





"""simple docstring"""
import inspect
import os
import re

from transformers.configuration_utils import PretrainedConfig
from transformers.utils import direct_transformers_import


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
_snake_case     :		Optional[Any]       						= """src/transformers"""


# This is to make sure the transformers module imported is the one in the repo.
_snake_case     :		List[str]       						= direct_transformers_import(PATH_TO_TRANSFORMERS)

_snake_case     :		Union[str, Any]       						= transformers.models.auto.configuration_auto.CONFIG_MAPPING

_snake_case     :		Union[str, Any]       						= {
    # used to compute the property `self.chunk_length`
    """EncodecConfig""": ["""overlap"""],
    # used as `self.bert_model = BertModel(config, ...)`
    """DPRConfig""": True,
    # not used in modeling files, but it's an important information
    """FSMTConfig""": ["""langs"""],
    # used internally in the configuration class file
    """GPTNeoConfig""": ["""attention_types"""],
    # used internally in the configuration class file
    """EsmConfig""": ["""is_folding_model"""],
    # used during training (despite we don't have training script for these models yet)
    """Mask2FormerConfig""": ["""ignore_value"""],
    # `ignore_value` used during training (despite we don't have training script for these models yet)
    # `norm` used in conversion script (despite not using in the modeling file)
    """OneFormerConfig""": ["""ignore_value""", """norm"""],
    # used during preprocessing and collation, see `collating_graphormer.py`
    """GraphormerConfig""": ["""spatial_pos_max"""],
    # used internally in the configuration class file
    """T5Config""": ["""feed_forward_proj"""],
    # used internally in the configuration class file
    # `tokenizer_class` get default value `T5Tokenizer` intentionally
    """MT5Config""": ["""feed_forward_proj""", """tokenizer_class"""],
    """UMT5Config""": ["""feed_forward_proj""", """tokenizer_class"""],
    # used internally in the configuration class file
    """LongT5Config""": ["""feed_forward_proj"""],
    # used internally in the configuration class file
    """SwitchTransformersConfig""": ["""feed_forward_proj"""],
    # having default values other than `1e-5` - we can't fix them without breaking
    """BioGptConfig""": ["""layer_norm_eps"""],
    # having default values other than `1e-5` - we can't fix them without breaking
    """GLPNConfig""": ["""layer_norm_eps"""],
    # having default values other than `1e-5` - we can't fix them without breaking
    """SegformerConfig""": ["""layer_norm_eps"""],
    # having default values other than `1e-5` - we can't fix them without breaking
    """CvtConfig""": ["""layer_norm_eps"""],
    # having default values other than `1e-5` - we can't fix them without breaking
    """PerceiverConfig""": ["""layer_norm_eps"""],
    # used internally to calculate the feature size
    """InformerConfig""": ["""num_static_real_features""", """num_time_features"""],
    # used internally to calculate the feature size
    """TimeSeriesTransformerConfig""": ["""num_static_real_features""", """num_time_features"""],
    # used internally to calculate the feature size
    """AutoformerConfig""": ["""num_static_real_features""", """num_time_features"""],
    # used internally to calculate `mlp_dim`
    """SamVisionConfig""": ["""mlp_ratio"""],
    # For (head) training, but so far not implemented
    """ClapAudioConfig""": ["""num_classes"""],
    # Not used, but providing useful information to users
    """SpeechT5HifiGanConfig""": ["""sampling_rate"""],
}


# TODO (ydshieh): Check the failing cases, try to fix them or move some cases to the above block once we are sure
SPECIAL_CASES_TO_ALLOW.update(
    {
        'CLIPSegConfig': True,
        'DeformableDetrConfig': True,
        'DetaConfig': True,
        'DinatConfig': True,
        'DonutSwinConfig': True,
        'EfficientFormerConfig': True,
        'FSMTConfig': True,
        'JukeboxConfig': True,
        'LayoutLMv2Config': True,
        'MaskFormerSwinConfig': True,
        'MT5Config': True,
        'NatConfig': True,
        'OneFormerConfig': True,
        'PerceiverConfig': True,
        'RagConfig': True,
        'SpeechT5Config': True,
        'SwinConfig': True,
        'Swin2SRConfig': True,
        'Swinv2Config': True,
        'SwitchTransformersConfig': True,
        'TableTransformerConfig': True,
        'TapasConfig': True,
        'TransfoXLConfig': True,
        'UniSpeechConfig': True,
        'UniSpeechSatConfig': True,
        'WavLMConfig': True,
        'WhisperConfig': True,
        # TODO: @Arthur (for `alignment_head` and `alignment_layer`)
        'JukeboxPriorConfig': True,
        # TODO: @Younes (for `is_decoder`)
        'Pix2StructTextConfig': True,
    }
)






def 					A__				(			UpperCamelCase					,  UpperCamelCase					,  UpperCamelCase					,  UpperCamelCase       ):
							A		=  False
							for attribute in attributes:
														for modeling_source in source_strings:
																					# check if we can find `config.xxx`, `getattr(config, "xxx", ...)` or `getattr(self.config, "xxx", ...)`
																					if (
																					    F"config.{attribute}" in modeling_source
																					    or F"getattr(config, \"{attribute}\"" in modeling_source
																					    or F"getattr(self.config, \"{attribute}\"" in modeling_source
																					):
																												A		=  True
																					# Deal with multi-line cases
																					elif (
																					    re.search(
																					        rF"getattr[ \t\v\n\r\f]*\([ \t\v\n\r\f]*(self\.)?config,[ \t\v\n\r\f]*\"{attribute}\""					,  _lowerCAmelCase					,  )
																					    is not None
																					):
																												A		=  True
																					# `SequenceSummary` is called with `SequenceSummary(config)`
																					elif attribute in [
																					    "summary_type",
																					    "summary_use_proj",
																					    "summary_activation",
																					    "summary_last_dropout",
																					    "summary_proj_to_labels",
																					    "summary_first_dropout",
																					]:
																												if "SequenceSummary" in modeling_source:
																																			A		=  True
																					if attribute_used:
																												break
														if attribute_used:
																					break

																					 # common and important attributes, even if they do not always appear in the modeling files
							A		=  [
							    "bos_index",
							    "eos_index",
							    "pad_index",
							    "unk_index",
							    "mask_index",
							    "image_size",
							    "use_cache",
							    "out_features",
							    "out_indices",
							]
							A		=  ["encoder_no_repeat_ngram_size"]

							# Special cases to be allowed
							A		=  True
							if not attribute_used:
														A		=  False
														for attribute in attributes:
																					# Allow if the default value in the configuration class is different from the one in `PretrainedConfig`
																					if attribute in ["is_encoder_decoder"] and default_value is True:
																												A		=  True
																					elif attribute in ["tie_word_embeddings"] and default_value is False:
																												A		=  True

																					# Allow cases without checking the default value in the configuration class
																					elif attribute in attributes_to_allow + attributes_used_in_generation:
																												A		=  True
																					elif attribute.endswith("_token_id"       ):
																												A		=  True

																					# configuration class specific cases
																					if not case_allowed:
																												A		=  SPECIAL_CASES_TO_ALLOW.get(config_class.__name__					,  []       )
																												A		=  allowed_cases is True or attribute in allowed_cases

							return attribute_used or case_allowed






def 					A__				(			UpperCamelCase       ):
							A		=  dict(inspect.signature(config_class.__init__       ).parameters       )
							A		=  [x for x in list(signature.keys()       ) if x not in ["self", "kwargs"]]
							A		=  [signature[param].default for param in parameter_names]

							# If `attribute_map` exists, an attribute can have different names to be used in the modeling files, and as long
							# as one variant is used, the test should pass
							A		=  {}
							if len(config_class.attribute_map       ) > 0:
														A		=  {v: k for k, v in config_class.attribute_map.items()}

							# Get the path to modeling source files
							A		=  inspect.getsourcefile(_lowerCAmelCase       )
							A		=  os.path.dirname(_lowerCAmelCase       )
							# Let's check against all frameworks: as long as one framework uses an attribute, we are good.
							A		=  [os.path.join(_lowerCAmelCase					,  _lowerCAmelCase       ) for fn in os.listdir(_lowerCAmelCase       ) if fn.startswith("modeling_"       )]

							# Get the source code strings
							A		=  []
							for path in modeling_paths:
														if os.path.isfile(_lowerCAmelCase       ):
																					with open(_lowerCAmelCase       ) as fp:
																												modeling_sources.append(fp.read()       )

							A		=  []
							for config_param, default_value in zip(_lowerCAmelCase					,  _lowerCAmelCase       ):
														# `attributes` here is all the variant names for `config_param`
														A		=  [config_param]
														# some configuration classes have non-empty `attribute_map`, and both names could be used in the
														# corresponding modeling files. As long as one of them appears, it is fine.
														if config_param in reversed_attribute_map:
																					attributes.append(reversed_attribute_map[config_param]       )

														if not check_attribute_being_used(_lowerCAmelCase					,  _lowerCAmelCase					,  _lowerCAmelCase					,  _lowerCAmelCase       ):
																					unused_attributes.append(attributes[0]       )

							return sorted(_lowerCAmelCase       )






def 					A__				(			):
							A		=  {}
							for _config_class in list(CONFIG_MAPPING.values()       ):
														# Skip deprecated models
														if "models.deprecated" in _config_class.__module__:
																					continue
														# Some config classes are not in `CONFIG_MAPPING` (e.g. `CLIPVisionConfig`, `Blip2VisionConfig`, etc.)
														A		=  [
														    cls
														    for name, cls in inspect.getmembers(
														        inspect.getmodule(_config_class       )					,  lambda UpperCamelCase       : inspect.isclass(_lowerCAmelCase       )
														        and issubclass(_lowerCAmelCase					,  _lowerCAmelCase       )
														        and inspect.getmodule(_lowerCAmelCase       ) == inspect.getmodule(_config_class       )					,  )
														]
														for config_class in config_classes_in_module:
																					A		=  check_config_attributes_being_used(_lowerCAmelCase       )
																					if len(_lowerCAmelCase       ) > 0:
																												A		=  unused_attributes

							if len(_lowerCAmelCase       ) > 0:
														A		=  "The following configuration classes contain unused attributes in the corresponding modeling files:\n"
														for name, attributes in configs_with_unused_attributes.items():
																					error += F"{name}: {attributes}\n"

														raise ValueError(_lowerCAmelCase       )


if __name__ == "__main__":
		check_config_attributes()









import pickle

import numpy as np
from matplotlib import pyplot as plt



class 					A__   :





      def __init__( self       ,							A_       ,							A_       ,							A_       ,							A_       ,							A_       ,							A_=0.2       ,							A_=0.2   ):
       '''simple docstring'''




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





      def        __UpperCamelCase( self       ,							A_   ):
       '''simple docstring'''




       UpperCamelCase	:					Optional[int]       	=   {
           "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(A_       ,							"wb"   ) as f:
        pickle.dump(A_       ,							A_   )

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





      @classmethod
      def        __UpperCamelCase( cls       ,							A_   ):
       '''simple docstring'''




       with open(A_       ,							"rb"   ) as f:
        UpperCamelCase	:					Optional[Any]       	=   pickle.load(A_   )  # noqa: S301

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





      def        __UpperCamelCase( self       ,							A_   ):
       '''simple docstring'''




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





      def        __UpperCamelCase( self       ,							A_   ):
       '''simple docstring'''




       return round(A_       ,							3   )





      def        __UpperCamelCase( self       ,							A_       ,							A_       ,							A_       ,							A_       ,							A_   ):
       '''simple docstring'''




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

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





      def        __UpperCamelCase( self       ,							A_       ,							A_       ,							A_="average_pool"   ):
       '''simple docstring'''




       UpperCamelCase	:					Any       	=   len(featuremaps[0]   )
       UpperCamelCase	:					str       	=   int(size_map / size_pooling   )
       UpperCamelCase	:					Optional[int]       	=   []
       for i_map in range(len(A_   )   ):
        UpperCamelCase	:					Tuple       	=   featuremaps[i_map]
        UpperCamelCase	:					Any       	=   []
        for i_focus in range(0       ,							A_       ,							A_   ):
         for j_focus in range(0       ,							A_       ,							A_   ):
          UpperCamelCase	:					int       	=   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(A_   )   )
          elif pooling_type == "max_pooling":
           # max pooling
           map_pooled.append(np.max(A_   )   )
        UpperCamelCase	:					Optional[Any]       	=   np.asmatrix(A_   ).reshape(A_       ,							A_   )
        featuremap_pooled.append(A_   )
       return featuremap_pooled





      def        __UpperCamelCase( self       ,							A_   ):
       '''simple docstring'''




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





      def        __UpperCamelCase( self       ,							A_   ):
       '''simple docstring'''




       UpperCamelCase	:					List[Any]       	=   np.asarray(A_   )
       UpperCamelCase	:					List[Any]       	=   np.shape(A_   )
       UpperCamelCase	:					Any       	=   data_mat.reshape(1       ,							shapes[0] * shapes[1]   )
       return data_expanded





      def        __UpperCamelCase( self       ,							A_       ,							A_       ,							A_       ,							A_       ,							A_   ):
       '''simple docstring'''




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





      def        __UpperCamelCase( self       ,							A_       ,							A_       ,							A_       ,							A_       ,							A_       ,							A_=bool   ):
       '''simple docstring'''




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

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

         # --------------Model Leaning ------------------------
         # calculate error and gradient---------------
         UpperCamelCase	:					List[Any]       	=   np.multiply(
             (data_teach - bp_outa)       ,							np.multiply(A_       ,							(1 - bp_outa)   )   )
         UpperCamelCase	:					str       	=   np.multiply(
             np.dot(A_       ,							self.wkj   )       ,							np.multiply(A_       ,							(1 - bp_outa)   )   )
         UpperCamelCase	:					Any       	=   np.dot(A_       ,							self.vji   )

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

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

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

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

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





      def        __UpperCamelCase( self       ,							A_   ):
       '''simple docstring'''




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

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





      def        __UpperCamelCase( self       ,							A_   ):
       '''simple docstring'''




       UpperCamelCase	:					Union[str, Any]       	=   np.asmatrix(A_   )
       UpperCamelCase	,    UpperCamelCase	:					List[Any]       	=   self.convolute(
           A_       ,							self.conva       ,							self.w_conva       ,							self.thre_conva       ,							conv_step=self.step_conva       ,							)
       UpperCamelCase	:					str       	=   self.pooling(A_       ,							self.size_poolinga   )

       return data_conveda, data_pooleda


if __name__ == "__main__":
    pass
