






"""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
_SCREAMING_SNAKE_CASE					:				Tuple					    =						{
    '/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      lowerCamelCase__       (							_lowerCamelCase       :		Optional[int] )      ->				Optional[int]:
  lowerCamelCase_         =						list(s_dict.keys() )
  for key in keys:
    lowerCamelCase_         =						r'.*/layers_(\d+)'
    lowerCamelCase_         =						key
    if re.match(_lowerCamelCase		,				_lowerCamelCase ):
      lowerCamelCase_         =						re.sub(r'layers_(\d+)'		,				r'block/\1/layer'		,				_lowerCamelCase )

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

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

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

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

    print(F'''{key} -> {new_key}''' )
    lowerCamelCase_         =						s_dict.pop(_lowerCamelCase )

  if "encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight" in s_dict:
    lowerCamelCase_         =						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:
    lowerCamelCase_         =						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:
      lowerCamelCase_         =						s_dict[key].shape[0]
      lowerCamelCase_         =						s_dict[key]
      for idx in range(_lowerCamelCase ):
        lowerCamelCase_         =						expert_weihts[idx]
        print(F'''{key} -> {key.replace("expert/"		,				"nested fstring" )}''' )

      s_dict.pop(_lowerCamelCase )

  return s_dict


_SCREAMING_SNAKE_CASE					:				Optional[int]					    =						{
    '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      lowerCamelCase__       (							_lowerCamelCase       :		Dict		,				_lowerCamelCase       :		Optional[Any] )      ->				Tuple:
  import regex as re

  with open(_lowerCamelCase		,				'r' ) as f:
    lowerCamelCase_         =						f.read()

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

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

  lowerCamelCase_         =						num_experts
  lowerCamelCase_         =						SwitchTransformersConfig(**_lowerCamelCase )
  return config







def      lowerCamelCase__       (							_lowerCamelCase       :		Union[str, Any]		,				_lowerCamelCase       :		Union[str, Any]		,				_lowerCamelCase       :		List[str]=None		,				_lowerCamelCase       :		List[Any]="./"		,				_lowerCamelCase       :		str=8 )      ->				str:
  print(F'''Loading flax weights from : {flax_checkpoint_path}''' )
  lowerCamelCase_         =						checkpoints.load_tax_checkpoint(_lowerCamelCase )

  if gin_file is not None:
    lowerCamelCase_         =						convert_gin_to_config(_lowerCamelCase		,				_lowerCamelCase )
  else:
    lowerCamelCase_         =						SwitchTransformersConfig.from_pretrained(_lowerCamelCase )

  lowerCamelCase_         =						SwitchTransformersForConditionalGeneration(_lowerCamelCase )

  lowerCamelCase_         =						flax_params['target']
  lowerCamelCase_         =						flatten_dict(_lowerCamelCase		,				sep='/' )
  lowerCamelCase_         =						rename_keys(_lowerCamelCase )
  lowerCamelCase_         =						unflatten_dict(_lowerCamelCase		,				sep='/' )

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

  print(F'''Save PyTorch model to {pytorch_dump_path}''' )
  pt_model.save_pretrained(_lowerCamelCase )


if __name__ == "__main__":
  _SCREAMING_SNAKE_CASE					:				int					    =						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''')
  _SCREAMING_SNAKE_CASE					:				Tuple					    =						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,
  )










from __future__ import annotations

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




def 			__magic_name__				( A		:					NDArray[floataa],  A		:					NDArray[floataa],  A		:					list[int],  A		:					int,  ):






			'''simple docstring'''







			a    ,		a    =			coefficient_matrix.shape
			a    ,		a    =			constant_matrix.shape

			if rowsa != colsa:
						a    =			F"""Coefficient matrix dimensions must be nxn but received {rowsa}x{colsa}"""
						raise ValueError(A   )

			if colsa != 1:
						a    =			F"""Constant matrix must be nx1 but received {rowsa}x{colsa}"""
						raise ValueError(A   )

			if rowsa != rowsa:
						a    =			(
						    "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:
						a    =			(
						    "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"   )

			a    =			np.concatenate(
			    (coefficient_matrix, constant_matrix),  axis=1   )

			a    ,		a    =			table.shape

			strictly_diagonally_dominant(A   )

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

			return [float(A   ) for i in new_val]




def 			__magic_name__				( A		:					NDArray[floataa]   ):






			'''simple docstring'''







			a    ,		a    =			table.shape

			a    =			True

			for i in range(0,  A   ):
						a    =			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()






