





import argparse
import collections

import numpy as np
import torch
from flax import traverse_util
from tax import checkpoints

from transformers import MTaConfig, UMTaEncoderModel, UMTaForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()







def    A				(							UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase				):
					return params[F"""{prefix}/{prefix}/relpos_bias/rel_embedding"""][:, i, :]







def    A				(							UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase="attention"				):
					_snake_case       :					Optional[int]				       =   np.ascontiguousarray(params[F"""{prefix}/{prefix}/{layer_name}/key/kernel"""][:, i, :, :]				)
					_snake_case       :					Dict				       =   k_tmp.reshape(k_tmp.shape[0]  ,  k_tmp.shape[1] * k_tmp.shape[2]				)
					_snake_case       :					Tuple				       =   np.ascontiguousarray(params[F"""{prefix}/{prefix}/{layer_name}/out/kernel"""][:, i, :, :]				)
					_snake_case       :					Tuple				       =   o_tmp.reshape(o_tmp.shape[0] * o_tmp.shape[1]  ,  o_tmp.shape[2]				)
					_snake_case       :					List[str]				       =   np.ascontiguousarray(params[F"""{prefix}/{prefix}/{layer_name}/query/kernel"""][:, i, :, :]				)
					_snake_case       :					Optional[int]				       =   q_tmp.reshape(q_tmp.shape[0]  ,  q_tmp.shape[1] * q_tmp.shape[2]				)
					_snake_case       :					Optional[Any]				       =   np.ascontiguousarray(params[F"""{prefix}/{prefix}/{layer_name}/value/kernel"""][:, i, :, :]				)
					_snake_case       :					int				       =   v_tmp.reshape(v_tmp.shape[0]  ,  v_tmp.shape[1] * v_tmp.shape[2]				)
					return k, o, q, v







def    A				(							UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase=False				):
					if split_mlp_wi:
										_snake_case       :					Optional[Any]				       =   params[F"""{prefix}/{prefix}/mlp/wi_0/kernel"""][:, i, :]
										_snake_case       :					str				       =   params[F"""{prefix}/{prefix}/mlp/wi_1/kernel"""][:, i, :]
										_snake_case       :					Optional[Any]				       =   (wi_a, wi_a)
					else:
										_snake_case       :					Union[str, Any]				       =   params[F"""{prefix}/{prefix}/mlp/wi/kernel"""][:, i, :]

					_snake_case       :					Optional[int]				       =   params[F"""{prefix}/{prefix}/mlp/wo/kernel"""][:, i, :]
					return wi, wo







def    A				(							UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase				):
					return params[F"""{prefix}/{prefix}/{layer_name}/scale"""][:, i]







def    A				(							UpperCAmelCase  ,  *, UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase = False				):
					_snake_case       :					Dict				       =   traverse_util.flatten_dict(variables["target"]				)
					_snake_case       :					List[Any]				       =   {"/".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       :					List[Any]				       =   "encoder/encoder/mlp/wi_0/kernel" in old
					print("Split MLP:"  ,  UpperCAmelCase				)

					_snake_case       :					List[str]				       =   collections.OrderedDict()

					# Shared embeddings.
					_snake_case       :					Dict				       =   old["token_embedder/embedding"]

					# Encoder.
					for i in range(UpperCAmelCase				):
										# Block i, layer 0 (Self Attention).
										_snake_case       :					str				       =   tax_layer_norm_lookup(UpperCAmelCase  ,  UpperCAmelCase  ,  "encoder"  ,  "pre_attention_layer_norm"				)
										_snake_case       , _snake_case       , _snake_case       , _snake_case       :					Any				       =   tax_attention_lookup(UpperCAmelCase  ,  UpperCAmelCase  ,  "encoder"  ,  "attention"				)
										_snake_case       :					Tuple				       =   layer_norm
										_snake_case       :					Dict				       =   k.T
										_snake_case       :					int				       =   o.T
										_snake_case       :					Optional[Any]				       =   q.T
										_snake_case       :					int				       =   v.T

										# Block i, layer 1 (MLP).
										_snake_case       :					str				       =   tax_layer_norm_lookup(UpperCAmelCase  ,  UpperCAmelCase  ,  "encoder"  ,  "pre_mlp_layer_norm"				)
										_snake_case       , _snake_case       :					Dict				       =   tax_mlp_lookup(UpperCAmelCase  ,  UpperCAmelCase  ,  "encoder"  ,  UpperCAmelCase				)
										_snake_case       :					List[Any]				       =   layer_norm
										if split_mlp_wi:
															_snake_case       :					Union[str, Any]				       =   wi[0].T
															_snake_case       :					List[str]				       =   wi[1].T
										else:
															_snake_case       :					Tuple				       =   wi.T
										_snake_case       :					Tuple				       =   wo.T
										if scalable_attention:
															# convert the rel_embedding of each layer
															_snake_case       :					Dict				       =   tax_relpos_bias_lookup(
															    UpperCAmelCase  ,  UpperCAmelCase  ,  "encoder"				).T

					_snake_case       :					List[Any]				       =   old["encoder/encoder_norm/scale"]

					if not scalable_attention:
										_snake_case       :					List[Any]				       =   tax_relpos_bias_lookup(
										    UpperCAmelCase  ,  0  ,  "encoder"				).T
										_snake_case       :					Union[str, Any]				       =   tax_relpos_bias_lookup(
										    UpperCAmelCase  ,  0  ,  "decoder"				).T

					if not is_encoder_only:
										# Decoder.
										for i in range(UpperCAmelCase				):
															# Block i, layer 0 (Self Attention).
															_snake_case       :					Dict				       =   tax_layer_norm_lookup(UpperCAmelCase  ,  UpperCAmelCase  ,  "decoder"  ,  "pre_self_attention_layer_norm"				)
															_snake_case       , _snake_case       , _snake_case       , _snake_case       :					Dict				       =   tax_attention_lookup(UpperCAmelCase  ,  UpperCAmelCase  ,  "decoder"  ,  "self_attention"				)
															_snake_case       :					Tuple				       =   layer_norm
															_snake_case       :					str				       =   k.T
															_snake_case       :					Any				       =   o.T
															_snake_case       :					List[str]				       =   q.T
															_snake_case       :					Any				       =   v.T

															# Block i, layer 1 (Cross Attention).
															_snake_case       :					Optional[int]				       =   tax_layer_norm_lookup(UpperCAmelCase  ,  UpperCAmelCase  ,  "decoder"  ,  "pre_cross_attention_layer_norm"				)
															_snake_case       , _snake_case       , _snake_case       , _snake_case       :					Optional[int]				       =   tax_attention_lookup(UpperCAmelCase  ,  UpperCAmelCase  ,  "decoder"  ,  "encoder_decoder_attention"				)
															_snake_case       :					Tuple				       =   layer_norm
															_snake_case       :					Optional[Any]				       =   k.T
															_snake_case       :					List[Any]				       =   o.T
															_snake_case       :					List[str]				       =   q.T
															_snake_case       :					Union[str, Any]				       =   v.T

															# Block i, layer 2 (MLP).
															_snake_case       :					str				       =   tax_layer_norm_lookup(UpperCAmelCase  ,  UpperCAmelCase  ,  "decoder"  ,  "pre_mlp_layer_norm"				)
															_snake_case       , _snake_case       :					str				       =   tax_mlp_lookup(UpperCAmelCase  ,  UpperCAmelCase  ,  "decoder"  ,  UpperCAmelCase				)
															_snake_case       :					Optional[int]				       =   layer_norm
															if split_mlp_wi:
																				_snake_case       :					List[Any]				       =   wi[0].T
																				_snake_case       :					Union[str, Any]				       =   wi[1].T
															else:
																				_snake_case       :					Optional[Any]				       =   wi.T
															_snake_case       :					int				       =   wo.T

															if scalable_attention:
																				# convert the rel_embedding of each layer
																				_snake_case       :					Union[str, Any]				       =   tax_relpos_bias_lookup(UpperCAmelCase  ,  UpperCAmelCase  ,  "decoder"				).T

										_snake_case       :					Any				       =   old["decoder/decoder_norm/scale"]

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

					return new







def    A				(							UpperCAmelCase  ,  UpperCAmelCase				):
					_snake_case       :					int				       =   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       :					Any				       =   state_dict["shared.weight"]

					if not is_encoder_only:
										if "decoder.embed_tokens.weight" not in state_dict:
															_snake_case       :					Optional[Any]				       =   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       :					Optional[Any]				       =   state_dict["shared.weight"]

					return state_dict







def    A				(							UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase				):
					_snake_case       :					Any				       =   checkpoints.load_tax_checkpoint(UpperCAmelCase				)
					_snake_case       :					str				       =   convert_tax_to_pytorch(
					    UpperCAmelCase  ,  num_layers=config.num_layers  ,  is_encoder_only=UpperCAmelCase  ,  scalable_attention=UpperCAmelCase				)
					_snake_case       :					Optional[Any]				       =   make_state_dict(UpperCAmelCase  ,  UpperCAmelCase				)
					model.load_state_dict(UpperCAmelCase  ,  strict=UpperCAmelCase				)







def    A				(							UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase = False  ,  UpperCAmelCase = False  ,  ):
					_snake_case       :					Optional[int]				       =   MTaConfig.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       :					Optional[int]				       =   UMTaEncoderModel(UpperCAmelCase				)
					else:
										_snake_case       :					Any				       =   UMTaForConditionalGeneration(UpperCAmelCase				)

					# Load weights from tf checkpoint
					load_tax_weights_in_ta(UpperCAmelCase  ,  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__":
							__lowerCAmelCase :str     =    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
							)
							parser.add_argument(
							    '--scalable_attention',
							    action='store_true',
							    help='Whether the model uses scaled attention (umt5 model)',
							    default=False,
							)
							__lowerCAmelCase :Any     =    parser.parse_args()
							convert_tax_checkpoint_to_pytorch(
							    args.tax_checkpoint_path,
							    args.config_file,
							    args.pytorch_dump_path,
							    args.is_encoder_only,
							    args.scalable_attention,
							)






import numpy as np

__lowerCAmelCase :Dict     =    [
    ['a', 'b', 'c', 'd', 'e'],
    ['f', 'g', 'h', 'i', 'k'],
    ['l', 'm', 'n', 'o', 'p'],
    ['q', 'r', 's', 't', 'u'],
    ['v', 'w', 'x', 'y', 'z'],
]






class        _a:







		def __init__(    self				)      ->      None:


							'''simple docstring'''

							_snake_case       :					Optional[Any]				       =   np.array(__snake_case				)







		def        lowercase       (    self ,						__snake_case				)      ->      np.ndarray:


							'''simple docstring'''

							_snake_case       , _snake_case       :					List[Any]				       =   np.where(letter == self.SQUARE				)
							_snake_case       :					Any				       =   np.concatenate([indexa + 1, indexa + 1]				)
							return indexes







		def        lowercase       (    self ,						__snake_case ,						__snake_case				)      ->      str:


							'''simple docstring'''

							_snake_case       :					int				       =   self.SQUARE[indexa - 1, indexa - 1]
							return letter







		def        lowercase       (    self ,						__snake_case				)      ->      str:


							'''simple docstring'''

							_snake_case       :					Optional[Any]				       =   message.lower()
							_snake_case       :					List[Any]				       =   message.replace(" " ,						""				)
							_snake_case       :					Any				       =   message.replace("j" ,						"i"				)

							_snake_case       :					Tuple				       =   np.empty((2, len(__snake_case				))				)
							for letter_index in range(len(__snake_case				)				):
												_snake_case       :					Dict				       =   self.letter_to_numbers(message[letter_index]				)

												_snake_case       :					Tuple				       =   numbers[0]
												_snake_case       :					List[Any]				       =   numbers[1]

							_snake_case       :					Any				       =   first_step.reshape(2 * len(__snake_case				)				)
							_snake_case       :					Optional[Any]				       =   ""
							for numbers_index in range(len(__snake_case				)				):
												_snake_case       :					Optional[int]				       =   int(second_step[numbers_index * 2]				)
												_snake_case       :					List[str]				       =   int(second_step[(numbers_index * 2) + 1]				)
												_snake_case       :					Any				       =   self.numbers_to_letter(__snake_case ,						__snake_case				)
												_snake_case       :					Any				       =   encoded_message + letter

							return encoded_message







		def        lowercase       (    self ,						__snake_case				)      ->      str:


							'''simple docstring'''

							_snake_case       :					Union[str, Any]				       =   message.lower()
							message.replace(" " ,						""				)
							_snake_case       :					Tuple				       =   np.empty(2 * len(__snake_case				)				)
							for letter_index in range(len(__snake_case				)				):
												_snake_case       :					Union[str, Any]				       =   self.letter_to_numbers(message[letter_index]				)
												_snake_case       :					Tuple				       =   numbers[0]
												_snake_case       :					Any				       =   numbers[1]

							_snake_case       :					Optional[Any]				       =   first_step.reshape((2, len(__snake_case				))				)
							_snake_case       :					Union[str, Any]				       =   ""
							for numbers_index in range(len(__snake_case				)				):
												_snake_case       :					Dict				       =   int(second_step[0, numbers_index]				)
												_snake_case       :					Optional[Any]				       =   int(second_step[1, numbers_index]				)
												_snake_case       :					Union[str, Any]				       =   self.numbers_to_letter(__snake_case ,						__snake_case				)
												_snake_case       :					str				       =   decoded_message + letter

							return decoded_message