import json
import os
import shutil
import tempfile
import unittest

from transformers.models.wavaveca import WavaVecaCTCTokenizer, WavaVecaFeatureExtractor, WavaVecaProcessor
from transformers.models.wavaveca.tokenization_wavaveca import VOCAB_FILES_NAMES
from transformers.utils import FEATURE_EXTRACTOR_NAME

from .test_feature_extraction_wavaveca import floats_list




class 					_snake_case						(						unittest.TestCase				):
					def    _lowerCamelCase (  self:				Dict     )    ->				int:
												__UpperCAmelCase						:			Optional[int]         =   "<pad> <s> </s> <unk> | E T A O N I H S R D L U M W C F G Y P B V K ' X J Q Z".split(" "     )
												__UpperCAmelCase						:			Optional[int]         =   dict(zip(__lowerCamelCase	,  range(len(__lowerCamelCase     )     )     )     )

												__UpperCAmelCase						:			Any         =   {
												    "pad_token": "<pad>",
												    "unk_token": "<unk>",
												    "bos_token": "<s>",
												    "eos_token": "</s>",
												}
												__UpperCAmelCase						:			Dict         =   {
												    "feature_size": 1,
												    "padding_value": 0.0,
												    "sampling_rate": 1_60_00,
												    "return_attention_mask": False,
												    "do_normalize": True,
												}

												__UpperCAmelCase						:			Optional[Any]         =   tempfile.mkdtemp()
												__UpperCAmelCase						:			Optional[int]         =   os.path.join(self.tmpdirname	,  VOCAB_FILES_NAMES["vocab_file"]     )
												__UpperCAmelCase						:			Optional[Any]         =   os.path.join(self.tmpdirname	,  __lowerCamelCase     )
												with open(self.vocab_file	,  "w"	,  encoding="utf-8"     ) as fp:
																			fp.write(json.dumps(__lowerCamelCase     ) + "\n"     )

												with open(self.feature_extraction_file	,  "w"	,  encoding="utf-8"     ) as fp:
																			fp.write(json.dumps(__lowerCamelCase     ) + "\n"     )
					def    _lowerCamelCase (  self:				Optional[int]	,  **__lowerCamelCase:				Optional[Any]     )    ->				Optional[int]:
												__UpperCAmelCase						:			int         =   self.add_kwargs_tokens_map.copy()
												kwargs.update(__lowerCamelCase     )
												return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname	,  **__lowerCamelCase     )
					def    _lowerCamelCase (  self:				str	,  **__lowerCamelCase:				Optional[int]     )    ->				str:
												return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname	,  **__lowerCamelCase     )
					def    _lowerCamelCase (  self:				Dict     )    ->				int:
												shutil.rmtree(self.tmpdirname     )
					def    _lowerCamelCase (  self:				Union[str, Any]     )    ->				Optional[int]:
												__UpperCAmelCase						:			Optional[Any]         =   self.get_tokenizer()
												__UpperCAmelCase						:			int         =   self.get_feature_extractor()

												__UpperCAmelCase						:			Optional[Any]         =   WavaVecaProcessor(tokenizer=__lowerCamelCase	,  feature_extractor=__lowerCamelCase     )

												processor.save_pretrained(self.tmpdirname     )
												__UpperCAmelCase						:			str         =   WavaVecaProcessor.from_pretrained(self.tmpdirname     )

												self.assertEqual(processor.tokenizer.get_vocab()	,  tokenizer.get_vocab()     )
												self.assertIsInstance(processor.tokenizer	,  __lowerCamelCase     )

												self.assertEqual(processor.feature_extractor.to_json_string()	,  feature_extractor.to_json_string()     )
												self.assertIsInstance(processor.feature_extractor	,  __lowerCamelCase     )
					def    _lowerCamelCase (  self:				Dict     )    ->				str:
												__UpperCAmelCase						:			List[str]         =   WavaVecaProcessor(tokenizer=self.get_tokenizer()	,  feature_extractor=self.get_feature_extractor()     )
												processor.save_pretrained(self.tmpdirname     )

												__UpperCAmelCase						:			Union[str, Any]         =   self.get_tokenizer(bos_token="(BOS)"	,  eos_token="(EOS)"     )
												__UpperCAmelCase						:			Any         =   self.get_feature_extractor(do_normalize=__lowerCamelCase	,  padding_value=1.0     )

												__UpperCAmelCase						:			Optional[Any]         =   WavaVecaProcessor.from_pretrained(
												    self.tmpdirname	,  bos_token="(BOS)"	,  eos_token="(EOS)"	,  do_normalize=__lowerCamelCase	,  padding_value=1.0     )

												self.assertEqual(processor.tokenizer.get_vocab()	,  tokenizer_add_kwargs.get_vocab()     )
												self.assertIsInstance(processor.tokenizer	,  __lowerCamelCase     )

												self.assertEqual(processor.feature_extractor.to_json_string()	,  feature_extractor_add_kwargs.to_json_string()     )
												self.assertIsInstance(processor.feature_extractor	,  __lowerCamelCase     )
					def    _lowerCamelCase (  self:				List[Any]     )    ->				Union[str, Any]:
												__UpperCAmelCase						:			Any         =   self.get_feature_extractor()
												__UpperCAmelCase						:			str         =   self.get_tokenizer()

												__UpperCAmelCase						:			List[Any]         =   WavaVecaProcessor(tokenizer=__lowerCamelCase	,  feature_extractor=__lowerCamelCase     )

												__UpperCAmelCase						:			Union[str, Any]         =   floats_list((3, 10_00)     )

												__UpperCAmelCase						:			Dict         =   feature_extractor(__lowerCamelCase	,  return_tensors="np"     )
												__UpperCAmelCase						:			str         =   processor(__lowerCamelCase	,  return_tensors="np"     )

												for key in input_feat_extract.keys():
																			self.assertAlmostEqual(input_feat_extract[key].sum()	,  input_processor[key].sum()	,  delta=1e-2     )
					def    _lowerCamelCase (  self:				Optional[Any]     )    ->				Optional[int]:
												__UpperCAmelCase						:			List[str]         =   self.get_feature_extractor()
												__UpperCAmelCase						:			Tuple         =   self.get_tokenizer()

												__UpperCAmelCase						:			Any         =   WavaVecaProcessor(tokenizer=__lowerCamelCase	,  feature_extractor=__lowerCamelCase     )

												__UpperCAmelCase						:			List[Any]         =   "This is a test string"

												__UpperCAmelCase						:			List[Any]         =   processor(text=__lowerCamelCase     )

												__UpperCAmelCase						:			int         =   tokenizer(__lowerCamelCase     )

												for key in encoded_tok.keys():
																			self.assertListEqual(encoded_tok[key]	,  encoded_processor[key]     )
					def    _lowerCamelCase (  self:				str     )    ->				Any:
												__UpperCAmelCase						:			Optional[Any]         =   self.get_feature_extractor()
												__UpperCAmelCase						:			Dict         =   self.get_tokenizer()

												__UpperCAmelCase						:			int         =   WavaVecaProcessor(tokenizer=__lowerCamelCase	,  feature_extractor=__lowerCamelCase     )

												__UpperCAmelCase						:			Optional[Any]         =   [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

												__UpperCAmelCase						:			int         =   processor.batch_decode(__lowerCamelCase     )
												__UpperCAmelCase						:			Union[str, Any]         =   tokenizer.batch_decode(__lowerCamelCase     )

												self.assertListEqual(__lowerCamelCase	,  __lowerCamelCase     )





					def    _lowerCamelCase (  self:				str     )    ->				Optional[int]:
												__UpperCAmelCase						:			List[str]         =   self.get_feature_extractor()
												__UpperCAmelCase						:			Union[str, Any]         =   self.get_tokenizer()

												__UpperCAmelCase						:			Tuple         =   WavaVecaProcessor(tokenizer=__lowerCamelCase	,  feature_extractor=__lowerCamelCase     )

												self.assertListEqual(
												    processor.model_input_names	,  feature_extractor.model_input_names	,  msg="`processor` and `feature_extractor` model input names do not match"	,  )




import itertools
from dataclasses import dataclass
from typing import Optional

import pyarrow as pa

import datasets
from datasets.table import table_cast


_snake_case			=						datasets.utils.logging.get_logger(__name__)




@dataclass
class 					_snake_case						(						datasets.BuilderConfig				):
					lowerCamelCase__:		Optional[datasets.Features]  =     None




class 					_snake_case						(						datasets.ArrowBasedBuilder				):
					lowerCamelCase__:		Union[str, Any]  =     ArrowConfig
					def    _lowerCamelCase (  self:				Optional[int]     )    ->				Optional[int]:
												return datasets.DatasetInfo(features=self.config.features     )
					def    _lowerCamelCase (  self:				List[Any]	,  __lowerCamelCase:				Tuple     )    ->				str:
												if not self.config.data_files:
																			raise ValueError(f'''At least one data file must be specified, but got data_files={self.config.data_files}'''     )
												__UpperCAmelCase						:			Dict         =   dl_manager.download_and_extract(self.config.data_files     )
												if isinstance(__lowerCamelCase	,  (str, list, tuple)     ):
																			__UpperCAmelCase						:			str         =   data_files
																			if isinstance(__lowerCamelCase	,  __lowerCamelCase     ):
																										__UpperCAmelCase						:			List[str]         =   [files]
																			# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
																			__UpperCAmelCase						:			List[Any]         =   [dl_manager.iter_files(__lowerCamelCase     ) for file in files]
																			return [datasets.SplitGenerator(name=datasets.Split.TRAIN	,  gen_kwargs={"files": files}     )]
												__UpperCAmelCase						:			Union[str, Any]         =   []
												for split_name, files in data_files.items():
																			if isinstance(__lowerCamelCase	,  __lowerCamelCase     ):
																										__UpperCAmelCase						:			Tuple         =   [files]
																			# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
																			__UpperCAmelCase						:			List[Any]         =   [dl_manager.iter_files(__lowerCamelCase     ) for file in files]
																			# Infer features is they are stoed in the arrow schema
																			if self.info.features is None:
																										for file in itertools.chain.from_iterable(__lowerCamelCase     ):
																																	with open(__lowerCamelCase	,  "rb"     ) as f:
																																								__UpperCAmelCase						:			Union[str, Any]         =   datasets.Features.from_arrow_schema(pa.ipc.open_stream(__lowerCamelCase     ).schema     )
																																	break
																			splits.append(datasets.SplitGenerator(name=__lowerCamelCase	,  gen_kwargs={"files": files}     )     )
												return splits
					def    _lowerCamelCase (  self:				str	,  __lowerCamelCase:				pa.Table     )    ->				pa.Table:
												if self.info.features is not None:
																			# more expensive cast to support nested features with keys in a different order
																			# allows str <-> int/float or str to Audio for example
																			__UpperCAmelCase						:			List[str]         =   table_cast(__lowerCamelCase	,  self.info.features.arrow_schema     )
												return pa_table





					def    _lowerCamelCase (  self:				Any	,  __lowerCamelCase:				Any     )    ->				Any:
												for file_idx, file in enumerate(itertools.chain.from_iterable(__lowerCamelCase     )     ):
																			with open(__lowerCamelCase	,  "rb"     ) as f:
																										try:
																																	for batch_idx, record_batch in enumerate(pa.ipc.open_stream(__lowerCamelCase     )     ):
																																								__UpperCAmelCase						:			Optional[int]         =   pa.Table.from_batches([record_batch]     )
																																								# Uncomment for debugging (will print the Arrow table size and elements)
																																								# logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
																																								# logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
																																								yield f'''{file_idx}_{batch_idx}''', self._cast_table(__lowerCamelCase     )
																										except ValueError as e:
																																	logger.error(f'''Failed to read file \'{file}\' with error {type(__lowerCamelCase     )}: {e}'''     )
																																	raise




