





import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    HubertConfig,
    HubertForCTC,
    HubertModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
_SCREAMING_SNAKE_CASE							=						logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE							=						{
    """post_extract_proj""": """feature_projection.projection""",
    """encoder.pos_conv.0""": """encoder.pos_conv_embed.conv""",
    """self_attn.k_proj""": """encoder.layers.*.attention.k_proj""",
    """self_attn.v_proj""": """encoder.layers.*.attention.v_proj""",
    """self_attn.q_proj""": """encoder.layers.*.attention.q_proj""",
    """self_attn.out_proj""": """encoder.layers.*.attention.out_proj""",
    """self_attn_layer_norm""": """encoder.layers.*.layer_norm""",
    """fc1""": """encoder.layers.*.feed_forward.intermediate_dense""",
    """fc2""": """encoder.layers.*.feed_forward.output_dense""",
    """final_layer_norm""": """encoder.layers.*.final_layer_norm""",
    """encoder.layer_norm""": """encoder.layer_norm""",
    """w2v_model.layer_norm""": """feature_projection.layer_norm""",
    """w2v_encoder.proj""": """lm_head""",
    """mask_emb""": """masked_spec_embed""",
}


def 							SCREAMING_SNAKE_CASE__  (			__a      ,       __a      ,       __a      ,       __a      ,       __a      ):

	for attribute in key.split('.'      ):
		snake_case_						:	List[str]      =		getattr(__a      ,       __a      )

	if weight_type is not None:
		snake_case_						:	int      =		getattr(__a      ,       __a      ).shape
	else:
		snake_case_						:	Dict      =		hf_pointer.shape

	assert hf_shape == value.shape, (
	    f"""Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be"""
	    f""" {value.shape} for {full_name}"""
	)

	if weight_type == "weight":
		snake_case_						:	Optional[int]      =		value
	elif weight_type == "weight_g":
		snake_case_						:	List[Any]      =		value
	elif weight_type == "weight_v":
		snake_case_						:	Optional[Any]      =		value
	elif weight_type == "bias":
		snake_case_						:	str      =		value
	else:
		snake_case_						:	int      =		value

	logger.info(f"""{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}."""      )


def 							SCREAMING_SNAKE_CASE__  (			__a      ,       __a      ,       __a      ):
	snake_case_						:	int      =		[]
	snake_case_						:	int      =		fairseq_model.state_dict()

	snake_case_						:	Dict      =		hf_model.hubert.feature_extractor if is_finetuned else hf_model.feature_extractor

	for name, value in fairseq_dict.items():
		snake_case_						:	Optional[int]      =		False
		if "conv_layers" in name:
			load_conv_layer(
			    __a      ,       __a      ,       __a      ,       __a      ,       hf_model.config.feat_extract_norm == 'group'      ,       )
			snake_case_						:	str      =		True
		else:
			for key, mapped_key in MAPPING.items():
				snake_case_						:	Optional[int]      =		'''hubert.''' + mapped_key if (is_finetuned and mapped_key != '''lm_head''') else mapped_key

				if key in name or (key.split('w2v_model.'      )[-1] == name.split('.'      )[0] and not is_finetuned):
					snake_case_						:	Union[str, Any]      =		True
					if "*" in mapped_key:
						snake_case_						:	List[Any]      =		name.split(__a      )[0].split('.'      )[-2]
						snake_case_						:	List[str]      =		mapped_key.replace('*'      ,       __a      )
					if "weight_g" in name:
						snake_case_						:	int      =		'''weight_g'''
					elif "weight_v" in name:
						snake_case_						:	Tuple      =		'''weight_v'''
					elif "weight" in name:
						snake_case_						:	Optional[int]      =		'''weight'''
					elif "bias" in name:
						snake_case_						:	str      =		'''bias'''
					else:
						snake_case_						:	Optional[Any]      =		None
					set_recursively(__a      ,       __a      ,       __a      ,       __a      ,       __a      )
				continue
		if not is_used:
			unused_weights.append(__a      )

	logger.warning(f"""Unused weights: {unused_weights}"""      )


def 							SCREAMING_SNAKE_CASE__  (			__a      ,       __a      ,       __a      ,       __a      ,       __a      ):
	snake_case_						:	int      =		full_name.split('conv_layers.'      )[-1]
	snake_case_						:	Any      =		name.split('.'      )
	snake_case_						:	List[str]      =		int(items[0]      )
	snake_case_						:	Optional[int]      =		int(items[1]      )

	if type_id == 0:
		if "bias" in name:
			assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
			    f"""{full_name} has size {value.shape}, but"""
			    f""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."""
			)
			snake_case_						:	Optional[Any]      =		value
			logger.info(f"""Feat extract conv layer {layer_id} was initialized from {full_name}."""      )
		elif "weight" in name:
			assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
			    f"""{full_name} has size {value.shape}, but"""
			    f""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."""
			)
			snake_case_						:	Tuple      =		value
			logger.info(f"""Feat extract conv layer {layer_id} was initialized from {full_name}."""      )
	elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
		if "bias" in name:
			assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
			    f"""{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was"""
			    " found."
			)
			snake_case_						:	Optional[int]      =		value
			logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""      )
		elif "weight" in name:
			assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
			    f"""{full_name} has size {value.shape}, but"""
			    f""" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found."""
			)
			snake_case_						:	Tuple      =		value
			logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""      )
	else:
		unused_weights.append(__a      )


@torch.no_grad()
def 							SCREAMING_SNAKE_CASE__  (			__a      ,       __a      ,       __a=None      ,       __a=None      ,       __a=True      ):

	if config_path is not None:
		snake_case_						:	Optional[Any]      =		HubertConfig.from_pretrained(__a      )
	else:
		snake_case_						:	List[str]      =		HubertConfig()

	if is_finetuned:
		if dict_path:
			snake_case_						:	Dict      =		Dictionary.load(__a      )

			# important change bos & pad token id since CTC symbol is <pad> and
			# not <s> as in fairseq
			snake_case_						:	Union[str, Any]      =		target_dict.pad_index
			snake_case_						:	Any      =		target_dict.bos_index
			snake_case_						:	Optional[int]      =		target_dict.eos_index
			snake_case_						:	Optional[Any]      =		len(target_dict.symbols      )
			snake_case_						:	int      =		os.path.join(__a      ,       'vocab.json'      )
			if not os.path.isdir(__a      ):
				logger.error('--pytorch_dump_folder_path ({}) should be a directory'.format(__a      )      )
				return
			os.makedirs(__a      ,       exist_ok=__a      )
			with open(__a      ,       'w'      ,       encoding='utf-8'      ) as vocab_handle:
				json.dump(target_dict.indices      ,       __a      )
			snake_case_						:	Tuple      =		WavaVecaCTCTokenizer(
			    __a      ,       unk_token=target_dict.unk_word      ,       pad_token=target_dict.pad_word      ,       bos_token=target_dict.bos_word      ,       eos_token=target_dict.eos_word      ,       word_delimiter_token='|'      ,       do_lower_case=__a      ,       )
			snake_case_						:	Tuple      =		True if config.feat_extract_norm == '''layer''' else False
			snake_case_						:	Tuple      =		WavaVecaFeatureExtractor(
			    feature_size=1      ,       sampling_rate=1_60_00      ,       padding_value=0      ,       do_normalize=__a      ,       return_attention_mask=__a      ,       )
			snake_case_						:	Tuple      =		WavaVecaProcessor(feature_extractor=__a      ,       tokenizer=__a      )
			processor.save_pretrained(__a      )

		snake_case_						:	Tuple      =		HubertForCTC(__a      )
	else:
		snake_case_						:	Optional[int]      =		HubertModel(__a      )

	if is_finetuned:
		snake_case_						:	List[str]      =		fairseq.checkpoint_utils.load_model_ensemble_and_task(
		    [checkpoint_path]      ,       arg_overrides={'data': '/'.join(dict_path.split('/'      )[:-1]      )}      )
	else:
		snake_case_						:	List[Any]      =		fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]      )

	snake_case_						:	int      =		model[0].eval()

	recursively_load_weights(__a      ,       __a      ,       __a      )

	hf_wavavec.save_pretrained(__a      )


if __name__ == "__main__":
						_SCREAMING_SNAKE_CASE							=						argparse.ArgumentParser()
						parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
						parser.add_argument("""--checkpoint_path""", default=None, type=str, help="""Path to fairseq checkpoint""")
						parser.add_argument("""--dict_path""", default=None, type=str, help="""Path to dict of fine-tuned model""")
						parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
						parser.add_argument(
						    """--not_finetuned""", action="""store_true""", help="""Whether the model to convert is a fine-tuned model or not"""
						)
						_SCREAMING_SNAKE_CASE							=						parser.parse_args()
						convert_hubert_checkpoint(
						    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
						)




import json
import os
import unittest

from transformers import AutoTokenizer, GPTaTokenizer, GPTaTokenizerFast
from transformers.models.gpta.tokenization_gpta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin

@require_tokenizers
class 				UpperCAmelCase_		(			__lowercase				,       unittest.TestCase   ):
							"""simple docstring"""







							UpperCAmelCase__       :						int	    =    GPTaTokenizer
							UpperCAmelCase__       :						str	    =    GPTaTokenizerFast
							UpperCAmelCase__       :						Tuple	    =    True
							UpperCAmelCase__       :						List[Any]	    =    {"add_prefix_space": True}
							UpperCAmelCase__       :						int	    =    False


							def 				__lowercase				(			self       )    ->    int:
												super().setUp()

												# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
												_a	:    str         =		[
												    '''l''',
												    '''o''',
												    '''w''',
												    '''e''',
												    '''r''',
												    '''s''',
												    '''t''',
												    '''i''',
												    '''d''',
												    '''n''',
												    '''\u0120''',
												    '''\u0120l''',
												    '''\u0120n''',
												    '''\u0120lo''',
												    '''\u0120low''',
												    '''er''',
												    '''\u0120lowest''',
												    '''\u0120newer''',
												    '''\u0120wider''',
												    '''<unk>''',
												    '''<|endoftext|>''',
												]
												_a	:    List[Any]         =		dict(zip(_a       ,							range(len(_a       )       )       )       )
												_a	:    Tuple         =		['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
												_a	:    List[str]         =		{'''unk_token''': '''<unk>'''}

												_a	:    List[str]         =		os.path.join(self.tmpdirname       ,							VOCAB_FILES_NAMES['''vocab_file''']       )
												_a	:    Tuple         =		os.path.join(self.tmpdirname       ,							VOCAB_FILES_NAMES['''merges_file''']       )
												with open(self.vocab_file       ,							'''w'''       ,							encoding='''utf-8'''       ) as fp:
																	fp.write(json.dumps(_a       ) + '''\n'''       )
												with open(self.merges_file       ,							'''w'''       ,							encoding='''utf-8'''       ) as fp:
																	fp.write('''\n'''.join(_a       )       )


							def 				__lowercase				(			self       ,							**_a       )    ->    List[str]:
												kwargs.update(self.special_tokens_map       )
												return GPTaTokenizer.from_pretrained(self.tmpdirname       ,							**_a       )


							def 				__lowercase				(			self       ,							**_a       )    ->    List[Any]:
												kwargs.update(self.special_tokens_map       )
												return GPTaTokenizerFast.from_pretrained(self.tmpdirname       ,							**_a       )


							def 				__lowercase				(			self       ,							_a       )    ->    Optional[Any]:
												_a	:    Tuple         =		'''lower newer'''
												_a	:    Tuple         =		'''lower newer'''
												return input_text, output_text


							def 				__lowercase				(			self       )    ->    Any:
												_a	:    Any         =		GPTaTokenizer(self.vocab_file       ,							self.merges_file       ,							**self.special_tokens_map       )
												_a	:    int         =		'''lower newer'''
												_a	:    int         =		['''\u0120low''', '''er''', '''\u0120''', '''n''', '''e''', '''w''', '''er''']
												_a	:    List[str]         =		tokenizer.tokenize(_a       ,							add_prefix_space=_a       )
												self.assertListEqual(_a       ,							_a       )

												_a	:    Optional[int]         =		tokens + [tokenizer.unk_token]
												_a	:    List[Any]         =		[1_4, 1_5, 1_0, 9, 3, 2, 1_5, 1_9]
												self.assertListEqual(tokenizer.convert_tokens_to_ids(_a       )       ,							_a       )


							def 				__lowercase				(			self       )    ->    Optional[int]:
												if not self.test_rust_tokenizer:
																	return

												_a	:    int         =		self.get_tokenizer()
												_a	:    Tuple         =		self.get_rust_tokenizer(add_prefix_space=_a       )

												_a	:    Tuple         =		'''lower newer'''

												# Testing tokenization
												_a	:    List[str]         =		tokenizer.tokenize(_a       ,							add_prefix_space=_a       )
												_a	:    Optional[Any]         =		rust_tokenizer.tokenize(_a       )
												self.assertListEqual(_a       ,							_a       )

												# Testing conversion to ids without special tokens
												_a	:    List[Any]         =		tokenizer.encode(_a       ,							add_special_tokens=_a       ,							add_prefix_space=_a       )
												_a	:    List[Any]         =		rust_tokenizer.encode(_a       ,							add_special_tokens=_a       )
												self.assertListEqual(_a       ,							_a       )

												# Testing conversion to ids with special tokens
												_a	:    List[str]         =		self.get_rust_tokenizer(add_prefix_space=_a       )
												_a	:    List[str]         =		tokenizer.encode(_a       ,							add_prefix_space=_a       )
												_a	:    Tuple         =		rust_tokenizer.encode(_a       )
												self.assertListEqual(_a       ,							_a       )

												# Testing the unknown token
												_a	:    Optional[Any]         =		tokens + [rust_tokenizer.unk_token]
												_a	:    str         =		[1_4, 1_5, 1_0, 9, 3, 2, 1_5, 1_9]
												self.assertListEqual(rust_tokenizer.convert_tokens_to_ids(_a       )       ,							_a       )


							def 				__lowercase				(			self       ,							*_a       ,							**_a       )    ->    int:
												# It's very difficult to mix/test pretokenization with byte-level
												# And get both GPT2 and Roberta to work at the same time (mostly an issue of adding a space before the string)
												pass


							def 				__lowercase				(			self       ,							_a=1_5       )    ->    str:
												for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																	with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""       ):
																						_a	:    Optional[int]         =		self.rust_tokenizer_class.from_pretrained(_a       ,							**_a       )

																						# Simple input
																						_a	:    List[str]         =		'''This is a simple input'''
																						_a	:    Optional[Any]         =		['''This is a simple input 1''', '''This is a simple input 2''']
																						_a	:    Tuple         =		('''This is a simple input''', '''This is a pair''')
																						_a	:    Optional[Any]         =		[
																						    ('''This is a simple input 1''', '''This is a simple input 2'''),
																						    ('''This is a simple pair 1''', '''This is a simple pair 2'''),
																						]

																						# Simple input tests
																						self.assertRaises(_a       ,							tokenizer_r.encode       ,							_a       ,							max_length=_a       ,							padding='''max_length'''       )

																						# Simple input
																						self.assertRaises(_a       ,							tokenizer_r.encode_plus       ,							_a       ,							max_length=_a       ,							padding='''max_length'''       )

																						# Simple input
																						self.assertRaises(
																						    _a       ,							tokenizer_r.batch_encode_plus       ,							_a       ,							max_length=_a       ,							padding='''max_length'''       ,							)

																						# Pair input
																						self.assertRaises(_a       ,							tokenizer_r.encode       ,							_a       ,							max_length=_a       ,							padding='''max_length'''       )

																						# Pair input
																						self.assertRaises(_a       ,							tokenizer_r.encode_plus       ,							_a       ,							max_length=_a       ,							padding='''max_length'''       )

																						# Pair input
																						self.assertRaises(
																						    _a       ,							tokenizer_r.batch_encode_plus       ,							_a       ,							max_length=_a       ,							padding='''max_length'''       ,							)


							def 				__lowercase				(			self       )    ->    List[Any]:
												_a	:    int         =		GPTaTokenizer.from_pretrained(self.tmpdirname       ,							pad_token='''<pad>'''       )

												# Simple input
												_a	:    int         =		'''This is a simple input'''
												_a	:    int         =		['''This is a simple input looooooooong''', '''This is a simple input''']
												_a	:    Any         =		('''This is a simple input''', '''This is a pair''')
												_a	:    Dict         =		[
												    ('''This is a simple input loooooong''', '''This is a simple input'''),
												    ('''This is a simple pair loooooong''', '''This is a simple pair'''),
												]

												_a	:    Optional[int]         =		tokenizer.pad_token_id

												_a	:    List[Any]         =		tokenizer(_a       ,							padding='''max_length'''       ,							max_length=3_0       ,							return_tensors='''np'''       )
												_a	:    Optional[int]         =		tokenizer(_a       ,							padding=_a       ,							truncate=_a       ,							return_tensors='''np'''       )
												_a	:    int         =		tokenizer(*_a       ,							padding='''max_length'''       ,							max_length=6_0       ,							return_tensors='''np'''       )
												_a	:    Union[str, Any]         =		tokenizer(_a       ,							padding=_a       ,							truncate=_a       ,							return_tensors='''np'''       )

												# s
												# test single string max_length padding
												self.assertEqual(out_s['''input_ids'''].shape[-1]       ,							3_0       )
												self.assertTrue(pad_token_id in out_s['''input_ids''']       )
												self.assertTrue(0 in out_s['''attention_mask''']       )

												# s2
												# test automatic padding
												self.assertEqual(out_sa['''input_ids'''].shape[-1]       ,							3_3       )
												# long slice doesn't have padding
												self.assertFalse(pad_token_id in out_sa['''input_ids'''][0]       )
												self.assertFalse(0 in out_sa['''attention_mask'''][0]       )
												# short slice does have padding
												self.assertTrue(pad_token_id in out_sa['''input_ids'''][1]       )
												self.assertTrue(0 in out_sa['''attention_mask'''][1]       )

												# p
												# test single pair max_length padding
												self.assertEqual(out_p['''input_ids'''].shape[-1]       ,							6_0       )
												self.assertTrue(pad_token_id in out_p['''input_ids''']       )
												self.assertTrue(0 in out_p['''attention_mask''']       )

												# p2
												# test automatic padding pair
												self.assertEqual(out_pa['''input_ids'''].shape[-1]       ,							5_2       )
												# long slice pair doesn't have padding
												self.assertFalse(pad_token_id in out_pa['''input_ids'''][0]       )
												self.assertFalse(0 in out_pa['''attention_mask'''][0]       )
												# short slice pair does have padding
												self.assertTrue(pad_token_id in out_pa['''input_ids'''][1]       )
												self.assertTrue(0 in out_pa['''attention_mask'''][1]       )


							def 				__lowercase				(			self       )    ->    Any:
												_a	:    Union[str, Any]         =		'''$$$'''
												_a	:    List[Any]         =		GPTaTokenizer.from_pretrained(self.tmpdirname       ,							bos_token=_a       ,							add_bos_token=_a       )

												_a	:    Any         =		'''This is a simple input'''
												_a	:    str         =		['''This is a simple input 1''', '''This is a simple input 2''']

												_a	:    Tuple         =		tokenizer.bos_token_id

												_a	:    Optional[Any]         =		tokenizer(_a       )
												_a	:    str         =		tokenizer(_a       )

												self.assertEqual(out_s.input_ids[0]       ,							_a       )
												self.assertTrue(all(o[0] == bos_token_id for o in out_sa.input_ids       )       )

												_a	:    str         =		tokenizer.decode(out_s.input_ids       )
												_a	:    Optional[Any]         =		tokenizer.batch_decode(out_sa.input_ids       )

												self.assertEqual(decode_s.split()[0]       ,							_a       )
												self.assertTrue(all(d.split()[0] == bos_token for d in decode_sa       )       )


							def 				__lowercase				(			self       )    ->    str:
												pass




							def 				__lowercase				(			self       )    ->    Dict:
												# TODO: change to self.get_tokenizers() when the fast version is implemented
												_a	:    Optional[int]         =		[self.get_tokenizer(do_lower_case=_a       ,							add_bos_token=_a       )]
												for tokenizer in tokenizers:
																	with self.subTest(F"""{tokenizer.__class__.__name__}"""       ):
																						_a	:    Tuple         =		'''Encode this.'''
																						_a	:    Optional[Any]         =		'''This one too please.'''
																						_a	:    List[Any]         =		tokenizer.encode(_a       ,							add_special_tokens=_a       )
																						encoded_sequence += tokenizer.encode(_a       ,							add_special_tokens=_a       )
																						_a	:    List[str]         =		tokenizer.encode_plus(
																						    _a       ,							_a       ,							add_special_tokens=_a       ,							return_special_tokens_mask=_a       ,							)
																						_a	:    int         =		encoded_sequence_dict['''input_ids''']
																						_a	:    int         =		encoded_sequence_dict['''special_tokens_mask''']
																						self.assertEqual(len(_a       )       ,							len(_a       )       )

																						_a	:    List[Any]         =		[
																						    (x if not special_tokens_mask[i] else None) for i, x in enumerate(_a       )
																						]
																						_a	:    str         =		[x for x in filtered_sequence if x is not None]
																						self.assertEqual(_a       ,							_a       )



@require_tokenizers
class 				UpperCAmelCase_		(			unittest.TestCase   ):
							"""simple docstring"""







							def 				__lowercase				(			self       )    ->    List[Any]:
												# More context:
												# https://huggingface.co/wjmcat/opt-350m-paddle/discussions/1
												# https://huggingface.slack.com/archives/C01N44FJDHT/p1653511495183519
												# https://github.com/huggingface/transformers/pull/17088#discussion_r871246439

												_a	:    Any         =		AutoTokenizer.from_pretrained('''facebook/opt-350m'''       ,							from_slow=_a       )
												_a	:    int         =		'''A photo of a cat'''

												_a	:    List[Any]         =		tokenizer.encode(
												    _a       ,							)
												self.assertEqual(_a       ,							[2, 2_5_0, 1_3_4_5, 9, 1_0, 4_7_5_8]       )
												tokenizer.save_pretrained('''test_opt'''       )

												_a	:    Union[str, Any]         =		AutoTokenizer.from_pretrained('''./test_opt'''       )
												_a	:    Any         =		tokenizer.encode(
												    _a       ,							)
												self.assertEqual(_a       ,							[2, 2_5_0, 1_3_4_5, 9, 1_0, 4_7_5_8]       )


							def 				__lowercase				(			self       )    ->    int:
												_a	:    Union[str, Any]         =		AutoTokenizer.from_pretrained('''facebook/opt-350m'''       ,							use_slow=_a       )
												_a	:    Any         =		'''A photo of a cat'''

												_a	:    Optional[int]         =		tokenizer.encode(
												    _a       ,							)
												# Same as above
												self.assertEqual(_a       ,							[2, 2_5_0, 1_3_4_5, 9, 1_0, 4_7_5_8]       )




							@unittest.skip('''This test is failing because of a bug in the fast tokenizer'''       )
							def 				__lowercase				(			self       )    ->    Any:
												_a	:    Union[str, Any]         =		AutoTokenizer.from_pretrained('''facebook/opt-350m'''       ,							from_slow=_a       )

												_a	:    Optional[Any]         =		'''bos'''
												_a	:    Optional[Any]         =		tokenizer.get_vocab()['''bos''']

												_a	:    str         =		'''A photo of a cat'''
												_a	:    int         =		tokenizer.encode(
												    _a       ,							)
												# We changed the bos token
												self.assertEqual(_a       ,							[3_1_9_5_7, 2_5_0, 1_3_4_5, 9, 1_0, 4_7_5_8]       )
												tokenizer.save_pretrained('''./tok'''       )
												_a	:    Dict         =		AutoTokenizer.from_pretrained('''./tok'''       )
												self.assertTrue(tokenizer.is_fast       )
												_a	:    Union[str, Any]         =		tokenizer.encode(
												    _a       ,							)
												self.assertEqual(_a       ,							[3_1_9_5_7, 2_5_0, 1_3_4_5, 9, 1_0, 4_7_5_8]       )



