





import argparse
import json
import sys
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import List

import torch
import torch.nn as nn
from huggingface_hub import cached_download, hf_hub_download
from torch import Tensor

from transformers import AutoImageProcessor, VanConfig, VanForImageClassification
from transformers.models.deprecated.van.modeling_van import VanLayerScaling
from transformers.utils import logging


logging.set_verbosity_info()
lowerCAmelCase_        =			logging.get_logger(__name__)



@dataclass
class 				_lowerCAmelCase			:


  '''simple docstring'''
  a_							:				nn.Module
  a_							:				List[nn.Module]					    =field(default_factory=UpperCAmelCase_							)
  a_							:				list					    =field(default_factory=UpperCAmelCase_							)







  def UpperCamelCase_       (					self					:    Any					, UpperCamelCase					:    List[Any]					, UpperCamelCase					:    Tensor					, UpperCamelCase					:    Tensor							):


    '''simple docstring'''





    _snake_case      : Optional[Any]														=		len(list(m.modules()							)							) == 1 or isinstance(UpperCamelCase					, nn.Convad							) or isinstance(UpperCamelCase					, nn.BatchNormad							)
    if has_not_submodules:
      if not isinstance(UpperCamelCase					, UpperCamelCase							):
        self.traced.append(UpperCamelCase							)







  def __call__(					self					:    List[Any]					, UpperCamelCase					:    Tensor							):


    '''simple docstring'''





    for m in self.module.modules():
      self.handles.append(m.register_forward_hook(self._forward_hook							)							)
    self.module(UpperCamelCase							)
    [x.remove() for x in self.handles]
    return self







  @property
  def UpperCamelCase_       (					self					:    Any							):


    '''simple docstring'''





    return list(filter(lambda UpperCamelCase							: len(list(x.state_dict().keys()							)							) > 0					, self.traced							)							)




@dataclass
class 				_lowerCAmelCase			:


  '''simple docstring'''
  a_							:				nn.Module
  a_							:				nn.Module
  a_							:				int					    =0
  a_							:				List					    =field(default_factory=UpperCAmelCase_							)
  a_							:				List					    =field(default_factory=UpperCAmelCase_							)







  def __call__(					self					:    Union[str, Any]					, UpperCamelCase					:    Tensor							):


    '''simple docstring'''





    _snake_case      : Dict														=		Tracker(self.dest							)(UpperCamelCase							).parametrized
    _snake_case      : List[Any]														=		Tracker(self.src							)(UpperCamelCase							).parametrized

    _snake_case      : List[str]														=		list(filter(lambda UpperCamelCase							: type(UpperCamelCase							) not in self.src_skip					, UpperCamelCase							)							)
    _snake_case      : Optional[int]														=		list(filter(lambda UpperCamelCase							: type(UpperCamelCase							) not in self.dest_skip					, UpperCamelCase							)							)

    if len(UpperCamelCase							) != len(UpperCamelCase							):
      raise Exception(
          f"""Numbers of operations are different. Source module has {len(UpperCamelCase							)} operations while"""
          f""" destination module has {len(UpperCamelCase							)}."""							)

    for dest_m, src_m in zip(UpperCamelCase					, UpperCamelCase							):
      dest_m.load_state_dict(src_m.state_dict()							)
      if self.verbose == 1:
        print(f"""Transfered from={src_m} to={dest_m}"""							)




def 			lowerCamelCase_		(					lowerCAmelCase:		nn.Module			,							lowerCAmelCase:		nn.Module )->						nn.Module:
  # nn.Parameter cannot be tracked by the Tracker, thus we need to manually convert them
  _snake_case      : List[str]														=		from_model.state_dict()
  _snake_case      : Optional[Any]														=		our_model.state_dict()
  _snake_case      : int														=		our_model.config
  _snake_case      : Optional[Any]														=		[]
  for stage_idx in range(len(config.hidden_sizes ) ):
    for block_id in range(config.depths[stage_idx] ):
      _snake_case      : int														=		F"""block{stage_idx + 1}.{block_id}.layer_scale_1"""
      _snake_case      : Tuple														=		F"""van.encoder.stages.{stage_idx}.layers.{block_id}.attention_scaling.weight"""

      all_keys.append((from_key, to_key) )
      _snake_case      : Tuple														=		F"""block{stage_idx + 1}.{block_id}.layer_scale_2"""
      _snake_case      : str														=		F"""van.encoder.stages.{stage_idx}.layers.{block_id}.mlp_scaling.weight"""

      all_keys.append((from_key, to_key) )

  for from_key, to_key in all_keys:
    _snake_case      : Union[str, Any]														=		from_state_dict.pop(lowerCAmelCase )

  our_model.load_state_dict(lowerCAmelCase )
  return our_model


def 			lowerCamelCase_		(					lowerCAmelCase:		str			,							lowerCAmelCase:		VanConfig			,							lowerCAmelCase:		str			,							lowerCAmelCase:		nn.Module			,							lowerCAmelCase:		Path			,							lowerCAmelCase:		bool = True			,							)->						Dict:
  print(F"""Downloading weights for {name}...""" )
  _snake_case      : Optional[Any]														=		cached_download(lowerCAmelCase )
  print(F"""Converting {name}...""" )
  _snake_case      : Optional[Any]														=		torch.load(lowerCAmelCase )['state_dict']
  from_model.load_state_dict(lowerCAmelCase )
  from_model.eval()
  with torch.no_grad():
    _snake_case      : Optional[Any]														=		VanForImageClassification(lowerCAmelCase ).eval()
    _snake_case      : Dict														=		ModuleTransfer(src=lowerCAmelCase			,							dest=lowerCAmelCase )
    _snake_case      : Any														=		torch.randn((1, 3, 2_24, 2_24) )
    module_transfer(lowerCAmelCase )
    _snake_case      : int														=		copy_parameters(lowerCAmelCase			,							lowerCAmelCase )

  if not torch.allclose(from_model(lowerCAmelCase )			,							our_model(lowerCAmelCase ).logits ):
    raise ValueError('The model logits don\'t match the original one.' )

  _snake_case      : Tuple														=		name
  print(lowerCAmelCase )

  if push_to_hub:
    our_model.push_to_hub(
        repo_path_or_name=save_directory / checkpoint_name			,							commit_message='Add model'			,							use_temp_dir=lowerCAmelCase			,							)

    # we can use the convnext one
    _snake_case      : Tuple														=		AutoImageProcessor.from_pretrained('facebook/convnext-base-224-22k-1k' )
    image_processor.push_to_hub(
        repo_path_or_name=save_directory / checkpoint_name			,							commit_message='Add image processor'			,							use_temp_dir=lowerCAmelCase			,							)

    print(F"""Pushed {checkpoint_name}""" )


def 			lowerCamelCase_		(					lowerCAmelCase:		Path			,							lowerCAmelCase:		str = None			,							lowerCAmelCase:		bool = True )->						Optional[Any]:
  _snake_case      : Optional[int]														=		'imagenet-1k-id2label.json'
  _snake_case      : Tuple														=		10_00

  _snake_case      : Optional[int]														=		'huggingface/label-files'
  _snake_case      : List[Any]														=		num_labels
  _snake_case      : str														=		json.load(open(hf_hub_download(lowerCAmelCase			,							lowerCAmelCase			,							repo_type='dataset' )			,							'r' ) )
  _snake_case      : Tuple														=		{int(lowerCAmelCase ): v for k, v in idalabel.items()}

  _snake_case      : Optional[int]														=		idalabel
  _snake_case      : str														=		{v: k for k, v in idalabel.items()}

  _snake_case      : Union[str, Any]														=		partial(lowerCAmelCase			,							num_labels=lowerCAmelCase			,							idalabel=lowerCAmelCase			,							labelaid=lowerCAmelCase )

  _snake_case      : Dict														=		{
      'van-tiny': ImageNetPreTrainedConfig(
          hidden_sizes=[32, 64, 1_60, 2_56]			,							depths=[3, 3, 5, 2]			,							mlp_ratios=[8, 8, 4, 4]			,							),
      'van-small': ImageNetPreTrainedConfig(
          hidden_sizes=[64, 1_28, 3_20, 5_12]			,							depths=[2, 2, 4, 2]			,							mlp_ratios=[8, 8, 4, 4]			,							),
      'van-base': ImageNetPreTrainedConfig(
          hidden_sizes=[64, 1_28, 3_20, 5_12]			,							depths=[3, 3, 12, 3]			,							mlp_ratios=[8, 8, 4, 4]			,							),
      'van-large': ImageNetPreTrainedConfig(
          hidden_sizes=[64, 1_28, 3_20, 5_12]			,							depths=[3, 5, 27, 3]			,							mlp_ratios=[8, 8, 4, 4]			,							),
  }

  _snake_case      : str														=		{
      'van-tiny': van_tiny,
      'van-small': van_small,
      'van-base': van_base,
      'van-large': van_large,
  }

  _snake_case      : Union[str, Any]														=		{
      'van-tiny': (
          'https://huggingface.co/Visual-Attention-Network/VAN-Tiny-original/resolve/main/van_tiny_754.pth.tar'
      ),
      'van-small': (
          'https://huggingface.co/Visual-Attention-Network/VAN-Small-original/resolve/main/van_small_811.pth.tar'
      ),
      'van-base': (
          'https://huggingface.co/Visual-Attention-Network/VAN-Base-original/resolve/main/van_base_828.pth.tar'
      ),
      'van-large': (
          'https://huggingface.co/Visual-Attention-Network/VAN-Large-original/resolve/main/van_large_839.pth.tar'
      ),
  }

  if model_name:
    convert_weight_and_push(
        lowerCAmelCase			,							names_to_config[model_name]			,							checkpoint=names_to_original_checkpoints[model_name]			,							from_model=names_to_original_models[model_name]()			,							save_directory=lowerCAmelCase			,							push_to_hub=lowerCAmelCase			,							)
  else:
    for model_name, config in names_to_config.items():
      convert_weight_and_push(
          lowerCAmelCase			,							lowerCAmelCase			,							checkpoint=names_to_original_checkpoints[model_name]			,							from_model=names_to_original_models[model_name]()			,							save_directory=lowerCAmelCase			,							push_to_hub=lowerCAmelCase			,							)


if __name__ == "__main__":
      lowerCAmelCase_        =			argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          """--model-name""",
          default=None,
          type=str,
          help=(
              """The name of the model you wish to convert, it must be one of the supported resnet* architecture,"""
              """ currently: van-tiny/small/base/large. If `None`, all of them will the converted."""
          ),
      )
      parser.add_argument(
          """--pytorch_dump_folder_path""",
          default=None,
          type=Path,
          required=True,
          help="""Path to the output PyTorch model directory.""",
      )
      parser.add_argument(
          """--van_dir""",
          required=True,
          type=Path,
          help=(
              """A path to VAN's original implementation directory. You can download from here:"""
              """ https://github.com/Visual-Attention-Network/VAN-Classification"""
          ),
      )
      parser.add_argument(
          """--push_to_hub""",
          default=True,
          type=bool,
          required=False,
          help="""If True, push model and image processor to the hub.""",
      )

      lowerCAmelCase_        =			parser.parse_args()
      lowerCAmelCase_        =			args.pytorch_dump_folder_path
      pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
      lowerCAmelCase_        =			args.van_dir
      # append the path to the parents to maskformer dir
      sys.path.append(str(van_dir.parent))
      from van.models.van import van_base, van_large, van_small, van_tiny

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











import datasets

from .evaluate import (
    apply_no_ans_threshold,
    find_all_best_thresh,
    get_raw_scores,
    make_eval_dict,
    make_qid_to_has_ans,
    merge_eval,
)


lowerCAmelCase_        =			"""\
@inproceedings{Rajpurkar2016SQuAD10,
  title={SQuAD: 100, 000+ Questions for Machine Comprehension of Text},
  author={Pranav Rajpurkar and Jian Zhang and Konstantin Lopyrev and Percy Liang},
  booktitle={EMNLP},
  year={2016}
}
"""

lowerCAmelCase_        =			"""
This metric wrap the official scoring script for version 2 of the Stanford Question
Answering Dataset (SQuAD).

Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by
crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span,
from the corresponding reading passage, or the question might be unanswerable.

SQuAD2.0 combines the 100,000 questions in SQuAD1.1 with over 50,000 unanswerable questions
written adversarially by crowdworkers to look similar to answerable ones.
To do well on SQuAD2.0, systems must not only answer questions when possible, but also
determine when no answer is supported by the paragraph and abstain from answering.
"""

lowerCAmelCase_        =			"""
Computes SQuAD v2 scores (F1 and EM).
Args:
    predictions: List of triple for question-answers to score with the following elements:
        - the question-answer 'id' field as given in the references (see below)
        - the text of the answer
        - the probability that the question has no answer
    references: List of question-answers dictionaries with the following key-values:
            - 'id': id of the question-answer pair (see above),
            - 'answers': a list of Dict {'text': text of the answer as a string}
    no_answer_threshold: float
        Probability threshold to decide that a question has no answer.
Returns:
    'exact': Exact match (the normalized answer exactly match the gold answer)
    'f1': The F-score of predicted tokens versus the gold answer
    'total': Number of score considered
    'HasAns_exact': Exact match (the normalized answer exactly match the gold answer)
    'HasAns_f1': The F-score of predicted tokens versus the gold answer
    'HasAns_total': Number of score considered
    'NoAns_exact': Exact match (the normalized answer exactly match the gold answer)
    'NoAns_f1': The F-score of predicted tokens versus the gold answer
    'NoAns_total': Number of score considered
    'best_exact': Best exact match (with varying threshold)
    'best_exact_thresh': No-answer probability threshold associated to the best exact match
    'best_f1': Best F1 (with varying threshold)
    'best_f1_thresh': No-answer probability threshold associated to the best F1
Examples:

    >>> predictions = [{'prediction_text': '1976', 'id': '56e10a3be3433e1400422b22', 'no_answer_probability': 0.}]
    >>> references = [{'answers': {'answer_start': [97], 'text': ['1976']}, 'id': '56e10a3be3433e1400422b22'}]
    >>> squad_v2_metric = datasets.load_metric(\"squad_v2\")
    >>> results = squad_v2_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'exact': 100.0, 'f1': 100.0, 'total': 1, 'HasAns_exact': 100.0, 'HasAns_f1': 100.0, 'HasAns_total': 1, 'best_exact': 100.0, 'best_exact_thresh': 0.0, 'best_f1': 100.0, 'best_f1_thresh': 0.0}
"""



@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION							,			_KWARGS_DESCRIPTION							)
class 				_lowerCAmelCase			(	datasets.Metric							):


  '''simple docstring'''







  def UpperCamelCase_       (					self					:    str							):


    '''simple docstring'''





    return datasets.MetricInfo(
        description=_DESCRIPTION					, citation=_CITATION					, inputs_description=_KWARGS_DESCRIPTION					, features=datasets.Features(
            {
                'predictions': {
                    'id': datasets.Value('string'							),
                    'prediction_text': datasets.Value('string'							),
                    'no_answer_probability': datasets.Value('float32'							),
                },
                'references': {
                    'id': datasets.Value('string'							),
                    'answers': datasets.features.Sequence(
                        {'text': datasets.Value('string'							), 'answer_start': datasets.Value('int32'							)}							),
                },
            }							)					, codebase_urls=['https://rajpurkar.github.io/SQuAD-explorer/']					, reference_urls=['https://rajpurkar.github.io/SQuAD-explorer/']					, )







  def UpperCamelCase_       (					self					:    Optional[Any]					, UpperCamelCase					:    List[str]					, UpperCamelCase					:    Optional[Any]					, UpperCamelCase					:    str=1.0							):


    '''simple docstring'''





    _snake_case      : Dict														=		{p['id']: p['no_answer_probability'] for p in predictions}
    _snake_case      : Dict														=		[{'paragraphs': [{'qas': references}]}]
    _snake_case      : Dict														=		{p['id']: p['prediction_text'] for p in predictions}

    _snake_case      : Optional[Any]														=		make_qid_to_has_ans(UpperCamelCase							)  # maps qid to True/False
    _snake_case      : str														=		[k for k, v in qid_to_has_ans.items() if v]
    _snake_case      : Optional[Any]														=		[k for k, v in qid_to_has_ans.items() if not v]

    _snake_case							,    _snake_case      : Union[str, Any]														=		get_raw_scores(UpperCamelCase					, UpperCamelCase							)
    _snake_case      : int														=		apply_no_ans_threshold(UpperCamelCase					, UpperCamelCase					, UpperCamelCase					, UpperCamelCase							)
    _snake_case      : List[Any]														=		apply_no_ans_threshold(UpperCamelCase					, UpperCamelCase					, UpperCamelCase					, UpperCamelCase							)
    _snake_case      : Optional[Any]														=		make_eval_dict(UpperCamelCase					, UpperCamelCase							)

    if has_ans_qids:
      _snake_case      : Optional[int]														=		make_eval_dict(UpperCamelCase					, UpperCamelCase					, qid_list=UpperCamelCase							)
      merge_eval(UpperCamelCase					, UpperCamelCase					, 'HasAns'							)
    if no_ans_qids:
      _snake_case      : List[str]														=		make_eval_dict(UpperCamelCase					, UpperCamelCase					, qid_list=UpperCamelCase							)
      merge_eval(UpperCamelCase					, UpperCamelCase					, 'NoAns'							)
    find_all_best_thresh(UpperCamelCase					, UpperCamelCase					, UpperCamelCase					, UpperCamelCase					, UpperCamelCase					, UpperCamelCase							)
    return dict(UpperCamelCase							)




