


'''simple docstring'''

import inspect
import unittest

from transformers import BitConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_backbone_common import BackboneTesterMixin
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 torch import nn

     from transformers import BitBackbone, BitForImageClassification, BitImageProcessor, BitModel
     from transformers.models.bit.modeling_bit import BIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
     from PIL import Image





class      snake_case    :





  """simple docstring"""




  def __init__(    self			,  UpperCamelCase			,  UpperCamelCase=3			,  UpperCamelCase=32			,  UpperCamelCase=3			,  UpperCamelCase=10			,  UpperCamelCase=[8, 16, 32, 64]			,  UpperCamelCase=[1, 1, 2, 1]			,  UpperCamelCase=True			,  UpperCamelCase=True			,  UpperCamelCase="relu"			,  UpperCamelCase=3			,  UpperCamelCase=None			,  UpperCamelCase=["stage2", "stage3", "stage4"]			,  UpperCamelCase=[2, 3, 4]			,  UpperCamelCase=1			,  ):







   """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(UpperCamelCase     )
   lowerCamelCase_      =  out_features
   lowerCamelCase_      =  out_indices
   lowerCamelCase_      =  num_groups






  def   snake_case		(    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   snake_case		(    self     ):







   """simple docstring"""
   return BitConfig(
       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			,  out_features=self.out_features			,  out_indices=self.out_indices			,  num_groups=self.num_groups			,  )






  def   snake_case		(    self			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase     ):







   """simple docstring"""
   lowerCamelCase_      =  BitModel(config=UpperCamelCase     )
   model.to(UpperCamelCase     )
   model.eval()
   lowerCamelCase_      =  model(UpperCamelCase     )
   self.parent.assertEqual(
       result.last_hidden_state.shape			,  (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)			,  )






  def   snake_case		(    self			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase     ):







   """simple docstring"""
   lowerCamelCase_      =  self.num_labels
   lowerCamelCase_      =  BitForImageClassification(UpperCamelCase     )
   model.to(UpperCamelCase     )
   model.eval()
   lowerCamelCase_      =  model(UpperCamelCase			,  labels=UpperCamelCase     )
   self.parent.assertEqual(result.logits.shape			,  (self.batch_size, self.num_labels)     )






  def   snake_case		(    self			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase     ):







   """simple docstring"""
   lowerCamelCase_      =  BitBackbone(config=UpperCamelCase     )
   model.to(UpperCamelCase     )
   model.eval()
   lowerCamelCase_      =  model(UpperCamelCase     )

   # verify feature maps
   self.parent.assertEqual(len(result.feature_maps     )			,  len(config.out_features     )     )
   self.parent.assertListEqual(list(result.feature_maps[0].shape     )			,  [self.batch_size, self.hidden_sizes[1], 4, 4]     )

   # verify channels
   self.parent.assertEqual(len(model.channels     )			,  len(config.out_features     )     )
   self.parent.assertListEqual(model.channels			,  config.hidden_sizes[1:]     )

   # verify backbone works with out_features=None
   lowerCamelCase_      =  None
   lowerCamelCase_      =  BitBackbone(config=UpperCamelCase     )
   model.to(UpperCamelCase     )
   model.eval()
   lowerCamelCase_      =  model(UpperCamelCase     )

   # verify feature maps
   self.parent.assertEqual(len(result.feature_maps     )			,  1     )
   self.parent.assertListEqual(list(result.feature_maps[0].shape     )			,  [self.batch_size, self.hidden_sizes[-1], 1, 1]     )

   # verify channels
   self.parent.assertEqual(len(model.channels     )			,  1     )
   self.parent.assertListEqual(model.channels			,  [config.hidden_sizes[-1]]     )







  def   snake_case		(    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_torch
class      snake_case    (    lowercase			,						lowercase			,						unittest.TestCase			):





  """simple docstring"""




  _lowerCamelCase		    =  (BitModel, BitForImageClassification, BitBackbone) if is_torch_available() else ()
  _lowerCamelCase		    =  (
      {"feature-extraction": BitModel, "image-classification": BitForImageClassification}
      if is_torch_available()
      else {}
  )

  _lowerCamelCase		    =  False
  _lowerCamelCase		    =  False
  _lowerCamelCase		    =  False
  _lowerCamelCase		    =  False
  _lowerCamelCase		    =  False






  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  BitModelTester(self     )
   lowerCamelCase_      =  ConfigTester(self			,  config_class=UpperCamelCase			,  has_text_modality=UpperCamelCase     )






  def   snake_case		(    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   snake_case		(    self     ):







   """simple docstring"""
   return






  @unittest.skip(reason="Bit does not output attentions"     )
  def   snake_case		(    self     ):







   """simple docstring"""
   pass






  @unittest.skip(reason="Bit does not use inputs_embeds"     )
  def   snake_case		(    self     ):







   """simple docstring"""
   pass






  @unittest.skip(reason="Bit does not support input and output embeddings"     )
  def   snake_case		(    self     ):







   """simple docstring"""
   pass






  def   snake_case		(    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(UpperCamelCase     )
    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]			,  UpperCamelCase     )






  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_model(*UpperCamelCase     )






  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_backbone(*UpperCamelCase     )






  def   snake_case		(    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(config=UpperCamelCase     )
    for name, module in model.named_modules():
     if isinstance(UpperCamelCase			,  (nn.BatchNormad, nn.GroupNorm)     ):
      self.assertTrue(
          torch.all(module.weight == 1     )			,  msg=f'''Parameter {name} of model {model_class} seems not properly initialized'''			,  )
      self.assertTrue(
          torch.all(module.bias == 0     )			,  msg=f'''Parameter {name} of model {model_class} seems not properly initialized'''			,  )






  def   snake_case		(    self     ):







   """simple docstring"""
   def check_hidden_states_output(UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase     ):
    lowerCamelCase_      =  model_class(UpperCamelCase     )
    model.to(UpperCamelCase     )
    model.eval()

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

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

    lowerCamelCase_      =  self.model_tester.num_stages
    self.assertEqual(len(UpperCamelCase     )			,  expected_num_stages + 1     )

    # Bit'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_      =  ["preactivation", "bottleneck"]
   for model_class in self.all_model_classes:
    for layer_type in layers_type:
     lowerCamelCase_      =  layer_type
     lowerCamelCase_      =  True
     check_hidden_states_output(UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase     )

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

     check_hidden_states_output(UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase     )






  @unittest.skip(reason="Bit does not use feedforward chunking"     )
  def   snake_case		(    self     ):







   """simple docstring"""
   pass






  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_image_classification(*UpperCamelCase     )







  @slow
  def   snake_case		(    self     ):







   """simple docstring"""
   for model_name in BIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
    lowerCamelCase_      =  BitModel.from_pretrained(UpperCamelCase     )
    self.assertIsNotNone(UpperCamelCase     )





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





@require_torch
@require_vision
class      snake_case    (    unittest.TestCase			):





  """simple docstring"""




  @cached_property
  def   snake_case		(    self     ):







   """simple docstring"""
   return (
       BitImageProcessor.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]     ) if is_vision_available() else None
   )







  @slow
  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  BitForImageClassification.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]     ).to(UpperCamelCase     )

   lowerCamelCase_      =  self.default_image_processor
   lowerCamelCase_      =  prepare_img()
   lowerCamelCase_      =  image_processor(images=UpperCamelCase			,  return_tensors="pt"     ).to(UpperCamelCase     )

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

   # verify the logits
   lowerCamelCase_      =  torch.Size((1, 1000)     )
   self.assertEqual(outputs.logits.shape			,  UpperCamelCase     )

   lowerCamelCase_      =  torch.tensor([[-0.6_526, -0.5_263, -1.4_398]]     ).to(UpperCamelCase     )

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





@require_torch
class      snake_case    (    lowercase			,						unittest.TestCase			):





  """simple docstring"""




  _lowerCamelCase		    =  (BitBackbone,) if is_torch_available() else ()
  _lowerCamelCase		    =  BitConfig

  _lowerCamelCase		    =  False






  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  BitModelTester(self     )






def 			A_      (    _UpperCAmelCase				,	_UpperCAmelCase = False    ):

						if not isinstance(_UpperCAmelCase				,	_UpperCAmelCase    ):
												SCREAMING_SNAKE_CASE_: str  		=       f"Expected string as input, found {type(_UpperCAmelCase    )}"
												raise ValueError(_UpperCAmelCase    )
						if not isinstance(_UpperCAmelCase				,	_UpperCAmelCase    ):
												SCREAMING_SNAKE_CASE_: Optional[Any]  		=       f"Expected boolean as use_pascal parameter, found {type(_UpperCAmelCase    )}"
												raise ValueError(_UpperCAmelCase    )

						SCREAMING_SNAKE_CASE_: Tuple  		=       input_str.split("_"    )

						SCREAMING_SNAKE_CASE_: str  		=       0 if use_pascal else 1

						SCREAMING_SNAKE_CASE_: int  		=       words[start_index:]

						SCREAMING_SNAKE_CASE_: List[str]  		=       [word[0].upper() + word[1:] for word in words_to_capitalize]

						SCREAMING_SNAKE_CASE_: List[Any]  		=       "" if use_pascal else words[0]

						return "".join([initial_word, *capitalized_words]    )


if __name__ == "__main__":
						from doctest import testmod

						testmod()






