






"""simple docstring"""







import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import Callable, Dict, List, Tuple

import timm
import torch
import torch.nn as nn
from classy_vision.models.regnet import RegNet, RegNetParams, RegNetYaagf, RegNetYaagf, RegNetYaaagf
from huggingface_hub import cached_download, hf_hub_url
from torch import Tensor
from vissl.models.model_helpers import get_trunk_forward_outputs

from transformers import AutoImageProcessor, RegNetConfig, RegNetForImageClassification, RegNetModel
from transformers.utils import logging


logging.set_verbosity_info()
_lowerCAmelCase            =	logging.get_logger()


@dataclass
class      UpperCamelCase		:
						_SCREAMING_SNAKE_CASE  : nn.Module
						_SCREAMING_SNAKE_CASE  : List[nn.Module]												=		field(default_factory=__snake_case     )
						_SCREAMING_SNAKE_CASE  : list												=		field(default_factory=__snake_case     )






						def      __snake_case      (		self  :Union[str, Any]			, __magic_name__  :Tuple			, __magic_name__  :Tensor			, __magic_name__  :Tensor				)      ->int:
										lowercase    :      Any       = len(list(m.modules()				)				) == 1 or isinstance(__magic_name__			, nn.Convad				) or isinstance(__magic_name__			, nn.BatchNormad				)
										if has_not_submodules:
														self.traced.append(__magic_name__				)






						def __call__(		self  :Any			, __magic_name__  :Tensor				)      ->Any:
										for m in self.module.modules():
														self.handles.append(m.register_forward_hook(self._forward_hook				)				)
										self.module(__magic_name__				)
										[x.remove() for x in self.handles]
										return self






						@property
						def      __snake_case      (		self  :List[Any]				)      ->Dict:
										# check the len of the state_dict keys to see if we have learnable params
										return list(filter(lambda __magic_name__				: len(list(x.state_dict().keys()				)				) > 0			, self.traced				)				)


@dataclass
class      UpperCamelCase		:
						_SCREAMING_SNAKE_CASE  : nn.Module
						_SCREAMING_SNAKE_CASE  : nn.Module
						_SCREAMING_SNAKE_CASE  : int												=		1
						_SCREAMING_SNAKE_CASE  : List												=		field(default_factory=__snake_case     )
						_SCREAMING_SNAKE_CASE  : List												=		field(default_factory=__snake_case     )
						_SCREAMING_SNAKE_CASE  : bool												=		True






						def __call__(		self  :Optional[int]			, __magic_name__  :Tensor				)      ->Any:
										lowercase    :      List[str]       = Tracker(self.dest				)(__magic_name__				).parametrized
										lowercase    :      str       = Tracker(self.src				)(__magic_name__				).parametrized

										lowercase    :      List[Any]       = list(filter(lambda __magic_name__				: type(__magic_name__				) not in self.src_skip			, __magic_name__				)				)
										lowercase    :      List[Any]       = list(filter(lambda __magic_name__				: type(__magic_name__				) not in self.dest_skip			, __magic_name__				)				)

										if len(__magic_name__				) != len(__magic_name__				) and self.raise_if_mismatch:
														raise Exception(
														    f"""Numbers of operations are different. Source module has {len(__magic_name__				)} operations while"""
														    f""" destination module has {len(__magic_name__				)}."""				)

										for dest_m, src_m in zip(__magic_name__			, __magic_name__				):
														dest_m.load_state_dict(src_m.state_dict()				)
														if self.verbose == 1:
																		print(f"""Transfered from={src_m} to={dest_m}"""				)


class      UpperCamelCase		(nn.Module     ):






						def __init__(		self  :Optional[int]			, __magic_name__  :nn.Module				)      ->Any:
										super().__init__()

										lowercase    :      List[Tuple[str, nn.Module]]       = []
										# - get the stem
										feature_blocks.append(("""conv1""", model.stem)				)
										# - get all the feature blocks
										for k, v in model.trunk_output.named_children():
														assert k.startswith("""block"""				), f"""Unexpected layer name {k}"""
														lowercase    :      Dict       = len(__magic_name__				) + 1
														feature_blocks.append((f"""res{block_index}""", v)				)

										lowercase    :      Union[str, Any]       = nn.ModuleDict(__magic_name__				)






						def      __snake_case      (		self  :Any			, __magic_name__  :Tensor				)      ->int:
										return get_trunk_forward_outputs(
										    __magic_name__			, out_feat_keys=__magic_name__			, feature_blocks=self._feature_blocks			, )


class      UpperCamelCase		(__snake_case     ):






						def      __snake_case      (		self  :Optional[Any]			, __magic_name__  :str				)      ->str:
										lowercase    :      Any       = x.split("""-"""				)
										return x_split[0] + x_split[1] + "_" + "".join(x_split[2:]				)






						def __getitem__(		self  :List[Any]			, __magic_name__  :str				)      ->Callable[[], Tuple[nn.Module, Dict]]:
										# default to timm!
										if x not in self:
														lowercase    :      List[Any]       = self.convert_name_to_timm(__magic_name__				)
														lowercase    :      Tuple       = partial(lambda: (timm.create_model(__magic_name__			, pretrained=__magic_name__				).eval(), None)				)

										else:
														lowercase    :      Any       = super().__getitem__(__magic_name__				)

										return val


class      UpperCamelCase		(__snake_case     ):






						def __getitem__(		self  :str			, __magic_name__  :str				)      ->Callable[[], nn.Module]:
										if "seer" in x and "in1k" not in x:
														lowercase    :      Tuple       = RegNetModel
										else:
														lowercase    :      Union[str, Any]       = RegNetForImageClassification
										return val


def      UpperCamelCase       ( _A  ,				_A  ,				_A )     ->       Optional[int]:
				for from_key, to_key in keys:
								lowercase    :      Dict       = from_state_dict[from_key].clone()
								print(F"""Copied key={from_key} to={to_key}""" )
				return to_state_dict


def      UpperCamelCase       ( _A  ,				_A  ,				_A  ,				_A  ,				_A  ,				_A = True  ,				)     ->       Optional[int]:
				print(F"""Converting {name}...""" )
				with torch.no_grad():
								lowercase      ,      lowercase    :      List[Any]       = from_model_func()
								lowercase    :      str       = our_model_func(_A ).eval()
								lowercase    :      List[str]       = ModuleTransfer(src=_A  ,				dest=_A  ,				raise_if_mismatch=_A )
								lowercase    :      str       = torch.randn((1, 3, 224, 224) )
								module_transfer(_A )

				if from_state_dict is not None:
								lowercase    :      Tuple       = []
								# for seer - in1k finetuned we have to manually copy the head
								if "seer" in name and "in1k" in name:
												lowercase    :      Any       = [("""0.clf.0.weight""", """classifier.1.weight"""), ("""0.clf.0.bias""", """classifier.1.bias""")]
								lowercase    :      Optional[Any]       = manually_copy_vissl_head(_A  ,				our_model.state_dict()  ,				_A )
								our_model.load_state_dict(_A )

				lowercase    :      str       = our_model(_A  ,				output_hidden_states=_A )
				lowercase    :      List[str]       = (
				    our_outputs.logits if isinstance(_A  ,				_A ) else our_outputs.last_hidden_state
				)

				lowercase    :      List[str]       = from_model(_A )
				lowercase    :      Optional[int]       = from_output[-1] if type(_A ) is list else from_output

				# now since I don't want to use any config files, vissl seer model doesn't actually have an head, so let's just check the last hidden state
				if "seer" in name and "in1k" in name:
								lowercase    :      Tuple       = our_outputs.hidden_states[-1]

				assert torch.allclose(_A  ,				_A ), "The model logits don't match the original one."

				if push_to_hub:
								our_model.push_to_hub(
								    repo_path_or_name=save_directory / name  ,				commit_message="""Add model"""  ,				use_temp_dir=_A  ,				)

								lowercase    :      List[str]       = 224 if """seer""" not in name else 384
								# we can use the convnext one
								lowercase    :      int       = AutoImageProcessor.from_pretrained("""facebook/convnext-base-224-22k-1k"""  ,				size=_A )
								image_processor.push_to_hub(
								    repo_path_or_name=save_directory / name  ,				commit_message="""Add image processor"""  ,				use_temp_dir=_A  ,				)

								print(F"""Pushed {name}""" )


def      UpperCamelCase       ( _A  ,				_A = None  ,				_A = True )     ->       Any:
				lowercase    :      Dict       = """imagenet-1k-id2label.json"""
				lowercase    :      List[str]       = 1_000
				lowercase    :      List[Any]       = (1, num_labels)

				lowercase    :      Optional[Any]       = """huggingface/label-files"""
				lowercase    :      Optional[int]       = num_labels
				lowercase    :      List[str]       = json.load(open(cached_download(hf_hub_url(_A  ,				_A  ,				repo_type="""dataset""" ) )  ,				"""r""" ) )
				lowercase    :      List[str]       = {int(_A ): v for k, v in idalabel.items()}

				lowercase    :      List[str]       = idalabel
				lowercase    :      List[str]       = {v: k for k, v in idalabel.items()}

				lowercase    :      int       = partial(_A  ,				num_labels=_A  ,				idalabel=_A  ,				labelaid=_A )

				lowercase    :      Dict       = {
				    """regnet-x-002""": ImageNetPreTrainedConfig(
				        depths=[1, 1, 4, 7]  ,				hidden_sizes=[24, 56, 152, 368]  ,				groups_width=8  ,				layer_type="""x""" ),
				    """regnet-x-004""": ImageNetPreTrainedConfig(
				        depths=[1, 2, 7, 12]  ,				hidden_sizes=[32, 64, 160, 384]  ,				groups_width=16  ,				layer_type="""x""" ),
				    """regnet-x-006""": ImageNetPreTrainedConfig(
				        depths=[1, 3, 5, 7]  ,				hidden_sizes=[48, 96, 240, 528]  ,				groups_width=24  ,				layer_type="""x""" ),
				    """regnet-x-008""": ImageNetPreTrainedConfig(
				        depths=[1, 3, 7, 5]  ,				hidden_sizes=[64, 128, 288, 672]  ,				groups_width=16  ,				layer_type="""x""" ),
				    """regnet-x-016""": ImageNetPreTrainedConfig(
				        depths=[2, 4, 10, 2]  ,				hidden_sizes=[72, 168, 408, 912]  ,				groups_width=24  ,				layer_type="""x""" ),
				    """regnet-x-032""": ImageNetPreTrainedConfig(
				        depths=[2, 6, 15, 2]  ,				hidden_sizes=[96, 192, 432, 1_008]  ,				groups_width=48  ,				layer_type="""x""" ),
				    """regnet-x-040""": ImageNetPreTrainedConfig(
				        depths=[2, 5, 14, 2]  ,				hidden_sizes=[80, 240, 560, 1_360]  ,				groups_width=40  ,				layer_type="""x""" ),
				    """regnet-x-064""": ImageNetPreTrainedConfig(
				        depths=[2, 4, 10, 1]  ,				hidden_sizes=[168, 392, 784, 1_624]  ,				groups_width=56  ,				layer_type="""x""" ),
				    """regnet-x-080""": ImageNetPreTrainedConfig(
				        depths=[2, 5, 15, 1]  ,				hidden_sizes=[80, 240, 720, 1_920]  ,				groups_width=120  ,				layer_type="""x""" ),
				    """regnet-x-120""": ImageNetPreTrainedConfig(
				        depths=[2, 5, 11, 1]  ,				hidden_sizes=[224, 448, 896, 2_240]  ,				groups_width=112  ,				layer_type="""x""" ),
				    """regnet-x-160""": ImageNetPreTrainedConfig(
				        depths=[2, 6, 13, 1]  ,				hidden_sizes=[256, 512, 896, 2_048]  ,				groups_width=128  ,				layer_type="""x""" ),
				    """regnet-x-320""": ImageNetPreTrainedConfig(
				        depths=[2, 7, 13, 1]  ,				hidden_sizes=[336, 672, 1_344, 2_520]  ,				groups_width=168  ,				layer_type="""x""" ),
				    # y variant
				    """regnet-y-002""": ImageNetPreTrainedConfig(depths=[1, 1, 4, 7]  ,				hidden_sizes=[24, 56, 152, 368]  ,				groups_width=8 ),
				    """regnet-y-004""": ImageNetPreTrainedConfig(
				        depths=[1, 3, 6, 6]  ,				hidden_sizes=[48, 104, 208, 440]  ,				groups_width=8 ),
				    """regnet-y-006""": ImageNetPreTrainedConfig(
				        depths=[1, 3, 7, 4]  ,				hidden_sizes=[48, 112, 256, 608]  ,				groups_width=16 ),
				    """regnet-y-008""": ImageNetPreTrainedConfig(
				        depths=[1, 3, 8, 2]  ,				hidden_sizes=[64, 128, 320, 768]  ,				groups_width=16 ),
				    """regnet-y-016""": ImageNetPreTrainedConfig(
				        depths=[2, 6, 17, 2]  ,				hidden_sizes=[48, 120, 336, 888]  ,				groups_width=24 ),
				    """regnet-y-032""": ImageNetPreTrainedConfig(
				        depths=[2, 5, 13, 1]  ,				hidden_sizes=[72, 216, 576, 1_512]  ,				groups_width=24 ),
				    """regnet-y-040""": ImageNetPreTrainedConfig(
				        depths=[2, 6, 12, 2]  ,				hidden_sizes=[128, 192, 512, 1_088]  ,				groups_width=64 ),
				    """regnet-y-064""": ImageNetPreTrainedConfig(
				        depths=[2, 7, 14, 2]  ,				hidden_sizes=[144, 288, 576, 1_296]  ,				groups_width=72 ),
				    """regnet-y-080""": ImageNetPreTrainedConfig(
				        depths=[2, 4, 10, 1]  ,				hidden_sizes=[168, 448, 896, 2_016]  ,				groups_width=56 ),
				    """regnet-y-120""": ImageNetPreTrainedConfig(
				        depths=[2, 5, 11, 1]  ,				hidden_sizes=[224, 448, 896, 2_240]  ,				groups_width=112 ),
				    """regnet-y-160""": ImageNetPreTrainedConfig(
				        depths=[2, 4, 11, 1]  ,				hidden_sizes=[224, 448, 1_232, 3_024]  ,				groups_width=112 ),
				    """regnet-y-320""": ImageNetPreTrainedConfig(
				        depths=[2, 5, 12, 1]  ,				hidden_sizes=[232, 696, 1_392, 3_712]  ,				groups_width=232 ),
				    # models created by SEER -> https://arxiv.org/abs/2202.08360
				    """regnet-y-320-seer""": RegNetConfig(depths=[2, 5, 12, 1]  ,				hidden_sizes=[232, 696, 1_392, 3_712]  ,				groups_width=232 ),
				    """regnet-y-640-seer""": RegNetConfig(depths=[2, 5, 12, 1]  ,				hidden_sizes=[328, 984, 1_968, 4_920]  ,				groups_width=328 ),
				    """regnet-y-1280-seer""": RegNetConfig(
				        depths=[2, 7, 17, 1]  ,				hidden_sizes=[528, 1_056, 2_904, 7_392]  ,				groups_width=264 ),
				    """regnet-y-2560-seer""": RegNetConfig(
				        depths=[3, 7, 16, 1]  ,				hidden_sizes=[640, 1_696, 2_544, 5_088]  ,				groups_width=640 ),
				    """regnet-y-10b-seer""": ImageNetPreTrainedConfig(
				        depths=[2, 7, 17, 1]  ,				hidden_sizes=[2_020, 4_040, 11_110, 28_280]  ,				groups_width=1_010 ),
				    # finetuned on imagenet
				    """regnet-y-320-seer-in1k""": ImageNetPreTrainedConfig(
				        depths=[2, 5, 12, 1]  ,				hidden_sizes=[232, 696, 1_392, 3_712]  ,				groups_width=232 ),
				    """regnet-y-640-seer-in1k""": ImageNetPreTrainedConfig(
				        depths=[2, 5, 12, 1]  ,				hidden_sizes=[328, 984, 1_968, 4_920]  ,				groups_width=328 ),
				    """regnet-y-1280-seer-in1k""": ImageNetPreTrainedConfig(
				        depths=[2, 7, 17, 1]  ,				hidden_sizes=[528, 1_056, 2_904, 7_392]  ,				groups_width=264 ),
				    """regnet-y-2560-seer-in1k""": ImageNetPreTrainedConfig(
				        depths=[3, 7, 16, 1]  ,				hidden_sizes=[640, 1_696, 2_544, 5_088]  ,				groups_width=640 ),
				    """regnet-y-10b-seer-in1k""": ImageNetPreTrainedConfig(
				        depths=[2, 7, 17, 1]  ,				hidden_sizes=[2_020, 4_040, 11_110, 28_280]  ,				groups_width=1_010 ),
				}

				lowercase    :      Optional[int]       = NameToOurModelFuncMap()
				lowercase    :      Tuple       = NameToFromModelFuncMap()
				# add seer weights logic

				def load_using_classy_vision(_A  ,				_A ) -> Tuple[nn.Module, Dict]:
								lowercase    :      Optional[int]       = torch.hub.load_state_dict_from_url(_A  ,				model_dir=str(_A )  ,				map_location="""cpu""" )
								lowercase    :      List[str]       = model_func()
								# check if we have a head, if yes add it
								lowercase    :      Union[str, Any]       = files["""classy_state_dict"""]["""base_model"""]["""model"""]
								lowercase    :      Optional[int]       = model_state_dict["""trunk"""]
								model.load_state_dict(_A )
								return model.eval(), model_state_dict["heads"]

				# pretrained
				lowercase    :      List[Any]       = partial(
				    _A  ,				"""https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet32d/seer_regnet32gf_model_iteration244000.torch"""  ,				lambda: FakeRegNetVisslWrapper(RegNetYaagf() )  ,				)

				lowercase    :      Dict       = partial(
				    _A  ,				"""https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet64/seer_regnet64gf_model_final_checkpoint_phase0.torch"""  ,				lambda: FakeRegNetVisslWrapper(RegNetYaagf() )  ,				)

				lowercase    :      Union[str, Any]       = partial(
				    _A  ,				"""https://dl.fbaipublicfiles.com/vissl/model_zoo/swav_ig1b_regnet128Gf_cnstant_bs32_node16_sinkhorn10_proto16k_syncBN64_warmup8k/model_final_checkpoint_phase0.torch"""  ,				lambda: FakeRegNetVisslWrapper(RegNetYaaagf() )  ,				)

				lowercase    :      Any       = partial(
				    _A  ,				"""https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet10B/model_iteration124500_conso.torch"""  ,				lambda: FakeRegNetVisslWrapper(
				        RegNet(RegNetParams(depth=27  ,				group_width=1_010  ,				w_a=1_744  ,				w_a=620.83  ,				w_m=2.52 ) ) )  ,				)

				# IN1K finetuned
				lowercase    :      int       = partial(
				    _A  ,				"""https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet32_finetuned_in1k_model_final_checkpoint_phase78.torch"""  ,				lambda: FakeRegNetVisslWrapper(RegNetYaagf() )  ,				)

				lowercase    :      Optional[int]       = partial(
				    _A  ,				"""https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet64_finetuned_in1k_model_final_checkpoint_phase78.torch"""  ,				lambda: FakeRegNetVisslWrapper(RegNetYaagf() )  ,				)

				lowercase    :      str       = partial(
				    _A  ,				"""https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet128_finetuned_in1k_model_final_checkpoint_phase78.torch"""  ,				lambda: FakeRegNetVisslWrapper(RegNetYaaagf() )  ,				)

				lowercase    :      int       = partial(
				    _A  ,				"""https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_10b_finetuned_in1k_model_phase28_conso.torch"""  ,				lambda: FakeRegNetVisslWrapper(
				        RegNet(RegNetParams(depth=27  ,				group_width=1_010  ,				w_a=1_744  ,				w_a=620.83  ,				w_m=2.52 ) ) )  ,				)

				if model_name:
								convert_weight_and_push(
								    _A  ,				names_to_from_model_map[model_name]  ,				names_to_ours_model_map[model_name]  ,				names_to_config[model_name]  ,				_A  ,				_A  ,				)
				else:
								for model_name, config in names_to_config.items():
												convert_weight_and_push(
												    _A  ,				names_to_from_model_map[model_name]  ,				names_to_ours_model_map[model_name]  ,				_A  ,				_A  ,				_A  ,				)
				return config, expected_shape


if __name__ == "__main__":
				_lowerCAmelCase            =	argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    '--model_name',
				    default=None,
				    type=str,
				    help=(
				        'The name of the model you wish to convert, it must be one of the supported regnet* architecture,'
				        ' currently: regnetx-*, regnety-*. If `None`, all of them will the converted.'
				    ),
				)
				parser.add_argument(
				    '--pytorch_dump_folder_path',
				    default=None,
				    type=Path,
				    required=True,
				    help='Path to the output PyTorch model directory.',
				)
				parser.add_argument(
				    '--push_to_hub',
				    default=True,
				    type=bool,
				    required=False,
				    help='If True, push model and image processor to the hub.',
				)

				_lowerCAmelCase            =	parser.parse_args()

				_lowerCAmelCase            =	args.pytorch_dump_folder_path
				pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
				convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)








"""simple docstring"""







import json
import os
import re
import unicodedata
from json.encoder import INFINITY
from typing import Any, Dict, List, Optional, Tuple, Union

import numpy as np
import regex

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, is_flax_available, is_tf_available, is_torch_available, logging
from ...utils.generic import _is_jax, _is_numpy


_lowerCAmelCase            =	logging.get_logger(__name__)

_lowerCAmelCase            =	{
    'artists_file': 'artists.json',
    'lyrics_file': 'lyrics.json',
    'genres_file': 'genres.json',
}

_lowerCAmelCase            =	{
    'artists_file': {
        'jukebox': 'https://huggingface.co/ArthurZ/jukebox/blob/main/artists.json',
    },
    'genres_file': {
        'jukebox': 'https://huggingface.co/ArthurZ/jukebox/blob/main/genres.json',
    },
    'lyrics_file': {
        'jukebox': 'https://huggingface.co/ArthurZ/jukebox/blob/main/lyrics.json',
    },
}

_lowerCAmelCase            =	{
    'jukebox': 5_12,
}


class      UpperCamelCase		(__snake_case     ):
						_SCREAMING_SNAKE_CASE  : Optional[int]												=		VOCAB_FILES_NAMES
						_SCREAMING_SNAKE_CASE  : Tuple												=		PRETRAINED_VOCAB_FILES_MAP
						_SCREAMING_SNAKE_CASE  : int												=		PRETRAINED_LYRIC_TOKENS_SIZES
						_SCREAMING_SNAKE_CASE  : Optional[int]												=		["""input_ids""", """attention_mask"""]






						def __init__(		self  :Tuple			, __magic_name__  :Dict			, __magic_name__  :int			, __magic_name__  :str			, __magic_name__  :List[Any]=["v3", "v2", "v2"]			, __magic_name__  :List[Any]=512			, __magic_name__  :Optional[Any]=5			, __magic_name__  :Tuple="<|endoftext|>"			, **__magic_name__  :Tuple			, )      ->Union[str, Any]:
										lowercase    :      Any       = AddedToken(__magic_name__			, lstrip=__magic_name__			, rstrip=__magic_name__				) if isinstance(__magic_name__			, __magic_name__				) else unk_token
										super().__init__(
										    unk_token=__magic_name__			, n_genres=__magic_name__			, version=__magic_name__			, max_n_lyric_tokens=__magic_name__			, **__magic_name__			, )
										lowercase    :      Optional[int]       = version
										lowercase    :      Optional[int]       = max_n_lyric_tokens
										lowercase    :      Optional[int]       = n_genres

										with open(__magic_name__			, encoding="""utf-8"""				) as vocab_handle:
														lowercase    :      Optional[int]       = json.load(__magic_name__				)

										with open(__magic_name__			, encoding="""utf-8"""				) as vocab_handle:
														lowercase    :      Any       = json.load(__magic_name__				)

										with open(__magic_name__			, encoding="""utf-8"""				) as vocab_handle:
														lowercase    :      List[str]       = json.load(__magic_name__				)

										lowercase    :      str       = r"""[^A-Za-z0-9.,:;!?\-'\"()\[\] \t\n]+"""
										# In v2, we had a n_vocab=80 and in v3 we missed + and so n_vocab=79 of characters.
										if len(self.lyrics_encoder				) == 79:
														lowercase    :      Dict       = oov.replace(r"""\-'"""			, r"""\-+'"""				)

										lowercase    :      Union[str, Any]       = regex.compile(__magic_name__				)
										lowercase    :      Tuple       = {v: k for k, v in self.artists_encoder.items()}
										lowercase    :      Tuple       = {v: k for k, v in self.genres_encoder.items()}
										lowercase    :      Dict       = {v: k for k, v in self.lyrics_encoder.items()}






						@property
						def      __snake_case      (		self  :str				)      ->Optional[Any]:
										return len(self.artists_encoder				) + len(self.genres_encoder				) + len(self.lyrics_encoder				)






						def      __snake_case      (		self  :int				)      ->Optional[int]:
										return dict(self.artists_encoder			, self.genres_encoder			, self.lyrics_encoder				)






						def      __snake_case      (		self  :str			, __magic_name__  :Optional[int]			, __magic_name__  :Dict			, __magic_name__  :Optional[Any]				)      ->List[str]:
										lowercase    :      Optional[int]       = [self.artists_encoder.get(__magic_name__			, 0				) for artist in list_artists]
										for genres in range(len(__magic_name__				)				):
														lowercase    :      int       = [self.genres_encoder.get(__magic_name__			, 0				) for genre in list_genres[genres]]
														lowercase    :      Optional[Any]       = list_genres[genres] + [-1] * (self.n_genres - len(list_genres[genres]				))

										lowercase    :      str       = [[self.lyrics_encoder.get(__magic_name__			, 0				) for character in list_lyrics[0]], [], []]
										return artists_id, list_genres, lyric_ids






						def      __snake_case      (		self  :Optional[int]			, __magic_name__  :Optional[Any]				)      ->List[str]:
										return list(__magic_name__				)






						def      __snake_case      (		self  :Tuple			, __magic_name__  :Tuple			, __magic_name__  :List[str]			, __magic_name__  :str			, **__magic_name__  :Union[str, Any]				)      ->Tuple:
										lowercase      ,      lowercase      ,      lowercase    :      List[str]       = self.prepare_for_tokenization(__magic_name__			, __magic_name__			, __magic_name__				)
										lowercase    :      List[str]       = self._tokenize(__magic_name__				)
										return artist, genre, lyrics






						def      __snake_case      (		self  :Optional[int]			, __magic_name__  :str			, __magic_name__  :str			, __magic_name__  :str			, __magic_name__  :bool = False				)      ->Tuple[str, str, str, Dict[str, Any]]:
										for idx in range(len(self.version				)				):
														if self.version[idx] == "v3":
																		lowercase    :      Tuple       = artists[idx].lower()
																		lowercase    :      Dict       = [genres[idx].lower()]
														else:
																		lowercase    :      str       = self._normalize(artists[idx]				) + """.v2"""
																		lowercase    :      str       = [
																		    self._normalize(__magic_name__				) + """.v2""" for genre in genres[idx].split("""_"""				)
																		]  # split is for the full dictionary with combined genres

										if self.version[0] == "v2":
														lowercase    :      Dict       = regex.compile(r"""[^A-Za-z0-9.,:;!?\-'\"()\[\] \t\n]+"""				)
														lowercase    :      Dict       = """ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789.,:;!?-+'\"()[] \t\n"""
														lowercase    :      Optional[Any]       = {vocab[index]: index + 1 for index in range(len(__magic_name__				)				)}
														lowercase    :      List[Any]       = 0
														lowercase    :      str       = len(__magic_name__				) + 1
														lowercase    :      List[Any]       = self.vocab
														lowercase    :      Optional[Any]       = {v: k for k, v in self.vocab.items()}
														lowercase    :      Tuple       = """"""
										else:
														lowercase    :      Optional[int]       = regex.compile(r"""[^A-Za-z0-9.,:;!?\-+'\"()\[\] \t\n]+"""				)

										lowercase    :      str       = self._run_strip_accents(__magic_name__				)
										lowercase    :      str       = lyrics.replace("""\\"""			, """\n"""				)
										lowercase    :      int       = self.out_of_vocab.sub(""""""			, __magic_name__				), [], []
										return artists, genres, lyrics






						def      __snake_case      (		self  :List[Any]			, __magic_name__  :Optional[Any]				)      ->List[str]:
										lowercase    :      Any       = unicodedata.normalize("""NFD"""			, __magic_name__				)
										lowercase    :      Optional[Any]       = []
										for char in text:
														lowercase    :      int       = unicodedata.category(__magic_name__				)
														if cat == "Mn":
																		continue
														output.append(__magic_name__				)
										return "".join(__magic_name__				)






						def      __snake_case      (		self  :Optional[int]			, __magic_name__  :str				)      ->str:
										lowercase    :      Tuple       = (
										    [chr(__magic_name__				) for i in range(ord("""a"""				)			, ord("""z"""				) + 1				)]
										    + [chr(__magic_name__				) for i in range(ord("""A"""				)			, ord("""Z"""				) + 1				)]
										    + [chr(__magic_name__				) for i in range(ord("""0"""				)			, ord("""9"""				) + 1				)]
										    + ["""."""]
										)
										lowercase    :      Dict       = frozenset(__magic_name__				)
										lowercase    :      Optional[int]       = re.compile(r"""_+"""				)
										lowercase    :      Optional[int]       = """""".join([c if c in accepted else """_""" for c in text.lower()]				)
										lowercase    :      Union[str, Any]       = pattern.sub("""_"""			, __magic_name__				).strip("""_"""				)
										return text






						def      __snake_case      (		self  :Optional[int]			, __magic_name__  :List[str]				)      ->str:
										return " ".join(__magic_name__				)






						def      __snake_case      (		self  :Tuple			, __magic_name__  :Optional[int]			, __magic_name__  :Optional[Union[str, TensorType]] = None			, __magic_name__  :bool = False				)      ->Tuple:
										# Convert to TensorType
										if not isinstance(__magic_name__			, __magic_name__				):
														lowercase    :      Optional[Any]       = TensorType(__magic_name__				)

										# Get a function reference for the correct framework
										if tensor_type == TensorType.TENSORFLOW:
														if not is_tf_available():
																		raise ImportError(
																		    """Unable to convert output to TensorFlow tensors format, TensorFlow is not installed."""				)
														import tensorflow as tf

														lowercase    :      int       = tf.constant
														lowercase    :      List[str]       = tf.is_tensor
										elif tensor_type == TensorType.PYTORCH:
														if not is_torch_available():
																		raise ImportError("""Unable to convert output to PyTorch tensors format, PyTorch is not installed."""				)
														import torch

														lowercase    :      List[Any]       = torch.tensor
														lowercase    :      List[str]       = torch.is_tensor
										elif tensor_type == TensorType.JAX:
														if not is_flax_available():
																		raise ImportError("""Unable to convert output to JAX tensors format, JAX is not installed."""				)
														import jax.numpy as jnp  # noqa: F811

														lowercase    :      int       = jnp.array
														lowercase    :      Dict       = _is_jax
										else:
														lowercase    :      List[Any]       = np.asarray
														lowercase    :      Tuple       = _is_numpy

										# Do the tensor conversion in batch

										try:
														if prepend_batch_axis:
																		lowercase    :      Dict       = [inputs]

														if not is_tensor(__magic_name__				):
																		lowercase    :      str       = as_tensor(__magic_name__				)
										except:  # noqa E722
														raise ValueError(
														    """Unable to create tensor, you should probably activate truncation and/or padding """
														    """with 'padding=True' 'truncation=True' to have batched tensors with the same length."""				)

										return inputs






						def __call__(		self  :Optional[int]			, __magic_name__  :Union[str, Any]			, __magic_name__  :str			, __magic_name__  :Optional[Any]=""			, __magic_name__  :Optional[int]="pt"				)      ->BatchEncoding:
										lowercase    :      Union[str, Any]       = [0, 0, 0]
										lowercase    :      Optional[int]       = [artist] * len(self.version				)
										lowercase    :      Dict       = [genres] * len(self.version				)

										lowercase      ,      lowercase      ,      lowercase    :      Dict       = self.tokenize(__magic_name__			, __magic_name__			, __magic_name__				)
										lowercase      ,      lowercase      ,      lowercase    :      Dict       = self._convert_token_to_id(__magic_name__			, __magic_name__			, __magic_name__				)

										lowercase    :      Tuple       = [-INFINITY] * len(full_tokens[-1]				)
										lowercase    :      Union[str, Any]       = [
										    self.convert_to_tensors(
										        [input_ids + [artists_id[i]] + genres_ids[i] + full_tokens[i]]			, tensor_type=__magic_name__				)
										    for i in range(len(self.version				)				)
										]
										return BatchEncoding({"""input_ids""": input_ids, """attention_masks""": attention_masks}				)






						def      __snake_case      (		self  :int			, __magic_name__  :str			, __magic_name__  :Optional[str] = None				)      ->Tuple[str]:
										if not os.path.isdir(__magic_name__				):
														logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""				)
														return

										lowercase    :      int       = os.path.join(
										    __magic_name__			, (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""artists_file"""]				)
										with open(__magic_name__			, """w"""			, encoding="""utf-8"""				) as f:
														f.write(json.dumps(self.artists_encoder			, ensure_ascii=__magic_name__				)				)

										lowercase    :      Any       = os.path.join(
										    __magic_name__			, (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""genres_file"""]				)
										with open(__magic_name__			, """w"""			, encoding="""utf-8"""				) as f:
														f.write(json.dumps(self.genres_encoder			, ensure_ascii=__magic_name__				)				)

										lowercase    :      Optional[int]       = os.path.join(
										    __magic_name__			, (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""lyrics_file"""]				)
										with open(__magic_name__			, """w"""			, encoding="""utf-8"""				) as f:
														f.write(json.dumps(self.lyrics_encoder			, ensure_ascii=__magic_name__				)				)

										return (artists_file, genres_file, lyrics_file)






						def      __snake_case      (		self  :int			, __magic_name__  :int			, __magic_name__  :Dict			, __magic_name__  :Union[str, Any]				)      ->List[str]:
										lowercase    :      Union[str, Any]       = self.artists_decoder.get(__magic_name__				)
										lowercase    :      int       = [self.genres_decoder.get(__magic_name__				) for genre in genres_index]
										lowercase    :      Union[str, Any]       = [self.lyrics_decoder.get(__magic_name__				) for character in lyric_index]
										return artist, genres, lyrics
