

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     lowercase__  :






  def __init__(					self      :					List[Any]		,						_lowercase      :					Optional[Any]		,						_lowercase      :					Optional[Any]=13		,						_lowercase      :					List[str]=7		,						_lowercase      :					str=True		,						_lowercase      :					Dict=True		,						_lowercase      :					int=True		,						_lowercase      :					Tuple=True		,						_lowercase      :					int=99		,						_lowercase      :					Union[str, Any]=16		,						_lowercase      :					Tuple=36		,						_lowercase      :					Tuple=6		,						_lowercase      :					Any=6		,						_lowercase      :					List[Any]=6		,						_lowercase      :					Optional[Any]=37		,						_lowercase      :					Tuple="gelu"		,						_lowercase      :					List[str]=0.1		,						_lowercase      :					Any=0.1		,						_lowercase      :					Optional[int]=5_12		,						_lowercase      :					Tuple=16		,						_lowercase      :					Any=2		,						_lowercase      :					Dict=0.0_2		,						_lowercase      :					Union[str, Any]=3		,						_lowercase      :					Optional[Any]=4		,						_lowercase      :					List[Any]=None		,						):




         """simple docstring"""







         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      :					Any ):




         """simple docstring"""







         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      :					Optional[Any] ):




         """simple docstring"""







         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      :					Any		,						_lowercase      :					Union[str, Any]		,						_lowercase      :					List[str]		,						_lowercase      :					int		,						_lowercase      :					Any		,						_lowercase      :					str		,						_lowercase      :					List[Any]		,						_lowercase      :					List[str] ):




         """simple docstring"""







         UpperCAmelCase__										=     AlbertModel(config=_lowercase )
         model.to(_lowercase )
         model.eval()
         UpperCAmelCase__										=     model(_lowercase		,						attention_mask=_lowercase		,						token_type_ids=_lowercase )
         UpperCAmelCase__										=     model(_lowercase		,						token_type_ids=_lowercase )
         UpperCAmelCase__										=     model(_lowercase )
         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      :					int		,						_lowercase      :					Optional[Any]		,						_lowercase      :					Dict		,						_lowercase      :					List[str]		,						_lowercase      :					List[Any]		,						_lowercase      :					Any		,						_lowercase      :					Optional[int]		,						_lowercase      :					List[Any] ):




         """simple docstring"""







         UpperCAmelCase__										=     AlbertForPreTraining(config=_lowercase )
         model.to(_lowercase )
         model.eval()
         UpperCAmelCase__										=     model(
             _lowercase		,						attention_mask=_lowercase		,						token_type_ids=_lowercase		,						labels=_lowercase		,						sentence_order_label=_lowercase		,						)
         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      :					Optional[int]		,						_lowercase      :					int		,						_lowercase      :					List[str]		,						_lowercase      :					Union[str, Any]		,						_lowercase      :					int		,						_lowercase      :					Dict		,						_lowercase      :					Tuple		,						_lowercase      :					Optional[Any] ):




         """simple docstring"""







         UpperCAmelCase__										=     AlbertForMaskedLM(config=_lowercase )
         model.to(_lowercase )
         model.eval()
         UpperCAmelCase__										=     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 			_UpperCAmelCase  (					self      :					Any		,						_lowercase      :					Any		,						_lowercase      :					int		,						_lowercase      :					Tuple		,						_lowercase      :					Tuple		,						_lowercase      :					List[Any]		,						_lowercase      :					List[str]		,						_lowercase      :					List[str] ):




         """simple docstring"""







         UpperCAmelCase__										=     AlbertForQuestionAnswering(config=_lowercase )
         model.to(_lowercase )
         model.eval()
         UpperCAmelCase__										=     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 			_UpperCAmelCase  (					self      :					Any		,						_lowercase      :					str		,						_lowercase      :					Optional[Any]		,						_lowercase      :					Tuple		,						_lowercase      :					Tuple		,						_lowercase      :					Any		,						_lowercase      :					str		,						_lowercase      :					int ):




         """simple docstring"""







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






  def 			_UpperCAmelCase  (					self      :					Dict		,						_lowercase      :					Union[str, Any]		,						_lowercase      :					Union[str, Any]		,						_lowercase      :					Optional[Any]		,						_lowercase      :					Union[str, Any]		,						_lowercase      :					Union[str, Any]		,						_lowercase      :					Any		,						_lowercase      :					int ):




         """simple docstring"""







         UpperCAmelCase__										=     self.num_labels
         UpperCAmelCase__										=     AlbertForTokenClassification(config=_lowercase )
         model.to(_lowercase )
         model.eval()
         UpperCAmelCase__										=     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 			_UpperCAmelCase  (					self      :					Tuple		,						_lowercase      :					List[str]		,						_lowercase      :					Any		,						_lowercase      :					int		,						_lowercase      :					Optional[int]		,						_lowercase      :					Tuple		,						_lowercase      :					Optional[Any]		,						_lowercase      :					Tuple ):




         """simple docstring"""







         UpperCAmelCase__										=     self.num_choices
         UpperCAmelCase__										=     AlbertForMultipleChoice(config=_lowercase )
         model.to(_lowercase )
         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(
             _lowercase		,						attention_mask=_lowercase		,						token_type_ids=_lowercase		,						labels=_lowercase		,						)
         self.parent.assertEqual(result.logits.shape		,						(self.batch_size, self.num_choices) )






  def 			_UpperCAmelCase  (					self      :					List[Any] ):




         """simple docstring"""







         UpperCAmelCase__										=     self.prepare_config_and_inputs()
         (
             (
             UpperCAmelCase__			
         )						,			(
             UpperCAmelCase__			
         )						,			(
             UpperCAmelCase__			
         )						,			(
             UpperCAmelCase__			
         )						,			(
             UpperCAmelCase__			
         )						,			(
             UpperCAmelCase__			
         )						,			(
             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     lowercase__  (					__SCREAMING_SNAKE_CASE					,							__SCREAMING_SNAKE_CASE					,							unittest.TestCase		):
  A__=  (
      (
          AlbertModel,
          AlbertForPreTraining,
          AlbertForMaskedLM,
          AlbertForMultipleChoice,
          AlbertForSequenceClassification,
          AlbertForTokenClassification,
          AlbertForQuestionAnswering,
      )
      if is_torch_available()
      else ()
  )
  A__=  (
      {
          'feature-extraction': AlbertModel,
          'fill-mask': AlbertForMaskedLM,
          'question-answering': AlbertForQuestionAnswering,
          'text-classification': AlbertForSequenceClassification,
          'token-classification': AlbertForTokenClassification,
          'zero-shot': AlbertForSequenceClassification,
      }
      if is_torch_available()
      else {}
  )
  A__=  True






  def 			_UpperCAmelCase  (					self      :					Optional[int]		,						_lowercase      :					List[str]		,						_lowercase      :					Any		,						_lowercase      :					int=False ):




         """simple docstring"""







         UpperCAmelCase__										=     super()._prepare_for_class(_lowercase		,						_lowercase		,						return_labels=_lowercase )

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






  def 			_UpperCAmelCase  (					self      :					List[Any] ):




         """simple docstring"""







         UpperCAmelCase__										=     AlbertModelTester(self )
         UpperCAmelCase__										=     ConfigTester(self		,						config_class=_lowercase		,						hidden_size=37 )






  def 			_UpperCAmelCase  (					self      :					Any ):




         """simple docstring"""







         self.config_tester.run_common_tests()






  def 			_UpperCAmelCase  (					self      :					Optional[Any] ):




         """simple docstring"""







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






  def 			_UpperCAmelCase  (					self      :					Optional[Any] ):




         """simple docstring"""







         UpperCAmelCase__										=     self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_pretraining(*_lowercase )






  def 			_UpperCAmelCase  (					self      :					int ):




         """simple docstring"""







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






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




         """simple docstring"""







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






  def 			_UpperCAmelCase  (					self      :					List[str] ):




         """simple docstring"""







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






  def 			_UpperCAmelCase  (					self      :					int ):




         """simple docstring"""







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






  def 			_UpperCAmelCase  (					self      :					Tuple ):




         """simple docstring"""







         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(*_lowercase )






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




         """simple docstring"""







         for model_name in ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                UpperCAmelCase__										=     AlbertModel.from_pretrained(_lowercase )
                self.assertIsNotNone(_lowercase )




@require_torch
class     lowercase__  (					unittest.TestCase		):






  @slow
  def 			_UpperCAmelCase  (					self      :					str ):




         """simple docstring"""







         UpperCAmelCase__										=     AlbertModel.from_pretrained("albert-base-v2" )
         UpperCAmelCase__										=     torch.tensor([[0, 3_45, 2_32, 3_28, 7_40, 1_40, 16_95, 69, 60_78, 15_88, 2]] )
         UpperCAmelCase__										=     torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]] )
         with torch.no_grad():
                UpperCAmelCase__										=     model(_lowercase		,						attention_mask=_lowercase )[0]
         UpperCAmelCase__										=     torch.Size((1, 11, 7_68) )
         self.assertEqual(output.shape		,						_lowercase )
         UpperCAmelCase__										=     torch.tensor(
             [[[-0.6_5_1_3, 1.5_0_3_5, -0.2_7_6_6], [-0.6_5_1_5, 1.5_0_4_6, -0.2_7_8_0], [-0.6_5_1_2, 1.5_0_4_9, -0.2_7_8_4]]] )

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









import os
import shutil
import tempfile
import unittest

import numpy as np

from transformers import AutoTokenizer, BarkProcessor
from transformers.testing_utils import require_torch, slow


@require_torch
class     lowercase__  (					unittest.TestCase		):






  def 			_UpperCAmelCase  (					self      :					Any ):




         """simple docstring"""







         UpperCAmelCase__										=     "ylacombe/bark-small"
         UpperCAmelCase__										=     tempfile.mkdtemp()
         UpperCAmelCase__										=     "en_speaker_1"
         UpperCAmelCase__										=     "This is a test string"
         UpperCAmelCase__										=     "speaker_embeddings_path.json"
         UpperCAmelCase__										=     "speaker_embeddings"






  def 			_UpperCAmelCase  (					self      :					List[str]		,						**_lowercase      :					str ):




         """simple docstring"""







         return AutoTokenizer.from_pretrained(self.checkpoint		,						**_lowercase )






  def 			_UpperCAmelCase  (					self      :					str ):




         """simple docstring"""







         shutil.rmtree(self.tmpdirname )






  def 			_UpperCAmelCase  (					self      :					List[Any] ):




         """simple docstring"""







         UpperCAmelCase__										=     self.get_tokenizer()

         UpperCAmelCase__										=     BarkProcessor(tokenizer=_lowercase )

         processor.save_pretrained(self.tmpdirname )
         UpperCAmelCase__										=     BarkProcessor.from_pretrained(self.tmpdirname )

         self.assertEqual(processor.tokenizer.get_vocab()		,						tokenizer.get_vocab() )






  @slow
  def 			_UpperCAmelCase  (					self      :					str ):




         """simple docstring"""







         UpperCAmelCase__										=     BarkProcessor.from_pretrained(
             pretrained_processor_name_or_path=self.checkpoint		,						speaker_embeddings_dict_path=self.speaker_embeddings_dict_path		,						)
         processor.save_pretrained(
             self.tmpdirname		,						speaker_embeddings_dict_path=self.speaker_embeddings_dict_path		,						speaker_embeddings_directory=self.speaker_embeddings_directory		,						)

         UpperCAmelCase__										=     self.get_tokenizer(bos_token="(BOS)"		,						eos_token="(EOS)" )

         UpperCAmelCase__										=     BarkProcessor.from_pretrained(
             self.tmpdirname		,						self.speaker_embeddings_dict_path		,						bos_token="(BOS)"		,						eos_token="(EOS)"		,						)

         self.assertEqual(processor.tokenizer.get_vocab()		,						tokenizer_add_kwargs.get_vocab() )






  def 			_UpperCAmelCase  (					self      :					Optional[Any] ):




         """simple docstring"""







         UpperCAmelCase__										=     BarkProcessor.from_pretrained(
             pretrained_processor_name_or_path=self.checkpoint		,						speaker_embeddings_dict_path=self.speaker_embeddings_dict_path		,						)

         UpperCAmelCase__										=     35
         UpperCAmelCase__										=     2
         UpperCAmelCase__										=     8

         UpperCAmelCase__										=     {
             "semantic_prompt": np.ones(_lowercase ),
             "coarse_prompt": np.ones((nb_codebooks_coarse, seq_len) ),
             "fine_prompt": np.ones((nb_codebooks_total, seq_len) ),
         }

         # test providing already loaded voice_preset
         UpperCAmelCase__										=     processor(text=self.input_string		,						voice_preset=_lowercase )

         UpperCAmelCase__										=     inputs["history_prompt"]
         for key in voice_preset:
                self.assertListEqual(voice_preset[key].tolist()		,						processed_voice_preset.get(_lowercase		,						np.array([] ) ).tolist() )

         # test loading voice preset from npz file
         UpperCAmelCase__										=     os.path.join(self.tmpdirname		,						"file.npz" )
         np.savez(_lowercase		,						**_lowercase )
         UpperCAmelCase__										=     processor(text=self.input_string		,						voice_preset=_lowercase )
         UpperCAmelCase__										=     inputs["history_prompt"]

         for key in voice_preset:
                self.assertListEqual(voice_preset[key].tolist()		,						processed_voice_preset.get(_lowercase		,						np.array([] ) ).tolist() )

         # test loading voice preset from the hub
         UpperCAmelCase__										=     processor(text=self.input_string		,						voice_preset=self.voice_preset )






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




         """simple docstring"""







         UpperCAmelCase__										=     self.get_tokenizer()

         UpperCAmelCase__										=     BarkProcessor(tokenizer=_lowercase )

         UpperCAmelCase__										=     processor(text=self.input_string )

         UpperCAmelCase__										=     tokenizer(
             self.input_string		,						padding="max_length"		,						max_length=2_56		,						add_special_tokens=_lowercase		,						return_attention_mask=_lowercase		,						return_token_type_ids=_lowercase		,						)

         for key in encoded_tok.keys():
                self.assertListEqual(encoded_tok[key]		,						encoded_processor[key].squeeze().tolist() )





