




'''simple docstring'''

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 __UpperCAmelCase	(   _UpperCAmelCase		:   Optional[Any]		,				_UpperCAmelCase		:   Optional[Any]		,				_UpperCAmelCase		:   Union[str, Any]		,				_UpperCAmelCase		:   Any="attention"						)      ->      Optional[int]:
			__snake_case			=		params[F'''{prefix}/layers_{i}/{layer_name}/key/kernel''']
			__snake_case			=		params[F'''{prefix}/layers_{i}/{layer_name}/out/kernel''']
			__snake_case			=		params[F'''{prefix}/layers_{i}/{layer_name}/query/kernel''']
			__snake_case			=		params[F'''{prefix}/layers_{i}/{layer_name}/value/kernel''']
			return k, o, q, v


def __UpperCAmelCase	(   _UpperCAmelCase		:   List[str]		,				_UpperCAmelCase		:   Optional[int]		,				_UpperCAmelCase		:   Any		,				_UpperCAmelCase		:   List[str]=False						)      ->      Optional[int]:
			if split_mlp_wi:
						__snake_case			=		params[F'''{prefix}/layers_{i}/mlp/wi_0/kernel''']
						__snake_case			=		params[F'''{prefix}/layers_{i}/mlp/wi_1/kernel''']
						__snake_case			=		(wi_a, wi_a)
			else:
						__snake_case			=		params[F'''{prefix}/layers_{i}/mlp/wi/kernel''']

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


def __UpperCAmelCase	(   _UpperCAmelCase		:   List[str]		,				_UpperCAmelCase		:   Dict		,				_UpperCAmelCase		:   Tuple		,				_UpperCAmelCase		:   Optional[int]						)      ->      int:
			return params[F'''{prefix}/layers_{i}/{layer_name}/scale''']


def __UpperCAmelCase	(   _UpperCAmelCase		:   dict		,				*, _UpperCAmelCase		:   int		,				_UpperCAmelCase		:   bool						)      ->      Optional[int]:
			__snake_case			=		traverse_util.flatten_dict(variables["target"]						)
			__snake_case			=		{"/".join(_UpperCAmelCase						): v for k, v in old.items()}

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

			__snake_case			=		collections.OrderedDict()

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

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

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

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

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

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

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

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

			return new


def __UpperCAmelCase	(   _UpperCAmelCase		:   Union[str, Any]		,				_UpperCAmelCase		:   bool						)      ->      Optional[int]:
			__snake_case			=		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:
						__snake_case			=		state_dict["shared.weight"]

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

			return state_dict


def __UpperCAmelCase	(   _UpperCAmelCase		:   Dict		,				_UpperCAmelCase		:   Dict		,				_UpperCAmelCase		:   Union[str, Any]		,				_UpperCAmelCase		:   Any						)      ->      Dict:
			__snake_case			=		checkpoints.load_tax_checkpoint(_UpperCAmelCase						)
			__snake_case			=		convert_tax_to_pytorch(_UpperCAmelCase		,				num_layers=config.num_layers		,				is_encoder_only=_UpperCAmelCase						)
			__snake_case			=		make_state_dict(_UpperCAmelCase		,				_UpperCAmelCase						)
			model.load_state_dict(_UpperCAmelCase		,				strict=_UpperCAmelCase						)


def __UpperCAmelCase	(   _UpperCAmelCase		:   str		,				_UpperCAmelCase		:   List[str]		,				_UpperCAmelCase		:   int		,				_UpperCAmelCase		:   bool = False						)      ->      Optional[Any]:
			__snake_case			=		TaConfig.from_json_file(_UpperCAmelCase						)
			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:
						__snake_case			=		TaEncoderModel(_UpperCAmelCase						)
			else:
						__snake_case			=		TaForConditionalGeneration(_UpperCAmelCase						)

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

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

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


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








a__												=						'''Input must be a string of 8 numbers plus letter'''
a__												=						'''TRWAGMYFPDXBNJZSQVHLCKE'''



def   __UpperCAmelCase					(      __a :     str      )     ->	bool:

					"""simple docstring"""

					if not isinstance(__a						,__a      ):
										_a      :       List[str]   =						F"""Expected string as input, found {type(__a      ).__name__}"""
										raise TypeError(__a      )

					_a      :       List[Any]   =						spanish_id.replace('''-'''						,''''''      ).upper()
					if len(__a      ) != 9:
										raise ValueError(__a      )

					try:
										_a      :       Any   =						int(spanish_id_clean[0:8]      )
										_a      :       str   =						spanish_id_clean[8]
					except ValueError as ex:
										raise ValueError(__a      ) from ex

					if letter.isdigit():
										raise ValueError(__a      )

					return letter == LOOKUP_LETTERS[number % 23]


if __name__ == "__main__":
			import doctest

			doctest.testmod()



