





"""simple docstring"""
from __future__ import annotations

import inspect
import unittest

import numpy as np

from transformers import ResNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
       import tensorflow as tf

       from transformers import TFResNetForImageClassification, TFResNetModel
       from transformers.models.resnet.modeling_tf_resnet import TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
       from PIL import Image

       from transformers import AutoImageProcessor







class       a							:



 def __init__(     self				,       _snake_case				,       _snake_case=3				,       _snake_case=32				,       _snake_case=3				,       _snake_case=10				,       _snake_case=[10, 20, 30, 40]				,       _snake_case=[1, 1, 2, 1]				,       _snake_case=True				,       _snake_case=True				,       _snake_case="relu"				,       _snake_case=3				,       _snake_case=None				,       ):







  """simple docstring"""






  lowerCAmelCase    				= parent
  lowerCAmelCase    				= batch_size
  lowerCAmelCase    				= image_size
  lowerCAmelCase    				= num_channels
  lowerCAmelCase    				= embeddings_size
  lowerCAmelCase    				= hidden_sizes
  lowerCAmelCase    				= depths
  lowerCAmelCase    				= is_training
  lowerCAmelCase    				= use_labels
  lowerCAmelCase    				= hidden_act
  lowerCAmelCase    				= num_labels
  lowerCAmelCase    				= scope
  lowerCAmelCase    				= len(_snake_case     )



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  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.num_labels     )

  lowerCAmelCase    				= self.get_config()

  return config, pixel_values, labels



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  return ResNetConfig(
      num_channels=self.num_channels				,       embeddings_size=self.embeddings_size				,       hidden_sizes=self.hidden_sizes				,       depths=self.depths				,       hidden_act=self.hidden_act				,       num_labels=self.num_labels				,       image_size=self.image_size				,       )



 def    UpperCamelCase__  (     self				,       _snake_case				,       _snake_case				,       _snake_case     ):







  """simple docstring"""






  lowerCAmelCase    				= TFResNetModel(config=_snake_case     )
  lowerCAmelCase    				= model(_snake_case     )
  # expected last hidden states: B, C, H // 32, W // 32
  self.parent.assertEqual(
      result.last_hidden_state.shape				,       (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)				,       )



 def    UpperCamelCase__  (     self				,       _snake_case				,       _snake_case				,       _snake_case     ):







  """simple docstring"""






  lowerCAmelCase    				= self.num_labels
  lowerCAmelCase    				= TFResNetForImageClassification(_snake_case     )
  lowerCAmelCase    				= model(_snake_case				,       labels=_snake_case     )
  self.parent.assertEqual(result.logits.shape				,       (self.batch_size, self.num_labels)     )



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  lowerCAmelCase    				= self.prepare_config_and_inputs()
  lowerCAmelCase				,lowerCAmelCase				,lowerCAmelCase    				= config_and_inputs
  lowerCAmelCase    				= {'pixel_values': pixel_values}
  return config, inputs_dict







@require_tf
class       a							(   a__   , a__   , unittest.TestCase  ):
 snake_case__             =       (TFResNetModel, TFResNetForImageClassification) if is_tf_available() else ()
 snake_case__             =       (
     {'''feature-extraction''': TFResNetModel, '''image-classification''': TFResNetForImageClassification}
     if is_tf_available()
     else {}
 )

 snake_case__             =       False
 snake_case__             =       False
 snake_case__             =       False
 snake_case__             =       False
 snake_case__             =       False



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  lowerCAmelCase    				= TFResNetModelTester(self     )
  lowerCAmelCase    				= ConfigTester(self				,       config_class=_snake_case				,       has_text_modality=_snake_case     )



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  self.create_and_test_config_common_properties()
  self.config_tester.create_and_test_config_to_json_string()
  self.config_tester.create_and_test_config_to_json_file()
  self.config_tester.create_and_test_config_from_and_save_pretrained()
  self.config_tester.create_and_test_config_with_num_labels()
  self.config_tester.check_config_can_be_init_without_params()
  self.config_tester.check_config_arguments_init()



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  return



 @unittest.skip(reason='ResNet does not use inputs_embeds'     )
 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  pass



 @unittest.skip(reason='ResNet does not support input and output embeddings'     )
 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  pass



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  lowerCAmelCase				,lowerCAmelCase    				= self.model_tester.prepare_config_and_inputs_for_common()

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

   lowerCAmelCase    				= ['pixel_values']
   self.assertListEqual(arg_names[:1]				,       _snake_case     )



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  lowerCAmelCase    				= self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_model(*_snake_case     )



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  def check_hidden_states_output(_snake_case				,       _snake_case				,       _snake_case     ):
   lowerCAmelCase    				= model_class(_snake_case     )
   lowerCAmelCase    				= model(**self._prepare_for_class(_snake_case				,       _snake_case     )     )

   lowerCAmelCase    				= outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

   lowerCAmelCase    				= self.model_tester.num_stages
   self.assertEqual(len(_snake_case     )				,       expected_num_stages + 1     )

   # ResNet's feature maps are of shape (batch_size, num_channels, height, width)
   self.assertListEqual(
       list(hidden_states[0].shape[-2:]     )				,       [self.model_tester.image_size // 4, self.model_tester.image_size // 4]				,       )

  lowerCAmelCase				,lowerCAmelCase    				= self.model_tester.prepare_config_and_inputs_for_common()
  lowerCAmelCase    				= ['basic', 'bottleneck']
  for model_class in self.all_model_classes:
   for layer_type in layers_type:
    lowerCAmelCase    				= layer_type
    lowerCAmelCase    				= True
    check_hidden_states_output(_snake_case				,       _snake_case				,       _snake_case     )

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

    check_hidden_states_output(_snake_case				,       _snake_case				,       _snake_case     )



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  lowerCAmelCase    				= self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_for_image_classification(*_snake_case     )



 @slow
 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  for model_name in TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
   lowerCAmelCase    				= TFResNetModel.from_pretrained(_snake_case     )
   self.assertIsNotNone(_snake_case     )


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







@require_tf
@require_vision
class       a							(   unittest.TestCase  ):



 @cached_property
 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  return (
      AutoImageProcessor.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]     )
      if is_vision_available()
      else None
  )



 @slow
 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  lowerCAmelCase    				= TFResNetForImageClassification.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]     )

  lowerCAmelCase    				= self.default_image_processor
  lowerCAmelCase    				= prepare_img()
  lowerCAmelCase    				= image_processor(images=_snake_case				,       return_tensors='tf'     )

  # forward pass
  lowerCAmelCase    				= model(**_snake_case     )

  # verify the logits
  lowerCAmelCase    				= tf.TensorShape((1, 10_00)     )
  self.assertEqual(outputs.logits.shape				,       _snake_case     )

  lowerCAmelCase    				= tf.constant([-11.1_069, -9.7_877, -8.3_777]     )

  self.assertTrue(np.allclose(outputs.logits[0, :3].numpy()				,       _snake_case				,       atol=1E-4     )     )












"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging


__UpperCamelCase     :						List[str]						       =  logging.get_logger(__name__)

__UpperCamelCase     :						Tuple						       =  {
    '''vinvino02/glpn-kitti''': '''https://huggingface.co/vinvino02/glpn-kitti/resolve/main/config.json''',
    # See all GLPN models at https://huggingface.co/models?filter=glpn
}







class       a							(   a__  ):
 snake_case__             =       '''glpn'''



 def __init__(     self				,       _snake_case=3				,       _snake_case=4				,       _snake_case=[2, 2, 2, 2]				,       _snake_case=[8, 4, 2, 1]				,       _snake_case=[32, 64, 1_60, 2_56]				,       _snake_case=[7, 3, 3, 3]				,       _snake_case=[4, 2, 2, 2]				,       _snake_case=[1, 2, 5, 8]				,       _snake_case=[4, 4, 4, 4]				,       _snake_case="gelu"				,       _snake_case=0.0				,       _snake_case=0.0				,       _snake_case=0.02				,       _snake_case=0.1				,       _snake_case=1E-6				,       _snake_case=64				,       _snake_case=10				,       _snake_case=-1				,       **_snake_case				,       ):







  """simple docstring"""






  super().__init__(**_snake_case     )

  lowerCAmelCase    				= num_channels
  lowerCAmelCase    				= num_encoder_blocks
  lowerCAmelCase    				= depths
  lowerCAmelCase    				= sr_ratios
  lowerCAmelCase    				= hidden_sizes
  lowerCAmelCase    				= patch_sizes
  lowerCAmelCase    				= strides
  lowerCAmelCase    				= mlp_ratios
  lowerCAmelCase    				= num_attention_heads
  lowerCAmelCase    				= hidden_act
  lowerCAmelCase    				= hidden_dropout_prob
  lowerCAmelCase    				= attention_probs_dropout_prob
  lowerCAmelCase    				= initializer_range
  lowerCAmelCase    				= drop_path_rate
  lowerCAmelCase    				= layer_norm_eps
  lowerCAmelCase    				= decoder_hidden_size
  lowerCAmelCase    				= max_depth
  lowerCAmelCase    				= head_in_index





