





"""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    (       SCREAMING_SNAKE_CASE							)		->       int:






							"""simple docstring"""




							_UpperCAmelCase     =     list(s_dict.keys()							)
							for key in keys:
														_UpperCAmelCase     =     R'.*/layers_(\d+)'
														_UpperCAmelCase     =     key
														if re.match(SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE							):
																					_UpperCAmelCase     =     re.sub(R'layers_(\d+)',R'block/\1/layer',SCREAMING_SNAKE_CASE							)

														_UpperCAmelCase     =     R'(encoder|decoder)\/'

														if re.match(SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE							):
																					_UpperCAmelCase     =     re.match(SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE							).groups()
																					if groups[0] == "encoder":
																												_UpperCAmelCase     =     re.sub(R'/mlp/',R'/1/mlp/',SCREAMING_SNAKE_CASE							)
																												_UpperCAmelCase     =     re.sub(R'/pre_mlp_layer_norm/',R'/1/layer_norm/',SCREAMING_SNAKE_CASE							)

																					elif groups[0] == "decoder":
																												_UpperCAmelCase     =     re.sub(R'/mlp/',R'/2/mlp/',SCREAMING_SNAKE_CASE							)
																												_UpperCAmelCase     =     re.sub(R'/pre_mlp_layer_norm/',R'/2/layer_norm/',SCREAMING_SNAKE_CASE							)

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

														print(F"""{key} -> {new_key}"""							)
														_UpperCAmelCase     =     s_dict.pop(SCREAMING_SNAKE_CASE							)

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

																					s_dict.pop(SCREAMING_SNAKE_CASE							)

							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    (       SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE							)		->       Optional[int]:






							"""simple docstring"""




							import regex as re

							with open(SCREAMING_SNAKE_CASE,'r'							) as f:
														_UpperCAmelCase     =     f.read()

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

							_UpperCAmelCase     =     re.findall(R'(.*activations) = \(\'(.*)\',\)',SCREAMING_SNAKE_CASE							)[0]
							_UpperCAmelCase     =     str(activation[1]							)

							_UpperCAmelCase     =     num_experts
							_UpperCAmelCase     =     SwitchTransformersConfig(**SCREAMING_SNAKE_CASE							)
							return config

def       __lowerCamelCase    (       SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE=None,SCREAMING_SNAKE_CASE="./",SCREAMING_SNAKE_CASE=8							)		->       Tuple:






							"""simple docstring"""




							print(F"""Loading flax weights from : {flax_checkpoint_path}"""							)
							_UpperCAmelCase     =     checkpoints.load_tax_checkpoint(SCREAMING_SNAKE_CASE							)

							if gin_file is not None:
														_UpperCAmelCase     =     convert_gin_to_config(SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE							)
							else:
														_UpperCAmelCase     =     SwitchTransformersConfig.from_pretrained(SCREAMING_SNAKE_CASE							)

							_UpperCAmelCase     =     SwitchTransformersForConditionalGeneration(SCREAMING_SNAKE_CASE							)

							_UpperCAmelCase     =     flax_params['target']
							_UpperCAmelCase     =     flatten_dict(SCREAMING_SNAKE_CASE,sep='/'							)
							_UpperCAmelCase     =     rename_keys(SCREAMING_SNAKE_CASE							)
							_UpperCAmelCase     =     unflatten_dict(SCREAMING_SNAKE_CASE,sep='/'							)

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

							print(F"""Save PyTorch model to {pytorch_dump_path}"""							)
							pt_model.save_pretrained(SCREAMING_SNAKE_CASE							)


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"""






def       __lowerCamelCase    (       SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE							)		->       int:






       """simple docstring"""




       return number | (1 << position)

def       __lowerCamelCase    (       SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE							)		->       int:






       """simple docstring"""




       return number & ~(1 << position)

def       __lowerCamelCase    (       SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE							)		->       int:






       """simple docstring"""




       return number ^ (1 << position)

def       __lowerCamelCase    (       SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE							)		->       bool:






       """simple docstring"""




       return ((number >> position) & 1) == 1

def       __lowerCamelCase    (       SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE							)		->       int:






       """simple docstring"""




       return int((number & (1 << position)) != 0							)


if __name__ == "__main__":
    import doctest

    doctest.testmod()






