





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()
_SCREAMING_SNAKE_CASE      	=		logging.get_logger(__name__)

set_seed(7_70)


_SCREAMING_SNAKE_CASE      	=		{
    """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""",
}


_SCREAMING_SNAKE_CASE      	=		{
    """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""",
    },
}

_SCREAMING_SNAKE_CASE      	=		os.path.dirname(os.path.abspath(__file__))
_SCREAMING_SNAKE_CASE      	=		os.path.join(os.path.expanduser("""~"""), """.cache""")
_SCREAMING_SNAKE_CASE      	=		os.path.join(os.getenv("""XDG_CACHE_HOME""", default_cache_dir), """suno""", """bark_v0""")


def 							SCREAMING_SNAKE_CASE__						(    __a   , __a=False   ):
	snake_case_		:			Dict											=	model_type
	if use_small:
		key += "_small"
	return os.path.join(__a   , REMOTE_MODEL_PATHS[key]['file_name']   )


def 							SCREAMING_SNAKE_CASE__						(    __a   , __a   ):
	os.makedirs(__a   , exist_ok=__a   )
	hf_hub_download(repo_id=__a   , filename=__a   , local_dir=__a   )


def 							SCREAMING_SNAKE_CASE__						(    __a   , __a   , __a=False   , __a="text"   ):
	if model_type == "text":
		snake_case_		:			Any											=	BarkSemanticModel
		snake_case_		:			Optional[int]											=	BarkSemanticConfig
		snake_case_		:			Any											=	BarkSemanticGenerationConfig
	elif model_type == "coarse":
		snake_case_		:			Tuple											=	BarkCoarseModel
		snake_case_		:			Any											=	BarkCoarseConfig
		snake_case_		:			Dict											=	BarkCoarseGenerationConfig
	elif model_type == "fine":
		snake_case_		:			Optional[int]											=	BarkFineModel
		snake_case_		:			Dict											=	BarkFineConfig
		snake_case_		:			Dict											=	BarkFineGenerationConfig
	else:
		raise NotImplementedError()
	snake_case_		:			Union[str, Any]											=	f"""{model_type}_small""" if use_small else model_type
	snake_case_		:			Union[str, 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']   )
	snake_case_		:			int											=	torch.load(__a   , map_location=__a   )
	# this is a hack
	snake_case_		:			List[str]											=	checkpoint['model_args']
	if "input_vocab_size" not in model_args:
		snake_case_		:			List[str]											=	model_args['vocab_size']
		snake_case_		:			Tuple											=	model_args['vocab_size']
		del model_args["vocab_size"]

	# convert Bark model arguments to HF Bark model arguments
	snake_case_		:			str											=	model_args.pop('n_head'   )
	snake_case_		:			Optional[int]											=	model_args.pop('n_embd'   )
	snake_case_		:			int											=	model_args.pop('n_layer'   )

	snake_case_		:			int											=	ConfigClass(**checkpoint['model_args']   )
	snake_case_		:			Dict											=	ModelClass(config=__a   )
	snake_case_		:			Dict											=	GenerationConfigClass()

	snake_case_		:			str											=	model_generation_config
	snake_case_		:			Optional[int]											=	checkpoint['model']
	# fixup checkpoint
	snake_case_		:			Dict											=	'_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
			snake_case_		:			Optional[Any]											=	k[len(__a   ) :]
			for old_layer_name in new_layer_name_dict:
				snake_case_		:			str											=	new_k.replace(__a   , new_layer_name_dict[old_layer_name]   )

			snake_case_		:			str											=	state_dict.pop(__a   )

	snake_case_		:			Optional[int]											=	set(state_dict.keys()   ) - set(model.state_dict().keys()   )
	snake_case_		:			List[Any]											=	{k for k in extra_keys if not k.endswith('.attn.bias'   )}
	snake_case_		:			Any											=	set(model.state_dict().keys()   ) - set(state_dict.keys()   )
	snake_case_		:			Optional[Any]											=	{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   )
	snake_case_		:			Any											=	model.num_parameters(exclude_embeddings=__a   )
	snake_case_		:			List[str]											=	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 							SCREAMING_SNAKE_CASE__						(    __a   , __a=False   , __a="text"   ):
	if model_type not in ("text", "coarse", "fine"):
		raise NotImplementedError()

	snake_case_		:			List[str]											=	'cpu'  # do conversion on cpu

	snake_case_		:			List[Any]											=	_get_ckpt_path(__a   , use_small=__a   )
	snake_case_		:			Tuple											=	_load_model(__a   , __a   , model_type=__a   , use_small=__a   )

	# load bark initial model
	snake_case_		:			List[str]											=	_bark_load_model(__a   , 'cpu'   , model_type=__a   , use_small=__a   )

	if model_type == "text":
		snake_case_		:			List[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
	snake_case_		:			Optional[int]											=	5
	snake_case_		:			Optional[int]											=	10

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

		snake_case_		:			List[str]											=	model(__a   )

		# take last logits
		snake_case_		:			Optional[Any]											=	output_new_model_total.logits[:, [-1], :]

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

		snake_case_		:			Any											=	model(__a   , __a   )
		snake_case_		:			Dict											=	bark_model(__a   , __a   )

		snake_case_		:			Dict											=	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 							SCREAMING_SNAKE_CASE__						(    __a   , __a   , __a   , __a   , __a   , __a   , ):
	snake_case_		:			str											=	os.path.join(__a   , __a   )

	snake_case_		:			Dict											=	BarkSemanticConfig.from_pretrained(os.path.join(__a   , 'config.json'   )   )
	snake_case_		:			Union[str, Any]											=	BarkCoarseConfig.from_pretrained(os.path.join(__a   , 'config.json'   )   )
	snake_case_		:			Dict											=	BarkFineConfig.from_pretrained(os.path.join(__a   , 'config.json'   )   )
	snake_case_		:			List[Any]											=	EncodecConfig.from_pretrained('facebook/encodec_24khz'   )

	snake_case_		:			Tuple											=	BarkSemanticModel.from_pretrained(__a   )
	snake_case_		:			Optional[Any]											=	BarkCoarseModel.from_pretrained(__a   )
	snake_case_		:			Optional[int]											=	BarkFineModel.from_pretrained(__a   )
	snake_case_		:			Optional[int]											=	EncodecModel.from_pretrained('facebook/encodec_24khz'   )

	snake_case_		:			Optional[int]											=	BarkConfig.from_sub_model_configs(
	    __a   , __a   , __a   , __a   )

	snake_case_		:			Tuple											=	BarkGenerationConfig.from_sub_model_configs(
	    semantic.generation_config   , coarseAcoustic.generation_config   , fineAcoustic.generation_config   )

	snake_case_		:			Optional[int]											=	BarkModel(__a   )

	snake_case_		:			Optional[int]											=	semantic
	snake_case_		:			Any											=	coarseAcoustic
	snake_case_		:			List[Any]											=	fineAcoustic
	snake_case_		:			Any											=	codec

	snake_case_		:			str											=	bark_generation_config

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


if __name__ == "__main__":
							_SCREAMING_SNAKE_CASE      	=		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.""")

							_SCREAMING_SNAKE_CASE      	=		parser.parse_args()

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








import gc
import unittest

from transformers import MODEL_FOR_MASKED_LM_MAPPING, TF_MODEL_FOR_MASKED_LM_MAPPING, FillMaskPipeline, pipeline
from transformers.pipelines import PipelineException
from transformers.testing_utils import (
    is_pipeline_test,
    is_torch_available,
    nested_simplify,
    require_tf,
    require_torch,
    require_torch_gpu,
    slow,
)

from .test_pipelines_common import ANY
@is_pipeline_test
class SCREAMING_SNAKE_CASE_						(					unittest.TestCase					):
						__magic_name__:			Union[str, Any]       =       MODEL_FOR_MASKED_LM_MAPPING
						__magic_name__:			Optional[int]       =       TF_MODEL_FOR_MASKED_LM_MAPPING






						def UpperCAmelCase_				(      self      :      str	)							->    str:







							"""simple docstring"""


							super().tearDown()
							# clean-up as much as possible GPU memory occupied by PyTorch
							gc.collect()
							if is_torch_available():
								import torch

								torch.cuda.empty_cache()






						@require_tf
						def UpperCAmelCase_				(      self      :      Optional[int]	)							->    Union[str, Any]:







							"""simple docstring"""


							snake_case_		:			int											=	pipeline(task='fill-mask'		,   model='sshleifer/tiny-distilroberta-base'		,   top_k=2		,   framework='tf'	)
							snake_case_		:			Optional[Any]											=	unmasker('My name is <mask>'	)
							self.assertEqual(
							    nested_simplify(_A		,   decimals=6	)		,   [
							        {'sequence': 'My name is grouped', 'score': 2.1E-05, 'token': 38015, 'token_str': ' grouped'},
							        {'sequence': 'My name is accuser', 'score': 2.1E-05, 'token': 25506, 'token_str': ' accuser'},
							    ]		,   )

							snake_case_		:			int											=	unmasker('The largest city in France is <mask>'	)
							self.assertEqual(
							    nested_simplify(_A		,   decimals=6	)		,   [
							        {
							            'sequence': 'The largest city in France is grouped',
							            'score': 2.1E-05,
							            'token': 38015,
							            'token_str': ' grouped',
							        },
							        {
							            'sequence': 'The largest city in France is accuser',
							            'score': 2.1E-05,
							            'token': 25506,
							            'token_str': ' accuser',
							        },
							    ]		,   )

							snake_case_		:			Any											=	unmasker('My name is <mask>'		,   targets=[' Patrick', ' Clara', ' Teven']		,   top_k=3	)
							self.assertEqual(
							    nested_simplify(_A		,   decimals=6	)		,   [
							        {'sequence': 'My name is Clara', 'score': 2E-05, 'token': 13606, 'token_str': ' Clara'},
							        {'sequence': 'My name is Patrick', 'score': 2E-05, 'token': 3499, 'token_str': ' Patrick'},
							        {'sequence': 'My name is Te', 'score': 1.9E-05, 'token': 2941, 'token_str': ' Te'},
							    ]		,   )






						@require_torch
						def UpperCAmelCase_				(      self      :      Union[str, Any]	)							->    Optional[Any]:







							"""simple docstring"""


							snake_case_		:			Tuple											=	pipeline(task='fill-mask'		,   model='sshleifer/tiny-distilroberta-base'		,   top_k=2		,   framework='pt'	)

							snake_case_		:			Tuple											=	unmasker('My name is <mask>'	)
							self.assertEqual(
							    nested_simplify(_A		,   decimals=6	)		,   [
							        {'sequence': 'My name is Maul', 'score': 2.2E-05, 'token': 35676, 'token_str': ' Maul'},
							        {'sequence': 'My name isELS', 'score': 2.2E-05, 'token': 16416, 'token_str': 'ELS'},
							    ]		,   )

							snake_case_		:			int											=	unmasker('The largest city in France is <mask>'	)
							self.assertEqual(
							    nested_simplify(_A		,   decimals=6	)		,   [
							        {
							            'sequence': 'The largest city in France is Maul',
							            'score': 2.2E-05,
							            'token': 35676,
							            'token_str': ' Maul',
							        },
							        {'sequence': 'The largest city in France isELS', 'score': 2.2E-05, 'token': 16416, 'token_str': 'ELS'},
							    ]		,   )

							snake_case_		:			Any											=	unmasker('My name is <mask>'		,   targets=[' Patrick', ' Clara', ' Teven']		,   top_k=3	)
							self.assertEqual(
							    nested_simplify(_A		,   decimals=6	)		,   [
							        {'sequence': 'My name is Patrick', 'score': 2.1E-05, 'token': 3499, 'token_str': ' Patrick'},
							        {'sequence': 'My name is Te', 'score': 2E-05, 'token': 2941, 'token_str': ' Te'},
							        {'sequence': 'My name is Clara', 'score': 2E-05, 'token': 13606, 'token_str': ' Clara'},
							    ]		,   )

							snake_case_		:			List[Any]											=	unmasker('My name is <mask> <mask>'		,   top_k=2	)

							self.assertEqual(
							    nested_simplify(_A		,   decimals=6	)		,   [
							        [
							            {
							                'score': 2.2E-05,
							                'token': 35676,
							                'token_str': ' Maul',
							                'sequence': '<s>My name is Maul<mask></s>',
							            },
							            {'score': 2.2E-05, 'token': 16416, 'token_str': 'ELS', 'sequence': '<s>My name isELS<mask></s>'},
							        ],
							        [
							            {
							                'score': 2.2E-05,
							                'token': 35676,
							                'token_str': ' Maul',
							                'sequence': '<s>My name is<mask> Maul</s>',
							            },
							            {'score': 2.2E-05, 'token': 16416, 'token_str': 'ELS', 'sequence': '<s>My name is<mask>ELS</s>'},
							        ],
							    ]		,   )






						@require_torch_gpu
						def UpperCAmelCase_				(      self      :      str	)							->    Any:







							"""simple docstring"""


							snake_case_		:			Union[str, Any]											=	pipeline('fill-mask'		,   model='hf-internal-testing/tiny-random-distilbert'		,   device=0		,   framework='pt'	)

							# convert model to fp16
							pipe.model.half()

							snake_case_		:			Tuple											=	pipe('Paris is the [MASK] of France.'	)
							# We actually don't care about the result, we just want to make sure
							# it works, meaning the float16 tensor got casted back to float32
							# for postprocessing.
							self.assertIsInstance(_A		,   _A	)






						@slow
						@require_torch
						def UpperCAmelCase_				(      self      :      str	)							->    List[Any]:







							"""simple docstring"""


							snake_case_		:			Optional[Any]											=	pipeline(task='fill-mask'		,   model='distilroberta-base'		,   top_k=2		,   framework='pt'	)
							self.run_large_test(_A	)






						@slow
						@require_tf
						def UpperCAmelCase_				(      self      :      Optional[int]	)							->    int:







							"""simple docstring"""


							snake_case_		:			Dict											=	pipeline(task='fill-mask'		,   model='distilroberta-base'		,   top_k=2		,   framework='tf'	)
							self.run_large_test(_A	)






						def UpperCAmelCase_				(      self      :      Dict		,   _A      :      List[Any]	)							->    Union[str, Any]:







							"""simple docstring"""


							snake_case_		:			List[Any]											=	unmasker('My name is <mask>'	)
							self.assertEqual(
							    nested_simplify(_A	)		,   [
							        {'sequence': 'My name is John', 'score': 0.0_0_8, 'token': 610, 'token_str': ' John'},
							        {'sequence': 'My name is Chris', 'score': 0.0_0_7, 'token': 1573, 'token_str': ' Chris'},
							    ]		,   )
							snake_case_		:			List[Any]											=	unmasker('The largest city in France is <mask>'	)
							self.assertEqual(
							    nested_simplify(_A	)		,   [
							        {
							            'sequence': 'The largest city in France is Paris',
							            'score': 0.2_5_1,
							            'token': 2201,
							            'token_str': ' Paris',
							        },
							        {
							            'sequence': 'The largest city in France is Lyon',
							            'score': 0.2_1_4,
							            'token': 12790,
							            'token_str': ' Lyon',
							        },
							    ]		,   )

							snake_case_		:			Tuple											=	unmasker('My name is <mask>'		,   targets=[' Patrick', ' Clara', ' Teven']		,   top_k=3	)
							self.assertEqual(
							    nested_simplify(_A	)		,   [
							        {'sequence': 'My name is Patrick', 'score': 0.0_0_5, 'token': 3499, 'token_str': ' Patrick'},
							        {'sequence': 'My name is Clara', 'score': 0.0_0_0, 'token': 13606, 'token_str': ' Clara'},
							        {'sequence': 'My name is Te', 'score': 0.0_0_0, 'token': 2941, 'token_str': ' Te'},
							    ]		,   )






						@require_torch
						def UpperCAmelCase_				(      self      :      Optional[int]	)							->    Any:







							"""simple docstring"""


							snake_case_		:			Optional[Any]											=	pipeline(task='fill-mask'		,   model='sshleifer/tiny-distilroberta-base'		,   framework='pt'	)
							snake_case_		:			Tuple											=	None
							snake_case_		:			str											=	None
							self.run_pipeline_test(_A		,   []	)






						@require_tf
						def UpperCAmelCase_				(      self      :      Tuple	)							->    Dict:







							"""simple docstring"""


							snake_case_		:			List[Any]											=	pipeline(task='fill-mask'		,   model='sshleifer/tiny-distilroberta-base'		,   framework='tf'	)
							snake_case_		:			List[str]											=	None
							snake_case_		:			List[str]											=	None
							self.run_pipeline_test(_A		,   []	)






						def UpperCAmelCase_				(      self      :      List[str]		,   _A      :      List[Any]		,   _A      :      Tuple		,   _A      :      Optional[int]	)							->    Optional[Any]:







							"""simple docstring"""


							if tokenizer is None or tokenizer.mask_token_id is None:
								self.skipTest('The provided tokenizer has no mask token, (probably reformer or wav2vec2)'	)

							snake_case_		:			Dict											=	FillMaskPipeline(model=_A		,   tokenizer=_A	)
							snake_case_		:			Optional[Any]											=	[
							    F"""This is another {tokenizer.mask_token} test""",
							]
							return fill_masker, examples






						def UpperCAmelCase_				(      self      :      Optional[Any]		,   _A      :      str		,   _A      :      List[Any]	)							->    int:







							"""simple docstring"""


							snake_case_		:			Optional[int]											=	fill_masker.tokenizer
							snake_case_		:			List[Any]											=	fill_masker.model

							snake_case_		:			int											=	fill_masker(
							    F"""This is a {tokenizer.mask_token}"""		,   )
							self.assertEqual(
							    _A		,   [
							        {'sequence': ANY(_A	), 'score': ANY(_A	), 'token': ANY(_A	), 'token_str': ANY(_A	)},
							        {'sequence': ANY(_A	), 'score': ANY(_A	), 'token': ANY(_A	), 'token_str': ANY(_A	)},
							        {'sequence': ANY(_A	), 'score': ANY(_A	), 'token': ANY(_A	), 'token_str': ANY(_A	)},
							        {'sequence': ANY(_A	), 'score': ANY(_A	), 'token': ANY(_A	), 'token_str': ANY(_A	)},
							        {'sequence': ANY(_A	), 'score': ANY(_A	), 'token': ANY(_A	), 'token_str': ANY(_A	)},
							    ]		,   )

							snake_case_		:			Dict											=	fill_masker([F"""This is a {tokenizer.mask_token}"""]	)
							self.assertEqual(
							    _A		,   [
							        {'sequence': ANY(_A	), 'score': ANY(_A	), 'token': ANY(_A	), 'token_str': ANY(_A	)},
							        {'sequence': ANY(_A	), 'score': ANY(_A	), 'token': ANY(_A	), 'token_str': ANY(_A	)},
							        {'sequence': ANY(_A	), 'score': ANY(_A	), 'token': ANY(_A	), 'token_str': ANY(_A	)},
							        {'sequence': ANY(_A	), 'score': ANY(_A	), 'token': ANY(_A	), 'token_str': ANY(_A	)},
							        {'sequence': ANY(_A	), 'score': ANY(_A	), 'token': ANY(_A	), 'token_str': ANY(_A	)},
							    ]		,   )

							snake_case_		:			Optional[int]											=	fill_masker([F"""This is a {tokenizer.mask_token}""", F"""Another {tokenizer.mask_token} great test."""]	)
							self.assertEqual(
							    _A		,   [
							        [
							            {'sequence': ANY(_A	), 'score': ANY(_A	), 'token': ANY(_A	), 'token_str': ANY(_A	)},
							            {'sequence': ANY(_A	), 'score': ANY(_A	), 'token': ANY(_A	), 'token_str': ANY(_A	)},
							            {'sequence': ANY(_A	), 'score': ANY(_A	), 'token': ANY(_A	), 'token_str': ANY(_A	)},
							            {'sequence': ANY(_A	), 'score': ANY(_A	), 'token': ANY(_A	), 'token_str': ANY(_A	)},
							            {'sequence': ANY(_A	), 'score': ANY(_A	), 'token': ANY(_A	), 'token_str': ANY(_A	)},
							        ],
							        [
							            {'sequence': ANY(_A	), 'score': ANY(_A	), 'token': ANY(_A	), 'token_str': ANY(_A	)},
							            {'sequence': ANY(_A	), 'score': ANY(_A	), 'token': ANY(_A	), 'token_str': ANY(_A	)},
							            {'sequence': ANY(_A	), 'score': ANY(_A	), 'token': ANY(_A	), 'token_str': ANY(_A	)},
							            {'sequence': ANY(_A	), 'score': ANY(_A	), 'token': ANY(_A	), 'token_str': ANY(_A	)},
							            {'sequence': ANY(_A	), 'score': ANY(_A	), 'token': ANY(_A	), 'token_str': ANY(_A	)},
							        ],
							    ]		,   )

							with self.assertRaises(_A	):
								fill_masker([None]	)
							# No mask_token is not supported
							with self.assertRaises(_A	):
								fill_masker('This is'	)

							self.run_test_top_k(_A		,   _A	)
							self.run_test_targets(_A		,   _A	)
							self.run_test_top_k_targets(_A		,   _A	)
							self.fill_mask_with_duplicate_targets_and_top_k(_A		,   _A	)
							self.fill_mask_with_multiple_masks(_A		,   _A	)






						def UpperCAmelCase_				(      self      :      Optional[Any]		,   _A      :      Any		,   _A      :      Optional[int]	)							->    Any:







							"""simple docstring"""


							snake_case_		:			Dict											=	tokenizer.get_vocab()
							snake_case_		:			List[Any]											=	sorted(vocab.keys()	)[:2]
							# Pipeline argument
							snake_case_		:			Dict											=	FillMaskPipeline(model=_A		,   tokenizer=_A		,   targets=_A	)
							snake_case_		:			Optional[int]											=	fill_masker(F"""This is a {tokenizer.mask_token}"""	)
							self.assertEqual(
							    _A		,   [
							        {'sequence': ANY(_A	), 'score': ANY(_A	), 'token': ANY(_A	), 'token_str': ANY(_A	)},
							        {'sequence': ANY(_A	), 'score': ANY(_A	), 'token': ANY(_A	), 'token_str': ANY(_A	)},
							    ]		,   )
							snake_case_		:			List[str]											=	{vocab[el] for el in targets}
							self.assertEqual({el['token'] for el in outputs}		,   _A	)
							snake_case_		:			int											=	[tokenizer.decode([x]	) for x in target_ids]
							self.assertEqual({el['token_str'] for el in outputs}		,   set(_A	)	)

							# Call argument
							snake_case_		:			Dict											=	FillMaskPipeline(model=_A		,   tokenizer=_A	)
							snake_case_		:			Optional[int]											=	fill_masker(F"""This is a {tokenizer.mask_token}"""		,   targets=_A	)
							self.assertEqual(
							    _A		,   [
							        {'sequence': ANY(_A	), 'score': ANY(_A	), 'token': ANY(_A	), 'token_str': ANY(_A	)},
							        {'sequence': ANY(_A	), 'score': ANY(_A	), 'token': ANY(_A	), 'token_str': ANY(_A	)},
							    ]		,   )
							snake_case_		:			Any											=	{vocab[el] for el in targets}
							self.assertEqual({el['token'] for el in outputs}		,   _A	)
							snake_case_		:			Tuple											=	[tokenizer.decode([x]	) for x in target_ids]
							self.assertEqual({el['token_str'] for el in outputs}		,   set(_A	)	)

							# Score equivalence
							snake_case_		:			Union[str, Any]											=	fill_masker(F"""This is a {tokenizer.mask_token}"""		,   targets=_A	)
							snake_case_		:			Any											=	[top_mask['token_str'] for top_mask in outputs]
							snake_case_		:			Optional[Any]											=	[top_mask['score'] for top_mask in outputs]

							# For some BPE tokenizers, `</w>` is removed during decoding, so `token_str` won't be the same as in `targets`.
							if set(_A	) == set(_A	):
								snake_case_		:			int											=	fill_masker(F"""This is a {tokenizer.mask_token}"""		,   targets=_A	)
								snake_case_		:			Union[str, Any]											=	[top_mask['score'] for top_mask in unmasked_targets]
								self.assertEqual(nested_simplify(_A	)		,   nested_simplify(_A	)	)

							# Raises with invalid
							with self.assertRaises(_A	):
								snake_case_		:			Dict											=	fill_masker(F"""This is a {tokenizer.mask_token}"""		,   targets=[]	)
							# For some tokenizers, `""` is actually in the vocabulary and the expected error won't raised
							if "" not in tokenizer.get_vocab():
								with self.assertRaises(_A	):
									snake_case_		:			Tuple											=	fill_masker(F"""This is a {tokenizer.mask_token}"""		,   targets=['']	)
								with self.assertRaises(_A	):
									snake_case_		:			Union[str, Any]											=	fill_masker(F"""This is a {tokenizer.mask_token}"""		,   targets=''	)






						def UpperCAmelCase_				(      self      :      Tuple		,   _A      :      Any		,   _A      :      Optional[Any]	)							->    Any:







							"""simple docstring"""


							snake_case_		:			str											=	FillMaskPipeline(model=_A		,   tokenizer=_A		,   top_k=2	)
							snake_case_		:			List[str]											=	fill_masker(F"""This is a {tokenizer.mask_token}"""	)
							self.assertEqual(
							    _A		,   [
							        {'sequence': ANY(_A	), 'score': ANY(_A	), 'token': ANY(_A	), 'token_str': ANY(_A	)},
							        {'sequence': ANY(_A	), 'score': ANY(_A	), 'token': ANY(_A	), 'token_str': ANY(_A	)},
							    ]		,   )

							snake_case_		:			Any											=	FillMaskPipeline(model=_A		,   tokenizer=_A	)
							snake_case_		:			int											=	fill_masker(F"""This is a {tokenizer.mask_token}"""		,   top_k=2	)
							self.assertEqual(
							    _A		,   [
							        {'sequence': ANY(_A	), 'score': ANY(_A	), 'token': ANY(_A	), 'token_str': ANY(_A	)},
							        {'sequence': ANY(_A	), 'score': ANY(_A	), 'token': ANY(_A	), 'token_str': ANY(_A	)},
							    ]		,   )
							self.assertEqual(nested_simplify(_A	)		,   nested_simplify(_A	)	)






						def UpperCAmelCase_				(      self      :      Tuple		,   _A      :      Any		,   _A      :      Dict	)							->    str:







							"""simple docstring"""


							snake_case_		:			str											=	tokenizer.get_vocab()
							snake_case_		:			Tuple											=	FillMaskPipeline(model=_A		,   tokenizer=_A	)

							# top_k=2, ntargets=3
							snake_case_		:			str											=	sorted(vocab.keys()	)[:3]
							snake_case_		:			Union[str, Any]											=	fill_masker(F"""This is a {tokenizer.mask_token}"""		,   top_k=2		,   targets=_A	)

							# If we use the most probably targets, and filter differently, we should still
							# have the same results
							snake_case_		:			Any											=	[el['token_str'] for el in sorted(_A		,   key=lambda _A	: x["score"]		,   reverse=_A	)]
							# For some BPE tokenizers, `</w>` is removed during decoding, so `token_str` won't be the same as in `targets`.
							if set(_A	).issubset(_A	):
								snake_case_		:			str											=	fill_masker(F"""This is a {tokenizer.mask_token}"""		,   top_k=3		,   targets=_A	)
								# They should yield exactly the same result
								self.assertEqual(nested_simplify(_A	)		,   nested_simplify(_A	)	)






						def UpperCAmelCase_				(      self      :      str		,   _A      :      Dict		,   _A      :      Tuple	)							->    Dict:







							"""simple docstring"""


							snake_case_		:			Tuple											=	FillMaskPipeline(model=_A		,   tokenizer=_A	)
							snake_case_		:			List[Any]											=	tokenizer.get_vocab()
							# String duplicates + id duplicates
							snake_case_		:			str											=	sorted(vocab.keys()	)[:3]
							snake_case_		:			Tuple											=	[targets[0], targets[1], targets[0], targets[2], targets[1]]
							snake_case_		:			str											=	fill_masker(F"""My name is {tokenizer.mask_token}"""		,   targets=_A		,   top_k=10	)

							# The target list contains duplicates, so we can't output more
							# than them
							self.assertEqual(len(_A	)		,   3	)






						def UpperCAmelCase_				(      self      :      List[str]		,   _A      :      str		,   _A      :      Union[str, Any]	)							->    Optional[Any]:







							"""simple docstring"""


							snake_case_		:			Union[str, Any]											=	FillMaskPipeline(model=_A		,   tokenizer=_A	)

							snake_case_		:			List[str]											=	fill_masker(
							    F"""This is a {tokenizer.mask_token} {tokenizer.mask_token} {tokenizer.mask_token}"""		,   top_k=2	)
							self.assertEqual(
							    _A		,   [
							        [
							            {'sequence': ANY(_A	), 'score': ANY(_A	), 'token': ANY(_A	), 'token_str': ANY(_A	)},
							            {'sequence': ANY(_A	), 'score': ANY(_A	), 'token': ANY(_A	), 'token_str': ANY(_A	)},
							        ],
							        [
							            {'sequence': ANY(_A	), 'score': ANY(_A	), 'token': ANY(_A	), 'token_str': ANY(_A	)},
							            {'sequence': ANY(_A	), 'score': ANY(_A	), 'token': ANY(_A	), 'token_str': ANY(_A	)},
							        ],
							        [
							            {'sequence': ANY(_A	), 'score': ANY(_A	), 'token': ANY(_A	), 'token_str': ANY(_A	)},
							            {'sequence': ANY(_A	), 'score': ANY(_A	), 'token': ANY(_A	), 'token_str': ANY(_A	)},
							        ],
							    ]		,   )

