


'''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 		UpperCAmelCase_      :





     def __init__( self       :      Dict    ,      UpperCAmelCase__       :      Union[str, Any]    ,      UpperCAmelCase__       :      List[str]=2    ,      UpperCAmelCase__       :      Optional[Any]=8    ,      UpperCAmelCase__       :      Optional[int]=True    ,      UpperCAmelCase__       :      Optional[Any]=True    ,      UpperCAmelCase__       :      Tuple=True    ,      UpperCAmelCase__       :      Optional[int]=True    ,      UpperCAmelCase__       :      Optional[Any]=9_9    ,      UpperCAmelCase__       :      int=1_6    ,      UpperCAmelCase__       :      Optional[int]=5    ,      UpperCAmelCase__       :      Any=2    ,      UpperCAmelCase__       :      int=3_6    ,      UpperCAmelCase__       :      Tuple="gelu"    ,      UpperCAmelCase__       :      Dict=0.0    ,      UpperCAmelCase__       :      str=0.0    ,      UpperCAmelCase__       :      Optional[int]=5_1_2    ,      UpperCAmelCase__       :      Union[str, Any]=1_6    ,      UpperCAmelCase__       :      Union[str, Any]=2    ,      UpperCAmelCase__       :      List[Any]=0.02    ,      UpperCAmelCase__       :      Dict=3    ,      UpperCAmelCase__       :      Optional[int]=4    ,      UpperCAmelCase__       :      Any=None    ,      )				->  int:
        lowerCAmelCase     =			parent
        lowerCAmelCase     =			batch_size
        lowerCAmelCase     =			seq_length
        lowerCAmelCase     =			is_training
        lowerCAmelCase     =			use_input_mask
        lowerCAmelCase     =			use_token_type_ids
        lowerCAmelCase     =			use_labels
        lowerCAmelCase     =			vocab_size
        lowerCAmelCase     =			hidden_size
        lowerCAmelCase     =			num_hidden_layers
        lowerCAmelCase     =			num_attention_heads
        lowerCAmelCase     =			intermediate_size
        lowerCAmelCase     =			hidden_act
        lowerCAmelCase     =			hidden_dropout_prob
        lowerCAmelCase     =			attention_probs_dropout_prob
        lowerCAmelCase     =			max_position_embeddings
        lowerCAmelCase     =			type_vocab_size
        lowerCAmelCase     =			type_sequence_label_size
        lowerCAmelCase     =			initializer_range
        lowerCAmelCase     =			num_labels
        lowerCAmelCase     =			num_choices
        lowerCAmelCase     =			scope





     def 							__UpperCAmelCase      ( self       :      Any    )				->  Tuple:
        lowerCAmelCase     =			ids_tensor([self.batch_size, self.seq_length]    ,      self.vocab_size    )

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

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

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

        lowerCAmelCase     =			self.get_config()

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





     def 							__UpperCAmelCase      ( self       :      Any    )				->  List[str]:
        return MraConfig(
            vocab_size=self.vocab_size    ,      hidden_size=self.hidden_size    ,      num_hidden_layers=self.num_hidden_layers    ,      num_attention_heads=self.num_attention_heads    ,      intermediate_size=self.intermediate_size    ,      hidden_act=self.hidden_act    ,      hidden_dropout_prob=self.hidden_dropout_prob    ,      attention_probs_dropout_prob=self.attention_probs_dropout_prob    ,      max_position_embeddings=self.max_position_embeddings    ,      type_vocab_size=self.type_vocab_size    ,      is_decoder=UpperCAmelCase__    ,      initializer_range=self.initializer_range    ,      )





     def 							__UpperCAmelCase      ( self       :      Dict    )				->  Dict:
        lowerCAmelCase     =			self.get_config()
        lowerCAmelCase     =			3_0_0
        return config





     def 							__UpperCAmelCase      ( self       :      str    )				->  List[str]:
        (
            (
            lowerCAmelCase
        )     ,  (
            lowerCAmelCase
        )     ,  (
            lowerCAmelCase
        )     ,  (
            lowerCAmelCase
        )     ,  (
            lowerCAmelCase
        )     ,  (
            lowerCAmelCase
        )     ,  (
            lowerCAmelCase
        )     ,  
        )     =			self.prepare_config_and_inputs()

        lowerCAmelCase     =			True
        lowerCAmelCase     =			floats_tensor([self.batch_size, self.seq_length, self.hidden_size]    )
        lowerCAmelCase     =			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 							__UpperCAmelCase      ( self       :      List[Any]    ,      UpperCAmelCase__       :      int    ,      UpperCAmelCase__       :      List[Any]    ,      UpperCAmelCase__       :      Tuple    ,      UpperCAmelCase__       :      Union[str, Any]    ,      UpperCAmelCase__       :      Optional[Any]    ,      UpperCAmelCase__       :      Tuple    ,      UpperCAmelCase__       :      int    )				->  int:
        lowerCAmelCase     =			MraModel(config=UpperCAmelCase__    )
        model.to(UpperCAmelCase__    )
        model.eval()
        lowerCAmelCase     =			model(UpperCAmelCase__    ,      attention_mask=UpperCAmelCase__    ,      token_type_ids=UpperCAmelCase__    )
        lowerCAmelCase     =			model(UpperCAmelCase__    ,      token_type_ids=UpperCAmelCase__    )
        lowerCAmelCase     =			model(UpperCAmelCase__    )
        self.parent.assertEqual(result.last_hidden_state.shape    ,      (self.batch_size, self.seq_length, self.hidden_size)    )





     def 							__UpperCAmelCase      ( self       :      List[str]    ,      UpperCAmelCase__       :      Any    ,      UpperCAmelCase__       :      int    ,      UpperCAmelCase__       :      int    ,      UpperCAmelCase__       :      List[str]    ,      UpperCAmelCase__       :      Optional[Any]    ,      UpperCAmelCase__       :      List[Any]    ,      UpperCAmelCase__       :      Tuple    ,      UpperCAmelCase__       :      Dict    ,      UpperCAmelCase__       :      Optional[int]    ,      )				->  List[str]:
        lowerCAmelCase     =			True
        lowerCAmelCase     =			MraModel(UpperCAmelCase__    )
        model.to(UpperCAmelCase__    )
        model.eval()
        lowerCAmelCase     =			model(
            UpperCAmelCase__    ,      attention_mask=UpperCAmelCase__    ,      token_type_ids=UpperCAmelCase__    ,      encoder_hidden_states=UpperCAmelCase__    ,      encoder_attention_mask=UpperCAmelCase__    ,      )
        lowerCAmelCase     =			model(
            UpperCAmelCase__    ,      attention_mask=UpperCAmelCase__    ,      token_type_ids=UpperCAmelCase__    ,      encoder_hidden_states=UpperCAmelCase__    ,      )
        lowerCAmelCase     =			model(UpperCAmelCase__    ,      attention_mask=UpperCAmelCase__    ,      token_type_ids=UpperCAmelCase__    )
        self.parent.assertEqual(result.last_hidden_state.shape    ,      (self.batch_size, self.seq_length, self.hidden_size)    )





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





     def 							__UpperCAmelCase      ( self       :      Optional[int]    ,      UpperCAmelCase__       :      Optional[Any]    ,      UpperCAmelCase__       :      Tuple    ,      UpperCAmelCase__       :      int    ,      UpperCAmelCase__       :      List[str]    ,      UpperCAmelCase__       :      Optional[Any]    ,      UpperCAmelCase__       :      Any    ,      UpperCAmelCase__       :      Union[str, Any]    )				->  int:
        lowerCAmelCase     =			MraForQuestionAnswering(config=UpperCAmelCase__    )
        model.to(UpperCAmelCase__    )
        model.eval()
        lowerCAmelCase     =			model(
            UpperCAmelCase__    ,      attention_mask=UpperCAmelCase__    ,      token_type_ids=UpperCAmelCase__    ,      start_positions=UpperCAmelCase__    ,      end_positions=UpperCAmelCase__    ,      )
        self.parent.assertEqual(result.start_logits.shape    ,      (self.batch_size, self.seq_length)    )
        self.parent.assertEqual(result.end_logits.shape    ,      (self.batch_size, self.seq_length)    )





     def 							__UpperCAmelCase      ( self       :      str    ,      UpperCAmelCase__       :      Optional[int]    ,      UpperCAmelCase__       :      Optional[int]    ,      UpperCAmelCase__       :      Union[str, Any]    ,      UpperCAmelCase__       :      Dict    ,      UpperCAmelCase__       :      Optional[int]    ,      UpperCAmelCase__       :      Any    ,      UpperCAmelCase__       :      Any    )				->  List[str]:
        lowerCAmelCase     =			self.num_labels
        lowerCAmelCase     =			MraForSequenceClassification(UpperCAmelCase__    )
        model.to(UpperCAmelCase__    )
        model.eval()
        lowerCAmelCase     =			model(UpperCAmelCase__    ,      attention_mask=UpperCAmelCase__    ,      token_type_ids=UpperCAmelCase__    ,      labels=UpperCAmelCase__    )
        self.parent.assertEqual(result.logits.shape    ,      (self.batch_size, self.num_labels)    )





     def 							__UpperCAmelCase      ( self       :      int    ,      UpperCAmelCase__       :      Optional[Any]    ,      UpperCAmelCase__       :      Optional[int]    ,      UpperCAmelCase__       :      str    ,      UpperCAmelCase__       :      str    ,      UpperCAmelCase__       :      Dict    ,      UpperCAmelCase__       :      Any    ,      UpperCAmelCase__       :      Union[str, Any]    )				->  Optional[int]:
        lowerCAmelCase     =			self.num_labels
        lowerCAmelCase     =			MraForTokenClassification(config=UpperCAmelCase__    )
        model.to(UpperCAmelCase__    )
        model.eval()
        lowerCAmelCase     =			model(UpperCAmelCase__    ,      attention_mask=UpperCAmelCase__    ,      token_type_ids=UpperCAmelCase__    ,      labels=UpperCAmelCase__    )
        self.parent.assertEqual(result.logits.shape    ,      (self.batch_size, self.seq_length, self.num_labels)    )





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





     def 							__UpperCAmelCase      ( self       :      Tuple    )				->  Optional[Any]:
        lowerCAmelCase     =			self.prepare_config_and_inputs()
        (
            (
            lowerCAmelCase
        )     ,  (
            lowerCAmelCase
        )     ,  (
            lowerCAmelCase
        )     ,  (
            lowerCAmelCase
        )     ,  (
            lowerCAmelCase
        )     ,  (
            lowerCAmelCase
        )     ,  (
            lowerCAmelCase
        )     ,  
        )     =			config_and_inputs
        lowerCAmelCase     =			{'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
        return config, inputs_dict




@require_torch
class 		UpperCAmelCase_      (  __lowercase       ,	unittest.TestCase		):
     lowerCamelCase				:	Optional[Any]		=     (
         (
             MraModel,
             MraForMaskedLM,
             MraForMultipleChoice,
             MraForQuestionAnswering,
             MraForSequenceClassification,
             MraForTokenClassification,
         )
         if is_torch_available()
         else ()
     )
     lowerCamelCase				:	Union[str, Any]		=     False
     lowerCamelCase				:	Dict		=     False
     lowerCamelCase				:	Union[str, Any]		=     False
     lowerCamelCase				:	int		=     False

     lowerCamelCase				:	List[Any]		=     ()





     def 							__UpperCAmelCase      ( self       :      Any    )				->  Optional[Any]:
        lowerCAmelCase     =			MraModelTester(self    )
        lowerCAmelCase     =			ConfigTester(self    ,      config_class=UpperCAmelCase__    ,      hidden_size=3_7    )





     def 							__UpperCAmelCase      ( self       :      str    )				->  Tuple:
        self.config_tester.run_common_tests()





     def 							__UpperCAmelCase      ( self       :      int    )				->  str:
        lowerCAmelCase     =			self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*UpperCAmelCase__    )





     def 							__UpperCAmelCase      ( self       :      Tuple    )				->  List[str]:
        lowerCAmelCase     =			self.model_tester.prepare_config_and_inputs()
        for type in ["absolute", "relative_key", "relative_key_query"]:
           lowerCAmelCase     =			type
           self.model_tester.create_and_check_model(*UpperCAmelCase__    )





     def 							__UpperCAmelCase      ( self       :      int    )				->  Optional[int]:
        lowerCAmelCase     =			self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_masked_lm(*UpperCAmelCase__    )





     def 							__UpperCAmelCase      ( self       :      Optional[Any]    )				->  List[Any]:
        lowerCAmelCase     =			self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_multiple_choice(*UpperCAmelCase__    )





     def 							__UpperCAmelCase      ( self       :      Union[str, Any]    )				->  Any:
        lowerCAmelCase     =			self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_question_answering(*UpperCAmelCase__    )





     def 							__UpperCAmelCase      ( self       :      Dict    )				->  List[Any]:
        lowerCAmelCase     =			self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_sequence_classification(*UpperCAmelCase__    )





     def 							__UpperCAmelCase      ( self       :      int    )				->  Optional[int]:
        lowerCAmelCase     =			self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_token_classification(*UpperCAmelCase__    )





     @slow
     def 							__UpperCAmelCase      ( self       :      str    )				->  str:
        for model_name in MRA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           lowerCAmelCase     =			MraModel.from_pretrained(UpperCAmelCase__    )
           self.assertIsNotNone(UpperCAmelCase__    )





     @unittest.skip(reason='MRA does not output attentions'    )
     def 							__UpperCAmelCase      ( self       :      Tuple    )				->  Optional[Any]:
        return




@require_torch
class 		UpperCAmelCase_      (  unittest.TestCase		):





     @slow
     def 							__UpperCAmelCase      ( self       :      Optional[Any]    )				->  str:
        lowerCAmelCase     =			MraModel.from_pretrained('uw-madison/mra-base-512-4'    )
        lowerCAmelCase     =			torch.arange(2_5_6    ).unsqueeze(0    )

        with torch.no_grad():
           lowerCAmelCase     =			model(UpperCAmelCase__    )[0]

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

        lowerCAmelCase     =			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]    ,      UpperCAmelCase__    ,      atol=1E-4    )    )





     @slow
     def 							__UpperCAmelCase      ( self       :      List[Any]    )				->  Optional[Any]:
        lowerCAmelCase     =			MraForMaskedLM.from_pretrained('uw-madison/mra-base-512-4'    )
        lowerCAmelCase     =			torch.arange(2_5_6    ).unsqueeze(0    )

        with torch.no_grad():
           lowerCAmelCase     =			model(UpperCAmelCase__    )[0]

        lowerCAmelCase     =			5_0_2_6_5

        lowerCAmelCase     =			torch.Size((1, 2_5_6, vocab_size)    )
        self.assertEqual(output.shape    ,      UpperCAmelCase__    )

        lowerCAmelCase     =			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]    ,      UpperCAmelCase__    ,      atol=1E-4    )    )





     @slow
     def 							__UpperCAmelCase      ( self       :      List[str]    )				->  Dict:
        lowerCAmelCase     =			MraForMaskedLM.from_pretrained('uw-madison/mra-base-4096-8-d3'    )
        lowerCAmelCase     =			torch.arange(4_0_9_6    ).unsqueeze(0    )

        with torch.no_grad():
           lowerCAmelCase     =			model(UpperCAmelCase__    )[0]

        lowerCAmelCase     =			5_0_2_6_5

        lowerCAmelCase     =			torch.Size((1, 4_0_9_6, vocab_size)    )
        self.assertEqual(output.shape    ,      UpperCAmelCase__    )

        lowerCAmelCase     =			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]    ,      UpperCAmelCase__    ,      atol=1E-4    )    )












'''simple docstring'''


lowerCAmelCase					:     str												='''
# Installazione di Transformers
! pip install transformers datasets
# Per installare dalla fonte invece dell\'ultima versione rilasciata, commenta il comando sopra e
# rimuovi la modalità commento al comando seguente.
# ! pip install git+https://github.com/huggingface/transformers.git
'''

lowerCAmelCase					:     int												=[{'''type''': '''code''', '''content''': INSTALL_CONTENT}]
lowerCAmelCase					:     List[str]												={
    '''{processor_class}''': '''FakeProcessorClass''',
    '''{model_class}''': '''FakeModelClass''',
    '''{object_class}''': '''FakeObjectClass''',
}


