






"""simple docstring"""
import json
import os
import pickle
import shutil
import tempfile
from unittest import TestCase
from unittest.mock import patch

import numpy as np
from datasets import Dataset

from transformers import is_faiss_available
from transformers.models.bart.configuration_bart import BartConfig
from transformers.models.bart.tokenization_bart import BartTokenizer
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES as DPR_VOCAB_FILES_NAMES
from transformers.models.dpr.configuration_dpr import DPRConfig
from transformers.models.dpr.tokenization_dpr import DPRContextEncoderTokenizer, DPRQuestionEncoderTokenizer
from transformers.models.rag.configuration_rag import RagConfig
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_faiss, require_sentencepiece, require_tokenizers, require_torch


if is_faiss_available():
			import faiss







@require_faiss
class      UpperCamelCase_			(__A	):





	def 	_SCREAMING_SNAKE_CASE ( self   :					Dict		)		->     int:
				UpperCAmelCase_	:					str     	=   tempfile.mkdtemp()
				UpperCAmelCase_	:					Dict     	=   8

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

				UpperCAmelCase_	:					int     	=   os.path.join(self.tmpdirname      ,      "bart_tokenizer"		)
				os.makedirs(lowerCAmelCase_      ,      exist_ok=lowerCAmelCase_		)
				UpperCAmelCase_	:					Tuple     	=   os.path.join(lowerCAmelCase_      ,      BART_VOCAB_FILES_NAMES["vocab_file"]		)
				UpperCAmelCase_	:					Tuple     	=   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 	_SCREAMING_SNAKE_CASE ( self   :					Any		)		->     DPRQuestionEncoderTokenizer:
				return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname      ,      "dpr_tokenizer"		)		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					int		)		->     DPRContextEncoderTokenizer:
				return DPRContextEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname      ,      "dpr_tokenizer"		)		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					List[str]		)		->     BartTokenizer:
				return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname      ,      "bart_tokenizer"		)		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					Any		)		->     Tuple:
				shutil.rmtree(self.tmpdirname		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					Union[str, Any]		)		->     Tuple:
				UpperCAmelCase_	:					Union[str, 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 	_SCREAMING_SNAKE_CASE ( self   :					Optional[Any]		)		->     str:
				UpperCAmelCase_	:					int     	=   self.get_dummy_dataset()
				UpperCAmelCase_	:					Dict     	=   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:
							UpperCAmelCase_	:					List[str]     	=   dataset
							UpperCAmelCase_	:					Dict     	=   RagRetriever(
							    lowerCAmelCase_      ,      question_encoder_tokenizer=self.get_dpr_tokenizer()      ,      generator_tokenizer=self.get_bart_tokenizer()      ,      )
				return retriever





	def 	_SCREAMING_SNAKE_CASE ( self   :					Optional[int]      ,      lowerCAmelCase_   :					bool		)		->     List[Any]:
				UpperCAmelCase_	:					Optional[Any]     	=   self.get_dummy_dataset()
				UpperCAmelCase_	:					str     	=   RagConfig(
				    retrieval_vector_size=self.retrieval_vector_size      ,      question_encoder=DPRConfig().to_dict()      ,      generator=BartConfig().to_dict()      ,      index_name="custom"      ,      )
				if from_disk:
							UpperCAmelCase_	:					List[str]     	=   os.path.join(self.tmpdirname      ,      "dataset"		)
							UpperCAmelCase_	:					List[Any]     	=   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
							UpperCAmelCase_	:					Optional[Any]     	=   RagRetriever(
							    lowerCAmelCase_      ,      question_encoder_tokenizer=self.get_dpr_tokenizer()      ,      generator_tokenizer=self.get_bart_tokenizer()      ,      )
				else:
							UpperCAmelCase_	:					List[Any]     	=   RagRetriever(
							    lowerCAmelCase_      ,      question_encoder_tokenizer=self.get_dpr_tokenizer()      ,      generator_tokenizer=self.get_bart_tokenizer()      ,      index=CustomHFIndex(config.retrieval_vector_size      ,      lowerCAmelCase_		)      ,      )
				return retriever





	def 	_SCREAMING_SNAKE_CASE ( self   :					Optional[int]		)		->     int:
				UpperCAmelCase_	:					Any     	=   Dataset.from_dict(
				    {
				        "id": ["0", "1"],
				        "text": ["foo", "bar"],
				        "title": ["Foo", "Bar"],
				        "embeddings": [np.ones(self.retrieval_vector_size + 1		), 2 * np.ones(self.retrieval_vector_size + 1		)],
				    }		)
				dataset.add_faiss_index("embeddings"      ,      string_factory="Flat"      ,      metric_type=faiss.METRIC_INNER_PRODUCT		)

				UpperCAmelCase_	:					Union[str, Any]     	=   os.path.join(self.tmpdirname      ,      "hf_bert_base.hnswSQ8_correct_phi_128.c_index"		)
				dataset.save_faiss_index("embeddings"      ,      index_file_name + ".index.dpr"		)
				pickle.dump(dataset["id"]      ,      open(index_file_name + ".index_meta.dpr"      ,      "wb"		)		)

				UpperCAmelCase_	:					Tuple     	=   os.path.join(self.tmpdirname      ,      "psgs_w100.tsv.pkl"		)
				UpperCAmelCase_	:					Optional[Any]     	=   {sample["id"]: [sample["text"], sample["title"]] for sample in dataset}
				pickle.dump(lowerCAmelCase_      ,      open(lowerCAmelCase_      ,      "wb"		)		)

				UpperCAmelCase_	:					int     	=   RagConfig(
				    retrieval_vector_size=self.retrieval_vector_size      ,      question_encoder=DPRConfig().to_dict()      ,      generator=BartConfig().to_dict()      ,      index_name="legacy"      ,      index_path=self.tmpdirname      ,      )
				UpperCAmelCase_	:					Optional[int]     	=   RagRetriever(
				    lowerCAmelCase_      ,      question_encoder_tokenizer=self.get_dpr_tokenizer()      ,      generator_tokenizer=self.get_bart_tokenizer()		)
				return retriever





	def 	_SCREAMING_SNAKE_CASE ( self   :					Dict		)		->     Tuple:
				UpperCAmelCase_	:					Dict     	=   1
				UpperCAmelCase_	:					Dict     	=   self.get_dummy_canonical_hf_index_retriever()
				UpperCAmelCase_	:					Dict     	=   np.array(
				    [np.ones(self.retrieval_vector_size		), -np.ones(self.retrieval_vector_size		)]      ,      dtype=np.floataa		)
				UpperCAmelCase_	,      UpperCAmelCase_	,      UpperCAmelCase_	:					Dict     	=   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 	_SCREAMING_SNAKE_CASE ( self   :					int		)		->     Any:
				UpperCAmelCase_	:					Optional[int]     	=   self.get_dummy_canonical_hf_index_retriever()
				with tempfile.TemporaryDirectory() as tmp_dirname:
							with patch("transformers.models.rag.retrieval_rag.load_dataset"		) as mock_load_dataset:
										UpperCAmelCase_	:					Dict     	=   self.get_dummy_dataset()
										retriever.save_pretrained(lowerCAmelCase_		)
										UpperCAmelCase_	:					Dict     	=   RagRetriever.from_pretrained(lowerCAmelCase_		)
										self.assertIsInstance(lowerCAmelCase_      ,      lowerCAmelCase_		)
										UpperCAmelCase_	:					Tuple     	=   np.array(
										    [np.ones(self.retrieval_vector_size		), -np.ones(self.retrieval_vector_size		)]      ,      dtype=np.floataa		)
										UpperCAmelCase_	:					Tuple     	=   retriever.retrieve(lowerCAmelCase_      ,      n_docs=1		)
										self.assertTrue(out is not None		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					Any		)		->     str:
				UpperCAmelCase_	:					Union[str, Any]     	=   1
				UpperCAmelCase_	:					Optional[int]     	=   self.get_dummy_custom_hf_index_retriever(from_disk=lowerCAmelCase_		)
				UpperCAmelCase_	:					Tuple     	=   np.array(
				    [np.ones(self.retrieval_vector_size		), -np.ones(self.retrieval_vector_size		)]      ,      dtype=np.floataa		)
				UpperCAmelCase_	,      UpperCAmelCase_	,      UpperCAmelCase_	:					Union[str, Any]     	=   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 	_SCREAMING_SNAKE_CASE ( self   :					List[Any]		)		->     Tuple:
				UpperCAmelCase_	:					Union[str, Any]     	=   self.get_dummy_custom_hf_index_retriever(from_disk=lowerCAmelCase_		)
				with tempfile.TemporaryDirectory() as tmp_dirname:
							retriever.save_pretrained(lowerCAmelCase_		)
							UpperCAmelCase_	:					Dict     	=   RagRetriever.from_pretrained(lowerCAmelCase_		)
							self.assertIsInstance(lowerCAmelCase_      ,      lowerCAmelCase_		)
							UpperCAmelCase_	:					List[Any]     	=   np.array(
							    [np.ones(self.retrieval_vector_size		), -np.ones(self.retrieval_vector_size		)]      ,      dtype=np.floataa		)
							UpperCAmelCase_	:					Any     	=   retriever.retrieve(lowerCAmelCase_      ,      n_docs=1		)
							self.assertTrue(out is not None		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					Any		)		->     Any:
				UpperCAmelCase_	:					Optional[int]     	=   1
				UpperCAmelCase_	:					Dict     	=   self.get_dummy_custom_hf_index_retriever(from_disk=lowerCAmelCase_		)
				UpperCAmelCase_	:					Optional[Any]     	=   np.array(
				    [np.ones(self.retrieval_vector_size		), -np.ones(self.retrieval_vector_size		)]      ,      dtype=np.floataa		)
				UpperCAmelCase_	,      UpperCAmelCase_	,      UpperCAmelCase_	:					Optional[int]     	=   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 	_SCREAMING_SNAKE_CASE ( self   :					Dict		)		->     Tuple:
				UpperCAmelCase_	:					Optional[int]     	=   self.get_dummy_custom_hf_index_retriever(from_disk=lowerCAmelCase_		)
				with tempfile.TemporaryDirectory() as tmp_dirname:
							retriever.save_pretrained(lowerCAmelCase_		)
							UpperCAmelCase_	:					List[Any]     	=   RagRetriever.from_pretrained(lowerCAmelCase_		)
							self.assertIsInstance(lowerCAmelCase_      ,      lowerCAmelCase_		)
							UpperCAmelCase_	:					Optional[int]     	=   np.array(
							    [np.ones(self.retrieval_vector_size		), -np.ones(self.retrieval_vector_size		)]      ,      dtype=np.floataa		)
							UpperCAmelCase_	:					str     	=   retriever.retrieve(lowerCAmelCase_      ,      n_docs=1		)
							self.assertTrue(out is not None		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					str		)		->     Any:
				UpperCAmelCase_	:					str     	=   1
				UpperCAmelCase_	:					int     	=   self.get_dummy_legacy_index_retriever()
				UpperCAmelCase_	:					Any     	=   np.array(
				    [np.ones(self.retrieval_vector_size		), -np.ones(self.retrieval_vector_size		)]      ,      dtype=np.floataa		)
				UpperCAmelCase_	,      UpperCAmelCase_	,      UpperCAmelCase_	:					int     	=   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]		)      ,      ["text", "title"]		)
				self.assertEqual(len(doc_dicts[0]["text"]		)      ,      lowerCAmelCase_		)
				self.assertEqual(doc_dicts[0]["text"][0]      ,      "bar"		)  # max inner product is reached with second doc
				self.assertEqual(doc_dicts[1]["text"][0]      ,      "foo"		)  # max inner product is reached with first doc
				self.assertListEqual(doc_ids.tolist()      ,      [[1], [0]]		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					Dict		)		->     int:
				UpperCAmelCase_	:					Optional[int]     	=   self.get_dummy_legacy_index_retriever()
				with tempfile.TemporaryDirectory() as tmp_dirname:
							retriever.save_pretrained(lowerCAmelCase_		)
							UpperCAmelCase_	:					Dict     	=   RagRetriever.from_pretrained(lowerCAmelCase_		)
							self.assertIsInstance(lowerCAmelCase_      ,      lowerCAmelCase_		)
							UpperCAmelCase_	:					List[str]     	=   np.array(
							    [np.ones(self.retrieval_vector_size		), -np.ones(self.retrieval_vector_size		)]      ,      dtype=np.floataa		)
							UpperCAmelCase_	:					int     	=   retriever.retrieve(lowerCAmelCase_      ,      n_docs=1		)
							self.assertTrue(out is not None		)





	@require_torch
	@require_tokenizers
	@require_sentencepiece
	def 	_SCREAMING_SNAKE_CASE ( self   :					Optional[int]		)		->     List[Any]:
				import torch

				UpperCAmelCase_	:					Any     	=   1
				UpperCAmelCase_	:					List[Any]     	=   self.get_dummy_canonical_hf_index_retriever()
				UpperCAmelCase_	:					str     	=   [[5, 7], [10, 11]]
				UpperCAmelCase_	:					Any     	=   np.array(
				    [np.ones(self.retrieval_vector_size		), -np.ones(self.retrieval_vector_size		)]      ,      dtype=np.floataa		)
				UpperCAmelCase_	:					Optional[Any]     	=   retriever(lowerCAmelCase_      ,      lowerCAmelCase_      ,      prefix=retriever.config.generator.prefix      ,      n_docs=lowerCAmelCase_		)
				UpperCAmelCase_	,      UpperCAmelCase_	,      UpperCAmelCase_	:					str     	=   (
				    out["context_input_ids"],
				    out["context_attention_mask"],
				    out["retrieved_doc_embeds"],
				)
				self.assertEqual(retrieved_doc_embeds.shape      ,      (2, n_docs, self.retrieval_vector_size)		)
				self.assertIsInstance(lowerCAmelCase_      ,      lowerCAmelCase_		)
				self.assertIsInstance(lowerCAmelCase_      ,      lowerCAmelCase_		)
				self.assertIsInstance(lowerCAmelCase_      ,      np.ndarray		)

				UpperCAmelCase_	:					List[str]     	=   retriever(
				    lowerCAmelCase_      ,      lowerCAmelCase_      ,      prefix=retriever.config.generator.prefix      ,      n_docs=lowerCAmelCase_      ,      return_tensors="pt"      ,      )
				UpperCAmelCase_	,      UpperCAmelCase_	,      UpperCAmelCase_	,      UpperCAmelCase_	:					Dict     	=   (  # noqa: F841
				    out["context_input_ids"],
				    out["context_attention_mask"],
				    out["retrieved_doc_embeds"],
				    out["doc_ids"],
				)
				self.assertEqual(retrieved_doc_embeds.shape      ,      (2, n_docs, self.retrieval_vector_size)		)
				self.assertIsInstance(lowerCAmelCase_      ,      torch.Tensor		)
				self.assertIsInstance(lowerCAmelCase_      ,      torch.Tensor		)
				self.assertIsInstance(lowerCAmelCase_      ,      torch.Tensor		)





	@require_torch
	@require_tokenizers
	@require_sentencepiece
	def 	_SCREAMING_SNAKE_CASE ( self   :					List[Any]		)		->     Tuple:
				UpperCAmelCase_	:					Optional[Any]     	=   self.get_dpr_ctx_encoder_tokenizer()
				UpperCAmelCase_	:					List[Any]     	=   1
				UpperCAmelCase_	:					Union[str, Any]     	=   self.get_dummy_custom_hf_index_retriever(from_disk=lowerCAmelCase_		)
				retriever.set_ctx_encoder_tokenizer(lowerCAmelCase_		)

				UpperCAmelCase_	:					Optional[Any]     	=   [[5, 7], [10, 11]]
				UpperCAmelCase_	:					Union[str, Any]     	=   np.array(
				    [np.ones(self.retrieval_vector_size		), -np.ones(self.retrieval_vector_size		)]      ,      dtype=np.floataa		)
				UpperCAmelCase_	:					List[str]     	=   retriever(lowerCAmelCase_      ,      lowerCAmelCase_      ,      prefix=retriever.config.generator.prefix      ,      n_docs=lowerCAmelCase_		)

				self.assertEqual(
				    len(lowerCAmelCase_		)      ,      6		)  # check whether the retriever output consist of 6 attributes including tokenized docs
				self.assertEqual(
				    all(k in out for k in ("tokenized_doc_ids", "tokenized_doc_attention_mask")		)      ,      lowerCAmelCase_		)  # check for doc token related keys in dictionary.











"""simple docstring"""
import timeit

import numpy as np

import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features.features import _ArrayXD

def 							snake_case (  A__		):
			def wrapper(*A__      ,**A__		):
						UpperCAmelCase_	:					Union[str, Any]     	=   timeit.default_timer()
						UpperCAmelCase_	:					Union[str, Any]     	=   func(*A__      ,**A__		)
						UpperCAmelCase_	:					Dict     	=   timeit.default_timer() - starttime
						return delta

			UpperCAmelCase_	:					Optional[Any]     	=   func.__name__

			return wrapper

def 							snake_case (  A__      ,A__=1_00      ,A__=None		):
			UpperCAmelCase_	:					str     	=   []
			UpperCAmelCase_	:					List[str]     	=   seq_shapes or {}
			for i in range(A__		):
						UpperCAmelCase_	:					int     	=   {}
						for col_id, (k, v) in enumerate(features.items()		):
									if isinstance(A__      ,_ArrayXD		):
												UpperCAmelCase_	:					Optional[int]     	=   np.random.rand(*v.shape		).astype(v.dtype		)
									elif isinstance(A__      ,datasets.Value		):
												if v.dtype == "string":
															UpperCAmelCase_	:					List[str]     	=   "The small grey turtle was surprisingly fast when challenged."
												else:
															UpperCAmelCase_	:					List[str]     	=   np.random.randint(10      ,size=1		).astype(v.dtype		).item()
									elif isinstance(A__      ,datasets.Sequence		):
												while isinstance(A__      ,datasets.Sequence		):
															UpperCAmelCase_	:					List[str]     	=   v.feature
												UpperCAmelCase_	:					List[Any]     	=   seq_shapes[k]
												UpperCAmelCase_	:					Dict     	=   np.random.rand(*A__		).astype(v.dtype		)
									UpperCAmelCase_	:					Dict     	=   data

						dummy_data.append((i, example)		)

			return dummy_data




def 							snake_case (  A__      ,A__      ,A__=1_00      ,A__=None		):
			UpperCAmelCase_	:					Optional[Any]     	=   generate_examples(A__      ,num_examples=A__      ,seq_shapes=A__		)

			with ArrowWriter(features=A__      ,path=A__		) as writer:
						for key, record in dummy_data:
									UpperCAmelCase_	:					Any     	=   features.encode_example(A__		)
									writer.write(A__		)

						UpperCAmelCase_	,      UpperCAmelCase_	:					Optional[int]     	=   writer.finalize()

			if not num_final_examples == num_examples:
						raise ValueError(
						    F"""Error writing the dataset, wrote {num_final_examples} examples but should have written {num_examples}."""		)

			UpperCAmelCase_	:					List[Any]     	=   datasets.Dataset.from_file(filename=A__      ,info=datasets.DatasetInfo(features=A__		)		)

			return dataset



