





import json
import os
import unittest

from transformers.models.ctrl.tokenization_ctrl import VOCAB_FILES_NAMES, CTRLTokenizer

from ...test_tokenization_common import TokenizerTesterMixin

class SCREAMING_SNAKE_CASE_ (					__SCREAMING_SNAKE_CASE							,       unittest.TestCase   ):





					"""simple docstring"""


					A__				 =    CTRLTokenizer
					A__				 =    False
					A__				 =    False





					def 					__magic_name__ ( self	):
										super().setUp()

										# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
										lowerCamelCase__       =       ["adapt", "re@@", "a@@", "apt", "c@@", "t", "<unk>"]
										lowerCamelCase__       =       dict(zip(_a	,  range(len(_a	)	)	)	)
										lowerCamelCase__       =       ["#version: 0.2", "a p", "ap t</w>", "r e", "a d", "ad apt</w>", ""]
										lowerCamelCase__       =       {"unk_token": "<unk>"}

										lowerCamelCase__       =       os.path.join(self.tmpdirname	,  VOCAB_FILES_NAMES["vocab_file"]	)
										lowerCamelCase__       =       os.path.join(self.tmpdirname	,  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 					__magic_name__ ( self	,  **_lowerCAmelCase	):
										kwargs.update(self.special_tokens_map	)
										return CTRLTokenizer.from_pretrained(self.tmpdirname	,  **_a	)





					def 					__magic_name__ ( self	,  _lowerCAmelCase	):
										lowerCamelCase__       =       "adapt react readapt apt"
										lowerCamelCase__       =       "adapt react readapt apt"
										return input_text, output_text






					def 					__magic_name__ ( self	):
										lowerCamelCase__       =       CTRLTokenizer(self.vocab_file	,  self.merges_file	,  **self.special_tokens_map	)
										lowerCamelCase__       =       "adapt react readapt apt"
										lowerCamelCase__       =       "adapt re@@ a@@ c@@ t re@@ adapt apt".split()
										lowerCamelCase__       =       tokenizer.tokenize(_a	)
										self.assertListEqual(_a	,  _a	)

										lowerCamelCase__       =       tokens + [tokenizer.unk_token]

										lowerCamelCase__       =       [0, 1, 2, 4, 5, 1, 0, 3, 6]
										self.assertListEqual(tokenizer.convert_tokens_to_ids(_a	)	,  _a	)








import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch

import datasets
import datasets.config

from .utils import require_beam

class SCREAMING_SNAKE_CASE_ (					datasets.BeamBasedBuilder   ):





					"""simple docstring"""


					def 					__magic_name__ ( self	):
										return datasets.DatasetInfo(
										    features=datasets.Features({"content": datasets.Value("string"	)}	)	,  supervised_keys=_lowerCAmelCase	,  )





					def 					__magic_name__ ( self	,  _lowerCAmelCase	,  _lowerCAmelCase	):
										return [datasets.SplitGenerator(name=datasets.Split.TRAIN	,  gen_kwargs={"examples": get_test_dummy_examples()}	)]






					def 					__magic_name__ ( self	,  _lowerCAmelCase	,  _lowerCAmelCase	):
										import apache_beam as beam

										return pipeline | "Load Examples" >> beam.Create(_lowerCAmelCase	)

class SCREAMING_SNAKE_CASE_ (					datasets.BeamBasedBuilder   ):





					"""simple docstring"""


					def 					__magic_name__ ( self	):
										return datasets.DatasetInfo(
										    features=datasets.Features({"a": datasets.Sequence({"b": datasets.Value("string"	)}	)}	)	,  supervised_keys=_lowerCAmelCase	,  )





					def 					__magic_name__ ( self	,  _lowerCAmelCase	,  _lowerCAmelCase	):
										return [
										    datasets.SplitGenerator(name=datasets.Split.TRAIN	,  gen_kwargs={"examples": get_test_nested_examples()}	)
										]






					def 					__magic_name__ ( self	,  _lowerCAmelCase	,  _lowerCAmelCase	):
										import apache_beam as beam

										return pipeline | "Load Examples" >> beam.Create(_lowerCAmelCase	)





def    __UpperCamelCase      (    ) ->Optional[Any]:
					return [(i, {"content": content}) for i, content in enumerate(["foo", "bar", "foobar"])]





def    __UpperCamelCase      (    ) ->Optional[Any]:
					return [(i, {"a": {"b": [content]}}) for i, content in enumerate(["foo", "bar", "foobar"])]







class SCREAMING_SNAKE_CASE_ (					lowercase_   ):





					"""simple docstring"""


					@require_beam
					def 					__magic_name__ ( self	):
										lowerCamelCase__       =       len(get_test_dummy_examples()	)
										with tempfile.TemporaryDirectory() as tmp_cache_dir:
															lowerCamelCase__       =       DummyBeamDataset(cache_dir=_lowerCAmelCase	,  beam_runner="DirectRunner"	)
															builder.download_and_prepare()
															self.assertTrue(
															    os.path.exists(
															        os.path.join(_lowerCAmelCase	,  builder.name	,  "default"	,  "0.0.0"	,  F"{builder.name}-train.arrow"	)	)	)
															self.assertDictEqual(builder.info.features	,  datasets.Features({"content": datasets.Value("string"	)}	)	)
															lowerCamelCase__       =       builder.as_dataset()
															self.assertEqual(dset["train"].num_rows	,  _lowerCAmelCase	)
															self.assertEqual(dset["train"].info.splits["train"].num_examples	,  _lowerCAmelCase	)
															self.assertDictEqual(dset["train"][0]	,  get_test_dummy_examples()[0][1]	)
															self.assertDictEqual(
															    dset["train"][expected_num_examples - 1]	,  get_test_dummy_examples()[expected_num_examples - 1][1]	)
															self.assertTrue(
															    os.path.exists(os.path.join(_lowerCAmelCase	,  builder.name	,  "default"	,  "0.0.0"	,  "dataset_info.json"	)	)	)
															del dset





					@require_beam
					def 					__magic_name__ ( self	):
										import apache_beam as beam

										lowerCamelCase__       =       beam.io.parquetio.WriteToParquet

										lowerCamelCase__       =       len(get_test_dummy_examples()	)
										with tempfile.TemporaryDirectory() as tmp_cache_dir:
															lowerCamelCase__       =       DummyBeamDataset(cache_dir=_lowerCAmelCase	,  beam_runner="DirectRunner"	)
															with patch("apache_beam.io.parquetio.WriteToParquet"	) as write_parquet_mock:
																				lowerCamelCase__       =       partial(_lowerCAmelCase	,  num_shards=2	)
																				builder.download_and_prepare()
															self.assertTrue(
															    os.path.exists(
															        os.path.join(
															            _lowerCAmelCase	,  builder.name	,  "default"	,  "0.0.0"	,  F"{builder.name}-train-00000-of-00002.arrow"	)	)	)
															self.assertTrue(
															    os.path.exists(
															        os.path.join(
															            _lowerCAmelCase	,  builder.name	,  "default"	,  "0.0.0"	,  F"{builder.name}-train-00000-of-00002.arrow"	)	)	)
															self.assertDictEqual(builder.info.features	,  datasets.Features({"content": datasets.Value("string"	)}	)	)
															lowerCamelCase__       =       builder.as_dataset()
															self.assertEqual(dset["train"].num_rows	,  _lowerCAmelCase	)
															self.assertEqual(dset["train"].info.splits["train"].num_examples	,  _lowerCAmelCase	)
															# Order is not preserved when sharding, so we just check that all the elements are there
															self.assertListEqual(sorted(dset["train"]["content"]	)	,  sorted(["foo", "bar", "foobar"]	)	)
															self.assertTrue(
															    os.path.exists(os.path.join(_lowerCAmelCase	,  builder.name	,  "default"	,  "0.0.0"	,  "dataset_info.json"	)	)	)
															del dset





					@require_beam
					def 					__magic_name__ ( self	):
										with tempfile.TemporaryDirectory() as tmp_cache_dir:
															lowerCamelCase__       =       DummyBeamDataset(cache_dir=_lowerCAmelCase	)
															self.assertRaises(datasets.builder.MissingBeamOptions	,  builder.download_and_prepare	)






					@require_beam
					def 					__magic_name__ ( self	):
										lowerCamelCase__       =       len(get_test_nested_examples()	)
										with tempfile.TemporaryDirectory() as tmp_cache_dir:
															lowerCamelCase__       =       NestedBeamDataset(cache_dir=_lowerCAmelCase	,  beam_runner="DirectRunner"	)
															builder.download_and_prepare()
															self.assertTrue(
															    os.path.exists(
															        os.path.join(_lowerCAmelCase	,  builder.name	,  "default"	,  "0.0.0"	,  F"{builder.name}-train.arrow"	)	)	)
															self.assertDictEqual(
															    builder.info.features	,  datasets.Features({"a": datasets.Sequence({"b": datasets.Value("string"	)}	)}	)	)
															lowerCamelCase__       =       builder.as_dataset()
															self.assertEqual(dset["train"].num_rows	,  _lowerCAmelCase	)
															self.assertEqual(dset["train"].info.splits["train"].num_examples	,  _lowerCAmelCase	)
															self.assertDictEqual(dset["train"][0]	,  get_test_nested_examples()[0][1]	)
															self.assertDictEqual(
															    dset["train"][expected_num_examples - 1]	,  get_test_nested_examples()[expected_num_examples - 1][1]	)
															self.assertTrue(
															    os.path.exists(os.path.join(_lowerCAmelCase	,  builder.name	,  "default"	,  "0.0.0"	,  "dataset_info.json"	)	)	)
															del dset

