



'''simple docstring'''



import unittest

from transformers.testing_utils import is_torch_available, require_torch, tooslow

from ...generation.test_utils import torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
 import torch

 from transformers import (
     CpmAntConfig,
     CpmAntForCausalLM,
     CpmAntModel,
     CpmAntTokenizer,
 )
@require_torch
class        UpperCAmelCase__  :



 def __init__(  self		,	lowercase		,	lowercase=2		,	lowercase=8		,	lowercase=True		,	lowercase=False		,	lowercase=False		,	lowercase=False		,	lowercase=False		,	lowercase=9_9		,	lowercase=3_2		,	lowercase=3		,	lowercase=4		,	lowercase=3_7		,	lowercase=3_2		,	lowercase=1_2_8		,	lowercase=8		,	lowercase=8		,	lowercase=8		,	lowercase=1.0		,	lowercase=True		,	)  ->							str:
  __UpperCamelCase				      =  parent
  __UpperCamelCase				      =  batch_size
  __UpperCamelCase				      =  seq_length
  __UpperCamelCase				      =  is_training
  __UpperCamelCase				      =  use_token_type_ids
  __UpperCamelCase				      =  use_input_mask
  __UpperCamelCase				      =  use_labels
  __UpperCamelCase				      =  use_mc_token_ids
  __UpperCamelCase				      =  vocab_size
  __UpperCamelCase				      =  hidden_size
  __UpperCamelCase				      =  num_hidden_layers
  __UpperCamelCase				      =  num_attention_heads
  __UpperCamelCase				      =  intermediate_size
  __UpperCamelCase				      =  num_buckets
  __UpperCamelCase				      =  max_distance
  __UpperCamelCase				      =  prompt_length
  __UpperCamelCase				      =  prompt_types
  __UpperCamelCase				      =  segment_types
  __UpperCamelCase				      =  init_std
  __UpperCamelCase				      =  return_dict



 def    __lowerCamelCase				(  self			)  ->							Optional[int]:
  __UpperCamelCase				      =  {}
  __UpperCamelCase				      =  ids_tensor([self.batch_size, self.seq_length]		,	self.vocab_size			).type(torch.intaa			)
  __UpperCamelCase				      =  False

  __UpperCamelCase				      =  self.get_config()

  return (config, input_ids)



 def    __lowerCamelCase				(  self			)  ->							Optional[int]:
  return CpmAntConfig(
      vocab_size=self.vocab_size		,	hidden_size=self.hidden_size		,	num_hidden_layers=self.num_hidden_layers		,	num_attention_heads=self.num_attention_heads		,	dim_ff=self.intermediate_size		,	position_bias_num_buckets=self.num_buckets		,	position_bias_max_distance=self.max_distance		,	prompt_types=self.prompt_types		,	prompt_length=self.prompt_length		,	segment_types=self.segment_types		,	use_cache=lowercase		,	init_std=self.init_std		,	return_dict=self.return_dict		,	)



 def    __lowerCamelCase				(  self		,	lowercase		,	lowercase		,	*lowercase			)  ->							Any:
  __UpperCamelCase				      =  CpmAntModel(config=lowercase			)
  model.to(lowercase			)
  model.eval()

  __UpperCamelCase				      =  model(**lowercase			).last_hidden_state

  self.parent.assertEqual(hidden_states.shape		,	(self.batch_size, self.seq_length, config.hidden_size)			)



 def    __lowerCamelCase				(  self		,	lowercase		,	lowercase		,	*lowercase			)  ->							Union[str, Any]:
  __UpperCamelCase				      =  CpmAntForCausalLM(lowercase			)
  model.to(lowercase			)
  __UpperCamelCase				      =  input_ids["""input_ids"""].to(lowercase			)
  model.eval()

  __UpperCamelCase				      =  model(**lowercase			)
  self.parent.assertEqual(
      model_output.logits.shape		,	(self.batch_size, self.seq_length, config.vocab_size + config.prompt_types * config.prompt_length)		,	)




 def    __lowerCamelCase				(  self			)  ->							Optional[Any]:
  __UpperCamelCase      ,		__UpperCamelCase				      =  self.prepare_config_and_inputs()
  return config, inputs_dict


@require_torch
class        UpperCAmelCase__  (    UpperCAmelCase_							,			UpperCAmelCase_							,			unittest.TestCase):
 __SCREAMING_SNAKE_CASE					      =				(CpmAntModel, CpmAntForCausalLM) if is_torch_available() else ()
 __SCREAMING_SNAKE_CASE					      =				(
     {'''feature-extraction''': CpmAntModel, '''text-generation''': CpmAntForCausalLM} if is_torch_available() else {}
 )

 __SCREAMING_SNAKE_CASE					      =				False
 __SCREAMING_SNAKE_CASE					      =				False
 __SCREAMING_SNAKE_CASE					      =				False
 __SCREAMING_SNAKE_CASE					      =				False
 __SCREAMING_SNAKE_CASE					      =				False



 def    __lowerCamelCase				(  self			)  ->							List[Any]:
  __UpperCamelCase				      =  CpmAntModelTester(self			)
  __UpperCamelCase				      =  ConfigTester(self		,	config_class=lowercase			)



 def    __lowerCamelCase				(  self			)  ->							Tuple:
  self.config_tester.create_and_test_config_common_properties()
  self.config_tester.create_and_test_config_to_json_string()
  self.config_tester.create_and_test_config_to_json_file()
  self.config_tester.create_and_test_config_from_and_save_pretrained()
  self.config_tester.check_config_can_be_init_without_params()
  self.config_tester.check_config_arguments_init()



 def    __lowerCamelCase				(  self			)  ->							Optional[int]:
  unittest.skip("""CPMAnt doesn't support input_embeds."""			)(self.test_inputs_embeds			)



 def    __lowerCamelCase				(  self			)  ->							Tuple:
  unittest.skip(
      """CPMAnt doesn't support retain grad in hidden_states or attentions, because prompt management will peel off the output.hidden_states from graph.\
                 So is attentions. We strongly recommand you use loss to tune model."""			)(self.test_retain_grad_hidden_states_attentions			)



 def    __lowerCamelCase				(  self			)  ->							Optional[int]:
  __UpperCamelCase      ,		__UpperCamelCase				      =  self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_cpmant_model(lowercase		,	lowercase			)




 def    __lowerCamelCase				(  self			)  ->							Dict:
  __UpperCamelCase      ,		__UpperCamelCase				      =  self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_lm_head_model(lowercase		,	lowercase			)


@require_torch
class        UpperCAmelCase__  (    unittest.TestCase):



 @tooslow
 def    __lowerCamelCase				(  self			)  ->							Optional[Any]:
  __UpperCamelCase				      =  """今天天气真好！"""
  __UpperCamelCase				      =  """openbmb/cpm-ant-10b"""
  __UpperCamelCase				      =  CpmAntModel.from_pretrained(lowercase			)
  __UpperCamelCase				      =  CpmAntTokenizer.from_pretrained(lowercase			)
  __UpperCamelCase				      =  tokenizer(lowercase		,	return_tensors="""pt"""			)
  __UpperCamelCase				      =  model(**lowercase			).last_hidden_state

  __UpperCamelCase				      =  torch.tensor(
      [[[6.1_708, 5.9_244, 1.0_835], [6.5_207, 6.2_893, -11.3_324], [-1.0_107, -0.0_576, -5.9_577]]]		,	)
  self.assertTrue(torch.allclose(hidden_states[:, :3, :3]		,	lowercase		,	atol=1E-2			)			)


@require_torch
class        UpperCAmelCase__  (    unittest.TestCase):



 @tooslow
 def    __lowerCamelCase				(  self			)  ->							Any:
  __UpperCamelCase				      =  """今天天气真好！"""
  __UpperCamelCase				      =  """openbmb/cpm-ant-10b"""
  __UpperCamelCase				      =  CpmAntForCausalLM.from_pretrained(lowercase			)
  __UpperCamelCase				      =  CpmAntTokenizer.from_pretrained(lowercase			)
  __UpperCamelCase				      =  tokenizer(lowercase		,	return_tensors="""pt"""			)
  __UpperCamelCase				      =  model(**lowercase			).logits

  __UpperCamelCase				      =  torch.tensor(
      [[[-6.4_267, -6.4_083, -6.3_958], [-5.8_802, -5.9_447, -5.7_811], [-5.3_896, -5.4_820, -5.4_295]]]		,	)
  self.assertTrue(torch.allclose(hidden_states[:, :3, :3]		,	lowercase		,	atol=1E-2			)			)



 @tooslow
 def    __lowerCamelCase				(  self			)  ->							int:
  __UpperCamelCase				      =  """openbmb/cpm-ant-10b"""
  __UpperCamelCase				      =  CpmAntForCausalLM.from_pretrained(lowercase			)
  __UpperCamelCase				      =  CpmAntTokenizer.from_pretrained(lowercase			)
  __UpperCamelCase				      =  """今天天气不错，"""
  __UpperCamelCase				      =  """今天天气不错，阳光明媚，我和妈妈一起去超市买东西。\n在超市里，我看到了一个很好玩的玩具，它的名字叫“机器人”。它有一个圆圆的脑袋，两只圆圆的眼睛，还有一个圆圆的"""
  __UpperCamelCase				      =  tokenizer(lowercase		,	return_tensors="""pt"""			)
  __UpperCamelCase				      =  model.generate(**lowercase			)
  __UpperCamelCase				      =  tokenizer.batch_decode(lowercase			)
  self.assertEqual(lowercase		,	lowercase			)




 @tooslow
 def    __lowerCamelCase				(  self			)  ->							Dict:
  __UpperCamelCase				      =  """openbmb/cpm-ant-10b"""
  __UpperCamelCase				      =  CpmAntForCausalLM.from_pretrained(lowercase			)
  __UpperCamelCase				      =  CpmAntTokenizer.from_pretrained(lowercase			)
  __UpperCamelCase				      =  ["""今天天气不错，""", """新年快乐，万事如意！"""]
  __UpperCamelCase				      =  [
      """今天天气不错，阳光明媚，我和妈妈一起去超市买东西。\n在超市里，我看到了一个很好玩的玩具，它的名字叫“机器人”。它有一个圆圆的脑袋，两只圆圆的眼睛，还有一个圆圆的""",
      """新年快乐，万事如意！在这辞旧迎新的美好时刻，我谨代表《农村新技术》杂志社全体同仁，向一直以来关心、支持《农村新技术》杂志发展的各级领导、各界朋友和广大读者致以最诚挚的""",
  ]
  __UpperCamelCase				      =  tokenizer(lowercase		,	return_tensors="""pt"""		,	padding=lowercase			)
  __UpperCamelCase				      =  model.generate(**lowercase			)
  __UpperCamelCase				      =  tokenizer.batch_decode(lowercase			)
  self.assertEqual(lowercase		,	lowercase			)










'''simple docstring'''



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


a__						:			Optional[int]										=			{
    'configuration_bark': [
        'BARK_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'BarkCoarseConfig',
        'BarkConfig',
        'BarkFineConfig',
        'BarkSemanticConfig',
    ],
    'processing_bark': ['BarkProcessor'],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a__						:			Any										=			[
     'BARK_PRETRAINED_MODEL_ARCHIVE_LIST',
     'BarkFineModel',
     'BarkSemanticModel',
     'BarkCoarseModel',
     'BarkModel',
     'BarkPreTrainedModel',
     'BarkCausalModel',
 ]

if TYPE_CHECKING:
 from .configuration_bark import (
     BARK_PRETRAINED_CONFIG_ARCHIVE_MAP,
     BarkCoarseConfig,
     BarkConfig,
     BarkFineConfig,
     BarkSemanticConfig,
 )
 from .processing_bark import BarkProcessor

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_bark import (
      BARK_PRETRAINED_MODEL_ARCHIVE_LIST,
      BarkCausalModel,
      BarkCoarseModel,
      BarkFineModel,
      BarkModel,
      BarkPreTrainedModel,
      BarkSemanticModel,
  )

else:
 import sys

 a__						:			Tuple										=			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





