'''simple docstring'''





import argparse
import json

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

from transformers import ViTImageProcessor, ViTMSNConfig, ViTMSNModel
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD


torch.set_grad_enabled(False)


def     __lowerCAmelCase						(       a_       ,      a_=False		)      ->	Tuple:





     '''simple docstring'''





     SCREAMING_SNAKE_CASE					:			List[str]    	=       []
     for i in range(config.num_hidden_layers		):
          # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
          rename_keys.append((f"""module.blocks.{i}.norm1.weight""", f"""vit.encoder.layer.{i}.layernorm_before.weight""")		)
          rename_keys.append((f"""module.blocks.{i}.norm1.bias""", f"""vit.encoder.layer.{i}.layernorm_before.bias""")		)
          rename_keys.append(
              (f"""module.blocks.{i}.attn.proj.weight""", f"""vit.encoder.layer.{i}.attention.output.dense.weight""")		)
          rename_keys.append((f"""module.blocks.{i}.attn.proj.bias""", f"""vit.encoder.layer.{i}.attention.output.dense.bias""")		)
          rename_keys.append((f"""module.blocks.{i}.norm2.weight""", f"""vit.encoder.layer.{i}.layernorm_after.weight""")		)
          rename_keys.append((f"""module.blocks.{i}.norm2.bias""", f"""vit.encoder.layer.{i}.layernorm_after.bias""")		)
          rename_keys.append((f"""module.blocks.{i}.mlp.fc1.weight""", f"""vit.encoder.layer.{i}.intermediate.dense.weight""")		)
          rename_keys.append((f"""module.blocks.{i}.mlp.fc1.bias""", f"""vit.encoder.layer.{i}.intermediate.dense.bias""")		)
          rename_keys.append((f"""module.blocks.{i}.mlp.fc2.weight""", f"""vit.encoder.layer.{i}.output.dense.weight""")		)
          rename_keys.append((f"""module.blocks.{i}.mlp.fc2.bias""", f"""vit.encoder.layer.{i}.output.dense.bias""")		)

     # projection layer + position embeddings
     rename_keys.extend(
         [
             ('module.cls_token', 'vit.embeddings.cls_token'),
             ('module.patch_embed.proj.weight', 'vit.embeddings.patch_embeddings.projection.weight'),
             ('module.patch_embed.proj.bias', 'vit.embeddings.patch_embeddings.projection.bias'),
             ('module.pos_embed', 'vit.embeddings.position_embeddings'),
         ]		)

     if base_model:
          # layernorm + pooler
          rename_keys.extend(
              [
                  ('module.norm.weight', 'layernorm.weight'),
                  ('module.norm.bias', 'layernorm.bias'),
              ]		)

          # if just the base model, we should remove "vit" from all keys that start with "vit"
          SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       [(pair[0], pair[1][4:]) if pair[1].startswith('vit'		) else pair for pair in rename_keys]
     else:
          # layernorm + classification head
          rename_keys.extend(
              [
                  ('norm.weight', 'vit.layernorm.weight'),
                  ('norm.bias', 'vit.layernorm.bias'),
                  ('head.weight', 'classifier.weight'),
                  ('head.bias', 'classifier.bias'),
              ]		)

     return rename_keys


def     __lowerCAmelCase						(       a_       ,      a_       ,      a_=False		)      ->	Optional[Any]:





     '''simple docstring'''





     for i in range(config.num_hidden_layers		):
          if base_model:
               SCREAMING_SNAKE_CASE					:			Optional[int]    	=       ''
          else:
               SCREAMING_SNAKE_CASE					:			Tuple    	=       'vit.'
          # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
          SCREAMING_SNAKE_CASE					:			Any    	=       state_dict.pop(f"""module.blocks.{i}.attn.qkv.weight"""		)
          SCREAMING_SNAKE_CASE					:			Dict    	=       state_dict.pop(f"""module.blocks.{i}.attn.qkv.bias"""		)
          # next, add query, keys and values (in that order) to the state dict
          SCREAMING_SNAKE_CASE					:			List[str]    	=       in_proj_weight[
              : config.hidden_size, :
          ]
          SCREAMING_SNAKE_CASE					:			Dict    	=       in_proj_bias[: config.hidden_size]
          SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       in_proj_weight[
              config.hidden_size : config.hidden_size * 2, :
          ]
          SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       in_proj_bias[
              config.hidden_size : config.hidden_size * 2
          ]
          SCREAMING_SNAKE_CASE					:			Optional[int]    	=       in_proj_weight[
              -config.hidden_size :, :
          ]
          SCREAMING_SNAKE_CASE					:			Tuple    	=       in_proj_bias[-config.hidden_size :]


def     __lowerCAmelCase						(       a_		)      ->	List[Any]:





     '''simple docstring'''





     SCREAMING_SNAKE_CASE					:			List[Any]    	=       ['head.weight', 'head.bias']
     for k in ignore_keys:
          state_dict.pop(_lowerCAmelCase       ,      _lowerCAmelCase		)


def     __lowerCAmelCase						(       a_		)      ->	str:





     '''simple docstring'''





     SCREAMING_SNAKE_CASE					:			int    	=       [
         'module.fc.fc1.weight',
         'module.fc.fc1.bias',
         'module.fc.bn1.weight',
         'module.fc.bn1.bias',
         'module.fc.bn1.running_mean',
         'module.fc.bn1.running_var',
         'module.fc.bn1.num_batches_tracked',
         'module.fc.fc2.weight',
         'module.fc.fc2.bias',
         'module.fc.bn2.weight',
         'module.fc.bn2.bias',
         'module.fc.bn2.running_mean',
         'module.fc.bn2.running_var',
         'module.fc.bn2.num_batches_tracked',
         'module.fc.fc3.weight',
         'module.fc.fc3.bias',
     ]
     for k in ignore_keys:
          state_dict.pop(_lowerCAmelCase       ,      _lowerCAmelCase		)


def     __lowerCAmelCase						(       a_       ,      a_       ,      a_		)      ->	str:





     '''simple docstring'''





     SCREAMING_SNAKE_CASE					:			Any    	=       dct.pop(_lowerCAmelCase		)
     SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       val


def     __lowerCAmelCase						(       a_       ,      a_		)      ->	Dict:





     '''simple docstring'''





     SCREAMING_SNAKE_CASE					:			List[Any]    	=       ViTMSNConfig()
     SCREAMING_SNAKE_CASE					:			Optional[int]    	=       1000

     SCREAMING_SNAKE_CASE					:			Dict    	=       'datasets/huggingface/label-files'
     SCREAMING_SNAKE_CASE					:			Any    	=       'imagenet-1k-id2label.json'
     SCREAMING_SNAKE_CASE					:			List[str]    	=       json.load(open(hf_hub_download(_lowerCAmelCase       ,      _lowerCAmelCase		)       ,      'r'		)		)
     SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       {int(_lowerCAmelCase		): v for k, v in idalabel.items()}
     SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       idalabel
     SCREAMING_SNAKE_CASE					:			Any    	=       {v: k for k, v in idalabel.items()}

     if "s16" in checkpoint_url:
          SCREAMING_SNAKE_CASE					:			List[str]    	=       384
          SCREAMING_SNAKE_CASE					:			Tuple    	=       1536
          SCREAMING_SNAKE_CASE					:			int    	=       6
     elif "l16" in checkpoint_url:
          SCREAMING_SNAKE_CASE					:			Tuple    	=       1024
          SCREAMING_SNAKE_CASE					:			int    	=       4096
          SCREAMING_SNAKE_CASE					:			Tuple    	=       24
          SCREAMING_SNAKE_CASE					:			Dict    	=       16
          SCREAMING_SNAKE_CASE					:			List[Any]    	=       0.1
     elif "b4" in checkpoint_url:
          SCREAMING_SNAKE_CASE					:			Optional[int]    	=       4
     elif "l7" in checkpoint_url:
          SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       7
          SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       1024
          SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       4096
          SCREAMING_SNAKE_CASE					:			Any    	=       24
          SCREAMING_SNAKE_CASE					:			str    	=       16
          SCREAMING_SNAKE_CASE					:			List[str]    	=       0.1

     SCREAMING_SNAKE_CASE					:			Optional[int]    	=       ViTMSNModel(_lowerCAmelCase		)

     SCREAMING_SNAKE_CASE					:			Optional[int]    	=       torch.hub.load_state_dict_from_url(_lowerCAmelCase       ,      map_location='cpu'		)['target_encoder']

     SCREAMING_SNAKE_CASE					:			str    	=       ViTImageProcessor(size=config.image_size		)

     remove_projection_head(_lowerCAmelCase		)
     SCREAMING_SNAKE_CASE					:			List[str]    	=       create_rename_keys(_lowerCAmelCase       ,      base_model=_lowerCAmelCase		)

     for src, dest in rename_keys:
          rename_key(_lowerCAmelCase       ,      _lowerCAmelCase       ,      _lowerCAmelCase		)
     read_in_q_k_v(_lowerCAmelCase       ,      _lowerCAmelCase       ,      base_model=_lowerCAmelCase		)

     model.load_state_dict(_lowerCAmelCase		)
     model.eval()

     SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       'http://images.cocodataset.org/val2017/000000039769.jpg'

     SCREAMING_SNAKE_CASE					:			int    	=       Image.open(requests.get(_lowerCAmelCase       ,      stream=_lowerCAmelCase		).raw		)
     SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       ViTImageProcessor(
         size=config.image_size       ,      image_mean=_lowerCAmelCase       ,      image_std=_lowerCAmelCase		)
     SCREAMING_SNAKE_CASE					:			int    	=       image_processor(images=_lowerCAmelCase       ,      return_tensors='pt'		)

     # forward pass
     torch.manual_seed(2		)
     SCREAMING_SNAKE_CASE					:			Tuple    	=       model(**_lowerCAmelCase		)
     SCREAMING_SNAKE_CASE					:			List[Any]    	=       outputs.last_hidden_state

     # The following Colab Notebook was used to generate these outputs:
     # https://colab.research.google.com/gist/sayakpaul/3672419a04f5997827503fd84079bdd1/scratchpad.ipynb
     if "s16" in checkpoint_url:
          SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       torch.tensor([[-1.0915, -1.4876, -1.1809]]		)
     elif "b16" in checkpoint_url:
          SCREAMING_SNAKE_CASE					:			Tuple    	=       torch.tensor([[14.2889, -18.9045, 11.7281]]		)
     elif "l16" in checkpoint_url:
          SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       torch.tensor([[41.5028, -22.8681, 45.6475]]		)
     elif "b4" in checkpoint_url:
          SCREAMING_SNAKE_CASE					:			List[str]    	=       torch.tensor([[-4.3868, 5.2932, -0.4137]]		)
     else:
          SCREAMING_SNAKE_CASE					:			Tuple    	=       torch.tensor([[-0.1792, -0.6465, 2.4263]]		)

     # verify logits
     assert torch.allclose(last_hidden_state[:, 0, :3]       ,      _lowerCAmelCase       ,      atol=1e-4		)

     print(f"""Saving model to {pytorch_dump_folder_path}"""		)
     model.save_pretrained(_lowerCAmelCase		)

     print(f"""Saving image processor to {pytorch_dump_folder_path}"""		)
     image_processor.save_pretrained(_lowerCAmelCase		)


if __name__ == "__main__":
 _lowerCAmelCase  :List[Any]  				=    argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     """--checkpoint_url""",
     default="""https://dl.fbaipublicfiles.com/msn/vits16_800ep.pth.tar""",
     type=str,
     help="""URL of the checkpoint you'd like to convert.""",
 )
 parser.add_argument(
     """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
 )

 _lowerCAmelCase  :Optional[Any]  				=    parser.parse_args()
 convert_vit_msn_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)

'''simple docstring'''





import unittest

from transformers import MPNetConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

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


if is_torch_available():
 import torch

 from transformers import (
     MPNetForMaskedLM,
     MPNetForMultipleChoice,
     MPNetForQuestionAnswering,
     MPNetForSequenceClassification,
     MPNetForTokenClassification,
     MPNetModel,
 )






class 				UpperCAmelCase :




    '''simple docstring'''







    def __init__(       self    ,							lowercase__    ,							lowercase__=13    ,							lowercase__=7    ,							lowercase__=True    ,							lowercase__=True    ,							lowercase__=False    ,							lowercase__=True    ,							lowercase__=99    ,							lowercase__=64    ,							lowercase__=5    ,							lowercase__=4    ,							lowercase__=64    ,							lowercase__="gelu"    ,							lowercase__=0.1    ,							lowercase__=0.1    ,							lowercase__=512    ,							lowercase__=16    ,							lowercase__=2    ,							lowercase__=0.0_2    ,							lowercase__=3    ,							lowercase__=4    ,							lowercase__=None    ,							)							-> int:
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       parent
         SCREAMING_SNAKE_CASE					:			int    	=       batch_size
         SCREAMING_SNAKE_CASE					:			Any    	=       seq_length
         SCREAMING_SNAKE_CASE					:			Any    	=       is_training
         SCREAMING_SNAKE_CASE					:			Any    	=       use_input_mask
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       use_token_type_ids
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       use_labels
         SCREAMING_SNAKE_CASE					:			int    	=       vocab_size
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       hidden_size
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       num_hidden_layers
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       num_attention_heads
         SCREAMING_SNAKE_CASE					:			Any    	=       intermediate_size
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       hidden_act
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       hidden_dropout_prob
         SCREAMING_SNAKE_CASE					:			Any    	=       attention_probs_dropout_prob
         SCREAMING_SNAKE_CASE					:			Tuple    	=       max_position_embeddings
         SCREAMING_SNAKE_CASE					:			Tuple    	=       type_vocab_size
         SCREAMING_SNAKE_CASE					:			List[str]    	=       type_sequence_label_size
         SCREAMING_SNAKE_CASE					:			int    	=       initializer_range
         SCREAMING_SNAKE_CASE					:			Dict    	=       num_labels
         SCREAMING_SNAKE_CASE					:			Dict    	=       num_choices
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       scope






    def       _UpperCamelCase    (       self )							-> Union[str, Any]:
         return MPNetConfig.from_pretrained('microsoft/mpnet-base' )






    def       _UpperCamelCase    (       self )							-> List[str]:
         SCREAMING_SNAKE_CASE					:			List[str]    	=       ids_tensor([self.batch_size, self.seq_length]    ,							self.vocab_size )

         SCREAMING_SNAKE_CASE					:			str    	=       None
         if self.use_input_mask:
              SCREAMING_SNAKE_CASE					:			Dict    	=       random_attention_mask([self.batch_size, self.seq_length] )

         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       None
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       None
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       None
         if self.use_labels:
              SCREAMING_SNAKE_CASE					:			Dict    	=       ids_tensor([self.batch_size]    ,							self.type_sequence_label_size )
              SCREAMING_SNAKE_CASE					:			Dict    	=       ids_tensor([self.batch_size, self.seq_length]    ,							self.num_labels )
              SCREAMING_SNAKE_CASE					:			Tuple    	=       ids_tensor([self.batch_size]    ,							self.num_choices )

         SCREAMING_SNAKE_CASE					:			Tuple    	=       self.get_config()
         return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels






    def       _UpperCamelCase    (       self )							-> Tuple:
         return MPNetConfig(
             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    ,							initializer_range=self.initializer_range    ,							)






    def       _UpperCamelCase    (       self    ,							lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__ )							-> List[str]:
         SCREAMING_SNAKE_CASE					:			List[str]    	=       MPNetModel(config=lowercase__ )
         model.to(lowercase__ )
         model.eval()
         SCREAMING_SNAKE_CASE					:			List[str]    	=       model(lowercase__    ,							lowercase__ )
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       model(lowercase__ )
         self.parent.assertEqual(result.last_hidden_state.shape    ,							(self.batch_size, self.seq_length, self.hidden_size) )
         self.parent.assertEqual(result.pooler_output.shape    ,							(self.batch_size, self.hidden_size) )






    def       _UpperCamelCase    (       self    ,							lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__ )							-> Union[str, Any]:
         SCREAMING_SNAKE_CASE					:			str    	=       MPNetForQuestionAnswering(config=lowercase__ )
         model.to(lowercase__ )
         model.eval()
         SCREAMING_SNAKE_CASE					:			Any    	=       model(
             lowercase__    ,							attention_mask=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       _UpperCamelCase    (       self    ,							lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__ )							-> Union[str, Any]:
         SCREAMING_SNAKE_CASE					:			str    	=       self.num_labels
         SCREAMING_SNAKE_CASE					:			Tuple    	=       MPNetForSequenceClassification(lowercase__ )
         model.to(lowercase__ )
         model.eval()
         SCREAMING_SNAKE_CASE					:			List[str]    	=       model(lowercase__    ,							attention_mask=lowercase__    ,							labels=lowercase__ )
         self.parent.assertEqual(result.logits.shape    ,							(self.batch_size, self.num_labels) )






    def       _UpperCamelCase    (       self    ,							lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__ )							-> List[Any]:
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       self.num_choices
         SCREAMING_SNAKE_CASE					:			Any    	=       MPNetForMultipleChoice(config=lowercase__ )
         model.to(lowercase__ )
         model.eval()
         SCREAMING_SNAKE_CASE					:			str    	=       input_ids.unsqueeze(1 ).expand(-1    ,							self.num_choices    ,							-1 ).contiguous()
         SCREAMING_SNAKE_CASE					:			List[str]    	=       input_mask.unsqueeze(1 ).expand(-1    ,							self.num_choices    ,							-1 ).contiguous()
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       model(
             lowercase__    ,							attention_mask=lowercase__    ,							labels=lowercase__    ,							)
         self.parent.assertEqual(result.logits.shape    ,							(self.batch_size, self.num_choices) )






    def       _UpperCamelCase    (       self    ,							lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__ )							-> List[str]:
         SCREAMING_SNAKE_CASE					:			str    	=       self.num_labels
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       MPNetForTokenClassification(config=lowercase__ )
         model.to(lowercase__ )
         model.eval()
         SCREAMING_SNAKE_CASE					:			Any    	=       model(lowercase__    ,							attention_mask=lowercase__    ,							labels=lowercase__ )
         self.parent.assertEqual(result.logits.shape    ,							(self.batch_size, self.seq_length, self.num_labels) )






    def       _UpperCamelCase    (       self )							-> List[Any]:
         SCREAMING_SNAKE_CASE					:			Any    	=       self.prepare_config_and_inputs()
         ((SCREAMING_SNAKE_CASE)	,   (SCREAMING_SNAKE_CASE)	,   (SCREAMING_SNAKE_CASE)	,   (SCREAMING_SNAKE_CASE)	,   (SCREAMING_SNAKE_CASE)	,   (SCREAMING_SNAKE_CASE))					:			str    	=       config_and_inputs
         SCREAMING_SNAKE_CASE					:			Dict    	=       {'input_ids': input_ids, 'attention_mask': input_mask}
         return config, inputs_dict






@require_torch
class 				UpperCAmelCase (       _SCREAMING_SNAKE_CASE   ,    _SCREAMING_SNAKE_CASE   ,    unittest.TestCase ):




    '''simple docstring'''







    snake_case__							:   Optional[int]							=						(
        (
            MPNetForMaskedLM,
            MPNetForMultipleChoice,
            MPNetForQuestionAnswering,
            MPNetForSequenceClassification,
            MPNetForTokenClassification,
            MPNetModel,
        )
        if is_torch_available()
        else ()
    )
    snake_case__							:   Optional[int]							=						(
        {
            "feature-extraction": MPNetModel,
            "fill-mask": MPNetForMaskedLM,
            "question-answering": MPNetForQuestionAnswering,
            "text-classification": MPNetForSequenceClassification,
            "token-classification": MPNetForTokenClassification,
            "zero-shot": MPNetForSequenceClassification,
        }
        if is_torch_available()
        else {}
    )
    snake_case__							:   List[str]							=						False
    snake_case__							:   int							=						True






    def       _UpperCamelCase    (       self )							-> Dict:
         SCREAMING_SNAKE_CASE					:			int    	=       MPNetModelTester(self )
         SCREAMING_SNAKE_CASE					:			int    	=       ConfigTester(self    ,							config_class=lowercase__    ,							hidden_size=37 )






    def       _UpperCamelCase    (       self )							-> Any:
         self.config_tester.run_common_tests()






    def       _UpperCamelCase    (       self )							-> Tuple:
         SCREAMING_SNAKE_CASE					:			int    	=       self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_mpnet_model(*lowercase__ )






    def       _UpperCamelCase    (       self )							-> Dict:
         SCREAMING_SNAKE_CASE					:			str    	=       self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_mpnet_for_sequence_classification(*lowercase__ )






    def       _UpperCamelCase    (       self )							-> List[Any]:
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_mpnet_for_multiple_choice(*lowercase__ )






    def       _UpperCamelCase    (       self )							-> Dict:
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_mpnet_for_token_classification(*lowercase__ )






    def       _UpperCamelCase    (       self )							-> Optional[int]:
         SCREAMING_SNAKE_CASE					:			str    	=       self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_mpnet_for_question_answering(*lowercase__ )






@require_torch
class 				UpperCAmelCase (       unittest.TestCase ):




    '''simple docstring'''







    @slow
    def       _UpperCamelCase    (       self )							-> List[str]:
         SCREAMING_SNAKE_CASE					:			Tuple    	=       MPNetModel.from_pretrained('microsoft/mpnet-base' )
         SCREAMING_SNAKE_CASE					:			str    	=       torch.tensor([[0, 345, 232, 328, 740, 140, 1_695, 69, 6_078, 1_588, 2]] )
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       model(lowercase__ )[0]
         SCREAMING_SNAKE_CASE					:			List[str]    	=       torch.Size((1, 11, 768) )
         self.assertEqual(output.shape    ,							lowercase__ )
         SCREAMING_SNAKE_CASE					:			str    	=       torch.tensor(
             [[[-0.0_5_5_0, 0.1_9_4_3, -0.0_7_4_0], [-0.0_5_6_2, 0.2_2_1_1, -0.0_5_7_9], [-0.0_4_3_7, 0.3_3_3_7, -0.0_6_4_1]]] )
         # compare the actual values for a slice.
         self.assertTrue(torch.allclose(output[:, :3, :3]    ,							lowercase__    ,							atol=1E-4 ) )



