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

						lowerCamelCase						:List[str]						  =     R'''(encoder|decoder)\/'''

						if re.match(lowerCAmelCase__						,			lowerCAmelCase__):
									lowerCamelCase						:str						  =     re.match(lowerCAmelCase__						,			lowerCAmelCase__).groups()
									if groups[0] == "encoder":
												lowerCamelCase						:Dict						  =     re.sub(R'''/mlp/'''						,			R'''/1/mlp/'''						,			lowerCAmelCase__)
												lowerCamelCase						:Tuple						  =     re.sub(R'''/pre_mlp_layer_norm/'''						,			R'''/1/layer_norm/'''						,			lowerCAmelCase__)

									elif groups[0] == "decoder":
												lowerCamelCase						:List[str]						  =     re.sub(R'''/mlp/'''						,			R'''/2/mlp/'''						,			lowerCAmelCase__)
												lowerCamelCase						:List[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						:Dict						  =     new_key.replace(lowerCAmelCase__						,			lowerCAmelCase__)

						print(F"{key} -> {new_key}")
						lowerCamelCase						:Optional[int]						  =     s_dict.pop(lowerCAmelCase__)

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

									s_dict.pop(lowerCAmelCase__)

			return s_dict


A__           =							{
    """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					(   a_				: Optional[Any]						,			a_				: Optional[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						:Optional[int]						  =     re.findall(R'''(.*) = ([0-9.]*)'''						,			lowerCAmelCase__)
			lowerCamelCase						:List[str]						  =     {}
			for param, value in regex_match:
						if param in GIN_TO_CONFIG_MAPPING and value != "":
									lowerCamelCase						:List[Any]						  =     float(lowerCAmelCase__) if '''.''' in value else int(lowerCAmelCase__)

			lowerCamelCase						:Optional[int]						  =     re.findall(R'''(.*activations) = \(\'(.*)\',\)'''						,			lowerCAmelCase__)[0]
			lowerCamelCase						:List[str]						  =     str(activation[1])

			lowerCamelCase						:Optional[int]						  =     num_experts
			lowerCamelCase						:int						  =     SwitchTransformersConfig(**lowerCAmelCase__)
			return config





def  _lowerCAmelCase					(   a_				: int						,			a_				: Optional[int]						,			a_				: Optional[int]=None						,			a_				: Tuple="./"						,			a_				: Dict=8):
			# 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						:Any						  =     convert_gin_to_config(lowerCAmelCase__						,			lowerCAmelCase__)
			else:
						lowerCamelCase						:List[Any]						  =     SwitchTransformersConfig.from_pretrained(lowerCAmelCase__)

			lowerCamelCase						:Any						  =     SwitchTransformersForConditionalGeneration(lowerCAmelCase__)

			lowerCamelCase						:Tuple						  =     flax_params['''target''']
			lowerCamelCase						:str						  =     flatten_dict(lowerCAmelCase__						,			sep='''/''')
			lowerCamelCase						:Optional[int]						  =     rename_keys(lowerCAmelCase__)
			lowerCamelCase						:int						  =     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__":
			A__           =							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""")
			A__           =							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 inspect
import os
import unittest

import torch

import accelerate
from accelerate import debug_launcher
from accelerate.test_utils import (
    execute_subprocess_async,
    require_cpu,
    require_huggingface_suite,
    require_multi_gpu,
    require_single_gpu,
)
from accelerate.utils import patch_environment
@require_huggingface_suite
class   _lowerCAmelCase		(     unittest.TestCase				):







							def 				snake_case  (				self     :    Tuple		):
										lowerCamelCase						:List[Any]						  =     inspect.getfile(accelerate.test_utils		)
										lowerCamelCase						:Dict						  =     os.path.sep.join(
										    mod_file.split(os.path.sep		)[:-1] + ['''scripts''', '''external_deps''', '''test_metrics.py''']		)

										from accelerate.test_utils.scripts.external_deps import test_metrics  # noqa: F401

										lowerCamelCase						:Any						  =     test_metrics







							@require_cpu
							def 				snake_case  (				self     :    Dict		):
										debug_launcher(self.test_metrics.main	,					num_processes=1		)







							@require_cpu
							def 				snake_case  (				self     :    int		):
										debug_launcher(self.test_metrics.main		)







							@require_single_gpu
							def 				snake_case  (				self     :    Any		):
										self.test_metrics.main()







							@require_multi_gpu
							def 				snake_case  (				self     :    Optional[int]		):
										print(F"Found {torch.cuda.device_count()} devices."		)
										lowerCamelCase						:Optional[int]						  =     ['''torchrun''', F"--nproc_per_node={torch.cuda.device_count()}", self.test_file_path]
										with patch_environment(omp_num_threads=1		):
													execute_subprocess_async(__snake_case	,					env=os.environ.copy()		)



