
      with torch.no_grad():
        _lowerCamelCase      =						model(**self._prepare_for_class(_lowerCamelCase	,		_lowerCamelCase		)		)
      _lowerCamelCase      =						outputs.attentions

      self.assertEqual(len(_lowerCamelCase		)	,		_lowerCamelCase		)

      # verify the first attentions (first block, first layer)
      _lowerCamelCase      =						(self.model_tester.image_size // 4) ** 2
      _lowerCamelCase      =						(self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
      self.assertListEqual(
          list(attentions[0].shape[-3:]		)	,		[self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len]	,		)

      # verify the last attentions (last block, last layer)
      _lowerCamelCase      =						(self.model_tester.image_size // 32) ** 2
      _lowerCamelCase      =						(self.model_tester.image_size // (32 * self.model_tester.sr_ratios[-1])) ** 2
      self.assertListEqual(
          list(attentions[-1].shape[-3:]		)	,		[self.model_tester.num_attention_heads[-1], expected_seq_len, expected_reduced_seq_len]	,		)
      _lowerCamelCase      =						len(_lowerCamelCase		)

      # Check attention is always last and order is fine
      _lowerCamelCase      =						True
      _lowerCamelCase      =						True
      _lowerCamelCase      =						model_class(_lowerCamelCase		)
      model.to(_lowerCamelCase		)
      model.eval()
      with torch.no_grad():
        _lowerCamelCase      =						model(**self._prepare_for_class(_lowerCamelCase	,		_lowerCamelCase		)		)

      self.assertEqual(out_len + 1	,		len(_lowerCamelCase		)		)

      _lowerCamelCase      =						outputs.attentions

      self.assertEqual(len(_lowerCamelCase		)	,		_lowerCamelCase		)
      # verify the first attentions (first block, first layer)
      _lowerCamelCase      =						(self.model_tester.image_size // 4) ** 2
      _lowerCamelCase      =						(self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
      self.assertListEqual(
          list(self_attentions[0].shape[-3:]		)	,		[self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len]	,		)


  def     _UpperCAmelCase  (					self		):
    def check_hidden_states_output(a__	,		a__	,		a__		):
      _lowerCamelCase      =						model_class(_lowerCamelCase		)
      model.to(_lowerCamelCase		)
      model.eval()

      with torch.no_grad():
        _lowerCamelCase      =						model(**self._prepare_for_class(_lowerCamelCase	,		_lowerCamelCase		)		)

      _lowerCamelCase      =						outputs.hidden_states

      _lowerCamelCase      =						self.model_tester.num_encoder_blocks
      self.assertEqual(len(_lowerCamelCase		)	,		_lowerCamelCase		)

      # verify the first hidden states (first block)
      self.assertListEqual(
          list(hidden_states[0].shape[-3:]		)	,		[
              self.model_tester.hidden_sizes[0],
              self.model_tester.image_size // 4,
              self.model_tester.image_size // 4,
          ]	,		)

    _lowerCamelCase      =						self.model_tester.prepare_config_and_inputs_for_common()

    for model_class in self.all_model_classes:
      _lowerCamelCase      =						True
      check_hidden_states_output(_lowerCamelCase	,		_lowerCamelCase	,		_lowerCamelCase		)

      # check that output_hidden_states also work using config
      del inputs_dict["output_hidden_states"]
      _lowerCamelCase      =						True

      check_hidden_states_output(_lowerCamelCase	,		_lowerCamelCase	,		_lowerCamelCase		)


  def     _UpperCAmelCase  (					self		):
    if not self.model_tester.is_training:
      return

    _lowerCamelCase      =						self.model_tester.prepare_config_and_inputs_for_common()
    _lowerCamelCase      =						True

    for model_class in self.all_model_classes:
      if model_class in get_values(_lowerCamelCase		):
        continue
      # TODO: remove the following 3 lines once we have a MODEL_FOR_DEPTH_ESTIMATION_MAPPING
      # this can then be incorporated into _prepare_for_class in test_modeling_common.py
      if model_class.__name__ == "GLPNForDepthEstimation":
        _lowerCamelCase      =						inputs_dict["""pixel_values"""].shape
        _lowerCamelCase      =						torch.zeros(
            [self.model_tester.batch_size, height, width]	,		device=_lowerCamelCase		).long()
      _lowerCamelCase      =						model_class(_lowerCamelCase		)
      model.to(_lowerCamelCase		)
      model.train()
      _lowerCamelCase      =						self._prepare_for_class(_lowerCamelCase	,		_lowerCamelCase	,		return_labels=_lowerCamelCase		)
      _lowerCamelCase      =						model(**_lowerCamelCase		).loss
      loss.backward()


  @slow
  def     _UpperCAmelCase  (					self		):
    for model_name in GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
      _lowerCamelCase      =						GLPNModel.from_pretrained(_lowerCamelCase		)
      self.assertIsNotNone(_lowerCamelCase		)







def 		_lowerCamelCase       (   ):
  """simple docstring"""




  _lowerCamelCase      =						Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''  )
  return image






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


  """simple docstring"""





  @slow
  def     _UpperCAmelCase  (					self		):
    _lowerCamelCase      =						GLPNImageProcessor.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]		)
    _lowerCamelCase      =						GLPNForDepthEstimation.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]		).to(_lowerCamelCase		)

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

    # forward pass
    with torch.no_grad():
      _lowerCamelCase      =						model(**_lowerCamelCase		)

    # verify the predicted depth
    _lowerCamelCase      =						torch.Size([1, 4_80, 6_40]		)
    self.assertEqual(outputs.predicted_depth.shape	,		_lowerCamelCase		)

    _lowerCamelCase      =						torch.tensor(
        [[3.4291, 2.7865, 2.5151], [3.2841, 2.7021, 2.3502], [3.1147, 2.4625, 2.2481]]		).to(_lowerCamelCase		)

    self.assertTrue(torch.allclose(outputs.predicted_depth[0, :3, :3]	,		_lowerCamelCase	,		atol=1E-4		)		)









'''simple docstring'''





import torch
import torch.nn.functional as F

from diffusers import VQDiffusionScheduler

from .test_schedulers import SchedulerCommonTest


class 							_lowerCAmelCase	(  __A							):


 """simple docstring"""

 lowerCamelCase									=     (VQDiffusionScheduler,)







 def       UpperCAmelCase_       (      self					,    **_lowerCamelCase  )    ->    Any:
    A_  :		List[Any]						=					{
        """num_vec_classes""": 4097,
        """num_train_timesteps""": 100,
    }

    config.update(**_lowerCamelCase  )
    return config







 def       UpperCAmelCase_       (      self					,    _lowerCamelCase  )    ->    Tuple:
    A_  :		Tuple						=					4
    A_  :		List[str]						=					8
    A_  :		Dict						=					8

    A_  :		int						=					torch.randint(0					,    _lowerCamelCase					,    (batch_size, height * width)  )

    return sample







 @property
 def       UpperCAmelCase_       (      self  )    ->    Union[str, Any]:
    assert False







 def       UpperCAmelCase_       (      self					,    _lowerCamelCase  )    ->    List[str]:
    def model(_lowerCamelCase					,    _lowerCamelCase					,    *_lowerCamelCase  ):
       A_						,  A_  :		List[Any]						=					sample.shape
       A_  :		int						=					torch.rand((batch_size, num_vec_classes - 1, num_latent_pixels)  )
       A_  :		Optional[int]						=					F.log_softmax(logits.double()					,    dim=1  ).float()
       return return_value

    return model







 def       UpperCAmelCase_       (      self  )    ->    Tuple:
    for timesteps in [2, 5, 100, 1000]:
       self.check_over_configs(num_train_timesteps=_lowerCamelCase  )







 def       UpperCAmelCase_       (      self  )    ->    Tuple:
    for num_vec_classes in [5, 100, 1000, 4000]:
       self.check_over_configs(num_vec_classes=_lowerCamelCase  )







 def       UpperCAmelCase_       (      self  )    ->    int:
    for t in [0, 50, 99]:
       self.check_over_forward(time_step=_lowerCamelCase  )







 def       UpperCAmelCase_       (      self  )    ->    Optional[int]:
    pass
