



'''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
UpperCamelCase__ :			int												=				{
    '''/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:     List[Any]      ):
		# 1. in HF T5, we have block.{x}.layer.{y}. which corresponds to layer.{x} in
		# the original model
		__SCREAMING_SNAKE_CASE :	List[str]					 =	list(s_dict.keys()      )
		for key in keys:
				__SCREAMING_SNAKE_CASE :	Optional[int]					 =	r""".*/layers_(\d+)"""
				__SCREAMING_SNAKE_CASE :	Any					 =	key
				if re.match(_lowerCamelCase ,   _lowerCamelCase      ):
						__SCREAMING_SNAKE_CASE :	Tuple					 =	re.sub(r"""layers_(\d+)""" ,   r"""block/\1/layer""" ,   _lowerCamelCase      )

				__SCREAMING_SNAKE_CASE :	Tuple					 =	r"""(encoder|decoder)\/"""

				if re.match(_lowerCamelCase ,   _lowerCamelCase      ):
						__SCREAMING_SNAKE_CASE :	Optional[int]					 =	re.match(_lowerCamelCase ,   _lowerCamelCase      ).groups()
						if groups[0] == "encoder":
								__SCREAMING_SNAKE_CASE :	Dict					 =	re.sub(r"""/mlp/""" ,   r"""/1/mlp/""" ,   _lowerCamelCase      )
								__SCREAMING_SNAKE_CASE :	int					 =	re.sub(r"""/pre_mlp_layer_norm/""" ,   r"""/1/layer_norm/""" ,   _lowerCamelCase      )

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

				print(F"{key} -> {new_key}"      )
				__SCREAMING_SNAKE_CASE :	str					 =	s_dict.pop(_lowerCamelCase      )

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

						s_dict.pop(_lowerCamelCase      )

		return s_dict


UpperCamelCase__ :			List[Any]												=				{
    '''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:     Tuple ,   _lowerCamelCase:     Optional[int]      ):
		# Convert a google style config to the hugging face fromat
		import regex as re

		with open(_lowerCamelCase ,   """r"""      ) as f:
				__SCREAMING_SNAKE_CASE :	List[Any]					 =	f.read()

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

		__SCREAMING_SNAKE_CASE :	List[str]					 =	re.findall(r"""(.*activations) = \(\'(.*)\',\)""" ,   _lowerCamelCase      )[0]
		__SCREAMING_SNAKE_CASE :	int					 =	str(activation[1]      )

		__SCREAMING_SNAKE_CASE :	Any					 =	num_experts
		__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	SwitchTransformersConfig(**_lowerCamelCase      )
		return config


def        lowerCAmelCase_     (			_lowerCamelCase:     int ,   _lowerCamelCase:     Dict ,   _lowerCamelCase:     Dict=None ,   _lowerCamelCase:     int="./" ,   _lowerCamelCase:     Tuple=8      ):
		# Initialise PyTorch model

		print(F"Loading flax weights from : {flax_checkpoint_path}"      )
		__SCREAMING_SNAKE_CASE :	Any					 =	checkpoints.load_tax_checkpoint(_lowerCamelCase      )

		if gin_file is not None:
				__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	convert_gin_to_config(_lowerCamelCase ,   _lowerCamelCase      )
		else:
				__SCREAMING_SNAKE_CASE :	Tuple					 =	SwitchTransformersConfig.from_pretrained(_lowerCamelCase      )

		__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	SwitchTransformersForConditionalGeneration(_lowerCamelCase      )

		__SCREAMING_SNAKE_CASE :	Optional[int]					 =	flax_params["""target"""]
		__SCREAMING_SNAKE_CASE :	Any					 =	flatten_dict(_lowerCamelCase ,   sep="""/"""      )
		__SCREAMING_SNAKE_CASE :	Dict					 =	rename_keys(_lowerCamelCase      )
		__SCREAMING_SNAKE_CASE :	Any					 =	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__":
						UpperCamelCase__ :			int												=				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''')
						UpperCamelCase__ :			Optional[Any]												=				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'''




UpperCamelCase__ :			List[str]												=				'''0.21.0'''

from .accelerator import Accelerator
from .big_modeling import (
    cpu_offload,
    cpu_offload_with_hook,
    disk_offload,
    dispatch_model,
    init_empty_weights,
    init_on_device,
    load_checkpoint_and_dispatch,
)
from .data_loader import skip_first_batches
from .launchers import debug_launcher, notebook_launcher
from .state import PartialState
from .utils import (
    DeepSpeedPlugin,
    DistributedDataParallelKwargs,
    DistributedType,
    FullyShardedDataParallelPlugin,
    GradScalerKwargs,
    InitProcessGroupKwargs,
    find_executable_batch_size,
    infer_auto_device_map,
    is_rich_available,
    load_checkpoint_in_model,
    synchronize_rng_states,
)


if is_rich_available():
						from .utils import rich