

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__							(	SCREAMING_SNAKE_CASE_:							Optional[int]      )			->      Union[str, Any]:




				'''simple docstring'''





				A__        =			list(s_dict.keys()      )
				for key in keys:
								A__        =			R".*/layers_(\d+)"
								A__        =			key
								if re.match(SCREAMING_SNAKE_CASE_	,       SCREAMING_SNAKE_CASE_      ):
												A__        =			re.sub(R"layers_(\d+)"	,       R"block/\1/layer"	,       SCREAMING_SNAKE_CASE_      )

								A__        =			R"(encoder|decoder)\/"

								if re.match(SCREAMING_SNAKE_CASE_	,       SCREAMING_SNAKE_CASE_      ):
												A__        =			re.match(SCREAMING_SNAKE_CASE_	,       SCREAMING_SNAKE_CASE_      ).groups()
												if groups[0] == "encoder":
																A__        =			re.sub(R"/mlp/"	,       R"/1/mlp/"	,       SCREAMING_SNAKE_CASE_      )
																A__        =			re.sub(R"/pre_mlp_layer_norm/"	,       R"/1/layer_norm/"	,       SCREAMING_SNAKE_CASE_      )

												elif groups[0] == "decoder":
																A__        =			re.sub(R"/mlp/"	,       R"/2/mlp/"	,       SCREAMING_SNAKE_CASE_      )
																A__        =			re.sub(R"/pre_mlp_layer_norm/"	,       R"/2/layer_norm/"	,       SCREAMING_SNAKE_CASE_      )

        # 2. Convert other classic mappings
								for old_key, temp_key in MOE_LAYER_NAME_MAPPING.items():
												if old_key in new_key:
																A__        =			new_key.replace(SCREAMING_SNAKE_CASE_	,       SCREAMING_SNAKE_CASE_      )

								print(F'{key} -> {new_key}'      )
								A__        =			s_dict.pop(SCREAMING_SNAKE_CASE_      )

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

												s_dict.pop(SCREAMING_SNAKE_CASE_      )

				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__							(	SCREAMING_SNAKE_CASE_:							str	,       SCREAMING_SNAKE_CASE_:							Tuple      )			->      List[str]:




				'''simple docstring'''





				import regex as re

				with open(SCREAMING_SNAKE_CASE_	,       "r"      ) as f:
								A__        =			f.read()

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

				A__        =			re.findall(R"(.*activations) = \(\'(.*)\',\)"	,       SCREAMING_SNAKE_CASE_      )[0]
				A__        =			str(activation[1]      )

				A__        =			num_experts
				A__        =			SwitchTransformersConfig(**SCREAMING_SNAKE_CASE_      )
				return config



def 					lowerCAmelCase__							(	SCREAMING_SNAKE_CASE_:							List[Any]	,       SCREAMING_SNAKE_CASE_:							Optional[int]	,       SCREAMING_SNAKE_CASE_:							List[str]=None	,       SCREAMING_SNAKE_CASE_:							Optional[Any]="./"	,       SCREAMING_SNAKE_CASE_:							Tuple=8      )			->      int:




				'''simple docstring'''





				print(F'Loading flax weights from : {flax_checkpoint_path}'      )
				A__        =			checkpoints.load_tax_checkpoint(SCREAMING_SNAKE_CASE_      )

				if gin_file is not None:
								A__        =			convert_gin_to_config(SCREAMING_SNAKE_CASE_	,       SCREAMING_SNAKE_CASE_      )
				else:
								A__        =			SwitchTransformersConfig.from_pretrained(SCREAMING_SNAKE_CASE_      )

				A__        =			SwitchTransformersForConditionalGeneration(SCREAMING_SNAKE_CASE_      )

				A__        =			flax_params["target"]
				A__        =			flatten_dict(SCREAMING_SNAKE_CASE_	,       sep="/"      )
				A__        =			rename_keys(SCREAMING_SNAKE_CASE_      )
				A__        =			unflatten_dict(SCREAMING_SNAKE_CASE_	,       sep="/"      )

				# Load the flax params in the PT model
				load_flax_weights_in_pytorch_model(SCREAMING_SNAKE_CASE_	,       SCREAMING_SNAKE_CASE_      )

				print(F'Save PyTorch model to {pytorch_dump_path}'      )
				pt_model.save_pretrained(SCREAMING_SNAKE_CASE_      )


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,
	)




from ..utils import is_flax_available, is_torch_available


if is_torch_available():
	from .autoencoder_kl import AutoencoderKL
	from .controlnet import ControlNetModel
	from .dual_transformer_ad import DualTransformeraDModel
	from .modeling_utils import ModelMixin
	from .prior_transformer import PriorTransformer
	from .ta_film_transformer import TaFilmDecoder
	from .transformer_ad import TransformeraDModel
	from .unet_ad import UNetaDModel
	from .unet_ad import UNetaDModel
	from .unet_ad_condition import UNetaDConditionModel
	from .unet_ad_condition import UNetaDConditionModel
	from .vq_model import VQModel

if is_flax_available():
	from .controlnet_flax import FlaxControlNetModel
	from .unet_ad_condition_flax import FlaxUNetaDConditionModel
	from .vae_flax import FlaxAutoencoderKL

