


from __future__ import annotations

from collections.abc import Callable

_SCREAMING_SNAKE_CASE     :					int      =       list[list[float | int]]
def      UpperCAmelCase__		(UpperCamelCase_				,UpperCamelCase_							):



     """simple docstring"""






     snake_case											=							len(UpperCamelCase_							)
     snake_case											=							[[0 for _ in range(size + 1							)] for _ in range(UpperCamelCase_							)]
     snake_case											=							42
     snake_case											=							42
     snake_case											=							42
     snake_case											=							42
     snake_case											=							42
     snake_case											=							42

     for row in range(UpperCamelCase_							):
          for col in range(UpperCamelCase_							):
               snake_case											=							matrix[row][col]

          snake_case											=							vector[row][0]

     snake_case											=							0
     snake_case											=							0
     while row < size and col < size:
          # pivoting
          snake_case											=							max((abs(augmented[rowa][col]							), rowa) for rowa in range(UpperCamelCase_				,UpperCamelCase_							)							)[
              1
          ]
          if augmented[pivot_row][col] == 0:
               col += 1
               continue
          else:
               snake_case					,    snake_case											=							augmented[pivot_row], augmented[row]

          for rowa in range(row + 1				,UpperCamelCase_							):
               snake_case											=							augmented[rowa][col] / augmented[row][col]
               snake_case											=							0
               for cola in range(col + 1				,size + 1							):
                    augmented[rowa][cola] -= augmented[row][cola] * ratio

          row += 1
          col += 1

     # back substitution
     for col in range(1				,UpperCamelCase_							):
          for row in range(UpperCamelCase_							):
               snake_case											=							augmented[row][col] / augmented[col][col]
               for cola in range(UpperCamelCase_				,size + 1							):
                    augmented[row][cola] -= augmented[col][cola] * ratio

    # round to get rid of numbers like 2.000000000000004
     return [
         [round(augmented[row][size] / augmented[row][row]				,10							)] for row in range(UpperCamelCase_							)
     ]
def      UpperCAmelCase__		(UpperCamelCase_							):



     """simple docstring"""






     snake_case											=							len(UpperCamelCase_							)
     snake_case											=							[[0 for _ in range(UpperCamelCase_							)] for _ in range(UpperCamelCase_							)]
     snake_case											=							[[0] for _ in range(UpperCamelCase_							)]
     snake_case											=							42
     snake_case											=							42
     snake_case											=							42
     snake_case											=							42

     for x_val, y_val in enumerate(UpperCamelCase_							):
          for col in range(UpperCamelCase_							):
               snake_case											=							(x_val + 1) ** (size - col - 1)
          snake_case											=							y_val

     snake_case											=							solve(UpperCamelCase_				,UpperCamelCase_							)

     def interpolated_func(UpperCamelCase_							) -> int:
          return sum(
              round(coeffs[x_val][0]							) * (var ** (size - x_val - 1))
              for x_val in range(UpperCamelCase_							)							)

     return interpolated_func
def      UpperCAmelCase__		(UpperCamelCase_							):



     """simple docstring"""






     return (
         1
         - variable
         + variable**2
         - variable**3
         + variable**4
         - variable**5
         + variable**6
         - variable**7
         + variable**8
         - variable**9
         + variable**10
     )
def      UpperCAmelCase__		(UpperCamelCase_ = question_function				,UpperCamelCase_ = 10							):



     """simple docstring"""






     snake_case											=							[func(UpperCamelCase_							) for x_val in range(1				,order + 1							)]

     snake_case											=							[
         interpolate(data_points[:max_coeff]							) for max_coeff in range(1				,order + 1							)
     ]

     snake_case											=							0
     snake_case											=							42
     snake_case											=							42

     for poly in polynomials:
          snake_case											=							1
          while func(UpperCamelCase_							) == poly(UpperCamelCase_							):
               x_val += 1

          ret += poly(UpperCamelCase_							)

     return ret


if __name__ == "__main__":
 print(f'''{solution() = }''')






"""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,
    )


