"""simple docstring"""


import inspect
import unittest

import numpy as np

from transformers import BeitConfig
from transformers.testing_utils import require_flax, require_vision, slow
from transformers.utils import cached_property, is_flax_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor


if is_flax_available():
    import jax

    from transformers import FlaxBeitForImageClassification, FlaxBeitForMaskedImageModeling, FlaxBeitModel

if is_vision_available():
    from PIL import Image

    from transformers import BeitImageProcessor


class 							a					(		unittest.TestCase ):


    def __init__(       self						:	Optional[Any] ,					__lowerCAmelCase						:	Tuple ,					__lowerCAmelCase						:	Optional[int]=100 ,					__lowerCAmelCase						:	Optional[Any]=13 ,					__lowerCAmelCase						:	int=30 ,					__lowerCAmelCase						:	int=2 ,					__lowerCAmelCase						:	Union[str, Any]=3 ,					__lowerCAmelCase						:	Tuple=True ,					__lowerCAmelCase						:	List[Any]=True ,					__lowerCAmelCase						:	List[Any]=32 ,					__lowerCAmelCase						:	Union[str, Any]=5 ,					__lowerCAmelCase						:	Optional[Any]=4 ,					__lowerCAmelCase						:	Any=37 ,					__lowerCAmelCase						:	Optional[int]="gelu" ,					__lowerCAmelCase						:	List[str]=0.1 ,					__lowerCAmelCase						:	Union[str, Any]=0.1 ,					__lowerCAmelCase						:	Tuple=10 ,					__lowerCAmelCase						:	Optional[Any]=0.02 ,					__lowerCAmelCase						:	int=3 ,					):
      _UpperCAmelCase    			=   parent
      _UpperCAmelCase    			=   vocab_size
      _UpperCAmelCase    			=   batch_size
      _UpperCAmelCase    			=   image_size
      _UpperCAmelCase    			=   patch_size
      _UpperCAmelCase    			=   num_channels
      _UpperCAmelCase    			=   is_training
      _UpperCAmelCase    			=   use_labels
      _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    			=   type_sequence_label_size
      _UpperCAmelCase    			=   initializer_range

      # in BeiT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
      _UpperCAmelCase    			=   (image_size // patch_size) ** 2
      _UpperCAmelCase    			=   num_patches + 1


    def   lowerCAmelCase_	(       self						:	Union[str, Any]       ):
      _UpperCAmelCase    			=   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )

      _UpperCAmelCase    			=   None
      if self.use_labels:
        _UpperCAmelCase    			=   ids_tensor([self.batch_size] ,					self.type_sequence_label_size       )

      _UpperCAmelCase    			=   BeitConfig(
          vocab_size=self.vocab_size ,					image_size=self.image_size ,					patch_size=self.patch_size ,					num_channels=self.num_channels ,					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 ,					is_decoder=__lowerCAmelCase ,					initializer_range=self.initializer_range ,					)

      return config, pixel_values, labels


    def   lowerCAmelCase_	(       self						:	Any ,					__lowerCAmelCase						:	Any ,					__lowerCAmelCase						:	Tuple ,					__lowerCAmelCase						:	int       ):
      _UpperCAmelCase    			=   FlaxBeitModel(config=__lowerCAmelCase       )
      _UpperCAmelCase    			=   model(__lowerCAmelCase       )
      self.parent.assertEqual(result.last_hidden_state.shape ,					(self.batch_size, self.seq_length, self.hidden_size)       )


    def   lowerCAmelCase_	(       self						:	Dict ,					__lowerCAmelCase						:	Optional[Any] ,					__lowerCAmelCase						:	List[str] ,					__lowerCAmelCase						:	Any       ):
      _UpperCAmelCase    			=   FlaxBeitForMaskedImageModeling(config=__lowerCAmelCase       )
      _UpperCAmelCase    			=   model(__lowerCAmelCase       )
      self.parent.assertEqual(result.logits.shape ,					(self.batch_size, self.seq_length - 1, self.vocab_size)       )


    def   lowerCAmelCase_	(       self						:	Dict ,					__lowerCAmelCase						:	int ,					__lowerCAmelCase						:	Optional[Any] ,					__lowerCAmelCase						:	List[Any]       ):
      _UpperCAmelCase    			=   self.type_sequence_label_size
      _UpperCAmelCase    			=   FlaxBeitForImageClassification(config=__lowerCAmelCase       )
      _UpperCAmelCase    			=   model(__lowerCAmelCase       )
      self.parent.assertEqual(result.logits.shape ,					(self.batch_size, self.type_sequence_label_size)       )

      # test greyscale images
      _UpperCAmelCase    			=   1
      _UpperCAmelCase    			=   FlaxBeitForImageClassification(__lowerCAmelCase       )

      _UpperCAmelCase    			=   floats_tensor([self.batch_size, 1, self.image_size, self.image_size]       )
      _UpperCAmelCase    			=   model(__lowerCAmelCase       )


    def   lowerCAmelCase_	(       self						:	Dict       ):
      _UpperCAmelCase    			=   self.prepare_config_and_inputs()
      (
          (
          _UpperCAmelCase
      )			,					(
          _UpperCAmelCase
      )			,					(
          _UpperCAmelCase
      )			,					
      )    			=   config_and_inputs
      _UpperCAmelCase    			=   {"""pixel_values""": pixel_values}
      return config, inputs_dict



@require_flax
class 							a					(		lowerCAmelCase_						,				unittest.TestCase ):
    _snake_case  :	Union[str, Any]  				=				(
        (FlaxBeitModel, FlaxBeitForImageClassification, FlaxBeitForMaskedImageModeling) if is_flax_available() else ()
    )


    def   lowerCAmelCase_	(       self						:	Dict       ):
      _UpperCAmelCase    			=   FlaxBeitModelTester(self       )
      _UpperCAmelCase    			=   ConfigTester(self ,					config_class=__lowerCAmelCase ,					has_text_modality=__lowerCAmelCase ,					hidden_size=37       )


    def   lowerCAmelCase_	(       self						:	Tuple       ):
      self.config_tester.run_common_tests()


    def   lowerCAmelCase_	(       self						:	List[str]       ):
      _UpperCAmelCase			,					_UpperCAmelCase    			=   self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
        _UpperCAmelCase    			=   model_class(__lowerCAmelCase       )
        _UpperCAmelCase    			=   inspect.signature(model.__call__       )
        # signature.parameters is an OrderedDict => so arg_names order is deterministic
        _UpperCAmelCase    			=   [*signature.parameters.keys()]

        _UpperCAmelCase    			=   ["""pixel_values"""]
        self.assertListEqual(arg_names[:1] ,					__lowerCAmelCase       )


    def   lowerCAmelCase_	(       self						:	Optional[Any]       ):
      _UpperCAmelCase			,					_UpperCAmelCase    			=   self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
        with self.subTest(model_class.__name__       ):
          _UpperCAmelCase    			=   self._prepare_for_class(__lowerCAmelCase ,					__lowerCAmelCase       )
          _UpperCAmelCase    			=   model_class(__lowerCAmelCase       )

          @jax.jit
          def model_jitted(__lowerCAmelCase						:	Optional[int] ,					**__lowerCAmelCase						:	Union[str, Any]       ):
            return model(pixel_values=__lowerCAmelCase ,					**__lowerCAmelCase       )

          with self.subTest("""JIT Enabled"""       ):
            _UpperCAmelCase    			=   model_jitted(**__lowerCAmelCase       ).to_tuple()

          with self.subTest("""JIT Disabled"""       ):
            with jax.disable_jit():
              _UpperCAmelCase    			=   model_jitted(**__lowerCAmelCase       ).to_tuple()

          self.assertEqual(len(__lowerCAmelCase       ) ,					len(__lowerCAmelCase       )       )
          for jitted_output, output in zip(__lowerCAmelCase ,					__lowerCAmelCase       ):
            self.assertEqual(jitted_output.shape ,					output.shape       )


    def   lowerCAmelCase_	(       self						:	int       ):
      _UpperCAmelCase    			=   self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*__lowerCAmelCase       )


    def   lowerCAmelCase_	(       self						:	str       ):
      _UpperCAmelCase    			=   self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_masked_lm(*__lowerCAmelCase       )


    def   lowerCAmelCase_	(       self						:	Union[str, Any]       ):
      _UpperCAmelCase    			=   self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_image_classification(*__lowerCAmelCase       )


    @slow
    def   lowerCAmelCase_	(       self						:	Optional[Any]       ):
      for model_class_name in self.all_model_classes:
        _UpperCAmelCase    			=   model_class_name.from_pretrained("""microsoft/beit-base-patch16-224"""       )
        _UpperCAmelCase    			=   model(np.ones((1, 3, 224, 224)       )       )
        self.assertIsNotNone(__lowerCAmelCase       )






def      __UpperCAmelCase       (      ):







  """simple docstring"""
  _UpperCAmelCase    			=   Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""		)
  return image


@require_vision
@require_flax
class 							a					(		unittest.TestCase ):


    @cached_property
    def   lowerCAmelCase_	(       self						:	Dict       ):
      return BeitImageProcessor.from_pretrained("""microsoft/beit-base-patch16-224"""       ) if is_vision_available() else None


    @slow
    def   lowerCAmelCase_	(       self						:	Optional[int]       ):
      _UpperCAmelCase    			=   FlaxBeitForMaskedImageModeling.from_pretrained("""microsoft/beit-base-patch16-224-pt22k"""       )

      _UpperCAmelCase    			=   self.default_image_processor
      _UpperCAmelCase    			=   prepare_img()
      _UpperCAmelCase    			=   image_processor(images=__lowerCAmelCase ,					return_tensors="""np"""       ).pixel_values

      # prepare bool_masked_pos
      _UpperCAmelCase    			=   np.ones((1, 196) ,					dtype=__lowerCAmelCase       )

      # forward pass
      _UpperCAmelCase    			=   model(pixel_values=__lowerCAmelCase ,					bool_masked_pos=__lowerCAmelCase       )
      _UpperCAmelCase    			=   outputs.logits

      # verify the logits
      _UpperCAmelCase    			=   (1, 196, 8192)
      self.assertEqual(logits.shape ,					__lowerCAmelCase       )

      _UpperCAmelCase    			=   np.array(
          [[-3.2_437, 0.5_072, -13.9_174], [-3.2_456, 0.4_948, -13.9_401], [-3.2_033, 0.5_121, -13.8_550]]       )

      self.assertTrue(np.allclose(logits[bool_masked_pos][:3, :3] ,					__lowerCAmelCase ,					atol=1e-2       )       )


    @slow
    def   lowerCAmelCase_	(       self						:	List[str]       ):
      _UpperCAmelCase    			=   FlaxBeitForImageClassification.from_pretrained("""microsoft/beit-base-patch16-224"""       )

      _UpperCAmelCase    			=   self.default_image_processor
      _UpperCAmelCase    			=   prepare_img()
      _UpperCAmelCase    			=   image_processor(images=__lowerCAmelCase ,					return_tensors="""np"""       )

      # forward pass
      _UpperCAmelCase    			=   model(**__lowerCAmelCase       )
      _UpperCAmelCase    			=   outputs.logits

      # verify the logits
      _UpperCAmelCase    			=   (1, 1000)
      self.assertEqual(logits.shape ,					__lowerCAmelCase       )

      _UpperCAmelCase    			=   np.array([-1.2_385, -1.0_987, -1.0_108]       )

      self.assertTrue(np.allclose(logits[0, :3] ,					__lowerCAmelCase ,					atol=1e-4       )       )

      _UpperCAmelCase    			=   281
      self.assertEqual(logits.argmax(-1       ).item() ,					__lowerCAmelCase       )


    @slow
    def   lowerCAmelCase_	(       self						:	Union[str, Any]       ):
      _UpperCAmelCase    			=   FlaxBeitForImageClassification.from_pretrained("""microsoft/beit-large-patch16-224-pt22k-ft22k"""       )

      _UpperCAmelCase    			=   self.default_image_processor
      _UpperCAmelCase    			=   prepare_img()
      _UpperCAmelCase    			=   image_processor(images=__lowerCAmelCase ,					return_tensors="""np"""       )

      # forward pass
      _UpperCAmelCase    			=   model(**__lowerCAmelCase       )
      _UpperCAmelCase    			=   outputs.logits

      # verify the logits
      _UpperCAmelCase    			=   (1, 2_1841)
      self.assertEqual(logits.shape ,					__lowerCAmelCase       )

      _UpperCAmelCase    			=   np.array([1.6_881, -0.2_787, 0.5_901]       )

      self.assertTrue(np.allclose(logits[0, :3] ,					__lowerCAmelCase ,					atol=1e-4       )       )

      _UpperCAmelCase    			=   2396
      self.assertEqual(logits.argmax(-1       ).item() ,					__lowerCAmelCase       )

"""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 							a					:


    def __init__(       self						:	Optional[int] ,					__lowerCAmelCase						:	Optional[Any] ,					__lowerCAmelCase						:	str=2 ,					__lowerCAmelCase						:	List[Any]=8 ,					__lowerCAmelCase						:	Dict=True ,					__lowerCAmelCase						:	List[Any]=True ,					__lowerCAmelCase						:	Optional[int]=True ,					__lowerCAmelCase						:	str=True ,					__lowerCAmelCase						:	Optional[Any]=99 ,					__lowerCAmelCase						:	Optional[Any]=16 ,					__lowerCAmelCase						:	Optional[int]=5 ,					__lowerCAmelCase						:	Optional[int]=2 ,					__lowerCAmelCase						:	Optional[int]=36 ,					__lowerCAmelCase						:	Any="gelu" ,					__lowerCAmelCase						:	Any=0.0 ,					__lowerCAmelCase						:	int=0.0 ,					__lowerCAmelCase						:	List[str]=512 ,					__lowerCAmelCase						:	List[str]=16 ,					__lowerCAmelCase						:	int=2 ,					__lowerCAmelCase						:	Any=0.02 ,					__lowerCAmelCase						:	List[str]=3 ,					__lowerCAmelCase						:	str=4 ,					__lowerCAmelCase						:	Dict=None ,					):
      _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   lowerCAmelCase_	(       self						:	Optional[int]       ):
      _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   lowerCAmelCase_	(       self						:	Any       ):
      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=__lowerCAmelCase ,					initializer_range=self.initializer_range ,					)


    def   lowerCAmelCase_	(       self						:	int       ):
      _UpperCAmelCase    			=   self.get_config()
      _UpperCAmelCase    			=   300
      return config


    def   lowerCAmelCase_	(       self						:	Any       ):
      (
          (
          _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   lowerCAmelCase_	(       self						:	List[str] ,					__lowerCAmelCase						:	Dict ,					__lowerCAmelCase						:	Dict ,					__lowerCAmelCase						:	Any ,					__lowerCAmelCase						:	int ,					__lowerCAmelCase						:	Dict ,					__lowerCAmelCase						:	List[Any] ,					__lowerCAmelCase						:	Tuple       ):
      _UpperCAmelCase    			=   MraModel(config=__lowerCAmelCase       )
      model.to(__lowerCAmelCase       )
      model.eval()
      _UpperCAmelCase    			=   model(__lowerCAmelCase ,					attention_mask=__lowerCAmelCase ,					token_type_ids=__lowerCAmelCase       )
      _UpperCAmelCase    			=   model(__lowerCAmelCase ,					token_type_ids=__lowerCAmelCase       )
      _UpperCAmelCase    			=   model(__lowerCAmelCase       )
      self.parent.assertEqual(result.last_hidden_state.shape ,					(self.batch_size, self.seq_length, self.hidden_size)       )


    def   lowerCAmelCase_	(       self						:	Tuple ,					__lowerCAmelCase						:	Dict ,					__lowerCAmelCase						:	Any ,					__lowerCAmelCase						:	Optional[Any] ,					__lowerCAmelCase						:	int ,					__lowerCAmelCase						:	Optional[int] ,					__lowerCAmelCase						:	Dict ,					__lowerCAmelCase						:	Dict ,					__lowerCAmelCase						:	Optional[int] ,					__lowerCAmelCase						:	Tuple ,					):
      _UpperCAmelCase    			=   True
      _UpperCAmelCase    			=   MraModel(__lowerCAmelCase       )
      model.to(__lowerCAmelCase       )
      model.eval()
      _UpperCAmelCase    			=   model(
          __lowerCAmelCase ,					attention_mask=__lowerCAmelCase ,					token_type_ids=__lowerCAmelCase ,					encoder_hidden_states=__lowerCAmelCase ,					encoder_attention_mask=__lowerCAmelCase ,					)
      _UpperCAmelCase    			=   model(
          __lowerCAmelCase ,					attention_mask=__lowerCAmelCase ,					token_type_ids=__lowerCAmelCase ,					encoder_hidden_states=__lowerCAmelCase ,					)
      _UpperCAmelCase    			=   model(__lowerCAmelCase ,					attention_mask=__lowerCAmelCase ,					token_type_ids=__lowerCAmelCase       )
      self.parent.assertEqual(result.last_hidden_state.shape ,					(self.batch_size, self.seq_length, self.hidden_size)       )


    def   lowerCAmelCase_	(       self						:	Dict ,					__lowerCAmelCase						:	Optional[int] ,					__lowerCAmelCase						:	List[Any] ,					__lowerCAmelCase						:	Optional[int] ,					__lowerCAmelCase						:	str ,					__lowerCAmelCase						:	Dict ,					__lowerCAmelCase						:	int ,					__lowerCAmelCase						:	str       ):
      _UpperCAmelCase    			=   MraForMaskedLM(config=__lowerCAmelCase       )
      model.to(__lowerCAmelCase       )
      model.eval()
      _UpperCAmelCase    			=   model(__lowerCAmelCase ,					attention_mask=__lowerCAmelCase ,					token_type_ids=__lowerCAmelCase ,					labels=__lowerCAmelCase       )
      self.parent.assertEqual(result.logits.shape ,					(self.batch_size, self.seq_length, self.vocab_size)       )


    def   lowerCAmelCase_	(       self						:	int ,					__lowerCAmelCase						:	Dict ,					__lowerCAmelCase						:	Optional[Any] ,					__lowerCAmelCase						:	int ,					__lowerCAmelCase						:	Tuple ,					__lowerCAmelCase						:	List[str] ,					__lowerCAmelCase						:	Any ,					__lowerCAmelCase						:	int       ):
      _UpperCAmelCase    			=   MraForQuestionAnswering(config=__lowerCAmelCase       )
      model.to(__lowerCAmelCase       )
      model.eval()
      _UpperCAmelCase    			=   model(
          __lowerCAmelCase ,					attention_mask=__lowerCAmelCase ,					token_type_ids=__lowerCAmelCase ,					start_positions=__lowerCAmelCase ,					end_positions=__lowerCAmelCase ,					)
      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   lowerCAmelCase_	(       self						:	List[Any] ,					__lowerCAmelCase						:	int ,					__lowerCAmelCase						:	List[str] ,					__lowerCAmelCase						:	Any ,					__lowerCAmelCase						:	Dict ,					__lowerCAmelCase						:	int ,					__lowerCAmelCase						:	Union[str, Any] ,					__lowerCAmelCase						:	int       ):
      _UpperCAmelCase    			=   self.num_labels
      _UpperCAmelCase    			=   MraForSequenceClassification(__lowerCAmelCase       )
      model.to(__lowerCAmelCase       )
      model.eval()
      _UpperCAmelCase    			=   model(__lowerCAmelCase ,					attention_mask=__lowerCAmelCase ,					token_type_ids=__lowerCAmelCase ,					labels=__lowerCAmelCase       )
      self.parent.assertEqual(result.logits.shape ,					(self.batch_size, self.num_labels)       )


    def   lowerCAmelCase_	(       self						:	List[str] ,					__lowerCAmelCase						:	Optional[int] ,					__lowerCAmelCase						:	Union[str, Any] ,					__lowerCAmelCase						:	List[Any] ,					__lowerCAmelCase						:	Optional[int] ,					__lowerCAmelCase						:	Tuple ,					__lowerCAmelCase						:	Optional[int] ,					__lowerCAmelCase						:	Optional[int]       ):
      _UpperCAmelCase    			=   self.num_labels
      _UpperCAmelCase    			=   MraForTokenClassification(config=__lowerCAmelCase       )
      model.to(__lowerCAmelCase       )
      model.eval()
      _UpperCAmelCase    			=   model(__lowerCAmelCase ,					attention_mask=__lowerCAmelCase ,					token_type_ids=__lowerCAmelCase ,					labels=__lowerCAmelCase       )
      self.parent.assertEqual(result.logits.shape ,					(self.batch_size, self.seq_length, self.num_labels)       )


    def   lowerCAmelCase_	(       self						:	Optional[Any] ,					__lowerCAmelCase						:	Any ,					__lowerCAmelCase						:	int ,					__lowerCAmelCase						:	List[Any] ,					__lowerCAmelCase						:	Any ,					__lowerCAmelCase						:	str ,					__lowerCAmelCase						:	List[str] ,					__lowerCAmelCase						:	Optional[Any]       ):
      _UpperCAmelCase    			=   self.num_choices
      _UpperCAmelCase    			=   MraForMultipleChoice(config=__lowerCAmelCase       )
      model.to(__lowerCAmelCase       )
      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(
          __lowerCAmelCase ,					attention_mask=__lowerCAmelCase ,					token_type_ids=__lowerCAmelCase ,					labels=__lowerCAmelCase ,					)
      self.parent.assertEqual(result.logits.shape ,					(self.batch_size, self.num_choices)       )


    def   lowerCAmelCase_	(       self						:	Tuple       ):
      _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 							a					(		lowerCAmelCase_						,				unittest.TestCase ):
    _snake_case  :	str  				=				(
        (
            MraModel,
            MraForMaskedLM,
            MraForMultipleChoice,
            MraForQuestionAnswering,
            MraForSequenceClassification,
            MraForTokenClassification,
        )
        if is_torch_available()
        else ()
    )
    _snake_case  :	Dict  				=				False
    _snake_case  :	Any  				=				False
    _snake_case  :	List[str]  				=				False
    _snake_case  :	Tuple  				=				False

    _snake_case  :	int  				=				()


    def   lowerCAmelCase_	(       self						:	Union[str, Any]       ):
      _UpperCAmelCase    			=   MraModelTester(self       )
      _UpperCAmelCase    			=   ConfigTester(self ,					config_class=__lowerCAmelCase ,					hidden_size=37       )


    def   lowerCAmelCase_	(       self						:	Optional[Any]       ):
      self.config_tester.run_common_tests()


    def   lowerCAmelCase_	(       self						:	Tuple       ):
      _UpperCAmelCase    			=   self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*__lowerCAmelCase       )


    def   lowerCAmelCase_	(       self						:	Any       ):
      _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(*__lowerCAmelCase       )


    def   lowerCAmelCase_	(       self						:	List[Any]       ):
      _UpperCAmelCase    			=   self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_masked_lm(*__lowerCAmelCase       )


    def   lowerCAmelCase_	(       self						:	str       ):
      _UpperCAmelCase    			=   self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_multiple_choice(*__lowerCAmelCase       )


    def   lowerCAmelCase_	(       self						:	str       ):
      _UpperCAmelCase    			=   self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_question_answering(*__lowerCAmelCase       )


    def   lowerCAmelCase_	(       self						:	List[str]       ):
      _UpperCAmelCase    			=   self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_sequence_classification(*__lowerCAmelCase       )


    def   lowerCAmelCase_	(       self						:	List[Any]       ):
      _UpperCAmelCase    			=   self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_token_classification(*__lowerCAmelCase       )


    @slow
    def   lowerCAmelCase_	(       self						:	Optional[Any]       ):
      for model_name in MRA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
        _UpperCAmelCase    			=   MraModel.from_pretrained(__lowerCAmelCase       )
        self.assertIsNotNone(__lowerCAmelCase       )


    @unittest.skip(reason="""MRA does not output attentions"""       )
    def   lowerCAmelCase_	(       self						:	str       ):
      return



@require_torch
class 							a					(		unittest.TestCase ):


    @slow
    def   lowerCAmelCase_	(       self						:	Dict       ):
      _UpperCAmelCase    			=   MraModel.from_pretrained("""uw-madison/mra-base-512-4"""       )
      _UpperCAmelCase    			=   torch.arange(256       ).unsqueeze(0       )

      with torch.no_grad():
        _UpperCAmelCase    			=   model(__lowerCAmelCase       )[0]

      _UpperCAmelCase    			=   torch.Size((1, 256, 768)       )
      self.assertEqual(output.shape ,					__lowerCAmelCase       )

      _UpperCAmelCase    			=   torch.tensor(
          [[[-0.0_140, 0.0_830, -0.0_381], [0.1_546, 0.1_402, 0.0_220], [0.1_162, 0.0_851, 0.0_165]]]       )

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


    @slow
    def   lowerCAmelCase_	(       self						:	str       ):
      _UpperCAmelCase    			=   MraForMaskedLM.from_pretrained("""uw-madison/mra-base-512-4"""       )
      _UpperCAmelCase    			=   torch.arange(256       ).unsqueeze(0       )

      with torch.no_grad():
        _UpperCAmelCase    			=   model(__lowerCAmelCase       )[0]

      _UpperCAmelCase    			=   5_0265

      _UpperCAmelCase    			=   torch.Size((1, 256, vocab_size)       )
      self.assertEqual(output.shape ,					__lowerCAmelCase       )

      _UpperCAmelCase    			=   torch.tensor(
          [[[9.2_595, -3.6_038, 11.8_819], [9.3_869, -3.2_693, 11.0_956], [11.8_524, -3.4_938, 13.1_210]]]       )

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


    @slow
    def   lowerCAmelCase_	(       self						:	Dict       ):
      _UpperCAmelCase    			=   MraForMaskedLM.from_pretrained("""uw-madison/mra-base-4096-8-d3"""       )
      _UpperCAmelCase    			=   torch.arange(4096       ).unsqueeze(0       )

      with torch.no_grad():
        _UpperCAmelCase    			=   model(__lowerCAmelCase       )[0]

      _UpperCAmelCase    			=   5_0265

      _UpperCAmelCase    			=   torch.Size((1, 4096, vocab_size)       )
      self.assertEqual(output.shape ,					__lowerCAmelCase       )

      _UpperCAmelCase    			=   torch.tensor(
          [[[5.4_789, -2.3_564, 7.5_064], [7.9_067, -1.3_369, 9.9_668], [9.0_712, -1.8_106, 7.0_380]]]       )

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