



'''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 	UpperCAmelCase	:


 '''simple docstring'''




 def __init__(   self							,  lowerCAmelCase_							,  lowerCAmelCase_=3							,  lowerCAmelCase_=3_2							,  lowerCAmelCase_=3							,  lowerCAmelCase_=1_0							,  lowerCAmelCase_=[8, 1_6, 3_2, 6_4]							,  lowerCAmelCase_=[1, 1, 2, 1]							,  lowerCAmelCase_=True							,  lowerCAmelCase_=True							,  lowerCAmelCase_="relu"							,  lowerCAmelCase_=3							,  lowerCAmelCase_=None							,  lowerCAmelCase_=["stage2", "stage3", "stage4"]							,  lowerCAmelCase_=[2, 3, 4]							,  lowerCAmelCase_=1							,  ) ->      int:



      """simple docstring"""







      a_       		=parent
      a_       		=batch_size
      a_       		=image_size
      a_       		=num_channels
      a_       		=embeddings_size
      a_       		=hidden_sizes
      a_       		=depths
      a_       		=is_training
      a_       		=use_labels
      a_       		=hidden_act
      a_       		=num_labels
      a_       		=scope
      a_       		=len(__lowerCamelCase)
      a_       		=out_features
      a_       		=out_indices
      a_       		=num_groups




 def       lowercase_  (   self) ->      Optional[Any]:



      """simple docstring"""







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

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

      a_       		=self.get_config()

      return config, pixel_values, labels




 def       lowercase_  (   self) ->      Union[str, Any]:



      """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       lowercase_  (   self							,  lowerCAmelCase_							,  lowerCAmelCase_							,  lowerCAmelCase_) ->      Optional[int]:



      """simple docstring"""







      a_       		=BitModel(config=__lowerCamelCase)
      model.to(__lowerCamelCase)
      model.eval()
      a_       		=model(__lowerCamelCase)
      self.parent.assertEqual(
          result.last_hidden_state.shape							,  (self.batch_size, self.hidden_sizes[-1], self.image_size // 3_2, self.image_size // 3_2)							,  )




 def       lowercase_  (   self							,  lowerCAmelCase_							,  lowerCAmelCase_							,  lowerCAmelCase_) ->      Tuple:



      """simple docstring"""







      a_       		=self.num_labels
      a_       		=BitForImageClassification(__lowerCamelCase)
      model.to(__lowerCamelCase)
      model.eval()
      a_       		=model(__lowerCamelCase							,  labels=__lowerCamelCase)
      self.parent.assertEqual(result.logits.shape							,  (self.batch_size, self.num_labels))




 def       lowercase_  (   self							,  lowerCAmelCase_							,  lowerCAmelCase_							,  lowerCAmelCase_) ->      List[Any]:



      """simple docstring"""







      a_       		=BitBackbone(config=__lowerCamelCase)
      model.to(__lowerCamelCase)
      model.eval()
      a_       		=model(__lowerCamelCase)

      # 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
      a_       		=None
      a_       		=BitBackbone(config=__lowerCamelCase)
      model.to(__lowerCamelCase)
      model.eval()
      a_       		=model(__lowerCamelCase)

      # 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       lowercase_  (   self) ->      Dict:



      """simple docstring"""







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







@require_torch
class 	UpperCAmelCase	(		_A					,	_A					,	unittest.TestCase):


 '''simple docstring'''




 __magic_name__					: str  =      (BitModel, BitForImageClassification, BitBackbone) if is_torch_available() else ()
 __magic_name__					: List[Any]  =      (
     {"feature-extraction": BitModel, "image-classification": BitForImageClassification}
     if is_torch_available()
     else {}
 )

 __magic_name__					: Tuple  =      False
 __magic_name__					: Optional[Any]  =      False
 __magic_name__					: List[str]  =      False
 __magic_name__					: Optional[int]  =      False
 __magic_name__					: Optional[Any]  =      False




 def       lowercase_  (   self) ->      Dict:



      """simple docstring"""







      a_       		=BitModelTester(self)
      a_       		=ConfigTester(self							,  config_class=__lowerCamelCase							,  has_text_modality=__lowerCamelCase)




 def       lowercase_  (   self) ->      List[Any]:



      """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       lowercase_  (   self) ->      Union[str, Any]:



      """simple docstring"""







      return




 @unittest.skip(reason="Bit does not output attentions")
 def       lowercase_  (   self) ->      List[str]:



      """simple docstring"""







      pass




 @unittest.skip(reason="Bit does not use inputs_embeds")
 def       lowercase_  (   self) ->      Optional[Any]:



      """simple docstring"""







      pass




 @unittest.skip(reason="Bit does not support input and output embeddings")
 def       lowercase_  (   self) ->      List[str]:



      """simple docstring"""







      pass




 def       lowercase_  (   self) ->      str:



      """simple docstring"""







      a_       		=self.model_tester.prepare_config_and_inputs_for_common()

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

           a_       		=["pixel_values"]
           self.assertListEqual(arg_names[:1]							,  __lowerCamelCase)




 def       lowercase_  (   self) ->      Union[str, Any]:



      """simple docstring"""







      a_       		=self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*__lowerCamelCase)




 def       lowercase_  (   self) ->      List[str]:



      """simple docstring"""







      a_       		=self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_backbone(*__lowerCamelCase)




 def       lowercase_  (   self) ->      Optional[int]:



      """simple docstring"""







      a_       		=self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
           a_       		=model_class(config=__lowerCamelCase)
           for name, module in model.named_modules():
                if isinstance(__lowerCamelCase							,  (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       lowercase_  (   self) ->      Optional[Any]:



      """simple docstring"""







      def check_hidden_states_output(lowerCAmelCase_							,  lowerCAmelCase_							,  lowerCAmelCase_):
           a_       		=model_class(__lowerCamelCase)
           model.to(__lowerCamelCase)
           model.eval()

           with torch.no_grad():
                a_       		=model(**self._prepare_for_class(__lowerCamelCase							,  __lowerCamelCase))

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

           a_       		=self.model_tester.num_stages
           self.assertEqual(len(__lowerCamelCase)							,  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]							,  )

      a_       		=self.model_tester.prepare_config_and_inputs_for_common()
      a_       		=["preactivation", "bottleneck"]
      for model_class in self.all_model_classes:
           for layer_type in layers_type:
                a_       		=layer_type
                a_       		=True
                check_hidden_states_output(__lowerCamelCase							,  __lowerCamelCase							,  __lowerCamelCase)

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

                check_hidden_states_output(__lowerCamelCase							,  __lowerCamelCase							,  __lowerCamelCase)




 @unittest.skip(reason="Bit does not use feedforward chunking")
 def       lowercase_  (   self) ->      Optional[Any]:



      """simple docstring"""







      pass




 def       lowercase_  (   self) ->      Optional[int]:



      """simple docstring"""







      a_       		=self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_image_classification(*__lowerCamelCase)




 @slow
 def       lowercase_  (   self) ->      List[Any]:



      """simple docstring"""







      for model_name in BIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           a_       		=BitModel.from_pretrained(__lowerCamelCase)
           self.assertIsNotNone(__lowerCamelCase)






def  UpperCAmelCase_       (		):


     '''simple docstring'''







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







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


 '''simple docstring'''




 @cached_property
 def       lowercase_  (   self) ->      Union[str, Any]:



      """simple docstring"""







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




 @slow
 def       lowercase_  (   self) ->      Dict:



      """simple docstring"""







      a_       		=BitForImageClassification.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]).to(__lowerCamelCase)

      a_       		=self.default_image_processor
      a_       		=prepare_img()
      a_       		=image_processor(images=__lowerCamelCase							,  return_tensors="pt").to(__lowerCamelCase)

      # forward pass
      with torch.no_grad():
           a_       		=model(**__lowerCamelCase)

      # verify the logits
      a_       		=torch.Size((1, 1_0_0_0))
      self.assertEqual(outputs.logits.shape							,  __lowerCamelCase)

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

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







@require_torch
class 	UpperCAmelCase	(		_A					,	unittest.TestCase):


 '''simple docstring'''




 __magic_name__					: Dict  =      (BitBackbone,) if is_torch_available() else ()
 __magic_name__					: int  =      BitConfig

 __magic_name__					: Any  =      False




 def       lowercase_  (   self) ->      Optional[int]:



      """simple docstring"""







      a_       		=BitModelTester(self)











'''simple docstring'''


import torch

from transformers import CamembertForMaskedLM, CamembertTokenizer






def  UpperCAmelCase_       (		lowercase__       ,	lowercase__       ,	lowercase__       ,	lowercase__=5       ):


     '''simple docstring'''







     assert masked_input.count("<mask>"       ) == 1
     a_       		=torch.tensor(tokenizer.encode(lowercase__       ,	add_special_tokens=lowercase__       )       ).unsqueeze(0       )  # Batch size 1
     a_       		=model(lowercase__       )[0]  # The last hidden-state is the first element of the output tuple
     a_       		=(input_ids.squeeze() == tokenizer.mask_token_id).nonzero().item()
     a_       		=logits[0, masked_index, :]
     a_       		=logits.softmax(dim=0       )
     a_		,						a_       		=prob.topk(k=lowercase__       ,	dim=0       )
     a_       		=" ".join(
         [tokenizer.convert_ids_to_tokens(indices[i].item()       ) for i in range(len(lowercase__       )       )]       )
     a_       		=tokenizer.mask_token
     a_       		=[]
     for index, predicted_token_bpe in enumerate(topk_predicted_token_bpe.split(" "       )       ):
          a_       		=predicted_token_bpe.replace("\u2581"       ,	" "       )
          if " {0}".format(lowercase__       ) in masked_input:
               topk_filled_outputs.append(
                   (
                       masked_input.replace(" {0}".format(lowercase__       )       ,	lowercase__       ),
                       values[index].item(),
                       predicted_token,
                   )       )
          else:
               topk_filled_outputs.append(
                   (
                       masked_input.replace(lowercase__       ,	lowercase__       ),
                       values[index].item(),
                       predicted_token,
                   )       )
     return topk_filled_outputs


lowercase							       =			CamembertTokenizer.from_pretrained('''camembert-base''')
lowercase							       =			CamembertForMaskedLM.from_pretrained('''camembert-base''')
model.eval()

lowercase							       =			'''Le camembert est <mask> :)'''

print(fill_mask(masked_input, model, tokenizer, topk=3))


