
'''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:
  a_     =  None

if is_ray_available():
  import ray  # noqa: E402 # isort:skip
  from distributed_ray_retriever import RagRayDistributedRetriever, RayRetriever  # noqa: E402 # isort:skip
else:
  a_     =  None
  a_     =  None
  a_     =  None

def   UpperCamelCase_       (		__SCREAMING_SNAKE_CASE    ):
    """simple docstring"""






    if not (is_datasets_available() and is_faiss_available() and is_psutil_available()):
        snake_case_	:      Any            =  unittest.skip("test requires Datasets, Faiss, psutil"    )(__SCREAMING_SNAKE_CASE    )
    return test_case






@require_distributed_retrieval
class 				UpperCAmelCase_			(		snake_case__   ):





 def     snake_case__						(      self):
     snake_case_	:      Union[str, Any]            =  tempfile.mkdtemp()
     snake_case_	:      str            =  8

     # DPR tok
     snake_case_	:      Dict            =  [
         "[UNK]",
         "[CLS]",
         "[SEP]",
         "[PAD]",
         "[MASK]",
         "want",
         "##want",
         "##ed",
         "wa",
         "un",
         "runn",
         "##ing",
         ",",
         "low",
         "lowest",
     ]
     snake_case_	:      Tuple            =  os.path.join(self.tmpdirname	,    "dpr_tokenizer")
     os.makedirs(lowercase_	,    exist_ok=lowercase_)
     snake_case_	:      Tuple            =  os.path.join(lowercase_	,    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
     snake_case_	:      List[str]            =  [
         "l",
         "o",
         "w",
         "e",
         "r",
         "s",
         "t",
         "i",
         "d",
         "n",
         "\u0120",
         "\u0120l",
         "\u0120n",
         "\u0120lo",
         "\u0120low",
         "er",
         "\u0120lowest",
         "\u0120newer",
         "\u0120wider",
         "<unk>",
     ]
     snake_case_	:      Any            =  dict(zip(lowercase_	,    range(len(lowercase_))))
     snake_case_	:      Any            =  ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
     snake_case_	:      Dict            =  {"unk_token": "<unk>"}

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





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





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





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





 def     snake_case__						(      self):
     snake_case_	:      List[Any]            =  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     snake_case__						(      self	,    lowercase_	,    lowercase_=1_23_45):
     snake_case_	:      int            =  self.get_dummy_dataset()
     snake_case_	:      Tuple            =  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:
         snake_case_	:      Optional[int]            =  dataset
         snake_case_	:      List[str]            =  RagPyTorchDistributedRetriever(
             lowercase_	,    question_encoder_tokenizer=self.get_dpr_tokenizer()	,    generator_tokenizer=self.get_bart_tokenizer()	,    )
         if init_retrieval:
             retriever.init_retrieval(lowercase_)
     return retriever





 def     snake_case__						(      self	,    lowercase_):
     # 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=lowercase_)
     snake_case_	:      Any            =  RagConfig(
         retrieval_vector_size=self.retrieval_vector_size	,    question_encoder=DPRConfig().to_dict()	,    generator=BartConfig().to_dict()	,    )
     snake_case_	:      Union[str, Any]            =  ray.remote(lowercase_)
     snake_case_	:      Tuple            =  [remote_cls.remote() for _ in range(1)]
     with patch("transformers.models.rag.retrieval_rag.load_dataset") as mock_load_dataset:
         snake_case_	:      Union[str, Any]            =  self.get_dummy_dataset()
         snake_case_	:      Union[str, Any]            =  RagRayDistributedRetriever(
             lowercase_	,    question_encoder_tokenizer=self.get_dpr_tokenizer()	,    generator_tokenizer=self.get_bart_tokenizer()	,    retrieval_workers=lowercase_	,    )
         if init_retrieval:
             retriever.init_retrieval()
     return retriever





 def     snake_case__						(      self	,    lowercase_	,    lowercase_	,    lowercase_=1_23_45):
     snake_case_	:      str            =  self.get_dummy_dataset()
     snake_case_	:      Optional[int]            =  RagConfig(
         retrieval_vector_size=self.retrieval_vector_size	,    question_encoder=DPRConfig().to_dict()	,    generator=BartConfig().to_dict()	,    index_name="custom"	,    )
     if from_disk:
         snake_case_	:      Optional[int]            =  os.path.join(self.tmpdirname	,    "dataset")
         snake_case_	:      Dict            =  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
         snake_case_	:      List[str]            =  RagPyTorchDistributedRetriever(
             lowercase_	,    question_encoder_tokenizer=self.get_dpr_tokenizer()	,    generator_tokenizer=self.get_bart_tokenizer()	,    )
     else:
         snake_case_	:      Optional[int]            =  RagPyTorchDistributedRetriever(
             lowercase_	,    question_encoder_tokenizer=self.get_dpr_tokenizer()	,    generator_tokenizer=self.get_bart_tokenizer()	,    index=CustomHFIndex(config.retrieval_vector_size	,    lowercase_)	,    )
     if init_retrieval:
         retriever.init_retrieval(lowercase_)
     return retriever





 def     snake_case__						(      self	,    lowercase_	,    lowercase_):
     # 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=lowercase_)
     snake_case_	:      List[Any]            =  self.get_dummy_dataset()
     snake_case_	:      Union[str, Any]            =  RagConfig(
         retrieval_vector_size=self.retrieval_vector_size	,    question_encoder=DPRConfig().to_dict()	,    generator=BartConfig().to_dict()	,    index_name="custom"	,    )
     snake_case_	:      Dict            =  ray.remote(lowercase_)
     snake_case_	:      Optional[int]            =  [remote_cls.remote() for _ in range(1)]
     if from_disk:
         snake_case_	:      int            =  os.path.join(self.tmpdirname	,    "dataset")
         snake_case_	:      int            =  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
         snake_case_	:      int            =  RagRayDistributedRetriever(
             lowercase_	,    question_encoder_tokenizer=self.get_dpr_tokenizer()	,    generator_tokenizer=self.get_bart_tokenizer()	,    retrieval_workers=lowercase_	,    index=CustomHFIndex.load_from_disk(
                 vector_size=config.retrieval_vector_size	,    dataset_path=config.passages_path	,    index_path=config.index_path	,    )	,    )
     else:
         snake_case_	:      Any            =  RagRayDistributedRetriever(
             lowercase_	,    question_encoder_tokenizer=self.get_dpr_tokenizer()	,    generator_tokenizer=self.get_bart_tokenizer()	,    retrieval_workers=lowercase_	,    index=CustomHFIndex(config.retrieval_vector_size	,    lowercase_)	,    )
     if init_retrieval:
         retriever.init_retrieval()
     return retriever





 def     snake_case__						(      self	,    lowercase_	,    lowercase_	,    lowercase_):
     snake_case_     ,	snake_case_     ,	snake_case_	:      Optional[Any]            =  retriever.retrieve(lowercase_	,    n_docs=lowercase_)
     self.assertEqual(retrieved_doc_embeds.shape	,    (2, n_docs, self.retrieval_vector_size))
     self.assertEqual(len(lowercase_)	,    2)
     self.assertEqual(sorted(doc_dicts[0])	,    ["embeddings", "id", "text", "title"])
     self.assertEqual(len(doc_dicts[0]["id"])	,    lowercase_)
     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     snake_case__						(      self):
     snake_case_	:      Dict            =  1
     snake_case_	:      Tuple            =  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=lowercase_)	,    lowercase_	,    lowercase_)





 def     snake_case__						(      self):
     snake_case_	:      Any            =  1
     snake_case_	:      Tuple            =  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=lowercase_	,    from_disk=lowercase_)	,    lowercase_	,    lowercase_	,    )





 def     snake_case__						(      self):
     snake_case_	:      List[Any]            =  1
     snake_case_	:      int            =  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=lowercase_	,    from_disk=lowercase_)	,    lowercase_	,    lowercase_	,    )





 @require_ray
 def     snake_case__						(      self):
     snake_case_	:      List[Any]            =  1
     snake_case_	:      Any            =  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=lowercase_)	,    lowercase_	,    lowercase_)
     ray.shutdown()





 @require_ray
 def     snake_case__						(      self):
     snake_case_	:      List[Any]            =  1
     snake_case_	:      Tuple            =  np.array(
         [np.ones(self.retrieval_vector_size), -np.ones(self.retrieval_vector_size)]	,    dtype=np.floataa)
     with self.assertRaises(lowercase_):
         self.distributed_retriever_check(
             self.get_dummy_custom_hf_index_ray_retriever(init_retrieval=lowercase_	,    from_disk=lowercase_)	,    lowercase_	,    lowercase_	,    )
     ray.shutdown()





 @require_ray
 def     snake_case__						(      self):
     snake_case_	:      str            =  1
     snake_case_	:      int            =  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=lowercase_	,    from_disk=lowercase_)	,    lowercase_	,    lowercase_)
     ray.shutdown()






'''simple docstring'''
from __future__ import annotations

from collections.abc import Callable

def   UpperCamelCase_       (		__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE = 1_0_0,						):
    """simple docstring"""






    snake_case_	:      Optional[Any]            =  x_start
    snake_case_	:      str            =  fnc(__SCREAMING_SNAKE_CASE    )
    snake_case_	:      List[str]            =  0.0

    for _ in range(__SCREAMING_SNAKE_CASE    ):
        # Approximates small segments of curve as linear and solve
        # for trapezoidal area
        snake_case_	:      List[str]            =  (x_end - x_start) / steps + xa
        snake_case_	:      Optional[int]            =  fnc(__SCREAMING_SNAKE_CASE    )
        area += abs(fxa + fxa    ) * (xa - xa) / 2

        # Increment step
        snake_case_	:      List[Any]            =  xa
        snake_case_	:      Optional[Any]            =  fxa
    return area


if __name__ == "__main__":

  def   UpperCamelCase_       (		__SCREAMING_SNAKE_CASE    ):
      """simple docstring"""






      return x**3

  print("f(x) = x^3")
  print("The area between the curve, x = -10, x = 10 and the x axis is:")
  a_     =  10
  while i <= 10_0000:
    a_     =  trapezoidal_area(f, -5, 5, i)
    print(f"""with {i} steps: {area}""")
    i *= 10




