
"""simple docstring"""




import doctest
import glob
import importlib
import inspect
import os
import re
from contextlib import contextmanager
from functools import wraps
from unittest.mock import patch

import numpy as np
import pytest
from absl.testing import parameterized

import datasets
from datasets import load_metric

from .utils import for_all_test_methods, local, slow


# mark all tests as integration
A__   :					Any							=   pytest.mark.integration


A__   :					List[str]							=   {'comet'}
A__   :					Optional[int]							=   importlib.util.find_spec('fairseq') is not None

A__   :					int							=   {'code_eval'}
A__   :					List[str]							=   os.name == 'nt'

A__   :					Optional[int]							=   {'bertscore', 'frugalscore', 'perplexity'}
A__   :					Union[str, Any]							=   importlib.util.find_spec('transformers') is not None




def  _lowerCAmelCase		(		_UpperCamelCase						):






  """simple docstring"""



  @wraps(_UpperCamelCase						)
  def wrapper(self ,				_UpperCamelCase						):
    if not _has_fairseq and metric_name in REQUIRE_FAIRSEQ:
      self.skipTest('''"test requires Fairseq"'''						)
    else:
      test_case(self ,				_UpperCamelCase						)

  return wrapper




def  _lowerCAmelCase		(		_UpperCamelCase						):






  """simple docstring"""



  @wraps(_UpperCamelCase						)
  def wrapper(self ,				_UpperCamelCase						):
    if not _has_transformers and metric_name in REQUIRE_TRANSFORMERS:
      self.skipTest('''"test requires transformers"'''						)
    else:
      test_case(self ,				_UpperCamelCase						)

  return wrapper




def  _lowerCAmelCase		(		_UpperCamelCase						):






  """simple docstring"""



  @wraps(_UpperCamelCase						)
  def wrapper(self ,				_UpperCamelCase						):
    if _on_windows and metric_name in UNSUPPORTED_ON_WINDOWS:
      self.skipTest('''"test not supported on Windows"'''						)
    else:
      test_case(self ,				_UpperCamelCase						)

  return wrapper




def  _lowerCAmelCase		(		):






  """simple docstring"""



  _lowercase:			Union[str, Any]       =     [metric_dir.split(os.sep						)[-2] for metric_dir in glob.glob('''./metrics/*/'''						)]
  return [{"testcase_name": x, "metric_name": x} for x in metrics if x != "gleu"]  # gleu is unfinished


@parameterized.named_parameters(get_local_metric_names() )
@for_all_test_methods(
    SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__ )
@local
class 			__magic_name__       (       parameterized.TestCase ):
       UpperCamelCase_   =		{}
       UpperCamelCase_   =		None
       @pytest.mark.filterwarnings('''ignore:metric_module_factory is deprecated:FutureWarning'''       )
       @pytest.mark.filterwarnings('''ignore:load_metric is deprecated:FutureWarning'''       )
       def 							lowercase_							(     self ,				A_       )     ->	str:





         """simple docstring"""




         _lowercase:			Dict       =     '''[...]'''
         _lowercase:			Optional[Any]       =     importlib.import_module(
             datasets.load.metric_module_factory(os.path.join('''metrics''' ,				A_       )       ).module_path       )
         _lowercase:			List[Any]       =     datasets.load.import_main_class(metric_module.__name__ ,				dataset=A_       )
         # check parameters
         _lowercase:			Optional[Any]       =     inspect.signature(metric._compute       ).parameters
         self.assertTrue(all(p.kind != p.VAR_KEYWORD for p in parameters.values()       )       )  # no **kwargs
         # run doctest
         with self.patch_intensive_calls(A_ ,				metric_module.__name__       ):
           with self.use_local_metrics():
             try:
               _lowercase:			List[str]       =     doctest.testmod(A_ ,				verbose=A_ ,				raise_on_error=A_       )
             except doctest.UnexpectedException as e:
               raise e.exc_info[1]  # raise the exception that doctest caught
         self.assertEqual(results.failed ,				0       )
         self.assertGreater(results.attempted ,				1       )
       @slow
       def 							lowercase_							(     self ,				A_       )     ->	Dict:





         """simple docstring"""




         _lowercase:			Optional[Any]       =     '''[...]'''
         _lowercase:			Optional[Any]       =     importlib.import_module(
             datasets.load.metric_module_factory(os.path.join('''metrics''' ,				A_       )       ).module_path       )
         # run doctest
         with self.use_local_metrics():
           _lowercase:			str       =     doctest.testmod(A_ ,				verbose=A_ ,				raise_on_error=A_       )
         self.assertEqual(results.failed ,				0       )
         self.assertGreater(results.attempted ,				1       )
       @contextmanager
       def 							lowercase_							(     self ,				A_ ,				A_       )     ->	int:





         """simple docstring"""




         if metric_name in self.INTENSIVE_CALLS_PATCHER:
           with self.INTENSIVE_CALLS_PATCHER[metric_name](A_       ):
             yield
         else:
           yield
       @contextmanager
       def 							lowercase_							(     self       )     ->	str:





         """simple docstring"""




         def load_local_metric(A_ ,				*A_ ,				**A_       ):
           return load_metric(os.path.join('''metrics''' ,				A_       ) ,				*A_ ,				**A_       )

         with patch('''datasets.load_metric'''       ) as mock_load_metric:
           _lowercase:			int       =     load_local_metric
           yield



       @classmethod
       def 							lowercase_							(     cls ,				A_       )     ->	Any:





         """simple docstring"""




         def wrapper(A_       ):
           _lowercase:			List[str]       =     contextmanager(A_       )
           _lowercase:			int       =     patcher
           return patcher

         return wrapper




@LocalMetricTest.register_intensive_calls_patcher('''bleurt'''						)
def  _lowerCAmelCase		(		_UpperCamelCase						):






  """simple docstring"""



  import tensorflow.compat.va as tf
  from bleurt.score import Predictor

  tf.flags.DEFINE_string('''sv''' ,				'''''' ,				''''''						)  # handle pytest cli flags


  class 			__magic_name__       (       SCREAMING_SNAKE_CASE__ ):
         def 							lowercase_							(     self ,				A_       )     ->	Tuple:





           """simple docstring"""




           assert len(input_dict['''input_ids''']       ) == 2
           return np.array([1.03, 1.04]       )

    # mock predict_fn which is supposed to do a forward pass with a bleurt model
  with patch('''bleurt.score._create_predictor'''						) as mock_create_predictor:
    _lowercase:			str       =     MockedPredictor()
    yield




@LocalMetricTest.register_intensive_calls_patcher('''bertscore'''						)
def  _lowerCAmelCase		(		_UpperCamelCase						):






  """simple docstring"""



  import torch

  def bert_cos_score_idf(_UpperCamelCase ,				_UpperCamelCase ,				*_UpperCamelCase ,				**_UpperCamelCase						):
    return torch.tensor([[1.0, 1.0, 1.0]] * len(_UpperCamelCase						)						)

  # mock get_model which is supposed to do download a bert model
  # mock bert_cos_score_idf which is supposed to do a forward pass with a bert model
  with patch('''bert_score.scorer.get_model'''						), patch(
      '''bert_score.scorer.bert_cos_score_idf'''						) as mock_bert_cos_score_idf:
    _lowercase:			List[Any]       =     bert_cos_score_idf
    yield




@LocalMetricTest.register_intensive_calls_patcher('''comet'''						)
def  _lowerCAmelCase		(		_UpperCamelCase						):






  """simple docstring"""



  def load_from_checkpoint(_UpperCamelCase						):


    class 			__magic_name__       :
           def 							lowercase_							(     self ,				A_ ,				*A_ ,				**A_       )     ->	Tuple:





             """simple docstring"""




             assert len(A_       ) == 2
             _lowercase:			List[Any]       =     [0.19, 0.92]
             return scores, sum(A_       ) / len(A_       )

    return Model()

  # mock load_from_checkpoint which is supposed to do download a bert model
  # mock load_from_checkpoint which is supposed to do download a bert model
  with patch('''comet.download_model'''						) as mock_download_model:
    _lowercase:			int       =     None
    with patch('''comet.load_from_checkpoint'''						) as mock_load_from_checkpoint:
      _lowercase:			Any       =     load_from_checkpoint
      yield







def  _lowerCAmelCase		(		):






  """simple docstring"""



  _lowercase:			Optional[int]       =     load_metric(os.path.join('''metrics''' ,				'''seqeval'''						)						)
  _lowercase:			Optional[int]       =     '''ERROR'''
  _lowercase:			Dict       =     f'''Scheme should be one of [IOB1, IOB2, IOE1, IOE2, IOBES, BILOU], got {wrong_scheme}'''
  with pytest.raises(_UpperCamelCase ,				match=re.escape(_UpperCamelCase						)						):
    metric.compute(predictions=[] ,				references=[] ,				scheme=_UpperCamelCase						)


"""simple docstring"""




import argparse
import json
from collections import OrderedDict
from functools import partial
from pathlib import Path

import timm
import torch
from huggingface_hub import hf_hub_download

from transformers import LevitConfig, LevitForImageClassificationWithTeacher, LevitImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
A__   :					List[Any]							=   logging.get_logger()




def  _lowerCAmelCase		(		_UpperCamelCase ,				_UpperCamelCase ,				_UpperCamelCase ,				_UpperCamelCase ,				_UpperCamelCase = True						):






  """simple docstring"""



  print(f'''Converting {name}...'''						)

  with torch.no_grad():
    if hidden_sizes == 128:
      if name[-1] == "S":
        _lowercase:			int       =     timm.create_model('''levit_128s''' ,				pretrained=_UpperCamelCase						)
      else:
        _lowercase:			int       =     timm.create_model('''levit_128''' ,				pretrained=_UpperCamelCase						)
    if hidden_sizes == 192:
      _lowercase:			str       =     timm.create_model('''levit_192''' ,				pretrained=_UpperCamelCase						)
    if hidden_sizes == 256:
      _lowercase:			int       =     timm.create_model('''levit_256''' ,				pretrained=_UpperCamelCase						)
    if hidden_sizes == 384:
      _lowercase:			Dict       =     timm.create_model('''levit_384''' ,				pretrained=_UpperCamelCase						)

    from_model.eval()
    _lowercase:			Any       =     LevitForImageClassificationWithTeacher(_UpperCamelCase						).eval()
    _lowercase:			Union[str, Any]       =     OrderedDict()

    _lowercase:			Optional[Any]       =     from_model.state_dict()
    _lowercase:			List[Any]       =     list(from_model.state_dict().keys()						)
    _lowercase:			int       =     list(our_model.state_dict().keys()						)
    print(len(_UpperCamelCase						) ,				len(_UpperCamelCase						)						)
    for i in range(len(_UpperCamelCase						)						):
      _lowercase:			Any       =     weights[og_keys[i]]
    our_model.load_state_dict(_UpperCamelCase						)

    _lowercase:			int       =     torch.randn((2, 3, 224, 224)						)
    _lowercase:			List[str]       =     from_model(_UpperCamelCase						)
    _lowercase:			Optional[int]       =     our_model(_UpperCamelCase						).logits

  assert torch.allclose(_UpperCamelCase ,				_UpperCamelCase						), "The model logits don't match the original one."

  _lowercase:			Union[str, Any]       =     name
  print(_UpperCamelCase						)

  if push_to_hub:
    our_model.save_pretrained(save_directory / checkpoint_name						)
    _lowercase:			Dict       =     LevitImageProcessor()
    image_processor.save_pretrained(save_directory / checkpoint_name						)

    print(f'''Pushed {checkpoint_name}'''						)




def  _lowerCAmelCase		(		_UpperCamelCase ,				_UpperCamelCase = None ,				_UpperCamelCase = True						):






  """simple docstring"""



  _lowercase:			int       =     '''imagenet-1k-id2label.json'''
  _lowercase:			int       =     1_000
  _lowercase:			Tuple       =     (1, num_labels)

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

  _lowercase:			int       =     idalabel
  _lowercase:			Tuple       =     {v: k for k, v in idalabel.items()}

  _lowercase:			Tuple       =     partial(_UpperCamelCase ,				num_labels=_UpperCamelCase ,				idalabel=_UpperCamelCase ,				labelaid=_UpperCamelCase						)

  _lowercase:			Union[str, Any]       =     {
      '''levit-128S''': 128,
      '''levit-128''': 128,
      '''levit-192''': 192,
      '''levit-256''': 256,
      '''levit-384''': 384,
  }

  _lowercase:			int       =     {
      '''levit-128S''': ImageNetPreTrainedConfig(
          hidden_sizes=[128, 256, 384] ,				num_attention_heads=[4, 6, 8] ,				depths=[2, 3, 4] ,				key_dim=[16, 16, 16] ,				drop_path_rate=0 ,				),
      '''levit-128''': ImageNetPreTrainedConfig(
          hidden_sizes=[128, 256, 384] ,				num_attention_heads=[4, 8, 12] ,				depths=[4, 4, 4] ,				key_dim=[16, 16, 16] ,				drop_path_rate=0 ,				),
      '''levit-192''': ImageNetPreTrainedConfig(
          hidden_sizes=[192, 288, 384] ,				num_attention_heads=[3, 5, 6] ,				depths=[4, 4, 4] ,				key_dim=[32, 32, 32] ,				drop_path_rate=0 ,				),
      '''levit-256''': ImageNetPreTrainedConfig(
          hidden_sizes=[256, 384, 512] ,				num_attention_heads=[4, 6, 8] ,				depths=[4, 4, 4] ,				key_dim=[32, 32, 32] ,				drop_path_rate=0 ,				),
      '''levit-384''': ImageNetPreTrainedConfig(
          hidden_sizes=[384, 512, 768] ,				num_attention_heads=[6, 9, 12] ,				depths=[4, 4, 4] ,				key_dim=[32, 32, 32] ,				drop_path_rate=0.1 ,				),
  }

  if model_name:
    convert_weight_and_push(
        names_to_hidden_sizes[model_name] ,				_UpperCamelCase ,				names_to_config[model_name] ,				_UpperCamelCase ,				_UpperCamelCase						)
  else:
    for model_name, config in names_to_config.items():
      convert_weight_and_push(names_to_hidden_sizes[model_name] ,				_UpperCamelCase ,				_UpperCamelCase ,				_UpperCamelCase ,				_UpperCamelCase						)
  return config, expected_shape


if __name__ == "__main__":
  A__   :					Any							=   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 Levit* architecture,',
  )
  parser.add_argument(
      '--pytorch_dump_folder_path',
      default='levit-dump-folder/',
      type=Path,
      required=False,
      help='Path to the output PyTorch model directory.',
  )
  parser.add_argument('--push_to_hub', action='store_true', help='Push model and image processor to the hub')
  parser.add_argument(
      '--no-push_to_hub',
      dest='push_to_hub',
      action='store_false',
      help='Do not push model and image processor to the hub',
  )

  A__   :					Optional[Any]							=   parser.parse_args()
  A__   :					Path							=   args.pytorch_dump_folder_path
  pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
  convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)
