

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_     (							UpperCAmelCase_							:     Union[str, Any]	)						->				List[str]:




	'''simple docstring'''




	_UpperCamelCase   :		Union[str, Any]			=						list(s_dict.keys()	)
	for key in keys:
		_UpperCamelCase   :		Optional[int]			=						R'.*/layers_(\d+)'
		_UpperCamelCase   :		int			=						key
		if re.match(UpperCAmelCase_							,       UpperCAmelCase_	):
			_UpperCamelCase   :		List[str]			=						re.sub(R'layers_(\d+)'							,       R'block/\1/layer'							,       UpperCAmelCase_	)

		_UpperCamelCase   :		Tuple			=						R'(encoder|decoder)\/'

		if re.match(UpperCAmelCase_							,       UpperCAmelCase_	):
			_UpperCamelCase   :		Optional[Any]			=						re.match(UpperCAmelCase_							,       UpperCAmelCase_	).groups()
			if groups[0] == "encoder":
				_UpperCamelCase   :		Tuple			=						re.sub(R'/mlp/'							,       R'/1/mlp/'							,       UpperCAmelCase_	)
				_UpperCamelCase   :		Optional[int]			=						re.sub(R'/pre_mlp_layer_norm/'							,       R'/1/layer_norm/'							,       UpperCAmelCase_	)

			elif groups[0] == "decoder":
				_UpperCamelCase   :		int			=						re.sub(R'/mlp/'							,       R'/2/mlp/'							,       UpperCAmelCase_	)
				_UpperCamelCase   :		Union[str, Any]			=						re.sub(R'/pre_mlp_layer_norm/'							,       R'/2/layer_norm/'							,       UpperCAmelCase_	)

        # 2. Convert other classic mappings
		for old_key, temp_key in MOE_LAYER_NAME_MAPPING.items():
			if old_key in new_key:
				_UpperCamelCase   :		Optional[int]			=						new_key.replace(UpperCAmelCase_							,       UpperCAmelCase_	)

		print(F'''{key} -> {new_key}'''	)
		_UpperCamelCase   :		Union[str, Any]			=						s_dict.pop(UpperCAmelCase_	)

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

			s_dict.pop(UpperCAmelCase_	)

	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_     (							UpperCAmelCase_							:     Union[str, Any]							,       UpperCAmelCase_							:     Dict	)						->				List[str]:




	'''simple docstring'''




	import regex as re

	with open(UpperCAmelCase_							,       'r'	) as f:
		_UpperCamelCase   :		str			=						f.read()

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

	_UpperCamelCase   :		Optional[Any]			=						re.findall(R'(.*activations) = \(\'(.*)\',\)'							,       UpperCAmelCase_	)[0]
	_UpperCamelCase   :		Union[str, Any]			=						str(activation[1]	)

	_UpperCamelCase   :		Any			=						num_experts
	_UpperCamelCase   :		Optional[Any]			=						SwitchTransformersConfig(**UpperCAmelCase_	)
	return config






def 					lowerCamelCase_     (							UpperCAmelCase_							:     Any							,       UpperCAmelCase_							:     Union[str, Any]							,       UpperCAmelCase_							:     Optional[Any]=None							,       UpperCAmelCase_							:     Dict="./"							,       UpperCAmelCase_							:     Optional[int]=8	)						->				Optional[int]:




	'''simple docstring'''




	print(F'''Loading flax weights from : {flax_checkpoint_path}'''	)
	_UpperCamelCase   :		Tuple			=						checkpoints.load_tax_checkpoint(UpperCAmelCase_	)

	if gin_file is not None:
		_UpperCamelCase   :		str			=						convert_gin_to_config(UpperCAmelCase_							,       UpperCAmelCase_	)
	else:
		_UpperCamelCase   :		Optional[Any]			=						SwitchTransformersConfig.from_pretrained(UpperCAmelCase_	)

	_UpperCamelCase   :		Union[str, Any]			=						SwitchTransformersForConditionalGeneration(UpperCAmelCase_	)

	_UpperCamelCase   :		Dict			=						flax_params['target']
	_UpperCamelCase   :		str			=						flatten_dict(UpperCAmelCase_							,       sep='/'	)
	_UpperCamelCase   :		Dict			=						rename_keys(UpperCAmelCase_	)
	_UpperCamelCase   :		Optional[int]			=						unflatten_dict(UpperCAmelCase_							,       sep='/'	)

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

	print(F'''Save PyTorch model to {pytorch_dump_path}'''	)
	pt_model.save_pretrained(UpperCAmelCase_	)


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,
							)







lowerCAmelCase__								=		range(2, 2_0 + 1)
lowerCAmelCase__								=		[1_0**k for k in range(ks[-1] + 1)]
lowerCAmelCase__								=		{}






def 					lowerCamelCase_     (							UpperCAmelCase_							:     Optional[Any]							,       UpperCAmelCase_							:     List[str]							,       UpperCAmelCase_							:     Any							,       UpperCAmelCase_							:     int	)						->				Tuple:




	'''simple docstring'''




	_UpperCamelCase   :		Dict			=						sum(a_i[j] for j in range(UpperCAmelCase_							,       len(UpperCAmelCase_	)	)	)
	_UpperCamelCase   :		Optional[int]			=						sum(a_i[j] * base[j] for j in range(min(len(UpperCAmelCase_	)							,       UpperCAmelCase_	)	)	)

	_UpperCamelCase	,		_UpperCamelCase   :		Dict			=						0, 0
	_UpperCamelCase   :		Optional[int]			=						n - i

	_UpperCamelCase   :		Union[str, Any]			=						memo.get(UpperCAmelCase_	)

	if sub_memo is not None:
		_UpperCamelCase   :		str			=						sub_memo.get(UpperCAmelCase_	)

		if jumps is not None and len(UpperCAmelCase_	) > 0:
			# find and make the largest jump without going over
			_UpperCamelCase   :		str			=						-1
			for _k in range(len(UpperCAmelCase_	) - 1							,       -1							,       -1	):
				if jumps[_k][2] <= k and jumps[_k][1] <= max_dn:
					_UpperCamelCase   :		Optional[Any]			=						_k
					break

			if max_jump >= 0:
				_UpperCamelCase	,		_UpperCamelCase	,		_UpperCamelCase   :		Optional[Any]			=						jumps[max_jump]
				# since the difference between jumps is cached, add c
				_UpperCamelCase   :		Tuple			=						diff + c
				for j in range(min(UpperCAmelCase_							,       len(UpperCAmelCase_	)	)	):
					_UpperCamelCase	,		_UpperCamelCase   :		Dict			=						divmod(UpperCAmelCase_							,       1_0	)
				if new_c > 0:
					add(UpperCAmelCase_							,       UpperCAmelCase_							,       UpperCAmelCase_	)

		else:
			_UpperCamelCase   :		Union[str, Any]			=						[]
	else:
		_UpperCamelCase   :		List[Any]			=						{c: []}
		_UpperCamelCase   :		Optional[int]			=						sub_memo

	if dn >= max_dn or c + diff >= base[k]:
		return diff, dn

	if k > ks[0]:
		while True:
			# keep doing smaller jumps
			_UpperCamelCase	,		_UpperCamelCase   :		Optional[Any]			=						next_term(UpperCAmelCase_							,       k - 1							,       i + dn							,       UpperCAmelCase_	)
			diff += _diff
			dn += terms_jumped

			if dn >= max_dn or c + diff >= base[k]:
				break
	else:
		# would be too small a jump, just compute sequential terms instead
		_UpperCamelCase	,		_UpperCamelCase   :		Any			=						compute(UpperCAmelCase_							,       UpperCAmelCase_							,       i + dn							,       UpperCAmelCase_	)
		diff += _diff
		dn += terms_jumped

	_UpperCamelCase   :		List[str]			=						sub_memo[c]

	# keep jumps sorted by # of terms skipped
	_UpperCamelCase   :		Union[str, Any]			=						0
	while j < len(UpperCAmelCase_	):
		if jumps[j][1] > dn:
			break
		j += 1

	# cache the jump for this value digitsum(b) and c
	sub_memo[c].insert(UpperCAmelCase_							,       (diff, dn, k)	)
	return (diff, dn)






def 					lowerCamelCase_     (							UpperCAmelCase_							:     int							,       UpperCAmelCase_							:     Dict							,       UpperCAmelCase_							:     Any							,       UpperCAmelCase_							:     Any	)						->				Dict:




	'''simple docstring'''




	if i >= n:
		return 0, i
	if k > len(UpperCAmelCase_	):
		a_i.extend([0 for _ in range(k - len(UpperCAmelCase_	)	)]	)

	# note: a_i -> b * 10^k + c
	# ds_b -> digitsum(b)
	# ds_c -> digitsum(c)
	_UpperCamelCase   :		Any			=						i
	_UpperCamelCase	,		_UpperCamelCase	,		_UpperCamelCase   :		Any			=						0, 0, 0
	for j in range(len(UpperCAmelCase_	)	):
		if j >= k:
			ds_b += a_i[j]
		else:
			ds_c += a_i[j]

	while i < n:
		i += 1
		_UpperCamelCase   :		Union[str, Any]			=						ds_c + ds_b
		diff += addend
		_UpperCamelCase   :		Union[str, Any]			=						0
		for j in range(UpperCAmelCase_	):
			_UpperCamelCase   :		Union[str, Any]			=						a_i[j] + addend
			_UpperCamelCase	,		_UpperCamelCase   :		Any			=						divmod(UpperCAmelCase_							,       1_0	)

			ds_c += a_i[j]

		if addend > 0:
			break

	if addend > 0:
		add(UpperCAmelCase_							,       UpperCAmelCase_							,       UpperCAmelCase_	)
	return diff, i - start_i






def 					lowerCamelCase_     (							UpperCAmelCase_							:     Union[str, Any]							,       UpperCAmelCase_							:     Optional[int]							,       UpperCAmelCase_							:     Any	)						->				Dict:




	'''simple docstring'''




	for j in range(UpperCAmelCase_							,       len(UpperCAmelCase_	)	):
		_UpperCamelCase   :		List[str]			=						digits[j] + addend
		if s >= 1_0:
			_UpperCamelCase	,		_UpperCamelCase   :		Any			=						divmod(UpperCAmelCase_							,       1_0	)
			_UpperCamelCase   :		Union[str, Any]			=						addend // 1_0 + quotient
		else:
			_UpperCamelCase   :		Dict			=						s
			_UpperCamelCase   :		Optional[Any]			=						addend // 1_0

		if addend == 0:
			break

	while addend > 0:
		_UpperCamelCase	,		_UpperCamelCase   :		Dict			=						divmod(UpperCAmelCase_							,       1_0	)
		digits.append(UpperCAmelCase_	)






def 					lowerCamelCase_     (							UpperCAmelCase_							:     int = 1_0**1_5	)						->				int:




	'''simple docstring'''




	_UpperCamelCase   :		Optional[Any]			=						[1]
	_UpperCamelCase   :		Optional[int]			=						1
	_UpperCamelCase   :		int			=						0
	while True:
		_UpperCamelCase	,		_UpperCamelCase   :		List[Any]			=						next_term(UpperCAmelCase_							,       2_0							,       i + dn							,       UpperCAmelCase_	)
		dn += terms_jumped
		if dn == n - i:
			break

	_UpperCamelCase   :		str			=						0
	for j in range(len(UpperCAmelCase_	)	):
		a_n += digits[j] * 1_0**j
	return a_n


if __name__ == "__main__":
							print(f'{solution() = }')




