





"""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							(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE="attention"     ):
						UpperCamelCase				:		List[str]				   =			params[f"""{prefix}/layers_{i}/{layer_name}/key/kernel"""]
						UpperCamelCase				:		str				   =			params[f"""{prefix}/layers_{i}/{layer_name}/out/kernel"""]
						UpperCamelCase				:		Optional[Any]				   =			params[f"""{prefix}/layers_{i}/{layer_name}/query/kernel"""]
						UpperCamelCase				:		str				   =			params[f"""{prefix}/layers_{i}/{layer_name}/value/kernel"""]
						return k, o, q, v

def      UpperCamelCase							(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE=False     ):
						if split_mlp_wi:
												UpperCamelCase				:		Optional[int]				   =			params[f"""{prefix}/layers_{i}/mlp/wi_0/kernel"""]
												UpperCamelCase				:		Optional[Any]				   =			params[f"""{prefix}/layers_{i}/mlp/wi_1/kernel"""]
												UpperCamelCase				:		int				   =			(wi_a, wi_a)
						else:
												UpperCamelCase				:		Optional[Any]				   =			params[f"""{prefix}/layers_{i}/mlp/wi/kernel"""]

						UpperCamelCase				:		Optional[Any]				   =			params[f"""{prefix}/layers_{i}/mlp/wo/kernel"""]
						return wi, wo

def      UpperCamelCase							(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     ):
						return params[f"""{prefix}/layers_{i}/{layer_name}/scale"""]

def      UpperCamelCase							(SCREAMING_SNAKE_CASE							,     *, SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     ):
						UpperCamelCase				:		Dict				   =			traverse_util.flatten_dict(variables["""target"""]     )
						UpperCamelCase				:		Optional[Any]				   =			{"""/""".join(SCREAMING_SNAKE_CASE     ): v for k, v in old.items()}

						# v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
						UpperCamelCase				:		Union[str, Any]				   =			"""encoder/layers_0/mlp/wi_0/kernel""" in old
						print("""Split MLP:"""							,     SCREAMING_SNAKE_CASE     )

						UpperCamelCase				:		Optional[int]				   =			collections.OrderedDict()

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

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

												# Block i, layer 1 (MLP).
												UpperCamelCase				:		Optional[int]				   =			tax_layer_norm_lookup(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE							,     """encoder"""							,     """pre_mlp_layer_norm"""     )
												UpperCamelCase   ,						UpperCamelCase				:		Tuple				   =			tax_mlp_lookup(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE							,     """encoder"""							,     SCREAMING_SNAKE_CASE     )
												UpperCamelCase				:		int				   =			layer_norm
												if split_mlp_wi:
																		UpperCamelCase				:		Optional[Any]				   =			wi[0].T
																		UpperCamelCase				:		Optional[int]				   =			wi[1].T
												else:
																		UpperCamelCase				:		str				   =			wi.T
												UpperCamelCase				:		Optional[Any]				   =			wo.T

						UpperCamelCase				:		List[str]				   =			old[
						    """encoder/relpos_bias/rel_embedding"""
						].T
						UpperCamelCase				:		Optional[Any]				   =			old["""encoder/encoder_norm/scale"""]

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

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

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

												UpperCamelCase				:		List[str]				   =			old["""decoder/decoder_norm/scale"""]
												UpperCamelCase				:		Union[str, Any]				   =			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:
																		UpperCamelCase				:		Dict				   =			old["""decoder/logits_dense/kernel"""].T

						return new

def      UpperCamelCase							(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     ):
						UpperCamelCase				:		Optional[Any]				   =			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:
												UpperCamelCase				:		Optional[int]				   =			state_dict["""shared.weight"""]

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

						return state_dict

def      UpperCamelCase							(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     ):
						UpperCamelCase				:		Tuple				   =			checkpoints.load_tax_checkpoint(SCREAMING_SNAKE_CASE     )
						UpperCamelCase				:		int				   =			convert_tax_to_pytorch(SCREAMING_SNAKE_CASE							,     num_layers=config.num_layers							,     is_encoder_only=SCREAMING_SNAKE_CASE     )
						UpperCamelCase				:		Optional[int]				   =			make_state_dict(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     )
						model.load_state_dict(SCREAMING_SNAKE_CASE							,     strict=SCREAMING_SNAKE_CASE     )

def      UpperCamelCase							(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE = False     ):
						UpperCamelCase				:		Optional[int]				   =			TaConfig.from_json_file(SCREAMING_SNAKE_CASE     )
						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:
												UpperCamelCase				:		int				   =			TaEncoderModel(SCREAMING_SNAKE_CASE     )
						else:
												UpperCamelCase				:		Union[str, Any]				   =			TaForConditionalGeneration(SCREAMING_SNAKE_CASE     )

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

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

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


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











UpperCAmelCase__      			=						'''ABCDEFGHIJKLMNOPQRSTUVWXYZ'''







def     a_   ()				->						None:



					"""simple docstring"""
					__a					:						Dict      =					input("Enter message: "      )
					__a					:						Dict      =					input("Enter key [alphanumeric]: "      )
					__a					:						str      =					input("Encrypt/Decrypt [e/d]: "      )

					if mode.lower().startswith("e"      ):
										__a					:						List[str]      =					"encrypt"
										__a					:						Union[str, Any]      =					encrypt_message(__A				,       __A      )
					elif mode.lower().startswith("d"      ):
										__a					:						int      =					"decrypt"
										__a					:						List[str]      =					decrypt_message(__A				,       __A      )

					print(f'\n{mode.title()}ed message:'      )
					print(__A      )







def     a_   (__A				,       __A      )				->						str:



					"""simple docstring"""
					return translate_message(__A				,       __A				,       "encrypt"      )







def     a_   (__A				,       __A      )				->						str:



					"""simple docstring"""
					return translate_message(__A				,       __A				,       "decrypt"      )







def     a_   (__A				,       __A				,       __A      )				->						str:



					"""simple docstring"""
					__a					:						Union[str, Any]      =					[]
					__a					:						Tuple      =					0
					__a					:						Dict      =					key.upper()

					for symbol in message:
										__a					:						Dict      =					LETTERS.find(symbol.upper()      )
										if num != -1:
															if mode == "encrypt":
																				num += LETTERS.find(key[key_index]      )
															elif mode == "decrypt":
																				num -= LETTERS.find(key[key_index]      )

															num %= len(__A      )

															if symbol.isupper():
																				translated.append(LETTERS[num]      )
															elif symbol.islower():
																				translated.append(LETTERS[num].lower()      )

															key_index += 1
															if key_index == len(__A      ):
																				__a					:						List[str]      =					0
										else:
															translated.append(__A      )
					return "".join(__A      )


if __name__ == "__main__":
			main()





