from dataclasses import dataclass
from typing import Any, Dict, Optional

import torch
import torch.nn.functional as F
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..models.embeddings import ImagePositionalEmbeddings
from ..utils import BaseOutput, deprecate
from .attention import BasicTransformerBlock
from .embeddings import PatchEmbed
from .modeling_utils import ModelMixin





@dataclass
class   lowerCAmelCase    (       _a      ):
     _SCREAMING_SNAKE_CASE  :  torch.FloatTensor





class   lowerCAmelCase    (       _a      ,		_a      ):



     @register_to_config
     def __init__(       self					,   lowerCAmelCase__ = 16					,   lowerCAmelCase__ = 88					,   lowerCAmelCase__ = None					,   lowerCAmelCase__ = None					,   lowerCAmelCase__ = 1					,   lowerCAmelCase__ = 0.0					,   lowerCAmelCase__ = 32					,   lowerCAmelCase__ = None					,   lowerCAmelCase__ = False					,   lowerCAmelCase__ = None					,   lowerCAmelCase__ = None					,   lowerCAmelCase__ = None					,   lowerCAmelCase__ = "geglu"					,   lowerCAmelCase__ = None					,   lowerCAmelCase__ = False					,   lowerCAmelCase__ = False					,   lowerCAmelCase__ = False					,   lowerCAmelCase__ = "layer_norm"					,   lowerCAmelCase__ = True					,   ):
            super().__init__()
            _A=						use_linear_projection
            _A=						num_attention_heads
            _A=						attention_head_dim
            _A=						num_attention_heads * attention_head_dim

            # 1. Transformer2DModel can process both standard continuous images of shape `(batch_size, num_channels, width, height)` as well as quantized image embeddings of shape `(batch_size, num_image_vectors)`
            # Define whether input is continuous or discrete depending on configuration
            _A=						(in_channels is not None) and (patch_size is None)
            _A=						num_vector_embeds is not None
            _A=						in_channels is not None and patch_size is not None

            if norm_type == "layer_norm" and num_embeds_ada_norm is not None:
                   _A=						(
                       f"The configuration file of this model: {self.__class__} is outdated. `norm_type` is either not set or"
                       ' incorrectly set to `\'layer_norm\'`.Make sure to set `norm_type` to `\'ada_norm\'` in the config.'
                       ' Please make sure to update the config accordingly as leaving `norm_type` might led to incorrect'
                       ' results in future versions. If you have downloaded this checkpoint from the Hugging Face Hub, it'
                       ' would be very nice if you could open a Pull request for the `transformer/config.json` file'
                   )
                   deprecate('norm_type!=num_embeds_ada_norm'					,   '1.0.0'					,   lowerCAmelCase__					,   standard_warn=lowerCAmelCase__							)
                   _A=						'ada_norm'

            if self.is_input_continuous and self.is_input_vectorized:
                   raise ValueError(
                       f"Cannot define both `in_channels`: {in_channels} and `num_vector_embeds`: {num_vector_embeds}. Make"
                       ' sure that either `in_channels` or `num_vector_embeds` is None.'							)
            elif self.is_input_vectorized and self.is_input_patches:
                   raise ValueError(
                       f"Cannot define both `num_vector_embeds`: {num_vector_embeds} and `patch_size`: {patch_size}. Make"
                       ' sure that either `num_vector_embeds` or `num_patches` is None.'							)
            elif not self.is_input_continuous and not self.is_input_vectorized and not self.is_input_patches:
                   raise ValueError(
                       f"Has to define `in_channels`: {in_channels}, `num_vector_embeds`: {num_vector_embeds}, or patch_size:"
                       f" {patch_size}. Make sure that `in_channels`, `num_vector_embeds` or `num_patches` is not None."							)

            # 2. Define input layers
            if self.is_input_continuous:
                   _A=						in_channels

                   _A=						torch.nn.GroupNorm(num_groups=lowerCAmelCase__					,   num_channels=lowerCAmelCase__					,   eps=1E-6					,   affine=lowerCAmelCase__							)
                   if use_linear_projection:
                          _A=						nn.Linear(lowerCAmelCase__					,   lowerCAmelCase__							)
                   else:
                          _A=						nn.Convad(lowerCAmelCase__					,   lowerCAmelCase__					,   kernel_size=1					,   stride=1					,   padding=0							)
            elif self.is_input_vectorized:
                   assert sample_size is not None, "Transformer2DModel over discrete input must provide sample_size"
                   assert num_vector_embeds is not None, "Transformer2DModel over discrete input must provide num_embed"

                   _A=						sample_size
                   _A=						sample_size
                   _A=						num_vector_embeds
                   _A=						self.height * self.width

                   _A=						ImagePositionalEmbeddings(
                       num_embed=lowerCAmelCase__					,   embed_dim=lowerCAmelCase__					,   height=self.height					,   width=self.width							)
            elif self.is_input_patches:
                   assert sample_size is not None, "Transformer2DModel over patched input must provide sample_size"

                   _A=						sample_size
                   _A=						sample_size

                   _A=						patch_size
                   _A=						PatchEmbed(
                       height=lowerCAmelCase__					,   width=lowerCAmelCase__					,   patch_size=lowerCAmelCase__					,   in_channels=lowerCAmelCase__					,   embed_dim=lowerCAmelCase__					,   )

            # 3. Define transformers blocks
            _A=						nn.ModuleList(
                [
                    BasicTransformerBlock(
                        lowerCAmelCase__					,   lowerCAmelCase__					,   lowerCAmelCase__					,   dropout=lowerCAmelCase__					,   cross_attention_dim=lowerCAmelCase__					,   activation_fn=lowerCAmelCase__					,   num_embeds_ada_norm=lowerCAmelCase__					,   attention_bias=lowerCAmelCase__					,   only_cross_attention=lowerCAmelCase__					,   upcast_attention=lowerCAmelCase__					,   norm_type=lowerCAmelCase__					,   norm_elementwise_affine=lowerCAmelCase__					,   )
                    for d in range(lowerCAmelCase__							)
                ]							)

            # 4. Define output layers
            _A=						in_channels if out_channels is None else out_channels
            if self.is_input_continuous:
                   # TODO: should use out_channels for continuous projections
                   if use_linear_projection:
                          _A=						nn.Linear(lowerCAmelCase__					,   lowerCAmelCase__							)
                   else:
                          _A=						nn.Convad(lowerCAmelCase__					,   lowerCAmelCase__					,   kernel_size=1					,   stride=1					,   padding=0							)
            elif self.is_input_vectorized:
                   _A=						nn.LayerNorm(lowerCAmelCase__							)
                   _A=						nn.Linear(lowerCAmelCase__					,   self.num_vector_embeds - 1							)
            elif self.is_input_patches:
                   _A=						nn.LayerNorm(lowerCAmelCase__					,   elementwise_affine=lowerCAmelCase__					,   eps=1E-6							)
                   _A=						nn.Linear(lowerCAmelCase__					,   2 * inner_dim							)
                   _A=						nn.Linear(lowerCAmelCase__					,   patch_size * patch_size * self.out_channels							)





     def       a__	(       self					,   lowerCAmelCase__					,   lowerCAmelCase__ = None					,   lowerCAmelCase__ = None					,   lowerCAmelCase__ = None					,   lowerCAmelCase__ = None					,   lowerCAmelCase__ = None					,   lowerCAmelCase__ = None					,   lowerCAmelCase__ = True					,   ):
            # ensure attention_mask is a bias, and give it a singleton query_tokens dimension.
            #   we may have done this conversion already, e.g. if we came here via UNet2DConditionModel#forward.
            #   we can tell by counting dims; if ndim == 2: it's a mask rather than a bias.
            # expects mask of shape:
            #   [batch, key_tokens]
            # adds singleton query_tokens dimension:
            #   [batch,                    1, key_tokens]
            # this helps to broadcast it as a bias over attention scores, which will be in one of the following shapes:
            #   [batch,  heads, query_tokens, key_tokens] (e.g. torch sdp attn)
            #   [batch * heads, query_tokens, key_tokens] (e.g. xformers or classic attn)
            if attention_mask is not None and attention_mask.ndim == 2:
                   # assume that mask is expressed as:
                   #   (1 = keep,      0 = discard)
                   # convert mask into a bias that can be added to attention scores:
                   #       (keep = +0,     discard = -10000.0)
                   _A=						(1 - attention_mask.to(hidden_states.dtype							)) * -10000.0
                   _A=						attention_mask.unsqueeze(1							)

            # convert encoder_attention_mask to a bias the same way we do for attention_mask
            if encoder_attention_mask is not None and encoder_attention_mask.ndim == 2:
                   _A=						(1 - encoder_attention_mask.to(hidden_states.dtype							)) * -10000.0
                   _A=						encoder_attention_mask.unsqueeze(1							)

            # 1. Input
            if self.is_input_continuous:
                   _A,      _A,      _A,      _A=						hidden_states.shape
                   _A=						hidden_states

                   _A=						self.norm(lowerCAmelCase__							)
                   if not self.use_linear_projection:
                          _A=						self.proj_in(lowerCAmelCase__							)
                          _A=						hidden_states.shape[1]
                          _A=						hidden_states.permute(0					,   2					,   3					,   1							).reshape(lowerCAmelCase__					,   height * width					,   lowerCAmelCase__							)
                   else:
                          _A=						hidden_states.shape[1]
                          _A=						hidden_states.permute(0					,   2					,   3					,   1							).reshape(lowerCAmelCase__					,   height * width					,   lowerCAmelCase__							)
                          _A=						self.proj_in(lowerCAmelCase__							)
            elif self.is_input_vectorized:
                   _A=						self.latent_image_embedding(lowerCAmelCase__							)
            elif self.is_input_patches:
                   _A=						self.pos_embed(lowerCAmelCase__							)

            # 2. Blocks
            for block in self.transformer_blocks:
                   _A=						block(
                       lowerCAmelCase__					,   attention_mask=lowerCAmelCase__					,   encoder_hidden_states=lowerCAmelCase__					,   encoder_attention_mask=lowerCAmelCase__					,   timestep=lowerCAmelCase__					,   cross_attention_kwargs=lowerCAmelCase__					,   class_labels=lowerCAmelCase__					,   )

            # 3. Output
            if self.is_input_continuous:
                   if not self.use_linear_projection:
                          _A=						hidden_states.reshape(lowerCAmelCase__					,   lowerCAmelCase__					,   lowerCAmelCase__					,   lowerCAmelCase__							).permute(0					,   3					,   1					,   2							).contiguous()
                          _A=						self.proj_out(lowerCAmelCase__							)
                   else:
                          _A=						self.proj_out(lowerCAmelCase__							)
                          _A=						hidden_states.reshape(lowerCAmelCase__					,   lowerCAmelCase__					,   lowerCAmelCase__					,   lowerCAmelCase__							).permute(0					,   3					,   1					,   2							).contiguous()

                   _A=						hidden_states + residual
            elif self.is_input_vectorized:
                   _A=						self.norm_out(lowerCAmelCase__							)
                   _A=						self.out(lowerCAmelCase__							)
                   # (batch, self.num_vector_embeds - 1, self.num_latent_pixels)
                   _A=						logits.permute(0					,   2					,   1							)

                   # log(p(x_0))
                   _A=						F.log_softmax(logits.double()					,   dim=1							).float()
            elif self.is_input_patches:
                   # TODO: cleanup!
                   _A=						self.transformer_blocks[0].norma.emb(
                       lowerCAmelCase__					,   lowerCAmelCase__					,   hidden_dtype=hidden_states.dtype							)
                   _A,      _A=						self.proj_out_a(F.silu(lowerCAmelCase__							)							).chunk(2					,   dim=1							)
                   _A=						self.norm_out(lowerCAmelCase__							) * (1 + scale[:, None]) + shift[:, None]
                   _A=						self.proj_out_a(lowerCAmelCase__							)

                   # unpatchify
                   _A=						_A=						int(hidden_states.shape[1] ** 0.5							)
                   _A=						hidden_states.reshape(
                       shape=(-1, height, width, self.patch_size, self.patch_size, self.out_channels)							)
                   _A=						torch.einsum('nhwpqc->nchpwq'					,   lowerCAmelCase__							)
                   _A=						hidden_states.reshape(
                       shape=(-1, self.out_channels, height * self.patch_size, width * self.patch_size)							)

            if not return_dict:
                   return (output,)

            return TransformeraDModelOutput(sample=lowerCAmelCase__							)
import copy
import os
from typing import Dict, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...utils import add_start_docstrings, logging
from ..auto import AutoConfig


UpperCAmelCase_    		=				logging.get_logger(__name__)


UpperCAmelCase_    		=				{
    '''suno/bark-small''': '''https://huggingface.co/suno/bark-small/resolve/main/config.json''',
    '''suno/bark''': '''https://huggingface.co/suno/bark/resolve/main/config.json''',
}

UpperCAmelCase_    		=				'''
    This is the configuration class to store the configuration of a [`{model}`]. It is used to instantiate the model
    according to the specified arguments, defining the model architecture. Instantiating a configuration with the
    defaults will yield a similar configuration to that of the Bark [suno/bark](https://huggingface.co/suno/bark)
    architecture.

    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
    documentation from [`PretrainedConfig`] for more information.

    Args:
        block_size (`int`, *optional*, defaults to 1024):
            The maximum sequence length that this model might ever be used with. Typically set this to something large
            just in case (e.g., 512 or 1024 or 2048).
        input_vocab_size (`int`, *optional*, defaults to 10_048):
            Vocabulary size of a Bark sub-model. Defines the number of different tokens that can be represented by the
            `inputs_ids` passed when calling [`{model}`]. Defaults to 10_048 but should be carefully thought with
            regards to the chosen sub-model.
        output_vocab_size (`int`, *optional*, defaults to 10_048):
            Output vocabulary size of a Bark sub-model. Defines the number of different tokens that can be represented
            by the: `output_ids` when passing forward a [`{model}`]. Defaults to 10_048 but should be carefully thought
            with regards to the chosen sub-model.
        num_layers (`int`, *optional*, defaults to 12):
            Number of hidden layers in the given sub-model.
        num_heads (`int`, *optional*, defaults to 12):
            Number of attention heads for each attention layer in the Transformer architecture.
        hidden_size (`int`, *optional*, defaults to 768):
            Dimensionality of the "intermediate" (often named feed-forward) layer in the architecture.
        dropout (`float`, *optional*, defaults to 0.0):
            The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
        bias (`bool`, *optional*, defaults to `True`):
            Whether or not to use bias in the linear layers and layer norm layers.
        initializer_range (`float`, *optional*, defaults to 0.02):
            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
        use_cache (`bool`, *optional*, defaults to `True`):
            Whether or not the model should return the last key/values attentions (not used by all models).
'''





class   lowerCAmelCase    (       _a      ):
     _SCREAMING_SNAKE_CASE  :  List[str]		="""bark_module"""
     _SCREAMING_SNAKE_CASE  :  str		=["""past_key_values"""]

     _SCREAMING_SNAKE_CASE  :  Tuple		={
         """num_attention_heads""": """num_heads""",
         """num_hidden_layers""": """num_layers""",
         """vocab_size""": """input_vocab_size""",
         """window_size""": """block_size""",
     }



     def __init__(       self					,   lowerCAmelCase__=1024					,   lowerCAmelCase__=10048					,   lowerCAmelCase__=10048					,   lowerCAmelCase__=12					,   lowerCAmelCase__=12					,   lowerCAmelCase__=768					,   lowerCAmelCase__=0.0					,   lowerCAmelCase__=True					,   lowerCAmelCase__=0.02					,   lowerCAmelCase__=True					,   **lowerCAmelCase__					,   ):
            _A=						block_size
            _A=						input_vocab_size
            _A=						output_vocab_size
            _A=						num_layers
            _A=						num_heads
            _A=						hidden_size
            _A=						dropout
            _A=						bias
            _A=						use_cache
            _A=						initializer_range

            super().__init__(**lowerCAmelCase__							)





     @classmethod
     def       a__	(       cls					,   lowerCAmelCase__					,   lowerCAmelCase__ = None					,   lowerCAmelCase__ = False					,   lowerCAmelCase__ = False					,   lowerCAmelCase__ = None					,   lowerCAmelCase__ = "main"					,   **lowerCAmelCase__					,   ):
            _A=						cache_dir
            _A=						force_download
            _A=						local_files_only
            _A=						revision

            cls._set_token_in_kwargs(lowerCAmelCase__					,   lowerCAmelCase__							)

            _A,      _A=						cls.get_config_dict(lowerCAmelCase__					,   **lowerCAmelCase__							)

            # get the config dict if we are loading from Bark
            if config_dict.get('model_type'							) == "bark":
                   _A=						config_dict[f"{cls.model_type}_config"]

            if "model_type" in config_dict and hasattr(cls					,   'model_type'							) and config_dict["model_type"] != cls.model_type:
                   logger.warning(
                       f"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
                       f"{cls.model_type}. This is not supported for all configurations of models and can yield errors."							)

            return cls.from_dict(lowerCAmelCase__					,   **lowerCAmelCase__							)





@add_start_docstrings(
    BARK_SUBMODELCONFIG_START_DOCSTRING.format(config="""BarkSemanticConfig"""      ,		model="""BarkSemanticModel"""      )      ,		"""
    Example:

    ```python
    >>> from transformers import BarkSemanticConfig, BarkSemanticModel

    >>> # Initializing a Bark sub-module style configuration
    >>> configuration = BarkSemanticConfig()

    >>> # Initializing a model (with random weights) from the suno/bark style configuration
    >>> model = BarkSemanticModel(configuration)

    >>> # Accessing the model configuration
    >>> configuration = model.config
    ```"""      ,		)
class   lowerCAmelCase    (       _a      ):
     _SCREAMING_SNAKE_CASE  :  Union[str, Any]		="""semantic"""





@add_start_docstrings(
    BARK_SUBMODELCONFIG_START_DOCSTRING.format(config="""BarkCoarseConfig"""      ,		model="""BarkCoarseModel"""      )      ,		"""
    Example:

    ```python
    >>> from transformers import BarkCoarseConfig, BarkCoarseModel

    >>> # Initializing a Bark sub-module style configuration
    >>> configuration = BarkCoarseConfig()

    >>> # Initializing a model (with random weights) from the suno/bark style configuration
    >>> model = BarkCoarseModel(configuration)

    >>> # Accessing the model configuration
    >>> configuration = model.config
    ```"""      ,		)
class   lowerCAmelCase    (       _a      ):
     _SCREAMING_SNAKE_CASE  :  str		="""coarse_acoustics"""





@add_start_docstrings(
    BARK_SUBMODELCONFIG_START_DOCSTRING.format(config="""BarkFineConfig"""      ,		model="""BarkFineModel"""      )      ,		"""
        n_codes_total (`int`, *optional*, defaults to 8):
            The total number of audio codebooks predicted. Used in the fine acoustics sub-model.
        n_codes_given (`int`, *optional*, defaults to 1):
            The number of audio codebooks predicted in the coarse acoustics sub-model. Used in the acoustics
            sub-models.
    Example:

    ```python
    >>> from transformers import BarkFineConfig, BarkFineModel

    >>> # Initializing a Bark sub-module style configuration
    >>> configuration = BarkFineConfig()

    >>> # Initializing a model (with random weights) from the suno/bark style configuration
    >>> model = BarkFineModel(configuration)

    >>> # Accessing the model configuration
    >>> configuration = model.config
    ```"""      ,		)
class   lowerCAmelCase    (       _a      ):
     _SCREAMING_SNAKE_CASE  :  str		="""fine_acoustics"""



     def __init__(       self					,   lowerCAmelCase__=True					,   lowerCAmelCase__=8					,   lowerCAmelCase__=1					,   **lowerCAmelCase__							):
            _A=						n_codes_total
            _A=						n_codes_given

            super().__init__(tie_word_embeddings=lowerCAmelCase__					,   **lowerCAmelCase__							)





class   lowerCAmelCase    (       _a      ):
     _SCREAMING_SNAKE_CASE  :  Optional[Any]		="""bark"""
     _SCREAMING_SNAKE_CASE  :  Any		=True



     def __init__(       self					,   lowerCAmelCase__ = None					,   lowerCAmelCase__ = None					,   lowerCAmelCase__ = None					,   lowerCAmelCase__ = None					,   lowerCAmelCase__=0.02					,   **lowerCAmelCase__					,   ):
            if semantic_config is None:
                   _A=						{}
                   logger.info('semantic_config is None. initializing the semantic model with default values.'							)

            if coarse_acoustics_config is None:
                   _A=						{}
                   logger.info('coarse_acoustics_config is None. initializing the coarse model with default values.'							)

            if fine_acoustics_config is None:
                   _A=						{}
                   logger.info('fine_acoustics_config is None. initializing the fine model with default values.'							)

            if codec_config is None:
                   _A=						{}
                   logger.info('codec_config is None. initializing the codec model with default values.'							)

            _A=						BarkSemanticConfig(**lowerCAmelCase__							)
            _A=						BarkCoarseConfig(**lowerCAmelCase__							)
            _A=						BarkFineConfig(**lowerCAmelCase__							)
            _A=						AutoConfig.for_model(**lowerCAmelCase__							)

            _A=						initializer_range

            super().__init__(**lowerCAmelCase__							)



     @classmethod
     def       a__	(       cls					,   lowerCAmelCase__					,   lowerCAmelCase__					,   lowerCAmelCase__					,   lowerCAmelCase__					,   **lowerCAmelCase__					,   ):
            return cls(
                semantic_config=semantic_config.to_dict()					,   coarse_acoustics_config=coarse_acoustics_config.to_dict()					,   fine_acoustics_config=fine_acoustics_config.to_dict()					,   codec_config=codec_config.to_dict()					,   **lowerCAmelCase__					,   )





     def       a__	(       self							):
            _A=						copy.deepcopy(self.__dict__							)

            _A=						self.semantic_config.to_dict()
            _A=						self.coarse_acoustics_config.to_dict()
            _A=						self.fine_acoustics_config.to_dict()
            _A=						self.codec_config.to_dict()

            _A=						self.__class__.model_type
            return output