


'''simple docstring'''




import argparse
import os
from pathlib import Path

import torch
from bark.generation import _load_model as _bark_load_model
from huggingface_hub import hf_hub_download

from transformers import EncodecConfig, EncodecModel, set_seed
from transformers.models.bark.configuration_bark import (
    BarkCoarseConfig,
    BarkConfig,
    BarkFineConfig,
    BarkSemanticConfig,
)
from transformers.models.bark.generation_configuration_bark import (
    BarkCoarseGenerationConfig,
    BarkFineGenerationConfig,
    BarkGenerationConfig,
    BarkSemanticGenerationConfig,
)
from transformers.models.bark.modeling_bark import BarkCoarseModel, BarkFineModel, BarkModel, BarkSemanticModel
from transformers.utils import logging


logging.set_verbosity_info()
_lowerCAmelCase  :		List[str]		     =	logging.get_logger(__name__)

set_seed(770)


_lowerCAmelCase  :		Optional[int]		     =	{
    'c_attn': 'att_proj',
    'c_proj': 'out_proj',
    'c_fc': 'in_proj',
    'transformer.': '',
    'h.': 'layers.',
    'ln_1': 'layernorm_1',
    'ln_2': 'layernorm_2',
    'ln_f': 'layernorm_final',
    'wpe': 'position_embeds_layer',
    'wte': 'input_embeds_layer',
}


_lowerCAmelCase  :		int		     =	{
    'text_small': {
        'repo_id': 'suno/bark',
        'file_name': 'text.pt',
    },
    'coarse_small': {
        'repo_id': 'suno/bark',
        'file_name': 'coarse.pt',
    },
    'fine_small': {
        'repo_id': 'suno/bark',
        'file_name': 'fine.pt',
    },
    'text': {
        'repo_id': 'suno/bark',
        'file_name': 'text_2.pt',
    },
    'coarse': {
        'repo_id': 'suno/bark',
        'file_name': 'coarse_2.pt',
    },
    'fine': {
        'repo_id': 'suno/bark',
        'file_name': 'fine_2.pt',
    },
}

_lowerCAmelCase  :		Any		     =	os.path.dirname(os.path.abspath(__file__))
_lowerCAmelCase  :		List[Any]		     =	os.path.join(os.path.expanduser('~'), '.cache')
_lowerCAmelCase  :		Optional[int]		     =	os.path.join(os.getenv('XDG_CACHE_HOME', default_cache_dir), 'suno', 'bark_v0')

def 			__UpperCamelCase    (    _A    :				List[str]					,   _A    :				List[Any]=False				)    ->      str:




 """simple docstring"""





 lowerCAmelCase					:    Dict      							= model_type
 if use_small:
  key += "_small"
 return os.path.join(_A					,   REMOTE_MODEL_PATHS[key]['file_name']				)

def 			__UpperCamelCase    (    _A    :				Union[str, Any]					,   _A    :				List[str]				)    ->      List[str]:




 """simple docstring"""





 os.makedirs(_A					,   exist_ok=_A				)
 hf_hub_download(repo_id=_A					,   filename=_A					,   local_dir=_A				)

def 			__UpperCamelCase    (    _A    :				str					,   _A    :				Union[str, Any]					,   _A    :				Any=False					,   _A    :				Union[str, Any]="text"				)    ->      Optional[int]:




 """simple docstring"""





 if model_type == "text":
  lowerCAmelCase					:    int      							= BarkSemanticModel
  lowerCAmelCase					:    Optional[Any]      							= BarkSemanticConfig
  lowerCAmelCase					:    List[Any]      							= BarkSemanticGenerationConfig
 elif model_type == "coarse":
  lowerCAmelCase					:    str      							= BarkCoarseModel
  lowerCAmelCase					:    Dict      							= BarkCoarseConfig
  lowerCAmelCase					:    Optional[int]      							= BarkCoarseGenerationConfig
 elif model_type == "fine":
  lowerCAmelCase					:    Optional[int]      							= BarkFineModel
  lowerCAmelCase					:    str      							= BarkFineConfig
  lowerCAmelCase					:    str      							= BarkFineGenerationConfig
 else:
  raise NotImplementedError()
 lowerCAmelCase					:    Optional[Any]      							= F"{model_type}_small" if use_small else model_type
 lowerCAmelCase					:    Any      							= REMOTE_MODEL_PATHS[model_key]
 if not os.path.exists(_A				):
  logger.info(F"{model_type} model not found, downloading into `{CACHE_DIR}`."				)
  _download(model_info['repo_id']					,   model_info['file_name']				)
 lowerCAmelCase					:    Optional[int]      							= torch.load(_A					,   map_location=_A				)
 # this is a hack
 lowerCAmelCase					:    List[Any]      							= checkpoint['model_args']
 if "input_vocab_size" not in model_args:
  lowerCAmelCase					:    Any      							= model_args['vocab_size']
  lowerCAmelCase					:    List[str]      							= model_args['vocab_size']
  del model_args["vocab_size"]

 # convert Bark model arguments to HF Bark model arguments
 lowerCAmelCase					:    Any      							= model_args.pop('n_head'				)
 lowerCAmelCase					:    Tuple      							= model_args.pop('n_embd'				)
 lowerCAmelCase					:    List[str]      							= model_args.pop('n_layer'				)

 lowerCAmelCase					:    Tuple      							= ConfigClass(**checkpoint['model_args']				)
 lowerCAmelCase					:    List[Any]      							= ModelClass(config=_A				)
 lowerCAmelCase					:    Union[str, Any]      							= GenerationConfigClass()

 lowerCAmelCase					:    Dict      							= model_generation_config
 lowerCAmelCase					:    List[Any]      							= checkpoint['model']
 # fixup checkpoint
 lowerCAmelCase					:    Any      							= '_orig_mod.'
 for k, v in list(state_dict.items()				):
  if k.startswith(_A				):
   # replace part of the key with corresponding layer name in HF implementation
   lowerCAmelCase					:    Union[str, Any]      							= k[len(_A				) :]
   for old_layer_name in new_layer_name_dict:
    lowerCAmelCase					:    int      							= new_k.replace(_A					,   new_layer_name_dict[old_layer_name]				)

   lowerCAmelCase					:    Dict      							= state_dict.pop(_A				)

 lowerCAmelCase					:    Any      							= set(state_dict.keys()				) - set(model.state_dict().keys()				)
 lowerCAmelCase					:    str      							= {k for k in extra_keys if not k.endswith('.attn.bias'				)}
 lowerCAmelCase					:    int      							= set(model.state_dict().keys()				) - set(state_dict.keys()				)
 lowerCAmelCase					:    Optional[int]      							= {k for k in missing_keys if not k.endswith('.attn.bias'				)}
 if len(_A				) != 0:
  raise ValueError(F"extra keys found: {extra_keys}"				)
 if len(_A				) != 0:
  raise ValueError(F"missing keys: {missing_keys}"				)
 model.load_state_dict(_A					,   strict=_A				)
 lowerCAmelCase					:    Tuple      							= model.num_parameters(exclude_embeddings=_A				)
 lowerCAmelCase					:    int      							= checkpoint['best_val_loss'].item()
 logger.info(F"model loaded: {round(n_params/1e6					,   1				)}M params, {round(_A					,   3				)} loss"				)
 model.eval()
 model.to(_A				)
 del checkpoint, state_dict

 return model

def 			__UpperCamelCase    (    _A    :				int					,   _A    :				List[str]=False					,   _A    :				Union[str, Any]="text"				)    ->      Optional[Any]:




 """simple docstring"""





 if model_type not in ("text", "coarse", "fine"):
  raise NotImplementedError()

 lowerCAmelCase					:    str      							= 'cpu'  # do conversion on cpu

 lowerCAmelCase					:    str      							= _get_ckpt_path(_A					,   use_small=_A				)
 lowerCAmelCase					:    List[Any]      							= _load_model(_A					,   _A					,   model_type=_A					,   use_small=_A				)

 # load bark initial model
 lowerCAmelCase					:    Any      							= _bark_load_model(_A					,   'cpu'					,   model_type=_A					,   use_small=_A				)

 if model_type == "text":
  lowerCAmelCase					:    Optional[Any]      							= bark_model['model']

 if model.num_parameters(exclude_embeddings=_A				) != bark_model.get_num_params():
  raise ValueError('initial and new models don\'t have the same number of parameters'				)

 # check if same output as the bark model
 lowerCAmelCase					:    Union[str, Any]      							= 5
 lowerCAmelCase					:    Optional[Any]      							= 10

 if model_type in ["text", "coarse"]:
  lowerCAmelCase					:    int      							= torch.randint(2_56					,   (batch_size, sequence_length)					,   dtype=torch.int				)
  lowerCAmelCase					:    Dict      							= bark_model(_A				)[0]

  lowerCAmelCase					:    int      							= model(_A				)

  # take last logits
  lowerCAmelCase					:    Tuple      							= output_new_model_total.logits[:, [-1], :]

 else:
  lowerCAmelCase					:    int      							= 3
  lowerCAmelCase					:    int      							= 8
  lowerCAmelCase					:    List[str]      							= torch.randint(2_56					,   (batch_size, sequence_length, n_codes_total)					,   dtype=torch.int				)

  lowerCAmelCase					:    Optional[Any]      							= model(_A					,   _A				)
  lowerCAmelCase					:    int      							= bark_model(_A					,   _A				)

  lowerCAmelCase					:    Any      							= output_new_model_total.logits

 # output difference should come from the difference of self-attention implementation design
 if output_new_model.shape != output_old_model.shape:
  raise ValueError('initial and new outputs don\'t have the same shape'				)
 if (output_new_model - output_old_model).abs().max().item() > 1e-3:
  raise ValueError('initial and new outputs are not equal'				)

 Path(_A				).mkdir(exist_ok=_A				)
 model.save_pretrained(_A				)

def 			__UpperCamelCase    (    _A    :				str					,   _A    :				Union[str, Any]					,   _A    :				Any					,   _A    :				Optional[Any]					,   _A    :				List[str]					,   _A    :				Tuple					,   )    ->      Union[str, Any]:




 """simple docstring"""





 lowerCAmelCase					:    Optional[int]      							= os.path.join(_A					,   _A				)

 lowerCAmelCase					:    Dict      							= BarkSemanticConfig.from_pretrained(os.path.join(_A					,   'config.json'				)				)
 lowerCAmelCase					:    int      							= BarkCoarseConfig.from_pretrained(os.path.join(_A					,   'config.json'				)				)
 lowerCAmelCase					:    Tuple      							= BarkFineConfig.from_pretrained(os.path.join(_A					,   'config.json'				)				)
 lowerCAmelCase					:    str      							= EncodecConfig.from_pretrained('facebook/encodec_24khz'				)

 lowerCAmelCase					:    int      							= BarkSemanticModel.from_pretrained(_A				)
 lowerCAmelCase					:    Optional[int]      							= BarkCoarseModel.from_pretrained(_A				)
 lowerCAmelCase					:    str      							= BarkFineModel.from_pretrained(_A				)
 lowerCAmelCase					:    List[str]      							= EncodecModel.from_pretrained('facebook/encodec_24khz'				)

 lowerCAmelCase					:    Optional[Any]      							= BarkConfig.from_sub_model_configs(
     _A					,   _A					,   _A					,   _A				)

 lowerCAmelCase					:    int      							= BarkGenerationConfig.from_sub_model_configs(
     semantic.generation_config					,   coarseAcoustic.generation_config					,   fineAcoustic.generation_config				)

 lowerCAmelCase					:    Dict      							= BarkModel(_A				)

 lowerCAmelCase					:    int      							= semantic
 lowerCAmelCase					:    Union[str, Any]      							= coarseAcoustic
 lowerCAmelCase					:    int      							= fineAcoustic
 lowerCAmelCase					:    Dict      							= codec

 lowerCAmelCase					:    List[Any]      							= bark_generation_config

 Path(_A				).mkdir(exist_ok=_A				)
 bark.save_pretrained(_A					,   repo_id=_A					,   push_to_hub=_A				)


if __name__ == "__main__":
    _lowerCAmelCase  :		List[str]		     =	argparse.ArgumentParser()
    # Required parameters

    parser.add_argument('model_type', type=str, help='text, coarse or fine.')
    parser.add_argument('pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
    parser.add_argument('--is_small', action='store_true', help='convert the small version instead of the large.')

    _lowerCAmelCase  :		str		     =	parser.parse_args()

    load_model(args.pytorch_dump_folder_path, model_type=args.model_type, use_small=args.is_small)




'''simple docstring'''




import unittest

from transformers import PegasusTokenizer, PegasusTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


_lowerCAmelCase  :		Union[str, Any]		     =	get_tests_dir('fixtures/test_sentencepiece_no_bos.model')



@require_sentencepiece
@require_tokenizers
class  lowerCAmelCase				(				a      ,				unittest.TestCase							):
	_lowerCamelCase   :					str			      = PegasusTokenizer
	_lowerCamelCase   :					Union[str, Any]			      = PegasusTokenizerFast
	_lowerCamelCase   :					Optional[Any]			      = True
	_lowerCamelCase   :					Optional[Any]			      = True



	def 				lowercase     (					self    ):
		super().setUp()

		# We have a SentencePiece fixture for testing
		lowerCAmelCase					:    List[Any]      							= PegasusTokenizer(snake_case__    )
		tokenizer.save_pretrained(self.tmpdirname    )



	@cached_property
	def 				lowercase     (					self    ):
		return PegasusTokenizer.from_pretrained('google/pegasus-large'    )



	def 				lowercase     (					self ,					**snake_case__    ):
		return PegasusTokenizer.from_pretrained(self.tmpdirname ,					**snake_case__    )



	def 				lowercase     (					self ,					snake_case__    ):
		return ("This is a test", "This is a test")



	def 				lowercase     (					self    ):
		lowerCAmelCase					:    Optional[int]      							= '</s>'
		lowerCAmelCase					:    int      							= 1

		self.assertEqual(self.get_tokenizer()._convert_token_to_id(snake_case__    ) ,					snake_case__    )
		self.assertEqual(self.get_tokenizer()._convert_id_to_token(snake_case__    ) ,					snake_case__    )



	def 				lowercase     (					self    ):
		lowerCAmelCase					:    Union[str, Any]      							= list(self.get_tokenizer().get_vocab().keys()    )

		self.assertEqual(vocab_keys[0] ,					'<pad>'    )
		self.assertEqual(vocab_keys[1] ,					'</s>'    )
		self.assertEqual(vocab_keys[-1] ,					'v'    )
		self.assertEqual(len(snake_case__    ) ,					1103    )



	def 				lowercase     (					self    ):
		self.assertEqual(self.get_tokenizer().vocab_size ,					1103    )



	def 				lowercase     (					self    ):
		lowerCAmelCase					:    List[Any]      							= self.rust_tokenizer_class.from_pretrained(self.tmpdirname    )
		lowerCAmelCase					:    List[Any]      							= self.tokenizer_class.from_pretrained(self.tmpdirname    )
		lowerCAmelCase					:    Optional[Any]      							= (
		    'Let\'s see which <unk> is the better <unk_token_11> one <mask_1> It seems like this <mask_2> was important'
		    ' </s> <pad> <pad> <pad>'
		)
		lowerCAmelCase					:    Optional[Any]      							= rust_tokenizer([raw_input_str] ,					return_tensors=snake_case__ ,					add_special_tokens=snake_case__    ).input_ids[0]
		lowerCAmelCase					:    Optional[int]      							= py_tokenizer([raw_input_str] ,					return_tensors=snake_case__ ,					add_special_tokens=snake_case__    ).input_ids[0]
		self.assertListEqual(snake_case__ ,					snake_case__    )



	def 				lowercase     (					self    ):
		lowerCAmelCase					:    Any      							= self._large_tokenizer
		# <mask_1> masks whole sentence while <mask_2> masks single word
		lowerCAmelCase					:    List[str]      							= '<mask_1> To ensure a <mask_2> flow of bank resolutions.'
		lowerCAmelCase					:    Optional[Any]      							= [2, 413, 615, 114, 3, 1971, 113, 1679, 1_0710, 107, 1]
		lowerCAmelCase					:    Optional[Any]      							= tokenizer([raw_input_str] ,					return_tensors=snake_case__    ).input_ids[0]
		self.assertListEqual(snake_case__ ,					snake_case__    )



	def 				lowercase     (					self    ):
		lowerCAmelCase					:    Optional[Any]      							= self._large_tokenizer
		# The tracebacks for the following asserts are **better** without messages or self.assertEqual
		assert tokenizer.vocab_size == 9_6103
		assert tokenizer.pad_token_id == 0
		assert tokenizer.eos_token_id == 1
		assert tokenizer.offset == 103
		assert tokenizer.unk_token_id == tokenizer.offset + 2 == 105
		assert tokenizer.unk_token == "<unk>"
		assert tokenizer.model_max_length == 1024
		lowerCAmelCase					:    List[Any]      							= 'To ensure a smooth flow of bank resolutions.'
		lowerCAmelCase					:    Optional[int]      							= [413, 615, 114, 2291, 1971, 113, 1679, 1_0710, 107, 1]
		lowerCAmelCase					:    Any      							= tokenizer([raw_input_str] ,					return_tensors=snake_case__    ).input_ids[0]
		self.assertListEqual(snake_case__ ,					snake_case__    )
		assert tokenizer.convert_ids_to_tokens([0, 1, 2, 3]    ) == ["<pad>", "</s>", "<mask_1>", "<mask_2>"]



	@require_torch
	def 				lowercase     (					self    ):
		lowerCAmelCase					:    Union[str, Any]      							= ['This is going to be way too long.' * 150, 'short example']
		lowerCAmelCase					:    int      							= ['not super long but more than 5 tokens', 'tiny']
		lowerCAmelCase					:    Dict      							= self._large_tokenizer(snake_case__ ,					padding=snake_case__ ,					truncation=snake_case__ ,					return_tensors='pt'    )
		lowerCAmelCase					:    Dict      							= self._large_tokenizer(
		    text_target=snake_case__ ,					max_length=5 ,					padding=snake_case__ ,					truncation=snake_case__ ,					return_tensors='pt'    )

		assert batch.input_ids.shape == (2, 1024)
		assert batch.attention_mask.shape == (2, 1024)
		assert targets["input_ids"].shape == (2, 5)
		assert len(snake_case__    ) == 2  # input_ids, attention_mask.



	@slow
	def 				lowercase     (					self    ):
		# fmt: off
		lowerCAmelCase					:    Tuple      							= {'input_ids': [[3_8979, 143, 1_8485, 606, 130, 2_6669, 8_7686, 121, 5_4189, 1129, 111, 2_6669, 8_7686, 121, 9114, 1_4787, 121, 1_3249, 158, 592, 956, 121, 1_4621, 3_1576, 143, 6_2613, 108, 9688, 930, 4_3430, 1_1562, 6_2613, 304, 108, 1_1443, 897, 108, 9314, 1_7415, 6_3399, 108, 1_1443, 7614, 1_8316, 118, 4284, 7148, 1_2430, 143, 1400, 2_5703, 158, 111, 4284, 7148, 1_1772, 143, 2_1297, 1064, 158, 122, 204, 3506, 1754, 1133, 1_4787, 1581, 115, 3_3224, 4482, 111, 1355, 110, 2_9173, 317, 5_0833, 108, 2_0147, 9_4665, 111, 7_7198, 107, 1], [110, 6_2613, 117, 638, 112, 1133, 121, 2_0098, 1355, 7_9050, 1_3872, 135, 1596, 5_3541, 1352, 141, 1_3039, 5542, 124, 302, 518, 111, 268, 2956, 115, 149, 4427, 107, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [139, 1235, 2799, 1_8289, 1_7780, 204, 109, 9474, 1296, 107, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
		# fmt: on

		self.tokenizer_integration_test_util(
		    expected_encoding=snake_case__ ,					model_name='google/bigbird-pegasus-large-arxiv' ,					revision='ba85d0851d708441f91440d509690f1ab6353415' ,					)



@require_sentencepiece
@require_tokenizers
class  lowerCAmelCase				(				a      ,				unittest.TestCase							):
	_lowerCamelCase   :					Optional[Any]			      = PegasusTokenizer
	_lowerCamelCase   :					str			      = PegasusTokenizerFast
	_lowerCamelCase   :					Tuple			      = True
	_lowerCamelCase   :					int			      = True



	def 				lowercase     (					self    ):
		super().setUp()

		# We have a SentencePiece fixture for testing
		lowerCAmelCase					:    int      							= PegasusTokenizer(snake_case__ ,					offset=0 ,					mask_token_sent=snake_case__ ,					mask_token='[MASK]'    )
		tokenizer.save_pretrained(self.tmpdirname    )



	@cached_property
	def 				lowercase     (					self    ):
		return PegasusTokenizer.from_pretrained('google/bigbird-pegasus-large-arxiv'    )



	def 				lowercase     (					self ,					**snake_case__    ):
		return PegasusTokenizer.from_pretrained(self.tmpdirname ,					**snake_case__    )



	def 				lowercase     (					self ,					snake_case__    ):
		return ("This is a test", "This is a test")



	def 				lowercase     (					self    ):
		lowerCAmelCase					:    Tuple      							= self.rust_tokenizer_class.from_pretrained(self.tmpdirname    )
		lowerCAmelCase					:    Union[str, Any]      							= self.tokenizer_class.from_pretrained(self.tmpdirname    )
		lowerCAmelCase					:    List[str]      							= (
		    'Let\'s see which <unk> is the better <unk_token> one [MASK] It seems like this [MASK] was important </s>'
		    ' <pad> <pad> <pad>'
		)
		lowerCAmelCase					:    Dict      							= rust_tokenizer([raw_input_str] ,					return_tensors=snake_case__ ,					add_special_tokens=snake_case__    ).input_ids[0]
		lowerCAmelCase					:    Union[str, Any]      							= py_tokenizer([raw_input_str] ,					return_tensors=snake_case__ ,					add_special_tokens=snake_case__    ).input_ids[0]
		self.assertListEqual(snake_case__ ,					snake_case__    )



	@require_torch
	def 				lowercase     (					self    ):
		lowerCAmelCase					:    Optional[int]      							= ['This is going to be way too long.' * 1000, 'short example']
		lowerCAmelCase					:    Union[str, Any]      							= ['not super long but more than 5 tokens', 'tiny']
		lowerCAmelCase					:    List[str]      							= self._large_tokenizer(snake_case__ ,					padding=snake_case__ ,					truncation=snake_case__ ,					return_tensors='pt'    )
		lowerCAmelCase					:    List[str]      							= self._large_tokenizer(
		    text_target=snake_case__ ,					max_length=5 ,					padding=snake_case__ ,					truncation=snake_case__ ,					return_tensors='pt'    )

		assert batch.input_ids.shape == (2, 4096)
		assert batch.attention_mask.shape == (2, 4096)
		assert targets["input_ids"].shape == (2, 5)
		assert len(snake_case__    ) == 2  # input_ids, attention_mask.



	def 				lowercase     (					self    ):
		lowerCAmelCase					:    List[str]      							= (
		    'This is an example string that is used to test the original TF implementation against the HF'
		    ' implementation'
		)

		lowerCAmelCase					:    Tuple      							= self._large_tokenizer(snake_case__    ).input_ids

		self.assertListEqual(
		    snake_case__ ,					[182, 117, 142, 587, 4211, 120, 117, 263, 112, 804, 109, 856, 2_5016, 3137, 464, 109, 2_6955, 3137, 1] ,					)
