





'''simple docstring'''


import argparse

from tax import checkpoints

from transformers import FlaxTaForConditionalGeneration, TaConfig




def    __a (     UpperCAmelCase       ,			UpperCAmelCase       ,			UpperCAmelCase    )      ->List[Any]:



	"""simple docstring"""







	A							  =      TaConfig.from_pretrained(UpperCAmelCase    )
	A							  =      FlaxTaForConditionalGeneration(config=UpperCAmelCase    )
	A							  =      checkpoints.load_tax_checkpoint(UpperCAmelCase    )

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

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

		# 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"""]

		# 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							  =      tax_attention_key
		A							  =      tax_attention_out
		A							  =      tax_attention_query
		A							  =      tax_attention_value

		A							  =      tax_attention_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

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

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

	# Decoder
	for layer_index in range(config.num_decoder_layers    ):
		A							  =      f"""layers_{str(UpperCAmelCase    )}"""

		# 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"""]["""key"""][
		    """kernel"""
		]
		A							  =      tax_model["""target"""]["""decoder"""][layer_name]["""encoder_decoder_attention"""]["""out"""][
		    """kernel"""
		]
		A							  =      tax_model["""target"""]["""decoder"""][layer_name]["""encoder_decoder_attention"""]["""query"""][
		    """kernel"""
		]
		A							  =      tax_model["""target"""]["""decoder"""][layer_name]["""encoder_decoder_attention"""]["""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							  =      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

	# 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 checkpoints)
	if "logits_dense" in tax_model["target"]["decoder"]:
		A							  =      tax_model["""target"""]["""decoder"""]["""logits_dense"""]["""kernel"""]

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


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








'''simple docstring'''


import logging
import random

import ray

from transformers import RagConfig, RagRetriever, RagTokenizer
from transformers.models.rag.retrieval_rag import CustomHFIndex


_lowerCamelCase				:	Tuple				    =       logging.getLogger(__name__)



class 							__UpperCAmelCase  :







		'''simple docstring'''







		def __init__(self						:      Union[str, Any]      ):
			A							  =      False







		def        A     (self						:      Any      ,      _lowerCAmelCase						:      str      ,      _lowerCAmelCase						:      List[str]      ,      _lowerCAmelCase						:      str      ,      _lowerCAmelCase						:      Any      ):
			if not self.initialized:
				A							  =      RagRetriever(
				    _lowerCAmelCase      ,      question_encoder_tokenizer=_lowerCAmelCase      ,      generator_tokenizer=_lowerCAmelCase      ,      index=_lowerCAmelCase      ,      init_retrieval=_lowerCAmelCase      ,      )
				A							  =      True







		def        A     (self						:      Optional[int]      ):
			self.retriever.index.init_index()







		def        A     (self						:      Optional[int]      ):
			# delete the old self.retriever object before assigning the new index
			del self.retriever
			A							  =      False







		def        A     (self						:      List[str]      ,      _lowerCAmelCase						:      int      ,      _lowerCAmelCase						:      List[str]      ):
			A  ,    A							  =      self.retriever._main_retrieve(_lowerCAmelCase      ,      _lowerCAmelCase      )
			A							  =      self.retriever.index.get_doc_dicts(_lowerCAmelCase      )
			return doc_ids, retrieved_doc_embeds, doc_dicts






class 							__UpperCAmelCase  (       A__						):







		'''simple docstring'''







		def __init__(self						:      Tuple      ,      _lowerCAmelCase						:      int      ,      _lowerCAmelCase						:      int      ,      _lowerCAmelCase						:      List[str]      ,      _lowerCAmelCase						:      Tuple      ,      _lowerCAmelCase						:      str=None      ):
			if index is not None and index.is_initialized() and len(_lowerCAmelCase      ) > 0:
				raise ValueError(
				    """When using Ray for distributed fine-tuning, """
				    """you'll need to provide the paths instead, """
				    """as the dataset and the index are loaded """
				    """separately. More info in examples/rag/use_own_knowledge_dataset.py """      )

			super().__init__(
			    _lowerCAmelCase      ,      question_encoder_tokenizer=_lowerCAmelCase      ,      generator_tokenizer=_lowerCAmelCase      ,      index=_lowerCAmelCase      ,      init_retrieval=_lowerCAmelCase      ,      )

			A							  =      retrieval_workers
			A							  =      question_encoder_tokenizer
			A							  =      generator_tokenizer
			if len(self.retrieval_workers      ) > 0:
				ray.get(
				    [
				        worker.create_rag_retriever.remote(_lowerCAmelCase      ,      _lowerCAmelCase      ,      _lowerCAmelCase      ,      _lowerCAmelCase      )
				        for worker in self.retrieval_workers
				    ]      )







		def        A     (self						:      List[Any]      ):
			logger.info("""initializing retrieval"""      )

			if len(self.retrieval_workers      ) > 0:
				ray.get([worker.init_retrieval.remote() for worker in self.retrieval_workers]      )
			else:
				# Non-distributed training. Load index into this same process.
				self.index.init_index()







		def        A     (self						:      str      ,      _lowerCAmelCase						:      Dict      ,      _lowerCAmelCase						:      Tuple      ):
			if len(self.retrieval_workers      ) > 0:
				# Select a random retrieval actor.
				A							  =      self.retrieval_workers[random.randint(0      ,      len(self.retrieval_workers      ) - 1      )]
				A  ,    A  ,    A							  =      ray.get(
				    random_worker.retrieve.remote(_lowerCAmelCase      ,      _lowerCAmelCase      )      )
			else:
				A  ,    A							  =      self._main_retrieve(_lowerCAmelCase      ,      _lowerCAmelCase      )
				A							  =      self.index.get_doc_dicts(_lowerCAmelCase      )
			return retrieved_doc_embeds, doc_ids, doc_dicts







		@classmethod
		def        A     (cls						:      Union[str, Any]      ,      _lowerCAmelCase						:      int      ,      _lowerCAmelCase						:      Optional[Any]=None      ,      **_lowerCAmelCase						:      Union[str, Any]      ):
			return super(_lowerCAmelCase      ,      cls      ).get_tokenizers(_lowerCAmelCase      ,      _lowerCAmelCase      ,      **_lowerCAmelCase      )







		@classmethod
		def        A     (cls						:      List[Any]      ,      _lowerCAmelCase						:      Optional[int]      ,      _lowerCAmelCase						:      List[str]      ,      _lowerCAmelCase						:      str=None      ,      **_lowerCAmelCase						:      List[str]      ):
			A							  =      kwargs.pop("""config"""      ,      _lowerCAmelCase      ) or RagConfig.from_pretrained(_lowerCAmelCase      ,      **_lowerCAmelCase      )
			A							  =      RagTokenizer.from_pretrained(_lowerCAmelCase      ,      config=_lowerCAmelCase      )
			A							  =      rag_tokenizer.question_encoder
			A							  =      rag_tokenizer.generator

			if indexed_dataset is not None:
				A							  =      """custom"""
				A							  =      CustomHFIndex(config.retrieval_vector_size      ,      _lowerCAmelCase      )
			else:
				A							  =      cls._build_index(_lowerCAmelCase      )

			return cls(
			    _lowerCAmelCase      ,      question_encoder_tokenizer=_lowerCAmelCase      ,      generator_tokenizer=_lowerCAmelCase      ,      retrieval_workers=_lowerCAmelCase      ,      index=_lowerCAmelCase      ,      )







		def        A     (self						:      List[Any]      ):
			logger.info("""re-loading the new dataset with embeddings"""      )
			# access from the training loop

			ray.get([worker.clear_object.remote() for worker in self.retrieval_workers]      )

			# build the index object again
			A							  =      self._build_index(self.config      )

			ray.get(
			    [
			        worker.create_rag_retriever.remote(
			            self.config      ,      self.question_encoder_tokenizer      ,      self.generator_tokenizer      ,      _lowerCAmelCase      )
			        for worker in self.retrieval_workers
			    ]      )

