

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      lowerCamelCase_ :




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




 def     a       (			self							):
   __UpperCAmelCase	:  Optional[Any]		=     {}
   __UpperCAmelCase	:  Any		=     ids_tensor([self.batch_size, self.seq_length]  ,  self.vocab_size							).type(torch.intaa							)
   __UpperCAmelCase	:  Union[str, Any]		=     False

   __UpperCAmelCase	:  List[str]		=     self.get_config()

   return (config, input_ids)




 def     a       (			self							):
   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=_UpperCAmelCase  ,  init_std=self.init_std  ,  return_dict=self.return_dict  ,  )




 def     a       (			self  ,  _UpperCAmelCase  ,  _UpperCAmelCase  ,  *_UpperCAmelCase							):
   __UpperCAmelCase	:  List[Any]		=     CpmAntModel(config=_UpperCAmelCase							)
   model.to(_UpperCAmelCase							)
   model.eval()

   __UpperCAmelCase	:  Dict		=     model(**_UpperCAmelCase							).last_hidden_state

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




 def     a       (			self  ,  _UpperCAmelCase  ,  _UpperCAmelCase  ,  *_UpperCAmelCase							):
   __UpperCAmelCase	:  List[str]		=     CpmAntForCausalLM(_UpperCAmelCase							)
   model.to(_UpperCAmelCase							)
   __UpperCAmelCase	:  Union[str, Any]		=     input_ids['input_ids'].to(_UpperCAmelCase							)
   model.eval()

   __UpperCAmelCase	:  Optional[Any]		=     model(**_UpperCAmelCase							)
   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							):
   __UpperCAmelCase,			__UpperCAmelCase	:  Any		=     self.prepare_config_and_inputs()
   return config, inputs_dict



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

 _lowerCAmelCase									=	False
 _lowerCAmelCase									=	False
 _lowerCAmelCase									=	False
 _lowerCAmelCase									=	False
 _lowerCAmelCase									=	False




 def     a       (			self							):
   __UpperCAmelCase	:  Any		=     CpmAntModelTester(self							)
   __UpperCAmelCase	:  int		=     ConfigTester(self  ,  config_class=_UpperCAmelCase							)




 def     a       (			self							):
   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							):
   unittest.skip('CPMAnt doesn\'t support input_embeds.'							)(self.test_inputs_embeds							)




 def     a       (			self							):
   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							):
   __UpperCAmelCase,			__UpperCAmelCase	:  str		=     self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_cpmant_model(_UpperCAmelCase  ,  _UpperCAmelCase							)




 def     a       (			self							):
   __UpperCAmelCase,			__UpperCAmelCase	:  Tuple		=     self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_lm_head_model(_UpperCAmelCase  ,  _UpperCAmelCase							)



@require_torch
class      lowerCamelCase_ (			unittest.TestCase):




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

   __UpperCAmelCase	:  Tuple		=     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]  ,  _UpperCAmelCase  ,  atol=1E-2							)							)



@require_torch
class      lowerCamelCase_ (			unittest.TestCase):




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

   __UpperCAmelCase	:  int		=     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]  ,  _UpperCAmelCase  ,  atol=1E-2							)							)




 @tooslow
 def     a       (			self							):
   __UpperCAmelCase	:  Any		=     'openbmb/cpm-ant-10b'
   __UpperCAmelCase	:  Any		=     CpmAntForCausalLM.from_pretrained(_UpperCAmelCase							)
   __UpperCAmelCase	:  str		=     CpmAntTokenizer.from_pretrained(_UpperCAmelCase							)
   __UpperCAmelCase	:  Dict		=     '今天天气不错，'
   __UpperCAmelCase	:  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'
   __UpperCAmelCase	:  Optional[Any]		=     tokenizer(_UpperCAmelCase  ,  return_tensors='pt'							)
   __UpperCAmelCase	:  Any		=     model.generate(**_UpperCAmelCase							)
   __UpperCAmelCase	:  Optional[Any]		=     tokenizer.batch_decode(_UpperCAmelCase							)
   self.assertEqual(_UpperCAmelCase  ,  _UpperCAmelCase							)




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






import numpy as np
from PIL import Image

def 				UpperCamelCase					(     lowerCAmelCase_ )		->    np.array:




       '''simple docstring'''






       _A,      _A,      _A=						rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
       return 0.2_989 * r + 0.5_870 * g + 0.1_140 * b

def 				UpperCamelCase					(     lowerCAmelCase_ )		->    np.array:




       '''simple docstring'''






       return (gray > 1_27) & (gray <= 2_55)

def 				UpperCamelCase					(     lowerCAmelCase_							,       lowerCAmelCase_ )		->    np.array:




       '''simple docstring'''






       _A=						np.zeros_like(lowerCAmelCase_ )
       _A=						np.zeros(
           (image.shape[0] + kernel.shape[0] - 1, image.shape[1] + kernel.shape[1] - 1) )

       # Copy image to padded image
       _A=						image

       # Iterate over image & apply kernel
       for x in range(image.shape[1] ):
              for y in range(image.shape[0] ):
                     _A=						(
                         kernel * image_padded[y : y + kernel.shape[0], x : x + kernel.shape[1]]
                     ).sum()
                     _A=						int(summation == 5 )
       return output


# kernel to be applied
UpperCAmelCase_    		=				np.array([[0, 1, 0], [1, 1, 1], [0, 1, 0]])

if __name__ == "__main__":
      # read original image
      UpperCAmelCase_    		=				np.array(Image.open(R'''..\image_data\lena.jpg'''))
      # Apply erosion operation to a binary image
      UpperCAmelCase_    		=				erosion(grayabinary(rgbagray(image)), structuring_element)
      # Save the output image
      UpperCAmelCase_    		=				Image.fromarray(output).convert('''RGB''')
      pil_img.save('''result_erosion.png''')