from typing import Optional, Tuple, Union

import tensorflow as tf

from ...activations_tf import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
from ...modeling_tf_outputs import (
    TFBaseModelOutputWithNoAttention,
    TFBaseModelOutputWithPoolingAndNoAttention,
    TFSequenceClassifierOutput,
)
from ...modeling_tf_utils import TFPreTrainedModel, TFSequenceClassificationLoss, keras_serializable, unpack_inputs
from ...tf_utils import shape_list
from ...utils import logging
from .configuration_regnet import RegNetConfig


a  :      int            =    logging.get_logger(__name__)

# General docstring
a  :      str            =    """RegNetConfig"""

# Base docstring
a  :      Tuple            =    """facebook/regnet-y-040"""
a  :      Any            =    [1, 1_0_8_8, 7, 7]

# Image classification docstring
a  :      Any            =    """facebook/regnet-y-040"""
a  :      Optional[Any]            =    """tabby, tabby cat"""

a  :      Optional[int]            =    [
    """facebook/regnet-y-040""",
    # See all regnet models at https://huggingface.co/models?filter=regnet
]

class 					_lowercase    (     tf.keras.layers.Layer			):






    '''simple docstring'''




    def __init__(    self       ,						lowerCamelCase__       ,						lowerCamelCase__ = 3       ,						lowerCamelCase__ = 1       ,						lowerCamelCase__ = 1       ,						lowerCamelCase__ = "relu"       ,						**lowerCamelCase__       ,						):
       super().__init__(**lowerCamelCase__ )
       # The padding and conv has been verified in
       # https://colab.research.google.com/gist/sayakpaul/854bc10eeaf21c9ee2119e0b9f3841a7/scratchpad.ipynb
       lowerCAmelCase_:							int       =	tf.keras.layers.ZeroPaddingaD(padding=kernel_size // 2 )
       lowerCAmelCase_:							Dict       =	tf.keras.layers.ConvaD(
           filters=lowerCamelCase__       ,						kernel_size=lowerCamelCase__       ,						strides=lowerCamelCase__       ,						padding="VALID"       ,						groups=lowerCamelCase__       ,						use_bias=lowerCamelCase__       ,						name="convolution"       ,						)
       lowerCAmelCase_:							Tuple       =	tf.keras.layers.BatchNormalization(epsilon=1E-5       ,						momentum=0.9       ,						name="normalization" )
       lowerCAmelCase_:							Union[str, Any]       =	ACTaFN[activation] if activation is not None else tf.identity




    def      _a    (    self       ,						lowerCamelCase__ ):
       lowerCAmelCase_:							List[str]       =	self.convolution(self.padding(lowerCamelCase__ ) )
       lowerCAmelCase_:							Dict       =	self.normalization(lowerCamelCase__ )
       lowerCAmelCase_:							List[Any]       =	self.activation(lowerCamelCase__ )
       return hidden_state





class 					_lowercase    (     tf.keras.layers.Layer			):






    '''simple docstring'''




    def __init__(    self       ,						lowerCamelCase__       ,						**lowerCamelCase__ ):
       super().__init__(**lowerCamelCase__ )
       lowerCAmelCase_:							str       =	config.num_channels
       lowerCAmelCase_:							int       =	TFRegNetConvLayer(
           out_channels=config.embedding_size       ,						kernel_size=3       ,						stride=2       ,						activation=config.hidden_act       ,						name="embedder"       ,						)




    def      _a    (    self       ,						lowerCamelCase__ ):
       lowerCAmelCase_:							Optional[Any]       =	shape_list(lowerCamelCase__ )[1]
       if tf.executing_eagerly() and num_channels != self.num_channels:
          raise ValueError(
              "Make sure that the channel dimension of the pixel values match with the one set in the configuration." )

       # When running on CPU, `tf.keras.layers.Conv2D` doesn't support `NCHW` format.
       # So change the input format from `NCHW` to `NHWC`.
       # shape = (batch_size, in_height, in_width, in_channels=num_channels)
       lowerCAmelCase_:							Any       =	tf.transpose(lowerCamelCase__       ,						perm=(0, 2, 3, 1) )
       lowerCAmelCase_:							List[Any]       =	self.embedder(lowerCamelCase__ )
       return hidden_state





class 					_lowercase    (     tf.keras.layers.Layer			):






    '''simple docstring'''




    def __init__(    self       ,						lowerCamelCase__       ,						lowerCamelCase__ = 2       ,						**lowerCamelCase__ ):
       super().__init__(**lowerCamelCase__ )
       lowerCAmelCase_:							Dict       =	tf.keras.layers.ConvaD(
           filters=lowerCamelCase__       ,						kernel_size=1       ,						strides=lowerCamelCase__       ,						use_bias=lowerCamelCase__       ,						name="convolution" )
       lowerCAmelCase_:							str       =	tf.keras.layers.BatchNormalization(epsilon=1E-5       ,						momentum=0.9       ,						name="normalization" )




    def      _a    (    self       ,						lowerCamelCase__       ,						lowerCamelCase__ = False ):
       return self.normalization(self.convolution(lowerCamelCase__ )       ,						training=lowerCamelCase__ )





class 					_lowercase    (     tf.keras.layers.Layer			):






    '''simple docstring'''




    def __init__(    self       ,						lowerCamelCase__       ,						lowerCamelCase__       ,						**lowerCamelCase__ ):
       super().__init__(**lowerCamelCase__ )
       lowerCAmelCase_:							List[str]       =	tf.keras.layers.GlobalAveragePoolingaD(keepdims=lowerCamelCase__       ,						name="pooler" )
       lowerCAmelCase_:							List[str]       =	[
           tf.keras.layers.ConvaD(filters=lowerCamelCase__       ,						kernel_size=1       ,						activation="relu"       ,						name="attention.0" ),
           tf.keras.layers.ConvaD(filters=lowerCamelCase__       ,						kernel_size=1       ,						activation="sigmoid"       ,						name="attention.2" ),
       ]




    def      _a    (    self       ,						lowerCamelCase__ ):
       # [batch_size, h, w, num_channels] -> [batch_size, 1, 1, num_channels]
       lowerCAmelCase_:							Optional[int]       =	self.pooler(lowerCamelCase__ )
       for layer_module in self.attention:
          lowerCAmelCase_:							Tuple       =	layer_module(lowerCamelCase__ )
       lowerCAmelCase_:							Optional[int]       =	hidden_state * pooled
       return hidden_state





class 					_lowercase    (     tf.keras.layers.Layer			):






    '''simple docstring'''




    def __init__(    self       ,						lowerCamelCase__       ,						lowerCamelCase__       ,						lowerCamelCase__       ,						lowerCamelCase__ = 1       ,						**lowerCamelCase__ ):
       super().__init__(**lowerCamelCase__ )
       lowerCAmelCase_:							str       =	in_channels != out_channels or stride != 1
       lowerCAmelCase_:							Optional[Any]       =	max(1       ,						out_channels // config.groups_width )
       lowerCAmelCase_:							Dict       =	(
           TFRegNetShortCut(lowerCamelCase__       ,						stride=lowerCamelCase__       ,						name="shortcut" )
           if should_apply_shortcut
           else tf.keras.layers.Activation("linear"       ,						name="shortcut" )
       )
       # `self.layers` instead of `self.layer` because that is a reserved argument.
       lowerCAmelCase_:							List[Any]       =	[
           TFRegNetConvLayer(lowerCamelCase__       ,						kernel_size=1       ,						activation=config.hidden_act       ,						name="layer.0" ),
           TFRegNetConvLayer(
               lowerCamelCase__       ,						stride=lowerCamelCase__       ,						groups=lowerCamelCase__       ,						activation=config.hidden_act       ,						name="layer.1" ),
           TFRegNetConvLayer(lowerCamelCase__       ,						kernel_size=1       ,						activation=lowerCamelCase__       ,						name="layer.2" ),
       ]
       lowerCAmelCase_:							Optional[Any]       =	ACTaFN[config.hidden_act]




    def      _a    (    self       ,						lowerCamelCase__ ):
       lowerCAmelCase_:							Union[str, Any]       =	hidden_state
       for layer_module in self.layers:
          lowerCAmelCase_:							Optional[int]       =	layer_module(lowerCamelCase__ )
       lowerCAmelCase_:							Optional[int]       =	self.shortcut(lowerCamelCase__ )
       hidden_state += residual
       lowerCAmelCase_:							Tuple       =	self.activation(lowerCamelCase__ )
       return hidden_state





class 					_lowercase    (     tf.keras.layers.Layer			):






    '''simple docstring'''




    def __init__(    self       ,						lowerCamelCase__       ,						lowerCamelCase__       ,						lowerCamelCase__       ,						lowerCamelCase__ = 1       ,						**lowerCamelCase__ ):
       super().__init__(**lowerCamelCase__ )
       lowerCAmelCase_:							Any       =	in_channels != out_channels or stride != 1
       lowerCAmelCase_:							Any       =	max(1       ,						out_channels // config.groups_width )
       lowerCAmelCase_:							Dict       =	(
           TFRegNetShortCut(lowerCamelCase__       ,						stride=lowerCamelCase__       ,						name="shortcut" )
           if should_apply_shortcut
           else tf.keras.layers.Activation("linear"       ,						name="shortcut" )
       )
       lowerCAmelCase_:							Tuple       =	[
           TFRegNetConvLayer(lowerCamelCase__       ,						kernel_size=1       ,						activation=config.hidden_act       ,						name="layer.0" ),
           TFRegNetConvLayer(
               lowerCamelCase__       ,						stride=lowerCamelCase__       ,						groups=lowerCamelCase__       ,						activation=config.hidden_act       ,						name="layer.1" ),
           TFRegNetSELayer(lowerCamelCase__       ,						reduced_channels=int(round(in_channels / 4 ) )       ,						name="layer.2" ),
           TFRegNetConvLayer(lowerCamelCase__       ,						kernel_size=1       ,						activation=lowerCamelCase__       ,						name="layer.3" ),
       ]
       lowerCAmelCase_:							Union[str, Any]       =	ACTaFN[config.hidden_act]




    def      _a    (    self       ,						lowerCamelCase__ ):
       lowerCAmelCase_:							List[str]       =	hidden_state
       for layer_module in self.layers:
          lowerCAmelCase_:							Optional[int]       =	layer_module(lowerCamelCase__ )
       lowerCAmelCase_:							Tuple       =	self.shortcut(lowerCamelCase__ )
       hidden_state += residual
       lowerCAmelCase_:							Optional[int]       =	self.activation(lowerCamelCase__ )
       return hidden_state





class 					_lowercase    (     tf.keras.layers.Layer			):






    '''simple docstring'''




    def __init__(    self       ,						lowerCamelCase__       ,						lowerCamelCase__       ,						lowerCamelCase__       ,						lowerCamelCase__ = 2       ,						lowerCamelCase__ = 2       ,						**lowerCamelCase__ ):
       super().__init__(**lowerCamelCase__ )

       lowerCAmelCase_:							Dict       =	TFRegNetXLayer if config.layer_type == "x" else TFRegNetYLayer
       lowerCAmelCase_:							Optional[int]       =	[
           # downsampling is done in the first layer with stride of 2
           layer(lowerCamelCase__       ,						lowerCamelCase__       ,						lowerCamelCase__       ,						stride=lowerCamelCase__       ,						name="layers.0" ),
           *[layer(lowerCamelCase__       ,						lowerCamelCase__       ,						lowerCamelCase__       ,						name=F'''layers.{i+1}''' ) for i in range(depth - 1 )],
       ]




    def      _a    (    self       ,						lowerCamelCase__ ):
       for layer_module in self.layers:
          lowerCAmelCase_:							Dict       =	layer_module(lowerCamelCase__ )
       return hidden_state





class 					_lowercase    (     tf.keras.layers.Layer			):






    '''simple docstring'''




    def __init__(    self       ,						lowerCamelCase__       ,						**lowerCamelCase__ ):
       super().__init__(**lowerCamelCase__ )
       lowerCAmelCase_:							Tuple       =	[]
       # based on `downsample_in_first_stage`, the first layer of the first stage may or may not downsample the input
       self.stages.append(
           TFRegNetStage(
               lowerCamelCase__       ,						config.embedding_size       ,						config.hidden_sizes[0]       ,						stride=2 if config.downsample_in_first_stage else 1       ,						depth=config.depths[0]       ,						name="stages.0"       ,						) )
       lowerCAmelCase_:							Any       =	zip(config.hidden_sizes       ,						config.hidden_sizes[1:] )
       for i, ((in_channels, out_channels), depth) in enumerate(zip(lowerCamelCase__       ,						config.depths[1:] ) ):
          self.stages.append(TFRegNetStage(lowerCamelCase__       ,						lowerCamelCase__       ,						lowerCamelCase__       ,						depth=lowerCamelCase__       ,						name=F'''stages.{i+1}''' ) )




    def      _a    (    self       ,						lowerCamelCase__       ,						lowerCamelCase__ = False       ,						lowerCamelCase__ = True ):
       lowerCAmelCase_:							int       =	() if output_hidden_states else None

       for stage_module in self.stages:
          if output_hidden_states:
             lowerCAmelCase_:							List[Any]       =	hidden_states + (hidden_state,)

          lowerCAmelCase_:							Dict       =	stage_module(lowerCamelCase__ )

       if output_hidden_states:
          lowerCAmelCase_:							Any       =	hidden_states + (hidden_state,)

       if not return_dict:
          return tuple(v for v in [hidden_state, hidden_states] if v is not None )

       return TFBaseModelOutputWithNoAttention(last_hidden_state=lowerCamelCase__       ,						hidden_states=lowerCamelCase__ )





@keras_serializable
class 					_lowercase    (     tf.keras.layers.Layer			):






    '''simple docstring'''
    SCREAMING_SNAKE_CASE:				str          =    RegNetConfig




    def __init__(    self       ,						lowerCamelCase__       ,						**lowerCamelCase__ ):
       super().__init__(**lowerCamelCase__ )
       lowerCAmelCase_:							Any       =	config
       lowerCAmelCase_:							str       =	TFRegNetEmbeddings(lowerCamelCase__       ,						name="embedder" )
       lowerCAmelCase_:							Optional[int]       =	TFRegNetEncoder(lowerCamelCase__       ,						name="encoder" )
       lowerCAmelCase_:							List[str]       =	tf.keras.layers.GlobalAveragePoolingaD(keepdims=lowerCamelCase__       ,						name="pooler" )




    @unpack_inputs
    def      _a    (    self       ,						lowerCamelCase__       ,						lowerCamelCase__ = None       ,						lowerCamelCase__ = None       ,						lowerCamelCase__ = False       ,						):
       lowerCAmelCase_:							List[Any]       =	(
           output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
       )
       lowerCAmelCase_:							Optional[Any]       =	return_dict if return_dict is not None else self.config.use_return_dict

       lowerCAmelCase_:							str       =	self.embedder(lowerCamelCase__       ,						training=lowerCamelCase__ )

       lowerCAmelCase_:							List[Any]       =	self.encoder(
           lowerCamelCase__       ,						output_hidden_states=lowerCamelCase__       ,						return_dict=lowerCamelCase__       ,						training=lowerCamelCase__ )

       lowerCAmelCase_:							Dict       =	encoder_outputs[0]
       lowerCAmelCase_:							str       =	self.pooler(lowerCamelCase__ )

       # Change to NCHW output format have uniformity in the modules
       lowerCAmelCase_:							Tuple       =	tf.transpose(lowerCamelCase__       ,						perm=(0, 3, 1, 2) )
       lowerCAmelCase_:							Optional[Any]       =	tf.transpose(lowerCamelCase__       ,						perm=(0, 3, 1, 2) )

       # Change the other hidden state outputs to NCHW as well
       if output_hidden_states:
          lowerCAmelCase_:							str       =	tuple([tf.transpose(lowerCamelCase__       ,						perm=(0, 3, 1, 2) ) for h in encoder_outputs[1]] )

       if not return_dict:
          return (last_hidden_state, pooled_output) + encoder_outputs[1:]

       return TFBaseModelOutputWithPoolingAndNoAttention(
           last_hidden_state=lowerCamelCase__       ,						pooler_output=lowerCamelCase__       ,						hidden_states=hidden_states if output_hidden_states else encoder_outputs.hidden_states       ,						)





class 					_lowercase    (     UpperCAmelCase__			):






    '''simple docstring'''
    SCREAMING_SNAKE_CASE:				str          =    RegNetConfig
    SCREAMING_SNAKE_CASE:				str          =    'regnet'
    SCREAMING_SNAKE_CASE:				int          =    'pixel_values'




    @property
    def      _a    (    self ):
       return {"pixel_values": tf.TensorSpec(shape=(None, self.config.num_channels, 224, 224)       ,						dtype=tf.floataa )}





a  :      Union[str, Any]            =    r"""
    Parameters:
    This model is a Tensorflow
    [tf.keras.layers.Layer](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Layer) sub-class. Use it as a
    regular Tensorflow Module and refer to the Tensorflow documentation for all matter related to general usage and
    behavior.
        config ([`RegNetConfig`]): Model configuration class with all the parameters of the model.
            Initializing with a config file does not load the weights associated with the model, only the
            configuration. Check out the [`~TFPreTrainedModel.from_pretrained`] method to load the model weights.
"""

a  :      str            =    r"""
    Args:
        pixel_values (`tf.Tensor` of shape `(batch_size, num_channels, height, width)`):
            Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See
            [`ConveNextImageProcessor.__call__`] for details.
        output_hidden_states (`bool`, *optional*):
            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
            more detail.
        return_dict (`bool`, *optional*):
            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""

@add_start_docstrings(
    'The bare RegNet model outputting raw features without any specific head on top.' ,  UpperCAmelCase__ ,  )
class 					_lowercase    (     UpperCAmelCase__			):






    '''simple docstring'''




    def __init__(    self       ,						lowerCamelCase__       ,						*lowerCamelCase__       ,						**lowerCamelCase__ ):
       super().__init__(lowerCamelCase__       ,						*lowerCamelCase__       ,						**lowerCamelCase__ )
       lowerCAmelCase_:							Optional[int]       =	TFRegNetMainLayer(lowerCamelCase__       ,						name="regnet" )




    @unpack_inputs
    @add_start_docstrings_to_model_forward(lowerCamelCase__ )
    @add_code_sample_docstrings(
        checkpoint=_CHECKPOINT_FOR_DOC       ,						output_type=lowerCamelCase__       ,						config_class=_CONFIG_FOR_DOC       ,						modality="vision"       ,						expected_output=_EXPECTED_OUTPUT_SHAPE       ,						)
    def      _a    (    self       ,						lowerCamelCase__       ,						lowerCamelCase__ = None       ,						lowerCamelCase__ = None       ,						lowerCamelCase__=False       ,						):
       lowerCAmelCase_:							Optional[int]       =	(
           output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
       )
       lowerCAmelCase_:							int       =	return_dict if return_dict is not None else self.config.use_return_dict

       lowerCAmelCase_:							List[Any]       =	self.regnet(
           pixel_values=lowerCamelCase__       ,						output_hidden_states=lowerCamelCase__       ,						return_dict=lowerCamelCase__       ,						training=lowerCamelCase__       ,						)
       if not return_dict:
          return (outputs[0],) + outputs[1:]

       return TFBaseModelOutputWithPoolingAndNoAttention(
           last_hidden_state=outputs.last_hidden_state       ,						pooler_output=outputs.pooler_output       ,						hidden_states=outputs.hidden_states       ,						)





@add_start_docstrings(
    '\n    RegNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for\n    ImageNet.\n    ' ,  UpperCAmelCase__ ,  )
class 					_lowercase    (     UpperCAmelCase__ ,  UpperCAmelCase__			):






    '''simple docstring'''




    def __init__(    self       ,						lowerCamelCase__       ,						*lowerCamelCase__       ,						**lowerCamelCase__ ):
       super().__init__(lowerCamelCase__       ,						*lowerCamelCase__       ,						**lowerCamelCase__ )
       lowerCAmelCase_:							List[Any]       =	config.num_labels
       lowerCAmelCase_:							List[str]       =	TFRegNetMainLayer(lowerCamelCase__       ,						name="regnet" )
       # classification head
       lowerCAmelCase_:							Tuple       =	[
           tf.keras.layers.Flatten(),
           tf.keras.layers.Dense(config.num_labels       ,						name="classifier.1" ) if config.num_labels > 0 else tf.identity,
       ]




    @unpack_inputs
    @add_start_docstrings_to_model_forward(lowerCamelCase__ )
    @add_code_sample_docstrings(
        checkpoint=_IMAGE_CLASS_CHECKPOINT       ,						output_type=lowerCamelCase__       ,						config_class=_CONFIG_FOR_DOC       ,						expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT       ,						)
    def      _a    (    self       ,						lowerCamelCase__ = None       ,						lowerCamelCase__ = None       ,						lowerCamelCase__ = None       ,						lowerCamelCase__ = None       ,						lowerCamelCase__=False       ,						):
       lowerCAmelCase_:							Optional[int]       =	(
           output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
       )
       lowerCAmelCase_:							List[Any]       =	return_dict if return_dict is not None else self.config.use_return_dict

       lowerCAmelCase_:							List[str]       =	self.regnet(
           lowerCamelCase__       ,						output_hidden_states=lowerCamelCase__       ,						return_dict=lowerCamelCase__       ,						training=lowerCamelCase__ )

       lowerCAmelCase_:							Tuple       =	outputs.pooler_output if return_dict else outputs[1]

       lowerCAmelCase_:							Dict       =	self.classifier[0](lowerCamelCase__ )
       lowerCAmelCase_:							List[Any]       =	self.classifier[1](lowerCamelCase__ )

       lowerCAmelCase_:							Optional[int]       =	None if labels is None else self.hf_compute_loss(labels=lowerCamelCase__       ,						logits=lowerCamelCase__ )

       if not return_dict:
          lowerCAmelCase_:							List[str]       =	(logits,) + outputs[2:]
          return ((loss,) + output) if loss is not None else output

       return TFSequenceClassifierOutput(loss=lowerCamelCase__       ,						logits=lowerCamelCase__       ,						hidden_states=outputs.hidden_states )
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  :      List[Any]            =    logging.get_logger(__name__)

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

a  :      str            =    {
    """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  :      Any            =    {
    """jukebox""": 5_1_2,
}

class 					_lowercase    (     UpperCAmelCase__			):






    '''simple docstring'''
    SCREAMING_SNAKE_CASE:				Optional[int]          =    VOCAB_FILES_NAMES
    SCREAMING_SNAKE_CASE:				Optional[Any]          =    PRETRAINED_VOCAB_FILES_MAP
    SCREAMING_SNAKE_CASE:				List[Any]          =    PRETRAINED_LYRIC_TOKENS_SIZES
    SCREAMING_SNAKE_CASE:				Dict          =    ['input_ids', 'attention_mask']




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

       with open(lowerCamelCase__       ,						encoding="utf-8" ) as vocab_handle:
          lowerCAmelCase_:							List[Any]       =	json.load(lowerCamelCase__ )

       with open(lowerCamelCase__       ,						encoding="utf-8" ) as vocab_handle:
          lowerCAmelCase_:							Tuple       =	json.load(lowerCamelCase__ )

       with open(lowerCamelCase__       ,						encoding="utf-8" ) as vocab_handle:
          lowerCAmelCase_:							Tuple       =	json.load(lowerCamelCase__ )

       lowerCAmelCase_:							List[str]       =	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_:							List[Any]       =	oov.replace(R"\-'"       ,						R"\-+'" )

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




    @property
    def      _a    (    self ):
       return len(self.artists_encoder ) + len(self.genres_encoder ) + len(self.lyrics_encoder )




    def      _a    (    self ):
       return dict(self.artists_encoder       ,						self.genres_encoder       ,						self.lyrics_encoder )




    def      _a    (    self       ,						lowerCamelCase__       ,						lowerCamelCase__       ,						lowerCamelCase__ ):
       lowerCAmelCase_:							Union[str, Any]       =	[self.artists_encoder.get(lowerCamelCase__       ,						0 ) for artist in list_artists]
       for genres in range(len(lowerCamelCase__ ) ):
          lowerCAmelCase_:							Tuple       =	[self.genres_encoder.get(lowerCamelCase__       ,						0 ) for genre in list_genres[genres]]
          lowerCAmelCase_:							Any       =	list_genres[genres] + [-1] * (self.n_genres - len(list_genres[genres] ))

       lowerCAmelCase_:							Optional[Any]       =	[[self.lyrics_encoder.get(lowerCamelCase__       ,						0 ) for character in list_lyrics[0]], [], []]
       return artists_id, list_genres, lyric_ids




    def      _a    (    self       ,						lowerCamelCase__ ):
       return list(lowerCamelCase__ )




    def      _a    (    self       ,						lowerCamelCase__       ,						lowerCamelCase__       ,						lowerCamelCase__       ,						**lowerCamelCase__ ):
       lowerCAmelCase_  ,	lowerCAmelCase_  ,	lowerCAmelCase_:							Union[str, Any]       =	self.prepare_for_tokenization(lowerCamelCase__       ,						lowerCamelCase__       ,						lowerCamelCase__ )
       lowerCAmelCase_:							Any       =	self._tokenize(lowerCamelCase__ )
       return artist, genre, lyrics




    def      _a    (    self       ,						lowerCamelCase__       ,						lowerCamelCase__       ,						lowerCamelCase__       ,						lowerCamelCase__ = False ):
       for idx in range(len(self.version ) ):
          if self.version[idx] == "v3":
             lowerCAmelCase_:							List[Any]       =	artists[idx].lower()
             lowerCAmelCase_:							Tuple       =	[genres[idx].lower()]
          else:
             lowerCAmelCase_:							Optional[Any]       =	self._normalize(artists[idx] ) + ".v2"
             lowerCAmelCase_:							int       =	[
                 self._normalize(lowerCamelCase__ ) + ".v2" for genre in genres[idx].split("_" )
             ]  # split is for the full dictionary with combined genres

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

       lowerCAmelCase_:							int       =	self._run_strip_accents(lowerCamelCase__ )
       lowerCAmelCase_:							Optional[Any]       =	lyrics.replace("\\"       ,						"\n" )
       lowerCAmelCase_:							List[str]       =	self.out_of_vocab.sub(""       ,						lowerCamelCase__ ), [], []
       return artists, genres, lyrics




    def      _a    (    self       ,						lowerCamelCase__ ):
       lowerCAmelCase_:							int       =	unicodedata.normalize("NFD"       ,						lowerCamelCase__ )
       lowerCAmelCase_:							Any       =	[]
       for char in text:
          lowerCAmelCase_:							Optional[Any]       =	unicodedata.category(lowerCamelCase__ )
          if cat == "Mn":
             continue
          output.append(lowerCamelCase__ )
       return "".join(lowerCamelCase__ )




    def      _a    (    self       ,						lowerCamelCase__ ):
       lowerCAmelCase_:							Tuple       =	(
           [chr(lowerCamelCase__ ) for i in range(ord("a" )       ,						ord("z" ) + 1 )]
           + [chr(lowerCamelCase__ ) for i in range(ord("A" )       ,						ord("Z" ) + 1 )]
           + [chr(lowerCamelCase__ ) for i in range(ord("0" )       ,						ord("9" ) + 1 )]
           + ["."]
       )
       lowerCAmelCase_:							List[str]       =	frozenset(lowerCamelCase__ )
       lowerCAmelCase_:							Any       =	re.compile(R"_+" )
       lowerCAmelCase_:							List[Any]       =	"".join([c if c in accepted else "_" for c in text.lower()] )
       lowerCAmelCase_:							Union[str, Any]       =	pattern.sub("_"       ,						lowerCamelCase__ ).strip("_" )
       return text




    def      _a    (    self       ,						lowerCamelCase__ ):
       return " ".join(lowerCamelCase__ )




    def      _a    (    self       ,						lowerCamelCase__       ,						lowerCamelCase__ = None       ,						lowerCamelCase__ = False ):
       # Convert to TensorType
       if not isinstance(lowerCamelCase__       ,						lowerCamelCase__ ):
          lowerCAmelCase_:							str       =	TensorType(lowerCamelCase__ )

       # 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_:							str       =	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_:							Optional[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_:							Optional[Any]       =	jnp.array
          lowerCAmelCase_:							List[str]       =	_is_jax
       else:
          lowerCAmelCase_:							Optional[int]       =	np.asarray
          lowerCAmelCase_:							int       =	_is_numpy

       # Do the tensor conversion in batch

       try:
          if prepend_batch_axis:
             lowerCAmelCase_:							Any       =	[inputs]

          if not is_tensor(lowerCamelCase__ ):
             lowerCAmelCase_:							Optional[int]       =	as_tensor(lowerCamelCase__ )
       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       ,						lowerCamelCase__       ,						lowerCamelCase__       ,						lowerCamelCase__=""       ,						lowerCamelCase__="pt" ):
       lowerCAmelCase_:							Any       =	[0, 0, 0]
       lowerCAmelCase_:							Dict       =	[artist] * len(self.version )
       lowerCAmelCase_:							int       =	[genres] * len(self.version )

       lowerCAmelCase_  ,	lowerCAmelCase_  ,	lowerCAmelCase_:							List[str]       =	self.tokenize(lowerCamelCase__       ,						lowerCamelCase__       ,						lowerCamelCase__ )
       lowerCAmelCase_  ,	lowerCAmelCase_  ,	lowerCAmelCase_:							str       =	self._convert_token_to_id(lowerCamelCase__       ,						lowerCamelCase__       ,						lowerCamelCase__ )

       lowerCAmelCase_:							List[Any]       =	[-INFINITY] * len(full_tokens[-1] )
       lowerCAmelCase_:							Optional[Any]       =	[
           self.convert_to_tensors(
               [input_ids + [artists_id[i]] + genres_ids[i] + full_tokens[i]]       ,						tensor_type=lowerCamelCase__ )
           for i in range(len(self.version ) )
       ]
       return BatchEncoding({"input_ids": input_ids, "attention_masks": attention_masks} )




    def      _a    (    self       ,						lowerCamelCase__       ,						lowerCamelCase__ = None ):
       if not os.path.isdir(lowerCamelCase__ ):
          logger.error(F'''Vocabulary path ({save_directory}) should be a directory''' )
          return

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

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

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

       return (artists_file, genres_file, lyrics_file)




    def      _a    (    self       ,						lowerCamelCase__       ,						lowerCamelCase__       ,						lowerCamelCase__ ):
       lowerCAmelCase_:							str       =	self.artists_decoder.get(lowerCamelCase__ )
       lowerCAmelCase_:							Optional[int]       =	[self.genres_decoder.get(lowerCamelCase__ ) for genre in genres_index]
       lowerCAmelCase_:							Optional[int]       =	[self.lyrics_decoder.get(lowerCamelCase__ ) for character in lyric_index]
       return artist, genres, lyrics