

import math

import torch
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from .attention_processor import Attention
from .embeddings import get_timestep_embedding
from .modeling_utils import ModelMixin



class SCREAMING_SNAKE_CASE     (  a_       ,							a_			):
  """simple docstring"""






  @register_to_config
  def __init__(      self			:   Union[str, Any]	,							lowerCAmelCase			:   int = 1_28	,							lowerCAmelCase			:   int = 2_56	,							lowerCAmelCase			:   float = 2000.0	,							lowerCAmelCase			:   int = 7_68	,							lowerCAmelCase			:   int = 12	,							lowerCAmelCase			:   int = 12	,							lowerCAmelCase			:   int = 64	,							lowerCAmelCase			:   int = 20_48	,							lowerCAmelCase			:   float = 0.1	,							)							->     Any:






     """simple docstring"""







     super().__init__()

     __lowerCAmelCase  :      Any    =						nn.Sequential(
         nn.Linear(lowerCAmelCase	,							d_model * 4	,							bias=lowerCAmelCase   )	,							nn.SiLU()	,							nn.Linear(d_model * 4	,							d_model * 4	,							bias=lowerCAmelCase   )	,							nn.SiLU()	,							)

     __lowerCAmelCase  :      Union[str, Any]    =						nn.Embedding(lowerCAmelCase	,							lowerCAmelCase   )
     __lowerCAmelCase  :      Tuple    =						False

     __lowerCAmelCase  :      Optional[int]    =						nn.Linear(lowerCAmelCase	,							lowerCAmelCase	,							bias=lowerCAmelCase   )

     __lowerCAmelCase  :      Optional[Any]    =						nn.Dropout(p=lowerCAmelCase   )

     __lowerCAmelCase  :      List[Any]    =						nn.ModuleList()
     for lyr_num in range(lowerCAmelCase   ):
        # FiLM conditional T5 decoder
        __lowerCAmelCase  :      List[str]    =						DecoderLayer(d_model=lowerCAmelCase	,							d_kv=lowerCAmelCase	,							num_heads=lowerCAmelCase	,							d_ff=lowerCAmelCase	,							dropout_rate=lowerCAmelCase   )
        self.decoders.append(lowerCAmelCase   )

     __lowerCAmelCase  :      List[Any]    =						TaLayerNorm(lowerCAmelCase   )

     __lowerCAmelCase  :      Union[str, Any]    =						nn.Dropout(p=lowerCAmelCase   )
     __lowerCAmelCase  :      int    =						nn.Linear(lowerCAmelCase	,							lowerCAmelCase	,							bias=lowerCAmelCase   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   List[Any]	,							lowerCAmelCase			:   Optional[Any]	,							lowerCAmelCase			:   int   )							->     Union[str, Any]:






     """simple docstring"""







     __lowerCAmelCase  :      List[Any]    =						torch.mul(query_input.unsqueeze(-1   )	,							key_input.unsqueeze(-2   )   )
     return mask.unsqueeze(-3   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   List[str]	,							lowerCAmelCase			:   Optional[Any]	,							lowerCAmelCase			:   Dict	,							lowerCAmelCase			:   Optional[Any]   )							->     str:






     """simple docstring"""







     __lowerCAmelCase   ,__lowerCAmelCase   ,__lowerCAmelCase  :      Optional[int]    =						decoder_input_tokens.shape
     assert decoder_noise_time.shape == (batch,)

     # decoder_noise_time is in [0, 1), so rescale to expected timing range.
     __lowerCAmelCase  :      Union[str, Any]    =						get_timestep_embedding(
         decoder_noise_time * self.config.max_decoder_noise_time	,							embedding_dim=self.config.d_model	,							max_period=self.config.max_decoder_noise_time	,							).to(dtype=self.dtype   )

     __lowerCAmelCase  :      List[str]    =						self.conditioning_emb(lowerCAmelCase   ).unsqueeze(1   )

     assert conditioning_emb.shape == (batch, 1, self.config.d_model * 4)

     __lowerCAmelCase  :      List[Any]    =						decoder_input_tokens.shape[1]

     # If we want to use relative positions for audio context, we can just offset
     # this sequence by the length of encodings_and_masks.
     __lowerCAmelCase  :      Optional[Any]    =						torch.broadcast_to(
         torch.arange(lowerCAmelCase	,							device=decoder_input_tokens.device   )	,							(batch, seq_length)	,							)

     __lowerCAmelCase  :      Tuple    =						self.position_encoding(lowerCAmelCase   )

     __lowerCAmelCase  :      Dict    =						self.continuous_inputs_projection(lowerCAmelCase   )
     inputs += position_encodings
     __lowerCAmelCase  :      Optional[int]    =						self.dropout(lowerCAmelCase   )

     # decoder: No padding present.
     __lowerCAmelCase  :      Union[str, Any]    =						torch.ones(
         decoder_input_tokens.shape[:2]	,							device=decoder_input_tokens.device	,							dtype=inputs.dtype   )

     # Translate encoding masks to encoder-decoder masks.
     __lowerCAmelCase  :      str    =						[(x, self.encoder_decoder_mask(lowerCAmelCase	,							lowerCAmelCase   )) for x, y in encodings_and_masks]

     # cross attend style: concat encodings
     __lowerCAmelCase  :      Dict    =						torch.cat([x[0] for x in encodings_and_encdec_masks]	,							dim=1   )
     __lowerCAmelCase  :      Any    =						torch.cat([x[1] for x in encodings_and_encdec_masks]	,							dim=-1   )

     for lyr in self.decoders:
        __lowerCAmelCase  :      Dict    =						lyr(
            lowerCAmelCase	,							conditioning_emb=lowerCAmelCase	,							encoder_hidden_states=lowerCAmelCase	,							encoder_attention_mask=lowerCAmelCase	,							)[0]

     __lowerCAmelCase  :      List[Any]    =						self.decoder_norm(lowerCAmelCase   )
     __lowerCAmelCase  :      Optional[Any]    =						self.post_dropout(lowerCAmelCase   )

     __lowerCAmelCase  :      Dict    =						self.spec_out(lowerCAmelCase   )
     return spec_out






class SCREAMING_SNAKE_CASE     (  nn.Module			):
  """simple docstring"""






  def __init__(      self			:   List[Any]	,							lowerCAmelCase			:   str	,							lowerCAmelCase			:   str	,							lowerCAmelCase			:   Optional[Any]	,							lowerCAmelCase			:   Any	,							lowerCAmelCase			:   Optional[Any]	,							lowerCAmelCase			:   Any=1e-6   )							->     int:






     """simple docstring"""







     super().__init__()
     __lowerCAmelCase  :      Optional[int]    =						nn.ModuleList()

     # cond self attention: layer 0
     self.layer.append(
         TaLayerSelfAttentionCond(d_model=lowerCAmelCase	,							d_kv=lowerCAmelCase	,							num_heads=lowerCAmelCase	,							dropout_rate=lowerCAmelCase   )   )

     # cross attention: layer 1
     self.layer.append(
         TaLayerCrossAttention(
             d_model=lowerCAmelCase	,							d_kv=lowerCAmelCase	,							num_heads=lowerCAmelCase	,							dropout_rate=lowerCAmelCase	,							layer_norm_epsilon=lowerCAmelCase	,							)   )

     # Film Cond MLP + dropout: last layer
     self.layer.append(
         TaLayerFFCond(d_model=lowerCAmelCase	,							d_ff=lowerCAmelCase	,							dropout_rate=lowerCAmelCase	,							layer_norm_epsilon=lowerCAmelCase   )   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   Optional[int]	,							lowerCAmelCase			:   Union[str, Any]	,							lowerCAmelCase			:   Optional[Any]=None	,							lowerCAmelCase			:   str=None	,							lowerCAmelCase			:   List[str]=None	,							lowerCAmelCase			:   Dict=None	,							lowerCAmelCase			:   str=None	,							)							->     Optional[Any]:






     """simple docstring"""







     __lowerCAmelCase  :      Union[str, Any]    =						self.layer[0](
         lowerCAmelCase	,							conditioning_emb=lowerCAmelCase	,							attention_mask=lowerCAmelCase	,							)

     if encoder_hidden_states is not None:
        __lowerCAmelCase  :      Any    =						torch.where(encoder_attention_mask > 0	,							0	,							-1e10   ).to(
            encoder_hidden_states.dtype   )

        __lowerCAmelCase  :      Union[str, Any]    =						self.layer[1](
            lowerCAmelCase	,							key_value_states=lowerCAmelCase	,							attention_mask=lowerCAmelCase	,							)

     # Apply Film Conditional Feed Forward layer
     __lowerCAmelCase  :      List[Any]    =						self.layer[-1](lowerCAmelCase	,							lowerCAmelCase   )

     return (hidden_states,)






class SCREAMING_SNAKE_CASE     (  nn.Module			):
  """simple docstring"""






  def __init__(      self			:   List[Any]	,							lowerCAmelCase			:   List[Any]	,							lowerCAmelCase			:   int	,							lowerCAmelCase			:   List[Any]	,							lowerCAmelCase			:   Tuple   )							->     str:






     """simple docstring"""







     super().__init__()
     __lowerCAmelCase  :      Optional[int]    =						TaLayerNorm(lowerCAmelCase   )
     __lowerCAmelCase  :      List[Any]    =						TaFiLMLayer(in_features=d_model * 4	,							out_features=lowerCAmelCase   )
     __lowerCAmelCase  :      Dict    =						Attention(query_dim=lowerCAmelCase	,							heads=lowerCAmelCase	,							dim_head=lowerCAmelCase	,							out_bias=lowerCAmelCase	,							scale_qk=lowerCAmelCase   )
     __lowerCAmelCase  :      Optional[int]    =						nn.Dropout(lowerCAmelCase   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   int	,							lowerCAmelCase			:   Tuple	,							lowerCAmelCase			:   Optional[int]=None	,							lowerCAmelCase			:   Tuple=None	,							)							->     Dict:






     """simple docstring"""







     __lowerCAmelCase  :      int    =						self.layer_norm(lowerCAmelCase   )

     if conditioning_emb is not None:
        __lowerCAmelCase  :      int    =						self.FiLMLayer(lowerCAmelCase	,							lowerCAmelCase   )

     # Self-attention block
     __lowerCAmelCase  :      List[Any]    =						self.attention(lowerCAmelCase   )

     __lowerCAmelCase  :      Optional[int]    =						hidden_states + self.dropout(lowerCAmelCase   )

     return hidden_states






class SCREAMING_SNAKE_CASE     (  nn.Module			):
  """simple docstring"""






  def __init__(      self			:   List[str]	,							lowerCAmelCase			:   Dict	,							lowerCAmelCase			:   List[str]	,							lowerCAmelCase			:   Dict	,							lowerCAmelCase			:   List[str]	,							lowerCAmelCase			:   Optional[int]   )							->     int:






     """simple docstring"""







     super().__init__()
     __lowerCAmelCase  :      Optional[Any]    =						Attention(query_dim=lowerCAmelCase	,							heads=lowerCAmelCase	,							dim_head=lowerCAmelCase	,							out_bias=lowerCAmelCase	,							scale_qk=lowerCAmelCase   )
     __lowerCAmelCase  :      List[str]    =						TaLayerNorm(lowerCAmelCase	,							eps=lowerCAmelCase   )
     __lowerCAmelCase  :      int    =						nn.Dropout(lowerCAmelCase   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   List[Any]	,							lowerCAmelCase			:   Optional[Any]	,							lowerCAmelCase			:   Tuple=None	,							lowerCAmelCase			:   int=None	,							)							->     int:






     """simple docstring"""







     __lowerCAmelCase  :      Dict    =						self.layer_norm(lowerCAmelCase   )
     __lowerCAmelCase  :      Optional[Any]    =						self.attention(
         lowerCAmelCase	,							encoder_hidden_states=lowerCAmelCase	,							attention_mask=attention_mask.squeeze(1   )	,							)
     __lowerCAmelCase  :      Union[str, Any]    =						hidden_states + self.dropout(lowerCAmelCase   )
     return layer_output






class SCREAMING_SNAKE_CASE     (  nn.Module			):
  """simple docstring"""






  def __init__(      self			:   Optional[Any]	,							lowerCAmelCase			:   int	,							lowerCAmelCase			:   Tuple	,							lowerCAmelCase			:   Union[str, Any]	,							lowerCAmelCase			:   Optional[int]   )							->     str:






     """simple docstring"""







     super().__init__()
     __lowerCAmelCase  :      Optional[Any]    =						TaDenseGatedActDense(d_model=lowerCAmelCase	,							d_ff=lowerCAmelCase	,							dropout_rate=lowerCAmelCase   )
     __lowerCAmelCase  :      Optional[Any]    =						TaFiLMLayer(in_features=d_model * 4	,							out_features=lowerCAmelCase   )
     __lowerCAmelCase  :      str    =						TaLayerNorm(lowerCAmelCase	,							eps=lowerCAmelCase   )
     __lowerCAmelCase  :      Any    =						nn.Dropout(lowerCAmelCase   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   Tuple	,							lowerCAmelCase			:   Union[str, Any]	,							lowerCAmelCase			:   Optional[int]=None   )							->     Any:






     """simple docstring"""







     __lowerCAmelCase  :      Tuple    =						self.layer_norm(lowerCAmelCase   )
     if conditioning_emb is not None:
        __lowerCAmelCase  :      Optional[int]    =						self.film(lowerCAmelCase	,							lowerCAmelCase   )

     __lowerCAmelCase  :      str    =						self.DenseReluDense(lowerCAmelCase   )
     __lowerCAmelCase  :      List[Any]    =						hidden_states + self.dropout(lowerCAmelCase   )
     return hidden_states






class SCREAMING_SNAKE_CASE     (  nn.Module			):
  """simple docstring"""






  def __init__(      self			:   Dict	,							lowerCAmelCase			:   Tuple	,							lowerCAmelCase			:   Optional[int]	,							lowerCAmelCase			:   Union[str, Any]   )							->     List[Any]:






     """simple docstring"""







     super().__init__()
     __lowerCAmelCase  :      int    =						nn.Linear(lowerCAmelCase	,							lowerCAmelCase	,							bias=lowerCAmelCase   )
     __lowerCAmelCase  :      Dict    =						nn.Linear(lowerCAmelCase	,							lowerCAmelCase	,							bias=lowerCAmelCase   )
     __lowerCAmelCase  :      List[Any]    =						nn.Linear(lowerCAmelCase	,							lowerCAmelCase	,							bias=lowerCAmelCase   )
     __lowerCAmelCase  :      List[Any]    =						nn.Dropout(lowerCAmelCase   )
     __lowerCAmelCase  :      Optional[int]    =						NewGELUActivation()







  def 	SCREAMING_SNAKE_CASE   (      self			:   int	,							lowerCAmelCase			:   Tuple   )							->     List[str]:






     """simple docstring"""







     __lowerCAmelCase  :      Union[str, Any]    =						self.act(self.wi_a(lowerCAmelCase   )   )
     __lowerCAmelCase  :      Optional[Any]    =						self.wi_a(lowerCAmelCase   )
     __lowerCAmelCase  :      Optional[int]    =						hidden_gelu * hidden_linear
     __lowerCAmelCase  :      Dict    =						self.dropout(lowerCAmelCase   )

     __lowerCAmelCase  :      Union[str, Any]    =						self.wo(lowerCAmelCase   )
     return hidden_states






class SCREAMING_SNAKE_CASE     (  nn.Module			):
  """simple docstring"""






  def __init__(      self			:   List[Any]	,							lowerCAmelCase			:   Optional[Any]	,							lowerCAmelCase			:   int=1e-6   )							->     Optional[int]:






     """simple docstring"""







     super().__init__()
     __lowerCAmelCase  :      List[str]    =						nn.Parameter(torch.ones(lowerCAmelCase   )   )
     __lowerCAmelCase  :      Optional[int]    =						eps







  def 	SCREAMING_SNAKE_CASE   (      self			:   Tuple	,							lowerCAmelCase			:   Tuple   )							->     int:






     """simple docstring"""







     __lowerCAmelCase  :      str    =						hidden_states.to(torch.floataa   ).pow(2   ).mean(-1	,							keepdim=lowerCAmelCase   )
     __lowerCAmelCase  :      Optional[Any]    =						hidden_states * torch.rsqrt(variance + self.variance_epsilon   )

     # convert into half-precision if necessary
     if self.weight.dtype in [torch.floataa, torch.bfloataa]:
        __lowerCAmelCase  :      str    =						hidden_states.to(self.weight.dtype   )

     return self.weight * hidden_states






class SCREAMING_SNAKE_CASE     (  nn.Module			):
  """simple docstring"""






  def 	SCREAMING_SNAKE_CASE   (      self			:   Union[str, Any]	,							lowerCAmelCase			:   torch.Tensor   )							->     torch.Tensor:






     """simple docstring"""







     return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi   ) * (input + 0.04_4715 * torch.pow(lowerCAmelCase	,							3.0   ))   ))






class SCREAMING_SNAKE_CASE     (  nn.Module			):
  """simple docstring"""






  def __init__(      self			:   Tuple	,							lowerCAmelCase			:   Dict	,							lowerCAmelCase			:   int   )							->     Optional[int]:






     """simple docstring"""







     super().__init__()
     __lowerCAmelCase  :      Union[str, Any]    =						nn.Linear(lowerCAmelCase	,							out_features * 2	,							bias=lowerCAmelCase   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   Tuple	,							lowerCAmelCase			:   Union[str, Any]	,							lowerCAmelCase			:   List[Any]   )							->     List[Any]:






     """simple docstring"""







     __lowerCAmelCase  :      str    =						self.scale_bias(lowerCAmelCase   )
     __lowerCAmelCase   ,__lowerCAmelCase  :      Dict    =						torch.chunk(lowerCAmelCase	,							2	,							-1   )
     __lowerCAmelCase  :      List[Any]    =						x * (1 + scale) + shift
     return x





# Lint as: python3
# pylint: enable=line-too-long
# pylint: disable=g-import-not-at-top,g-bad-import-order,wrong-import-position

__UpperCAmelCase								=			"""2.13.1"""

import platform

import pyarrow
from packaging import version


if version.parse(platform.python_version()) < version.parse("""3.7"""):
   raise ImportWarning(
       """To use `datasets`, Python>=3.7 is required, and the current version of Python doesn't match this condition."""
   )

if version.parse(pyarrow.__version__).major < 8:
   raise ImportWarning(
       """To use `datasets`, the module `pyarrow>=8.0.0` is required, and the current version of `pyarrow` doesn't match this condition.\n"""
       """If you are running this in a Google Colab, you should probably just restart the runtime to use the right version of `pyarrow`."""
   )

del platform
del pyarrow
del version

from .arrow_dataset import Dataset
from .arrow_reader import ReadInstruction
from .builder import ArrowBasedBuilder, BeamBasedBuilder, BuilderConfig, DatasetBuilder, GeneratorBasedBuilder
from .combine import concatenate_datasets, interleave_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .download import *
from .features import *
from .fingerprint import disable_caching, enable_caching, is_caching_enabled, set_caching_enabled
from .info import DatasetInfo, MetricInfo
from .inspect import (
    get_dataset_config_info,
    get_dataset_config_names,
    get_dataset_infos,
    get_dataset_split_names,
    inspect_dataset,
    inspect_metric,
    list_datasets,
    list_metrics,
)
from .iterable_dataset import IterableDataset
from .load import load_dataset, load_dataset_builder, load_from_disk, load_metric
from .metric import Metric
from .splits import (
    NamedSplit,
    NamedSplitAll,
    Split,
    SplitBase,
    SplitDict,
    SplitGenerator,
    SplitInfo,
    SubSplitInfo,
    percent,
)
from .tasks import *
from .utils import *
from .utils import logging


# deprecated modules
from datasets import arrow_dataset as _arrow_dataset  # isort:skip
from datasets import utils as _utils  # isort:skip
from datasets.utils import download_manager as _deprecated_download_manager  # isort:skip

__UpperCAmelCase								=			concatenate_datasets
__UpperCAmelCase								=			DownloadConfig
__UpperCAmelCase								=			DownloadManager
__UpperCAmelCase								=			DownloadMode
__UpperCAmelCase								=			DownloadConfig
__UpperCAmelCase								=			DownloadMode
__UpperCAmelCase								=			DownloadManager

del _arrow_dataset, _utils, _deprecated_download_manager


