




"""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


a         =      logging.get_logger(__name__)

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

a         =      {
    '''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''',
    },
}

a         =      {
    '''jukebox''': 512,
}





class     lowercase_	(					__lowerCAmelCase							):



 '''simple docstring'''
 UpperCAmelCase   :					Optional[int]				  =     VOCAB_FILES_NAMES
 UpperCAmelCase   :					Tuple				  =     PRETRAINED_VOCAB_FILES_MAP
 UpperCAmelCase   :					Tuple				  =     PRETRAINED_LYRIC_TOKENS_SIZES
 UpperCAmelCase   :					Optional[Any]				  =     ['''input_ids''', '''attention_mask''']

 def __init__(			self	:	Optional[int]   ,							_UpperCAmelCase	:	Optional[int]   ,							_UpperCAmelCase	:	Optional[Any]   ,							_UpperCAmelCase	:	Dict   ,							_UpperCAmelCase	:	List[Any]=["v3", "v2", "v2"]   ,							_UpperCAmelCase	:	Optional[Any]=512   ,							_UpperCAmelCase	:	Optional[Any]=5   ,							_UpperCAmelCase	:	Any="<|endoftext|>"   ,							**_UpperCAmelCase	:	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       lowerCAmelCase_							(			self	:	Any		):
       return len(self.artists_encoder		) + len(self.genres_encoder		) + len(self.lyrics_encoder		)

 def       lowerCAmelCase_							(			self	:	List[Any]		):
       return dict(self.artists_encoder   ,							self.genres_encoder   ,							self.lyrics_encoder		)

 def       lowerCAmelCase_							(			self	:	str   ,							_UpperCAmelCase	:	List[Any]   ,							_UpperCAmelCase	:	Union[str, Any]   ,							_UpperCAmelCase	:	List[str]		):
       _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       lowerCAmelCase_							(			self	:	Dict   ,							_UpperCAmelCase	:	Dict		):
       return list(_UpperCAmelCase		)

 def       lowerCAmelCase_							(			self	:	Union[str, Any]   ,							_UpperCAmelCase	:	int   ,							_UpperCAmelCase	:	int   ,							_UpperCAmelCase	:	Optional[int]   ,							**_UpperCAmelCase	:	List[str]		):
       _A       ,   _A       ,   _A        =       self.prepare_for_tokenization(_UpperCAmelCase   ,							_UpperCAmelCase   ,							_UpperCAmelCase		)
       _A        =       self._tokenize(_UpperCAmelCase		)
       return artist, genre, lyrics

 def       lowerCAmelCase_							(			self	:	List[Any]   ,							_UpperCAmelCase	:	str   ,							_UpperCAmelCase	:	str   ,							_UpperCAmelCase	:	str   ,							_UpperCAmelCase	:	bool = False		):
       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       lowerCAmelCase_							(			self	:	Any   ,							_UpperCAmelCase	:	str		):
       _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       lowerCAmelCase_							(			self	:	List[Any]   ,							_UpperCAmelCase	:	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       lowerCAmelCase_							(			self	:	List[Any]   ,							_UpperCAmelCase	:	List[str]		):
       return " ".join(_UpperCAmelCase		)

 def       lowerCAmelCase_							(			self	:	Any   ,							_UpperCAmelCase	:	Any   ,							_UpperCAmelCase	:	Optional[Union[str, TensorType]] = None   ,							_UpperCAmelCase	:	bool = False		):
       # Convert to TensorType
       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	:	Optional[Any]   ,							_UpperCAmelCase	:	Optional[Any]   ,							_UpperCAmelCase	:	Optional[Any]   ,							_UpperCAmelCase	:	Dict=""   ,							_UpperCAmelCase	:	Any="pt"		):
       _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       lowerCAmelCase_							(			self	:	Optional[Any]   ,							_UpperCAmelCase	:	str   ,							_UpperCAmelCase	:	Optional[str] = None		):
       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       lowerCAmelCase_							(			self	:	Optional[int]   ,							_UpperCAmelCase	:	Tuple   ,							_UpperCAmelCase	:	str   ,							_UpperCAmelCase	:	Optional[int]		):
       _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 typing import List, Optional, Union

import torch

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)


a         =      logging.get_logger(__name__)  # pylint: disable=invalid-name

a         =      '''
    Examples:
        ```py
        >>> from diffusers import KandinskyV22Pipeline, KandinskyV22PriorPipeline
        >>> import torch

        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained("kandinsky-community/kandinsky-2-2-prior")
        >>> pipe_prior.to("cuda")
        >>> prompt = "red cat, 4k photo"
        >>> out = pipe_prior(prompt)
        >>> image_emb = out.image_embeds
        >>> zero_image_emb = out.negative_image_embeds
        >>> pipe = KandinskyV22Pipeline.from_pretrained("kandinsky-community/kandinsky-2-2-decoder")
        >>> pipe.to("cuda")
        >>> image = pipe(
        ...     image_embeds=image_emb,
        ...     negative_image_embeds=zero_image_emb,
        ...     height=768,
        ...     width=768,
        ...     num_inference_steps=50,
        ... ).images
        >>> image[0].save("cat.png")
        ```
'''






def 						_snake_case					( _snake_case							: Dict    ,  _snake_case							: List[Any]    ,  _snake_case							: List[Any]=8							)   -> Optional[int]:






      '''simple docstring'''


      _A        =       height // scale_factor**2
      if height % scale_factor**2 != 0:
            new_height += 1
      _A        =       width // scale_factor**2
      if width % scale_factor**2 != 0:
            new_width += 1
      return new_height * scale_factor, new_width * scale_factor







class     lowercase_	(					__lowerCAmelCase							):



 '''simple docstring'''

 def __init__(			self	:	Dict   ,							_UpperCAmelCase	:	UNetaDConditionModel   ,							_UpperCAmelCase	:	DDPMScheduler   ,							_UpperCAmelCase	:	VQModel   ,							):
       super().__init__()

       self.register_modules(
           unet=_UpperCAmelCase   ,							scheduler=_UpperCAmelCase   ,							movq=_UpperCAmelCase   ,							)
       _A        =       2 ** (len(self.movq.config.block_out_channels		) - 1)

 def       lowerCAmelCase_							(			self	:	Any   ,							_UpperCAmelCase	:	str   ,							_UpperCAmelCase	:	Tuple   ,							_UpperCAmelCase	:	Optional[Any]   ,							_UpperCAmelCase	:	Dict   ,							_UpperCAmelCase	:	List[str]   ,							_UpperCAmelCase	:	int		):
       if latents is None:
             _A        =       randn_tensor(_UpperCAmelCase   ,							generator=_UpperCAmelCase   ,							device=_UpperCAmelCase   ,							dtype=_UpperCAmelCase		)
       else:
             if latents.shape != shape:
                   raise ValueError(F'''Unexpected latents shape, got {latents.shape}, expected {shape}'''		)
             _A        =       latents.to(_UpperCAmelCase		)

       _A        =       latents * scheduler.init_noise_sigma
       return latents

 def       lowerCAmelCase_							(			self	:	Tuple   ,							_UpperCAmelCase	:	Tuple=0		):
       if is_accelerate_available():
             from accelerate import cpu_offload
       else:
             raise ImportError('Please install accelerate via `pip install accelerate`'		)

       _A        =       torch.device(F'''cuda:{gpu_id}'''		)

       _A        =       [
           self.unet,
           self.movq,
       ]
       for cpu_offloaded_model in models:
             if cpu_offloaded_model is not None:
                   cpu_offload(_UpperCAmelCase   ,							_UpperCAmelCase		)

 def       lowerCAmelCase_							(			self	:	Union[str, Any]   ,							_UpperCAmelCase	:	Union[str, Any]=0		):
       if is_accelerate_available() and is_accelerate_version('>='   ,							'0.17.0.dev0'		):
             from accelerate import cpu_offload_with_hook
       else:
             raise ImportError('`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.'		)

       _A        =       torch.device(F'''cuda:{gpu_id}'''		)

       if self.device.type != "cpu":
             self.to('cpu'   ,							silence_dtype_warnings=_UpperCAmelCase		)
             torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

       _A        =       None
       for cpu_offloaded_model in [self.unet, self.movq]:
             _A       ,   _A        =       cpu_offload_with_hook(_UpperCAmelCase   ,							_UpperCAmelCase   ,							prev_module_hook=_UpperCAmelCase		)

       # We'll offload the last model manually.
       _A        =       hook

 @property
 # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
 def       lowerCAmelCase_							(			self	:	int		):
       if not hasattr(self.unet   ,							'_hf_hook'		):
             return self.device
       for module in self.unet.modules():
             if (
                 hasattr(_UpperCAmelCase   ,							'_hf_hook'		)
                 and hasattr(module._hf_hook   ,							'execution_device'		)
                 and module._hf_hook.execution_device is not None
             ):
                   return torch.device(module._hf_hook.execution_device		)
       return self.device



 @torch.no_grad()
 @replace_example_docstring(_UpperCAmelCase		)
 def __call__(			self	:	Union[str, Any]   ,							_UpperCAmelCase	:	Union[torch.FloatTensor, List[torch.FloatTensor]]   ,							_UpperCAmelCase	:	Union[torch.FloatTensor, List[torch.FloatTensor]]   ,							_UpperCAmelCase	:	int = 512   ,							_UpperCAmelCase	:	int = 512   ,							_UpperCAmelCase	:	int = 100   ,							_UpperCAmelCase	:	float = 4.0   ,							_UpperCAmelCase	:	int = 1   ,							_UpperCAmelCase	:	Optional[Union[torch.Generator, List[torch.Generator]]] = None   ,							_UpperCAmelCase	:	Optional[torch.FloatTensor] = None   ,							_UpperCAmelCase	:	Optional[str] = "pil"   ,							_UpperCAmelCase	:	bool = True   ,							):
       _A        =       self._execution_device

       _A        =       guidance_scale > 1.0

       if isinstance(_UpperCAmelCase   ,							_UpperCAmelCase		):
             _A        =       torch.cat(_UpperCAmelCase   ,							dim=0		)
       _A        =       image_embeds.shape[0] * num_images_per_prompt
       if isinstance(_UpperCAmelCase   ,							_UpperCAmelCase		):
             _A        =       torch.cat(_UpperCAmelCase   ,							dim=0		)

       if do_classifier_free_guidance:
             _A        =       image_embeds.repeat_interleave(_UpperCAmelCase   ,							dim=0		)
             _A        =       negative_image_embeds.repeat_interleave(_UpperCAmelCase   ,							dim=0		)

       _A        =       torch.cat([negative_image_embeds, image_embeds]   ,							dim=0		).to(dtype=self.unet.dtype   ,							device=_UpperCAmelCase		)

       self.scheduler.set_timesteps(_UpperCAmelCase   ,							device=_UpperCAmelCase		)
       _A        =       self.scheduler.timesteps

       _A        =       self.unet.config.in_channels

       _A       ,   _A        =       downscale_height_and_width(_UpperCAmelCase   ,							_UpperCAmelCase   ,							self.movq_scale_factor		)

       # create initial latent
       _A        =       self.prepare_latents(
           (batch_size, num_channels_latents, height, width)   ,							image_embeds.dtype   ,							_UpperCAmelCase   ,							_UpperCAmelCase   ,							_UpperCAmelCase   ,							self.scheduler   ,							)

       for i, t in enumerate(self.progress_bar(_UpperCAmelCase		)		):
             # expand the latents if we are doing classifier free guidance
             _A        =       torch.cat([latents] * 2		) if do_classifier_free_guidance else latents

             _A        =       {'image_embeds': image_embeds}
             _A        =       self.unet(
                 sample=_UpperCAmelCase   ,							timestep=_UpperCAmelCase   ,							encoder_hidden_states=_UpperCAmelCase   ,							added_cond_kwargs=_UpperCAmelCase   ,							return_dict=_UpperCAmelCase   ,							)[0]

             if do_classifier_free_guidance:
                   _A       ,   _A        =       noise_pred.split(latents.shape[1]   ,							dim=1		)
                   _A       ,   _A        =       noise_pred.chunk(2		)
                   _A       ,   _A        =       variance_pred.chunk(2		)
                   _A        =       noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
                   _A        =       torch.cat([noise_pred, variance_pred_text]   ,							dim=1		)

             if not (
                 hasattr(self.scheduler.config   ,							'variance_type'		)
                 and self.scheduler.config.variance_type in ["learned", "learned_range"]
             ):
                   _A       ,   _A        =       noise_pred.split(latents.shape[1]   ,							dim=1		)

             # compute the previous noisy sample x_t -> x_t-1
             _A        =       self.scheduler.step(
                 _UpperCAmelCase   ,							_UpperCAmelCase   ,							_UpperCAmelCase   ,							generator=_UpperCAmelCase   ,							)[0]
       # post-processing
       _A        =       self.movq.decode(_UpperCAmelCase   ,							force_not_quantize=_UpperCAmelCase		)['sample']

       if output_type not in ["pt", "np", "pil"]:
             raise ValueError(F'''Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}'''		)

       if output_type in ["np", "pil"]:
             _A        =       image * 0.5 + 0.5
             _A        =       image.clamp(0   ,							1		)
             _A        =       image.cpu().permute(0   ,							2   ,							3   ,							1		).float().numpy()

       if output_type == "pil":
             _A        =       self.numpy_to_pil(_UpperCAmelCase		)

       if not return_dict:
             return (image,)

       return ImagePipelineOutput(images=_UpperCAmelCase		)

