




'''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_					:

    '''simple docstring'''






    def __init__(    self   :			str      ,				UpperCAmelCase__   :			List[str]      ,				UpperCAmelCase__   :			Dict=13      ,				UpperCAmelCase__   :			Union[str, Any]=32      ,				UpperCAmelCase__   :			int=2      ,				UpperCAmelCase__   :			int=3      ,				UpperCAmelCase__   :			Optional[Any]=16      ,				UpperCAmelCase__   :			Optional[int]=[1, 2, 1]      ,				UpperCAmelCase__   :			str=[2, 2, 4]      ,				UpperCAmelCase__   :			Optional[Any]=2      ,				UpperCAmelCase__   :			Any=2.0      ,				UpperCAmelCase__   :			Optional[Any]=True      ,				UpperCAmelCase__   :			Optional[Any]=0.0      ,				UpperCAmelCase__   :			Union[str, Any]=0.0      ,				UpperCAmelCase__   :			List[str]=0.1      ,				UpperCAmelCase__   :			Optional[int]="gelu"      ,				UpperCAmelCase__   :			Tuple=False      ,				UpperCAmelCase__   :			Any=True      ,				UpperCAmelCase__   :			Tuple=0.02      ,				UpperCAmelCase__   :			Dict=1e-5      ,				UpperCAmelCase__   :			str=True      ,				UpperCAmelCase__   :			int=None      ,				UpperCAmelCase__   :			Union[str, Any]=True      ,				UpperCAmelCase__   :			int=10      ,				UpperCAmelCase__   :			Optional[int]=8      ,				UpperCAmelCase__   :			Union[str, Any]=["stage1", "stage2", "stage3"]      ,				UpperCAmelCase__   :			List[Any]=[1, 2, 3]      ,				)		->Optional[Any]:





           '''simple docstring'''

           A__   				=					parent
           A__   				=					batch_size
           A__   				=					image_size
           A__   				=					patch_size
           A__   				=					num_channels
           A__   				=					embed_dim
           A__   				=					depths
           A__   				=					num_heads
           A__   				=					window_size
           A__   				=					mlp_ratio
           A__   				=					qkv_bias
           A__   				=					hidden_dropout_prob
           A__   				=					attention_probs_dropout_prob
           A__   				=					drop_path_rate
           A__   				=					hidden_act
           A__   				=					use_absolute_embeddings
           A__   				=					patch_norm
           A__   				=					layer_norm_eps
           A__   				=					initializer_range
           A__   				=					is_training
           A__   				=					scope
           A__   				=					use_labels
           A__   				=					type_sequence_label_size
           A__   				=					encoder_stride
           A__   				=					out_features
           A__   				=					out_indices


    def  SCREAMING_SNAKE_CASE							(    self   :			List[Any])		->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.type_sequence_label_size)

           A__   				=					self.get_config()

           return config, pixel_values, labels


    def  SCREAMING_SNAKE_CASE							(    self   :			int)		->int:





           '''simple docstring'''

           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  SCREAMING_SNAKE_CASE							(    self   :			Optional[int]      ,				UpperCAmelCase__   :			Optional[Any]      ,				UpperCAmelCase__   :			List[str]      ,				UpperCAmelCase__   :			List[str])		->Union[str, Any]:





           '''simple docstring'''

           A__   				=					MaskFormerSwinModel(config=A_)
           model.to(A_)
           model.eval()
           A__   				=					model(A_)

           A__   				=					((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths) - 1))
           A__   				=					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  SCREAMING_SNAKE_CASE							(    self   :			Optional[Any]      ,				UpperCAmelCase__   :			str      ,				UpperCAmelCase__   :			Optional[int]      ,				UpperCAmelCase__   :			Tuple)		->int:





           '''simple docstring'''

           A__   				=					MaskFormerSwinBackbone(config=A_)
           model.to(A_)
           model.eval()
           A__   				=					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)      ,				[13, 16, 16, 16])

           # verify channels
           self.parent.assertEqual(len(model.channels)      ,				len(config.out_features))
           self.parent.assertListEqual(model.channels      ,				[16, 32, 64])

           # verify ValueError
           with self.parent.assertRaises(A_):
                  A__   				=					['''stem''']
                  A__   				=					MaskFormerSwinBackbone(config=A_)





    def  SCREAMING_SNAKE_CASE							(    self   :			List[Any])		->Optional[Any]:





           '''simple docstring'''

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


@require_torch
class        UpperCamelCase_					(				_UpperCAmelCase	,      _UpperCAmelCase	,      unittest.TestCase  ):

    '''simple docstring'''






    UpperCAmelCase__													=   (
        (
            MaskFormerSwinModel,
            MaskFormerSwinBackbone,
        )
        if is_torch_available()
        else ()
    )
    UpperCAmelCase__													=   {'''feature-extraction''': MaskFormerSwinModel} if is_torch_available() else {}
    UpperCAmelCase__													=   False
    UpperCAmelCase__													=   False
    UpperCAmelCase__													=   False
    UpperCAmelCase__													=   False
    UpperCAmelCase__													=   False


    def  SCREAMING_SNAKE_CASE							(    self   :			int)		->List[str]:





           '''simple docstring'''

           A__   				=					MaskFormerSwinModelTester(self)
           A__   				=					ConfigTester(self      ,				config_class=A_      ,				embed_dim=37)


    @require_torch_multi_gpu
    @unittest.skip(
        reason=(
            '''`MaskFormerSwinModel` outputs `hidden_states_spatial_dimensions` which doesn\'t work well with'''
            ''' `nn.DataParallel`'''
        ))
    def  SCREAMING_SNAKE_CASE							(    self   :			Any)		->Any:





           '''simple docstring'''

           pass


    def  SCREAMING_SNAKE_CASE							(    self   :			str)		->Tuple:





           '''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  SCREAMING_SNAKE_CASE							(    self   :			int)		->Dict:





           '''simple docstring'''

           return


    def  SCREAMING_SNAKE_CASE							(    self   :			Tuple)		->Any:





           '''simple docstring'''

           A__   				=					self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*A_)


    def  SCREAMING_SNAKE_CASE							(    self   :			List[str])		->Optional[int]:





           '''simple docstring'''

           A__   				=					self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_backbone(*A_)


    @unittest.skip('''Swin does not use inputs_embeds''')
    def  SCREAMING_SNAKE_CASE							(    self   :			List[str])		->List[str]:





           '''simple docstring'''

           pass


    @unittest.skip('''Swin does not support feedforward chunking''')
    def  SCREAMING_SNAKE_CASE							(    self   :			str)		->Any:





           '''simple docstring'''

           pass


    def  SCREAMING_SNAKE_CASE							(    self   :			List[str])		->int:





           '''simple docstring'''

           A__				,							A__   				=					self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                  A__   				=					model_class(A_)
                  self.assertIsInstance(model.get_input_embeddings()      ,				(nn.Module))
                  A__   				=					model.get_output_embeddings()
                  self.assertTrue(x is None or isinstance(A_      ,				nn.Linear))


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





           '''simple docstring'''

           A__				,							A__   				=					self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                  A__   				=					model_class(A_)
                  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]      ,				A_)


    @unittest.skip(reason='''MaskFormerSwin is only used as backbone and doesn\'t support output_attentions''')
    def  SCREAMING_SNAKE_CASE							(    self   :			Tuple)		->Any:





           '''simple docstring'''

           pass


    @unittest.skip(reason='''MaskFormerSwin is only used as an internal backbone''')
    def  SCREAMING_SNAKE_CASE							(    self   :			Any)		->List[Any]:





           '''simple docstring'''

           pass


    def  SCREAMING_SNAKE_CASE							(    self   :			Dict      ,				UpperCAmelCase__   :			List[Any]      ,				UpperCAmelCase__   :			Tuple      ,				UpperCAmelCase__   :			List[Any]      ,				UpperCAmelCase__   :			Union[str, Any])		->int:





           '''simple docstring'''

           A__   				=					model_class(A_)
           model.to(A_)
           model.eval()

           with torch.no_grad():
                  A__   				=					model(**self._prepare_for_class(A_      ,				A_))

           A__   				=					outputs.hidden_states

           A__   				=					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
           A__   				=					(
               config.patch_size
               if isinstance(config.patch_size      ,				collections.abc.Iterable)
               else (config.patch_size, config.patch_size)
           )

           A__   				=					(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  SCREAMING_SNAKE_CASE							(    self   :			Dict)		->Union[str, Any]:





           '''simple docstring'''

           A__				,							A__   				=					self.model_tester.prepare_config_and_inputs_for_common()

           A__   				=					(
               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:
                  A__   				=					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"]
                  A__   				=					True

                  self.check_hidden_states_output(A_      ,				A_      ,				A_      ,				A_)


    def  SCREAMING_SNAKE_CASE							(    self   :			Any)		->Dict:





           '''simple docstring'''

           A__				,							A__   				=					self.model_tester.prepare_config_and_inputs_for_common()
           A__   				=					3

           A__   				=					(
               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)
           )
           A__   				=					(
               config.patch_size
               if isinstance(config.patch_size      ,				collections.abc.Iterable)
               else (config.patch_size, config.patch_size)
           )

           A__   				=					image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
           A__   				=					image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

           for model_class in self.all_model_classes:
                  A__   				=					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"]
                  A__   				=					True
                  self.check_hidden_states_output(A_      ,				A_      ,				A_      ,				(padded_height, padded_width))


    @unittest.skip(reason='''MaskFormerSwin doesn\'t have pretrained checkpoints''')
    def  SCREAMING_SNAKE_CASE							(    self   :			Optional[Any])		->List[str]:





           '''simple docstring'''

           pass


    @unittest.skip(reason='''This will be fixed once MaskFormerSwin is replaced by native Swin''')
    def  SCREAMING_SNAKE_CASE							(    self   :			Tuple)		->Optional[int]:





           '''simple docstring'''

           pass


    @unittest.skip(reason='''This will be fixed once MaskFormerSwin is replaced by native Swin''')
    def  SCREAMING_SNAKE_CASE							(    self   :			List[str])		->Union[str, Any]:





           '''simple docstring'''

           pass





    def  SCREAMING_SNAKE_CASE							(    self   :			Optional[Any])		->int:





           '''simple docstring'''

           A__				,							A__   				=					self.model_tester.prepare_config_and_inputs_for_common()

           def set_nan_tensor_to_zero(UpperCAmelCase__   :			List[str]):
                  A__   				=					0
                  return t

           def check_equivalence(UpperCAmelCase__   :			Union[str, Any]      ,				UpperCAmelCase__   :			List[Any]      ,				UpperCAmelCase__   :			Optional[int]      ,				UpperCAmelCase__   :			List[str]={}):
                  with torch.no_grad():
                         A__   				=					model(**A_      ,				return_dict=A_      ,				**A_)
                         A__   				=					model(**A_      ,				return_dict=A_      ,				**A_).to_tuple()

                         def recursive_check(UpperCAmelCase__   :			List[Any]      ,				UpperCAmelCase__   :			List[str]):
                                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:
                  A__   				=					model_class(A_)
                  model.to(A_)
                  model.eval()

                  A__   				=					self._prepare_for_class(A_      ,				A_)
                  A__   				=					self._prepare_for_class(A_      ,				A_)
                  check_equivalence(A_      ,				A_      ,				A_)

                  A__   				=					self._prepare_for_class(A_      ,				A_      ,				return_labels=A_)
                  A__   				=					self._prepare_for_class(A_      ,				A_      ,				return_labels=A_)
                  check_equivalence(A_      ,				A_      ,				A_)

                  A__   				=					self._prepare_for_class(A_      ,				A_)
                  A__   				=					self._prepare_for_class(A_      ,				A_)
                  check_equivalence(A_      ,				A_      ,				A_      ,				{'''output_hidden_states''': True})

                  A__   				=					self._prepare_for_class(A_      ,				A_      ,				return_labels=A_)
                  A__   				=					self._prepare_for_class(A_      ,				A_      ,				return_labels=A_)
                  check_equivalence(A_      ,				A_      ,				A_      ,				{'''output_hidden_states''': True})


@require_torch
class        UpperCamelCase_					(				unittest.TestCase	,      _UpperCAmelCase  ):

    '''simple docstring'''






    UpperCAmelCase__													=   (MaskFormerSwinBackbone,) if is_torch_available() else ()
    UpperCAmelCase__													=   MaskFormerSwinConfig


    def  SCREAMING_SNAKE_CASE							(    self   :			Optional[Any])		->List[str]:





           '''simple docstring'''

           A__   				=					MaskFormerSwinModelTester(self)





    def  SCREAMING_SNAKE_CASE							(    self   :			Optional[int])		->List[Any]:





           '''simple docstring'''

           A__				,							A__   				=					self.model_tester.prepare_config_and_inputs_for_common()
           A__   				=					inputs_dict['''pixel_values'''].shape[0]

           for backbone_class in self.all_model_classes:
                  A__   				=					backbone_class(A_)
                  backbone.to(A_)
                  backbone.eval()

                  A__   				=					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
                  A__   				=					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)
                                A__				,							A__				,							A__   				=					hidden_state.shape
                                self.assertTrue((h_batch_size, h_n_channels)      ,				(batch_size, n_channels))

            # Test output_attentions=True
                  if self.has_attentions:
                         A__   				=					backbone(**A_      ,				output_attentions=A_)
                         self.assertIsNotNone(outputs.attentions)








from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_lowerCamelCase			:	List[Any]							 =     logging.get_logger(__name__)

_lowerCamelCase			:	Dict							 =     {
    """xlm-mlm-en-2048""": """https://huggingface.co/xlm-mlm-en-2048/resolve/main/config.json""",
    """xlm-mlm-ende-1024""": """https://huggingface.co/xlm-mlm-ende-1024/resolve/main/config.json""",
    """xlm-mlm-enfr-1024""": """https://huggingface.co/xlm-mlm-enfr-1024/resolve/main/config.json""",
    """xlm-mlm-enro-1024""": """https://huggingface.co/xlm-mlm-enro-1024/resolve/main/config.json""",
    """xlm-mlm-tlm-xnli15-1024""": """https://huggingface.co/xlm-mlm-tlm-xnli15-1024/resolve/main/config.json""",
    """xlm-mlm-xnli15-1024""": """https://huggingface.co/xlm-mlm-xnli15-1024/resolve/main/config.json""",
    """xlm-clm-enfr-1024""": """https://huggingface.co/xlm-clm-enfr-1024/resolve/main/config.json""",
    """xlm-clm-ende-1024""": """https://huggingface.co/xlm-clm-ende-1024/resolve/main/config.json""",
    """xlm-mlm-17-1280""": """https://huggingface.co/xlm-mlm-17-1280/resolve/main/config.json""",
    """xlm-mlm-100-1280""": """https://huggingface.co/xlm-mlm-100-1280/resolve/main/config.json""",
}


class        UpperCamelCase_					(				UpperCAmelCase__  ):

    '''simple docstring'''






    UpperCAmelCase__													=   '''xlm'''
    UpperCAmelCase__													=   {
        '''hidden_size''': '''emb_dim''',
        '''num_attention_heads''': '''n_heads''',
        '''num_hidden_layers''': '''n_layers''',
        '''n_words''': '''vocab_size''',  # For backward compatibility
    }


    def __init__(    self   :			Tuple      ,				UpperCAmelCase__   :			Optional[Any]=30_145      ,				UpperCAmelCase__   :			List[str]=2_048      ,				UpperCAmelCase__   :			str=12      ,				UpperCAmelCase__   :			List[Any]=16      ,				UpperCAmelCase__   :			Dict=0.1      ,				UpperCAmelCase__   :			Tuple=0.1      ,				UpperCAmelCase__   :			str=True      ,				UpperCAmelCase__   :			Union[str, Any]=False      ,				UpperCAmelCase__   :			str=False      ,				UpperCAmelCase__   :			Optional[int]=False      ,				UpperCAmelCase__   :			List[str]=1      ,				UpperCAmelCase__   :			List[Any]=True      ,				UpperCAmelCase__   :			Union[str, Any]=512      ,				UpperCAmelCase__   :			List[str]=2_048**-0.5      ,				UpperCAmelCase__   :			List[Any]=1e-12      ,				UpperCAmelCase__   :			Any=0.02      ,				UpperCAmelCase__   :			str=0      ,				UpperCAmelCase__   :			int=1      ,				UpperCAmelCase__   :			Union[str, Any]=2      ,				UpperCAmelCase__   :			List[Any]=3      ,				UpperCAmelCase__   :			int=5      ,				UpperCAmelCase__   :			Any=True      ,				UpperCAmelCase__   :			Tuple="first"      ,				UpperCAmelCase__   :			Optional[int]=True      ,				UpperCAmelCase__   :			List[str]=None      ,				UpperCAmelCase__   :			Tuple=True      ,				UpperCAmelCase__   :			Dict=0.1      ,				UpperCAmelCase__   :			Any=5      ,				UpperCAmelCase__   :			List[Any]=5      ,				UpperCAmelCase__   :			int=0      ,				UpperCAmelCase__   :			Dict=0      ,				UpperCAmelCase__   :			Dict=2      ,				UpperCAmelCase__   :			str=0      ,				**UpperCAmelCase__   :			List[str]      ,				)		->str:





           '''simple docstring'''

           A__   				=					vocab_size
           A__   				=					emb_dim
           A__   				=					n_layers
           A__   				=					n_heads
           A__   				=					dropout
           A__   				=					attention_dropout
           A__   				=					gelu_activation
           A__   				=					sinusoidal_embeddings
           A__   				=					causal
           A__   				=					asm
           A__   				=					n_langs
           A__   				=					use_lang_emb
           A__   				=					layer_norm_eps
           A__   				=					bos_index
           A__   				=					eos_index
           A__   				=					pad_index
           A__   				=					unk_index
           A__   				=					mask_index
           A__   				=					is_encoder
           A__   				=					max_position_embeddings
           A__   				=					embed_init_std
           A__   				=					init_std
           A__   				=					summary_type
           A__   				=					summary_use_proj
           A__   				=					summary_activation
           A__   				=					summary_proj_to_labels
           A__   				=					summary_first_dropout
           A__   				=					start_n_top
           A__   				=					end_n_top
           A__   				=					mask_token_id
           A__   				=					lang_id

           if "n_words" in kwargs:
                  A__   				=					kwargs['''n_words''']

           super().__init__(pad_token_id=UpperCAmelCase__      ,				bos_token_id=UpperCAmelCase__      ,				**UpperCAmelCase__)


class        UpperCamelCase_					(				UpperCAmelCase__  ):

    '''simple docstring'''






    @property
    def  SCREAMING_SNAKE_CASE							(    self   :			Dict)		->Mapping[str, Mapping[int, str]]:





           '''simple docstring'''

           if self.task == "multiple-choice":
                  A__   				=					{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
           else:
                  A__   				=					{0: '''batch''', 1: '''sequence'''}
           return OrderedDict(
               [
                   ('''input_ids''', dynamic_axis),
                   ('''attention_mask''', dynamic_axis),
                   ('''token_type_ids''', dynamic_axis),
               ])


