

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__				(						SCREAMING_SNAKE_CASE_:				Optional[Any]						,							SCREAMING_SNAKE_CASE_:				str						,							SCREAMING_SNAKE_CASE_:				Any						,							SCREAMING_SNAKE_CASE_:				Optional[int]="attention"   )      ->   Tuple:




				'''simple docstring'''





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



def        lowerCAmelCase__				(						SCREAMING_SNAKE_CASE_:				Dict						,							SCREAMING_SNAKE_CASE_:				List[str]						,							SCREAMING_SNAKE_CASE_:				Dict						,							SCREAMING_SNAKE_CASE_:				Dict=False   )      ->   Any:




				'''simple docstring'''





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

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



def        lowerCAmelCase__				(						SCREAMING_SNAKE_CASE_:				Dict						,							SCREAMING_SNAKE_CASE_:				Any						,							SCREAMING_SNAKE_CASE_:				Optional[int]						,							SCREAMING_SNAKE_CASE_:				List[Any]   )      ->   Optional[int]:




				'''simple docstring'''





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



def        lowerCAmelCase__				(						SCREAMING_SNAKE_CASE_:				List[Any]						,							*, SCREAMING_SNAKE_CASE_:				Optional[int]						,							SCREAMING_SNAKE_CASE_:				List[Any]   )      ->   List[Any]:




				'''simple docstring'''





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

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

				A__          =      collections.OrderedDict()

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

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

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

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

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

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

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

								A__          =      old["decoder/decoder_norm/scale"]
								A__          =      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:
												A__          =      old["decoder/logits_dense/kernel"].T

				return new



def        lowerCAmelCase__				(						SCREAMING_SNAKE_CASE_:				Dict						,							SCREAMING_SNAKE_CASE_:				Union[str, Any]   )      ->   Dict:




				'''simple docstring'''





				A__          =      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:
								A__          =      state_dict["shared.weight"]

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

				return state_dict



def        lowerCAmelCase__				(						SCREAMING_SNAKE_CASE_:				Any						,							SCREAMING_SNAKE_CASE_:				Optional[Any]						,							SCREAMING_SNAKE_CASE_:				Optional[int]						,							SCREAMING_SNAKE_CASE_:				Any   )      ->   str:




				'''simple docstring'''





				A__          =      checkpoints.load_tax_checkpoint(__A   )
				A__          =      convert_tax_to_pytorch(__A						,							num_layers=config.num_layers						,							is_encoder_only=__A   )
				A__          =      make_state_dict(__A						,							__A   )
				model.load_state_dict(__A						,							strict=__A   )



def        lowerCAmelCase__				(						SCREAMING_SNAKE_CASE_:				Optional[Any]						,							SCREAMING_SNAKE_CASE_:				Any						,							SCREAMING_SNAKE_CASE_:				Tuple						,							SCREAMING_SNAKE_CASE_:				str = False   )      ->   Optional[int]:




				'''simple docstring'''





				A__          =      TaConfig.from_json_file(__A   )
				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:
								A__          =      TaEncoderModel(__A   )
				else:
								A__          =      TaForConditionalGeneration(__A   )

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

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

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


if __name__ == "__main__":
	lowerCAmelCase__						 =			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
	)
	lowerCAmelCase__						 =			parser.parse_args()
	convert_tax_checkpoint_to_pytorch(
	    args.tax_checkpoint_path, args.config_file, args.pytorch_dump_path, args.is_encoder_only
	)






'''simple docstring'''





from __future__ import annotations




def lowerCAmelCase    (__A):



     """simple docstring"""



     _a           =    2
     _a           =    []
     while i * i <= n:
          if n % i:
               i += 1
          else:
               n //= i
               factors.append(__A)
     if n > 1:
          factors.append(__A)
     return factors


if __name__ == "__main__":
  import doctest

  doctest.testmod()



