



import pickle

import numpy as np
from matplotlib import pyplot as plt



class     UpperCAmelCase_       :







							def __init__(  self,     __a,     __a,     __a,     __a,     __a,     __a=0.2,     __a=0.2):

														'''simple docstring'''

														_lowerCAmelCase	:				List[str]					  =       bp_numa
														_lowerCAmelCase	:				Dict					  =       bp_numa
														_lowerCAmelCase	:				str					  =       bp_numa
														_lowerCAmelCase	:				Tuple					  =       conva_get[:2]
														_lowerCAmelCase	:				List[Any]					  =       conva_get[2]
														_lowerCAmelCase	:				Tuple					  =       size_pa
														_lowerCAmelCase	:				List[str]					  =       rate_w
														_lowerCAmelCase	:				int					  =       rate_t
														_lowerCAmelCase	:				Union[str, Any]					  =       [
														    np.mat(-1 * np.random.rand(self.conva[0],     self.conva[0]) + 0.5)
														    for i in range(self.conva[1])
														]
														_lowerCAmelCase	:				Dict					  =       np.mat(-1 * np.random.rand(self.num_bpa,     self.num_bpa) + 0.5)
														_lowerCAmelCase	:				Tuple					  =       np.mat(-1 * np.random.rand(self.num_bpa,     self.num_bpa) + 0.5)
														_lowerCAmelCase	:				Dict					  =       -2 * np.random.rand(self.conva[1]) + 1
														_lowerCAmelCase	:				Dict					  =       -2 * np.random.rand(self.num_bpa) + 1
														_lowerCAmelCase	:				Tuple					  =       -2 * np.random.rand(self.num_bpa) + 1







							def   snake_case__ (  self,     __a):

														'''simple docstring'''

														_lowerCAmelCase	:				Optional[Any]					  =       {
														    "num_bp1": self.num_bpa,
														    "num_bp2": self.num_bpa,
														    "num_bp3": self.num_bpa,
														    "conv1": self.conva,
														    "step_conv1": self.step_conva,
														    "size_pooling1": self.size_poolinga,
														    "rate_weight": self.rate_weight,
														    "rate_thre": self.rate_thre,
														    "w_conv1": self.w_conva,
														    "wkj": self.wkj,
														    "vji": self.vji,
														    "thre_conv1": self.thre_conva,
														    "thre_bp2": self.thre_bpa,
														    "thre_bp3": self.thre_bpa,
														}
														with open(__UpperCamelCase,     "wb") as f:
																					pickle.dump(__UpperCamelCase,     __UpperCamelCase)

														print(f"Model saved： {save_path}")







							@classmethod
							def   snake_case__ (  cls,     __a):

														'''simple docstring'''

														with open(__UpperCamelCase,     "rb") as f:
																					_lowerCAmelCase	:				Dict					  =       pickle.load(__UpperCamelCase)  # noqa: S301

														_lowerCAmelCase	:				Union[str, Any]					  =       model_dic.get("conv1")
														conv_get.append(model_dic.get("step_conv1"))
														_lowerCAmelCase	:				Optional[int]					  =       model_dic.get("size_pooling1")
														_lowerCAmelCase	:				Optional[Any]					  =       model_dic.get("num_bp1")
														_lowerCAmelCase	:				Union[str, Any]					  =       model_dic.get("num_bp2")
														_lowerCAmelCase	:				int					  =       model_dic.get("num_bp3")
														_lowerCAmelCase	:				Any					  =       model_dic.get("rate_weight")
														_lowerCAmelCase	:				str					  =       model_dic.get("rate_thre")
														# create model instance
														_lowerCAmelCase	:				int					  =       CNN(__UpperCamelCase,     __UpperCamelCase,     __UpperCamelCase,     __UpperCamelCase,     __UpperCamelCase,     __UpperCamelCase,     __UpperCamelCase)
														# modify model parameter
														_lowerCAmelCase	:				List[str]					  =       model_dic.get("w_conv1")
														_lowerCAmelCase	:				Any					  =       model_dic.get("wkj")
														_lowerCAmelCase	:				Union[str, Any]					  =       model_dic.get("vji")
														_lowerCAmelCase	:				Tuple					  =       model_dic.get("thre_conv1")
														_lowerCAmelCase	:				int					  =       model_dic.get("thre_bp2")
														_lowerCAmelCase	:				Optional[int]					  =       model_dic.get("thre_bp3")
														return conv_ins







							def   snake_case__ (  self,     __a):

														'''simple docstring'''

														return 1 / (1 + np.exp(-1 * x))







							def   snake_case__ (  self,     __a):

														'''simple docstring'''

														return round(__UpperCamelCase,     3)







							def   snake_case__ (  self,     __a,     __a,     __a,     __a,     __a):

														'''simple docstring'''

														_lowerCAmelCase	:				str					  =       convs[0]
														_lowerCAmelCase	:				Tuple					  =       convs[1]
														_lowerCAmelCase	:				str					  =       np.shape(__UpperCamelCase)[0]
														# get the data slice of original image data, data_focus
														_lowerCAmelCase	:				str					  =       []
														for i_focus in range(0,     size_data - size_conv + 1,     __UpperCamelCase):
																					for j_focus in range(0,     size_data - size_conv + 1,     __UpperCamelCase):
																												_lowerCAmelCase	:				int					  =       data[
																												    i_focus : i_focus + size_conv, j_focus : j_focus + size_conv
																												]
																												data_focus.append(__UpperCamelCase)
        # calculate the feature map of every single kernel, and saved as list of matrix
														_lowerCAmelCase	:				str					  =       []
														_lowerCAmelCase	:				List[Any]					  =       int((size_data - size_conv) / conv_step + 1)
														for i_map in range(__UpperCamelCase):
																					_lowerCAmelCase	:				Tuple					  =       []
																					for i_focus in range(len(__UpperCamelCase)):
																												_lowerCAmelCase	:				List[str]					  =       (
																												    np.sum(np.multiply(data_focus[i_focus],     w_convs[i_map]))
																												    - thre_convs[i_map]
																												)
																												featuremap.append(self.sig(__UpperCamelCase))
																					_lowerCAmelCase	:				str					  =       np.asmatrix(__UpperCamelCase).reshape(
																					    __UpperCamelCase,     __UpperCamelCase)
																					data_featuremap.append(__UpperCamelCase)

														# expanding the data slice to One dimenssion
														_lowerCAmelCase	:				Dict					  =       []
														for each_focus in data_focus:
																					focusa_list.extend(self.Expand_Mat(__UpperCamelCase))
														_lowerCAmelCase	:				Optional[Any]					  =       np.asarray(__UpperCamelCase)
														return focus_list, data_featuremap







							def   snake_case__ (  self,     __a,     __a,     __a="average_pool"):

														'''simple docstring'''

														_lowerCAmelCase	:				int					  =       len(featuremaps[0])
														_lowerCAmelCase	:				Union[str, Any]					  =       int(size_map / size_pooling)
														_lowerCAmelCase	:				List[Any]					  =       []
														for i_map in range(len(__UpperCamelCase)):
																					_lowerCAmelCase	:				Union[str, Any]					  =       featuremaps[i_map]
																					_lowerCAmelCase	:				Union[str, Any]					  =       []
																					for i_focus in range(0,     __UpperCamelCase,     __UpperCamelCase):
																												for j_focus in range(0,     __UpperCamelCase,     __UpperCamelCase):
																																			_lowerCAmelCase	:				Dict					  =       feature_map[
																																			    i_focus : i_focus + size_pooling,
																																			    j_focus : j_focus + size_pooling,
																																			]
																																			if pooling_type == "average_pool":
																																										# average pooling
																																										map_pooled.append(np.average(__UpperCamelCase))
																																			elif pooling_type == "max_pooling":
																																										# max pooling
																																										map_pooled.append(np.max(__UpperCamelCase))
																					_lowerCAmelCase	:				Optional[Any]					  =       np.asmatrix(__UpperCamelCase).reshape(__UpperCamelCase,     __UpperCamelCase)
																					featuremap_pooled.append(__UpperCamelCase)
														return featuremap_pooled







							def   snake_case__ (  self,     __a):

														'''simple docstring'''

														_lowerCAmelCase	:				List[str]					  =       []
														for i in range(len(__UpperCamelCase)):
																					_lowerCAmelCase	:				Optional[Any]					  =       np.shape(data[i])
																					_lowerCAmelCase	:				int					  =       data[i].reshape(1,     shapes[0] * shapes[1])
																					_lowerCAmelCase	:				str					  =       data_listed.getA().tolist()[0]
																					data_expanded.extend(__UpperCamelCase)
														_lowerCAmelCase	:				Any					  =       np.asarray(__UpperCamelCase)
														return data_expanded







							def   snake_case__ (  self,     __a):

														'''simple docstring'''

														_lowerCAmelCase	:				str					  =       np.asarray(__UpperCamelCase)
														_lowerCAmelCase	:				Dict					  =       np.shape(__UpperCamelCase)
														_lowerCAmelCase	:				Any					  =       data_mat.reshape(1,     shapes[0] * shapes[1])
														return data_expanded







							def   snake_case__ (  self,     __a,     __a,     __a,     __a,     __a):

														'''simple docstring'''

														_lowerCAmelCase	:				Optional[Any]					  =       []
														_lowerCAmelCase	:				Union[str, Any]					  =       0
														for i_map in range(__UpperCamelCase):
																					_lowerCAmelCase	:				Optional[int]					  =       np.ones((size_map, size_map))
																					for i in range(0,     __UpperCamelCase,     __UpperCamelCase):
																												for j in range(0,     __UpperCamelCase,     __UpperCamelCase):
																																			_lowerCAmelCase	:				str					  =       pd_pool[
																																			    i_pool
																																			]
																																			_lowerCAmelCase	:				Optional[int]					  =       i_pool + 1
																					_lowerCAmelCase	:				Tuple					  =       np.multiply(
																					    __UpperCamelCase,     np.multiply(out_map[i_map],     (1 - out_map[i_map])))
																					pd_all.append(__UpperCamelCase)
														return pd_all







							def   snake_case__ (  self,     __a,     __a,     __a,     __a,     __a,     __a=bool):

														'''simple docstring'''

														print("----------------------Start Training-------------------------")
														print((" - - Shape: Train_Data  ", np.shape(__UpperCamelCase)))
														print((" - - Shape: Teach_Data  ", np.shape(__UpperCamelCase)))
														_lowerCAmelCase	:				int					  =       0
														_lowerCAmelCase	:				Optional[Any]					  =       []
														_lowerCAmelCase	:				Dict					  =       1_0000
														while rp < n_repeat and mse >= error_accuracy:
																					_lowerCAmelCase	:				Optional[Any]					  =       0
																					print(f"-------------Learning Time {rp}--------------")
																					for p in range(len(__UpperCamelCase)):
																												# print('------------Learning Image: %d--------------'%p)
																												_lowerCAmelCase	:				Dict					  =       np.asmatrix(datas_train[p])
																												_lowerCAmelCase	:				str					  =       np.asarray(datas_teach[p])
																												_lowerCAmelCase  ,       _lowerCAmelCase	:				int					  =       self.convolute(
																												    __UpperCamelCase,     self.conva,     self.w_conva,     self.thre_conva,     conv_step=self.step_conva,     )
																												_lowerCAmelCase	:				List[str]					  =       self.pooling(__UpperCamelCase,     self.size_poolinga)
																												_lowerCAmelCase	:				List[Any]					  =       np.shape(__UpperCamelCase)
																												_lowerCAmelCase	:				Tuple					  =       self._expand(__UpperCamelCase)
																												_lowerCAmelCase	:				Optional[int]					  =       data_bp_input

																												_lowerCAmelCase	:				Optional[int]					  =       np.dot(__UpperCamelCase,     self.vji.T) - self.thre_bpa
																												_lowerCAmelCase	:				Optional[int]					  =       self.sig(__UpperCamelCase)
																												_lowerCAmelCase	:				List[str]					  =       np.dot(__UpperCamelCase,     self.wkj.T) - self.thre_bpa
																												_lowerCAmelCase	:				Any					  =       self.sig(__UpperCamelCase)

																												# --------------Model Leaning ------------------------
																												# calculate error and gradient---------------
																												_lowerCAmelCase	:				Union[str, Any]					  =       np.multiply(
																												    (data_teach - bp_outa),     np.multiply(__UpperCamelCase,     (1 - bp_outa)))
																												_lowerCAmelCase	:				Union[str, Any]					  =       np.multiply(
																												    np.dot(__UpperCamelCase,     self.wkj),     np.multiply(__UpperCamelCase,     (1 - bp_outa)))
																												_lowerCAmelCase	:				Dict					  =       np.dot(__UpperCamelCase,     self.vji)

																												_lowerCAmelCase	:				List[Any]					  =       pd_i_all / (self.size_poolinga * self.size_poolinga)
																												_lowerCAmelCase	:				int					  =       pd_conva_pooled.T.getA().tolist()
																												_lowerCAmelCase	:				Dict					  =       self._calculate_gradient_from_pool(
																												    __UpperCamelCase,     __UpperCamelCase,     shape_featuremapa[0],     shape_featuremapa[1],     self.size_poolinga,     )
																												# weight and threshold learning process---------
																												# convolution layer
																												for k_conv in range(self.conva[1]):
																																			_lowerCAmelCase	:				Any					  =       self._expand_mat(pd_conva_all[k_conv])
																																			_lowerCAmelCase	:				List[str]					  =       self.rate_weight * np.dot(__UpperCamelCase,     __UpperCamelCase)

																																			_lowerCAmelCase	:				str					  =       self.w_conva[k_conv] + delta_w.reshape(
																																			    (self.conva[0], self.conva[0]))

																																			_lowerCAmelCase	:				int					  =       (
																																			    self.thre_conva[k_conv]
																																			    - np.sum(pd_conva_all[k_conv]) * self.rate_thre
																																			)
																												# all connected layer
																												_lowerCAmelCase	:				Union[str, Any]					  =       self.wkj + pd_k_all.T * bp_outa * self.rate_weight
																												_lowerCAmelCase	:				Union[str, Any]					  =       self.vji + pd_j_all.T * bp_outa * self.rate_weight
																												_lowerCAmelCase	:				List[str]					  =       self.thre_bpa - pd_k_all * self.rate_thre
																												_lowerCAmelCase	:				Dict					  =       self.thre_bpa - pd_j_all * self.rate_thre
																												# calculate the sum error of all single image
																												_lowerCAmelCase	:				Dict					  =       np.sum(abs(data_teach - bp_outa))
																												error_count += errors
																												# print('   ----Teach      ',data_teach)
																												# print('   ----BP_output  ',bp_out3)
																					_lowerCAmelCase	:				List[Any]					  =       rp + 1
																					_lowerCAmelCase	:				Union[str, Any]					  =       error_count / patterns
																					all_mse.append(__UpperCamelCase)

														def draw_error():
																					_lowerCAmelCase	:				List[Any]					  =       [error_accuracy for i in range(int(n_repeat * 1.2))]
																					plt.plot(__UpperCamelCase,     "+-")
																					plt.plot(__UpperCamelCase,     "r--")
																					plt.xlabel("Learning Times")
																					plt.ylabel("All_mse")
																					plt.grid(__UpperCamelCase,     alpha=0.5)
																					plt.show()

														print("------------------Training Complished---------------------")
														print((" - - Training epoch: ", rp, f"     - - Mse: {mse:.6f}"))
														if draw_e:
																					draw_error()
														return mse







							def   snake_case__ (  self,     __a):

														'''simple docstring'''

														_lowerCAmelCase	:				Tuple					  =       []
														print("-------------------Start Testing-------------------------")
														print((" - - Shape: Test_Data  ", np.shape(__UpperCamelCase)))
														for p in range(len(__UpperCamelCase)):
																					_lowerCAmelCase	:				Union[str, Any]					  =       np.asmatrix(datas_test[p])
																					_lowerCAmelCase  ,       _lowerCAmelCase	:				Dict					  =       self.convolute(
																					    __UpperCamelCase,     self.conva,     self.w_conva,     self.thre_conva,     conv_step=self.step_conva,     )
																					_lowerCAmelCase	:				Tuple					  =       self.pooling(__UpperCamelCase,     self.size_poolinga)
																					_lowerCAmelCase	:				Optional[int]					  =       self._expand(__UpperCamelCase)

																					_lowerCAmelCase	:				Optional[int]					  =       data_bp_input
																					_lowerCAmelCase	:				Union[str, Any]					  =       bp_outa * self.vji.T - self.thre_bpa
																					_lowerCAmelCase	:				List[str]					  =       self.sig(__UpperCamelCase)
																					_lowerCAmelCase	:				Dict					  =       bp_outa * self.wkj.T - self.thre_bpa
																					_lowerCAmelCase	:				Any					  =       self.sig(__UpperCamelCase)
																					produce_out.extend(bp_outa.getA().tolist())
														_lowerCAmelCase	:				List[str]					  =       [list(map(self.do_round,     __UpperCamelCase)) for each in produce_out]
														return np.asarray(__UpperCamelCase)







							def   snake_case__ (  self,     __a):

														'''simple docstring'''

														_lowerCAmelCase	:				Dict					  =       np.asmatrix(__UpperCamelCase)
														_lowerCAmelCase  ,       _lowerCAmelCase	:				int					  =       self.convolute(
														    __UpperCamelCase,     self.conva,     self.w_conva,     self.thre_conva,     conv_step=self.step_conva,     )
														_lowerCAmelCase	:				Optional[Any]					  =       self.pooling(__UpperCamelCase,     self.size_poolinga)

														return data_conveda, data_pooleda


if __name__ == "__main__":
						pass








import shutil
import tempfile
import unittest

from transformers import ClapFeatureExtractor, ClapProcessor, RobertaTokenizer, RobertaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torchaudio

from .test_feature_extraction_clap import floats_list



@require_torchaudio
@require_sentencepiece
class 				_UpperCamelCase      (unittest.TestCase      ):


      def 			__UpperCAmelCase		(						self						)->						Optional[int]:
           __lowerCAmelCase             =       "laion/clap-htsat-unfused"
           __lowerCAmelCase             =       tempfile.mkdtemp()


      def 			__UpperCAmelCase		(						self       ,    **__UpperCamelCase						)->						Optional[int]:
           return RobertaTokenizer.from_pretrained(self.checkpoint       ,    **__UpperCamelCase						)


      def 			__UpperCAmelCase		(						self       ,    **__UpperCamelCase						)->						List[Any]:
           return ClapFeatureExtractor.from_pretrained(self.checkpoint       ,    **__UpperCamelCase						)


      def 			__UpperCAmelCase		(						self						)->						Union[str, Any]:
           shutil.rmtree(self.tmpdirname						)


      def 			__UpperCAmelCase		(						self						)->						Dict:
           __lowerCAmelCase             =       self.get_tokenizer()
           __lowerCAmelCase             =       self.get_feature_extractor()

           __lowerCAmelCase             =       ClapProcessor(tokenizer=__UpperCamelCase       ,    feature_extractor=__UpperCamelCase						)

           processor.save_pretrained(self.tmpdirname						)
           __lowerCAmelCase             =       ClapProcessor.from_pretrained(self.tmpdirname						)

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

           self.assertEqual(processor.feature_extractor.to_json_string()       ,    feature_extractor.to_json_string()						)
           self.assertIsInstance(processor.feature_extractor       ,    __UpperCamelCase						)


      def 			__UpperCAmelCase		(						self						)->						List[str]:
           __lowerCAmelCase             =       ClapProcessor(tokenizer=self.get_tokenizer()       ,    feature_extractor=self.get_feature_extractor()						)
           processor.save_pretrained(self.tmpdirname						)

           __lowerCAmelCase             =       self.get_tokenizer(bos_token="(BOS)"       ,    eos_token="(EOS)"						)
           __lowerCAmelCase             =       self.get_feature_extractor(do_normalize=__UpperCamelCase       ,    padding_value=1.0						)

           __lowerCAmelCase             =       ClapProcessor.from_pretrained(
               self.tmpdirname       ,    bos_token="(BOS)"       ,    eos_token="(EOS)"       ,    do_normalize=__UpperCamelCase       ,    padding_value=1.0						)

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

           self.assertEqual(processor.feature_extractor.to_json_string()       ,    feature_extractor_add_kwargs.to_json_string()						)
           self.assertIsInstance(processor.feature_extractor       ,    __UpperCamelCase						)


      def 			__UpperCAmelCase		(						self						)->						List[str]:
           __lowerCAmelCase             =       self.get_feature_extractor()
           __lowerCAmelCase             =       self.get_tokenizer()

           __lowerCAmelCase             =       ClapProcessor(tokenizer=__UpperCamelCase       ,    feature_extractor=__UpperCamelCase						)

           __lowerCAmelCase             =       floats_list((3, 1_0_0_0)						)

           __lowerCAmelCase             =       feature_extractor(__UpperCamelCase       ,    return_tensors="np"						)
           __lowerCAmelCase             =       processor(audios=__UpperCamelCase       ,    return_tensors="np"						)

           for key in input_feat_extract.keys():
                self.assertAlmostEqual(input_feat_extract[key].sum()       ,    input_processor[key].sum()       ,    delta=1e-2						)


      def 			__UpperCAmelCase		(						self						)->						Dict:
           __lowerCAmelCase             =       self.get_feature_extractor()
           __lowerCAmelCase             =       self.get_tokenizer()

           __lowerCAmelCase             =       ClapProcessor(tokenizer=__UpperCamelCase       ,    feature_extractor=__UpperCamelCase						)

           __lowerCAmelCase             =       "This is a test string"

           __lowerCAmelCase             =       processor(text=__UpperCamelCase						)

           __lowerCAmelCase             =       tokenizer(__UpperCamelCase						)

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


      def 			__UpperCAmelCase		(						self						)->						int:
           __lowerCAmelCase             =       self.get_feature_extractor()
           __lowerCAmelCase             =       self.get_tokenizer()

           __lowerCAmelCase             =       ClapProcessor(tokenizer=__UpperCamelCase       ,    feature_extractor=__UpperCamelCase						)

           __lowerCAmelCase             =       [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

           __lowerCAmelCase             =       processor.batch_decode(__UpperCamelCase						)
           __lowerCAmelCase             =       tokenizer.batch_decode(__UpperCamelCase						)

           self.assertListEqual(__UpperCamelCase       ,    __UpperCamelCase						)


      def 			__UpperCAmelCase		(						self						)->						Dict:
           __lowerCAmelCase             =       self.get_feature_extractor()
           __lowerCAmelCase             =       self.get_tokenizer()

           __lowerCAmelCase             =       ClapProcessor(tokenizer=__UpperCamelCase       ,    feature_extractor=__UpperCamelCase						)

           self.assertListEqual(
               processor.model_input_names[2:]       ,    feature_extractor.model_input_names       ,    msg="`processor` and `feature_extractor` model input names do not match"       ,    )






