"""simple docstring"""
import argparse

import requests
import torch

# pip3 install salesforce-lavis
# I'm actually installing a slightly modified version: pip3 install git+https://github.com/nielsrogge/LAVIS.git@fix_lavis
from lavis.models import load_model_and_preprocess
from PIL import Image

from transformers import (
    AutoTokenizer,
    BlipaConfig,
    BlipaForConditionalGeneration,
    BlipaProcessor,
    BlipaVisionConfig,
    BlipImageProcessor,
    OPTConfig,
    TaConfig,
)
from transformers.utils.constants import OPENAI_CLIP_MEAN, OPENAI_CLIP_STD







def 						__a       (     )     ->Dict:
  a__:					int      							=     'https://storage.googleapis.com/sfr-vision-language-research/LAVIS/assets/merlion.png'
  a__:					Any      							=     Image.open(requests.get(_SCREAMING_SNAKE_CASE		,					stream=_SCREAMING_SNAKE_CASE       ).raw       ).convert('RGB'       )

  return image







def 						__a       (     _SCREAMING_SNAKE_CASE       )     ->List[str]:
  a__:					List[Any]      							=     []
  # fmt: off

  # vision encoder
  rename_keys.append(('visual_encoder.cls_token', 'vision_model.embeddings.class_embedding')       )
  rename_keys.append(('visual_encoder.pos_embed', 'vision_model.embeddings.position_embedding')       )
  rename_keys.append(('visual_encoder.patch_embed.proj.weight', 'vision_model.embeddings.patch_embedding.weight')       )
  rename_keys.append(('visual_encoder.patch_embed.proj.bias', 'vision_model.embeddings.patch_embedding.bias')       )
  rename_keys.append(('ln_vision.weight', 'vision_model.post_layernorm.weight')       )
  rename_keys.append(('ln_vision.bias', 'vision_model.post_layernorm.bias')       )

  for i in range(config.vision_config.num_hidden_layers       ):
    rename_keys.append((F'visual_encoder.blocks.{i}.norm1.weight', F'vision_model.encoder.layers.{i}.layer_norm1.weight')       )
    rename_keys.append((F'visual_encoder.blocks.{i}.norm1.bias', F'vision_model.encoder.layers.{i}.layer_norm1.bias')       )
    rename_keys.append((F'visual_encoder.blocks.{i}.norm2.weight', F'vision_model.encoder.layers.{i}.layer_norm2.weight')       )
    rename_keys.append((F'visual_encoder.blocks.{i}.norm2.bias', F'vision_model.encoder.layers.{i}.layer_norm2.bias')       )
    rename_keys.append((F'visual_encoder.blocks.{i}.attn.qkv.weight', F'vision_model.encoder.layers.{i}.self_attn.qkv.weight')       )
    rename_keys.append((F'visual_encoder.blocks.{i}.attn.proj.weight', F'vision_model.encoder.layers.{i}.self_attn.projection.weight',)       )
    rename_keys.append((F'visual_encoder.blocks.{i}.attn.proj.bias', F'vision_model.encoder.layers.{i}.self_attn.projection.bias')       )
    rename_keys.append((F'visual_encoder.blocks.{i}.mlp.fc1.weight', F'vision_model.encoder.layers.{i}.mlp.fc1.weight')       )
    rename_keys.append((F'visual_encoder.blocks.{i}.mlp.fc1.bias', F'vision_model.encoder.layers.{i}.mlp.fc1.bias')       )
    rename_keys.append((F'visual_encoder.blocks.{i}.mlp.fc2.weight', F'vision_model.encoder.layers.{i}.mlp.fc2.weight')       )
    rename_keys.append((F'visual_encoder.blocks.{i}.mlp.fc2.bias', F'vision_model.encoder.layers.{i}.mlp.fc2.bias')       )

  # QFormer
  rename_keys.append(('Qformer.bert.embeddings.LayerNorm.weight', 'qformer.layernorm.weight')       )
  rename_keys.append(('Qformer.bert.embeddings.LayerNorm.bias', 'qformer.layernorm.bias')       )

  # fmt: on
  return rename_keys







def 						__a       (     _SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE       )     ->List[str]:
  a__:					int      							=     dct.pop(_SCREAMING_SNAKE_CASE       )
  a__:					Tuple      							=     val







def 						__a       (     _SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE       )     ->Optional[Any]:
  for i in range(config.vision_config.num_hidden_layers       ):
    # read in original q and v biases
    a__:					List[str]      							=     state_dict.pop(F'visual_encoder.blocks.{i}.attn.q_bias'       )
    a__:					Union[str, Any]      							=     state_dict.pop(F'visual_encoder.blocks.{i}.attn.v_bias'       )

    # next, set bias in the state dict
    a__:					str      							=     torch.cat((q_bias, torch.zeros_like(_SCREAMING_SNAKE_CASE		,					requires_grad=_SCREAMING_SNAKE_CASE       ), v_bias)       )
    a__:					Union[str, Any]      							=     qkv_bias







def 						__a       (     _SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE       )     ->Any:
  a__:					List[Any]      							=     364 if 'coco' in model_name else 224
  a__:					Union[str, Any]      							=     BlipaVisionConfig(image_size=_SCREAMING_SNAKE_CASE       ).to_dict()

  # make sure the models have proper bos_token_id and eos_token_id set (important for generation)
  # seems like flan-T5 models don't have bos_token_id properly set?
  if "opt-2.7b" in model_name:
    a__:					List[Any]      							=     OPTConfig.from_pretrained('facebook/opt-2.7b'		,					eos_token_id=_SCREAMING_SNAKE_CASE       ).to_dict()
  elif "opt-6.7b" in model_name:
    a__:					List[str]      							=     OPTConfig.from_pretrained('facebook/opt-6.7b'		,					eos_token_id=_SCREAMING_SNAKE_CASE       ).to_dict()
  elif "t5-xl" in model_name:
    a__:					Dict      							=     TaConfig.from_pretrained('google/flan-t5-xl'		,					dense_act_fn='gelu'		,					bos_token_id=1       ).to_dict()
  elif "t5-xxl" in model_name:
    a__:					Tuple      							=     TaConfig.from_pretrained('google/flan-t5-xxl'		,					dense_act_fn='gelu'		,					bos_token_id=1       ).to_dict()

  a__:					Any      							=     BlipaConfig(vision_config=_SCREAMING_SNAKE_CASE		,					text_config=_SCREAMING_SNAKE_CASE       )

  return config, image_size







@torch.no_grad()
def 						__a       (     _SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE=None		,					_SCREAMING_SNAKE_CASE=False       )     ->Optional[int]:
  a__:					Optional[Any]      							=     (
      AutoTokenizer.from_pretrained('facebook/opt-2.7b'       )
      if 'opt' in model_name
      else AutoTokenizer.from_pretrained('google/flan-t5-xl'       )
  )
  a__:					int      							=     tokenizer('\n'		,					add_special_tokens=_SCREAMING_SNAKE_CASE       ).input_ids[0]
  a__:					List[Any]      							=     get_blipa_config(_SCREAMING_SNAKE_CASE		,					eos_token_id=_SCREAMING_SNAKE_CASE       )

  a__:					Dict      							=     BlipaForConditionalGeneration(_SCREAMING_SNAKE_CASE       ).eval()

  a__:					List[Any]      							=     {
      'blip2-opt-2.7b': ('blip2_opt', 'pretrain_opt2.7b'),
      'blip2-opt-6.7b': ('blip2_opt', 'pretrain_opt6.7b'),
      'blip2-opt-2.7b-coco': ('blip2_opt', 'caption_coco_opt2.7b'),
      'blip2-opt-6.7b-coco': ('blip2_opt', 'caption_coco_opt6.7b'),
      'blip2-flan-t5-xl': ('blip2_t5', 'pretrain_flant5xl'),
      'blip2-flan-t5-xl-coco': ('blip2_t5', 'caption_coco_flant5xl'),
      'blip2-flan-t5-xxl': ('blip2_t5', 'pretrain_flant5xxl'),
  }

  a__:					Dict      							=     model_name_to_original[model_name]

  # load original model
  print('Loading original model...'       )
  a__:					Tuple      							=     'cuda' if torch.cuda.is_available() else 'cpu'
  a__:					Tuple      							=     load_model_and_preprocess(
      name=_SCREAMING_SNAKE_CASE		,					model_type=_SCREAMING_SNAKE_CASE		,					is_eval=_SCREAMING_SNAKE_CASE		,					device=_SCREAMING_SNAKE_CASE       )
  original_model.eval()
  print('Done!'       )

  # update state dict keys
  a__:					int      							=     original_model.state_dict()
  a__:					List[str]      							=     create_rename_keys(_SCREAMING_SNAKE_CASE       )
  for src, dest in rename_keys:
    rename_key(_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE       )

  # some keys can be renamed efficiently
  for key, val in state_dict.copy().items():
    a__:					Tuple      							=     state_dict.pop(_SCREAMING_SNAKE_CASE       )
    if key.startswith('Qformer.bert'       ):
      a__:					List[Any]      							=     key.replace('Qformer.bert'		,					'qformer'       )
    if "attention.self" in key:
      a__:					Tuple      							=     key.replace('self'		,					'attention'       )
    if "opt_proj" in key:
      a__:					int      							=     key.replace('opt_proj'		,					'language_projection'       )
    if "t5_proj" in key:
      a__:					List[str]      							=     key.replace('t5_proj'		,					'language_projection'       )
    if key.startswith('opt'       ):
      a__:					Union[str, Any]      							=     key.replace('opt'		,					'language'       )
    if key.startswith('t5'       ):
      a__:					List[Any]      							=     key.replace('t5'		,					'language'       )
    a__:					str      							=     val

  # read in qv biases
  read_in_q_v_bias(_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE       )

  a__:					Dict      							=     hf_model.load_state_dict(_SCREAMING_SNAKE_CASE		,					strict=_SCREAMING_SNAKE_CASE       )
  assert len(_SCREAMING_SNAKE_CASE       ) == 0
  assert unexpected_keys == ["qformer.embeddings.position_ids"]

  a__:					Optional[Any]      							=     load_demo_image()
  a__:					Tuple      							=     vis_processors['eval'](_SCREAMING_SNAKE_CASE       ).unsqueeze(0       ).to(_SCREAMING_SNAKE_CASE       )
  a__:					Optional[int]      							=     tokenizer(['\n']		,					return_tensors='pt'       ).input_ids.to(_SCREAMING_SNAKE_CASE       )

  # create processor
  a__:					Optional[Any]      							=     BlipImageProcessor(
      size={'height': image_size, 'width': image_size}		,					image_mean=_SCREAMING_SNAKE_CASE		,					image_std=_SCREAMING_SNAKE_CASE       )
  a__:					int      							=     BlipaProcessor(image_processor=_SCREAMING_SNAKE_CASE		,					tokenizer=_SCREAMING_SNAKE_CASE       )
  a__:					Dict      							=     processor(images=_SCREAMING_SNAKE_CASE		,					return_tensors='pt'       ).pixel_values.to(_SCREAMING_SNAKE_CASE       )

  # make sure processor creates exact same pixel values
  assert torch.allclose(_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE       )

  original_model.to(_SCREAMING_SNAKE_CASE       )
  hf_model.to(_SCREAMING_SNAKE_CASE       )
  with torch.no_grad():
    if "opt" in model_name:
      a__:					Dict      							=     original_model({'image': original_pixel_values, 'text_input': ['']}       ).logits
      a__:					Union[str, Any]      							=     hf_model(_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE       ).logits
    else:
      a__:					Any      							=     original_model(
          {'image': original_pixel_values, 'text_input': ['\n'], 'text_output': ['\n']}       ).logits
      a__:					str      							=     input_ids.masked_fill(input_ids == tokenizer.pad_token_id		,					-100       )
      a__:					Any      							=     hf_model(_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE		,					labels=_SCREAMING_SNAKE_CASE       ).logits

  assert original_logits.shape == logits.shape
  print('First values of original logits:'		,					original_logits[0, :3, :3]       )
  print('First values of HF logits:'		,					logits[0, :3, :3]       )

  # assert values
  if model_name == "blip2-flan-t5-xl":
    a__:					int      							=     torch.tensor(
        [[-41.5_850, -4.4_440, -8.9_922], [-47.4_322, -5.9_143, -1.7_340]]		,					device=_SCREAMING_SNAKE_CASE       )
    assert torch.allclose(logits[0, :3, :3]		,					_SCREAMING_SNAKE_CASE		,					atol=1e-4       )
  elif model_name == "blip2-flan-t5-xl-coco":
    a__:					List[str]      							=     torch.tensor(
        [[-57.0_109, -9.8_967, -12.6_280], [-68.6_578, -12.7_191, -10.5_065]]		,					device=_SCREAMING_SNAKE_CASE       )
  else:
    # cast to same type
    a__:					Optional[int]      							=     logits.dtype
    assert torch.allclose(original_logits.to(_SCREAMING_SNAKE_CASE       )		,					_SCREAMING_SNAKE_CASE		,					atol=1e-2       )
  print('Looks ok!'       )

  print('Generating a caption...'       )
  a__:					List[Any]      							=     ''
  a__:					Any      							=     tokenizer(_SCREAMING_SNAKE_CASE		,					return_tensors='pt'       ).input_ids.to(_SCREAMING_SNAKE_CASE       )

  a__:					Any      							=     original_model.generate({'image': original_pixel_values}       )
  a__:					str      							=     hf_model.generate(
      _SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE		,					do_sample=_SCREAMING_SNAKE_CASE		,					num_beams=5		,					max_length=30		,					min_length=1		,					top_p=0.9		,					repetition_penalty=1.0		,					length_penalty=1.0		,					temperature=1		,					)
  print('Original generation:'		,					_SCREAMING_SNAKE_CASE       )
  a__:					Union[str, Any]      							=     input_ids.shape[1]
  a__:					Tuple      							=     processor.batch_decode(outputs[:, prompt_length:]		,					skip_special_tokens=_SCREAMING_SNAKE_CASE       )
  a__:					Optional[Any]      							=     [text.strip() for text in output_text]
  print('HF generation:'		,					_SCREAMING_SNAKE_CASE       )

  if pytorch_dump_folder_path is not None:
    processor.save_pretrained(_SCREAMING_SNAKE_CASE       )
    hf_model.save_pretrained(_SCREAMING_SNAKE_CASE       )

  if push_to_hub:
    processor.push_to_hub(F'nielsr/{model_name}'       )
    hf_model.push_to_hub(F'nielsr/{model_name}'       )


if __name__ == "__main__":
 lowercase__ 	=     argparse.ArgumentParser()
 lowercase__ 	=     [
     'blip2-opt-2.7b',
     'blip2-opt-6.7b',
     'blip2-opt-2.7b-coco',
     'blip2-opt-6.7b-coco',
     'blip2-flan-t5-xl',
     'blip2-flan-t5-xl-coco',
     'blip2-flan-t5-xxl',
 ]
 parser.add_argument(
     '--model_name',
     default='blip2-opt-2.7b',
     choices=choices,
     type=str,
     help='Path to hf config.json of model to convert',
 )
 parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
 parser.add_argument(
     '--push_to_hub',
     action='store_true',
     help='Whether to push the model and processor to the hub after converting',
 )

 lowercase__ 	=     parser.parse_args()

 convert_blipa_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)



"""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 import BertTokenizer


lowercase__ 	=     logging.get_logger(__name__)

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

lowercase__ 	=     {
    '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'
        ),
    },
}
lowercase__ 	=     {
    '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'
        ),
    },
}
lowercase__ 	=     {
    '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'
        ),
    },
}

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


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




class        __snake_case	(					__lowerCAmelCase    ):
      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        __snake_case	(					__lowerCAmelCase    ):
      a__				=		VOCAB_FILES_NAMES
      a__				=		QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
      a__				=		QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      a__				=		QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION







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

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


lowercase__ 	=     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(__lowerCAmelCase    )
class        __snake_case	:



      def __call__(						self   ,      lowercase   ,      lowercase = None   ,      lowercase = None   ,      lowercase = False   ,      lowercase = False   ,      lowercase = None   ,      lowercase = None   ,      lowercase = None   ,      **lowercase   ,      )     ->      BatchEncoding:





        '''simple docstring'''





        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:
          a__:					str      							=     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   ,      )
        a__:					Tuple      							=     titles if not isinstance(lowercase   ,      lowercase) else [titles]
        a__:					Optional[int]      							=     texts if not isinstance(lowercase   ,      lowercase) else [texts]
        a__:					Dict      							=     len(lowercase)
        a__:					Dict      							=     questions if not isinstance(lowercase   ,      lowercase) else [questions] * n_passages
        if len(lowercase) != len(lowercase):
          raise ValueError(
              f'There should be as many titles than texts but got {len(lowercase)} titles and {len(lowercase)} texts.')
        a__:					List[str]      							=     super().__call__(lowercase   ,      lowercase   ,      padding=lowercase   ,      truncation=lowercase)['input_ids']
        a__:					List[Any]      							=     super().__call__(lowercase   ,      add_special_tokens=lowercase   ,      padding=lowercase   ,      truncation=lowercase)['input_ids']
        a__:					Optional[Any]      							=     {
            '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:
          a__:					Dict      							=     []
          for input_ids in encoded_inputs["input_ids"]:
            attention_mask.append([int(input_id != self.pad_token_id) for input_id in input_ids])
          a__:					int      							=     attention_mask
        return self.pad(lowercase   ,      padding=lowercase   ,      max_length=lowercase   ,      return_tensors=lowercase)



      def   lowerCamelCase_       (						self   ,      lowercase   ,      lowercase   ,      lowercase = 16   ,      lowercase = 64   ,      lowercase = 4   ,      )     ->      List[DPRSpanPrediction]:





        '''simple docstring'''





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

          a__:					Optional[int]      							=     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   lowerCamelCase_       (						self   ,      lowercase   ,      lowercase   ,      lowercase   ,      lowercase   ,      )     ->      List[DPRSpanPrediction]:





        '''simple docstring'''





        a__:					Optional[Any]      							=     []
        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))
        a__:					str      							=     sorted(lowercase   ,      key=lambda lowercase: x[1]   ,      reverse=lowercase)
        a__:					Optional[Any]      							=     []
        for (start_index, end_index), score in scores:
          if start_index > end_index:
            raise ValueError(f'Wrong span indices: [{start_index}:{end_index}]')
          a__:					str      							=     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(lowercase) == top_spans:
            break
        return chosen_span_intervals







@add_end_docstrings(__lowerCAmelCase    )
class        __snake_case	(					__lowerCAmelCase	,				__lowerCAmelCase    ):
      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"""]


