






"""simple docstring"""







import unittest

from transformers import AlbertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
  import torch

  from transformers import (
      MODEL_FOR_PRETRAINING_MAPPING,
      AlbertForMaskedLM,
      AlbertForMultipleChoice,
      AlbertForPreTraining,
      AlbertForQuestionAnswering,
      AlbertForSequenceClassification,
      AlbertForTokenClassification,
      AlbertModel,
  )
  from transformers.models.albert.modeling_albert import ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST







class  lowerCamelCase__ :






    def __init__(			self     ,A     ,A=13     ,A=7     ,A=True     ,A=True     ,A=True     ,A=True     ,A=99     ,A=16     ,A=36     ,A=6     ,A=6     ,A=6     ,A=37     ,A="gelu"     ,A=0.1     ,A=0.1     ,A=512     ,A=16     ,A=2     ,A=0.02     ,A=3     ,A=4     ,A=None     ,):
          UpperCAmelCase          =	parent
          UpperCAmelCase          =	batch_size
          UpperCAmelCase          =	seq_length
          UpperCAmelCase          =	is_training
          UpperCAmelCase          =	use_input_mask
          UpperCAmelCase          =	use_token_type_ids
          UpperCAmelCase          =	use_labels
          UpperCAmelCase          =	vocab_size
          UpperCAmelCase          =	embedding_size
          UpperCAmelCase          =	hidden_size
          UpperCAmelCase          =	num_hidden_layers
          UpperCAmelCase          =	num_hidden_groups
          UpperCAmelCase          =	num_attention_heads
          UpperCAmelCase          =	intermediate_size
          UpperCAmelCase          =	hidden_act
          UpperCAmelCase          =	hidden_dropout_prob
          UpperCAmelCase          =	attention_probs_dropout_prob
          UpperCAmelCase          =	max_position_embeddings
          UpperCAmelCase          =	type_vocab_size
          UpperCAmelCase          =	type_sequence_label_size
          UpperCAmelCase          =	initializer_range
          UpperCAmelCase          =	num_labels
          UpperCAmelCase          =	num_choices
          UpperCAmelCase          =	scope






    def     _UpperCamelCase							(			self ):
          UpperCAmelCase          =	ids_tensor([self.batch_size, self.seq_length]     ,self.vocab_size )

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

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

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

          UpperCAmelCase          =	self.get_config()

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






    def     _UpperCamelCase							(			self ):
          return AlbertConfig(
              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     ,initializer_range=self.initializer_range     ,num_hidden_groups=self.num_hidden_groups     ,)






    def     _UpperCamelCase							(			self     ,A     ,A     ,A     ,A     ,A     ,A     ,A ):
          UpperCAmelCase          =	AlbertModel(config=snake_case_ )
          model.to(snake_case_ )
          model.eval()
          UpperCAmelCase          =	model(snake_case_     ,attention_mask=snake_case_     ,token_type_ids=snake_case_ )
          UpperCAmelCase          =	model(snake_case_     ,token_type_ids=snake_case_ )
          UpperCAmelCase          =	model(snake_case_ )
          self.parent.assertEqual(result.last_hidden_state.shape     ,(self.batch_size, self.seq_length, self.hidden_size) )
          self.parent.assertEqual(result.pooler_output.shape     ,(self.batch_size, self.hidden_size) )






    def     _UpperCamelCase							(			self     ,A     ,A     ,A     ,A     ,A     ,A     ,A ):
          UpperCAmelCase          =	AlbertForPreTraining(config=snake_case_ )
          model.to(snake_case_ )
          model.eval()
          UpperCAmelCase          =	model(
              snake_case_     ,attention_mask=snake_case_     ,token_type_ids=snake_case_     ,labels=snake_case_     ,sentence_order_label=snake_case_     ,)
          self.parent.assertEqual(result.prediction_logits.shape     ,(self.batch_size, self.seq_length, self.vocab_size) )
          self.parent.assertEqual(result.sop_logits.shape     ,(self.batch_size, config.num_labels) )






    def     _UpperCamelCase							(			self     ,A     ,A     ,A     ,A     ,A     ,A     ,A ):
          UpperCAmelCase          =	AlbertForMaskedLM(config=snake_case_ )
          model.to(snake_case_ )
          model.eval()
          UpperCAmelCase          =	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     _UpperCamelCase							(			self     ,A     ,A     ,A     ,A     ,A     ,A     ,A ):
          UpperCAmelCase          =	AlbertForQuestionAnswering(config=snake_case_ )
          model.to(snake_case_ )
          model.eval()
          UpperCAmelCase          =	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     _UpperCamelCase							(			self     ,A     ,A     ,A     ,A     ,A     ,A     ,A ):
          UpperCAmelCase          =	self.num_labels
          UpperCAmelCase          =	AlbertForSequenceClassification(snake_case_ )
          model.to(snake_case_ )
          model.eval()
          UpperCAmelCase          =	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     _UpperCamelCase							(			self     ,A     ,A     ,A     ,A     ,A     ,A     ,A ):
          UpperCAmelCase          =	self.num_labels
          UpperCAmelCase          =	AlbertForTokenClassification(config=snake_case_ )
          model.to(snake_case_ )
          model.eval()
          UpperCAmelCase          =	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     _UpperCamelCase							(			self     ,A     ,A     ,A     ,A     ,A     ,A     ,A ):
          UpperCAmelCase          =	self.num_choices
          UpperCAmelCase          =	AlbertForMultipleChoice(config=snake_case_ )
          model.to(snake_case_ )
          model.eval()
          UpperCAmelCase          =	input_ids.unsqueeze(1 ).expand(-1     ,self.num_choices     ,-1 ).contiguous()
          UpperCAmelCase          =	token_type_ids.unsqueeze(1 ).expand(-1     ,self.num_choices     ,-1 ).contiguous()
          UpperCAmelCase          =	input_mask.unsqueeze(1 ).expand(-1     ,self.num_choices     ,-1 ).contiguous()
          UpperCAmelCase          =	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     _UpperCamelCase							(			self ):
          UpperCAmelCase          =	self.prepare_config_and_inputs()
          (
              UpperCAmelCase    
          )          =	config_and_inputs
          UpperCAmelCase          =	{"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
          return config, inputs_dict







@require_torch
class  lowerCamelCase__ (	_a   ,					_a   ,					unittest.TestCase			):
    SCREAMING_SNAKE_CASE		=      (
        (
            AlbertModel,
            AlbertForPreTraining,
            AlbertForMaskedLM,
            AlbertForMultipleChoice,
            AlbertForSequenceClassification,
            AlbertForTokenClassification,
            AlbertForQuestionAnswering,
        )
        if is_torch_available()
        else ()
    )
    SCREAMING_SNAKE_CASE		=      (
        {
            '''feature-extraction''': AlbertModel,
            '''fill-mask''': AlbertForMaskedLM,
            '''question-answering''': AlbertForQuestionAnswering,
            '''text-classification''': AlbertForSequenceClassification,
            '''token-classification''': AlbertForTokenClassification,
            '''zero-shot''': AlbertForSequenceClassification,
        }
        if is_torch_available()
        else {}
    )
    SCREAMING_SNAKE_CASE		=      True






    def     _UpperCamelCase							(			self     ,A     ,A     ,A=False ):
          UpperCAmelCase          =	super()._prepare_for_class(snake_case_     ,snake_case_     ,return_labels=snake_case_ )

          if return_labels:
                if model_class in get_values(snake_case_ ):
                      UpperCAmelCase          =	torch.zeros(
                          (self.model_tester.batch_size, self.model_tester.seq_length)     ,dtype=torch.long     ,device=snake_case_ )
                      UpperCAmelCase          =	torch.zeros(
                          self.model_tester.batch_size     ,dtype=torch.long     ,device=snake_case_ )
          return inputs_dict






    def     _UpperCamelCase							(			self ):
          UpperCAmelCase          =	AlbertModelTester(self )
          UpperCAmelCase          =	ConfigTester(self     ,config_class=snake_case_     ,hidden_size=37 )






    def     _UpperCamelCase							(			self ):
          self.config_tester.run_common_tests()






    def     _UpperCamelCase							(			self ):
          UpperCAmelCase          =	self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*snake_case_ )






    def     _UpperCamelCase							(			self ):
          UpperCAmelCase          =	self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_pretraining(*snake_case_ )






    def     _UpperCamelCase							(			self ):
          UpperCAmelCase          =	self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_masked_lm(*snake_case_ )






    def     _UpperCamelCase							(			self ):
          UpperCAmelCase          =	self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_multiple_choice(*snake_case_ )






    def     _UpperCamelCase							(			self ):
          UpperCAmelCase          =	self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_question_answering(*snake_case_ )






    def     _UpperCamelCase							(			self ):
          UpperCAmelCase          =	self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_sequence_classification(*snake_case_ )






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






    @slow
    def     _UpperCamelCase							(			self ):
          for model_name in ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                UpperCAmelCase          =	AlbertModel.from_pretrained(snake_case_ )
                self.assertIsNotNone(snake_case_ )







@require_torch
class  lowerCamelCase__ (	unittest.TestCase			):






    @slow
    def     _UpperCamelCase							(			self ):
          UpperCAmelCase          =	AlbertModel.from_pretrained("""albert-base-v2""" )
          UpperCAmelCase          =	torch.tensor([[0, 345, 232, 328, 740, 140, 1_695, 69, 6_078, 1_588, 2]] )
          UpperCAmelCase          =	torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]] )
          with torch.no_grad():
                UpperCAmelCase          =	model(snake_case_     ,attention_mask=snake_case_ )[0]
          UpperCAmelCase          =	torch.Size((1, 11, 768) )
          self.assertEqual(output.shape     ,snake_case_ )
          UpperCAmelCase          =	torch.tensor(
              [[[-0.6513, 1.5035, -0.2766], [-0.6515, 1.5046, -0.2780], [-0.6512, 1.5049, -0.2784]]] )

          self.assertTrue(torch.allclose(output[:, 1:4, 1:4]     ,snake_case_     ,atol=1e-4 ) )











"""simple docstring"""







import io
import json
import unittest

from parameterized import parameterized

from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu


_UpperCamelCase					      =  get_tests_dir() + """/test_data/fsmt/fsmt_val_data.json"""
with io.open(filename, """r""", encoding="""utf-8""") as f:
  _UpperCamelCase					      =  json.load(f)







@require_torch
class  lowerCamelCase__ (	unittest.TestCase			):






    def     _UpperCamelCase							(			self     ,A ):
          return FSMTTokenizer.from_pretrained(A )






    def     _UpperCamelCase							(			self     ,A ):
          UpperCAmelCase          =	FSMTForConditionalGeneration.from_pretrained(A ).to(A )
          if torch_device == "cuda":
                model.half()
          return model






    @parameterized.expand(
        [
            ["""en-ru""", 26.0],
            ["""ru-en""", 22.0],
            ["""en-de""", 22.0],
            ["""de-en""", 29.0],
        ] )
    @slow
    def     _UpperCamelCase							(			self     ,A     ,A ):
          # note: this test is not testing the best performance since it only evals a small batch
          # but it should be enough to detect a regression in the output quality
          UpperCAmelCase          =	F'''facebook/wmt19-{pair}'''
          UpperCAmelCase          =	self.get_tokenizer(A )
          UpperCAmelCase          =	self.get_model(A )

          UpperCAmelCase          =	bleu_data[pair]["""src"""]
          UpperCAmelCase          =	bleu_data[pair]["""tgt"""]

          UpperCAmelCase          =	tokenizer(A     ,return_tensors="""pt"""     ,truncation=A     ,padding="""longest""" ).to(A )
          UpperCAmelCase          =	model.generate(
              input_ids=batch.input_ids     ,num_beams=8     ,)
          UpperCAmelCase          =	tokenizer.batch_decode(
              A     ,skip_special_tokens=A     ,clean_up_tokenization_spaces=A )
          UpperCAmelCase          =	calculate_bleu(A     ,A )
          print(A )
          self.assertGreaterEqual(scores["""bleu"""]     ,A )



