"""simple docstring"""





import torch

from transformers import CamembertForMaskedLM, CamembertTokenizer





def        a_  (   _lowerCAmelCase						:				Optional[Any]     ,				_lowerCAmelCase						:				Optional[int]     ,				_lowerCAmelCase						:				List[Any]     ,				_lowerCAmelCase						:				str=5  ):





       '''simple docstring'''

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


_UpperCamelCase     :		Tuple											=					CamembertTokenizer.from_pretrained("camembert-base")
_UpperCamelCase     :		List[Any]											=					CamembertForMaskedLM.from_pretrained("camembert-base")
model.eval()

_UpperCamelCase     :		Tuple											=					"Le camembert est <mask> :)"






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


"""simple docstring"""





import collections
import inspect
import unittest
from typing import Dict, List, Tuple

from transformers import MaskFormerSwinConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, torch_device
from transformers.utils import is_torch_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 MaskFormerSwinBackbone
       from transformers.models.maskformer import MaskFormerSwinModel


class   UpperCAmelCase_			:


      def __init__(     self	,    a	,    a=1_3	,    a=3_2	,    a=2	,    a=3	,    a=1_6	,    a=[1, 2, 1]	,    a=[2, 2, 4]	,    a=2	,    a=2.0	,    a=True	,    a=0.0	,    a=0.0	,    a=0.1	,    a="gelu"	,    a=False	,    a=True	,    a=0.02	,    a=1e-5	,    a=True	,    a=None	,    a=True	,    a=1_0	,    a=8	,    a=["stage1", "stage2", "stage3"]	,    a=[1, 2, 3]	,    ) ->      int:
             lowercase__    :	int     					=			parent
             lowercase__    :	Union[str, Any]     					=			batch_size
             lowercase__    :	Dict     					=			image_size
             lowercase__    :	str     					=			patch_size
             lowercase__    :	Optional[Any]     					=			num_channels
             lowercase__    :	List[str]     					=			embed_dim
             lowercase__    :	Any     					=			depths
             lowercase__    :	Dict     					=			num_heads
             lowercase__    :	List[str]     					=			window_size
             lowercase__    :	int     					=			mlp_ratio
             lowercase__    :	Tuple     					=			qkv_bias
             lowercase__    :	Union[str, Any]     					=			hidden_dropout_prob
             lowercase__    :	str     					=			attention_probs_dropout_prob
             lowercase__    :	Tuple     					=			drop_path_rate
             lowercase__    :	List[str]     					=			hidden_act
             lowercase__    :	Optional[Any]     					=			use_absolute_embeddings
             lowercase__    :	Optional[Any]     					=			patch_norm
             lowercase__    :	Any     					=			layer_norm_eps
             lowercase__    :	List[Any]     					=			initializer_range
             lowercase__    :	List[str]     					=			is_training
             lowercase__    :	int     					=			scope
             lowercase__    :	Optional[int]     					=			use_labels
             lowercase__    :	List[Any]     					=			type_sequence_label_size
             lowercase__    :	List[str]     					=			encoder_stride
             lowercase__    :	Optional[Any]     					=			out_features
             lowercase__    :	Dict     					=			out_indices


      def 	_UpperCAmelCase (     self							) ->      Tuple:
             lowercase__    :	Any     					=			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

             lowercase__    :	Optional[Any]     					=			None
             if self.use_labels:
                    lowercase__    :	List[Any]     					=			ids_tensor([self.batch_size]	,    self.type_sequence_label_size							)

             lowercase__    :	Tuple     					=			self.get_config()

             return config, pixel_values, labels


      def 	_UpperCAmelCase (     self							) ->      Union[str, Any]:
             return MaskFormerSwinConfig(
                 image_size=self.image_size	,    patch_size=self.patch_size	,    num_channels=self.num_channels	,    embed_dim=self.embed_dim	,    depths=self.depths	,    num_heads=self.num_heads	,    window_size=self.window_size	,    mlp_ratio=self.mlp_ratio	,    qkv_bias=self.qkv_bias	,    hidden_dropout_prob=self.hidden_dropout_prob	,    attention_probs_dropout_prob=self.attention_probs_dropout_prob	,    drop_path_rate=self.drop_path_rate	,    hidden_act=self.hidden_act	,    use_absolute_embeddings=self.use_absolute_embeddings	,    path_norm=self.patch_norm	,    layer_norm_eps=self.layer_norm_eps	,    initializer_range=self.initializer_range	,    encoder_stride=self.encoder_stride	,    out_features=self.out_features	,    out_indices=self.out_indices	,    )


      def 	_UpperCAmelCase (     self	,    a	,    a	,    a							) ->      Dict:
             lowercase__    :	Tuple     					=			MaskFormerSwinModel(config=a							)
             model.to(a							)
             model.eval()
             lowercase__    :	str     					=			model(a							)

             lowercase__    :	str     					=			((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths							) - 1))
             lowercase__    :	Dict     					=			int(config.embed_dim * 2 ** (len(config.depths							) - 1)							)

             self.parent.assertEqual(result.last_hidden_state.shape	,    (self.batch_size, expected_seq_len, expected_dim)							)


      def 	_UpperCAmelCase (     self	,    a	,    a	,    a							) ->      Optional[int]:
             lowercase__    :	List[Any]     					=			MaskFormerSwinBackbone(config=a							)
             model.to(a							)
             model.eval()
             lowercase__    :	int     					=			model(a							)

             # verify feature maps
             self.parent.assertEqual(len(result.feature_maps							)	,    len(config.out_features							)							)
             self.parent.assertListEqual(list(result.feature_maps[0].shape							)	,    [1_3, 1_6, 1_6, 1_6]							)

             # verify channels
             self.parent.assertEqual(len(model.channels							)	,    len(config.out_features							)							)
             self.parent.assertListEqual(model.channels	,    [1_6, 3_2, 6_4]							)

             # verify ValueError
             with self.parent.assertRaises(a							):
                    lowercase__    :	Dict     					=			['stem']
                    lowercase__    :	List[str]     					=			MaskFormerSwinBackbone(config=a							)


      def 	_UpperCAmelCase (     self							) ->      str:
             lowercase__    :	int     					=			self.prepare_config_and_inputs()
             lowercase__					,   lowercase__					,   lowercase__    :	Tuple     					=			config_and_inputs
             lowercase__    :	Union[str, Any]     					=			{'pixel_values': pixel_values}
             return config, inputs_dict





@require_torch
class   UpperCAmelCase_			(					_a				,							_a				,							unittest.TestCase):
      lowerCamelCase__	:					Optional[int]										=						(
          (
              MaskFormerSwinModel,
              MaskFormerSwinBackbone,
          )
          if is_torch_available()
          else ()
      )
      lowerCamelCase__	:					List[str]										=						{"feature-extraction": MaskFormerSwinModel} if is_torch_available() else {}
      lowerCamelCase__	:					str										=						False
      lowerCamelCase__	:					Dict										=						False
      lowerCamelCase__	:					Any										=						False
      lowerCamelCase__	:					Dict										=						False
      lowerCamelCase__	:					int										=						False


      def 	_UpperCAmelCase (     self							) ->      List[Any]:
             lowercase__    :	str     					=			MaskFormerSwinModelTester(self							)
             lowercase__    :	Tuple     					=			ConfigTester(self	,    config_class=a	,    embed_dim=3_7							)


      @require_torch_multi_gpu
      @unittest.skip(
          reason=(
              '`MaskFormerSwinModel` outputs `hidden_states_spatial_dimensions` which doesn\'t work well with'
              ' `nn.DataParallel`'
          )							)
      def 	_UpperCAmelCase (     self							) ->      Optional[int]:
             pass


      def 	_UpperCAmelCase (     self							) ->      Tuple:
             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							) ->      str:
             return


      def 	_UpperCAmelCase (     self							) ->      List[Any]:
             lowercase__    :	Optional[Any]     					=			self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_model(*a							)


      def 	_UpperCAmelCase (     self							) ->      List[Any]:
             lowercase__    :	str     					=			self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_backbone(*a							)


      @unittest.skip('Swin does not use inputs_embeds'							)
      def 	_UpperCAmelCase (     self							) ->      Tuple:
             pass


      @unittest.skip('Swin does not support feedforward chunking'							)
      def 	_UpperCAmelCase (     self							) ->      Tuple:
             pass


      def 	_UpperCAmelCase (     self							) ->      Optional[Any]:
             lowercase__					,   lowercase__    :	Union[str, Any]     					=			self.model_tester.prepare_config_and_inputs_for_common()

             for model_class in self.all_model_classes:
                    lowercase__    :	List[str]     					=			model_class(a							)
                    self.assertIsInstance(model.get_input_embeddings()	,    (nn.Module)							)
                    lowercase__    :	Union[str, Any]     					=			model.get_output_embeddings()
                    self.assertTrue(x is None or isinstance(a	,    nn.Linear							)							)


      def 	_UpperCAmelCase (     self							) ->      str:
             lowercase__					,   lowercase__    :	Any     					=			self.model_tester.prepare_config_and_inputs_for_common()

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

                    lowercase__    :	List[Any]     					=			['pixel_values']
                    self.assertListEqual(arg_names[:1]	,    a							)


      @unittest.skip(reason='MaskFormerSwin is only used as backbone and doesn\'t support output_attentions'							)
      def 	_UpperCAmelCase (     self							) ->      List[Any]:
             pass


      @unittest.skip(reason='MaskFormerSwin is only used as an internal backbone'							)
      def 	_UpperCAmelCase (     self							) ->      int:
             pass


      def 	_UpperCAmelCase (     self	,    a	,    a	,    a	,    a							) ->      Tuple:
             lowercase__    :	Dict     					=			model_class(a							)
             model.to(a							)
             model.eval()

             with torch.no_grad():
                    lowercase__    :	str     					=			model(**self._prepare_for_class(a	,    a							)							)

             lowercase__    :	List[Any]     					=			outputs.hidden_states

             lowercase__    :	str     					=			getattr(
                 self.model_tester	,    'expected_num_hidden_layers'	,    len(self.model_tester.depths							) + 1							)
             self.assertEqual(len(a							)	,    a							)

             # Swin has a different seq_length
             lowercase__    :	Dict     					=			(
                 config.patch_size
                 if isinstance(config.patch_size	,    collections.abc.Iterable							)
                 else (config.patch_size, config.patch_size)
             )

             lowercase__    :	Tuple     					=			(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

             self.assertListEqual(
                 list(hidden_states[0].shape[-2:]							)	,    [num_patches, self.model_tester.embed_dim]	,    )


      def 	_UpperCAmelCase (     self							) ->      Optional[Any]:
             lowercase__					,   lowercase__    :	Dict     					=			self.model_tester.prepare_config_and_inputs_for_common()

             lowercase__    :	List[Any]     					=			(
                 self.model_tester.image_size
                 if isinstance(self.model_tester.image_size	,    collections.abc.Iterable							)
                 else (self.model_tester.image_size, self.model_tester.image_size)
             )

             for model_class in self.all_model_classes:
                    lowercase__    :	List[str]     					=			True
                    self.check_hidden_states_output(a	,    a	,    a	,    a							)

                    # check that output_hidden_states also work using config
                    del inputs_dict["output_hidden_states"]
                    lowercase__    :	List[str]     					=			True

                    self.check_hidden_states_output(a	,    a	,    a	,    a							)


      def 	_UpperCAmelCase (     self							) ->      Optional[int]:
             lowercase__					,   lowercase__    :	Any     					=			self.model_tester.prepare_config_and_inputs_for_common()
             lowercase__    :	Union[str, Any]     					=			3

             lowercase__    :	str     					=			(
                 self.model_tester.image_size
                 if isinstance(self.model_tester.image_size	,    collections.abc.Iterable							)
                 else (self.model_tester.image_size, self.model_tester.image_size)
             )
             lowercase__    :	Tuple     					=			(
                 config.patch_size
                 if isinstance(config.patch_size	,    collections.abc.Iterable							)
                 else (config.patch_size, config.patch_size)
             )

             lowercase__    :	Optional[int]     					=			image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
             lowercase__    :	List[str]     					=			image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

             for model_class in self.all_model_classes:
                    lowercase__    :	List[str]     					=			True
                    self.check_hidden_states_output(a	,    a	,    a	,    (padded_height, padded_width)							)

                    # check that output_hidden_states also work using config
                    del inputs_dict["output_hidden_states"]
                    lowercase__    :	int     					=			True
                    self.check_hidden_states_output(a	,    a	,    a	,    (padded_height, padded_width)							)


      @unittest.skip(reason='MaskFormerSwin doesn\'t have pretrained checkpoints'							)
      def 	_UpperCAmelCase (     self							) ->      Optional[int]:
             pass


      @unittest.skip(reason='This will be fixed once MaskFormerSwin is replaced by native Swin'							)
      def 	_UpperCAmelCase (     self							) ->      Any:
             pass


      @unittest.skip(reason='This will be fixed once MaskFormerSwin is replaced by native Swin'							)
      def 	_UpperCAmelCase (     self							) ->      Any:
             pass


      def 	_UpperCAmelCase (     self							) ->      Any:
             lowercase__					,   lowercase__    :	str     					=			self.model_tester.prepare_config_and_inputs_for_common()

             def set_nan_tensor_to_zero(a							):
                    lowercase__    :	Union[str, Any]     					=			0
                    return t

             def check_equivalence(a	,    a	,    a	,    a={}							):
                    with torch.no_grad():
                           lowercase__    :	Optional[Any]     					=			model(**a	,    return_dict=a	,    **a							)
                           lowercase__    :	Optional[int]     					=			model(**a	,    return_dict=a	,    **a							).to_tuple()

                           def recursive_check(a	,    a							):
                                  if isinstance(a	,    (List, Tuple)							):
                                         for tuple_iterable_value, dict_iterable_value in zip(a	,    a							):
                                                recursive_check(a	,    a							)
                                  elif isinstance(a	,    a							):
                                         for tuple_iterable_value, dict_iterable_value in zip(
                                             tuple_object.values()	,    dict_object.values()							):
                                                recursive_check(a	,    a							)
                                  elif tuple_object is None:
                                         return
                                  else:
                                         self.assertTrue(
                                             torch.allclose(
                                                 set_nan_tensor_to_zero(a							)	,    set_nan_tensor_to_zero(a							)	,    atol=1e-5							)	,    msg=(
                                                 'Tuple and dict output are not equal. Difference:'
                                                 f""" {torch.max(torch.abs(tuple_object - dict_object							)							)}. Tuple has `nan`:"""
                                                 f""" {torch.isnan(a							).any()} and `inf`: {torch.isinf(a							)}. Dict has"""
                                                 f""" `nan`: {torch.isnan(a							).any()} and `inf`: {torch.isinf(a							)}."""
                                             )	,    )

                           recursive_check(a	,    a							)

             for model_class in self.all_model_classes:
                    lowercase__    :	Any     					=			model_class(a							)
                    model.to(a							)
                    model.eval()

                    lowercase__    :	Tuple     					=			self._prepare_for_class(a	,    a							)
                    lowercase__    :	Optional[Any]     					=			self._prepare_for_class(a	,    a							)
                    check_equivalence(a	,    a	,    a							)

                    lowercase__    :	Any     					=			self._prepare_for_class(a	,    a	,    return_labels=a							)
                    lowercase__    :	List[Any]     					=			self._prepare_for_class(a	,    a	,    return_labels=a							)
                    check_equivalence(a	,    a	,    a							)

                    lowercase__    :	Any     					=			self._prepare_for_class(a	,    a							)
                    lowercase__    :	int     					=			self._prepare_for_class(a	,    a							)
                    check_equivalence(a	,    a	,    a	,    {'output_hidden_states': True}							)

                    lowercase__    :	Dict     					=			self._prepare_for_class(a	,    a	,    return_labels=a							)
                    lowercase__    :	Optional[int]     					=			self._prepare_for_class(a	,    a	,    return_labels=a							)
                    check_equivalence(a	,    a	,    a	,    {'output_hidden_states': True}							)





@require_torch
class   UpperCAmelCase_			(					unittest.TestCase				,							_a):
      lowerCamelCase__	:					Dict										=						(MaskFormerSwinBackbone,) if is_torch_available() else ()
      lowerCamelCase__	:					Optional[int]										=						MaskFormerSwinConfig


      def 	_UpperCAmelCase (     self							) ->      Dict:
             lowercase__    :	Optional[int]     					=			MaskFormerSwinModelTester(self							)


      def 	_UpperCAmelCase (     self							) ->      Optional[Any]:
             lowercase__					,   lowercase__    :	Any     					=			self.model_tester.prepare_config_and_inputs_for_common()
             lowercase__    :	int     					=			inputs_dict['pixel_values'].shape[0]

             for backbone_class in self.all_model_classes:
                    lowercase__    :	Optional[Any]     					=			backbone_class(a							)
                    backbone.to(a							)
                    backbone.eval()

                    lowercase__    :	Union[str, Any]     					=			backbone(**a							)

                    # Test default outputs and verify feature maps
                    self.assertIsInstance(outputs.feature_maps	,    a							)
                    self.assertTrue(len(outputs.feature_maps							) == len(backbone.channels							)							)
                    for feature_map, n_channels in zip(outputs.feature_maps	,    backbone.channels							):
                           self.assertTrue(feature_map.shape[:2]	,    (batch_size, n_channels)							)
                    self.assertIsNone(outputs.hidden_states							)
                    self.assertIsNone(outputs.attentions							)

                    # Test output_hidden_states=True
                    lowercase__    :	List[str]     					=			backbone(**a	,    output_hidden_states=a							)
                    self.assertIsNotNone(outputs.hidden_states							)
                    self.assertTrue(len(outputs.hidden_states							)	,    len(backbone.stage_names							)							)
                    # We skip the stem layer
                    for hidden_states, n_channels in zip(outputs.hidden_states[1:]	,    backbone.channels							):
                           for hidden_state in hidden_states:
                                  # Hidden states are in the format (batch_size, (height * width), n_channels)
                                  lowercase__					,   lowercase__					,   lowercase__    :	int     					=			hidden_state.shape
                                  self.assertTrue((h_batch_size, h_n_channels)	,    (batch_size, n_channels)							)

            # Test output_attentions=True
                    if self.has_attentions:
                           lowercase__    :	List[Any]     					=			backbone(**a	,    output_attentions=a							)
                           self.assertIsNotNone(outputs.attentions							)

