



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

				_a	: Any								=				r'(encoder|decoder)\/'

				if re.match(lowerCAmelCase_		,     lowerCAmelCase_    ):
						_a	: List[str]								=				re.match(lowerCAmelCase_		,     lowerCAmelCase_    ).groups()
						if groups[0] == "encoder":
								_a	: int								=				re.sub(r'/mlp/'		,     r'/1/mlp/'		,     lowerCAmelCase_    )
								_a	: Optional[Any]								=				re.sub(r'/pre_mlp_layer_norm/'		,     r'/1/layer_norm/'		,     lowerCAmelCase_    )

						elif groups[0] == "decoder":
								_a	: Optional[int]								=				re.sub(r'/mlp/'		,     r'/2/mlp/'		,     lowerCAmelCase_    )
								_a	: Optional[int]								=				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:
								_a	: Dict								=				new_key.replace(lowerCAmelCase_		,     lowerCAmelCase_    )

				print(f"""{key} -> {new_key}"""    )
				_a	: int								=				s_dict.pop(lowerCAmelCase_    )

		if "encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight" in s_dict:
				_a	: List[str]								=				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:
				_a	: 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:
						_a	: Optional[int]								=				s_dict[key].shape[0]
						_a	: List[Any]								=				s_dict[key]
						for idx in range(lowerCAmelCase_    ):
								_a	: List[Any]								=				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 	__lowerCamelCase	( lowerCAmelCase_		,     lowerCAmelCase_    )     -> Union[str, Any]:
		# Convert a google style config to the hugging face fromat
		import regex as re

		with open(lowerCAmelCase_		,     'r'    ) as f:
				_a	: Optional[int]								=				f.read()

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

		_a	: List[Any]								=				re.findall(r'(.*activations) = \(\'(.*)\',\)'		,     lowerCAmelCase_    )[0]
		_a	: str								=				str(activation[1]    )

		_a	: int								=				num_experts
		_a	: List[Any]								=				SwitchTransformersConfig(**lowerCAmelCase_    )
		return config



def 	__lowerCamelCase	( lowerCAmelCase_		,     lowerCAmelCase_		,     lowerCAmelCase_=None		,     lowerCAmelCase_="./"		,     lowerCAmelCase_=8    )     -> int:
		# Initialise PyTorch model

		print(f"""Loading flax weights from : {flax_checkpoint_path}"""    )
		_a	: int								=				checkpoints.load_tax_checkpoint(lowerCAmelCase_    )

		if gin_file is not None:
				_a	: int								=				convert_gin_to_config(lowerCAmelCase_		,     lowerCAmelCase_    )
		else:
				_a	: Tuple								=				SwitchTransformersConfig.from_pretrained(lowerCAmelCase_    )

		_a	: Any								=				SwitchTransformersForConditionalGeneration(lowerCAmelCase_    )

		_a	: Tuple								=				flax_params['target']
		_a	: Dict								=				flatten_dict(lowerCAmelCase_		,     sep='/'    )
		_a	: Optional[Any]								=				rename_keys(lowerCAmelCase_    )
		_a	: List[str]								=				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'''

from __future__ import annotations

from math import gcd



def 	__lowerCamelCase	( lowerCAmelCase_		,     lowerCAmelCase_ = 2		,     lowerCAmelCase_ = 1		,     lowerCAmelCase_ = 3		,     )     -> int | None:
		# A value less than 2 can cause an infinite loop in the algorithm.
		if num < 2:
				raise ValueError('The input value cannot be less than 2'    )

		# Because of the relationship between ``f(f(x))`` and ``f(x)``, this
		# algorithm struggles to find factors that are divisible by two.
		# As a workaround, we specifically check for two and even inputs.
		#   See: https://math.stackexchange.com/a/2856214/165820
		if num > 2 and num % 2 == 0:
				return 2

		# Pollard's Rho algorithm requires a function that returns pseudorandom
		# values between 0 <= X < ``num``.  It doesn't need to be random in the
		# sense that the output value is cryptographically secure or difficult
		# to calculate, it only needs to be random in the sense that all output
		# values should be equally likely to appear.
		# For this reason, Pollard suggested using ``f(x) = (x**2 - 1) % num``
		# However, the success of Pollard's algorithm isn't guaranteed and is
		# determined in part by the initial seed and the chosen random function.
		# To make retries easier, we will instead use ``f(x) = (x**2 + C) % num``
		# where ``C`` is a value that we can modify between each attempt.
		def rand_fn(lowerCAmelCase_		,     lowerCAmelCase_		,     lowerCAmelCase_    ) -> int:
				return (pow(lowerCAmelCase_		,     2    ) + step) % modulus

		for _ in range(lowerCAmelCase_    ):
				# These track the position within the cycle detection logic.
				_a	: Optional[int]								=				seed
				_a	: str								=				seed

				while True:
						# At each iteration, the tortoise moves one step and the hare moves two.
						_a	: str								=				rand_fn(lowerCAmelCase_		,     lowerCAmelCase_		,     lowerCAmelCase_    )
						_a	: Dict								=				rand_fn(lowerCAmelCase_		,     lowerCAmelCase_		,     lowerCAmelCase_    )
						_a	: Tuple								=				rand_fn(lowerCAmelCase_		,     lowerCAmelCase_		,     lowerCAmelCase_    )

						# At some point both the tortoise and the hare will enter a cycle whose
						# length ``p`` is a divisor of ``num``.  Once in that cycle, at some point
						# the tortoise and hare will end up on the same value modulo ``p``.
						# We can detect when this happens because the position difference between
						# the tortoise and the hare will share a common divisor with ``num``.
						_a	: Optional[Any]								=				gcd(hare - tortoise		,     lowerCAmelCase_    )

						if divisor == 1:
								# No common divisor yet, just keep searching.
								continue
						else:
								# We found a common divisor!
								if divisor == num:
										# Unfortunately, the divisor is ``num`` itself and is useless.
										break
								else:
										# The divisor is a nontrivial factor of ``num``!
										return divisor

        # If we made it here, then this attempt failed.
        # We need to pick a new starting seed for the tortoise and hare
        # in addition to a new step value for the random function.
        # To keep this example implementation deterministic, the
        # new values will be generated based on currently available
        # values instead of using something like ``random.randint``.

        # We can use the hare's position as the new seed.
        # This is actually what Richard Brent's the "optimized" variant does.
				_a	: Union[str, Any]								=				hare

				# The new step value for the random function can just be incremented.
				# At first the results will be similar to what the old function would
				# have produced, but the value will quickly diverge after a bit.
				step += 1

		# We haven't found a divisor within the requested number of attempts.
		# We were unlucky or ``num`` itself is actually prime.
		return None


if __name__ == "__main__":
							import argparse

							__lowerCAmelCase		     =  argparse.ArgumentParser()
							parser.add_argument(
							    '''num''',
							    type=int,
							    help='''The value to find a divisor of''',
							)
							parser.add_argument(
							    '''--attempts''',
							    type=int,
							    default=3,
							    help='''The number of attempts before giving up''',
							)
							__lowerCAmelCase		     =  parser.parse_args()

							__lowerCAmelCase		     =  pollard_rho(args.num, attempts=args.attempts)
							if divisor is None:
														print(f"""{args.num} is probably prime""")
							else:
														__lowerCAmelCase		     =  args.num // divisor
														print(f"""{args.num} = {divisor} * {quotient}""")




