

import argparse

from tax import checkpoints

from transformers import FlaxTaForConditionalGeneration, TaConfig







def  UpperCAmelCase_   (_lowerCAmelCase :		Optional[Any]     ,			_lowerCAmelCase :		List[str]     ,			_lowerCAmelCase :		Optional[int]		):
			__UpperCamelCase    :    int								=      TaConfig.from_pretrained(lowercase__		)
			__UpperCamelCase    :    List[str]								=      FlaxTaForConditionalGeneration(config=lowercase__		)
			__UpperCamelCase    :    Any								=      checkpoints.load_tax_checkpoint(lowercase__		)

			__UpperCamelCase    :    Any								=      "wi_0" in tax_model["target"]["encoder"]["layers_0"]["mlp"]

			# Encoder
			for layer_index in range(config.num_layers		):
						__UpperCamelCase    :    Any								=      F'''layers_{str(lowercase__		)}'''

						# Self-Attention
						__UpperCamelCase    :    Dict								=      tax_model["target"]["encoder"][layer_name]["attention"]["key"]["kernel"]
						__UpperCamelCase    :    Optional[Any]								=      tax_model["target"]["encoder"][layer_name]["attention"]["out"]["kernel"]
						__UpperCamelCase    :    Any								=      tax_model["target"]["encoder"][layer_name]["attention"]["query"]["kernel"]
						__UpperCamelCase    :    List[str]								=      tax_model["target"]["encoder"][layer_name]["attention"]["value"]["kernel"]

						# Layer Normalization
						__UpperCamelCase    :    int								=      tax_model["target"]["encoder"][layer_name]["pre_attention_layer_norm"]["scale"]

						if split_mlp_wi:
									__UpperCamelCase    :    Tuple								=      tax_model["target"]["encoder"][layer_name]["mlp"]["wi_0"]["kernel"]
									__UpperCamelCase    :    Any								=      tax_model["target"]["encoder"][layer_name]["mlp"]["wi_1"]["kernel"]
						else:
									__UpperCamelCase    :    Any								=      tax_model["target"]["encoder"][layer_name]["mlp"]["wi"]["kernel"]

						__UpperCamelCase    :    Dict								=      tax_model["target"]["encoder"][layer_name]["mlp"]["wo"]["kernel"]

						# Layer Normalization
						__UpperCamelCase    :    Optional[Any]								=      tax_model["target"]["encoder"][layer_name]["pre_mlp_layer_norm"]["scale"]

						# Assigning
						__UpperCamelCase    :    Tuple								=      tax_attention_key
						__UpperCamelCase    :    int								=      tax_attention_out
						__UpperCamelCase    :    List[Any]								=      tax_attention_query
						__UpperCamelCase    :    Union[str, Any]								=      tax_attention_value

						__UpperCamelCase    :    Any								=      tax_attention_layer_norm

						if split_mlp_wi:
									__UpperCamelCase    :    Tuple								=      tax_mlp_wi_a
									__UpperCamelCase    :    List[str]								=      tax_mlp_wi_a
						else:
									__UpperCamelCase    :    Optional[int]								=      tax_mlp_wi

						__UpperCamelCase    :    Optional[Any]								=      tax_mlp_wo
						__UpperCamelCase    :    Optional[Any]								=      tax_mlp_layer_norm

			# Only for layer 0:
			__UpperCamelCase    :    int								=      tax_model["target"]["encoder"]["relpos_bias"]["rel_embedding"].T
			__UpperCamelCase    :    List[Any]								=      tax_encoder_rel_embedding

			# Assigning
			__UpperCamelCase    :    Union[str, Any]								=      tax_model["target"]["encoder"]["encoder_norm"]["scale"]
			__UpperCamelCase    :    List[str]								=      tax_encoder_norm

			# Decoder
			for layer_index in range(config.num_decoder_layers		):
						__UpperCamelCase    :    Optional[Any]								=      F'''layers_{str(lowercase__		)}'''

						# Self-Attention
						__UpperCamelCase    :    int								=      tax_model["target"]["decoder"][layer_name]["self_attention"]["key"]["kernel"]
						__UpperCamelCase    :    str								=      tax_model["target"]["decoder"][layer_name]["self_attention"]["out"]["kernel"]
						__UpperCamelCase    :    List[Any]								=      tax_model["target"]["decoder"][layer_name]["self_attention"]["query"]["kernel"]
						__UpperCamelCase    :    List[Any]								=      tax_model["target"]["decoder"][layer_name]["self_attention"]["value"]["kernel"]

						# Layer Normalization
						__UpperCamelCase    :    int								=      tax_model["target"]["decoder"][layer_name]["pre_self_attention_layer_norm"][
						    "scale"
						]

						# Encoder-Decoder-Attention
						__UpperCamelCase    :    str								=      tax_model["target"]["decoder"][layer_name]["encoder_decoder_attention"]["key"][
						    "kernel"
						]
						__UpperCamelCase    :    Optional[int]								=      tax_model["target"]["decoder"][layer_name]["encoder_decoder_attention"]["out"][
						    "kernel"
						]
						__UpperCamelCase    :    List[str]								=      tax_model["target"]["decoder"][layer_name]["encoder_decoder_attention"]["query"][
						    "kernel"
						]
						__UpperCamelCase    :    Tuple								=      tax_model["target"]["decoder"][layer_name]["encoder_decoder_attention"]["value"][
						    "kernel"
						]

						# Layer Normalization
						__UpperCamelCase    :    int								=      tax_model["target"]["decoder"][layer_name]["pre_cross_attention_layer_norm"]["scale"]

						# MLP
						if split_mlp_wi:
									__UpperCamelCase    :    List[str]								=      tax_model["target"]["decoder"][layer_name]["mlp"]["wi_0"]["kernel"]
									__UpperCamelCase    :    Tuple								=      tax_model["target"]["decoder"][layer_name]["mlp"]["wi_1"]["kernel"]
						else:
									__UpperCamelCase    :    Optional[Any]								=      tax_model["target"]["decoder"][layer_name]["mlp"]["wi"]["kernel"]

						__UpperCamelCase    :    Any								=      tax_model["target"]["decoder"][layer_name]["mlp"]["wo"]["kernel"]

						# Layer Normalization
						__UpperCamelCase    :    Optional[Any]								=      tax_model["target"]["decoder"][layer_name]["pre_mlp_layer_norm"]["scale"]

						# Assigning
						__UpperCamelCase    :    Dict								=      tax_attention_key
						__UpperCamelCase    :    List[Any]								=      tax_attention_out
						__UpperCamelCase    :    List[Any]								=      tax_attention_query
						__UpperCamelCase    :    List[Any]								=      tax_attention_value

						__UpperCamelCase    :    Optional[int]								=      tax_pre_attention_layer_norm

						__UpperCamelCase    :    int								=      tax_enc_dec_attention_key
						__UpperCamelCase    :    Optional[Any]								=      tax_enc_dec_attention_out
						__UpperCamelCase    :    List[str]								=      tax_enc_dec_attention_query
						__UpperCamelCase    :    Tuple								=      tax_enc_dec_attention_value

						__UpperCamelCase    :    Optional[Any]								=      tax_cross_layer_norm

						if split_mlp_wi:
									__UpperCamelCase    :    Any								=      tax_mlp_wi_a
									__UpperCamelCase    :    List[Any]								=      tax_mlp_wi_a
						else:
									__UpperCamelCase    :    Union[str, Any]								=      tax_mlp_wi

						__UpperCamelCase    :    Tuple								=      tax_mlp_wo

						__UpperCamelCase    :    Union[str, Any]								=      txa_mlp_layer_norm

			# Decoder Normalization
			__UpperCamelCase    :    Optional[Any]								=      tax_model["target"]["decoder"]["decoder_norm"]["scale"]
			__UpperCamelCase    :    Optional[Any]								=      txa_decoder_norm

			# Only for layer 0:
			__UpperCamelCase    :    str								=      tax_model["target"]["decoder"]["relpos_bias"]["rel_embedding"].T
			__UpperCamelCase    :    Tuple								=      tax_decoder_rel_embedding

			# Token Embeddings
			__UpperCamelCase    :    int								=      tax_model["target"]["token_embedder"]["embedding"]
			__UpperCamelCase    :    Union[str, Any]								=      txa_token_embeddings

			# LM Head (only in v1.1 checkpoints)
			if "logits_dense" in tax_model["target"]["decoder"]:
						__UpperCamelCase    :    Union[str, Any]								=      tax_model["target"]["decoder"]["logits_dense"]["kernel"]

			flax_model.save_pretrained(lowercase__		)
			print("T5X Model was sucessfully converted!"		)


if __name__ == "__main__":
					lowercase     :       Any        =       argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    "--t5x_checkpoint_path", default=None, type=str, required=True, help="Path the TX5 checkpoint."
					)
					parser.add_argument("--config_name", default=None, type=str, required=True, help="Config name of T5 model.")
					parser.add_argument(
					    "--flax_dump_folder_path", default=None, type=str, required=True, help="Path to the output FLAX model."
					)
					lowercase     :       List[str]        =       parser.parse_args()
					convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)


import numpy as np

lowercase     :       Union[str, Any]        =       [
    ["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 		SCREAMING_SNAKE_CASE__					:







						"""simple docstring"""




						def __init__(				self    )							->	None:




									'''simple docstring'''


									__UpperCamelCase    :    Union[str, Any]								=      np.array(__UpperCamelCase    )

						def __lowerCamelCase					(				self , __UpperCamelCase    )							->	np.ndarray:




									'''simple docstring'''


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

						def __lowerCamelCase					(				self , __UpperCamelCase , __UpperCamelCase    )							->	str:




									'''simple docstring'''


									return self.SQUARE[indexa - 1, indexa - 1]

						def __lowerCamelCase					(				self , __UpperCamelCase    )							->	str:




									'''simple docstring'''


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

									__UpperCamelCase    :    Dict								=      ""
									for letter_index in range(len(__UpperCamelCase    )    ):
												if message[letter_index] != " ":
															__UpperCamelCase    :    int								=      self.letter_to_numbers(message[letter_index]    )
															__UpperCamelCase    :    Optional[int]								=      encoded_message + str(numbers[0]    ) + str(numbers[1]    )
												elif message[letter_index] == " ":
															__UpperCamelCase    :    List[str]								=      encoded_message + " "

									return encoded_message




						def __lowerCamelCase					(				self , __UpperCamelCase    )							->	str:




									'''simple docstring'''


									__UpperCamelCase    :    Dict								=      message.replace(" " , "  "    )
									__UpperCamelCase    :    Optional[int]								=      ""
									for numbers_index in range(int(len(__UpperCamelCase    ) / 2    )    ):
												if message[numbers_index * 2] != " ":
															__UpperCamelCase    :    int								=      message[numbers_index * 2]
															__UpperCamelCase    :    Optional[int]								=      message[numbers_index * 2 + 1]

															__UpperCamelCase    :    str								=      self.numbers_to_letter(int(__UpperCamelCase    ) , int(__UpperCamelCase    )    )
															__UpperCamelCase    :    Optional[Any]								=      decoded_message + letter
												elif message[numbers_index * 2] == " ":
															__UpperCamelCase    :    str								=      decoded_message + " "

									return decoded_message