

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__     :					Optional[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__     :					Tuple								=			None
       UpperCamelCase__     :					Union[str, Any]								=			None
       UpperCamelCase__     :					Optional[Any]								=			None


def     _UpperCAmelCase				(     _SCREAMING_SNAKE_CASE    :						Tuple				):





    """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 					__snake_case     (			lowerCAmelCase__				):

  def   lowerCAmelCase__   (    self):
      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   lowerCAmelCase__   (    self):
      return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname  ,						'dpr_tokenizer'))

  def   lowerCAmelCase__   (    self):
      return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname  ,						'bart_tokenizer'))

  def   lowerCAmelCase__   (    self):
      shutil.rmtree(self.tmpdirname)

  def   lowerCAmelCase__   (    self):
      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   lowerCAmelCase__   (    self  ,						_A  ,						_A=12345):
      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   lowerCAmelCase__   (    self  ,						_A):
      # 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   lowerCAmelCase__   (    self  ,						_A  ,						_A  ,						_A=12345):
      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   lowerCAmelCase__   (    self  ,						_A  ,						_A):
      # 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   lowerCAmelCase__   (    self  ,						_A  ,						_A  ,						_A):
      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   lowerCAmelCase__   (    self):
      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   lowerCAmelCase__   (    self):
      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   lowerCAmelCase__   (    self):
      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   lowerCAmelCase__   (    self):
      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   lowerCAmelCase__   (    self):
      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   lowerCAmelCase__   (    self):
      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()





import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig


UpperCamelCase__     :					Optional[Any]								=			logging.get_logger(__name__)

UpperCamelCase__     :					List[Any]								=			{
    "facebook/musicgen-small": "https://huggingface.co/facebook/musicgen-small/resolve/main/config.json",
    # See all Musicgen models at https://huggingface.co/models?filter=musicgen
}



class 					__snake_case     (			lowerCAmelCase__				):
  __lowerCAmelCase   :		Any         =     'musicgen_decoder'
  __lowerCAmelCase   :		Optional[Any]         =     ['past_key_values']

  def __init__(    self  ,						_A=2048  ,						_A=2048  ,						_A=24  ,						_A=4096  ,						_A=16  ,						_A=0.0  ,						_A=True  ,						_A="gelu"  ,						_A=1024  ,						_A=0.1  ,						_A=0.0  ,						_A=0.0  ,						_A=0.0_2  ,						_A=False  ,						_A=4  ,						_A=2048  ,						_A=2048  ,						_A=None  ,						_A=False  ,						**_A  ,						):
      SCREAMING_SNAKE_CASE_													=  vocab_size
      SCREAMING_SNAKE_CASE_													=  max_position_embeddings
      SCREAMING_SNAKE_CASE_													=  hidden_size
      SCREAMING_SNAKE_CASE_													=  ffn_dim
      SCREAMING_SNAKE_CASE_													=  num_hidden_layers
      SCREAMING_SNAKE_CASE_													=  num_attention_heads
      SCREAMING_SNAKE_CASE_													=  dropout
      SCREAMING_SNAKE_CASE_													=  attention_dropout
      SCREAMING_SNAKE_CASE_													=  activation_dropout
      SCREAMING_SNAKE_CASE_													=  activation_function
      SCREAMING_SNAKE_CASE_													=  initializer_factor
      SCREAMING_SNAKE_CASE_													=  layerdrop
      SCREAMING_SNAKE_CASE_													=  use_cache
      SCREAMING_SNAKE_CASE_													=  scale_embedding  # scale factor will be sqrt(d_model) if True
      SCREAMING_SNAKE_CASE_													=  num_codebooks
      super().__init__(
          pad_token_id=_A  ,						bos_token_id=_A  ,						eos_token_id=_A  ,						tie_word_embeddings=_A  ,						**_A  ,						)






class 					__snake_case     (			lowerCAmelCase__				):
  __lowerCAmelCase   :		int         =     'musicgen'
  __lowerCAmelCase   :		Optional[Any]         =     True

  def __init__(    self  ,						**_A):
      super().__init__(**_A)
      if "text_encoder" not in kwargs or "audio_encoder" not in kwargs or "decoder" not in kwargs:
          raise ValueError('Config has to be initialized with text_encoder, audio_encoder and decoder config')

      SCREAMING_SNAKE_CASE_													=  kwargs.pop('text_encoder')
      SCREAMING_SNAKE_CASE_													=  text_encoder_config.pop('model_type')

      SCREAMING_SNAKE_CASE_													=  kwargs.pop('audio_encoder')
      SCREAMING_SNAKE_CASE_													=  audio_encoder_config.pop('model_type')

      SCREAMING_SNAKE_CASE_													=  kwargs.pop('decoder')

      SCREAMING_SNAKE_CASE_													=  AutoConfig.for_model(_A  ,						**_A)
      SCREAMING_SNAKE_CASE_													=  AutoConfig.for_model(_A  ,						**_A)
      SCREAMING_SNAKE_CASE_													=  MusicgenDecoderConfig(**_A)
      SCREAMING_SNAKE_CASE_													=  True

  @classmethod
  def   lowerCAmelCase__   (    cls  ,						_A  ,						_A  ,						_A  ,						**_A  ,						):
      return cls(
          text_encoder=text_encoder_config.to_dict()  ,						audio_encoder=audio_encoder_config.to_dict()  ,						decoder=decoder_config.to_dict()  ,						**_A  ,						)


  def   lowerCAmelCase__   (    self):
      SCREAMING_SNAKE_CASE_													=  copy.deepcopy(self.__dict__)
      SCREAMING_SNAKE_CASE_													=  self.text_encoder.to_dict()
      SCREAMING_SNAKE_CASE_													=  self.audio_encoder.to_dict()
      SCREAMING_SNAKE_CASE_													=  self.decoder.to_dict()
      SCREAMING_SNAKE_CASE_													=  self.__class__.model_type
      return output


