
'''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


UpperCamelCase__      :		Optional[Any]       							=  logging.get_logger(__name__)

UpperCamelCase__      :		Optional[int]       							=  {"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}

UpperCamelCase__      :		Tuple       							=  {
    "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__      :		int       							=  {
    "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__      :		Optional[int]       							=  {
    "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__      :		List[Any]       							=  {
    "facebook/dpr-ctx_encoder-single-nq-base": 512,
    "facebook/dpr-ctx_encoder-multiset-base": 512,
}
UpperCamelCase__      :		Optional[Any]       							=  {
    "facebook/dpr-question_encoder-single-nq-base": 512,
    "facebook/dpr-question_encoder-multiset-base": 512,
}
UpperCamelCase__      :		Optional[int]       							=  {
    "facebook/dpr-reader-single-nq-base": 512,
    "facebook/dpr-reader-multiset-base": 512,
}


UpperCamelCase__      :		Optional[Any]       							=  {
    "facebook/dpr-ctx_encoder-single-nq-base": {"do_lower_case": True},
    "facebook/dpr-ctx_encoder-multiset-base": {"do_lower_case": True},
}
UpperCamelCase__      :		Any       							=  {
    "facebook/dpr-question_encoder-single-nq-base": {"do_lower_case": True},
    "facebook/dpr-question_encoder-multiset-base": {"do_lower_case": True},
}
UpperCamelCase__      :		str       							=  {
    "facebook/dpr-reader-single-nq-base": {"do_lower_case": True},
    "facebook/dpr-reader-multiset-base": {"do_lower_case": True},
}

class    _a				(_lowerCamelCase):





       """simple docstring"""




       SCREAMING_SNAKE_CASE      =  VOCAB_FILES_NAMES
       SCREAMING_SNAKE_CASE      =  CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
       SCREAMING_SNAKE_CASE      =  CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       SCREAMING_SNAKE_CASE      =  CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION






class    _a				(_lowerCamelCase):





       """simple docstring"""




       SCREAMING_SNAKE_CASE      =  VOCAB_FILES_NAMES
       SCREAMING_SNAKE_CASE      =  QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
       SCREAMING_SNAKE_CASE      =  QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       SCREAMING_SNAKE_CASE      =  QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION






UpperCamelCase__      :		Optional[Any]       							=  collections.namedtuple(
    "DPRSpanPrediction", ["span_score", "relevance_score", "doc_id", "start_index", "end_index", "text"]
)

UpperCamelCase__      :		Union[str, Any]       							=  collections.namedtuple("DPRReaderOutput", ["start_logits", "end_logits", "relevance_logits"])


UpperCamelCase__      :		Union[str, Any]       							=  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    _a				:





       """simple docstring"""




       def __call__(							self       ,							A__       ,							A__ = None       ,							A__ = None       ,							A__ = False       ,							A__ = False       ,							A__ = None       ,							A__ = None       ,							A__ = None       ,							**A__       ,							)    ->			BatchEncoding:
          if titles is None and texts is None:
             return super().__call__(
                 A__       ,							padding=A__       ,							truncation=A__       ,							max_length=A__       ,							return_tensors=A__       ,							return_attention_mask=A__       ,							**A__       ,							)
          elif titles is None or texts is None:
             _SCREAMING_SNAKE_CASE      =				titles if texts is None else texts
             return super().__call__(
                 A__       ,							A__       ,							padding=A__       ,							truncation=A__       ,							max_length=A__       ,							return_tensors=A__       ,							return_attention_mask=A__       ,							**A__       ,							)
          _SCREAMING_SNAKE_CASE      =				titles if not isinstance(A__       ,							A__      ) else [titles]
          _SCREAMING_SNAKE_CASE      =				texts if not isinstance(A__       ,							A__      ) else [texts]
          _SCREAMING_SNAKE_CASE      =				len(A__      )
          _SCREAMING_SNAKE_CASE      =				questions if not isinstance(A__       ,							A__      ) else [questions] * n_passages
          if len(A__      ) != len(A__      ):
             raise ValueError(
                 F"There should be as many titles than texts but got {len(A__      )} titles and {len(A__      )} texts."      )
          _SCREAMING_SNAKE_CASE      =				super().__call__(A__       ,							A__       ,							padding=A__       ,							truncation=A__      )["""input_ids"""]
          _SCREAMING_SNAKE_CASE      =				super().__call__(A__       ,							add_special_tokens=A__       ,							padding=A__       ,							truncation=A__      )["""input_ids"""]
          _SCREAMING_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(A__       ,							A__      )
              ]
          }
          if return_attention_mask is not False:
             _SCREAMING_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]      )
             _SCREAMING_SNAKE_CASE      =				attention_mask
          return self.pad(A__       ,							padding=A__       ,							max_length=A__       ,							return_tensors=A__      )






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

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






       def        UpperCamelCase				(							self       ,							A__       ,							A__       ,							A__       ,							A__       ,							)    ->			List[DPRSpanPrediction]:
          _SCREAMING_SNAKE_CASE      =				[]
          for start_index, start_score in enumerate(A__      ):
             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)      )
          _SCREAMING_SNAKE_CASE      =				sorted(A__       ,							key=lambda A__      : x[1]       ,							reverse=A__      )
          _SCREAMING_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}]"      )
             _SCREAMING_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(A__      ) == top_spans:
                break
          return chosen_span_intervals






@add_end_docstrings(_lowerCamelCase)
class    _a				(_lowerCamelCase  ,					_lowerCamelCase):





       """simple docstring"""




       SCREAMING_SNAKE_CASE      =  VOCAB_FILES_NAMES
       SCREAMING_SNAKE_CASE      =  READER_PRETRAINED_VOCAB_FILES_MAP
       SCREAMING_SNAKE_CASE      =  READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       SCREAMING_SNAKE_CASE      =  READER_PRETRAINED_INIT_CONFIGURATION
       SCREAMING_SNAKE_CASE      =  ['input_ids', 'attention_mask']








'''simple docstring'''



import logging
import os

import quant_trainer
import torch
from torch.utils.data import DataLoader

from transformers import Trainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput


UpperCamelCase__      :		Tuple       							=  logging.getLogger(__name__)

if is_torch_tpu_available(check_device=False):
     import torch_xla.core.xla_model as xm
     import torch_xla.debug.metrics as met

class    _a				(_lowerCamelCase):





       """simple docstring"""




       def __init__(							self       ,							*A__       ,							A__=None       ,							A__=None       ,							A__=None       ,							**A__      )    ->			Optional[int]:
          super().__init__(*A__       ,							**A__      )
          _SCREAMING_SNAKE_CASE      =				eval_examples
          _SCREAMING_SNAKE_CASE      =				post_process_function
          _SCREAMING_SNAKE_CASE      =				quant_trainer_args
          _SCREAMING_SNAKE_CASE      =				1_28  # default number of calibration samples






       def        UpperCamelCase				(							self       ,							A__=None      )    ->			Union[str, Any]:
          if calib_dataset is None and self.calib_dataset is None:
             raise ValueError("""Trainer: calibration requires an calib_dataset."""      )
          _SCREAMING_SNAKE_CASE      =				calib_dataset if calib_dataset is not None else self.calib_dataset

          _SCREAMING_SNAKE_CASE      =				self._remove_unused_columns(A__       ,							description="""Calibration"""      )

          return DataLoader(
              A__       ,							batch_size=self.args.eval_batch_size       ,							collate_fn=self.data_collator       ,							drop_last=self.args.dataloader_drop_last       ,							num_workers=self.args.dataloader_num_workers       ,							pin_memory=self.args.dataloader_pin_memory       ,							shuffle=A__       ,							)






       def        UpperCamelCase				(							self       ,							A__=None      )    ->			str:
          _SCREAMING_SNAKE_CASE      =				self.train_dataset if calib_dataset is None else calib_dataset
          _SCREAMING_SNAKE_CASE      =				self.get_calib_dataloader(A__      )

          _SCREAMING_SNAKE_CASE      =				self.model
          quant_trainer.configure_model(A__       ,							self.quant_trainer_args       ,							calib=A__      )
          model.eval()
          quant_trainer.enable_calibration(A__      )

          logger.info("""***** Running calibration *****"""      )
          logger.info(F"  Num examples = {self.calib_num}"      )
          logger.info(F"  Batch size = {calib_dataloader.batch_size}"      )

          for step, inputs in enumerate(A__      ):
             # Prediction step
             _SCREAMING_SNAKE_CASE ,							_SCREAMING_SNAKE_CASE ,							_SCREAMING_SNAKE_CASE      =				self.prediction_step(A__       ,							A__       ,							prediction_loss_only=A__      )
             if (step + 1) * calib_dataloader.batch_size >= self.calib_num:
                break

          quant_trainer.finish_calibration(A__       ,							self.quant_trainer_args      )
          _SCREAMING_SNAKE_CASE      =				model






       def        UpperCamelCase				(							self       ,							A__=None       ,							A__=None       ,							A__=None       ,							A__ = "eval"      )    ->			List[Any]:
          _SCREAMING_SNAKE_CASE      =				self.eval_dataset if eval_dataset is None else eval_dataset
          _SCREAMING_SNAKE_CASE      =				self.get_eval_dataloader(A__      )
          _SCREAMING_SNAKE_CASE      =				self.eval_examples if eval_examples is None else eval_examples

          # Temporarily disable metric computation, we will do it in the loop here.
          _SCREAMING_SNAKE_CASE      =				self.compute_metrics
          _SCREAMING_SNAKE_CASE      =				None
          _SCREAMING_SNAKE_CASE      =				self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
          try:
             _SCREAMING_SNAKE_CASE      =				eval_loop(
                 A__       ,							description="""Evaluation"""       ,							prediction_loss_only=True if compute_metrics is None else None       ,							ignore_keys=A__       ,							)
          finally:
             _SCREAMING_SNAKE_CASE      =				compute_metrics

          if self.post_process_function is not None and self.compute_metrics is not None:
             _SCREAMING_SNAKE_CASE      =				self.post_process_function(A__       ,							A__       ,							output.predictions      )
             _SCREAMING_SNAKE_CASE      =				self.compute_metrics(A__      )

             # Prefix all keys with metric_key_prefix + '_'
             for key in list(metrics.keys()      ):
                if not key.startswith(F"{metric_key_prefix}_"      ):
                   _SCREAMING_SNAKE_CASE      =				metrics.pop(A__      )

             self.log(A__      )
          else:
             _SCREAMING_SNAKE_CASE      =				{}

          if self.args.tpu_metrics_debug or self.args.debug:
             # tpu-comment: Logging debug metrics for PyTorch/XLA (compile, execute times, ops, etc.)
             xm.master_print(met.metrics_report()      )

          _SCREAMING_SNAKE_CASE      =				self.callback_handler.on_evaluate(self.args       ,							self.state       ,							self.control       ,							A__      )
          return metrics






       def        UpperCamelCase				(							self       ,							A__       ,							A__       ,							A__=None       ,							A__ = "test"      )    ->			List[str]:
          _SCREAMING_SNAKE_CASE      =				self.get_test_dataloader(A__      )

          # Temporarily disable metric computation, we will do it in the loop here.
          _SCREAMING_SNAKE_CASE      =				self.compute_metrics
          _SCREAMING_SNAKE_CASE      =				None
          _SCREAMING_SNAKE_CASE      =				self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
          try:
             _SCREAMING_SNAKE_CASE      =				eval_loop(
                 A__       ,							description="""Prediction"""       ,							prediction_loss_only=True if compute_metrics is None else None       ,							ignore_keys=A__       ,							)
          finally:
             _SCREAMING_SNAKE_CASE      =				compute_metrics

          if self.post_process_function is None or self.compute_metrics is None:
             return output

          _SCREAMING_SNAKE_CASE      =				self.post_process_function(A__       ,							A__       ,							output.predictions       ,							"""predict"""      )
          _SCREAMING_SNAKE_CASE      =				self.compute_metrics(A__      )

          # Prefix all keys with metric_key_prefix + '_'
          for key in list(metrics.keys()      ):
             if not key.startswith(F"{metric_key_prefix}_"      ):
                _SCREAMING_SNAKE_CASE      =				metrics.pop(A__      )

          return PredictionOutput(predictions=predictions.predictions       ,							label_ids=predictions.label_ids       ,							metrics=A__      )






       def        UpperCamelCase				(							self       ,							A__="./"      )    ->			Tuple:
          _SCREAMING_SNAKE_CASE      =				self.eval_dataset
          _SCREAMING_SNAKE_CASE      =				self.get_eval_dataloader(A__      )

          _SCREAMING_SNAKE_CASE      =				next(iter(A__      )      )

          # saving device - to make it consistent
          _SCREAMING_SNAKE_CASE      =				torch.device("""cuda""" if torch.cuda.is_available() else """cpu"""      )

          # convert to tuple
          _SCREAMING_SNAKE_CASE      =				tuple(v.to(A__      ) for k, v in batch.items()      )

          logger.info("""Converting model to be onnx compatible"""      )
          from pytorch_quantization.nn import TensorQuantizer

          _SCREAMING_SNAKE_CASE      =				True

          _SCREAMING_SNAKE_CASE      =				self.model.to(A__      )

          model.eval()
          model.float()

          _SCREAMING_SNAKE_CASE      =				model.module if hasattr(A__       ,							"""module"""      ) else model
          quant_trainer.configure_model(A__       ,							self.quant_trainer_args      )

          _SCREAMING_SNAKE_CASE      =				os.path.join(A__       ,							"""model.onnx"""      )
          logger.info(F"exporting model to {output_model_file}"      )

          _SCREAMING_SNAKE_CASE      =				{0: """batch_size""", 1: """seq_len"""}

          torch.onnx.export(
              A__       ,							A__       ,							A__       ,							export_params=A__       ,							opset_version=13       ,							do_constant_folding=A__       ,							input_names=["""input_ids""", """attention_mask""", """token_type_ids"""]       ,							output_names=["""output_start_logits""", """output_end_logits"""]       ,							dynamic_axes={
                  """input_ids""": axes,
                  """attention_mask""": axes,
                  """token_type_ids""": axes,
                  """output_start_logits""": axes,
                  """output_end_logits""": axes,
              }       ,							verbose=A__       ,							)
          logger.info("""onnx export finished"""      )






