

"""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


_A							=  logging.get_logger(__name__)

_A							=  {"""vocab_file""": """vocab.txt""", """tokenizer_file""": """tokenizer.json"""}

_A							=  {
    """vocab_file""": {
        """facebook/dpr-ctx_encoder-single-nq-base""": (
            """https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/vocab.txt"""
        ),
        """facebook/dpr-ctx_encoder-multiset-base""": (
            """https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/vocab.txt"""
        ),
    },
    """tokenizer_file""": {
        """facebook/dpr-ctx_encoder-single-nq-base""": (
            """https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/tokenizer.json"""
        ),
        """facebook/dpr-ctx_encoder-multiset-base""": (
            """https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/tokenizer.json"""
        ),
    },
}
_A							=  {
    """vocab_file""": {
        """facebook/dpr-question_encoder-single-nq-base""": (
            """https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/vocab.txt"""
        ),
        """facebook/dpr-question_encoder-multiset-base""": (
            """https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/vocab.txt"""
        ),
    },
    """tokenizer_file""": {
        """facebook/dpr-question_encoder-single-nq-base""": (
            """https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/tokenizer.json"""
        ),
        """facebook/dpr-question_encoder-multiset-base""": (
            """https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/tokenizer.json"""
        ),
    },
}
_A							=  {
    """vocab_file""": {
        """facebook/dpr-reader-single-nq-base""": (
            """https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/vocab.txt"""
        ),
        """facebook/dpr-reader-multiset-base""": (
            """https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/vocab.txt"""
        ),
    },
    """tokenizer_file""": {
        """facebook/dpr-reader-single-nq-base""": (
            """https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/tokenizer.json"""
        ),
        """facebook/dpr-reader-multiset-base""": (
            """https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/tokenizer.json"""
        ),
    },
}

_A							=  {
    """facebook/dpr-ctx_encoder-single-nq-base""": 5_12,
    """facebook/dpr-ctx_encoder-multiset-base""": 5_12,
}
_A							=  {
    """facebook/dpr-question_encoder-single-nq-base""": 5_12,
    """facebook/dpr-question_encoder-multiset-base""": 5_12,
}
_A							=  {
    """facebook/dpr-reader-single-nq-base""": 5_12,
    """facebook/dpr-reader-multiset-base""": 5_12,
}


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

class       lowerCamelCase	(       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
  SCREAMING_SNAKE_CASE					      =  DPRContextEncoderTokenizer




class       lowerCamelCase	(       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
  SCREAMING_SNAKE_CASE					      =  DPRQuestionEncoderTokenizer




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

_A							=  collections.namedtuple("""DPRReaderOutput""", ["""start_logits""", """end_logits""", """relevance_logits"""])


_A							=  R"""\n    Return a dictionary with the token ids of the input strings and other information to give to `.decode_best_spans`.\n    It converts the strings of a question and different passages (title and text) in a sequence of IDs (integers),\n    using the tokenizer and vocabulary. The resulting `input_ids` is a matrix of size `(n_passages, sequence_length)`\n    with the format:\n\n    [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(lowerCAmelCase__   )
class       lowerCamelCase	:


  '''simple docstring'''







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


        """simple docstring"""

        if titles is None and texts is None:
              return super().__call__(
                  _lowerCamelCase							,       padding=_lowerCamelCase							,       truncation=_lowerCamelCase							,       max_length=_lowerCamelCase							,       return_tensors=_lowerCamelCase							,       return_attention_mask=_lowerCamelCase							,       **_lowerCamelCase							,       )
        elif titles is None or texts is None:
              UpperCAmelCase__			:							Dict							=    titles if texts is None else texts
              return super().__call__(
                  _lowerCamelCase							,       _lowerCamelCase							,       padding=_lowerCamelCase							,       truncation=_lowerCamelCase							,       max_length=_lowerCamelCase							,       return_tensors=_lowerCamelCase							,       return_attention_mask=_lowerCamelCase							,       **_lowerCamelCase							,       )
        UpperCAmelCase__			:							int							=    titles if not isinstance(_lowerCamelCase							,       _lowerCamelCase	) else [titles]
        UpperCAmelCase__			:							int							=    texts if not isinstance(_lowerCamelCase							,       _lowerCamelCase	) else [texts]
        UpperCAmelCase__			:							int							=    len(_lowerCamelCase	)
        UpperCAmelCase__			:							Union[str, Any]							=    questions if not isinstance(_lowerCamelCase							,       _lowerCamelCase	) else [questions] * n_passages
        assert len(_lowerCamelCase	) == len(
            _lowerCamelCase	), F"""There should be as many titles than texts but got {len(_lowerCamelCase	)} titles and {len(_lowerCamelCase	)} texts."""
        UpperCAmelCase__			:							List[Any]							=    super().__call__(_lowerCamelCase							,       _lowerCamelCase							,       padding=_lowerCamelCase							,       truncation=_lowerCamelCase	)["""input_ids"""]
        UpperCAmelCase__			:							Dict							=    super().__call__(_lowerCamelCase							,       add_special_tokens=_lowerCamelCase							,       padding=_lowerCamelCase							,       truncation=_lowerCamelCase	)["""input_ids"""]
        UpperCAmelCase__			:							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(_lowerCamelCase							,       _lowerCamelCase	)
            ]
        }
        if return_attention_mask is not False:
              UpperCAmelCase__			:							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]	)
              UpperCAmelCase__			:							Tuple							=    attention_mask
        return self.pad(_lowerCamelCase							,       padding=_lowerCamelCase							,       max_length=_lowerCamelCase							,       return_tensors=_lowerCamelCase	)



  def  _a      (self							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase = 16							,       _lowerCamelCase = 64							,       _lowerCamelCase = 4							,       ):


        """simple docstring"""

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

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




  def  _a      (self							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase							,       ):


        """simple docstring"""

        UpperCAmelCase__			:							List[Any]							=    []
        for start_index, start_score in enumerate(_lowerCamelCase	):
              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)	)
        UpperCAmelCase__			:							int							=    sorted(_lowerCamelCase							,       key=lambda _lowerCamelCase	: x[1]							,       reverse=_lowerCamelCase	)
        UpperCAmelCase__			:							Union[str, Any]							=    []
        for (start_index, end_index), score in scores:
              assert start_index <= end_index, F"""Wrong span indices: [{start_index}:{end_index}]"""
              UpperCAmelCase__			:							Optional[Any]							=    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(_lowerCamelCase	) == top_spans:
                    break
        return chosen_span_intervals




@add_end_docstrings(lowerCAmelCase__   )
class       lowerCamelCase	(       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']
  SCREAMING_SNAKE_CASE					      =  DPRReaderTokenizer






'''simple docstring'''





import argparse
import logging
import os
from datetime import datetime

import numpy as np
import torch
from torch import nn
from torch.utils.data import DataLoader, RandomSampler, TensorDataset
from tqdm import tqdm

from transformers import GPTaLMHeadModel


lowercase_									=    logging.getLogger(__name__)




def lowerCAmelCase    (__A    ,      __A):



     """simple docstring"""



     if os.path.exists(__A):
          if os.path.exists(os.path.join(__A    ,      '''config.json''')) and os.path.isfile(
              os.path.join(__A    ,      '''config.json''')):
               os.remove(os.path.join(__A    ,      '''config.json'''))
          if os.path.exists(os.path.join(__A    ,      '''pytorch_model.bin''')) and os.path.isfile(
              os.path.join(__A    ,      '''pytorch_model.bin''')):
               os.remove(os.path.join(__A    ,      '''pytorch_model.bin'''))
     else:
          os.makedirs(__A)
     model.save_pretrained(__A)




def lowerCAmelCase    (__A    ,      __A=False):



     """simple docstring"""



     _a           =    2
     if unlogit:
          _a           =    torch.pow(__A    ,      __A)
     _a           =    p * torch.log(__A)
     _a           =    0
     return -plogp.sum(dim=-1)




def lowerCAmelCase    (__A):



     """simple docstring"""



     logger.info('''lv, h >\t''' + '''\t'''.join(F'''{x + 1}''' for x in range(len(__A))))
     for row in range(len(__A)):
          if tensor.dtype != torch.long:
               logger.info(F'''layer {row + 1}:\t''' + '''\t'''.join(F'''{x:.5f}''' for x in tensor[row].cpu().data))
          else:
               logger.info(F'''layer {row + 1}:\t''' + '''\t'''.join(F'''{x:d}''' for x in tensor[row].cpu().data))




def lowerCAmelCase    (__A    ,      __A    ,      __A    ,      __A=True    ,      __A=True    ,      __A=None    ,      __A=False):



     """simple docstring"""



     _a    ,       _a           =    model.config.num_hidden_layers, model.config.num_attention_heads
     _a           =    torch.zeros(__A    ,      __A).to(args.device)
     _a           =    torch.zeros(__A    ,      __A).to(args.device)

     if head_mask is None:
          _a           =    torch.ones(__A    ,      __A).to(args.device)

     head_mask.requires_grad_(requires_grad=__A)
     # If actually pruned attention multi-head, set head mask to None to avoid shape mismatch
     if actually_pruned:
          _a           =    None

     _a           =    0.0
     _a           =    0.0
     for step, inputs in enumerate(tqdm(__A    ,      desc='''Iteration'''    ,      disable=args.local_rank not in [-1, 0])):
          _a           =    tuple(t.to(args.device) for t in inputs)
          ((_a)    ,       )           =    inputs

          # Do a forward pass (not with torch.no_grad() since we need gradients for importance score - see below)
          _a           =    model(__A    ,      labels=__A    ,      head_mask=__A)
          #  (loss), lm_logits, presents, (all hidden_states), (attentions)
          _a    ,       _a    ,       _a           =    (
              outputs[0],
              outputs[1],
              outputs[-1],
          )  # Loss and logits are the first, attention the last
          loss.backward()  # Backpropagate to populate the gradients in the head mask
          total_loss += loss.detach().cpu().numpy()
          if compute_entropy:
               for layer, attn in enumerate(__A):
                    _a           =    entropy(attn.detach()    ,      __A)
                    attn_entropy[layer] += masked_entropy.sum(-1).sum(0).sum(0).detach()

          if compute_importance:
               head_importance += head_mask.grad.abs().detach()
          tot_tokens += torch.ones_like(__A).float().detach().sum().data

     # Normalize
     attn_entropy /= tot_tokens
     head_importance /= tot_tokens
     # Layerwise importance normalization
     if not args.dont_normalize_importance_by_layer:
          _a           =    2
          _a           =    torch.pow(torch.pow(__A    ,      __A).sum(-1)    ,      1 / exponent)
          head_importance /= norm_by_layer.unsqueeze(-1) + 1e-20

     if not args.dont_normalize_global_importance:
          _a           =    (head_importance - head_importance.min()) / (head_importance.max() - head_importance.min())

     # Print matrices
     if compute_entropy:
          logger.info('''Attention entropies''')
          print_ad_tensor(__A)
     if compute_importance:
          logger.info('''Head importance scores''')
          print_ad_tensor(__A)
     logger.info('''Head ranked by importance scores''')
     _a           =    torch.zeros(head_importance.numel()    ,      dtype=torch.long    ,      device=args.device)
     _a           =    torch.arange(
         head_importance.numel()    ,      device=args.device)
     _a           =    head_ranks.view_as(__A)
     print_ad_tensor(__A)
     return attn_entropy, head_importance, total_loss




def lowerCAmelCase    (__A    ,      __A    ,      __A):



     """simple docstring"""



     _a    ,       _a    ,       _a           =    compute_heads_importance(__A    ,      __A    ,      __A    ,      compute_entropy=__A)
     _a           =    1 / loss  # instead of downsteam score use the LM loss
     logger.info('''Pruning: original score: %f, threshold: %f'''    ,      __A    ,      original_score * args.masking_threshold)

     _a           =    torch.ones_like(__A)
     _a           =    max(1    ,      int(new_head_mask.numel() * args.masking_amount))

     _a           =    original_score
     while current_score >= original_score * args.masking_threshold:
          _a           =    new_head_mask.clone().detach()  # save current head mask
          # heads from least important to most - keep only not-masked heads
          _a           =    float('''Inf''')
          _a           =    head_importance.view(-1).sort()[1]

          if len(__A) <= num_to_mask:
               print('''BREAK BY num_to_mask''')
               break

          # mask heads
          _a           =    current_heads_to_mask[:num_to_mask]
          logger.info('''Heads to mask: %s'''    ,      str(current_heads_to_mask.tolist()))
          _a           =    new_head_mask.view(-1)
          _a           =    0.0
          _a           =    new_head_mask.view_as(__A)
          _a           =    new_head_mask.clone().detach()
          print_ad_tensor(__A)

          # Compute metric and head importance again
          _a    ,       _a    ,       _a           =    compute_heads_importance(
              __A    ,      __A    ,      __A    ,      compute_entropy=__A    ,      head_mask=__A)
          _a           =    1 / loss
          logger.info(
              '''Masking: current score: %f, remaining heads %d (%.1f percents)'''    ,      __A    ,      new_head_mask.sum()    ,      new_head_mask.sum() / new_head_mask.numel() * 100    ,      )

     logger.info('''Final head mask''')
     print_ad_tensor(__A)
     np.save(os.path.join(args.output_dir    ,      '''head_mask.npy''')    ,      head_mask.detach().cpu().numpy())

     return head_mask




def lowerCAmelCase    (__A    ,      __A    ,      __A    ,      __A):



     """simple docstring"""



     _a           =    datetime.now()
     _a    ,       _a    ,       _a           =    compute_heads_importance(
         __A    ,      __A    ,      __A    ,      compute_entropy=__A    ,      compute_importance=__A    ,      head_mask=__A)
     _a           =    1 / loss
     _a           =    datetime.now() - before_time

     _a           =    sum(p.numel() for p in model.parameters())
     _a           =    {
         layer: (1 - head_mask[layer].long()).nonzero().squeeze().tolist() for layer in range(len(__A))
     }

     for k, v in heads_to_prune.items():
          if isinstance(__A    ,      __A):
               _a           =    [
                   v,
               ]

     assert sum(len(__A) for h in heads_to_prune.values()) == (1 - head_mask.long()).sum().item()
     model.prune_heads(__A)
     _a           =    sum(p.numel() for p in model.parameters())

     _a           =    datetime.now()
     _a    ,       _a    ,       _a           =    compute_heads_importance(
         __A    ,      __A    ,      __A    ,      compute_entropy=__A    ,      compute_importance=__A    ,      head_mask=__A    ,      actually_pruned=__A    ,      )

     _a           =    1 / loss
     _a           =    datetime.now() - before_time

     logger.info(
         '''Pruning: original num of params: %.2e, after pruning %.2e (%.1f percents)'''    ,      __A    ,      __A    ,      pruned_num_params / original_num_params * 100    ,      )
     logger.info('''Pruning: score with masking: %f score with pruning: %f'''    ,      __A    ,      __A)
     logger.info('''Pruning: speed ratio (original timing / new timing): %f percents'''    ,      original_time / new_time * 100)
     save_model(__A    ,      args.output_dir)




def lowerCAmelCase    ():



     """simple docstring"""



     _a           =    argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         '''--data_dir'''    ,      default=__A    ,      type=__A    ,      required=__A    ,      help='''The input data dir. Should contain the .tsv files (or other data files) for the task.'''    ,      )
     parser.add_argument(
         '''--model_name_or_path'''    ,      default=__A    ,      type=__A    ,      required=__A    ,      help='''Path to pretrained model or model identifier from huggingface.co/models'''    ,      )
     parser.add_argument(
         '''--output_dir'''    ,      default=__A    ,      type=__A    ,      required=__A    ,      help='''The output directory where the model predictions and checkpoints will be written.'''    ,      )

     # Other parameters
     parser.add_argument(
         '''--config_name'''    ,      default=''''''    ,      type=__A    ,      help='''Pretrained config name or path if not the same as model_name_or_path'''    ,      )
     parser.add_argument(
         '''--tokenizer_name'''    ,      default=''''''    ,      type=__A    ,      help='''Pretrained tokenizer name or path if not the same as model_name_or_path'''    ,      )
     parser.add_argument(
         '''--cache_dir'''    ,      default=__A    ,      type=__A    ,      help='''Where do you want to store the pre-trained models downloaded from s3'''    ,      )
     parser.add_argument(
         '''--data_subset'''    ,      type=__A    ,      default=-1    ,      help='''If > 0: limit the data to a subset of data_subset instances.''')
     parser.add_argument(
         '''--overwrite_output_dir'''    ,      action='''store_true'''    ,      help='''Whether to overwrite data in output directory''')
     parser.add_argument(
         '''--overwrite_cache'''    ,      action='''store_true'''    ,      help='''Overwrite the cached training and evaluation sets''')

     parser.add_argument(
         '''--dont_normalize_importance_by_layer'''    ,      action='''store_true'''    ,      help='''Don\'t normalize importance score by layers''')
     parser.add_argument(
         '''--dont_normalize_global_importance'''    ,      action='''store_true'''    ,      help='''Don\'t normalize all importance scores between 0 and 1'''    ,      )

     parser.add_argument(
         '''--try_masking'''    ,      action='''store_true'''    ,      help='''Whether to try to mask head until a threshold of accuracy.''')
     parser.add_argument(
         '''--masking_threshold'''    ,      default=0.9    ,      type=__A    ,      help='''masking threshold in term of metrics (stop masking when metric < threshold * original metric value).'''    ,      )
     parser.add_argument(
         '''--masking_amount'''    ,      default=0.1    ,      type=__A    ,      help='''Amount to heads to masking at each masking step.''')
     parser.add_argument('''--metric_name'''    ,      default='''acc'''    ,      type=__A    ,      help='''Metric to use for head masking.''')

     parser.add_argument(
         '''--max_seq_length'''    ,      default=128    ,      type=__A    ,      help=(
             '''The maximum total input sequence length after WordPiece tokenization. \n'''
             '''Sequences longer than this will be truncated, sequences shorter padded.'''
         )    ,      )
     parser.add_argument('''--batch_size'''    ,      default=1    ,      type=__A    ,      help='''Batch size.''')

     parser.add_argument('''--seed'''    ,      type=__A    ,      default=42)
     parser.add_argument('''--local_rank'''    ,      type=__A    ,      default=-1    ,      help='''local_rank for distributed training on gpus''')
     parser.add_argument('''--no_cuda'''    ,      action='''store_true'''    ,      help='''Whether not to use CUDA when available''')
     parser.add_argument('''--server_ip'''    ,      type=__A    ,      default=''''''    ,      help='''Can be used for distant debugging.''')
     parser.add_argument('''--server_port'''    ,      type=__A    ,      default=''''''    ,      help='''Can be used for distant debugging.''')
     _a           =    parser.parse_args()

     if args.server_ip and args.server_port:
          # Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
          import ptvsd

          print('''Waiting for debugger attach''')
          ptvsd.enable_attach(address=(args.server_ip, args.server_port)    ,      redirect_output=__A)
          ptvsd.wait_for_attach()

     # Setup devices and distributed training
     if args.local_rank == -1 or args.no_cuda:
          _a           =    torch.device('''cuda''' if torch.cuda.is_available() and not args.no_cuda else '''cpu''')
          _a           =    0 if args.no_cuda else torch.cuda.device_count()
     else:
          torch.cuda.set_device(args.local_rank)
          _a           =    torch.device('''cuda'''    ,      args.local_rank)
          _a           =    1
          torch.distributed.init_process_group(backend='''nccl''')  # Initializes the distributed backend

     # Setup logging
     logging.basicConfig(level=logging.INFO if args.local_rank in [-1, 0] else logging.WARN)
     logger.info('''device: {} n_gpu: {}, distributed: {}'''.format(args.device    ,      args.n_gpu    ,      bool(args.local_rank != -1)))

     _a           =    GPTaLMHeadModel.from_pretrained(args.model_name_or_path)

     # Distributed and parallel training
     model.to(args.device)
     if args.local_rank != -1:
          _a           =    nn.parallel.DistributedDataParallel(
              __A    ,      device_ids=[args.local_rank]    ,      output_device=args.local_rank    ,      find_unused_parameters=__A)
     elif args.n_gpu > 1:
          _a           =    nn.DataParallel(__A)

     # Print/save training arguments
     os.makedirs(args.output_dir    ,      exist_ok=__A)
     torch.save(__A    ,      os.path.join(args.output_dir    ,      '''run_args.bin'''))
     logger.info('''Training/evaluation parameters %s'''    ,      __A)

     # Prepare dataset
     _a           =    np.concatenate(
         [
             np.loadtxt(args.data_dir    ,      dtype=np.intaa),
         ])
     _a           =    (torch.from_numpy(__A),)
     _a           =    TensorDataset(*__A)
     _a           =    RandomSampler(__A)
     _a           =    DataLoader(__A    ,      sampler=__A    ,      batch_size=args.batch_size)

     # Compute head entropy and importance score
     compute_heads_importance(__A    ,      __A    ,      __A)

     # Try head masking (set heads to zero until the score goes under a threshole)
     # and head pruning (remove masked heads and see the effect on the network)
     if args.try_masking and args.masking_threshold > 0.0 and args.masking_threshold < 1.0:
          _a           =    mask_heads(__A    ,      __A    ,      __A)
          prune_heads(__A    ,      __A    ,      __A    ,      __A)


if __name__ == "__main__":
  main()



