



import collections
from typing import List, Optional, Union

from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert import BertTokenizer


lowerCamelCase__	=							logging.get_logger(__name__)

lowerCamelCase__	=							{"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}

lowerCamelCase__	=							{
    "vocab_file": {
        "facebook/dpr-ctx_encoder-single-nq-base": (
            "https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/vocab.txt"
        ),
        "facebook/dpr-ctx_encoder-multiset-base": (
            "https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/vocab.txt"
        ),
    },
    "tokenizer_file": {
        "facebook/dpr-ctx_encoder-single-nq-base": (
            "https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/tokenizer.json"
        ),
        "facebook/dpr-ctx_encoder-multiset-base": (
            "https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/tokenizer.json"
        ),
    },
}
lowerCamelCase__	=							{
    "vocab_file": {
        "facebook/dpr-question_encoder-single-nq-base": (
            "https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/vocab.txt"
        ),
        "facebook/dpr-question_encoder-multiset-base": (
            "https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/vocab.txt"
        ),
    },
    "tokenizer_file": {
        "facebook/dpr-question_encoder-single-nq-base": (
            "https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/tokenizer.json"
        ),
        "facebook/dpr-question_encoder-multiset-base": (
            "https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/tokenizer.json"
        ),
    },
}
lowerCamelCase__	=							{
    "vocab_file": {
        "facebook/dpr-reader-single-nq-base": (
            "https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/vocab.txt"
        ),
        "facebook/dpr-reader-multiset-base": (
            "https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/vocab.txt"
        ),
    },
    "tokenizer_file": {
        "facebook/dpr-reader-single-nq-base": (
            "https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/tokenizer.json"
        ),
        "facebook/dpr-reader-multiset-base": (
            "https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/tokenizer.json"
        ),
    },
}

lowerCamelCase__	=							{
    "facebook/dpr-ctx_encoder-single-nq-base": 512,
    "facebook/dpr-ctx_encoder-multiset-base": 512,
}
lowerCamelCase__	=							{
    "facebook/dpr-question_encoder-single-nq-base": 512,
    "facebook/dpr-question_encoder-multiset-base": 512,
}
lowerCamelCase__	=							{
    "facebook/dpr-reader-single-nq-base": 512,
    "facebook/dpr-reader-multiset-base": 512,
}


lowerCamelCase__	=							{
    "facebook/dpr-ctx_encoder-single-nq-base": {"do_lower_case": True},
    "facebook/dpr-ctx_encoder-multiset-base": {"do_lower_case": True},
}
lowerCamelCase__	=							{
    "facebook/dpr-question_encoder-single-nq-base": {"do_lower_case": True},
    "facebook/dpr-question_encoder-multiset-base": {"do_lower_case": True},
}
lowerCamelCase__	=							{
    "facebook/dpr-reader-single-nq-base": {"do_lower_case": True},
    "facebook/dpr-reader-multiset-base": {"do_lower_case": True},
}







class        lowerCAmelCase__      (   __UpperCAmelCase	):
						UpperCamelCase_							: Any	=		VOCAB_FILES_NAMES
						UpperCamelCase_							: int	=		CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
						UpperCamelCase_							: Dict	=		CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						UpperCamelCase_							: Any	=		CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION







class        lowerCAmelCase__      (   __UpperCAmelCase	):
						UpperCamelCase_							: Optional[int]	=		VOCAB_FILES_NAMES
						UpperCamelCase_							: Any	=		QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
						UpperCamelCase_							: str	=		QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						UpperCamelCase_							: Tuple	=		QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION




lowerCamelCase__	=							collections.namedtuple(
    "DPRSpanPrediction", ["span_score", "relevance_score", "doc_id", "start_index", "end_index", "text"]
)

lowerCamelCase__	=							collections.namedtuple("DPRReaderOutput", ["start_logits", "end_logits", "relevance_logits"])


lowerCamelCase__	=							r"\n    Return a dictionary with the token ids of the input strings and other information to give to `.decode_best_spans`.\n    It converts the strings of a question and different passages (title and text) in a sequence of IDs (integers),\n    using the tokenizer and vocabulary. The resulting `input_ids` is a matrix of size `(n_passages, sequence_length)`\n    with the format:\n\n    ```\n    [CLS] <question token ids> [SEP] <titles ids> [SEP] <texts ids>\n    ```\n\n    Args:\n        questions (`str` or `List[str]`):\n            The questions to be encoded. You can specify one question for many passages. In this case, the question\n            will be duplicated like `[questions] * n_passages`. Otherwise you have to specify as many questions as in\n            `titles` or `texts`.\n        titles (`str` or `List[str]`):\n            The passages titles to be encoded. This can be a string or a list of strings if there are several passages.\n        texts (`str` or `List[str]`):\n            The passages texts to be encoded. This can be a string or a list of strings if there are several passages.\n        padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `False`):\n            Activates and controls padding. Accepts the following values:\n\n            - `True` or `'longest'`: Pad to the longest sequence in the batch (or no padding if only a single sequence\n              if provided).\n            - `'max_length'`: Pad to a maximum length specified with the argument `max_length` or to the maximum\n              acceptable input length for the model if that argument is not provided.\n            - `False` or `'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of different\n              lengths).\n        truncation (`bool`, `str` or [`~tokenization_utils_base.TruncationStrategy`], *optional*, defaults to `False`):\n            Activates and controls truncation. Accepts the following values:\n\n            - `True` or `'longest_first'`: Truncate to a maximum length specified with the argument `max_length` or to\n              the maximum acceptable input length for the model if that argument is not provided. This will truncate\n              token by token, removing a token from the longest sequence in the pair if a pair of sequences (or a batch\n              of pairs) is provided.\n            - `'only_first'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum\n              acceptable input length for the model if that argument is not provided. This will only truncate the first\n              sequence of a pair if a pair of sequences (or a batch of pairs) is provided.\n            - `'only_second'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum\n              acceptable input length for the model if that argument is not provided. This will only truncate the\n              second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.\n            - `False` or `'do_not_truncate'` (default): No truncation (i.e., can output batch with sequence lengths\n              greater than the model maximum admissible input size).\n        max_length (`int`, *optional*):\n                Controls the maximum length to use by one of the truncation/padding parameters.\n\n                If left unset or set to `None`, this will use the predefined model maximum length if a maximum length\n                is required by one of the truncation/padding parameters. If the model has no specific maximum input\n                length (like XLNet) truncation/padding to a maximum length will be deactivated.\n        return_tensors (`str` or [`~utils.TensorType`], *optional*):\n                If set, will return tensors instead of list of python integers. Acceptable values are:\n\n                - `'tf'`: Return TensorFlow `tf.constant` objects.\n                - `'pt'`: Return PyTorch `torch.Tensor` objects.\n                - `'np'`: Return Numpy `np.ndarray` objects.\n        return_attention_mask (`bool`, *optional*):\n            Whether or not to return the attention mask. If not set, will return the attention mask according to the\n            specific tokenizer's default, defined by the `return_outputs` attribute.\n\n            [What are attention masks?](../glossary#attention-mask)\n\n    Returns:\n        `Dict[str, List[List[int]]]`: A dictionary with the following keys:\n\n        - `input_ids`: List of token ids to be fed to a model.\n        - `attention_mask`: List of indices specifying which tokens should be attended to by the model.\n    "







@add_start_docstrings(__UpperCAmelCase	)
class        lowerCAmelCase__      :

						def __call__(			self       ,							a       ,							a = None       ,							a = None       ,							a = False       ,							a = False       ,							a = None       ,							a = None       ,							a = None       ,							**a       ,							)					->      Union[str, Any]:


								'''simple docstring'''

								if titles is None and texts is None:
										return super().__call__(
										    lowerCAmelCase_       ,							padding=lowerCAmelCase_       ,							truncation=lowerCAmelCase_       ,							max_length=lowerCAmelCase_       ,							return_tensors=lowerCAmelCase_       ,							return_attention_mask=lowerCAmelCase_       ,							**lowerCAmelCase_       ,							)
								elif titles is None or texts is None:
										_UpperCamelCase						    =	titles if texts is None else texts
										return super().__call__(
										    lowerCAmelCase_       ,							lowerCAmelCase_       ,							padding=lowerCAmelCase_       ,							truncation=lowerCAmelCase_       ,							max_length=lowerCAmelCase_       ,							return_tensors=lowerCAmelCase_       ,							return_attention_mask=lowerCAmelCase_       ,							**lowerCAmelCase_       ,							)
								_UpperCamelCase						    =	titles if not isinstance(lowerCAmelCase_       ,							lowerCAmelCase_				) else [titles]
								_UpperCamelCase						    =	texts if not isinstance(lowerCAmelCase_       ,							lowerCAmelCase_				) else [texts]
								_UpperCamelCase						    =	len(lowerCAmelCase_				)
								_UpperCamelCase						    =	questions if not isinstance(lowerCAmelCase_       ,							lowerCAmelCase_				) else [questions] * n_passages
								if len(lowerCAmelCase_				) != len(lowerCAmelCase_				):
										raise ValueError(
										    F'There should be as many titles than texts but got {len(lowerCAmelCase_				)} titles and {len(lowerCAmelCase_				)} texts.'				)
								_UpperCamelCase						    =	super().__call__(lowerCAmelCase_       ,							lowerCAmelCase_       ,							padding=lowerCAmelCase_       ,							truncation=lowerCAmelCase_				)["""input_ids"""]
								_UpperCamelCase						    =	super().__call__(lowerCAmelCase_       ,							add_special_tokens=lowerCAmelCase_       ,							padding=lowerCAmelCase_       ,							truncation=lowerCAmelCase_				)["""input_ids"""]
								_UpperCamelCase						    =	{
								    """input_ids""": [
								        (encoded_question_and_title + encoded_text)[:max_length]
								        if max_length is not None and truncation
								        else encoded_question_and_title + encoded_text
								        for encoded_question_and_title, encoded_text in zip(lowerCAmelCase_       ,							lowerCAmelCase_				)
								    ]
								}
								if return_attention_mask is not False:
										_UpperCamelCase						    =	[]
										for input_ids in encoded_inputs["input_ids"]:
												attention_mask.append([int(input_id != self.pad_token_id				) for input_id in input_ids]				)
										_UpperCamelCase						    =	attention_mask
								return self.pad(lowerCAmelCase_       ,							padding=lowerCAmelCase_       ,							max_length=lowerCAmelCase_       ,							return_tensors=lowerCAmelCase_				)

						def  A_				(			self       ,							a       ,							a       ,							a = 16       ,							a = 64       ,							a = 4       ,							)					->      Tuple:


								'''simple docstring'''

								_UpperCamelCase						    =	reader_input["""input_ids"""]
								_UpperCamelCase    ,	_UpperCamelCase    ,	_UpperCamelCase						    =	reader_output[:3]
								_UpperCamelCase						    =	len(lowerCAmelCase_				)
								_UpperCamelCase						    =	sorted(range(lowerCAmelCase_				)       ,							reverse=lowerCAmelCase_       ,							key=relevance_logits.__getitem__				)
								_UpperCamelCase						    =	[]
								for doc_id in sorted_docs:
										_UpperCamelCase						    =	list(input_ids[doc_id]				)
										# assuming question & title information is at the beginning of the sequence
										_UpperCamelCase						    =	sequence_ids.index(self.sep_token_id       ,							2				) + 1  # second sep id
										if sequence_ids[-1] == self.pad_token_id:
												_UpperCamelCase						    =	sequence_ids.index(self.pad_token_id				)
										else:
												_UpperCamelCase						    =	len(lowerCAmelCase_				)

										_UpperCamelCase						    =	self._get_best_spans(
										    start_logits=start_logits[doc_id][passage_offset:sequence_len]       ,							end_logits=end_logits[doc_id][passage_offset:sequence_len]       ,							max_answer_length=lowerCAmelCase_       ,							top_spans=lowerCAmelCase_       ,							)
										for start_index, end_index in best_spans:
												start_index += passage_offset
												end_index += passage_offset
												nbest_spans_predictions.append(
												    DPRSpanPrediction(
												        span_score=start_logits[doc_id][start_index] + end_logits[doc_id][end_index]       ,							relevance_score=relevance_logits[doc_id]       ,							doc_id=lowerCAmelCase_       ,							start_index=lowerCAmelCase_       ,							end_index=lowerCAmelCase_       ,							text=self.decode(sequence_ids[start_index : end_index + 1]				)       ,							)				)
										if len(lowerCAmelCase_				) >= num_spans:
												break
								return nbest_spans_predictions[:num_spans]







						def  A_				(			self       ,							a       ,							a       ,							a       ,							a       ,							)					->      Tuple:


								'''simple docstring'''

								_UpperCamelCase						    =	[]
								for start_index, start_score in enumerate(lowerCAmelCase_				):
										for answer_length, end_score in enumerate(end_logits[start_index : start_index + max_answer_length]				):
												scores.append(((start_index, start_index + answer_length), start_score + end_score)				)
								_UpperCamelCase						    =	sorted(lowerCAmelCase_       ,							key=lambda a				: x[1]       ,							reverse=lowerCAmelCase_				)
								_UpperCamelCase						    =	[]
								for (start_index, end_index), score in scores:
										if start_index > end_index:
												raise ValueError(F'Wrong span indices: [{start_index}:{end_index}]'				)
										_UpperCamelCase						    =	end_index - start_index + 1
										if length > max_answer_length:
												raise ValueError(F'Span is too long: {length} > {max_answer_length}'				)
										if any(
										    start_index <= prev_start_index <= prev_end_index <= end_index
										    or prev_start_index <= start_index <= end_index <= prev_end_index
										    for (prev_start_index, prev_end_index) in chosen_span_intervals				):
												continue
										chosen_span_intervals.append((start_index, end_index)				)

										if len(lowerCAmelCase_				) == top_spans:
												break
								return chosen_span_intervals







@add_end_docstrings(__UpperCAmelCase	)
class        lowerCAmelCase__      (   __UpperCAmelCase		,	__UpperCAmelCase	):
						UpperCamelCase_							: Tuple	=		VOCAB_FILES_NAMES
						UpperCamelCase_							: str	=		READER_PRETRAINED_VOCAB_FILES_MAP
						UpperCamelCase_							: List[Any]	=		READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						UpperCamelCase_							: Optional[int]	=		READER_PRETRAINED_INIT_CONFIGURATION
						UpperCamelCase_							: Optional[int]	=		["input_ids", "attention_mask"]





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

from ...utils import logging


lowerCamelCase__	=							logging.get_logger(__name__)


def 							__A(lowerCAmelCase						,  lowerCAmelCase )		->				List[str]:

		"""simple docstring"""






		_UpperCamelCase						    =	nn.functional.normalize(lowerCAmelCase )
		_UpperCamelCase						    =	nn.functional.normalize(lowerCAmelCase )
		return torch.mm(lowerCAmelCase						,  normalized_text_embeds.t() )







class        lowerCAmelCase__      (   __lowercase	):
						UpperCamelCase_							: Tuple	=		CLIPConfig

						UpperCamelCase_							: str	=		["CLIPEncoderLayer"]

						def __init__(			self       ,							a				)					->      Optional[Any]:


								'''simple docstring'''

								super().__init__(a				)

								_UpperCamelCase						    =	CLIPVisionModel(config.vision_config				)
								_UpperCamelCase						    =	nn.Linear(config.vision_config.hidden_size       ,							config.projection_dim       ,							bias=a				)

								_UpperCamelCase						    =	nn.Parameter(torch.ones(17       ,							config.projection_dim				)       ,							requires_grad=a				)
								_UpperCamelCase						    =	nn.Parameter(torch.ones(3       ,							config.projection_dim				)       ,							requires_grad=a				)

								_UpperCamelCase						    =	nn.Parameter(torch.ones(17				)       ,							requires_grad=a				)
								_UpperCamelCase						    =	nn.Parameter(torch.ones(3				)       ,							requires_grad=a				)

						@torch.no_grad()
						def  A_				(			self       ,							a       ,							a				)					->      str:


								'''simple docstring'''

								_UpperCamelCase						    =	self.vision_model(a				)[1]  # pooled_output
								_UpperCamelCase						    =	self.visual_projection(a				)

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

								_UpperCamelCase						    =	[]
								_UpperCamelCase						    =	image_embeds.shape[0]
								for i in range(a				):
										_UpperCamelCase						    =	{"""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
										_UpperCamelCase						    =	0.0

										for concept_idx in range(len(special_cos_dist[0]				)				):
												_UpperCamelCase						    =	special_cos_dist[i][concept_idx]
												_UpperCamelCase						    =	self.special_care_embeds_weights[concept_idx].item()
												_UpperCamelCase						    =	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]}				)
														_UpperCamelCase						    =	0.01

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

										result.append(a				)

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

								return images, has_nsfw_concepts







						@torch.no_grad()
						def  A_				(			self       ,							a       ,							a				)					->      Any:


								'''simple docstring'''

								_UpperCamelCase						    =	self.vision_model(a				)[1]  # pooled_output
								_UpperCamelCase						    =	self.visual_projection(a				)

								_UpperCamelCase						    =	cosine_distance(a       ,							self.special_care_embeds				)
								_UpperCamelCase						    =	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
								_UpperCamelCase						    =	0.0

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

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

								return images, has_nsfw_concepts



