
import argparse

from tax import checkpoints

from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM



def  UpperCamelCase					(							lowerCAmelCase__				,    lowerCAmelCase__				,    lowerCAmelCase__	):
					'''simple docstring'''
					lowercase					=		AutoConfig.from_pretrained(lowerCAmelCase__	)
					lowercase					=		FlaxAutoModelForSeqaSeqLM.from_config(config=lowerCAmelCase__	)
					lowercase					=		checkpoints.load_tax_checkpoint(lowerCAmelCase__	)

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

					if config.model_type == "t5":
										lowercase					=		'''SelfAttention'''
					if config.model_type == "longt5" and config.encoder_attention_type == "local":
										lowercase					=		'''LocalSelfAttention'''
					elif config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
										lowercase					=		'''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	):
										lowercase					=		f'layers_{str(lowerCAmelCase__	)}'

										# Self-Attention
										lowercase					=		tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''key''']['''kernel''']
										lowercase					=		tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''out''']['''kernel''']
										lowercase					=		tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''query''']['''kernel''']
										lowercase					=		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":
															lowercase					=		tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''T5LayerNorm_0''']['''scale''']

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

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

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

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

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

										lowercase					=		tax_attention_layer_norm

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

										if split_mlp_wi:
															lowercase					=		tax_mlp_wi_a
															lowercase					=		tax_mlp_wi_a
										else:
															lowercase					=		tax_mlp_wi

										lowercase					=		tax_mlp_wo
										lowercase					=		tax_mlp_layer_norm

										lowercase					=		flax_model_encoder_layer_block

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

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

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

					# Decoder
					for layer_index in range(config.num_layers	):
										lowercase					=		f'layers_{str(lowerCAmelCase__	)}'

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

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

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

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

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

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

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

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

										lowercase					=		tax_pre_attention_layer_norm

										lowercase					=		tax_enc_dec_attention_key
										lowercase					=		tax_enc_dec_attention_out
										lowercase					=		tax_enc_dec_attention_query
										lowercase					=		tax_enc_dec_attention_value

										lowercase					=		tax_cross_layer_norm

										if split_mlp_wi:
															lowercase					=		tax_mlp_wi_a
															lowercase					=		tax_mlp_wi_a
										else:
															lowercase					=		tax_mlp_wi

										lowercase					=		tax_mlp_wo

										lowercase					=		txa_mlp_layer_norm

										lowercase					=		flax_model_decoder_layer_block

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

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

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

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

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


if __name__ == "__main__":
							lowercase__	:Union[str, Any]										=					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."
							)
							lowercase__	:Dict										=					parser.parse_args()
							convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


lowercase__	:Tuple										=					{
    "configuration_biogpt": ["BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "BioGptConfig"],
    "tokenization_biogpt": ["BioGptTokenizer"],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowercase__	:Union[str, Any]										=					[
							    "BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "BioGptForCausalLM",
							    "BioGptForTokenClassification",
							    "BioGptForSequenceClassification",
							    "BioGptModel",
							    "BioGptPreTrainedModel",
							]


if TYPE_CHECKING:
							from .configuration_biogpt import BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, BioGptConfig
							from .tokenization_biogpt import BioGptTokenizer

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_biogpt import (
														    BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    BioGptForCausalLM,
														    BioGptForSequenceClassification,
														    BioGptForTokenClassification,
														    BioGptModel,
														    BioGptPreTrainedModel,
														)


else:
							import sys

							lowercase__	:Any										=					_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




