


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


_lowercase				     = logging.get_logger(__name__)

# General docstring
_lowercase				     = 'ResNetConfig'

# Base docstring
_lowercase				     = 'microsoft/resnet-50'
_lowercase				     = [1, 2_048, 7, 7]

# Image classification docstring
_lowercase				     = 'microsoft/resnet-50'
_lowercase				     = 'tiger cat'

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



class 	lowerCamelCase__			(      nn.Module      ):




   def __init__(   self						:						Dict    ,    __a						:						int    ,    __a						:						int    ,    __a						:						int = 3    ,    __a						:						int = 1    ,    __a						:						str = "relu"							):




         '''simple docstring'''







         super().__init__()
         lowerCamelCase__:						List[str]							   = nn.Convad(
             __UpperCAmelCase    ,    __UpperCAmelCase    ,    kernel_size=__UpperCAmelCase    ,    stride=__UpperCAmelCase    ,    padding=kernel_size // 2    ,    bias=__UpperCAmelCase							)
         lowerCamelCase__:						str							   = nn.BatchNormad(__UpperCAmelCase							)
         lowerCamelCase__:						Optional[int]							   = ACTaFN[activation] if activation is not None else nn.Identity()




   def 							lowerCamelCase_   (   self						:						List[str]    ,    __a						:						Tensor							):




         '''simple docstring'''







         lowerCamelCase__:						int							   = self.convolution(__UpperCAmelCase							)
         lowerCamelCase__:						int							   = self.normalization(__UpperCAmelCase							)
         lowerCamelCase__:						Optional[int]							   = self.activation(__UpperCAmelCase							)
         return hidden_state



class 	lowerCamelCase__			(      nn.Module      ):




   def __init__(   self						:						int    ,    __a						:						ResNetConfig							):




         '''simple docstring'''







         super().__init__()
         lowerCamelCase__:						List[str]							   = ResNetConvLayer(
             config.num_channels    ,    config.embedding_size    ,    kernel_size=7    ,    stride=2    ,    activation=config.hidden_act							)
         lowerCamelCase__:						Tuple							   = nn.MaxPoolad(kernel_size=3    ,    stride=2    ,    padding=1							)
         lowerCamelCase__:						List[Any]							   = config.num_channels




   def 							lowerCamelCase_   (   self						:						str    ,    __a						:						Tensor							):




         '''simple docstring'''







         lowerCamelCase__:						Union[str, Any]							   = 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__:						Dict							   = self.embedder(__UpperCAmelCase							)
         lowerCamelCase__:						Optional[int]							   = self.pooler(__UpperCAmelCase							)
         return embedding



class 	lowerCamelCase__			(      nn.Module      ):




   def __init__(   self						:						List[Any]    ,    __a						:						int    ,    __a						:						int    ,    __a						:						int = 2							):




         '''simple docstring'''







         super().__init__()
         lowerCamelCase__:						Union[str, Any]							   = nn.Convad(__UpperCAmelCase    ,    __UpperCAmelCase    ,    kernel_size=1    ,    stride=__UpperCAmelCase    ,    bias=__UpperCAmelCase							)
         lowerCamelCase__:						Tuple							   = nn.BatchNormad(__UpperCAmelCase							)




   def 							lowerCamelCase_   (   self						:						List[Any]    ,    __a						:						Tensor							):




         '''simple docstring'''







         lowerCamelCase__:						str							   = self.convolution(__UpperCAmelCase							)
         lowerCamelCase__:						Optional[int]							   = self.normalization(__UpperCAmelCase							)
         return hidden_state



class 	lowerCamelCase__			(      nn.Module      ):




   def __init__(   self						:						int    ,    __a						:						int    ,    __a						:						int    ,    __a						:						int = 1    ,    __a						:						str = "relu"							):




         '''simple docstring'''







         super().__init__()
         lowerCamelCase__:						List[Any]							   = in_channels != out_channels or stride != 1
         lowerCamelCase__:						Tuple							   = (
             ResNetShortCut(__UpperCAmelCase    ,    __UpperCAmelCase    ,    stride=__UpperCAmelCase							) if should_apply_shortcut else nn.Identity()
         )
         lowerCamelCase__:						Optional[int]							   = nn.Sequential(
             ResNetConvLayer(__UpperCAmelCase    ,    __UpperCAmelCase    ,    stride=__UpperCAmelCase							)    ,    ResNetConvLayer(__UpperCAmelCase    ,    __UpperCAmelCase    ,    activation=__UpperCAmelCase							)    ,    )
         lowerCamelCase__:						List[Any]							   = ACTaFN[activation]




   def 							lowerCamelCase_   (   self						:						Any    ,    __a						:						str							):




         '''simple docstring'''







         lowerCamelCase__:						str							   = hidden_state
         lowerCamelCase__:						Any							   = self.layer(__UpperCAmelCase							)
         lowerCamelCase__:						Union[str, Any]							   = self.shortcut(__UpperCAmelCase							)
         hidden_state += residual
         lowerCamelCase__:						int							   = self.activation(__UpperCAmelCase							)
         return hidden_state



class 	lowerCamelCase__			(      nn.Module      ):




   def __init__(   self						:						Dict    ,    __a						:						int    ,    __a						:						int    ,    __a						:						int = 1    ,    __a						:						str = "relu"    ,    __a						:						int = 4							):




         '''simple docstring'''







         super().__init__()
         lowerCamelCase__:						Any							   = in_channels != out_channels or stride != 1
         lowerCamelCase__:						Optional[Any]							   = out_channels // reduction
         lowerCamelCase__:						int							   = (
             ResNetShortCut(__UpperCAmelCase    ,    __UpperCAmelCase    ,    stride=__UpperCAmelCase							) if should_apply_shortcut else nn.Identity()
         )
         lowerCamelCase__:						Any							   = nn.Sequential(
             ResNetConvLayer(__UpperCAmelCase    ,    __UpperCAmelCase    ,    kernel_size=1							)    ,    ResNetConvLayer(__UpperCAmelCase    ,    __UpperCAmelCase    ,    stride=__UpperCAmelCase							)    ,    ResNetConvLayer(__UpperCAmelCase    ,    __UpperCAmelCase    ,    kernel_size=1    ,    activation=__UpperCAmelCase							)    ,    )
         lowerCamelCase__:						Any							   = ACTaFN[activation]




   def 							lowerCamelCase_   (   self						:						Optional[Any]    ,    __a						:						List[str]							):




         '''simple docstring'''







         lowerCamelCase__:						List[Any]							   = hidden_state
         lowerCamelCase__:						List[Any]							   = self.layer(__UpperCAmelCase							)
         lowerCamelCase__:						List[str]							   = self.shortcut(__UpperCAmelCase							)
         hidden_state += residual
         lowerCamelCase__:						List[Any]							   = self.activation(__UpperCAmelCase							)
         return hidden_state



class 	lowerCamelCase__			(      nn.Module      ):




   def __init__(   self						:						Tuple    ,    __a						:						ResNetConfig    ,    __a						:						int    ,    __a						:						int    ,    __a						:						int = 2    ,    __a						:						int = 2    ,    ):




         '''simple docstring'''







         super().__init__()

         lowerCamelCase__:						int							   = ResNetBottleNeckLayer if config.layer_type == "bottleneck" else ResNetBasicLayer

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




   def 							lowerCamelCase_   (   self						:						List[Any]    ,    __a						:						Tensor							):




         '''simple docstring'''







         lowerCamelCase__:						Union[str, Any]							   = input
         for layer in self.layers:
               lowerCamelCase__:						Tuple							   = layer(__UpperCAmelCase							)
         return hidden_state



class 	lowerCamelCase__			(      nn.Module      ):




   def __init__(   self						:						Union[str, Any]    ,    __a						:						ResNetConfig							):




         '''simple docstring'''







         super().__init__()
         lowerCamelCase__:						int							   = 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(
                 __UpperCAmelCase    ,    config.embedding_size    ,    config.hidden_sizes[0]    ,    stride=2 if config.downsample_in_first_stage else 1    ,    depth=config.depths[0]    ,    )							)
         lowerCamelCase__:						int							   = zip(config.hidden_sizes    ,    config.hidden_sizes[1:]							)
         for (in_channels, out_channels), depth in zip(__UpperCAmelCase    ,    config.depths[1:]							):
               self.stages.append(ResNetStage(__UpperCAmelCase    ,    __UpperCAmelCase    ,    __UpperCAmelCase    ,    depth=__UpperCAmelCase							)							)




   def 							lowerCamelCase_   (   self						:						Optional[int]    ,    __a						:						Tensor    ,    __a						:						bool = False    ,    __a						:						bool = True							):




         '''simple docstring'''







         lowerCamelCase__:						str							   = () if output_hidden_states else None

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

               lowerCamelCase__:						List[str]							   = stage_module(__UpperCAmelCase							)

         if output_hidden_states:
               lowerCamelCase__:						int							   = 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=__UpperCAmelCase    ,    hidden_states=__UpperCAmelCase    ,    )



class 	lowerCamelCase__			(      __UpperCamelCase      ):
   __lowerCamelCase			      =  ResNetConfig
   __lowerCamelCase			      =  """resnet"""
   __lowerCamelCase			      =  """pixel_values"""
   __lowerCamelCase			      =  True




   def 							lowerCamelCase_   (   self						:						Any    ,    __a						:						List[Any]							):




         '''simple docstring'''







         if isinstance(__UpperCAmelCase    ,    nn.Convad							):
               nn.init.kaiming_normal_(module.weight    ,    mode="""fan_out"""    ,    nonlinearity="""relu"""							)
         elif isinstance(__UpperCAmelCase    ,    (nn.BatchNormad, nn.GroupNorm)							):
               nn.init.constant_(module.weight    ,    1							)
               nn.init.constant_(module.bias    ,    0							)




   def 							lowerCamelCase_   (   self						:						Optional[Any]    ,    __a						:						Union[str, Any]    ,    __a						:						Optional[Any]=False							):




         '''simple docstring'''







         if isinstance(__UpperCAmelCase    ,    __UpperCAmelCase							):
               lowerCamelCase__:						Optional[int]							   = value
_lowercase				     = r'\n    This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it\n    as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and\n    behavior.\n\n    Parameters:\n        config ([`ResNetConfig`]): Model configuration class with all the parameters of the model.\n            Initializing with a config file does not load the weights associated with the model, only the\n            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.\n'

_lowercase				     = r'\n    Args:\n        pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):\n            Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See\n            [`ConvNextImageProcessor.__call__`] for details.\n\n        output_hidden_states (`bool`, *optional*):\n            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for\n            more detail.\n        return_dict (`bool`, *optional*):\n            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.\n'



@add_start_docstrings(
    """The bare ResNet model outputting raw features without any specific head on top."""		,	__UpperCamelCase		,	)
class 	lowerCamelCase__			(      __UpperCamelCase      ):




   def __init__(   self						:						Tuple    ,    __a						:						List[Any]							):




         '''simple docstring'''







         super().__init__(__UpperCAmelCase							)
         lowerCamelCase__:						List[Any]							   = config
         lowerCamelCase__:						Dict							   = ResNetEmbeddings(__UpperCAmelCase							)
         lowerCamelCase__:						Dict							   = ResNetEncoder(__UpperCAmelCase							)
         lowerCamelCase__:						Any							   = nn.AdaptiveAvgPoolad((1, 1)							)
         # Initialize weights and apply final processing
         self.post_init()




   @add_start_docstrings_to_model_forward(__UpperCAmelCase							)
   @add_code_sample_docstrings(
       checkpoint=_CHECKPOINT_FOR_DOC    ,    output_type=__UpperCAmelCase    ,    config_class=_CONFIG_FOR_DOC    ,    modality="""vision"""    ,    expected_output=_EXPECTED_OUTPUT_SHAPE    ,    )
   def 							lowerCamelCase_   (   self						:						List[Any]    ,    __a						:						Tensor    ,    __a						:						Optional[bool] = None    ,    __a						:						Optional[bool] = None							):




         '''simple docstring'''







         lowerCamelCase__:						Any							   = (
             output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
         )
         lowerCamelCase__:						Optional[Any]							   = return_dict if return_dict is not None else self.config.use_return_dict

         lowerCamelCase__:						str							   = self.embedder(__UpperCAmelCase							)

         lowerCamelCase__:						Optional[Any]							   = self.encoder(
             __UpperCAmelCase    ,    output_hidden_states=__UpperCAmelCase    ,    return_dict=__UpperCAmelCase							)

         lowerCamelCase__:						List[Any]							   = encoder_outputs[0]

         lowerCamelCase__:						Dict							   = self.pooler(__UpperCAmelCase							)

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

         return BaseModelOutputWithPoolingAndNoAttention(
             last_hidden_state=__UpperCAmelCase    ,    pooler_output=__UpperCAmelCase    ,    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 	lowerCamelCase__			(      __UpperCamelCase      ):




   def __init__(   self						:						List[Any]    ,    __a						:						Union[str, Any]							):




         '''simple docstring'''







         super().__init__(__UpperCAmelCase							)
         lowerCamelCase__:						str							   = config.num_labels
         lowerCamelCase__:						str							   = ResNetModel(__UpperCAmelCase							)
         # classification head
         lowerCamelCase__:						int							   = 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(__UpperCAmelCase							)
   @add_code_sample_docstrings(
       checkpoint=_IMAGE_CLASS_CHECKPOINT    ,    output_type=__UpperCAmelCase    ,    config_class=_CONFIG_FOR_DOC    ,    expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT    ,    )
   def 							lowerCamelCase_   (   self						:						int    ,    __a						:						Optional[torch.FloatTensor] = None    ,    __a						:						Optional[torch.LongTensor] = None    ,    __a						:						Optional[bool] = None    ,    __a						:						Optional[bool] = None    ,    ):




         '''simple docstring'''







         lowerCamelCase__:						Optional[int]							   = return_dict if return_dict is not None else self.config.use_return_dict

         lowerCamelCase__:						Any							   = self.resnet(__UpperCAmelCase    ,    output_hidden_states=__UpperCAmelCase    ,    return_dict=__UpperCAmelCase							)

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

         lowerCamelCase__:						Tuple							   = self.classifier(__UpperCAmelCase							)

         lowerCamelCase__:						str							   = None

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

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

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



@add_start_docstrings(
    """
    ResNet backbone, to be used with frameworks like DETR and MaskFormer.
    """		,	__UpperCamelCase		,	)
class 	lowerCamelCase__			(      __UpperCamelCase		,	__UpperCamelCase      ):




   def __init__(   self						:						List[str]    ,    __a						:						Tuple							):




         '''simple docstring'''







         super().__init__(__UpperCAmelCase							)
         super()._init_backbone(__UpperCAmelCase							)

         lowerCamelCase__:						Any							   = [config.embedding_size] + config.hidden_sizes
         lowerCamelCase__:						int							   = ResNetEmbeddings(__UpperCAmelCase							)
         lowerCamelCase__:						Any							   = ResNetEncoder(__UpperCAmelCase							)

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




   @add_start_docstrings_to_model_forward(__UpperCAmelCase							)
   @replace_return_docstrings(output_type=__UpperCAmelCase    ,    config_class=_CONFIG_FOR_DOC							)
   def 							lowerCamelCase_   (   self						:						Any    ,    __a						:						Tensor    ,    __a						:						Optional[bool] = None    ,    __a						:						Optional[bool] = None							):




         '''simple docstring'''







         lowerCamelCase__:						Optional[Any]							   = return_dict if return_dict is not None else self.config.use_return_dict
         lowerCamelCase__:						List[Any]							   = (
             output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
         )

         lowerCamelCase__:						Dict							   = self.embedder(__UpperCAmelCase							)

         lowerCamelCase__:						Optional[int]							   = self.encoder(__UpperCAmelCase    ,    output_hidden_states=__UpperCAmelCase    ,    return_dict=__UpperCAmelCase							)

         lowerCamelCase__:						List[str]							   = outputs.hidden_states

         lowerCamelCase__:						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__:						str							   = (feature_maps,)
               if output_hidden_states:
                     output += (outputs.hidden_states,)
               return output

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














import json
import os
import unittest

from transformers import BatchEncoding, MvpTokenizer, MvpTokenizerFast
from transformers.models.roberta.tokenization_roberta 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, filter_roberta_detectors




@require_tokenizers
class 							snake_case_       (    __UpperCamelCase		,							unittest.TestCase      ):

						"""simple docstring"""



						snake_case__        =      MvpTokenizer
						snake_case__        =      MvpTokenizerFast
						snake_case__        =      True
						snake_case__        =      filter_roberta_detectors



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




											'''simple docstring'''



											super().setUp()
											__a					:						Dict      =					[
											    "l",
											    "o",
											    "w",
											    "e",
											    "r",
											    "s",
											    "t",
											    "i",
											    "d",
											    "n",
											    "\u0120",
											    "\u0120l",
											    "\u0120n",
											    "\u0120lo",
											    "\u0120low",
											    "er",
											    "\u0120lowest",
											    "\u0120newer",
											    "\u0120wider",
											    "<unk>",
											]
											__a					:						Optional[int]      =					dict(zip(__UpperCAmelCase ,					range(len(__UpperCAmelCase   )   )   )   )
											__a					:						Dict      =					["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
											__a					:						Optional[int]      =					{"unk_token": "<unk>"}

											__a					:						Tuple      =					os.path.join(self.tmpdirname ,					VOCAB_FILES_NAMES["vocab_file"]   )
											__a					:						List[str]      =					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(__UpperCAmelCase   ) + "\n"   )
											with open(self.merges_file ,					"w" ,					encoding="utf-8"   ) as fp:
																fp.write("\n".join(__UpperCAmelCase   )   )



						def        UpperCAmelCase__					(self:					List[Any] ,					**__UpperCAmelCase:					Dict   )    ->     int:




											'''simple docstring'''



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



						def        UpperCAmelCase__					(self:					str ,					**__UpperCAmelCase:					Tuple   )    ->     Tuple:




											'''simple docstring'''



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



						def        UpperCAmelCase__					(self:					Any ,					__UpperCAmelCase:					int   )    ->     List[str]:




											'''simple docstring'''



											return "lower newer", "lower newer"



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




											'''simple docstring'''



											return MvpTokenizer.from_pretrained("RUCAIBox/mvp"   )



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




											'''simple docstring'''



											return MvpTokenizerFast.from_pretrained("RUCAIBox/mvp"   )



						@require_torch
						def        UpperCAmelCase__					(self:					Dict   )    ->     List[Any]:




											'''simple docstring'''



											__a					:						Union[str, Any]      =					["A long paragraph for summarization.", "Another paragraph for summarization."]
											__a					:						List[Any]      =					[0, 250, 251, 17818, 13, 39186, 1938, 4, 2]

											for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																__a					:						Optional[Any]      =					tokenizer(__UpperCAmelCase ,					max_length=len(__UpperCAmelCase   ) ,					padding=__UpperCAmelCase ,					return_tensors="pt"   )
																self.assertIsInstance(__UpperCAmelCase ,					__UpperCAmelCase   )

																self.assertEqual((2, 9) ,					batch.input_ids.shape   )
																self.assertEqual((2, 9) ,					batch.attention_mask.shape   )
																__a					:						Optional[int]      =					batch.input_ids.tolist()[0]
																self.assertListEqual(__UpperCAmelCase ,					__UpperCAmelCase   )
																# Test that special tokens are reset



						@require_torch
						def        UpperCAmelCase__					(self:					Optional[int]   )    ->     List[Any]:




											'''simple docstring'''



											__a					:						int      =					["A long paragraph for summarization.", "Another paragraph for summarization."]
											for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																__a					:						int      =					tokenizer(__UpperCAmelCase ,					padding=__UpperCAmelCase ,					return_tensors="pt"   )
																# check if input_ids are returned and no labels
																self.assertIn("input_ids" ,					__UpperCAmelCase   )
																self.assertIn("attention_mask" ,					__UpperCAmelCase   )
																self.assertNotIn("labels" ,					__UpperCAmelCase   )
																self.assertNotIn("decoder_attention_mask" ,					__UpperCAmelCase   )



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




											'''simple docstring'''



											__a					:						List[Any]      =					[
											    "Summary of the text.",
											    "Another summary.",
											]
											for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																__a					:						int      =					tokenizer(text_target=__UpperCAmelCase ,					max_length=32 ,					padding="max_length" ,					return_tensors="pt"   )
																self.assertEqual(32 ,					targets["input_ids"].shape[1]   )



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




											'''simple docstring'''



											for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																__a					:						str      =					tokenizer(
																    ["I am a small frog" * 1024, "I am a small frog"] ,					padding=__UpperCAmelCase ,					truncation=__UpperCAmelCase ,					return_tensors="pt"   )
																self.assertIsInstance(__UpperCAmelCase ,					__UpperCAmelCase   )
																self.assertEqual(batch.input_ids.shape ,					(2, 1024)   )



						@require_torch
						def        UpperCAmelCase__					(self:					str   )    ->     Any:




											'''simple docstring'''



											__a					:						Optional[int]      =					["A long paragraph for summarization."]
											__a					:						Tuple      =					[
											    "Summary of the text.",
											]
											for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																__a					:						Dict      =					tokenizer(__UpperCAmelCase ,					text_target=__UpperCAmelCase ,					return_tensors="pt"   )
																__a					:						str      =					inputs["input_ids"]
																__a					:						Optional[Any]      =					inputs["labels"]
																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()   )



						def        UpperCAmelCase__					(self:					Dict   )    ->     str:




											'''simple docstring'''



											pass



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




											'''simple docstring'''



											for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																with self.subTest(f'{tokenizer.__class__.__name__} ({pretrained_name})'   ):
																					__a					:						Tuple      =					self.rust_tokenizer_class.from_pretrained(__UpperCAmelCase ,					**__UpperCAmelCase   )
																					__a					:						List[Any]      =					self.tokenizer_class.from_pretrained(__UpperCAmelCase ,					**__UpperCAmelCase   )
																					__a					:						Tuple      =					"A, <mask> AllenNLP sentence."
																					__a					:						Dict      =					tokenizer_r.encode_plus(__UpperCAmelCase ,					add_special_tokens=__UpperCAmelCase ,					return_token_type_ids=__UpperCAmelCase   )
																					__a					:						List[Any]      =					tokenizer_p.encode_plus(__UpperCAmelCase ,					add_special_tokens=__UpperCAmelCase ,					return_token_type_ids=__UpperCAmelCase   )

																					# token_type_ids should put 0 everywhere
																					self.assertEqual(sum(tokens_r["token_type_ids"]   ) ,					sum(tokens_p["token_type_ids"]   )   )

																					# attention_mask should put 1 everywhere, so sum over length should be 1
																					self.assertEqual(
																					    sum(tokens_r["attention_mask"]   ) / len(tokens_r["attention_mask"]   ) ,					sum(tokens_p["attention_mask"]   ) / len(tokens_p["attention_mask"]   ) ,					)

																					__a					:						Optional[int]      =					tokenizer_r.convert_ids_to_tokens(tokens_r["input_ids"]   )
																					__a					:						Union[str, Any]      =					tokenizer_p.convert_ids_to_tokens(tokens_p["input_ids"]   )

																					# Rust correctly handles the space before the mask while python doesnt
																					self.assertSequenceEqual(tokens_p["input_ids"] ,					[0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2]   )
																					self.assertSequenceEqual(tokens_r["input_ids"] ,					[0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2]   )

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





