




'''simple docstring'''



import subprocess
from typing import Union

import numpy as np
import requests

from ..utils import add_end_docstrings, is_torch_available, is_torchaudio_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_torch_available():
 from ..models.auto.modeling_auto import MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING

lowerCamelCase_	 =		logging.get_logger(__name__)
def 				SCREAMING_SNAKE_CASE_				(							__A       :				bytes ,  __A       :				int						)				->				np.array:
   _SCREAMING_SNAKE_CASE =   f"""{sampling_rate}"""
   _SCREAMING_SNAKE_CASE =   "1"
   _SCREAMING_SNAKE_CASE =   "f32le"
   _SCREAMING_SNAKE_CASE =   [
       "ffmpeg",
       "-i",
       "pipe:0",
       "-ac",
       ac,
       "-ar",
       ar,
       "-f",
       format_for_conversion,
       "-hide_banner",
       "-loglevel",
       "quiet",
       "pipe:1",
   ]

   try:
      _SCREAMING_SNAKE_CASE =   subprocess.Popen(__A ,  stdin=subprocess.PIPE ,  stdout=subprocess.PIPE						)
   except FileNotFoundError:
      raise ValueError("ffmpeg was not found but is required to load audio files from filename"						)
   _SCREAMING_SNAKE_CASE =   ffmpeg_process.communicate(__A						)
   _SCREAMING_SNAKE_CASE =   output_stream[0]

   _SCREAMING_SNAKE_CASE =   np.frombuffer(__A ,  np.floataa						)
   if audio.shape[0] == 0:
      raise ValueError("Malformed soundfile"						)
   return audio







@add_end_docstrings(A     )
class 	lowercase_					(				A     ):


   """simple docstring"""

   def __init__(					self      :			Tuple						,   *__lowerCamelCase      :			str						,   **__lowerCamelCase      :			Tuple					):

      """simple docstring"""
      # Default, might be overriden by the model.config.
      _SCREAMING_SNAKE_CASE =   5
      super().__init__(*__lowerCamelCase						,   **__lowerCamelCase					)

      if self.framework != "pt":
         raise ValueError(F"""The {self.__class__} is only available in PyTorch."""					)

      self.check_model_type(__lowerCamelCase					)



   def __call__(					self      :			List[Any]						,   __lowerCamelCase      :			Union[np.ndarray, bytes, str]						,   **__lowerCamelCase      :			Tuple						,   ):

      """simple docstring"""
      return super().__call__(__lowerCamelCase						,   **__lowerCamelCase					)



   def 							lowerCAmelCase_    (					self      :			str						,   __lowerCamelCase      :			Optional[int]=None						,   **__lowerCamelCase      :			Optional[int]					):

      """simple docstring"""
      # No parameters on this pipeline right now
      _SCREAMING_SNAKE_CASE =   {}
      if top_k is not None:
         if top_k > self.model.config.num_labels:
            _SCREAMING_SNAKE_CASE =   self.model.config.num_labels
         _SCREAMING_SNAKE_CASE =   top_k
      return {}, {}, postprocess_params



   def 							lowerCAmelCase_    (					self      :			Optional[int]						,   __lowerCamelCase      :			str					):

      """simple docstring"""
      if isinstance(__lowerCamelCase						,   __lowerCamelCase					):
         if inputs.startswith("http://"					) or inputs.startswith("https://"					):
            # We need to actually check for a real protocol, otherwise it's impossible to use a local file
            # like http_huggingface_co.png
            _SCREAMING_SNAKE_CASE =   requests.get(__lowerCamelCase					).content
         else:
            with open(__lowerCamelCase						,   "rb"					) as f:
               _SCREAMING_SNAKE_CASE =   f.read()

      if isinstance(__lowerCamelCase						,   __lowerCamelCase					):
         _SCREAMING_SNAKE_CASE =   ffmpeg_read(__lowerCamelCase						,   self.feature_extractor.sampling_rate					)

      if isinstance(__lowerCamelCase						,   __lowerCamelCase					):
         # Accepting `"array"` which is the key defined in `datasets` for
         # better integration
         if not ("sampling_rate" in inputs and ("raw" in inputs or "array" in inputs)):
            raise ValueError(
                "When passing a dictionary to AudioClassificationPipeline, the dict needs to contain a "
                "\"raw\" key containing the numpy array representing the audio and a \"sampling_rate\" key, "
                "containing the sampling_rate associated with that array"					)

         _SCREAMING_SNAKE_CASE =   inputs.pop("raw"						,   __lowerCamelCase					)
         if _inputs is None:
            # Remove path which will not be used from `datasets`.
            inputs.pop("path"						,   __lowerCamelCase					)
            _SCREAMING_SNAKE_CASE =   inputs.pop("array"						,   __lowerCamelCase					)
         _SCREAMING_SNAKE_CASE =   inputs.pop("sampling_rate"					)
         _SCREAMING_SNAKE_CASE =   _inputs
         if in_sampling_rate != self.feature_extractor.sampling_rate:
            import torch

            if is_torchaudio_available():
               from torchaudio import functional as F
            else:
               raise ImportError(
                   "torchaudio is required to resample audio samples in AudioClassificationPipeline. "
                   "The torchaudio package can be installed through: `pip install torchaudio`."					)

            _SCREAMING_SNAKE_CASE =   F.resample(
                torch.from_numpy(__lowerCamelCase					)						,   __lowerCamelCase						,   self.feature_extractor.sampling_rate					).numpy()

      if not isinstance(__lowerCamelCase						,   np.ndarray					):
         raise ValueError("We expect a numpy ndarray as input"					)
      if len(inputs.shape					) != 1:
         raise ValueError("We expect a single channel audio input for AudioClassificationPipeline"					)

      _SCREAMING_SNAKE_CASE =   self.feature_extractor(
          __lowerCamelCase						,   sampling_rate=self.feature_extractor.sampling_rate						,   return_tensors="pt"					)
      return processed



   def 							lowerCAmelCase_    (					self      :			Any						,   __lowerCamelCase      :			Optional[Any]					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   self.model(**__lowerCamelCase					)
      return model_outputs



   def 							lowerCAmelCase_    (					self      :			Union[str, Any]						,   __lowerCamelCase      :			int						,   __lowerCamelCase      :			Any=5					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   model_outputs.logits[0].softmax(-1					)
      _SCREAMING_SNAKE_CASE, _SCREAMING_SNAKE_CASE =   probs.topk(__lowerCamelCase					)

      _SCREAMING_SNAKE_CASE =   scores.tolist()
      _SCREAMING_SNAKE_CASE =   ids.tolist()

      _SCREAMING_SNAKE_CASE =   [{"score": score, "label": self.model.config.idalabel[_id]} for score, _id in zip(__lowerCamelCase						,   __lowerCamelCase					)]

      return labels







'''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:
 lowerCamelCase_	 =		None

if is_ray_available():
 import ray  # noqa: E402 # isort:skip
 from distributed_ray_retriever import RagRayDistributedRetriever, RayRetriever  # noqa: E402 # isort:skip
else:
 lowerCamelCase_	 =		None
 lowerCamelCase_	 =		None
 lowerCamelCase_	 =		None
def 				SCREAMING_SNAKE_CASE_				(							__A       :				Any						)				->				Union[str, Any]:
   if not (is_datasets_available() and is_faiss_available() and is_psutil_available()):
      _SCREAMING_SNAKE_CASE =   unittest.skip("test requires Datasets, Faiss, psutil"						)(__A						)
   return test_case







@require_distributed_retrieval
class 	lowercase_					(				A     ):


   """simple docstring"""

   def 							lowerCAmelCase_    (					self      :			List[str]					):

      """simple docstring"""
      _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(__lowerCamelCase						,   exist_ok=__lowerCamelCase					)
      _SCREAMING_SNAKE_CASE =   os.path.join(__lowerCamelCase						,   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(__lowerCamelCase						,   range(len(__lowerCamelCase					)					)					)					)
      _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(__lowerCamelCase						,   exist_ok=__lowerCamelCase					)
      _SCREAMING_SNAKE_CASE =   os.path.join(__lowerCamelCase						,   BART_VOCAB_FILES_NAMES["vocab_file"]					)
      _SCREAMING_SNAKE_CASE =   os.path.join(__lowerCamelCase						,   BART_VOCAB_FILES_NAMES["merges_file"]					)
      with open(self.vocab_file						,   "w"						,   encoding="utf-8"					) as fp:
         fp.write(json.dumps(__lowerCamelCase					) + "\n"					)
      with open(self.merges_file						,   "w"						,   encoding="utf-8"					) as fp:
         fp.write("\n".join(__lowerCamelCase					)					)



   def 							lowerCAmelCase_    (					self      :			List[Any]					):

      """simple docstring"""
      return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname						,   "dpr_tokenizer"					)					)



   def 							lowerCAmelCase_    (					self      :			Any					):

      """simple docstring"""
      return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname						,   "bart_tokenizer"					)					)



   def 							lowerCAmelCase_    (					self      :			List[Any]					):

      """simple docstring"""
      shutil.rmtree(self.tmpdirname					)



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

      """simple docstring"""
      _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      :			List[Any]						,   __lowerCamelCase      :			bool						,   __lowerCamelCase      :			Optional[Any]=1_2_3_4_5					):

      """simple docstring"""
      _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(
             __lowerCamelCase						,   question_encoder_tokenizer=self.get_dpr_tokenizer()						,   generator_tokenizer=self.get_bart_tokenizer()						,   )
         if init_retrieval:
            retriever.init_retrieval(__lowerCamelCase					)
      return retriever



   def 							lowerCAmelCase_    (					self      :			Union[str, Any]						,   __lowerCamelCase      :			bool					):

      """simple docstring"""
      # 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=__lowerCamelCase					)
      _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(__lowerCamelCase					)
      _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(
             __lowerCamelCase						,   question_encoder_tokenizer=self.get_dpr_tokenizer()						,   generator_tokenizer=self.get_bart_tokenizer()						,   retrieval_workers=__lowerCamelCase						,   )
         if init_retrieval:
            retriever.init_retrieval()
      return retriever



   def 							lowerCAmelCase_    (					self      :			Dict						,   __lowerCamelCase      :			bool						,   __lowerCamelCase      :			bool						,   __lowerCamelCase      :			List[str]=1_2_3_4_5					):

      """simple docstring"""
      _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(
             __lowerCamelCase						,   question_encoder_tokenizer=self.get_dpr_tokenizer()						,   generator_tokenizer=self.get_bart_tokenizer()						,   )
      else:
         _SCREAMING_SNAKE_CASE =   RagPyTorchDistributedRetriever(
             __lowerCamelCase						,   question_encoder_tokenizer=self.get_dpr_tokenizer()						,   generator_tokenizer=self.get_bart_tokenizer()						,   index=CustomHFIndex(config.retrieval_vector_size						,   __lowerCamelCase					)						,   )
      if init_retrieval:
         retriever.init_retrieval(__lowerCamelCase					)
      return retriever



   def 							lowerCAmelCase_    (					self      :			List[Any]						,   __lowerCamelCase      :			bool						,   __lowerCamelCase      :			bool					):

      """simple docstring"""
      # 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=__lowerCamelCase					)
      _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(__lowerCamelCase					)
      _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(
             __lowerCamelCase						,   question_encoder_tokenizer=self.get_dpr_tokenizer()						,   generator_tokenizer=self.get_bart_tokenizer()						,   retrieval_workers=__lowerCamelCase						,   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(
             __lowerCamelCase						,   question_encoder_tokenizer=self.get_dpr_tokenizer()						,   generator_tokenizer=self.get_bart_tokenizer()						,   retrieval_workers=__lowerCamelCase						,   index=CustomHFIndex(config.retrieval_vector_size						,   __lowerCamelCase					)						,   )
      if init_retrieval:
         retriever.init_retrieval()
      return retriever



   def 							lowerCAmelCase_    (					self      :			int						,   __lowerCamelCase      :			RagRetriever						,   __lowerCamelCase      :			np.array						,   __lowerCamelCase      :			int					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE, _SCREAMING_SNAKE_CASE, _SCREAMING_SNAKE_CASE =   retriever.retrieve(__lowerCamelCase						,   n_docs=__lowerCamelCase					)
      self.assertEqual(retrieved_doc_embeds.shape						,   (2, n_docs, self.retrieval_vector_size)					)
      self.assertEqual(len(__lowerCamelCase					)						,   2					)
      self.assertEqual(sorted(doc_dicts[0]					)						,   ["embeddings", "id", "text", "title"]					)
      self.assertEqual(len(doc_dicts[0]["id"]					)						,   __lowerCamelCase					)
      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      :			Optional[Any]					):

      """simple docstring"""
      _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=__lowerCamelCase					)						,   __lowerCamelCase						,   __lowerCamelCase					)



   def 							lowerCAmelCase_    (					self      :			Any					):

      """simple docstring"""
      _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=__lowerCamelCase						,   from_disk=__lowerCamelCase					)						,   __lowerCamelCase						,   __lowerCamelCase						,   )



   def 							lowerCAmelCase_    (					self      :			int					):

      """simple docstring"""
      _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=__lowerCamelCase						,   from_disk=__lowerCamelCase					)						,   __lowerCamelCase						,   __lowerCamelCase						,   )



   @require_ray
   def 							lowerCAmelCase_    (					self      :			Optional[int]					):

      """simple docstring"""
      _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=__lowerCamelCase					)						,   __lowerCamelCase						,   __lowerCamelCase					)
      ray.shutdown()



   @require_ray
   def 							lowerCAmelCase_    (					self      :			Any					):

      """simple docstring"""
      _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(__lowerCamelCase					):
         self.distributed_retriever_check(
             self.get_dummy_custom_hf_index_ray_retriever(init_retrieval=__lowerCamelCase						,   from_disk=__lowerCamelCase					)						,   __lowerCamelCase						,   __lowerCamelCase						,   )
      ray.shutdown()



   @require_ray
   def 							lowerCAmelCase_    (					self      :			Optional[int]					):

      """simple docstring"""
      _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=__lowerCamelCase						,   from_disk=__lowerCamelCase					)						,   __lowerCamelCase						,   __lowerCamelCase					)
      ray.shutdown()

