

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": 512,
}


class 			A						(							_UpperCAmelCase			):







    """simple docstring"""





    lowerCamelCase		     =							VOCAB_FILES_NAMES
    lowerCamelCase		     =							PRETRAINED_VOCAB_FILES_MAP
    lowerCamelCase		     =							PRETRAINED_LYRIC_TOKENS_SIZES
    lowerCamelCase		     =							['input_ids', 'attention_mask']


    def __init__(				self			:	List[Any],lowercase_			:	Optional[Any],lowercase_			:	List[str],lowercase_			:	Optional[int],lowercase_			:	str=["v3", "v2", "v2"],lowercase_			:	List[Any]=5_1_2,lowercase_			:	List[Any]=5,lowercase_			:	Any="<|endoftext|>",**lowercase_			:	Optional[Any],)->   Optional[int]:





        '''simple docstring'''







        A__   				=       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_,)
        A__   				=       version
        A__   				=       max_n_lyric_tokens
        A__   				=       n_genres

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

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

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

        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		) == 7_9:
            A__   				=       oov.replace(r'\-\'',r'\-+\''		)

        A__   				=       regex.compile(lowercase_		)
        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 						snake_case__					(				self			:	Any		)->   Any:





        '''simple docstring'''







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


    def 						snake_case__					(				self			:	List[str]		)->   int:





        '''simple docstring'''







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


    def 						snake_case__					(				self			:	Tuple,lowercase_			:	int,lowercase_			:	Dict,lowercase_			:	Optional[int]		)->   List[str]:





        '''simple docstring'''







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

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


    def 						snake_case__					(				self			:	Union[str, Any],lowercase_			:	str		)->   Union[str, Any]:





        '''simple docstring'''







        return list(lowercase_		)


    def 						snake_case__					(				self			:	List[Any],lowercase_			:	Optional[int],lowercase_			:	Dict,lowercase_			:	Optional[int],**lowercase_			:	List[Any]		)->   Tuple:





        '''simple docstring'''







        A__				,						A__				,						A__   				=       self.prepare_for_tokenization(lowercase_,lowercase_,lowercase_		)
        A__   				=       self._tokenize(lowercase_		)
        return artist, genre, lyrics


    def 						snake_case__					(				self			:	Dict,lowercase_			:	str,lowercase_			:	str,lowercase_			:	str,lowercase_			:	bool = False		)->   Tuple[str, str, str, Dict[str, Any]]:





        '''simple docstring'''







        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(lowercase_		) + '.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(lowercase_		)		)}
            A__   				=       0
            A__   				=       len(lowercase_		) + 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(lowercase_		)
        A__   				=       lyrics.replace('\\','\n'		)
        A__   				=       self.out_of_vocab.sub('',lowercase_		), [], []
        return artists, genres, lyrics


    def 						snake_case__					(				self			:	str,lowercase_			:	Optional[int]		)->   int:





        '''simple docstring'''







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


    def 						snake_case__					(				self			:	Dict,lowercase_			:	str		)->   str:





        '''simple docstring'''







        A__   				=       (
            [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		)]
            + ['.']
        )
        A__   				=       frozenset(lowercase_		)
        A__   				=       re.compile(r'_+'		)
        A__   				=       ''.join([c if c in accepted else '_' for c in text.lower()]		)
        A__   				=       pattern.sub('_',lowercase_		).strip('_'		)
        return text


    def 						snake_case__					(				self			:	Optional[int],lowercase_			:	List[str]		)->   str:





        '''simple docstring'''







        return " ".join(lowercase_		)


    def 						snake_case__					(				self			:	List[str],lowercase_			:	List[Any],lowercase_			:	Optional[Union[str, TensorType]] = None,lowercase_			:	bool = False		)->   Optional[int]:





        '''simple docstring'''







        if not isinstance(lowercase_,lowercase_		):
            A__   				=       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

            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(lowercase_		):
                A__   				=       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			:	Union[str, Any],lowercase_			:	Optional[int],lowercase_			:	Union[str, Any],lowercase_			:	Dict="",lowercase_			:	List[str]="pt"		)->   BatchEncoding:





        '''simple docstring'''







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

        A__				,						A__				,						A__   				=       self.tokenize(lowercase_,lowercase_,lowercase_		)
        A__				,						A__				,						A__   				=       self._convert_token_to_id(lowercase_,lowercase_,lowercase_		)

        A__   				=       [-INFINITY] * len(full_tokens[-1]		)
        A__   				=       [
            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 						snake_case__					(				self			:	List[Any],lowercase_			:	str,lowercase_			:	Optional[str] = None		)->   Tuple[str]:





        '''simple docstring'''







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

        A__   				=       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_		)		)

        A__   				=       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_		)		)

        A__   				=       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 						snake_case__					(				self			:	Any,lowercase_			:	List[Any],lowercase_			:	Any,lowercase_			:	str		)->   str:





        '''simple docstring'''







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




import unittest
from typing import Dict, List, Optional, Union

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
     import torch

if is_vision_available():
     from PIL import Image

     from transformers import BridgeTowerImageProcessor


class 			A						(							unittest.TestCase			):







    """simple docstring"""





    def __init__(				self			:	List[str],lowercase_			:	List[str],lowercase_			:	bool = True,lowercase_			:	Dict[str, int] = None,lowercase_			:	int = 3_2,lowercase_			:	bool = True,lowercase_			:	Union[int, float] = 1 / 2_5_5,lowercase_			:	bool = True,lowercase_			:	bool = True,lowercase_			:	Optional[Union[float, List[float]]] = [0.48_145_466, 0.4_578_275, 0.40_821_073],lowercase_			:	Optional[Union[float, List[float]]] = [0.26_862_954, 0.26_130_258, 0.27_577_711],lowercase_			:	bool = True,lowercase_			:	Tuple=7,lowercase_			:	str=3_0,lowercase_			:	Union[str, Any]=4_0_0,lowercase_			:	Dict=3,)->   List[Any]:





        '''simple docstring'''







        A__   				=       parent
        A__   				=       do_resize
        A__   				=       size if size is not None else {'shortest_edge': 2_8_8}
        A__   				=       size_divisor
        A__   				=       do_rescale
        A__   				=       rescale_factor
        A__   				=       do_normalize
        A__   				=       do_center_crop
        A__   				=       image_mean
        A__   				=       image_std
        A__   				=       do_pad
        A__   				=       batch_size
        A__   				=       num_channels
        A__   				=       min_resolution
        A__   				=       max_resolution


    def 						snake_case__					(				self			:	Optional[Any]		)->   Optional[int]:





        '''simple docstring'''







        return {
            "image_mean": self.image_mean,
            "image_std": self.image_std,
            "do_normalize": self.do_normalize,
            "do_resize": self.do_resize,
            "size": self.size,
            "size_divisor": self.size_divisor,
        }


    def 						snake_case__					(				self			:	int,lowercase_			:	Optional[int],lowercase_			:	List[str]=False		)->   Any:





        '''simple docstring'''







        if not batched:
            A__   				=       self.size['shortest_edge']
            A__   				=       image_inputs[0]
            if isinstance(lowercase_,Image.Image		):
                A__				,						A__   				=       image.size
            else:
                A__				,						A__   				=       image.shape[1], image.shape[2]
            A__   				=       size / min(lowercase_,lowercase_		)
            if h < w:
                A__				,						A__   				=       size, scale * w
            else:
                A__				,						A__   				=       scale * h, size

            A__   				=       int((1_3_3_3 / 8_0_0) * size		)
            if max(lowercase_,lowercase_		) > max_size:
                A__   				=       max_size / max(lowercase_,lowercase_		)
                A__   				=       newh * scale
                A__   				=       neww * scale

            A__				,						A__   				=       int(newh + 0.5		), int(neww + 0.5		)
            A__				,						A__   				=       (
                newh // self.size_divisor * self.size_divisor,
                neww // self.size_divisor * self.size_divisor,
            )

        else:
            A__   				=       []
            for image in image_inputs:
                A__				,						A__   				=       self.get_expected_values([image]		)
                expected_values.append((expected_height, expected_width)		)
            A__   				=       max(lowercase_,key=lambda lowercase_		: item[0]		)[0]
            A__   				=       max(lowercase_,key=lambda lowercase_		: item[1]		)[1]

        return expected_height, expected_width




@require_torch
@require_vision
class 			A						(							_UpperCAmelCase   ,							unittest.TestCase			):







    """simple docstring"""





    lowerCamelCase		     =							BridgeTowerImageProcessor if is_vision_available() else None


    def 						snake_case__					(				self			:	str		)->   Optional[int]:





        '''simple docstring'''







        A__   				=       BridgeTowerImageProcessingTester(self		)


    @property
    def 						snake_case__					(				self			:	Dict		)->   List[str]:





        '''simple docstring'''







        return self.image_processor_tester.prepare_image_processor_dict()


    def 						snake_case__					(				self			:	Optional[Any]		)->   Tuple:





        '''simple docstring'''







        A__   				=       self.image_processing_class(**self.image_processor_dict		)
        self.assertTrue(hasattr(lowercase_,'image_mean'		)		)
        self.assertTrue(hasattr(lowercase_,'image_std'		)		)
        self.assertTrue(hasattr(lowercase_,'do_normalize'		)		)
        self.assertTrue(hasattr(lowercase_,'do_resize'		)		)
        self.assertTrue(hasattr(lowercase_,'size'		)		)
        self.assertTrue(hasattr(lowercase_,'size_divisor'		)		)


    def 						snake_case__					(				self			:	Any		)->   List[str]:





        '''simple docstring'''







        pass


    def 						snake_case__					(				self			:	int		)->   Any:





        '''simple docstring'''







        A__   				=       self.image_processing_class(**self.image_processor_dict		)
        # create random PIL images
        A__   				=       prepare_image_inputs(self.image_processor_tester,equal_resolution=lowercase_		)
        for image in image_inputs:
            self.assertIsInstance(lowercase_,Image.Image		)

        # Test not batched input
        A__   				=       image_processing(image_inputs[0],return_tensors='pt'		).pixel_values

        A__				,						A__   				=       self.image_processor_tester.get_expected_values(lowercase_		)
        self.assertEqual(
            encoded_images.shape,(1, self.image_processor_tester.num_channels, expected_height, expected_width),)

        # Test batched
        A__   				=       image_processing(lowercase_,return_tensors='pt'		).pixel_values

        A__				,						A__   				=       self.image_processor_tester.get_expected_values(lowercase_,batched=lowercase_		)
        self.assertEqual(
            encoded_images.shape,(
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                expected_height,
                expected_width,
            ),)


    def 						snake_case__					(				self			:	List[str]		)->   Tuple:





        '''simple docstring'''







        A__   				=       self.image_processing_class(**self.image_processor_dict		)
        # create random numpy tensors
        A__   				=       prepare_image_inputs(self.image_processor_tester,equal_resolution=lowercase_,numpify=lowercase_		)
        for image in image_inputs:
            self.assertIsInstance(lowercase_,np.ndarray		)

        # Test not batched input
        A__   				=       image_processing(image_inputs[0],return_tensors='pt'		).pixel_values

        A__				,						A__   				=       self.image_processor_tester.get_expected_values(lowercase_		)
        self.assertEqual(
            encoded_images.shape,(1, self.image_processor_tester.num_channels, expected_height, expected_width),)

        # Test batched
        A__   				=       image_processing(lowercase_,return_tensors='pt'		).pixel_values

        A__				,						A__   				=       self.image_processor_tester.get_expected_values(lowercase_,batched=lowercase_		)
        self.assertEqual(
            encoded_images.shape,(
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                expected_height,
                expected_width,
            ),)


    def 						snake_case__					(				self			:	Optional[Any]		)->   List[Any]:





        '''simple docstring'''







        A__   				=       self.image_processing_class(**self.image_processor_dict		)
        # create random PyTorch tensors
        A__   				=       prepare_image_inputs(self.image_processor_tester,equal_resolution=lowercase_,torchify=lowercase_		)
        for image in image_inputs:
            self.assertIsInstance(lowercase_,torch.Tensor		)

        # Test not batched input
        A__   				=       image_processing(image_inputs[0],return_tensors='pt'		).pixel_values

        A__				,						A__   				=       self.image_processor_tester.get_expected_values(lowercase_		)
        self.assertEqual(
            encoded_images.shape,(1, self.image_processor_tester.num_channels, expected_height, expected_width),)

        # Test batched
        A__   				=       image_processing(lowercase_,return_tensors='pt'		).pixel_values

        A__				,						A__   				=       self.image_processor_tester.get_expected_values(lowercase_,batched=lowercase_		)
        self.assertEqual(
            encoded_images.shape,(
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                expected_height,
                expected_width,
            ),)

