
"""simple docstring"""



import pickle

import numpy as np
from matplotlib import pyplot as plt




class 		a_						:







    def __init__(			self			:       Dict     ,					__UpperCamelCase			:       str     ,					__UpperCamelCase			:       Dict     ,					__UpperCamelCase			:       List[str]     ,					__UpperCamelCase			:       Any     ,					__UpperCamelCase			:       Optional[Any]     ,					__UpperCamelCase			:       Any=0.2     ,					__UpperCamelCase			:       Dict=0.2     )		->Any:






        '''simple docstring'''






        _UpperCAmelCase          =   bp_numa
        _UpperCAmelCase          =   bp_numa
        _UpperCAmelCase          =   bp_numa
        _UpperCAmelCase          =   conva_get[:2]
        _UpperCAmelCase          =   conva_get[2]
        _UpperCAmelCase          =   size_pa
        _UpperCAmelCase          =   rate_w
        _UpperCAmelCase          =   rate_t
        _UpperCAmelCase          =   [
            np.mat(-1 * np.random.rand(self.conva[0]     ,					self.conva[0]     ) + 0.5     )
            for i in range(self.conva[1]     )
        ]
        _UpperCAmelCase          =   np.mat(-1 * np.random.rand(self.num_bpa     ,					self.num_bpa     ) + 0.5     )
        _UpperCAmelCase          =   np.mat(-1 * np.random.rand(self.num_bpa     ,					self.num_bpa     ) + 0.5     )
        _UpperCAmelCase          =   -2 * np.random.rand(self.conva[1]     ) + 1
        _UpperCAmelCase          =   -2 * np.random.rand(self.num_bpa     ) + 1
        _UpperCAmelCase          =   -2 * np.random.rand(self.num_bpa     ) + 1







    def      _snake_case					(			self			:       List[Any]     ,					__UpperCamelCase			:       List[Any]     )		->Optional[int]:






        '''simple docstring'''






        _UpperCAmelCase          =   {
            """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(_lowerCAmelCase     ,					"""wb"""     ) as f:
            pickle.dump(_lowerCAmelCase     ,					_lowerCAmelCase     )

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







    @classmethod
    def      _snake_case					(			cls			:       Dict     ,					__UpperCamelCase			:       List[Any]     )		->List[str]:






        '''simple docstring'''






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

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







    def      _snake_case					(			self			:       Optional[Any]     ,					__UpperCamelCase			:       Dict     )		->Optional[Any]:






        '''simple docstring'''






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







    def      _snake_case					(			self			:       str     ,					__UpperCamelCase			:       List[str]     )		->Tuple:






        '''simple docstring'''






        return round(_lowerCAmelCase     ,					3     )







    def      _snake_case					(			self			:       Union[str, Any]     ,					__UpperCamelCase			:       Optional[int]     ,					__UpperCamelCase			:       Tuple     ,					__UpperCamelCase			:       Dict     ,					__UpperCamelCase			:       int     ,					__UpperCamelCase			:       Dict     )		->Union[str, Any]:






        '''simple docstring'''






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

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







    def      _snake_case					(			self			:       Union[str, Any]     ,					__UpperCamelCase			:       Tuple     ,					__UpperCamelCase			:       Any     ,					__UpperCamelCase			:       int="average_pool"     )		->Any:






        '''simple docstring'''






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







    def      _snake_case					(			self			:       int     ,					__UpperCamelCase			:       str     )		->Tuple:






        '''simple docstring'''






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







    def      _snake_case					(			self			:       Union[str, Any]     ,					__UpperCamelCase			:       str     )		->Any:






        '''simple docstring'''






        _UpperCAmelCase          =   np.asarray(_lowerCAmelCase     )
        _UpperCAmelCase          =   np.shape(_lowerCAmelCase     )
        _UpperCAmelCase          =   data_mat.reshape(1     ,					shapes[0] * shapes[1]     )
        return data_expanded







    def      _snake_case					(			self			:       List[Any]     ,					__UpperCamelCase			:       int     ,					__UpperCamelCase			:       List[str]     ,					__UpperCamelCase			:       Union[str, Any]     ,					__UpperCamelCase			:       Tuple     ,					__UpperCamelCase			:       Dict     )		->Optional[Any]:






        '''simple docstring'''






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







    def      _snake_case					(			self			:       str     ,					__UpperCamelCase			:       Tuple     ,					__UpperCamelCase			:       str     ,					__UpperCamelCase			:       Optional[Any]     ,					__UpperCamelCase			:       List[Any]     ,					__UpperCamelCase			:       Optional[int]     ,					__UpperCamelCase			:       Optional[int]=bool     )		->Optional[Any]:






        '''simple docstring'''






        print("""----------------------Start Training-------------------------"""     )
        print((""" - - Shape: Train_Data  """, np.shape(_lowerCAmelCase     ))     )
        print((""" - - Shape: Teach_Data  """, np.shape(_lowerCAmelCase     ))     )
        _UpperCAmelCase          =   0
        _UpperCAmelCase          =   []
        _UpperCAmelCase          =   1_00_00
        while rp < n_repeat and mse >= error_accuracy:
            _UpperCAmelCase          =   0
            print(f"""-------------Learning Time {rp}--------------"""     )
            for p in range(len(_lowerCAmelCase     )     ):
                # print('------------Learning Image: %d--------------'%p)
                _UpperCAmelCase          =   np.asmatrix(datas_train[p]     )
                _UpperCAmelCase          =   np.asarray(datas_teach[p]     )
                _UpperCAmelCase    ,_UpperCAmelCase          =   self.convolute(
                    _lowerCAmelCase     ,					self.conva     ,					self.w_conva     ,					self.thre_conva     ,					conv_step=self.step_conva     ,					)
                _UpperCAmelCase          =   self.pooling(_lowerCAmelCase     ,					self.size_poolinga     )
                _UpperCAmelCase          =   np.shape(_lowerCAmelCase     )
                _UpperCAmelCase          =   self._expand(_lowerCAmelCase     )
                _UpperCAmelCase          =   data_bp_input

                _UpperCAmelCase          =   np.dot(_lowerCAmelCase     ,					self.vji.T     ) - self.thre_bpa
                _UpperCAmelCase          =   self.sig(_lowerCAmelCase     )
                _UpperCAmelCase          =   np.dot(_lowerCAmelCase     ,					self.wkj.T     ) - self.thre_bpa
                _UpperCAmelCase          =   self.sig(_lowerCAmelCase     )

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

                _UpperCAmelCase          =   pd_i_all / (self.size_poolinga * self.size_poolinga)
                _UpperCAmelCase          =   pd_conva_pooled.T.getA().tolist()
                _UpperCAmelCase          =   self._calculate_gradient_from_pool(
                    _lowerCAmelCase     ,					_lowerCAmelCase     ,					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          =   self._expand_mat(pd_conva_all[k_conv]     )
                    _UpperCAmelCase          =   self.rate_weight * np.dot(_lowerCAmelCase     ,					_lowerCAmelCase     )

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

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

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






        '''simple docstring'''






        _UpperCAmelCase          =   []
        print("""-------------------Start Testing-------------------------"""     )
        print((""" - - Shape: Test_Data  """, np.shape(_lowerCAmelCase     ))     )
        for p in range(len(_lowerCAmelCase     )     ):
            _UpperCAmelCase          =   np.asmatrix(datas_test[p]     )
            _UpperCAmelCase    ,_UpperCAmelCase          =   self.convolute(
                _lowerCAmelCase     ,					self.conva     ,					self.w_conva     ,					self.thre_conva     ,					conv_step=self.step_conva     ,					)
            _UpperCAmelCase          =   self.pooling(_lowerCAmelCase     ,					self.size_poolinga     )
            _UpperCAmelCase          =   self._expand(_lowerCAmelCase     )

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







    def      _snake_case					(			self			:       Dict     ,					__UpperCamelCase			:       List[str]     )		->Tuple:






        '''simple docstring'''






        _UpperCAmelCase          =   np.asmatrix(_lowerCAmelCase     )
        _UpperCAmelCase    ,_UpperCAmelCase          =   self.convolute(
            _lowerCAmelCase     ,					self.conva     ,					self.w_conva     ,					self.thre_conva     ,					conv_step=self.step_conva     ,					)
        _UpperCAmelCase          =   self.pooling(_lowerCAmelCase     ,					self.size_poolinga     )

        return data_conveda, data_pooleda


if __name__ == "__main__":
 pass

"""simple docstring"""



import enum
import warnings

from ..tokenization_utils import TruncationStrategy
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_tf_available():
 import tensorflow as tf

 from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING

if is_torch_available():
 from ..models.auto.modeling_auto import MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING

a					:	List[str]							=		logging.get_logger(__name__)




class 		a_						( enum.Enum	):
    a      :				Optional[Any]     =				0
    a      :				Dict     =				1







@add_end_docstrings(_UpperCAmelCase	)
class 		a_						( _UpperCAmelCase	):
    a      :				List[Any]     =				'generated'







    def __init__(			self			:       int     ,					*__UpperCamelCase			:       Optional[int]     ,					**__UpperCamelCase			:       str     )		->Any:






        '''simple docstring'''






        super().__init__(*__UpperCamelCase     ,					**__UpperCamelCase     )

        self.check_model_type(
            TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
            if self.framework == """tf"""
            else MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING     )







    def      _snake_case					(			self			:       Optional[int]     ,					__UpperCamelCase			:       List[Any]=None     ,					__UpperCamelCase			:       Union[str, Any]=None     ,					__UpperCamelCase			:       Any=None     ,					__UpperCamelCase			:       int=None     ,					__UpperCamelCase			:       Tuple=None     ,					__UpperCamelCase			:       Dict=None     ,					**__UpperCamelCase			:       Any     ,					)		->Tuple:






        '''simple docstring'''






        _UpperCAmelCase          =   {}
        if truncation is not None:
            _UpperCAmelCase          =   truncation

        _UpperCAmelCase          =   generate_kwargs

        _UpperCAmelCase          =   {}
        if return_tensors is not None and return_type is None:
            _UpperCAmelCase          =   ReturnType.TENSORS if return_tensors else ReturnType.TEXT
        if return_type is not None:
            _UpperCAmelCase          =   return_type

        if clean_up_tokenization_spaces is not None:
            _UpperCAmelCase          =   clean_up_tokenization_spaces

        if stop_sequence is not None:
            _UpperCAmelCase          =   self.tokenizer.encode(__UpperCamelCase     ,					add_special_tokens=__UpperCamelCase     )
            if len(__UpperCamelCase     ) > 1:
                warnings.warn(
                    """Stopping on a multiple token sequence is not yet supported on transformers. The first token of"""
                    """ the stop sequence will be used as the stop sequence string in the interim."""     )
            _UpperCAmelCase          =   stop_sequence_ids[0]

        return preprocess_params, forward_params, postprocess_params







    def      _snake_case					(			self			:       int     ,					__UpperCamelCase			:       int     ,					__UpperCamelCase			:       int     ,					__UpperCamelCase			:       int     )		->Any:






        '''simple docstring'''






        return True







    def      _snake_case					(			self			:       Optional[Any]     ,					*__UpperCamelCase			:       Any     ,					__UpperCamelCase			:       Dict     )		->Dict:






        '''simple docstring'''






        _UpperCAmelCase          =   self.model.config.prefix if self.model.config.prefix is not None else """"""
        if isinstance(args[0]     ,					__UpperCamelCase     ):
            if self.tokenizer.pad_token_id is None:
                raise ValueError("""Please make sure that the tokenizer has a pad_token_id when using a batch input"""     )
            _UpperCAmelCase          =   ([prefix + arg for arg in args[0]],)
            _UpperCAmelCase          =   True

        elif isinstance(args[0]     ,					__UpperCamelCase     ):
            _UpperCAmelCase          =   (prefix + args[0],)
            _UpperCAmelCase          =   False
        else:
            raise ValueError(
                f""" `args[0]`: {args[0]} have the wrong format. The should be either of type `str` or type `list`"""     )
        _UpperCAmelCase          =   self.tokenizer(*__UpperCamelCase     ,					padding=__UpperCamelCase     ,					truncation=__UpperCamelCase     ,					return_tensors=self.framework     )
        # This is produced by tokenizers but is an invalid generate kwargs
        if "token_type_ids" in inputs:
            del inputs["token_type_ids"]
        return inputs







    def __call__(			self			:       Dict     ,					*__UpperCamelCase			:       str     ,					**__UpperCamelCase			:       Dict     )		->Optional[Any]:






        '''simple docstring'''






        _UpperCAmelCase          =   super().__call__(*__UpperCamelCase     ,					**__UpperCamelCase     )
        if (
            isinstance(args[0]     ,					__UpperCamelCase     )
            and all(isinstance(__UpperCamelCase     ,					__UpperCamelCase     ) for el in args[0]     )
            and all(len(__UpperCamelCase     ) == 1 for res in result     )
        ):
            return [res[0] for res in result]
        return result







    def      _snake_case					(			self			:       List[str]     ,					__UpperCamelCase			:       Any     ,					__UpperCamelCase			:       str=TruncationStrategy.DO_NOT_TRUNCATE     ,					**__UpperCamelCase			:       Optional[int]     )		->Optional[int]:






        '''simple docstring'''






        _UpperCAmelCase          =   self._parse_and_tokenize(__UpperCamelCase     ,					truncation=__UpperCamelCase     ,					**__UpperCamelCase     )
        return inputs







    def      _snake_case					(			self			:       str     ,					__UpperCamelCase			:       Dict     ,					**__UpperCamelCase			:       Dict     )		->Tuple:






        '''simple docstring'''






        if self.framework == "pt":
            _UpperCAmelCase    ,_UpperCAmelCase          =   model_inputs["""input_ids"""].shape
        elif self.framework == "tf":
            _UpperCAmelCase    ,_UpperCAmelCase          =   tf.shape(model_inputs["""input_ids"""]     ).numpy()

        _UpperCAmelCase          =   generate_kwargs.get("""min_length"""     ,					self.model.config.min_length     )
        _UpperCAmelCase          =   generate_kwargs.get("""max_length"""     ,					self.model.config.max_length     )
        self.check_inputs(__UpperCamelCase     ,					generate_kwargs["""min_length"""]     ,					generate_kwargs["""max_length"""]     )
        _UpperCAmelCase          =   self.model.generate(**__UpperCamelCase     ,					**__UpperCamelCase     )
        _UpperCAmelCase          =   output_ids.shape[0]
        if self.framework == "pt":
            _UpperCAmelCase          =   output_ids.reshape(__UpperCamelCase     ,					out_b // in_b     ,					*output_ids.shape[1:]     )
        elif self.framework == "tf":
            _UpperCAmelCase          =   tf.reshape(__UpperCamelCase     ,					(in_b, out_b // in_b, *output_ids.shape[1:])     )
        return {"output_ids": output_ids}







    def      _snake_case					(			self			:       Tuple     ,					__UpperCamelCase			:       str     ,					__UpperCamelCase			:       Union[str, Any]=ReturnType.TEXT     ,					__UpperCamelCase			:       int=False     )		->Any:






        '''simple docstring'''






        _UpperCAmelCase          =   []
        for output_ids in model_outputs["output_ids"][0]:
            if return_type == ReturnType.TENSORS:
                _UpperCAmelCase          =   {f"""{self.return_name}_token_ids""": output_ids}
            elif return_type == ReturnType.TEXT:
                _UpperCAmelCase          =   {
                    f"""{self.return_name}_text""": self.tokenizer.decode(
                        __UpperCamelCase     ,					skip_special_tokens=__UpperCamelCase     ,					clean_up_tokenization_spaces=__UpperCamelCase     ,					)
                }
            records.append(__UpperCamelCase     )
        return records







@add_end_docstrings(_UpperCAmelCase	)
class 		a_						( _UpperCAmelCase	):
    a      :				List[Any]     =				'summary'







    def __call__(			self			:       Optional[Any]     ,					*__UpperCamelCase			:       Optional[Any]     ,					**__UpperCamelCase			:       Optional[int]     )		->Any:






        '''simple docstring'''






        return super().__call__(*__UpperCamelCase     ,					**__UpperCamelCase     )







    def      _snake_case					(			self			:       str     ,					__UpperCamelCase			:       int     ,					__UpperCamelCase			:       int     ,					__UpperCamelCase			:       int     )		->bool:






        '''simple docstring'''






        if max_length < min_length:
            logger.warning(f"""Your min_length={min_length} must be inferior than your max_length={max_length}."""     )

        if input_length < max_length:
            logger.warning(
                f"""Your max_length is set to {max_length}, but your input_length is only {input_length}. Since this is """
                """a summarization task, where outputs shorter than the input are typically wanted, you might """
                f"""consider decreasing max_length manually, e.g. summarizer('...', max_length={input_length//2})"""     )







@add_end_docstrings(_UpperCAmelCase	)
class 		a_						( _UpperCAmelCase	):
    a      :				Optional[int]     =				'translation'







    def      _snake_case					(			self			:       Union[str, Any]     ,					__UpperCamelCase			:       int     ,					__UpperCamelCase			:       int     ,					__UpperCamelCase			:       int     )		->Any:






        '''simple docstring'''






        if input_length > 0.9 * max_length:
            logger.warning(
                f"""Your input_length: {input_length} is bigger than 0.9 * max_length: {max_length}. You might consider """
                """increasing your max_length manually, e.g. translator('...', max_length=400)"""     )
        return True







    def      _snake_case					(			self			:       Tuple     ,					*__UpperCamelCase			:       List[str]     ,					__UpperCamelCase			:       Tuple=TruncationStrategy.DO_NOT_TRUNCATE     ,					__UpperCamelCase			:       Tuple=None     ,					__UpperCamelCase			:       Union[str, Any]=None     )		->Tuple:






        '''simple docstring'''






        if getattr(self.tokenizer     ,					"""_build_translation_inputs"""     ,					__UpperCamelCase     ):
            return self.tokenizer._build_translation_inputs(
                *__UpperCamelCase     ,					return_tensors=self.framework     ,					truncation=__UpperCamelCase     ,					src_lang=__UpperCamelCase     ,					tgt_lang=__UpperCamelCase     )
        else:
            return super()._parse_and_tokenize(*__UpperCamelCase     ,					truncation=__UpperCamelCase     )







    def      _snake_case					(			self			:       List[str]     ,					__UpperCamelCase			:       int=None     ,					__UpperCamelCase			:       int=None     ,					**__UpperCamelCase			:       Any     )		->int:






        '''simple docstring'''






        _UpperCAmelCase    ,_UpperCAmelCase    ,_UpperCAmelCase          =   super()._sanitize_parameters(**__UpperCamelCase     )
        if src_lang is not None:
            _UpperCAmelCase          =   src_lang
        if tgt_lang is not None:
            _UpperCAmelCase          =   tgt_lang
        if src_lang is None and tgt_lang is None:
            # Backward compatibility, direct arguments use is preferred.
            _UpperCAmelCase          =   kwargs.get("""task"""     ,					self.task     )
            _UpperCAmelCase          =   task.split("""_"""     )
            if task and len(__UpperCamelCase     ) == 4:
                # translation, XX, to YY
                _UpperCAmelCase          =   items[1]
                _UpperCAmelCase          =   items[3]
        return preprocess_params, forward_params, postprocess_params







    def __call__(			self			:       List[str]     ,					*__UpperCamelCase			:       str     ,					**__UpperCamelCase			:       Optional[Any]     )		->int:






        '''simple docstring'''






        return super().__call__(*__UpperCamelCase     ,					**__UpperCamelCase     )