


'''simple docstring'''







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

from ...utils import logging


A      :     int										= logging.get_logger(__name__)






def       lowerCAmelCase__       (				lowerCamelCase     :			List[str]  ,lowerCamelCase     :			Any   ):
       _A     :	int      =					nn.functional.normalize(lowercase_   )
       _A     :	int      =					nn.functional.normalize(lowercase_   )
       return torch.mm(lowercase_  ,normalized_text_embeds.t()   )


class  __lowerCamelCase (       UpperCAmelCase_       ):
    """simple docstring"""
    a													=    CLIPConfig

    a													=    ["CLIPEncoderLayer"]


    def __init__(  self			:							int      ,							SCREAMING_SNAKE_CASE			:							Optional[int]):
           super().__init__(__UpperCamelCase)

           _A     :	Optional[Any]      =					CLIPVisionModel(config.vision_config)
           _A     :	Any      =					nn.Linear(config.vision_config.hidden_size      ,							config.projection_dim      ,							bias=__UpperCamelCase)

           _A     :	str      =					nn.Parameter(torch.ones(17      ,							config.projection_dim)      ,							requires_grad=__UpperCamelCase)
           _A     :	int      =					nn.Parameter(torch.ones(3      ,							config.projection_dim)      ,							requires_grad=__UpperCamelCase)

           _A     :	List[Any]      =					nn.Parameter(torch.ones(17)      ,							requires_grad=__UpperCamelCase)
           _A     :	Dict      =					nn.Parameter(torch.ones(3)      ,							requires_grad=__UpperCamelCase)


    @torch.no_grad()
    def    A    (  self			:							List[str]      ,							SCREAMING_SNAKE_CASE			:							Union[str, Any]      ,							SCREAMING_SNAKE_CASE			:							Optional[int]):
           _A     :	Any      =					self.vision_model(__UpperCamelCase)[1]  # pooled_output
           _A     :	Dict      =					self.visual_projection(__UpperCamelCase)

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

           _A     :	List[Any]      =					[]
           _A     :	List[str]      =					image_embeds.shape[0]
           for i in range(__UpperCamelCase):
                  _A     :	Dict      =					{'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
                  _A     :	List[Any]      =					0.0

                  for concept_idx in range(len(special_cos_dist[0])):
                         _A     :	int      =					special_cos_dist[i][concept_idx]
                         _A     :	List[str]      =					self.special_care_embeds_weights[concept_idx].item()
                         _A     :	int      =					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]})
                                _A     :	List[Any]      =					0.01

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

                  result.append(__UpperCamelCase)

           _A     :	Dict      =					[len(res['bad_concepts']) > 0 for res in result]

           for idx, has_nsfw_concept in enumerate(__UpperCamelCase):
                  if has_nsfw_concept:
                         if torch.is_tensor(__UpperCamelCase) or torch.is_tensor(images[0]):
                                _A     :	Dict      =					torch.zeros_like(images[idx])  # black image
                         else:
                                _A     :	List[Any]      =					np.zeros(images[idx].shape)  # black image

           if any(__UpperCamelCase):
                  logger.warning(
                      'Potential NSFW content was detected in one or more images. A black image will be returned instead.'
                      ' Try again with a different prompt and/or seed.')

           return images, has_nsfw_concepts


    @torch.no_grad()
    def    A    (  self			:							int      ,							SCREAMING_SNAKE_CASE			:							Dict      ,							SCREAMING_SNAKE_CASE			:							Optional[Any]):
           _A     :	Union[str, Any]      =					self.vision_model(__UpperCamelCase)[1]  # pooled_output
           _A     :	List[str]      =					self.visual_projection(__UpperCamelCase)

           _A     :	str      =					cosine_distance(__UpperCamelCase      ,							self.special_care_embeds)
           _A     :	Optional[Any]      =					cosine_distance(__UpperCamelCase      ,							self.concept_embeds)

           # increase this value to create a stronger `nsfw` filter
           # at the cost of increasing the possibility of filtering benign images
           _A     :	Union[str, Any]      =					0.0

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

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

           _A     :	str      =					0.0  # black image

           return images, has_nsfw_concepts












'''simple docstring'''







import importlib
import inspect
from functools import wraps
from typing import TYPE_CHECKING, Optional, Union

from .download.streaming_download_manager import (
    xbasename,
    xdirname,
    xet_parse,
    xexists,
    xgetsize,
    xglob,
    xgzip_open,
    xisdir,
    xisfile,
    xjoin,
    xlistdir,
    xnumpy_load,
    xopen,
    xpandas_read_csv,
    xpandas_read_excel,
    xPath,
    xrelpath,
    xsio_loadmat,
    xsplit,
    xsplitext,
    xwalk,
    xxml_dom_minidom_parse,
)
from .utils.logging import get_logger
from .utils.patching import patch_submodule
from .utils.py_utils import get_imports


_lowerCamelCase							:						int								=  get_logger(__name__)


if TYPE_CHECKING:
						from .builder import DatasetBuilder






def 		_lowercase		(	lowercase_						,							lowercase_ = None  ):



		'''simple docstring'''







		_snake_case	       =       importlib.import_module(lowercase_  )

		# TODO(QL): always update the module to add subsequent new authentication
		if hasattr(lowercase_						,							"_patched_for_streaming"  ) and module._patched_for_streaming:
				return

		def wrap_auth(lowercase_  ):
				@wraps(lowercase_  )
				def wrapper(*lowercase_						,							**lowercase_  ):
						return function(*lowercase_						,							use_auth_token=lowercase_						,							**lowercase_  )

				_snake_case	       =       "wrap_auth"
				return wrapper

		# open files in a streaming fashion
		patch_submodule(lowercase_						,							"open"						,							wrap_auth(lowercase_  )  ).start()
		patch_submodule(lowercase_						,							"os.listdir"						,							wrap_auth(lowercase_  )  ).start()
		patch_submodule(lowercase_						,							"os.walk"						,							wrap_auth(lowercase_  )  ).start()
		patch_submodule(lowercase_						,							"glob.glob"						,							wrap_auth(lowercase_  )  ).start()
		# allow to navigate in remote zip files
		patch_submodule(lowercase_						,							"os.path.join"						,							lowercase_  ).start()
		patch_submodule(lowercase_						,							"os.path.dirname"						,							lowercase_  ).start()
		patch_submodule(lowercase_						,							"os.path.basename"						,							lowercase_  ).start()
		patch_submodule(lowercase_						,							"os.path.relpath"						,							lowercase_  ).start()
		patch_submodule(lowercase_						,							"os.path.split"						,							lowercase_  ).start()
		patch_submodule(lowercase_						,							"os.path.splitext"						,							lowercase_  ).start()
		# allow checks on paths
		patch_submodule(lowercase_						,							"os.path.exists"						,							wrap_auth(lowercase_  )  ).start()
		patch_submodule(lowercase_						,							"os.path.isdir"						,							wrap_auth(lowercase_  )  ).start()
		patch_submodule(lowercase_						,							"os.path.isfile"						,							wrap_auth(lowercase_  )  ).start()
		patch_submodule(lowercase_						,							"os.path.getsize"						,							wrap_auth(lowercase_  )  ).start()
		patch_submodule(lowercase_						,							"pathlib.Path"						,							lowercase_  ).start()
		# file readers
		patch_submodule(lowercase_						,							"gzip.open"						,							wrap_auth(lowercase_  )  ).start()
		patch_submodule(lowercase_						,							"numpy.load"						,							wrap_auth(lowercase_  )  ).start()
		patch_submodule(lowercase_						,							"pandas.read_csv"						,							wrap_auth(lowercase_  )						,							attrs=["__version__"]  ).start()
		patch_submodule(lowercase_						,							"pandas.read_excel"						,							wrap_auth(lowercase_  )						,							attrs=["__version__"]  ).start()
		patch_submodule(lowercase_						,							"scipy.io.loadmat"						,							wrap_auth(lowercase_  )						,							attrs=["__version__"]  ).start()
		patch_submodule(lowercase_						,							"xml.etree.ElementTree.parse"						,							wrap_auth(lowercase_  )  ).start()
		patch_submodule(lowercase_						,							"xml.dom.minidom.parse"						,							wrap_auth(lowercase_  )  ).start()
		_snake_case	       =       True






def 		_lowercase		(	lowercase_  ):



		'''simple docstring'''







		extend_module_for_streaming(builder.__module__						,							use_auth_token=builder.use_auth_token  )
		# if needed, we also have to extend additional internal imports (like wmt14 -> wmt_utils)
		if not builder.__module__.startswith("datasets."  ):  # check that it's not a packaged builder like csv
				for imports in get_imports(inspect.getfile(builder.__class__  )  ):
						if imports[0] == "internal":
								_snake_case	       =       imports[1]
								_snake_case	       =       ".".join(builder.__module__.split("."  )[:-1] + [internal_import_name]  )
								extend_module_for_streaming(lowercase_						,							use_auth_token=builder.use_auth_token  )

    # builders can inherit from other builders that might use streaming functionality
    # (for example, ImageFolder and AudioFolder inherit from FolderBuilder which implements examples generation)
    # but these parents builders are not patched automatically as they are not instantiated, so we patch them here
		from .builder import DatasetBuilder

		_snake_case	       =       [
		    cls.__module__
		    for cls in type(lowercase_  ).__mro__[1:]  # make sure it's not the same module we've already patched
		    if issubclass(cls						,							lowercase_  ) and cls.__module__ != DatasetBuilder.__module__
		]  # check it's not a standard builder from datasets.builder
		for module in parent_builder_modules:
				extend_module_for_streaming(lowercase_						,							use_auth_token=builder.use_auth_token  )





