


"""simple docstring"""


import argparse

from tax import checkpoints

from transformers import FlaxTaForConditionalGeneration, TaConfig






def   a__			(					__lowercase  ,      __lowercase  ,      __lowercase )						->						Dict:
				_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_											=     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_											=     parser.parse_args()
		convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)



"""simple docstring"""


from math import factorial






def   a__			(					__lowercase  ,      __lowercase )						->						List[Any]:
				return factorial(__lowercase ) / (factorial(__lowercase ) * factorial(n - r ))






def   a__			(					)						->						List[str]:
				_A       	=      0

				for i in range(1  ,      101 ):
								for j in range(1  ,      i + 1 ):
												if combinations(__lowercase  ,      __lowercase ) > 1E6:
																total += 1
				return total


if __name__ == "__main__":
		print(solution())