
"""simple docstring"""





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 A_      (lowercase__ ):




  '''simple docstring'''







  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     UpperCAmelCase_   :   Any     	=       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 A_      :




  '''simple docstring'''







  def __init__(						self							,  lowercase_							,  lowercase_=13							,  lowercase_=64							,  lowercase_=3							,  lowercase_=4							,  lowercase_=[2, 2, 2, 2]							,  lowercase_=[8, 4, 2, 1]							,  lowercase_=[16, 32, 64, 128]							,  lowercase_=[1, 4, 8, 16]							,  lowercase_=[1, 2, 4, 8]							,  lowercase_=True							,  lowercase_=True							,  lowercase_="gelu"							,  lowercase_=0.1							,  lowercase_=0.1							,  lowercase_=0.02							,  lowercase_=16							,  lowercase_=3							,  lowercase_=None							,  ):

     """simple docstring"""
     UpperCAmelCase_   :   str     	=       parent
     UpperCAmelCase_   :   Dict     	=       batch_size
     UpperCAmelCase_   :   Tuple     	=       image_size
     UpperCAmelCase_   :   Any     	=       num_channels
     UpperCAmelCase_   :   Tuple     	=       num_encoder_blocks
     UpperCAmelCase_   :   Union[str, Any]     	=       sr_ratios
     UpperCAmelCase_   :   int     	=       depths
     UpperCAmelCase_   :   Any     	=       hidden_sizes
     UpperCAmelCase_   :   Optional[int]     	=       downsampling_rates
     UpperCAmelCase_   :   str     	=       num_attention_heads
     UpperCAmelCase_   :   List[Any]     	=       is_training
     UpperCAmelCase_   :   Dict     	=       use_labels
     UpperCAmelCase_   :   Optional[Any]     	=       hidden_act
     UpperCAmelCase_   :   Any     	=       hidden_dropout_prob
     UpperCAmelCase_   :   List[str]     	=       attention_probs_dropout_prob
     UpperCAmelCase_   :   Dict     	=       initializer_range
     UpperCAmelCase_   :   List[str]     	=       decoder_hidden_size
     UpperCAmelCase_   :   Optional[Any]     	=       num_labels
     UpperCAmelCase_   :   List[str]     	=       scope







  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     UpperCAmelCase_   :   int     	=       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]    )

     UpperCAmelCase_   :   int     	=       None
     if self.use_labels:
        UpperCAmelCase_   :   Optional[Any]     	=       ids_tensor([self.batch_size, self.image_size, self.image_size]							,  self.num_labels    )

     UpperCAmelCase_   :   Optional[Any]     	=       self.get_config()
     return config, pixel_values, labels







  def 			UpperCamelCase__   (						self    ):

     """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							,  lowercase_							,  lowercase_							,  lowercase_    ):

     """simple docstring"""
     UpperCAmelCase_   :   Optional[int]     	=       GLPNModel(config=lowercase_    )
     model.to(lowercase_    )
     model.eval()
     UpperCAmelCase_   :   Dict     	=       model(lowercase_    )
     UpperCAmelCase_   :   Optional[int]     	=       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							,  lowercase_							,  lowercase_							,  lowercase_    ):

     """simple docstring"""
     UpperCAmelCase_   :   Dict     	=       self.num_labels
     UpperCAmelCase_   :   Union[str, Any]     	=       GLPNForDepthEstimation(lowercase_    )
     model.to(lowercase_    )
     model.eval()
     UpperCAmelCase_   :   Tuple     	=       model(lowercase_    )
     self.parent.assertEqual(result.predicted_depth.shape							,  (self.batch_size, self.image_size, self.image_size)    )
     UpperCAmelCase_   :   str     	=       model(lowercase_							,  labels=lowercase_    )
     self.parent.assertEqual(result.predicted_depth.shape							,  (self.batch_size, self.image_size, self.image_size)    )







  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     UpperCAmelCase_   :   str     	=       self.prepare_config_and_inputs()
     UpperCAmelCase_	,  UpperCAmelCase_	,  UpperCAmelCase_   :   Tuple     	=       config_and_inputs
     UpperCAmelCase_   :   Dict     	=       {"pixel_values": pixel_values}
     return config, inputs_dict




@require_torch
class A_      (lowercase__							,lowercase__							,unittest.TestCase ):




  '''simple docstring'''
  SCREAMING_SNAKE_CASE__     :		Union[str, Any]    	=						(GLPNModel, GLPNForDepthEstimation) if is_torch_available() else ()
  SCREAMING_SNAKE_CASE__     :		Any    	=						(
      {"""depth-estimation""": GLPNForDepthEstimation, """feature-extraction""": GLPNModel} if is_torch_available() else {}
  )

  SCREAMING_SNAKE_CASE__     :		Union[str, Any]    	=						False
  SCREAMING_SNAKE_CASE__     :		Optional[int]    	=						False
  SCREAMING_SNAKE_CASE__     :		Any    	=						False







  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     UpperCAmelCase_   :   Any     	=       GLPNModelTester(self    )
     UpperCAmelCase_   :   Optional[Any]     	=       GLPNConfigTester(self							,  config_class=lowercase_    )







  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     self.config_tester.run_common_tests()







  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     UpperCAmelCase_   :   Any     	=       self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*lowercase_    )







  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     UpperCAmelCase_   :   int     	=       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    ):

     """simple docstring"""
     pass







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

     """simple docstring"""
     pass







  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     UpperCAmelCase_	,  UpperCAmelCase_   :   Optional[Any]     	=       self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
        UpperCAmelCase_   :   List[str]     	=       model_class(lowercase_    )
        UpperCAmelCase_   :   List[Any]     	=       inspect.signature(model.forward    )
        # signature.parameters is an OrderedDict => so arg_names order is deterministic
        UpperCAmelCase_   :   Optional[int]     	=       [*signature.parameters.keys()]

        UpperCAmelCase_   :   Any     	=       ["pixel_values"]
        self.assertListEqual(arg_names[:1]							,  lowercase_    )







  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     UpperCAmelCase_	,  UpperCAmelCase_   :   Optional[int]     	=       self.model_tester.prepare_config_and_inputs_for_common()
     UpperCAmelCase_   :   Union[str, Any]     	=       True

     for model_class in self.all_model_classes:
        UpperCAmelCase_   :   Optional[int]     	=       True
        UpperCAmelCase_   :   int     	=       False
        UpperCAmelCase_   :   List[str]     	=       True
        UpperCAmelCase_   :   Union[str, Any]     	=       model_class(lowercase_    )
        model.to(lowercase_    )
        model.eval()
        with torch.no_grad():
           UpperCAmelCase_   :   Optional[int]     	=       model(**self._prepare_for_class(lowercase_							,  lowercase_    )    )
        UpperCAmelCase_   :   Union[str, Any]     	=       outputs.attentions

        UpperCAmelCase_   :   int     	=       sum(self.model_tester.depths    )
        self.assertEqual(len(lowercase_    )							,  lowercase_    )

        # check that output_attentions also work using config
        del inputs_dict["output_attentions"]
        UpperCAmelCase_   :   Optional[int]     	=       True
        UpperCAmelCase_   :   Dict     	=       model_class(lowercase_    )
        model.to(lowercase_    )
        model.eval()
        with torch.no_grad():
           UpperCAmelCase_   :   Tuple     	=       model(**self._prepare_for_class(lowercase_							,  lowercase_    )    )
        UpperCAmelCase_   :   Union[str, Any]     	=       outputs.attentions

        self.assertEqual(len(lowercase_    )							,  lowercase_    )

        # verify the first attentions (first block, first layer)
        UpperCAmelCase_   :   Union[str, Any]     	=       (self.model_tester.image_size // 4) ** 2
        UpperCAmelCase_   :   Optional[Any]     	=       (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)
        UpperCAmelCase_   :   int     	=       (self.model_tester.image_size // 32) ** 2
        UpperCAmelCase_   :   Tuple     	=       (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]							,  )
        UpperCAmelCase_   :   Union[str, Any]     	=       len(lowercase_    )

        # Check attention is always last and order is fine
        UpperCAmelCase_   :   Optional[Any]     	=       True
        UpperCAmelCase_   :   Any     	=       True
        UpperCAmelCase_   :   str     	=       model_class(lowercase_    )
        model.to(lowercase_    )
        model.eval()
        with torch.no_grad():
           UpperCAmelCase_   :   str     	=       model(**self._prepare_for_class(lowercase_							,  lowercase_    )    )

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

        UpperCAmelCase_   :   List[str]     	=       outputs.attentions

        self.assertEqual(len(lowercase_    )							,  lowercase_    )
        # verify the first attentions (first block, first layer)
        UpperCAmelCase_   :   Tuple     	=       (self.model_tester.image_size // 4) ** 2
        UpperCAmelCase_   :   Any     	=       (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    ):

     """simple docstring"""
     def check_hidden_states_output(lowercase_							,  lowercase_							,  lowercase_    ):
        UpperCAmelCase_   :   str     	=       model_class(lowercase_    )
        model.to(lowercase_    )
        model.eval()

        with torch.no_grad():
           UpperCAmelCase_   :   Union[str, Any]     	=       model(**self._prepare_for_class(lowercase_							,  lowercase_    )    )

        UpperCAmelCase_   :   Optional[int]     	=       outputs.hidden_states

        UpperCAmelCase_   :   Union[str, Any]     	=       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,
            ]							,  )

     UpperCAmelCase_	,  UpperCAmelCase_   :   List[str]     	=       self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
        UpperCAmelCase_   :   Dict     	=       True
        check_hidden_states_output(lowercase_							,  lowercase_							,  lowercase_    )

        # check that output_hidden_states also work using config
        del inputs_dict["output_hidden_states"]
        UpperCAmelCase_   :   Dict     	=       True

        check_hidden_states_output(lowercase_							,  lowercase_							,  lowercase_    )







  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     if not self.model_tester.is_training:
        return

     UpperCAmelCase_	,  UpperCAmelCase_   :   Optional[int]     	=       self.model_tester.prepare_config_and_inputs_for_common()
     UpperCAmelCase_   :   Optional[int]     	=       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":
           UpperCAmelCase_	,  UpperCAmelCase_	,  UpperCAmelCase_	,  UpperCAmelCase_   :   List[str]     	=       inputs_dict["pixel_values"].shape
           UpperCAmelCase_   :   str     	=       torch.zeros(
               [self.model_tester.batch_size, height, width]							,  device=lowercase_    ).long()
        UpperCAmelCase_   :   Tuple     	=       model_class(lowercase_    )
        model.to(lowercase_    )
        model.train()
        UpperCAmelCase_   :   Union[str, Any]     	=       self._prepare_for_class(lowercase_							,  lowercase_							,  return_labels=lowercase_    )
        UpperCAmelCase_   :   Optional[Any]     	=       model(**lowercase_    ).loss
        loss.backward()







  @slow
  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     for model_name in GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
        UpperCAmelCase_   :   List[str]     	=       GLPNModel.from_pretrained(lowercase_    )
        self.assertIsNotNone(lowercase_    )







def       __a	(						):
   UpperCAmelCase_   :   Optional[int]     	=       Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"    )
   return image


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




  '''simple docstring'''







  @slow
  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     UpperCAmelCase_   :   List[Any]     	=       GLPNImageProcessor.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]    )
     UpperCAmelCase_   :   List[Any]     	=       GLPNForDepthEstimation.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]    ).to(lowercase_    )

     UpperCAmelCase_   :   List[Any]     	=       prepare_img()
     UpperCAmelCase_   :   str     	=       image_processor(images=lowercase_							,  return_tensors="pt"    ).to(lowercase_    )

     # forward pass
     with torch.no_grad():
        UpperCAmelCase_   :   Any     	=       model(**lowercase_    )

     # verify the predicted depth
     UpperCAmelCase_   :   Optional[int]     	=       torch.Size([1, 480, 640]    )
     self.assertEqual(outputs.predicted_depth.shape							,  lowercase_    )

     UpperCAmelCase_   :   Dict     	=       torch.tensor(
         [[3.42_91, 2.78_65, 2.51_51], [3.28_41, 2.70_21, 2.35_02], [3.11_47, 2.46_25, 2.24_81]]    ).to(lowercase_    )

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



'''simple docstring'''




from typing import TYPE_CHECKING

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


_A										= {
    'configuration_rwkv': ['RWKV_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RwkvConfig', 'RwkvOnnxConfig'],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _A										= [
     'RWKV_PRETRAINED_MODEL_ARCHIVE_LIST',
     'RwkvForCausalLM',
     'RwkvModel',
     'RwkvPreTrainedModel',
 ]


if TYPE_CHECKING:
 from .configuration_rwkv import RWKV_PRETRAINED_CONFIG_ARCHIVE_MAP, RwkvConfig, RwkvOnnxConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_rwkv import (
      RWKV_PRETRAINED_MODEL_ARCHIVE_LIST,
      RwkvForCausalLM,
      RwkvModel,
      RwkvPreTrainedModel,
  )
else:
 import sys

 _A										= _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




