'''simple docstring'''





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 	__A				(			UpperCamelCase__		,     UpperCamelCase__     ):



    @register_to_config
    def __init__(self   :   int       ,      __a   :   int = 128       ,      __a   :   int = 256       ,      __a   :   float = 20_00.0       ,      __a   :   int = 768       ,      __a   :   int = 12       ,      __a   :   int = 12       ,      __a   :   int = 64       ,      __a   :   int = 2048       ,      __a   :   float = 0.1       ,      ):
         super().__init__()

         UpperCAmelCase_	   =       nn.Sequential(
             nn.Linear(__a       ,      d_model * 4       ,      bias=__a			)       ,      nn.SiLU()       ,      nn.Linear(d_model * 4       ,      d_model * 4       ,      bias=__a			)       ,      nn.SiLU()       ,      )

         UpperCAmelCase_	   =       nn.Embedding(__a       ,      __a			)
         UpperCAmelCase_	   =       False

         UpperCAmelCase_	   =       nn.Linear(__a       ,      __a       ,      bias=__a			)

         UpperCAmelCase_	   =       nn.Dropout(p=__a			)

         UpperCAmelCase_	   =       nn.ModuleList()
         for lyr_num in range(__a			):
              # FiLM conditional T5 decoder
              UpperCAmelCase_	   =       DecoderLayer(d_model=__a       ,      d_kv=__a       ,      num_heads=__a       ,      d_ff=__a       ,      dropout_rate=__a			)
              self.decoders.append(__a			)

         UpperCAmelCase_	   =       TaLayerNorm(__a			)

         UpperCAmelCase_	   =       nn.Dropout(p=__a			)
         UpperCAmelCase_	   =       nn.Linear(__a       ,      __a       ,      bias=__a			)



    def  _lowercase						(self   :   List[str]       ,      __a   :   str       ,      __a   :   List[str]			):
         UpperCAmelCase_	   =       torch.mul(query_input.unsqueeze(-1			)       ,      key_input.unsqueeze(-2			)			)
         return mask.unsqueeze(-3			)






    def  _lowercase						(self   :   Dict       ,      __a   :   Union[str, Any]       ,      __a   :   Optional[int]       ,      __a   :   Any			):
         UpperCAmelCase_   ,			UpperCAmelCase_   ,			UpperCAmelCase_	   =       decoder_input_tokens.shape
         assert decoder_noise_time.shape == (batch,)

         # decoder_noise_time is in [0, 1), so rescale to expected timing range.
         UpperCAmelCase_	   =       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			)

         UpperCAmelCase_	   =       self.conditioning_emb(__a			).unsqueeze(1			)

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

         UpperCAmelCase_	   =       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.
         UpperCAmelCase_	   =       torch.broadcast_to(
             torch.arange(__a       ,      device=decoder_input_tokens.device			)       ,      (batch, seq_length)       ,      )

         UpperCAmelCase_	   =       self.position_encoding(__a			)

         UpperCAmelCase_	   =       self.continuous_inputs_projection(__a			)
         inputs += position_encodings
         UpperCAmelCase_	   =       self.dropout(__a			)

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

         # Translate encoding masks to encoder-decoder masks.
         UpperCAmelCase_	   =       [(x, self.encoder_decoder_mask(__a       ,      __a			)) for x, y in encodings_and_masks]

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

         for lyr in self.decoders:
              UpperCAmelCase_	   =       lyr(
                  __a       ,      conditioning_emb=__a       ,      encoder_hidden_states=__a       ,      encoder_attention_mask=__a       ,      )[0]

         UpperCAmelCase_	   =       self.decoder_norm(__a			)
         UpperCAmelCase_	   =       self.post_dropout(__a			)

         UpperCAmelCase_	   =       self.spec_out(__a			)
         return spec_out




class 	__A				(			nn.Module     ):



    def __init__(self   :   Optional[Any]       ,      __a   :   Union[str, Any]       ,      __a   :   int       ,      __a   :   Any       ,      __a   :   List[Any]       ,      __a   :   Optional[int]       ,      __a   :   Dict=1E-6			):
         super().__init__()
         UpperCAmelCase_	   =       nn.ModuleList()

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

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

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






    def  _lowercase						(self   :   int       ,      __a   :   Dict       ,      __a   :   Optional[int]=None       ,      __a   :   Union[str, Any]=None       ,      __a   :   List[str]=None       ,      __a   :   Dict=None       ,      __a   :   Optional[Any]=None       ,      ):
         UpperCAmelCase_	   =       self.layer[0](
             __a       ,      conditioning_emb=__a       ,      attention_mask=__a       ,      )

         if encoder_hidden_states is not None:
              UpperCAmelCase_	   =       torch.where(encoder_attention_mask > 0       ,      0       ,      -1E10			).to(
                  encoder_hidden_states.dtype			)

              UpperCAmelCase_	   =       self.layer[1](
                  __a       ,      key_value_states=__a       ,      attention_mask=__a       ,      )

         # Apply Film Conditional Feed Forward layer
         UpperCAmelCase_	   =       self.layer[-1](__a       ,      __a			)

         return (hidden_states,)




class 	__A				(			nn.Module     ):



    def __init__(self   :   int       ,      __a   :   List[str]       ,      __a   :   Optional[int]       ,      __a   :   int       ,      __a   :   List[Any]			):
         super().__init__()
         UpperCAmelCase_	   =       TaLayerNorm(__a			)
         UpperCAmelCase_	   =       TaFiLMLayer(in_features=d_model * 4       ,      out_features=__a			)
         UpperCAmelCase_	   =       Attention(query_dim=__a       ,      heads=__a       ,      dim_head=__a       ,      out_bias=__a       ,      scale_qk=__a			)
         UpperCAmelCase_	   =       nn.Dropout(__a			)






    def  _lowercase						(self   :   Optional[Any]       ,      __a   :   Optional[Any]       ,      __a   :   Dict=None       ,      __a   :   Optional[Any]=None       ,      ):
         # pre_self_attention_layer_norm
         UpperCAmelCase_	   =       self.layer_norm(__a			)

         if conditioning_emb is not None:
              UpperCAmelCase_	   =       self.FiLMLayer(__a       ,      __a			)

         # Self-attention block
         UpperCAmelCase_	   =       self.attention(__a			)

         UpperCAmelCase_	   =       hidden_states + self.dropout(__a			)

         return hidden_states




class 	__A				(			nn.Module     ):



    def __init__(self   :   Dict       ,      __a   :   int       ,      __a   :   Union[str, Any]       ,      __a   :   int       ,      __a   :   int       ,      __a   :   List[Any]			):
         super().__init__()
         UpperCAmelCase_	   =       Attention(query_dim=__a       ,      heads=__a       ,      dim_head=__a       ,      out_bias=__a       ,      scale_qk=__a			)
         UpperCAmelCase_	   =       TaLayerNorm(__a       ,      eps=__a			)
         UpperCAmelCase_	   =       nn.Dropout(__a			)






    def  _lowercase						(self   :   Optional[int]       ,      __a   :   str       ,      __a   :   List[str]=None       ,      __a   :   Tuple=None       ,      ):
         UpperCAmelCase_	   =       self.layer_norm(__a			)
         UpperCAmelCase_	   =       self.attention(
             __a       ,      encoder_hidden_states=__a       ,      attention_mask=attention_mask.squeeze(1			)       ,      )
         UpperCAmelCase_	   =       hidden_states + self.dropout(__a			)
         return layer_output




class 	__A				(			nn.Module     ):



    def __init__(self   :   int       ,      __a   :   Union[str, Any]       ,      __a   :   Union[str, Any]       ,      __a   :   int       ,      __a   :   Optional[int]			):
         super().__init__()
         UpperCAmelCase_	   =       TaDenseGatedActDense(d_model=__a       ,      d_ff=__a       ,      dropout_rate=__a			)
         UpperCAmelCase_	   =       TaFiLMLayer(in_features=d_model * 4       ,      out_features=__a			)
         UpperCAmelCase_	   =       TaLayerNorm(__a       ,      eps=__a			)
         UpperCAmelCase_	   =       nn.Dropout(__a			)






    def  _lowercase						(self   :   Optional[Any]       ,      __a   :   Any       ,      __a   :   List[str]=None			):
         UpperCAmelCase_	   =       self.layer_norm(__a			)
         if conditioning_emb is not None:
              UpperCAmelCase_	   =       self.film(__a       ,      __a			)

         UpperCAmelCase_	   =       self.DenseReluDense(__a			)
         UpperCAmelCase_	   =       hidden_states + self.dropout(__a			)
         return hidden_states




class 	__A				(			nn.Module     ):



    def __init__(self   :   Dict       ,      __a   :   Union[str, Any]       ,      __a   :   List[Any]       ,      __a   :   int			):
         super().__init__()
         UpperCAmelCase_	   =       nn.Linear(__a       ,      __a       ,      bias=__a			)
         UpperCAmelCase_	   =       nn.Linear(__a       ,      __a       ,      bias=__a			)
         UpperCAmelCase_	   =       nn.Linear(__a       ,      __a       ,      bias=__a			)
         UpperCAmelCase_	   =       nn.Dropout(__a			)
         UpperCAmelCase_	   =       NewGELUActivation()






    def  _lowercase						(self   :   Optional[Any]       ,      __a   :   int			):
         UpperCAmelCase_	   =       self.act(self.wi_a(__a			)			)
         UpperCAmelCase_	   =       self.wi_a(__a			)
         UpperCAmelCase_	   =       hidden_gelu * hidden_linear
         UpperCAmelCase_	   =       self.dropout(__a			)

         UpperCAmelCase_	   =       self.wo(__a			)
         return hidden_states




class 	__A				(			nn.Module     ):



    def __init__(self   :   Optional[Any]       ,      __a   :   str       ,      __a   :   List[Any]=1E-6			):
         super().__init__()
         UpperCAmelCase_	   =       nn.Parameter(torch.ones(__a			)			)
         UpperCAmelCase_	   =       eps






    def  _lowercase						(self   :   Union[str, Any]       ,      __a   :   Dict			):
         # T5 uses a layer_norm which only scales and doesn't shift, which is also known as Root Mean
         # Square Layer Normalization https://arxiv.org/abs/1910.07467 thus variance is calculated
         # w/o mean and there is no bias. Additionally we want to make sure that the accumulation for
         # half-precision inputs is done in fp32

         UpperCAmelCase_	   =       hidden_states.to(torch.floataa			).pow(2			).mean(-1       ,      keepdim=__a			)
         UpperCAmelCase_	   =       hidden_states * torch.rsqrt(variance + self.variance_epsilon			)

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

         return self.weight * hidden_states




class 	__A				(			nn.Module     ):



    def  _lowercase						(self   :   Optional[int]       ,      __a   :   torch.Tensor			):
         return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi			) * (input + 0.04_47_15 * torch.pow(__a       ,      3.0			))			))




class 	__A				(			nn.Module     ):



    def __init__(self   :   int       ,      __a   :   Union[str, Any]       ,      __a   :   List[Any]			):
         super().__init__()
         UpperCAmelCase_	   =       nn.Linear(__a       ,      out_features * 2       ,      bias=__a			)






    def  _lowercase						(self   :   Dict       ,      __a   :   Union[str, Any]       ,      __a   :   Any			):
         UpperCAmelCase_	   =       self.scale_bias(__a			)
         UpperCAmelCase_   ,			UpperCAmelCase_	   =       torch.chunk(__a       ,      2       ,      -1			)
         UpperCAmelCase_	   =       x * (1 + scale) + shift
         return x





'''simple docstring'''





# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING, Dict, Optional

import numpy as np
import pyarrow as pa

from .. import config
from ..utils.logging import get_logger
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter


if TYPE_CHECKING:
    import jax
    import jaxlib

SCREAMING_SNAKE_CASE_:					List[str]  =get_logger()

SCREAMING_SNAKE_CASE_:					Optional[dict]  =None




class 	__A				(			TensorFormatter[Mapping, """jax.Array""", Mapping]     ):



    def __init__(self   :   List[Any]       ,      __a   :   Optional[int]=None       ,      __a   :   Any=None       ,      **__a   :   Dict			):
         super().__init__(features=__a			)
         import jax
         from jaxlib.xla_client import Device

         if isinstance(__a       ,      __a			):
              raise ValueError(
                  f"""Expected {device} to be a `str` not {type(__a			)}, as `jaxlib.xla_extension.Device` """
                  "is not serializable neither with `pickle` nor with `dill`. Instead you can surround "
                  "the device with `str()` to get its string identifier that will be internally mapped "
                  "to the actual `jaxlib.xla_extension.Device`."			)
         UpperCAmelCase_	   =       device if isinstance(__a       ,      __a			) else str(jax.devices()[0]			)
         # using global variable since `jaxlib.xla_extension.Device` is not serializable neither
         # with `pickle` nor with `dill`, so we need to use a global variable instead
         global DEVICE_MAPPING
         if DEVICE_MAPPING is None:
              UpperCAmelCase_	   =       self._map_devices_to_str()
         if self.device not in list(DEVICE_MAPPING.keys()			):
              logger.warning(
                  f"""Device with string identifier {self.device} not listed among the available """
                  f"""devices: {list(DEVICE_MAPPING.keys()			)}, so falling back to the default """
                  f"""device: {str(jax.devices()[0]			)}."""			)
              UpperCAmelCase_	   =       str(jax.devices()[0]			)
         UpperCAmelCase_	   =       jnp_array_kwargs



    @staticmethod
    def  _lowercase						():
         import jax

         return {str(__a			): device for device in jax.devices()}



    def  _lowercase						(self   :   str       ,      __a   :   Tuple			):
         import jax
         import jax.numpy as jnp

         if isinstance(__a       ,      __a			) and column:
              if all(
                  isinstance(__a       ,      jax.Array			) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column			):
                   return jnp.stack(__a       ,      axis=0			)
         return column



    def  _lowercase						(self   :   Any       ,      __a   :   Optional[int]			):
         import jax
         import jax.numpy as jnp

         if isinstance(__a       ,      (str, bytes, type(__a			))			):
              return value
         elif isinstance(__a       ,      (np.character, np.ndarray)			) and np.issubdtype(value.dtype       ,      np.character			):
              return value.tolist()

         UpperCAmelCase_	   =       {}

         if isinstance(__a       ,      (np.number, np.ndarray)			) and np.issubdtype(value.dtype       ,      np.integer			):
              # the default int precision depends on the jax config
              # see https://jax.readthedocs.io/en/latest/notebooks/Common_Gotchas_in_JAX.html#double-64bit-precision
              if jax.config.jax_enable_xaa:
                   UpperCAmelCase_	   =       {"dtype": jnp.intaa}
              else:
                   UpperCAmelCase_	   =       {"dtype": jnp.intaa}
         elif isinstance(__a       ,      (np.number, np.ndarray)			) and np.issubdtype(value.dtype       ,      np.floating			):
              UpperCAmelCase_	   =       {"dtype": jnp.floataa}
         elif config.PIL_AVAILABLE and "PIL" in sys.modules:
              import PIL.Image

              if isinstance(__a       ,      PIL.Image.Image			):
                   UpperCAmelCase_	   =       np.asarray(__a			)

        # using global variable since `jaxlib.xla_extension.Device` is not serializable neither
        # with `pickle` nor with `dill`, so we need to use a global variable instead
         global DEVICE_MAPPING
         if DEVICE_MAPPING is None:
              UpperCAmelCase_	   =       self._map_devices_to_str()

         with jax.default_device(DEVICE_MAPPING[self.device]			):
              # calling jnp.array on a np.ndarray does copy the data
              # see https://github.com/google/jax/issues/4486
              return jnp.array(__a       ,      **{**default_dtype, **self.jnp_array_kwargs}			)



    def  _lowercase						(self   :   int       ,      __a   :   Any			):
         import jax

         # support for torch, tf, jax etc.
         if config.TORCH_AVAILABLE and "torch" in sys.modules:
              import torch

              if isinstance(__a       ,      torch.Tensor			):
                   return self._tensorize(data_struct.detach().cpu().numpy()[()]			)
         if hasattr(__a       ,      "__array__"			) and not isinstance(__a       ,      jax.Array			):
              UpperCAmelCase_	   =       data_struct.__array__()
         # support for nested types like struct of list of struct
         if isinstance(__a       ,      np.ndarray			):
              if data_struct.dtype == object:  # jax arrays cannot be instantied from an array of objects
                   return self._consolidate([self.recursive_tensorize(__a			) for substruct in data_struct]			)
         elif isinstance(__a       ,      (list, tuple)			):
              return self._consolidate([self.recursive_tensorize(__a			) for substruct in data_struct]			)
         return self._tensorize(__a			)



    def  _lowercase						(self   :   Union[str, Any]       ,      __a   :   dict			):
         return map_nested(self._recursive_tensorize       ,      __a       ,      map_list=__a			)



    def  _lowercase						(self   :   str       ,      __a   :   pa.Table			):
         UpperCAmelCase_	   =       self.numpy_arrow_extractor().extract_row(__a			)
         UpperCAmelCase_	   =       self.python_features_decoder.decode_row(__a			)
         return self.recursive_tensorize(__a			)



    def  _lowercase						(self   :   Tuple       ,      __a   :   pa.Table			):
         UpperCAmelCase_	   =       self.numpy_arrow_extractor().extract_column(__a			)
         UpperCAmelCase_	   =       self.python_features_decoder.decode_column(__a       ,      pa_table.column_names[0]			)
         UpperCAmelCase_	   =       self.recursive_tensorize(__a			)
         UpperCAmelCase_	   =       self._consolidate(__a			)
         return column






    def  _lowercase						(self   :   str       ,      __a   :   pa.Table			):
         UpperCAmelCase_	   =       self.numpy_arrow_extractor().extract_batch(__a			)
         UpperCAmelCase_	   =       self.python_features_decoder.decode_batch(__a			)
         UpperCAmelCase_	   =       self.recursive_tensorize(__a			)
         for column_name in batch:
              UpperCAmelCase_	   =       self._consolidate(batch[column_name]			)
         return batch




