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

from ...utils import logging


lowercase_      							=       logging.get_logger(__name__)


def 				a__ (				snake_case    ,       snake_case				):





  """simple docstring"""
  __SCREAMING_SNAKE_CASE	:     Any =						nn.functional.normalize(__UpperCamelCase				)
  __SCREAMING_SNAKE_CASE	:     Optional[int] =						nn.functional.normalize(__UpperCamelCase				)
  return torch.mm(__UpperCamelCase    ,       normalized_text_embeds.t()				)





class   __UpperCamelCase      (				UpperCamelCase_    ):






     """simple docstring"""







     lowerCAmelCase_									=       CLIPConfig

     lowerCAmelCase_									=       ['''CLIPEncoderLayer''']



     def __init__(							self				:				Dict			,		_A				:				CLIPConfig      ):







       """simple docstring"""




       super().__init__(A_      )

       __SCREAMING_SNAKE_CASE	:     Optional[Any] =						CLIPVisionModel(config.vision_config      )
       __SCREAMING_SNAKE_CASE	:     Dict =						nn.Linear(config.vision_config.hidden_size			,		config.projection_dim			,		bias=A_      )

       __SCREAMING_SNAKE_CASE	:     List[Any] =						nn.Parameter(torch.ones(17			,		config.projection_dim      )			,		requires_grad=A_      )
       __SCREAMING_SNAKE_CASE	:     Tuple =						nn.Parameter(torch.ones(3			,		config.projection_dim      )			,		requires_grad=A_      )

       __SCREAMING_SNAKE_CASE	:     int =						nn.Parameter(torch.ones(17      )			,		requires_grad=A_      )
       __SCREAMING_SNAKE_CASE	:     int =						nn.Parameter(torch.ones(3      )			,		requires_grad=A_      )



     @torch.no_grad()
     def       UpperCAmelCase__				(							self				:				int			,		_A				:				int			,		_A				:				int      ):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE	:     int =						self.vision_model(A_      )[1]  # pooled_output
       __SCREAMING_SNAKE_CASE	:     Optional[Any] =						self.visual_projection(A_      )

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

       __SCREAMING_SNAKE_CASE	:     Any =						[]
       __SCREAMING_SNAKE_CASE	:     Optional[Any] =						image_embeds.shape[0]
       for i in range(A_      ):
         __SCREAMING_SNAKE_CASE	:     int =						{'''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
         __SCREAMING_SNAKE_CASE	:     Any =						0.0

         for concept_idx in range(len(special_cos_dist[0]      )      ):
           __SCREAMING_SNAKE_CASE	:     Optional[int] =						special_cos_dist[i][concept_idx]
           __SCREAMING_SNAKE_CASE	:     Optional[int] =						self.special_care_embeds_weights[concept_idx].item()
           __SCREAMING_SNAKE_CASE	:     Dict =						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]}      )
             __SCREAMING_SNAKE_CASE	:     str =						0.01

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

         result.append(A_      )

       __SCREAMING_SNAKE_CASE	:     str =						[len(res['''bad_concepts''']      ) > 0 for res in result]

       return images, has_nsfw_concepts




     @torch.no_grad()
     def       UpperCAmelCase__				(							self				:				str			,		_A				:				torch.FloatTensor			,		_A				:				torch.FloatTensor      ):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE	:     str =						self.vision_model(A_      )[1]  # pooled_output
       __SCREAMING_SNAKE_CASE	:     Optional[int] =						self.visual_projection(A_      )

       __SCREAMING_SNAKE_CASE	:     List[str] =						cosine_distance(A_			,		self.special_care_embeds      )
       __SCREAMING_SNAKE_CASE	:     Union[str, Any] =						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
       __SCREAMING_SNAKE_CASE	:     Any =						0.0

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

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

       return images, has_nsfw_concepts





# Lint as: python3
import os
import re
import urllib.parse
from pathlib import Path
from typing import Callable, List, Optional, Union
from zipfile import ZipFile

from ..utils.file_utils import cached_path, hf_github_url
from ..utils.logging import get_logger
from ..utils.version import Version


A__  :  Tuple								=					get_logger(__name__)



class 				__snake_case						:
							_a			      = '''dummy_data'''
							_a			      = '''datasets'''
							_a			      = False




							def __init__(       self     :       Optional[Any]				,				A_     :       str				,				A_     :       str				,				A_     :       Union[Version, str]				,				A_     :       Optional[str] = None				,				A_     :       bool = False				,				A_     :       bool = True				,				A_     :       Optional[List[Callable]] = None				,				):
									lowerCAmelCase_			:     List[Any]									= 0
									lowerCAmelCase_			:     Any									= dataset_name
									lowerCAmelCase_			:     Union[str, Any]									= cache_dir
									lowerCAmelCase_			:     List[Any]									= use_local_dummy_data
									lowerCAmelCase_			:     Optional[Any]									= config
									# download_callbacks take a single url as input
									lowerCAmelCase_			:     List[Callable]									= download_callbacks or []
									# if False, it doesn't load existing files and it returns the paths of the dummy files relative
									# to the dummy_data zip file root
									lowerCAmelCase_			:     Tuple									= load_existing_dummy_data

									# TODO(PVP, QL) might need to make this more general
									lowerCAmelCase_			:     int									= str(A_)
									# to be downloaded
									lowerCAmelCase_			:     Dict									= None
									lowerCAmelCase_			:     Optional[int]									= None




							@property
							def    UpperCAmelCase__			(       self     :       List[str]):
									if self._dummy_file is None:
											lowerCAmelCase_			:     int									= self.download_dummy_data()
									return self._dummy_file




							@property
							def    UpperCAmelCase__			(       self     :       str):
									if self.config is not None:
											# structure is dummy / config_name / version_name
											return os.path.join('''dummy'''				,				self.config.name				,				self.version_name)
									# structure is dummy / version_name
									return os.path.join('''dummy'''				,				self.version_name)




							@property
							def    UpperCAmelCase__			(       self     :       str):
									return os.path.join(self.dummy_data_folder				,				'''dummy_data.zip''')




							def    UpperCAmelCase__			(       self     :       Any):
									lowerCAmelCase_			:     Any									= (
									    self.local_path_to_dummy_data if self.use_local_dummy_data is True else self.github_path_to_dummy_data
									)

									lowerCAmelCase_			:     Union[str, Any]									= cached_path(
									    A_				,				cache_dir=self.cache_dir				,				extract_compressed_file=A_				,				force_extract=A_)

									return os.path.join(A_				,				self.dummy_file_name)




							@property
							def    UpperCAmelCase__			(       self     :       List[str]):
									return os.path.join(self.datasets_scripts_dir				,				self.dataset_name				,				self.dummy_zip_file)




							@property
							def    UpperCAmelCase__			(       self     :       Optional[int]):
									if self._bucket_url is None:
											lowerCAmelCase_			:     str									= hf_github_url(self.dataset_name				,				self.dummy_zip_file.replace(os.sep				,				'''/'''))
									return self._bucket_url




							@property
							def    UpperCAmelCase__			(       self     :       List[Any]):
									# return full path if its a dir
									if os.path.isdir(self.dummy_file):
											return self.dummy_file
									# else cut off path to file -> example `xsum`.
									return "/".join(self.dummy_file.replace(os.sep				,				'''/''').split('''/''')[:-1])




							def    UpperCAmelCase__			(       self     :       Union[str, Any]				,				A_     :       Dict				,				*A_     :       List[Any]):
									if self.load_existing_dummy_data:
											# dummy data is downloaded and tested
											lowerCAmelCase_			:     Union[str, Any]									= self.dummy_file
									else:
											# dummy data cannot be downloaded and only the path to dummy file is returned
											lowerCAmelCase_			:     Optional[int]									= self.dummy_file_name

									# special case when data_url is a dict
									if isinstance(A_				,				A_):
											return self.create_dummy_data_dict(A_				,				A_)
									elif isinstance(A_				,				(list, tuple)):
											return self.create_dummy_data_list(A_				,				A_)
									else:
											return self.create_dummy_data_single(A_				,				A_)




							def    UpperCAmelCase__			(       self     :       Optional[int]				,				A_     :       Tuple				,				*A_     :       int):
									return self.download_and_extract(A_)




							def    UpperCAmelCase__			(       self     :       Tuple				,				A_     :       List[str]				,				A_     :       Optional[Any]):
									return self.download_and_extract(A_)




							def    UpperCAmelCase__			(       self     :       int				,				A_     :       Optional[int]				,				*A_     :       str				,				**A_     :       List[Any]):
									return path




							def    UpperCAmelCase__			(       self     :       Tuple):
									return {}




							def    UpperCAmelCase__			(       self     :       Optional[Any]				,				A_     :       Union[str, Any]				,				A_     :       List[Any]):
									lowerCAmelCase_			:     Union[str, Any]									= {}
									for key, single_urls in data_url.items():
											for download_callback in self.download_callbacks:
													if isinstance(A_				,				A_):
															for single_url in single_urls:
																	download_callback(A_)
													else:
															lowerCAmelCase_			:     Any									= single_urls
															download_callback(A_)
            # we force the name of each key to be the last file / folder name of the url path
            # if the url has arguments, we need to encode them with urllib.parse.quote_plus
											if isinstance(A_				,				A_):
													lowerCAmelCase_			:     Any									= [os.path.join(A_				,				urllib.parse.quote_plus(Path(A_).name)) for x in single_urls]
											else:
													lowerCAmelCase_			:     Optional[int]									= single_urls
													lowerCAmelCase_			:     List[str]									= os.path.join(A_				,				urllib.parse.quote_plus(Path(A_).name))
											lowerCAmelCase_			:     Dict									= value

									# make sure that values are unique
									if all(isinstance(A_				,				A_) for i in dummy_data_dict.values()) and len(set(dummy_data_dict.values())) < len(
									    dummy_data_dict.values()):
											# append key to value to make its name unique
											lowerCAmelCase_			:     Tuple									= {key: value + key for key, value in dummy_data_dict.items()}

									return dummy_data_dict




							def    UpperCAmelCase__			(       self     :       Dict				,				A_     :       List[str]				,				A_     :       str):
									lowerCAmelCase_			:     Optional[Any]									= []
									# trick: if there are many shards named like `data.txt-000001-of-00300`, only use the first one
									lowerCAmelCase_			:     str									= all(bool(re.findall('''[0-9]{3,}-of-[0-9]{3,}'''				,				A_)) for url in data_url)
									lowerCAmelCase_			:     Optional[Any]									= all(
									    url.startswith('''https://ftp.ncbi.nlm.nih.gov/pubmed/baseline/pubmed''') for url in data_url)
									if data_url and (is_tf_records or is_pubmed_records):
											lowerCAmelCase_			:     Any									= [data_url[0]] * len(A_)
									for single_url in data_url:
											for download_callback in self.download_callbacks:
													download_callback(A_)
											# we force the name of each key to be the last file / folder name of the url path
											# if the url has arguments, we need to encode them with urllib.parse.quote_plus
											lowerCAmelCase_			:     int									= os.path.join(A_				,				urllib.parse.quote_plus(single_url.split('''/''')[-1]))
											dummy_data_list.append(A_)
									return dummy_data_list




							def    UpperCAmelCase__			(       self     :       List[str]				,				A_     :       Optional[Any]				,				A_     :       Tuple):
									for download_callback in self.download_callbacks:
											download_callback(A_)
									# we force the name of each key to be the last file / folder name of the url path
									# if the url has arguments, we need to encode them with urllib.parse.quote_plus
									lowerCAmelCase_			:     Tuple									= os.path.join(A_				,				urllib.parse.quote_plus(data_url.split('''/''')[-1]))
									if os.path.exists(A_) or not self.load_existing_dummy_data:
											return value
									else:
											# Backward compatibility, maybe deprecate at one point.
											# For many datasets with single url calls to dl_manager.download_and_extract,
											# the dummy_data.zip file is actually the zipped downloaded file
											# while now we expected the dummy_data.zip file to be a directory containing
											# the downloaded file.
											return path_to_dummy_data




							def    UpperCAmelCase__			(       self     :       int):
									pass




							def    UpperCAmelCase__			(       self     :       Optional[int]):
									pass




							def    UpperCAmelCase__			(       self     :       List[str]				,				A_     :       str):
									def _iter_archive_members(A_     :       Any):
											# this preserves the order of the members inside the ZIP archive
											lowerCAmelCase_			:     Optional[int]									= Path(self.dummy_file).parent
											lowerCAmelCase_			:     Optional[int]									= path.relative_to(A_)
											with ZipFile(self.local_path_to_dummy_data) as zip_file:
													lowerCAmelCase_			:     Tuple									= zip_file.namelist()
											for member in members:
													if member.startswith(relative_path.as_posix()):
															yield dummy_parent_path.joinpath(A_)

									lowerCAmelCase_			:     List[Any]									= Path(A_)
									lowerCAmelCase_			:     Optional[int]									= _iter_archive_members(A_) if self.use_local_dummy_data else path.rglob('''*''')
									for file_path in file_paths:
											if file_path.is_file() and not file_path.name.startswith(('''.''', '''__''')):
													yield file_path.relative_to(A_).as_posix(), file_path.open('''rb''')







							def    UpperCAmelCase__			(       self     :       Dict				,				A_     :       Any):
									if not isinstance(A_				,				A_):
											lowerCAmelCase_			:     Dict									= [paths]
									for path in paths:
											if os.path.isfile(A_):
													if os.path.basename(A_).startswith(('''.''', '''__''')):
															return
													yield path
											else:
													for dirpath, dirnames, filenames in os.walk(A_):
															if os.path.basename(A_).startswith(('''.''', '''__''')):
																	continue
															dirnames.sort()
															for filename in sorted(A_):
																	if filename.startswith(('''.''', '''__''')):
																			continue
																	yield os.path.join(A_				,				A_)





