
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 		UpperCamelCase__  :
   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=9_9 ,    snake_case_			: int=1_6 ,    snake_case_			: Optional[int]=5 ,    snake_case_			: int=2 ,    snake_case_			: Tuple=3_6 ,    snake_case_			: Optional[Any]="gelu" ,    snake_case_			: str=0.0 ,    snake_case_			: Optional[int]=0.0 ,    snake_case_			: Tuple=5_1_2 ,    snake_case_			: str=1_6 ,    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 				lowerCAmelCase      (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 				lowerCAmelCase      (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 				lowerCAmelCase      (self			: List[str]       ):
    __a			:  List[Any]		    =  self.get_config()
    __a			:  Optional[Any]		    =  3_0_0
    return config
   def 				lowerCAmelCase      (self			: Union[str, Any]       ):
    (
        __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 				lowerCAmelCase      (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 				lowerCAmelCase      (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 				lowerCAmelCase      (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 				lowerCAmelCase      (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 				lowerCAmelCase      (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 				lowerCAmelCase      (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 				lowerCAmelCase      (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 				lowerCAmelCase      (self			: Optional[Any]       ):
    __a			:  Union[str, Any]		    =  self.prepare_config_and_inputs()
    (
        __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 		UpperCamelCase__  (	lowerCamelCase__   ,unittest.TestCase   ):
   _SCREAMING_SNAKE_CASE				:							Optional[Any]     	=				(
       (
           MraModel,
           MraForMaskedLM,
           MraForMultipleChoice,
           MraForQuestionAnswering,
           MraForSequenceClassification,
           MraForTokenClassification,
       )
       if is_torch_available()
       else ()
   )
   _SCREAMING_SNAKE_CASE				:							Tuple     	=				False
   _SCREAMING_SNAKE_CASE				:							Optional[int]     	=				False
   _SCREAMING_SNAKE_CASE				:							Any     	=				False
   _SCREAMING_SNAKE_CASE				:							Tuple     	=				False

   _SCREAMING_SNAKE_CASE				:							Tuple     	=				()
   def 				lowerCAmelCase      (self			: Any       ):
    __a			:  Tuple		    =  MraModelTester(self       )
    __a			:  str		    =  ConfigTester(self ,    config_class=__snake_case ,    hidden_size=3_7       )
   def 				lowerCAmelCase      (self			: List[str]       ):
    self.config_tester.run_common_tests()
   def 				lowerCAmelCase      (self			: List[str]       ):
    __a			:  List[Any]		    =  self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_model(*__snake_case       )
   def 				lowerCAmelCase      (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 				lowerCAmelCase      (self			: List[Any]       ):
    __a			:  Dict		    =  self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_masked_lm(*__snake_case       )
   def 				lowerCAmelCase      (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 				lowerCAmelCase      (self			: List[Any]       ):
    __a			:  int		    =  self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_question_answering(*__snake_case       )
   def 				lowerCAmelCase      (self			: Tuple       ):
    __a			:  Tuple		    =  self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_sequence_classification(*__snake_case       )
   def 				lowerCAmelCase      (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 				lowerCAmelCase      (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 				lowerCAmelCase      (self			: Union[str, Any]       ):
    return





@require_torch
class 		UpperCamelCase__  (	unittest.TestCase   ):
   @slow
   def 				lowerCAmelCase      (self			: Union[str, Any]       ):
    __a			:  Union[str, Any]		    =  MraModel.from_pretrained('''uw-madison/mra-base-512-4'''       )
    __a			:  List[str]		    =  torch.arange(2_5_6       ).unsqueeze(0       )

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

    __a			:  Any		    =  torch.Size((1, 2_5_6, 7_6_8)       )
    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 				lowerCAmelCase      (self			: Optional[int]       ):
    __a			:  Dict		    =  MraForMaskedLM.from_pretrained('''uw-madison/mra-base-512-4'''       )
    __a			:  Optional[int]		    =  torch.arange(2_5_6       ).unsqueeze(0       )

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

    __a			:  Union[str, Any]		    =  5_0_2_6_5

    __a			:  Dict		    =  torch.Size((1, 2_5_6, 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 				lowerCAmelCase      (self			: Any       ):
    __a			:  Dict		    =  MraForMaskedLM.from_pretrained('''uw-madison/mra-base-4096-8-d3'''       )
    __a			:  Optional[int]		    =  torch.arange(4_0_9_6       ).unsqueeze(0       )

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

    __a			:  List[Any]		    =  5_0_2_6_5

    __a			:  str		    =  torch.Size((1, 4_0_9_6, 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       )       )


import os
import unittest

from transformers.models.phobert.tokenization_phobert import VOCAB_FILES_NAMES, PhobertTokenizer

from ...test_tokenization_common import TokenizerTesterMixin



class 		UpperCamelCase__  (	__lowercase   ,unittest.TestCase   ):
   _SCREAMING_SNAKE_CASE				:							Union[str, Any]     	=				PhobertTokenizer
   _SCREAMING_SNAKE_CASE				:							int     	=				False
   def 				lowerCAmelCase      (self			: Optional[int]       ):
    super().setUp()

    # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
    __a			:  Optional[int]		    =  ['''T@@''', '''i''', '''I''', '''R@@''', '''r''', '''e@@''']
    __a			:  Tuple		    =  dict(zip(snake_case_ ,    range(len(snake_case_       )       )       )       )
    __a			:  int		    =  ['''#version: 0.2''', '''l à</w>''']
    __a			:  List[Any]		    =  {'''unk_token''': '''<unk>'''}

    __a			:  Any		    =  os.path.join(self.tmpdirname ,    VOCAB_FILES_NAMES['''vocab_file''']       )
    __a			:  List[Any]		    =  os.path.join(self.tmpdirname ,    VOCAB_FILES_NAMES['''merges_file''']       )

    with open(self.vocab_file ,    '''w''' ,    encoding='''utf-8'''       ) as fp:
     for token in vocab_tokens:
      fp.write(f"{token} {vocab_tokens[token]}\n"       )
    with open(self.merges_file ,    '''w''' ,    encoding='''utf-8'''       ) as fp:
     fp.write('''\n'''.join(snake_case_       )       )
   def 				lowerCAmelCase      (self			: Union[str, Any] ,    **snake_case_			: List[str]       ):
    kwargs.update(self.special_tokens_map       )
    return PhobertTokenizer.from_pretrained(self.tmpdirname ,    **snake_case_       )
   def 				lowerCAmelCase      (self			: Any ,    snake_case_			: Dict       ):
    __a			:  Union[str, Any]		    =  '''Tôi là VinAI Research'''
    __a			:  int		    =  '''T<unk> i <unk> <unk> <unk> <unk> <unk> <unk> I Re<unk> e<unk> <unk> <unk> <unk>'''
    return input_text, output_text







   def 				lowerCAmelCase      (self			: Optional[Any]       ):
    __a			:  Optional[int]		    =  PhobertTokenizer(self.vocab_file ,    self.merges_file ,    **self.special_tokens_map       )
    __a			:  Any		    =  '''Tôi là VinAI Research'''
    __a			:  Union[str, Any]		    =  '''T@@ ô@@ i l@@ à V@@ i@@ n@@ A@@ I R@@ e@@ s@@ e@@ a@@ r@@ c@@ h'''.split()
    __a			:  List[str]		    =  tokenizer.tokenize(snake_case_       )
    print(snake_case_       )
    self.assertListEqual(snake_case_ ,    snake_case_       )

    __a			:  str		    =  tokens + [tokenizer.unk_token]

    __a			:  str		    =  [4, 3, 5, 3, 3, 3, 3, 3, 3, 6, 7, 9, 3, 9, 3, 3, 3, 3, 3]
    self.assertListEqual(tokenizer.convert_tokens_to_ids(snake_case_       ) ,    snake_case_       )
