



'''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   _A       (   _lowerCAmelCase					):



							"""simple docstring"""



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

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

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

																					elif groups[0] == "decoder":
																												__lowercase       	=re.sub(r'/mlp/'  ,   r'/2/mlp/'  ,   _lowerCAmelCase					)
																												__lowercase       	=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:
																												__lowercase       	=new_key.replace(_lowerCAmelCase  ,   _lowerCAmelCase					)

														print(f"""{key} -> {new_key}"""					)
														__lowercase       	=s_dict.pop(_lowerCAmelCase					)

							if "encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight" in s_dict:
														__lowercase       	=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:
														__lowercase       	=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:
																					__lowercase       	=s_dict[key].shape[0]
																					__lowercase       	=s_dict[key]
																					for idx in range(_lowerCAmelCase					):
																												__lowercase       	=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   _A       (   _lowerCAmelCase  ,   _lowerCAmelCase					):



							"""simple docstring"""



							import regex as re

							with open(_lowerCAmelCase  ,   'r'					) as f:
														__lowercase       	=f.read()

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

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

							__lowercase       	=num_experts
							__lowercase       	=SwitchTransformersConfig(**_lowerCAmelCase					)
							return config



def   _A       (   _lowerCAmelCase  ,   _lowerCAmelCase  ,   _lowerCAmelCase=None  ,   _lowerCAmelCase="./"  ,   _lowerCAmelCase=8					):



							"""simple docstring"""



							print(f"""Loading flax weights from : {flax_checkpoint_path}"""					)
							__lowercase       	=checkpoints.load_tax_checkpoint(_lowerCAmelCase					)

							if gin_file is not None:
														__lowercase       	=convert_gin_to_config(_lowerCAmelCase  ,   _lowerCAmelCase					)
							else:
														__lowercase       	=SwitchTransformersConfig.from_pretrained(_lowerCAmelCase					)

							__lowercase       	=SwitchTransformersForConditionalGeneration(_lowerCAmelCase					)

							__lowercase       	=flax_params['target']
							__lowercase       	=flatten_dict(_lowerCAmelCase  ,   sep='/'					)
							__lowercase       	=rename_keys(_lowerCAmelCase					)
							__lowercase       	=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'''



def   _A       (   _lowerCAmelCase					):



							"""simple docstring"""



							if number > 0:
														raise ValueError('input must be a negative integer'					)
							__lowercase       	=len(bin(_lowerCAmelCase					)[3:]					)
							__lowercase       	=bin(abs(_lowerCAmelCase					) - (1 << binary_number_length)					)[3:]
							__lowercase       	=(
							    (
							        '1'
							        + '0' * (binary_number_length - len(_lowerCAmelCase					))
							        + twos_complement_number
							    )
							    if number < 0
							    else '0'
							)
							return "0b" + twos_complement_number


if __name__ == "__main__":
		import doctest

		doctest.testmod()



