





'''simple docstring'''







import argparse

from tax import checkpoints

from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM






def  _lowerCamelCase				( lowercase						:      List[str]    ,							lowercase						:      Optional[Any]    ,							lowercase						:      Dict						)	->	Optional[Any]:
				_a								= AutoConfig.from_pretrained(lowercase						)
				_a								= FlaxAutoModelForSeqaSeqLM.from_config(config=lowercase						)
				_a								= checkpoints.load_tax_checkpoint(lowercase						)

				_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(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"]

								# 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(lowercase						)]["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(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"]
								_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(lowercase						)]["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(lowercase						)
				print("T5X Model was sucessfully converted!"						)


if __name__ == "__main__":
					lowerCAmelCase_					:    Dict      	=			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_					:    Optional[Any]      	=			parser.parse_args()
					convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)







'''simple docstring'''







from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase_					:    Dict      	=			logging.get_logger(__name__)

lowerCAmelCase_					:    int      	=			{
    'bigcode/gpt_bigcode-santacoder': 'https://huggingface.co/bigcode/gpt_bigcode-santacoder/resolve/main/config.json',
}
class 				__SCREAMING_SNAKE_CASE  (lowerCamelCase_      ):






				"""simple docstring"""







				__a            ='gpt_bigcode'
				__a            =['past_key_values']
				__a            ={
				    'hidden_size': 'n_embd',
				    'max_position_embeddings': 'n_positions',
				    'num_attention_heads': 'n_head',
				    'num_hidden_layers': 'n_layer',
				}




				def __init__(  self     :					Optional[Any]					,				__a     :					Tuple=5_02_57					,				__a     :					str=10_24					,				__a     :					Dict=7_68					,				__a     :					Tuple=12					,				__a     :					str=12					,				__a     :					Optional[int]=None					,				__a     :					Dict="gelu_pytorch_tanh"					,				__a     :					Tuple=0.1					,				__a     :					Tuple=0.1					,				__a     :					Union[str, Any]=0.1					,				__a     :					Tuple=1e-5					,				__a     :					str=0.02					,				__a     :					Dict=True					,				__a     :					Union[str, Any]=True					,				__a     :					Optional[int]=5_02_56					,				__a     :					Optional[int]=5_02_56					,				__a     :					Union[str, Any]=True					,				__a     :					Dict=True					,				__a     :					Union[str, Any]=True					,				**__a     :					List[Any]					,				):
								_a								= vocab_size
								_a								= n_positions
								_a								= n_embd
								_a								= n_layer
								_a								= n_head
								_a								= n_inner
								_a								= activation_function
								_a								= resid_pdrop
								_a								= embd_pdrop
								_a								= attn_pdrop
								_a								= layer_norm_epsilon
								_a								= initializer_range
								_a								= scale_attn_weights
								_a								= use_cache
								_a								= attention_softmax_in_fpaa
								_a								= scale_attention_softmax_in_fpaa
								_a								= multi_query

								_a								= bos_token_id
								_a								= eos_token_id

								super().__init__(bos_token_id=__a					,				eos_token_id=__a					,				**__a					)
