
import argparse

from tax import checkpoints

from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM



def 					lowerCAmelCase__							(	SCREAMING_SNAKE_CASE_:							str	,       SCREAMING_SNAKE_CASE_:							Tuple	,       SCREAMING_SNAKE_CASE_:							Optional[int]      )			->      Dict:




				'''simple docstring'''





				A__        =			AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE_      )
				A__        =			FlaxAutoModelForSeqaSeqLM.from_config(config=SCREAMING_SNAKE_CASE_      )
				A__        =			checkpoints.load_tax_checkpoint(SCREAMING_SNAKE_CASE_      )

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

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

								# 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"]

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

								# 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__        =			flax_model.params["encoder"]["block"][str(SCREAMING_SNAKE_CASE_      )]["layer"]
								A__        =			tax_attention_key
								A__        =			tax_attention_out
								A__        =			tax_attention_query
								A__        =			tax_attention_value

								A__        =			tax_attention_layer_norm

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

								A__        =			flax_model_encoder_layer_block

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

				# Side/global relative position_bias + layer norm
				if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
								A__        =			tax_model["target"]["encoder"]["side_relpos_bias"]["rel_embedding"].T
								A__        =			tax_encoder_global_rel_embedding

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

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

								# 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"]
								A__        =			tax_enc_dec_attention_module["key"]["kernel"]
								A__        =			tax_enc_dec_attention_module["out"]["kernel"]
								A__        =			tax_enc_dec_attention_module["query"]["kernel"]
								A__        =			tax_enc_dec_attention_module["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__        =			flax_model.params["decoder"]["block"][str(SCREAMING_SNAKE_CASE_      )]["layer"]
								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

								A__        =			flax_model_decoder_layer_block

				# 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 and LongT5 checkpoints)
				if "logits_dense" in tax_model["target"]["decoder"]:
								A__        =			tax_model["target"]["decoder"]["logits_dense"]["kernel"]

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


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




from ..utils import is_flax_available, is_torch_available


if is_torch_available():
	from .autoencoder_kl import AutoencoderKL
	from .controlnet import ControlNetModel
	from .dual_transformer_ad import DualTransformeraDModel
	from .modeling_utils import ModelMixin
	from .prior_transformer import PriorTransformer
	from .ta_film_transformer import TaFilmDecoder
	from .transformer_ad import TransformeraDModel
	from .unet_ad import UNetaDModel
	from .unet_ad import UNetaDModel
	from .unet_ad_condition import UNetaDConditionModel
	from .unet_ad_condition import UNetaDConditionModel
	from .vq_model import VQModel

if is_flax_available():
	from .controlnet_flax import FlaxControlNetModel
	from .unet_ad_condition_flax import FlaxUNetaDConditionModel
	from .vae_flax import FlaxAutoencoderKL

