





'''simple docstring'''




import argparse
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration


_A:					Optional[int]							=				[
    # tf -> hf
    ("/", "."),
    ("layer_", "layers."),
    ("kernel", "weight"),
    ("beta", "bias"),
    ("gamma", "weight"),
    ("pegasus", "model"),
]
_A:					Any							=				[
    (".output.dense", ".fc2"),
    ("intermediate.LayerNorm", "final_layer_norm"),
    ("intermediate.dense", "fc1"),
]

_A:					Any							=				(
    INIT_COMMON
    + [
        ("attention.self.LayerNorm", "self_attn_layer_norm"),
        ("attention.output.dense", "self_attn.out_proj"),
        ("attention.self", "self_attn"),
        ("attention.encdec.LayerNorm", "encoder_attn_layer_norm"),
        ("attention.encdec_output.dense", "encoder_attn.out_proj"),
        ("attention.encdec", "encoder_attn"),
        ("key", "k_proj"),
        ("value", "v_proj"),
        ("query", "q_proj"),
        ("decoder.LayerNorm", "decoder.layernorm_embedding"),
    ]
    + END_COMMON
)

_A:					Tuple							=				(
    INIT_COMMON
    + [
        ("embeddings.word_embeddings", "shared.weight"),
        ("embeddings.position_embeddings", "embed_positions.weight"),
        ("attention.self.LayerNorm", "self_attn_layer_norm"),
        ("attention.output.dense", "self_attn.output"),
        ("attention.self", "self_attn.self"),
        ("encoder.LayerNorm", "encoder.layernorm_embedding"),
    ]
    + END_COMMON
)

_A:					List[Any]							=				[
    "encdec/key/bias",
    "encdec/query/bias",
    "encdec/value/bias",
    "self/key/bias",
    "self/query/bias",
    "self/value/bias",
    "encdec_output/dense/bias",
    "attention/output/dense/bias",
]







def _lowerCAmelCase     (      _lowerCAmelCase       ,    _lowerCAmelCase    )->     Union[str, Any]:
 for tf_name, hf_name in patterns:
  __UpperCAmelCase					=   k.replace(__A       ,    __A    )
 return k







def _lowerCAmelCase     (      _lowerCAmelCase       ,    _lowerCAmelCase    )->     Optional[int]:
 __UpperCAmelCase					=   BigBirdPegasusConfig(**__A    )
 __UpperCAmelCase					=   BigBirdPegasusForConditionalGeneration(__A    )
 __UpperCAmelCase					=   torch_model.state_dict()
 __UpperCAmelCase					=   {}

 # separating decoder weights
 __UpperCAmelCase					=   {k: tf_weights[k] for k in tf_weights if k.startswith('pegasus/decoder'    )}
 __UpperCAmelCase					=   {k: tf_weights[k] for k in tf_weights if not k.startswith('pegasus/decoder'    )}

 for k, v in tqdm(decoder_weights.items()       ,    'tf -> hf conversion'    ):
  __UpperCAmelCase					=   [k.endswith(__A    ) for ending in KEYS_TO_IGNORE]
  if any(__A    ):
   continue
  __UpperCAmelCase					=   DECODER_PATTERNS
  __UpperCAmelCase					=   rename_state_dict_key(__A       ,    __A    )
  if new_k not in state_dict:
   raise ValueError(F'could not find new key {new_k} in state dict. (converted from {k})'    )
  if any(True if i in k else False for i in ['dense', 'query', 'key', 'value']    ):
   __UpperCAmelCase					=   v.T
  __UpperCAmelCase					=   torch.from_numpy(__A    )
  assert v.shape == state_dict[new_k].shape, F'{new_k}, {k}, {v.shape}, {state_dict[new_k].shape}'

 for k, v in tqdm(remaining_weights.items()       ,    'tf -> hf conversion'    ):
  __UpperCAmelCase					=   [k.endswith(__A    ) for ending in KEYS_TO_IGNORE]
  if any(__A    ):
   continue
  __UpperCAmelCase					=   REMAINING_PATTERNS
  __UpperCAmelCase					=   rename_state_dict_key(__A       ,    __A    )
  if new_k not in state_dict and k != "pegasus/embeddings/position_embeddings":
   raise ValueError(F'could not find new key {new_k} in state dict. (converted from {k})'    )
  if any(True if i in k else False for i in ['dense', 'query', 'key', 'value']    ):
   __UpperCAmelCase					=   v.T
  __UpperCAmelCase					=   torch.from_numpy(__A    )
  if k != "pegasus/embeddings/position_embeddings":
   assert v.shape == state_dict[new_k].shape, F'{new_k}, {k}, {v.shape}, {state_dict[new_k].shape}'

 __UpperCAmelCase					=   mapping["""model.embed_positions.weight"""]
 __UpperCAmelCase					=   mapping.pop('model.embed_positions.weight'    )
 __UpperCAmelCase					=   torch_model.load_state_dict(__A       ,    strict=__A    )
 __UpperCAmelCase					=   [
     k
     for k in missing
     if k
     not in [
         """final_logits_bias""",
         """model.encoder.embed_tokens.weight""",
         """model.decoder.embed_tokens.weight""",
         """lm_head.weight""",
     ]
 ]
 assert unexpected_missing == [], F'no matches found for the following torch keys {unexpected_missing}'
 assert extra == [], F'no matches found for the following tf keys {extra}'
 return torch_model







def _lowerCAmelCase     (      _lowerCAmelCase    )->     Tuple:
 __UpperCAmelCase					=   tf.train.list_variables(__A    )
 __UpperCAmelCase					=   {}
 __UpperCAmelCase					=   ["""global_step"""]
 for name, shape in tqdm(__A       ,    desc='converting tf checkpoint to dict'    ):
  __UpperCAmelCase					=   any(pat in name for pat in ignore_name    )
  if skip_key:
   continue
  __UpperCAmelCase					=   tf.train.load_variable(__A       ,    __A    )
  __UpperCAmelCase					=   array
 return tf_weights







def _lowerCAmelCase     (      _lowerCAmelCase       ,    _lowerCAmelCase       ,    _lowerCAmelCase    )->     Dict:
 __UpperCAmelCase					=   get_tf_weights_as_numpy(__A    )
 __UpperCAmelCase					=   convert_bigbird_pegasus(__A       ,    __A    )
 torch_model.save_pretrained(__A    )


if __name__ == "__main__":
    _A:					Any							=				argparse.ArgumentParser()
    parser.add_argument("""--tf_ckpt_path""", type=str, help="""passed to tf.train.list_variables""")
    parser.add_argument("""--save_dir""", default=None, type=str, help="""Path to the output PyTorch model.""")
    _A:					int							=				parser.parse_args()
    _A:					List[str]							=				{}
    convert_bigbird_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir, config_update=config_update)












'''simple docstring'''




import logging
import random

import ray

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


_A:					int							=				logging.getLogger(__name__)






class UpperCAmelCase							:



    def __init__(  self					):
     __UpperCAmelCase					=   False



    def 					__lowerCamelCase						(  self     ,			__A     ,			__A     ,			__A     ,			__A					):
     if not self.initialized:
      __UpperCAmelCase					=   RagRetriever(
          __A     ,			question_encoder_tokenizer=__A     ,			generator_tokenizer=__A     ,			index=__A     ,			init_retrieval=__A     ,			)
      __UpperCAmelCase					=   True



    def 					__lowerCamelCase						(  self					):
     self.retriever.index.init_index()




    def 					__lowerCamelCase						(  self     ,			__A     ,			__A					):
     __UpperCAmelCase					,    __UpperCAmelCase					=   self.retriever._main_retrieve(__A     ,			__A					)
     return doc_ids, retrieved_doc_embeds






class UpperCAmelCase							(		UpperCAmelCase_							):



    def __init__(  self     ,			__A     ,			__A     ,			__A     ,			__A     ,			__A=None					):
     if index is not None and index.is_initialized() and len(__A					) > 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__(
         __A     ,			question_encoder_tokenizer=__A     ,			generator_tokenizer=__A     ,			index=__A     ,			init_retrieval=__A     ,			)
     __UpperCAmelCase					=   retrieval_workers
     if len(self.retrieval_workers					) > 0:
      ray.get(
          [
              worker.create_rag_retriever.remote(__A     ,			__A     ,			__A     ,			__A					)
              for worker in self.retrieval_workers
          ]					)



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



    @classmethod
    def 					__lowerCamelCase						(  cls     ,			__A     ,			__A=None     ,			**__A					):
     return super(__A     ,			cls					).get_tokenizers(__A     ,			__A     ,			**__A					)




    @classmethod
    def 					__lowerCamelCase						(  cls     ,			__A     ,			__A     ,			__A=None     ,			**__A					):
     __UpperCAmelCase					=   kwargs.pop('config'     ,			__A					) or RagConfig.from_pretrained(__A     ,			**__A					)
     __UpperCAmelCase					=   RagTokenizer.from_pretrained(__A     ,			config=__A					)
     __UpperCAmelCase					=   rag_tokenizer.question_encoder
     __UpperCAmelCase					=   rag_tokenizer.generator
     if indexed_dataset is not None:
      __UpperCAmelCase					=   'custom'
      __UpperCAmelCase					=   CustomHFIndex(config.retrieval_vector_size     ,			__A					)
     else:
      __UpperCAmelCase					=   cls._build_index(__A					)
     return cls(
         __A     ,			question_encoder_tokenizer=__A     ,			generator_tokenizer=__A     ,			retrieval_workers=__A     ,			index=__A     ,			)





