
"""simple docstring"""


import pickle

import numpy as np
from matplotlib import pyplot as plt







class 					lowerCAmelCase_					:



						'''simple docstring'''


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


						def     _SCREAMING_SNAKE_CASE   (			self  :   Tuple	,A_  :   int   )  -> Optional[Any]:
							# save model dict with pickle
							A     = {
							    '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     _SCREAMING_SNAKE_CASE   (			cls  :   Optional[int]	,A_  :   int   )  -> Dict:
							# read saved model
							with open(A_	,'rb'   ) as f:
								A     = pickle.load(A_   )  # noqa: S301

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


						def     _SCREAMING_SNAKE_CASE   (			self  :   Any	,A_  :   List[str]   )  -> List[Any]:
							return 1 / (1 + np.exp(-1 * x   ))


						def     _SCREAMING_SNAKE_CASE   (			self  :   List[Any]	,A_  :   Dict   )  -> List[str]:
							return round(A_	,3   )


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

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


						def     _SCREAMING_SNAKE_CASE   (			self  :   Dict	,A_  :   Tuple	,A_  :   List[str]	,A_  :   Tuple="average_pool"   )  -> List[Any]:
							# pooling process
							A     = len(featuremaps[0]   )
							A     = int(size_map / size_pooling   )
							A     = []
							for i_map in range(len(A_   )   ):
								A     = featuremaps[i_map]
								A     = []
								for i_focus in range(0	,A_	,A_   ):
									for j_focus in range(0	,A_	,A_   ):
										A     = 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_   )   )
								A     = np.asmatrix(A_   ).reshape(A_	,A_   )
								featuremap_pooled.append(A_   )
							return featuremap_pooled


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


						def     _SCREAMING_SNAKE_CASE   (			self  :   Optional[Any]	,A_  :   str   )  -> Optional[Any]:
							# expanding matrix to one dimension list
							A     = np.asarray(A_   )
							A     = np.shape(A_   )
							A     = data_mat.reshape(1	,shapes[0] * shapes[1]   )
							return data_expanded


						def     _SCREAMING_SNAKE_CASE   (			self  :   Optional[Any]	,A_  :   Dict	,A_  :   List[Any]	,A_  :   int	,A_  :   Optional[Any]	,A_  :   List[Any]   )  -> Tuple:
							A     = []
							A     = 0
							for i_map in range(A_   ):
								A     = np.ones((size_map, size_map)   )
								for i in range(0	,A_	,A_   ):
									for j in range(0	,A_	,A_   ):
										A     = pd_pool[
										    i_pool
										]
										A     = i_pool + 1
								A     = np.multiply(
								    A_	,np.multiply(out_map[i_map]	,(1 - out_map[i_map])   )   )
								pd_all.append(A_   )
							return pd_all


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

									A     = np.dot(A_	,self.vji.T   ) - self.thre_bpa
									A     = self.sig(A_   )
									A     = np.dot(A_	,self.wkj.T   ) - self.thre_bpa
									A     = self.sig(A_   )

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

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

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

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

							def draw_error():
								A     = [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     _SCREAMING_SNAKE_CASE   (			self  :   List[Any]	,A_  :   List[Any]   )  -> Union[str, Any]:
							# model predict
							A     = []
							print('-------------------Start Testing-------------------------'   )
							print((' - - Shape: Test_Data  ', np.shape(A_   ))   )
							for p in range(len(A_   )   ):
								A     = np.asmatrix(datas_test[p]   )
								A ,   A     = self.convolute(
								    A_	,self.conva	,self.w_conva	,self.thre_conva	,conv_step=self.step_conva	,)
								A     = self.pooling(A_	,self.size_poolinga   )
								A     = self._expand(A_   )

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




						def     _SCREAMING_SNAKE_CASE   (			self  :   Optional[int]	,A_  :   Optional[Any]   )  -> Any:
							# return the data of image after convoluting process so we can check it out
							A     = np.asmatrix(A_   )
							A ,   A     = self.convolute(
							    A_	,self.conva	,self.w_conva	,self.thre_conva	,conv_step=self.step_conva	,)
							A     = self.pooling(A_	,self.size_poolinga   )

							return data_conveda, data_pooleda


if __name__ == "__main__":
		pass

"""simple docstring"""




import argparse
import re

from flax.traverse_util import flatten_dict, unflatten_dict
from tax import checkpoints

from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration
from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model
from transformers.utils import logging


logging.set_verbosity_info()


# should not include what is already done by the `from_pt` argument
a		:       int     						=       {
    '/attention/': '/0/SelfAttention/',
    '/self_attention/': '/0/SelfAttention/',
    '/encoder_decoder_attention/': '/1/EncDecAttention/',
    'value': 'v',
    'query': 'q',
    'key': 'k',
    'out': 'o',
    'pre_self_attention_layer_norm': '0/layer_norm',
    'pre_cross_attention_layer_norm': '1/layer_norm',
    'pre_attention_layer_norm': '0/layer_norm',  # previously 1, but seems wrong
    'token_embedder': 'shared',
    'encoder_norm': 'final_layer_norm',
    'decoder_norm': 'final_layer_norm',
    'relpos_bias/rel_embedding': 'block/0/layer/0/SelfAttention/relative_attention_bias/weight',
    'router/router_weights/w/': 'router/classifier/',
    'roer/roer_weights/w/': 'router/classifier/',
    'logits_dense': 'lm_head',
}



def     __magic_name__		(					UpperCamelCase     :		List[Any]     )     ->						Union[str, Any]:
    # 1. in HF T5, we have block.{x}.layer.{y}. which corresponds to layer.{x} in
    # the original model
    a__		=			list(s_dict.keys()     )
    for key in keys:
        a__		=			r'.*/layers_(\d+)'
        a__		=			key
        if re.match(UpperCamelCase							,		UpperCamelCase     ):
            a__		=			re.sub(r'layers_(\d+)'							,		r'block/\1/layer'							,		UpperCamelCase     )

        a__		=			r'(encoder|decoder)\/'

        if re.match(UpperCamelCase							,		UpperCamelCase     ):
            a__		=			re.match(UpperCamelCase							,		UpperCamelCase     ).groups()
            if groups[0] == "encoder":
                a__		=			re.sub(r'/mlp/'							,		r'/1/mlp/'							,		UpperCamelCase     )
                a__		=			re.sub(r'/pre_mlp_layer_norm/'							,		r'/1/layer_norm/'							,		UpperCamelCase     )

            elif groups[0] == "decoder":
                a__		=			re.sub(r'/mlp/'							,		r'/2/mlp/'							,		UpperCamelCase     )
                a__		=			re.sub(r'/pre_mlp_layer_norm/'							,		r'/2/layer_norm/'							,		UpperCamelCase     )

        # 2. Convert other classic mappings
        for old_key, temp_key in MOE_LAYER_NAME_MAPPING.items():
            if old_key in new_key:
                a__		=			new_key.replace(UpperCamelCase							,		UpperCamelCase     )

        print(f'{key} -> {new_key}'     )
        a__		=			s_dict.pop(UpperCamelCase     )

    if "encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight" in s_dict:
        a__		=			s_dict[
            'encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight'
        ].T
    if "decoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight" in s_dict:
        a__		=			s_dict[
            'decoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight'
        ].T

    # 3. Take extra care of the EXPERTS layer
    for key in list(s_dict.keys()     ):
        if "expert" in key:
            a__		=			s_dict[key].shape[0]
            a__		=			s_dict[key]
            for idx in range(UpperCamelCase     ):
                a__		=			expert_weihts[idx]
                print(f'{key} -> {key.replace("expert/"							,		"nested fstring"     )}'     )

            s_dict.pop(UpperCamelCase     )

    return s_dict


a		:       List[str]     						=       {
    'NUM_ENCODER_LAYERS': 'num_layers',
    'NUM_DECODER_LAYERS': 'num_decoder_layers',
    'NUM_HEADS': 'num_heads',
    'HEAD_DIM': 'd_kv',
    'EMBED_DIM': 'd_model',
    'MLP_DIM': 'd_ff',
    'NUM_SELECTED_EXPERTS': 'num_selected_experts',
    'NUM_ENCODER_SPARSE_LAYERS': 'num_sparse_encoder_layers',
    'NUM_DECODER_SPARSE_LAYERS': 'num_sparse_decoder_layers',
    'dense.MlpBlock.activations': 'feed_forward_proj',
}



def     __magic_name__		(					UpperCamelCase     :		Optional[int]							,		UpperCamelCase     :		Optional[int]     )     ->						Optional[int]:
    # Convert a google style config to the hugging face fromat
    import regex as re

    with open(UpperCamelCase							,		'r'     ) as f:
        a__		=			f.read()

    a__		=			re.findall(r'(.*) = ([0-9.]*)'							,		UpperCamelCase     )
    a__		=			{}
    for param, value in regex_match:
        if param in GIN_TO_CONFIG_MAPPING and value != "":
            a__		=			float(UpperCamelCase     ) if '.' in value else int(UpperCamelCase     )

    a__		=			re.findall(r'(.*activations) = \(\'(.*)\',\)'							,		UpperCamelCase     )[0]
    a__		=			str(activation[1]     )

    a__		=			num_experts
    a__		=			SwitchTransformersConfig(**UpperCamelCase     )
    return config



def     __magic_name__		(					UpperCamelCase     :		str							,		UpperCamelCase     :		Tuple							,		UpperCamelCase     :		Union[str, Any]=None							,		UpperCamelCase     :		List[str]="./"							,		UpperCamelCase     :		Any=8     )     ->						Union[str, Any]:
    # Initialise PyTorch model

    print(f'Loading flax weights from : {flax_checkpoint_path}'     )
    a__		=			checkpoints.load_tax_checkpoint(UpperCamelCase     )

    if gin_file is not None:
        a__		=			convert_gin_to_config(UpperCamelCase							,		UpperCamelCase     )
    else:
        a__		=			SwitchTransformersConfig.from_pretrained(UpperCamelCase     )

    a__		=			SwitchTransformersForConditionalGeneration(UpperCamelCase     )

    a__		=			flax_params['target']
    a__		=			flatten_dict(UpperCamelCase							,		sep='/'     )
    a__		=			rename_keys(UpperCamelCase     )
    a__		=			unflatten_dict(UpperCamelCase							,		sep='/'     )

    # Load the flax params in the PT model
    load_flax_weights_in_pytorch_model(UpperCamelCase							,		UpperCamelCase     )

    print(f'Save PyTorch model to {pytorch_dump_path}'     )
    pt_model.save_pretrained(UpperCamelCase     )


if __name__ == "__main__":
    a		:       Dict     						=       argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        '--switch_t5x_checkpoint_path',
        default=None,
        type=str,
        required=True,
        help=(
            'The config json file corresponding to the pre-trained SwitchTransformers model. \nThis specifies the'
            ' model architecture. If not provided, a `gin_file` has to be provided.'
        ),
    )
    parser.add_argument(
        '--gin_file',
        default=None,
        type=str,
        required=False,
        help='Path to the gin config file. If not provided, a `config_file` has to be passed   ',
    )
    parser.add_argument(
        '--config_name', default=None, type=str, required=False, help='Config name of SwitchTransformers model.'
    )
    parser.add_argument(
        '--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output pytorch model.'
    )
    parser.add_argument('--num_experts', default=8, type=int, required=False, help='Number of experts')
    a		:       List[str]     						=       parser.parse_args()
    convert_flax_checkpoint_to_pytorch(
        args.switch_tax_checkpoint_path,
        args.config_name,
        args.gin_file,
        args.pytorch_dump_folder_path,
        args.num_experts,
    )


