"""simple docstring"""




import inspect
import math
import tempfile
import unittest

import numpy as np

from transformers import ViTMAEConfig
from transformers.testing_utils import require_torch, 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, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
				import torch
				from torch import nn

				from transformers import ViTMAEForPreTraining, ViTMAEModel
				from transformers.models.vit.modeling_vit import VIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
				from PIL import Image

				from transformers import ViTImageProcessor
class     UpperCamelCase__:





						def __init__(       self		,__UpperCAmelCase		,__UpperCAmelCase=13		,__UpperCAmelCase=30		,__UpperCAmelCase=2		,__UpperCAmelCase=3		,__UpperCAmelCase=True		,__UpperCAmelCase=True		,__UpperCAmelCase=32		,__UpperCAmelCase=5		,__UpperCAmelCase=4		,__UpperCAmelCase=37		,__UpperCAmelCase="gelu"		,__UpperCAmelCase=0.1		,__UpperCAmelCase=0.1		,__UpperCAmelCase=10		,__UpperCAmelCase=0.0_2		,__UpperCAmelCase=3		,__UpperCAmelCase=0.6		,__UpperCAmelCase=None		,)					->				Optional[int]:
													A__	     =							parent
													A__	     =							batch_size
													A__	     =							image_size
													A__	     =							patch_size
													A__	     =							num_channels
													A__	     =							is_training
													A__	     =							use_labels
													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__	     =							type_sequence_label_size
													A__	     =							initializer_range
													A__	     =							mask_ratio
													A__	     =							scope

													# in ViTMAE, the expected sequence length = (num_patches + 1) * (1 - config.mask_ratio), rounded above
													# (we add 1 for the [CLS] token)
													A__	     =							(image_size // patch_size) ** 2
													A__	     =							int(math.ceil((1 - mask_ratio) * (num_patches + 1)     )     )





						def 				snake_case__	(       self     )					->				Dict:
													A__	     =							floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]     )

													A__	     =							None
													if self.use_labels:
																				A__	     =							ids_tensor([self.batch_size]		,self.type_sequence_label_size     )

													A__	     =							self.get_config()

													return config, pixel_values, labels





						def 				snake_case__	(       self     )					->				int:
													return ViTMAEConfig(
													    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=__UpperCAmelCase		,initializer_range=self.initializer_range		,mask_ratio=self.mask_ratio		,)





						def 				snake_case__	(       self		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase     )					->				Dict:
													A__	     =							ViTMAEModel(config=__UpperCAmelCase     )
													model.to(__UpperCAmelCase     )
													model.eval()
													A__	     =							model(__UpperCAmelCase     )
													self.parent.assertEqual(result.last_hidden_state.shape		,(self.batch_size, self.seq_length, self.hidden_size)     )





						def 				snake_case__	(       self		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase     )					->				Dict:
													A__	     =							ViTMAEForPreTraining(__UpperCAmelCase     )
													model.to(__UpperCAmelCase     )
													model.eval()
													A__	     =							model(__UpperCAmelCase     )
													A__	     =							(self.image_size // self.patch_size) ** 2
													A__	     =							self.patch_size**2 * self.num_channels
													self.parent.assertEqual(result.logits.shape		,(self.batch_size, num_patches, expected_num_channels)     )

													# test greyscale images
													A__	     =							1
													A__	     =							ViTMAEForPreTraining(__UpperCAmelCase     )
													model.to(__UpperCAmelCase     )
													model.eval()
													A__	     =							floats_tensor([self.batch_size, 1, self.image_size, self.image_size]     )
													A__	     =							model(__UpperCAmelCase     )
													A__	     =							self.patch_size**2
													self.parent.assertEqual(result.logits.shape		,(self.batch_size, num_patches, expected_num_channels)     )





						def 				snake_case__	(       self     )					->				List[str]:
													A__	     =							self.prepare_config_and_inputs()
													A__     ,  A__     ,  A__	     =							config_and_inputs
													A__	     =							{'pixel_values': pixel_values}
													return config, inputs_dict






@require_torch
class     UpperCamelCase__(			__A		,						__A		,						unittest.TestCase ):
						lowerCAmelCase__  :						str													=		(ViTMAEModel, ViTMAEForPreTraining) if is_torch_available() else ()
						lowerCAmelCase__  :						Dict													=		{'feature-extraction': ViTMAEModel} if is_torch_available() else {}

						lowerCAmelCase__  :						List[str]													=		False
						lowerCAmelCase__  :						List[Any]													=		False
						lowerCAmelCase__  :						Optional[Any]													=		False
						lowerCAmelCase__  :						Optional[Any]													=		False





						def 				snake_case__	(       self     )					->				Any:
													A__	     =							ViTMAEModelTester(self     )
													A__	     =							ConfigTester(self		,config_class=__UpperCAmelCase		,has_text_modality=__UpperCAmelCase		,hidden_size=37     )





						def 				snake_case__	(       self     )					->				Dict:
													self.config_tester.run_common_tests()





						@unittest.skip(reason='ViTMAE does not use inputs_embeds'     )
						def 				snake_case__	(       self     )					->				Tuple:
													pass





						def 				snake_case__	(       self     )					->				Dict:
													A__     ,  A__	     =							self.model_tester.prepare_config_and_inputs_for_common()

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





						def 				snake_case__	(       self     )					->				List[Any]:
													A__     ,  A__	     =							self.model_tester.prepare_config_and_inputs_for_common()

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

																				A__	     =							['pixel_values']
																				self.assertListEqual(arg_names[:1]		,__UpperCAmelCase     )





						def 				snake_case__	(       self     )					->				int:
													A__	     =							self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_model(*__UpperCAmelCase     )





						def 				snake_case__	(       self     )					->				int:
													A__	     =							self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_for_pretraining(*__UpperCAmelCase     )





						def 				snake_case__	(       self		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase     )					->				Optional[Any]:
													# make masks reproducible
													np.random.seed(2     )

													A__	     =							int((pt_model.config.image_size // pt_model.config.patch_size) ** 2     )
													A__	     =							np.random.uniform(size=(self.model_tester.batch_size, num_patches)     )
													A__	     =							torch.from_numpy(__UpperCAmelCase     )

													# Add `noise` argument.
													# PT inputs will be prepared in `super().check_pt_tf_models()` with this added `noise` argument
													A__	     =							pt_noise

													super().check_pt_tf_models(__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase     )





						def 				snake_case__	(       self     )					->				Dict:
													A__     ,  A__	     =							self.model_tester.prepare_config_and_inputs_for_common()

													for model_class in self.all_model_classes:
																				A__	     =							model_class(__UpperCAmelCase     )
																				model.to(__UpperCAmelCase     )
																				model.eval()
																				# make random mask reproducible
																				torch.manual_seed(2     )
																				with torch.no_grad():
																											A__	     =							model(**self._prepare_for_class(__UpperCAmelCase		,__UpperCAmelCase     )     )

																				A__	     =							outputs[0].cpu().numpy()
																				A__	     =							0

																				with tempfile.TemporaryDirectory() as tmpdirname:
																											model.save_pretrained(__UpperCAmelCase     )
																											A__	     =							model_class.from_pretrained(__UpperCAmelCase     )
																											model.to(__UpperCAmelCase     )
																											# make random mask reproducible
																											torch.manual_seed(2     )
																											with torch.no_grad():
																																		A__	     =							model(**self._prepare_for_class(__UpperCAmelCase		,__UpperCAmelCase     )     )

																											# Make sure we don't have nans
																											A__	     =							after_outputs[0].cpu().numpy()
																											A__	     =							0
																											A__	     =							np.amax(np.abs(out_a - out_a     )     )
																											self.assertLessEqual(__UpperCAmelCase		,1e-5     )





						@unittest.skip(
						    reason='ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n    to get deterministic results.'     )
						def 				snake_case__	(       self     )					->				List[str]:
													pass





						@unittest.skip(
						    reason='ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n    to get deterministic results.'     )
						def 				snake_case__	(       self     )					->				List[str]:
													pass





						@unittest.skip(
						    reason='ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n    to get deterministic results.'     )
						def 				snake_case__	(       self     )					->				Union[str, Any]:
													pass





						@unittest.skip(reason='ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load'     )
						def 				snake_case__	(       self     )					->				str:
													pass





						@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'     )
						def 				snake_case__	(       self     )					->				Any:
													pass





						@slow
						def 				snake_case__	(       self     )					->				str:
													for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																				A__	     =							ViTMAEModel.from_pretrained(__UpperCAmelCase     )
																				self.assertIsNotNone(__UpperCAmelCase     )






def   UpperCAmelCase					(	):






							"""simple docstring"""





							A__	     =							Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'				)
							return image


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





						@cached_property
						def 				snake_case__	(       self     )					->				Optional[int]:
													return ViTImageProcessor.from_pretrained('facebook/vit-mae-base'     ) if is_vision_available() else None





						@slow
						def 				snake_case__	(       self     )					->				str:
													# make random mask reproducible across the PT and TF model
													np.random.seed(2     )

													A__	     =							ViTMAEForPreTraining.from_pretrained('facebook/vit-mae-base'     ).to(__UpperCAmelCase     )

													A__	     =							self.default_image_processor
													A__	     =							prepare_img()
													A__	     =							image_processor(images=__UpperCAmelCase		,return_tensors='pt'     ).to(__UpperCAmelCase     )

													# prepare a noise vector that will be also used for testing the TF model
													# (this way we can ensure that the PT and TF models operate on the same inputs)
													A__	     =							ViTMAEConfig()
													A__	     =							int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2     )
													A__	     =							np.random.uniform(size=(1, num_patches)     )

													# forward pass
													with torch.no_grad():
																				A__	     =							model(**__UpperCAmelCase		,noise=torch.from_numpy(__UpperCAmelCase     ).to(device=__UpperCAmelCase     )     )

													# verify the logits
													A__	     =							torch.Size((1, 1_96, 7_68)     )
													self.assertEqual(outputs.logits.shape		,__UpperCAmelCase     )

													A__	     =							torch.tensor(
													    [[-0.0_5_4_8, -1.7_0_2_3, -0.9_3_2_5], [0.3_7_2_1, -0.5_6_7_0, -0.2_2_3_3], [0.8_2_3_5, -1.3_8_7_8, -0.3_5_2_4]]     )

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






"""simple docstring"""




import os
import unittest

from transformers.models.cpmant.tokenization_cpmant import VOCAB_FILES_NAMES, CpmAntTokenizer
from transformers.testing_utils import require_jieba, tooslow

from ...test_tokenization_common import TokenizerTesterMixin
@require_jieba
class     UpperCamelCase__(			__A		,						unittest.TestCase ):
						lowerCAmelCase__  :						Optional[int]													=		CpmAntTokenizer
						lowerCAmelCase__  :						List[str]													=		False





						def 				snake_case__	(       self     )					->				Dict:
													super().setUp()

													A__	     =							[
													    '<d>',
													    '</d>',
													    '<s>',
													    '</s>',
													    '</_>',
													    '<unk>',
													    '<pad>',
													    '</n>',
													    '我',
													    '是',
													    'C',
													    'P',
													    'M',
													    'A',
													    'n',
													    't',
													]
													A__	     =							os.path.join(self.tmpdirname		,VOCAB_FILES_NAMES['vocab_file']     )
													with open(self.vocab_file		,'w'		,encoding='utf-8'     ) as vocab_writer:
																				vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]     )     )





						@tooslow
						def 				snake_case__	(       self     )					->				List[Any]:
													A__	     =							CpmAntTokenizer.from_pretrained('openbmb/cpm-ant-10b'     )
													A__	     =							'今天天气真好！'
													A__	     =							['今天', '天气', '真', '好', '！']
													A__	     =							tokenizer.tokenize(__UpperCAmelCase     )
													self.assertListEqual(__UpperCAmelCase		,__UpperCAmelCase     )
													A__	     =							'今天天气真好！'
													A__	     =							[tokenizer.bos_token] + tokens

													A__	     =							[6, 98_02, 1_49_62, 20_82, 8_31, 2_44]
													self.assertListEqual(tokenizer.convert_tokens_to_ids(__UpperCAmelCase     )		,__UpperCAmelCase     )

													A__	     =							tokenizer.decode(__UpperCAmelCase     )
													self.assertEqual(__UpperCAmelCase		,__UpperCAmelCase     )





