

"""simple docstring"""




import inspect
import unittest

from datasets import load_dataset
from packaging import version

from transformers import BeitConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_multi_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, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
						import torch
						from torch import nn

						from transformers import (
						    MODEL_MAPPING,
						    BeitForImageClassification,
						    BeitForMaskedImageModeling,
						    BeitForSemanticSegmentation,
						    BeitModel,
						)
						from transformers.models.beit.modeling_beit import BEIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
						import PIL
						from PIL import Image

						from transformers import BeitImageProcessor





class  snake_case     :

							def __init__(self       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_=1_00       ,       SCREAMING_SNAKE_CASE_=13       ,       SCREAMING_SNAKE_CASE_=30       ,       SCREAMING_SNAKE_CASE_=2       ,       SCREAMING_SNAKE_CASE_=3       ,       SCREAMING_SNAKE_CASE_=True       ,       SCREAMING_SNAKE_CASE_=True       ,       SCREAMING_SNAKE_CASE_=32       ,       SCREAMING_SNAKE_CASE_=4       ,       SCREAMING_SNAKE_CASE_=4       ,       SCREAMING_SNAKE_CASE_=37       ,       SCREAMING_SNAKE_CASE_="gelu"       ,       SCREAMING_SNAKE_CASE_=0.1       ,       SCREAMING_SNAKE_CASE_=0.1       ,       SCREAMING_SNAKE_CASE_=10       ,       SCREAMING_SNAKE_CASE_=0.02       ,       SCREAMING_SNAKE_CASE_=3       ,       SCREAMING_SNAKE_CASE_=None       ,       SCREAMING_SNAKE_CASE_=[0, 1, 2, 3]       ,       ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = parent
									SCREAMING_SNAKE_CASE_						 = 1_00
									SCREAMING_SNAKE_CASE_						 = batch_size
									SCREAMING_SNAKE_CASE_						 = image_size
									SCREAMING_SNAKE_CASE_						 = patch_size
									SCREAMING_SNAKE_CASE_						 = num_channels
									SCREAMING_SNAKE_CASE_						 = is_training
									SCREAMING_SNAKE_CASE_						 = use_labels
									SCREAMING_SNAKE_CASE_						 = hidden_size
									SCREAMING_SNAKE_CASE_						 = num_hidden_layers
									SCREAMING_SNAKE_CASE_						 = num_attention_heads
									SCREAMING_SNAKE_CASE_						 = intermediate_size
									SCREAMING_SNAKE_CASE_						 = hidden_act
									SCREAMING_SNAKE_CASE_						 = hidden_dropout_prob
									SCREAMING_SNAKE_CASE_						 = attention_probs_dropout_prob
									SCREAMING_SNAKE_CASE_						 = type_sequence_label_size
									SCREAMING_SNAKE_CASE_						 = initializer_range
									SCREAMING_SNAKE_CASE_						 = scope
									SCREAMING_SNAKE_CASE_						 = out_indices
									SCREAMING_SNAKE_CASE_						 = num_labels

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

							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]    )

									SCREAMING_SNAKE_CASE_						 = None
									SCREAMING_SNAKE_CASE_						 = None
									if self.use_labels:
											SCREAMING_SNAKE_CASE_						 = ids_tensor([self.batch_size]       ,       self.type_sequence_label_size    )
											SCREAMING_SNAKE_CASE_						 = ids_tensor([self.batch_size, self.image_size, self.image_size]       ,       self.num_labels    )

									SCREAMING_SNAKE_CASE_						 = self.get_config()

									return config, pixel_values, labels, pixel_labels

							def _lowercase					(self    ):

									"""simple docstring"""







									return BeitConfig(
									    vocab_size=self.vocab_size       ,       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=snake_case__       ,       initializer_range=self.initializer_range       ,       out_indices=self.out_indices       ,       )

							def _lowercase					(self       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = BeitModel(config=snake_case__    )
									model.to(snake_case__    )
									model.eval()
									SCREAMING_SNAKE_CASE_						 = model(snake_case__    )
									self.parent.assertEqual(result.last_hidden_state.shape       ,       (self.batch_size, self.seq_length, self.hidden_size)    )

							def _lowercase					(self       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = BeitForMaskedImageModeling(config=snake_case__    )
									model.to(snake_case__    )
									model.eval()
									SCREAMING_SNAKE_CASE_						 = model(snake_case__    )
									self.parent.assertEqual(result.logits.shape       ,       (self.batch_size, self.seq_length - 1, self.vocab_size)    )

							def _lowercase					(self       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = self.type_sequence_label_size
									SCREAMING_SNAKE_CASE_						 = BeitForImageClassification(snake_case__    )
									model.to(snake_case__    )
									model.eval()
									SCREAMING_SNAKE_CASE_						 = model(snake_case__       ,       labels=snake_case__    )
									self.parent.assertEqual(result.logits.shape       ,       (self.batch_size, self.type_sequence_label_size)    )

									# test greyscale images
									SCREAMING_SNAKE_CASE_						 = 1
									SCREAMING_SNAKE_CASE_						 = BeitForImageClassification(snake_case__    )
									model.to(snake_case__    )
									model.eval()

									SCREAMING_SNAKE_CASE_						 = floats_tensor([self.batch_size, 1, self.image_size, self.image_size]    )
									SCREAMING_SNAKE_CASE_						 = model(snake_case__       ,       labels=snake_case__    )
									self.parent.assertEqual(result.logits.shape       ,       (self.batch_size, self.type_sequence_label_size)    )

							def _lowercase					(self       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = self.num_labels
									SCREAMING_SNAKE_CASE_						 = BeitForSemanticSegmentation(snake_case__    )
									model.to(snake_case__    )
									model.eval()
									SCREAMING_SNAKE_CASE_						 = model(snake_case__    )
									self.parent.assertEqual(
									    result.logits.shape       ,       (self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)    )
									SCREAMING_SNAKE_CASE_						 = model(snake_case__       ,       labels=snake_case__    )
									self.parent.assertEqual(
									    result.logits.shape       ,       (self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)    )







							def _lowercase					(self    ):

									"""simple docstring"""







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







@require_torch
class  snake_case     (							SCREAMING_SNAKE_CASE__       ,			SCREAMING_SNAKE_CASE__       ,			unittest.TestCase   ):
							UpperCAmelCase__						     =      (
							    (BeitModel, BeitForImageClassification, BeitForMaskedImageModeling, BeitForSemanticSegmentation)
							    if is_torch_available()
							    else ()
							)
							UpperCAmelCase__						     =      (
							    {
							        '''feature-extraction''': BeitModel,
							        '''image-classification''': BeitForImageClassification,
							        '''image-segmentation''': BeitForSemanticSegmentation,
							    }
							    if is_torch_available()
							    else {}
							)

							UpperCAmelCase__						     =      False
							UpperCAmelCase__						     =      False
							UpperCAmelCase__						     =      False

							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = BeitModelTester(self    )
									SCREAMING_SNAKE_CASE_						 = ConfigTester(self       ,       config_class=snake_case__       ,       has_text_modality=snake_case__       ,       hidden_size=37    )

							def _lowercase					(self    ):

									"""simple docstring"""







									self.config_tester.run_common_tests()

							@unittest.skip(reason='''BEiT does not use inputs_embeds'''    )
							def _lowercase					(self    ):

									"""simple docstring"""







									pass

							@require_torch_multi_gpu
							@unittest.skip(reason='''BEiT has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`'''    )
							def _lowercase					(self    ):

									"""simple docstring"""







									pass

							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = self.model_tester.prepare_config_and_inputs_for_common()

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

							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = self.model_tester.prepare_config_and_inputs_for_common()

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

											SCREAMING_SNAKE_CASE_						 = ["pixel_values"]
											self.assertListEqual(arg_names[:1]       ,       snake_case__    )

							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*snake_case__    )

							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_masked_lm(*snake_case__    )

							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_image_classification(*snake_case__    )

							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_semantic_segmentation(*snake_case__    )

							def _lowercase					(self    ):

									"""simple docstring"""







									if not self.model_tester.is_training:
											return

									SCREAMING_SNAKE_CASE_						 = self.model_tester.prepare_config_and_inputs_for_common()
									SCREAMING_SNAKE_CASE_						 = True

									for model_class in self.all_model_classes:
											# we don't test BeitForMaskedImageModeling
											if model_class in [*get_values(snake_case__    ), BeitForMaskedImageModeling]:
													continue

											SCREAMING_SNAKE_CASE_						 = model_class(snake_case__    )
											model.to(snake_case__    )
											model.train()
											SCREAMING_SNAKE_CASE_						 = self._prepare_for_class(snake_case__       ,       snake_case__       ,       return_labels=snake_case__    )
											SCREAMING_SNAKE_CASE_						 = model(**snake_case__    ).loss
											loss.backward()

							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = self.model_tester.prepare_config_and_inputs_for_common()
									if not self.model_tester.is_training:
											return

									SCREAMING_SNAKE_CASE_						 = False
									SCREAMING_SNAKE_CASE_						 = True

									for model_class in self.all_model_classes:
											# we don't test BeitForMaskedImageModeling
											if (
											    model_class in [*get_values(snake_case__    ), BeitForMaskedImageModeling]
											    or not model_class.supports_gradient_checkpointing
											):
													continue

											SCREAMING_SNAKE_CASE_						 = model_class(snake_case__    )
											model.gradient_checkpointing_enable()
											model.to(snake_case__    )
											model.train()
											SCREAMING_SNAKE_CASE_						 = self._prepare_for_class(snake_case__       ,       snake_case__       ,       return_labels=snake_case__    )
											SCREAMING_SNAKE_CASE_						 = model(**snake_case__    ).loss
											loss.backward()

							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = self.model_tester.prepare_config_and_inputs_for_common()

									SCREAMING_SNAKE_CASE_						 = _config_zero_init(snake_case__    )
									for model_class in self.all_model_classes:
											SCREAMING_SNAKE_CASE_						 = model_class(config=snake_case__    )
											for name, param in model.named_parameters():
													# we skip lambda parameters as these require special initial values
													# determined by config.layer_scale_init_value
													if "lambda" in name:
															continue
													if param.requires_grad:
															self.assertIn(
															    ((param.data.mean() * 1e9).round() / 1e9).item()       ,       [0.0, 1.0]       ,       msg=f'Parameter {name} of model {model_class} seems not properly initialized'       ,       )







							@slow
							def _lowercase					(self    ):

									"""simple docstring"""







									for model_name in BEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											SCREAMING_SNAKE_CASE_						 = BeitModel.from_pretrained(snake_case__    )
											self.assertIsNotNone(snake_case__    )







def        _lowerCamelCase ( ):
		SCREAMING_SNAKE_CASE_						 = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''    )
		return image





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

							@cached_property
							def _lowercase					(self    ):

									"""simple docstring"""







									return BeitImageProcessor.from_pretrained('''microsoft/beit-base-patch16-224'''    ) if is_vision_available() else None

							@slow
							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = BeitForMaskedImageModeling.from_pretrained('''microsoft/beit-base-patch16-224-pt22k'''    ).to(snake_case__    )

									SCREAMING_SNAKE_CASE_						 = self.default_image_processor
									SCREAMING_SNAKE_CASE_						 = prepare_img()
									SCREAMING_SNAKE_CASE_						 = image_processor(images=snake_case__       ,       return_tensors='''pt'''    ).pixel_values.to(snake_case__    )

									# prepare bool_masked_pos
									SCREAMING_SNAKE_CASE_						 = torch.ones((1, 1_96)       ,       dtype=torch.bool    ).to(snake_case__    )

									# forward pass
									with torch.no_grad():
											SCREAMING_SNAKE_CASE_						 = model(pixel_values=snake_case__       ,       bool_masked_pos=snake_case__    )
									SCREAMING_SNAKE_CASE_						 = outputs.logits

									# verify the logits
									SCREAMING_SNAKE_CASE_						 = torch.Size((1, 1_96, 81_92)    )
									self.assertEqual(logits.shape       ,       snake_case__    )

									SCREAMING_SNAKE_CASE_						 = torch.tensor(
									    [[-3.24_37, 0.50_72, -13.91_74], [-3.24_56, 0.49_48, -13.94_01], [-3.20_33, 0.51_21, -13.85_50]]    ).to(snake_case__    )

									self.assertTrue(torch.allclose(logits[bool_masked_pos][:3, :3]       ,       snake_case__       ,       atol=1e-2    )    )

							@slow
							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = BeitForImageClassification.from_pretrained('''microsoft/beit-base-patch16-224'''    ).to(snake_case__    )

									SCREAMING_SNAKE_CASE_						 = self.default_image_processor
									SCREAMING_SNAKE_CASE_						 = prepare_img()
									SCREAMING_SNAKE_CASE_						 = image_processor(images=snake_case__       ,       return_tensors='''pt'''    ).to(snake_case__    )

									# forward pass
									with torch.no_grad():
											SCREAMING_SNAKE_CASE_						 = model(**snake_case__    )
									SCREAMING_SNAKE_CASE_						 = outputs.logits

									# verify the logits
									SCREAMING_SNAKE_CASE_						 = torch.Size((1, 10_00)    )
									self.assertEqual(logits.shape       ,       snake_case__    )

									SCREAMING_SNAKE_CASE_						 = torch.tensor([-1.23_85, -1.09_87, -1.01_08]    ).to(snake_case__    )

									self.assertTrue(torch.allclose(logits[0, :3]       ,       snake_case__       ,       atol=1e-4    )    )

									SCREAMING_SNAKE_CASE_						 = 2_81
									self.assertEqual(logits.argmax(-1    ).item()       ,       snake_case__    )

							@slow
							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = BeitForImageClassification.from_pretrained('''microsoft/beit-large-patch16-224-pt22k-ft22k'''    ).to(
									    snake_case__    )

									SCREAMING_SNAKE_CASE_						 = self.default_image_processor
									SCREAMING_SNAKE_CASE_						 = prepare_img()
									SCREAMING_SNAKE_CASE_						 = image_processor(images=snake_case__       ,       return_tensors='''pt'''    ).to(snake_case__    )

									# forward pass
									with torch.no_grad():
											SCREAMING_SNAKE_CASE_						 = model(**snake_case__    )
									SCREAMING_SNAKE_CASE_						 = outputs.logits

									# verify the logits
									SCREAMING_SNAKE_CASE_						 = torch.Size((1, 2_18_41)    )
									self.assertEqual(logits.shape       ,       snake_case__    )

									SCREAMING_SNAKE_CASE_						 = torch.tensor([1.68_81, -0.27_87, 0.59_01]    ).to(snake_case__    )

									self.assertTrue(torch.allclose(logits[0, :3]       ,       snake_case__       ,       atol=1e-4    )    )

									SCREAMING_SNAKE_CASE_						 = 23_96
									self.assertEqual(logits.argmax(-1    ).item()       ,       snake_case__    )

							@slow
							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = BeitForSemanticSegmentation.from_pretrained('''microsoft/beit-base-finetuned-ade-640-640'''    )
									SCREAMING_SNAKE_CASE_						 = model.to(snake_case__    )

									SCREAMING_SNAKE_CASE_						 = BeitImageProcessor(do_resize=snake_case__       ,       size=6_40       ,       do_center_crop=snake_case__    )

									SCREAMING_SNAKE_CASE_						 = load_dataset('''hf-internal-testing/fixtures_ade20k'''       ,       split='''test'''    )
									SCREAMING_SNAKE_CASE_						 = Image.open(ds[0]['''file''']    )
									SCREAMING_SNAKE_CASE_						 = image_processor(images=snake_case__       ,       return_tensors='''pt'''    ).to(snake_case__    )

									# forward pass
									with torch.no_grad():
											SCREAMING_SNAKE_CASE_						 = model(**snake_case__    )
									SCREAMING_SNAKE_CASE_						 = outputs.logits

									# verify the logits
									SCREAMING_SNAKE_CASE_						 = torch.Size((1, 1_50, 1_60, 1_60)    )
									self.assertEqual(logits.shape       ,       snake_case__    )

									SCREAMING_SNAKE_CASE_						 = version.parse(PIL.__version__    ) < version.parse('''9.0.0'''    )

									if is_pillow_less_than_a:
											SCREAMING_SNAKE_CASE_						 = torch.tensor(
											    [
											        [[-4.92_25, -2.39_54, -3.05_22], [-2.88_22, -1.00_46, -1.75_61], [-2.95_49, -1.32_28, -2.13_47]],
											        [[-5.81_68, -3.41_29, -4.07_78], [-3.86_51, -2.22_14, -3.02_77], [-3.83_56, -2.46_43, -3.35_35]],
											        [[-0.00_78, 3.99_52, 4.07_54], [2.98_56, 4.69_44, 5.00_35], [3.24_13, 4.78_13, 4.99_69]],
											    ]       ,       device=snake_case__       ,       )
									else:
											SCREAMING_SNAKE_CASE_						 = torch.tensor(
											    [
											        [[-4.89_60, -2.36_88, -3.03_55], [-2.84_78, -0.98_36, -1.74_18], [-2.94_49, -1.33_32, -2.14_56]],
											        [[-5.80_81, -3.41_24, -4.10_06], [-3.85_61, -2.20_81, -3.03_23], [-3.83_65, -2.46_01, -3.36_69]],
											        [[-0.03_09, 3.98_68, 4.05_40], [2.96_40, 4.68_77, 4.99_76], [3.20_81, 4.76_90, 4.99_42]],
											    ]       ,       device=snake_case__       ,       )

									self.assertTrue(torch.allclose(logits[0, :3, :3, :3]       ,       snake_case__       ,       atol=1e-4    )    )







							@slow
							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = BeitForSemanticSegmentation.from_pretrained('''microsoft/beit-base-finetuned-ade-640-640'''    )
									SCREAMING_SNAKE_CASE_						 = model.to(snake_case__    )

									SCREAMING_SNAKE_CASE_						 = BeitImageProcessor(do_resize=snake_case__       ,       size=6_40       ,       do_center_crop=snake_case__    )

									SCREAMING_SNAKE_CASE_						 = load_dataset('''hf-internal-testing/fixtures_ade20k'''       ,       split='''test'''    )
									SCREAMING_SNAKE_CASE_						 = Image.open(ds[0]['''file''']    )
									SCREAMING_SNAKE_CASE_						 = image_processor(images=snake_case__       ,       return_tensors='''pt'''    ).to(snake_case__    )

									# forward pass
									with torch.no_grad():
											SCREAMING_SNAKE_CASE_						 = model(**snake_case__    )

									SCREAMING_SNAKE_CASE_						 = outputs.logits.detach().cpu()

									SCREAMING_SNAKE_CASE_						 = image_processor.post_process_semantic_segmentation(outputs=snake_case__       ,       target_sizes=[(5_00, 3_00)]    )
									SCREAMING_SNAKE_CASE_						 = torch.Size((5_00, 3_00)    )
									self.assertEqual(segmentation[0].shape       ,       snake_case__    )

									SCREAMING_SNAKE_CASE_						 = image_processor.post_process_semantic_segmentation(outputs=snake_case__    )
									SCREAMING_SNAKE_CASE_						 = torch.Size((1_60, 1_60)    )
									self.assertEqual(segmentation[0].shape       ,       snake_case__    )

"""simple docstring"""




import argparse

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


logging.set_verbosity_info()



def        _lowerCamelCase ( __a,							__a,							__a,							__a    ):
		# Initialise PyTorch model
		SCREAMING_SNAKE_CASE_						 = BigBirdConfig.from_json_file(__a    )
		print(F'Building PyTorch model from configuration: {config}'    )

		if is_trivia_qa:
				SCREAMING_SNAKE_CASE_						 = BigBirdForQuestionAnswering(__a    )
		else:
				SCREAMING_SNAKE_CASE_						 = 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__":
						lowerCAmelCase__							=				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.'
						)
						lowerCAmelCase__							=				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
						)