



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 				_snake_case			(					lowercase__    ,	unittest.TestCase):
				UpperCamelCase__				:					Optional[Any]     		=WavaVecaPhonemeCTCTokenizer
				UpperCamelCase__				:					List[Any]     		=False




				def 							A__      (						self					:						str						):
							super().setUp()

							lowercase__					  =				(
							    "<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(" "						)
							lowercase__					  =				dict(zip(__lowercase,					range(len(__lowercase						)						)						)						)

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

							lowercase__					  =				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 							A__      (						self					:						Optional[Any],					__lowercase					:						Union[str, Any],					__lowercase					:						Optional[int]=False,					__lowercase					:						Optional[int]=20,					__lowercase					:						str=5						):
							lowercase__					  =				[(i, tokenizer.decode([i],					clean_up_tokenization_spaces=__lowercase						)) for i in range(len(__lowercase						)						)]
							lowercase__					  =				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:
										lowercase__					  =				toks[:max_length]
							if min_length is not None and len(__lowercase						) < min_length and len(__lowercase						) > 0:
										while len(__lowercase						) < min_length:
													lowercase__					  =				toks + toks
        # toks_str = [t[1] for t in toks]
							lowercase__					  =				[t[0] for t in toks]

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




				def 							A__      (						self					:						Dict,					**__lowercase					:						Optional[int]						):
							kwargs.update(self.special_tokens_map						)
							return WavaVecaPhonemeCTCTokenizer.from_pretrained(self.tmpdirname,					**__lowercase						)




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

							# check adding a single token
							tokenizer.add_tokens("xxx"						)
							lowercase__					  =				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"]						)
							lowercase__					  =				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

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




				def 							A__      (						self					:						Union[str, Any]						):
							lowercase__					  =				self.tokenizer_class.from_pretrained("facebook/wav2vec2-lv-60-espeak-cv-ft"						)

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




				def 							A__      (						self					:						List[str]						):
							lowercase__					  =				self.tokenizer_class.from_pretrained("facebook/wav2vec2-lv-60-espeak-cv-ft"						)

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




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

							lowercase__					  =				tokenizer.decode(tokenizer(__lowercase						).input_ids						)

							self.assertEqual(__lowercase,					__lowercase						)




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

							lowercase__					  =				[
							    [11, 5, 15, tokenizer.pad_token_id, 15, 8, 98],
							    [24, 22, 5, 24, 22, 5, 77],
							]
							lowercase__					  =				tokenizer.decode(sample_ids[0]						)
							lowercase__					  =				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 							A__      (						self					:						str						):
							lowercase__					  =				self.tokenizer_class.from_pretrained(
							    "facebook/wav2vec2-lv-60-espeak-cv-ft",					word_delimiter_token="|"						)
							tokenizer.add_tokens("|"						)

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




				def 							A__      (						self					:						Union[str, Any]						):
							lowercase__					  =				self.tokenizer_class.from_pretrained(
							    "facebook/wav2vec2-lv-60-espeak-cv-ft",					word_delimiter_token="|"						)
							tokenizer.add_tokens("|"						)

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




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

							# fmt: off
							lowercase__					  =				[
							    [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
							lowercase__					  =				tokenizer.decode(sample_ids[0]						)
							lowercase__					  =				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
							lowercase__					  =				tokenizer.decode(sample_ids[0],					filter_word_delimiter_token=__lowercase						)
							lowercase__					  =				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 							A__      (						self					:						int						):
							lowercase__					  =				self.tokenizer_class.from_pretrained(
							    "facebook/wav2vec2-lv-60-espeak-cv-ft",					word_delimiter_token="|"						)
							tokenizer.add_tokens("|"						)

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

							lowercase__					  =				tokenizer.decode(tokenizer(__lowercase						).input_ids,					filter_word_delimiter_token=__lowercase						)

							self.assertEqual(__lowercase,					__lowercase						)




				def 							A__      (						self					:						Optional[int]						):
							lowercase__					  =				self.tokenizer_class.from_pretrained(
							    "facebook/wav2vec2-lv-60-espeak-cv-ft",					word_delimiter_token="|"						)
							tokenizer.add_tokens("|"						)

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

							lowercase__					  =				tokenizer.decode(tokenizer(__lowercase						).input_ids,					filter_word_delimiter_token=__lowercase						)

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




				def 							A__      (						self					:						Optional[int]						):
							lowercase__					  =				self.tokenizer_class.from_pretrained(
							    "facebook/wav2vec2-lv-60-espeak-cv-ft",					word_delimiter_token=__lowercase						)
							lowercase__					  =				"Hello how are you"

							lowercase__					  =				tokenizer(__lowercase,					phonemizer_lang="en-us"						).input_ids
							lowercase__					  =				tokenizer(__lowercase,					phonemizer_lang="fr-fr"						).input_ids

							self.assertNotEqual(__lowercase,					__lowercase						)

							lowercase__					  =				tokenizer.decode(__lowercase						)
							lowercase__					  =				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 							A__      (						self					:						List[Any]						):
							lowercase__					  =				self.tokenizer_class.from_pretrained("facebook/wav2vec2-lv-60-espeak-cv-ft"						)
							lowercase__					  =				"Hello how Are you"
							lowercase__					  =				"hello how are you"

							lowercase__					  =				tokenizer(__lowercase						).input_ids
							lowercase__					  =				tokenizer(__lowercase						).input_ids

							self.assertEqual(__lowercase,					__lowercase						)




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

							# fmt: off
							lowercase__					  =				[
							    [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

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




				@staticmethod
				def 							A__      (						__lowercase					:						Tuple,					__lowercase					:						Any						):
							lowercase__					  =				[d[key] for d in offsets]
							return retrieved_list




				def 							A__      (						self					:						List[Any]						):
							lowercase__					  =				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"
							lowercase__					  =				[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

							lowercase__					  =				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 							A__      (						self					:						List[Any]						):
							lowercase__					  =				self.get_tokenizer(word_delimiter_token="|"						)

							def check_list_tuples_equal(__lowercase					:						Optional[int],					__lowercase					:						Optional[int]						):
										self.assertTrue(isinstance(__lowercase,					__lowercase						)						)
										self.assertTrue(isinstance(outputs_list[0],					__lowercase						)						)

										# transform list to ModelOutput
										lowercase__					  =				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					:						Dict,					__lowercase					:						Tuple						):
													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
							lowercase__					  =				[
							    [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
							lowercase__					  =				tokenizer.batch_decode(__lowercase,					output_char_offsets=__lowercase						)
							lowercase__					  =				[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 							A__      (						self					:						Optional[int]						):
							pass




				@unittest.skip("Wav2Vec2PhonemeTokenizer always puts spaces between phonemes"						)
				def 							A__      (						self					:						Optional[int]						):
							pass




				@unittest.skip("encodes to text to ids, but decodes ids to phonemes -> not possible to have internal consistency"						)
				def 							A__      (						self					:						Any						):
							pass




				@unittest.skip("Wav2Vec2PhonemeModel has no max model length => no testing"						)
				def 							A__      (						self					:						Dict						):
							pass




				def 							A__      (						self					:						Optional[Any]						):
							lowercase__					  =				self.get_tokenizers(do_lower_case=__lowercase						)
							for tokenizer in tokenizers:
										with self.subTest(F'''{tokenizer.__class__.__name__}'''						):
													lowercase__					  =				tokenizer.vocab_size
													lowercase__					  =				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)

													lowercase__					  =				["aaaaa bbbbbb", "cccccccccdddddddd"]
													lowercase__					  =				tokenizer.add_tokens(__lowercase						)
													lowercase__					  =				tokenizer.vocab_size
													lowercase__					  =				len(__lowercase						)

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

													lowercase__					  =				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						)

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

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

													lowercase__					  =				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 							A__      (						self					:						List[Any]						):
							pass




				@unittest.skip("The tokenizer shouldn't be used to encode input IDs (except for labels), only to decode."						)
				def 							A__      (						self					:						str						):
							pass




				def 							A__      (						self					:						int						):
							# The default common tokenizer tests assumes that the output of `convert_tokens_to_string` is a string which
							# is not the case for Wav2Vec2PhonemeCTCTokenizer.
							lowercase__					  =				self.get_tokenizers(fast=__lowercase,					do_lower_case=__lowercase						)
							for tokenizer in tokenizers:
										with self.subTest(F'''{tokenizer.__class__.__name__}'''						):
													lowercase__					  =				["ð", "ɪ", "s", "ɪ", "z", "ɐ", "t", "ɛ", "k", "s", "t"]
													lowercase__					  =				tokenizer.convert_tokens_to_string(__lowercase						)

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









import unittest

import numpy as np

from transformers.testing_utils import is_flaky, require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
							import torch

if is_vision_available():
							from PIL import Image

							from transformers import DonutImageProcessor
class 				_snake_case			(					unittest.TestCase):




				def __init__(						self					:						Dict,					__lowercase					:						int,					__lowercase					:						Union[str, Any]=7,					__lowercase					:						Union[str, Any]=3,					__lowercase					:						Any=18,					__lowercase					:						Union[str, Any]=30,					__lowercase					:						Any=400,					__lowercase					:						List[str]=True,					__lowercase					:						Dict=None,					__lowercase					:						List[str]=True,					__lowercase					:						int=False,					__lowercase					:						Union[str, Any]=True,					__lowercase					:						str=True,					__lowercase					:						Optional[int]=[0.5, 0.5, 0.5],					__lowercase					:						List[Any]=[0.5, 0.5, 0.5],					):
							lowercase__					  =				parent
							lowercase__					  =				batch_size
							lowercase__					  =				num_channels
							lowercase__					  =				image_size
							lowercase__					  =				min_resolution
							lowercase__					  =				max_resolution
							lowercase__					  =				do_resize
							lowercase__					  =				size if size is not None else {"height": 18, "width": 20}
							lowercase__					  =				do_thumbnail
							lowercase__					  =				do_align_axis
							lowercase__					  =				do_pad
							lowercase__					  =				do_normalize
							lowercase__					  =				image_mean
							lowercase__					  =				image_std




				def 							A__      (						self					:						Optional[Any]						):
							return {
							    "do_resize": self.do_resize,
							    "size": self.size,
							    "do_thumbnail": self.do_thumbnail,
							    "do_align_long_axis": self.do_align_axis,
							    "do_pad": self.do_pad,
							    "do_normalize": self.do_normalize,
							    "image_mean": self.image_mean,
							    "image_std": self.image_std,
							}
@require_torch
@require_vision
class 				_snake_case			(					lowercase__    ,	unittest.TestCase):
				UpperCamelCase__				:					Optional[int]     		=DonutImageProcessor if is_vision_available() else None




				def 							A__      (						self					:						str						):
							lowercase__					  =				DonutImageProcessingTester(self						)




				@property
				def 							A__      (						self					:						List[str]						):
							return self.image_processor_tester.prepare_image_processor_dict()




				def 							A__      (						self					:						Optional[Any]						):
							lowercase__					  =				self.image_processing_class(**self.image_processor_dict						)
							self.assertTrue(hasattr(__lowercase,					"do_resize"						)						)
							self.assertTrue(hasattr(__lowercase,					"size"						)						)
							self.assertTrue(hasattr(__lowercase,					"do_thumbnail"						)						)
							self.assertTrue(hasattr(__lowercase,					"do_align_long_axis"						)						)
							self.assertTrue(hasattr(__lowercase,					"do_pad"						)						)
							self.assertTrue(hasattr(__lowercase,					"do_normalize"						)						)
							self.assertTrue(hasattr(__lowercase,					"image_mean"						)						)
							self.assertTrue(hasattr(__lowercase,					"image_std"						)						)




				def 							A__      (						self					:						str						):
							lowercase__					  =				self.image_processing_class.from_dict(self.image_processor_dict						)
							self.assertEqual(image_processor.size,					{"height": 18, "width": 20}						)

							lowercase__					  =				self.image_processing_class.from_dict(self.image_processor_dict,					size=42						)
							self.assertEqual(image_processor.size,					{"height": 42, "width": 42}						)

							# Previous config had dimensions in (width, height) order
							lowercase__					  =				self.image_processing_class.from_dict(self.image_processor_dict,					size=(42, 84)						)
							self.assertEqual(image_processor.size,					{"height": 84, "width": 42}						)




				def 							A__      (						self					:						List[str]						):
							pass




				@is_flaky()
				def 							A__      (						self					:						Dict						):
							# Initialize image_processing
							lowercase__					  =				self.image_processing_class(**self.image_processor_dict						)
							# create random PIL images
							lowercase__					  =				prepare_image_inputs(self.image_processor_tester,					equal_resolution=__lowercase						)
							for image in image_inputs:
										self.assertIsInstance(__lowercase,					Image.Image						)

							# Test not batched input
							lowercase__					  =				image_processing(image_inputs[0],					return_tensors="pt"						).pixel_values
							self.assertEqual(
							    encoded_images.shape,					(
							        1,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.size["height"],
							        self.image_processor_tester.size["width"],
							    ),					)

							# Test batched
							lowercase__					  =				image_processing(__lowercase,					return_tensors="pt"						).pixel_values
							self.assertEqual(
							    encoded_images.shape,					(
							        self.image_processor_tester.batch_size,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.size["height"],
							        self.image_processor_tester.size["width"],
							    ),					)




				@is_flaky()
				def 							A__      (						self					:						Optional[Any]						):
							# Initialize image_processing
							lowercase__					  =				self.image_processing_class(**self.image_processor_dict						)
							# create random numpy tensors
							lowercase__					  =				prepare_image_inputs(self.image_processor_tester,					equal_resolution=__lowercase,					numpify=__lowercase						)
							for image in image_inputs:
										self.assertIsInstance(__lowercase,					np.ndarray						)

							# Test not batched input
							lowercase__					  =				image_processing(image_inputs[0],					return_tensors="pt"						).pixel_values
							self.assertEqual(
							    encoded_images.shape,					(
							        1,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.size["height"],
							        self.image_processor_tester.size["width"],
							    ),					)

							# Test batched
							lowercase__					  =				image_processing(__lowercase,					return_tensors="pt"						).pixel_values
							self.assertEqual(
							    encoded_images.shape,					(
							        self.image_processor_tester.batch_size,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.size["height"],
							        self.image_processor_tester.size["width"],
							    ),					)




				@is_flaky()
				def 							A__      (						self					:						Tuple						):
							# Initialize image_processing
							lowercase__					  =				self.image_processing_class(**self.image_processor_dict						)
							# create random PyTorch tensors
							lowercase__					  =				prepare_image_inputs(self.image_processor_tester,					equal_resolution=__lowercase,					torchify=__lowercase						)
							for image in image_inputs:
										self.assertIsInstance(__lowercase,					torch.Tensor						)

							# Test not batched input
							lowercase__					  =				image_processing(image_inputs[0],					return_tensors="pt"						).pixel_values
							self.assertEqual(
							    encoded_images.shape,					(
							        1,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.size["height"],
							        self.image_processor_tester.size["width"],
							    ),					)

							# Test batched
							lowercase__					  =				image_processing(__lowercase,					return_tensors="pt"						).pixel_values
							self.assertEqual(
							    encoded_images.shape,					(
							        self.image_processor_tester.batch_size,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.size["height"],
							        self.image_processor_tester.size["width"],
							    ),					)




