
"""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


_lowercase							   =				logging.get_logger(__name__)

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

_lowercase							   =				{
    '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',
    },
}

_lowercase							   =				{
    'jukebox': 5_12,
}







class 				lowerCAmelCase_				(      snake_case__    ):



						'''simple docstring'''


						_lowerCamelCase:    int      =    VOCAB_FILES_NAMES
						_lowerCamelCase:    Tuple      =    PRETRAINED_VOCAB_FILES_MAP
						_lowerCamelCase:    int      =    PRETRAINED_LYRIC_TOKENS_SIZES
						_lowerCamelCase:    str      =    ["""input_ids""", """attention_mask"""]


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

							with open(UpperCAmelCase_  ,encoding='utf-8'      ) as vocab_handle:
								A						   =   json.load(UpperCAmelCase_      )

							with open(UpperCAmelCase_  ,encoding='utf-8'      ) as vocab_handle:
								A						   =   json.load(UpperCAmelCase_      )

							with open(UpperCAmelCase_  ,encoding='utf-8'      ) as vocab_handle:
								A						   =   json.load(UpperCAmelCase_      )

							A						   =   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:
								A						   =   oov.replace(R'\-\''  ,R'\-+\''      )

							A						   =   regex.compile(UpperCAmelCase_      )
							A						   =   {v: k for k, v in self.artists_encoder.items()}
							A						   =   {v: k for k, v in self.genres_encoder.items()}
							A						   =   {v: k for k, v in self.lyrics_encoder.items()}


						@property
						def 						_SCREAMING_SNAKE_CASE      ( self     :      Union[str, Any]      )     ->   int:
							return len(self.artists_encoder      ) + len(self.genres_encoder      ) + len(self.lyrics_encoder      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Tuple      )     ->   Union[str, Any]:
							return dict(self.artists_encoder  ,self.genres_encoder  ,self.lyrics_encoder      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Dict  ,A_     :      Tuple  ,A_     :      str  ,A_     :      List[str]      )     ->   int:
							A						   =   [self.artists_encoder.get(UpperCAmelCase_  ,0      ) for artist in list_artists]
							for genres in range(len(UpperCAmelCase_      )      ):
								A						   =   [self.genres_encoder.get(UpperCAmelCase_  ,0      ) for genre in list_genres[genres]]
								A						   =   list_genres[genres] + [-1] * (self.n_genres - len(list_genres[genres]      ))

							A						   =   [[self.lyrics_encoder.get(UpperCAmelCase_  ,0      ) for character in list_lyrics[0]], [], []]
							return artists_id, list_genres, lyric_ids


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Dict  ,A_     :      List[str]      )     ->   Any:
							return list(UpperCAmelCase_      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      List[str]  ,A_     :      str  ,A_     :      int  ,A_     :      List[Any]  ,**A_     :      Optional[Any]      )     ->   List[str]:
							A   ,      A   ,      A						   =   self.prepare_for_tokenization(UpperCAmelCase_  ,UpperCAmelCase_  ,UpperCAmelCase_      )
							A						   =   self._tokenize(UpperCAmelCase_      )
							return artist, genre, lyrics


						def 						_SCREAMING_SNAKE_CASE      ( self     :      List[str]  ,A_     :      str  ,A_     :      str  ,A_     :      str  ,A_     :      bool = False      )     ->   Tuple[str, str, str, Dict[str, Any]]:



							for idx in range(len(self.version      )      ):
								if self.version[idx] == "v3":
									A						   =   artists[idx].lower()
									A						   =   [genres[idx].lower()]
								else:
									A						   =   self._normalize(artists[idx]      ) + '.v2'
									A						   =   [
									    self._normalize(UpperCAmelCase_      ) + '.v2' for genre in genres[idx].split('_'      )
									]  # split is for the full dictionary with combined genres

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

							A						   =   self._run_strip_accents(UpperCAmelCase_      )
							A						   =   lyrics.replace('\\'  ,'\n'      )
							A						   =   self.out_of_vocab.sub(''  ,UpperCAmelCase_      ), [], []
							return artists, genres, lyrics


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Tuple  ,A_     :      List[str]      )     ->   Tuple:
							A						   =   unicodedata.normalize('NFD'  ,UpperCAmelCase_      )
							A						   =   []
							for char in text:
								A						   =   unicodedata.category(UpperCAmelCase_      )
								if cat == "Mn":
									continue
								output.append(UpperCAmelCase_      )
							return "".join(UpperCAmelCase_      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      List[Any]  ,A_     :      str      )     ->   str:
							A						   =   (
							    [chr(UpperCAmelCase_      ) for i in range(ord('a'      )  ,ord('z'      ) + 1      )]
							    + [chr(UpperCAmelCase_      ) for i in range(ord('A'      )  ,ord('Z'      ) + 1      )]
							    + [chr(UpperCAmelCase_      ) for i in range(ord('0'      )  ,ord('9'      ) + 1      )]
							    + ['.']
							)
							A						   =   frozenset(UpperCAmelCase_      )
							A						   =   re.compile(R'_+'      )
							A						   =   ''.join([c if c in accepted else '_' for c in text.lower()]      )
							A						   =   pattern.sub('_'  ,UpperCAmelCase_      ).strip('_'      )
							return text


						def 						_SCREAMING_SNAKE_CASE      ( self     :      List[Any]  ,A_     :      List[str]      )     ->   str:
							return " ".join(UpperCAmelCase_      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Tuple  ,A_     :      str  ,A_     :      Optional[Union[str, TensorType]] = None  ,A_     :      bool = False      )     ->   Union[str, Any]:



							if not isinstance(UpperCAmelCase_  ,UpperCAmelCase_      ):
								A						   =   TensorType(UpperCAmelCase_      )

							# 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

								A						   =   tf.constant
								A						   =   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

								A						   =   torch.tensor
								A						   =   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

								A						   =   jnp.array
								A						   =   _is_jax
							else:
								A						   =   np.asarray
								A						   =   _is_numpy

							# Do the tensor conversion in batch

							try:
								if prepend_batch_axis:
									A						   =   [inputs]

								if not is_tensor(UpperCAmelCase_      ):
									A						   =   as_tensor(UpperCAmelCase_      )
							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     :      Any  ,A_     :      List[str]  ,A_     :      List[str]  ,A_     :      List[Any]=""  ,A_     :      List[Any]="pt"      )     ->   BatchEncoding:
							A						   =   [0, 0, 0]
							A						   =   [artist] * len(self.version      )
							A						   =   [genres] * len(self.version      )

							A   ,      A   ,      A						   =   self.tokenize(UpperCAmelCase_  ,UpperCAmelCase_  ,UpperCAmelCase_      )
							A   ,      A   ,      A						   =   self._convert_token_to_id(UpperCAmelCase_  ,UpperCAmelCase_  ,UpperCAmelCase_      )

							A						   =   [-INFINITY] * len(full_tokens[-1]      )
							A						   =   [
							    self.convert_to_tensors(
							        [input_ids + [artists_id[i]] + genres_ids[i] + full_tokens[i]]  ,tensor_type=UpperCAmelCase_      )
							    for i in range(len(self.version      )      )
							]
							return BatchEncoding({'input_ids': input_ids, 'attention_masks': attention_masks}      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Optional[Any]  ,A_     :      str  ,A_     :      Optional[str] = None      )     ->   Tuple[str]:



							if not os.path.isdir(UpperCAmelCase_      ):
								logger.error(F'Vocabulary path ({save_directory}) should be a directory'      )
								return

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

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

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

							return (artists_file, genres_file, lyrics_file)




						def 						_SCREAMING_SNAKE_CASE      ( self     :      Union[str, Any]  ,A_     :      List[str]  ,A_     :      Dict  ,A_     :      List[Any]      )     ->   Union[str, Any]:
							A						   =   self.artists_decoder.get(UpperCAmelCase_      )
							A						   =   [self.genres_decoder.get(UpperCAmelCase_      ) for genre in genres_index]
							A						   =   [self.lyrics_decoder.get(UpperCAmelCase_      ) for character in lyric_index]
							return artist, genres, lyrics

"""simple docstring"""


from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_lowercase							   =				logging.get_logger(__name__)

_lowercase							   =				{
    '''hustvl/yolos-small''': '''https://huggingface.co/hustvl/yolos-small/resolve/main/config.json''',
    # See all YOLOS models at https://huggingface.co/models?filter=yolos
}







class 				lowerCAmelCase_				(      _lowercase    ):



						'''simple docstring'''


						_lowerCamelCase:    List[str]      =    '''yolos'''


						def __init__( self     :      Dict  ,A_     :      Optional[Any]=768  ,A_     :      int=12  ,A_     :      List[str]=12  ,A_     :      str=3072  ,A_     :      Tuple="gelu"  ,A_     :      Dict=0.0  ,A_     :      List[Any]=0.0  ,A_     :      Any=0.02  ,A_     :      str=1e-12  ,A_     :      List[Any]=[512, 864]  ,A_     :      Union[str, Any]=16  ,A_     :      List[str]=3  ,A_     :      Optional[int]=True  ,A_     :      Tuple=100  ,A_     :      str=True  ,A_     :      Optional[Any]=False  ,A_     :      Any=1  ,A_     :      Optional[Any]=5  ,A_     :      Optional[Any]=2  ,A_     :      Optional[int]=5  ,A_     :      List[Any]=2  ,A_     :      Union[str, Any]=0.1  ,**A_     :      Tuple  ,)     ->   Any:
							super().__init__(**A_      )

							A						   =   hidden_size
							A						   =   num_hidden_layers
							A						   =   num_attention_heads
							A						   =   intermediate_size
							A						   =   hidden_act
							A						   =   hidden_dropout_prob
							A						   =   attention_probs_dropout_prob
							A						   =   initializer_range
							A						   =   layer_norm_eps
							A						   =   image_size
							A						   =   patch_size
							A						   =   num_channels
							A						   =   qkv_bias
							A						   =   num_detection_tokens
							A						   =   use_mid_position_embeddings
							A						   =   auxiliary_loss
							# Hungarian matcher
							A						   =   class_cost
							A						   =   bbox_cost
							A						   =   giou_cost
							# Loss coefficients
							A						   =   bbox_loss_coefficient
							A						   =   giou_loss_coefficient
							A						   =   eos_coefficient







class 				lowerCAmelCase_				(      _lowercase    ):



						'''simple docstring'''


						_lowerCamelCase:    Any      =    version.parse('''1.11'''    )


						@property
						def 						_SCREAMING_SNAKE_CASE      ( self     :      Optional[int]      )     ->   Mapping[str, Mapping[int, str]]:
							return OrderedDict(
							    [
							        ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
							    ]      )


						@property
						def 						_SCREAMING_SNAKE_CASE      ( self     :      Any      )     ->   float:
							return 1e-4




						@property
						def 						_SCREAMING_SNAKE_CASE      ( self     :      Tuple      )     ->   int:
							return 12