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__		:					Union[str, Any]						=					{
    '/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     _a				(      lowercase__  :			Tuple ):







					'''simple docstring'''


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

										SCREAMING_SNAKE_CASE__				:				Tuple			=		r'(encoder|decoder)\/'

										if re.match(A_     ,  A_ ):
															SCREAMING_SNAKE_CASE__				:				Dict			=		re.match(A_     ,  A_ ).groups()
															if groups[0] == "encoder":
																				SCREAMING_SNAKE_CASE__				:				List[str]			=		re.sub(r'/mlp/'     ,  r'/1/mlp/'     ,  A_ )
																				SCREAMING_SNAKE_CASE__				:				int			=		re.sub(r'/pre_mlp_layer_norm/'     ,  r'/1/layer_norm/'     ,  A_ )

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

										print(f'''{key} -> {new_key}''' )
										SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		s_dict.pop(A_ )

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

															s_dict.pop(A_ )

					return s_dict


SCREAMING_SNAKE_CASE__		:					Dict						=					{
    '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     _a				(      lowercase__  :			List[Any]     ,  lowercase__  :			str ):







					'''simple docstring'''


					import regex as re

					with open(A_     ,  'r' ) as f:
										SCREAMING_SNAKE_CASE__				:				str			=		f.read()

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

					SCREAMING_SNAKE_CASE__				:				int			=		re.findall(r'(.*activations) = \(\'(.*)\',\)'     ,  A_ )[0]
					SCREAMING_SNAKE_CASE__				:				int			=		str(activation[1] )

					SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		num_experts
					SCREAMING_SNAKE_CASE__				:				Any			=		SwitchTransformersConfig(**A_ )
					return config







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







					'''simple docstring'''


					print(f'''Loading flax weights from : {flax_checkpoint_path}''' )
					SCREAMING_SNAKE_CASE__				:				Optional[int]			=		checkpoints.load_tax_checkpoint(A_ )

					if gin_file is not None:
										SCREAMING_SNAKE_CASE__				:				int			=		convert_gin_to_config(A_     ,  A_ )
					else:
										SCREAMING_SNAKE_CASE__				:				Tuple			=		SwitchTransformersConfig.from_pretrained(A_ )

					SCREAMING_SNAKE_CASE__				:				List[Any]			=		SwitchTransformersForConditionalGeneration(A_ )

					SCREAMING_SNAKE_CASE__				:				List[Any]			=		flax_params['target']
					SCREAMING_SNAKE_CASE__				:				List[str]			=		flatten_dict(A_     ,  sep='/' )
					SCREAMING_SNAKE_CASE__				:				int			=		rename_keys(A_ )
					SCREAMING_SNAKE_CASE__				:				int			=		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__":
						SCREAMING_SNAKE_CASE__		:					Tuple						=					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__		:					Dict						=					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,
						)




def     _a				(      lowercase__  :			Optional[int] ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__				:				Optional[int]			=		[]
     SCREAMING_SNAKE_CASE__				:				List[Any]			=		set({'(', '[', '{'} )
     SCREAMING_SNAKE_CASE__				:				Optional[int]			=		set({')', ']', '}'} )
     SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		{'{': '}', '[': ']', '(': ')'}

     for i in range(len(lowercase__ ) ):
          if s[i] in open_brackets:
               stack.append(s[i] )

          elif s[i] in closed_brackets and (
              len(lowercase__ ) == 0 or (len(lowercase__ ) > 0 and open_to_closed[stack.pop()] != s[i])
          ):
               return False

     return len(lowercase__ ) == 0







def     _a				(      ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__				:				Tuple			=		input('Enter sequence of brackets: ' )
     if is_balanced(lowercase__ ):
          print(lowercase__     ,  'is balanced' )
     else:
          print(lowercase__     ,  'is not balanced' )


if __name__ == "__main__":
      main()



