'''simple docstring'''




import logging
import random

import ray

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


__lowercase          =  logging.getLogger(__name__)






class 				a__:


			'''simple docstring'''






			def __init__(   self):




					"""simple docstring"""


					lowerCAmelCase   					=  False



			def 							a_       (   self       ,    __lowerCAmelCase       ,    __lowerCAmelCase       ,    __lowerCAmelCase       ,    __lowerCAmelCase):




					"""simple docstring"""


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



			def 							a_       (   self):




					"""simple docstring"""


					self.retriever.index.init_index()







			def 							a_       (   self       ,    __lowerCAmelCase       ,    __lowerCAmelCase):




					"""simple docstring"""


					lowerCAmelCase   					=  self.retriever._main_retrieve(_a       ,    _a)
					return doc_ids, retrieved_doc_embeds







class 				a__(					UpperCamelCase__	):


			'''simple docstring'''






			def __init__(   self       ,    __lowerCAmelCase       ,    __lowerCAmelCase       ,    __lowerCAmelCase       ,    __lowerCAmelCase       ,    __lowerCAmelCase=None):




					"""simple docstring"""


					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       ,    )
					lowerCAmelCase   					=  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 							a_       (   self):




					"""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 							a_       (   self       ,    __lowerCAmelCase       ,    __lowerCAmelCase):




					"""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   					=  ray.get(random_worker.retrieve.remote(_a       ,    _a))
					else:
							lowerCAmelCase   					=  self._main_retrieve(_a       ,    _a)
					return retrieved_doc_embeds, doc_ids, self.index.get_doc_dicts(_a)



			@classmethod
			def 							a_       (   cls       ,    __lowerCAmelCase       ,    __lowerCAmelCase=None       ,    **__lowerCAmelCase):




					"""simple docstring"""


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







			@classmethod
			def 							a_       (   cls       ,    __lowerCAmelCase       ,    __lowerCAmelCase       ,    __lowerCAmelCase=None       ,    **__lowerCAmelCase):




					"""simple docstring"""


					lowerCAmelCase   					=  kwargs.pop("""config"""       ,    _a) or RagConfig.from_pretrained(_a       ,    **_a)
					lowerCAmelCase   					=  RagTokenizer.from_pretrained(_a       ,    config=_a)
					lowerCAmelCase   					=  rag_tokenizer.question_encoder
					lowerCAmelCase   					=  rag_tokenizer.generator
					if indexed_dataset is not None:
							lowerCAmelCase   					=  """custom"""
							lowerCAmelCase   					=  CustomHFIndex(config.retrieval_vector_size       ,    _a)
					else:
							lowerCAmelCase   					=  cls._build_index(_a)
					return cls(
					    _a       ,    question_encoder_tokenizer=_a       ,    generator_tokenizer=_a       ,    retrieval_workers=_a       ,    index=_a       ,    )










import unittest

from transformers import (
    MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TextaTextGenerationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch
from transformers.utils import is_torch_available

from .test_pipelines_common import ANY


if is_torch_available():
  import torch







@is_pipeline_test
class 						lowercase							(   unittest.TestCase		):
     _a							 =						MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
     _a							 =						TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING

     def 					a__	(  self		,     _a		,     _a		,     _a				)						->							int:
           _A							:     str			=						TextaTextGenerationPipeline(model=_a		,     tokenizer=_a				)
           return generator, ["Something to write", "Something else"]

     def 					a__	(  self		,     _a		,     _a				)						->							Dict:
           _A							:     Any			=						generator("""Something there"""				)
           self.assertEqual(_a		,     [{"""generated_text""": ANY(_a				)}]				)
           # These are encoder decoder, they don't just append to incoming string
           self.assertFalse(outputs[0]["""generated_text"""].startswith("""Something there"""				)				)

           _A							:     List[Any]			=						generator(["""This is great !""", """Something else"""]		,     num_return_sequences=2		,     do_sample=_a				)
           self.assertEqual(
               _a		,     [
                   [{"""generated_text""": ANY(_a				)}, {"""generated_text""": ANY(_a				)}],
                   [{"""generated_text""": ANY(_a				)}, {"""generated_text""": ANY(_a				)}],
               ]		,     )

           _A							:     Optional[int]			=						generator(
               ["""This is great !""", """Something else"""]		,     num_return_sequences=2		,     batch_size=2		,     do_sample=_a				)
           self.assertEqual(
               _a		,     [
                   [{"""generated_text""": ANY(_a				)}, {"""generated_text""": ANY(_a				)}],
                   [{"""generated_text""": ANY(_a				)}, {"""generated_text""": ANY(_a				)}],
               ]		,     )

           with self.assertRaises(_a				):
                 generator(4				)

     @require_torch
     def 					a__	(  self				)						->							List[str]:
           _A							:     Any			=						pipeline("""text2text-generation"""		,     model="""patrickvonplaten/t5-tiny-random"""		,     framework="""pt"""				)
           # do_sample=False necessary for reproducibility
           _A							:     Dict			=						generator("""Something there"""		,     do_sample=_a				)
           self.assertEqual(_a		,     [{"""generated_text""": """"""}]				)

           _A							:     Any			=						3
           _A							:     Any			=						generator(
               """Something there"""		,     num_return_sequences=_a		,     num_beams=_a		,     )
           _A							:     Optional[int]			=						[
               {"""generated_text""": """Beide Beide Beide Beide Beide Beide Beide Beide Beide"""},
               {"""generated_text""": """Beide Beide Beide Beide Beide Beide Beide Beide"""},
               {"""generated_text""": """"""},
           ]
           self.assertEqual(_a		,     _a				)

           _A							:     Dict			=						generator("""This is a test"""		,     do_sample=_a		,     num_return_sequences=2		,     return_tensors=_a				)
           self.assertEqual(
               _a		,     [
                   {"""generated_token_ids""": ANY(torch.Tensor				)},
                   {"""generated_token_ids""": ANY(torch.Tensor				)},
               ]		,     )
           _A							:     Dict			=						generator.model.config.eos_token_id
           _A							:     List[str]			=						"""<pad>"""
           _A							:     Dict			=						generator(
               ["""This is a test""", """This is a second test"""]		,     do_sample=_a		,     num_return_sequences=2		,     batch_size=2		,     return_tensors=_a		,     )
           self.assertEqual(
               _a		,     [
                   [
                       {"""generated_token_ids""": ANY(torch.Tensor				)},
                       {"""generated_token_ids""": ANY(torch.Tensor				)},
                   ],
                   [
                       {"""generated_token_ids""": ANY(torch.Tensor				)},
                       {"""generated_token_ids""": ANY(torch.Tensor				)},
                   ],
               ]		,     )



     @require_tf
     def 					a__	(  self				)						->							int:
           _A							:     Optional[Any]			=						pipeline("""text2text-generation"""		,     model="""patrickvonplaten/t5-tiny-random"""		,     framework="""tf"""				)
           # do_sample=False necessary for reproducibility
           _A							:     str			=						generator("""Something there"""		,     do_sample=_a				)
           self.assertEqual(_a		,     [{"""generated_text""": """"""}]				)

