"""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 						__UpperCAmelCase    :







			'''simple docstring'''





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







										'''simple docstring'''


										A__       :     Union[str, Any]								=			parent
										A__       :     Union[str, Any]								=			100
										A__       :     Any								=			batch_size
										A__       :     Dict								=			image_size
										A__       :     Union[str, Any]								=			patch_size
										A__       :     str								=			num_channels
										A__       :     Union[str, Any]								=			is_training
										A__       :     Union[str, Any]								=			use_labels
										A__       :     List[Any]								=			hidden_size
										A__       :     Optional[int]								=			num_hidden_layers
										A__       :     List[str]								=			num_attention_heads
										A__       :     List[str]								=			intermediate_size
										A__       :     Any								=			hidden_act
										A__       :     List[Any]								=			hidden_dropout_prob
										A__       :     Dict								=			attention_probs_dropout_prob
										A__       :     Any								=			type_sequence_label_size
										A__       :     List[Any]								=			initializer_range
										A__       :     Any								=			scope
										A__       :     str								=			out_indices
										A__       :     Tuple								=			num_labels

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





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										A__       :     List[Any]								=			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

										A__       :     Optional[Any]								=			None
										A__       :     str								=			None
										if self.use_labels:
																	A__       :     str								=			ids_tensor([self.batch_size]   ,      self.type_sequence_label_size							)
																	A__       :     Union[str, Any]								=			ids_tensor([self.batch_size, self.image_size, self.image_size]   ,      self.num_labels							)

										A__       :     Dict								=			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=snake_case_   ,      initializer_range=self.initializer_range   ,      out_indices=self.out_indices   ,      )





			def 		lowerCamelCase					(			self   ,      snake_case_   ,      snake_case_   ,      snake_case_   ,      snake_case_							):







										'''simple docstring'''


										A__       :     Union[str, Any]								=			BeitModel(config=snake_case_							)
										model.to(snake_case_							)
										model.eval()
										A__       :     Optional[Any]								=			model(snake_case_							)
										self.parent.assertEqual(result.last_hidden_state.shape   ,      (self.batch_size, self.seq_length, self.hidden_size)							)





			def 		lowerCamelCase					(			self   ,      snake_case_   ,      snake_case_   ,      snake_case_   ,      snake_case_							):







										'''simple docstring'''


										A__       :     int								=			BeitForMaskedImageModeling(config=snake_case_							)
										model.to(snake_case_							)
										model.eval()
										A__       :     List[str]								=			model(snake_case_							)
										self.parent.assertEqual(result.logits.shape   ,      (self.batch_size, self.seq_length - 1, self.vocab_size)							)





			def 		lowerCamelCase					(			self   ,      snake_case_   ,      snake_case_   ,      snake_case_   ,      snake_case_							):







										'''simple docstring'''


										A__       :     int								=			self.type_sequence_label_size
										A__       :     Optional[int]								=			BeitForImageClassification(snake_case_							)
										model.to(snake_case_							)
										model.eval()
										A__       :     List[Any]								=			model(snake_case_   ,      labels=snake_case_							)
										self.parent.assertEqual(result.logits.shape   ,      (self.batch_size, self.type_sequence_label_size)							)

										# test greyscale images
										A__       :     Optional[Any]								=			1
										A__       :     Union[str, Any]								=			BeitForImageClassification(snake_case_							)
										model.to(snake_case_							)
										model.eval()

										A__       :     Optional[int]								=			floats_tensor([self.batch_size, 1, self.image_size, self.image_size]							)
										A__       :     Any								=			model(snake_case_   ,      labels=snake_case_							)
										self.parent.assertEqual(result.logits.shape   ,      (self.batch_size, self.type_sequence_label_size)							)





			def 		lowerCamelCase					(			self   ,      snake_case_   ,      snake_case_   ,      snake_case_   ,      snake_case_							):







										'''simple docstring'''


										A__       :     str								=			self.num_labels
										A__       :     Optional[int]								=			BeitForSemanticSegmentation(snake_case_							)
										model.to(snake_case_							)
										model.eval()
										A__       :     List[Any]								=			model(snake_case_							)
										self.parent.assertEqual(
										    result.logits.shape   ,      (self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)							)
										A__       :     Union[str, Any]								=			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 		lowerCamelCase					(			self							):







										'''simple docstring'''


										A__       :     Optional[Any]								=			self.prepare_config_and_inputs()
										A__				,							A__				,							A__				,							A__       :     Optional[int]								=			config_and_inputs
										A__       :     List[Any]								=			{"""pixel_values""": pixel_values}
										return config, inputs_dict





@require_torch
class 						__UpperCAmelCase    (__A							,   __A							,   unittest.TestCase     ):







			'''simple docstring'''
			_UpperCamelCase  :						Union[str, Any]					     =				(
			    (BeitModel, BeitForImageClassification, BeitForMaskedImageModeling, BeitForSemanticSegmentation)
			    if is_torch_available()
			    else ()
			)
			_UpperCamelCase  :						List[Any]					     =				(
			    {
			        'feature-extraction': BeitModel,
			        'image-classification': BeitForImageClassification,
			        'image-segmentation': BeitForSemanticSegmentation,
			    }
			    if is_torch_available()
			    else {}
			)

			_UpperCamelCase  :						Union[str, Any]					     =				False
			_UpperCamelCase  :						Any					     =				False
			_UpperCamelCase  :						Tuple					     =				False





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										A__       :     str								=			BeitModelTester(self							)
										A__       :     List[str]								=			ConfigTester(self   ,      config_class=snake_case_   ,      has_text_modality=snake_case_   ,      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'''


										A__				,							A__       :     Optional[int]								=			self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
																	A__       :     Union[str, Any]								=			model_class(snake_case_							)
																	self.assertIsInstance(model.get_input_embeddings()   ,      (nn.Module)							)
																	A__       :     List[Any]								=			model.get_output_embeddings()
																	self.assertTrue(x is None or isinstance(snake_case_   ,      nn.Linear							)							)





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										A__				,							A__       :     Union[str, Any]								=			self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
																	A__       :     List[str]								=			model_class(snake_case_							)
																	A__       :     Dict								=			inspect.signature(model.forward							)
																	# signature.parameters is an OrderedDict => so arg_names order is deterministic
																	A__       :     List[Any]								=			[*signature.parameters.keys()]

																	A__       :     Optional[Any]								=			["""pixel_values"""]
																	self.assertListEqual(arg_names[:1]   ,      snake_case_							)





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										A__       :     int								=			self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*snake_case_							)





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										A__       :     Dict								=			self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_masked_lm(*snake_case_							)





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										A__       :     str								=			self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_image_classification(*snake_case_							)





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										A__       :     Tuple								=			self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_semantic_segmentation(*snake_case_							)





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										if not self.model_tester.is_training:
																	return

										A__				,							A__       :     Optional[Any]								=			self.model_tester.prepare_config_and_inputs_for_common()
										A__       :     Optional[Any]								=			True

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

																	A__       :     str								=			model_class(snake_case_							)
																	model.to(snake_case_							)
																	model.train()
																	A__       :     Any								=			self._prepare_for_class(snake_case_   ,      snake_case_   ,      return_labels=snake_case_							)
																	A__       :     Dict								=			model(**snake_case_							).loss
																	loss.backward()





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										A__				,							A__       :     Optional[Any]								=			self.model_tester.prepare_config_and_inputs_for_common()
										if not self.model_tester.is_training:
																	return

										A__       :     Any								=			False
										A__       :     List[str]								=			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

																	A__       :     Union[str, Any]								=			model_class(snake_case_							)
																	model.gradient_checkpointing_enable()
																	model.to(snake_case_							)
																	model.train()
																	A__       :     str								=			self._prepare_for_class(snake_case_   ,      snake_case_   ,      return_labels=snake_case_							)
																	A__       :     List[str]								=			model(**snake_case_							).loss
																	loss.backward()





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										A__				,							A__       :     str								=			self.model_tester.prepare_config_and_inputs_for_common()

										A__       :     Any								=			_config_zero_init(snake_case_							)
										for model_class in self.all_model_classes:
																	A__       :     List[Any]								=			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 		lowerCamelCase					(			self							):







										'''simple docstring'''


										for model_name in BEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																	A__       :     str								=			BeitModel.from_pretrained(snake_case_							)
																	self.assertIsNotNone(snake_case_							)





def  _A(		):
							A__       :     List[Any]								=			Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""							)
							return image



@require_torch
@require_vision
class 						__UpperCAmelCase    (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'''


										A__       :     Optional[int]								=			BeitForMaskedImageModeling.from_pretrained("""microsoft/beit-base-patch16-224-pt22k"""							).to(snake_case_							)

										A__       :     List[Any]								=			self.default_image_processor
										A__       :     Dict								=			prepare_img()
										A__       :     List[Any]								=			image_processor(images=snake_case_   ,      return_tensors="""pt"""							).pixel_values.to(snake_case_							)

										# prepare bool_masked_pos
										A__       :     Tuple								=			torch.ones((1, 196)   ,      dtype=torch.bool							).to(snake_case_							)

										# forward pass
										with torch.no_grad():
																	A__       :     Optional[Any]								=			model(pixel_values=snake_case_   ,      bool_masked_pos=snake_case_							)
										A__       :     Optional[int]								=			outputs.logits

										# verify the logits
										A__       :     List[str]								=			torch.Size((1, 196, 8_192)							)
										self.assertEqual(logits.shape   ,      snake_case_							)

										A__       :     Any								=			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 		lowerCamelCase					(			self							):







										'''simple docstring'''


										A__       :     str								=			BeitForImageClassification.from_pretrained("""microsoft/beit-base-patch16-224"""							).to(snake_case_							)

										A__       :     List[str]								=			self.default_image_processor
										A__       :     Any								=			prepare_img()
										A__       :     Union[str, Any]								=			image_processor(images=snake_case_   ,      return_tensors="""pt"""							).to(snake_case_							)

										# forward pass
										with torch.no_grad():
																	A__       :     int								=			model(**snake_case_							)
										A__       :     str								=			outputs.logits

										# verify the logits
										A__       :     Union[str, Any]								=			torch.Size((1, 1_000)							)
										self.assertEqual(logits.shape   ,      snake_case_							)

										A__       :     str								=			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							)							)

										A__       :     Optional[int]								=			281
										self.assertEqual(logits.argmax(-1							).item()   ,      snake_case_							)





			@slow
			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										A__       :     List[Any]								=			BeitForImageClassification.from_pretrained("""microsoft/beit-large-patch16-224-pt22k-ft22k"""							).to(
										    snake_case_							)

										A__       :     Dict								=			self.default_image_processor
										A__       :     Union[str, Any]								=			prepare_img()
										A__       :     Any								=			image_processor(images=snake_case_   ,      return_tensors="""pt"""							).to(snake_case_							)

										# forward pass
										with torch.no_grad():
																	A__       :     Any								=			model(**snake_case_							)
										A__       :     int								=			outputs.logits

										# verify the logits
										A__       :     Union[str, Any]								=			torch.Size((1, 21_841)							)
										self.assertEqual(logits.shape   ,      snake_case_							)

										A__       :     int								=			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							)							)

										A__       :     Union[str, Any]								=			2_396
										self.assertEqual(logits.argmax(-1							).item()   ,      snake_case_							)





			@slow
			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										A__       :     List[Any]								=			BeitForSemanticSegmentation.from_pretrained("""microsoft/beit-base-finetuned-ade-640-640"""							)
										A__       :     Union[str, Any]								=			model.to(snake_case_							)

										A__       :     Tuple								=			BeitImageProcessor(do_resize=snake_case_   ,      size=640   ,      do_center_crop=snake_case_							)

										A__       :     Optional[int]								=			load_dataset("""hf-internal-testing/fixtures_ade20k"""   ,      split="""test"""							)
										A__       :     Dict								=			Image.open(ds[0]["""file"""]							)
										A__       :     Any								=			image_processor(images=snake_case_   ,      return_tensors="""pt"""							).to(snake_case_							)

										# forward pass
										with torch.no_grad():
																	A__       :     List[str]								=			model(**snake_case_							)
										A__       :     Optional[Any]								=			outputs.logits

										# verify the logits
										A__       :     int								=			torch.Size((1, 150, 160, 160)							)
										self.assertEqual(logits.shape   ,      snake_case_							)

										A__       :     Optional[int]								=			version.parse(PIL.__version__							) < version.parse("""9.0.0"""							)

										if is_pillow_less_than_a:
																	A__       :     Optional[int]								=			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:
																	A__       :     Dict								=			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 		lowerCamelCase					(			self							):







										'''simple docstring'''


										A__       :     Optional[int]								=			BeitForSemanticSegmentation.from_pretrained("""microsoft/beit-base-finetuned-ade-640-640"""							)
										A__       :     Optional[Any]								=			model.to(snake_case_							)

										A__       :     Tuple								=			BeitImageProcessor(do_resize=snake_case_   ,      size=640   ,      do_center_crop=snake_case_							)

										A__       :     str								=			load_dataset("""hf-internal-testing/fixtures_ade20k"""   ,      split="""test"""							)
										A__       :     Optional[Any]								=			Image.open(ds[0]["""file"""]							)
										A__       :     Optional[int]								=			image_processor(images=snake_case_   ,      return_tensors="""pt"""							).to(snake_case_							)

										# forward pass
										with torch.no_grad():
																	A__       :     Optional[Any]								=			model(**snake_case_							)

										A__       :     Dict								=			outputs.logits.detach().cpu()

										A__       :     List[str]								=			image_processor.post_process_semantic_segmentation(outputs=snake_case_   ,      target_sizes=[(500, 300)]							)
										A__       :     Optional[int]								=			torch.Size((500, 300)							)
										self.assertEqual(segmentation[0].shape   ,      snake_case_							)

										A__       :     List[str]								=			image_processor.post_process_semantic_segmentation(outputs=snake_case_							)
										A__       :     List[str]								=			torch.Size((160, 160)							)
										self.assertEqual(segmentation[0].shape   ,      snake_case_							)






"""simple docstring"""



from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging


_UpperCamelCase								=		logging.get_logger(__name__)

_UpperCamelCase								=		{
    "EleutherAI/gpt-neo-1.3B": "https://huggingface.co/EleutherAI/gpt-neo-1.3B/resolve/main/config.json",
    # See all GPTNeo models at https://huggingface.co/models?filter=gpt_neo
}
class 						__UpperCAmelCase    (__A     ):







			'''simple docstring'''
			_UpperCamelCase  :						Tuple					     =				'gpt_neo'
			_UpperCamelCase  :						Optional[Any]					     =				['past_key_values']
			_UpperCamelCase  :						int					     =				{'num_attention_heads': 'num_heads', 'num_hidden_layers': 'num_layers'}





			def __init__(			self   ,      snake_case_=50_257   ,      snake_case_=2_048   ,      snake_case_=2_048   ,      snake_case_=24   ,      snake_case_=[[["global", "local"], 12]]   ,      snake_case_=16   ,      snake_case_=None   ,      snake_case_=256   ,      snake_case_="gelu_new"   ,      snake_case_=0.0   ,      snake_case_=0.0   ,      snake_case_=0.0   ,      snake_case_=0.1   ,      snake_case_=1E-5   ,      snake_case_=0.02   ,      snake_case_=True   ,      snake_case_=50_256   ,      snake_case_=50_256   ,      **snake_case_   ,      ):







										'''simple docstring'''


										A__       :     int								=			vocab_size
										A__       :     Optional[Any]								=			max_position_embeddings
										A__       :     int								=			hidden_size
										A__       :     List[Any]								=			num_layers
										A__       :     Any								=			num_heads
										A__       :     List[str]								=			intermediate_size
										A__       :     Dict								=			window_size
										A__       :     Optional[int]								=			activation_function
										A__       :     Optional[int]								=			resid_dropout
										A__       :     List[str]								=			embed_dropout
										A__       :     str								=			attention_dropout
										A__       :     List[str]								=			classifier_dropout
										A__       :     str								=			layer_norm_epsilon
										A__       :     str								=			initializer_range
										A__       :     Any								=			use_cache

										A__       :     List[str]								=			bos_token_id
										A__       :     Any								=			eos_token_id

										A__       :     Optional[Any]								=			attention_types
										A__       :     List[Any]								=			self.expand_attention_types_params(snake_case_							)

										if len(self.attention_layers							) != self.num_layers:
																	raise ValueError(
																	    """Configuration for convolutional module is incorrect. """
																	    """It is required that `len(config.attention_layers)` == `config.num_layers` """
																	    F'''but is `len(config.attention_layers) = {len(self.attention_layers							)}`, '''
																	    F'''`config.num_layers = {self.num_layers}`. '''
																	    """`config.attention_layers` is prepared using `config.attention_types`. """
																	    """Please verify the value of `config.attention_types` argument."""							)

										super().__init__(bos_token_id=snake_case_   ,      eos_token_id=snake_case_   ,      **snake_case_							)





			@staticmethod
			def 		lowerCamelCase					(			snake_case_							):







										'''simple docstring'''


										A__       :     Optional[Any]								=			[]
										for item in attention_types:
																	for _ in range(item[1]							):
																								attentions.extend(item[0]							)
										return attentions





def  _A(		lowerCAmelCase			,  lowerCAmelCase			,  lowerCAmelCase			,  lowerCAmelCase							):
							import torch

							A__       :     Union[str, Any]								=			input.size()
							A__       :     str								=			len(lowerCAmelCase							)
							A__       :     Union[str, Any]								=			shape[dimension]

							A__       :     Dict								=			torch.arange(0			,  lowerCAmelCase			,  lowerCAmelCase							)
							A__       :     Dict								=			torch.div(sizedim - size			,  lowerCAmelCase			,  rounding_mode="""floor"""							) + 1
							A__       :     str								=			torch.arange(lowerCAmelCase							) + low_indices[:min_length][:, None]

							A__       :     str								=			[slice(lowerCAmelCase							)] * rank
							A__       :     Optional[Any]								=			indices
							A__       :     Tuple								=			input[s]

							A__       :     List[str]								=			list(range(0			,  rank + 1							)							)
							perm.append(perm.pop(dimension + 1							)							)

							return sliced.permute(lowerCAmelCase							)

def  _A(		lowerCAmelCase			,  lowerCAmelCase							):
							import torch

							A__       :     Optional[Any]								=			torch.arange(1			,  lowerCAmelCase							)
							A__       :     int								=			torch.remainder(lowerCAmelCase			,  lowerCAmelCase							)
							A__       :     str								=			remainders == 0
							A__       :     Tuple								=			candidates[divisor_indices]
							A__       :     Any								=			torch.max(lowerCAmelCase							)
							return largest_divisor, torch.div(lowerCAmelCase			,  lowerCAmelCase			,  rounding_mode="""floor"""							)



class 						__UpperCAmelCase    (__A     ):







			'''simple docstring'''





			@property
			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										A__       :     Any								=			OrderedDict({"""input_ids""": {0: """batch""", 1: """sequence"""}}							)
										if self.use_past:
																	self.fill_with_past_key_values_(snake_case_   ,      direction="""inputs"""							)
																	A__       :     str								=			{0: """batch""", 1: """past_sequence + sequence"""}
										else:
																	A__       :     Optional[Any]								=			{0: """batch""", 1: """sequence"""}

										return common_inputs





			@property
			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										return self._config.num_heads





			def 		lowerCamelCase					(			self   ,      snake_case_   ,      snake_case_ = -1   ,      snake_case_ = -1   ,      snake_case_ = False   ,      snake_case_ = None   ,      ):







										'''simple docstring'''


										A__       :     Optional[int]								=			super(snake_case_   ,      self							).generate_dummy_inputs(
										    snake_case_   ,      batch_size=snake_case_   ,      seq_length=snake_case_   ,      is_pair=snake_case_   ,      framework=snake_case_							)

										# We need to order the input in the way they appears in the forward()
										A__       :     List[Any]								=			OrderedDict({"""input_ids""": common_inputs["""input_ids"""]}							)

										# Need to add the past_keys
										if self.use_past:
																	if not is_torch_available():
																								raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed."""							)
																	else:
																								import torch

																								A__				,							A__       :     Dict								=			common_inputs["""input_ids"""].shape
																								# Not using the same length for past_key_values
																								A__       :     Any								=			seqlen + 2
																								A__       :     Any								=			(
																								    batch,
																								    self.num_attention_heads,
																								    past_key_values_length,
																								    self._config.hidden_size // self.num_attention_heads,
																								)
																								A__       :     List[Any]								=			[
																								    (torch.zeros(snake_case_							), torch.zeros(snake_case_							)) for _ in range(self.num_layers							)
																								]

										A__       :     Tuple								=			common_inputs["""attention_mask"""]
										if self.use_past:
																	A__       :     List[Any]								=			ordered_inputs["""attention_mask"""].dtype
																	A__       :     Optional[Any]								=			torch.cat(
																	    [ordered_inputs["""attention_mask"""], torch.ones(snake_case_   ,      snake_case_   ,      dtype=snake_case_							)]   ,      dim=1							)

										return ordered_inputs





			@property
			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										return 13





