



'''simple docstring'''






import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple

from transformers import AddedToken, BatchEncoding, PerceiverTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available

from ...test_tokenization_common import TokenizerTesterMixin


if is_torch_available():
     lowerCAmelCase: List[str]							=						'pt'
elif is_tf_available():
     lowerCAmelCase: Any							=						'tf'
else:
     lowerCAmelCase: str							=						'jax'

class 							a__( lowerCamelCase__		,			unittest.TestCase     ):
    lowercase__			=   PerceiverTokenizer
    lowercase__			=   False







    def       lowercase_	(				self			:  Tuple     ):
      super().setUp()
      a						: Tuple					       =					PerceiverTokenizer()
      tokenizer.save_pretrained(self.tmpdirname     )







    @cached_property
    def       lowercase_	(				self			:  Dict     ):
      return PerceiverTokenizer.from_pretrained('deepmind/language-perceiver'     )







    def       lowercase_	(				self			:  List[Any]						,      **__snake_case			:  Tuple     ):
      return self.tokenizer_class.from_pretrained(self.tmpdirname						,      **__snake_case     )







    def       lowercase_	(				self			:  Union[str, Any]						,      __snake_case			:  Optional[Any]						,      __snake_case			:  Union[str, Any]=False						,      __snake_case			:  str=20						,      __snake_case			:  List[str]=5     ):
      # XXX The default common tokenizer tests assume that every ID is decodable on its own.
      # This assumption is invalid for Perceiver because single bytes might not be
      # valid utf-8 (byte 128 for instance).
      # Here we're overriding the smallest possible method to provide
      # a clean sequence without making the same assumption.

      a						: Union[str, Any]					       =					[]
      for i in range(len(__snake_case     )     ):
        try:
          a						: int					       =					tokenizer.decode([i]						,      clean_up_tokenization_spaces=__snake_case     )
        except UnicodeDecodeError:
          pass
        toks.append((i, tok)     )

      a						: Any					       =					list(filter(lambda __snake_case     : re.match(r'^[ a-zA-Z]+$'						,      t[1]     )						,      __snake_case     )     )
      a						: Any					       =					list(filter(lambda __snake_case     : [t[0]] == tokenizer.encode(t[1]						,      add_special_tokens=__snake_case     )						,      __snake_case     )     )
      if max_length is not None and len(__snake_case     ) > max_length:
        a						: Any					       =					toks[:max_length]
      if min_length is not None and len(__snake_case     ) < min_length and len(__snake_case     ) > 0:
        while len(__snake_case     ) < min_length:
          a						: Any					       =					toks + toks
        # toks_str = [t[1] for t in toks]
      a						: str					       =					[t[0] for t in toks]

      # Ensure consistency
      a						: Tuple					       =					tokenizer.decode(__snake_case						,      clean_up_tokenization_spaces=__snake_case     )
      if " " not in output_txt and len(__snake_case     ) > 1:
        a						: List[Any]					       =					(
            tokenizer.decode([toks_ids[0]]						,      clean_up_tokenization_spaces=__snake_case     )
            + ' '
            + tokenizer.decode(toks_ids[1:]						,      clean_up_tokenization_spaces=__snake_case     )
        )
      if with_prefix_space:
        a						: List[str]					       =					' ' + output_txt
      a						: List[str]					       =					tokenizer.encode(__snake_case						,      add_special_tokens=__snake_case     )
      return output_txt, output_ids







    def       lowercase_	(				self			:  Optional[Any]     ):
      a						: str					       =					self.perceiver_tokenizer
      a						: Optional[Any]					       =					'Unicode €.'
      a						: Tuple					       =					tokenizer(__snake_case     )
      a						: Optional[int]					       =					[4, 91, 1_16, 1_11, 1_05, 1_17, 1_06, 1_07, 38, 2_32, 1_36, 1_78, 52, 5]
      self.assertEqual(encoded['input_ids']						,      __snake_case     )

      # decoding
      a						: Any					       =					tokenizer.decode(__snake_case     )
      self.assertEqual(__snake_case						,      '[CLS]Unicode €.[SEP]'     )

      a						: List[str]					       =					tokenizer('e è é ê ë'     )
      a						: Dict					       =					[4, 1_07, 38, 2_01, 1_74, 38, 2_01, 1_75, 38, 2_01, 1_76, 38, 2_01, 1_77, 5]
      self.assertEqual(encoded['input_ids']						,      __snake_case     )
      # decoding
      a						: Tuple					       =					tokenizer.decode(__snake_case     )
      self.assertEqual(__snake_case						,      '[CLS]e è é ê ë[SEP]'     )

      # encode/decode, but with `encode` instead of `__call__`
      self.assertEqual(tokenizer.decode(tokenizer.encode('e è é ê ë'     )     )						,      '[CLS]e è é ê ë[SEP]'     )







    def       lowercase_	(				self			:  str     ):
      a						: List[str]					       =					self.perceiver_tokenizer
      a						: Optional[int]					       =					['A long paragraph for summarization.', 'Another paragraph for summarization.']
      # fmt: off
      a						: Dict					       =					[4, 71, 38, 1_14, 1_17, 1_16, 1_09, 38, 1_18, 1_03, 1_20, 1_03, 1_09, 1_20, 1_03, 1_18, 1_10, 38, 1_08, 1_17, 1_20, 38, 1_21, 1_23, 1_15, 1_15, 1_03, 1_20, 1_11, 1_28, 1_03, 1_22, 1_11, 1_17, 1_16, 52, 5, 0]
      # fmt: on
      a						: Optional[int]					       =					tokenizer(__snake_case						,      padding=__snake_case						,      return_tensors=__snake_case     )
      self.assertIsInstance(__snake_case						,      __snake_case     )

      if FRAMEWORK != "jax":
        a						: Any					       =					list(batch.input_ids.numpy()[0]     )
      else:
        a						: Optional[int]					       =					list(batch.input_ids.tolist()[0]     )

      self.assertListEqual(__snake_case						,      __snake_case     )

      self.assertEqual((2, 38)						,      batch.input_ids.shape     )
      self.assertEqual((2, 38)						,      batch.attention_mask.shape     )







    def       lowercase_	(				self			:  List[str]     ):
      a						: Optional[int]					       =					self.perceiver_tokenizer
      a						: Dict					       =					['A long paragraph for summarization.', 'Another paragraph for summarization.']
      a						: str					       =					tokenizer(__snake_case						,      padding=__snake_case						,      return_tensors=__snake_case     )
      # check if input_ids are returned and no decoder_input_ids
      self.assertIn('input_ids'						,      __snake_case     )
      self.assertIn('attention_mask'						,      __snake_case     )
      self.assertNotIn('decoder_input_ids'						,      __snake_case     )
      self.assertNotIn('decoder_attention_mask'						,      __snake_case     )







    def       lowercase_	(				self			:  Optional[int]     ):
      a						: List[Any]					       =					self.perceiver_tokenizer
      a						: Union[str, Any]					       =					[
          'Summary of the text.',
          'Another summary.',
      ]
      a						: List[str]					       =					tokenizer(
          text_target=__snake_case						,      max_length=32						,      padding='max_length'						,      truncation=__snake_case						,      return_tensors=__snake_case     )
      self.assertEqual(32						,      targets['input_ids'].shape[1]     )







    def       lowercase_	(				self			:  List[Any]     ):
      # safety check on max_len default value so we are sure the test works
      a						: Union[str, Any]					       =					self.get_tokenizers()
      for tokenizer in tokenizers:
        with self.subTest(F"""{tokenizer.__class__.__name__}"""     ):
          self.assertNotEqual(tokenizer.model_max_length						,      42     )

        # Now let's start the test
      a						: List[str]					       =					self.get_tokenizers()
      for tokenizer in tokenizers:
        with self.subTest(F"""{tokenizer.__class__.__name__}"""     ):
          # Isolate this from the other tests because we save additional tokens/etc
          a						: Optional[int]					       =					tempfile.mkdtemp()

          a						: Tuple					       =					' He is very happy, UNwant\u00E9d,running'
          a						: Any					       =					tokenizer.encode(__snake_case						,      add_special_tokens=__snake_case     )
          tokenizer.save_pretrained(__snake_case     )

          a						: List[Any]					       =					tokenizer.__class__.from_pretrained(__snake_case     )
          a						: int					       =					after_tokenizer.encode(__snake_case						,      add_special_tokens=__snake_case     )
          self.assertListEqual(__snake_case						,      __snake_case     )

          shutil.rmtree(__snake_case     )

      a						: Dict					       =					self.get_tokenizers(model_max_length=42     )
      for tokenizer in tokenizers:
        with self.subTest(F"""{tokenizer.__class__.__name__}"""     ):
          # Isolate this from the other tests because we save additional tokens/etc
          a						: List[str]					       =					tempfile.mkdtemp()

          a						: int					       =					' He is very happy, UNwant\u00E9d,running'
          tokenizer.add_tokens(['bim', 'bambam']     )
          a						: List[str]					       =					tokenizer.additional_special_tokens
          additional_special_tokens.append('new_additional_special_token'     )
          tokenizer.add_special_tokens({'additional_special_tokens': additional_special_tokens}     )
          a						: Union[str, Any]					       =					tokenizer.encode(__snake_case						,      add_special_tokens=__snake_case     )
          tokenizer.save_pretrained(__snake_case     )

          a						: int					       =					tokenizer.__class__.from_pretrained(__snake_case     )
          a						: List[Any]					       =					after_tokenizer.encode(__snake_case						,      add_special_tokens=__snake_case     )
          self.assertListEqual(__snake_case						,      __snake_case     )
          self.assertIn('new_additional_special_token'						,      after_tokenizer.additional_special_tokens     )
          self.assertEqual(after_tokenizer.model_max_length						,      42     )

          a						: str					       =					tokenizer.__class__.from_pretrained(__snake_case						,      model_max_length=43     )
          self.assertEqual(tokenizer.model_max_length						,      43     )

          shutil.rmtree(__snake_case     )







    def       lowercase_	(				self			:  List[str]     ):
      a						: str					       =					[]
      if self.test_slow_tokenizer:
        tokenizer_list.append((self.tokenizer_class, self.get_tokenizer())     )

      if self.test_rust_tokenizer:
        tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer())     )

      for tokenizer_class, tokenizer_utils in tokenizer_list:
        with tempfile.TemporaryDirectory() as tmp_dir:
          tokenizer_utils.save_pretrained(__snake_case     )

          with open(os.path.join(__snake_case						,      'special_tokens_map.json'     )						,      encoding='utf-8'     ) as json_file:
            a						: Dict					       =					json.load(__snake_case     )

          with open(os.path.join(__snake_case						,      'tokenizer_config.json'     )						,      encoding='utf-8'     ) as json_file:
            a						: Union[str, Any]					       =					json.load(__snake_case     )

          a						: Dict					       =					[F"""<extra_id_{i}>""" for i in range(1_25     )]

          a						: str					       =					added_tokens_extra_ids + [
              'an_additional_special_token'
          ]
          a						: Dict					       =					added_tokens_extra_ids + [
              'an_additional_special_token'
          ]

          with open(os.path.join(__snake_case						,      'special_tokens_map.json'     )						,      'w'						,      encoding='utf-8'     ) as outfile:
            json.dump(__snake_case						,      __snake_case     )
          with open(os.path.join(__snake_case						,      'tokenizer_config.json'     )						,      'w'						,      encoding='utf-8'     ) as outfile:
            json.dump(__snake_case						,      __snake_case     )

          # the following checks allow us to verify that our test works as expected, i.e. that the tokenizer takes
          # into account the new value of additional_special_tokens given in the "tokenizer_config.json" and
          # "special_tokens_map.json" files
          a						: Tuple					       =					tokenizer_class.from_pretrained(
              __snake_case						,      )
          self.assertIn(
              'an_additional_special_token'						,      tokenizer_without_change_in_init.additional_special_tokens     )
          self.assertEqual(
              ['an_additional_special_token']						,      tokenizer_without_change_in_init.convert_ids_to_tokens(
                  tokenizer_without_change_in_init.convert_tokens_to_ids(['an_additional_special_token']     )     )						,      )

          # Now we test that we can change the value of additional_special_tokens in the from_pretrained
          a						: Dict					       =					added_tokens_extra_ids + [AddedToken('a_new_additional_special_token'						,      lstrip=__snake_case     )]
          a						: Tuple					       =					tokenizer_class.from_pretrained(
              __snake_case						,      additional_special_tokens=__snake_case						,      )

          self.assertIn('a_new_additional_special_token'						,      tokenizer.additional_special_tokens     )
          self.assertEqual(
              ['a_new_additional_special_token']						,      tokenizer.convert_ids_to_tokens(
                  tokenizer.convert_tokens_to_ids(['a_new_additional_special_token']     )     )						,      )







    def       lowercase_	(				self			:  Tuple     ):
      a						: Tuple					       =					self.perceiver_tokenizer
      self.assertEqual(tokenizer.decode([1_78]     )						,      '�'     )







    def       lowercase_	(				self			:  Union[str, Any]     ):
      pass







    def       lowercase_	(				self			:  int     ):
      pass







    def       lowercase_	(				self			:  Optional[int]     ):
      pass







    def       lowercase_	(				self			:  str     ):
      pass







    def       lowercase_	(				self			:  Optional[Any]     ):
      # The default common tokenizer tests uses invalid tokens for Perceiver that can only accept one-character
      # strings and special added tokens as tokens
      a						: str					       =					self.get_tokenizers(fast=__snake_case						,      do_lower_case=__snake_case     )
      for tokenizer in tokenizers:
        with self.subTest(F"""{tokenizer.__class__.__name__}"""     ):
          a						: Optional[Any]					       =					['[CLS]', 't', 'h', 'i', 's', ' ', 'i', 's', ' ', 'a', ' ', 't', 'e', 's', 't', '[SEP]']
          a						: Tuple					       =					tokenizer.convert_tokens_to_string(__snake_case     )

          self.assertIsInstance(__snake_case						,      __snake_case     )




'''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			:  Union[str, Any]						,      __snake_case			:  Optional[int]						,      __snake_case			:  Optional[int]=2						,      __snake_case			:  Union[str, Any]=8						,      __snake_case			:  List[str]=True						,      __snake_case			:  Dict=True						,      __snake_case			:  Optional[Any]=True						,      __snake_case			:  List[str]=True						,      __snake_case			:  Tuple=99						,      __snake_case			:  int=16						,      __snake_case			:  Optional[int]=5						,      __snake_case			:  int=2						,      __snake_case			:  Tuple=36						,      __snake_case			:  Optional[Any]="gelu"						,      __snake_case			:  str=0.0						,      __snake_case			:  Optional[int]=0.0						,      __snake_case			:  Tuple=5_12						,      __snake_case			:  str=16						,      __snake_case			:  str=2						,      __snake_case			:  int=0.02						,      __snake_case			:  Optional[int]=3						,      __snake_case			:  List[Any]=4						,      __snake_case			:  Any=None						,      ):
      a						: int					       =					parent
      a						: Any					       =					batch_size
      a						: Optional[int]					       =					seq_length
      a						: List[str]					       =					is_training
      a						: Dict					       =					use_input_mask
      a						: Union[str, Any]					       =					use_token_type_ids
      a						: Tuple					       =					use_labels
      a						: Dict					       =					vocab_size
      a						: Optional[int]					       =					hidden_size
      a						: List[Any]					       =					num_hidden_layers
      a						: Optional[Any]					       =					num_attention_heads
      a						: str					       =					intermediate_size
      a						: Dict					       =					hidden_act
      a						: str					       =					hidden_dropout_prob
      a						: Tuple					       =					attention_probs_dropout_prob
      a						: Optional[Any]					       =					max_position_embeddings
      a						: Tuple					       =					type_vocab_size
      a						: int					       =					type_sequence_label_size
      a						: List[Any]					       =					initializer_range
      a						: List[str]					       =					num_labels
      a						: List[str]					       =					num_choices
      a						: Optional[Any]					       =					scope







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

      a						: Optional[Any]					       =					None
      if self.use_input_mask:
        a						: List[Any]					       =					random_attention_mask([self.batch_size, self.seq_length]     )

      a						: Tuple					       =					None
      if self.use_token_type_ids:
        a						: Optional[Any]					       =					ids_tensor([self.batch_size, self.seq_length]						,      self.type_vocab_size     )

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

      a						: Dict					       =					self.get_config()

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







    def       lowercase_	(				self			:  Union[str, 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=__snake_case						,      initializer_range=self.initializer_range						,      )







    def       lowercase_	(				self			:  List[str]     ):
      a						: List[Any]					       =					self.get_config()
      a						: Optional[Any]					       =					3_00
      return config







    def       lowercase_	(				self			:  Union[str, Any]     ):
      (
          (
          a
      )       ,     (
          a
      )       ,     (
          a
      )       ,     (
          a
      )       ,     (
          a
      )       ,     (
          a
      )       ,     (
          a
      )       ,     
      )						: Optional[Any]					       =					self.prepare_config_and_inputs()

      a						: Union[str, Any]					       =					True
      a						: Tuple					       =					floats_tensor([self.batch_size, self.seq_length, self.hidden_size]     )
      a						: Dict					       =					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       lowercase_	(				self			:  int						,      __snake_case			:  int						,      __snake_case			:  List[Any]						,      __snake_case			:  Union[str, Any]						,      __snake_case			:  List[Any]						,      __snake_case			:  Union[str, Any]						,      __snake_case			:  Dict						,      __snake_case			:  Any     ):
      a						: Dict					       =					MraModel(config=__snake_case     )
      model.to(__snake_case     )
      model.eval()
      a						: str					       =					model(__snake_case						,      attention_mask=__snake_case						,      token_type_ids=__snake_case     )
      a						: List[str]					       =					model(__snake_case						,      token_type_ids=__snake_case     )
      a						: Union[str, Any]					       =					model(__snake_case     )
      self.parent.assertEqual(result.last_hidden_state.shape						,      (self.batch_size, self.seq_length, self.hidden_size)     )







    def       lowercase_	(				self			:  List[str]						,      __snake_case			:  Tuple						,      __snake_case			:  List[str]						,      __snake_case			:  str						,      __snake_case			:  Optional[Any]						,      __snake_case			:  Union[str, Any]						,      __snake_case			:  Any						,      __snake_case			:  Optional[Any]						,      __snake_case			:  Optional[int]						,      __snake_case			:  List[Any]						,      ):
      a						: Optional[Any]					       =					True
      a						: Optional[int]					       =					MraModel(__snake_case     )
      model.to(__snake_case     )
      model.eval()
      a						: List[Any]					       =					model(
          __snake_case						,      attention_mask=__snake_case						,      token_type_ids=__snake_case						,      encoder_hidden_states=__snake_case						,      encoder_attention_mask=__snake_case						,      )
      a						: Any					       =					model(
          __snake_case						,      attention_mask=__snake_case						,      token_type_ids=__snake_case						,      encoder_hidden_states=__snake_case						,      )
      a						: Optional[int]					       =					model(__snake_case						,      attention_mask=__snake_case						,      token_type_ids=__snake_case     )
      self.parent.assertEqual(result.last_hidden_state.shape						,      (self.batch_size, self.seq_length, self.hidden_size)     )







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







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







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







    def       lowercase_	(				self			:  Any						,      __snake_case			:  Any						,      __snake_case			:  str						,      __snake_case			:  Dict						,      __snake_case			:  List[Any]						,      __snake_case			:  Any						,      __snake_case			:  Any						,      __snake_case			:  str     ):
      a						: Optional[int]					       =					self.num_choices
      a						: int					       =					MraForMultipleChoice(config=__snake_case     )
      model.to(__snake_case     )
      model.eval()
      a						: Tuple					       =					input_ids.unsqueeze(1     ).expand(-1						,      self.num_choices						,      -1     ).contiguous()
      a						: Optional[int]					       =					token_type_ids.unsqueeze(1     ).expand(-1						,      self.num_choices						,      -1     ).contiguous()
      a						: Optional[Any]					       =					input_mask.unsqueeze(1     ).expand(-1						,      self.num_choices						,      -1     ).contiguous()
      a						: int					       =					model(
          __snake_case						,      attention_mask=__snake_case						,      token_type_ids=__snake_case						,      labels=__snake_case						,      )
      self.parent.assertEqual(result.logits.shape						,      (self.batch_size, self.num_choices)     )







    def       lowercase_	(				self			:  Optional[Any]     ):
      a						: Union[str, Any]					       =					self.prepare_config_and_inputs()
      (
          (
          a
      )       ,     (
          a
      )       ,     (
          a
      )       ,     (
          a
      )       ,     (
          a
      )       ,     (
          a
      )       ,     (
          a
      )       ,     
      )						: Union[str, Any]					       =					config_and_inputs
      a						: List[str]					       =					{'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     ):
    lowercase__			=   (
        (
            MraModel,
            MraForMaskedLM,
            MraForMultipleChoice,
            MraForQuestionAnswering,
            MraForSequenceClassification,
            MraForTokenClassification,
        )
        if is_torch_available()
        else ()
    )
    lowercase__			=   False
    lowercase__			=   False
    lowercase__			=   False
    lowercase__			=   False

    lowercase__			=   ()







    def       lowercase_	(				self			:  Any     ):
      a						: Tuple					       =					MraModelTester(self     )
      a						: str					       =					ConfigTester(self						,      config_class=__snake_case						,      hidden_size=37     )







    def       lowercase_	(				self			:  List[str]     ):
      self.config_tester.run_common_tests()







    def       lowercase_	(				self			:  List[str]     ):
      a						: List[Any]					       =					self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*__snake_case     )







    def       lowercase_	(				self			:  Any     ):
      a						: Union[str, Any]					       =					self.model_tester.prepare_config_and_inputs()
      for type in ["absolute", "relative_key", "relative_key_query"]:
        a						: Dict					       =					type
        self.model_tester.create_and_check_model(*__snake_case     )







    def       lowercase_	(				self			:  List[Any]     ):
      a						: Dict					       =					self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_masked_lm(*__snake_case     )







    def       lowercase_	(				self			:  Optional[Any]     ):
      a						: List[str]					       =					self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_multiple_choice(*__snake_case     )







    def       lowercase_	(				self			:  List[Any]     ):
      a						: int					       =					self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_question_answering(*__snake_case     )







    def       lowercase_	(				self			:  Tuple     ):
      a						: Tuple					       =					self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_sequence_classification(*__snake_case     )







    def       lowercase_	(				self			:  Optional[Any]     ):
      a						: Dict					       =					self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_token_classification(*__snake_case     )







    @slow
    def       lowercase_	(				self			:  int     ):
      for model_name in MRA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
        a						: Dict					       =					MraModel.from_pretrained(__snake_case     )
        self.assertIsNotNone(__snake_case     )







    @unittest.skip(reason='MRA does not output attentions'     )
    def       lowercase_	(				self			:  Union[str, Any]     ):
      return

@require_torch
class 							a__( unittest.TestCase     ):







    @slow
    def       lowercase_	(				self			:  Union[str, Any]     ):
      a						: Union[str, Any]					       =					MraModel.from_pretrained('uw-madison/mra-base-512-4'     )
      a						: List[str]					       =					torch.arange(2_56     ).unsqueeze(0     )

      with torch.no_grad():
        a						: Optional[int]					       =					model(__snake_case     )[0]

      a						: Any					       =					torch.Size((1, 2_56, 7_68)     )
      self.assertEqual(output.shape						,      __snake_case     )

      a						: str					       =					torch.tensor(
          [[[-0.0140, 0.0830, -0.0381], [0.1546, 0.1402, 0.0220], [0.1162, 0.0851, 0.0165]]]     )

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







    @slow
    def       lowercase_	(				self			:  Optional[int]     ):
      a						: Dict					       =					MraForMaskedLM.from_pretrained('uw-madison/mra-base-512-4'     )
      a						: Optional[int]					       =					torch.arange(2_56     ).unsqueeze(0     )

      with torch.no_grad():
        a						: Dict					       =					model(__snake_case     )[0]

      a						: Union[str, Any]					       =					5_02_65

      a						: Dict					       =					torch.Size((1, 2_56, vocab_size)     )
      self.assertEqual(output.shape						,      __snake_case     )

      a						: Dict					       =					torch.tensor(
          [[[9.2595, -3.6038, 11.8819], [9.3869, -3.2693, 11.0956], [11.8524, -3.4938, 13.1210]]]     )

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







    @slow
    def       lowercase_	(				self			:  Any     ):
      a						: Dict					       =					MraForMaskedLM.from_pretrained('uw-madison/mra-base-4096-8-d3'     )
      a						: Optional[int]					       =					torch.arange(40_96     ).unsqueeze(0     )

      with torch.no_grad():
        a						: Tuple					       =					model(__snake_case     )[0]

      a						: List[Any]					       =					5_02_65

      a						: str					       =					torch.Size((1, 40_96, vocab_size)     )
      self.assertEqual(output.shape						,      __snake_case     )

      a						: int					       =					torch.tensor(
          [[[5.4789, -2.3564, 7.5064], [7.9067, -1.3369, 9.9668], [9.0712, -1.8106, 7.0380]]]     )

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