





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 					lowerCAmelCase							(   lowercase_					):







  def   UpperCAmelCase     (			self  :Union[str, Any]					):






     '''simple docstring'''





     lowercase__    =  self.config_class(**self.inputs_dict					)
     self.parent.assertTrue(hasattr(_lowercase    ,							"hidden_sizes"					)					)
     self.parent.assertTrue(hasattr(_lowercase    ,							"num_attention_heads"					)					)
     self.parent.assertTrue(hasattr(_lowercase    ,							"num_encoder_blocks"					)					)





class 					lowerCAmelCase							:







  def __init__(			self  :Dict    ,							_lowercase  :List[str]    ,							_lowercase  :str=13    ,							_lowercase  :Optional[Any]=64    ,							_lowercase  :Tuple=3    ,							_lowercase  :str=4    ,							_lowercase  :List[str]=[2, 2, 2, 2]    ,							_lowercase  :Dict=[8, 4, 2, 1]    ,							_lowercase  :Any=[16, 32, 64, 1_28]    ,							_lowercase  :List[Any]=[1, 4, 8, 16]    ,							_lowercase  :Optional[Any]=[1, 2, 4, 8]    ,							_lowercase  :Union[str, Any]=True    ,							_lowercase  :str=True    ,							_lowercase  :Union[str, Any]="gelu"    ,							_lowercase  :int=0.1    ,							_lowercase  :Union[str, Any]=0.1    ,							_lowercase  :Any=0.02    ,							_lowercase  :Optional[Any]=16    ,							_lowercase  :str=3    ,							_lowercase  :Dict=None    ,							):






     '''simple docstring'''





     lowercase__    =  parent
     lowercase__    =  batch_size
     lowercase__    =  image_size
     lowercase__    =  num_channels
     lowercase__    =  num_encoder_blocks
     lowercase__    =  sr_ratios
     lowercase__    =  depths
     lowercase__    =  hidden_sizes
     lowercase__    =  downsampling_rates
     lowercase__    =  num_attention_heads
     lowercase__    =  is_training
     lowercase__    =  use_labels
     lowercase__    =  hidden_act
     lowercase__    =  hidden_dropout_prob
     lowercase__    =  attention_probs_dropout_prob
     lowercase__    =  initializer_range
     lowercase__    =  decoder_hidden_size
     lowercase__    =  num_labels
     lowercase__    =  scope







  def   UpperCAmelCase     (			self  :int					):






     '''simple docstring'''





     lowercase__    =  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]					)

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

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







  def   UpperCAmelCase     (			self  :Tuple					):






     '''simple docstring'''





     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  :int    ,							_lowercase  :int    ,							_lowercase  :Union[str, Any]    ,							_lowercase  :List[str]					):






     '''simple docstring'''





     lowercase__    =  GLPNModel(config=_lowercase					)
     model.to(_lowercase					)
     model.eval()
     lowercase__    =  model(_lowercase					)
     lowercase__    =  lowercase__    =  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  :int    ,							_lowercase  :str    ,							_lowercase  :Dict    ,							_lowercase  :Any					):






     '''simple docstring'''





     lowercase__    =  self.num_labels
     lowercase__    =  GLPNForDepthEstimation(_lowercase					)
     model.to(_lowercase					)
     model.eval()
     lowercase__    =  model(_lowercase					)
     self.parent.assertEqual(result.predicted_depth.shape    ,							(self.batch_size, self.image_size, self.image_size)					)
     lowercase__    =  model(_lowercase    ,							labels=_lowercase					)
     self.parent.assertEqual(result.predicted_depth.shape    ,							(self.batch_size, self.image_size, self.image_size)					)







  def   UpperCAmelCase     (			self  :Union[str, Any]					):






     '''simple docstring'''





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





@require_torch
class 					lowerCAmelCase							(   lowercase_							,							lowercase_							,							unittest.TestCase					):
  __lowerCamelCase		=			(GLPNModel, GLPNForDepthEstimation) if is_torch_available() else ()
  __lowerCamelCase		=			(
      {'depth-estimation': GLPNForDepthEstimation, 'feature-extraction': GLPNModel} if is_torch_available() else {}
  )

  __lowerCamelCase		=			False
  __lowerCamelCase		=			False
  __lowerCamelCase		=			False







  def   UpperCAmelCase     (			self  :int					):






     '''simple docstring'''





     lowercase__    =  GLPNModelTester(self					)
     lowercase__    =  GLPNConfigTester(self    ,							config_class=_lowercase					)







  def   UpperCAmelCase     (			self  :Any					):






     '''simple docstring'''





     self.config_tester.run_common_tests()







  def   UpperCAmelCase     (			self  :Optional[int]					):






     '''simple docstring'''





     lowercase__    =  self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*_lowercase					)







  def   UpperCAmelCase     (			self  :Any					):






     '''simple docstring'''





     lowercase__    =  self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_depth_estimation(*_lowercase					)







  @unittest.skip("GLPN does not use inputs_embeds"					)
  def   UpperCAmelCase     (			self  :Any					):






     '''simple docstring'''





     pass







  @unittest.skip("GLPN does not have get_input_embeddings method and get_output_embeddings methods"					)
  def   UpperCAmelCase     (			self  :Dict					):






     '''simple docstring'''





     pass







  def   UpperCAmelCase     (			self  :int					):






     '''simple docstring'''





     lowercase__   ,							lowercase__    =  self.model_tester.prepare_config_and_inputs_for_common()

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

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







  def   UpperCAmelCase     (			self  :Tuple					):






     '''simple docstring'''





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

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

        lowercase__    =  sum(self.model_tester.depths					)
        self.assertEqual(len(_lowercase					)    ,							_lowercase					)

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

        self.assertEqual(len(_lowercase					)    ,							_lowercase					)

        # verify the first attentions (first block, first layer)
        lowercase__    =  (self.model_tester.image_size // 4) ** 2
        lowercase__    =  (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)
        lowercase__    =  (self.model_tester.image_size // 32) ** 2
        lowercase__    =  (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]    ,							)
        lowercase__    =  len(_lowercase					)

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

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

        lowercase__    =  outputs.attentions

        self.assertEqual(len(_lowercase					)    ,							_lowercase					)
        # verify the first attentions (first block, first layer)
        lowercase__    =  (self.model_tester.image_size // 4) ** 2
        lowercase__    =  (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  :Dict					):






     '''simple docstring'''





     def check_hidden_states_output(_lowercase  :Tuple    ,							_lowercase  :str    ,							_lowercase  :Optional[int]					):
        lowercase__    =  model_class(_lowercase					)
        model.to(_lowercase					)
        model.eval()

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

        lowercase__    =  outputs.hidden_states

        lowercase__    =  self.model_tester.num_encoder_blocks
        self.assertEqual(len(_lowercase					)    ,							_lowercase					)

        # 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,
            ]    ,							)

     lowercase__   ,							lowercase__    =  self.model_tester.prepare_config_and_inputs_for_common()

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

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

        check_hidden_states_output(_lowercase    ,							_lowercase    ,							_lowercase					)







  def   UpperCAmelCase     (			self  :List[str]					):






     '''simple docstring'''





     if not self.model_tester.is_training:
        return

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

     for model_class in self.all_model_classes:
        if model_class in get_values(_lowercase					):
           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":
           lowercase__   ,							lowercase__   ,							lowercase__   ,							lowercase__    =  inputs_dict["pixel_values"].shape
           lowercase__    =  torch.zeros(
               [self.model_tester.batch_size, height, width]    ,							device=_lowercase					).long()
        lowercase__    =  model_class(_lowercase					)
        model.to(_lowercase					)
        model.train()
        lowercase__    =  self._prepare_for_class(_lowercase    ,							_lowercase    ,							return_labels=_lowercase					)
        lowercase__    =  model(**_lowercase					).loss
        loss.backward()







  @slow
  def   UpperCAmelCase     (			self  :List[Any]					):






     '''simple docstring'''





     for model_name in GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
        lowercase__    =  GLPNModel.from_pretrained(_lowercase					)
        self.assertIsNotNone(_lowercase					)


def _A   (    ):
   lowercase__    =  Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"       )
   return image







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







  @slow
  def   UpperCAmelCase     (			self  :List[str]					):






     '''simple docstring'''





     lowercase__    =  GLPNImageProcessor.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]					)
     lowercase__    =  GLPNForDepthEstimation.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]					).to(_lowercase					)

     lowercase__    =  prepare_img()
     lowercase__    =  image_processor(images=_lowercase    ,							return_tensors="pt"					).to(_lowercase					)

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

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

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

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













from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


_snake_case				= {"""configuration_regnet""": ["""REGNET_PRETRAINED_CONFIG_ARCHIVE_MAP""", """RegNetConfig"""]}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _snake_case				= [
      """REGNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """RegNetForImageClassification""",
      """RegNetModel""",
      """RegNetPreTrainedModel""",
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _snake_case				= [
      """TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """TFRegNetForImageClassification""",
      """TFRegNetModel""",
      """TFRegNetPreTrainedModel""",
  ]

try:
  if not is_flax_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _snake_case				= [
      """FlaxRegNetForImageClassification""",
      """FlaxRegNetModel""",
      """FlaxRegNetPreTrainedModel""",
  ]


if TYPE_CHECKING:
  from .configuration_regnet import REGNET_PRETRAINED_CONFIG_ARCHIVE_MAP, RegNetConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_regnet import (
        REGNET_PRETRAINED_MODEL_ARCHIVE_LIST,
        RegNetForImageClassification,
        RegNetModel,
        RegNetPreTrainedModel,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_regnet import (
        TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFRegNetForImageClassification,
        TFRegNetModel,
        TFRegNetPreTrainedModel,
    )

  try:
    if not is_flax_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_flax_regnet import (
        FlaxRegNetForImageClassification,
        FlaxRegNetModel,
        FlaxRegNetPreTrainedModel,
    )


else:
  import sys

  _snake_case				= _LazyModule(__name__, globals()["""__file__"""], _import_structure)






