




'''simple docstring'''

import torch
import torch.nn as nn
from transformers import CLIPConfig, CLIPVisionModel, PreTrainedModel

from ...utils import logging


a       :						Union[str, Any]								=		logging.get_logger(__name__)


def __UpperCAmelCase	(   _UpperCAmelCase		:   int		,				_UpperCAmelCase		:   int						)      ->      Any:
			__snake_case			=		nn.functional.normalize(_UpperCAmelCase						)
			__snake_case			=		nn.functional.normalize(_UpperCAmelCase						)
			return torch.mm(_UpperCAmelCase		,				normalized_text_embeds.t()						)






class 		SCREAMING_SNAKE_CASE__  (			_UpperCamelCase	):
			__SCREAMING_SNAKE_CASE    =			CLIPConfig

			__SCREAMING_SNAKE_CASE    =			["""CLIPEncoderLayer"""]





			def __init__(      self						:    List[str]      ,		a_						:    CLIPConfig							):



						"""simple docstring"""




						super().__init__(a_							)

						__snake_case			=		CLIPVisionModel(config.vision_config							)
						__snake_case			=		nn.Linear(config.vision_config.hidden_size      ,		config.projection_dim      ,		bias=a_							)

						__snake_case			=		nn.Parameter(torch.ones(17      ,		config.projection_dim							)      ,		requires_grad=a_							)
						__snake_case			=		nn.Parameter(torch.ones(3      ,		config.projection_dim							)      ,		requires_grad=a_							)

						__snake_case			=		nn.Parameter(torch.ones(17							)      ,		requires_grad=a_							)
						__snake_case			=		nn.Parameter(torch.ones(3							)      ,		requires_grad=a_							)





			@torch.no_grad()
			def 							A (      self						:    Tuple      ,		a_						:    Optional[Any]      ,		a_						:    Tuple							):



						"""simple docstring"""




						__snake_case			=		self.vision_model(a_							)[1]  # pooled_output
						__snake_case			=		self.visual_projection(a_							)

						# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
						__snake_case			=		cosine_distance(a_      ,		self.special_care_embeds							).cpu().float().numpy()
						__snake_case			=		cosine_distance(a_      ,		self.concept_embeds							).cpu().float().numpy()

						__snake_case			=		[]
						__snake_case			=		image_embeds.shape[0]
						for i in range(a_							):
									__snake_case			=		{"special_scores": {}, "special_care": [], "concept_scores": {}, "bad_concepts": []}

									# increase this value to create a stronger `nfsw` filter
									# at the cost of increasing the possibility of filtering benign images
									__snake_case			=		0.0

									for concept_idx in range(len(special_cos_dist[0]							)							):
												__snake_case			=		special_cos_dist[i][concept_idx]
												__snake_case			=		self.special_care_embeds_weights[concept_idx].item()
												__snake_case			=		round(concept_cos - concept_threshold + adjustment      ,		3							)
												if result_img["special_scores"][concept_idx] > 0:
															result_img["special_care"].append({concept_idx, result_img["special_scores"][concept_idx]}							)
															__snake_case			=		0.01

									for concept_idx in range(len(cos_dist[0]							)							):
												__snake_case			=		cos_dist[i][concept_idx]
												__snake_case			=		self.concept_embeds_weights[concept_idx].item()
												__snake_case			=		round(concept_cos - concept_threshold + adjustment      ,		3							)
												if result_img["concept_scores"][concept_idx] > 0:
															result_img["bad_concepts"].append(a_							)

									result.append(a_							)

						__snake_case			=		[len(res["bad_concepts"]							) > 0 for res in result]

						return images, has_nsfw_concepts






			@torch.no_grad()
			def 							A (      self						:    List[Any]      ,		a_						:    torch.FloatTensor      ,		a_						:    torch.FloatTensor							):



						"""simple docstring"""




						__snake_case			=		self.vision_model(a_							)[1]  # pooled_output
						__snake_case			=		self.visual_projection(a_							)

						__snake_case			=		cosine_distance(a_      ,		self.special_care_embeds							)
						__snake_case			=		cosine_distance(a_      ,		self.concept_embeds							)

						# increase this value to create a stronger `nsfw` filter
						# at the cost of increasing the possibility of filtering benign images
						__snake_case			=		0.0

						__snake_case			=		special_cos_dist - self.special_care_embeds_weights + adjustment
						# special_scores = special_scores.round(decimals=3)
						__snake_case			=		torch.any(special_scores > 0      ,		dim=1							)
						__snake_case			=		special_care * 0.01
						__snake_case			=		special_adjustment.unsqueeze(1							).expand(-1      ,		cos_dist.shape[1]							)

						__snake_case			=		(cos_dist - self.concept_embeds_weights) + special_adjustment
						# concept_scores = concept_scores.round(decimals=3)
						__snake_case			=		torch.any(concept_scores > 0      ,		dim=1							)

						return images, has_nsfw_concepts







import os
from typing import BinaryIO, Optional, Union

import numpy as np
import pyarrow.parquet as pq

from .. import Audio, Dataset, Features, Image, NamedSplit, Value, config
from ..features.features import FeatureType, _visit
from ..formatting import query_table
from ..packaged_modules import _PACKAGED_DATASETS_MODULES
from ..packaged_modules.parquet.parquet import Parquet
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader


def 				__A(lowerCAmelCase			)		->					Optional[int]:

		"""simple docstring"""






		_UpperCamelCase         =						np.inf

		def set_batch_size(lowerCAmelCase			) -> None:
				nonlocal batch_size
				if isinstance(lowerCAmelCase    ,	lowerCAmelCase			):
						_UpperCamelCase         =						min(lowerCAmelCase    ,	config.PARQUET_ROW_GROUP_SIZE_FOR_IMAGE_DATASETS			)
				elif isinstance(lowerCAmelCase    ,	lowerCAmelCase			):
						_UpperCamelCase         =						min(lowerCAmelCase    ,	config.PARQUET_ROW_GROUP_SIZE_FOR_AUDIO_DATASETS			)
				elif isinstance(lowerCAmelCase    ,	lowerCAmelCase			) and feature.dtype == "binary":
						_UpperCamelCase         =						min(lowerCAmelCase    ,	config.PARQUET_ROW_GROUP_SIZE_FOR_BINARY_DATASETS			)

		_visit(lowerCAmelCase    ,	lowerCAmelCase			)

		return None if batch_size is np.inf else batch_size







class 							lowerCAmelCase__   ( __lowercase						):

						def __init__(      self							,				a							,				a = None							,				a = None							,				a = None							,				a = False							,				a = False							,				a = None							,				**a							,				)       ->   Tuple:


								'''simple docstring'''

								super().__init__(
								    a							,				split=a							,				features=a							,				cache_dir=a							,				keep_in_memory=a							,				streaming=a							,				num_proc=a							,				**a							,				)
								_UpperCamelCase         =						path_or_paths if isinstance(a							,				a					) else {self.split: path_or_paths}
								_UpperCamelCase         =						_PACKAGED_DATASETS_MODULES["""parquet"""][1]
								_UpperCamelCase         =						Parquet(
								    cache_dir=a							,				data_files=a							,				features=a							,				hash=a							,				**a							,				)







						def 			A_					(      self					)       ->   Optional[int]:


								'''simple docstring'''

								if self.streaming:
										_UpperCamelCase         =						self.builder.as_streaming_dataset(split=self.split					)
								# Build regular (map-style) dataset
								else:
										_UpperCamelCase         =						None
										_UpperCamelCase         =						None
										_UpperCamelCase         =						None
										_UpperCamelCase         =						None

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







class 							lowerCAmelCase__   :

						def __init__(      self							,				a							,				a							,				a = None							,				**a							,				)       ->   str:


								'''simple docstring'''

								_UpperCamelCase         =						dataset
								_UpperCamelCase         =						path_or_buf
								_UpperCamelCase         =						batch_size or get_writer_batch_size(dataset.features					)
								_UpperCamelCase         =						parquet_writer_kwargs

						def 			A_					(      self					)       ->   int:


								'''simple docstring'''

								_UpperCamelCase         =						self.batch_size if self.batch_size else config.DEFAULT_MAX_BATCH_SIZE

								if isinstance(self.path_or_buf							,				(str, bytes, os.PathLike)					):
										with open(self.path_or_buf							,				"""wb+"""					) as buffer:
												_UpperCamelCase         =						self._write(file_obj=a							,				batch_size=a							,				**self.parquet_writer_kwargs					)
								else:
										_UpperCamelCase         =						self._write(file_obj=self.path_or_buf							,				batch_size=a							,				**self.parquet_writer_kwargs					)
								return written







						def 			A_					(      self							,				a							,				a							,				**a					)       ->   int:


								'''simple docstring'''

								_UpperCamelCase         =						0
								_UpperCamelCase         =						parquet_writer_kwargs.pop("""path_or_buf"""							,				a					)
								_UpperCamelCase         =						self.dataset.features.arrow_schema

								_UpperCamelCase         =						pq.ParquetWriter(a							,				schema=a							,				**a					)

								for offset in logging.tqdm(
								    range(0							,				len(self.dataset					)							,				a					)							,				unit="""ba"""							,				disable=not logging.is_progress_bar_enabled()							,				desc="""Creating parquet from Arrow format"""							,				):
										_UpperCamelCase         =						query_table(
										    table=self.dataset._data							,				key=slice(a							,				offset + batch_size					)							,				indices=self.dataset._indices if self.dataset._indices is not None else None							,				)
										writer.write_table(a					)
										written += batch.nbytes
								writer.close()
								return written



