


import collections.abc
from typing import Optional, Tuple, Union

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

from ...activations import ACTaFN
from ...modeling_outputs import BaseModelOutputWithNoAttention, ImageClassifierOutputWithNoAttention
from ...modeling_utils import PreTrainedModel
from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging
from .configuration_poolformer import PoolFormerConfig


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

# General docstring
snake_case_						:			Union[str, Any]													=							'''PoolFormerConfig'''

# Base docstring
snake_case_						:			List[str]													=							'''sail/poolformer_s12'''
snake_case_						:			str													=							[1, 5_12, 7, 7]

# Image classification docstring
snake_case_						:			Optional[int]													=							'''sail/poolformer_s12'''
snake_case_						:			Dict													=							'''tabby, tabby cat'''

snake_case_						:			List[Any]													=							[
    '''sail/poolformer_s12''',
    # See all PoolFormer models at https://huggingface.co/models?filter=poolformer
]







def 						lowerCamelCase(   a__	,a__ = 0.0	,a__ = False):
				if drop_prob == 0.0 or not training:
								return input
				_SCREAMING_SNAKE_CASE			  =1 - drop_prob
				_SCREAMING_SNAKE_CASE			  =(input.shape[0],) + (1,) * (input.ndim - 1)  # work with diff dim tensors, not just 2D ConvNets
				_SCREAMING_SNAKE_CASE			  =keep_prob + torch.rand(a__	,dtype=input.dtype	,device=input.device)
				random_tensor.floor_()  # binarize
				_SCREAMING_SNAKE_CASE			  =input.div(a__) * random_tensor
				return output

class 						A__							(		nn.Module  ):




						def __init__(       self					:      List[Any]				,    _a					:      Optional[float] = None  )							->     None:


										"""simple docstring"""





										super().__init__()
										_SCREAMING_SNAKE_CASE			  =drop_prob




						def 							__UpperCamelCase				(       self					:      str				,    _a					:      torch.Tensor  )							->     torch.Tensor:


										"""simple docstring"""





										return drop_path(_a				,    self.drop_prob				,    self.training  )




						def 							__UpperCamelCase				(       self					:      Any  )							->     str:


										"""simple docstring"""





										return "p={}".format(self.drop_prob  )

class 						A__							(		nn.Module  ):




						def __init__(       self					:      int				,    _a					:      Optional[int]				,    _a					:      Union[str, Any]				,    _a					:      Optional[int]				,    _a					:      List[str]				,    _a					:      Dict				,    _a					:      Any=None  )							->     List[Any]:


										"""simple docstring"""





										super().__init__()
										_SCREAMING_SNAKE_CASE			  =patch_size if isinstance(_a				,    collections.abc.Iterable  ) else (patch_size, patch_size)
										_SCREAMING_SNAKE_CASE			  =stride if isinstance(_a				,    collections.abc.Iterable  ) else (stride, stride)
										_SCREAMING_SNAKE_CASE			  =padding if isinstance(_a				,    collections.abc.Iterable  ) else (padding, padding)

										_SCREAMING_SNAKE_CASE			  =nn.Convad(_a				,    _a				,    kernel_size=_a				,    stride=_a				,    padding=_a  )
										_SCREAMING_SNAKE_CASE			  =norm_layer(_a  ) if norm_layer else nn.Identity()




						def 							__UpperCamelCase				(       self					:      str				,    _a					:      str  )							->     int:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =self.projection(_a  )
										_SCREAMING_SNAKE_CASE			  =self.norm(_a  )
										return embeddings

class 						A__							(		nn.GroupNorm  ):




						def __init__(       self					:      List[Any]				,    _a					:      List[str]				,    **_a					:      Dict  )							->     Optional[int]:


										"""simple docstring"""





										super().__init__(1				,    _a				,    **_a  )

class 						A__							(		nn.Module  ):




						def __init__(       self					:      List[str]				,    _a					:      int  )							->     Optional[Any]:


										"""simple docstring"""





										super().__init__()
										_SCREAMING_SNAKE_CASE			  =nn.AvgPoolad(_a				,    stride=1				,    padding=pool_size // 2				,    count_include_pad=_a  )




						def 							__UpperCamelCase				(       self					:      Union[str, Any]				,    _a					:      Union[str, Any]  )							->     int:


										"""simple docstring"""





										return self.pool(_a  ) - hidden_states

class 						A__							(		nn.Module  ):




						def __init__(       self					:      Tuple				,    _a					:      Optional[int]				,    _a					:      str				,    _a					:      Optional[int]				,    _a					:      List[str]  )							->     List[Any]:


										"""simple docstring"""





										super().__init__()
										_SCREAMING_SNAKE_CASE			  =nn.Convad(_a				,    _a				,    1  )
										_SCREAMING_SNAKE_CASE			  =nn.Convad(_a				,    _a				,    1  )
										_SCREAMING_SNAKE_CASE			  =PoolFormerDropPath(_a  )
										if isinstance(config.hidden_act				,    _a  ):
														_SCREAMING_SNAKE_CASE			  =ACTaFN[config.hidden_act]
										else:
														_SCREAMING_SNAKE_CASE			  =config.hidden_act




						def 							__UpperCamelCase				(       self					:      Tuple				,    _a					:      int  )							->     List[Any]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =self.conva(_a  )
										_SCREAMING_SNAKE_CASE			  =self.act_fn(_a  )
										_SCREAMING_SNAKE_CASE			  =self.drop(_a  )
										_SCREAMING_SNAKE_CASE			  =self.conva(_a  )
										_SCREAMING_SNAKE_CASE			  =self.drop(_a  )

										return hidden_states

class 						A__							(		nn.Module  ):




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


										"""simple docstring"""





										super().__init__()
										_SCREAMING_SNAKE_CASE			  =PoolFormerPooling(_a  )
										_SCREAMING_SNAKE_CASE			  =PoolFormerOutput(_a				,    _a				,    _a				,    _a  )
										_SCREAMING_SNAKE_CASE			  =PoolFormerGroupNorm(_a  )
										_SCREAMING_SNAKE_CASE			  =PoolFormerGroupNorm(_a  )

										# Useful for training neural nets
										_SCREAMING_SNAKE_CASE			  =PoolFormerDropPath(_a  ) if drop_path > 0.0 else nn.Identity()
										_SCREAMING_SNAKE_CASE			  =config.use_layer_scale
										if config.use_layer_scale:
														_SCREAMING_SNAKE_CASE			  =nn.Parameter(
														    config.layer_scale_init_value * torch.ones((_a)  )				,    requires_grad=_a  )
														_SCREAMING_SNAKE_CASE			  =nn.Parameter(
														    config.layer_scale_init_value * torch.ones((_a)  )				,    requires_grad=_a  )




						def 							__UpperCamelCase				(       self					:      int				,    _a					:      Tuple  )							->     List[Any]:


										"""simple docstring"""





										if self.use_layer_scale:
														_SCREAMING_SNAKE_CASE			  =self.pooling(self.before_norm(_a  )  )
														_SCREAMING_SNAKE_CASE			  =self.layer_scale_a.unsqueeze(-1  ).unsqueeze(-1  ) * pooling_output
														# First residual connection
														_SCREAMING_SNAKE_CASE			  =hidden_states + self.drop_path(_a  )
														_SCREAMING_SNAKE_CASE			  =()

														_SCREAMING_SNAKE_CASE			  =self.output(self.after_norm(_a  )  )
														_SCREAMING_SNAKE_CASE			  =self.layer_scale_a.unsqueeze(-1  ).unsqueeze(-1  ) * layer_output
														# Second residual connection
														_SCREAMING_SNAKE_CASE			  =hidden_states + self.drop_path(_a  )

														_SCREAMING_SNAKE_CASE			  =(output,) + outputs
														return outputs

										else:
														_SCREAMING_SNAKE_CASE			  =self.drop_path(self.pooling(self.before_norm(_a  )  )  )
														# First residual connection
														_SCREAMING_SNAKE_CASE			  =pooling_output + hidden_states
														_SCREAMING_SNAKE_CASE			  =()

														# Second residual connection inside the PoolFormerOutput block
														_SCREAMING_SNAKE_CASE			  =self.drop_path(self.output(self.after_norm(_a  )  )  )
														_SCREAMING_SNAKE_CASE			  =hidden_states + layer_output

														_SCREAMING_SNAKE_CASE			  =(output,) + outputs
														return outputs

class 						A__							(		nn.Module  ):




						def __init__(       self					:      Optional[Any]				,    _a					:      List[str]  )							->     Optional[Any]:


										"""simple docstring"""





										super().__init__()
										_SCREAMING_SNAKE_CASE			  =config
										# stochastic depth decay rule
										_SCREAMING_SNAKE_CASE			  =[x.item() for x in torch.linspace(0				,    config.drop_path_rate				,    sum(config.depths  )  )]

										# patch embeddings
										_SCREAMING_SNAKE_CASE			  =[]
										for i in range(config.num_encoder_blocks  ):
														embeddings.append(
														    PoolFormerEmbeddings(
														        patch_size=config.patch_sizes[i]				,    stride=config.strides[i]				,    padding=config.padding[i]				,    num_channels=config.num_channels if i == 0 else config.hidden_sizes[i - 1]				,    hidden_size=config.hidden_sizes[i]				,    )  )
										_SCREAMING_SNAKE_CASE			  =nn.ModuleList(_a  )

										# Transformer blocks
										_SCREAMING_SNAKE_CASE			  =[]
										_SCREAMING_SNAKE_CASE			  =0
										for i in range(config.num_encoder_blocks  ):
														# each block consists of layers
														_SCREAMING_SNAKE_CASE			  =[]
														if i != 0:
																		cur += config.depths[i - 1]
														for j in range(config.depths[i]  ):
																		layers.append(
																		    PoolFormerLayer(
																		        _a				,    num_channels=config.hidden_sizes[i]				,    pool_size=config.pool_size				,    hidden_size=config.hidden_sizes[i]				,    intermediate_size=int(config.hidden_sizes[i] * config.mlp_ratio  )				,    drop_path=dpr[cur + j]				,    )  )
														blocks.append(nn.ModuleList(_a  )  )

										_SCREAMING_SNAKE_CASE			  =nn.ModuleList(_a  )




						def 							__UpperCamelCase				(       self					:      Dict				,    _a					:      int				,    _a					:      str=False				,    _a					:      Optional[Any]=True  )							->     Any:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =() if output_hidden_states else None

										_SCREAMING_SNAKE_CASE			  =pixel_values
										for idx, layers in enumerate(zip(self.patch_embeddings				,    self.block  )  ):
														_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE			  =layers
														# Get patch embeddings from hidden_states
														_SCREAMING_SNAKE_CASE			  =embedding_layer(_a  )
														# Send the embeddings through the blocks
														for _, blk in enumerate(_a  ):
																		_SCREAMING_SNAKE_CASE			  =blk(_a  )
																		_SCREAMING_SNAKE_CASE			  =layer_outputs[0]

														if output_hidden_states:
																		_SCREAMING_SNAKE_CASE			  =all_hidden_states + (hidden_states,)

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

										return BaseModelOutputWithNoAttention(last_hidden_state=_a				,    hidden_states=_a  )

class 						A__							(		UpperCamelCase__  ):
						UpperCAmelCase             =						PoolFormerConfig
						UpperCAmelCase             =						"poolformer"
						UpperCAmelCase             =						"pixel_values"
						UpperCAmelCase             =						True




						def 							__UpperCamelCase				(       self					:      List[Any]				,    _a					:      Optional[Any]  )							->     Dict:


										"""simple docstring"""





										if isinstance(_a				,    (nn.Linear, nn.Convad)  ):
														module.weight.data.normal_(mean=0.0				,    std=self.config.initializer_range  )
														if module.bias is not None:
																		module.bias.data.zero_()
										elif isinstance(_a				,    nn.LayerNorm  ):
														module.bias.data.zero_()
														module.weight.data.fill_(1.0  )




						def 							__UpperCamelCase				(       self					:      Dict				,    _a					:      Optional[int]				,    _a					:      Union[str, Any]=False  )							->     Union[str, Any]:


										"""simple docstring"""





										if isinstance(_a				,    _a  ):
														_SCREAMING_SNAKE_CASE			  =value
snake_case_						:			List[str]													=							R'''
    This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use
    it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
    behavior.

    Parameters:
        config ([`PoolFormerConfig`]): 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.
'''

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

@add_start_docstrings(
    "The bare PoolFormer Model transformer outputting raw hidden-states without any specific head on top."   ,     UpperCamelCase__   ,     )
class 						A__							(		UpperCamelCase__  ):




						def __init__(       self					:      Any				,    _a					:      Any  )							->     Any:


										"""simple docstring"""





										super().__init__(_a  )
										_SCREAMING_SNAKE_CASE			  =config

										_SCREAMING_SNAKE_CASE			  =PoolFormerEncoder(_a  )

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




						def 							__UpperCamelCase				(       self					:      int  )							->     int:


										"""simple docstring"""





										return self.embeddings.patch_embeddings




						@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[Any]				,    _a					:      Optional[torch.FloatTensor] = None				,    _a					:      Optional[bool] = None				,    _a					:      Optional[bool] = None				,    )							->     Union[Tuple, BaseModelOutputWithNoAttention]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =(
										    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
										)
										_SCREAMING_SNAKE_CASE			  =return_dict if return_dict is not None else self.config.use_return_dict

										if pixel_values is None:
														raise ValueError('''You have to specify pixel_values'''  )

										_SCREAMING_SNAKE_CASE			  =self.encoder(
										    _a				,    output_hidden_states=_a				,    return_dict=_a				,    )
										_SCREAMING_SNAKE_CASE			  =encoder_outputs[0]

										if not return_dict:
														return (sequence_output, None) + encoder_outputs[1:]

										return BaseModelOutputWithNoAttention(
										    last_hidden_state=_a				,    hidden_states=encoder_outputs.hidden_states				,    )

class 						A__							(		nn.Module  ):




						def __init__(       self					:      Optional[Any]				,    _a					:      List[str]  )							->     Optional[int]:


										"""simple docstring"""





										super().__init__()
										_SCREAMING_SNAKE_CASE			  =nn.Linear(config.hidden_size				,    config.hidden_size  )




						def 							__UpperCamelCase				(       self					:      Optional[Any]				,    _a					:      int  )							->     Any:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =self.dense(_a  )
										return output

@add_start_docstrings(
    "\n    PoolFormer Model transformer with an image classification head on top\n    "   ,     UpperCamelCase__   ,     )
class 						A__							(		UpperCamelCase__  ):




						def __init__(       self					:      Union[str, Any]				,    _a					:      List[Any]  )							->     Optional[Any]:


										"""simple docstring"""





										super().__init__(_a  )
										_SCREAMING_SNAKE_CASE			  =config.num_labels
										_SCREAMING_SNAKE_CASE			  =PoolFormerModel(_a  )

										# Final norm
										_SCREAMING_SNAKE_CASE			  =PoolFormerGroupNorm(config.hidden_sizes[-1]  )
										# Classifier head
										_SCREAMING_SNAKE_CASE			  =(
										    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					:      Tuple				,    _a					:      Optional[torch.FloatTensor] = None				,    _a					:      Optional[torch.LongTensor] = None				,    _a					:      Optional[bool] = None				,    _a					:      Optional[bool] = None				,    )							->     Union[Tuple, ImageClassifierOutputWithNoAttention]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =return_dict if return_dict is not None else self.config.use_return_dict

										_SCREAMING_SNAKE_CASE			  =self.poolformer(
										    _a				,    output_hidden_states=_a				,    return_dict=_a				,    )

										_SCREAMING_SNAKE_CASE			  =outputs[0]

										_SCREAMING_SNAKE_CASE			  =self.classifier(self.norm(_a  ).mean([-2, -1]  )  )

										_SCREAMING_SNAKE_CASE			  =None
										if labels is not None:
														if self.config.problem_type is None:
																		if self.num_labels == 1:
																						_SCREAMING_SNAKE_CASE			  ='''regression'''
																		elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
																						_SCREAMING_SNAKE_CASE			  ='''single_label_classification'''
																		else:
																						_SCREAMING_SNAKE_CASE			  ='''multi_label_classification'''

														if self.config.problem_type == "regression":
																		_SCREAMING_SNAKE_CASE			  =MSELoss()
																		if self.num_labels == 1:
																						_SCREAMING_SNAKE_CASE			  =loss_fct(logits.squeeze()				,    labels.squeeze()  )
																		else:
																						_SCREAMING_SNAKE_CASE			  =loss_fct(_a				,    _a  )
														elif self.config.problem_type == "single_label_classification":
																		_SCREAMING_SNAKE_CASE			  =CrossEntropyLoss()
																		_SCREAMING_SNAKE_CASE			  =loss_fct(logits.view(-1				,    self.num_labels  )				,    labels.view(-1  )  )
														elif self.config.problem_type == "multi_label_classification":
																		_SCREAMING_SNAKE_CASE			  =BCEWithLogitsLoss()
																		_SCREAMING_SNAKE_CASE			  =loss_fct(_a				,    _a  )

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

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



import json
import os
import unittest

from transformers import BatchEncoding, LEDTokenizer, LEDTokenizerFast
from transformers.models.led.tokenization_led import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin

@require_tokenizers
class 						A__							(		UpperCamelCase__   ,     unittest.TestCase  ):
						UpperCAmelCase             =						LEDTokenizer
						UpperCAmelCase             =						LEDTokenizerFast
						UpperCAmelCase             =						True




						def 							__UpperCamelCase				(       self					:      Dict  )							->     Optional[int]:


										"""simple docstring"""





										super().setUp()
										_SCREAMING_SNAKE_CASE			  =[
										    '''l''',
										    '''o''',
										    '''w''',
										    '''e''',
										    '''r''',
										    '''s''',
										    '''t''',
										    '''i''',
										    '''d''',
										    '''n''',
										    '''\u0120''',
										    '''\u0120l''',
										    '''\u0120n''',
										    '''\u0120lo''',
										    '''\u0120low''',
										    '''er''',
										    '''\u0120lowest''',
										    '''\u0120newer''',
										    '''\u0120wider''',
										    '''<unk>''',
										]
										_SCREAMING_SNAKE_CASE			  =dict(zip(_a				,    range(len(_a  )  )  )  )
										_SCREAMING_SNAKE_CASE			  =['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
										_SCREAMING_SNAKE_CASE			  ={'''unk_token''': '''<unk>'''}

										_SCREAMING_SNAKE_CASE			  =os.path.join(self.tmpdirname				,    VOCAB_FILES_NAMES['''vocab_file''']  )
										_SCREAMING_SNAKE_CASE			  =os.path.join(self.tmpdirname				,    VOCAB_FILES_NAMES['''merges_file''']  )
										with open(self.vocab_file				,    '''w'''				,    encoding='''utf-8'''  ) as fp:
														fp.write(json.dumps(_a  ) + '''\n'''  )
										with open(self.merges_file				,    '''w'''				,    encoding='''utf-8'''  ) as fp:
														fp.write('''\n'''.join(_a  )  )




						def 							__UpperCamelCase				(       self					:      str				,    **_a					:      int  )							->     Optional[Any]:


										"""simple docstring"""





										kwargs.update(self.special_tokens_map  )
										return self.tokenizer_class.from_pretrained(self.tmpdirname				,    **_a  )




						def 							__UpperCamelCase				(       self					:      List[str]				,    **_a					:      List[Any]  )							->     str:


										"""simple docstring"""





										kwargs.update(self.special_tokens_map  )
										return self.rust_tokenizer_class.from_pretrained(self.tmpdirname				,    **_a  )




						def 							__UpperCamelCase				(       self					:      Dict				,    _a					:      Any  )							->     Dict:


										"""simple docstring"""





										return "lower newer", "lower newer"




						@cached_property
						def 							__UpperCamelCase				(       self					:      Optional[Any]  )							->     Dict:


										"""simple docstring"""





										return LEDTokenizer.from_pretrained('''allenai/led-base-16384'''  )




						@cached_property
						def 							__UpperCamelCase				(       self					:      List[Any]  )							->     Any:


										"""simple docstring"""





										return LEDTokenizerFast.from_pretrained('''allenai/led-base-16384'''  )




						@require_torch
						def 							__UpperCamelCase				(       self					:      str  )							->     Dict:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
										_SCREAMING_SNAKE_CASE			  =[0, 250, 251, 1_7818, 13, 3_9186, 1938, 4, 2]

										for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
														_SCREAMING_SNAKE_CASE			  =tokenizer(_a				,    max_length=len(_a  )				,    padding=_a				,    return_tensors='''pt'''  )
														self.assertIsInstance(_a				,    _a  )

														self.assertEqual((2, 9)				,    batch.input_ids.shape  )
														self.assertEqual((2, 9)				,    batch.attention_mask.shape  )
														_SCREAMING_SNAKE_CASE			  =batch.input_ids.tolist()[0]
														self.assertListEqual(_a				,    _a  )




						@require_torch
						def 							__UpperCamelCase				(       self					:      Dict  )							->     Dict:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
										for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
														_SCREAMING_SNAKE_CASE			  =tokenizer(_a				,    padding=_a				,    return_tensors='''pt'''  )
														self.assertIn('''input_ids'''				,    _a  )
														self.assertIn('''attention_mask'''				,    _a  )
														self.assertNotIn('''labels'''				,    _a  )
														self.assertNotIn('''decoder_attention_mask'''				,    _a  )




						@require_torch
						def 							__UpperCamelCase				(       self					:      Dict  )							->     str:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =[
										    '''Summary of the text.''',
										    '''Another summary.''',
										]
										for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
														_SCREAMING_SNAKE_CASE			  =tokenizer(text_target=_a				,    max_length=32				,    padding='''max_length'''				,    return_tensors='''pt'''  )
														self.assertEqual(32				,    targets['''input_ids'''].shape[1]  )




						@require_torch
						def 							__UpperCamelCase				(       self					:      Any  )							->     Union[str, Any]:


										"""simple docstring"""





										for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
														_SCREAMING_SNAKE_CASE			  =tokenizer(
														    ['''I am a small frog''' * 1024, '''I am a small frog''']				,    padding=_a				,    truncation=_a				,    return_tensors='''pt'''  )
														self.assertIsInstance(_a				,    _a  )
														self.assertEqual(batch.input_ids.shape				,    (2, 5122)  )




						@require_torch
						def 							__UpperCamelCase				(       self					:      int  )							->     Dict:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =['''A long paragraph for summarization.''']
										_SCREAMING_SNAKE_CASE			  =[
										    '''Summary of the text.''',
										]
										for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
														_SCREAMING_SNAKE_CASE			  =tokenizer(_a				,    return_tensors='''pt'''  )
														_SCREAMING_SNAKE_CASE			  =tokenizer(text_target=_a				,    return_tensors='''pt'''  )
														_SCREAMING_SNAKE_CASE			  =inputs['''input_ids''']
														_SCREAMING_SNAKE_CASE			  =targets['''input_ids''']
														self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item()  )
														self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item()  )
														self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item()  )
														self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item()  )




						@require_torch
						def 							__UpperCamelCase				(       self					:      Optional[Any]  )							->     str:


										"""simple docstring"""





										for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
														_SCREAMING_SNAKE_CASE			  =['''Summary of the text.''', '''Another summary.''']
														_SCREAMING_SNAKE_CASE			  =[[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, -1, -1]]

														_SCREAMING_SNAKE_CASE			  =tokenizer(_a				,    padding=_a  )
														_SCREAMING_SNAKE_CASE			  =[[0] * len(_a  ) for x in encoded_output['''input_ids''']]
														_SCREAMING_SNAKE_CASE			  =tokenizer.pad(_a  )
														self.assertSequenceEqual(outputs['''global_attention_mask''']				,    _a  )




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


										"""simple docstring"""





										pass




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


										"""simple docstring"""





										for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
														with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"  ):
																		_SCREAMING_SNAKE_CASE			  =self.rust_tokenizer_class.from_pretrained(_a				,    **_a  )
																		_SCREAMING_SNAKE_CASE			  =self.tokenizer_class.from_pretrained(_a				,    **_a  )
																		_SCREAMING_SNAKE_CASE			  ='''A, <mask> AllenNLP sentence.'''
																		_SCREAMING_SNAKE_CASE			  =tokenizer_r.encode_plus(_a				,    add_special_tokens=_a				,    return_token_type_ids=_a  )
																		_SCREAMING_SNAKE_CASE			  =tokenizer_p.encode_plus(_a				,    add_special_tokens=_a				,    return_token_type_ids=_a  )
																		self.assertEqual(sum(tokens_r['''token_type_ids''']  )				,    sum(tokens_p['''token_type_ids''']  )  )
																		self.assertEqual(
																		    sum(tokens_r['''attention_mask''']  ) / len(tokens_r['''attention_mask''']  )				,    sum(tokens_p['''attention_mask''']  ) / len(tokens_p['''attention_mask''']  )				,    )

																		_SCREAMING_SNAKE_CASE			  =tokenizer_r.convert_ids_to_tokens(tokens_r['''input_ids''']  )
																		_SCREAMING_SNAKE_CASE			  =tokenizer_p.convert_ids_to_tokens(tokens_p['''input_ids''']  )
																		self.assertSequenceEqual(tokens_p['''input_ids''']				,    [0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2]  )
																		self.assertSequenceEqual(tokens_r['''input_ids''']				,    [0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2]  )

																		self.assertSequenceEqual(
																		    _a				,    ['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>''']  )
																		self.assertSequenceEqual(
																		    _a				,    ['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>''']  )