

"""simple docstring"""




import argparse

from tax import checkpoints

from transformers import FlaxTaForConditionalGeneration, TaConfig
def   __SCREAMING_SNAKE_CASE		(		lowercase__     ,				lowercase__     ,				lowercase__ ):
					"""simple docstring"""
					A     =   TaConfig.from_pretrained(lowercase__ )
					A     =   FlaxTaForConditionalGeneration(config=lowercase__ )
					A     =   checkpoints.load_tax_checkpoint(lowercase__ )

					A     =   "wi_0" in tax_model["target"]["encoder"]["layers_0"]["mlp"]

					# Encoder
					for layer_index in range(config.num_layers ):
										A     =   F"""layers_{str(lowercase__ )}"""

										# Self-Attention
										A     =   tax_model["target"]["encoder"][layer_name]["attention"]["key"]["kernel"]
										A     =   tax_model["target"]["encoder"][layer_name]["attention"]["out"]["kernel"]
										A     =   tax_model["target"]["encoder"][layer_name]["attention"]["query"]["kernel"]
										A     =   tax_model["target"]["encoder"][layer_name]["attention"]["value"]["kernel"]

										# Layer Normalization
										A     =   tax_model["target"]["encoder"][layer_name]["pre_attention_layer_norm"]["scale"]

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

										A     =   tax_model["target"]["encoder"][layer_name]["mlp"]["wo"]["kernel"]

										# Layer Normalization
										A     =   tax_model["target"]["encoder"][layer_name]["pre_mlp_layer_norm"]["scale"]

										# Assigning
										A     =   tax_attention_key
										A     =   tax_attention_out
										A     =   tax_attention_query
										A     =   tax_attention_value

										A     =   tax_attention_layer_norm

										if split_mlp_wi:
															A     =   tax_mlp_wi_a
															A     =   tax_mlp_wi_a
										else:
															A     =   tax_mlp_wi

										A     =   tax_mlp_wo
										A     =   tax_mlp_layer_norm

					# Only for layer 0:
					A     =   tax_model["target"]["encoder"]["relpos_bias"]["rel_embedding"].T
					A     =   tax_encoder_rel_embedding

					# Assigning
					A     =   tax_model["target"]["encoder"]["encoder_norm"]["scale"]
					A     =   tax_encoder_norm

					# Decoder
					for layer_index in range(config.num_decoder_layers ):
										A     =   F"""layers_{str(lowercase__ )}"""

										# Self-Attention
										A     =   tax_model["target"]["decoder"][layer_name]["self_attention"]["key"]["kernel"]
										A     =   tax_model["target"]["decoder"][layer_name]["self_attention"]["out"]["kernel"]
										A     =   tax_model["target"]["decoder"][layer_name]["self_attention"]["query"]["kernel"]
										A     =   tax_model["target"]["decoder"][layer_name]["self_attention"]["value"]["kernel"]

										# Layer Normalization
										A     =   tax_model["target"]["decoder"][layer_name]["pre_self_attention_layer_norm"][
										    "scale"
										]

										# Encoder-Decoder-Attention
										A     =   tax_model["target"]["decoder"][layer_name]["encoder_decoder_attention"]["key"][
										    "kernel"
										]
										A     =   tax_model["target"]["decoder"][layer_name]["encoder_decoder_attention"]["out"][
										    "kernel"
										]
										A     =   tax_model["target"]["decoder"][layer_name]["encoder_decoder_attention"]["query"][
										    "kernel"
										]
										A     =   tax_model["target"]["decoder"][layer_name]["encoder_decoder_attention"]["value"][
										    "kernel"
										]

										# Layer Normalization
										A     =   tax_model["target"]["decoder"][layer_name]["pre_cross_attention_layer_norm"]["scale"]

										# MLP
										if split_mlp_wi:
															A     =   tax_model["target"]["decoder"][layer_name]["mlp"]["wi_0"]["kernel"]
															A     =   tax_model["target"]["decoder"][layer_name]["mlp"]["wi_1"]["kernel"]
										else:
															A     =   tax_model["target"]["decoder"][layer_name]["mlp"]["wi"]["kernel"]

										A     =   tax_model["target"]["decoder"][layer_name]["mlp"]["wo"]["kernel"]

										# Layer Normalization
										A     =   tax_model["target"]["decoder"][layer_name]["pre_mlp_layer_norm"]["scale"]

										# Assigning
										A     =   tax_attention_key
										A     =   tax_attention_out
										A     =   tax_attention_query
										A     =   tax_attention_value

										A     =   tax_pre_attention_layer_norm

										A     =   tax_enc_dec_attention_key
										A     =   tax_enc_dec_attention_out
										A     =   tax_enc_dec_attention_query
										A     =   tax_enc_dec_attention_value

										A     =   tax_cross_layer_norm

										if split_mlp_wi:
															A     =   tax_mlp_wi_a
															A     =   tax_mlp_wi_a
										else:
															A     =   tax_mlp_wi

										A     =   tax_mlp_wo

										A     =   txa_mlp_layer_norm

					# Decoder Normalization
					A     =   tax_model["target"]["decoder"]["decoder_norm"]["scale"]
					A     =   txa_decoder_norm

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

					# Token Embeddings
					A     =   tax_model["target"]["token_embedder"]["embedding"]
					A     =   txa_token_embeddings

					# LM Head (only in v1.1 checkpoints)
					if "logits_dense" in tax_model["target"]["decoder"]:
										A     =   tax_model["target"]["decoder"]["logits_dense"]["kernel"]

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


if __name__ == "__main__":
	__A	:       Tuple        =						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.'
	)
	__A	:       Dict        =						parser.parse_args()
	convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)








"""simple docstring"""




import sys

sys.set_int_max_str_digits(0)  # type: ignore
def   __SCREAMING_SNAKE_CASE		(		lowercase__ ):
					"""simple docstring"""
					A     =   [0] * 11
					A     =   [0] * 11

					# mark last 9 numbers
					for _ in range(9 ):
										A     =   1
										A     =   number // 10
					# flag
					A     =   True

					# check last 9 numbers for pandigitality

					for x in range(9 ):
										if not check_last[x + 1]:
															A     =   False
					if not f:
										return f

					# mark first 9 numbers
					A     =   int(str(lowercase__ )[:9] )

					for _ in range(9 ):
										A     =   1
										A     =   number // 10

					# check first 9 numbers for pandigitality

					for x in range(9 ):
										if not check_front[x + 1]:
															A     =   False
					return f
def   __SCREAMING_SNAKE_CASE		(		lowercase__ ):
					"""simple docstring"""
					A     =   [0] * 11

					# mark last 9 numbers
					for _ in range(9 ):
										A     =   1
										A     =   number // 10
					# flag
					A     =   True

					# check last 9 numbers for pandigitality

					for x in range(9 ):
										if not check_last[x + 1]:
															A     =   False
					return f
def   __SCREAMING_SNAKE_CASE		(		):
					"""simple docstring"""
					A     =   1
					A     =   1
					A     =   2
					# temporary Fibonacci numbers

					A     =   1
					A     =   1
					A     =   2
					# temporary Fibonacci numbers mod 1e9

					# mod m=1e9, done for fast optimisation
					A     =   [0] * 1_000_000
					A     =   1_000_000_000

					for x in range(1_000_000 ):
										A     =   (aa + ba) % m
										A     =   ba % m
										A     =   ca % m
										if checka(lowercase__ ):
															A     =   1

					for x in range(1_000_000 ):
										A     =   a + b
										A     =   b
										A     =   c
										# perform check only if in tocheck
										if tocheck[x + 3] and check(lowercase__ ):
															return x + 3  # first 2 already done
					return -1


if __name__ == "__main__":
	print(f"""{solution() = }""")





