

'''simple docstring'''






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_fast import BertTokenizerFast
from .tokenization_dpr import DPRContextEncoderTokenizer, DPRQuestionEncoderTokenizer, DPRReaderTokenizer


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     lowercase_			(lowerCamelCase__   ):




  """simple docstring"""




  SCREAMING_SNAKE_CASE					:						Dict           =    VOCAB_FILES_NAMES
  SCREAMING_SNAKE_CASE					:						Dict           =    CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
  SCREAMING_SNAKE_CASE					:						Union[str, Any]           =    CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  SCREAMING_SNAKE_CASE					:						List[Any]           =    CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION
  SCREAMING_SNAKE_CASE					:						str           =    DPRContextEncoderTokenizer






class     lowercase_			(lowerCamelCase__   ):




  """simple docstring"""




  SCREAMING_SNAKE_CASE					:						Optional[Any]           =    VOCAB_FILES_NAMES
  SCREAMING_SNAKE_CASE					:						int           =    QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
  SCREAMING_SNAKE_CASE					:						Optional[int]           =    QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  SCREAMING_SNAKE_CASE					:						Tuple           =    QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION
  SCREAMING_SNAKE_CASE					:						Any           =    DPRQuestionEncoderTokenizer
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'''
    Return a dictionary with the token ids of the input strings and other information to give to `.decode_best_spans`.
    It converts the strings of a question and different passages (title and text) in a sequence of IDs (integers),
    using the tokenizer and vocabulary. The resulting `input_ids` is a matrix of size `(n_passages, sequence_length)`
    with the format:

    [CLS] <question token ids> [SEP] <titles ids> [SEP] <texts ids>

    Args:
        questions (`str` or `List[str]`):
            The questions to be encoded. You can specify one question for many passages. In this case, the question
            will be duplicated like `[questions] * n_passages`. Otherwise you have to specify as many questions as in
            `titles` or `texts`.
        titles (`str` or `List[str]`):
            The passages titles to be encoded. This can be a string or a list of strings if there are several passages.
        texts (`str` or `List[str]`):
            The passages texts to be encoded. This can be a string or a list of strings if there are several passages.
        padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `False`):
            Activates and controls padding. Accepts the following values:

            - `True` or `\'longest\'`: Pad to the longest sequence in the batch (or no padding if only a single sequence
              if provided).
            - `\'max_length\'`: Pad to a maximum length specified with the argument `max_length` or to the maximum
              acceptable input length for the model if that argument is not provided.
            - `False` or `\'do_not_pad\'` (default): No padding (i.e., can output a batch with sequences of different
              lengths).
        truncation (`bool`, `str` or [`~tokenization_utils_base.TruncationStrategy`], *optional*, defaults to `False`):
            Activates and controls truncation. Accepts the following values:

            - `True` or `\'longest_first\'`: Truncate to a maximum length specified with the argument `max_length` or to
              the maximum acceptable input length for the model if that argument is not provided. This will truncate
              token by token, removing a token from the longest sequence in the pair if a pair of sequences (or a batch
              of pairs) is provided.
            - `\'only_first\'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum
              acceptable input length for the model if that argument is not provided. This will only truncate the first
              sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
            - `\'only_second\'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum
              acceptable input length for the model if that argument is not provided. This will only truncate the
              second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
            - `False` or `\'do_not_truncate\'` (default): No truncation (i.e., can output batch with sequence lengths
              greater than the model maximum admissible input size).
        max_length (`int`, *optional*):
                Controls the maximum length to use by one of the truncation/padding parameters.

                If left unset or set to `None`, this will use the predefined model maximum length if a maximum length
                is required by one of the truncation/padding parameters. If the model has no specific maximum input
                length (like XLNet) truncation/padding to a maximum length will be deactivated.
        return_tensors (`str` or [`~utils.TensorType`], *optional*):
                If set, will return tensors instead of list of python integers. Acceptable values are:

                - `\'tf\'`: Return TensorFlow `tf.constant` objects.
                - `\'pt\'`: Return PyTorch `torch.Tensor` objects.
                - `\'np\'`: Return Numpy `np.ndarray` objects.
        return_attention_mask (`bool`, *optional*):
            Whether or not to return the attention mask. If not set, will return the attention mask according to the
            specific tokenizer\'s default, defined by the `return_outputs` attribute.

            [What are attention masks?](../glossary#attention-mask)

    Return:
        `Dict[str, List[List[int]]]`: A dictionary with the following keys:

        - `input_ids`: List of token ids to be fed to a model.
        - `attention_mask`: List of indices specifying which tokens should be attended to by the model.
    '''






@add_start_docstrings(lowerCamelCase__   )
class     lowercase_			:




  """simple docstring"""




  def __call__(   self     :  Union[str, Any]      ,lowercase__     :  Optional[Any]      ,lowercase__     :  Optional[str] = None      ,lowercase__     :  Optional[str] = None      ,lowercase__     :  Union[bool, str] = False      ,lowercase__     :  Union[bool, str] = False      ,lowercase__     :  Optional[int] = None      ,lowercase__     :  Optional[Union[str, TensorType]] = None      ,lowercase__     :  Optional[bool] = None      ,**lowercase__     :  str      ,):
   if titles is None and texts is None:
    return super().__call__(
        lowercase__      ,padding=lowercase__      ,truncation=lowercase__      ,max_length=lowercase__      ,return_tensors=lowercase__      ,return_attention_mask=lowercase__      ,**lowercase__      ,)
   elif titles is None or texts is None:
    __lowercase													=				titles if texts is None else texts
    return super().__call__(
        lowercase__      ,lowercase__      ,padding=lowercase__      ,truncation=lowercase__      ,max_length=lowercase__      ,return_tensors=lowercase__      ,return_attention_mask=lowercase__      ,**lowercase__      ,)
   __lowercase													=				titles if not isinstance(lowercase__      ,lowercase__    ) else [titles]
   __lowercase													=				texts if not isinstance(lowercase__      ,lowercase__    ) else [texts]
   __lowercase													=				len(lowercase__    )
   __lowercase													=				questions if not isinstance(lowercase__      ,lowercase__    ) else [questions] * n_passages
   assert len(lowercase__    ) == len(
       lowercase__    ), F"There should be as many titles than texts but got {len(lowercase__    )} titles and {len(lowercase__    )} texts."
   __lowercase													=				super().__call__(lowercase__      ,lowercase__      ,padding=lowercase__      ,truncation=lowercase__    )['''input_ids''']
   __lowercase													=				super().__call__(lowercase__      ,add_special_tokens=lowercase__      ,padding=lowercase__      ,truncation=lowercase__    )['''input_ids''']
   __lowercase													=				{
       '''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(lowercase__      ,lowercase__    )
       ]
   }
   if return_attention_mask is not False:
    __lowercase													=				[]
    for input_ids in encoded_inputs["input_ids"]:
     attention_mask.append([int(input_id != self.pad_token_id    ) for input_id in input_ids]    )
    __lowercase													=				attention_mask
   return self.pad(lowercase__      ,padding=lowercase__      ,max_length=lowercase__      ,return_tensors=lowercase__    )






  def       SCREAMING_SNAKE_CASE   (   self     :  Dict      ,lowercase__     :  BatchEncoding      ,lowercase__     :  DPRReaderOutput      ,lowercase__     :  int = 1_6      ,lowercase__     :  int = 6_4      ,lowercase__     :  int = 4      ,):
   __lowercase													=				reader_input['''input_ids''']
   __lowercase						,			__lowercase						,			__lowercase													=				reader_output[:3]
   __lowercase													=				len(lowercase__    )
   __lowercase													=				sorted(range(lowercase__    )      ,reverse=lowercase__      ,key=relevance_logits.__getitem__    )
   __lowercase													=				[]
   for doc_id in sorted_docs:
    __lowercase													=				list(input_ids[doc_id]    )
    # assuming question & title information is at the beginning of the sequence
    __lowercase													=				sequence_ids.index(self.sep_token_id      ,2    ) + 1  # second sep id
    if sequence_ids[-1] == self.pad_token_id:
     __lowercase													=				sequence_ids.index(self.pad_token_id    )
    else:
     __lowercase													=				len(lowercase__    )

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






  def       SCREAMING_SNAKE_CASE   (   self     :  Union[str, Any]      ,lowercase__     :  List[int]      ,lowercase__     :  List[int]      ,lowercase__     :  int      ,lowercase__     :  int      ,):
   __lowercase													=				[]
   for start_index, start_score in enumerate(lowercase__    ):
    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)    )
   __lowercase													=				sorted(lowercase__      ,key=lambda lowercase__    : x[1]      ,reverse=lowercase__    )
   __lowercase													=				[]
   for (start_index, end_index), score in scores:
    assert start_index <= end_index, F"Wrong span indices: [{start_index}:{end_index}]"
    __lowercase													=				end_index - start_index + 1
    assert length <= max_answer_length, 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(lowercase__    ) == top_spans:
     break
   return chosen_span_intervals






@add_end_docstrings(lowerCamelCase__   )
class     lowercase_			(lowerCamelCase__       , lowerCamelCase__   ):




  """simple docstring"""




  SCREAMING_SNAKE_CASE					:						Tuple           =    VOCAB_FILES_NAMES
  SCREAMING_SNAKE_CASE					:						Optional[Any]           =    READER_PRETRAINED_VOCAB_FILES_MAP
  SCREAMING_SNAKE_CASE					:						Union[str, Any]           =    READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  SCREAMING_SNAKE_CASE					:						Any           =    READER_PRETRAINED_INIT_CONFIGURATION
  SCREAMING_SNAKE_CASE					:						Tuple           =    ['input_ids', 'attention_mask']
  SCREAMING_SNAKE_CASE					:						Dict           =    DPRReaderTokenizer




'''simple docstring'''






import glob
import os
import random
from string import ascii_lowercase, digits

import cva
import numpy as np

# Parrameters
lowerCAmelCase__      		=			(720, 1280)  # Height, Width
lowerCAmelCase__      		=			(0.4, 0.6)  # if height or width lower than this scale, drop it.
lowerCAmelCase__      		=			1 / 100
lowerCAmelCase__      		=			''''''
lowerCAmelCase__      		=			''''''
lowerCAmelCase__      		=			''''''
lowerCAmelCase__      		=			250


def 	_A  (					):






 """simple docstring"""



 __lowercase						,			__lowercase													=				get_dataset(A__  ,       A__   )
 for index in range(A__   ):
  __lowercase													=				random.sample(range(len(A__   )   )  ,       4   )
  __lowercase						,			__lowercase						,			__lowercase													=				update_image_and_anno(
      A__  ,       A__  ,       A__  ,       A__  ,       A__  ,       filter_scale=A__  ,       )

  # Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
  __lowercase													=				random_chars(32   )
  __lowercase													=				path.split(os.sep   )[-1].rsplit('''.'''  ,       1   )[0]
  __lowercase													=				F"{OUTPUT_DIR}/{file_name}_MOSAIC_{letter_code}"
  cva.imwrite(F"{file_root}.jpg"  ,       A__  ,       [cva.IMWRITE_JPEG_QUALITY, 85]   )
  print(F"Succeeded {index+1}/{NUMBER_IMAGES} with {file_name}"   )
  __lowercase													=				[]
  for anno in new_annos:
   __lowercase													=				anno[3] - anno[1]
   __lowercase													=				anno[4] - anno[2]
   __lowercase													=				anno[1] + width / 2
   __lowercase													=				anno[2] + height / 2
   __lowercase													=				F"{anno[0]} {x_center} {y_center} {width} {height}"
   annos_list.append(A__   )
  with open(F"{file_root}.txt"  ,       '''w'''   ) as outfile:
   outfile.write('''\n'''.join(line for line in annos_list   )   )


def 	_A  (					A__  ,       A__   ):






 """simple docstring"""



 __lowercase													=				[]
 __lowercase													=				[]
 for label_file in glob.glob(os.path.join(A__  ,       '''*.txt'''   )   ):
  __lowercase													=				label_file.split(os.sep   )[-1].rsplit('''.'''  ,       1   )[0]
  with open(A__   ) as in_file:
   __lowercase													=				in_file.readlines()
  __lowercase													=				os.path.join(A__  ,       F"{label_name}.jpg"   )

  __lowercase													=				[]
  for obj_list in obj_lists:
   __lowercase													=				obj_list.rstrip('''\n'''   ).split(''' '''   )
   __lowercase													=				float(obj[1]   ) - float(obj[3]   ) / 2
   __lowercase													=				float(obj[2]   ) - float(obj[4]   ) / 2
   __lowercase													=				float(obj[1]   ) + float(obj[3]   ) / 2
   __lowercase													=				float(obj[2]   ) + float(obj[4]   ) / 2

   boxes.append([int(obj[0]   ), xmin, ymin, xmax, ymax]   )
  if not boxes:
   continue
  img_paths.append(A__   )
  labels.append(A__   )
 return img_paths, labels


def 	_A  (					A__  ,       A__  ,       A__  ,       A__  ,       A__  ,       A__ = 0.0  ,       ):






 """simple docstring"""



 __lowercase													=				np.zeros([output_size[0], output_size[1], 3]  ,       dtype=np.uinta   )
 __lowercase													=				scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
 __lowercase													=				scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
 __lowercase													=				int(scale_x * output_size[1]   )
 __lowercase													=				int(scale_y * output_size[0]   )

 __lowercase													=				[]
 __lowercase													=				[]
 for i, index in enumerate(A__   ):
  __lowercase													=				all_img_list[index]
  path_list.append(A__   )
  __lowercase													=				all_annos[index]
  __lowercase													=				cva.imread(A__   )
  if i == 0:  # top-left
   __lowercase													=				cva.resize(A__  ,       (divid_point_x, divid_point_y)   )
   __lowercase													=				img
   for bbox in img_annos:
    __lowercase													=				bbox[1] * scale_x
    __lowercase													=				bbox[2] * scale_y
    __lowercase													=				bbox[3] * scale_x
    __lowercase													=				bbox[4] * scale_y
    new_anno.append([bbox[0], xmin, ymin, xmax, ymax]   )
  elif i == 1:  # top-right
   __lowercase													=				cva.resize(A__  ,       (output_size[1] - divid_point_x, divid_point_y)   )
   __lowercase													=				img
   for bbox in img_annos:
    __lowercase													=				scale_x + bbox[1] * (1 - scale_x)
    __lowercase													=				bbox[2] * scale_y
    __lowercase													=				scale_x + bbox[3] * (1 - scale_x)
    __lowercase													=				bbox[4] * scale_y
    new_anno.append([bbox[0], xmin, ymin, xmax, ymax]   )
  elif i == 2:  # bottom-left
   __lowercase													=				cva.resize(A__  ,       (divid_point_x, output_size[0] - divid_point_y)   )
   __lowercase													=				img
   for bbox in img_annos:
    __lowercase													=				bbox[1] * scale_x
    __lowercase													=				scale_y + bbox[2] * (1 - scale_y)
    __lowercase													=				bbox[3] * scale_x
    __lowercase													=				scale_y + bbox[4] * (1 - scale_y)
    new_anno.append([bbox[0], xmin, ymin, xmax, ymax]   )
  else:  # bottom-right
   __lowercase													=				cva.resize(
       A__  ,       (output_size[1] - divid_point_x, output_size[0] - divid_point_y)   )
   __lowercase													=				img
   for bbox in img_annos:
    __lowercase													=				scale_x + bbox[1] * (1 - scale_x)
    __lowercase													=				scale_y + bbox[2] * (1 - scale_y)
    __lowercase													=				scale_x + bbox[3] * (1 - scale_x)
    __lowercase													=				scale_y + bbox[4] * (1 - scale_y)
    new_anno.append([bbox[0], xmin, ymin, xmax, ymax]   )

    # Remove bounding box small than scale of filter
 if filter_scale > 0:
  __lowercase													=				[
      anno
      for anno in new_anno
      if filter_scale < (anno[3] - anno[1]) and filter_scale < (anno[4] - anno[2])
  ]

 return output_img, new_anno, path_list[0]


def 	_A  (					A__   ):






 """simple docstring"""



 assert number_char > 1, "The number of character should greater than 1"
 __lowercase													=				ascii_lowercase + digits
 return "".join(random.choice(A__   ) for _ in range(A__   )   )


if __name__ == "__main__":
 main()
 print('''DONE ✅''')

