

import json
import os
import unittest
from typing import Tuple

from transformers import WavaVecaPhonemeCTCTokenizer
from transformers.models.wavaveca.tokenization_wavaveca import VOCAB_FILES_NAMES
from transformers.models.wavaveca_phoneme.tokenization_wavaveca_phoneme import WavaVecaPhonemeCTCTokenizerOutput
from transformers.testing_utils import require_phonemizer

from ...test_tokenization_common import TokenizerTesterMixin

@require_phonemizer
class     a__  (    snake_case							,		unittest.TestCase	):
			"""simple docstring"""







			__lowerCamelCase       				=				WavaVecaPhonemeCTCTokenizer
			__lowerCamelCase       				=				False


			def    UpperCamelCase						(			self				)     ->  Optional[Any]:
							'''simple docstring'''






							super().setUp()

							A__						=	(
							    "<s> <pad> </s> <unk> n s t ə l a i k d m ɛ ɾ e ɪ p o ɐ z ð f j v b ɹ ʁ ʊ iː r w ʌ u ɡ æ aɪ ʃ h ɔ ɑː "
							    "ŋ ɚ eɪ β uː y ɑ̃ oʊ ᵻ eː θ aʊ ts oː ɔ̃ ɣ ɜ ɑ dʒ əl x ɜː ç ʒ tʃ ɔː ɑːɹ ɛ̃ ʎ ɔːɹ ʋ aː ɕ œ ø oːɹ ɲ yː "
							    "ʔ iə i5 s. tɕ ?? nʲ ɛː œ̃ ɭ ɔø ʑ tʲ ɨ ɛɹ ts. rʲ ɪɹ ɭʲ i.5 ɔɪ q sʲ u5 ʊɹ iɜ a5 iɛ5 øː ʕ ja əɜ th ɑ5 "
							    "oɪ dʲ ə5 tɕh ts.h mʲ ɯ dʑ vʲ e̞ tʃʲ ei5 o5 onɡ5 ɑu5 iɑ5 ai5 aɪɚ kh ə1 ʐ i2 ʉ ħ t[ aɪə ʲ ju ə2 u2 oɜ "
							    "pː iɛɜ ou5 y5 uɜ tː uo5 d[ uoɜ tsh ɑɜ ɵ i̪5 uei5 ɟ aɜ ɑɨ i.ɜ eʊ o2 ɐ̃ ä pʲ kʲ n̩ ɒ ph ɑu2 uɨ əɪ ɫ ɬ "
							    "yɜ bʲ ɑ2 s̪ aiɜ χ ɐ̃ʊ̃ 1 ə4 yæɜ a2 ɨː t̪ iouɜ ũ onɡɜ aɨ iɛ2 ɔɨ ɑuɜ o̞ ei2 iou2 c kː y2 ɖ oe dˤ yɛɜ "
							    "əʊ S ɡʲ onɡ2 u\" eiɜ ʈ ɯᵝ iou5 dZ r̝̊ i.2 tS s^ ʝ yə5 iɑɜ uə5 pf ɨu iɑ2 ou2 ər2 fʲ ai2 r̝ uəɜ ɳ əɨ "
							    "ua5 uɪ ɽ bː yu5 uo2 yɛ5 l̩ ɻ ərɜ ʂ i̪2 ouɜ uaɜ a. a.ː yæ5 dː r̩ ee ɪu ər5 i̪ ɜ æi u: i.ː t^ o1 ɪ^ "
							    "ai ueiɜ æː ɛɪ eə i. ɴ ie ua2 ɑ1 o4 tʃː o: ɑ: u1 N i̪1 au yæ2 u. qː yəɜ y: kʰ tʃʰ iʊ sx õ uo tʰ "
							    "uai5 bʰ u.ː uə2 ʊə d^ s̪ː yiɜ dʰ r. oe: i1 ɟː yu2 nʲʲ i̪4 uei2 tsʲ ɸ ĩ ɑ4 t̪ː eɑ u4 e: tsː ʈʰ ɡʰ "
							    "ɯɯ dʒʲ ʂʲ X ɵː uaiɜ tɕʲ ã t^ː ẽː yɛ2 cː i.1 ɛʊ dˤdˤ dʒː i4 ɡː yi ɕʲ ɟʰ pʰ dʑʲ yuɜ ua1 ua4 æiː ɐɐ "
							    "ui iou1 ʊː a1 iou4 cʰ iɛ1 yə2 ɖʰ ẽ ʒʲ ää ər4 iːː ɪː iɑ1 ər1 œː øi ɪuː cʰcʰ əː1 iː1 ũ kʰː o̞o̞ xʲ "
							    "ou1 iɛ4 e̞e̞ y1 dzː dʲʲ dʰː ɯᵝɯᵝ lː uo1 i.4 i: yɛ5ʲ a4"
							).split(" "				)
							A__						=	dict(zip(lowercase					,					range(len(lowercase				)				)				)				)

							A__						=	{"pad_token": "<pad>", "unk_token": "<unk>", "bos_token": "<s>", "eos_token": "</s>"}

							A__						=	os.path.join(self.tmpdirname					,					VOCAB_FILES_NAMES["vocab_file"]				)
							with open(self.vocab_file					,					"w"					,					encoding="utf-8"				) as fp:
											fp.write(json.dumps(lowercase				) + "\n"				)


			def    UpperCamelCase						(			self					,					lowercase					,					lowercase=False					,					lowercase=20					,					lowercase=5				)     ->  Tuple[str, list]:
							'''simple docstring'''






							A__						=	[(i, tokenizer.decode([i]					,					clean_up_tokenization_spaces=lowercase				)) for i in range(len(lowercase				)				)]
							A__						=	list(filter(lambda lowercase				: [t[0]] == tokenizer.encode(t[1]					,					do_phonemize=lowercase				)					,					lowercase				)				)
							if max_length is not None and len(lowercase				) > max_length:
											A__						=	toks[:max_length]
							if min_length is not None and len(lowercase				) < min_length and len(lowercase				) > 0:
											while len(lowercase				) < min_length:
															A__						=	toks + toks
        # toks_str = [t[1] for t in toks]
							A__						=	[t[0] for t in toks]

							# Ensure consistency
							A__						=	tokenizer.decode(lowercase					,					clean_up_tokenization_spaces=lowercase				)
							if " " not in output_txt and len(lowercase				) > 1:
											A__						=	(
											    tokenizer.decode([toks_ids[0]]					,					clean_up_tokenization_spaces=lowercase				)
											    + " "
											    + tokenizer.decode(toks_ids[1:]					,					clean_up_tokenization_spaces=lowercase				)
											)
							if with_prefix_space:
											A__						=	" " + output_txt
							A__						=	tokenizer.encode(lowercase					,					add_special_tokens=lowercase				)
							return output_txt, output_ids


			def    UpperCamelCase						(			self					,					**lowercase				)     ->  Dict:
							'''simple docstring'''






							kwargs.update(self.special_tokens_map				)
							return WavaVecaPhonemeCTCTokenizer.from_pretrained(self.tmpdirname					,					**lowercase				)


			def    UpperCamelCase						(			self				)     ->  str:
							'''simple docstring'''






							A__						=	self.tokenizer_class.from_pretrained("facebook/wav2vec2-lv-60-espeak-cv-ft"				)

							# check adding a single token
							tokenizer.add_tokens("xxx"				)
							A__						=	tokenizer("m xxx ɪ"					,					do_phonemize=lowercase				).input_ids
							self.assertEqual(lowercase					,					[13, 392, 17]				)  # xxx should be last token

							tokenizer.add_tokens(["aaa", "bbb", "ccc"]				)
							A__						=	tokenizer("m aaa ɪ ccc"					,					do_phonemize=lowercase				).input_ids
							self.assertEqual(lowercase					,					[13, 393, 17, 395]				)  # aaa and ccc should be after xxx and 2 after aaa

							A__						=	tokenizer("maɪ c"					,					do_phonemize=lowercase				).input_ids
							self.assertEqual(lowercase					,					[3, 200]				)  # mai should be <unk> (=3)


			def    UpperCamelCase						(			self				)     ->  Any:
							'''simple docstring'''






							A__						=	self.tokenizer_class.from_pretrained("facebook/wav2vec2-lv-60-espeak-cv-ft"				)

							A__						=	"Hello how are you"
							A__						=	tokenizer.phonemize(lowercase					,					phonemizer_lang="en-us"				)
							self.assertEqual(lowercase					,					"h ə l oʊ h aʊ ɑːɹ j uː"				)


			def    UpperCamelCase						(			self				)     ->  int:
							'''simple docstring'''






							A__						=	self.tokenizer_class.from_pretrained("facebook/wav2vec2-lv-60-espeak-cv-ft"				)

							A__						=	"Hello how are you"
							A__						=	tokenizer.phonemize(lowercase					,					phonemizer_lang="en-us"				)
							self.assertEqual(tokenizer(lowercase				).input_ids					,					tokenizer(lowercase					,					do_phonemize=lowercase				).input_ids				)


			def    UpperCamelCase						(			self				)     ->  Union[str, Any]:
							'''simple docstring'''






							A__						=	self.tokenizer_class.from_pretrained("facebook/wav2vec2-lv-60-espeak-cv-ft"				)
							A__						=	"Hello how are you"
							A__						=	tokenizer.phonemize(lowercase					,					phonemizer_lang="en-us"				)

							A__						=	tokenizer.decode(tokenizer(lowercase				).input_ids				)

							self.assertEqual(lowercase					,					lowercase				)


			def    UpperCamelCase						(			self				)     ->  Tuple:
							'''simple docstring'''






							A__						=	self.tokenizer_class.from_pretrained("facebook/wav2vec2-lv-60-espeak-cv-ft"				)

							A__						=	[
							    [11, 5, 15, tokenizer.pad_token_id, 15, 8, 98],
							    [24, 22, 5, 24, 22, 5, 77],
							]
							A__						=	tokenizer.decode(sample_ids[0]				)
							A__						=	tokenizer.batch_decode(lowercase				)
							self.assertEqual(lowercase					,					batch_tokens[0]				)
							self.assertEqual(lowercase					,					["k s ɾ ɾ l ɭʲ", "j ð s j ð s oːɹ"]				)


			def    UpperCamelCase						(			self				)     ->  Any:
							'''simple docstring'''






							A__						=	self.tokenizer_class.from_pretrained(
							    "facebook/wav2vec2-lv-60-espeak-cv-ft"					,					word_delimiter_token="|"				)
							tokenizer.add_tokens("|"				)

							A__						=	"Hello how are you"
							A__						=	tokenizer.phonemize(lowercase					,					phonemizer_lang="en-us"				)
							self.assertEqual(lowercase					,					"h ə l oʊ | h aʊ | ɑːɹ | j uː |"				)


			def    UpperCamelCase						(			self				)     ->  Union[str, Any]:
							'''simple docstring'''






							A__						=	self.tokenizer_class.from_pretrained(
							    "facebook/wav2vec2-lv-60-espeak-cv-ft"					,					word_delimiter_token="|"				)
							tokenizer.add_tokens("|"				)

							A__						=	"Hello how are you"
							A__						=	tokenizer.phonemize(lowercase					,					phonemizer_lang="en-us"				)
							self.assertEqual(tokenizer(lowercase				).input_ids					,					tokenizer(lowercase					,					do_phonemize=lowercase				).input_ids				)


			def    UpperCamelCase						(			self				)     ->  List[str]:
							'''simple docstring'''






							A__						=	self.tokenizer_class.from_pretrained(
							    "facebook/wav2vec2-lv-60-espeak-cv-ft"					,					word_delimiter_token="|"				)
							tokenizer.add_tokens("|"				)

							# fmt: off
							A__						=	[
							    [11, 5, 15, tokenizer.pad_token_id, tokenizer.word_delimiter_token_id, 15, 8, tokenizer.word_delimiter_token_id, 98],
							    [tokenizer.word_delimiter_token_id, 24, 22, tokenizer.word_delimiter_token_id, 5, 24, 22, 5, 77],
							]
							# fmt: on

							# decode with word_del_token filter
							A__						=	tokenizer.decode(sample_ids[0]				)
							A__						=	tokenizer.batch_decode(lowercase				)
							self.assertEqual(lowercase					,					batch_tokens[0]				)
							self.assertEqual(lowercase					,					["k s ɾ ɾ l ɭʲ", "j ð s j ð s oːɹ"]				)

							# decode with no word_del_token filter
							A__						=	tokenizer.decode(sample_ids[0]					,					filter_word_delimiter_token=lowercase				)
							A__						=	tokenizer.batch_decode(lowercase					,					filter_word_delimiter_token=lowercase				)
							self.assertEqual(lowercase					,					batch_tokens[0]				)
							self.assertEqual(lowercase					,					["k s ɾ | ɾ l | ɭʲ", "| j ð | s j ð s oːɹ"]				)


			def    UpperCamelCase						(			self				)     ->  Union[str, Any]:
							'''simple docstring'''






							A__						=	self.tokenizer_class.from_pretrained(
							    "facebook/wav2vec2-lv-60-espeak-cv-ft"					,					word_delimiter_token="|"				)
							tokenizer.add_tokens("|"				)

							A__						=	"Hello how are you"
							A__						=	tokenizer.phonemize(lowercase					,					phonemizer_lang="en-us"				)

							A__						=	tokenizer.decode(tokenizer(lowercase				).input_ids					,					filter_word_delimiter_token=lowercase				)

							self.assertEqual(lowercase					,					lowercase				)


			def    UpperCamelCase						(			self				)     ->  Optional[int]:
							'''simple docstring'''






							A__						=	self.tokenizer_class.from_pretrained(
							    "facebook/wav2vec2-lv-60-espeak-cv-ft"					,					word_delimiter_token="|"				)
							tokenizer.add_tokens("|"				)

							A__						=	"Hello how are you"
							A__						=	tokenizer.phonemize(lowercase					,					phonemizer_lang="en-us"				)

							A__						=	tokenizer.decode(tokenizer(lowercase				).input_ids					,					filter_word_delimiter_token=lowercase				)

							self.assertEqual(" ".join([p.strip() for p in phonemes.split(" |"				)]				).strip()					,					lowercase				)


			def    UpperCamelCase						(			self				)     ->  Tuple:
							'''simple docstring'''






							A__						=	self.tokenizer_class.from_pretrained(
							    "facebook/wav2vec2-lv-60-espeak-cv-ft"					,					word_delimiter_token=lowercase				)
							A__						=	"Hello how are you"

							A__						=	tokenizer(lowercase					,					phonemizer_lang="en-us"				).input_ids
							A__						=	tokenizer(lowercase					,					phonemizer_lang="fr-fr"				).input_ids

							self.assertNotEqual(lowercase					,					lowercase				)

							A__						=	tokenizer.decode(lowercase				)
							A__						=	tokenizer.decode(lowercase				)

							self.assertEqual(lowercase					,					"h ə l oʊ h aʊ ɑːɹ j uː"				)
							self.assertEqual(lowercase					,					"ɛ l o h aʊ a ʁ j u"				)


			def    UpperCamelCase						(			self				)     ->  List[Any]:
							'''simple docstring'''






							A__						=	self.tokenizer_class.from_pretrained("facebook/wav2vec2-lv-60-espeak-cv-ft"				)
							A__						=	"Hello how Are you"
							A__						=	"hello how are you"

							A__						=	tokenizer(lowercase				).input_ids
							A__						=	tokenizer(lowercase				).input_ids

							self.assertEqual(lowercase					,					lowercase				)


			def    UpperCamelCase						(			self				)     ->  List[Any]:
							'''simple docstring'''






							A__						=	self.tokenizer_class.from_pretrained("facebook/wav2vec2-lv-60-espeak-cv-ft"				)
							tokenizer.add_tokens(["!", "?"]				)
							tokenizer.add_special_tokens({"cls_token": "$$$"}				)

							# fmt: off
							A__						=	[
							    [11, 5, 15, tokenizer.pad_token_id, 15, 8, 98, 392, 392, 393, 392, 392, 393, 394, 394],
							    [24, 22, 5, 24, 22, 5, 77, tokenizer.pad_token_id, 394, 394],
							]
							# fmt: on

							A__						=	tokenizer.batch_decode(lowercase				)
							self.assertEqual(lowercase					,					["k s ɾ ɾ l ɭʲ!?!? $$$", "j ð s j ð s oːɹ $$$"]				)


			@staticmethod
			def    UpperCamelCase						(			lowercase					,					lowercase				)     ->  List[str]:
							'''simple docstring'''






							A__						=	[d[key] for d in offsets]
							return retrieved_list


			def    UpperCamelCase						(			self				)     ->  int:
							'''simple docstring'''






							A__						=	self.get_tokenizer(word_delimiter_token="|"				)
							tokenizer.add_tokens("|"				)

							# fmt: off
							# ksssɾɾ|ɾɾ<pad>ɾɾ|<pad>ɾlll|ɭʲ -> k s ɾ ɾ | ɾ l | ɭʲ"
							A__						=	[11, 5, 5, 5, 15, 15, tokenizer.pad_token_id, 15, 15, tokenizer.word_delimiter_token_id, tokenizer.pad_token_id, 15, 8, 8, 8, tokenizer.word_delimiter_token_id, 98]
							# fmt: on

							A__						=	tokenizer.decode(lowercase					,					output_char_offsets=lowercase					,					filter_word_delimiter_token=lowercase				)
							# check Wav2Vec2CTCTokenizerOutput keys for char
							self.assertEqual(len(outputs.keys()				)					,					2				)
							self.assertTrue("text" in outputs				)
							self.assertTrue("char_offsets" in outputs				)
							self.assertTrue(isinstance(lowercase					,					lowercase				)				)

							# check that order of chars is correct and identical for both outputs
							self.assertEqual(" ".join(self.get_from_offsets(outputs["char_offsets"]					,					"char"				)				)					,					outputs.text				)
							self.assertListEqual(
							    self.get_from_offsets(outputs["char_offsets"]					,					"char"				)					,					["k", "s", "ɾ", "ɾ", "|", "ɾ", "l", "|", "ɭʲ"]				)

							# check that offsets are actually correct for char
							# 0-1 is 11, 1-4 is 5, 4-6 is first 15, 6-7 is <pad> (thus not shown), 7-9 is second 15, 9-10 is word_delimiter_token,
							# 10-11 is <pad> (thus not shown), 11-12 is third 15, 12-15 is 8, 15-16 is word_delimiter_token, 16-17 is 98
							self.assertListEqual(
							    self.get_from_offsets(outputs["char_offsets"]					,					"start_offset"				)					,					[0, 1, 4, 7, 9, 11, 12, 15, 16]				)
							self.assertListEqual(
							    self.get_from_offsets(outputs["char_offsets"]					,					"end_offset"				)					,					[1, 4, 6, 9, 10, 12, 15, 16, 17]				)


			def    UpperCamelCase						(			self				)     ->  Dict:
							'''simple docstring'''






							A__						=	self.get_tokenizer(word_delimiter_token="|"				)

							def check_list_tuples_equal(lowercase					,					lowercase				):
											self.assertTrue(isinstance(lowercase					,					lowercase				)				)
											self.assertTrue(isinstance(outputs_list[0]					,					lowercase				)				)

											# transform list to ModelOutput
											A__						=	WavaVecaPhonemeCTCTokenizerOutput(
											    {k: [d[k] for d in outputs_list] for k in outputs_list[0]}				)

											self.assertListEqual(outputs_batch["text"]					,					outputs_batch_a["text"]				)

											def recursive_check(lowercase					,					lowercase				):
															if isinstance(lowercase					,					lowercase				):
																			[recursive_check(lowercase					,					lowercase				) for la, la in zip(lowercase					,					lowercase				)]
															self.assertEqual(lowercase					,					lowercase				)

											if "char_offsets" in outputs_batch:
															recursive_check(outputs_batch["char_offsets"]					,					outputs_batch_a["char_offsets"]				)

        # fmt: off
							A__						=	[
							    [11, 5, 15, tokenizer.pad_token_id, 15, 4, 8, 98, 32, 32, 32, 32, 4, 33, tokenizer.word_delimiter_token_id, 32, 32, 33, 34, 34],
							    [24, 22, 5, tokenizer.word_delimiter_token_id, tokenizer.word_delimiter_token_id, 24, 22, 22, 22, 4, 5, 77, tokenizer.pad_token_id, 22, 22, 4, 34, 34, 34, 34],
							]
							# fmt: on

							# We assume that `decode` works as expected. All we will check now is
							# the output type is correct and the output is identical to `decode`

							# char
							A__						=	tokenizer.batch_decode(lowercase					,					output_char_offsets=lowercase				)
							A__						=	[tokenizer.decode(lowercase					,					output_char_offsets=lowercase				) for ids in sample_ids]
							check_list_tuples_equal(lowercase					,					lowercase				)


			@unittest.skip("Wav2Vec2PhonemeTokenizer always lower cases letters to correctly map to phonemes"				)
			def    UpperCamelCase						(			self				)     ->  Union[str, Any]:
							'''simple docstring'''






							pass


			@unittest.skip("Wav2Vec2PhonemeTokenizer always puts spaces between phonemes"				)
			def    UpperCamelCase						(			self				)     ->  Any:
							'''simple docstring'''






							pass


			@unittest.skip("encodes to text to ids, but decodes ids to phonemes -> not possible to have internal consistency"				)
			def    UpperCamelCase						(			self				)     ->  int:
							'''simple docstring'''






							pass


			@unittest.skip("Wav2Vec2PhonemeModel has no max model length => no testing"				)
			def    UpperCamelCase						(			self				)     ->  Tuple:
							'''simple docstring'''






							pass


			def    UpperCamelCase						(			self				)     ->  Any:
							'''simple docstring'''






							A__						=	self.get_tokenizers(do_lower_case=lowercase				)
							for tokenizer in tokenizers:
											with self.subTest(F'{tokenizer.__class__.__name__}'				):
															A__						=	tokenizer.vocab_size
															A__						=	len(lowercase				)

															self.assertNotEqual(lowercase					,					0				)

															# We usually have added tokens from the start in tests because our vocab fixtures are
															# smaller than the original vocabs - let's not assert this
															# self.assertEqual(vocab_size, all_size)

															A__						=	["aaaaa bbbbbb", "cccccccccdddddddd"]
															A__						=	tokenizer.add_tokens(lowercase				)
															A__						=	tokenizer.vocab_size
															A__						=	len(lowercase				)

															self.assertNotEqual(lowercase					,					0				)
															self.assertEqual(lowercase					,					lowercase				)
															self.assertEqual(lowercase					,					len(lowercase				)				)
															self.assertEqual(lowercase					,					all_size + len(lowercase				)				)

															A__						=	tokenizer.encode("aaaaa bbbbbb low cccccccccdddddddd l"					,					add_special_tokens=lowercase				)

															self.assertGreaterEqual(len(lowercase				)					,					4				)
															self.assertGreater(tokens[0]					,					tokenizer.vocab_size - 1				)
															self.assertGreater(tokens[-3]					,					tokenizer.vocab_size - 1				)

															A__						=	{"eos_token": ">>>>|||<||<<|<<", "pad_token": "<<<<<|||>|>>>>|>"}
															A__						=	tokenizer.add_special_tokens(lowercase				)
															A__						=	tokenizer.vocab_size
															A__						=	len(lowercase				)

															self.assertNotEqual(lowercase					,					0				)
															self.assertEqual(lowercase					,					lowercase				)
															self.assertEqual(lowercase					,					len(lowercase				)				)
															self.assertEqual(lowercase					,					all_size_a + len(lowercase				)				)

															A__						=	tokenizer.encode(
															    ">>>>|||<||<<|<< aaaaabbbbbb low cccccccccdddddddd <<<<<|||>|>>>>|> l"					,					add_special_tokens=lowercase				)

															self.assertGreaterEqual(len(lowercase				)					,					6				)
															self.assertGreater(tokens[0]					,					tokenizer.vocab_size - 1				)
															self.assertGreater(tokens[0]					,					tokens[1]				)
															self.assertGreater(tokens[-3]					,					tokenizer.vocab_size - 1				)
															self.assertGreater(tokens[-3]					,					tokens[-4]				)
															self.assertEqual(tokens[0]					,					tokenizer.eos_token_id				)
															self.assertEqual(tokens[-3]					,					tokenizer.pad_token_id				)


			@unittest.skip("The tokenizer shouldn't be used to encode input IDs (except for labels), only to decode."				)
			def    UpperCamelCase						(			self				)     ->  int:
							'''simple docstring'''






							pass


			@unittest.skip("The tokenizer shouldn't be used to encode input IDs (except for labels), only to decode."				)
			def    UpperCamelCase						(			self				)     ->  Optional[int]:
							'''simple docstring'''






							pass


			def    UpperCamelCase						(			self				)     ->  List[str]:
							'''simple docstring'''






							A__						=	self.get_tokenizers(fast=lowercase					,					do_lower_case=lowercase				)
							for tokenizer in tokenizers:
											with self.subTest(F'{tokenizer.__class__.__name__}'				):
															A__						=	["ð", "ɪ", "s", "ɪ", "z", "ɐ", "t", "ɛ", "k", "s", "t"]
															A__						=	tokenizer.convert_tokens_to_string(lowercase				)

															self.assertIsInstance(output["text"]					,					lowercase				)






"""simple docstring"""
import argparse
import re
from typing import Dict

import torch
from datasets import Audio, Dataset, load_dataset, load_metric

from transformers import AutoFeatureExtractor, pipeline

def       _snake_case						(						UpperCamelCase						:		Dataset			,		UpperCamelCase						:		Dict[str, str]						):
 UpperCAmelCase	:					Any	=	args.log_outputs
 UpperCAmelCase	:					Any	=	"""_""".join(args.dataset.split("""/"""						) + [args.config, args.split]						)

 # load metric
 UpperCAmelCase	:					List[Any]	=	load_metric("""wer"""						)
 UpperCAmelCase	:					Any	=	load_metric("""cer"""						)

 # compute metrics
 UpperCAmelCase	:					int	=	wer.compute(references=result["""target"""]			,		predictions=result["""prediction"""]						)
 UpperCAmelCase	:					str	=	cer.compute(references=result["""target"""]			,		predictions=result["""prediction"""]						)

 # print & log results
 UpperCAmelCase	:					Tuple	=	F"WER: {wer_result}\nCER: {cer_result}"
 print(UpperCamelCase						)

 with open(F"{dataset_id}_eval_results.txt"			,		"""w"""						) as f:
  f.write(UpperCamelCase						)

 # log all results in text file. Possibly interesting for analysis
 if log_outputs is not None:
  UpperCAmelCase	:					str	=	F"log_{dataset_id}_predictions.txt"
  UpperCAmelCase	:					Tuple	=	F"log_{dataset_id}_targets.txt"

  with open(UpperCamelCase			,		"""w"""						) as p, open(UpperCamelCase			,		"""w"""						) as t:
   # mapping function to write output
   def write_to_file(UpperCamelCase						:		List[Any]			,		UpperCamelCase						:		List[Any]						):
    p.write(F"{i}" + """\n"""						)
    p.write(batch["""prediction"""] + """\n"""						)
    t.write(F"{i}" + """\n"""						)
    t.write(batch["""target"""] + """\n"""						)

   result.map(UpperCamelCase			,		with_indices=UpperCamelCase						)

def       _snake_case						(						UpperCamelCase						:		str						):
 UpperCAmelCase	:					List[str]	=	"""[,?.!\-\;\:\"“%‘”�—’…–]"""  # noqa: W605 IMPORTANT: this should correspond to the chars that were ignored during training

 UpperCAmelCase	:					Dict	=	re.sub(UpperCamelCase			,		""""""			,		text.lower()						)

 # In addition, we can normalize the target text, e.g. removing new lines characters etc...
 # note that order is important here!
 UpperCAmelCase	:					List[str]	=	["""\n\n""", """\n""", """   """, """  """]

 for t in token_sequences_to_ignore:
  UpperCAmelCase	:					Optional[Any]	=	""" """.join(text.split(UpperCamelCase						)						)

 return text

def       _snake_case						(						UpperCamelCase						:		Tuple						):
 # load dataset
 UpperCAmelCase	:					Union[str, Any]	=	load_dataset(args.dataset			,		args.config			,		split=args.split			,		use_auth_token=UpperCamelCase						)

 # for testing: only process the first two examples as a test
 # dataset = dataset.select(range(10))

 # load processor
 UpperCAmelCase	:					Optional[int]	=	AutoFeatureExtractor.from_pretrained(args.model_id						)
 UpperCAmelCase	:					Any	=	feature_extractor.sampling_rate

 # resample audio
 UpperCAmelCase	:					List[str]	=	dataset.cast_column("""audio"""			,		Audio(sampling_rate=UpperCamelCase						)						)

 # load eval pipeline
 if args.device is None:
  UpperCAmelCase	:					Optional[int]	=	0 if torch.cuda.is_available() else -1
 UpperCAmelCase	:					Tuple	=	pipeline("""automatic-speech-recognition"""			,		model=args.model_id			,		device=args.device						)

 # map function to decode audio
 def map_to_pred(UpperCamelCase						:		Any						):
  UpperCAmelCase	:					Any	=	asr(
      batch["""audio"""]["""array"""]			,		chunk_length_s=args.chunk_length_s			,		stride_length_s=args.stride_length_s						)

  UpperCAmelCase	:					Tuple	=	prediction["""text"""]
  UpperCAmelCase	:					List[str]	=	normalize_text(batch["""sentence"""]						)
  return batch

 # run inference on all examples
 UpperCAmelCase	:					int	=	dataset.map(UpperCamelCase			,		remove_columns=dataset.column_names						)

 # compute and log_results
 # do not change function below
 log_results(UpperCamelCase			,		UpperCamelCase						)


if __name__ == "__main__":
      A:   List[Any]								=		argparse.ArgumentParser()

      parser.add_argument(
          "--model_id", type=str, required=True, help="Model identifier. Should be loadable with 🤗 Transformers"
      )
      parser.add_argument(
          "--dataset",
          type=str,
          required=True,
          help="Dataset name to evaluate the `model_id`. Should be loadable with 🤗 Datasets",
      )
      parser.add_argument(
          "--config", type=str, required=True, help="Config of the dataset. *E.g.* `'en'`  for Common Voice"
      )
      parser.add_argument("--split", type=str, required=True, help="Split of the dataset. *E.g.* `'test'`")
      parser.add_argument(
          "--chunk_length_s", type=float, default=None, help="Chunk length in seconds. Defaults to 5 seconds."
      )
      parser.add_argument(
          "--stride_length_s", type=float, default=None, help="Stride of the audio chunks. Defaults to 1 second."
      )
      parser.add_argument(
          "--log_outputs", action="store_true", help="If defined, write outputs to log file for analysis."
      )
      parser.add_argument(
          "--device",
          type=int,
          default=None,
          help="The device to run the pipeline on. -1 for CPU (default), 0 for the first GPU and so on.",
      )
      A:   Union[str, Any]								=		parser.parse_args()

      main(args)
