






'''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


UpperCAmelCase__						:str          =			logging.get_logger(__name__)

UpperCAmelCase__						:Dict          =			{
    """artists_file""": """artists.json""",
    """lyrics_file""": """lyrics.json""",
    """genres_file""": """genres.json""",
}

UpperCAmelCase__						:Union[str, Any]          =			{
    """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""",
    },
}

UpperCAmelCase__						:Any          =			{
    """jukebox""": 512,
}







class 	SCREAMING_SNAKE_CASE	(      lowerCAmelCase_  ):
       snake_case__    :     int   		=			VOCAB_FILES_NAMES
       snake_case__    :     Optional[Any]   		=			PRETRAINED_VOCAB_FILES_MAP
       snake_case__    :     Tuple   		=			PRETRAINED_LYRIC_TOKENS_SIZES
       snake_case__    :     Any   		=			['input_ids', 'attention_mask']




       def __init__(					self      : Optional[Any]	,    A__      : str	,    A__      : int	,    A__      : Any	,    A__      : str=["v3", "v2", "v2"]	,    A__      : List[str]=512	,    A__      : Dict=5	,    A__      : List[Any]="<|endoftext|>"	,    **A__      : Any	,    ):


            """simple docstring"""







            __lowerCamelCase						:       Tuple				   =				AddedToken(A__	,    lstrip=A__	,    rstrip=A__					) if isinstance(A__	,    A__					) else unk_token
            super().__init__(
                unk_token=A__	,    n_genres=A__	,    version=A__	,    max_n_lyric_tokens=A__	,    **A__	,    )
            __lowerCamelCase						:       List[str]				   =				version
            __lowerCamelCase						:       Optional[int]				   =				max_n_lyric_tokens
            __lowerCamelCase						:       Optional[int]				   =				n_genres

            with open(A__	,    encoding="""utf-8"""					) as vocab_handle:
                 __lowerCamelCase						:       Optional[Any]				   =				json.load(A__					)

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

            with open(A__	,    encoding="""utf-8"""					) as vocab_handle:
                 __lowerCamelCase						:       str				   =				json.load(A__					)

            __lowerCamelCase						:       List[Any]				   =				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:
                 __lowerCamelCase						:       int				   =				oov.replace(r"""\-'"""	,    r"""\-+'"""					)

            __lowerCamelCase						:       List[str]				   =				regex.compile(A__					)
            __lowerCamelCase						:       List[str]				   =				{v: k for k, v in self.artists_encoder.items()}
            __lowerCamelCase						:       int				   =				{v: k for k, v in self.genres_encoder.items()}
            __lowerCamelCase						:       Any				   =				{v: k for k, v in self.lyrics_encoder.items()}




       @property
       def 					a_					(					self      : Optional[int]					):


            """simple docstring"""







            return len(self.artists_encoder					) + len(self.genres_encoder					) + len(self.lyrics_encoder					)




       def 					a_					(					self      : int					):


            """simple docstring"""







            return dict(self.artists_encoder	,    self.genres_encoder	,    self.lyrics_encoder					)




       def 					a_					(					self      : Any	,    A__      : List[Any]	,    A__      : List[str]	,    A__      : Union[str, Any]					):


            """simple docstring"""







            __lowerCamelCase						:       Tuple				   =				[self.artists_encoder.get(A__	,    0					) for artist in list_artists]
            for genres in range(len(A__					)					):
                 __lowerCamelCase						:       int				   =				[self.genres_encoder.get(A__	,    0					) for genre in list_genres[genres]]
                 __lowerCamelCase						:       List[str]				   =				list_genres[genres] + [-1] * (self.n_genres - len(list_genres[genres]					))

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




       def 					a_					(					self      : List[Any]	,    A__      : Optional[int]					):


            """simple docstring"""







            return list(A__					)




       def 					a_					(					self      : Tuple	,    A__      : Optional[Any]	,    A__      : Tuple	,    A__      : str	,    **A__      : Optional[int]					):


            """simple docstring"""







            __lowerCamelCase   ,      __lowerCamelCase   ,      __lowerCamelCase						:       Optional[Any]				   =				self.prepare_for_tokenization(A__	,    A__	,    A__					)
            __lowerCamelCase						:       Dict				   =				self._tokenize(A__					)
            return artist, genre, lyrics




       def 					a_					(					self      : Union[str, Any]	,    A__      : str	,    A__      : str	,    A__      : str	,    A__      : bool = False					):


            """simple docstring"""







            for idx in range(len(self.version					)					):
                 if self.version[idx] == "v3":
                      __lowerCamelCase						:       Union[str, Any]				   =				artists[idx].lower()
                      __lowerCamelCase						:       Tuple				   =				[genres[idx].lower()]
                 else:
                      __lowerCamelCase						:       Tuple				   =				self._normalize(artists[idx]					) + """.v2"""
                      __lowerCamelCase						:       Union[str, Any]				   =				[
                          self._normalize(A__					) + """.v2""" for genre in genres[idx].split("""_"""					)
                      ]  # split is for the full dictionary with combined genres

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

            __lowerCamelCase						:       Any				   =				self._run_strip_accents(A__					)
            __lowerCamelCase						:       Optional[int]				   =				lyrics.replace("""\\"""	,    """\n"""					)
            __lowerCamelCase						:       Optional[int]				   =				self.out_of_vocab.sub(""""""	,    A__					), [], []
            return artists, genres, lyrics




       def 					a_					(					self      : int	,    A__      : Dict					):


            """simple docstring"""







            __lowerCamelCase						:       Union[str, Any]				   =				unicodedata.normalize("""NFD"""	,    A__					)
            __lowerCamelCase						:       Optional[Any]				   =				[]
            for char in text:
                 __lowerCamelCase						:       Dict				   =				unicodedata.category(A__					)
                 if cat == "Mn":
                      continue
                 output.append(A__					)
            return "".join(A__					)




       def 					a_					(					self      : Any	,    A__      : str					):


            """simple docstring"""







            __lowerCamelCase						:       List[Any]				   =				(
                [chr(A__					) for i in range(ord("""a"""					)	,    ord("""z"""					) + 1					)]
                + [chr(A__					) for i in range(ord("""A"""					)	,    ord("""Z"""					) + 1					)]
                + [chr(A__					) for i in range(ord("""0"""					)	,    ord("""9"""					) + 1					)]
                + ["""."""]
            )
            __lowerCamelCase						:       Dict				   =				frozenset(A__					)
            __lowerCamelCase						:       Dict				   =				re.compile(r"""_+"""					)
            __lowerCamelCase						:       Optional[int]				   =				"""""".join([c if c in accepted else """_""" for c in text.lower()]					)
            __lowerCamelCase						:       Optional[int]				   =				pattern.sub("""_"""	,    A__					).strip("""_"""					)
            return text




       def 					a_					(					self      : Optional[int]	,    A__      : List[str]					):


            """simple docstring"""







            return " ".join(A__					)




       def 					a_					(					self      : Any	,    A__      : int	,    A__      : Optional[Union[str, TensorType]] = None	,    A__      : bool = False					):


            """simple docstring"""







            if not isinstance(A__	,    A__					):
                 __lowerCamelCase						:       Union[str, Any]				   =				TensorType(A__					)

            # 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

                 __lowerCamelCase						:       Optional[int]				   =				tf.constant
                 __lowerCamelCase						:       Tuple				   =				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

                 __lowerCamelCase						:       Union[str, Any]				   =				torch.tensor
                 __lowerCamelCase						:       Optional[Any]				   =				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

                 __lowerCamelCase						:       Tuple				   =				jnp.array
                 __lowerCamelCase						:       Tuple				   =				_is_jax
            else:
                 __lowerCamelCase						:       Dict				   =				np.asarray
                 __lowerCamelCase						:       Union[str, Any]				   =				_is_numpy

            # Do the tensor conversion in batch

            try:
                 if prepend_batch_axis:
                      __lowerCamelCase						:       Optional[Any]				   =				[inputs]

                 if not is_tensor(A__					):
                      __lowerCamelCase						:       Optional[int]				   =				as_tensor(A__					)
            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      : List[Any]	,    A__      : List[str]	,    A__      : Dict	,    A__      : int=""	,    A__      : Union[str, Any]="pt"					):


            """simple docstring"""







            __lowerCamelCase						:       Union[str, Any]				   =				[0, 0, 0]
            __lowerCamelCase						:       Optional[int]				   =				[artist] * len(self.version					)
            __lowerCamelCase						:       List[Any]				   =				[genres] * len(self.version					)

            __lowerCamelCase   ,      __lowerCamelCase   ,      __lowerCamelCase						:       str				   =				self.tokenize(A__	,    A__	,    A__					)
            __lowerCamelCase   ,      __lowerCamelCase   ,      __lowerCamelCase						:       Any				   =				self._convert_token_to_id(A__	,    A__	,    A__					)

            __lowerCamelCase						:       int				   =				[-INFINITY] * len(full_tokens[-1]					)
            __lowerCamelCase						:       Dict				   =				[
                self.convert_to_tensors(
                    [input_ids + [artists_id[i]] + genres_ids[i] + full_tokens[i]]	,    tensor_type=A__					)
                for i in range(len(self.version					)					)
            ]
            return BatchEncoding({"""input_ids""": input_ids, """attention_masks""": attention_masks}					)




       def 					a_					(					self      : Optional[Any]	,    A__      : str	,    A__      : Optional[str] = None					):


            """simple docstring"""







            if not os.path.isdir(A__					):
                 logger.error(f"Vocabulary path ({save_directory}) should be a directory"					)
                 return

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

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

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

            return (artists_file, genres_file, lyrics_file)




       def 					a_					(					self      : int	,    A__      : List[str]	,    A__      : Dict	,    A__      : Union[str, Any]					):


            """simple docstring"""







            __lowerCamelCase						:       List[str]				   =				self.artists_decoder.get(A__					)
            __lowerCamelCase						:       Optional[Any]				   =				[self.genres_decoder.get(A__					) for genre in genres_index]
            __lowerCamelCase						:       List[str]				   =				[self.lyrics_decoder.get(A__					) for character in lyric_index]
            return artist, genres, lyrics











'''simple docstring'''

from __future__ import annotations

import typing
from collections.abc import Iterable

import numpy as np

UpperCAmelCase__						:List[str]          =			typing.Union[Iterable[float], Iterable[int], np.ndarray]  # noqa: UP007
UpperCAmelCase__						:Optional[int]          =			typing.Union[np.floataa, int, float]  # noqa: UP007
def     __lowercase  (_lowercase,						_lowercase     )   ->     VectorOut:


     """simple docstring"""
     return np.sqrt(np.sum((np.asarray(_lowercase     ) - np.asarray(_lowercase     )) ** 2     )     )
def     __lowercase  (_lowercase,						_lowercase     )   ->     VectorOut:


     """simple docstring"""
     return sum((va - va) ** 2 for va, va in zip(_lowercase,						_lowercase     )     ) ** (1 / 2)


if __name__ == "__main__":
   def     __lowercase  ()   ->     None:


        """simple docstring"""
        from timeit import timeit

        print("""Without Numpy"""     )
        print(
            timeit(
                """euclidean_distance_no_np([1, 2, 3], [4, 5, 6])""",						number=10_000,						globals=globals(),						)     )
        print("""With Numpy"""     )
        print(
            timeit(
                """euclidean_distance([1, 2, 3], [4, 5, 6])""",						number=10_000,						globals=globals(),						)     )
   benchmark()



