




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_   :

    '''simple docstring'''






    def __init__(      self				:     List[Any] ,			UpperCAmelCase__				:     str ,			UpperCAmelCase__				:     Union[str, Any]=2 ,			UpperCAmelCase__				:     Tuple=8 ,			UpperCAmelCase__				:     List[Any]=True ,			UpperCAmelCase__				:     Tuple=True ,			UpperCAmelCase__				:     Union[str, Any]=True ,			UpperCAmelCase__				:     Dict=True ,			UpperCAmelCase__				:     Optional[int]=99 ,			UpperCAmelCase__				:     Union[str, Any]=16 ,			UpperCAmelCase__				:     Dict=5 ,			UpperCAmelCase__				:     List[Any]=2 ,			UpperCAmelCase__				:     List[Any]=36 ,			UpperCAmelCase__				:     Optional[int]="gelu" ,			UpperCAmelCase__				:     Dict=0.0 ,			UpperCAmelCase__				:     str=0.0 ,			UpperCAmelCase__				:     int=512 ,			UpperCAmelCase__				:     Union[str, Any]=16 ,			UpperCAmelCase__				:     Dict=2 ,			UpperCAmelCase__				:     Optional[Any]=0.02 ,			UpperCAmelCase__				:     Dict=3 ,			UpperCAmelCase__				:     Optional[Any]=4 ,			UpperCAmelCase__				:     Tuple=None ,			)						->Union[str, Any]:





           '''simple docstring'''

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


    def 	SCREAMING_SNAKE_CASE							(      self				:     Optional[int])						->Any:





           '''simple docstring'''

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

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

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

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

           A__							=   self.get_config()

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


    def 	SCREAMING_SNAKE_CASE							(      self				:     Optional[int])						->Union[str, 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=UpperCAmelCase__ ,			initializer_range=self.initializer_range ,			)


    def 	SCREAMING_SNAKE_CASE							(      self				:     Optional[int])						->Any:





           '''simple docstring'''

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


    def 	SCREAMING_SNAKE_CASE							(      self				:     List[str])						->List[Any]:





           '''simple docstring'''

           (
               (
               A__
           )			,							(
               A__
           )			,							(
               A__
           )			,							(
               A__
           )			,							(
               A__
           )			,							(
               A__
           )			,							(
               A__
           )			,							
           )							=   self.prepare_config_and_inputs()

           A__							=   True
           A__							=   floats_tensor([self.batch_size, self.seq_length, self.hidden_size])
           A__							=   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 	SCREAMING_SNAKE_CASE							(      self				:     Optional[int] ,			UpperCAmelCase__				:     int ,			UpperCAmelCase__				:     Dict ,			UpperCAmelCase__				:     Tuple ,			UpperCAmelCase__				:     Optional[Any] ,			UpperCAmelCase__				:     Optional[int] ,			UpperCAmelCase__				:     Optional[int] ,			UpperCAmelCase__				:     Tuple)						->List[str]:





           '''simple docstring'''

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


    def 	SCREAMING_SNAKE_CASE							(      self				:     Dict ,			UpperCAmelCase__				:     Tuple ,			UpperCAmelCase__				:     int ,			UpperCAmelCase__				:     List[Any] ,			UpperCAmelCase__				:     Optional[int] ,			UpperCAmelCase__				:     Optional[Any] ,			UpperCAmelCase__				:     Dict ,			UpperCAmelCase__				:     List[str] ,			UpperCAmelCase__				:     Optional[int] ,			UpperCAmelCase__				:     Optional[int] ,			)						->List[Any]:





           '''simple docstring'''

           A__							=   True
           A__							=   MraModel(UpperCAmelCase__)
           model.to(UpperCAmelCase__)
           model.eval()
           A__							=   model(
               UpperCAmelCase__ ,			attention_mask=UpperCAmelCase__ ,			token_type_ids=UpperCAmelCase__ ,			encoder_hidden_states=UpperCAmelCase__ ,			encoder_attention_mask=UpperCAmelCase__ ,			)
           A__							=   model(
               UpperCAmelCase__ ,			attention_mask=UpperCAmelCase__ ,			token_type_ids=UpperCAmelCase__ ,			encoder_hidden_states=UpperCAmelCase__ ,			)
           A__							=   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 	SCREAMING_SNAKE_CASE							(      self				:     Any ,			UpperCAmelCase__				:     Dict ,			UpperCAmelCase__				:     Optional[Any] ,			UpperCAmelCase__				:     Any ,			UpperCAmelCase__				:     Union[str, Any] ,			UpperCAmelCase__				:     Union[str, Any] ,			UpperCAmelCase__				:     Dict ,			UpperCAmelCase__				:     str)						->int:





           '''simple docstring'''

           A__							=   MraForMaskedLM(config=UpperCAmelCase__)
           model.to(UpperCAmelCase__)
           model.eval()
           A__							=   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 	SCREAMING_SNAKE_CASE							(      self				:     Optional[Any] ,			UpperCAmelCase__				:     Union[str, Any] ,			UpperCAmelCase__				:     Tuple ,			UpperCAmelCase__				:     Tuple ,			UpperCAmelCase__				:     List[Any] ,			UpperCAmelCase__				:     Tuple ,			UpperCAmelCase__				:     Any ,			UpperCAmelCase__				:     Any)						->Any:





           '''simple docstring'''

           A__							=   MraForQuestionAnswering(config=UpperCAmelCase__)
           model.to(UpperCAmelCase__)
           model.eval()
           A__							=   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 	SCREAMING_SNAKE_CASE							(      self				:     Dict ,			UpperCAmelCase__				:     Optional[int] ,			UpperCAmelCase__				:     List[str] ,			UpperCAmelCase__				:     Optional[Any] ,			UpperCAmelCase__				:     Tuple ,			UpperCAmelCase__				:     Tuple ,			UpperCAmelCase__				:     Any ,			UpperCAmelCase__				:     Any)						->List[Any]:





           '''simple docstring'''

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


    def 	SCREAMING_SNAKE_CASE							(      self				:     Optional[Any] ,			UpperCAmelCase__				:     Tuple ,			UpperCAmelCase__				:     Any ,			UpperCAmelCase__				:     Optional[int] ,			UpperCAmelCase__				:     int ,			UpperCAmelCase__				:     Dict ,			UpperCAmelCase__				:     int ,			UpperCAmelCase__				:     List[str])						->str:





           '''simple docstring'''

           A__							=   self.num_labels
           A__							=   MraForTokenClassification(config=UpperCAmelCase__)
           model.to(UpperCAmelCase__)
           model.eval()
           A__							=   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 	SCREAMING_SNAKE_CASE							(      self				:     List[Any] ,			UpperCAmelCase__				:     Optional[int] ,			UpperCAmelCase__				:     Any ,			UpperCAmelCase__				:     str ,			UpperCAmelCase__				:     int ,			UpperCAmelCase__				:     str ,			UpperCAmelCase__				:     List[Any] ,			UpperCAmelCase__				:     Optional[int])						->Optional[int]:





           '''simple docstring'''

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





    def 	SCREAMING_SNAKE_CASE							(      self				:     List[Any])						->int:





           '''simple docstring'''

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


@require_torch
class     UpperCamelCase_   (			UpperCAmelCase__		, unittest.TestCase					):

    '''simple docstring'''






    UpperCAmelCase__							  =				(
        (
            MraModel,
            MraForMaskedLM,
            MraForMultipleChoice,
            MraForQuestionAnswering,
            MraForSequenceClassification,
            MraForTokenClassification,
        )
        if is_torch_available()
        else ()
    )
    UpperCAmelCase__							  =				False
    UpperCAmelCase__							  =				False
    UpperCAmelCase__							  =				False
    UpperCAmelCase__							  =				False

    UpperCAmelCase__							  =				()


    def 	SCREAMING_SNAKE_CASE							(      self				:     Optional[int])						->Tuple:





           '''simple docstring'''

           A__							=   MraModelTester(self)
           A__							=   ConfigTester(self ,			config_class=UpperCAmelCase__ ,			hidden_size=37)


    def 	SCREAMING_SNAKE_CASE							(      self				:     List[Any])						->Union[str, Any]:





           '''simple docstring'''

           self.config_tester.run_common_tests()


    def 	SCREAMING_SNAKE_CASE							(      self				:     Tuple)						->Dict:





           '''simple docstring'''

           A__							=   self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*UpperCAmelCase__)


    def 	SCREAMING_SNAKE_CASE							(      self				:     str)						->List[Any]:





           '''simple docstring'''

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


    def 	SCREAMING_SNAKE_CASE							(      self				:     List[Any])						->List[str]:





           '''simple docstring'''

           A__							=   self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_masked_lm(*UpperCAmelCase__)


    def 	SCREAMING_SNAKE_CASE							(      self				:     List[str])						->str:





           '''simple docstring'''

           A__							=   self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_multiple_choice(*UpperCAmelCase__)


    def 	SCREAMING_SNAKE_CASE							(      self				:     Optional[Any])						->str:





           '''simple docstring'''

           A__							=   self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_question_answering(*UpperCAmelCase__)


    def 	SCREAMING_SNAKE_CASE							(      self				:     Tuple)						->Any:





           '''simple docstring'''

           A__							=   self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_sequence_classification(*UpperCAmelCase__)


    def 	SCREAMING_SNAKE_CASE							(      self				:     Any)						->List[str]:





           '''simple docstring'''

           A__							=   self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_token_classification(*UpperCAmelCase__)


    @slow
    def 	SCREAMING_SNAKE_CASE							(      self				:     str)						->Optional[int]:





           '''simple docstring'''

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





    @unittest.skip(reason='''MRA does not output attentions''')
    def 	SCREAMING_SNAKE_CASE							(      self				:     Tuple)						->Any:





           '''simple docstring'''

           return


@require_torch
class     UpperCamelCase_   (			unittest.TestCase					):

    '''simple docstring'''






    @slow
    def 	SCREAMING_SNAKE_CASE							(      self				:     Dict)						->Tuple:





           '''simple docstring'''

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

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

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

           A__							=   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] ,			UpperCAmelCase__ ,			atol=1e-4))


    @slow
    def 	SCREAMING_SNAKE_CASE							(      self				:     List[str])						->Optional[int]:





           '''simple docstring'''

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

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

           A__							=   50_265

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

           A__							=   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] ,			UpperCAmelCase__ ,			atol=1e-4))





    @slow
    def 	SCREAMING_SNAKE_CASE							(      self				:     Union[str, Any])						->Optional[int]:





           '''simple docstring'''

           A__							=   MraForMaskedLM.from_pretrained('''uw-madison/mra-base-4096-8-d3''')
           A__							=   torch.arange(4_096).unsqueeze(0)

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

           A__							=   50_265

           A__							=   torch.Size((1, 4_096, vocab_size))
           self.assertEqual(output.shape ,			UpperCAmelCase__)

           A__							=   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] ,			UpperCAmelCase__ ,			atol=1e-4))








# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny model through reduction of a normal pre-trained model, but keeping the
# full vocab, merges file, and thus also resulting in a larger model due to a large vocab size.
# This gives ~3MB in total for all files.
#
# If you want a 50 times smaller than this see `fsmt-make-super-tiny-model.py`, which is slightly more complicated
#
#
# It will be used then as "stas/tiny-wmt19-en-de"

# Build
from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration

_lowerCamelCase     :		Optional[Any]								=       """facebook/wmt19-en-de"""
_lowerCamelCase     :		Optional[Any]								=       FSMTTokenizer.from_pretrained(mname)
# get the correct vocab sizes, etc. from the master model
_lowerCamelCase     :		int								=       FSMTConfig.from_pretrained(mname)
config.update(
    dict(
        d_model=4,
        encoder_layers=1,
        decoder_layers=1,
        encoder_ffn_dim=4,
        decoder_ffn_dim=4,
        encoder_attention_heads=1,
        decoder_attention_heads=1,
    )
)

_lowerCamelCase     :		Union[str, Any]								=       FSMTForConditionalGeneration(config)
print(F'''num of params {tiny_model.num_parameters()}''')

# Test
_lowerCamelCase     :		int								=       tokenizer(["""Making tiny model"""], return_tensors="""pt""")
_lowerCamelCase     :		int								=       tiny_model(**batch)

print("""test output:""", len(outputs.logits[0]))

# Save
_lowerCamelCase     :		str								=       """tiny-wmt19-en-de"""
tiny_model.half()  # makes it smaller
tiny_model.save_pretrained(mname_tiny)
tokenizer.save_pretrained(mname_tiny)

print(F'''Generated {mname_tiny}''')

# Upload
# transformers-cli upload tiny-wmt19-en-de


