
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       :				Tuple       =			logging.get_logger(__name__)

_lowerCamelCase       :				Optional[Any]       =			{
    '''artists_file''': '''artists.json''',
    '''lyrics_file''': '''lyrics.json''',
    '''genres_file''': '''genres.json''',
}

_lowerCamelCase       :				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''',
    },
}

_lowerCamelCase       :				Optional[Any]       =			{
    '''jukebox''': 512,
}




class    SCREAMING_SNAKE_CASE__     (	UpperCAmelCase					):





	'''simple docstring'''



	_UpperCAmelCase       :		Optional[int]       =					VOCAB_FILES_NAMES
	_UpperCAmelCase       :		List[Any]       =					PRETRAINED_VOCAB_FILES_MAP
	_UpperCAmelCase       :		int       =					PRETRAINED_LYRIC_TOKENS_SIZES
	_UpperCAmelCase       :		Optional[Any]       =					["input_ids", "attention_mask"]



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


				'''simple docstring'''


				_snake_case		=			AddedToken(lowercase			,  lstrip=lowercase			,  rstrip=lowercase	) if isinstance(lowercase			,  lowercase	) else unk_token
				super().__init__(
				    unk_token=lowercase			,  n_genres=lowercase			,  version=lowercase			,  max_n_lyric_tokens=lowercase			,  **lowercase			,  )
				_snake_case		=			version
				_snake_case		=			max_n_lyric_tokens
				_snake_case		=			n_genres

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

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

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

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

				_snake_case		=			regex.compile(lowercase	)
				_snake_case		=			{v: k for k, v in self.artists_encoder.items()}
				_snake_case		=			{v: k for k, v in self.genres_encoder.items()}
				_snake_case		=			{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	:    Tuple	):


				'''simple docstring'''


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



	def  A							(  self	:    Optional[int]			,  lowercase	:    List[Any]			,  lowercase	:    List[str]			,  lowercase	:    str	):


				'''simple docstring'''


				_snake_case		=			[self.artists_encoder.get(lowercase			,  0	) for artist in list_artists]
				for genres in range(len(lowercase	)	):
							_snake_case		=			[self.genres_encoder.get(lowercase			,  0	) for genre in list_genres[genres]]
							_snake_case		=			list_genres[genres] + [-1] * (self.n_genres - len(list_genres[genres]	))

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



	def  A							(  self	:    List[Any]			,  lowercase	:    List[str]	):


				'''simple docstring'''


				return list(lowercase	)



	def  A							(  self	:    Tuple			,  lowercase	:    Tuple			,  lowercase	:    Dict			,  lowercase	:    Optional[int]			,  **lowercase	:    List[Any]	):


				'''simple docstring'''


				_snake_case	,					_snake_case	,					_snake_case		=			self.prepare_for_tokenization(lowercase			,  lowercase			,  lowercase	)
				_snake_case		=			self._tokenize(lowercase	)
				return artist, genre, lyrics



	def  A							(  self	:    Any			,  lowercase	:    str			,  lowercase	:    str			,  lowercase	:    str			,  lowercase	:    bool = False	):


				'''simple docstring'''


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

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

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



	def  A							(  self	:    Union[str, Any]			,  lowercase	:    List[str]	):


				'''simple docstring'''


				_snake_case		=			unicodedata.normalize('NFD'			,  lowercase	)
				_snake_case		=			[]
				for char in text:
							_snake_case		=			unicodedata.category(lowercase	)
							if cat == "Mn":
										continue
							output.append(lowercase	)
				return "".join(lowercase	)



	def  A							(  self	:    Tuple			,  lowercase	:    str	):


				'''simple docstring'''


				_snake_case		=			(
				    [chr(lowercase	) for i in range(ord('a'	)			,  ord('z'	) + 1	)]
				    + [chr(lowercase	) for i in range(ord('A'	)			,  ord('Z'	) + 1	)]
				    + [chr(lowercase	) for i in range(ord('0'	)			,  ord('9'	) + 1	)]
				    + ['.']
				)
				_snake_case		=			frozenset(lowercase	)
				_snake_case		=			re.compile(R'_+'	)
				_snake_case		=			''.join([c if c in accepted else '_' for c in text.lower()]	)
				_snake_case		=			pattern.sub('_'			,  lowercase	).strip('_'	)
				return text



	def  A							(  self	:    Any			,  lowercase	:    List[str]	):


				'''simple docstring'''


				return " ".join(lowercase	)



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


				'''simple docstring'''


				if not isinstance(lowercase			,  lowercase	):
							_snake_case		=			TensorType(lowercase	)

				# 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

							_snake_case		=			tf.constant
							_snake_case		=			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

							_snake_case		=			torch.tensor
							_snake_case		=			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

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

				# Do the tensor conversion in batch

				try:
							if prepend_batch_axis:
										_snake_case		=			[inputs]

							if not is_tensor(lowercase	):
										_snake_case		=			as_tensor(lowercase	)
				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[Any]			,  lowercase	:    Optional[int]			,  lowercase	:    Tuple			,  lowercase	:    Tuple=""			,  lowercase	:    Any="pt"	):


				'''simple docstring'''


				_snake_case		=			[0, 0, 0]
				_snake_case		=			[artist] * len(self.version	)
				_snake_case		=			[genres] * len(self.version	)

				_snake_case	,					_snake_case	,					_snake_case		=			self.tokenize(lowercase			,  lowercase			,  lowercase	)
				_snake_case	,					_snake_case	,					_snake_case		=			self._convert_token_to_id(lowercase			,  lowercase			,  lowercase	)

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



	def  A							(  self	:    Dict			,  lowercase	:    str			,  lowercase	:    Optional[str] = None	):


				'''simple docstring'''


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

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

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

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

				return (artists_file, genres_file, lyrics_file)





	def  A							(  self	:    Optional[Any]			,  lowercase	:    str			,  lowercase	:    List[str]			,  lowercase	:    Any	):


				'''simple docstring'''


				_snake_case		=			self.artists_decoder.get(lowercase	)
				_snake_case		=			[self.genres_decoder.get(lowercase	) for genre in genres_index]
				_snake_case		=			[self.lyrics_decoder.get(lowercase	) for character in lyric_index]
				return artist, genres, lyrics

import os
from typing import Any, Callable, Dict, List, Optional, Tuple, Union

import torch
from torch import nn

from ...models.controlnet import ControlNetModel, ControlNetOutput
from ...models.modeling_utils import ModelMixin
from ...utils import logging


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




class    SCREAMING_SNAKE_CASE__     (	UpperCAmelCase					):





	'''simple docstring'''



	def __init__(  self	:    Dict			,  lowercase	:    Union[List[ControlNetModel], Tuple[ControlNetModel]]	):


				'''simple docstring'''


				super().__init__()
				_snake_case		=			nn.ModuleList(lowercase	)



	def  A							(  self	:    Optional[int]			,  lowercase	:    torch.FloatTensor			,  lowercase	:    Union[torch.Tensor, float, int]			,  lowercase	:    torch.Tensor			,  lowercase	:    List[torch.tensor]			,  lowercase	:    List[float]			,  lowercase	:    Optional[torch.Tensor] = None			,  lowercase	:    Optional[torch.Tensor] = None			,  lowercase	:    Optional[torch.Tensor] = None			,  lowercase	:    Optional[Dict[str, Any]] = None			,  lowercase	:    bool = False			,  lowercase	:    bool = True			,  ):


				'''simple docstring'''


				for i, (image, scale, controlnet) in enumerate(zip(lowercase			,  lowercase			,  self.nets	)	):
							_snake_case	,					_snake_case		=			controlnet(
							    lowercase			,  lowercase			,  lowercase			,  lowercase			,  lowercase			,  lowercase			,  lowercase			,  lowercase			,  lowercase			,  lowercase			,  lowercase			,  )

							# merge samples
							if i == 0:
										_snake_case	,					_snake_case		=			down_samples, mid_sample
							else:
										_snake_case		=			[
										    samples_prev + samples_curr
										    for samples_prev, samples_curr in zip(lowercase			,  lowercase	)
										]
										mid_block_res_sample += mid_sample

				return down_block_res_samples, mid_block_res_sample



	def  A							(  self	:    Dict			,  lowercase	:    Union[str, os.PathLike]			,  lowercase	:    bool = True			,  lowercase	:    Callable = None			,  lowercase	:    bool = False			,  lowercase	:    Optional[str] = None			,  ):


				'''simple docstring'''


				_snake_case		=			0
				_snake_case		=			save_directory
				for controlnet in self.nets:
							controlnet.save_pretrained(
							    lowercase			,  is_main_process=lowercase			,  save_function=lowercase			,  safe_serialization=lowercase			,  variant=lowercase			,  )

							idx += 1
							_snake_case		=			model_path_to_save + f'''_{idx}'''





	@classmethod
	def  A							(  cls	:    Any			,  lowercase	:    Optional[Union[str, os.PathLike]]			,  **lowercase	:    List[str]	):


				'''simple docstring'''


				_snake_case		=			0
				_snake_case		=			[]

				# load controlnet and append to list until no controlnet directory exists anymore
				# first controlnet has to be saved under `./mydirectory/controlnet` to be compliant with `DiffusionPipeline.from_prertained`
				# second, third, ... controlnets have to be saved under `./mydirectory/controlnet_1`, `./mydirectory/controlnet_2`, ...
				_snake_case		=			pretrained_model_path
				while os.path.isdir(lowercase	):
							_snake_case		=			ControlNetModel.from_pretrained(lowercase			,  **lowercase	)
							controlnets.append(lowercase	)

							idx += 1
							_snake_case		=			pretrained_model_path + f'''_{idx}'''

				logger.info(f'''{len(lowercase	)} controlnets loaded from {pretrained_model_path}.'''	)

				if len(lowercase	) == 0:
							raise ValueError(
							    f'''No ControlNets found under {os.path.dirname(lowercase	)}. Expected at least {pretrained_model_path + '_0'}.'''	)

				return cls(lowercase	)