



from typing import Optional

import torch
import torch.utils.checkpoint
from torch import Tensor, nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss

from ...activations import ACTaFN
from ...modeling_outputs import (
    BackboneOutput,
    BaseModelOutputWithNoAttention,
    BaseModelOutputWithPoolingAndNoAttention,
    ImageClassifierOutputWithNoAttention,
)
from ...modeling_utils import PreTrainedModel
from ...utils import (
    add_code_sample_docstrings,
    add_start_docstrings,
    add_start_docstrings_to_model_forward,
    logging,
    replace_return_docstrings,
)
from ...utils.backbone_utils import BackboneMixin
from .configuration_resnet import ResNetConfig


A__  :  str								=					logging.get_logger(__name__)

# General docstring
A__  :  List[str]								=					'''ResNetConfig'''

# Base docstring
A__  :  Tuple								=					'''microsoft/resnet-50'''
A__  :  List[str]								=					[1, 2048, 7, 7]

# Image classification docstring
A__  :  str								=					'''microsoft/resnet-50'''
A__  :  List[str]								=					'''tiger cat'''

A__  :  Dict								=					[
    '''microsoft/resnet-50''',
    # See all resnet models at https://huggingface.co/models?filter=resnet
]



class 				__snake_case						(     nn.Module						):




							def __init__(       self     :       int				,				A_     :       int				,				A_     :       int				,				A_     :       int = 3				,				A_     :       int = 1				,				A_     :       str = "relu"):
									super().__init__()
									lowerCAmelCase_			:     Optional[Any]									= nn.Convad(
									    A_				,				A_				,				kernel_size=A_				,				stride=A_				,				padding=kernel_size // 2				,				bias=A_)
									lowerCAmelCase_			:     Any									= nn.BatchNormad(A_)
									lowerCAmelCase_			:     Optional[int]									= ACTaFN[activation] if activation is not None else nn.Identity()







							def    UpperCAmelCase__			(       self     :       Union[str, Any]				,				A_     :       Tensor):
									lowerCAmelCase_			:     Tuple									= self.convolution(A_)
									lowerCAmelCase_			:     str									= self.normalization(A_)
									lowerCAmelCase_			:     Tuple									= self.activation(A_)
									return hidden_state





class 				__snake_case						(     nn.Module						):




							def __init__(       self     :       Optional[Any]				,				A_     :       ResNetConfig):
									super().__init__()
									lowerCAmelCase_			:     Union[str, Any]									= ResNetConvLayer(
									    config.num_channels				,				config.embedding_size				,				kernel_size=7				,				stride=2				,				activation=config.hidden_act)
									lowerCAmelCase_			:     Any									= nn.MaxPoolad(kernel_size=3				,				stride=2				,				padding=1)
									lowerCAmelCase_			:     Dict									= config.num_channels







							def    UpperCAmelCase__			(       self     :       Any				,				A_     :       Tensor):
									lowerCAmelCase_			:     Tuple									= pixel_values.shape[1]
									if num_channels != self.num_channels:
											raise ValueError(
											    '''Make sure that the channel dimension of the pixel values match with the one set in the configuration.''')
									lowerCAmelCase_			:     int									= self.embedder(A_)
									lowerCAmelCase_			:     List[str]									= self.pooler(A_)
									return embedding





class 				__snake_case						(     nn.Module						):




							def __init__(       self     :       List[str]				,				A_     :       int				,				A_     :       int				,				A_     :       int = 2):
									super().__init__()
									lowerCAmelCase_			:     Optional[Any]									= nn.Convad(A_				,				A_				,				kernel_size=1				,				stride=A_				,				bias=A_)
									lowerCAmelCase_			:     int									= nn.BatchNormad(A_)







							def    UpperCAmelCase__			(       self     :       Any				,				A_     :       Tensor):
									lowerCAmelCase_			:     str									= self.convolution(A_)
									lowerCAmelCase_			:     Optional[int]									= self.normalization(A_)
									return hidden_state





class 				__snake_case						(     nn.Module						):




							def __init__(       self     :       int				,				A_     :       int				,				A_     :       int				,				A_     :       int = 1				,				A_     :       str = "relu"):
									super().__init__()
									lowerCAmelCase_			:     Tuple									= in_channels != out_channels or stride != 1
									lowerCAmelCase_			:     str									= (
									    ResNetShortCut(A_				,				A_				,				stride=A_) if should_apply_shortcut else nn.Identity()
									)
									lowerCAmelCase_			:     str									= nn.Sequential(
									    ResNetConvLayer(A_				,				A_				,				stride=A_)				,				ResNetConvLayer(A_				,				A_				,				activation=A_)				,				)
									lowerCAmelCase_			:     List[Any]									= ACTaFN[activation]







							def    UpperCAmelCase__			(       self     :       Union[str, Any]				,				A_     :       Dict):
									lowerCAmelCase_			:     Optional[int]									= hidden_state
									lowerCAmelCase_			:     Any									= self.layer(A_)
									lowerCAmelCase_			:     Optional[int]									= self.shortcut(A_)
									hidden_state += residual
									lowerCAmelCase_			:     Tuple									= self.activation(A_)
									return hidden_state





class 				__snake_case						(     nn.Module						):




							def __init__(       self     :       Optional[int]				,				A_     :       int				,				A_     :       int				,				A_     :       int = 1				,				A_     :       str = "relu"				,				A_     :       int = 4):
									super().__init__()
									lowerCAmelCase_			:     Union[str, Any]									= in_channels != out_channels or stride != 1
									lowerCAmelCase_			:     List[str]									= out_channels // reduction
									lowerCAmelCase_			:     Union[str, Any]									= (
									    ResNetShortCut(A_				,				A_				,				stride=A_) if should_apply_shortcut else nn.Identity()
									)
									lowerCAmelCase_			:     List[Any]									= nn.Sequential(
									    ResNetConvLayer(A_				,				A_				,				kernel_size=1)				,				ResNetConvLayer(A_				,				A_				,				stride=A_)				,				ResNetConvLayer(A_				,				A_				,				kernel_size=1				,				activation=A_)				,				)
									lowerCAmelCase_			:     int									= ACTaFN[activation]







							def    UpperCAmelCase__			(       self     :       Union[str, Any]				,				A_     :       Dict):
									lowerCAmelCase_			:     List[str]									= hidden_state
									lowerCAmelCase_			:     Optional[Any]									= self.layer(A_)
									lowerCAmelCase_			:     Optional[Any]									= self.shortcut(A_)
									hidden_state += residual
									lowerCAmelCase_			:     Dict									= self.activation(A_)
									return hidden_state





class 				__snake_case						(     nn.Module						):




							def __init__(       self     :       int				,				A_     :       ResNetConfig				,				A_     :       int				,				A_     :       int				,				A_     :       int = 2				,				A_     :       int = 2				,				):
									super().__init__()

									lowerCAmelCase_			:     List[str]									= ResNetBottleNeckLayer if config.layer_type == '''bottleneck''' else ResNetBasicLayer

									lowerCAmelCase_			:     int									= nn.Sequential(
									    # downsampling is done in the first layer with stride of 2
									    layer(A_				,				A_				,				stride=A_				,				activation=config.hidden_act)				,				*[layer(A_				,				A_				,				activation=config.hidden_act) for _ in range(depth - 1)]				,				)







							def    UpperCAmelCase__			(       self     :       Tuple				,				A_     :       Tensor):
									lowerCAmelCase_			:     Union[str, Any]									= input
									for layer in self.layers:
											lowerCAmelCase_			:     List[str]									= layer(A_)
									return hidden_state





class 				__snake_case						(     nn.Module						):




							def __init__(       self     :       List[str]				,				A_     :       ResNetConfig):
									super().__init__()
									lowerCAmelCase_			:     Union[str, Any]									= nn.ModuleList([])
									# based on `downsample_in_first_stage` the first layer of the first stage may or may not downsample the input
									self.stages.append(
									    ResNetStage(
									        A_				,				config.embedding_size				,				config.hidden_sizes[0]				,				stride=2 if config.downsample_in_first_stage else 1				,				depth=config.depths[0]				,				))
									lowerCAmelCase_			:     Tuple									= zip(config.hidden_sizes				,				config.hidden_sizes[1:])
									for (in_channels, out_channels), depth in zip(A_				,				config.depths[1:]):
											self.stages.append(ResNetStage(A_				,				A_				,				A_				,				depth=A_))







							def    UpperCAmelCase__			(       self     :       Union[str, Any]				,				A_     :       Tensor				,				A_     :       bool = False				,				A_     :       bool = True):
									lowerCAmelCase_			:     Union[str, Any]									= () if output_hidden_states else None

									for stage_module in self.stages:
											if output_hidden_states:
													lowerCAmelCase_			:     int									= hidden_states + (hidden_state,)

											lowerCAmelCase_			:     int									= stage_module(A_)

									if output_hidden_states:
											lowerCAmelCase_			:     List[Any]									= hidden_states + (hidden_state,)

									if not return_dict:
											return tuple(v for v in [hidden_state, hidden_states] if v is not None)

									return BaseModelOutputWithNoAttention(
									    last_hidden_state=A_				,				hidden_states=A_				,				)





class 				__snake_case						(     UpperCamelCase_						):
							_a			      = ResNetConfig
							_a			      = '''resnet'''
							_a			      = '''pixel_values'''
							_a			      = True




							def    UpperCAmelCase__			(       self     :       Optional[Any]				,				A_     :       Any):
									if isinstance(A_				,				nn.Convad):
											nn.init.kaiming_normal_(module.weight				,				mode='''fan_out'''				,				nonlinearity='''relu''')
									elif isinstance(A_				,				(nn.BatchNormad, nn.GroupNorm)):
											nn.init.constant_(module.weight				,				1)
											nn.init.constant_(module.bias				,				0)







							def    UpperCAmelCase__			(       self     :       str				,				A_     :       int				,				A_     :       Union[str, Any]=False):
									if isinstance(A_				,				A_):
											lowerCAmelCase_			:     Union[str, Any]									= value





A__  :  Any								=					R'''
    This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it
    as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
    behavior.

    Parameters:
        config ([`ResNetConfig`]): Model configuration class with all the parameters of the model.
            Initializing with a config file does not load the weights associated with the model, only the
            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
'''

A__  :  Union[str, Any]								=					R'''
    Args:
        pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
            Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See
            [`ConvNextImageProcessor.__call__`] for details.

        output_hidden_states (`bool`, *optional*):
            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
            more detail.
        return_dict (`bool`, *optional*):
            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
'''



@add_start_docstrings(
    '''The bare ResNet model outputting raw features without any specific head on top.'''						,UpperCamelCase_						,)
class 				__snake_case						(     UpperCamelCase_						):




							def __init__(       self     :       Any				,				A_     :       Any):
									super().__init__(A_)
									lowerCAmelCase_			:     str									= config
									lowerCAmelCase_			:     Union[str, Any]									= ResNetEmbeddings(A_)
									lowerCAmelCase_			:     Dict									= ResNetEncoder(A_)
									lowerCAmelCase_			:     Any									= nn.AdaptiveAvgPoolad((1, 1))
									# Initialize weights and apply final processing
									self.post_init()







							@add_start_docstrings_to_model_forward(A_)
							@add_code_sample_docstrings(
							    checkpoint=_CHECKPOINT_FOR_DOC				,				output_type=A_				,				config_class=_CONFIG_FOR_DOC				,				modality='''vision'''				,				expected_output=_EXPECTED_OUTPUT_SHAPE				,				)
							def    UpperCAmelCase__			(       self     :       Optional[int]				,				A_     :       Tensor				,				A_     :       Optional[bool] = None				,				A_     :       Optional[bool] = None):
									lowerCAmelCase_			:     Optional[int]									= (
									    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
									)
									lowerCAmelCase_			:     Optional[int]									= return_dict if return_dict is not None else self.config.use_return_dict

									lowerCAmelCase_			:     Tuple									= self.embedder(A_)

									lowerCAmelCase_			:     Tuple									= self.encoder(
									    A_				,				output_hidden_states=A_				,				return_dict=A_)

									lowerCAmelCase_			:     List[Any]									= encoder_outputs[0]

									lowerCAmelCase_			:     str									= self.pooler(A_)

									if not return_dict:
											return (last_hidden_state, pooled_output) + encoder_outputs[1:]

									return BaseModelOutputWithPoolingAndNoAttention(
									    last_hidden_state=A_				,				pooler_output=A_				,				hidden_states=encoder_outputs.hidden_states				,				)





@add_start_docstrings(
    '''
    ResNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for
    ImageNet.
    '''						,UpperCamelCase_						,)
class 				__snake_case						(     UpperCamelCase_						):




							def __init__(       self     :       Dict				,				A_     :       List[str]):
									super().__init__(A_)
									lowerCAmelCase_			:     Optional[Any]									= config.num_labels
									lowerCAmelCase_			:     Union[str, Any]									= ResNetModel(A_)
									# classification head
									lowerCAmelCase_			:     List[str]									= nn.Sequential(
									    nn.Flatten()				,				nn.Linear(config.hidden_sizes[-1]				,				config.num_labels) if config.num_labels > 0 else nn.Identity()				,				)
									# initialize weights and apply final processing
									self.post_init()







							@add_start_docstrings_to_model_forward(A_)
							@add_code_sample_docstrings(
							    checkpoint=_IMAGE_CLASS_CHECKPOINT				,				output_type=A_				,				config_class=_CONFIG_FOR_DOC				,				expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT				,				)
							def    UpperCAmelCase__			(       self     :       int				,				A_     :       Optional[torch.FloatTensor] = None				,				A_     :       Optional[torch.LongTensor] = None				,				A_     :       Optional[bool] = None				,				A_     :       Optional[bool] = None				,				):
									lowerCAmelCase_			:     Dict									= return_dict if return_dict is not None else self.config.use_return_dict

									lowerCAmelCase_			:     Dict									= self.resnet(A_				,				output_hidden_states=A_				,				return_dict=A_)

									lowerCAmelCase_			:     Optional[Any]									= outputs.pooler_output if return_dict else outputs[1]

									lowerCAmelCase_			:     List[str]									= self.classifier(A_)

									lowerCAmelCase_			:     List[str]									= None

									if labels is not None:
											if self.config.problem_type is None:
													if self.num_labels == 1:
															lowerCAmelCase_			:     List[Any]									= '''regression'''
													elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
															lowerCAmelCase_			:     Optional[Any]									= '''single_label_classification'''
													else:
															lowerCAmelCase_			:     str									= '''multi_label_classification'''
											if self.config.problem_type == "regression":
													lowerCAmelCase_			:     List[str]									= MSELoss()
													if self.num_labels == 1:
															lowerCAmelCase_			:     List[Any]									= loss_fct(logits.squeeze()				,				labels.squeeze())
													else:
															lowerCAmelCase_			:     Dict									= loss_fct(A_				,				A_)
											elif self.config.problem_type == "single_label_classification":
													lowerCAmelCase_			:     int									= CrossEntropyLoss()
													lowerCAmelCase_			:     Optional[int]									= loss_fct(logits.view(-1				,				self.num_labels)				,				labels.view(-1))
											elif self.config.problem_type == "multi_label_classification":
													lowerCAmelCase_			:     List[str]									= BCEWithLogitsLoss()
													lowerCAmelCase_			:     Tuple									= loss_fct(A_				,				A_)

									if not return_dict:
											lowerCAmelCase_			:     int									= (logits,) + outputs[2:]
											return (loss,) + output if loss is not None else output

									return ImageClassifierOutputWithNoAttention(loss=A_				,				logits=A_				,				hidden_states=outputs.hidden_states)





@add_start_docstrings(
    '''
    ResNet backbone, to be used with frameworks like DETR and MaskFormer.
    '''						,UpperCamelCase_						,)
class 				__snake_case						(     UpperCamelCase_						,UpperCamelCase_						):




							def __init__(       self     :       Dict				,				A_     :       int):
									super().__init__(A_)
									super()._init_backbone(A_)

									lowerCAmelCase_			:     List[Any]									= [config.embedding_size] + config.hidden_sizes
									lowerCAmelCase_			:     Dict									= ResNetEmbeddings(A_)
									lowerCAmelCase_			:     Any									= ResNetEncoder(A_)

									# initialize weights and apply final processing
									self.post_init()







							@add_start_docstrings_to_model_forward(A_)
							@replace_return_docstrings(output_type=A_				,				config_class=_CONFIG_FOR_DOC)
							def    UpperCAmelCase__			(       self     :       List[str]				,				A_     :       Tensor				,				A_     :       Optional[bool] = None				,				A_     :       Optional[bool] = None):
									lowerCAmelCase_			:     Optional[Any]									= return_dict if return_dict is not None else self.config.use_return_dict
									lowerCAmelCase_			:     Optional[int]									= (
									    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
									)

									lowerCAmelCase_			:     Any									= self.embedder(A_)

									lowerCAmelCase_			:     str									= self.encoder(A_				,				output_hidden_states=A_				,				return_dict=A_)

									lowerCAmelCase_			:     Union[str, Any]									= outputs.hidden_states

									lowerCAmelCase_			:     List[Any]									= ()
									for idx, stage in enumerate(self.stage_names):
											if stage in self.out_features:
													feature_maps += (hidden_states[idx],)

									if not return_dict:
											lowerCAmelCase_			:     Any									= (feature_maps,)
											if output_hidden_states:
													output += (outputs.hidden_states,)
											return output

									return BackboneOutput(
									    feature_maps=A_				,				hidden_states=outputs.hidden_states if output_hidden_states else None				,				attentions=A_				,				)












import unittest

from transformers import BertGenerationTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


_UpperCAmelCase				:       Optional[Any]	   =				"""▁"""

_UpperCAmelCase				:       Optional[Any]	   =				get_tests_dir("""fixtures/test_sentencepiece.model""")

@require_sentencepiece
class    lowerCAmelCase							(					__UpperCamelCase,  unittest.TestCase			):
 UpperCAmelCase__													=		BertGenerationTokenizer
 UpperCAmelCase__													=		False
 UpperCAmelCase__													=		True
 def      A_	(      self  :      List[Any]	)			->	List[str]:
     super().setUp()

     lowerCamelCase__    :      Dict					 =		BertGenerationTokenizer(UpperCAmelCase			,     keep_accents=UpperCAmelCase	)
     tokenizer.save_pretrained(self.tmpdirname	)
 def      A_	(      self  :      Optional[Any]	)			->	Dict:
     lowerCamelCase__    :      List[str]					 =		'<s>'
     lowerCamelCase__    :      Optional[int]					 =		1

     self.assertEqual(self.get_tokenizer()._convert_token_to_id(UpperCAmelCase	)			,     UpperCAmelCase	)
     self.assertEqual(self.get_tokenizer()._convert_id_to_token(UpperCAmelCase	)			,     UpperCAmelCase	)
 def      A_	(      self  :      List[str]	)			->	Optional[int]:
     lowerCamelCase__    :      Optional[Any]					 =		list(self.get_tokenizer().get_vocab().keys()	)

     self.assertEqual(vocab_keys[0]			,     '<unk>'	)
     self.assertEqual(vocab_keys[1]			,     '<s>'	)
     self.assertEqual(vocab_keys[-1]			,     '<pad>'	)
     self.assertEqual(len(UpperCAmelCase	)			,     1002	)
 def      A_	(      self  :      List[Any]	)			->	Optional[Any]:
     self.assertEqual(self.get_tokenizer().vocab_size			,     1000	)
 def      A_	(      self  :      Union[str, Any]	)			->	List[Any]:
     lowerCamelCase__    :      Union[str, Any]					 =		BertGenerationTokenizer(UpperCAmelCase			,     keep_accents=UpperCAmelCase	)

     lowerCamelCase__    :      List[str]					 =		tokenizer.tokenize('This is a test'	)
     self.assertListEqual(UpperCAmelCase			,     ['▁This', '▁is', '▁a', '▁t', 'est']	)

     self.assertListEqual(
         tokenizer.convert_tokens_to_ids(UpperCAmelCase	)			,     [285, 46, 10, 170, 382]			,     )

     lowerCamelCase__    :      List[str]					 =		tokenizer.tokenize('I was born in 92000, and this is falsé.'	)
     self.assertListEqual(
         UpperCAmelCase			,     [
             SPIECE_UNDERLINE + 'I',
             SPIECE_UNDERLINE + 'was',
             SPIECE_UNDERLINE + 'b',
             'or',
             'n',
             SPIECE_UNDERLINE + 'in',
             SPIECE_UNDERLINE + '',
             '9',
             '2',
             '0',
             '0',
             '0',
             ',',
             SPIECE_UNDERLINE + 'and',
             SPIECE_UNDERLINE + 'this',
             SPIECE_UNDERLINE + 'is',
             SPIECE_UNDERLINE + 'f',
             'al',
             's',
             'é',
             '.',
         ]			,     )
     lowerCamelCase__    :      Optional[int]					 =		tokenizer.convert_tokens_to_ids(UpperCAmelCase	)
     self.assertListEqual(
         UpperCAmelCase			,     [8, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 0, 4]			,     )

     lowerCamelCase__    :      Optional[Any]					 =		tokenizer.convert_ids_to_tokens(UpperCAmelCase	)
     self.assertListEqual(
         UpperCAmelCase			,     [
             SPIECE_UNDERLINE + 'I',
             SPIECE_UNDERLINE + 'was',
             SPIECE_UNDERLINE + 'b',
             'or',
             'n',
             SPIECE_UNDERLINE + 'in',
             SPIECE_UNDERLINE + '',
             '<unk>',
             '2',
             '0',
             '0',
             '0',
             ',',
             SPIECE_UNDERLINE + 'and',
             SPIECE_UNDERLINE + 'this',
             SPIECE_UNDERLINE + 'is',
             SPIECE_UNDERLINE + 'f',
             'al',
             's',
             '<unk>',
             '.',
         ]			,     )
 @cached_property
 def      A_	(      self  :      Dict	)			->	Tuple:
     return BertGenerationTokenizer.from_pretrained('google/bert_for_seq_generation_L-24_bbc_encoder'	)
 @slow
 def      A_	(      self  :      Optional[int]	)			->	List[str]:
     lowerCamelCase__    :      Union[str, Any]					 =		'Hello World!'
     lowerCamelCase__    :      Dict					 =		[18536, 2260, 101]

     self.assertListEqual(UpperCAmelCase			,     self.big_tokenizer.encode(UpperCAmelCase	)	)
 @slow
 def      A_	(      self  :      Optional[Any]	)			->	str:
     lowerCamelCase__    :      List[Any]					 =		(
         'This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) " [ ] ! : - . Also we will'
         ' add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth'
     )
     lowerCamelCase__    :      Any					 =		[
         871,
         419,
         358,
         946,
         991,
         2521,
         452,
         358,
         1357,
         387,
         7751,
         3536,
         112,
         985,
         456,
         126,
         865,
         938,
         5400,
         5734,
         458,
         1368,
         467,
         786,
         2462,
         5246,
         1159,
         633,
         865,
         4519,
         457,
         582,
         852,
         2557,
         427,
         916,
         508,
         405,
         34324,
         497,
         391,
         408,
         11342,
         1244,
         385,
         100,
         938,
         985,
         456,
         574,
         362,
         12597,
         3200,
         3129,
         1172,
     ]

     self.assertListEqual(UpperCAmelCase			,     self.big_tokenizer.encode(UpperCAmelCase	)	)
 @require_torch
 @slow
 def      A_	(      self  :      int	)			->	Optional[Any]:
     import torch

     from transformers import BertGenerationConfig, BertGenerationEncoder

     # Build sequence
     lowerCamelCase__    :      str					 =		list(self.big_tokenizer.get_vocab().keys()	)[:10]
     lowerCamelCase__    :      int					 =		' '.join(UpperCAmelCase	)
     lowerCamelCase__    :      Union[str, Any]					 =		self.big_tokenizer.encode_plus(UpperCAmelCase			,     return_tensors='pt'			,     return_token_type_ids=UpperCAmelCase	)
     lowerCamelCase__    :      Optional[Any]					 =		self.big_tokenizer.batch_encode_plus(
         [sequence + ' ' + sequence]			,     return_tensors='pt'			,     return_token_type_ids=UpperCAmelCase	)

     lowerCamelCase__    :      Tuple					 =		BertGenerationConfig()
     lowerCamelCase__    :      Optional[Any]					 =		BertGenerationEncoder(UpperCAmelCase	)

     assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size

     with torch.no_grad():
         model(**UpperCAmelCase	)
         model(**UpperCAmelCase	)







 @slow
 def      A_	(      self  :      Optional[int]	)			->	List[Any]:
     # fmt: off
     lowerCamelCase__    :      Any					 =		{'input_ids': [[39286, 458, 36335, 2001, 456, 13073, 13266, 455, 113, 7746, 1741, 11157, 391, 13073, 13266, 455, 113, 3967, 35412, 113, 4936, 109, 3870, 2377, 113, 30084, 45720, 458, 134, 17496, 112, 503, 11672, 113, 118, 112, 5665, 13347, 38687, 112, 1496, 31389, 112, 3268, 47264, 134, 962, 112, 16377, 8035, 23130, 430, 12169, 15518, 28592, 458, 146, 41697, 109, 391, 12169, 15518, 16689, 458, 146, 41358, 109, 452, 726, 4034, 111, 763, 35412, 5082, 388, 1903, 111, 9051, 391, 2870, 48918, 1900, 1123, 550, 998, 112, 9586, 15985, 455, 391, 410, 22955, 37636, 114], [448, 17496, 419, 3663, 385, 763, 113, 27533, 2870, 3283, 13043, 1639, 24713, 523, 656, 24013, 18550, 2521, 517, 27014, 21244, 420, 1212, 1465, 391, 927, 4833, 388, 578, 11786, 114, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [484, 2169, 7687, 21932, 18146, 726, 363, 17032, 3391, 114, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
     # fmt: on

     self.tokenizer_integration_test_util(
         expected_encoding=UpperCAmelCase			,     model_name='google/bert_for_seq_generation_L-24_bbc_encoder'			,     revision='c817d1fd1be2ffa69431227a1fe320544943d4db'			,     )





