
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(				UpperCAmelCase     ,		unittest.TestCase       ):
					SCREAMING_SNAKE_CASE			    =    WavaVecaPhonemeCTCTokenizer
					SCREAMING_SNAKE_CASE			    =    False

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

												lowerCamelCase_							  =					(
												    """<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(""" """   )
												lowerCamelCase_							  =					dict(zip(__UpperCamelCase   ,					range(len(__UpperCamelCase   )   )   )   )

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

												lowerCamelCase_							  =					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(__UpperCamelCase   ) + """\n"""   )

					def    lowercase__      (	self					:     Union[str, Any]   ,					__UpperCamelCase					:     List[Any]   ,					__UpperCamelCase					:     Any=False   ,					__UpperCamelCase					:     List[Any]=2_0   ,					__UpperCamelCase					:     List[Any]=5   ):
												lowerCamelCase_							  =					[(i, tokenizer.decode([i]   ,					clean_up_tokenization_spaces=__UpperCamelCase   )) for i in range(len(__UpperCamelCase   )   )]
												lowerCamelCase_							  =					list(filter(lambda __UpperCamelCase   : [t[0]] == tokenizer.encode(t[1]   ,					do_phonemize=__UpperCamelCase   )   ,					__UpperCamelCase   )   )
												if max_length is not None and len(__UpperCamelCase   ) > max_length:
																			lowerCamelCase_							  =					toks[:max_length]
												if min_length is not None and len(__UpperCamelCase   ) < min_length and len(__UpperCamelCase   ) > 0:
																			while len(__UpperCamelCase   ) < min_length:
																										lowerCamelCase_							  =					toks + toks
        # toks_str = [t[1] for t in toks]
												lowerCamelCase_							  =					[t[0] for t in toks]

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

					def    lowercase__      (	self					:     str   ,					**__UpperCamelCase					:     str   ):
												kwargs.update(self.special_tokens_map   )
												return WavaVecaPhonemeCTCTokenizer.from_pretrained(self.tmpdirname   ,					**__UpperCamelCase   )

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

												# check adding a single token
												tokenizer.add_tokens("""xxx"""   )
												lowerCamelCase_							  =					tokenizer("""m xxx ɪ"""   ,					do_phonemize=__UpperCamelCase   ).input_ids
												self.assertEqual(__UpperCamelCase   ,					[1_3, 3_9_2, 1_7]   )  # xxx should be last token

												tokenizer.add_tokens(["""aaa""", """bbb""", """ccc"""]   )
												lowerCamelCase_							  =					tokenizer("""m aaa ɪ ccc"""   ,					do_phonemize=__UpperCamelCase   ).input_ids
												self.assertEqual(__UpperCamelCase   ,					[1_3, 3_9_3, 1_7, 3_9_5]   )  # aaa and ccc should be after xxx and 2 after aaa

												lowerCamelCase_							  =					tokenizer("""maɪ c"""   ,					do_phonemize=__UpperCamelCase   ).input_ids
												self.assertEqual(__UpperCamelCase   ,					[3, 2_0_0]   )  # mai should be <unk> (=3)

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

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

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

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

					def    lowercase__      (	self					:     List[Any]   ):
												lowerCamelCase_							  =					self.tokenizer_class.from_pretrained("""facebook/wav2vec2-lv-60-espeak-cv-ft"""   )
												lowerCamelCase_							  =					"""Hello how are you"""
												lowerCamelCase_							  =					tokenizer.phonemize(__UpperCamelCase   ,					phonemizer_lang="""en-us"""   )

												lowerCamelCase_							  =					tokenizer.decode(tokenizer(__UpperCamelCase   ).input_ids   )

												self.assertEqual(__UpperCamelCase   ,					__UpperCamelCase   )

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

												lowerCamelCase_							  =					[
												    [1_1, 5, 1_5, tokenizer.pad_token_id, 1_5, 8, 9_8],
												    [2_4, 2_2, 5, 2_4, 2_2, 5, 7_7],
												]
												lowerCamelCase_							  =					tokenizer.decode(sample_ids[0]   )
												lowerCamelCase_							  =					tokenizer.batch_decode(__UpperCamelCase   )
												self.assertEqual(__UpperCamelCase   ,					batch_tokens[0]   )
												self.assertEqual(__UpperCamelCase   ,					["""k s ɾ ɾ l ɭʲ""", """j ð s j ð s oːɹ"""]   )

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

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

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

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

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

												# fmt: off
												lowerCamelCase_							  =					[
												    [1_1, 5, 1_5, tokenizer.pad_token_id, tokenizer.word_delimiter_token_id, 1_5, 8, tokenizer.word_delimiter_token_id, 9_8],
												    [tokenizer.word_delimiter_token_id, 2_4, 2_2, tokenizer.word_delimiter_token_id, 5, 2_4, 2_2, 5, 7_7],
												]
												# fmt: on

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

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

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

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

												lowerCamelCase_							  =					tokenizer.decode(tokenizer(__UpperCamelCase   ).input_ids   ,					filter_word_delimiter_token=__UpperCamelCase   )

												self.assertEqual(__UpperCamelCase   ,					__UpperCamelCase   )

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

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

												lowerCamelCase_							  =					tokenizer.decode(tokenizer(__UpperCamelCase   ).input_ids   ,					filter_word_delimiter_token=__UpperCamelCase   )

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

					def    lowercase__      (	self					:     Dict   ):
												lowerCamelCase_							  =					self.tokenizer_class.from_pretrained(
												    """facebook/wav2vec2-lv-60-espeak-cv-ft"""   ,					word_delimiter_token=__UpperCamelCase   )
												lowerCamelCase_							  =					"""Hello how are you"""

												lowerCamelCase_							  =					tokenizer(__UpperCamelCase   ,					phonemizer_lang="""en-us"""   ).input_ids
												lowerCamelCase_							  =					tokenizer(__UpperCamelCase   ,					phonemizer_lang="""fr-fr"""   ).input_ids

												self.assertNotEqual(__UpperCamelCase   ,					__UpperCamelCase   )

												lowerCamelCase_							  =					tokenizer.decode(__UpperCamelCase   )
												lowerCamelCase_							  =					tokenizer.decode(__UpperCamelCase   )

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

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

												lowerCamelCase_							  =					tokenizer(__UpperCamelCase   ).input_ids
												lowerCamelCase_							  =					tokenizer(__UpperCamelCase   ).input_ids

												self.assertEqual(__UpperCamelCase   ,					__UpperCamelCase   )

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

												# fmt: off
												lowerCamelCase_							  =					[
												    [1_1, 5, 1_5, tokenizer.pad_token_id, 1_5, 8, 9_8, 3_9_2, 3_9_2, 3_9_3, 3_9_2, 3_9_2, 3_9_3, 3_9_4, 3_9_4],
												    [2_4, 2_2, 5, 2_4, 2_2, 5, 7_7, tokenizer.pad_token_id, 3_9_4, 3_9_4],
												]
												# fmt: on

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

					@staticmethod
					def    lowercase__      (	__UpperCamelCase					:     str   ,					__UpperCamelCase					:     int   ):
												lowerCamelCase_							  =					[d[key] for d in offsets]
												return retrieved_list

					def    lowercase__      (	self					:     int   ):
												lowerCamelCase_							  =					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"
												lowerCamelCase_							  =					[1_1, 5, 5, 5, 1_5, 1_5, tokenizer.pad_token_id, 1_5, 1_5, tokenizer.word_delimiter_token_id, tokenizer.pad_token_id, 1_5, 8, 8, 8, tokenizer.word_delimiter_token_id, 9_8]
												# fmt: on

												lowerCamelCase_							  =					tokenizer.decode(__UpperCamelCase   ,					output_char_offsets=__UpperCamelCase   ,					filter_word_delimiter_token=__UpperCamelCase   )
												# 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(__UpperCamelCase   ,					__UpperCamelCase   )   )

												# 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, 1_1, 1_2, 1_5, 1_6]   )
												self.assertListEqual(
												    self.get_from_offsets(outputs["""char_offsets"""]   ,					"""end_offset"""   )   ,					[1, 4, 6, 9, 1_0, 1_2, 1_5, 1_6, 1_7]   )

					def    lowercase__      (	self					:     str   ):
												lowerCamelCase_							  =					self.get_tokenizer(word_delimiter_token="""|"""   )

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

																			# transform list to ModelOutput
																			lowerCamelCase_							  =					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(__UpperCamelCase					:     Dict   ,					__UpperCamelCase					:     Optional[Any]   ):
																										if isinstance(__UpperCamelCase   ,					__UpperCamelCase   ):
																																	[recursive_check(__UpperCamelCase   ,					__UpperCamelCase   ) for la, la in zip(__UpperCamelCase   ,					__UpperCamelCase   )]
																										self.assertEqual(__UpperCamelCase   ,					__UpperCamelCase   )

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

        # fmt: off
												lowerCamelCase_							  =					[
												    [1_1, 5, 1_5, tokenizer.pad_token_id, 1_5, 4, 8, 9_8, 3_2, 3_2, 3_2, 3_2, 4, 3_3, tokenizer.word_delimiter_token_id, 3_2, 3_2, 3_3, 3_4, 3_4],
												    [2_4, 2_2, 5, tokenizer.word_delimiter_token_id, tokenizer.word_delimiter_token_id, 2_4, 2_2, 2_2, 2_2, 4, 5, 7_7, tokenizer.pad_token_id, 2_2, 2_2, 4, 3_4, 3_4, 3_4, 3_4],
												]
												# 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
												lowerCamelCase_							  =					tokenizer.batch_decode(__UpperCamelCase   ,					output_char_offsets=__UpperCamelCase   )
												lowerCamelCase_							  =					[tokenizer.decode(__UpperCamelCase   ,					output_char_offsets=__UpperCamelCase   ) for ids in sample_ids]
												check_list_tuples_equal(__UpperCamelCase   ,					__UpperCamelCase   )

					@unittest.skip("""Wav2Vec2PhonemeTokenizer always lower cases letters to correctly map to phonemes"""   )
					def    lowercase__      (	self					:     str   ):
												pass

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

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

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

					def    lowercase__      (	self					:     Tuple   ):
												lowerCamelCase_							  =					self.get_tokenizers(do_lower_case=__UpperCamelCase   )
												for tokenizer in tokenizers:
																			with self.subTest(F'''{tokenizer.__class__.__name__}'''   ):
																										lowerCamelCase_							  =					tokenizer.vocab_size
																										lowerCamelCase_							  =					len(__UpperCamelCase   )

																										self.assertNotEqual(__UpperCamelCase   ,					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)

																										lowerCamelCase_							  =					["""aaaaa bbbbbb""", """cccccccccdddddddd"""]
																										lowerCamelCase_							  =					tokenizer.add_tokens(__UpperCamelCase   )
																										lowerCamelCase_							  =					tokenizer.vocab_size
																										lowerCamelCase_							  =					len(__UpperCamelCase   )

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

																										lowerCamelCase_							  =					tokenizer.encode("""aaaaa bbbbbb low cccccccccdddddddd l"""   ,					add_special_tokens=__UpperCamelCase   )

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

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

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

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

																										self.assertGreaterEqual(len(__UpperCamelCase   )   ,					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    lowercase__      (	self					:     List[str]   ):
												pass

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



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

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







from __future__ import annotations

import inspect
import unittest
from typing import List, Tuple

from transformers import RegNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
		import tensorflow as tf

		from transformers import TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST, TFRegNetForImageClassification, TFRegNetModel


if is_vision_available():
		from PIL import Image

		from transformers import AutoImageProcessor





class       __A:

					def __init__(	self					:     int   ,					__UpperCamelCase					:     Union[str, Any]   ,					__UpperCamelCase					:     Union[str, Any]=3   ,					__UpperCamelCase					:     int=3_2   ,					__UpperCamelCase					:     Any=3   ,					__UpperCamelCase					:     List[str]=1_0   ,					__UpperCamelCase					:     int=[1_0, 2_0, 3_0, 4_0]   ,					__UpperCamelCase					:     List[str]=[1, 1, 2, 1]   ,					__UpperCamelCase					:     str=True   ,					__UpperCamelCase					:     List[str]=True   ,					__UpperCamelCase					:     Dict="relu"   ,					__UpperCamelCase					:     int=3   ,					__UpperCamelCase					:     Dict=None   ,					):
												lowerCamelCase_							  =					parent
												lowerCamelCase_							  =					batch_size
												lowerCamelCase_							  =					image_size
												lowerCamelCase_							  =					num_channels
												lowerCamelCase_							  =					embeddings_size
												lowerCamelCase_							  =					hidden_sizes
												lowerCamelCase_							  =					depths
												lowerCamelCase_							  =					is_training
												lowerCamelCase_							  =					use_labels
												lowerCamelCase_							  =					hidden_act
												lowerCamelCase_							  =					num_labels
												lowerCamelCase_							  =					scope
												lowerCamelCase_							  =					len(__UpperCamelCase   )

					def    lowercase__      (	self					:     Any   ):
												lowerCamelCase_							  =					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]   )

												lowerCamelCase_							  =					None
												if self.use_labels:
																			lowerCamelCase_							  =					ids_tensor([self.batch_size]   ,					self.num_labels   )

												lowerCamelCase_							  =					self.get_config()
												return config, pixel_values, labels

					def    lowercase__      (	self					:     str   ):
												return RegNetConfig(
												    num_channels=self.num_channels   ,					embeddings_size=self.embeddings_size   ,					hidden_sizes=self.hidden_sizes   ,					depths=self.depths   ,					hidden_act=self.hidden_act   ,					num_labels=self.num_labels   ,					)

					def    lowercase__      (	self					:     Optional[int]   ,					__UpperCamelCase					:     int   ,					__UpperCamelCase					:     Tuple   ,					__UpperCamelCase					:     Union[str, Any]   ):
												lowerCamelCase_							  =					TFRegNetModel(config=__UpperCamelCase   )
												lowerCamelCase_							  =					model(__UpperCamelCase   ,					training=__UpperCamelCase   )
												# expected last hidden states: B, C, H // 32, W // 32
												self.parent.assertEqual(
												    result.last_hidden_state.shape   ,					(self.batch_size, self.hidden_sizes[-1], self.image_size // 3_2, self.image_size // 3_2)   ,					)

					def    lowercase__      (	self					:     Union[str, Any]   ,					__UpperCamelCase					:     int   ,					__UpperCamelCase					:     List[Any]   ,					__UpperCamelCase					:     List[str]   ):
												lowerCamelCase_							  =					self.num_labels
												lowerCamelCase_							  =					TFRegNetForImageClassification(__UpperCamelCase   )
												lowerCamelCase_							  =					model(__UpperCamelCase   ,					labels=__UpperCamelCase   ,					training=__UpperCamelCase   )
												self.parent.assertEqual(result.logits.shape   ,					(self.batch_size, self.num_labels)   )



					def    lowercase__      (	self					:     int   ):
												lowerCamelCase_							  =					self.prepare_config_and_inputs()
												lowerCamelCase_  ,   lowerCamelCase_  ,   lowerCamelCase_							  =					config_and_inputs
												lowerCamelCase_							  =					{"""pixel_values""": pixel_values}
												return config, inputs_dict





@require_tf
class       __A(				UpperCAmelCase     ,		UpperCAmelCase     ,		unittest.TestCase       ):
					SCREAMING_SNAKE_CASE			    =    (TFRegNetModel, TFRegNetForImageClassification) if is_tf_available() else ()
					SCREAMING_SNAKE_CASE			    =    (
					    {'''feature-extraction''': TFRegNetModel, '''image-classification''': TFRegNetForImageClassification}
					    if is_tf_available()
					    else {}
					)

					SCREAMING_SNAKE_CASE			    =    False
					SCREAMING_SNAKE_CASE			    =    False
					SCREAMING_SNAKE_CASE			    =    False
					SCREAMING_SNAKE_CASE			    =    False
					SCREAMING_SNAKE_CASE			    =    False

					def    lowercase__      (	self					:     Tuple   ):
												lowerCamelCase_							  =					TFRegNetModelTester(self   )
												lowerCamelCase_							  =					ConfigTester(self   ,					config_class=__UpperCamelCase   ,					has_text_modality=__UpperCamelCase   )

					def    lowercase__      (	self					:     Union[str, Any]   ):
												return

					@unittest.skip(reason="""RegNet does not use inputs_embeds"""   )
					def    lowercase__      (	self					:     Optional[int]   ):
												pass

					@unittest.skipIf(
					    not is_tf_available() or len(tf.config.list_physical_devices("""GPU"""   )   ) == 0   ,					reason="""TF does not support backprop for grouped convolutions on CPU."""   ,					)
					@slow
					def    lowercase__      (	self					:     Tuple   ):
												super().test_keras_fit()

					@unittest.skip(reason="""RegNet does not support input and output embeddings"""   )
					def    lowercase__      (	self					:     Optional[Any]   ):
												pass

					def    lowercase__      (	self					:     Dict   ):
												lowerCamelCase_  ,   lowerCamelCase_							  =					self.model_tester.prepare_config_and_inputs_for_common()

												for model_class in self.all_model_classes:
																			lowerCamelCase_							  =					model_class(__UpperCamelCase   )
																			lowerCamelCase_							  =					inspect.signature(model.call   )
																			# signature.parameters is an OrderedDict => so arg_names order is deterministic
																			lowerCamelCase_							  =					[*signature.parameters.keys()]

																			lowerCamelCase_							  =					["""pixel_values"""]
																			self.assertListEqual(arg_names[:1]   ,					__UpperCamelCase   )

					def    lowercase__      (	self					:     Any   ):
												lowerCamelCase_							  =					self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_model(*__UpperCamelCase   )

					def    lowercase__      (	self					:     int   ):
												def check_hidden_states_output(__UpperCamelCase					:     Union[str, Any]   ,					__UpperCamelCase					:     str   ,					__UpperCamelCase					:     Any   ):
																			lowerCamelCase_							  =					model_class(__UpperCamelCase   )
																			lowerCamelCase_							  =					model(**self._prepare_for_class(__UpperCamelCase   ,					__UpperCamelCase   )   ,					training=__UpperCamelCase   )

																			lowerCamelCase_							  =					outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

																			lowerCamelCase_							  =					self.model_tester.num_stages
																			self.assertEqual(len(__UpperCamelCase   )   ,					expected_num_stages + 1   )

																			# RegNet's feature maps are of shape (batch_size, num_channels, height, width)
																			self.assertListEqual(
																			    list(hidden_states[0].shape[-2:]   )   ,					[self.model_tester.image_size // 2, self.model_tester.image_size // 2]   ,					)

												lowerCamelCase_  ,   lowerCamelCase_							  =					self.model_tester.prepare_config_and_inputs_for_common()
												lowerCamelCase_							  =					["""basic""", """bottleneck"""]
												for model_class in self.all_model_classes:
																			for layer_type in layers_type:
																										lowerCamelCase_							  =					layer_type
																										lowerCamelCase_							  =					True
																										check_hidden_states_output(__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase   )

																										# check that output_hidden_states also work using config
																										del inputs_dict["output_hidden_states"]
																										lowerCamelCase_							  =					True

																										check_hidden_states_output(__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase   )

					def    lowercase__      (	self					:     Optional[Any]   ):
												lowerCamelCase_  ,   lowerCamelCase_							  =					self.model_tester.prepare_config_and_inputs_for_common()

												def check_equivalence(__UpperCamelCase					:     List[str]   ,					__UpperCamelCase					:     Optional[Any]   ,					__UpperCamelCase					:     Tuple   ,					__UpperCamelCase					:     List[Any]={}   ):
																			lowerCamelCase_							  =					model(__UpperCamelCase   ,					return_dict=__UpperCamelCase   ,					**__UpperCamelCase   )
																			lowerCamelCase_							  =					model(__UpperCamelCase   ,					return_dict=__UpperCamelCase   ,					**__UpperCamelCase   ).to_tuple()

																			def recursive_check(__UpperCamelCase					:     Union[str, Any]   ,					__UpperCamelCase					:     Dict   ):
																										if isinstance(__UpperCamelCase   ,					(List, Tuple)   ):
																																	for tuple_iterable_value, dict_iterable_value in zip(__UpperCamelCase   ,					__UpperCamelCase   ):
																																								recursive_check(__UpperCamelCase   ,					__UpperCamelCase   )
																										elif tuple_object is None:
																																	return
																										else:
																																	self.assertTrue(
																																	    all(tf.equal(__UpperCamelCase   ,					__UpperCamelCase   )   )   ,					msg=(
																																	        """Tuple and dict output are not equal. Difference:"""
																																	        F''' {tf.math.reduce_max(tf.abs(tuple_object - dict_object   )   )}'''
																																	    )   ,					)

																										recursive_check(__UpperCamelCase   ,					__UpperCamelCase   )

												for model_class in self.all_model_classes:
																			lowerCamelCase_							  =					model_class(__UpperCamelCase   )

																			lowerCamelCase_							  =					self._prepare_for_class(__UpperCamelCase   ,					__UpperCamelCase   )
																			lowerCamelCase_							  =					self._prepare_for_class(__UpperCamelCase   ,					__UpperCamelCase   )
																			check_equivalence(__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase   )

																			lowerCamelCase_							  =					self._prepare_for_class(__UpperCamelCase   ,					__UpperCamelCase   ,					return_labels=__UpperCamelCase   )
																			lowerCamelCase_							  =					self._prepare_for_class(__UpperCamelCase   ,					__UpperCamelCase   ,					return_labels=__UpperCamelCase   )
																			check_equivalence(__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase   )

																			lowerCamelCase_							  =					self._prepare_for_class(__UpperCamelCase   ,					__UpperCamelCase   )
																			lowerCamelCase_							  =					self._prepare_for_class(__UpperCamelCase   ,					__UpperCamelCase   )
																			check_equivalence(__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase   ,					{"""output_hidden_states""": True}   )

																			lowerCamelCase_							  =					self._prepare_for_class(__UpperCamelCase   ,					__UpperCamelCase   ,					return_labels=__UpperCamelCase   )
																			lowerCamelCase_							  =					self._prepare_for_class(__UpperCamelCase   ,					__UpperCamelCase   ,					return_labels=__UpperCamelCase   )
																			check_equivalence(__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase   ,					{"""output_hidden_states""": True}   )

					def    lowercase__      (	self					:     Union[str, Any]   ):
												lowerCamelCase_							  =					self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_image_classification(*__UpperCamelCase   )



					@slow
					def    lowercase__      (	self					:     List[Any]   ):
												for model_name in TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																			lowerCamelCase_							  =					TFRegNetModel.from_pretrained(__UpperCamelCase   )
																			self.assertIsNotNone(__UpperCamelCase   )



def      __lowerCAmelCase						(							)  ->    Optional[int]:
							lowerCamelCase_							  =					Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""       )
							return image





@require_tf
@require_vision
class       __A(				unittest.TestCase       ):

					@cached_property
					def    lowercase__      (	self					:     Union[str, Any]   ):
												return (
												    AutoImageProcessor.from_pretrained(TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]   )
												    if is_vision_available()
												    else None
												)



					@slow
					def    lowercase__      (	self					:     List[str]   ):
												lowerCamelCase_							  =					TFRegNetForImageClassification.from_pretrained(TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]   )

												lowerCamelCase_							  =					self.default_image_processor
												lowerCamelCase_							  =					prepare_img()
												lowerCamelCase_							  =					image_processor(images=__UpperCamelCase   ,					return_tensors="""tf"""   )

												# forward pass
												lowerCamelCase_							  =					model(**__UpperCamelCase   ,					training=__UpperCamelCase   )

												# verify the logits
												lowerCamelCase_							  =					tf.TensorShape((1, 1_0_0_0)   )
												self.assertEqual(outputs.logits.shape   ,					__UpperCamelCase   )

												lowerCamelCase_							  =					tf.constant([-0.4180, -1.5051, -3.4836]   )

												tf.debugging.assert_near(outputs.logits[0, :3]   ,					__UpperCamelCase   ,					atol=1E-4   )





