'''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=  {
    '''/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  _UpperCamelCase (     _a	:	Any			):




    """simple docstring"""



    __UpperCamelCase	:	Union[str, Any]		    =  list(s_dict.keys()			)
    for key in keys:
        __UpperCamelCase	:	Union[str, Any]		    =  r'.*/layers_(\d+)'
        __UpperCamelCase	:	Any		    =  key
        if re.match(_a				,     _a			):
            __UpperCamelCase	:	Optional[int]		    =  re.sub(r'layers_(\d+)'				,     r'block/\1/layer'				,     _a			)

        __UpperCamelCase	:	Any		    =  r'(encoder|decoder)\/'

        if re.match(_a				,     _a			):
            __UpperCamelCase	:	Any		    =  re.match(_a				,     _a			).groups()
            if groups[0] == "encoder":
                __UpperCamelCase	:	str		    =  re.sub(r'/mlp/'				,     r'/1/mlp/'				,     _a			)
                __UpperCamelCase	:	Union[str, Any]		    =  re.sub(r'/pre_mlp_layer_norm/'				,     r'/1/layer_norm/'				,     _a			)

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

        # 2. Convert other classic mappings
        for old_key, temp_key in MOE_LAYER_NAME_MAPPING.items():
            if old_key in new_key:
                __UpperCamelCase	:	Optional[int]		    =  new_key.replace(_a				,     _a			)

        print(f"""{key} -> {new_key}"""			)
        __UpperCamelCase	:	Tuple		    =  s_dict.pop(_a			)

    if "encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight" in s_dict:
        __UpperCamelCase	:	Union[str, Any]		    =  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:
        __UpperCamelCase	:	Tuple		    =  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:
            __UpperCamelCase	:	str		    =  s_dict[key].shape[0]
            __UpperCamelCase	:	Any		    =  s_dict[key]
            for idx in range(_a			):
                __UpperCamelCase	:	int		    =  expert_weihts[idx]
                print(f"""{key} -> {key.replace('expert/'				,     'nested fstring'			)}"""			)

            s_dict.pop(_a			)

    return s_dict


a=  {
    '''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  _UpperCamelCase (     _a	:	Dict				,     _a	:	int			):




    """simple docstring"""



    import regex as re

    with open(_a				,     'r'			) as f:
        __UpperCamelCase	:	str		    =  f.read()

    __UpperCamelCase	:	Dict		    =  re.findall(r'(.*) = ([0-9.]*)'				,     _a			)
    __UpperCamelCase	:	Optional[Any]		    =  {}
    for param, value in regex_match:
        if param in GIN_TO_CONFIG_MAPPING and value != "":
            __UpperCamelCase	:	str		    =  float(_a			) if '.' in value else int(_a			)

    __UpperCamelCase	:	Any		    =  re.findall(r'(.*activations) = \(\'(.*)\',\)'				,     _a			)[0]
    __UpperCamelCase	:	List[Any]		    =  str(activation[1]			)

    __UpperCamelCase	:	int		    =  num_experts
    __UpperCamelCase	:	Any		    =  SwitchTransformersConfig(**_a			)
    return config







def  _UpperCamelCase (     _a	:	Union[str, Any]				,     _a	:	Any				,     _a	:	str=None				,     _a	:	Any="./"				,     _a	:	Optional[Any]=8			):




    """simple docstring"""



    print(f"""Loading flax weights from : {flax_checkpoint_path}"""			)
    __UpperCamelCase	:	Any		    =  checkpoints.load_tax_checkpoint(_a			)

    if gin_file is not None:
        __UpperCamelCase	:	List[Any]		    =  convert_gin_to_config(_a				,     _a			)
    else:
        __UpperCamelCase	:	int		    =  SwitchTransformersConfig.from_pretrained(_a			)

    __UpperCamelCase	:	Dict		    =  SwitchTransformersForConditionalGeneration(_a			)

    __UpperCamelCase	:	List[Any]		    =  flax_params['target']
    __UpperCamelCase	:	Dict		    =  flatten_dict(_a				,     sep='/'			)
    __UpperCamelCase	:	str		    =  rename_keys(_a			)
    __UpperCamelCase	:	Union[str, Any]		    =  unflatten_dict(_a				,     sep='/'			)

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

    print(f"""Save PyTorch model to {pytorch_dump_path}"""			)
    pt_model.save_pretrained(_a			)


if __name__ == "__main__":
     a=  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=  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,
     )




'''simple docstring'''
from __future__ import annotations

import numpy as np
from numpy import floataa
from numpy.typing import NDArray







def  _UpperCamelCase (     _a	:	NDArray[floataa]				,     _a	:	NDArray[floataa]				,     _a	:	list[int]				,     _a	:	int				,     ):




    """simple docstring"""



    __UpperCamelCase    ,					__UpperCamelCase	:	Union[str, Any]		    =  coefficient_matrix.shape
    __UpperCamelCase    ,					__UpperCamelCase	:	Union[str, Any]		    =  constant_matrix.shape

    if rowsa != colsa:
        __UpperCamelCase	:	Tuple		    =  f"""Coefficient matrix dimensions must be nxn but received {rowsa}x{colsa}"""
        raise ValueError(_a			)

    if colsa != 1:
        __UpperCamelCase	:	List[Any]		    =  f"""Constant matrix must be nx1 but received {rowsa}x{colsa}"""
        raise ValueError(_a			)

    if rowsa != rowsa:
        __UpperCamelCase	:	Optional[int]		    =  (
            'Coefficient and constant matrices dimensions must be nxn and nx1 but '
            f"""received {rowsa}x{colsa} and {rowsa}x{colsa}"""
        )
        raise ValueError(_a			)

    if len(_a			) != rowsa:
        __UpperCamelCase	:	Union[str, Any]		    =  (
            'Number of initial values must be equal to number of rows in coefficient '
            f"""matrix but received {len(_a			)} and {rowsa}"""
        )
        raise ValueError(_a			)

    if iterations <= 0:
        raise ValueError('Iterations must be at least 1'			)

    __UpperCamelCase	:	NDArray[floataa]		    =  np.concatenate(
        (coefficient_matrix, constant_matrix)				,     axis=1			)

    __UpperCamelCase    ,					__UpperCamelCase	:	Union[str, Any]		    =  table.shape

    strictly_diagonally_dominant(_a			)

    # Iterates the whole matrix for given number of times
    for _ in range(_a			):
        __UpperCamelCase	:	List[Any]		    =  []
        for row in range(_a			):
            __UpperCamelCase	:	List[Any]		    =  0
            for col in range(_a			):
                if col == row:
                    __UpperCamelCase	:	Optional[int]		    =  table[row][col]
                elif col == cols - 1:
                    __UpperCamelCase	:	str		    =  table[row][col]
                else:
                    temp += (-1) * table[row][col] * init_val[col]
            __UpperCamelCase	:	Any		    =  (temp + val) / denom
            new_val.append(_a			)
        __UpperCamelCase	:	List[Any]		    =  new_val

    return [float(_a			) for i in new_val]







def  _UpperCamelCase (     _a	:	NDArray[floataa]			):




    """simple docstring"""



    __UpperCamelCase    ,					__UpperCamelCase	:	str		    =  table.shape

    __UpperCamelCase	:	str		    =  True

    for i in range(0				,     _a			):
        __UpperCamelCase	:	Optional[Any]		    =  0
        for j in range(0				,     cols - 1			):
            if i == j:
                continue
            else:
                total += table[i][j]

        if table[i][i] <= total:
            raise ValueError('Coefficient matrix is not strictly diagonally dominant'			)

    return is_diagonally_dominant


# Test Cases
if __name__ == "__main__":
  import doctest

  doctest.testmod()



