


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

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

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

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

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

		if "encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight" in s_dict:
				_UpperCAmelCase						:				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:
				_UpperCAmelCase						:				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:
						_UpperCAmelCase						:				List[str]	    =					s_dict[key].shape[0]
						_UpperCAmelCase						:				Tuple	    =					s_dict[key]
						for idx in range(_lowerCamelCase      ):
								_UpperCAmelCase						:				Union[str, 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      ):
		# Convert a google style config to the hugging face fromat
		import regex as re

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

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

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

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






def        __lowerCAmelCase       (__lowerCAmelCase					,       __lowerCAmelCase					,       __lowerCAmelCase=None					,       __lowerCAmelCase="./"					,       __lowerCAmelCase=8      ):
		# Initialise PyTorch model

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

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

		_UpperCAmelCase						:				Optional[int]	    =					SwitchTransformersForConditionalGeneration(_lowerCamelCase      )

		_UpperCAmelCase						:				Optional[int]	    =					flax_params["""target"""]
		_UpperCAmelCase						:				Union[str, Any]	    =					flatten_dict(_lowerCamelCase					,       sep="/"      )
		_UpperCAmelCase						:				int	    =					rename_keys(_lowerCamelCase      )
		_UpperCAmelCase						:				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__":
						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'''




import string

# frequency taken from https://en.wikipedia.org/wiki/Letter_frequency
UpperCamelCase__       :	List[Any]    = {
    '''E''': 1_2.7_0,
    '''T''': 9.0_6,
    '''A''': 8.1_7,
    '''O''': 7.5_1,
    '''I''': 6.9_7,
    '''N''': 6.7_5,
    '''S''': 6.3_3,
    '''H''': 6.0_9,
    '''R''': 5.9_9,
    '''D''': 4.2_5,
    '''L''': 4.0_3,
    '''C''': 2.7_8,
    '''U''': 2.7_6,
    '''M''': 2.4_1,
    '''W''': 2.3_6,
    '''F''': 2.2_3,
    '''G''': 2.0_2,
    '''Y''': 1.9_7,
    '''P''': 1.9_3,
    '''B''': 1.2_9,
    '''V''': 0.9_8,
    '''K''': 0.7_7,
    '''J''': 0.1_5,
    '''X''': 0.1_5,
    '''Q''': 0.1_0,
    '''Z''': 0.0_7,
}
UpperCamelCase__       :	Optional[Any]    = '''ETAOINSHRDLCUMWFGYPBVKJXQZ'''
UpperCamelCase__       :	Dict    = '''ABCDEFGHIJKLMNOPQRSTUVWXYZ'''


def 				lowerCAmelCase_   (	_lowerCamelCase:   str					):
		__SCREAMING_SNAKE_CASE       :		int           =		{letter: 0 for letter in string.ascii_uppercase}
		for letter in message.upper():
				if letter in LETTERS:
						letter_count[letter] += 1

		return letter_count


def 				lowerCAmelCase_   (	_lowerCamelCase:   tuple					):
		return x[0]


def 				lowerCAmelCase_   (	_lowerCamelCase:   str					):
		__SCREAMING_SNAKE_CASE       :		Dict           =		get_letter_count(_lowerCamelCase					)
		__SCREAMING_SNAKE_CASE       :		dict[int, list[str]]           =		{
		    freq: [] for letter, freq in letter_to_freq.items()
		}
		for letter in LETTERS:
				freq_to_letter[letter_to_freq[letter]].append(_lowerCamelCase					)

		__SCREAMING_SNAKE_CASE       :		dict[int, str]           =		{}

		for freq in freq_to_letter:
				freq_to_letter[freq].sort(key=ETAOIN.find       ,      reverse=_lowerCamelCase					)
				__SCREAMING_SNAKE_CASE       :		Tuple           =		"""""".join(freq_to_letter[freq]					)

		__SCREAMING_SNAKE_CASE       :		str           =		list(freq_to_letter_str.items()					)
		freq_pairs.sort(key=_lowerCamelCase       ,      reverse=_lowerCamelCase					)

		__SCREAMING_SNAKE_CASE       :		list[str]           =		[freq_pair[1] for freq_pair in freq_pairs]

		return "".join(_lowerCamelCase					)


def 				lowerCAmelCase_   (	_lowerCamelCase:   str					):
		__SCREAMING_SNAKE_CASE       :		List[Any]           =		get_frequency_order(_lowerCamelCase					)
		__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		0
		for common_letter in ETAOIN[:6]:
				if common_letter in freq_order[:6]:
						match_score += 1

		for uncommon_letter in ETAOIN[-6:]:
				if uncommon_letter in freq_order[-6:]:
						match_score += 1

		return match_score


if __name__ == "__main__":
						import doctest

						doctest.testmod()