
"""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_      ):





		'''simple docstring'''


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

				lowerCamelCase					:      List[str]          =   r'(encoder|decoder)\/'

				if re.match(a_,						a_      ):
						lowerCamelCase					:      List[Any]          =   re.match(a_,						a_      ).groups()
						if groups[0] == "encoder":
								lowerCamelCase					:      List[Any]          =   re.sub(r'/mlp/',						r'/1/mlp/',						a_      )
								lowerCamelCase					:      Optional[int]          =   re.sub(r'/pre_mlp_layer_norm/',						r'/1/layer_norm/',						a_      )

						elif groups[0] == "decoder":
								lowerCamelCase					:      List[Any]          =   re.sub(r'/mlp/',						r'/2/mlp/',						a_      )
								lowerCamelCase					:      Union[str, 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:
								lowerCamelCase					:      Tuple          =   new_key.replace(a_,						a_      )

				print(F"""{key} -> {new_key}"""      )
				lowerCamelCase					:      Tuple          =   s_dict.pop(a_      )

		if "encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight" in s_dict:
				lowerCamelCase					:      str          =   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					:      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:
						lowerCamelCase					:      Any          =   s_dict[key].shape[0]
						lowerCamelCase					:      Optional[int]          =   s_dict[key]
						for idx in range(a_      ):
								lowerCamelCase					:      List[Any]          =   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_,						a_      ):





		'''simple docstring'''


		import regex as re

		with open(a_,						'r'      ) as f:
				lowerCamelCase					:      Any          =   f.read()

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

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

		lowerCamelCase					:      Dict          =   num_experts
		lowerCamelCase					:      int          =   SwitchTransformersConfig(**a_      )
		return config
def 		UpperCAmelCase (		a_,						a_,						a_=None,						a_="./",						a_=8      ):





		'''simple docstring'''


		print(F"""Loading flax weights from : {flax_checkpoint_path}"""      )
		lowerCamelCase					:      List[Any]          =   checkpoints.load_tax_checkpoint(a_      )

		if gin_file is not None:
				lowerCamelCase					:      Dict          =   convert_gin_to_config(a_,						a_      )
		else:
				lowerCamelCase					:      Optional[int]          =   SwitchTransformersConfig.from_pretrained(a_      )

		lowerCamelCase					:      Union[str, Any]          =   SwitchTransformersForConditionalGeneration(a_      )

		lowerCamelCase					:      List[str]          =   flax_params['target']
		lowerCamelCase					:      List[str]          =   flatten_dict(a_,						sep='/'      )
		lowerCamelCase					:      Dict          =   rename_keys(a_      )
		lowerCamelCase					:      List[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"""

def 		UpperCAmelCase (		a_      ):





		'''simple docstring'''


		return str(a_      ) == str(a_      )[::-1]
def 		UpperCAmelCase (		a_      ):





		'''simple docstring'''


		return int(a_      ) + int(str(a_      )[::-1]      )
def 		UpperCAmelCase (		a_ = 1_0000      ):





		'''simple docstring'''


		lowerCamelCase					:      Optional[Any]          =   []
		for num in range(1,						a_      ):
				lowerCamelCase					:      List[str]          =   0
				lowerCamelCase					:      Union[str, Any]          =   num
				while iterations < 50:
						lowerCamelCase					:      Optional[int]          =   sum_reverse(a_      )
						iterations += 1
						if is_palindrome(a_      ):
								break
				else:
						lychrel_nums.append(a_      )
		return len(a_      )


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






