






import argparse
import os

import torch

from transformers import (
    XLNetConfig,
    XLNetForQuestionAnswering,
    XLNetForSequenceClassification,
    XLNetLMHeadModel,
    load_tf_weights_in_xlnet,
)
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


__a:     List[str]   					=					{
    '''cola''': 2,
    '''mnli''': 3,
    '''mrpc''': 2,
    '''sst-2''': 2,
    '''sts-b''': 1,
    '''qqp''': 2,
    '''qnli''': 2,
    '''rte''': 2,
    '''wnli''': 2,
}


logging.set_verbosity_info()




def       _SCREAMING_SNAKE_CASE (							__snake_case   ,      __snake_case   ,      __snake_case   ,      __snake_case=None )				->						Any:
       # Initialise PyTorch model
       _UpperCAmelCase							=    XLNetConfig.from_json_file(__snake_case )

       _UpperCAmelCase							=    finetuning_task.lower() if finetuning_task is not None else """"""
       if finetuning_task in GLUE_TASKS_NUM_LABELS:
              print(f"""Building PyTorch XLNetForSequenceClassification model from configuration: {config}""" )
              _UpperCAmelCase							=    finetuning_task
              _UpperCAmelCase							=    GLUE_TASKS_NUM_LABELS[finetuning_task]
              _UpperCAmelCase							=    XLNetForSequenceClassification(__snake_case )
       elif "squad" in finetuning_task:
              _UpperCAmelCase							=    finetuning_task
              _UpperCAmelCase							=    XLNetForQuestionAnswering(__snake_case )
       else:
              _UpperCAmelCase							=    XLNetLMHeadModel(__snake_case )

       # Load weights from tf checkpoint
       load_tf_weights_in_xlnet(__snake_case   ,      __snake_case   ,      __snake_case )

       # Save pytorch-model
       _UpperCAmelCase							=    os.path.join(__snake_case   ,      __snake_case )
       _UpperCAmelCase							=    os.path.join(__snake_case   ,      __snake_case )
       print(f"""Save PyTorch model to {os.path.abspath(__snake_case )}""" )
       torch.save(model.state_dict()   ,      __snake_case )
       print(f"""Save configuration file to {os.path.abspath(__snake_case )}""" )
       with open(__snake_case   ,      """w"""   ,      encoding="""utf-8""" ) as f:
              f.write(config.to_json_string() )


if __name__ == "__main__":
  __a:     str   					=					argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      '''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
  )
  parser.add_argument(
      '''--xlnet_config_file''',
      default=None,
      type=str,
      required=True,
      help=(
          '''The config json file corresponding to the pre-trained XLNet model. \n'''
          '''This specifies the model architecture.'''
      ),
  )
  parser.add_argument(
      '''--pytorch_dump_folder_path''',
      default=None,
      type=str,
      required=True,
      help='''Path to the folder to store the PyTorch model or dataset/vocab.''',
  )
  parser.add_argument(
      '''--finetuning_task''',
      default=None,
      type=str,
      help='''Name of a task on which the XLNet TensorFlow model was fine-tuned''',
  )
  __a:     Optional[int]   					=					parser.parse_args()
  print(args)

  convert_xlnet_checkpoint_to_pytorch(
      args.tf_checkpoint_path, args.xlnet_config_file, args.pytorch_dump_folder_path, args.finetuning_task
  )







import inspect
import unittest

from transformers import ConvNextConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
  import torch

  from transformers import ConvNextBackbone, ConvNextForImageClassification, ConvNextModel
  from transformers.models.convnext.modeling_convnext import CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
  from PIL import Image

  from transformers import AutoImageProcessor
class 		SCREAMING_SNAKE_CASE__	:

  '''simple docstring'''







  def __init__(      self		:			Dict       ,				lowerCamelCase		:			List[str]       ,				lowerCamelCase		:			Optional[Any]=13       ,				lowerCamelCase		:			Dict=32       ,				lowerCamelCase		:			Optional[int]=3       ,				lowerCamelCase		:			List[Any]=4       ,				lowerCamelCase		:			Tuple=[10, 20, 30, 40]       ,				lowerCamelCase		:			str=[2, 2, 3, 2]       ,				lowerCamelCase		:			Optional[int]=True       ,				lowerCamelCase		:			List[Any]=True       ,				lowerCamelCase		:			Union[str, Any]=37       ,				lowerCamelCase		:			Any="gelu"       ,				lowerCamelCase		:			Tuple=10       ,				lowerCamelCase		:			Optional[Any]=0.02       ,				lowerCamelCase		:			Union[str, Any]=["stage2", "stage3", "stage4"]       ,				lowerCamelCase		:			Any=[2, 3, 4]       ,				lowerCamelCase		:			Tuple=None       ,				)						->	int:


         """simple docstring"""
         _UpperCAmelCase							=    parent
         _UpperCAmelCase							=    batch_size
         _UpperCAmelCase							=    image_size
         _UpperCAmelCase							=    num_channels
         _UpperCAmelCase							=    num_stages
         _UpperCAmelCase							=    hidden_sizes
         _UpperCAmelCase							=    depths
         _UpperCAmelCase							=    is_training
         _UpperCAmelCase							=    use_labels
         _UpperCAmelCase							=    intermediate_size
         _UpperCAmelCase							=    hidden_act
         _UpperCAmelCase							=    num_labels
         _UpperCAmelCase							=    initializer_range
         _UpperCAmelCase							=    out_features
         _UpperCAmelCase							=    out_indices
         _UpperCAmelCase							=    scope


  def  lowerCamelCase       (      self		:			Union[str, Any]							)						->	Union[str, Any]:


         """simple docstring"""
         _UpperCAmelCase							=    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

         _UpperCAmelCase							=    None
         if self.use_labels:
                _UpperCAmelCase							=    ids_tensor([self.batch_size]       ,				self.num_labels							)

         _UpperCAmelCase							=    self.get_config()
         return config, pixel_values, labels


  def  lowerCamelCase       (      self		:			Optional[Any]							)						->	Any:


         """simple docstring"""
         return ConvNextConfig(
             num_channels=self.num_channels       ,				hidden_sizes=self.hidden_sizes       ,				depths=self.depths       ,				num_stages=self.num_stages       ,				hidden_act=self.hidden_act       ,				is_decoder=lowerCamelCase       ,				initializer_range=self.initializer_range       ,				out_features=self.out_features       ,				out_indices=self.out_indices       ,				num_labels=self.num_labels       ,				)


  def  lowerCamelCase       (      self		:			Union[str, Any]       ,				lowerCamelCase		:			List[str]       ,				lowerCamelCase		:			Optional[int]       ,				lowerCamelCase		:			Any							)						->	str:


         """simple docstring"""
         _UpperCAmelCase							=    ConvNextModel(config=lowerCamelCase							)
         model.to(lowerCamelCase							)
         model.eval()
         _UpperCAmelCase							=    model(lowerCamelCase							)
         # expected last hidden states: B, C, H // 32, W // 32
         self.parent.assertEqual(
             result.last_hidden_state.shape       ,				(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)       ,				)


  def  lowerCamelCase       (      self		:			Dict       ,				lowerCamelCase		:			Tuple       ,				lowerCamelCase		:			Tuple       ,				lowerCamelCase		:			List[Any]							)						->	int:


         """simple docstring"""
         _UpperCAmelCase							=    ConvNextForImageClassification(lowerCamelCase							)
         model.to(lowerCamelCase							)
         model.eval()
         _UpperCAmelCase							=    model(lowerCamelCase       ,				labels=lowerCamelCase							)
         self.parent.assertEqual(result.logits.shape       ,				(self.batch_size, self.num_labels)							)


  def  lowerCamelCase       (      self		:			Dict       ,				lowerCamelCase		:			List[Any]       ,				lowerCamelCase		:			Tuple       ,				lowerCamelCase		:			Dict							)						->	Dict:


         """simple docstring"""
         _UpperCAmelCase							=    ConvNextBackbone(config=lowerCamelCase							)
         model.to(lowerCamelCase							)
         model.eval()
         _UpperCAmelCase							=    model(lowerCamelCase							)

         # verify hidden states
         self.parent.assertEqual(len(result.feature_maps							)       ,				len(config.out_features							)							)
         self.parent.assertListEqual(list(result.feature_maps[0].shape							)       ,				[self.batch_size, self.hidden_sizes[1], 4, 4]							)

         # verify channels
         self.parent.assertEqual(len(model.channels							)       ,				len(config.out_features							)							)
         self.parent.assertListEqual(model.channels       ,				config.hidden_sizes[1:]							)

         # verify backbone works with out_features=None
         _UpperCAmelCase							=    None
         _UpperCAmelCase							=    ConvNextBackbone(config=lowerCamelCase							)
         model.to(lowerCamelCase							)
         model.eval()
         _UpperCAmelCase							=    model(lowerCamelCase							)

         # verify feature maps
         self.parent.assertEqual(len(result.feature_maps							)       ,				1							)
         self.parent.assertListEqual(list(result.feature_maps[0].shape							)       ,				[self.batch_size, self.hidden_sizes[-1], 1, 1]							)

         # verify channels
         self.parent.assertEqual(len(model.channels							)       ,				1							)
         self.parent.assertListEqual(model.channels       ,				[config.hidden_sizes[-1]]							)





  def  lowerCamelCase       (      self		:			str							)						->	Union[str, Any]:


         """simple docstring"""
         _UpperCAmelCase							=    self.prepare_config_and_inputs()
         _UpperCAmelCase	,			_UpperCAmelCase	,			_UpperCAmelCase							=    config_and_inputs
         _UpperCAmelCase							=    {"""pixel_values""": pixel_values}
         return config, inputs_dict



@require_torch
class 		SCREAMING_SNAKE_CASE__	(  UpperCAmelCase					,      UpperCAmelCase					,      unittest.TestCase     ):

  '''simple docstring'''







  _lowerCamelCase    =  (
      (
          ConvNextModel,
          ConvNextForImageClassification,
          ConvNextBackbone,
      )
      if is_torch_available()
      else ()
  )
  _lowerCamelCase    =  (
      {'''feature-extraction''': ConvNextModel, '''image-classification''': ConvNextForImageClassification}
      if is_torch_available()
      else {}
  )

  _lowerCamelCase    =  True
  _lowerCamelCase    =  False
  _lowerCamelCase    =  False
  _lowerCamelCase    =  False
  _lowerCamelCase    =  False


  def  lowerCamelCase       (      self		:			Dict							)						->	int:


         """simple docstring"""
         _UpperCAmelCase							=    ConvNextModelTester(self							)
         _UpperCAmelCase							=    ConfigTester(self       ,				config_class=lowerCamelCase       ,				has_text_modality=lowerCamelCase       ,				hidden_size=37							)


  def  lowerCamelCase       (      self		:			int							)						->	int:


         """simple docstring"""
         self.create_and_test_config_common_properties()
         self.config_tester.create_and_test_config_to_json_string()
         self.config_tester.create_and_test_config_to_json_file()
         self.config_tester.create_and_test_config_from_and_save_pretrained()
         self.config_tester.create_and_test_config_with_num_labels()
         self.config_tester.check_config_can_be_init_without_params()
         self.config_tester.check_config_arguments_init()


  def  lowerCamelCase       (      self		:			str							)						->	Union[str, Any]:


         """simple docstring"""
         return


  @unittest.skip(reason="""ConvNext does not use inputs_embeds"""							)
  def  lowerCamelCase       (      self		:			List[Any]							)						->	Any:


         """simple docstring"""
         pass


  @unittest.skip(reason="""ConvNext does not support input and output embeddings"""							)
  def  lowerCamelCase       (      self		:			int							)						->	int:


         """simple docstring"""
         pass


  @unittest.skip(reason="""ConvNext does not use feedforward chunking"""							)
  def  lowerCamelCase       (      self		:			Optional[int]							)						->	int:


         """simple docstring"""
         pass


  def  lowerCamelCase       (      self		:			List[Any]							)						->	Optional[int]:


         """simple docstring"""
         _UpperCAmelCase	,			_UpperCAmelCase							=    self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
                _UpperCAmelCase							=    model_class(lowerCamelCase							)
                _UpperCAmelCase							=    inspect.signature(model.forward							)
                # signature.parameters is an OrderedDict => so arg_names order is deterministic
                _UpperCAmelCase							=    [*signature.parameters.keys()]

                _UpperCAmelCase							=    ["""pixel_values"""]
                self.assertListEqual(arg_names[:1]       ,				lowerCamelCase							)


  def  lowerCamelCase       (      self		:			Tuple							)						->	Any:


         """simple docstring"""
         _UpperCAmelCase							=    self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*lowerCamelCase							)


  def  lowerCamelCase       (      self		:			Union[str, Any]							)						->	Optional[Any]:


         """simple docstring"""
         _UpperCAmelCase							=    self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_backbone(*lowerCamelCase							)


  def  lowerCamelCase       (      self		:			int							)						->	Any:


         """simple docstring"""
         def check_hidden_states_output(lowerCamelCase		:			List[Any]       ,				lowerCamelCase		:			Tuple       ,				lowerCamelCase		:			Optional[int]							):
                _UpperCAmelCase							=    model_class(lowerCamelCase							)
                model.to(lowerCamelCase							)
                model.eval()

                with torch.no_grad():
                       _UpperCAmelCase							=    model(**self._prepare_for_class(lowerCamelCase       ,				lowerCamelCase							)							)

                _UpperCAmelCase							=    outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

                _UpperCAmelCase							=    self.model_tester.num_stages
                self.assertEqual(len(lowerCamelCase							)       ,				expected_num_stages + 1							)

                # ConvNext's feature maps are of shape (batch_size, num_channels, height, width)
                self.assertListEqual(
                    list(hidden_states[0].shape[-2:]							)       ,				[self.model_tester.image_size // 4, self.model_tester.image_size // 4]       ,				)

         _UpperCAmelCase	,			_UpperCAmelCase							=    self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
                _UpperCAmelCase							=    True
                check_hidden_states_output(lowerCamelCase       ,				lowerCamelCase       ,				lowerCamelCase							)

                # check that output_hidden_states also work using config
                del inputs_dict["output_hidden_states"]
                _UpperCAmelCase							=    True

                check_hidden_states_output(lowerCamelCase       ,				lowerCamelCase       ,				lowerCamelCase							)


  def  lowerCamelCase       (      self		:			Any							)						->	Optional[int]:


         """simple docstring"""
         _UpperCAmelCase							=    self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_image_classification(*lowerCamelCase							)





  @slow
  def  lowerCamelCase       (      self		:			Optional[int]							)						->	List[Any]:


         """simple docstring"""
         for model_name in CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                _UpperCAmelCase							=    ConvNextModel.from_pretrained(lowerCamelCase							)
                self.assertIsNotNone(lowerCamelCase							)




def       _SCREAMING_SNAKE_CASE (							)				->						Tuple:
       _UpperCAmelCase							=    Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
       return image
@require_torch
@require_vision
class 		SCREAMING_SNAKE_CASE__	(  unittest.TestCase     ):

  '''simple docstring'''







  @cached_property
  def  lowerCamelCase       (      self		:			Dict							)						->	int:


         """simple docstring"""
         return AutoImageProcessor.from_pretrained("""facebook/convnext-tiny-224"""							) if is_vision_available() else None





  @slow
  def  lowerCamelCase       (      self		:			List[Any]							)						->	int:


         """simple docstring"""
         _UpperCAmelCase							=    ConvNextForImageClassification.from_pretrained("""facebook/convnext-tiny-224"""							).to(lowerCamelCase							)

         _UpperCAmelCase							=    self.default_image_processor
         _UpperCAmelCase							=    prepare_img()
         _UpperCAmelCase							=    image_processor(images=lowerCamelCase       ,				return_tensors="""pt"""							).to(lowerCamelCase							)

         # forward pass
         with torch.no_grad():
                _UpperCAmelCase							=    model(**lowerCamelCase							)

         # verify the logits
         _UpperCAmelCase							=    torch.Size((1, 1000)							)
         self.assertEqual(outputs.logits.shape       ,				lowerCamelCase							)

         _UpperCAmelCase							=    torch.tensor([-0.0260, -0.4739, 0.1911]							).to(lowerCamelCase							)

         self.assertTrue(torch.allclose(outputs.logits[0, :3]       ,				lowerCamelCase       ,				atol=1E-4							)							)



@require_torch
class 		SCREAMING_SNAKE_CASE__	(  unittest.TestCase					,      UpperCAmelCase     ):

  '''simple docstring'''







  _lowerCamelCase    =  (ConvNextBackbone,) if is_torch_available() else ()
  _lowerCamelCase    =  ConvNextConfig

  _lowerCamelCase    =  False


  def  lowerCamelCase       (      self		:			List[Any]							)						->	Optional[int]:


         """simple docstring"""
         _UpperCAmelCase							=    ConvNextModelTester(self							)