





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
_SCREAMING_SNAKE_CASE 		=						{
    "/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 							SCREAMING_SNAKE_CASE__  (      __a				):
	snake_case_						:     List[str]     =		list(s_dict.keys()				)
	for key in keys:
		snake_case_						:     Optional[Any]     =		r'.*/layers_(\d+)'
		snake_case_						:     Optional[Any]     =		key
		if re.match(lowerCAmelCase__				,	lowerCAmelCase__				):
			snake_case_						:     Optional[Any]     =		re.sub(r'layers_(\d+)'				,	r'block/\1/layer'				,	lowerCAmelCase__				)

		snake_case_						:     int     =		r'(encoder|decoder)\/'

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

			elif groups[0] == "decoder":
				snake_case_						:     Optional[Any]     =		re.sub(r'/mlp/'				,	r'/2/mlp/'				,	lowerCAmelCase__				)
				snake_case_						:     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:
				snake_case_						:     List[str]     =		new_key.replace(lowerCAmelCase__				,	lowerCAmelCase__				)

		print(f"""{key} -> {new_key}"""				)
		snake_case_						:     List[str]     =		s_dict.pop(lowerCAmelCase__				)

	if "encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight" in s_dict:
		snake_case_						:     Union[str, 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:
		snake_case_						:     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:
			snake_case_						:     int     =		s_dict[key].shape[0]
			snake_case_						:     Tuple     =		s_dict[key]
			for idx in range(lowerCAmelCase__				):
				snake_case_						:     str     =		expert_weihts[idx]
				print(f"""{key} -> {key.replace('expert/'				,	'nested fstring'				)}"""				)

			s_dict.pop(lowerCAmelCase__				)

	return s_dict


_SCREAMING_SNAKE_CASE 		=						{
    "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 							SCREAMING_SNAKE_CASE__  (      __a				,	__a				):
	import regex as re

	with open(lowerCAmelCase__				,	'r'				) as f:
		snake_case_						:     Any     =		f.read()

	snake_case_						:     Tuple     =		re.findall(r'(.*) = ([0-9.]*)'				,	lowerCAmelCase__				)
	snake_case_						:     Tuple     =		{}
	for param, value in regex_match:
		if param in GIN_TO_CONFIG_MAPPING and value != "":
			snake_case_						:     Optional[int]     =		float(lowerCAmelCase__				) if '.' in value else int(lowerCAmelCase__				)

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

	snake_case_						:     Dict     =		num_experts
	snake_case_						:     Union[str, Any]     =		SwitchTransformersConfig(**lowerCAmelCase__				)
	return config


def 							SCREAMING_SNAKE_CASE__  (      __a				,	__a				,	__a=None				,	__a="./"				,	__a=8				):
	print(f"""Loading flax weights from : {flax_checkpoint_path}"""				)
	snake_case_						:     Any     =		checkpoints.load_tax_checkpoint(lowerCAmelCase__				)

	if gin_file is not None:
		snake_case_						:     Any     =		convert_gin_to_config(lowerCAmelCase__				,	lowerCAmelCase__				)
	else:
		snake_case_						:     Tuple     =		SwitchTransformersConfig.from_pretrained(lowerCAmelCase__				)

	snake_case_						:     Dict     =		SwitchTransformersForConditionalGeneration(lowerCAmelCase__				)

	snake_case_						:     Tuple     =		flax_params['target']
	snake_case_						:     Union[str, Any]     =		flatten_dict(lowerCAmelCase__				,	sep='/'				)
	snake_case_						:     Union[str, Any]     =		rename_keys(lowerCAmelCase__				)
	snake_case_						:     Tuple     =		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__":
						_SCREAMING_SNAKE_CASE 		=						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""")
						_SCREAMING_SNAKE_CASE 		=						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 warnings

from ..trainer import Trainer
from ..utils import logging


_SCREAMING_SNAKE_CASE 		=						logging.get_logger(__name__)
class SCREAMING_SNAKE_CASE_  (   snake_case_   ):






						def __init__( self : Any						,				_A : str=None						,				**_A : Union[str, Any]		)							->      Any:







							"""simple docstring"""


							warnings.warn(
							    '`SageMakerTrainer` is deprecated and will be removed in v5 of Transformers. You can use `Trainer` '
							    'instead.'						,				_A						,				)
							super().__init__(args=_A						,				**_A		)

