'''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 				_lowercase     :


 '''simple docstring'''






 def __init__(      self   :	List[Any]      ,       SCREAMING_SNAKE_CASE__   :	Union[str, Any]      ,       SCREAMING_SNAKE_CASE__   :	List[Any]=3      ,       SCREAMING_SNAKE_CASE__   :	List[str]=32      ,       SCREAMING_SNAKE_CASE__   :	Optional[int]=3      ,       SCREAMING_SNAKE_CASE__   :	Any=10      ,       SCREAMING_SNAKE_CASE__   :	Union[str, Any]=[8, 16, 32, 64]      ,       SCREAMING_SNAKE_CASE__   :	Union[str, Any]=[1, 1, 2, 1]      ,       SCREAMING_SNAKE_CASE__   :	Optional[int]=True      ,       SCREAMING_SNAKE_CASE__   :	Tuple=True      ,       SCREAMING_SNAKE_CASE__   :	Optional[Any]="relu"      ,       SCREAMING_SNAKE_CASE__   :	Optional[Any]=3      ,       SCREAMING_SNAKE_CASE__   :	Any=None      ,       SCREAMING_SNAKE_CASE__   :	List[str]=["stage2", "stage3", "stage4"]      ,       SCREAMING_SNAKE_CASE__   :	Tuple=[2, 3, 4]      ,       SCREAMING_SNAKE_CASE__   :	Tuple=1      ,       )  ->    List[str]:
  __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(SCREAMING_SNAKE_CASE__							)
  __lowerCAmelCase											=						out_features
  __lowerCAmelCase											=						out_indices
  __lowerCAmelCase											=						num_groups
 def a					(      self   :	Optional[Any]							)  ->    List[str]:
  __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 a					(      self   :	Optional[Any]							)  ->    Optional[Any]:
  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 a					(      self   :	Any      ,       SCREAMING_SNAKE_CASE__   :	Any      ,       SCREAMING_SNAKE_CASE__   :	Tuple      ,       SCREAMING_SNAKE_CASE__   :	Tuple							)  ->    Tuple:
  __lowerCAmelCase											=						BitModel(config=SCREAMING_SNAKE_CASE__							)
  model.to(SCREAMING_SNAKE_CASE__							)
  model.eval()
  __lowerCAmelCase											=						model(SCREAMING_SNAKE_CASE__							)
  self.parent.assertEqual(
      result.last_hidden_state.shape      ,       (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)      ,       )
 def a					(      self   :	Optional[Any]      ,       SCREAMING_SNAKE_CASE__   :	Dict      ,       SCREAMING_SNAKE_CASE__   :	Optional[int]      ,       SCREAMING_SNAKE_CASE__   :	Any							)  ->    Optional[int]:
  __lowerCAmelCase											=						self.num_labels
  __lowerCAmelCase											=						BitForImageClassification(SCREAMING_SNAKE_CASE__							)
  model.to(SCREAMING_SNAKE_CASE__							)
  model.eval()
  __lowerCAmelCase											=						model(SCREAMING_SNAKE_CASE__      ,       labels=SCREAMING_SNAKE_CASE__							)
  self.parent.assertEqual(result.logits.shape      ,       (self.batch_size, self.num_labels)							)
 def a					(      self   :	str      ,       SCREAMING_SNAKE_CASE__   :	Optional[int]      ,       SCREAMING_SNAKE_CASE__   :	Tuple      ,       SCREAMING_SNAKE_CASE__   :	str							)  ->    List[Any]:
  __lowerCAmelCase											=						BitBackbone(config=SCREAMING_SNAKE_CASE__							)
  model.to(SCREAMING_SNAKE_CASE__							)
  model.eval()
  __lowerCAmelCase											=						model(SCREAMING_SNAKE_CASE__							)

  # 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=SCREAMING_SNAKE_CASE__							)
  model.to(SCREAMING_SNAKE_CASE__							)
  model.eval()
  __lowerCAmelCase											=						model(SCREAMING_SNAKE_CASE__							)

  # 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 a					(      self   :	List[str]							)  ->    Dict:
  __lowerCAmelCase											=						self.prepare_config_and_inputs()
  __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase											=						config_and_inputs
  __lowerCAmelCase											=						{"""pixel_values""": pixel_values}
  return config, inputs_dict







@require_torch
class 				_lowercase     (				UpperCAmelCase__  ,							UpperCAmelCase__  ,							unittest.TestCase			):


 '''simple docstring'''






 _SCREAMING_SNAKE_CASE     :       Optional[Any]      	=					(BitModel, BitForImageClassification, BitBackbone) if is_torch_available() else ()
 _SCREAMING_SNAKE_CASE     :       Optional[int]      	=					(
     {"""feature-extraction""": BitModel, """image-classification""": BitForImageClassification}
     if is_torch_available()
     else {}
 )

 _SCREAMING_SNAKE_CASE     :       List[str]      	=					False
 _SCREAMING_SNAKE_CASE     :       Optional[Any]      	=					False
 _SCREAMING_SNAKE_CASE     :       Dict      	=					False
 _SCREAMING_SNAKE_CASE     :       Optional[int]      	=					False
 _SCREAMING_SNAKE_CASE     :       str      	=					False
 def a					(      self   :	Dict							)  ->    Union[str, Any]:
  __lowerCAmelCase											=						BitModelTester(self							)
  __lowerCAmelCase											=						ConfigTester(self      ,       config_class=SCREAMING_SNAKE_CASE__      ,       has_text_modality=SCREAMING_SNAKE_CASE__							)
 def a					(      self   :	List[Any]							)  ->    Any:
  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 a					(      self   :	Optional[int]							)  ->    Dict:
  return
 @unittest.skip(reason="""Bit does not output attentions"""							)
 def a					(      self   :	List[Any]							)  ->    Optional[Any]:
  pass
 @unittest.skip(reason="""Bit does not use inputs_embeds"""							)
 def a					(      self   :	Optional[Any]							)  ->    List[str]:
  pass
 @unittest.skip(reason="""Bit does not support input and output embeddings"""							)
 def a					(      self   :	List[Any]							)  ->    List[Any]:
  pass
 def a					(      self   :	Any							)  ->    int:
  __lowerCAmelCase							,       __lowerCAmelCase											=						self.model_tester.prepare_config_and_inputs_for_common()

  for model_class in self.all_model_classes:
   __lowerCAmelCase											=						model_class(SCREAMING_SNAKE_CASE__							)
   __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]      ,       SCREAMING_SNAKE_CASE__							)
 def a					(      self   :	str							)  ->    List[str]:
  __lowerCAmelCase											=						self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE__							)
 def a					(      self   :	int							)  ->    Tuple:
  __lowerCAmelCase											=						self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_backbone(*SCREAMING_SNAKE_CASE__							)
 def a					(      self   :	int							)  ->    Tuple:
  __lowerCAmelCase							,       __lowerCAmelCase											=						self.model_tester.prepare_config_and_inputs_for_common()

  for model_class in self.all_model_classes:
   __lowerCAmelCase											=						model_class(config=SCREAMING_SNAKE_CASE__							)
   for name, module in model.named_modules():
    if isinstance(SCREAMING_SNAKE_CASE__      ,       (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 a					(      self   :	Tuple							)  ->    Optional[int]:
  def check_hidden_states_output(SCREAMING_SNAKE_CASE__   :	Any      ,       SCREAMING_SNAKE_CASE__   :	Optional[int]      ,       SCREAMING_SNAKE_CASE__   :	Tuple							):
   __lowerCAmelCase											=						model_class(SCREAMING_SNAKE_CASE__							)
   model.to(SCREAMING_SNAKE_CASE__							)
   model.eval()

   with torch.no_grad():
    __lowerCAmelCase											=						model(**self._prepare_for_class(SCREAMING_SNAKE_CASE__      ,       SCREAMING_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(SCREAMING_SNAKE_CASE__							)      ,       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(SCREAMING_SNAKE_CASE__      ,       SCREAMING_SNAKE_CASE__      ,       SCREAMING_SNAKE_CASE__							)

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

    check_hidden_states_output(SCREAMING_SNAKE_CASE__      ,       SCREAMING_SNAKE_CASE__      ,       SCREAMING_SNAKE_CASE__							)
 @unittest.skip(reason="""Bit does not use feedforward chunking"""							)
 def a					(      self   :	str							)  ->    Any:
  pass
 def a					(      self   :	Union[str, Any]							)  ->    Dict:
  __lowerCAmelCase											=						self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_for_image_classification(*SCREAMING_SNAKE_CASE__							)





 @slow
 def a					(      self   :	List[Any]							)  ->    str:
  for model_name in BIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
   __lowerCAmelCase											=						BitModel.from_pretrained(SCREAMING_SNAKE_CASE__							)
   self.assertIsNotNone(SCREAMING_SNAKE_CASE__							)







def      UpperCamelCase_						(					)					->     List[Any]:






 '''simple docstring'''

 __lowerCAmelCase											=						Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""      )
 return image







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


 '''simple docstring'''






 @cached_property
 def a					(      self   :	List[Any]							)  ->    List[Any]:
  return (
      BitImageProcessor.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]							) if is_vision_available() else None
  )





 @slow
 def a					(      self   :	List[str]							)  ->    List[Any]:
  __lowerCAmelCase											=						BitForImageClassification.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]							).to(SCREAMING_SNAKE_CASE__							)

  __lowerCAmelCase											=						self.default_image_processor
  __lowerCAmelCase											=						prepare_img()
  __lowerCAmelCase											=						image_processor(images=SCREAMING_SNAKE_CASE__      ,       return_tensors="""pt"""							).to(SCREAMING_SNAKE_CASE__							)

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

  # verify the logits
  __lowerCAmelCase											=						torch.Size((1, 10_00)							)
  self.assertEqual(outputs.logits.shape      ,       SCREAMING_SNAKE_CASE__							)

  __lowerCAmelCase											=						torch.tensor([[-0.6_5_2_6, -0.5_2_6_3, -1.4_3_9_8]]							).to(SCREAMING_SNAKE_CASE__							)

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







@require_torch
class 				_lowercase     (				UpperCAmelCase__  ,							unittest.TestCase			):


 '''simple docstring'''






 _SCREAMING_SNAKE_CASE     :       Dict      	=					(BitBackbone,) if is_torch_available() else ()
 _SCREAMING_SNAKE_CASE     :       Optional[Any]      	=					BitConfig

 _SCREAMING_SNAKE_CASE     :       Any      	=					False
 def a					(      self   :	Any							)  ->    Tuple:
  __lowerCAmelCase											=						BitModelTester(self							)







'''simple docstring'''

from ...configuration_utils import PretrainedConfig
from ...utils import logging


_A			:      int					      =				logging.get_logger(__name__)

_A			:      Any					      =				{'''ctrl''': '''https://huggingface.co/ctrl/resolve/main/config.json'''}







class 				_lowercase     (				UpperCAmelCase__			):


 '''simple docstring'''






 _SCREAMING_SNAKE_CASE     :       Any      	=					"""ctrl"""
 _SCREAMING_SNAKE_CASE     :       Optional[Any]      	=					["""past_key_values"""]
 _SCREAMING_SNAKE_CASE     :       Union[str, Any]      	=					{
     """max_position_embeddings""": """n_positions""",
     """hidden_size""": """n_embd""",
     """num_attention_heads""": """n_head""",
     """num_hidden_layers""": """n_layer""",
 }
 def __init__(      self   :	Any      ,       SCREAMING_SNAKE_CASE__   :	Optional[Any]=24_65_34      ,       SCREAMING_SNAKE_CASE__   :	Optional[int]=2_56      ,       SCREAMING_SNAKE_CASE__   :	Optional[Any]=12_80      ,       SCREAMING_SNAKE_CASE__   :	str=81_92      ,       SCREAMING_SNAKE_CASE__   :	List[str]=48      ,       SCREAMING_SNAKE_CASE__   :	Tuple=16      ,       SCREAMING_SNAKE_CASE__   :	str=0.1      ,       SCREAMING_SNAKE_CASE__   :	Dict=0.1      ,       SCREAMING_SNAKE_CASE__   :	Dict=1e-6      ,       SCREAMING_SNAKE_CASE__   :	Any=0.0_2      ,       SCREAMING_SNAKE_CASE__   :	Tuple=True      ,       **SCREAMING_SNAKE_CASE__   :	Optional[int]      ,       )  ->    Tuple:
  __lowerCAmelCase											=						vocab_size
  __lowerCAmelCase											=						n_positions
  __lowerCAmelCase											=						n_embd
  __lowerCAmelCase											=						n_layer
  __lowerCAmelCase											=						n_head
  __lowerCAmelCase											=						dff
  __lowerCAmelCase											=						resid_pdrop
  __lowerCAmelCase											=						embd_pdrop
  __lowerCAmelCase											=						layer_norm_epsilon
  __lowerCAmelCase											=						initializer_range

  __lowerCAmelCase											=						use_cache

  super().__init__(**SCREAMING_SNAKE_CASE__							)






