import argparse
import json
from collections import OrderedDict
from functools import partial
from pathlib import Path

import timm
import torch
from huggingface_hub import hf_hub_download

from transformers import LevitConfig, LevitForImageClassificationWithTeacher, LevitImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
UpperCamelCase__											=					logging.get_logger()




def    lowerCAmelCase_ (     __A,  __A,  __A,  __A,  __A = True							)     ->				str:







      '''simple docstring'''


      print(f"""Converting {name}..."""							)

      with torch.no_grad():
            if hidden_sizes == 128:
                  if name[-1] == "S":
                        UpperCAmelCase__								=  timm.create_model("levit_128s",  pretrained=__A							)
                  else:
                        UpperCAmelCase__								=  timm.create_model("levit_128",  pretrained=__A							)
            if hidden_sizes == 192:
                  UpperCAmelCase__								=  timm.create_model("levit_192",  pretrained=__A							)
            if hidden_sizes == 256:
                  UpperCAmelCase__								=  timm.create_model("levit_256",  pretrained=__A							)
            if hidden_sizes == 384:
                  UpperCAmelCase__								=  timm.create_model("levit_384",  pretrained=__A							)

            from_model.eval()
            UpperCAmelCase__								=  LevitForImageClassificationWithTeacher(__A							).eval()
            UpperCAmelCase__								=  OrderedDict()

            UpperCAmelCase__								=  from_model.state_dict()
            UpperCAmelCase__								=  list(from_model.state_dict().keys()							)
            UpperCAmelCase__								=  list(our_model.state_dict().keys()							)
            print(len(__A							),  len(__A							)							)
            for i in range(len(__A							)							):
                  UpperCAmelCase__								=  weights[og_keys[i]]
            our_model.load_state_dict(__A							)

            UpperCAmelCase__								=  torch.randn((2, 3, 224, 224)							)
            UpperCAmelCase__								=  from_model(__A							)
            UpperCAmelCase__								=  our_model(__A							).logits

      assert torch.allclose(__A,  __A							), "The model logits don't match the original one."

      UpperCAmelCase__								=  name
      print(__A							)

      if push_to_hub:
            our_model.save_pretrained(save_directory / checkpoint_name							)
            UpperCAmelCase__								=  LevitImageProcessor()
            image_processor.save_pretrained(save_directory / checkpoint_name							)

            print(f"""Pushed {checkpoint_name}"""							)




def    lowerCAmelCase_ (     __A,  __A = None,  __A = True							)     ->				Tuple:







      '''simple docstring'''


      UpperCAmelCase__								=  "imagenet-1k-id2label.json"
      UpperCAmelCase__								=  1_000
      UpperCAmelCase__								=  (1, num_labels)

      UpperCAmelCase__								=  "huggingface/label-files"
      UpperCAmelCase__								=  num_labels
      UpperCAmelCase__								=  json.load(open(hf_hub_download(__A,  __A,  repo_type="dataset"							),  "r"							)							)
      UpperCAmelCase__								=  {int(__A							): v for k, v in idalabel.items()}

      UpperCAmelCase__								=  idalabel
      UpperCAmelCase__								=  {v: k for k, v in idalabel.items()}

      UpperCAmelCase__								=  partial(__A,  num_labels=__A,  idalabel=__A,  labelaid=__A							)

      UpperCAmelCase__								=  {
          "levit-128S": 128,
          "levit-128": 128,
          "levit-192": 192,
          "levit-256": 256,
          "levit-384": 384,
      }

      UpperCAmelCase__								=  {
          "levit-128S": ImageNetPreTrainedConfig(
              hidden_sizes=[128, 256, 384],  num_attention_heads=[4, 6, 8],  depths=[2, 3, 4],  key_dim=[16, 16, 16],  drop_path_rate=0,  ),
          "levit-128": ImageNetPreTrainedConfig(
              hidden_sizes=[128, 256, 384],  num_attention_heads=[4, 8, 12],  depths=[4, 4, 4],  key_dim=[16, 16, 16],  drop_path_rate=0,  ),
          "levit-192": ImageNetPreTrainedConfig(
              hidden_sizes=[192, 288, 384],  num_attention_heads=[3, 5, 6],  depths=[4, 4, 4],  key_dim=[32, 32, 32],  drop_path_rate=0,  ),
          "levit-256": ImageNetPreTrainedConfig(
              hidden_sizes=[256, 384, 512],  num_attention_heads=[4, 6, 8],  depths=[4, 4, 4],  key_dim=[32, 32, 32],  drop_path_rate=0,  ),
          "levit-384": ImageNetPreTrainedConfig(
              hidden_sizes=[384, 512, 768],  num_attention_heads=[6, 9, 12],  depths=[4, 4, 4],  key_dim=[32, 32, 32],  drop_path_rate=0.1,  ),
      }

      if model_name:
            convert_weight_and_push(
                names_to_hidden_sizes[model_name],  __A,  names_to_config[model_name],  __A,  __A							)
      else:
            for model_name, config in names_to_config.items():
                  convert_weight_and_push(names_to_hidden_sizes[model_name],  __A,  __A,  __A,  __A							)
      return config, expected_shape


if __name__ == "__main__":
      UpperCamelCase__											=					argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          '--model_name',
          default=None,
          type=str,
          help='The name of the model you wish to convert, it must be one of the supported Levit* architecture,',
      )
      parser.add_argument(
          '--pytorch_dump_folder_path',
          default='levit-dump-folder/',
          type=Path,
          required=False,
          help='Path to the output PyTorch model directory.',
      )
      parser.add_argument('--push_to_hub', action='store_true', help='Push model and image processor to the hub')
      parser.add_argument(
          '--no-push_to_hub',
          dest='push_to_hub',
          action='store_false',
          help='Do not push model and image processor to the hub',
      )

      UpperCamelCase__											=					parser.parse_args()
      UpperCamelCase__											=					args.pytorch_dump_folder_path
      pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
      convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)








'''simple docstring'''


import unittest

from transformers import MraConfig, 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, floats_tensor, ids_tensor, random_attention_mask


if is_torch_available():
   import torch

   from transformers import (
       MraForMaskedLM,
       MraForMultipleChoice,
       MraForQuestionAnswering,
       MraForSequenceClassification,
       MraForTokenClassification,
       MraModel,
   )
   from transformers.models.mra.modeling_mra import MRA_PRETRAINED_MODEL_ARCHIVE_LIST

class  SCREAMING_SNAKE_CASE :






    """simple docstring"""





    def __init__(  self      :					Any  ,						UpperCamelCase__      :					Dict  ,						UpperCamelCase__      :					Any=2  ,						UpperCamelCase__      :					Union[str, Any]=8  ,						UpperCamelCase__      :					List[Any]=True  ,						UpperCamelCase__      :					Any=True  ,						UpperCamelCase__      :					str=True  ,						UpperCamelCase__      :					Dict=True  ,						UpperCamelCase__      :					List[Any]=9_9  ,						UpperCamelCase__      :					List[Any]=1_6  ,						UpperCamelCase__      :					List[str]=5  ,						UpperCamelCase__      :					Dict=2  ,						UpperCamelCase__      :					Optional[int]=3_6  ,						UpperCamelCase__      :					str="gelu"  ,						UpperCamelCase__      :					Dict=0.0  ,						UpperCamelCase__      :					Dict=0.0  ,						UpperCamelCase__      :					Optional[int]=5_1_2  ,						UpperCamelCase__      :					Dict=1_6  ,						UpperCamelCase__      :					List[str]=2  ,						UpperCamelCase__      :					Any=0.0_2  ,						UpperCamelCase__      :					str=3  ,						UpperCamelCase__      :					Tuple=4  ,						UpperCamelCase__      :					Union[str, Any]=None  ,						):


       """simple docstring"""







       UpperCamelCase					=  parent
       UpperCamelCase					=  batch_size
       UpperCamelCase					=  seq_length
       UpperCamelCase					=  is_training
       UpperCamelCase					=  use_input_mask
       UpperCamelCase					=  use_token_type_ids
       UpperCamelCase					=  use_labels
       UpperCamelCase					=  vocab_size
       UpperCamelCase					=  hidden_size
       UpperCamelCase					=  num_hidden_layers
       UpperCamelCase					=  num_attention_heads
       UpperCamelCase					=  intermediate_size
       UpperCamelCase					=  hidden_act
       UpperCamelCase					=  hidden_dropout_prob
       UpperCamelCase					=  attention_probs_dropout_prob
       UpperCamelCase					=  max_position_embeddings
       UpperCamelCase					=  type_vocab_size
       UpperCamelCase					=  type_sequence_label_size
       UpperCamelCase					=  initializer_range
       UpperCamelCase					=  num_labels
       UpperCamelCase					=  num_choices
       UpperCamelCase					=  scope






    def  A       (  self      :					int ):


       """simple docstring"""







       UpperCamelCase					=  ids_tensor([self.batch_size, self.seq_length]  ,						self.vocab_size )

       UpperCamelCase					=  None
       if self.use_input_mask:
          UpperCamelCase					=  random_attention_mask([self.batch_size, self.seq_length] )

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

       UpperCamelCase					=  None
       UpperCamelCase					=  None
       UpperCamelCase					=  None
       if self.use_labels:
          UpperCamelCase					=  ids_tensor([self.batch_size]  ,						self.type_sequence_label_size )
          UpperCamelCase					=  ids_tensor([self.batch_size, self.seq_length]  ,						self.num_labels )
          UpperCamelCase					=  ids_tensor([self.batch_size]  ,						self.num_choices )

       UpperCamelCase					=  self.get_config()

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






    def  A       (  self      :					Optional[int] ):


       """simple docstring"""







       return MraConfig(
           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  ,						is_decoder=UpperCamelCase__  ,						initializer_range=self.initializer_range  ,						)






    def  A       (  self      :					Any ):


       """simple docstring"""







       UpperCamelCase					=  self.get_config()
       UpperCamelCase					=  3_0_0
       return config






    def  A       (  self      :					Tuple ):


       """simple docstring"""







       (
           (
           UpperCamelCase
       )				,      (
           UpperCamelCase
       )				,      (
           UpperCamelCase
       )				,      (
           UpperCamelCase
       )				,      (
           UpperCamelCase
       )				,      (
           UpperCamelCase
       )				,      (
           UpperCamelCase
       )				,      
       )					=  self.prepare_config_and_inputs()

       UpperCamelCase					=  True
       UpperCamelCase					=  floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
       UpperCamelCase					=  ids_tensor([self.batch_size, self.seq_length]  ,						vocab_size=2 )

       return (
           config,
           input_ids,
           token_type_ids,
           input_mask,
           sequence_labels,
           token_labels,
           choice_labels,
           encoder_hidden_states,
           encoder_attention_mask,
       )






    def  A       (  self      :					Tuple  ,						UpperCamelCase__      :					Tuple  ,						UpperCamelCase__      :					int  ,						UpperCamelCase__      :					Any  ,						UpperCamelCase__      :					Dict  ,						UpperCamelCase__      :					int  ,						UpperCamelCase__      :					List[str]  ,						UpperCamelCase__      :					Dict ):


       """simple docstring"""







       UpperCamelCase					=  MraModel(config=UpperCamelCase__ )
       model.to(UpperCamelCase__ )
       model.eval()
       UpperCamelCase					=  model(UpperCamelCase__  ,						attention_mask=UpperCamelCase__  ,						token_type_ids=UpperCamelCase__ )
       UpperCamelCase					=  model(UpperCamelCase__  ,						token_type_ids=UpperCamelCase__ )
       UpperCamelCase					=  model(UpperCamelCase__ )
       self.parent.assertEqual(result.last_hidden_state.shape  ,						(self.batch_size, self.seq_length, self.hidden_size) )






    def  A       (  self      :					List[str]  ,						UpperCamelCase__      :					Dict  ,						UpperCamelCase__      :					List[Any]  ,						UpperCamelCase__      :					Union[str, Any]  ,						UpperCamelCase__      :					Union[str, Any]  ,						UpperCamelCase__      :					Dict  ,						UpperCamelCase__      :					List[Any]  ,						UpperCamelCase__      :					str  ,						UpperCamelCase__      :					Dict  ,						UpperCamelCase__      :					Optional[Any]  ,						):


       """simple docstring"""







       UpperCamelCase					=  True
       UpperCamelCase					=  MraModel(UpperCamelCase__ )
       model.to(UpperCamelCase__ )
       model.eval()
       UpperCamelCase					=  model(
           UpperCamelCase__  ,						attention_mask=UpperCamelCase__  ,						token_type_ids=UpperCamelCase__  ,						encoder_hidden_states=UpperCamelCase__  ,						encoder_attention_mask=UpperCamelCase__  ,						)
       UpperCamelCase					=  model(
           UpperCamelCase__  ,						attention_mask=UpperCamelCase__  ,						token_type_ids=UpperCamelCase__  ,						encoder_hidden_states=UpperCamelCase__  ,						)
       UpperCamelCase					=  model(UpperCamelCase__  ,						attention_mask=UpperCamelCase__  ,						token_type_ids=UpperCamelCase__ )
       self.parent.assertEqual(result.last_hidden_state.shape  ,						(self.batch_size, self.seq_length, self.hidden_size) )






    def  A       (  self      :					int  ,						UpperCamelCase__      :					Optional[int]  ,						UpperCamelCase__      :					List[Any]  ,						UpperCamelCase__      :					Any  ,						UpperCamelCase__      :					List[Any]  ,						UpperCamelCase__      :					List[str]  ,						UpperCamelCase__      :					Union[str, Any]  ,						UpperCamelCase__      :					List[str] ):


       """simple docstring"""







       UpperCamelCase					=  MraForMaskedLM(config=UpperCamelCase__ )
       model.to(UpperCamelCase__ )
       model.eval()
       UpperCamelCase					=  model(UpperCamelCase__  ,						attention_mask=UpperCamelCase__  ,						token_type_ids=UpperCamelCase__  ,						labels=UpperCamelCase__ )
       self.parent.assertEqual(result.logits.shape  ,						(self.batch_size, self.seq_length, self.vocab_size) )






    def  A       (  self      :					Any  ,						UpperCamelCase__      :					Any  ,						UpperCamelCase__      :					Union[str, Any]  ,						UpperCamelCase__      :					List[Any]  ,						UpperCamelCase__      :					Any  ,						UpperCamelCase__      :					List[str]  ,						UpperCamelCase__      :					Tuple  ,						UpperCamelCase__      :					Optional[Any] ):


       """simple docstring"""







       UpperCamelCase					=  MraForQuestionAnswering(config=UpperCamelCase__ )
       model.to(UpperCamelCase__ )
       model.eval()
       UpperCamelCase					=  model(
           UpperCamelCase__  ,						attention_mask=UpperCamelCase__  ,						token_type_ids=UpperCamelCase__  ,						start_positions=UpperCamelCase__  ,						end_positions=UpperCamelCase__  ,						)
       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  A       (  self      :					Optional[int]  ,						UpperCamelCase__      :					Union[str, Any]  ,						UpperCamelCase__      :					Tuple  ,						UpperCamelCase__      :					Optional[int]  ,						UpperCamelCase__      :					List[Any]  ,						UpperCamelCase__      :					int  ,						UpperCamelCase__      :					Optional[Any]  ,						UpperCamelCase__      :					Tuple ):


       """simple docstring"""







       UpperCamelCase					=  self.num_labels
       UpperCamelCase					=  MraForSequenceClassification(UpperCamelCase__ )
       model.to(UpperCamelCase__ )
       model.eval()
       UpperCamelCase					=  model(UpperCamelCase__  ,						attention_mask=UpperCamelCase__  ,						token_type_ids=UpperCamelCase__  ,						labels=UpperCamelCase__ )
       self.parent.assertEqual(result.logits.shape  ,						(self.batch_size, self.num_labels) )






    def  A       (  self      :					Any  ,						UpperCamelCase__      :					Any  ,						UpperCamelCase__      :					str  ,						UpperCamelCase__      :					List[Any]  ,						UpperCamelCase__      :					Optional[int]  ,						UpperCamelCase__      :					int  ,						UpperCamelCase__      :					int  ,						UpperCamelCase__      :					Optional[Any] ):


       """simple docstring"""







       UpperCamelCase					=  self.num_labels
       UpperCamelCase					=  MraForTokenClassification(config=UpperCamelCase__ )
       model.to(UpperCamelCase__ )
       model.eval()
       UpperCamelCase					=  model(UpperCamelCase__  ,						attention_mask=UpperCamelCase__  ,						token_type_ids=UpperCamelCase__  ,						labels=UpperCamelCase__ )
       self.parent.assertEqual(result.logits.shape  ,						(self.batch_size, self.seq_length, self.num_labels) )






    def  A       (  self      :					int  ,						UpperCamelCase__      :					Optional[Any]  ,						UpperCamelCase__      :					Any  ,						UpperCamelCase__      :					Optional[int]  ,						UpperCamelCase__      :					Tuple  ,						UpperCamelCase__      :					Dict  ,						UpperCamelCase__      :					str  ,						UpperCamelCase__      :					Dict ):


       """simple docstring"""







       UpperCamelCase					=  self.num_choices
       UpperCamelCase					=  MraForMultipleChoice(config=UpperCamelCase__ )
       model.to(UpperCamelCase__ )
       model.eval()
       UpperCamelCase					=  input_ids.unsqueeze(1 ).expand(-1  ,						self.num_choices  ,						-1 ).contiguous()
       UpperCamelCase					=  token_type_ids.unsqueeze(1 ).expand(-1  ,						self.num_choices  ,						-1 ).contiguous()
       UpperCamelCase					=  input_mask.unsqueeze(1 ).expand(-1  ,						self.num_choices  ,						-1 ).contiguous()
       UpperCamelCase					=  model(
           UpperCamelCase__  ,						attention_mask=UpperCamelCase__  ,						token_type_ids=UpperCamelCase__  ,						labels=UpperCamelCase__  ,						)
       self.parent.assertEqual(result.logits.shape  ,						(self.batch_size, self.num_choices) )






    def  A       (  self      :					int ):


       """simple docstring"""







       UpperCamelCase					=  self.prepare_config_and_inputs()
       (
           (
           UpperCamelCase
       )				,      (
           UpperCamelCase
       )				,      (
           UpperCamelCase
       )				,      (
           UpperCamelCase
       )				,      (
           UpperCamelCase
       )				,      (
           UpperCamelCase
       )				,      (
           UpperCamelCase
       )				,      
       )					=  config_and_inputs
       UpperCamelCase					=  {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
       return config, inputs_dict

@require_torch
class  SCREAMING_SNAKE_CASE (  _a   ,						unittest.TestCase		):






    """simple docstring"""





    _SCREAMING_SNAKE_CASE	  =  (
        (
            MraModel,
            MraForMaskedLM,
            MraForMultipleChoice,
            MraForQuestionAnswering,
            MraForSequenceClassification,
            MraForTokenClassification,
        )
        if is_torch_available()
        else ()
    )
    _SCREAMING_SNAKE_CASE	  =  False
    _SCREAMING_SNAKE_CASE	  =  False
    _SCREAMING_SNAKE_CASE	  =  False
    _SCREAMING_SNAKE_CASE	  =  False

    _SCREAMING_SNAKE_CASE	  =  ()






    def  A       (  self      :					str ):


       """simple docstring"""







       UpperCamelCase					=  MraModelTester(self )
       UpperCamelCase					=  ConfigTester(self  ,						config_class=UpperCamelCase__  ,						hidden_size=3_7 )






    def  A       (  self      :					str ):


       """simple docstring"""







       self.config_tester.run_common_tests()






    def  A       (  self      :					Optional[Any] ):


       """simple docstring"""







       UpperCamelCase					=  self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*UpperCamelCase__ )






    def  A       (  self      :					str ):


       """simple docstring"""







       UpperCamelCase					=  self.model_tester.prepare_config_and_inputs()
       for type in ["absolute", "relative_key", "relative_key_query"]:
          UpperCamelCase					=  type
          self.model_tester.create_and_check_model(*UpperCamelCase__ )






    def  A       (  self      :					Optional[int] ):


       """simple docstring"""







       UpperCamelCase					=  self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_masked_lm(*UpperCamelCase__ )






    def  A       (  self      :					List[Any] ):


       """simple docstring"""







       UpperCamelCase					=  self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_multiple_choice(*UpperCamelCase__ )






    def  A       (  self      :					Tuple ):


       """simple docstring"""







       UpperCamelCase					=  self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_question_answering(*UpperCamelCase__ )






    def  A       (  self      :					Any ):


       """simple docstring"""







       UpperCamelCase					=  self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_sequence_classification(*UpperCamelCase__ )






    def  A       (  self      :					Any ):


       """simple docstring"""







       UpperCamelCase					=  self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_token_classification(*UpperCamelCase__ )






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


       """simple docstring"""







       for model_name in MRA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
          UpperCamelCase					=  MraModel.from_pretrained(UpperCamelCase__ )
          self.assertIsNotNone(UpperCamelCase__ )






    @unittest.skip(reason='MRA does not output attentions' )
    def  A       (  self      :					List[str] ):


       """simple docstring"""







       return

@require_torch
class  SCREAMING_SNAKE_CASE (  unittest.TestCase		):






    """simple docstring"""





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


       """simple docstring"""







       UpperCamelCase					=  MraModel.from_pretrained('uw-madison/mra-base-512-4' )
       UpperCamelCase					=  torch.arange(2_5_6 ).unsqueeze(0 )

       with torch.no_grad():
          UpperCamelCase					=  model(UpperCamelCase__ )[0]

       UpperCamelCase					=  torch.Size((1, 2_5_6, 7_6_8) )
       self.assertEqual(output.shape  ,						UpperCamelCase__ )

       UpperCamelCase					=  torch.tensor(
           [[[-0.0_1_4_0, 0.0_8_3_0, -0.0_3_8_1], [0.1_5_4_6, 0.1_4_0_2, 0.0_2_2_0], [0.1_1_6_2, 0.0_8_5_1, 0.0_1_6_5]]] )

       self.assertTrue(torch.allclose(output[:, :3, :3]  ,						UpperCamelCase__  ,						atol=1E-4 ) )






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


       """simple docstring"""







       UpperCamelCase					=  MraForMaskedLM.from_pretrained('uw-madison/mra-base-512-4' )
       UpperCamelCase					=  torch.arange(2_5_6 ).unsqueeze(0 )

       with torch.no_grad():
          UpperCamelCase					=  model(UpperCamelCase__ )[0]

       UpperCamelCase					=  5_0_2_6_5

       UpperCamelCase					=  torch.Size((1, 2_5_6, vocab_size) )
       self.assertEqual(output.shape  ,						UpperCamelCase__ )

       UpperCamelCase					=  torch.tensor(
           [[[9.2_5_9_5, -3.6_0_3_8, 1_1.8_8_1_9], [9.3_8_6_9, -3.2_6_9_3, 1_1.0_9_5_6], [1_1.8_5_2_4, -3.4_9_3_8, 1_3.1_2_1_0]]] )

       self.assertTrue(torch.allclose(output[:, :3, :3]  ,						UpperCamelCase__  ,						atol=1E-4 ) )






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


       """simple docstring"""







       UpperCamelCase					=  MraForMaskedLM.from_pretrained('uw-madison/mra-base-4096-8-d3' )
       UpperCamelCase					=  torch.arange(4_0_9_6 ).unsqueeze(0 )

       with torch.no_grad():
          UpperCamelCase					=  model(UpperCamelCase__ )[0]

       UpperCamelCase					=  5_0_2_6_5

       UpperCamelCase					=  torch.Size((1, 4_0_9_6, vocab_size) )
       self.assertEqual(output.shape  ,						UpperCamelCase__ )

       UpperCamelCase					=  torch.tensor(
           [[[5.4_7_8_9, -2.3_5_6_4, 7.5_0_6_4], [7.9_0_6_7, -1.3_3_6_9, 9.9_6_6_8], [9.0_7_1_2, -1.8_1_0_6, 7.0_3_8_0]]] )

       self.assertTrue(torch.allclose(output[:, :3, :3]  ,						UpperCamelCase__  ,						atol=1E-4 ) )

