
'''simple docstring'''



import json
import os
import shutil
import sys
import tempfile
import unittest
from unittest import TestCase
from unittest.mock import patch

import faiss
import numpy as np
from datasets import Dataset

from transformers import BartConfig, BartTokenizer, DPRConfig, DPRQuestionEncoderTokenizer, RagConfig
from transformers.file_utils import is_datasets_available, is_faiss_available, is_psutil_available, is_torch_available
from transformers.integrations import is_ray_available
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES as DPR_VOCAB_FILES_NAMES
from transformers.models.rag.retrieval_rag import CustomHFIndex, RagRetriever
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES as BART_VOCAB_FILES_NAMES
from transformers.testing_utils import require_ray


sys.path.append(os.path.join(os.getcwd()))  # noqa: E402 # noqa: E402 # isort:skip

if is_torch_available():
     from distributed_pytorch_retriever import RagPyTorchDistributedRetriever  # noqa: E402 # isort:skip
else:
     UpperCamelCase__					:						List[Any]											=     None

if is_ray_available():
     import ray  # noqa: E402 # isort:skip
     from distributed_ray_retriever import RagRayDistributedRetriever, RayRetriever  # noqa: E402 # isort:skip
else:
     UpperCamelCase__					:						Union[str, Any]											=     None
     UpperCamelCase__					:						Dict											=     None
     UpperCamelCase__					:						Optional[int]											=     None
def       lowerCAmelCase_		( SCREAMING_SNAKE_CASE_  )   ->   Union[str, Any]:




   """simple docstring"""
   if not (is_datasets_available() and is_faiss_available() and is_psutil_available()):
      _SCREAMING_SNAKE_CASE          = unittest.skip("""test requires Datasets, Faiss, psutil"""  )(SCREAMING_SNAKE_CASE_  )
   return test_case







@require_distributed_retrieval
class       _a (_lowerCamelCase):





       """simple docstring"""




       def 				UpperCamelCase (							self					)			->	str:
          _SCREAMING_SNAKE_CASE          = tempfile.mkdtemp()
          _SCREAMING_SNAKE_CASE          = 8

          # DPR tok
          _SCREAMING_SNAKE_CASE          = [
              """[UNK]""",
              """[CLS]""",
              """[SEP]""",
              """[PAD]""",
              """[MASK]""",
              """want""",
              """##want""",
              """##ed""",
              """wa""",
              """un""",
              """runn""",
              """##ing""",
              """,""",
              """low""",
              """lowest""",
          ]
          _SCREAMING_SNAKE_CASE          = os.path.join(self.tmpdirname				,							"""dpr_tokenizer"""					)
          os.makedirs(A__				,							exist_ok=A__					)
          _SCREAMING_SNAKE_CASE          = os.path.join(A__				,							DPR_VOCAB_FILES_NAMES["""vocab_file"""]					)
          with open(self.vocab_file				,							"""w"""				,							encoding="""utf-8"""					) as vocab_writer:
             vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens]					)					)

          # BART tok
          _SCREAMING_SNAKE_CASE          = [
              """l""",
              """o""",
              """w""",
              """e""",
              """r""",
              """s""",
              """t""",
              """i""",
              """d""",
              """n""",
              """\u0120""",
              """\u0120l""",
              """\u0120n""",
              """\u0120lo""",
              """\u0120low""",
              """er""",
              """\u0120lowest""",
              """\u0120newer""",
              """\u0120wider""",
              """<unk>""",
          ]
          _SCREAMING_SNAKE_CASE          = dict(zip(A__				,							range(len(A__					)					)					)					)
          _SCREAMING_SNAKE_CASE          = ["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
          _SCREAMING_SNAKE_CASE          = {"""unk_token""": """<unk>"""}

          _SCREAMING_SNAKE_CASE          = os.path.join(self.tmpdirname				,							"""bart_tokenizer"""					)
          os.makedirs(A__				,							exist_ok=A__					)
          _SCREAMING_SNAKE_CASE          = os.path.join(A__				,							BART_VOCAB_FILES_NAMES["""vocab_file"""]					)
          _SCREAMING_SNAKE_CASE          = os.path.join(A__				,							BART_VOCAB_FILES_NAMES["""merges_file"""]					)
          with open(self.vocab_file				,							"""w"""				,							encoding="""utf-8"""					) as fp:
             fp.write(json.dumps(A__					) + """\n"""					)
          with open(self.merges_file				,							"""w"""				,							encoding="""utf-8"""					) as fp:
             fp.write("""\n""".join(A__					)					)






       def 				UpperCamelCase (							self					)			->	DPRQuestionEncoderTokenizer:
          return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname				,							"""dpr_tokenizer"""					)					)






       def 				UpperCamelCase (							self					)			->	BartTokenizer:
          return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname				,							"""bart_tokenizer"""					)					)






       def 				UpperCamelCase (							self					)			->	Optional[int]:
          shutil.rmtree(self.tmpdirname					)






       def 				UpperCamelCase (							self					)			->	List[Any]:
          _SCREAMING_SNAKE_CASE          = Dataset.from_dict(
              {
                  """id""": ["""0""", """1"""],
                  """text""": ["""foo""", """bar"""],
                  """title""": ["""Foo""", """Bar"""],
                  """embeddings""": [np.ones(self.retrieval_vector_size					), 2 * np.ones(self.retrieval_vector_size					)],
              }					)
          dataset.add_faiss_index("""embeddings"""				,							string_factory="""Flat"""				,							metric_type=faiss.METRIC_INNER_PRODUCT					)
          return dataset






       def 				UpperCamelCase (							self				,							A__				,							A__=1_23_45					)			->	RagPyTorchDistributedRetriever:
          _SCREAMING_SNAKE_CASE          = self.get_dummy_dataset()
          _SCREAMING_SNAKE_CASE          = RagConfig(
              retrieval_vector_size=self.retrieval_vector_size				,							question_encoder=DPRConfig().to_dict()				,							generator=BartConfig().to_dict()				,							)
          with patch("""transformers.models.rag.retrieval_rag.load_dataset"""					) as mock_load_dataset:
             _SCREAMING_SNAKE_CASE          = dataset
             _SCREAMING_SNAKE_CASE          = RagPyTorchDistributedRetriever(
                 A__				,							question_encoder_tokenizer=self.get_dpr_tokenizer()				,							generator_tokenizer=self.get_bart_tokenizer()				,							)
             if init_retrieval:
                retriever.init_retrieval(A__					)
          return retriever






       def 				UpperCamelCase (							self				,							A__					)			->	RagRayDistributedRetriever:
          # Have to run in local mode because sys.path modifications at top of
          # file are not propogated to remote workers.
          # https://stackoverflow.com/questions/54338013/parallel-import-a-python-file-from-sibling-folder
          ray.init(local_mode=A__					)
          _SCREAMING_SNAKE_CASE          = RagConfig(
              retrieval_vector_size=self.retrieval_vector_size				,							question_encoder=DPRConfig().to_dict()				,							generator=BartConfig().to_dict()				,							)
          _SCREAMING_SNAKE_CASE          = ray.remote(A__					)
          _SCREAMING_SNAKE_CASE          = [remote_cls.remote() for _ in range(1					)]
          with patch("""transformers.models.rag.retrieval_rag.load_dataset"""					) as mock_load_dataset:
             _SCREAMING_SNAKE_CASE          = self.get_dummy_dataset()
             _SCREAMING_SNAKE_CASE          = RagRayDistributedRetriever(
                 A__				,							question_encoder_tokenizer=self.get_dpr_tokenizer()				,							generator_tokenizer=self.get_bart_tokenizer()				,							retrieval_workers=A__				,							)
             if init_retrieval:
                retriever.init_retrieval()
          return retriever






       def 				UpperCamelCase (							self				,							A__				,							A__				,							A__=1_23_45					)			->	Optional[Any]:
          _SCREAMING_SNAKE_CASE          = self.get_dummy_dataset()
          _SCREAMING_SNAKE_CASE          = RagConfig(
              retrieval_vector_size=self.retrieval_vector_size				,							question_encoder=DPRConfig().to_dict()				,							generator=BartConfig().to_dict()				,							index_name="""custom"""				,							)
          if from_disk:
             _SCREAMING_SNAKE_CASE          = os.path.join(self.tmpdirname				,							"""dataset"""					)
             _SCREAMING_SNAKE_CASE          = os.path.join(self.tmpdirname				,							"""index.faiss"""					)
             dataset.get_index("""embeddings"""					).save(os.path.join(self.tmpdirname				,							"""index.faiss"""					)					)
             dataset.drop_index("""embeddings"""					)
             dataset.save_to_disk(os.path.join(self.tmpdirname				,							"""dataset"""					)					)
             del dataset
             _SCREAMING_SNAKE_CASE          = RagPyTorchDistributedRetriever(
                 A__				,							question_encoder_tokenizer=self.get_dpr_tokenizer()				,							generator_tokenizer=self.get_bart_tokenizer()				,							)
          else:
             _SCREAMING_SNAKE_CASE          = RagPyTorchDistributedRetriever(
                 A__				,							question_encoder_tokenizer=self.get_dpr_tokenizer()				,							generator_tokenizer=self.get_bart_tokenizer()				,							index=CustomHFIndex(config.retrieval_vector_size				,							A__					)				,							)
          if init_retrieval:
             retriever.init_retrieval(A__					)
          return retriever






       def 				UpperCamelCase (							self				,							A__				,							A__					)			->	Optional[Any]:
          # Have to run in local mode because sys.path modifications at top of
          # file are not propogated to remote workers.
          # https://stackoverflow.com/questions/54338013/parallel-import-a-python-file-from-sibling-folder
          ray.init(local_mode=A__					)
          _SCREAMING_SNAKE_CASE          = self.get_dummy_dataset()
          _SCREAMING_SNAKE_CASE          = RagConfig(
              retrieval_vector_size=self.retrieval_vector_size				,							question_encoder=DPRConfig().to_dict()				,							generator=BartConfig().to_dict()				,							index_name="""custom"""				,							)
          _SCREAMING_SNAKE_CASE          = ray.remote(A__					)
          _SCREAMING_SNAKE_CASE          = [remote_cls.remote() for _ in range(1					)]
          if from_disk:
             _SCREAMING_SNAKE_CASE          = os.path.join(self.tmpdirname				,							"""dataset"""					)
             _SCREAMING_SNAKE_CASE          = os.path.join(self.tmpdirname				,							"""index.faiss"""					)
             dataset.get_index("""embeddings"""					).save(os.path.join(self.tmpdirname				,							"""index.faiss"""					)					)
             dataset.drop_index("""embeddings"""					)
             dataset.save_to_disk(os.path.join(self.tmpdirname				,							"""dataset"""					)					)
             del dataset
             _SCREAMING_SNAKE_CASE          = RagRayDistributedRetriever(
                 A__				,							question_encoder_tokenizer=self.get_dpr_tokenizer()				,							generator_tokenizer=self.get_bart_tokenizer()				,							retrieval_workers=A__				,							index=CustomHFIndex.load_from_disk(
                     vector_size=config.retrieval_vector_size				,							dataset_path=config.passages_path				,							index_path=config.index_path				,							)				,							)
          else:
             _SCREAMING_SNAKE_CASE          = RagRayDistributedRetriever(
                 A__				,							question_encoder_tokenizer=self.get_dpr_tokenizer()				,							generator_tokenizer=self.get_bart_tokenizer()				,							retrieval_workers=A__				,							index=CustomHFIndex(config.retrieval_vector_size				,							A__					)				,							)
          if init_retrieval:
             retriever.init_retrieval()
          return retriever






       def 				UpperCamelCase (							self				,							A__				,							A__				,							A__					)			->	None:
          _SCREAMING_SNAKE_CASE       ,							_SCREAMING_SNAKE_CASE       ,							_SCREAMING_SNAKE_CASE          = retriever.retrieve(A__				,							n_docs=A__					)
          self.assertEqual(retrieved_doc_embeds.shape				,							(2, n_docs, self.retrieval_vector_size)					)
          self.assertEqual(len(A__					)				,							2					)
          self.assertEqual(sorted(doc_dicts[0]					)				,							["""embeddings""", """id""", """text""", """title"""]					)
          self.assertEqual(len(doc_dicts[0]["""id"""]					)				,							A__					)
          self.assertEqual(doc_dicts[0]["""id"""][0]				,							"""1"""					)  # max inner product is reached with second doc
          self.assertEqual(doc_dicts[1]["""id"""][0]				,							"""0"""					)  # max inner product is reached with first doc
          self.assertListEqual(doc_ids.tolist()				,							[[1], [0]]					)






       def 				UpperCamelCase (							self					)			->	Tuple:
          _SCREAMING_SNAKE_CASE          = 1
          _SCREAMING_SNAKE_CASE          = np.array(
              [np.ones(self.retrieval_vector_size					), -np.ones(self.retrieval_vector_size					)]				,							dtype=np.floataa					)

          self.distributed_retriever_check(
              self.get_dummy_pytorch_distributed_retriever(init_retrieval=A__					)				,							A__				,							A__					)






       def 				UpperCamelCase (							self					)			->	int:
          _SCREAMING_SNAKE_CASE          = 1
          _SCREAMING_SNAKE_CASE          = np.array(
              [np.ones(self.retrieval_vector_size					), -np.ones(self.retrieval_vector_size					)]				,							dtype=np.floataa					)

          self.distributed_retriever_check(
              self.get_dummy_custom_hf_index_pytorch_retriever(init_retrieval=A__				,							from_disk=A__					)				,							A__				,							A__				,							)






       def 				UpperCamelCase (							self					)			->	Tuple:
          _SCREAMING_SNAKE_CASE          = 1
          _SCREAMING_SNAKE_CASE          = np.array(
              [np.ones(self.retrieval_vector_size					), -np.ones(self.retrieval_vector_size					)]				,							dtype=np.floataa					)

          self.distributed_retriever_check(
              self.get_dummy_custom_hf_index_pytorch_retriever(init_retrieval=A__				,							from_disk=A__					)				,							A__				,							A__				,							)






       @require_ray
       def 				UpperCamelCase (							self					)			->	str:
          _SCREAMING_SNAKE_CASE          = 1
          _SCREAMING_SNAKE_CASE          = np.array(
              [np.ones(self.retrieval_vector_size					), -np.ones(self.retrieval_vector_size					)]				,							dtype=np.floataa					)

          self.distributed_retriever_check(
              self.get_dummy_ray_distributed_retriever(init_retrieval=A__					)				,							A__				,							A__					)
          ray.shutdown()






       @require_ray
       def 				UpperCamelCase (							self					)			->	Tuple:
          _SCREAMING_SNAKE_CASE          = 1
          _SCREAMING_SNAKE_CASE          = np.array(
              [np.ones(self.retrieval_vector_size					), -np.ones(self.retrieval_vector_size					)]				,							dtype=np.floataa					)
          with self.assertRaises(A__					):
             self.distributed_retriever_check(
                 self.get_dummy_custom_hf_index_ray_retriever(init_retrieval=A__				,							from_disk=A__					)				,							A__				,							A__				,							)
          ray.shutdown()






       @require_ray
       def 				UpperCamelCase (							self					)			->	str:
          _SCREAMING_SNAKE_CASE          = 1
          _SCREAMING_SNAKE_CASE          = np.array(
              [np.ones(self.retrieval_vector_size					), -np.ones(self.retrieval_vector_size					)]				,							dtype=np.floataa					)

          self.distributed_retriever_check(
              self.get_dummy_custom_hf_index_ray_retriever(init_retrieval=A__				,							from_disk=A__					)				,							A__				,							A__					)
          ray.shutdown()








'''simple docstring'''



import argparse

from . import (
    BertConfig,
    BertGenerationConfig,
    BertGenerationDecoder,
    BertGenerationEncoder,
    load_tf_weights_in_bert_generation,
    logging,
)


logging.set_verbosity_info()
def       lowerCAmelCase_		( SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_  )   ->   Any:




   """simple docstring"""
   # Initialise PyTorch model
   _SCREAMING_SNAKE_CASE          = BertConfig.from_pretrained(
       """bert-large-cased"""			,					vocab_size=SCREAMING_SNAKE_CASE_			,					max_position_embeddings=5_12			,					is_decoder=SCREAMING_SNAKE_CASE_			,					add_cross_attention=SCREAMING_SNAKE_CASE_			,					)
   _SCREAMING_SNAKE_CASE          = bert_config.to_dict()
   del bert_config_dict["type_vocab_size"]
   _SCREAMING_SNAKE_CASE          = BertGenerationConfig(**SCREAMING_SNAKE_CASE_  )
   if is_encoder:
      _SCREAMING_SNAKE_CASE          = BertGenerationEncoder(SCREAMING_SNAKE_CASE_  )
   else:
      _SCREAMING_SNAKE_CASE          = BertGenerationDecoder(SCREAMING_SNAKE_CASE_  )
   print(F"Building PyTorch model from configuration: {config}"  )

   # Load weights from tf checkpoint
   load_tf_weights_in_bert_generation(
       SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_			,					model_class="""bert"""			,					is_encoder_named_decoder=SCREAMING_SNAKE_CASE_			,					is_encoder=SCREAMING_SNAKE_CASE_			,					)

   # Save pytorch-model
   print(F"Save PyTorch model and config to {pytorch_dump_path}"  )
   model.save_pretrained(SCREAMING_SNAKE_CASE_  )


if __name__ == "__main__":
     UpperCamelCase__					:						int											=     argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         "--tf_hub_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
     )
     parser.add_argument(
         "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
     )
     parser.add_argument(
         "--is_encoder_named_decoder",
         action="store_true",
         help="If decoder has to be renamed to encoder in PyTorch model.",
     )
     parser.add_argument("--is_encoder", action="store_true", help="If model is an encoder.")
     parser.add_argument("--vocab_size", default=50_358, type=int, help="Vocab size of model")
     UpperCamelCase__					:						int											=     parser.parse_args()
     convert_tf_checkpoint_to_pytorch(
         args.tf_hub_path,
         args.pytorch_dump_path,
         args.is_encoder_named_decoder,
         args.vocab_size,
         is_encoder=args.is_encoder,
     )






