



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        lowerCAmelCase__					:


				def __init__(							self      :  Optional[int]						, __UpperCamelCase      :  Optional[int]						, __UpperCamelCase      :  int=13						, __UpperCamelCase      :  List[Any]=7						, __UpperCamelCase      :  Any=True						, __UpperCamelCase      :  Tuple=True						, __UpperCamelCase      :  int=True						, __UpperCamelCase      :  List[Any]=True						, __UpperCamelCase      :  Optional[int]=99						, __UpperCamelCase      :  str=32						, __UpperCamelCase      :  Tuple=5						, __UpperCamelCase      :  str=4						, __UpperCamelCase      :  Any=37						, __UpperCamelCase      :  Tuple="gelu"						, __UpperCamelCase      :  Optional[int]=0.1						, __UpperCamelCase      :  str=0.1						, __UpperCamelCase      :  Optional[Any]=512						, __UpperCamelCase      :  List[str]=16						, __UpperCamelCase      :  str=2						, __UpperCamelCase      :  Dict=0.0_2						, __UpperCamelCase      :  Optional[Any]=3						, __UpperCamelCase      :  int=4						, __UpperCamelCase      :  Any=None						, )		->     List[Any]:
											A      						=    parent
											A      						=    batch_size
											A      						=    seq_length
											A      						=    is_training
											A      						=    use_input_mask
											A      						=    use_token_type_ids
											A      						=    use_labels
											A      						=    vocab_size
											A      						=    hidden_size
											A      						=    num_hidden_layers
											A      						=    num_attention_heads
											A      						=    intermediate_size
											A      						=    hidden_act
											A      						=    hidden_dropout_prob
											A      						=    attention_probs_dropout_prob
											A      						=    max_position_embeddings
											A      						=    type_vocab_size
											A      						=    type_sequence_label_size
											A      						=    initializer_range
											A      						=    num_labels
											A      						=    num_choices
											A      						=    scope


				def __UpperCamelCase						(							self      :  int       )		->     int:
											A      						=    ids_tensor([self.batch_size, self.seq_length]						, self.vocab_size       )

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

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

											A      						=    None
											A      						=    None
											A      						=    None
											if self.use_labels:
																		A      						=    ids_tensor([self.batch_size]						, self.type_sequence_label_size       )
																		A      						=    ids_tensor([self.batch_size, self.seq_length]						, self.num_labels       )
																		A      						=    ids_tensor([self.batch_size]						, self.num_choices       )

											A      						=    self.get_config()

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


				def __UpperCamelCase						(							self      :  Optional[int]       )		->     Any:
											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      :  Any						, __UpperCamelCase      :  Any						, __UpperCamelCase      :  List[str]						, __UpperCamelCase      :  Union[str, Any]						, __UpperCamelCase      :  Optional[Any]						, __UpperCamelCase      :  str						, __UpperCamelCase      :  Optional[int]						, __UpperCamelCase      :  Optional[Any]       )		->     str:
											A      						=    NystromformerModel(config=__UpperCamelCase       )
											model.to(__UpperCamelCase       )
											model.eval()
											A      						=    model(__UpperCamelCase						, attention_mask=__UpperCamelCase						, token_type_ids=__UpperCamelCase       )
											A      						=    model(__UpperCamelCase						, token_type_ids=__UpperCamelCase       )
											A      						=    model(__UpperCamelCase       )
											self.parent.assertEqual(result.last_hidden_state.shape						, (self.batch_size, self.seq_length, self.hidden_size)       )


				def __UpperCamelCase						(							self      :  List[Any]						, __UpperCamelCase      :  List[Any]						, __UpperCamelCase      :  Dict						, __UpperCamelCase      :  Optional[int]						, __UpperCamelCase      :  List[str]						, __UpperCamelCase      :  int						, __UpperCamelCase      :  List[Any]						, __UpperCamelCase      :  Union[str, Any]       )		->     Optional[Any]:
											A      						=    NystromformerForMaskedLM(config=__UpperCamelCase       )
											model.to(__UpperCamelCase       )
											model.eval()
											A      						=    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      :  int						, __UpperCamelCase      :  str						, __UpperCamelCase      :  Tuple						, __UpperCamelCase      :  Dict						, __UpperCamelCase      :  str						, __UpperCamelCase      :  Any						, __UpperCamelCase      :  int						, __UpperCamelCase      :  Dict       )		->     Optional[int]:
											A      						=    NystromformerForQuestionAnswering(config=__UpperCamelCase       )
											model.to(__UpperCamelCase       )
											model.eval()
											A      						=    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      :  Any						, __UpperCamelCase      :  Any						, __UpperCamelCase      :  Any						, __UpperCamelCase      :  Optional[Any]						, __UpperCamelCase      :  List[str]						, __UpperCamelCase      :  List[Any]						, __UpperCamelCase      :  Optional[int]						, __UpperCamelCase      :  str       )		->     Tuple:
											A      						=    self.num_labels
											A      						=    NystromformerForSequenceClassification(__UpperCamelCase       )
											model.to(__UpperCamelCase       )
											model.eval()
											A      						=    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      :  List[Any]						, __UpperCamelCase      :  str						, __UpperCamelCase      :  Optional[Any]						, __UpperCamelCase      :  Dict						, __UpperCamelCase      :  Optional[int]						, __UpperCamelCase      :  List[Any]						, __UpperCamelCase      :  Optional[int]						, __UpperCamelCase      :  List[str]       )		->     Dict:
											A      						=    self.num_labels
											A      						=    NystromformerForTokenClassification(config=__UpperCamelCase       )
											model.to(__UpperCamelCase       )
											model.eval()
											A      						=    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      :  Union[str, Any]						, __UpperCamelCase      :  List[str]						, __UpperCamelCase      :  Tuple						, __UpperCamelCase      :  Optional[Any]						, __UpperCamelCase      :  Union[str, Any]						, __UpperCamelCase      :  Optional[Any]						, __UpperCamelCase      :  str						, __UpperCamelCase      :  List[str]       )		->     str:
											A      						=    self.num_choices
											A      						=    NystromformerForMultipleChoice(config=__UpperCamelCase       )
											model.to(__UpperCamelCase       )
											model.eval()
											A      						=    input_ids.unsqueeze(1       ).expand(-1						, self.num_choices						, -1       ).contiguous()
											A      						=    token_type_ids.unsqueeze(1       ).expand(-1						, self.num_choices						, -1       ).contiguous()
											A      						=    input_mask.unsqueeze(1       ).expand(-1						, self.num_choices						, -1       ).contiguous()
											A      						=    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      :  Tuple       )		->     str:
											A      						=    self.prepare_config_and_inputs()
											(
											    (
											    A
											)						,   (
											    A
											)						,   (
											    A
											)						,   (
											    A
											)						,   (
											    A
											)						,   (
											    A
											)						,   (
											    A
											)						,   
											)      						=    config_and_inputs
											A      						=    {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
											return config, inputs_dict





@require_torch
class        lowerCAmelCase__					(  _lowerCamelCase ,				_lowerCamelCase ,				unittest.TestCase       ):
				A_   :     Optional[Any]								=     (
				    (
				        NystromformerModel,
				        NystromformerForMaskedLM,
				        NystromformerForMultipleChoice,
				        NystromformerForQuestionAnswering,
				        NystromformerForSequenceClassification,
				        NystromformerForTokenClassification,
				    )
				    if is_torch_available()
				    else ()
				)
				A_   :     Optional[Any]								=     (
				    {
				        'feature-extraction': NystromformerModel,
				        'fill-mask': NystromformerForMaskedLM,
				        'question-answering': NystromformerForQuestionAnswering,
				        'text-classification': NystromformerForSequenceClassification,
				        'token-classification': NystromformerForTokenClassification,
				        'zero-shot': NystromformerForSequenceClassification,
				    }
				    if is_torch_available()
				    else {}
				)
				A_   :     Dict								=     False
				A_   :     int								=     False


				def __UpperCamelCase						(							self      :  Dict       )		->     int:
											A      						=    NystromformerModelTester(self       )
											A      						=    ConfigTester(self						, config_class=__UpperCamelCase						, hidden_size=37       )


				def __UpperCamelCase						(							self      :  List[Any]       )		->     Tuple:
											self.config_tester.run_common_tests()


				def __UpperCamelCase						(							self      :  Tuple       )		->     str:
											A      						=    self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*__UpperCamelCase       )


				def __UpperCamelCase						(							self      :  str       )		->     Any:
											A      						=    self.model_tester.prepare_config_and_inputs()
											for type in ["absolute", "relative_key", "relative_key_query"]:
																		A      						=    type
																		self.model_tester.create_and_check_model(*__UpperCamelCase       )


				def __UpperCamelCase						(							self      :  Optional[Any]       )		->     Dict:
											A      						=    self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_masked_lm(*__UpperCamelCase       )


				def __UpperCamelCase						(							self      :  Optional[Any]       )		->     List[Any]:
											A      						=    self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_multiple_choice(*__UpperCamelCase       )


				def __UpperCamelCase						(							self      :  Dict       )		->     List[str]:
											A      						=    self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_question_answering(*__UpperCamelCase       )


				def __UpperCamelCase						(							self      :  Tuple       )		->     str:
											A      						=    self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_sequence_classification(*__UpperCamelCase       )


				def __UpperCamelCase						(							self      :  List[str]       )		->     Optional[int]:
											A      						=    self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_token_classification(*__UpperCamelCase       )


				@slow
				def __UpperCamelCase						(							self      :  int       )		->     Optional[int]:
											for model_name in NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																		A      						=    NystromformerModel.from_pretrained(__UpperCamelCase       )
																		self.assertIsNotNone(__UpperCamelCase       )





@require_torch
class        lowerCAmelCase__					(  unittest.TestCase       ):


				@slow
				def __UpperCamelCase						(							self      :  int       )		->     Dict:
											A      						=    NystromformerModel.from_pretrained('uw-madison/nystromformer-512'       )
											A      						=    torch.tensor([[0, 1, 2, 3, 4, 5]]       )

											with torch.no_grad():
																		A      						=    model(__UpperCamelCase       )[0]

											A      						=    torch.Size((1, 6, 768)       )
											self.assertEqual(output.shape						, __UpperCamelCase       )

											A      						=    torch.tensor(
											    [[[-0.4_5_3_2, -0.0_9_3_6, 0.5_1_3_7], [-0.2_6_7_6, 0.0_6_2_8, 0.6_1_8_6], [-0.3_6_2_9, -0.1_7_2_6, 0.4_7_1_6]]]       )

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


				@slow
				def __UpperCamelCase						(							self      :  List[Any]       )		->     int:
											A      						=    'the [MASK] of Belgium is Brussels'

											A      						=    AutoTokenizer.from_pretrained('uw-madison/nystromformer-512'       )
											A      						=    NystromformerForMaskedLM.from_pretrained('uw-madison/nystromformer-512'       )

											A      						=    tokenizer(__UpperCamelCase						, return_tensors='pt'       )

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

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

											self.assertEqual(tokenizer.decode(__UpperCamelCase       )						, 'capital'       )

import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path

import torch_xla.distributed.xla_multiprocessing as xmp

def  __snake_case							(	):







				'''simple docstring'''


				lowercase         =      ArgumentParser(
				    description=(
				        "PyTorch TPU distributed training launch helper utility that will spawn up multiple distributed processes"
				    )						)

				# Optional arguments for the launch helper
				parser.add_argument("--num_cores"		,    type=__magic_name__		,    default=1		,    help="Number of TPU cores to use (1 or 8)."						)

				# positional
				parser.add_argument(
				    "training_script"		,    type=__magic_name__		,    help=(
				        "The full path to the single TPU training "
				        "program/script to be launched in parallel, "
				        "followed by all the arguments for the "
				        "training script"
				    )		,    )

				# rest from the training program
				parser.add_argument("training_script_args"		,    nargs=__magic_name__						)

				return parser.parse_args()

def  __snake_case							(	):







				'''simple docstring'''


				lowercase         =      parse_args()

				# Import training_script as a module.
				lowercase         =      Path(args.training_script						)
				sys.path.append(str(script_fpath.parent.resolve()						)						)
				lowercase         =      script_fpath.stem
				lowercase         =      importlib.import_module(__magic_name__						)

				# Patch sys.argv
				lowercase         =      [args.training_script] + args.training_script_args + ["--tpu_num_cores", str(args.num_cores						)]

				xmp.spawn(mod._mp_fn		,    args=()		,    nprocs=args.num_cores						)


if __name__ == "__main__":
						main()
