import argparse

from transformers import BigBirdConfig, BigBirdForPreTraining, BigBirdForQuestionAnswering, load_tf_weights_in_big_bird
from transformers.utils import logging


logging.set_verbosity_info()



def   __A		(			_A							,				_A							,				_A							,				_A					):



 """simple docstring"""


 __a       =  BigBirdConfig.from_json_file(a__					)
 print(f"""Building PyTorch model from configuration: {config}"""					)

 if is_trivia_qa:
  __a       =  BigBirdForQuestionAnswering(a__					)
 else:
  __a       =  BigBirdForPreTraining(a__					)

 # Load weights from tf checkpoint
 load_tf_weights_in_big_bird(a__							,				a__							,				is_trivia_qa=a__					)

 # Save pytorch-model
 print(f"""Save PyTorch model to {pytorch_dump_path}"""					)
 model.save_pretrained(a__					)


if __name__ == "__main__":
  SCREAMING_SNAKE_CASE :	Optional[Any]							     =		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(
      """--big_bird_config_file""",
      default=None,
      type=str,
      required=True,
      help=(
          """The config json file corresponding to the pre-trained BERT model. \n"""
          """This specifies the model architecture."""
      ),
  )
  parser.add_argument(
      """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
  )
  parser.add_argument(
      """--is_trivia_qa""", action="""store_true""", help="""Whether to convert a model with a trivia_qa head."""
  )
  SCREAMING_SNAKE_CASE :	List[Any]							     =		parser.parse_args()
  convert_tf_checkpoint_to_pytorch(
      args.tf_checkpoint_path, args.big_bird_config_file, args.pytorch_dump_path, args.is_trivia_qa
  )



import inspect
import unittest

from transformers import ViTConfig
from transformers.testing_utils import (
    require_accelerate,
    require_torch,
    require_torch_gpu,
    require_vision,
    slow,
    torch_device,
)
from transformers.utils import cached_property, is_torch_available, is_vision_available

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 torch import nn

  from transformers import ViTForImageClassification, ViTForMaskedImageModeling, ViTModel
  from transformers.models.vit.modeling_vit import VIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
  from PIL import Image

  from transformers import ViTImageProcessor
class    A_ :

  def __init__(		self :      Union[str, Any]     ,							__SCREAMING_SNAKE_CASE :      Optional[int]     ,							__SCREAMING_SNAKE_CASE :      List[Any]=13     ,							__SCREAMING_SNAKE_CASE :      Optional[Any]=30     ,							__SCREAMING_SNAKE_CASE :      Optional[Any]=2     ,							__SCREAMING_SNAKE_CASE :      Optional[int]=3     ,							__SCREAMING_SNAKE_CASE :      List[Any]=True     ,							__SCREAMING_SNAKE_CASE :      Dict=True     ,							__SCREAMING_SNAKE_CASE :      Optional[int]=32     ,							__SCREAMING_SNAKE_CASE :      str=5     ,							__SCREAMING_SNAKE_CASE :      List[str]=4     ,							__SCREAMING_SNAKE_CASE :      List[Any]=37     ,							__SCREAMING_SNAKE_CASE :      int="gelu"     ,							__SCREAMING_SNAKE_CASE :      List[Any]=0.1     ,							__SCREAMING_SNAKE_CASE :      Optional[Any]=0.1     ,							__SCREAMING_SNAKE_CASE :      List[Any]=10     ,							__SCREAMING_SNAKE_CASE :      int=0.02     ,							__SCREAMING_SNAKE_CASE :      Dict=None     ,							__SCREAMING_SNAKE_CASE :      str=2     ,							):
   __a       =  parent
   __a       =  batch_size
   __a       =  image_size
   __a       =  patch_size
   __a       =  num_channels
   __a       =  is_training
   __a       =  use_labels
   __a       =  hidden_size
   __a       =  num_hidden_layers
   __a       =  num_attention_heads
   __a       =  intermediate_size
   __a       =  hidden_act
   __a       =  hidden_dropout_prob
   __a       =  attention_probs_dropout_prob
   __a       =  type_sequence_label_size
   __a       =  initializer_range
   __a       =  scope
   __a       =  encoder_stride

   # in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
   __a       =  (image_size // patch_size) ** 2
   __a       =  num_patches + 1

  def 		_UpperCAmelCase   (		self :      Any       ):
   __a       =  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )

   __a       =  None
   if self.use_labels:
    __a       =  ids_tensor([self.batch_size]     ,							self.type_sequence_label_size       )

   __a       =  self.get_config()

   return config, pixel_values, labels

  def 		_UpperCAmelCase   (		self :      str       ):
   return ViTConfig(
       image_size=self.image_size     ,							patch_size=self.patch_size     ,							num_channels=self.num_channels     ,							hidden_size=self.hidden_size     ,							num_hidden_layers=self.num_hidden_layers     ,							num_attention_heads=self.num_attention_heads     ,							intermediate_size=self.intermediate_size     ,							hidden_act=self.hidden_act     ,							hidden_dropout_prob=self.hidden_dropout_prob     ,							attention_probs_dropout_prob=self.attention_probs_dropout_prob     ,							is_decoder=__SCREAMING_SNAKE_CASE     ,							initializer_range=self.initializer_range     ,							encoder_stride=self.encoder_stride     ,							)

  def 		_UpperCAmelCase   (		self :      Dict     ,							__SCREAMING_SNAKE_CASE :      List[Any]     ,							__SCREAMING_SNAKE_CASE :      Dict     ,							__SCREAMING_SNAKE_CASE :      Any       ):
   __a       =  ViTModel(config=__SCREAMING_SNAKE_CASE       )
   model.to(__SCREAMING_SNAKE_CASE       )
   model.eval()
   __a       =  model(__SCREAMING_SNAKE_CASE       )
   self.parent.assertEqual(result.last_hidden_state.shape     ,							(self.batch_size, self.seq_length, self.hidden_size)       )

  def 		_UpperCAmelCase   (		self :      Union[str, Any]     ,							__SCREAMING_SNAKE_CASE :      Tuple     ,							__SCREAMING_SNAKE_CASE :      str     ,							__SCREAMING_SNAKE_CASE :      Dict       ):
   __a       =  ViTForMaskedImageModeling(config=__SCREAMING_SNAKE_CASE       )
   model.to(__SCREAMING_SNAKE_CASE       )
   model.eval()
   __a       =  model(__SCREAMING_SNAKE_CASE       )
   self.parent.assertEqual(
       result.reconstruction.shape     ,							(self.batch_size, self.num_channels, self.image_size, self.image_size)       )

   # test greyscale images
   __a       =  1
   __a       =  ViTForMaskedImageModeling(__SCREAMING_SNAKE_CASE       )
   model.to(__SCREAMING_SNAKE_CASE       )
   model.eval()

   __a       =  floats_tensor([self.batch_size, 1, self.image_size, self.image_size]       )
   __a       =  model(__SCREAMING_SNAKE_CASE       )
   self.parent.assertEqual(result.reconstruction.shape     ,							(self.batch_size, 1, self.image_size, self.image_size)       )

  def 		_UpperCAmelCase   (		self :      Optional[Any]     ,							__SCREAMING_SNAKE_CASE :      Tuple     ,							__SCREAMING_SNAKE_CASE :      Union[str, Any]     ,							__SCREAMING_SNAKE_CASE :      List[str]       ):
   __a       =  self.type_sequence_label_size
   __a       =  ViTForImageClassification(__SCREAMING_SNAKE_CASE       )
   model.to(__SCREAMING_SNAKE_CASE       )
   model.eval()
   __a       =  model(__SCREAMING_SNAKE_CASE     ,							labels=__SCREAMING_SNAKE_CASE       )
   self.parent.assertEqual(result.logits.shape     ,							(self.batch_size, self.type_sequence_label_size)       )

   # test greyscale images
   __a       =  1
   __a       =  ViTForImageClassification(__SCREAMING_SNAKE_CASE       )
   model.to(__SCREAMING_SNAKE_CASE       )
   model.eval()

   __a       =  floats_tensor([self.batch_size, 1, self.image_size, self.image_size]       )
   __a       =  model(__SCREAMING_SNAKE_CASE       )
   self.parent.assertEqual(result.logits.shape     ,							(self.batch_size, self.type_sequence_label_size)       )

  def 		_UpperCAmelCase   (		self :      List[Any]       ):
   __a       =  self.prepare_config_and_inputs()
   (
       (
       __a
   )       ,     (
       __a
   )       ,     (
       __a
   )       ,     
   )       =  config_and_inputs
   __a       =  {"pixel_values": pixel_values}
   return config, inputs_dict
@require_torch
class    A_ (       a_			,       a_			,       unittest.TestCase ):
  _SCREAMING_SNAKE_CASE    				=			(
      (
          ViTModel,
          ViTForImageClassification,
          ViTForMaskedImageModeling,
      )
      if is_torch_available()
      else ()
  )
  _SCREAMING_SNAKE_CASE    				=			(
      {"""feature-extraction""": ViTModel, """image-classification""": ViTForImageClassification}
      if is_torch_available()
      else {}
  )
  _SCREAMING_SNAKE_CASE    				=			True

  _SCREAMING_SNAKE_CASE    				=			False
  _SCREAMING_SNAKE_CASE    				=			False
  _SCREAMING_SNAKE_CASE    				=			False

  def 		_UpperCAmelCase   (		self :      Optional[Any]       ):
   __a       =  ViTModelTester(self       )
   __a       =  ConfigTester(self     ,							config_class=__SCREAMING_SNAKE_CASE     ,							has_text_modality=__SCREAMING_SNAKE_CASE     ,							hidden_size=37       )

  def 		_UpperCAmelCase   (		self :      List[str]       ):
   self.config_tester.run_common_tests()

  @unittest.skip(reason="ViT does not use inputs_embeds"       )
  def 		_UpperCAmelCase   (		self :      Optional[Any]       ):
   pass

  def 		_UpperCAmelCase   (		self :      int       ):
   __a       ,     __a       =  self.model_tester.prepare_config_and_inputs_for_common()

   for model_class in self.all_model_classes:
    __a       =  model_class(__SCREAMING_SNAKE_CASE       )
    self.assertIsInstance(model.get_input_embeddings()     ,							(nn.Module)       )
    __a       =  model.get_output_embeddings()
    self.assertTrue(x is None or isinstance(__SCREAMING_SNAKE_CASE     ,							nn.Linear       )       )

  def 		_UpperCAmelCase   (		self :      Optional[int]       ):
   __a       ,     __a       =  self.model_tester.prepare_config_and_inputs_for_common()

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

    __a       =  ["pixel_values"]
    self.assertListEqual(arg_names[:1]     ,							__SCREAMING_SNAKE_CASE       )

  def 		_UpperCAmelCase   (		self :      List[str]       ):
   __a       =  self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_model(*__SCREAMING_SNAKE_CASE       )

  def 		_UpperCAmelCase   (		self :      Any       ):
   __a       =  self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_masked_image_modeling(*__SCREAMING_SNAKE_CASE       )

  def 		_UpperCAmelCase   (		self :      Tuple       ):
   __a       =  self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_image_classification(*__SCREAMING_SNAKE_CASE       )

  @slow
  def 		_UpperCAmelCase   (		self :      Dict       ):
   for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
    __a       =  ViTModel.from_pretrained(__SCREAMING_SNAKE_CASE       )
    self.assertIsNotNone(__SCREAMING_SNAKE_CASE       )



def   __A		(			):



 """simple docstring"""


 __a       =  Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"					)
 return image






@require_torch
@require_vision
class    A_ (       unittest.TestCase ):

  @cached_property
  def 		_UpperCAmelCase   (		self :      List[str]       ):
   return ViTImageProcessor.from_pretrained("google/vit-base-patch16-224"       ) if is_vision_available() else None

  @slow
  def 		_UpperCAmelCase   (		self :      str       ):
   __a       =  ViTForImageClassification.from_pretrained("google/vit-base-patch16-224"       ).to(__SCREAMING_SNAKE_CASE       )

   __a       =  self.default_image_processor
   __a       =  prepare_img()
   __a       =  image_processor(images=__SCREAMING_SNAKE_CASE     ,							return_tensors="pt"       ).to(__SCREAMING_SNAKE_CASE       )

   # forward pass
   with torch.no_grad():
    __a       =  model(**__SCREAMING_SNAKE_CASE       )

   # verify the logits
   __a       =  torch.Size((1, 10_00)       )
   self.assertEqual(outputs.logits.shape     ,							__SCREAMING_SNAKE_CASE       )

   __a       =  torch.tensor([-0.27_44, 0.82_15, -0.08_36]       ).to(__SCREAMING_SNAKE_CASE       )

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

  @slow
  def 		_UpperCAmelCase   (		self :      Union[str, Any]       ):
   # ViT models have an `interpolate_pos_encoding` argument in their forward method,
   # allowing to interpolate the pre-trained position embeddings in order to use
   # the model on higher resolutions. The DINO model by Facebook AI leverages this
   # to visualize self-attention on higher resolution images.
   __a       =  ViTModel.from_pretrained("facebook/dino-vits8"       ).to(__SCREAMING_SNAKE_CASE       )

   __a       =  ViTImageProcessor.from_pretrained("facebook/dino-vits8"     ,							size=4_80       )
   __a       =  prepare_img()
   __a       =  image_processor(images=__SCREAMING_SNAKE_CASE     ,							return_tensors="pt"       )
   __a       =  inputs.pixel_values.to(__SCREAMING_SNAKE_CASE       )

   # forward pass
   with torch.no_grad():
    __a       =  model(__SCREAMING_SNAKE_CASE     ,							interpolate_pos_encoding=__SCREAMING_SNAKE_CASE       )

   # verify the logits
   __a       =  torch.Size((1, 36_01, 3_84)       )
   self.assertEqual(outputs.last_hidden_state.shape     ,							__SCREAMING_SNAKE_CASE       )

   __a       =  torch.tensor(
       [[4.23_40, 4.39_06, -6.66_92], [4.54_63, 1.89_28, -6.72_57], [4.44_29, 0.84_96, -5.85_85]]       ).to(__SCREAMING_SNAKE_CASE       )

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

  @slow
  @require_accelerate
  @require_torch_gpu
  def 		_UpperCAmelCase   (		self :      Any       ):
   __a       =  ViTModel.from_pretrained("facebook/dino-vits8"     ,							torch_dtype=torch.floataa     ,							device_map="auto"       )
   __a       =  self.default_image_processor

   __a       =  prepare_img()
   __a       =  image_processor(images=__SCREAMING_SNAKE_CASE     ,							return_tensors="pt"       )
   __a       =  inputs.pixel_values.to(__SCREAMING_SNAKE_CASE       )

   # forward pass to make sure inference works in fp16
   with torch.no_grad():
    __a       =  model(__SCREAMING_SNAKE_CASE       )


