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        lowercase_  :



	"""simple docstring"""





	def __init__(							self		,      __SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE=2		,      __SCREAMING_SNAKE_CASE=8		,      __SCREAMING_SNAKE_CASE=True		,      __SCREAMING_SNAKE_CASE=False		,      __SCREAMING_SNAKE_CASE=False		,      __SCREAMING_SNAKE_CASE=False		,      __SCREAMING_SNAKE_CASE=False		,      __SCREAMING_SNAKE_CASE=99		,      __SCREAMING_SNAKE_CASE=32		,      __SCREAMING_SNAKE_CASE=3		,      __SCREAMING_SNAKE_CASE=4		,      __SCREAMING_SNAKE_CASE=37		,      __SCREAMING_SNAKE_CASE=32		,      __SCREAMING_SNAKE_CASE=128		,      __SCREAMING_SNAKE_CASE=8		,      __SCREAMING_SNAKE_CASE=8		,      __SCREAMING_SNAKE_CASE=8		,      __SCREAMING_SNAKE_CASE=1.0		,      __SCREAMING_SNAKE_CASE=True		,      )						->List[Any]:
								lowerCAmelCase     		=		parent
								lowerCAmelCase     		=		batch_size
								lowerCAmelCase     		=		seq_length
								lowerCAmelCase     		=		is_training
								lowerCAmelCase     		=		use_token_type_ids
								lowerCAmelCase     		=		use_input_mask
								lowerCAmelCase     		=		use_labels
								lowerCAmelCase     		=		use_mc_token_ids
								lowerCAmelCase     		=		vocab_size
								lowerCAmelCase     		=		hidden_size
								lowerCAmelCase     		=		num_hidden_layers
								lowerCAmelCase     		=		num_attention_heads
								lowerCAmelCase     		=		intermediate_size
								lowerCAmelCase     		=		num_buckets
								lowerCAmelCase     		=		max_distance
								lowerCAmelCase     		=		prompt_length
								lowerCAmelCase     		=		prompt_types
								lowerCAmelCase     		=		segment_types
								lowerCAmelCase     		=		init_std
								lowerCAmelCase     		=		return_dict


	def       SCREAMING_SNAKE_CASE_    (							self    )						->Tuple:
								lowerCAmelCase     		=		{}
								lowerCAmelCase     		=		ids_tensor([self.batch_size, self.seq_length]		,      self.vocab_size    ).type(torch.intaa    )
								lowerCAmelCase     		=		False

								lowerCAmelCase     		=		self.get_config()

								return (config, input_ids)


	def       SCREAMING_SNAKE_CASE_    (							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=__SCREAMING_SNAKE_CASE		,      init_std=self.init_std		,      return_dict=self.return_dict		,      )


	def       SCREAMING_SNAKE_CASE_    (							self		,      __SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE		,      *__SCREAMING_SNAKE_CASE    )						->Any:
								lowerCAmelCase     		=		CpmAntModel(config=__SCREAMING_SNAKE_CASE    )
								model.to(__SCREAMING_SNAKE_CASE    )
								model.eval()

								lowerCAmelCase     		=		model(**__SCREAMING_SNAKE_CASE    ).last_hidden_state

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


	def       SCREAMING_SNAKE_CASE_    (							self		,      __SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE		,      *__SCREAMING_SNAKE_CASE    )						->Tuple:
								lowerCAmelCase     		=		CpmAntForCausalLM(__SCREAMING_SNAKE_CASE    )
								model.to(__SCREAMING_SNAKE_CASE    )
								lowerCAmelCase     		=		input_ids['''input_ids'''].to(__SCREAMING_SNAKE_CASE    )
								model.eval()

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




	def       SCREAMING_SNAKE_CASE_    (							self    )						->str:
								lowerCAmelCase		,				lowerCAmelCase     		=		self.prepare_config_and_inputs()
								return config, inputs_dict




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



	"""simple docstring"""





	UpperCAmelCase_  : Dict       =    (CpmAntModel, CpmAntForCausalLM) if is_torch_available() else ()
	UpperCAmelCase_  : List[str]       =    (
	    {"""feature-extraction""": CpmAntModel, """text-generation""": CpmAntForCausalLM} if is_torch_available() else {}
	)

	UpperCAmelCase_  : Optional[int]       =    False
	UpperCAmelCase_  : Dict       =    False
	UpperCAmelCase_  : Union[str, Any]       =    False
	UpperCAmelCase_  : List[Any]       =    False
	UpperCAmelCase_  : Optional[int]       =    False


	def       SCREAMING_SNAKE_CASE_    (							self    )						->Tuple:
								lowerCAmelCase     		=		CpmAntModelTester(self    )
								lowerCAmelCase     		=		ConfigTester(self		,      config_class=__SCREAMING_SNAKE_CASE    )


	def       SCREAMING_SNAKE_CASE_    (							self    )						->Any:
								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       SCREAMING_SNAKE_CASE_    (							self    )						->Optional[Any]:
								unittest.skip('''CPMAnt doesn\'t support input_embeds.'''    )(self.test_inputs_embeds    )


	def       SCREAMING_SNAKE_CASE_    (							self    )						->Optional[int]:
								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       SCREAMING_SNAKE_CASE_    (							self    )						->Optional[Any]:
								lowerCAmelCase		,				lowerCAmelCase     		=		self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_cpmant_model(__SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE    )




	def       SCREAMING_SNAKE_CASE_    (							self    )						->Any:
								lowerCAmelCase		,				lowerCAmelCase     		=		self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_lm_head_model(__SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE    )




@require_torch
class        lowercase_  (  unittest.TestCase						):



	"""simple docstring"""





	@tooslow
	def       SCREAMING_SNAKE_CASE_    (							self    )						->List[Any]:
								lowerCAmelCase     		=		'''今天天气真好！'''
								lowerCAmelCase     		=		'''openbmb/cpm-ant-10b'''
								lowerCAmelCase     		=		CpmAntModel.from_pretrained(__SCREAMING_SNAKE_CASE    )
								lowerCAmelCase     		=		CpmAntTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE    )
								lowerCAmelCase     		=		tokenizer(__SCREAMING_SNAKE_CASE		,      return_tensors='''pt'''    )
								lowerCAmelCase     		=		model(**__SCREAMING_SNAKE_CASE    ).last_hidden_state

								lowerCAmelCase     		=		torch.tensor(
								    [[[6.1_7_0_8, 5.9_2_4_4, 1.0_8_3_5], [6.5_2_0_7, 6.2_8_9_3, -1_1.3_3_2_4], [-1.0_1_0_7, -0.0_5_7_6, -5.9_5_7_7]]]		,      )
								self.assertTrue(torch.allclose(hidden_states[:, :3, :3]		,      __SCREAMING_SNAKE_CASE		,      atol=1e-2    )    )




@require_torch
class        lowercase_  (  unittest.TestCase						):



	"""simple docstring"""





	@tooslow
	def       SCREAMING_SNAKE_CASE_    (							self    )						->List[Any]:
								lowerCAmelCase     		=		'''今天天气真好！'''
								lowerCAmelCase     		=		'''openbmb/cpm-ant-10b'''
								lowerCAmelCase     		=		CpmAntForCausalLM.from_pretrained(__SCREAMING_SNAKE_CASE    )
								lowerCAmelCase     		=		CpmAntTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE    )
								lowerCAmelCase     		=		tokenizer(__SCREAMING_SNAKE_CASE		,      return_tensors='''pt'''    )
								lowerCAmelCase     		=		model(**__SCREAMING_SNAKE_CASE    ).logits

								lowerCAmelCase     		=		torch.tensor(
								    [[[-6.4_2_6_7, -6.4_0_8_3, -6.3_9_5_8], [-5.8_8_0_2, -5.9_4_4_7, -5.7_8_1_1], [-5.3_8_9_6, -5.4_8_2_0, -5.4_2_9_5]]]		,      )
								self.assertTrue(torch.allclose(hidden_states[:, :3, :3]		,      __SCREAMING_SNAKE_CASE		,      atol=1e-2    )    )


	@tooslow
	def       SCREAMING_SNAKE_CASE_    (							self    )						->List[str]:
								lowerCAmelCase     		=		'''openbmb/cpm-ant-10b'''
								lowerCAmelCase     		=		CpmAntForCausalLM.from_pretrained(__SCREAMING_SNAKE_CASE    )
								lowerCAmelCase     		=		CpmAntTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE    )
								lowerCAmelCase     		=		'''今天天气不错，'''
								lowerCAmelCase     		=		'''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'''
								lowerCAmelCase     		=		tokenizer(__SCREAMING_SNAKE_CASE		,      return_tensors='''pt'''    )
								lowerCAmelCase     		=		model.generate(**__SCREAMING_SNAKE_CASE    )
								lowerCAmelCase     		=		tokenizer.batch_decode(__SCREAMING_SNAKE_CASE    )
								self.assertEqual(__SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE    )




	@tooslow
	def       SCREAMING_SNAKE_CASE_    (							self    )						->List[Any]:
								lowerCAmelCase     		=		'''openbmb/cpm-ant-10b'''
								lowerCAmelCase     		=		CpmAntForCausalLM.from_pretrained(__SCREAMING_SNAKE_CASE    )
								lowerCAmelCase     		=		CpmAntTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE    )
								lowerCAmelCase     		=		['''今天天气不错，''', '''新年快乐，万事如意！''']
								lowerCAmelCase     		=		[
								    '''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''',
								]
								lowerCAmelCase     		=		tokenizer(__SCREAMING_SNAKE_CASE		,      return_tensors='''pt'''		,      padding=__SCREAMING_SNAKE_CASE    )
								lowerCAmelCase     		=		model.generate(**__SCREAMING_SNAKE_CASE    )
								lowerCAmelCase     		=		tokenizer.batch_decode(__SCREAMING_SNAKE_CASE    )
								self.assertEqual(__SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE    )




from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier

# Load iris file
lowercase__      :    int          =     load_iris()
iris.keys()


print(f'Target names: \n {iris.target_names} ')
print(f'\n Features: \n {iris.feature_names}')

# Train set e Test set
lowercase__     ,   lowercase__     ,   lowercase__     ,   lowercase__      :    Any          =     train_test_split(
    iris['''data'''], iris['''target'''], random_state=4
)

# KNN

lowercase__      :    int          =     KNeighborsClassifier(n_neighbors=1)
knn.fit(X_train, y_train)

# new array to test
lowercase__      :    List[str]          =     [[1, 2, 1, 4], [2, 3, 4, 5]]

lowercase__      :    int          =     knn.predict(X_new)

print(
    f'\nNew array: \n {X_new}\n\nTarget Names Prediction: \n'
    f' {iris["target_names"][prediction]}'
)



