'''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 lowercase_       (		_UpperCamelCase    ,    _UpperCamelCase			):







   """simple docstring"""

   @register_to_config
   def __init__(      self      :			Dict, UpperCamelCase__      :			int = 1_28, UpperCamelCase__      :			int = 2_56, UpperCamelCase__      :			float = 2_000.0, UpperCamelCase__      :			int = 7_68, UpperCamelCase__      :			int = 12, UpperCamelCase__      :			int = 12, UpperCamelCase__      :			int = 64, UpperCamelCase__      :			int = 20_48, UpperCamelCase__      :			float = 0.1, )  ->			Tuple:
        super().__init__()

        _A    		=  nn.Sequential(
            nn.Linear(UpperCamelCase__, d_model * 4, bias=UpperCamelCase__     ), nn.SiLU(), nn.Linear(d_model * 4, d_model * 4, bias=UpperCamelCase__     ), nn.SiLU(), )

        _A    		=  nn.Embedding(UpperCamelCase__, UpperCamelCase__     )
        _A    		=  False

        _A    		=  nn.Linear(UpperCamelCase__, UpperCamelCase__, bias=UpperCamelCase__     )

        _A    		=  nn.Dropout(p=UpperCamelCase__     )

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

        _A    		=  TaLayerNorm(UpperCamelCase__     )

        _A    		=  nn.Dropout(p=UpperCamelCase__     )
        _A    		=  nn.Linear(UpperCamelCase__, UpperCamelCase__, bias=UpperCamelCase__     )
   def 	__UpperCAmelCase   (      self      :			int, UpperCamelCase__      :			Optional[Any], UpperCamelCase__      :			int     )  ->			List[str]:
        _A    		=  torch.mul(query_input.unsqueeze(-1     ), key_input.unsqueeze(-2     )     )
        return mask.unsqueeze(-3     )

   def 	__UpperCAmelCase   (      self      :			List[Any], UpperCamelCase__      :			List[Any], UpperCamelCase__      :			Dict, UpperCamelCase__      :			int     )  ->			Any:
        _A		,       _A		,       _A    		=  decoder_input_tokens.shape
        assert decoder_noise_time.shape == (batch,)

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

        _A    		=  self.conditioning_emb(UpperCamelCase__     ).unsqueeze(1     )

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

        _A    		=  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.
        _A    		=  torch.broadcast_to(
            torch.arange(UpperCamelCase__, device=decoder_input_tokens.device     ), (batch, seq_length), )

        _A    		=  self.position_encoding(UpperCamelCase__     )

        _A    		=  self.continuous_inputs_projection(UpperCamelCase__     )
        inputs += position_encodings
        _A    		=  self.dropout(UpperCamelCase__     )

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

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

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

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

        _A    		=  self.decoder_norm(UpperCamelCase__     )
        _A    		=  self.post_dropout(UpperCamelCase__     )

        _A    		=  self.spec_out(UpperCamelCase__     )
        return spec_out







class lowercase_       (		nn.Module			):







   """simple docstring"""

   def __init__(      self      :			Any, UpperCamelCase__      :			Tuple, UpperCamelCase__      :			Dict, UpperCamelCase__      :			str, UpperCamelCase__      :			Union[str, Any], UpperCamelCase__      :			Union[str, Any], UpperCamelCase__      :			Union[str, Any]=1e-6     )  ->			List[str]:
        super().__init__()
        _A    		=  nn.ModuleList()

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

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

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

   def 	__UpperCAmelCase   (      self      :			int, UpperCamelCase__      :			Any, UpperCamelCase__      :			Any=None, UpperCamelCase__      :			Optional[Any]=None, UpperCamelCase__      :			Any=None, UpperCamelCase__      :			List[str]=None, UpperCamelCase__      :			Any=None, )  ->			Dict:
        _A    		=  self.layer[0](
            UpperCamelCase__, conditioning_emb=UpperCamelCase__, attention_mask=UpperCamelCase__, )

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

             _A    		=  self.layer[1](
                 UpperCamelCase__, key_value_states=UpperCamelCase__, attention_mask=UpperCamelCase__, )

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

        return (hidden_states,)







class lowercase_       (		nn.Module			):







   """simple docstring"""

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

   def 	__UpperCAmelCase   (      self      :			str, UpperCamelCase__      :			Optional[Any], UpperCamelCase__      :			Tuple=None, UpperCamelCase__      :			Dict=None, )  ->			Optional[int]:
        _A    		=  self.layer_norm(UpperCamelCase__     )

        if conditioning_emb is not None:
             _A    		=  self.FiLMLayer(UpperCamelCase__, UpperCamelCase__     )

        # Self-attention block
        _A    		=  self.attention(UpperCamelCase__     )

        _A    		=  hidden_states + self.dropout(UpperCamelCase__     )

        return hidden_states







class lowercase_       (		nn.Module			):







   """simple docstring"""

   def __init__(      self      :			int, UpperCamelCase__      :			Union[str, Any], UpperCamelCase__      :			Dict, UpperCamelCase__      :			Optional[int], UpperCamelCase__      :			Tuple, UpperCamelCase__      :			Optional[Any]     )  ->			List[str]:
        super().__init__()
        _A    		=  Attention(query_dim=UpperCamelCase__, heads=UpperCamelCase__, dim_head=UpperCamelCase__, out_bias=UpperCamelCase__, scale_qk=UpperCamelCase__     )
        _A    		=  TaLayerNorm(UpperCamelCase__, eps=UpperCamelCase__     )
        _A    		=  nn.Dropout(UpperCamelCase__     )

   def 	__UpperCAmelCase   (      self      :			Optional[int], UpperCamelCase__      :			Union[str, Any], UpperCamelCase__      :			str=None, UpperCamelCase__      :			str=None, )  ->			str:
        _A    		=  self.layer_norm(UpperCamelCase__     )
        _A    		=  self.attention(
            UpperCamelCase__, encoder_hidden_states=UpperCamelCase__, attention_mask=attention_mask.squeeze(1     ), )
        _A    		=  hidden_states + self.dropout(UpperCamelCase__     )
        return layer_output







class lowercase_       (		nn.Module			):







   """simple docstring"""

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

   def 	__UpperCAmelCase   (      self      :			Union[str, Any], UpperCamelCase__      :			Optional[int], UpperCamelCase__      :			Optional[Any]=None     )  ->			Dict:
        _A    		=  self.layer_norm(UpperCamelCase__     )
        if conditioning_emb is not None:
             _A    		=  self.film(UpperCamelCase__, UpperCamelCase__     )

        _A    		=  self.DenseReluDense(UpperCamelCase__     )
        _A    		=  hidden_states + self.dropout(UpperCamelCase__     )
        return hidden_states







class lowercase_       (		nn.Module			):







   """simple docstring"""

   def __init__(      self      :			int, UpperCamelCase__      :			Optional[int], UpperCamelCase__      :			Any, UpperCamelCase__      :			Optional[int]     )  ->			List[Any]:
        super().__init__()
        _A    		=  nn.Linear(UpperCamelCase__, UpperCamelCase__, bias=UpperCamelCase__     )
        _A    		=  nn.Linear(UpperCamelCase__, UpperCamelCase__, bias=UpperCamelCase__     )
        _A    		=  nn.Linear(UpperCamelCase__, UpperCamelCase__, bias=UpperCamelCase__     )
        _A    		=  nn.Dropout(UpperCamelCase__     )
        _A    		=  NewGELUActivation()

   def 	__UpperCAmelCase   (      self      :			Optional[Any], UpperCamelCase__      :			str     )  ->			List[Any]:
        _A    		=  self.act(self.wi_a(UpperCamelCase__     )     )
        _A    		=  self.wi_a(UpperCamelCase__     )
        _A    		=  hidden_gelu * hidden_linear
        _A    		=  self.dropout(UpperCamelCase__     )

        _A    		=  self.wo(UpperCamelCase__     )
        return hidden_states







class lowercase_       (		nn.Module			):







   """simple docstring"""

   def __init__(      self      :			List[Any], UpperCamelCase__      :			Union[str, Any], UpperCamelCase__      :			Optional[int]=1e-6     )  ->			Union[str, Any]:
        super().__init__()
        _A    		=  nn.Parameter(torch.ones(UpperCamelCase__     )     )
        _A    		=  eps

   def 	__UpperCAmelCase   (      self      :			Union[str, Any], UpperCamelCase__      :			Dict     )  ->			Optional[Any]:
        _A    		=  hidden_states.to(torch.floataa     ).pow(2     ).mean(-1, keepdim=UpperCamelCase__     )
        _A    		=  hidden_states * torch.rsqrt(variance + self.variance_epsilon     )

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

        return self.weight * hidden_states







class lowercase_       (		nn.Module			):







   """simple docstring"""

   def 	__UpperCAmelCase   (      self      :			Any, UpperCamelCase__      :			torch.Tensor     )  ->			torch.Tensor:
        return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi     ) * (input + 0.044_715 * torch.pow(UpperCamelCase__, 3.0     ))     ))







class lowercase_       (		nn.Module			):







   """simple docstring"""

   def __init__(      self      :			List[Any], UpperCamelCase__      :			Any, UpperCamelCase__      :			str     )  ->			Optional[int]:
        super().__init__()
        _A    		=  nn.Linear(UpperCamelCase__, out_features * 2, bias=UpperCamelCase__     )

   def 	__UpperCAmelCase   (      self      :			Optional[int], UpperCamelCase__      :			str, UpperCamelCase__      :			List[Any]     )  ->			List[str]:
        _A    		=  self.scale_bias(UpperCamelCase__     )
        _A		,       _A    		=  torch.chunk(UpperCamelCase__, 2, -1     )
        _A    		=  x * (1 + scale) + shift
        return x











import io
import itertools
import json
from dataclasses import dataclass
from typing import Optional

import pyarrow as pa
import pyarrow.json as paj

import datasets
from datasets.table import table_cast
from datasets.utils.file_utils import readline


__a:     Tuple   					=					datasets.utils.logging.get_logger(__name__)
@dataclass
class 		SCREAMING_SNAKE_CASE__	(  datasets.BuilderConfig     ):

  '''simple docstring'''







  _lowerCamelCase    =  None
  _lowerCamelCase    =  "utf-8"
  _lowerCamelCase    =  None
  _lowerCamelCase    =  None
  _lowerCamelCase    =  True  # deprecated
  _lowerCamelCase    =  None  # deprecated
  _lowerCamelCase    =  10 << 20  # 10MB
  _lowerCamelCase    =  None



class 		SCREAMING_SNAKE_CASE__	(  datasets.ArrowBasedBuilder     ):

  '''simple docstring'''







  _lowerCamelCase    =  JsonConfig


  def  lowerCamelCase       (      self		:			Any							)						->	Any:


         """simple docstring"""
         if self.config.block_size is not None:
                logger.warning("""The JSON loader parameter `block_size` is deprecated. Please use `chunksize` instead"""							)
                _UpperCAmelCase							=    self.config.block_size
         if self.config.use_threads is not True:
                logger.warning(
                    """The JSON loader parameter `use_threads` is deprecated and doesn't have any effect anymore."""							)
         if self.config.newlines_in_values is not None:
                raise ValueError("""The JSON loader parameter `newlines_in_values` is no longer supported"""							)
         return datasets.DatasetInfo(features=self.config.features							)


  def  lowerCamelCase       (      self		:			List[Any]       ,				lowerCamelCase		:			Optional[int]							)						->	Dict:


         """simple docstring"""
         if not self.config.data_files:
                raise ValueError(f"""At least one data file must be specified, but got data_files={self.config.data_files}"""							)
         _UpperCAmelCase							=    dl_manager.download_and_extract(self.config.data_files							)
         if isinstance(lowerCamelCase       ,				(str, list, tuple)							):
                _UpperCAmelCase							=    data_files
                if isinstance(lowerCamelCase       ,				lowerCamelCase							):
                       _UpperCAmelCase							=    [files]
                _UpperCAmelCase							=    [dl_manager.iter_files(lowerCamelCase							) for file in files]
                return [datasets.SplitGenerator(name=datasets.Split.TRAIN       ,				gen_kwargs={"""files""": files}							)]
         _UpperCAmelCase							=    []
         for split_name, files in data_files.items():
                if isinstance(lowerCamelCase       ,				lowerCamelCase							):
                       _UpperCAmelCase							=    [files]
                _UpperCAmelCase							=    [dl_manager.iter_files(lowerCamelCase							) for file in files]
                splits.append(datasets.SplitGenerator(name=lowerCamelCase       ,				gen_kwargs={"""files""": files}							)							)
         return splits


  def  lowerCamelCase       (      self		:			Tuple       ,				lowerCamelCase		:			pa.Table							)						->	pa.Table:


         """simple docstring"""
         if self.config.features is not None:
                # adding missing columns
                for column_name in set(self.config.features							) - set(pa_table.column_names							):
                       _UpperCAmelCase							=    self.config.features.arrow_schema.field(lowerCamelCase							).type
                       _UpperCAmelCase							=    pa_table.append_column(lowerCamelCase       ,				pa.array([None] * len(lowerCamelCase							)       ,				type=lowerCamelCase							)							)
                # more expensive cast to support nested structures with keys in a different order
                # allows str <-> int/float or str to Audio for example
                _UpperCAmelCase							=    table_cast(lowerCamelCase       ,				self.config.features.arrow_schema							)
         return pa_table





  def  lowerCamelCase       (      self		:			Union[str, Any]       ,				lowerCamelCase		:			Optional[int]							)						->	str:


         """simple docstring"""
         for file_idx, file in enumerate(itertools.chain.from_iterable(lowerCamelCase							)							):
                # If the file is one json object and if we need to look at the list of items in one specific field
                if self.config.field is not None:
                       with open(lowerCamelCase       ,				encoding=self.config.encoding       ,				errors=self.config.encoding_errors							) as f:
                              _UpperCAmelCase							=    json.load(lowerCamelCase							)

                       # We keep only the field we are interested in
                       _UpperCAmelCase							=    dataset[self.config.field]

                       # We accept two format: a list of dicts or a dict of lists
                       if isinstance(lowerCamelCase       ,				(list, tuple)							):
                              _UpperCAmelCase							=    set().union(*[row.keys() for row in dataset]							)
                              _UpperCAmelCase							=    {col: [row.get(lowerCamelCase							) for row in dataset] for col in keys}
                       else:
                              _UpperCAmelCase							=    dataset
                       _UpperCAmelCase							=    pa.Table.from_pydict(lowerCamelCase							)
                       yield file_idx, self._cast_table(lowerCamelCase							)

                # If the file has one json object per line
                else:
                       with open(lowerCamelCase       ,				"""rb"""							) as f:
                              _UpperCAmelCase							=    0
                              # Use block_size equal to the chunk size divided by 32 to leverage multithreading
                              # Set a default minimum value of 16kB if the chunk size is really small
                              _UpperCAmelCase							=    max(self.config.chunksize // 32       ,				16 << 10							)
                              _UpperCAmelCase							=    (
                                  self.config.encoding_errors if self.config.encoding_errors is not None else """strict"""
                              )
                              while True:
                                     _UpperCAmelCase							=    f.read(self.config.chunksize							)
                                     if not batch:
                                            break
                                     # Finish current line
                                     try:
                                            batch += f.readline()
                                     except (AttributeError, io.UnsupportedOperation):
                                            batch += readline(lowerCamelCase							)
                                     # PyArrow only accepts utf-8 encoded bytes
                                     if self.config.encoding != "utf-8":
                                            _UpperCAmelCase							=    batch.decode(self.config.encoding       ,				errors=lowerCamelCase							).encode("""utf-8"""							)
                                     try:
                                            while True:
                                                   try:
                                                          _UpperCAmelCase							=    paj.read_json(
                                                              io.BytesIO(lowerCamelCase							)       ,				read_options=paj.ReadOptions(block_size=lowerCamelCase							)							)
                                                          break
                                                   except (pa.ArrowInvalid, pa.ArrowNotImplementedError) as e:
                                                          if (
                                                              isinstance(lowerCamelCase       ,				pa.ArrowInvalid							)
                                                              and "straddling" not in str(lowerCamelCase							)
                                                              or block_size > len(lowerCamelCase							)
                                                          ):
                                                                 raise
                                                          else:
                                                                 # Increase the block size in case it was too small.
                                                                 # The block size will be reset for the next file.
                                                                 logger.debug(
                                                                     f"""Batch of {len(lowerCamelCase							)} bytes couldn't be parsed with block_size={block_size}. Retrying with block_size={block_size * 2}."""							)
                                                                 block_size *= 2
                                     except pa.ArrowInvalid as e:
                                            try:
                                                   with open(
                                                       lowerCamelCase       ,				encoding=self.config.encoding       ,				errors=self.config.encoding_errors							) as f:
                                                          _UpperCAmelCase							=    json.load(lowerCamelCase							)
                                            except json.JSONDecodeError:
                                                   logger.error(f"""Failed to read file '{file}' with error {type(lowerCamelCase							)}: {e}"""							)
                                                   raise e
                                            # If possible, parse the file as a list of json objects and exit the loop
                                            if isinstance(lowerCamelCase       ,				lowerCamelCase							):  # list is the only sequence type supported in JSON
                                                   try:
                                                          _UpperCAmelCase							=    set().union(*[row.keys() for row in dataset]							)
                                                          _UpperCAmelCase							=    {col: [row.get(lowerCamelCase							) for row in dataset] for col in keys}
                                                          _UpperCAmelCase							=    pa.Table.from_pydict(lowerCamelCase							)
                                                   except (pa.ArrowInvalid, AttributeError) as e:
                                                          logger.error(f"""Failed to read file '{file}' with error {type(lowerCamelCase							)}: {e}"""							)
                                                          raise ValueError(f"""Not able to read records in the JSON file at {file}."""							) from None
                                                   yield file_idx, self._cast_table(lowerCamelCase							)
                                                   break
                                            else:
                                                   logger.error(f"""Failed to read file '{file}' with error {type(lowerCamelCase							)}: {e}"""							)
                                                   raise ValueError(
                                                       f"""Not able to read records in the JSON file at {file}. """
                                                       f"""You should probably indicate the field of the JSON file containing your records. """
                                                       f"""This JSON file contain the following fields: {str(list(dataset.keys()							)							)}. """
                                                       f"""Select the correct one and provide it as `field='XXX'` to the dataset loading method. """							) from None
                        # Uncomment for debugging (will print the Arrow table size and elements)
                        # logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
                        # logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
                                     yield (file_idx, batch_idx), self._cast_table(lowerCamelCase							)
                                     batch_idx += 1