



'''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
__lowerCAmelCase						      =       {
    '''/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_  )      ->				str:
		# 1. in HF T5, we have block.{x}.layer.{y}. which corresponds to layer.{x} in
		# the original model
		_a					:				List[str]				=							list(s_dict.keys()  )
		for key in keys:
				_a					:				List[Any]				=							r'.*/layers_(\d+)'
				_a					:				Union[str, Any]				=							key
				if re.match(lowerCAmelCase_						,      lowerCAmelCase_  ):
						_a					:				int				=							re.sub(r'layers_(\d+)'						,      r'block/\1/layer'						,      lowerCAmelCase_  )

				_a					:				Dict				=							r'(encoder|decoder)\/'

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

						elif groups[0] == "decoder":
								_a					:				Dict				=							re.sub(r'/mlp/'						,      r'/2/mlp/'						,      lowerCAmelCase_  )
								_a					:				Dict				=							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:
								_a					:				int				=							new_key.replace(lowerCAmelCase_						,      lowerCAmelCase_  )

				print(f"""{key} -> {new_key}"""  )
				_a					:				Optional[int]				=							s_dict.pop(lowerCAmelCase_  )

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

						s_dict.pop(lowerCAmelCase_  )

		return s_dict


__lowerCAmelCase						      =       {
    '''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_						,      lowerCAmelCase_  )      ->				Optional[int]:
		# Convert a google style config to the hugging face fromat
		import regex as re

		with open(lowerCAmelCase_						,      'r'  ) as f:
				_a					:				List[str]				=							f.read()

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

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

		_a					:				Optional[Any]				=							num_experts
		_a					:				List[Any]				=							SwitchTransformersConfig(**lowerCAmelCase_  )
		return config



def 					__lowerCamelCase					(				lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_=None						,      lowerCAmelCase_="./"						,      lowerCAmelCase_=8  )      ->				Dict:
		# Initialise PyTorch model

		print(f"""Loading flax weights from : {flax_checkpoint_path}"""  )
		_a					:				Union[str, Any]				=							checkpoints.load_tax_checkpoint(lowerCAmelCase_  )

		if gin_file is not None:
				_a					:				int				=							convert_gin_to_config(lowerCAmelCase_						,      lowerCAmelCase_  )
		else:
				_a					:				Dict				=							SwitchTransformersConfig.from_pretrained(lowerCAmelCase_  )

		_a					:				Optional[int]				=							SwitchTransformersForConditionalGeneration(lowerCAmelCase_  )

		_a					:				Union[str, Any]				=							flax_params['target']
		_a					:				Optional[Any]				=							flatten_dict(lowerCAmelCase_						,      sep='/'  )
		_a					:				List[Any]				=							rename_keys(lowerCAmelCase_  )
		_a					:				Dict				=							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__":
							__lowerCAmelCase						      =       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''')
							__lowerCAmelCase						      =       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 collections
import pprint
from pathlib import Path



def 					__lowerCamelCase					(				lowerCAmelCase_  )      ->				str:
		return "".join(sorted(lowerCAmelCase_  )  )



def 					__lowerCamelCase					(				lowerCAmelCase_  )      ->				list[str]:
		return word_by_signature[signature(lowerCAmelCase_  )]


__lowerCAmelCase						      =       Path(__file__).parent.joinpath('''words.txt''').read_text(encoding='''utf-8''')
__lowerCAmelCase						      =       sorted({word.strip().lower() for word in data.splitlines()})

__lowerCAmelCase						      =       collections.defaultdict(list)
for word in word_list:
							word_by_signature[signature(word)].append(word)

if __name__ == "__main__":
							__lowerCAmelCase						      =       {word: anagram(word) for word in word_list if len(anagram(word)) > 1}

							with open('''anagrams.txt''', '''w''') as file:
														file.write('''all_anagrams = \n ''')
														file.write(pprint.pformat(all_anagrams))




