



'''simple docstring'''


import argparse
import collections
import json
from pathlib import Path

import requests
import torch
import yaml
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    MobileViTImageProcessor,
    MobileViTVaConfig,
    MobileViTVaForImageClassification,
    MobileViTVaForSemanticSegmentation,
)
from transformers.utils import logging


logging.set_verbosity_info()
lowerCAmelCase :					List[str] 							=logging.get_logger(__name__)
def 							UpperCAmelCase_  (						__lowerCamelCase : Tuple			):
   print("Loading config file..."			)

   def flatten_yaml_as_dict(__lowerCamelCase : List[Any]     ,__lowerCamelCase : Optional[int]=""     ,__lowerCamelCase : List[Any]="."			):
      lowercase_      :List[str]         =			[]
      for k, v in d.items():
         lowercase_      :Union[str, Any]         =			parent_key + sep + k if parent_key else k
         if isinstance(SCREAMING_SNAKE_CASE__     ,collections.abc.MutableMapping			):
            items.extend(flatten_yaml_as_dict(SCREAMING_SNAKE_CASE__     ,SCREAMING_SNAKE_CASE__     ,sep=SCREAMING_SNAKE_CASE__			).items()			)
         else:
            items.append((new_key, v)			)
      return dict(SCREAMING_SNAKE_CASE__			)

   lowercase_      :Tuple         =			argparse.Namespace()
   with open(SCREAMING_SNAKE_CASE__     ,"r"			) as yaml_file:
      try:
         lowercase_      :Optional[Any]         =			yaml.load(SCREAMING_SNAKE_CASE__     ,Loader=yaml.FullLoader			)

         lowercase_      :Optional[Any]         =			flatten_yaml_as_dict(SCREAMING_SNAKE_CASE__			)
         for k, v in flat_cfg.items():
            setattr(SCREAMING_SNAKE_CASE__     ,SCREAMING_SNAKE_CASE__     ,SCREAMING_SNAKE_CASE__			)
      except yaml.YAMLError as exc:
         logger.error("Error while loading config file: {}. Error message: {}".format(SCREAMING_SNAKE_CASE__     ,str(SCREAMING_SNAKE_CASE__			)			)			)
   return config
def 							UpperCAmelCase_  (						__lowerCamelCase : str     ,__lowerCamelCase : int			):
   lowercase_      :Optional[Any]         =			MobileViTVaConfig()

   lowercase_      :Tuple         =			False

   # dataset
   if task_name.startswith("imagenet1k_"			):
      lowercase_      :Optional[Any]         =			10_00
      if int(task_name.strip().split("_"			)[-1]			) == 3_84:
         lowercase_      :List[Any]         =			3_84
      else:
         lowercase_      :Tuple         =			2_56
      lowercase_      :List[str]         =			'''imagenet-1k-id2label.json'''
   elif task_name.startswith("imagenet21k_to_1k_"			):
      lowercase_      :Any         =			2_10_00
      if int(task_name.strip().split("_"			)[-1]			) == 3_84:
         lowercase_      :List[Any]         =			3_84
      else:
         lowercase_      :Dict         =			2_56
      lowercase_      :int         =			'''imagenet-22k-id2label.json'''
   elif task_name.startswith("ade20k_"			):
      lowercase_      :Optional[int]         =			1_51
      lowercase_      :List[Any]         =			5_12
      lowercase_      :int         =			'''ade20k-id2label.json'''
      lowercase_      :Tuple         =			True
   elif task_name.startswith("voc_"			):
      lowercase_      :Tuple         =			21
      lowercase_      :Union[str, Any]         =			5_12
      lowercase_      :List[Any]         =			'''pascal-voc-id2label.json'''
      lowercase_      :Dict         =			True

   # orig_config
   lowercase_      :Optional[int]         =			load_orig_config_file(SCREAMING_SNAKE_CASE__			)
   assert getattr(SCREAMING_SNAKE_CASE__     ,"model.classification.name"     ,-1			) == "mobilevit_v2", "Invalid model"
   lowercase_      :Dict         =			getattr(SCREAMING_SNAKE_CASE__     ,"model.classification.mitv2.width_multiplier"     ,1.0			)
   assert (
       getattr(SCREAMING_SNAKE_CASE__     ,"model.classification.mitv2.attn_norm_layer"     ,-1			) == "layer_norm_2d"
   ), "Norm layers other than layer_norm_2d is not supported"
   lowercase_      :str         =			getattr(SCREAMING_SNAKE_CASE__     ,"model.classification.activation.name"     ,"swish"			)
   # config.image_size == getattr(orig_config,  'sampler.bs.crop_size_width', 256)

   if is_segmentation_model:
      lowercase_      :List[str]         =			getattr(SCREAMING_SNAKE_CASE__     ,"model.segmentation.output_stride"     ,16			)
      if "_deeplabv3" in task_name:
         lowercase_      :Dict         =			getattr(SCREAMING_SNAKE_CASE__     ,"model.segmentation.deeplabv3.aspp_rates"     ,[12, 24, 36]			)
         lowercase_      :Dict         =			getattr(SCREAMING_SNAKE_CASE__     ,"model.segmentation.deeplabv3.aspp_out_channels"     ,5_12			)
         lowercase_      :List[str]         =			getattr(SCREAMING_SNAKE_CASE__     ,"model.segmentation.deeplabv3.aspp_dropout"     ,0.1			)

    # id2label
   lowercase_      :Tuple         =			'''huggingface/label-files'''
   lowercase_      :Optional[Any]         =			json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE__     ,SCREAMING_SNAKE_CASE__     ,repo_type="dataset"			)     ,"r"			)			)
   lowercase_      :List[str]         =			{int(SCREAMING_SNAKE_CASE__			): v for k, v in idalabel.items()}
   lowercase_      :Optional[int]         =			idalabel
   lowercase_      :Optional[Any]         =			{v: k for k, v in idalabel.items()}

   return config
def 							UpperCAmelCase_  (						__lowerCamelCase : List[str]     ,__lowerCamelCase : Any     ,__lowerCamelCase : List[str]			):
   lowercase_      :int         =			dct.pop(SCREAMING_SNAKE_CASE__			)
   lowercase_      :Dict         =			val
def 							UpperCAmelCase_  (						__lowerCamelCase : Optional[Any]     ,__lowerCamelCase : str=False			):


   if base_model:
      lowercase_      :Optional[Any]         =			''''''
   else:
      lowercase_      :Dict         =			'''mobilevitv2.'''

   lowercase_      :str         =			[]
   for k in state_dict.keys():
      if k[:8] == "encoder.":
         lowercase_      :Optional[int]         =			k[8:]
      else:
         lowercase_      :int         =			k

      if ".block." in k:
         lowercase_      :int         =			k_new.replace(".block."     ,"."			)
      if ".conv." in k:
         lowercase_      :int         =			k_new.replace(".conv."     ,".convolution."			)
      if ".norm." in k:
         lowercase_      :int         =			k_new.replace(".norm."     ,".normalization."			)

      if "conv_1." in k:
         lowercase_      :Union[str, Any]         =			k_new.replace("conv_1."     ,F'{model_prefix}conv_stem.'			)
      for i in [1, 2]:
         if F'layer_{i}.' in k:
            lowercase_      :List[str]         =			k_new.replace(F'layer_{i}.'     ,F'{model_prefix}encoder.layer.{i-1}.layer.'			)
      if ".exp_1x1." in k:
         lowercase_      :Optional[int]         =			k_new.replace(".exp_1x1."     ,".expand_1x1."			)
      if ".red_1x1." in k:
         lowercase_      :Optional[int]         =			k_new.replace(".red_1x1."     ,".reduce_1x1."			)

      for i in [3, 4, 5]:
         if F'layer_{i}.0.' in k:
            lowercase_      :int         =			k_new.replace(F'layer_{i}.0.'     ,F'{model_prefix}encoder.layer.{i-1}.downsampling_layer.'			)
         if F'layer_{i}.1.local_rep.0.' in k:
            lowercase_      :Optional[Any]         =			k_new.replace(F'layer_{i}.1.local_rep.0.'     ,F'{model_prefix}encoder.layer.{i-1}.conv_kxk.'			)
         if F'layer_{i}.1.local_rep.1.' in k:
            lowercase_      :List[str]         =			k_new.replace(F'layer_{i}.1.local_rep.1.'     ,F'{model_prefix}encoder.layer.{i-1}.conv_1x1.'			)

      for i in [3, 4, 5]:
         if i == 3:
            lowercase_      :Union[str, Any]         =			[0, 1]
         elif i == 4:
            lowercase_      :List[str]         =			[0, 1, 2, 3]
         elif i == 5:
            lowercase_      :Optional[int]         =			[0, 1, 2]

         for j in j_in:
            if F'layer_{i}.1.global_rep.{j}.' in k:
               lowercase_      :Any         =			k_new.replace(
                   F'layer_{i}.1.global_rep.{j}.'     ,F'{model_prefix}encoder.layer.{i-1}.transformer.layer.{j}.'			)
         if F'layer_{i}.1.global_rep.{j+1}.' in k:
            lowercase_      :str         =			k_new.replace(
                F'layer_{i}.1.global_rep.{j+1}.'     ,F'{model_prefix}encoder.layer.{i-1}.layernorm.'			)

         if F'layer_{i}.1.conv_proj.' in k:
            lowercase_      :Optional[int]         =			k_new.replace(F'layer_{i}.1.conv_proj.'     ,F'{model_prefix}encoder.layer.{i-1}.conv_projection.'			)

      if "pre_norm_attn.0." in k:
         lowercase_      :Dict         =			k_new.replace("pre_norm_attn.0."     ,"layernorm_before."			)
      if "pre_norm_attn.1." in k:
         lowercase_      :Tuple         =			k_new.replace("pre_norm_attn.1."     ,"attention."			)
      if "pre_norm_ffn.0." in k:
         lowercase_      :Optional[Any]         =			k_new.replace("pre_norm_ffn.0."     ,"layernorm_after."			)
      if "pre_norm_ffn.1." in k:
         lowercase_      :List[Any]         =			k_new.replace("pre_norm_ffn.1."     ,"ffn.conv1."			)
      if "pre_norm_ffn.3." in k:
         lowercase_      :Optional[Any]         =			k_new.replace("pre_norm_ffn.3."     ,"ffn.conv2."			)

      if "classifier.1." in k:
         lowercase_      :str         =			k_new.replace("classifier.1."     ,"classifier."			)

      if "seg_head." in k:
         lowercase_      :Tuple         =			k_new.replace("seg_head."     ,"segmentation_head."			)
      if ".aspp_layer." in k:
         lowercase_      :Optional[int]         =			k_new.replace(".aspp_layer."     ,"."			)
      if ".aspp_pool." in k:
         lowercase_      :List[str]         =			k_new.replace(".aspp_pool."     ,"."			)

      rename_keys.append((k, k_new)			)
   return rename_keys
def 							UpperCAmelCase_  (						__lowerCamelCase : Optional[Any]			):
   lowercase_      :Optional[int]         =			[]
   for k in state_dict.keys():
      if k.startswith("seg_head.aux_head."			):
         keys_to_ignore.append(SCREAMING_SNAKE_CASE__			)
   for k in keys_to_ignore:
      state_dict.pop(SCREAMING_SNAKE_CASE__     ,SCREAMING_SNAKE_CASE__			)
def 							UpperCAmelCase_  (						):
   lowercase_      :Optional[Any]         =			'''http://images.cocodataset.org/val2017/000000039769.jpg'''
   # url = "https://cdn.britannica.com/86/141086-050-9D7C75EE/Gulfstream-G450-business-jet-passengers.jpg"
   lowercase_      :List[Any]         =			Image.open(requests.get(SCREAMING_SNAKE_CASE__     ,stream=SCREAMING_SNAKE_CASE__			).raw			)
   return im
@torch.no_grad()
def 							UpperCAmelCase_  (						__lowerCamelCase : Optional[int]     ,__lowerCamelCase : List[str]     ,__lowerCamelCase : List[str]     ,__lowerCamelCase : Tuple			):
   lowercase_      :List[str]         =			get_mobilevitva_config(SCREAMING_SNAKE_CASE__     ,SCREAMING_SNAKE_CASE__			)

   # load original state_dict
   lowercase_      :Optional[Any]         =			torch.load(SCREAMING_SNAKE_CASE__     ,map_location="cpu"			)

   # load huggingface model
   if task_name.startswith("ade20k_"			) or task_name.startswith("voc_"			):
      lowercase_      :Optional[int]         =			MobileViTVaForSemanticSegmentation(SCREAMING_SNAKE_CASE__			).eval()
      lowercase_      :Union[str, Any]         =			False
   else:
      lowercase_      :Any         =			MobileViTVaForImageClassification(SCREAMING_SNAKE_CASE__			).eval()
      lowercase_      :List[Any]         =			False

   # remove and rename some keys of load the original model
   lowercase_      :Any         =			checkpoint
   remove_unused_keys(SCREAMING_SNAKE_CASE__			)
   lowercase_      :List[Any]         =			create_rename_keys(SCREAMING_SNAKE_CASE__     ,base_model=SCREAMING_SNAKE_CASE__			)
   for rename_key_src, rename_key_dest in rename_keys:
      rename_key(SCREAMING_SNAKE_CASE__     ,SCREAMING_SNAKE_CASE__     ,SCREAMING_SNAKE_CASE__			)

   # load modified state_dict
   model.load_state_dict(SCREAMING_SNAKE_CASE__			)

   # Check outputs on an image, prepared by MobileViTImageProcessor
   lowercase_      :Optional[Any]         =			MobileViTImageProcessor(crop_size=config.image_size     ,size=config.image_size + 32			)
   lowercase_      :Optional[int]         =			image_processor(images=prepare_img()     ,return_tensors="pt"			)
   lowercase_      :Optional[Any]         =			model(**SCREAMING_SNAKE_CASE__			)

   # verify classification model
   if task_name.startswith("imagenet"			):
      lowercase_      :str         =			outputs.logits
      lowercase_      :Any         =			logits.argmax(-1			).item()
      print("Predicted class:"     ,model.config.idalabel[predicted_class_idx]			)
      if task_name.startswith("imagenet1k_256"			) and config.width_multiplier == 1.0:
         # expected_logits for base variant
         lowercase_      :List[str]         =			torch.tensor([-1.63_36e00, -7.32_04e-02, -5.18_83e-01]			)
         assert torch.allclose(logits[0, :3]     ,SCREAMING_SNAKE_CASE__     ,atol=1e-4			)

   Path(SCREAMING_SNAKE_CASE__			).mkdir(exist_ok=SCREAMING_SNAKE_CASE__			)
   print(F'Saving model {task_name} to {pytorch_dump_folder_path}'			)
   model.save_pretrained(SCREAMING_SNAKE_CASE__			)
   print(F'Saving image processor to {pytorch_dump_folder_path}'			)
   image_processor.save_pretrained(SCREAMING_SNAKE_CASE__			)


if __name__ == "__main__":
 lowerCAmelCase :					List[Any] 							=argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '''--task''',
     default='''imagenet1k_256''',
     type=str,
     help=(
         '''Name of the task for which the MobileViTV2 model you\'d like to convert is trained on . '''
         '''\n                Classification (ImageNet-1k)\n                    - MobileViTV2 (256x256) : imagenet1k_256\n                    - MobileViTV2 (Trained on 256x256 and Finetuned on 384x384) : imagenet1k_384\n                    - MobileViTV2 (Trained on ImageNet-21k and Finetuned on ImageNet-1k 256x256) :\n                      imagenet21k_to_1k_256\n                    - MobileViTV2 (Trained on ImageNet-21k, Finetuned on ImageNet-1k 256x256, and Finetuned on\n                      ImageNet-1k 384x384) : imagenet21k_to_1k_384\n                Segmentation\n                    - ADE20K Dataset : ade20k_deeplabv3\n                    - Pascal VOC 2012 Dataset: voc_deeplabv3\n            '''
     ),
     choices=[
         '''imagenet1k_256''',
         '''imagenet1k_384''',
         '''imagenet21k_to_1k_256''',
         '''imagenet21k_to_1k_384''',
         '''ade20k_deeplabv3''',
         '''voc_deeplabv3''',
     ],
 )

 parser.add_argument(
     '''--orig_checkpoint_path''', required=True, type=str, help='''Path to the original state dict (.pt file).'''
 )
 parser.add_argument('''--orig_config_path''', required=True, type=str, help='''Path to the original config file.''')
 parser.add_argument(
     '''--pytorch_dump_folder_path''', required=True, type=str, help='''Path to the output PyTorch model directory.'''
 )

 lowerCAmelCase :					str 							=parser.parse_args()
 convert_mobilevitva_checkpoint(
     args.task, args.orig_checkpoint_path, args.orig_config_path, args.pytorch_dump_folder_path
 )









'''simple docstring'''


import unittest

from transformers import DebertaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
 import torch

 from transformers import (
     DebertaForMaskedLM,
     DebertaForQuestionAnswering,
     DebertaForSequenceClassification,
     DebertaForTokenClassification,
     DebertaModel,
 )
 from transformers.models.deberta.modeling_deberta import DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST
class 				a_       (    _lowerCAmelCase							):





     def __init__(			self		: Any     ,				lowercase		: int     ,				lowercase		: Union[str, Any]=13     ,				lowercase		: List[str]=7     ,				lowercase		: List[str]=True     ,				lowercase		: int=True     ,				lowercase		: Tuple=True     ,				lowercase		: int=True     ,				lowercase		: List[Any]=99     ,				lowercase		: Optional[int]=32     ,				lowercase		: Dict=5     ,				lowercase		: Optional[int]=4     ,				lowercase		: List[str]=37     ,				lowercase		: Tuple="gelu"     ,				lowercase		: List[Any]=0.1     ,				lowercase		: Tuple=0.1     ,				lowercase		: List[str]=512     ,				lowercase		: str=16     ,				lowercase		: Tuple=2     ,				lowercase		: List[Any]=0.02     ,				lowercase		: Dict=False     ,				lowercase		: Dict=True     ,				lowercase		: int="None"     ,				lowercase		: Optional[Any]=3     ,				lowercase		: Dict=4     ,				lowercase		: List[Any]=None     ,				):





        """simple docstring"""




        lowercase_      :int         =			parent
        lowercase_      :str         =			batch_size
        lowercase_      :Tuple         =			seq_length
        lowercase_      :Union[str, Any]         =			is_training
        lowercase_      :Dict         =			use_input_mask
        lowercase_      :Any         =			use_token_type_ids
        lowercase_      :Tuple         =			use_labels
        lowercase_      :Dict         =			vocab_size
        lowercase_      :Tuple         =			hidden_size
        lowercase_      :Union[str, Any]         =			num_hidden_layers
        lowercase_      :int         =			num_attention_heads
        lowercase_      :List[Any]         =			intermediate_size
        lowercase_      :Tuple         =			hidden_act
        lowercase_      :str         =			hidden_dropout_prob
        lowercase_      :Any         =			attention_probs_dropout_prob
        lowercase_      :List[Any]         =			max_position_embeddings
        lowercase_      :Union[str, Any]         =			type_vocab_size
        lowercase_      :Union[str, Any]         =			type_sequence_label_size
        lowercase_      :Any         =			initializer_range
        lowercase_      :List[Any]         =			num_labels
        lowercase_      :str         =			num_choices
        lowercase_      :Optional[Any]         =			relative_attention
        lowercase_      :Tuple         =			position_biased_input
        lowercase_      :Union[str, Any]         =			pos_att_type
        lowercase_      :Tuple         =			scope





     def 							lowercase__				(			self		: Dict						):





        """simple docstring"""




        lowercase_      :List[str]         =			ids_tensor([self.batch_size, self.seq_length]     ,				self.vocab_size						)

        lowercase_      :Union[str, Any]         =			None
        if self.use_input_mask:
           lowercase_      :int         =			ids_tensor([self.batch_size, self.seq_length]     ,				vocab_size=2						)

        lowercase_      :List[Any]         =			None
        if self.use_token_type_ids:
           lowercase_      :Any         =			ids_tensor([self.batch_size, self.seq_length]     ,				self.type_vocab_size						)

        lowercase_      :str         =			None
        lowercase_      :Union[str, Any]         =			None
        lowercase_      :List[str]         =			None
        if self.use_labels:
           lowercase_      :List[Any]         =			ids_tensor([self.batch_size]     ,				self.type_sequence_label_size						)
           lowercase_      :Any         =			ids_tensor([self.batch_size, self.seq_length]     ,				self.num_labels						)
           lowercase_      :str         =			ids_tensor([self.batch_size]     ,				self.num_choices						)

        lowercase_      :Optional[Any]         =			self.get_config()

        return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels





     def 							lowercase__				(			self		: Optional[Any]						):





        """simple docstring"""




        return DebertaConfig(
            vocab_size=self.vocab_size     ,				hidden_size=self.hidden_size     ,				num_hidden_layers=self.num_hidden_layers     ,				num_attention_heads=self.num_attention_heads     ,				intermediate_size=self.intermediate_size     ,				hidden_act=self.hidden_act     ,				hidden_dropout_prob=self.hidden_dropout_prob     ,				attention_probs_dropout_prob=self.attention_probs_dropout_prob     ,				max_position_embeddings=self.max_position_embeddings     ,				type_vocab_size=self.type_vocab_size     ,				initializer_range=self.initializer_range     ,				relative_attention=self.relative_attention     ,				position_biased_input=self.position_biased_input     ,				pos_att_type=self.pos_att_type     ,				)





     def 							lowercase__				(			self		: Optional[int]						):





        """simple docstring"""




        lowercase_      :Dict         =			self.get_config()
        lowercase_      :Optional[Any]         =			300
        return config





     def 							lowercase__				(			self		: Optional[Any]     ,				lowercase		: Dict						):





        """simple docstring"""




        self.parent.assertListEqual(list(result.loss.size()						)     ,				[]						)





     def 							lowercase__				(			self		: Optional[Any]     ,				lowercase		: Union[str, Any]     ,				lowercase		: List[Any]     ,				lowercase		: List[Any]     ,				lowercase		: Tuple     ,				lowercase		: str     ,				lowercase		: Optional[Any]     ,				lowercase		: Optional[int]						):





        """simple docstring"""




        lowercase_      :str         =			DebertaModel(config=lowercase						)
        model.to(lowercase						)
        model.eval()
        lowercase_      :Optional[int]         =			model(lowercase     ,				attention_mask=lowercase     ,				token_type_ids=lowercase						)[0]
        lowercase_      :Union[str, Any]         =			model(lowercase     ,				token_type_ids=lowercase						)[0]
        lowercase_      :Dict         =			model(lowercase						)[0]

        self.parent.assertListEqual(list(sequence_output.size()						)     ,				[self.batch_size, self.seq_length, self.hidden_size]						)





     def 							lowercase__				(			self		: Dict     ,				lowercase		: Union[str, Any]     ,				lowercase		: Optional[int]     ,				lowercase		: Tuple     ,				lowercase		: Dict     ,				lowercase		: Dict     ,				lowercase		: str     ,				lowercase		: Tuple						):





        """simple docstring"""




        lowercase_      :Dict         =			DebertaForMaskedLM(config=lowercase						)
        model.to(lowercase						)
        model.eval()
        lowercase_      :Optional[Any]         =			model(lowercase     ,				attention_mask=lowercase     ,				token_type_ids=lowercase     ,				labels=lowercase						)

        self.parent.assertEqual(result.logits.shape     ,				(self.batch_size, self.seq_length, self.vocab_size)						)





     def 							lowercase__				(			self		: Optional[int]     ,				lowercase		: Union[str, Any]     ,				lowercase		: Union[str, Any]     ,				lowercase		: Union[str, Any]     ,				lowercase		: Dict     ,				lowercase		: List[Any]     ,				lowercase		: int     ,				lowercase		: Dict						):





        """simple docstring"""




        lowercase_      :Dict         =			self.num_labels
        lowercase_      :int         =			DebertaForSequenceClassification(lowercase						)
        model.to(lowercase						)
        model.eval()
        lowercase_      :Union[str, Any]         =			model(lowercase     ,				attention_mask=lowercase     ,				token_type_ids=lowercase     ,				labels=lowercase						)
        self.parent.assertListEqual(list(result.logits.size()						)     ,				[self.batch_size, self.num_labels]						)
        self.check_loss_output(lowercase						)





     def 							lowercase__				(			self		: List[Any]     ,				lowercase		: List[str]     ,				lowercase		: Tuple     ,				lowercase		: Optional[Any]     ,				lowercase		: Optional[int]     ,				lowercase		: Union[str, Any]     ,				lowercase		: Dict     ,				lowercase		: int						):





        """simple docstring"""




        lowercase_      :List[str]         =			self.num_labels
        lowercase_      :Optional[int]         =			DebertaForTokenClassification(config=lowercase						)
        model.to(lowercase						)
        model.eval()
        lowercase_      :Dict         =			model(lowercase     ,				attention_mask=lowercase     ,				token_type_ids=lowercase     ,				labels=lowercase						)
        self.parent.assertEqual(result.logits.shape     ,				(self.batch_size, self.seq_length, self.num_labels)						)





     def 							lowercase__				(			self		: Union[str, Any]     ,				lowercase		: Tuple     ,				lowercase		: Any     ,				lowercase		: List[Any]     ,				lowercase		: List[Any]     ,				lowercase		: Union[str, Any]     ,				lowercase		: Optional[int]     ,				lowercase		: List[Any]						):





        """simple docstring"""




        lowercase_      :Any         =			DebertaForQuestionAnswering(config=lowercase						)
        model.to(lowercase						)
        model.eval()
        lowercase_      :List[Any]         =			model(
            lowercase     ,				attention_mask=lowercase     ,				token_type_ids=lowercase     ,				start_positions=lowercase     ,				end_positions=lowercase     ,				)
        self.parent.assertEqual(result.start_logits.shape     ,				(self.batch_size, self.seq_length)						)
        self.parent.assertEqual(result.end_logits.shape     ,				(self.batch_size, self.seq_length)						)







     def 							lowercase__				(			self		: Any						):





        """simple docstring"""




        lowercase_      :Optional[int]         =			self.prepare_config_and_inputs()
        (
            (
            lowercase_
        )      ,							(
            lowercase_
        )      ,							(
            lowercase_
        )      ,							(
            lowercase_
        )      ,							(
            lowercase_
        )      ,							(
            lowercase_
        )      ,							(
            lowercase_
        )      ,							
        )      :List[str]         =			config_and_inputs
        lowercase_      :Tuple         =			{"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
        return config, inputs_dict







@require_torch
class 				a_       (    _lowerCAmelCase     ,						_lowerCAmelCase     ,						unittest.TestCase							):
     __A								=      (
         (
             DebertaModel,
             DebertaForMaskedLM,
             DebertaForSequenceClassification,
             DebertaForTokenClassification,
             DebertaForQuestionAnswering,
         )
         if is_torch_available()
         else ()
     )
     __A								=      (
         {
             "feature-extraction": DebertaModel,
             "fill-mask": DebertaForMaskedLM,
             "question-answering": DebertaForQuestionAnswering,
             "text-classification": DebertaForSequenceClassification,
             "token-classification": DebertaForTokenClassification,
             "zero-shot": DebertaForSequenceClassification,
         }
         if is_torch_available()
         else {}
     )

     __A								=      True
     __A								=      False
     __A								=      False
     __A								=      False
     __A								=      False





     def 							lowercase__				(			self		: Optional[int]						):





        """simple docstring"""




        lowercase_      :List[Any]         =			DebertaModelTester(self						)
        lowercase_      :str         =			ConfigTester(self     ,				config_class=lowercase     ,				hidden_size=37						)





     def 							lowercase__				(			self		: Union[str, Any]						):





        """simple docstring"""




        self.config_tester.run_common_tests()





     def 							lowercase__				(			self		: Union[str, Any]						):





        """simple docstring"""




        lowercase_      :str         =			self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_deberta_model(*lowercase						)





     def 							lowercase__				(			self		: Union[str, Any]						):





        """simple docstring"""




        lowercase_      :Optional[Any]         =			self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_deberta_for_sequence_classification(*lowercase						)





     def 							lowercase__				(			self		: Union[str, Any]						):





        """simple docstring"""




        lowercase_      :Optional[int]         =			self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_deberta_for_masked_lm(*lowercase						)





     def 							lowercase__				(			self		: int						):





        """simple docstring"""




        lowercase_      :Union[str, Any]         =			self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_deberta_for_question_answering(*lowercase						)





     def 							lowercase__				(			self		: Tuple						):





        """simple docstring"""




        lowercase_      :Union[str, Any]         =			self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_deberta_for_token_classification(*lowercase						)







     @slow
     def 							lowercase__				(			self		: Optional[int]						):





        """simple docstring"""




        for model_name in DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           lowercase_      :Tuple         =			DebertaModel.from_pretrained(lowercase						)
           self.assertIsNotNone(lowercase						)







@require_torch
@require_sentencepiece
@require_tokenizers
class 				a_       (    unittest.TestCase							):





     @unittest.skip(reason="Model not available yet"						)
     def 							lowercase__				(			self		: Dict						):





        """simple docstring"""




        pass







     @slow
     def 							lowercase__				(			self		: List[Any]						):





        """simple docstring"""




        lowercase_      :Optional[Any]         =			DebertaModel.from_pretrained("microsoft/deberta-base"						)

        lowercase_      :Dict         =			torch.tensor([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]]						)
        lowercase_      :Optional[Any]         =			torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]						)
        with torch.no_grad():
           lowercase_      :Optional[int]         =			model(lowercase     ,				attention_mask=lowercase						)[0]
        # compare the actual values for a slice.
        lowercase_      :List[Any]         =			torch.tensor(
            [[[-0.59_86, -0.80_55, -0.84_62], [1.44_84, -0.93_48, -0.80_59], [0.31_23, 0.00_32, -1.41_31]]]						)
        self.assertTrue(torch.allclose(output[:, 1:4, 1:4]     ,				lowercase     ,				atol=1e-4						)     ,				F'{output[:, 1:4, 1:4]}'						)


