

'''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_     (							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,
							)







import logging

from transformers.configuration_utils import PretrainedConfig


lowerCAmelCase__								=		logging.getLogger(__name__)







class 	lowercase    (				_lowercase							):


		"""simple docstring"""




		a__					       =  "masked_bert"


		def __init__(				self	,       __snake_case=3_05_22	,       __snake_case=7_68	,       __snake_case=12	,       __snake_case=12	,       __snake_case=30_72	,       __snake_case="gelu"	,       __snake_case=0.1	,       __snake_case=0.1	,       __snake_case=5_12	,       __snake_case=2	,       __snake_case=0.0_2	,       __snake_case=1e-12	,       __snake_case=0	,       __snake_case="topK"	,       __snake_case="constant"	,       __snake_case=0.0	,       **__snake_case	,       ):
			super().__init__(pad_token_id=__snake_case	,       **__snake_case)

			_UpperCamelCase   :		List[Any]			=						vocab_size
			_UpperCamelCase   :		Union[str, Any]			=						hidden_size
			_UpperCamelCase   :		Optional[int]			=						num_hidden_layers
			_UpperCamelCase   :		Any			=						num_attention_heads
			_UpperCamelCase   :		int			=						hidden_act
			_UpperCamelCase   :		str			=						intermediate_size
			_UpperCamelCase   :		str			=						hidden_dropout_prob
			_UpperCamelCase   :		Any			=						attention_probs_dropout_prob
			_UpperCamelCase   :		Tuple			=						max_position_embeddings
			_UpperCamelCase   :		Dict			=						type_vocab_size
			_UpperCamelCase   :		str			=						initializer_range
			_UpperCamelCase   :		List[Any]			=						layer_norm_eps
			_UpperCamelCase   :		Tuple			=						pruning_method
			_UpperCamelCase   :		Tuple			=						mask_init
			_UpperCamelCase   :		Dict			=						mask_scale




