


import argparse
import json

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import ViTImageProcessor, ViTMSNConfig, ViTMSNModel
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD


torch.set_grad_enabled(False)


def        _a  (     UpperCamelCase_					:				Optional[int]    ,   UpperCamelCase_					:				str=False   ) ->   Optional[Any]:
						"""simple docstring"""







						lowerCAmelCase__       =						[]
						for i in range(config.num_hidden_layers   ):
												# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
												rename_keys.append((F"module.blocks.{i}.norm1.weight", F"vit.encoder.layer.{i}.layernorm_before.weight")   )
												rename_keys.append((F"module.blocks.{i}.norm1.bias", F"vit.encoder.layer.{i}.layernorm_before.bias")   )
												rename_keys.append(
												    (F"module.blocks.{i}.attn.proj.weight", F"vit.encoder.layer.{i}.attention.output.dense.weight")   )
												rename_keys.append((F"module.blocks.{i}.attn.proj.bias", F"vit.encoder.layer.{i}.attention.output.dense.bias")   )
												rename_keys.append((F"module.blocks.{i}.norm2.weight", F"vit.encoder.layer.{i}.layernorm_after.weight")   )
												rename_keys.append((F"module.blocks.{i}.norm2.bias", F"vit.encoder.layer.{i}.layernorm_after.bias")   )
												rename_keys.append((F"module.blocks.{i}.mlp.fc1.weight", F"vit.encoder.layer.{i}.intermediate.dense.weight")   )
												rename_keys.append((F"module.blocks.{i}.mlp.fc1.bias", F"vit.encoder.layer.{i}.intermediate.dense.bias")   )
												rename_keys.append((F"module.blocks.{i}.mlp.fc2.weight", F"vit.encoder.layer.{i}.output.dense.weight")   )
												rename_keys.append((F"module.blocks.{i}.mlp.fc2.bias", F"vit.encoder.layer.{i}.output.dense.bias")   )

						# projection layer + position embeddings
						rename_keys.extend(
						    [
						        ("module.cls_token", "vit.embeddings.cls_token"),
						        ("module.patch_embed.proj.weight", "vit.embeddings.patch_embeddings.projection.weight"),
						        ("module.patch_embed.proj.bias", "vit.embeddings.patch_embeddings.projection.bias"),
						        ("module.pos_embed", "vit.embeddings.position_embeddings"),
						    ]   )

						if base_model:
												# layernorm + pooler
												rename_keys.extend(
												    [
												        ("module.norm.weight", "layernorm.weight"),
												        ("module.norm.bias", "layernorm.bias"),
												    ]   )

												# if just the base model, we should remove "vit" from all keys that start with "vit"
												lowerCAmelCase__       =						[(pair[0], pair[1][4:]) if pair[1].startswith("vit"   ) else pair for pair in rename_keys]
						else:
												# layernorm + classification head
												rename_keys.extend(
												    [
												        ("norm.weight", "vit.layernorm.weight"),
												        ("norm.bias", "vit.layernorm.bias"),
												        ("head.weight", "classifier.weight"),
												        ("head.bias", "classifier.bias"),
												    ]   )

						return rename_keys


def        _a  (     UpperCamelCase_					:				Union[str, Any]    ,   UpperCamelCase_					:				Union[str, Any]    ,   UpperCamelCase_					:				Tuple=False   ) ->   Optional[int]:
						"""simple docstring"""







						for i in range(config.num_hidden_layers   ):
												if base_model:
																		lowerCAmelCase__       =						""
												else:
																		lowerCAmelCase__       =						"vit."
												# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
												lowerCAmelCase__       =						state_dict.pop(F"module.blocks.{i}.attn.qkv.weight"   )
												lowerCAmelCase__       =						state_dict.pop(F"module.blocks.{i}.attn.qkv.bias"   )
												# next, add query, keys and values (in that order) to the state dict
												lowerCAmelCase__       =						in_proj_weight[
												    : config.hidden_size, :
												]
												lowerCAmelCase__       =						in_proj_bias[: config.hidden_size]
												lowerCAmelCase__       =						in_proj_weight[
												    config.hidden_size : config.hidden_size * 2, :
												]
												lowerCAmelCase__       =						in_proj_bias[
												    config.hidden_size : config.hidden_size * 2
												]
												lowerCAmelCase__       =						in_proj_weight[
												    -config.hidden_size :, :
												]
												lowerCAmelCase__       =						in_proj_bias[-config.hidden_size :]


def        _a  (     UpperCamelCase_					:				Any   ) ->   Optional[Any]:
						"""simple docstring"""







						lowerCAmelCase__       =						["head.weight", "head.bias"]
						for k in ignore_keys:
												state_dict.pop(UpperCamelCase_    ,   UpperCamelCase_   )


def        _a  (     UpperCamelCase_					:				int   ) ->   Optional[Any]:
						"""simple docstring"""







						lowerCAmelCase__       =						[
						    "module.fc.fc1.weight",
						    "module.fc.fc1.bias",
						    "module.fc.bn1.weight",
						    "module.fc.bn1.bias",
						    "module.fc.bn1.running_mean",
						    "module.fc.bn1.running_var",
						    "module.fc.bn1.num_batches_tracked",
						    "module.fc.fc2.weight",
						    "module.fc.fc2.bias",
						    "module.fc.bn2.weight",
						    "module.fc.bn2.bias",
						    "module.fc.bn2.running_mean",
						    "module.fc.bn2.running_var",
						    "module.fc.bn2.num_batches_tracked",
						    "module.fc.fc3.weight",
						    "module.fc.fc3.bias",
						]
						for k in ignore_keys:
												state_dict.pop(UpperCamelCase_    ,   UpperCamelCase_   )


def        _a  (     UpperCamelCase_					:				Tuple    ,   UpperCamelCase_					:				Dict    ,   UpperCamelCase_					:				Any   ) ->   Union[str, Any]:
						"""simple docstring"""







						lowerCAmelCase__       =						dct.pop(UpperCamelCase_   )
						lowerCAmelCase__       =						val


def        _a  (     UpperCamelCase_					:				Tuple    ,   UpperCamelCase_					:				Optional[Any]   ) ->   List[Any]:
						"""simple docstring"""







						lowerCAmelCase__       =						ViTMSNConfig()
						lowerCAmelCase__       =						1_000

						lowerCAmelCase__       =						"datasets/huggingface/label-files"
						lowerCAmelCase__       =						"imagenet-1k-id2label.json"
						lowerCAmelCase__       =						json.load(open(hf_hub_download(UpperCamelCase_    ,   UpperCamelCase_   )    ,   "r"   )   )
						lowerCAmelCase__       =						{int(UpperCamelCase_   ): v for k, v in idalabel.items()}
						lowerCAmelCase__       =						idalabel
						lowerCAmelCase__       =						{v: k for k, v in idalabel.items()}

						if "s16" in checkpoint_url:
												lowerCAmelCase__       =						384
												lowerCAmelCase__       =						1_536
												lowerCAmelCase__       =						6
						elif "l16" in checkpoint_url:
												lowerCAmelCase__       =						1_024
												lowerCAmelCase__       =						4_096
												lowerCAmelCase__       =						24
												lowerCAmelCase__       =						16
												lowerCAmelCase__       =						0.1
						elif "b4" in checkpoint_url:
												lowerCAmelCase__       =						4
						elif "l7" in checkpoint_url:
												lowerCAmelCase__       =						7
												lowerCAmelCase__       =						1_024
												lowerCAmelCase__       =						4_096
												lowerCAmelCase__       =						24
												lowerCAmelCase__       =						16
												lowerCAmelCase__       =						0.1

						lowerCAmelCase__       =						ViTMSNModel(UpperCamelCase_   )

						lowerCAmelCase__       =						torch.hub.load_state_dict_from_url(UpperCamelCase_    ,   map_location="cpu"   )["target_encoder"]

						lowerCAmelCase__       =						ViTImageProcessor(size=config.image_size   )

						remove_projection_head(UpperCamelCase_   )
						lowerCAmelCase__       =						create_rename_keys(UpperCamelCase_    ,   base_model=UpperCamelCase_   )

						for src, dest in rename_keys:
												rename_key(UpperCamelCase_    ,   UpperCamelCase_    ,   UpperCamelCase_   )
						read_in_q_k_v(UpperCamelCase_    ,   UpperCamelCase_    ,   base_model=UpperCamelCase_   )

						model.load_state_dict(UpperCamelCase_   )
						model.eval()

						lowerCAmelCase__       =						"http://images.cocodataset.org/val2017/000000039769.jpg"

						lowerCAmelCase__       =						Image.open(requests.get(UpperCamelCase_    ,   stream=UpperCamelCase_   ).raw   )
						lowerCAmelCase__       =						ViTImageProcessor(
						    size=config.image_size    ,   image_mean=UpperCamelCase_    ,   image_std=UpperCamelCase_   )
						lowerCAmelCase__       =						image_processor(images=UpperCamelCase_    ,   return_tensors="pt"   )

						# forward pass
						torch.manual_seed(2   )
						lowerCAmelCase__       =						model(**UpperCamelCase_   )
						lowerCAmelCase__       =						outputs.last_hidden_state

						# The following Colab Notebook was used to generate these outputs:
						# https://colab.research.google.com/gist/sayakpaul/3672419a04f5997827503fd84079bdd1/scratchpad.ipynb
						if "s16" in checkpoint_url:
												lowerCAmelCase__       =						torch.tensor([[-1.0_915, -1.4_876, -1.1_809]]   )
						elif "b16" in checkpoint_url:
												lowerCAmelCase__       =						torch.tensor([[14.2_889, -18.9_045, 11.7_281]]   )
						elif "l16" in checkpoint_url:
												lowerCAmelCase__       =						torch.tensor([[41.5_028, -22.8_681, 45.6_475]]   )
						elif "b4" in checkpoint_url:
												lowerCAmelCase__       =						torch.tensor([[-4.3_868, 5.2_932, -0.4_137]]   )
						else:
												lowerCAmelCase__       =						torch.tensor([[-0.1_792, -0.6_465, 2.4_263]]   )

						# verify logits
						assert torch.allclose(last_hidden_state[:, 0, :3]    ,   UpperCamelCase_    ,   atol=1e-4   )

						print(F"Saving model to {pytorch_dump_folder_path}"   )
						model.save_pretrained(UpperCamelCase_   )

						print(F"Saving image processor to {pytorch_dump_folder_path}"   )
						image_processor.save_pretrained(UpperCamelCase_   )


if __name__ == "__main__":
	a_							=    argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    '''--checkpoint_url''',
	    default='''https://dl.fbaipublicfiles.com/msn/vits16_800ep.pth.tar''',
	    type=str,
	    help='''URL of the checkpoint you\'d like to convert.''',
	)
	parser.add_argument(
	    '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
	)

	a_							=    parser.parse_args()
	convert_vit_msn_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)














import unittest

from transformers import AutoTokenizer, NystromformerConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
	import torch

	from transformers import (
	    NystromformerForMaskedLM,
	    NystromformerForMultipleChoice,
	    NystromformerForQuestionAnswering,
	    NystromformerForSequenceClassification,
	    NystromformerForTokenClassification,
	    NystromformerModel,
	)
	from transformers.models.nystromformer.modeling_nystromformer import NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST


class      lowercase__     :

	def __init__(							self		,    __UpperCAmelCase		,    __UpperCAmelCase=13		,    __UpperCAmelCase=7		,    __UpperCAmelCase=True		,    __UpperCAmelCase=True		,    __UpperCAmelCase=True		,    __UpperCAmelCase=True		,    __UpperCAmelCase=99		,    __UpperCAmelCase=32		,    __UpperCAmelCase=5		,    __UpperCAmelCase=4		,    __UpperCAmelCase=37		,    __UpperCAmelCase="gelu"		,    __UpperCAmelCase=0.1		,    __UpperCAmelCase=0.1		,    __UpperCAmelCase=512		,    __UpperCAmelCase=16		,    __UpperCAmelCase=2		,    __UpperCAmelCase=0.02		,    __UpperCAmelCase=3		,    __UpperCAmelCase=4		,    __UpperCAmelCase=None		,    )->					Any:







							'''simple docstring'''





							lowerCAmelCase__       =						parent
							lowerCAmelCase__       =						batch_size
							lowerCAmelCase__       =						seq_length
							lowerCAmelCase__       =						is_training
							lowerCAmelCase__       =						use_input_mask
							lowerCAmelCase__       =						use_token_type_ids
							lowerCAmelCase__       =						use_labels
							lowerCAmelCase__       =						vocab_size
							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__       =						max_position_embeddings
							lowerCAmelCase__       =						type_vocab_size
							lowerCAmelCase__       =						type_sequence_label_size
							lowerCAmelCase__       =						initializer_range
							lowerCAmelCase__       =						num_labels
							lowerCAmelCase__       =						num_choices
							lowerCAmelCase__       =						scope

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







							'''simple docstring'''





							lowerCAmelCase__       =						ids_tensor([self.batch_size, self.seq_length]		,    self.vocab_size       )

							lowerCAmelCase__       =						None
							if self.use_input_mask:
													lowerCAmelCase__       =						random_attention_mask([self.batch_size, self.seq_length]       )

							lowerCAmelCase__       =						None
							if self.use_token_type_ids:
													lowerCAmelCase__       =						ids_tensor([self.batch_size, self.seq_length]		,    self.type_vocab_size       )

							lowerCAmelCase__       =						None
							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.seq_length]		,    self.num_labels       )
													lowerCAmelCase__       =						ids_tensor([self.batch_size]		,    self.num_choices       )

							lowerCAmelCase__       =						self.get_config()

							return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels

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







							'''simple docstring'''





							return NystromformerConfig(
							    vocab_size=self.vocab_size		,    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		,    max_position_embeddings=self.max_position_embeddings		,    type_vocab_size=self.type_vocab_size		,    is_decoder=__UpperCAmelCase		,    initializer_range=self.initializer_range		,    )

	def 		UpperCAmelCase  (							self		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase       )->					int:







							'''simple docstring'''





							lowerCAmelCase__       =						NystromformerModel(config=__UpperCAmelCase       )
							model.to(__UpperCAmelCase       )
							model.eval()
							lowerCAmelCase__       =						model(__UpperCAmelCase		,    attention_mask=__UpperCAmelCase		,    token_type_ids=__UpperCAmelCase       )
							lowerCAmelCase__       =						model(__UpperCAmelCase		,    token_type_ids=__UpperCAmelCase       )
							lowerCAmelCase__       =						model(__UpperCAmelCase       )
							self.parent.assertEqual(result.last_hidden_state.shape		,    (self.batch_size, self.seq_length, self.hidden_size)       )

	def 		UpperCAmelCase  (							self		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase       )->					str:







							'''simple docstring'''





							lowerCAmelCase__       =						NystromformerForMaskedLM(config=__UpperCAmelCase       )
							model.to(__UpperCAmelCase       )
							model.eval()
							lowerCAmelCase__       =						model(__UpperCAmelCase		,    attention_mask=__UpperCAmelCase		,    token_type_ids=__UpperCAmelCase		,    labels=__UpperCAmelCase       )
							self.parent.assertEqual(result.logits.shape		,    (self.batch_size, self.seq_length, self.vocab_size)       )

	def 		UpperCAmelCase  (							self		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase       )->					Any:







							'''simple docstring'''





							lowerCAmelCase__       =						NystromformerForQuestionAnswering(config=__UpperCAmelCase       )
							model.to(__UpperCAmelCase       )
							model.eval()
							lowerCAmelCase__       =						model(
							    __UpperCAmelCase		,    attention_mask=__UpperCAmelCase		,    token_type_ids=__UpperCAmelCase		,    start_positions=__UpperCAmelCase		,    end_positions=__UpperCAmelCase		,    )
							self.parent.assertEqual(result.start_logits.shape		,    (self.batch_size, self.seq_length)       )
							self.parent.assertEqual(result.end_logits.shape		,    (self.batch_size, self.seq_length)       )

	def 		UpperCAmelCase  (							self		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase       )->					Dict:







							'''simple docstring'''





							lowerCAmelCase__       =						self.num_labels
							lowerCAmelCase__       =						NystromformerForSequenceClassification(__UpperCAmelCase       )
							model.to(__UpperCAmelCase       )
							model.eval()
							lowerCAmelCase__       =						model(__UpperCAmelCase		,    attention_mask=__UpperCAmelCase		,    token_type_ids=__UpperCAmelCase		,    labels=__UpperCAmelCase       )
							self.parent.assertEqual(result.logits.shape		,    (self.batch_size, self.num_labels)       )

	def 		UpperCAmelCase  (							self		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase       )->					str:







							'''simple docstring'''





							lowerCAmelCase__       =						self.num_labels
							lowerCAmelCase__       =						NystromformerForTokenClassification(config=__UpperCAmelCase       )
							model.to(__UpperCAmelCase       )
							model.eval()
							lowerCAmelCase__       =						model(__UpperCAmelCase		,    attention_mask=__UpperCAmelCase		,    token_type_ids=__UpperCAmelCase		,    labels=__UpperCAmelCase       )
							self.parent.assertEqual(result.logits.shape		,    (self.batch_size, self.seq_length, self.num_labels)       )

	def 		UpperCAmelCase  (							self		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase       )->					Optional[int]:







							'''simple docstring'''





							lowerCAmelCase__       =						self.num_choices
							lowerCAmelCase__       =						NystromformerForMultipleChoice(config=__UpperCAmelCase       )
							model.to(__UpperCAmelCase       )
							model.eval()
							lowerCAmelCase__       =						input_ids.unsqueeze(1       ).expand(-1		,    self.num_choices		,    -1       ).contiguous()
							lowerCAmelCase__       =						token_type_ids.unsqueeze(1       ).expand(-1		,    self.num_choices		,    -1       ).contiguous()
							lowerCAmelCase__       =						input_mask.unsqueeze(1       ).expand(-1		,    self.num_choices		,    -1       ).contiguous()
							lowerCAmelCase__       =						model(
							    __UpperCAmelCase		,    attention_mask=__UpperCAmelCase		,    token_type_ids=__UpperCAmelCase		,    labels=__UpperCAmelCase		,    )
							self.parent.assertEqual(result.logits.shape		,    (self.batch_size, self.num_choices)       )


	def 		UpperCAmelCase  (							self       )->					Any:







							'''simple docstring'''





							lowerCAmelCase__       =						self.prepare_config_and_inputs()
							(
							    (
							    lowerCAmelCase__
							)      ,							(
							    lowerCAmelCase__
							)      ,							(
							    lowerCAmelCase__
							)      ,							(
							    lowerCAmelCase__
							)      ,							(
							    lowerCAmelCase__
							)      ,							(
							    lowerCAmelCase__
							)      ,							(
							    lowerCAmelCase__
							)      ,							
							)       =						config_and_inputs
							lowerCAmelCase__       =						{"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
							return config, inputs_dict





@require_torch
class      lowercase__     (			_UpperCAmelCase,					_UpperCAmelCase,					unittest.TestCase	):
	a_         =(
	    (
	        NystromformerModel,
	        NystromformerForMaskedLM,
	        NystromformerForMultipleChoice,
	        NystromformerForQuestionAnswering,
	        NystromformerForSequenceClassification,
	        NystromformerForTokenClassification,
	    )
	    if is_torch_available()
	    else ()
	)
	a_         =(
	    {
	        """feature-extraction""": NystromformerModel,
	        """fill-mask""": NystromformerForMaskedLM,
	        """question-answering""": NystromformerForQuestionAnswering,
	        """text-classification""": NystromformerForSequenceClassification,
	        """token-classification""": NystromformerForTokenClassification,
	        """zero-shot""": NystromformerForSequenceClassification,
	    }
	    if is_torch_available()
	    else {}
	)
	a_         =False
	a_         =False

	def 		UpperCAmelCase  (							self       )->					int:







							'''simple docstring'''





							lowerCAmelCase__       =						NystromformerModelTester(self       )
							lowerCAmelCase__       =						ConfigTester(self		,    config_class=__UpperCAmelCase		,    hidden_size=37       )

	def 		UpperCAmelCase  (							self       )->					Dict:







							'''simple docstring'''





							self.config_tester.run_common_tests()

	def 		UpperCAmelCase  (							self       )->					List[Any]:







							'''simple docstring'''





							lowerCAmelCase__       =						self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*__UpperCAmelCase       )

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







							'''simple docstring'''





							lowerCAmelCase__       =						self.model_tester.prepare_config_and_inputs()
							for type in ["absolute", "relative_key", "relative_key_query"]:
													lowerCAmelCase__       =						type
													self.model_tester.create_and_check_model(*__UpperCAmelCase       )

	def 		UpperCAmelCase  (							self       )->					List[Any]:







							'''simple docstring'''





							lowerCAmelCase__       =						self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_masked_lm(*__UpperCAmelCase       )

	def 		UpperCAmelCase  (							self       )->					Optional[int]:







							'''simple docstring'''





							lowerCAmelCase__       =						self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_multiple_choice(*__UpperCAmelCase       )

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







							'''simple docstring'''





							lowerCAmelCase__       =						self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_question_answering(*__UpperCAmelCase       )

	def 		UpperCAmelCase  (							self       )->					int:







							'''simple docstring'''





							lowerCAmelCase__       =						self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_sequence_classification(*__UpperCAmelCase       )

	def 		UpperCAmelCase  (							self       )->					Any:







							'''simple docstring'''





							lowerCAmelCase__       =						self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_token_classification(*__UpperCAmelCase       )


	@slow
	def 		UpperCAmelCase  (							self       )->					Optional[int]:







							'''simple docstring'''





							for model_name in NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													lowerCAmelCase__       =						NystromformerModel.from_pretrained(__UpperCAmelCase       )
													self.assertIsNotNone(__UpperCAmelCase       )





@require_torch
class      lowercase__     (			unittest.TestCase	):

	@slow
	def 		UpperCAmelCase  (							self       )->					Dict:







							'''simple docstring'''





							lowerCAmelCase__       =						NystromformerModel.from_pretrained("uw-madison/nystromformer-512"       )
							lowerCAmelCase__       =						torch.tensor([[0, 1, 2, 3, 4, 5]]       )

							with torch.no_grad():
													lowerCAmelCase__       =						model(__UpperCAmelCase       )[0]

							lowerCAmelCase__       =						torch.Size((1, 6, 768)       )
							self.assertEqual(output.shape		,    __UpperCAmelCase       )

							lowerCAmelCase__       =						torch.tensor(
							    [[[-0.4_532, -0.0_936, 0.5_137], [-0.2_676, 0.0_628, 0.6_186], [-0.3_629, -0.1_726, 0.4_716]]]       )

							self.assertTrue(torch.allclose(output[:, :3, :3]		,    __UpperCAmelCase		,    atol=1E-4       )       )


	@slow
	def 		UpperCAmelCase  (							self       )->					Any:







							'''simple docstring'''





							lowerCAmelCase__       =						"the [MASK] of Belgium is Brussels"

							lowerCAmelCase__       =						AutoTokenizer.from_pretrained("uw-madison/nystromformer-512"       )
							lowerCAmelCase__       =						NystromformerForMaskedLM.from_pretrained("uw-madison/nystromformer-512"       )

							lowerCAmelCase__       =						tokenizer(__UpperCAmelCase		,    return_tensors="pt"       )

							with torch.no_grad():
													lowerCAmelCase__       =						model(encoding.input_ids       ).logits

							lowerCAmelCase__       =						token_logits[:, 2, :].argmax(-1       )[0]

							self.assertEqual(tokenizer.decode(__UpperCAmelCase       )		,    "capital"       )






