
import argparse
import collections

import torch
from flax import traverse_util
from tax import checkpoints

from transformers import TaConfig, TaEncoderModel, TaForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()






def    lowerCamelCase__				(				__lowerCAmelCase  :  str    ,							__lowerCAmelCase  :  str    ,							__lowerCAmelCase  :  Any    ,							__lowerCAmelCase  :  Dict="attention"				):




			"""simple docstring"""


			lowerCAmelCase_						=	params[F"""{prefix}/layers_{i}/{layer_name}/key/kernel"""]
			lowerCAmelCase_						=	params[F"""{prefix}/layers_{i}/{layer_name}/out/kernel"""]
			lowerCAmelCase_						=	params[F"""{prefix}/layers_{i}/{layer_name}/query/kernel"""]
			lowerCAmelCase_						=	params[F"""{prefix}/layers_{i}/{layer_name}/value/kernel"""]
			return k, o, q, v






def    lowerCamelCase__				(				__lowerCAmelCase  :  Optional[Any]    ,							__lowerCAmelCase  :  Dict    ,							__lowerCAmelCase  :  List[str]    ,							__lowerCAmelCase  :  Union[str, Any]=False				):




			"""simple docstring"""


			if split_mlp_wi:
						lowerCAmelCase_						=	params[F"""{prefix}/layers_{i}/mlp/wi_0/kernel"""]
						lowerCAmelCase_						=	params[F"""{prefix}/layers_{i}/mlp/wi_1/kernel"""]
						lowerCAmelCase_						=	(wi_a, wi_a)
			else:
						lowerCAmelCase_						=	params[F"""{prefix}/layers_{i}/mlp/wi/kernel"""]

			lowerCAmelCase_						=	params[F"""{prefix}/layers_{i}/mlp/wo/kernel"""]
			return wi, wo






def    lowerCamelCase__				(				__lowerCAmelCase  :  Union[str, Any]    ,							__lowerCAmelCase  :  Tuple    ,							__lowerCAmelCase  :  List[Any]    ,							__lowerCAmelCase  :  Any				):




			"""simple docstring"""


			return params[F"""{prefix}/layers_{i}/{layer_name}/scale"""]






def    lowerCamelCase__				(				__lowerCAmelCase  :  dict    ,							*, __lowerCAmelCase  :  int    ,							__lowerCAmelCase  :  bool				):




			"""simple docstring"""


			lowerCAmelCase_						=	traverse_util.flatten_dict(variables["target"]				)
			lowerCAmelCase_						=	{"/".join(__lowerCAmelCase				): v for k, v in old.items()}

			# v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
			lowerCAmelCase_						=	"encoder/layers_0/mlp/wi_0/kernel" in old
			print("Split MLP:"    ,							__lowerCAmelCase				)

			lowerCAmelCase_						=	collections.OrderedDict()

			# Shared embeddings.
			lowerCAmelCase_						=	old["token_embedder/embedding"]

			# Encoder.
			for i in range(__lowerCAmelCase				):
						# Block i, layer 0 (Self Attention).
						lowerCAmelCase_						=	tax_layer_norm_lookup(__lowerCAmelCase    ,							__lowerCAmelCase    ,							"encoder"    ,							"pre_attention_layer_norm"				)
						lowerCAmelCase_	,  lowerCAmelCase_	,  lowerCAmelCase_	,  lowerCAmelCase_						=	tax_attention_lookup(__lowerCAmelCase    ,							__lowerCAmelCase    ,							"encoder"    ,							"attention"				)
						lowerCAmelCase_						=	layer_norm
						lowerCAmelCase_						=	k.T
						lowerCAmelCase_						=	o.T
						lowerCAmelCase_						=	q.T
						lowerCAmelCase_						=	v.T

						# Block i, layer 1 (MLP).
						lowerCAmelCase_						=	tax_layer_norm_lookup(__lowerCAmelCase    ,							__lowerCAmelCase    ,							"encoder"    ,							"pre_mlp_layer_norm"				)
						lowerCAmelCase_	,  lowerCAmelCase_						=	tax_mlp_lookup(__lowerCAmelCase    ,							__lowerCAmelCase    ,							"encoder"    ,							__lowerCAmelCase				)
						lowerCAmelCase_						=	layer_norm
						if split_mlp_wi:
									lowerCAmelCase_						=	wi[0].T
									lowerCAmelCase_						=	wi[1].T
						else:
									lowerCAmelCase_						=	wi.T
						lowerCAmelCase_						=	wo.T

			lowerCAmelCase_						=	old[
			    "encoder/relpos_bias/rel_embedding"
			].T
			lowerCAmelCase_						=	old["encoder/encoder_norm/scale"]

			if not is_encoder_only:
						# Decoder.
						for i in range(__lowerCAmelCase				):
									# Block i, layer 0 (Self Attention).
									lowerCAmelCase_						=	tax_layer_norm_lookup(__lowerCAmelCase    ,							__lowerCAmelCase    ,							"decoder"    ,							"pre_self_attention_layer_norm"				)
									lowerCAmelCase_	,  lowerCAmelCase_	,  lowerCAmelCase_	,  lowerCAmelCase_						=	tax_attention_lookup(__lowerCAmelCase    ,							__lowerCAmelCase    ,							"decoder"    ,							"self_attention"				)
									lowerCAmelCase_						=	layer_norm
									lowerCAmelCase_						=	k.T
									lowerCAmelCase_						=	o.T
									lowerCAmelCase_						=	q.T
									lowerCAmelCase_						=	v.T

									# Block i, layer 1 (Cross Attention).
									lowerCAmelCase_						=	tax_layer_norm_lookup(__lowerCAmelCase    ,							__lowerCAmelCase    ,							"decoder"    ,							"pre_cross_attention_layer_norm"				)
									lowerCAmelCase_	,  lowerCAmelCase_	,  lowerCAmelCase_	,  lowerCAmelCase_						=	tax_attention_lookup(__lowerCAmelCase    ,							__lowerCAmelCase    ,							"decoder"    ,							"encoder_decoder_attention"				)
									lowerCAmelCase_						=	layer_norm
									lowerCAmelCase_						=	k.T
									lowerCAmelCase_						=	o.T
									lowerCAmelCase_						=	q.T
									lowerCAmelCase_						=	v.T

									# Block i, layer 2 (MLP).
									lowerCAmelCase_						=	tax_layer_norm_lookup(__lowerCAmelCase    ,							__lowerCAmelCase    ,							"decoder"    ,							"pre_mlp_layer_norm"				)
									lowerCAmelCase_	,  lowerCAmelCase_						=	tax_mlp_lookup(__lowerCAmelCase    ,							__lowerCAmelCase    ,							"decoder"    ,							__lowerCAmelCase				)
									lowerCAmelCase_						=	layer_norm
									if split_mlp_wi:
												lowerCAmelCase_						=	wi[0].T
												lowerCAmelCase_						=	wi[1].T
									else:
												lowerCAmelCase_						=	wi.T
									lowerCAmelCase_						=	wo.T

						lowerCAmelCase_						=	old["decoder/decoder_norm/scale"]
						lowerCAmelCase_						=	old[
						    "decoder/relpos_bias/rel_embedding"
						].T

						# LM Head (only in v1.1 checkpoints, in v1.0 embeddings are used instead)
						if "decoder/logits_dense/kernel" in old:
									lowerCAmelCase_						=	old["decoder/logits_dense/kernel"].T

			return new






def    lowerCamelCase__				(				__lowerCAmelCase  :  int    ,							__lowerCAmelCase  :  bool				):




			"""simple docstring"""


			lowerCAmelCase_						=	collections.OrderedDict([(k, torch.from_numpy(v.copy()				)) for (k, v) in converted_params.items()]				)

			# Add what is missing.
			if "encoder.embed_tokens.weight" not in state_dict:
						lowerCAmelCase_						=	state_dict["shared.weight"]

			if not is_encoder_only:
						if "decoder.embed_tokens.weight" not in state_dict:
									lowerCAmelCase_						=	state_dict["shared.weight"]

						if "lm_head.weight" not in state_dict:  # For old 1.0 models.
									print("Using shared word embeddings as lm_head."				)
									lowerCAmelCase_						=	state_dict["shared.weight"]

			return state_dict






def    lowerCamelCase__				(				__lowerCAmelCase  :  Optional[int]    ,							__lowerCAmelCase  :  Any    ,							__lowerCAmelCase  :  Optional[int]    ,							__lowerCAmelCase  :  Optional[Any]				):




			"""simple docstring"""


			lowerCAmelCase_						=	checkpoints.load_tax_checkpoint(__lowerCAmelCase				)
			lowerCAmelCase_						=	convert_tax_to_pytorch(__lowerCAmelCase    ,							num_layers=config.num_layers    ,							is_encoder_only=__lowerCAmelCase				)
			lowerCAmelCase_						=	make_state_dict(__lowerCAmelCase    ,							__lowerCAmelCase				)
			model.load_state_dict(__lowerCAmelCase    ,							strict=__lowerCAmelCase				)






def    lowerCamelCase__				(				__lowerCAmelCase  :  Any    ,							__lowerCAmelCase  :  Dict    ,							__lowerCAmelCase  :  int    ,							__lowerCAmelCase  :  bool = False				):




			"""simple docstring"""


			lowerCAmelCase_						=	TaConfig.from_json_file(__lowerCAmelCase				)
			print(F"""Building PyTorch model from configuration: {config}"""				)
			# Non-v1.1 checkpoints could also use T5Model, but this works for all.
			# The v1.0 checkpoints will simply have an LM head that is the word embeddings.
			if is_encoder_only:
						lowerCAmelCase_						=	TaEncoderModel(__lowerCAmelCase				)
			else:
						lowerCAmelCase_						=	TaForConditionalGeneration(__lowerCAmelCase				)

			# Load weights from tf checkpoint
			load_tax_weights_in_ta(__lowerCAmelCase    ,							__lowerCAmelCase    ,							__lowerCAmelCase    ,							__lowerCAmelCase				)

			# Save pytorch-model
			print(F"""Save PyTorch model to {pytorch_dump_path}"""				)
			model.save_pretrained(__lowerCAmelCase				)

			# Verify that we can load the checkpoint.
			model.from_pretrained(__lowerCAmelCase				)
			print("Done"				)


if __name__ == "__main__":
			_A   =     argparse.ArgumentParser(description="Converts a native T5X checkpoint into a PyTorch checkpoint.")
			# Required parameters
			parser.add_argument(
			    "--t5x_checkpoint_path", default=None, type=str, required=True, help="Path to the T5X checkpoint."
			)
			parser.add_argument(
			    "--config_file",
			    default=None,
			    type=str,
			    required=True,
			    help="The config json file corresponding to the pre-trained T5 model.\nThis specifies the model architecture.",
			)
			parser.add_argument(
			    "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
			)
			parser.add_argument(
			    "--is_encoder_only", action="store_true", help="Check if the model is encoder-decoder model", default=False
			)
			_A   =     parser.parse_args()
			convert_tax_checkpoint_to_pytorch(
			    args.tax_checkpoint_path, args.config_file, args.pytorch_dump_path, args.is_encoder_only
			)





import functools






def    lowerCamelCase__				(				__lowerCAmelCase  :  str    ,							__lowerCAmelCase  :  str				):




			"""simple docstring"""


			lowerCAmelCase_						=	len(__lowerCAmelCase				)
			lowerCAmelCase_						=	len(__lowerCAmelCase				)

			@functools.cache
			def min_distance(__lowerCAmelCase  :  int    ,							__lowerCAmelCase  :  int				) -> int:
						# if first word index is overflow - delete all from the second word
						if indexa >= len_worda:
									return len_worda - indexa
						# if second word index is overflow - delete all from the first word
						if indexa >= len_worda:
									return len_worda - indexa
						lowerCAmelCase_						=	int(worda[indexa] != worda[indexa]				)  # current letters not identical
						return min(
						    1 + min_distance(indexa + 1    ,							__lowerCAmelCase				)    ,							1 + min_distance(__lowerCAmelCase    ,							indexa + 1				)    ,							diff + min_distance(indexa + 1    ,							indexa + 1				)    ,							)

			return min_distance(0    ,							0				)


if __name__ == "__main__":
			import doctest

			doctest.testmod()



