






"""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        _SCREAMING_SNAKE_CASE						:





							"""simple docstring"""





							def __init__(   self:     Union[str, Any]				,		__A:     str				,		__A:     Dict=13				,		__A:     Optional[Any]=30				,		__A:     str=2				,		__A:     Optional[int]=3				,		__A:     Dict=True				,		__A:     str=True				,		__A:     Tuple=32				,		__A:     List[Any]=5				,		__A:     Any=4				,		__A:     Optional[Any]=37				,		__A:     Optional[int]="gelu"				,		__A:     Tuple=0.1				,		__A:     int=0.1				,		__A:     Dict=10				,		__A:     List[Any]=0.0_2				,		__A:     List[Any]=3				,		__A:     List[str]=0.6				,		__A:     List[str]=None				,		):

												'''simple docstring'''

												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  lowercase		(   self:     List[str]    ):

												'''simple docstring'''

												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  lowercase		(   self:     int    ):

												'''simple docstring'''

												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=__A				,		initializer_range=self.initializer_range				,		mask_ratio=self.mask_ratio				,		)




							def  lowercase		(   self:     str				,		__A:     Any				,		__A:     Tuple				,		__A:     int    ):

												'''simple docstring'''

												a__          =  ViTMAEModel(config=__A    )
												model.to(__A    )
												model.eval()
												a__          =  model(__A    )
												self.parent.assertEqual(result.last_hidden_state.shape				,		(self.batch_size, self.seq_length, self.hidden_size)    )




							def  lowercase		(   self:     Any				,		__A:     Optional[int]				,		__A:     List[Any]				,		__A:     Dict    ):

												'''simple docstring'''

												a__          =  ViTMAEForPreTraining(__A    )
												model.to(__A    )
												model.eval()
												a__          =  model(__A    )
												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(__A    )
												model.to(__A    )
												model.eval()
												a__          =  floats_tensor([self.batch_size, 1, self.image_size, self.image_size]    )
												a__          =  model(__A    )
												a__          =  self.patch_size**2
												self.parent.assertEqual(result.logits.shape				,		(self.batch_size, num_patches, expected_num_channels)    )





							def  lowercase		(   self:     Optional[int]    ):

												'''simple docstring'''

												a__          =  self.prepare_config_and_inputs()
												a__       ,a__       ,a__          =  config_and_inputs
												a__          =  {'''pixel_values''': pixel_values}
												return config, inputs_dict







@require_torch
class        _SCREAMING_SNAKE_CASE						(  __snake_case       , __snake_case       , unittest.TestCase							):





							"""simple docstring"""





							_SCREAMING_SNAKE_CASE    =(ViTMAEModel, ViTMAEForPreTraining) if is_torch_available() else ()
							_SCREAMING_SNAKE_CASE    ={'feature-extraction': ViTMAEModel} if is_torch_available() else {}

							_SCREAMING_SNAKE_CASE    =False
							_SCREAMING_SNAKE_CASE    =False
							_SCREAMING_SNAKE_CASE    =False
							_SCREAMING_SNAKE_CASE    =False




							def  lowercase		(   self:     List[str]    ):

												'''simple docstring'''

												a__          =  ViTMAEModelTester(self    )
												a__          =  ConfigTester(self				,		config_class=__A				,		has_text_modality=__A				,		hidden_size=37    )




							def  lowercase		(   self:     Union[str, Any]    ):

												'''simple docstring'''

												self.config_tester.run_common_tests()




							@unittest.skip(reason='''ViTMAE does not use inputs_embeds'''    )
							def  lowercase		(   self:     Optional[int]    ):

												'''simple docstring'''

												pass




							def  lowercase		(   self:     Tuple    ):

												'''simple docstring'''

												a__       ,a__          =  self.model_tester.prepare_config_and_inputs_for_common()

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




							def  lowercase		(   self:     List[str]    ):

												'''simple docstring'''

												a__       ,a__          =  self.model_tester.prepare_config_and_inputs_for_common()

												for model_class in self.all_model_classes:
																	a__          =  model_class(__A    )
																	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]				,		__A    )




							def  lowercase		(   self:     str    ):

												'''simple docstring'''

												a__          =  self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_model(*__A    )




							def  lowercase		(   self:     Tuple    ):

												'''simple docstring'''

												a__          =  self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_pretraining(*__A    )




							def  lowercase		(   self:     Optional[int]				,		__A:     List[Any]				,		__A:     Optional[Any]				,		__A:     Optional[int]    ):

												'''simple docstring'''

												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(__A    )

												# 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(__A				,		__A				,		__A    )




							def  lowercase		(   self:     List[str]    ):

												'''simple docstring'''

												a__       ,a__          =  self.model_tester.prepare_config_and_inputs_for_common()

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

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

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

																						# 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(__A				,		1e-5    )




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

												'''simple docstring'''

												pass




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

												'''simple docstring'''

												pass




							@unittest.skip(
							    reason='''ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load
    to get deterministic results.'''    )
							def  lowercase		(   self:     Tuple    ):

												'''simple docstring'''

												pass




							@unittest.skip(reason='''ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load'''    )
							def  lowercase		(   self:     Optional[Any]    ):

												'''simple docstring'''

												pass




							@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''    )
							def  lowercase		(   self:     List[str]    ):

												'''simple docstring'''

												pass





							@slow
							def  lowercase		(   self:     Dict    ):

												'''simple docstring'''

												for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																	a__          =  ViTMAEModel.from_pretrained(__A    )
																	self.assertIsNotNone(__A    )





def        SCREAMING_SNAKE_CASE       (      ):
					a__          =  Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''')
					return image







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





							"""simple docstring"""





							@cached_property
							def  lowercase		(   self:     Optional[int]    ):

												'''simple docstring'''

												return ViTImageProcessor.from_pretrained('''facebook/vit-mae-base'''    ) if is_vision_available() else None





							@slow
							def  lowercase		(   self:     Union[str, Any]    ):

												'''simple docstring'''

												np.random.seed(2    )

												a__          =  ViTMAEForPreTraining.from_pretrained('''facebook/vit-mae-base'''    ).to(__A    )

												a__          =  self.default_image_processor
												a__          =  prepare_img()
												a__          =  image_processor(images=__A				,		return_tensors='''pt'''    ).to(__A    )

												# 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(**__A				,		noise=torch.from_numpy(__A    ).to(device=__A    )    )

												# verify the logits
												a__          =  torch.Size((1, 196, 768)    )
												self.assertEqual(outputs.logits.shape				,		__A    )

												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(__A    )				,		atol=1e-4    )    )










"""simple docstring"""







import json
import logging
import os
import sys
from pathlib import Path

import finetune_rag

from transformers.file_utils import is_apex_available
from transformers.testing_utils import (
    TestCasePlus,
    execute_subprocess_async,
    require_ray,
    require_torch_gpu,
    require_torch_multi_gpu,
)


logging.basicConfig(level=logging.DEBUG)
__a       :	Optional[int]             = logging.getLogger()

__a       :	Any             = logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)







class        _SCREAMING_SNAKE_CASE						(  __snake_case							):





							"""simple docstring"""





							def  lowercase		(   self:     List[Any]				,		__A:     List[Any]    ):

												'''simple docstring'''

												os.makedirs(__A				,		exist_ok=__A    )
												a__          =  {'''source''': '''What is love ?''', '''target''': '''life'''}
												a__          =  {'''train''': 12, '''val''': 2, '''test''': 2}
												for split in ["train", "test", "val"]:
																	for field in ["source", "target"]:
																						a__          =  '''\n'''.join([contents[field]] * n_lines[split]    )
																						with open(os.path.join(__A				,		F'{split}.{field}'    )				,		'''w'''    ) as f:
																											f.write(__A    )




							def  lowercase		(   self:     Optional[int]				,		__A:     int				,		__A:     str = "pytorch"    ):

												'''simple docstring'''

												a__          =  self.get_auto_remove_tmp_dir()
												a__          =  os.path.join(__A				,		'''output'''    )
												a__          =  os.path.join(__A				,		'''data'''    )
												self._create_dummy_data(data_dir=__A    )

												a__          =  F'\n                --data_dir {data_dir} \\n                --output_dir {output_dir} \\n                --model_name_or_path facebook/rag-sequence-base \\n                --model_type rag_sequence \\n                --do_train \\n                --do_predict \\n                --n_val -1 \\n                --val_check_interval 1.0 \\n                --train_batch_size 2 \\n                --eval_batch_size 1 \\n                --max_source_length 25 \\n                --max_target_length 25 \\n                --val_max_target_length 25 \\n                --test_max_target_length 25 \\n                --label_smoothing 0.1 \\n                --dropout 0.1 \\n                --attention_dropout 0.1 \\n                --weight_decay 0.001 \\n                --adam_epsilon 1e-08 \\n                --max_grad_norm 0.1 \\n                --lr_scheduler polynomial \\n                --learning_rate 3e-04 \\n                --num_train_epochs 1 \\n                --warmup_steps 4 \\n                --gradient_accumulation_steps 1 \\n                --distributed-port 8787 \\n                --use_dummy_dataset 1 \\n                --distributed_retriever {distributed_retriever} \\n            '.split()

												if gpus > 0:
																	testargs.append(F'--gpus={gpus}'    )
																	if is_apex_available():
																						testargs.append('''--fp16'''    )
												else:
																	testargs.append('''--gpus=0'''    )
																	testargs.append('''--distributed_backend=ddp_cpu'''    )
																	testargs.append('''--num_processes=2'''    )

												a__          =  [sys.executable, str(Path(finetune_rag.__file__    ).resolve()    )] + testargs
												execute_subprocess_async(__A				,		env=self.get_env()    )

												a__          =  os.path.join(__A				,		'''metrics.json'''    )
												with open(__A    ) as f:
																	a__          =  json.load(__A    )
												return result




							@require_torch_gpu
							def  lowercase		(   self:     Optional[int]    ):

												'''simple docstring'''

												a__          =  self._run_finetune(gpus=1    )
												self.assertGreaterEqual(result['''test'''][0]['''test_avg_em''']				,		0.2    )




							@require_torch_multi_gpu
							def  lowercase		(   self:     str    ):

												'''simple docstring'''

												a__          =  self._run_finetune(gpus=2    )
												self.assertGreaterEqual(result['''test'''][0]['''test_avg_em''']				,		0.2    )




							@require_torch_gpu
							@require_ray
							def  lowercase		(   self:     Dict    ):

												'''simple docstring'''

												a__          =  self._run_finetune(gpus=1				,		distributed_retriever='''ray'''    )
												self.assertGreaterEqual(result['''test'''][0]['''test_avg_em''']				,		0.2    )





							@require_torch_multi_gpu
							@require_ray
							def  lowercase		(   self:     str    ):

												'''simple docstring'''

												a__          =  self._run_finetune(gpus=1				,		distributed_retriever='''ray'''    )
												self.assertGreaterEqual(result['''test'''][0]['''test_avg_em''']				,		0.2    )


