

'''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 	lowerCAmelCase  :







       def __init__(				self      :			int    ,   __lowercase      :			Dict    ,   __lowercase      :			int=2    ,   __lowercase      :			List[Any]=8    ,   __lowercase      :			Any=True    ,   __lowercase      :			Optional[Any]=True    ,   __lowercase      :			Optional[Any]=True    ,   __lowercase      :			int=True    ,   __lowercase      :			int=99    ,   __lowercase      :			Union[str, Any]=16    ,   __lowercase      :			Optional[int]=5    ,   __lowercase      :			Tuple=2    ,   __lowercase      :			Dict=36    ,   __lowercase      :			Optional[int]="gelu"    ,   __lowercase      :			Optional[Any]=0.0    ,   __lowercase      :			int=0.0    ,   __lowercase      :			Tuple=512    ,   __lowercase      :			Dict=16    ,   __lowercase      :			int=2    ,   __lowercase      :			Tuple=0.0_2    ,   __lowercase      :			Any=3    ,   __lowercase      :			Optional[Any]=4    ,   __lowercase      :			Dict=None    ,   ):







            """simple docstring"""







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







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







            """simple docstring"""







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

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

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

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

            __lowercase      		=self.get_config()

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







       def       snake_case (				self      :			int   ):







            """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       snake_case (				self      :			Union[str, Any]   ):







            """simple docstring"""







            __lowercase      		=self.get_config()
            __lowercase      		=300
            return config







       def       snake_case (				self      :			str   ):







            """simple docstring"""







            (
                (
                __lowercase
            )		,  (
                __lowercase
            )		,  (
                __lowercase
            )		,  (
                __lowercase
            )		,  (
                __lowercase
            )		,  (
                __lowercase
            )		,  (
                __lowercase
            )		,  
            )      		=self.prepare_config_and_inputs()

            __lowercase      		=True
            __lowercase      		=floats_tensor([self.batch_size, self.seq_length, self.hidden_size]   )
            __lowercase      		=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       snake_case (				self      :			Union[str, Any]    ,   __lowercase      :			Union[str, Any]    ,   __lowercase      :			Union[str, Any]    ,   __lowercase      :			List[str]    ,   __lowercase      :			List[Any]    ,   __lowercase      :			str    ,   __lowercase      :			List[str]    ,   __lowercase      :			str   ):







            """simple docstring"""







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







       def       snake_case (				self      :			Optional[Any]    ,   __lowercase      :			Optional[int]    ,   __lowercase      :			List[Any]    ,   __lowercase      :			Optional[int]    ,   __lowercase      :			List[str]    ,   __lowercase      :			Dict    ,   __lowercase      :			List[str]    ,   __lowercase      :			str    ,   __lowercase      :			Any    ,   __lowercase      :			List[str]    ,   ):







            """simple docstring"""







            __lowercase      		=True
            __lowercase      		=MraModel(__lowercase   )
            model.to(__lowercase   )
            model.eval()
            __lowercase      		=model(
                __lowercase    ,   attention_mask=__lowercase    ,   token_type_ids=__lowercase    ,   encoder_hidden_states=__lowercase    ,   encoder_attention_mask=__lowercase    ,   )
            __lowercase      		=model(
                __lowercase    ,   attention_mask=__lowercase    ,   token_type_ids=__lowercase    ,   encoder_hidden_states=__lowercase    ,   )
            __lowercase      		=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       snake_case (				self      :			Union[str, Any]    ,   __lowercase      :			List[Any]    ,   __lowercase      :			Dict    ,   __lowercase      :			List[Any]    ,   __lowercase      :			Optional[Any]    ,   __lowercase      :			str    ,   __lowercase      :			Optional[Any]    ,   __lowercase      :			Optional[Any]   ):







            """simple docstring"""







            __lowercase      		=MraForMaskedLM(config=__lowercase   )
            model.to(__lowercase   )
            model.eval()
            __lowercase      		=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       snake_case (				self      :			List[str]    ,   __lowercase      :			Dict    ,   __lowercase      :			Optional[int]    ,   __lowercase      :			int    ,   __lowercase      :			Tuple    ,   __lowercase      :			Tuple    ,   __lowercase      :			Optional[Any]    ,   __lowercase      :			str   ):







            """simple docstring"""







            __lowercase      		=MraForQuestionAnswering(config=__lowercase   )
            model.to(__lowercase   )
            model.eval()
            __lowercase      		=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       snake_case (				self      :			Any    ,   __lowercase      :			List[Any]    ,   __lowercase      :			str    ,   __lowercase      :			List[Any]    ,   __lowercase      :			Tuple    ,   __lowercase      :			int    ,   __lowercase      :			Any    ,   __lowercase      :			Any   ):







            """simple docstring"""







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







       def       snake_case (				self      :			Dict    ,   __lowercase      :			Any    ,   __lowercase      :			Optional[int]    ,   __lowercase      :			Union[str, Any]    ,   __lowercase      :			Tuple    ,   __lowercase      :			Dict    ,   __lowercase      :			int    ,   __lowercase      :			Union[str, Any]   ):







            """simple docstring"""







            __lowercase      		=self.num_labels
            __lowercase      		=MraForTokenClassification(config=__lowercase   )
            model.to(__lowercase   )
            model.eval()
            __lowercase      		=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       snake_case (				self      :			List[Any]    ,   __lowercase      :			int    ,   __lowercase      :			List[str]    ,   __lowercase      :			str    ,   __lowercase      :			Optional[Any]    ,   __lowercase      :			int    ,   __lowercase      :			List[Any]    ,   __lowercase      :			Tuple   ):







            """simple docstring"""







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







       def       snake_case (				self      :			str   ):







            """simple docstring"""







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






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

       lowerCAmelCase_												=			()







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







            """simple docstring"""







            __lowercase      		=MraModelTester(self   )
            __lowercase      		=ConfigTester(self    ,   config_class=__lowercase    ,   hidden_size=37   )







       def       snake_case (				self      :			Dict   ):







            """simple docstring"""







            self.config_tester.run_common_tests()







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







            """simple docstring"""







            __lowercase      		=self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_model(*__lowercase   )







       def       snake_case (				self      :			str   ):







            """simple docstring"""







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







       def       snake_case (				self      :			int   ):







            """simple docstring"""







            __lowercase      		=self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_masked_lm(*__lowercase   )







       def       snake_case (				self      :			Optional[int]   ):







            """simple docstring"""







            __lowercase      		=self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_multiple_choice(*__lowercase   )







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







            """simple docstring"""







            __lowercase      		=self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_question_answering(*__lowercase   )







       def       snake_case (				self      :			Optional[int]   ):







            """simple docstring"""







            __lowercase      		=self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_sequence_classification(*__lowercase   )







       def       snake_case (				self      :			Dict   ):







            """simple docstring"""







            __lowercase      		=self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_token_classification(*__lowercase   )







       @slow
       def       snake_case (				self      :			Union[str, Any]   ):







            """simple docstring"""







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







       @unittest.skip(reason='MRA does not output attentions'   )
       def       snake_case (				self      :			str   ):







            """simple docstring"""







            return






@require_torch
class 	lowerCAmelCase  (   unittest.TestCase ):







       @slow
       def       snake_case (				self      :			Optional[int]   ):







            """simple docstring"""







            __lowercase      		=MraModel.from_pretrained('uw-madison/mra-base-512-4'   )
            __lowercase      		=torch.arange(256   ).unsqueeze(0   )

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

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

            __lowercase      		=torch.tensor(
                [[[-0.0_1_4_0, 0.0_8_3_0, -0.0_3_8_1], [0.1_5_4_6, 0.1_4_0_2, 0.0_2_2_0], [0.1_1_6_2, 0.0_8_5_1, 0.0_1_6_5]]]   )

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







       @slow
       def       snake_case (				self      :			str   ):







            """simple docstring"""







            __lowercase      		=MraForMaskedLM.from_pretrained('uw-madison/mra-base-512-4'   )
            __lowercase      		=torch.arange(256   ).unsqueeze(0   )

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

            __lowercase      		=50265

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

            __lowercase      		=torch.tensor(
                [[[9.2_5_9_5, -3.6_0_3_8, 1_1.8_8_1_9], [9.3_8_6_9, -3.2_6_9_3, 1_1.0_9_5_6], [1_1.8_5_2_4, -3.4_9_3_8, 1_3.1_2_1_0]]]   )

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







       @slow
       def       snake_case (				self      :			Optional[int]   ):







            """simple docstring"""







            __lowercase      		=MraForMaskedLM.from_pretrained('uw-madison/mra-base-4096-8-d3'   )
            __lowercase      		=torch.arange(4096   ).unsqueeze(0   )

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

            __lowercase      		=50265

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

            __lowercase      		=torch.tensor(
                [[[5.4_7_8_9, -2.3_5_6_4, 7.5_0_6_4], [7.9_0_6_7, -1.3_3_6_9, 9.9_6_6_8], [9.0_7_1_2, -1.8_1_0_6, 7.0_3_8_0]]]   )

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





'''simple docstring'''

class 	lowerCAmelCase  :







       def __init__(				self      :			List[Any]    ,   __lowercase      :			str    ,   __lowercase      :			Any    ,   __lowercase      :			str   ):







            """simple docstring"""







            __lowercase      		=name
            __lowercase      		=value
            __lowercase      		=weight







       def __repr__(				self      :			List[str]   ):







            """simple docstring"""







            return f'''{self.__class__.__name__}({self.name}, {self.value}, {self.weight})'''







       def       snake_case (				self      :			List[Any]   ):







            """simple docstring"""







            return self.value







       def       snake_case (				self      :			str   ):







            """simple docstring"""







            return self.name







       def       snake_case (				self      :			Optional[Any]   ):







            """simple docstring"""







            return self.weight







       def       snake_case (				self      :			Tuple   ):







            """simple docstring"""







            return self.value / self.weight






def 					__UpperCamelCase				(						lowercase__    :	Dict,       lowercase__    :	List[str],       lowercase__    :	str			):






     '''simple docstring'''





     __lowercase      		=[]
     for i in range(len(lowercase__			)			):
          menu.append(Things(name[i],       value[i],       weight[i]			)			)
     return menu

def 					__UpperCamelCase				(						lowercase__    :	List[Any],       lowercase__    :	List[Any],       lowercase__    :	str			):






     '''simple docstring'''





     __lowercase      		=sorted(lowercase__,       key=lowercase__,       reverse=lowercase__			)
     __lowercase      		=[]
     __lowercase		,  __lowercase      		=0.0, 0.0
     for i in range(len(lowercase__			)			):
          if (total_cost + items_copy[i].get_weight()) <= max_cost:
               result.append(items_copy[i]			)
               total_cost += items_copy[i].get_weight()
               total_value += items_copy[i].get_value()
     return (result, total_value)

def 					__UpperCamelCase				(						):






     '''simple docstring'''







if __name__ == "__main__":
       import doctest

       doctest.testmod()


