

'''simple docstring'''





import argparse

from tax import checkpoints

from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM





def       __lowerCamelCase			(				UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__   ):







							'''simple docstring'''






							snake_case_           =					AutoConfig.from_pretrained(UpperCamelCase__   )
							snake_case_           =					FlaxAutoModelForSeqaSeqLM.from_config(config=UpperCamelCase__   )
							snake_case_           =					checkpoints.load_tax_checkpoint(UpperCamelCase__   )

							snake_case_           =					'wi_0' in tax_model['target']['encoder']['layers_0']['mlp']

							if config.model_type == "t5":
														snake_case_           =					'SelfAttention'
							if config.model_type == "longt5" and config.encoder_attention_type == "local":
														snake_case_           =					'LocalSelfAttention'
							elif config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
														snake_case_           =					'TransientGlobalSelfAttention'
							else:
														raise ValueError(
														    'Given config is expected to have `model_type=\'t5\'`, or `model_type=\'longt5` with `encoder_attention_type`'
														    ' attribute with a value from [\'local\', \'transient-global].'   )

							# Encoder
							for layer_index in range(config.num_layers   ):
														snake_case_           =					F'''layers_{str(UpperCamelCase__   )}'''

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

														# Global input layer norm
														if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
																					snake_case_           =					tax_model['target']['encoder'][layer_name]['attention']['T5LayerNorm_0']['scale']

														# Layer Normalization
														snake_case_           =					tax_model['target']['encoder'][layer_name]['pre_attention_layer_norm']['scale']

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

														snake_case_           =					tax_model['target']['encoder'][layer_name]['mlp']['wo']['kernel']

														# Layer Normalization
														snake_case_           =					tax_model['target']['encoder'][layer_name]['pre_mlp_layer_norm']['scale']

														# Assigning
														snake_case_           =					flax_model.params['encoder']['block'][str(UpperCamelCase__   )]['layer']
														snake_case_           =					tax_attention_key
														snake_case_           =					tax_attention_out
														snake_case_           =					tax_attention_query
														snake_case_           =					tax_attention_value

														snake_case_           =					tax_attention_layer_norm

														# Global input layer norm
														if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
																					snake_case_           =					tax_global_layer_norm

														if split_mlp_wi:
																					snake_case_           =					tax_mlp_wi_a
																					snake_case_           =					tax_mlp_wi_a
														else:
																					snake_case_           =					tax_mlp_wi

														snake_case_           =					tax_mlp_wo
														snake_case_           =					tax_mlp_layer_norm

														snake_case_           =					flax_model_encoder_layer_block

							# Only for layer 0:
							snake_case_           =					tax_model['target']['encoder']['relpos_bias']['rel_embedding'].T
							snake_case_           =					tax_encoder_rel_embedding

							# Side/global relative position_bias + layer norm
							if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
														snake_case_           =					tax_model['target']['encoder']['side_relpos_bias']['rel_embedding'].T
														snake_case_           =					tax_encoder_global_rel_embedding

							# Assigning
							snake_case_           =					tax_model['target']['encoder']['encoder_norm']['scale']
							snake_case_           =					tax_encoder_norm

							# Decoder
							for layer_index in range(config.num_layers   ):
														snake_case_           =					F'''layers_{str(UpperCamelCase__   )}'''

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

														# Layer Normalization
														snake_case_           =					tax_model['target']['decoder'][layer_name]['pre_self_attention_layer_norm'][
														    'scale'
														]

														# Encoder-Decoder-Attention
														snake_case_           =					tax_model['target']['decoder'][layer_name]['encoder_decoder_attention']
														snake_case_           =					tax_enc_dec_attention_module['key']['kernel']
														snake_case_           =					tax_enc_dec_attention_module['out']['kernel']
														snake_case_           =					tax_enc_dec_attention_module['query']['kernel']
														snake_case_           =					tax_enc_dec_attention_module['value']['kernel']

														# Layer Normalization
														snake_case_           =					tax_model['target']['decoder'][layer_name]['pre_cross_attention_layer_norm']['scale']

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

														snake_case_           =					tax_model['target']['decoder'][layer_name]['mlp']['wo']['kernel']

														# Layer Normalization
														snake_case_           =					tax_model['target']['decoder'][layer_name]['pre_mlp_layer_norm']['scale']

														# Assigning
														snake_case_           =					flax_model.params['decoder']['block'][str(UpperCamelCase__   )]['layer']
														snake_case_           =					tax_attention_key
														snake_case_           =					tax_attention_out
														snake_case_           =					tax_attention_query
														snake_case_           =					tax_attention_value

														snake_case_           =					tax_pre_attention_layer_norm

														snake_case_           =					tax_enc_dec_attention_key
														snake_case_           =					tax_enc_dec_attention_out
														snake_case_           =					tax_enc_dec_attention_query
														snake_case_           =					tax_enc_dec_attention_value

														snake_case_           =					tax_cross_layer_norm

														if split_mlp_wi:
																					snake_case_           =					tax_mlp_wi_a
																					snake_case_           =					tax_mlp_wi_a
														else:
																					snake_case_           =					tax_mlp_wi

														snake_case_           =					tax_mlp_wo

														snake_case_           =					txa_mlp_layer_norm

														snake_case_           =					flax_model_decoder_layer_block

							# Decoder Normalization
							snake_case_           =					tax_model['target']['decoder']['decoder_norm']['scale']
							snake_case_           =					txa_decoder_norm

							# Only for layer 0:
							snake_case_           =					tax_model['target']['decoder']['relpos_bias']['rel_embedding'].T
							snake_case_           =					tax_decoder_rel_embedding

							# Token Embeddings
							snake_case_           =					tax_model['target']['token_embedder']['embedding']
							snake_case_           =					txa_token_embeddings

							# LM Head (only in v1.1 and LongT5 checkpoints)
							if "logits_dense" in tax_model["target"]["decoder"]:
														snake_case_           =					tax_model['target']['decoder']['logits_dense']['kernel']

							flax_model.save_pretrained(UpperCamelCase__   )
							print('T5X Model was sucessfully converted!'   )


if __name__ == "__main__":
					_UpperCAmelCase	:     Tuple		       = argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    """--t5x_checkpoint_path""", default=None, type=str, required=True, help="""Path the T5X checkpoint."""
					)
					parser.add_argument("""--config_name""", default=None, type=str, required=True, help="""Config name of LongT5/T5 model.""")
					parser.add_argument(
					    """--flax_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output FLAX model."""
					)
					_UpperCAmelCase	:     List[str]		       = parser.parse_args()
					convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)







_UpperCAmelCase	:     str		       = [0, 2, 4, 6, 8]
_UpperCAmelCase	:     Any		       = [1, 3, 5, 7, 9]





def       __lowerCamelCase			(				UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__   ):







							'''simple docstring'''






							if remaining_length == 0:
														if digits[0] == 0 or digits[-1] == 0:
																					return 0

														for i in range(length // 2 - 1     ,							-1     ,							-1   ):
																					remainder += digits[i] + digits[length - i - 1]

																					if remainder % 2 == 0:
																												return 0

																					remainder //= 10

														return 1

							if remaining_length == 1:
														if remainder % 2 == 0:
																					return 0

														snake_case_           =					0
														for digit in range(10   ):
																					snake_case_           =					digit
																					result += reversible_numbers(
																					    0     ,							(remainder + 2 * digit) // 10     ,							UpperCamelCase__     ,							UpperCamelCase__   )
														return result

							snake_case_           =					0
							for digita in range(10   ):
														snake_case_           =					digita

														if (remainder + digita) % 2 == 0:
																					snake_case_           =					ODD_DIGITS
														else:
																					snake_case_           =					EVEN_DIGITS

														for digita in other_parity_digits:
																					snake_case_           =					digita
																					result += reversible_numbers(
																					    remaining_length - 2     ,							(remainder + digita + digita) // 10     ,							UpperCamelCase__     ,							UpperCamelCase__     ,							)
							return result





def       __lowerCamelCase			(				UpperCamelCase__ = 9   ):







							'''simple docstring'''






							snake_case_           =					0
							for length in range(1     ,							max_power + 1   ):
														result += reversible_numbers(UpperCamelCase__     ,							0     ,							[0] * length     ,							UpperCamelCase__   )
							return result


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




