






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        SCREAMING_SNAKE_CASE	:
     '''simple docstring'''


     def __init__( self				:		Optional[int]    ,       UpperCAmelCase_				:		Tuple    ,       UpperCAmelCase_				:		Tuple=2    ,       UpperCAmelCase_				:		Optional[Any]=8    ,       UpperCAmelCase_				:		List[str]=True    ,       UpperCAmelCase_				:		Any=False    ,       UpperCAmelCase_				:		List[Any]=False    ,       UpperCAmelCase_				:		Dict=False    ,       UpperCAmelCase_				:		Optional[int]=False    ,       UpperCAmelCase_				:		Any=99    ,       UpperCAmelCase_				:		Tuple=32    ,       UpperCAmelCase_				:		Any=3    ,       UpperCAmelCase_				:		List[str]=4    ,       UpperCAmelCase_				:		Optional[Any]=37    ,       UpperCAmelCase_				:		Optional[Any]=32    ,       UpperCAmelCase_				:		Optional[Any]=128    ,       UpperCAmelCase_				:		Union[str, Any]=8    ,       UpperCAmelCase_				:		str=8    ,       UpperCAmelCase_				:		Any=8    ,       UpperCAmelCase_				:		Optional[int]=1.0    ,       UpperCAmelCase_				:		List[str]=True    ,       ):
       SCREAMING_SNAKE_CASE				:					Any		  =			parent
       SCREAMING_SNAKE_CASE				:					Optional[int]		  =			batch_size
       SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			seq_length
       SCREAMING_SNAKE_CASE				:					int		  =			is_training
       SCREAMING_SNAKE_CASE				:					Any		  =			use_token_type_ids
       SCREAMING_SNAKE_CASE				:					List[str]		  =			use_input_mask
       SCREAMING_SNAKE_CASE				:					Tuple		  =			use_labels
       SCREAMING_SNAKE_CASE				:					Any		  =			use_mc_token_ids
       SCREAMING_SNAKE_CASE				:					Tuple		  =			vocab_size
       SCREAMING_SNAKE_CASE				:					Tuple		  =			hidden_size
       SCREAMING_SNAKE_CASE				:					Tuple		  =			num_hidden_layers
       SCREAMING_SNAKE_CASE				:					int		  =			num_attention_heads
       SCREAMING_SNAKE_CASE				:					Tuple		  =			intermediate_size
       SCREAMING_SNAKE_CASE				:					Optional[int]		  =			num_buckets
       SCREAMING_SNAKE_CASE				:					Dict		  =			max_distance
       SCREAMING_SNAKE_CASE				:					int		  =			prompt_length
       SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			prompt_types
       SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			segment_types
       SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			init_std
       SCREAMING_SNAKE_CASE				:					Dict		  =			return_dict


     def  _A				( self				:		Optional[int]					):
       SCREAMING_SNAKE_CASE				:					List[Any]		  =			{}
       SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			ids_tensor([self.batch_size, self.seq_length]    ,       self.vocab_size					).type(torch.intaa					)
       SCREAMING_SNAKE_CASE				:					Dict		  =			False

       SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			self.get_config()

       return (config, input_ids)


     def  _A				( self				:		str					):
       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=A_    ,       init_std=self.init_std    ,       return_dict=self.return_dict    ,       )


     def  _A				( self				:		List[str]    ,       UpperCAmelCase_				:		List[Any]    ,       UpperCAmelCase_				:		Any    ,       *UpperCAmelCase_				:		List[Any]					):
       SCREAMING_SNAKE_CASE				:					Tuple		  =			CpmAntModel(config=A_					)
       model.to(A_					)
       model.eval()

       SCREAMING_SNAKE_CASE				:					List[str]		  =			model(**A_					).last_hidden_state

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


     def  _A				( self				:		List[str]    ,       UpperCAmelCase_				:		List[Any]    ,       UpperCAmelCase_				:		List[str]    ,       *UpperCAmelCase_				:		Dict					):
       SCREAMING_SNAKE_CASE				:					List[str]		  =			CpmAntForCausalLM(A_					)
       model.to(A_					)
       SCREAMING_SNAKE_CASE				:					Optional[int]		  =			input_ids["input_ids"].to(A_					)
       model.eval()

       SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			model(**A_					)
       self.parent.assertEqual(
           model_output.logits.shape    ,       (self.batch_size, self.seq_length, config.vocab_size + config.prompt_types * config.prompt_length)    ,       )






     def  _A				( self				:		Optional[Any]					):
       SCREAMING_SNAKE_CASE  ,     SCREAMING_SNAKE_CASE				:					List[str]		  =			self.prepare_config_and_inputs()
       return config, inputs_dict





@require_torch
class        SCREAMING_SNAKE_CASE	(	snake_case_					,						snake_case_					,						unittest.TestCase    ):
     '''simple docstring'''


     UpperCamelCase_							:						Union[str, Any]								=		(CpmAntModel, CpmAntForCausalLM) if is_torch_available() else ()
     UpperCamelCase_							:						Any								=		(
         {'''feature-extraction''': CpmAntModel, '''text-generation''': CpmAntForCausalLM} if is_torch_available() else {}
     )

     UpperCamelCase_							:						Optional[Any]								=		False
     UpperCamelCase_							:						Dict								=		False
     UpperCamelCase_							:						Dict								=		False
     UpperCamelCase_							:						List[str]								=		False
     UpperCamelCase_							:						Dict								=		False


     def  _A				( self				:		int					):
       SCREAMING_SNAKE_CASE				:					List[Any]		  =			CpmAntModelTester(self					)
       SCREAMING_SNAKE_CASE				:					List[str]		  =			ConfigTester(self    ,       config_class=A_					)


     def  _A				( 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  _A				( self				:		List[str]					):
       unittest.skip("CPMAnt doesn\'t support input_embeds."					)(self.test_inputs_embeds					)


     def  _A				( 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  _A				( self				:		Optional[Any]					):
       SCREAMING_SNAKE_CASE  ,     SCREAMING_SNAKE_CASE				:					str		  =			self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_cpmant_model(A_    ,       A_					)






     def  _A				( self				:		List[str]					):
       SCREAMING_SNAKE_CASE  ,     SCREAMING_SNAKE_CASE				:					List[str]		  =			self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_lm_head_model(A_    ,       A_					)





@require_torch
class        SCREAMING_SNAKE_CASE	(	unittest.TestCase    ):
     '''simple docstring'''


     @tooslow
     def  _A				( self				:		Optional[Any]					):
       SCREAMING_SNAKE_CASE				:					Dict		  =			"今天天气真好！"
       SCREAMING_SNAKE_CASE				:					int		  =			"openbmb/cpm-ant-10b"
       SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			CpmAntModel.from_pretrained(A_					)
       SCREAMING_SNAKE_CASE				:					Tuple		  =			CpmAntTokenizer.from_pretrained(A_					)
       SCREAMING_SNAKE_CASE				:					int		  =			tokenizer(A_    ,       return_tensors="pt"					)
       SCREAMING_SNAKE_CASE				:					Dict		  =			model(**A_					).last_hidden_state

       SCREAMING_SNAKE_CASE				:					Any		  =			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]    ,       A_    ,       atol=1E-2					)					)





@require_torch
class        SCREAMING_SNAKE_CASE	(	unittest.TestCase    ):
     '''simple docstring'''


     @tooslow
     def  _A				( self				:		Optional[Any]					):
       SCREAMING_SNAKE_CASE				:					Optional[int]		  =			"今天天气真好！"
       SCREAMING_SNAKE_CASE				:					List[Any]		  =			"openbmb/cpm-ant-10b"
       SCREAMING_SNAKE_CASE				:					int		  =			CpmAntForCausalLM.from_pretrained(A_					)
       SCREAMING_SNAKE_CASE				:					Dict		  =			CpmAntTokenizer.from_pretrained(A_					)
       SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			tokenizer(A_    ,       return_tensors="pt"					)
       SCREAMING_SNAKE_CASE				:					str		  =			model(**A_					).logits

       SCREAMING_SNAKE_CASE				:					int		  =			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]    ,       A_    ,       atol=1E-2					)					)


     @tooslow
     def  _A				( self				:		Optional[int]					):
       SCREAMING_SNAKE_CASE				:					Tuple		  =			"openbmb/cpm-ant-10b"
       SCREAMING_SNAKE_CASE				:					Tuple		  =			CpmAntForCausalLM.from_pretrained(A_					)
       SCREAMING_SNAKE_CASE				:					Optional[int]		  =			CpmAntTokenizer.from_pretrained(A_					)
       SCREAMING_SNAKE_CASE				:					Optional[int]		  =			"今天天气不错，"
       SCREAMING_SNAKE_CASE				:					Tuple		  =			"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"
       SCREAMING_SNAKE_CASE				:					List[str]		  =			tokenizer(A_    ,       return_tensors="pt"					)
       SCREAMING_SNAKE_CASE				:					Optional[int]		  =			model.generate(**A_					)
       SCREAMING_SNAKE_CASE				:					int		  =			tokenizer.batch_decode(A_					)
       self.assertEqual(A_    ,       A_					)






     @tooslow
     def  _A				( self				:		Dict					):
       SCREAMING_SNAKE_CASE				:					Tuple		  =			"openbmb/cpm-ant-10b"
       SCREAMING_SNAKE_CASE				:					Dict		  =			CpmAntForCausalLM.from_pretrained(A_					)
       SCREAMING_SNAKE_CASE				:					Optional[int]		  =			CpmAntTokenizer.from_pretrained(A_					)
       SCREAMING_SNAKE_CASE				:					List[Any]		  =			["今天天气不错，", "新年快乐，万事如意！"]
       SCREAMING_SNAKE_CASE				:					List[Any]		  =			[
           "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",
       ]
       SCREAMING_SNAKE_CASE				:					str		  =			tokenizer(A_    ,       return_tensors="pt"    ,       padding=A_					)
       SCREAMING_SNAKE_CASE				:					Optional[int]		  =			model.generate(**A_					)
       SCREAMING_SNAKE_CASE				:					Dict		  =			tokenizer.batch_decode(A_					)
       self.assertEqual(A_    ,       A_					)








'''simple docstring'''
def 						A_(			A		:						list[list[int]]):
							if len(A) == 0 or not all(len(A) == 2 for pair in coordinates):
														raise ValueError('The program cannot work out a fitting polynomial.')

							if len({tuple(A) for pair in coordinates}) != len(A):
														raise ValueError('The program cannot work out a fitting polynomial.')

							UpperCamelCase					       =			{x for x, _ in coordinates}
							if len(A) == 1:
														return f'''x={coordinates[0][0]}'''

							if len(A) != len(A):
														raise ValueError('The program cannot work out a fitting polynomial.')

							UpperCamelCase					       =			len(A)

							UpperCamelCase					       =			0
							UpperCamelCase					       =			[]
							# put the x and x to the power values in a matrix
							while count_of_line < x:
														UpperCamelCase					       =			0
														UpperCamelCase					       =			coordinates[count_of_line][0]
														UpperCamelCase					       =			[]
														while count_in_line < x:
																					count_line.append(a ** (x - (count_in_line + 1)))
																					count_in_line += 1
														matrix.append(A)
														count_of_line += 1

							UpperCamelCase					       =			0
							# put the y values into a vector
							UpperCamelCase					       =			[]
							while count_of_line < x:
														vector.append(coordinates[count_of_line][1])
														count_of_line += 1

							UpperCamelCase					       =			0

							while count < x:
														UpperCamelCase					       =			0
														while zahlen < x:
																					if count == zahlen:
																												zahlen += 1
																					if zahlen == x:
																												break
																					UpperCamelCase					       =			matrix[zahlen][count] / matrix[count][count]
																					for counting_columns, item in enumerate(matrix[count]):
																												# manipulating all the values in the matrix
																												matrix[zahlen][counting_columns] -= item * bruch
																					# manipulating the values in the vector
																					vector[zahlen] -= vector[count] * bruch
																					zahlen += 1
														count += 1

							UpperCamelCase					       =			0
							# make solutions
							UpperCamelCase					       =			[]
							while count < x:
														solution.append(str(vector[count] / matrix[count][count]))
														count += 1

							UpperCamelCase					       =			0
							UpperCamelCase					       =			'f(x)='

							while count < x:
														UpperCamelCase					       =			solution[count].split('E')
														if len(A) > 1:
																					UpperCamelCase					       =			f'''{remove_e[0]}*10^{remove_e[1]}'''
														solved += f'''x^{x - (count + 1)}*{solution[count]}'''
														if count + 1 != x:
																					solved += "+"
														count += 1

							return solved


if __name__ == "__main__":
						print(points_to_polynomial([]))
						print(points_to_polynomial([[]]))
						print(points_to_polynomial([[1, 0], [2, 0], [3, 0]]))
						print(points_to_polynomial([[1, 1], [2, 1], [3, 1]]))
						print(points_to_polynomial([[1, 3], [2, 3], [3, 3]]))
						print(points_to_polynomial([[1, 1], [2, 2], [3, 3]]))
						print(points_to_polynomial([[1, 1], [2, 4], [3, 9]]))
						print(points_to_polynomial([[1, 3], [2, 6], [3, 11]]))
						print(points_to_polynomial([[1, -3], [2, -6], [3, -11]]))
						print(points_to_polynomial([[1, 5], [2, 2], [3, 9]]))




