




import argparse
import os
import sys
from unittest.mock import patch

import pytorch_lightning as pl
import timeout_decorator
import torch
from distillation import SummarizationDistiller, distill_main
from finetune import SummarizationModule, main

from transformers import MarianMTModel
from transformers.file_utils import cached_path
from transformers.testing_utils import TestCasePlus, require_torch_gpu, slow
from utils import load_json


UpperCamelCase								=					'sshleifer/mar_enro_6_3_student'





class 			__lowerCamelCase	(   UpperCamelCase__   ):




    """simple docstring"""

    def        a				(	self					:					List[Any]       )				->	Optional[Any]:
         super().setUp()

         lowerCAmelCase__          =			cached_path(
             "https://cdn-datasets.huggingface.co/translation/wmt_en_ro-tr40k-va0.5k-te0.5k.tar.gz"       ,     extract_compressed_file=SCREAMING_SNAKE_CASE__       ,     )
         lowerCAmelCase__          =			f'{data_cached}/wmt_en_ro-tr40k-va0.5k-te0.5k'


    @slow
    @require_torch_gpu
    def        a				(	self					:					List[Any]       )				->	List[Any]:
         MarianMTModel.from_pretrained(SCREAMING_SNAKE_CASE__       )


    @slow
    @require_torch_gpu
    def        a				(	self					:					Tuple       )				->	Optional[Any]:
         lowerCAmelCase__          =			{
             "$MAX_LEN": 64,
             "$BS": 64,
             "$GAS": 1,
             "$ENRO_DIR": self.data_dir,
             "facebook/mbart-large-cc25": MARIAN_MODEL,
             # "val_check_interval=0.25": "val_check_interval=1.0",
             "--learning_rate=3e-5": "--learning_rate 3e-4",
             "--num_train_epochs 6": "--num_train_epochs 1",
         }

         # Clean up bash script
         lowerCAmelCase__          =			(self.test_file_dir / "train_mbart_cc25_enro.sh").open().read().split("finetune.py"       )[1].strip()
         lowerCAmelCase__          =			bash_script.replace("\\\n"       ,     ""       ).strip().replace("\"$@\""       ,     ""       )
         for k, v in env_vars_to_replace.items():
              lowerCAmelCase__          =			bash_script.replace(SCREAMING_SNAKE_CASE__       ,     str(SCREAMING_SNAKE_CASE__       )       )
         lowerCAmelCase__          =			self.get_auto_remove_tmp_dir()

         # bash_script = bash_script.replace("--fp16 ", "")
         lowerCAmelCase__          =			f'\n            --output_dir {output_dir}\n            --tokenizer_name Helsinki-NLP/opus-mt-en-ro\n            --sortish_sampler\n            --do_predict\n            --gpus 1\n            --freeze_encoder\n            --n_train 40000\n            --n_val 500\n            --n_test 500\n            --fp16_opt_level O1\n            --num_sanity_val_steps 0\n            --eval_beams 2\n        '.split()
         # XXX: args.gpus > 1 : handle multi_gpu in the future

         lowerCAmelCase__          =			["finetune.py"] + bash_script.split() + args
         with patch.object(SCREAMING_SNAKE_CASE__       ,     "argv"       ,     SCREAMING_SNAKE_CASE__       ):
              lowerCAmelCase__          =			argparse.ArgumentParser()
              lowerCAmelCase__          =			pl.Trainer.add_argparse_args(SCREAMING_SNAKE_CASE__       )
              lowerCAmelCase__          =			SummarizationModule.add_model_specific_args(SCREAMING_SNAKE_CASE__       ,     os.getcwd()       )
              lowerCAmelCase__          =			parser.parse_args()
              lowerCAmelCase__          =			main(SCREAMING_SNAKE_CASE__       )

         # Check metrics
         lowerCAmelCase__          =			load_json(model.metrics_save_path       )
         lowerCAmelCase__          =			metrics["val"][0]
         lowerCAmelCase__          =			metrics["val"][-1]
         self.assertEqual(len(metrics["val"]       )       ,     (args.max_epochs / args.val_check_interval)       )
         assert isinstance(last_step_stats[f'val_avg_{model.val_metric}']       ,     SCREAMING_SNAKE_CASE__       )

         self.assertGreater(last_step_stats["val_avg_gen_time"]       ,     0.01       )
         # model hanging on generate. Maybe bad config was saved. (XXX: old comment/assert?)
         self.assertLessEqual(last_step_stats["val_avg_gen_time"]       ,     1.0       )

         # test learning requirements:

         # 1. BLEU improves over the course of training by more than 2 pts
         self.assertGreater(last_step_stats["val_avg_bleu"] - first_step_stats["val_avg_bleu"]       ,     2       )

         # 2. BLEU finishes above 17
         self.assertGreater(last_step_stats["val_avg_bleu"]       ,     17       )

         # 3. test BLEU and val BLEU within ~1.1 pt.
         self.assertLess(abs(metrics["val"][-1]["val_avg_bleu"] - metrics["test"][-1]["test_avg_bleu"]       )       ,     1.1       )

         # check lightning ckpt can be loaded and has a reasonable statedict
         lowerCAmelCase__          =			os.listdir(SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			[x for x in contents if x.endswith(".ckpt"       )][0]
         lowerCAmelCase__          =			os.path.join(args.output_dir       ,     SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			torch.load(SCREAMING_SNAKE_CASE__       ,     map_location="cpu"       )
         lowerCAmelCase__          =			"model.model.decoder.layers.0.encoder_attn_layer_norm.weight"
         assert expected_key in ckpt["state_dict"]
         assert ckpt["state_dict"]["model.model.decoder.layers.0.encoder_attn_layer_norm.weight"].dtype == torch.floataa

         # TODO: turn on args.do_predict when PL bug fixed.
         if args.do_predict:
              lowerCAmelCase__          =			{os.path.basename(SCREAMING_SNAKE_CASE__       ) for p in contents}
              assert "test_generations.txt" in contents
              assert "test_results.txt" in contents
              # assert len(metrics["val"]) ==  desired_n_evals
              assert len(metrics["test"]       ) == 1






class 			__lowerCamelCase	(   UpperCamelCase__   ):




    """simple docstring"""

    @timeout_decorator.timeout(600       )
    @slow
    @require_torch_gpu
    def        a				(	self					:					Any       )				->	List[Any]:
         lowerCAmelCase__          =			f'{self.test_file_dir_str}/test_data/wmt_en_ro'
         lowerCAmelCase__          =			{
             "--fp16_opt_level=O1": "",
             "$MAX_LEN": 128,
             "$BS": 16,
             "$GAS": 1,
             "$ENRO_DIR": data_dir,
             "$m": "sshleifer/student_marian_en_ro_6_1",
             "val_check_interval=0.25": "val_check_interval=1.0",
         }

         # Clean up bash script
         lowerCAmelCase__          =			(
             (self.test_file_dir / "distil_marian_no_teacher.sh").open().read().split("distillation.py"       )[1].strip()
         )
         lowerCAmelCase__          =			bash_script.replace("\\\n"       ,     ""       ).strip().replace("\"$@\""       ,     ""       )
         lowerCAmelCase__          =			bash_script.replace("--fp16 "       ,     " "       )

         for k, v in env_vars_to_replace.items():
              lowerCAmelCase__          =			bash_script.replace(SCREAMING_SNAKE_CASE__       ,     str(SCREAMING_SNAKE_CASE__       )       )
         lowerCAmelCase__          =			self.get_auto_remove_tmp_dir()
         lowerCAmelCase__          =			bash_script.replace("--fp16"       ,     ""       )
         lowerCAmelCase__          =			6
         lowerCAmelCase__          =			(
             ["distillation.py"]
             + bash_script.split()
             + [
                 f'--output_dir={output_dir}',
                 "--gpus=1",
                 "--learning_rate=1e-3",
                 f'--num_train_epochs={epochs}',
                 "--warmup_steps=10",
                 "--val_check_interval=1.0",
                 "--do_predict",
             ]
         )
         with patch.object(SCREAMING_SNAKE_CASE__       ,     "argv"       ,     SCREAMING_SNAKE_CASE__       ):
              lowerCAmelCase__          =			argparse.ArgumentParser()
              lowerCAmelCase__          =			pl.Trainer.add_argparse_args(SCREAMING_SNAKE_CASE__       )
              lowerCAmelCase__          =			SummarizationDistiller.add_model_specific_args(SCREAMING_SNAKE_CASE__       ,     os.getcwd()       )
              lowerCAmelCase__          =			parser.parse_args()
              # assert args.gpus == gpus THIS BREAKS for multi_gpu

              lowerCAmelCase__          =			distill_main(SCREAMING_SNAKE_CASE__       )

         # Check metrics
         lowerCAmelCase__          =			load_json(model.metrics_save_path       )
         lowerCAmelCase__          =			metrics["val"][0]
         lowerCAmelCase__          =			metrics["val"][-1]
         assert len(metrics["val"]       ) >= (args.max_epochs / args.val_check_interval)  # +1 accounts for val_sanity_check

         assert last_step_stats["val_avg_gen_time"] >= 0.01

         assert first_step_stats["val_avg_bleu"] < last_step_stats["val_avg_bleu"]  # model learned nothing
         assert 1.0 >= last_step_stats["val_avg_gen_time"]  # model hanging on generate. Maybe bad config was saved.
         assert isinstance(last_step_stats[f'val_avg_{model.val_metric}']       ,     SCREAMING_SNAKE_CASE__       )

         # check lightning ckpt can be loaded and has a reasonable statedict
         lowerCAmelCase__          =			os.listdir(SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			[x for x in contents if x.endswith(".ckpt"       )][0]
         lowerCAmelCase__          =			os.path.join(args.output_dir       ,     SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			torch.load(SCREAMING_SNAKE_CASE__       ,     map_location="cpu"       )
         lowerCAmelCase__          =			"model.model.decoder.layers.0.encoder_attn_layer_norm.weight"
         assert expected_key in ckpt["state_dict"]
         assert ckpt["state_dict"]["model.model.decoder.layers.0.encoder_attn_layer_norm.weight"].dtype == torch.floataa

         # TODO: turn on args.do_predict when PL bug fixed.
         if args.do_predict:
              lowerCAmelCase__          =			{os.path.basename(SCREAMING_SNAKE_CASE__       ) for p in contents}
              assert "test_generations.txt" in contents
              assert "test_results.txt" in contents
              # assert len(metrics["val"]) ==  desired_n_evals
              assert len(metrics["test"]       ) == 1






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


UpperCamelCase								=					logging.get_logger(__name__)

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

UpperCamelCase								=					{
    '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'
        ),
    },
}
UpperCamelCase								=					{
    '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'
        ),
    },
}
UpperCamelCase								=					{
    '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'
        ),
    },
}

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


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





class 			__lowerCamelCase	(   UpperCamelCase__   ):




    """simple docstring"""

    snake_case__ =				VOCAB_FILES_NAMES
    snake_case__ =				CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
    snake_case__ =				CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    snake_case__ =				CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION
    snake_case__ =				DPRContextEncoderTokenizer






class 			__lowerCamelCase	(   UpperCamelCase__   ):




    """simple docstring"""

    snake_case__ =				VOCAB_FILES_NAMES
    snake_case__ =				QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
    snake_case__ =				QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    snake_case__ =				QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION
    snake_case__ =				DPRQuestionEncoderTokenizer






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

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


UpperCamelCase								=					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    [CLS] <question token ids> [SEP] <titles ids> [SEP] <texts ids>\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    Return:\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	:




    """simple docstring"""

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


    def        a				(	self					:					Dict       ,     SCREAMING_SNAKE_CASE__					:					BatchEncoding       ,     SCREAMING_SNAKE_CASE__					:					DPRReaderOutput       ,     SCREAMING_SNAKE_CASE__					:					int = 16       ,     SCREAMING_SNAKE_CASE__					:					int = 64       ,     SCREAMING_SNAKE_CASE__					:					int = 4       ,     )				->	List[DPRSpanPrediction]:
         lowerCAmelCase__          =			reader_input["input_ids"]
         lowerCAmelCase__     ,		lowerCAmelCase__     ,		lowerCAmelCase__          =			reader_output[:3]
         lowerCAmelCase__          =			len(SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			sorted(range(SCREAMING_SNAKE_CASE__       )       ,     reverse=SCREAMING_SNAKE_CASE__       ,     key=relevance_logits.__getitem__       )
         lowerCAmelCase__          =			[]
         for doc_id in sorted_docs:
              lowerCAmelCase__          =			list(input_ids[doc_id]       )
              # assuming question & title information is at the beginning of the sequence
              lowerCAmelCase__          =			sequence_ids.index(self.sep_token_id       ,     2       ) + 1  # second sep id
              if sequence_ids[-1] == self.pad_token_id:
                   lowerCAmelCase__          =			sequence_ids.index(self.pad_token_id       )
              else:
                   lowerCAmelCase__          =			len(SCREAMING_SNAKE_CASE__       )

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


    def        a				(	self					:					int       ,     SCREAMING_SNAKE_CASE__					:					List[int]       ,     SCREAMING_SNAKE_CASE__					:					List[int]       ,     SCREAMING_SNAKE_CASE__					:					int       ,     SCREAMING_SNAKE_CASE__					:					int       ,     )				->	List[DPRSpanPrediction]:
         lowerCAmelCase__          =			[]
         for start_index, start_score in enumerate(SCREAMING_SNAKE_CASE__       ):
              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)       )
         lowerCAmelCase__          =			sorted(SCREAMING_SNAKE_CASE__       ,     key=lambda SCREAMING_SNAKE_CASE__       : x[1]       ,     reverse=SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			[]
         for (start_index, end_index), score in scores:
              assert start_index <= end_index, f'Wrong span indices: [{start_index}:{end_index}]'
              lowerCAmelCase__          =			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(SCREAMING_SNAKE_CASE__       ) == top_spans:
                   break
         return chosen_span_intervals






@add_end_docstrings(UpperCamelCase__   )
class 			__lowerCamelCase	(   UpperCamelCase__     ,    UpperCamelCase__   ):




    """simple docstring"""

    snake_case__ =				VOCAB_FILES_NAMES
    snake_case__ =				READER_PRETRAINED_VOCAB_FILES_MAP
    snake_case__ =				READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    snake_case__ =				READER_PRETRAINED_INIT_CONFIGURATION
    snake_case__ =				["input_ids", "attention_mask"]
    snake_case__ =				DPRReaderTokenizer
