




import logging
import random

import ray

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


__A						:		str          =						logging.getLogger(__name__)

class 		lowercase_       :




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


									'''simple docstring'''


									__lowerCAmelCase					=  False




					def     _lowercase						(   self:		str,     _lowercase:		Tuple,     _lowercase:		List[str],     _lowercase:		List[str],     _lowercase:		Union[str, Any]):


									'''simple docstring'''


									if not self.initialized:
													__lowerCAmelCase					=  RagRetriever(
													    _lowercase,     question_encoder_tokenizer=_lowercase,     generator_tokenizer=_lowercase,     index=_lowercase,     init_retrieval=_lowercase,     )
													__lowerCAmelCase					=  True




					def     _lowercase						(   self:		str):


									'''simple docstring'''


									self.retriever.index.init_index()




					def     _lowercase						(   self:		List[str]):


									'''simple docstring'''


									del self.retriever
									__lowerCAmelCase					=  False







					def     _lowercase						(   self:		Any,     _lowercase:		Optional[int],     _lowercase:		Dict):


									'''simple docstring'''


									__lowerCAmelCase	,				__lowerCAmelCase					=  self.retriever._main_retrieve(_lowercase,     _lowercase)
									__lowerCAmelCase					=  self.retriever.index.get_doc_dicts(_lowercase)
									return doc_ids, retrieved_doc_embeds, doc_dicts





class 		lowercase_       (	lowerCAmelCase__				):




					def __init__(   self:		str,     _lowercase:		List[Any],     _lowercase:		str,     _lowercase:		Dict,     _lowercase:		List[str],     _lowercase:		str=None):


									'''simple docstring'''


									if index is not None and index.is_initialized() and len(_lowercase) > 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__(
									    _lowercase,     question_encoder_tokenizer=_lowercase,     generator_tokenizer=_lowercase,     index=_lowercase,     init_retrieval=_lowercase,     )

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




					def     _lowercase						(   self:		Tuple):


									'''simple docstring'''


									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     _lowercase						(   self:		Optional[Any],     _lowercase:		Optional[Any],     _lowercase:		List[str]):


									'''simple docstring'''


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




					@classmethod
					def     _lowercase						(   cls:		Union[str, Any],     _lowercase:		Optional[int],     _lowercase:		Any=None,     **_lowercase:		str):


									'''simple docstring'''


									return super(_lowercase,     cls).get_tokenizers(_lowercase,     _lowercase,     **_lowercase)




					@classmethod
					def     _lowercase						(   cls:		Union[str, Any],     _lowercase:		List[Any],     _lowercase:		List[Any],     _lowercase:		List[str]=None,     **_lowercase:		Any):


									'''simple docstring'''


									__lowerCAmelCase					=  kwargs.pop("""config""",     _lowercase) or RagConfig.from_pretrained(_lowercase,     **_lowercase)
									__lowerCAmelCase					=  RagTokenizer.from_pretrained(_lowercase,     config=_lowercase)
									__lowerCAmelCase					=  rag_tokenizer.question_encoder
									__lowerCAmelCase					=  rag_tokenizer.generator

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

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







					def     _lowercase						(   self:		int):


									'''simple docstring'''


									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
									__lowerCAmelCase					=  self._build_index(self.config)

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







import argparse
import os


_A							=       {
    '''pytorch''': {
        '''1.13''': {
            '''torch''': '''1.13.1''',
            '''torchvision''': '''0.14.1''',
            '''torchaudio''': '''0.13.1''',
            '''python''': 3.9,
            '''cuda''': '''cu116''',
            '''install''': (
                '''python3 -m pip install --no-cache-dir -U torch==1.13.1 torchvision==0.14.1 torchaudio==0.13.1'''
                ''' --extra-index-url https://download.pytorch.org/whl/cu116'''
            ),
            '''base_image''': '''nvidia/cuda:11.6.2-cudnn8-devel-ubuntu20.04''',
        },
        '''1.12''': {
            '''torch''': '''1.12.1''',
            '''torchvision''': '''0.13.1''',
            '''torchaudio''': '''0.12.1''',
            '''python''': 3.9,
            '''cuda''': '''cu113''',
            '''install''': (
                '''python3 -m pip install --no-cache-dir -U torch==1.12.1 torchvision==0.13.1 torchaudio==0.12.1'''
                ''' --extra-index-url https://download.pytorch.org/whl/cu113'''
            ),
            '''base_image''': '''nvidia/cuda:11.2.2-cudnn8-devel-ubuntu20.04''',
        },
        '''1.11''': {
            '''torch''': '''1.11.0''',
            '''torchvision''': '''0.12.0''',
            '''torchaudio''': '''0.11.0''',
            '''python''': 3.9,
            '''cuda''': '''cu113''',
            '''install''': (
                '''python3 -m pip install --no-cache-dir -U torch==1.11.0 torchvision==0.12.0 torchaudio==0.11.0'''
                ''' --extra-index-url https://download.pytorch.org/whl/cu113'''
            ),
            '''base_image''': '''nvidia/cuda:11.2.2-cudnn8-devel-ubuntu20.04''',
        },
        '''1.10''': {
            '''torch''': '''1.10.2''',
            '''torchvision''': '''0.11.3''',
            '''torchaudio''': '''0.10.2''',
            '''python''': 3.9,
            '''cuda''': '''cu113''',
            '''install''': (
                '''python3 -m pip install --no-cache-dir -U torch==1.10.2 torchvision==0.11.3 torchaudio==0.10.2'''
                ''' --extra-index-url https://download.pytorch.org/whl/cu113'''
            ),
            '''base_image''': '''nvidia/cuda:11.2.2-cudnn8-devel-ubuntu20.04''',
        },
        # torchaudio < 0.10 has no CUDA-enabled binary distributions
        '''1.9''': {
            '''torch''': '''1.9.1''',
            '''torchvision''': '''0.10.1''',
            '''torchaudio''': '''0.9.1''',
            '''python''': 3.9,
            '''cuda''': '''cu111''',
            '''install''': (
                '''python3 -m pip install --no-cache-dir -U torch==1.9.1 torchvision==0.10.1 torchaudio==0.9.1'''
                ''' --extra-index-url https://download.pytorch.org/whl/cu111'''
            ),
            '''base_image''': '''nvidia/cuda:11.2.2-cudnn8-devel-ubuntu20.04''',
        },
    },
    '''tensorflow''': {
        '''2.11''': {
            '''tensorflow''': '''2.11.1''',
            '''install''': '''python3 -m pip install --no-cache-dir -U tensorflow==2.11.1''',
            '''base_image''': '''nvidia/cuda:11.2.2-cudnn8-devel-ubuntu20.04''',
        },
        '''2.10''': {
            '''tensorflow''': '''2.10.1''',
            '''install''': '''python3 -m pip install --no-cache-dir -U tensorflow==2.10.1''',
            '''base_image''': '''nvidia/cuda:11.2.2-cudnn8-devel-ubuntu20.04''',
        },
        '''2.9''': {
            '''tensorflow''': '''2.9.3''',
            '''install''': '''python3 -m pip install --no-cache-dir -U tensorflow==2.9.3''',
            '''base_image''': '''nvidia/cuda:11.2.2-cudnn8-devel-ubuntu20.04''',
        },
        '''2.8''': {
            '''tensorflow''': '''2.8.2''',
            '''install''': '''python3 -m pip install --no-cache-dir -U tensorflow==2.8.2''',
            '''base_image''': '''nvidia/cuda:11.2.2-cudnn8-devel-ubuntu20.04''',
        },
        '''2.7''': {
            '''tensorflow''': '''2.7.3''',
            '''install''': '''python3 -m pip install --no-cache-dir -U tensorflow==2.7.3''',
            '''base_image''': '''nvidia/cuda:11.2.2-cudnn8-devel-ubuntu20.04''',
        },
        '''2.6''': {
            '''tensorflow''': '''2.6.5''',
            '''install''': '''python3 -m pip install --no-cache-dir -U tensorflow==2.6.5''',
            '''base_image''': '''nvidia/cuda:11.2.2-cudnn8-devel-ubuntu20.04''',
        },
        '''2.5''': {
            '''tensorflow''': '''2.5.3''',
            '''install''': '''python3 -m pip install --no-cache-dir -U tensorflow==2.5.3''',
            '''base_image''': '''nvidia/cuda:11.2.2-cudnn8-devel-ubuntu20.04''',
        },
    },
}


if __name__ == "__main__":
      _A							=       argparse.ArgumentParser('''Choose the framework and version to install''')
      parser.add_argument(
          '''--framework''', help='''The framework to install. Should be `torch` or `tensorflow`''', type=str, required=True
      )
      parser.add_argument('''--version''', help='''The version of the framework to install.''', type=str, required=True)
      _A							=       parser.parse_args()

      _A							=       past_versions_testing[args.framework][args.version]

      os.system(F'''echo "export INSTALL_CMD=\'{info['install']}\'" >> ~/.profile''')
      print(F'''echo "export INSTALL_CMD=\'{info['install']}\'" >> ~/.profile''')

      _A							=       ''''''
      if args.framework == "pytorch":
            _A							=       info['''cuda''']
      os.system(F'''echo \"export CUDA=\'{cuda}\'\" >> ~/.profile''')
      print(F'''echo \"export CUDA=\'{cuda}\'\" >> ~/.profile''')



