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 __snake_case    :




   def __init__(   self      :    str	,     _lowercase      :    Any	,     _lowercase      :    Union[str, Any]=2	,     _lowercase      :    Optional[int]=8	,     _lowercase      :    str=True	,     _lowercase      :    str=True	,     _lowercase      :    Tuple=True	,     _lowercase      :    Union[str, Any]=True	,     _lowercase      :    Tuple=99	,     _lowercase      :    Optional[Any]=16	,     _lowercase      :    List[str]=5	,     _lowercase      :    Tuple=2	,     _lowercase      :    Optional[int]=36	,     _lowercase      :    List[str]="gelu"	,     _lowercase      :    Tuple=0.0	,     _lowercase      :    List[str]=0.0	,     _lowercase      :    int=5_12	,     _lowercase      :    Optional[int]=16	,     _lowercase      :    Dict=2	,     _lowercase      :    List[str]=0.02	,     _lowercase      :    Optional[int]=3	,     _lowercase      :    Tuple=4	,     _lowercase      :    Any=None	,     ):



     """simple docstring"""






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




   def      __a						(   self      :    Union[str, Any]				):



     """simple docstring"""






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

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

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

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

     SCREAMING_SNAKE_CASE__      							=				self.get_config()

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




   def      __a						(   self      :    List[Any]				):



     """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=_lowercase	,     initializer_range=self.initializer_range	,     )




   def      __a						(   self      :    Tuple				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				self.get_config()
     SCREAMING_SNAKE_CASE__      							=				3_00
     return config




   def      __a						(   self      :    str				):



     """simple docstring"""






     (
         (
         SCREAMING_SNAKE_CASE__
     )							,					(
         SCREAMING_SNAKE_CASE__
     )							,					(
         SCREAMING_SNAKE_CASE__
     )							,					(
         SCREAMING_SNAKE_CASE__
     )							,					(
         SCREAMING_SNAKE_CASE__
     )							,					(
         SCREAMING_SNAKE_CASE__
     )							,					(
         SCREAMING_SNAKE_CASE__
     )							,					
     )      							=				self.prepare_config_and_inputs()

     SCREAMING_SNAKE_CASE__      							=				True
     SCREAMING_SNAKE_CASE__      							=				floats_tensor([self.batch_size, self.seq_length, self.hidden_size]				)
     SCREAMING_SNAKE_CASE__      							=				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      :    str	,     _lowercase      :    Optional[Any]	,     _lowercase      :    Dict	,     _lowercase      :    Tuple	,     _lowercase      :    Optional[int]	,     _lowercase      :    Optional[int]	,     _lowercase      :    List[str]	,     _lowercase      :    List[str]				):



     """simple docstring"""






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




   def      __a						(   self      :    List[Any]	,     _lowercase      :    Optional[int]	,     _lowercase      :    Optional[int]	,     _lowercase      :    str	,     _lowercase      :    int	,     _lowercase      :    Any	,     _lowercase      :    Optional[Any]	,     _lowercase      :    Optional[Any]	,     _lowercase      :    Any	,     _lowercase      :    str	,     ):



     """simple docstring"""






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




   def      __a						(   self      :    Optional[int]	,     _lowercase      :    Dict	,     _lowercase      :    Optional[Any]	,     _lowercase      :    Union[str, Any]	,     _lowercase      :    Any	,     _lowercase      :    List[Any]	,     _lowercase      :    Tuple	,     _lowercase      :    int				):



     """simple docstring"""






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




   def      __a						(   self      :    List[str]	,     _lowercase      :    Union[str, Any]	,     _lowercase      :    Any	,     _lowercase      :    List[Any]	,     _lowercase      :    Tuple	,     _lowercase      :    Union[str, Any]	,     _lowercase      :    Optional[Any]	,     _lowercase      :    int				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				MraForQuestionAnswering(config=_lowercase				)
     model.to(_lowercase				)
     model.eval()
     SCREAMING_SNAKE_CASE__      							=				model(
         _lowercase	,     attention_mask=_lowercase	,     token_type_ids=_lowercase	,     start_positions=_lowercase	,     end_positions=_lowercase	,     )
     self.parent.assertEqual(result.start_logits.shape	,     (self.batch_size, self.seq_length)				)
     self.parent.assertEqual(result.end_logits.shape	,     (self.batch_size, self.seq_length)				)




   def      __a						(   self      :    Tuple	,     _lowercase      :    int	,     _lowercase      :    Tuple	,     _lowercase      :    Dict	,     _lowercase      :    Tuple	,     _lowercase      :    Optional[Any]	,     _lowercase      :    Optional[Any]	,     _lowercase      :    Optional[Any]				):



     """simple docstring"""






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




   def      __a						(   self      :    Dict	,     _lowercase      :    List[str]	,     _lowercase      :    List[Any]	,     _lowercase      :    Optional[int]	,     _lowercase      :    Dict	,     _lowercase      :    int	,     _lowercase      :    Optional[int]	,     _lowercase      :    Any				):



     """simple docstring"""






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




   def      __a						(   self      :    Any	,     _lowercase      :    Union[str, Any]	,     _lowercase      :    List[Any]	,     _lowercase      :    Any	,     _lowercase      :    List[Any]	,     _lowercase      :    str	,     _lowercase      :    Any	,     _lowercase      :    Optional[Any]				):



     """simple docstring"""






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




   def      __a						(   self      :    Dict				):



     """simple docstring"""






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







@require_torch
class __snake_case    (						lowerCamelCase_       ,		unittest.TestCase		):
   lowerCAmelCase_														=							(
       (
           MraModel,
           MraForMaskedLM,
           MraForMultipleChoice,
           MraForQuestionAnswering,
           MraForSequenceClassification,
           MraForTokenClassification,
       )
       if is_torch_available()
       else ()
   )
   lowerCAmelCase_														=							False
   lowerCAmelCase_														=							False
   lowerCAmelCase_														=							False
   lowerCAmelCase_														=							False

   lowerCAmelCase_														=							()




   def      __a						(   self      :    List[str]				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				MraModelTester(self				)
     SCREAMING_SNAKE_CASE__      							=				ConfigTester(self	,     config_class=_lowercase	,     hidden_size=37				)




   def      __a						(   self      :    Union[str, Any]				):



     """simple docstring"""






     self.config_tester.run_common_tests()




   def      __a						(   self      :    Dict				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*_lowercase				)




   def      __a						(   self      :    Tuple				):



     """simple docstring"""






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




   def      __a						(   self      :    Tuple				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_masked_lm(*_lowercase				)




   def      __a						(   self      :    Tuple				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_multiple_choice(*_lowercase				)




   def      __a						(   self      :    List[str]				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_question_answering(*_lowercase				)




   def      __a						(   self      :    Union[str, Any]				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_sequence_classification(*_lowercase				)




   def      __a						(   self      :    Dict				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_token_classification(*_lowercase				)




   @slow
   def      __a						(   self      :    Dict				):



     """simple docstring"""






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




   @unittest.skip(reason="""MRA does not output attentions"""				)
   def      __a						(   self      :    Any				):



     """simple docstring"""






     return







@require_torch
class __snake_case    (						unittest.TestCase		):




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



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				MraModel.from_pretrained("""uw-madison/mra-base-512-4"""				)
     SCREAMING_SNAKE_CASE__      							=				torch.arange(2_56				).unsqueeze(0				)

     with torch.no_grad():
       SCREAMING_SNAKE_CASE__      							=				model(_lowercase				)[0]

     SCREAMING_SNAKE_CASE__      							=				torch.Size((1, 2_56, 7_68)				)
     self.assertEqual(output.shape	,     _lowercase				)

     SCREAMING_SNAKE_CASE__      							=				torch.tensor(
         [[[-0.01_40, 0.08_30, -0.03_81], [0.15_46, 0.14_02, 0.02_20], [0.11_62, 0.08_51, 0.01_65]]]				)

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




   @slow
   def      __a						(   self      :    str				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				MraForMaskedLM.from_pretrained("""uw-madison/mra-base-512-4"""				)
     SCREAMING_SNAKE_CASE__      							=				torch.arange(2_56				).unsqueeze(0				)

     with torch.no_grad():
       SCREAMING_SNAKE_CASE__      							=				model(_lowercase				)[0]

     SCREAMING_SNAKE_CASE__      							=				5_02_65

     SCREAMING_SNAKE_CASE__      							=				torch.Size((1, 2_56, vocab_size)				)
     self.assertEqual(output.shape	,     _lowercase				)

     SCREAMING_SNAKE_CASE__      							=				torch.tensor(
         [[[9.25_95, -3.60_38, 11.88_19], [9.38_69, -3.26_93, 11.09_56], [11.85_24, -3.49_38, 13.12_10]]]				)

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




   @slow
   def      __a						(   self      :    Optional[Any]				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				MraForMaskedLM.from_pretrained("""uw-madison/mra-base-4096-8-d3"""				)
     SCREAMING_SNAKE_CASE__      							=				torch.arange(40_96				).unsqueeze(0				)

     with torch.no_grad():
       SCREAMING_SNAKE_CASE__      							=				model(_lowercase				)[0]

     SCREAMING_SNAKE_CASE__      							=				5_02_65

     SCREAMING_SNAKE_CASE__      							=				torch.Size((1, 40_96, vocab_size)				)
     self.assertEqual(output.shape	,     _lowercase				)

     SCREAMING_SNAKE_CASE__      							=				torch.tensor(
         [[[5.47_89, -2.35_64, 7.50_64], [7.90_67, -1.33_69, 9.96_68], [9.07_12, -1.81_06, 7.03_80]]]				)

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

import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
   from transformers import AutoModelForSeqaSeqLM, AutoTokenizer







@require_torch
@require_sentencepiece
@require_tokenizers
class __snake_case    (						unittest.TestCase		):




   @slow
   def      __a						(   self      :    List[str]				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				AutoModelForSeqaSeqLM.from_pretrained("""google/mt5-small"""	,     return_dict=_lowercase				).to(_lowercase				)
     SCREAMING_SNAKE_CASE__      							=				AutoTokenizer.from_pretrained("""google/mt5-small"""				)

     SCREAMING_SNAKE_CASE__      							=				tokenizer("""Hello there"""	,     return_tensors="""pt"""				).input_ids
     SCREAMING_SNAKE_CASE__      							=				tokenizer("""Hi I am"""	,     return_tensors="""pt"""				).input_ids

     SCREAMING_SNAKE_CASE__      							=				model(input_ids.to(_lowercase				)	,     labels=labels.to(_lowercase				)				).loss
     SCREAMING_SNAKE_CASE__      							=				-(labels.shape[-1] * loss.item())

     SCREAMING_SNAKE_CASE__      							=				-84.91_27
     self.assertTrue(abs(mtf_score - EXPECTED_SCORE				) < 1E-4				)
