


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

								lowerCAmelCase_ :      List[Any]    				=				r'''(encoder|decoder)\/'''

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

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

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

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

												s_dict.pop(lowerCAmelCase_ )

				return s_dict


_UpperCAmelCase			:  Dict     ={
    """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:
								lowerCAmelCase_ :      Any    				=				f.read()

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

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

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



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

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

				if gin_file is not None:
								lowerCAmelCase_ :      Optional[int]    				=				convert_gin_to_config(lowerCAmelCase_ ,   lowerCAmelCase_ )
				else:
								lowerCAmelCase_ :      Optional[int]    				=				SwitchTransformersConfig.from_pretrained(lowerCAmelCase_ )

				lowerCAmelCase_ :      Union[str, Any]    				=				SwitchTransformersForConditionalGeneration(lowerCAmelCase_ )

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



# We ignore warnings about stepping the scheduler since we step it ourselves during gradient accumulation

import warnings

from .state import AcceleratorState, GradientState


warnings.filterwarnings("""ignore""", category=UserWarning, module="""torch.optim.lr_scheduler""")







class 				snake_case__:







						'''simple docstring'''




						def __init__(				self   ,						__lowercase   ,						__lowercase   ,						__lowercase = True   ,						__lowercase = False							)					->    Tuple:
										lowerCAmelCase_ :      Optional[int]    				=				scheduler
										lowerCAmelCase_ :      Dict    				=				optimizers if isinstance(__lowercase   ,						(list, tuple)							) else [optimizers]
										lowerCAmelCase_ :      str    				=				split_batches
										lowerCAmelCase_ :      Any    				=				step_with_optimizer
										lowerCAmelCase_ :      Optional[Any]    				=				GradientState()




						def 						lowercase_ (				self   ,						*__lowercase   ,						**__lowercase							)					->    Any:
										if not self.step_with_optimizer:
														# No link between scheduler and optimizer -> just step
														self.scheduler.step(*__lowercase   ,						**__lowercase							)
														return

										# Otherwise, first make sure the optimizer was stepped.
										if not self.gradient_state.sync_gradients:
														if self.gradient_state.adjust_scheduler:
																		self.scheduler._step_count += 1
														return

										for opt in self.optimizers:
														if opt.step_was_skipped:
																		return
										if self.split_batches:
														# Split batches -> the training dataloader batch size is not changed so one step per training step
														self.scheduler.step(*__lowercase   ,						**__lowercase							)
										else:
														# Otherwise the training dataloader batch size was multiplied by `num_processes`, so we need to do
														# num_processes steps per training step
														lowerCAmelCase_ :      Optional[Any]    				=				AcceleratorState().num_processes
														for _ in range(__lowercase							):
																		# Special case when using OneCycle and `drop_last` was not used
																		if hasattr(self.scheduler   ,						'''total_steps'''							):
																						if self.scheduler._step_count <= self.scheduler.total_steps:
																										self.scheduler.step(*__lowercase   ,						**__lowercase							)
																		else:
																						self.scheduler.step(*__lowercase   ,						**__lowercase							)




						def 						lowercase_ (				self							)					->    Union[str, Any]:
										return self.scheduler.get_last_lr()




						def 						lowercase_ (				self							)					->    List[str]:
										return self.scheduler.state_dict()




						def 						lowercase_ (				self   ,						__lowercase							)					->    int:
										self.scheduler.load_state_dict(__lowercase							)




						def 						lowercase_ (				self							)					->    Tuple:
										return self.scheduler.get_lr()




						def 						lowercase_ (				self   ,						*__lowercase   ,						**__lowercase							)					->    int:
										return self.scheduler.print_lr(*__lowercase   ,						**__lowercase							)