




'''simple docstring'''

from typing import Optional

from torch import nn

from .transformer_ad import TransformeraDModel, TransformeraDModelOutput






class 		a							(  nn.Module	):


				def __init__(						self       :   Any			,							lowercase_       :   int = 16			,							lowercase_       :   int = 88			,							lowercase_       :   Optional[int] = None			,							lowercase_       :   int = 1			,							lowercase_       :   float = 0.0			,							lowercase_       :   int = 32			,							lowercase_       :   Optional[int] = None			,							lowercase_       :   bool = False			,							lowercase_       :   Optional[int] = None			,							lowercase_       :   Optional[int] = None			,							lowercase_       :   str = "geglu"			,							lowercase_       :   Optional[int] = None			,							):
									super().__init__()
									snake_case_		     =							nn.ModuleList(
									    [
									        TransformeraDModel(
									            num_attention_heads=lowercase_			,							attention_head_dim=lowercase_			,							in_channels=lowercase_			,							num_layers=lowercase_			,							dropout=lowercase_			,							norm_num_groups=lowercase_			,							cross_attention_dim=lowercase_			,							attention_bias=lowercase_			,							sample_size=lowercase_			,							num_vector_embeds=lowercase_			,							activation_fn=lowercase_			,							num_embeds_ada_norm=lowercase_			,							)
									        for _ in range(2   )
									    ]   )

									# Variables that can be set by a pipeline:

									# The ratio of transformer1 to transformer2's output states to be combined during inference
									snake_case_		     =							0.5

									# The shape of `encoder_hidden_states` is expected to be
									# `(batch_size, condition_lengths[0]+condition_lengths[1], num_features)`
									snake_case_		     =							[77, 257]

									# Which transformer to use to encode which condition.
									# E.g. `(1, 0)` means that we'll use `transformers[1](conditions[0])` and `transformers[0](conditions[1])`
									snake_case_		     =							[1, 0]


				def     A_					(						self       :   Any			,							lowercase_       :   Optional[Any]			,							lowercase_       :   List[Any]			,							lowercase_       :   Union[str, Any]=None			,							lowercase_       :   Optional[Any]=None			,							lowercase_       :   str=None			,							lowercase_       :   bool = True			,							):
									snake_case_		     =							hidden_states

									snake_case_		     =							[]
									snake_case_		     =							0
									# attention_mask is not used yet
									for i in range(2   ):
														# for each of the two transformers, pass the corresponding condition tokens
														snake_case_		     =							encoder_hidden_states[:, tokens_start : tokens_start + self.condition_lengths[i]]
														snake_case_		     =							self.transformer_index_for_condition[i]
														snake_case_		     =							self.transformers[transformer_index](
														    lowercase_			,							encoder_hidden_states=lowercase_			,							timestep=lowercase_			,							cross_attention_kwargs=lowercase_			,							return_dict=lowercase_			,							)[0]
														encoded_states.append(encoded_state - input_states   )
														tokens_start += self.condition_lengths[i]

									snake_case_		     =							encoded_states[0] * self.mix_ratio + encoded_states[1] * (1 - self.mix_ratio)
									snake_case_		     =							output_states + input_states

									if not return_dict:
														return (output_states,)

									return TransformeraDModelOutput(sample=lowercase_   )







'''simple docstring'''

import multiprocessing
import os
from typing import BinaryIO, Optional, Union

import fsspec

from .. import Dataset, Features, NamedSplit, config
from ..formatting import query_table
from ..packaged_modules.json.json import Json
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader






class 		a							(  _lowerCamelCase	):


				def __init__(						self       :   Optional[int]			,							lowercase_       :   NestedDataStructureLike[PathLike]			,							lowercase_       :   Optional[NamedSplit] = None			,							lowercase_       :   Optional[Features] = None			,							lowercase_       :   str = None			,							lowercase_       :   bool = False			,							lowercase_       :   bool = False			,							lowercase_       :   Optional[str] = None			,							lowercase_       :   Optional[int] = None			,							**lowercase_       :   Optional[Any]			,							):
									super().__init__(
									    lowercase_			,							split=lowercase_			,							features=lowercase_			,							cache_dir=lowercase_			,							keep_in_memory=lowercase_			,							streaming=lowercase_			,							num_proc=lowercase_			,							**lowercase_			,							)
									snake_case_		     =							field
									snake_case_		     =							path_or_paths if isinstance(lowercase_			,							lowercase_   ) else {self.split: path_or_paths}
									snake_case_		     =							Json(
									    cache_dir=lowercase_			,							data_files=lowercase_			,							features=lowercase_			,							field=lowercase_			,							**lowercase_			,							)


				def     A_					(						self       :   Optional[Any]   ):
									# Build iterable dataset
									if self.streaming:
														snake_case_		     =							self.builder.as_streaming_dataset(split=self.split   )
									# Build regular (map-style) dataset
									else:
														snake_case_		     =							None
														snake_case_		     =							None
														snake_case_		     =							None
														snake_case_		     =							None

														self.builder.download_and_prepare(
														    download_config=lowercase_			,							download_mode=lowercase_			,							verification_mode=lowercase_			,							base_path=lowercase_			,							num_proc=self.num_proc			,							)
														snake_case_		     =							self.builder.as_dataset(
														    split=self.split			,							verification_mode=lowercase_			,							in_memory=self.keep_in_memory   )
									return dataset







class 		a							:


				def __init__(						self       :   Dict			,							lowercase_       :   Dataset			,							lowercase_       :   Union[PathLike, BinaryIO]			,							lowercase_       :   Optional[int] = None			,							lowercase_       :   Optional[int] = None			,							**lowercase_       :   List[str]			,							):
									if num_proc is not None and num_proc <= 0:
														raise ValueError(F"num_proc {num_proc} must be an integer > 0."   )

									snake_case_		     =							dataset
									snake_case_		     =							path_or_buf
									snake_case_		     =							batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
									snake_case_		     =							num_proc
									snake_case_		     =							'''utf-8'''
									snake_case_		     =							to_json_kwargs


				def     A_					(						self       :   str   ):
									snake_case_		     =							self.to_json_kwargs.pop('''path_or_buf'''			,							lowercase_   )
									snake_case_		     =							self.to_json_kwargs.pop('''orient'''			,							'''records'''   )
									snake_case_		     =							self.to_json_kwargs.pop('''lines'''			,							True if orient == '''records''' else False   )
									snake_case_		     =							self.to_json_kwargs.pop('''index'''			,							False if orient in ['''split''', '''table'''] else True   )
									snake_case_		     =							self.to_json_kwargs.pop('''compression'''			,							lowercase_   )

									if compression not in [None, "infer", "gzip", "bz2", "xz"]:
														raise NotImplementedError(F"`datasets` currently does not support {compression} compression"   )

									if isinstance(self.path_or_buf			,							(str, bytes, os.PathLike)   ):
														with fsspec.open(self.path_or_buf			,							'''wb'''			,							compression=lowercase_   ) as buffer:
																			snake_case_		     =							self._write(file_obj=lowercase_			,							orient=lowercase_			,							lines=lowercase_			,							index=lowercase_			,							**self.to_json_kwargs   )
									else:
														if compression:
																			raise NotImplementedError(
																			    F"The compression parameter is not supported when writing to a buffer, but compression={compression}"
																			    ''' was passed. Please provide a local path instead.'''   )
														snake_case_		     =							self._write(
														    file_obj=self.path_or_buf			,							orient=lowercase_			,							lines=lowercase_			,							index=lowercase_			,							**self.to_json_kwargs   )
									return written


				def     A_					(						self       :   List[str]			,							lowercase_       :   Dict   ):
									snake_case_     ,snake_case_     ,snake_case_     ,snake_case_     ,snake_case_		     =							args

									snake_case_		     =							query_table(
									    table=self.dataset.data			,							key=slice(lowercase_			,							offset + self.batch_size   )			,							indices=self.dataset._indices			,							)
									snake_case_		     =							batch.to_pandas().to_json(
									    path_or_buf=lowercase_			,							orient=lowercase_			,							lines=lowercase_			,							index=lowercase_			,							**lowercase_   )
									if not json_str.endswith('''\n'''   ):
														json_str += "\n"
									return json_str.encode(self.encoding   )


				def     A_					(						self       :   Optional[int]			,							lowercase_       :   BinaryIO			,							lowercase_       :   List[Any]			,							lowercase_       :   str			,							lowercase_       :   int			,							**lowercase_       :   Optional[int]			,							):
									snake_case_		     =							0

									if self.num_proc is None or self.num_proc == 1:
														for offset in logging.tqdm(
														    range(0			,							len(self.dataset   )			,							self.batch_size   )			,							unit='''ba'''			,							disable=not logging.is_progress_bar_enabled()			,							desc='''Creating json from Arrow format'''			,							):
																			snake_case_		     =							self._batch_json((offset, orient, lines, index, to_json_kwargs)   )
																			written += file_obj.write(lowercase_   )
									else:
														snake_case_     ,snake_case_		     =							len(self.dataset   ), self.batch_size
														with multiprocessing.Pool(self.num_proc   ) as pool:
																			for json_str in logging.tqdm(
																			    pool.imap(
																			        self._batch_json			,							[(offset, orient, lines, index, to_json_kwargs) for offset in range(0			,							lowercase_			,							lowercase_   )]			,							)			,							total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size			,							unit='''ba'''			,							disable=not logging.is_progress_bar_enabled()			,							desc='''Creating json from Arrow format'''			,							):
																								written += file_obj.write(lowercase_   )

									return written

