




"""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 					__magic_name__							:






				'''simple docstring'''





				def __init__( self	,				_a	,				_a=100	,				_a=13	,				_a=30	,				_a=2	,				_a=3	,				_a=True	,				_a=True	,				_a=32	,				_a=4	,				_a=4	,				_a=37	,				_a="gelu"	,				_a=0.1	,				_a=0.1	,				_a=10	,				_a=0.02	,				_a=3	,				_a=None	,				_a=[0, 1, 2, 3]	,				):





							"""simple docstring"""
							lowerCamelCase       =      parent
							lowerCamelCase       =      100
							lowerCamelCase       =      batch_size
							lowerCamelCase       =      image_size
							lowerCamelCase       =      patch_size
							lowerCamelCase       =      num_channels
							lowerCamelCase       =      is_training
							lowerCamelCase       =      use_labels
							lowerCamelCase       =      hidden_size
							lowerCamelCase       =      num_hidden_layers
							lowerCamelCase       =      num_attention_heads
							lowerCamelCase       =      intermediate_size
							lowerCamelCase       =      hidden_act
							lowerCamelCase       =      hidden_dropout_prob
							lowerCamelCase       =      attention_probs_dropout_prob
							lowerCamelCase       =      type_sequence_label_size
							lowerCamelCase       =      initializer_range
							lowerCamelCase       =      scope
							lowerCamelCase       =      out_indices
							lowerCamelCase       =      num_labels

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





				def      _lowerCAmelCase       ( self				):





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

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

							lowerCamelCase       =      self.get_config()

							return config, pixel_values, labels, pixel_labels





				def      _lowerCAmelCase       ( 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=_a	,				initializer_range=self.initializer_range	,				out_indices=self.out_indices	,				)





				def      _lowerCAmelCase       ( self	,				_a	,				_a	,				_a	,				_a				):





							"""simple docstring"""
							lowerCamelCase       =      BeitModel(config=_a				)
							model.to(_a				)
							model.eval()
							lowerCamelCase       =      model(_a				)
							self.parent.assertEqual(result.last_hidden_state.shape	,				(self.batch_size, self.seq_length, self.hidden_size)				)





				def      _lowerCAmelCase       ( self	,				_a	,				_a	,				_a	,				_a				):





							"""simple docstring"""
							lowerCamelCase       =      BeitForMaskedImageModeling(config=_a				)
							model.to(_a				)
							model.eval()
							lowerCamelCase       =      model(_a				)
							self.parent.assertEqual(result.logits.shape	,				(self.batch_size, self.seq_length - 1, self.vocab_size)				)





				def      _lowerCAmelCase       ( self	,				_a	,				_a	,				_a	,				_a				):





							"""simple docstring"""
							lowerCamelCase       =      self.type_sequence_label_size
							lowerCamelCase       =      BeitForImageClassification(_a				)
							model.to(_a				)
							model.eval()
							lowerCamelCase       =      model(_a	,				labels=_a				)
							self.parent.assertEqual(result.logits.shape	,				(self.batch_size, self.type_sequence_label_size)				)

							# test greyscale images
							lowerCamelCase       =      1
							lowerCamelCase       =      BeitForImageClassification(_a				)
							model.to(_a				)
							model.eval()

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





				def      _lowerCAmelCase       ( self	,				_a	,				_a	,				_a	,				_a				):





							"""simple docstring"""
							lowerCamelCase       =      self.num_labels
							lowerCamelCase       =      BeitForSemanticSegmentation(_a				)
							model.to(_a				)
							model.eval()
							lowerCamelCase       =      model(_a				)
							self.parent.assertEqual(
							    result.logits.shape	,				(self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)				)
							lowerCamelCase       =      model(_a	,				labels=_a				)
							self.parent.assertEqual(
							    result.logits.shape	,				(self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)				)





				def      _lowerCAmelCase       ( self				):





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






@require_torch
class 					__magic_name__							( UpperCAmelCase__		,      UpperCAmelCase__		,      unittest.TestCase       ):






				'''simple docstring'''
				__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      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							lowerCamelCase       =      BeitModelTester(self				)
							lowerCamelCase       =      ConfigTester(self	,				config_class=_a	,				has_text_modality=_a	,				hidden_size=37				)





				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							self.config_tester.run_common_tests()





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





							"""simple docstring"""
							pass





				def      _lowerCAmelCase       ( self				):





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

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





				def      _lowerCAmelCase       ( self				):





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

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

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





				def      _lowerCAmelCase       ( self				):





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





				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							lowerCamelCase       =      self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_masked_lm(*_a				)





				def      _lowerCAmelCase       ( self				):





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





				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							lowerCamelCase       =      self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_semantic_segmentation(*_a				)





				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							if not self.model_tester.is_training:
										return

							lowerCamelCase   ,     lowerCamelCase       =      self.model_tester.prepare_config_and_inputs_for_common()
							lowerCamelCase       =      True

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

										lowerCamelCase       =      model_class(_a				)
										model.to(_a				)
										model.train()
										lowerCamelCase       =      self._prepare_for_class(_a	,				_a	,				return_labels=_a				)
										lowerCamelCase       =      model(**_a				).loss
										loss.backward()





				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							lowerCamelCase   ,     lowerCamelCase       =      self.model_tester.prepare_config_and_inputs_for_common()
							if not self.model_tester.is_training:
										return

							lowerCamelCase       =      False
							lowerCamelCase       =      True

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

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





				def      _lowerCAmelCase       ( self				):





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

							lowerCamelCase       =      _config_zero_init(_a				)
							for model_class in self.all_model_classes:
										lowerCamelCase       =      model_class(config=_a				)
										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      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							for model_name in BEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
										lowerCamelCase       =      BeitModel.from_pretrained(_a				)
										self.assertIsNotNone(_a				)




def 					a__				(       )		->      Tuple:
			lowerCamelCase       =      Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""       )
			return image






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






				'''simple docstring'''





				@cached_property
				def      _lowerCAmelCase       ( self				):





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





				@slow
				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							lowerCamelCase       =      BeitForMaskedImageModeling.from_pretrained("""microsoft/beit-base-patch16-224-pt22k"""				).to(_a				)

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

							# prepare bool_masked_pos
							lowerCamelCase       =      torch.ones((1, 196)	,				dtype=torch.bool				).to(_a				)

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

							# verify the logits
							lowerCamelCase       =      torch.Size((1, 196, 8_192)				)
							self.assertEqual(logits.shape	,				_a				)

							lowerCamelCase       =      torch.tensor(
							    [[-3.2_437, 0.5_072, -13.9_174], [-3.2_456, 0.4_948, -13.9_401], [-3.2_033, 0.5_121, -13.8_550]]				).to(_a				)

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





				@slow
				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							lowerCamelCase       =      BeitForImageClassification.from_pretrained("""microsoft/beit-base-patch16-224"""				).to(_a				)

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

							# forward pass
							with torch.no_grad():
										lowerCamelCase       =      model(**_a				)
							lowerCamelCase       =      outputs.logits

							# verify the logits
							lowerCamelCase       =      torch.Size((1, 1_000)				)
							self.assertEqual(logits.shape	,				_a				)

							lowerCamelCase       =      torch.tensor([-1.2_385, -1.0_987, -1.0_108]				).to(_a				)

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

							lowerCamelCase       =      281
							self.assertEqual(logits.argmax(-1				).item()	,				_a				)





				@slow
				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							lowerCamelCase       =      BeitForImageClassification.from_pretrained("""microsoft/beit-large-patch16-224-pt22k-ft22k"""				).to(
							    _a				)

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

							# forward pass
							with torch.no_grad():
										lowerCamelCase       =      model(**_a				)
							lowerCamelCase       =      outputs.logits

							# verify the logits
							lowerCamelCase       =      torch.Size((1, 21_841)				)
							self.assertEqual(logits.shape	,				_a				)

							lowerCamelCase       =      torch.tensor([1.6_881, -0.2_787, 0.5_901]				).to(_a				)

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

							lowerCamelCase       =      2_396
							self.assertEqual(logits.argmax(-1				).item()	,				_a				)





				@slow
				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							lowerCamelCase       =      BeitForSemanticSegmentation.from_pretrained("""microsoft/beit-base-finetuned-ade-640-640"""				)
							lowerCamelCase       =      model.to(_a				)

							lowerCamelCase       =      BeitImageProcessor(do_resize=_a	,				size=640	,				do_center_crop=_a				)

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

							# forward pass
							with torch.no_grad():
										lowerCamelCase       =      model(**_a				)
							lowerCamelCase       =      outputs.logits

							# verify the logits
							lowerCamelCase       =      torch.Size((1, 150, 160, 160)				)
							self.assertEqual(logits.shape	,				_a				)

							lowerCamelCase       =      version.parse(PIL.__version__				) < version.parse("""9.0.0"""				)

							if is_pillow_less_than_a:
										lowerCamelCase       =      torch.tensor(
										    [
										        [[-4.9_225, -2.3_954, -3.0_522], [-2.8_822, -1.0_046, -1.7_561], [-2.9_549, -1.3_228, -2.1_347]],
										        [[-5.8_168, -3.4_129, -4.0_778], [-3.8_651, -2.2_214, -3.0_277], [-3.8_356, -2.4_643, -3.3_535]],
										        [[-0.0_078, 3.9_952, 4.0_754], [2.9_856, 4.6_944, 5.0_035], [3.2_413, 4.7_813, 4.9_969]],
										    ]	,				device=_a	,				)
							else:
										lowerCamelCase       =      torch.tensor(
										    [
										        [[-4.8_960, -2.3_688, -3.0_355], [-2.8_478, -0.9_836, -1.7_418], [-2.9_449, -1.3_332, -2.1_456]],
										        [[-5.8_081, -3.4_124, -4.1_006], [-3.8_561, -2.2_081, -3.0_323], [-3.8_365, -2.4_601, -3.3_669]],
										        [[-0.0_309, 3.9_868, 4.0_540], [2.9_640, 4.6_877, 4.9_976], [3.2_081, 4.7_690, 4.9_942]],
										    ]	,				device=_a	,				)

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





				@slow
				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							lowerCamelCase       =      BeitForSemanticSegmentation.from_pretrained("""microsoft/beit-base-finetuned-ade-640-640"""				)
							lowerCamelCase       =      model.to(_a				)

							lowerCamelCase       =      BeitImageProcessor(do_resize=_a	,				size=640	,				do_center_crop=_a				)

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

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

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

							lowerCamelCase       =      image_processor.post_process_semantic_segmentation(outputs=_a	,				target_sizes=[(500, 300)]				)
							lowerCamelCase       =      torch.Size((500, 300)				)
							self.assertEqual(segmentation[0].shape	,				_a				)

							lowerCamelCase       =      image_processor.post_process_semantic_segmentation(outputs=_a				)
							lowerCamelCase       =      torch.Size((160, 160)				)
							self.assertEqual(segmentation[0].shape	,				_a				)









"""simple docstring"""




def 					a__				(       snake_case__       )		->      list:
			if n_term == "":
						return []
			lowerCamelCase       =      []
			for temp in range(int(snake_case__       )       ):
						series.append(F'1/{temp + 1}' if series else """1"""       )
			return series


if __name__ == "__main__":
					lowerCAmelCase							:    Optional[int]  							=							input("""Enter the last number (nth term) of the Harmonic Series""")
					print("""Formula of Harmonic Series => 1+1/2+1/3 ..... 1/n""")
					print(harmonic_series(nth_term))



