
"""simple docstring"""
import argparse

from tax import checkpoints

from transformers import FlaxTaForConditionalGeneration, TaConfig


def     lowerCamelCase_    (	lowerCamelCase_	,				lowerCamelCase_	,				lowerCamelCase_ )     ->			Dict:


					'''simple docstring'''
					__a:						Optional[int]		    =					TaConfig.from_pretrained(__a )
					__a:						Dict		    =					FlaxTaForConditionalGeneration(config=__a )
					__a:						List[Any]		    =					checkpoints.load_tax_checkpoint(__a )

					__a:						List[str]		    =					'''wi_0''' in tax_model['''target''']['''encoder''']['''layers_0''']['''mlp''']

					# Encoder
					for layer_index in range(config.num_layers ):
										__a:						str		    =					f"""layers_{str(__a )}"""

										# Self-Attention
										__a:						Dict		    =					tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''key''']['''kernel''']
										__a:						int		    =					tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''out''']['''kernel''']
										__a:						int		    =					tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''query''']['''kernel''']
										__a:						Tuple		    =					tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''value''']['''kernel''']

										# Layer Normalization
										__a:						Optional[int]		    =					tax_model['''target''']['''encoder'''][layer_name]['''pre_attention_layer_norm''']['''scale''']

										if split_mlp_wi:
															__a:						Optional[int]		    =					tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_0''']['''kernel''']
															__a:						Tuple		    =					tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_1''']['''kernel''']
										else:
															__a:						int		    =					tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi''']['''kernel''']

										__a:						int		    =					tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']

										# Layer Normalization
										__a:						Optional[Any]		    =					tax_model['''target''']['''encoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']

										# Assigning
										__a:						Dict		    =					tax_attention_key
										__a:						str		    =					tax_attention_out
										__a:						Any		    =					tax_attention_query
										__a:						str		    =					tax_attention_value

										__a:						List[Any]		    =					tax_attention_layer_norm

										if split_mlp_wi:
															__a:						int		    =					tax_mlp_wi_a
															__a:						Dict		    =					tax_mlp_wi_a
										else:
															__a:						Tuple		    =					tax_mlp_wi

										__a:						List[str]		    =					tax_mlp_wo
										__a:						Union[str, Any]		    =					tax_mlp_layer_norm

					# Only for layer 0:
					__a:						Optional[Any]		    =					tax_model['''target''']['''encoder''']['''relpos_bias''']['''rel_embedding'''].T
					__a:						Union[str, Any]		    =					tax_encoder_rel_embedding

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

					# Decoder
					for layer_index in range(config.num_decoder_layers ):
										__a:						Optional[int]		    =					f"""layers_{str(__a )}"""

										# Self-Attention
										__a:						Optional[Any]		    =					tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''key''']['''kernel''']
										__a:						str		    =					tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''out''']['''kernel''']
										__a:						Dict		    =					tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''query''']['''kernel''']
										__a:						int		    =					tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''value''']['''kernel''']

										# Layer Normalization
										__a:						Tuple		    =					tax_model['''target''']['''decoder'''][layer_name]['''pre_self_attention_layer_norm'''][
										    '''scale'''
										]

										# Encoder-Decoder-Attention
										__a:						List[str]		    =					tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']['''key'''][
										    '''kernel'''
										]
										__a:						List[str]		    =					tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']['''out'''][
										    '''kernel'''
										]
										__a:						Optional[Any]		    =					tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']['''query'''][
										    '''kernel'''
										]
										__a:						List[Any]		    =					tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']['''value'''][
										    '''kernel'''
										]

										# Layer Normalization
										__a:						Any		    =					tax_model['''target''']['''decoder'''][layer_name]['''pre_cross_attention_layer_norm''']['''scale''']

										# MLP
										if split_mlp_wi:
															__a:						List[Any]		    =					tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi_0''']['''kernel''']
															__a:						Any		    =					tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi_1''']['''kernel''']
										else:
															__a:						Union[str, Any]		    =					tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi''']['''kernel''']

										__a:						Dict		    =					tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']

										# Layer Normalization
										__a:						Optional[int]		    =					tax_model['''target''']['''decoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']

										# Assigning
										__a:						Optional[int]		    =					tax_attention_key
										__a:						Tuple		    =					tax_attention_out
										__a:						Optional[int]		    =					tax_attention_query
										__a:						Dict		    =					tax_attention_value

										__a:						List[Any]		    =					tax_pre_attention_layer_norm

										__a:						Optional[int]		    =					tax_enc_dec_attention_key
										__a:						Optional[int]		    =					tax_enc_dec_attention_out
										__a:						Optional[Any]		    =					tax_enc_dec_attention_query
										__a:						List[Any]		    =					tax_enc_dec_attention_value

										__a:						List[Any]		    =					tax_cross_layer_norm

										if split_mlp_wi:
															__a:						Tuple		    =					tax_mlp_wi_a
															__a:						Tuple		    =					tax_mlp_wi_a
										else:
															__a:						Any		    =					tax_mlp_wi

										__a:						int		    =					tax_mlp_wo

										__a:						Dict		    =					txa_mlp_layer_norm

					# Decoder Normalization
					__a:						int		    =					tax_model['''target''']['''decoder''']['''decoder_norm''']['''scale''']
					__a:						Tuple		    =					txa_decoder_norm

					# Only for layer 0:
					__a:						List[str]		    =					tax_model['''target''']['''decoder''']['''relpos_bias''']['''rel_embedding'''].T
					__a:						Tuple		    =					tax_decoder_rel_embedding

					# Token Embeddings
					__a:						List[str]		    =					tax_model['''target''']['''token_embedder''']['''embedding''']
					__a:						Union[str, Any]		    =					txa_token_embeddings

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

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


if __name__ == "__main__":
			lowercase		:						str			=					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		:						Tuple			=					parser.parse_args()
			convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)


"""simple docstring"""
lowercase		:						str =					{
    '0000': '0',
    '0001': '1',
    '0010': '2',
    '0011': '3',
    '0100': '4',
    '0101': '5',
    '0110': '6',
    '0111': '7',
    '1000': '8',
    '1001': '9',
    '1010': 'a',
    '1011': 'b',
    '1100': 'c',
    '1101': 'd',
    '1110': 'e',
    '1111': 'f',
}


def     lowerCamelCase_    (	lowerCamelCase_ )     ->			str:


					'''simple docstring'''
					__a:						Optional[int]		    =					str(lowerCamelCase_ ).strip()

					# Exceptions
					if not binary_str:
										raise ValueError("""Empty string was passed to the function""" )
					__a:						List[Any]		    =					binary_str[0] == """-"""
					__a:						List[str]		    =					binary_str[1:] if is_negative else binary_str
					if not all(char in """01""" for char in binary_str ):
										raise ValueError("""Non-binary value was passed to the function""" )

					__a:						Any		    =					(
					    """0""" * (4 * (divmod(len(lowerCamelCase_ )	,				4 )[0] + 1) - len(lowerCamelCase_ )) + binary_str
					)

					__a:						int		    =					[]
					for x in range(0	,				len(lowerCamelCase_ )	,				4 ):
										hexadecimal.append(BITS_TO_HEX[binary_str[x : x + 4]] )
					__a:						Tuple		    =					"""0x""" + """""".join(lowerCamelCase_ )

					return "-" + hexadecimal_str if is_negative else hexadecimal_str


if __name__ == "__main__":
			from doctest import testmod

			testmod()
