



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

				__SCREAMING_SNAKE_CASE       :		List[str]           =		r"""(encoder|decoder)\/"""

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

						elif groups[0] == "decoder":
								__SCREAMING_SNAKE_CASE       :		List[str]           =		re.sub(r"""/mlp/"""       ,      r"""/2/mlp/"""       ,      _lowerCamelCase					)
								__SCREAMING_SNAKE_CASE       :		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       :		Any           =		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       :		List[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       :		List[str]           =		s_dict[key].shape[0]
						__SCREAMING_SNAKE_CASE       :		Tuple           =		s_dict[key]
						for idx in range(_lowerCamelCase					):
								__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		expert_weihts[idx]
								print(F"{key} -> {key.replace('expert/'       ,      'nested fstring'					)}"					)

						s_dict.pop(_lowerCamelCase					)

		return s_dict


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

		with open(_lowerCamelCase       ,      """r"""					) as f:
				__SCREAMING_SNAKE_CASE       :		Tuple           =		f.read()

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

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

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


def 				lowerCAmelCase_   (	_lowerCamelCase:   Dict       ,      _lowerCamelCase:   str       ,      _lowerCamelCase:   Dict=None       ,      _lowerCamelCase:   Dict="./"       ,      _lowerCamelCase:   Optional[Any]=8					):
		# Initialise PyTorch model

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

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

		__SCREAMING_SNAKE_CASE       :		Optional[int]           =		SwitchTransformersForConditionalGeneration(_lowerCamelCase					)

		__SCREAMING_SNAKE_CASE       :		Optional[int]           =		flax_params["""target"""]
		__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		flatten_dict(_lowerCamelCase       ,      sep="""/"""					)
		__SCREAMING_SNAKE_CASE       :		int           =		rename_keys(_lowerCamelCase					)
		__SCREAMING_SNAKE_CASE       :		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__":
						UpperCamelCase__       :	List[Any]    = 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__       :	List[str]    = 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__       :	Optional[Any]    = [
    (10_00, '''M'''),
    (9_00, '''CM'''),
    (5_00, '''D'''),
    (4_00, '''CD'''),
    (1_00, '''C'''),
    (90, '''XC'''),
    (50, '''L'''),
    (40, '''XL'''),
    (10, '''X'''),
    (9, '''IX'''),
    (5, '''V'''),
    (4, '''IV'''),
    (1, '''I'''),
]


def 				lowerCAmelCase_   (	_lowerCamelCase:   str					):
		__SCREAMING_SNAKE_CASE       :		List[Any]           =		{"""I""": 1, """V""": 5, """X""": 10, """L""": 50, """C""": 1_00, """D""": 5_00, """M""": 10_00}
		__SCREAMING_SNAKE_CASE       :		Tuple           =		0
		__SCREAMING_SNAKE_CASE       :		str           =		0
		while place < len(_lowerCamelCase					):
				if (place + 1 < len(_lowerCamelCase					)) and (vals[roman[place]] < vals[roman[place + 1]]):
						total += vals[roman[place + 1]] - vals[roman[place]]
						place += 2
				else:
						total += vals[roman[place]]
						place += 1
		return total


def 				lowerCAmelCase_   (	_lowerCamelCase:   int					):
		__SCREAMING_SNAKE_CASE       :		Any           =		[]
		for arabic, roman in ROMAN:
				((__SCREAMING_SNAKE_CASE)       ,						(__SCREAMING_SNAKE_CASE))       :		str           =		divmod(_lowerCamelCase       ,      _lowerCamelCase					)
				result.append(roman * factor					)
				if number == 0:
						break
		return "".join(_lowerCamelCase					)


if __name__ == "__main__":
						import doctest

						doctest.testmod()