

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     ,A__     ,A__=1_3     ,A__=7     ,A__=True     ,A__=True     ,A__=True     ,A__=True     ,A__=9_9     ,A__=3_2     ,A__=5     ,A__=4     ,A__=3_7     ,A__="gelu"     ,A__=0.1     ,A__=0.1     ,A__=5_1_2     ,A__=1_6     ,A__=2     ,A__=0.02     ,A__=3     ,A__=4     ,A__=None     ,):
												lowercase					=		parent
												lowercase					=		batch_size
												lowercase					=		seq_length
												lowercase					=		is_training
												lowercase					=		use_input_mask
												lowercase					=		use_token_type_ids
												lowercase					=		use_labels
												lowercase					=		vocab_size
												lowercase					=		hidden_size
												lowercase					=		num_hidden_layers
												lowercase					=		num_attention_heads
												lowercase					=		intermediate_size
												lowercase					=		hidden_act
												lowercase					=		hidden_dropout_prob
												lowercase					=		attention_probs_dropout_prob
												lowercase					=		max_position_embeddings
												lowercase					=		type_vocab_size
												lowercase					=		type_sequence_label_size
												lowercase					=		initializer_range
												lowercase					=		num_labels
												lowercase					=		num_choices
												lowercase					=		scope




							def 				A__   (       self):
												lowercase					=		ids_tensor([self.batch_size, self.seq_length]     ,self.vocab_size)

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

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

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

												lowercase					=		self.get_config()

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




							def 				A__   (       self):
												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=A__     ,initializer_range=self.initializer_range     ,)




							def 				A__   (       self     ,A__     ,A__     ,A__     ,A__     ,A__     ,A__     ,A__):
												lowercase					=		NystromformerModel(config=A__)
												model.to(A__)
												model.eval()
												lowercase					=		model(A__     ,attention_mask=A__     ,token_type_ids=A__)
												lowercase					=		model(A__     ,token_type_ids=A__)
												lowercase					=		model(A__)
												self.parent.assertEqual(result.last_hidden_state.shape     ,(self.batch_size, self.seq_length, self.hidden_size))




							def 				A__   (       self     ,A__     ,A__     ,A__     ,A__     ,A__     ,A__     ,A__):
												lowercase					=		NystromformerForMaskedLM(config=A__)
												model.to(A__)
												model.eval()
												lowercase					=		model(A__     ,attention_mask=A__     ,token_type_ids=A__     ,labels=A__)
												self.parent.assertEqual(result.logits.shape     ,(self.batch_size, self.seq_length, self.vocab_size))




							def 				A__   (       self     ,A__     ,A__     ,A__     ,A__     ,A__     ,A__     ,A__):
												lowercase					=		NystromformerForQuestionAnswering(config=A__)
												model.to(A__)
												model.eval()
												lowercase					=		model(
												    A__     ,attention_mask=A__     ,token_type_ids=A__     ,start_positions=A__     ,end_positions=A__     ,)
												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 				A__   (       self     ,A__     ,A__     ,A__     ,A__     ,A__     ,A__     ,A__):
												lowercase					=		self.num_labels
												lowercase					=		NystromformerForSequenceClassification(A__)
												model.to(A__)
												model.eval()
												lowercase					=		model(A__     ,attention_mask=A__     ,token_type_ids=A__     ,labels=A__)
												self.parent.assertEqual(result.logits.shape     ,(self.batch_size, self.num_labels))




							def 				A__   (       self     ,A__     ,A__     ,A__     ,A__     ,A__     ,A__     ,A__):
												lowercase					=		self.num_labels
												lowercase					=		NystromformerForTokenClassification(config=A__)
												model.to(A__)
												model.eval()
												lowercase					=		model(A__     ,attention_mask=A__     ,token_type_ids=A__     ,labels=A__)
												self.parent.assertEqual(result.logits.shape     ,(self.batch_size, self.seq_length, self.num_labels))




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





							def 				A__   (       self):
												lowercase					=		self.prepare_config_and_inputs()
												(
												    (
												    lowercase		
												)			,		(
												    lowercase		
												)			,		(
												    lowercase		
												)			,		(
												    lowercase		
												)			,		(
												    lowercase		
												)			,		(
												    lowercase		
												)			,		(
												    lowercase		
												)			,				
												)					=		config_and_inputs
												lowercase					=		{'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
												return config, inputs_dict







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




							def 				A__   (       self):
												lowercase					=		NystromformerModelTester(self)
												lowercase					=		ConfigTester(self     ,config_class=A__     ,hidden_size=3_7)




							def 				A__   (       self):
												self.config_tester.run_common_tests()




							def 				A__   (       self):
												lowercase					=		self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_model(*A__)




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




							def 				A__   (       self):
												lowercase					=		self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_masked_lm(*A__)




							def 				A__   (       self):
												lowercase					=		self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_multiple_choice(*A__)




							def 				A__   (       self):
												lowercase					=		self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_question_answering(*A__)




							def 				A__   (       self):
												lowercase					=		self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_sequence_classification(*A__)




							def 				A__   (       self):
												lowercase					=		self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_token_classification(*A__)





							@slow
							def 				A__   (       self):
												for model_name in NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																	lowercase					=		NystromformerModel.from_pretrained(A__)
																	self.assertIsNotNone(A__)







@require_torch
class        lowercase	(      unittest.TestCase	):




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

												with torch.no_grad():
																	lowercase					=		model(A__)[0]

												lowercase					=		torch.Size((1, 6, 7_6_8))
												self.assertEqual(output.shape     ,A__)

												lowercase					=		torch.tensor(
												    [[[-0.4532, -0.0936, 0.5137], [-0.2676, 0.0628, 0.6186], [-0.3629, -0.1726, 0.4716]]])

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





							@slow
							def 				A__   (       self):
												lowercase					=		'''the [MASK] of Belgium is Brussels'''

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

												lowercase					=		tokenizer(A__     ,return_tensors='''pt''')

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

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

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






import asyncio
import os
import shutil
import subprocess
import sys
import tempfile
import unittest
from distutils.util import strtobool
from functools import partial
from pathlib import Path
from typing import List, Union
from unittest import mock

import torch

from ..state import AcceleratorState, PartialState
from ..utils import (
    gather,
    is_bnb_available,
    is_comet_ml_available,
    is_datasets_available,
    is_deepspeed_available,
    is_mps_available,
    is_safetensors_available,
    is_tensorboard_available,
    is_torch_version,
    is_tpu_available,
    is_transformers_available,
    is_wandb_available,
    is_xpu_available,
)



def  UpperCamelCase					(							lowerCAmelCase__				,    lowerCAmelCase__=False	):
					'''simple docstring'''
					try:
										lowercase					=		os.environ[key]
					except KeyError:
										# KEY isn't set, default to `default`.
										lowercase					=		default
					else:
										# KEY is set, convert it to True or False.
										try:
															lowercase					=		strtobool(lowerCAmelCase__	)
										except ValueError:
															# More values are supported, but let's keep the message simple.
															raise ValueError(f'If set, {key} must be yes or no.'	)
					return _value


lowercase__	:Dict										=					parse_flag_from_env("RUN_SLOW", default=False)



def  UpperCamelCase					(							lowerCAmelCase__	):
					'''simple docstring'''
					return unittest.skip('''Test was skipped'''	)(lowerCAmelCase__	)



def  UpperCamelCase					(							lowerCAmelCase__	):
					'''simple docstring'''
					return unittest.skipUnless(_run_slow_tests				,    '''test is slow'''	)(lowerCAmelCase__	)



def  UpperCamelCase					(							lowerCAmelCase__	):
					'''simple docstring'''
					return unittest.skipUnless(not torch.cuda.is_available()				,    '''test requires only a CPU'''	)(lowerCAmelCase__	)



def  UpperCamelCase					(							lowerCAmelCase__	):
					'''simple docstring'''
					return unittest.skipUnless(torch.cuda.is_available()				,    '''test requires a GPU'''	)(lowerCAmelCase__	)



def  UpperCamelCase					(							lowerCAmelCase__	):
					'''simple docstring'''
					return unittest.skipUnless(is_xpu_available()				,    '''test requires a XPU'''	)(lowerCAmelCase__	)



def  UpperCamelCase					(							lowerCAmelCase__	):
					'''simple docstring'''
					return unittest.skipUnless(is_mps_available()				,    '''test requires a `mps` backend support in `torch`'''	)(lowerCAmelCase__	)



def  UpperCamelCase					(							lowerCAmelCase__	):
					'''simple docstring'''
					return unittest.skipUnless(
					    is_transformers_available() and is_datasets_available()				,    '''test requires the Hugging Face suite'''	)(lowerCAmelCase__	)



def  UpperCamelCase					(							lowerCAmelCase__	):
					'''simple docstring'''
					return unittest.skipUnless(is_bnb_available()				,    '''test requires the bitsandbytes library'''	)(lowerCAmelCase__	)



def  UpperCamelCase					(							lowerCAmelCase__	):
					'''simple docstring'''
					return unittest.skipUnless(is_tpu_available()				,    '''test requires TPU'''	)(lowerCAmelCase__	)



def  UpperCamelCase					(							lowerCAmelCase__	):
					'''simple docstring'''
					return unittest.skipUnless(torch.cuda.device_count() == 1				,    '''test requires a GPU'''	)(lowerCAmelCase__	)



def  UpperCamelCase					(							lowerCAmelCase__	):
					'''simple docstring'''
					return unittest.skipUnless(torch.xpu.device_count() == 1				,    '''test requires a XPU'''	)(lowerCAmelCase__	)



def  UpperCamelCase					(							lowerCAmelCase__	):
					'''simple docstring'''
					return unittest.skipUnless(torch.cuda.device_count() > 1				,    '''test requires multiple GPUs'''	)(lowerCAmelCase__	)



def  UpperCamelCase					(							lowerCAmelCase__	):
					'''simple docstring'''
					return unittest.skipUnless(torch.xpu.device_count() > 1				,    '''test requires multiple XPUs'''	)(lowerCAmelCase__	)



def  UpperCamelCase					(							lowerCAmelCase__	):
					'''simple docstring'''
					return unittest.skipUnless(is_safetensors_available()				,    '''test requires safetensors'''	)(lowerCAmelCase__	)



def  UpperCamelCase					(							lowerCAmelCase__	):
					'''simple docstring'''
					return unittest.skipUnless(is_deepspeed_available()				,    '''test requires DeepSpeed'''	)(lowerCAmelCase__	)



def  UpperCamelCase					(							lowerCAmelCase__	):
					'''simple docstring'''
					return unittest.skipUnless(is_torch_version('''>='''				,    '''1.12.0'''	)				,    '''test requires torch version >= 1.12.0'''	)(lowerCAmelCase__	)



def  UpperCamelCase					(							lowerCAmelCase__=None				,    lowerCAmelCase__=None	):
					'''simple docstring'''
					if test_case is None:
										return partial(lowerCAmelCase__				,    version=lowerCAmelCase__	)
					return unittest.skipUnless(is_torch_version('''>='''				,    lowerCAmelCase__	)				,    f'test requires torch version >= {version}'	)(lowerCAmelCase__	)



def  UpperCamelCase					(							lowerCAmelCase__	):
					'''simple docstring'''
					return unittest.skipUnless(is_tensorboard_available()				,    '''test requires Tensorboard'''	)(lowerCAmelCase__	)



def  UpperCamelCase					(							lowerCAmelCase__	):
					'''simple docstring'''
					return unittest.skipUnless(is_wandb_available()				,    '''test requires wandb'''	)(lowerCAmelCase__	)



def  UpperCamelCase					(							lowerCAmelCase__	):
					'''simple docstring'''
					return unittest.skipUnless(is_comet_ml_available()				,    '''test requires comet_ml'''	)(lowerCAmelCase__	)


lowercase__	:Dict										=					(
    any([is_wandb_available(), is_tensorboard_available()]) and not is_comet_ml_available()
)



def  UpperCamelCase					(							lowerCAmelCase__	):
					'''simple docstring'''
					return unittest.skipUnless(
					    _atleast_one_tracker_available				,    '''test requires at least one tracker to be available and for `comet_ml` to not be installed'''				,    )(lowerCAmelCase__	)







class        lowercase	(      unittest.TestCase	):
							lowercase_ : int       =True




							@classmethod
							def 				A__   (       cls):
												lowercase					=		tempfile.mkdtemp()




							@classmethod
							def 				A__   (       cls):
												if os.path.exists(cls.tmpdir):
																	shutil.rmtree(cls.tmpdir)





							def 				A__   (       self):
												if self.clear_on_setup:
																	for path in Path(self.tmpdir).glob('''**/*'''):
																						if path.is_file():
																											path.unlink()
																						elif path.is_dir():
																											shutil.rmtree(A__)







class        lowercase	(      unittest.TestCase	):




							def 				A__   (       self):
												super().tearDown()
												# Reset the state of the AcceleratorState singleton.
												AcceleratorState._reset_state()
												PartialState._reset_state()







class        lowercase	(      unittest.TestCase	):




							def 				A__   (       self     ,A__):
												lowercase					=		mocks if isinstance(A__     ,(tuple, list)) else [mocks]
												for m in self.mocks:
																	m.start()
																	self.addCleanup(m.stop)



def  UpperCamelCase					(							lowerCAmelCase__	):
					'''simple docstring'''
					lowercase					=		AcceleratorState()
					lowercase					=		tensor[None].clone().to(state.device	)
					lowercase					=		gather(lowerCAmelCase__	).cpu()
					lowercase					=		tensor[0].cpu()
					for i in range(tensors.shape[0]	):
										if not torch.equal(tensors[i]				,    lowerCAmelCase__	):
															return False
					return True







class        lowercase	:




							def __init__(       self     ,A__     ,A__     ,A__):
												lowercase					=		returncode
												lowercase					=		stdout
												lowercase					=		stderr



async def  UpperCamelCase					(							lowerCAmelCase__				,    lowerCAmelCase__	):
					'''simple docstring'''
					while True:
										lowercase					=		await stream.readline()
										if line:
															callback(lowerCAmelCase__	)
										else:
															break



async def  UpperCamelCase					(							lowerCAmelCase__				,    lowerCAmelCase__=None				,    lowerCAmelCase__=None				,    lowerCAmelCase__=None				,    lowerCAmelCase__=False				,    lowerCAmelCase__=False	):
					'''simple docstring'''
					if echo:
										print('''\nRunning: '''				,    ''' '''.join(lowerCAmelCase__	)	)

					lowercase					=		await asyncio.create_subprocess_exec(
					    cmd[0]				,    *cmd[1:]				,    stdin=lowerCAmelCase__				,    stdout=asyncio.subprocess.PIPE				,    stderr=asyncio.subprocess.PIPE				,    env=lowerCAmelCase__				,    )

					# note: there is a warning for a possible deadlock when using `wait` with huge amounts of data in the pipe
					# https://docs.python.org/3/library/asyncio-subprocess.html#asyncio.asyncio.subprocess.Process.wait
					#
					# If it starts hanging, will need to switch to the following code. The problem is that no data
					# will be seen until it's done and if it hangs for example there will be no debug info.
					# out, err = await p.communicate()
					# return _RunOutput(p.returncode, out, err)

					lowercase					=		[]
					lowercase					=		[]

					def tee(lowerCAmelCase__				,    lowerCAmelCase__				,    lowerCAmelCase__				,    lowerCAmelCase__=""	):
										lowercase					=		line.decode('''utf-8'''	).rstrip()
										sink.append(lowerCAmelCase__	)
										if not quiet:
															print(lowerCAmelCase__				,    lowerCAmelCase__				,    file=lowerCAmelCase__	)

    # XXX: the timeout doesn't seem to make any difference here
					await asyncio.wait(
					    [
					        asyncio.create_task(_read_stream(p.stdout				,    lambda lowerCAmelCase__	: tee(lowerCAmelCase__				,    lowerCAmelCase__				,    sys.stdout				,    label='''stdout:'''	)	)	),
					        asyncio.create_task(_read_stream(p.stderr				,    lambda lowerCAmelCase__	: tee(lowerCAmelCase__				,    lowerCAmelCase__				,    sys.stderr				,    label='''stderr:'''	)	)	),
					    ]				,    timeout=lowerCAmelCase__				,    )
					return _RunOutput(await p.wait()				,    lowerCAmelCase__				,    lowerCAmelCase__	)



def  UpperCamelCase					(							lowerCAmelCase__				,    lowerCAmelCase__=None				,    lowerCAmelCase__=None				,    lowerCAmelCase__=180				,    lowerCAmelCase__=False				,    lowerCAmelCase__=True	):
					'''simple docstring'''
					lowercase					=		asyncio.get_event_loop()
					lowercase					=		loop.run_until_complete(
					    _stream_subprocess(lowerCAmelCase__				,    env=lowerCAmelCase__				,    stdin=lowerCAmelCase__				,    timeout=lowerCAmelCase__				,    quiet=lowerCAmelCase__				,    echo=lowerCAmelCase__	)	)

					lowercase					=		''' '''.join(lowerCAmelCase__	)
					if result.returncode > 0:
										lowercase					=		'''\n'''.join(result.stderr	)
										raise RuntimeError(
										    f'\'{cmd_str}\' failed with returncode {result.returncode}\n\n'
										    f'The combined stderr from workers follows:\n{stderr}'	)

					return result







class        lowercase	(      SCREAMING_SNAKE_CASE__	):
							pass



def  UpperCamelCase					(							lowerCAmelCase__				,    lowerCAmelCase__=False	):
					'''simple docstring'''
					try:
										lowercase					=		subprocess.check_output(lowerCAmelCase__				,    stderr=subprocess.STDOUT	)
										if return_stdout:
															if hasattr(lowerCAmelCase__				,    '''decode'''	):
																				lowercase					=		output.decode('''utf-8'''	)
															return output
					except subprocess.CalledProcessError as e:
										raise SubprocessCallException(
										    f'Command `{" ".join(lowerCAmelCase__	)}` failed with the following error:\n\n{e.output.decode()}'	) from e




