import pickle

import numpy as np
from matplotlib import pyplot as plt


class      lowerCamelCase    :
  """simple docstring"""







  def __init__(							self       :				Dict     ,   __magic_name__       :				int     ,   __magic_name__       :				Any     ,   __magic_name__       :				Union[str, Any]     ,   __magic_name__       :				str     ,   __magic_name__       :				Optional[Any]     ,   __magic_name__       :				Optional[Any]=0.2     ,   __magic_name__       :				str=0.2      )     ->		str:
        SCREAMING_SNAKE_CASE_      =     bp_numa
        SCREAMING_SNAKE_CASE_      =     bp_numa
        SCREAMING_SNAKE_CASE_      =     bp_numa
        SCREAMING_SNAKE_CASE_      =     conva_get[:2]
        SCREAMING_SNAKE_CASE_      =     conva_get[2]
        SCREAMING_SNAKE_CASE_      =     size_pa
        SCREAMING_SNAKE_CASE_      =     rate_w
        SCREAMING_SNAKE_CASE_      =     rate_t
        SCREAMING_SNAKE_CASE_      =     [
            np.mat(-1 * np.random.rand(self.conva[0]     ,   self.conva[0]      ) + 0.5      )
            for i in range(self.conva[1]      )
        ]
        SCREAMING_SNAKE_CASE_      =     np.mat(-1 * np.random.rand(self.num_bpa     ,   self.num_bpa      ) + 0.5      )
        SCREAMING_SNAKE_CASE_      =     np.mat(-1 * np.random.rand(self.num_bpa     ,   self.num_bpa      ) + 0.5      )
        SCREAMING_SNAKE_CASE_      =     -2 * np.random.rand(self.conva[1]      ) + 1
        SCREAMING_SNAKE_CASE_      =     -2 * np.random.rand(self.num_bpa      ) + 1
        SCREAMING_SNAKE_CASE_      =     -2 * np.random.rand(self.num_bpa      ) + 1







  def       __A   (							self       :				List[Any]     ,   __magic_name__       :				Any      )     ->		Optional[Any]:
        # save model dict with pickle
        SCREAMING_SNAKE_CASE_      =     {
            """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       __A   (							cls       :				Tuple     ,   __magic_name__       :				Dict      )     ->		List[Any]:
        # read saved model
        with open(__UpperCAmelCase     ,   "rb"      ) as f:
              SCREAMING_SNAKE_CASE_      =     pickle.load(__UpperCAmelCase      )  # noqa: S301

        SCREAMING_SNAKE_CASE_      =     model_dic.get("conv1"      )
        conv_get.append(model_dic.get("step_conv1"      )      )
        SCREAMING_SNAKE_CASE_      =     model_dic.get("size_pooling1"      )
        SCREAMING_SNAKE_CASE_      =     model_dic.get("num_bp1"      )
        SCREAMING_SNAKE_CASE_      =     model_dic.get("num_bp2"      )
        SCREAMING_SNAKE_CASE_      =     model_dic.get("num_bp3"      )
        SCREAMING_SNAKE_CASE_      =     model_dic.get("rate_weight"      )
        SCREAMING_SNAKE_CASE_      =     model_dic.get("rate_thre"      )
        # create model instance
        SCREAMING_SNAKE_CASE_      =     CNN(__UpperCAmelCase     ,   __UpperCAmelCase     ,   __UpperCAmelCase     ,   __UpperCAmelCase     ,   __UpperCAmelCase     ,   __UpperCAmelCase     ,   __UpperCAmelCase      )
        # modify model parameter
        SCREAMING_SNAKE_CASE_      =     model_dic.get("w_conv1"      )
        SCREAMING_SNAKE_CASE_      =     model_dic.get("wkj"      )
        SCREAMING_SNAKE_CASE_      =     model_dic.get("vji"      )
        SCREAMING_SNAKE_CASE_      =     model_dic.get("thre_conv1"      )
        SCREAMING_SNAKE_CASE_      =     model_dic.get("thre_bp2"      )
        SCREAMING_SNAKE_CASE_      =     model_dic.get("thre_bp3"      )
        return conv_ins







  def       __A   (							self       :				Union[str, Any]     ,   __magic_name__       :				List[str]      )     ->		Dict:
        return 1 / (1 + np.exp(-1 * x      ))







  def       __A   (							self       :				int     ,   __magic_name__       :				List[Any]      )     ->		List[Any]:
        return round(__UpperCAmelCase     ,   3      )







  def       __A   (							self       :				Union[str, Any]     ,   __magic_name__       :				Union[str, Any]     ,   __magic_name__       :				int     ,   __magic_name__       :				str     ,   __magic_name__       :				Optional[int]     ,   __magic_name__       :				Any      )     ->		List[str]:
        # convolution process
        SCREAMING_SNAKE_CASE_      =     convs[0]
        SCREAMING_SNAKE_CASE_      =     convs[1]
        SCREAMING_SNAKE_CASE_      =     np.shape(__UpperCAmelCase      )[0]
        # get the data slice of original image data, data_focus
        SCREAMING_SNAKE_CASE_      =     []
        for i_focus in range(0     ,   size_data - size_conv + 1     ,   __UpperCAmelCase      ):
              for j_focus in range(0     ,   size_data - size_conv + 1     ,   __UpperCAmelCase      ):
                    SCREAMING_SNAKE_CASE_      =     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
        SCREAMING_SNAKE_CASE_      =     []
        SCREAMING_SNAKE_CASE_      =     int((size_data - size_conv) / conv_step + 1      )
        for i_map in range(__UpperCAmelCase      ):
              SCREAMING_SNAKE_CASE_      =     []
              for i_focus in range(len(__UpperCAmelCase      )      ):
                    SCREAMING_SNAKE_CASE_      =     (
                        np.sum(np.multiply(data_focus[i_focus]     ,   w_convs[i_map]      )      )
                        - thre_convs[i_map]
                    )
                    featuremap.append(self.sig(__UpperCAmelCase      )      )
              SCREAMING_SNAKE_CASE_      =     np.asmatrix(__UpperCAmelCase      ).reshape(
                  __UpperCAmelCase     ,   __UpperCAmelCase      )
              data_featuremap.append(__UpperCAmelCase      )

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







  def       __A   (							self       :				Optional[Any]     ,   __magic_name__       :				int     ,   __magic_name__       :				Optional[int]     ,   __magic_name__       :				Any="average_pool"      )     ->		List[Any]:
        # pooling process
        SCREAMING_SNAKE_CASE_      =     len(featuremaps[0]      )
        SCREAMING_SNAKE_CASE_      =     int(size_map / size_pooling      )
        SCREAMING_SNAKE_CASE_      =     []
        for i_map in range(len(__UpperCAmelCase      )      ):
              SCREAMING_SNAKE_CASE_      =     featuremaps[i_map]
              SCREAMING_SNAKE_CASE_      =     []
              for i_focus in range(0     ,   __UpperCAmelCase     ,   __UpperCAmelCase      ):
                    for j_focus in range(0     ,   __UpperCAmelCase     ,   __UpperCAmelCase      ):
                          SCREAMING_SNAKE_CASE_      =     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      )      )
              SCREAMING_SNAKE_CASE_      =     np.asmatrix(__UpperCAmelCase      ).reshape(__UpperCAmelCase     ,   __UpperCAmelCase      )
              featuremap_pooled.append(__UpperCAmelCase      )
        return featuremap_pooled







  def       __A   (							self       :				Any     ,   __magic_name__       :				Any      )     ->		Tuple:
        # expanding three dimension data to one dimension list
        SCREAMING_SNAKE_CASE_      =     []
        for i in range(len(__UpperCAmelCase      )      ):
              SCREAMING_SNAKE_CASE_      =     np.shape(data[i]      )
              SCREAMING_SNAKE_CASE_      =     data[i].reshape(1     ,   shapes[0] * shapes[1]      )
              SCREAMING_SNAKE_CASE_      =     data_listed.getA().tolist()[0]
              data_expanded.extend(__UpperCAmelCase      )
        SCREAMING_SNAKE_CASE_      =     np.asarray(__UpperCAmelCase      )
        return data_expanded







  def       __A   (							self       :				Any     ,   __magic_name__       :				Dict      )     ->		int:
        # expanding matrix to one dimension list
        SCREAMING_SNAKE_CASE_      =     np.asarray(__UpperCAmelCase      )
        SCREAMING_SNAKE_CASE_      =     np.shape(__UpperCAmelCase      )
        SCREAMING_SNAKE_CASE_      =     data_mat.reshape(1     ,   shapes[0] * shapes[1]      )
        return data_expanded







  def       __A   (							self       :				Dict     ,   __magic_name__       :				Tuple     ,   __magic_name__       :				Dict     ,   __magic_name__       :				str     ,   __magic_name__       :				Optional[Any]     ,   __magic_name__       :				str      )     ->		Union[str, Any]:
        SCREAMING_SNAKE_CASE_      =     []
        SCREAMING_SNAKE_CASE_      =     0
        for i_map in range(__UpperCAmelCase      ):
              SCREAMING_SNAKE_CASE_      =     np.ones((size_map, size_map)      )
              for i in range(0     ,   __UpperCAmelCase     ,   __UpperCAmelCase      ):
                    for j in range(0     ,   __UpperCAmelCase     ,   __UpperCAmelCase      ):
                          SCREAMING_SNAKE_CASE_      =     pd_pool[
                              i_pool
                          ]
                          SCREAMING_SNAKE_CASE_      =     i_pool + 1
              SCREAMING_SNAKE_CASE_      =     np.multiply(
                  __UpperCAmelCase     ,   np.multiply(out_map[i_map]     ,   (1 - out_map[i_map])      )      )
              pd_all.append(__UpperCAmelCase      )
        return pd_all







  def       __A   (							self       :				List[str]     ,   __magic_name__       :				Tuple     ,   __magic_name__       :				Tuple     ,   __magic_name__       :				str     ,   __magic_name__       :				int     ,   __magic_name__       :				List[str]     ,   __magic_name__       :				Any=bool      )     ->		Tuple:
        # model traning
        print("----------------------Start Training-------------------------"      )
        print((" - - Shape: Train_Data  ", np.shape(__UpperCAmelCase      ))      )
        print((" - - Shape: Teach_Data  ", np.shape(__UpperCAmelCase      ))      )
        SCREAMING_SNAKE_CASE_      =     0
        SCREAMING_SNAKE_CASE_      =     []
        SCREAMING_SNAKE_CASE_      =     10_000
        while rp < n_repeat and mse >= error_accuracy:
              SCREAMING_SNAKE_CASE_      =     0
              print(F'''-------------Learning Time {rp}--------------'''      )
              for p in range(len(__UpperCAmelCase      )      ):
                    # print('------------Learning Image: %d--------------'%p)
                    SCREAMING_SNAKE_CASE_      =     np.asmatrix(datas_train[p]      )
                    SCREAMING_SNAKE_CASE_      =     np.asarray(datas_teach[p]      )
                    SCREAMING_SNAKE_CASE_      =     self.convolute(
                        __UpperCAmelCase     ,   self.conva     ,   self.w_conva     ,   self.thre_conva     ,   conv_step=self.step_conva     ,   )
                    SCREAMING_SNAKE_CASE_      =     self.pooling(__UpperCAmelCase     ,   self.size_poolinga      )
                    SCREAMING_SNAKE_CASE_      =     np.shape(__UpperCAmelCase      )
                    SCREAMING_SNAKE_CASE_      =     self._expand(__UpperCAmelCase      )
                    SCREAMING_SNAKE_CASE_      =     data_bp_input

                    SCREAMING_SNAKE_CASE_      =     np.dot(__UpperCAmelCase     ,   self.vji.T      ) - self.thre_bpa
                    SCREAMING_SNAKE_CASE_      =     self.sig(__UpperCAmelCase      )
                    SCREAMING_SNAKE_CASE_      =     np.dot(__UpperCAmelCase     ,   self.wkj.T      ) - self.thre_bpa
                    SCREAMING_SNAKE_CASE_      =     self.sig(__UpperCAmelCase      )

                    # --------------Model Leaning ------------------------
                    # calculate error and gradient---------------
                    SCREAMING_SNAKE_CASE_      =     np.multiply(
                        (data_teach - bp_outa)     ,   np.multiply(__UpperCAmelCase     ,   (1 - bp_outa)      )      )
                    SCREAMING_SNAKE_CASE_      =     np.multiply(
                        np.dot(__UpperCAmelCase     ,   self.wkj      )     ,   np.multiply(__UpperCAmelCase     ,   (1 - bp_outa)      )      )
                    SCREAMING_SNAKE_CASE_      =     np.dot(__UpperCAmelCase     ,   self.vji      )

                    SCREAMING_SNAKE_CASE_      =     pd_i_all / (self.size_poolinga * self.size_poolinga)
                    SCREAMING_SNAKE_CASE_      =     pd_conva_pooled.T.getA().tolist()
                    SCREAMING_SNAKE_CASE_      =     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]      ):
                          SCREAMING_SNAKE_CASE_      =     self._expand_mat(pd_conva_all[k_conv]      )
                          SCREAMING_SNAKE_CASE_      =     self.rate_weight * np.dot(__UpperCAmelCase     ,   __UpperCAmelCase      )

                          SCREAMING_SNAKE_CASE_      =     self.w_conva[k_conv] + delta_w.reshape(
                              (self.conva[0], self.conva[0])      )

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

        def draw_error():
              SCREAMING_SNAKE_CASE_      =     [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       __A   (							self       :				Optional[Any]     ,   __magic_name__       :				Union[str, Any]      )     ->		Dict:
        # model predict
        SCREAMING_SNAKE_CASE_      =     []
        print("-------------------Start Testing-------------------------"      )
        print((" - - Shape: Test_Data  ", np.shape(__UpperCAmelCase      ))      )
        for p in range(len(__UpperCAmelCase      )      ):
              SCREAMING_SNAKE_CASE_      =     np.asmatrix(datas_test[p]      )
              SCREAMING_SNAKE_CASE_      =     self.convolute(
                  __UpperCAmelCase     ,   self.conva     ,   self.w_conva     ,   self.thre_conva     ,   conv_step=self.step_conva     ,   )
              SCREAMING_SNAKE_CASE_      =     self.pooling(__UpperCAmelCase     ,   self.size_poolinga      )
              SCREAMING_SNAKE_CASE_      =     self._expand(__UpperCAmelCase      )

              SCREAMING_SNAKE_CASE_      =     data_bp_input
              SCREAMING_SNAKE_CASE_      =     bp_outa * self.vji.T - self.thre_bpa
              SCREAMING_SNAKE_CASE_      =     self.sig(__UpperCAmelCase      )
              SCREAMING_SNAKE_CASE_      =     bp_outa * self.wkj.T - self.thre_bpa
              SCREAMING_SNAKE_CASE_      =     self.sig(__UpperCAmelCase      )
              produce_out.extend(bp_outa.getA().tolist()      )
        SCREAMING_SNAKE_CASE_      =     [list(map(self.do_round     ,   __UpperCAmelCase      )      ) for each in produce_out]
        return np.asarray(__UpperCAmelCase      )







  def       __A   (							self       :				List[Any]     ,   __magic_name__       :				int      )     ->		List[str]:
        # return the data of image after convoluting process so we can check it out
        SCREAMING_SNAKE_CASE_      =     np.asmatrix(__UpperCAmelCase      )
        SCREAMING_SNAKE_CASE_      =     self.convolute(
            __UpperCAmelCase     ,   self.conva     ,   self.w_conva     ,   self.thre_conva     ,   conv_step=self.step_conva     ,   )
        SCREAMING_SNAKE_CASE_      =     self.pooling(__UpperCAmelCase     ,   self.size_poolinga      )

        return data_conveda, data_pooleda


if __name__ == "__main__":
      pass







'''simple docstring'''





import json
import os
import subprocess
import unittest
from ast import literal_eval

import pytest
from parameterized import parameterized, parameterized_class

from . import is_sagemaker_available


if is_sagemaker_available():
 from sagemaker import Session, TrainingJobAnalytics
 from sagemaker.huggingface import HuggingFace






@pytest.mark.skipif(
    literal_eval(os.getenv('''TEST_SAGEMAKER'''						, '''False'''     )     ) is not True						, reason='''Skipping test because should only be run when releasing minor transformers version'''						, )
@pytest.mark.usefixtures('''sm_env'''     )
@parameterized_class(
    [
        {
            '''framework''': '''pytorch''',
            '''script''': '''run_glue.py''',
            '''model_name_or_path''': '''distilbert-base-cased''',
            '''instance_type''': '''ml.p3.16xlarge''',
            '''results''': {'''train_runtime''': 6_5_0, '''eval_accuracy''': 0.7, '''eval_loss''': 0.6},
        },
        {
            '''framework''': '''pytorch''',
            '''script''': '''run_ddp.py''',
            '''model_name_or_path''': '''distilbert-base-cased''',
            '''instance_type''': '''ml.p3.16xlarge''',
            '''results''': {'''train_runtime''': 6_0_0, '''eval_accuracy''': 0.7, '''eval_loss''': 0.6},
        },
        {
            '''framework''': '''tensorflow''',
            '''script''': '''run_tf_dist.py''',
            '''model_name_or_path''': '''distilbert-base-cased''',
            '''instance_type''': '''ml.p3.16xlarge''',
            '''results''': {'''train_runtime''': 6_0_0, '''eval_accuracy''': 0.6, '''eval_loss''': 0.7},
        },
    ]     )
class        lowerCAmelCase_(				unittest.TestCase     ):
  '''simple docstring'''




  def 							UpperCAmelCase_	(    self      )    ->		Optional[Any]:
      if self.framework == "pytorch":
          subprocess.run(
              F"""cp ./examples/pytorch/text-classification/run_glue.py {self.env.test_path}/run_glue.py""".split()  ,encoding="""utf-8"""  ,check=__UpperCAmelCase  ,)
      assert hasattr(self  ,"""env"""      )




  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase      )    ->		Optional[Any]:
      lowerCAmelCase__ :  Optional[int]							      =					F"""{self.env.base_job_name}-{instance_count}-{'ddp' if 'ddp' in self.script else 'smd'}"""
      # distributed data settings
      lowerCAmelCase__ :  Any							      =					{"""smdistributed""": {"""dataparallel""": {"""enabled""": True}}} if self.script != """run_ddp.py""" else None

      # creates estimator
      return HuggingFace(
          entry_point=self.script  ,source_dir=self.env.test_path  ,role=self.env.role  ,image_uri=self.env.image_uri  ,base_job_name=__UpperCAmelCase  ,instance_count=__UpperCAmelCase  ,instance_type=self.instance_type  ,debugger_hook_config=__UpperCAmelCase  ,hyperparameters={**self.env.distributed_hyperparameters, """model_name_or_path""": self.model_name_or_path}  ,metric_definitions=self.env.metric_definitions  ,distribution=__UpperCAmelCase  ,py_version="""py36"""  ,)




  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase      )    ->		Optional[Any]:
      TrainingJobAnalytics(__UpperCAmelCase      ).export_csv(F"""{self.env.test_path}/{job_name}_metrics.csv"""      )






  @parameterized.expand([(2,)]      )
  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase      )    ->		Any:
      # create estimator
      lowerCAmelCase__ :  List[Any]							      =					self.create_estimator(__UpperCAmelCase      )

      # run training
      estimator.fit()

      # result dataframe
      lowerCAmelCase__ :  Any							      =					TrainingJobAnalytics(estimator.latest_training_job.name      ).dataframe()

      # extract kpis
      lowerCAmelCase__ :  int							      =					list(result_metrics_df[result_metrics_df.metric_name == """eval_accuracy"""]["""value"""]      )
      lowerCAmelCase__ :  List[Any]							      =					list(result_metrics_df[result_metrics_df.metric_name == """eval_loss"""]["""value"""]      )
      # get train time from SageMaker job, this includes starting, preprocessing, stopping
      lowerCAmelCase__ :  List[str]							      =					(
          Session().describe_training_job(estimator.latest_training_job.name      ).get("""TrainingTimeInSeconds"""  ,99_9999      )
      )

      # assert kpis
      assert train_runtime <= self.results["train_runtime"]
      assert all(t >= self.results["""eval_accuracy"""] for t in eval_accuracy      )
      assert all(t <= self.results["""eval_loss"""] for t in eval_loss      )

      # dump tests result into json file to share in PR
      with open(F"""{estimator.latest_training_job.name}.json"""  ,"""w"""      ) as outfile:
          json.dump({"""train_time""": train_runtime, """eval_accuracy""": eval_accuracy, """eval_loss""": eval_loss}  ,__UpperCAmelCase      )


