

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


A											=     logging.get_logger(__name__)

A											=     {'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}

A											=     {
    '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'
        ),
    },
}
A											=     {
    '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'
        ),
    },
}
A											=     {
    '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'
        ),
    },
}

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


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


class SCREAMING_SNAKE_CASE  (		__snake_case							):






			"""simple docstring"""




			__A						  =  VOCAB_FILES_NAMES
			__A						  =  CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
			__A						  =  CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			__A						  =  CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION







class SCREAMING_SNAKE_CASE  (		__snake_case							):






			"""simple docstring"""




			__A						  =  VOCAB_FILES_NAMES
			__A						  =  QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
			__A						  =  QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			__A						  =  QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION







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

A											=     collections.namedtuple('DPRReaderOutput', ['start_logits', 'end_logits', 'relevance_logits'])


A											=     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(__snake_case							)
class SCREAMING_SNAKE_CASE  :






			"""simple docstring"""




			def __call__(    self ,				__UpperCamelCase ,				__UpperCamelCase = None ,				__UpperCamelCase = None ,				__UpperCamelCase = False ,				__UpperCamelCase = False ,				__UpperCamelCase = None ,				__UpperCamelCase = None ,				__UpperCamelCase = None ,				**__UpperCamelCase ,				):

				"""simple docstring"""


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


			def 			__lowerCAmelCase (    self ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase = 16 ,				__UpperCamelCase = 64 ,				__UpperCamelCase = 4 ,				):

				"""simple docstring"""


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

					snake_case_      		= 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=__UpperCamelCase ,				top_spans=__UpperCamelCase ,				)
					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=__UpperCamelCase ,				start_index=__UpperCamelCase ,				end_index=__UpperCamelCase ,				text=self.decode(sequence_ids[start_index : end_index + 1]							) ,				)							)
					if len(__UpperCamelCase							) >= num_spans:
						break
				return nbest_spans_predictions[:num_spans]







			def 			__lowerCAmelCase (    self ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase ,				):

				"""simple docstring"""


				snake_case_      		= []
				for start_index, start_score in enumerate(__UpperCamelCase							):
					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)							)
				snake_case_      		= sorted(__UpperCamelCase ,				key=lambda __UpperCamelCase							: x[1] ,				reverse=__UpperCamelCase							)
				snake_case_      		= []
				for (start_index, end_index), score in scores:
					if start_index > end_index:
						raise ValueError(f"""Wrong span indices: [{start_index}:{end_index}]"""							)
					snake_case_      		= 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(__UpperCamelCase							) == top_spans:
						break
				return chosen_span_intervals







@add_end_docstrings(__snake_case							)
class SCREAMING_SNAKE_CASE  (		__snake_case							,						__snake_case							):






			"""simple docstring"""




			__A						  =  VOCAB_FILES_NAMES
			__A						  =  READER_PRETRAINED_VOCAB_FILES_MAP
			__A						  =  READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			__A						  =  READER_PRETRAINED_INIT_CONFIGURATION
			__A						  =  ["""input_ids""", """attention_mask"""]




import argparse
import os
from io import BytesIO
from pathlib import Path

import requests
from clip_retrieval.clip_client import ClipClient
from PIL import Image
from tqdm import tqdm





def a(lowercase__			, lowercase__			, lowercase__	):





	'''simple docstring'''
	snake_case_      		= 1.5
	snake_case_      		= int(factor * num_class_images	)
	snake_case_      		= ClipClient(
	    url='https://knn.laion.ai/knn-service'			, indice_name='laion_400m'			, num_images=lowercase__			, aesthetic_weight=0.1	)

	os.makedirs(f"""{class_data_dir}/images"""			, exist_ok=lowercase__	)
	if len(list(Path(f"""{class_data_dir}/images"""	).iterdir()	)	) >= num_class_images:
		return

	while True:
		snake_case_      		= client.query(text=lowercase__	)
		if len(lowercase__	) >= factor * num_class_images or num_images > 1e4:
			break
		else:
			snake_case_      		= int(factor * num_images	)
			snake_case_      		= ClipClient(
			    url='https://knn.laion.ai/knn-service'			, indice_name='laion_400m'			, num_images=lowercase__			, aesthetic_weight=0.1			, )

	snake_case_      		= 0
	snake_case_      		= 0
	snake_case_      		= tqdm(desc='downloading real regularization images'			, total=lowercase__	)

	with open(f"""{class_data_dir}/caption.txt"""			, 'w'	) as fa, open(f"""{class_data_dir}/urls.txt"""			, 'w'	) as fa, open(
	    f"""{class_data_dir}/images.txt"""			, 'w'	) as fa:
		while total < num_class_images:
			snake_case_      		= class_images[count]
			count += 1
			try:
				snake_case_      		= requests.get(images['url']	)
				if img.status_code == 200:
					snake_case_      		= Image.open(BytesIO(img.content	)	)
					with open(f"""{class_data_dir}/images/{total}.jpg"""			, 'wb'	) as f:
						f.write(img.content	)
					fa.write(images['caption'] + '\n'	)
					fa.write(images['url'] + '\n'	)
					fa.write(f"""{class_data_dir}/images/{total}.jpg""" + '\n'	)
					total += 1
					pbar.update(1	)
				else:
					continue
			except Exception:
				continue
	return





def a():





	'''simple docstring'''
	snake_case_      		= argparse.ArgumentParser(''			, add_help=lowercase__	)
	parser.add_argument('--class_prompt'			, help='text prompt to retrieve images'			, required=lowercase__			, type=lowercase__	)
	parser.add_argument('--class_data_dir'			, help='path to save images'			, required=lowercase__			, type=lowercase__	)
	parser.add_argument('--num_class_images'			, help='number of images to download'			, default=200			, type=lowercase__	)
	return parser.parse_args()


if __name__ == "__main__":
		A											=     parse_args()
		retrieve(args.class_prompt, args.class_data_dir, args.num_class_images)
