



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 				lowerCAmelCase_		:
       def __init__(			self,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_=13,			SCREAMING_SNAKE_CASE_=30,			SCREAMING_SNAKE_CASE_=2,			SCREAMING_SNAKE_CASE_=3,			SCREAMING_SNAKE_CASE_=True,			SCREAMING_SNAKE_CASE_=True,			SCREAMING_SNAKE_CASE_=32,			SCREAMING_SNAKE_CASE_=5,			SCREAMING_SNAKE_CASE_=4,			SCREAMING_SNAKE_CASE_=37,			SCREAMING_SNAKE_CASE_="gelu",			SCREAMING_SNAKE_CASE_=0.1,			SCREAMING_SNAKE_CASE_=0.1,			SCREAMING_SNAKE_CASE_=10,			SCREAMING_SNAKE_CASE_=0.02,			SCREAMING_SNAKE_CASE_=3,			SCREAMING_SNAKE_CASE_=0.6,			SCREAMING_SNAKE_CASE_=None,			)     ->		Optional[int]:
          UpperCamelCase    :  int							=	parent
          UpperCamelCase    :  int							=	batch_size
          UpperCamelCase    :  List[str]							=	image_size
          UpperCamelCase    :  Any							=	patch_size
          UpperCamelCase    :  Optional[int]							=	num_channels
          UpperCamelCase    :  Tuple							=	is_training
          UpperCamelCase    :  Optional[int]							=	use_labels
          UpperCamelCase    :  Optional[int]							=	hidden_size
          UpperCamelCase    :  List[Any]							=	num_hidden_layers
          UpperCamelCase    :  Any							=	num_attention_heads
          UpperCamelCase    :  Optional[Any]							=	intermediate_size
          UpperCamelCase    :  str							=	hidden_act
          UpperCamelCase    :  str							=	hidden_dropout_prob
          UpperCamelCase    :  int							=	attention_probs_dropout_prob
          UpperCamelCase    :  str							=	type_sequence_label_size
          UpperCamelCase    :  str							=	initializer_range
          UpperCamelCase    :  Union[str, Any]							=	mask_ratio
          UpperCamelCase    :  Any							=	scope

          # in ViTMAE, the expected sequence length = (num_patches + 1) * (1 - config.mask_ratio), rounded above
          # (we add 1 for the [CLS] token)
          UpperCamelCase    :  str							=	(image_size // patch_size) ** 2
          UpperCamelCase    :  Optional[int]							=	int(math.ceil((1 - mask_ratio) * (num_patches + 1)       )       )
       def 					snake_case_       (			self       )     ->		int:
          UpperCamelCase    :  Optional[Any]							=	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )

          UpperCamelCase    :  Optional[Any]							=	None
          if self.use_labels:
             UpperCamelCase    :  Union[str, Any]							=	ids_tensor([self.batch_size],			self.type_sequence_label_size       )

          UpperCamelCase    :  Optional[Any]							=	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=SCREAMING_SNAKE_CASE_,			initializer_range=self.initializer_range,			mask_ratio=self.mask_ratio,			)
       def 					snake_case_       (			self,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_       )     ->		Optional[Any]:
          UpperCamelCase    :  List[str]							=	ViTMAEModel(config=SCREAMING_SNAKE_CASE_       )
          model.to(SCREAMING_SNAKE_CASE_       )
          model.eval()
          UpperCamelCase    :  Any							=	model(SCREAMING_SNAKE_CASE_       )
          self.parent.assertEqual(result.last_hidden_state.shape,			(self.batch_size, self.seq_length, self.hidden_size)       )
       def 					snake_case_       (			self,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_       )     ->		List[str]:
          UpperCamelCase    :  Union[str, Any]							=	ViTMAEForPreTraining(SCREAMING_SNAKE_CASE_       )
          model.to(SCREAMING_SNAKE_CASE_       )
          model.eval()
          UpperCamelCase    :  Dict							=	model(SCREAMING_SNAKE_CASE_       )
          UpperCamelCase    :  Optional[Any]							=	(self.image_size // self.patch_size) ** 2
          UpperCamelCase    :  int							=	self.patch_size**2 * self.num_channels
          self.parent.assertEqual(result.logits.shape,			(self.batch_size, num_patches, expected_num_channels)       )

          # test greyscale images
          UpperCamelCase    :  Union[str, Any]							=	1
          UpperCamelCase    :  int							=	ViTMAEForPreTraining(SCREAMING_SNAKE_CASE_       )
          model.to(SCREAMING_SNAKE_CASE_       )
          model.eval()
          UpperCamelCase    :  Union[str, Any]							=	floats_tensor([self.batch_size, 1, self.image_size, self.image_size]       )
          UpperCamelCase    :  Dict							=	model(SCREAMING_SNAKE_CASE_       )
          UpperCamelCase    :  str							=	self.patch_size**2
          self.parent.assertEqual(result.logits.shape,			(self.batch_size, num_patches, expected_num_channels)       )






       def 					snake_case_       (			self       )     ->		int:
          UpperCamelCase    :  Union[str, Any]							=	self.prepare_config_and_inputs()
          UpperCamelCase	,     UpperCamelCase	,     UpperCamelCase    :  Any							=	config_and_inputs
          UpperCamelCase    :  str							=	{'pixel_values': pixel_values}
          return config, inputs_dict






@require_torch
class 				lowerCAmelCase_		(  a__       ,    a__       ,    unittest.TestCase							):
       UpperCAmelCase__				:					str        =  (ViTMAEModel, ViTMAEForPreTraining) if is_torch_available() else ()
       UpperCAmelCase__				:					List[str]        =  {"feature-extraction": ViTMAEModel} if is_torch_available() else {}

       UpperCAmelCase__				:					str        =  False
       UpperCAmelCase__				:					List[str]        =  False
       UpperCAmelCase__				:					str        =  False
       UpperCAmelCase__				:					List[Any]        =  False
       def 					snake_case_       (			self       )     ->		Optional[int]:
          UpperCamelCase    :  Union[str, Any]							=	ViTMAEModelTester(self       )
          UpperCamelCase    :  Any							=	ConfigTester(self,			config_class=SCREAMING_SNAKE_CASE_,			has_text_modality=SCREAMING_SNAKE_CASE_,			hidden_size=37       )
       def 					snake_case_       (			self       )     ->		Union[str, Any]:
          self.config_tester.run_common_tests()
       @unittest.skip(reason='ViTMAE does not use inputs_embeds'       )
       def 					snake_case_       (			self       )     ->		str:
          pass
       def 					snake_case_       (			self       )     ->		List[str]:
          UpperCamelCase	,     UpperCamelCase    :  Dict							=	self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
             UpperCamelCase    :  Optional[Any]							=	model_class(SCREAMING_SNAKE_CASE_       )
             self.assertIsInstance(model.get_input_embeddings(),			(nn.Module)       )
             UpperCamelCase    :  Any							=	model.get_output_embeddings()
             self.assertTrue(x is None or isinstance(SCREAMING_SNAKE_CASE_,			nn.Linear       )       )
       def 					snake_case_       (			self       )     ->		List[Any]:
          UpperCamelCase	,     UpperCamelCase    :  Any							=	self.model_tester.prepare_config_and_inputs_for_common()

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

             UpperCamelCase    :  List[Any]							=	['pixel_values']
             self.assertListEqual(arg_names[:1],			SCREAMING_SNAKE_CASE_       )
       def 					snake_case_       (			self       )     ->		str:
          UpperCamelCase    :  Optional[Any]							=	self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_       )
       def 					snake_case_       (			self       )     ->		Any:
          UpperCamelCase    :  List[str]							=	self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_pretraining(*SCREAMING_SNAKE_CASE_       )
       def 					snake_case_       (			self,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_       )     ->		Optional[Any]:
          # make masks reproducible
          np.random.seed(2       )

          UpperCamelCase    :  List[Any]							=	int((pt_model.config.image_size // pt_model.config.patch_size) ** 2       )
          UpperCamelCase    :  Any							=	np.random.uniform(size=(self.model_tester.batch_size, num_patches)       )
          UpperCamelCase    :  Optional[Any]							=	torch.from_numpy(SCREAMING_SNAKE_CASE_       )

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

          super().check_pt_tf_models(SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_       )
       def 					snake_case_       (			self       )     ->		Optional[Any]:
          UpperCamelCase	,     UpperCamelCase    :  Tuple							=	self.model_tester.prepare_config_and_inputs_for_common()

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

             UpperCamelCase    :  Any							=	outputs[0].cpu().numpy()
             UpperCamelCase    :  str							=	0

             with tempfile.TemporaryDirectory() as tmpdirname:
                model.save_pretrained(SCREAMING_SNAKE_CASE_       )
                UpperCamelCase    :  Optional[int]							=	model_class.from_pretrained(SCREAMING_SNAKE_CASE_       )
                model.to(SCREAMING_SNAKE_CASE_       )
                # make random mask reproducible
                torch.manual_seed(2       )
                with torch.no_grad():
                   UpperCamelCase    :  str							=	model(**self._prepare_for_class(SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_       )       )

                # Make sure we don't have nans
                UpperCamelCase    :  Optional[int]							=	after_outputs[0].cpu().numpy()
                UpperCamelCase    :  Union[str, Any]							=	0
                UpperCamelCase    :  List[Any]							=	np.amax(np.abs(out_a - out_a       )       )
                self.assertLessEqual(SCREAMING_SNAKE_CASE_,			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       )     ->		Tuple:
          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       )     ->		str:
          pass
       @unittest.skip(reason='ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load'       )
       def 					snake_case_       (			self       )     ->		Optional[int]:
          pass
       @unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'       )
       def 					snake_case_       (			self       )     ->		int:
          pass






       @slow
       def 					snake_case_       (			self       )     ->		str:
          for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             UpperCamelCase    :  Optional[Any]							=	ViTMAEModel.from_pretrained(SCREAMING_SNAKE_CASE_       )
             self.assertIsNotNone(SCREAMING_SNAKE_CASE_       )






def 		UpperCamelCase	(   )	->     Optional[int]:
   UpperCamelCase    :  str							=	Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'	)
   return image



@require_torch
@require_vision
class 				lowerCAmelCase_		(  unittest.TestCase							):
       @cached_property
       def 					snake_case_       (			self       )     ->		Any:
          return ViTImageProcessor.from_pretrained('facebook/vit-mae-base'       ) if is_vision_available() else None






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

          UpperCamelCase    :  Optional[Any]							=	ViTMAEForPreTraining.from_pretrained('facebook/vit-mae-base'       ).to(SCREAMING_SNAKE_CASE_       )

          UpperCamelCase    :  Dict							=	self.default_image_processor
          UpperCamelCase    :  List[Any]							=	prepare_img()
          UpperCamelCase    :  str							=	image_processor(images=SCREAMING_SNAKE_CASE_,			return_tensors='pt'       ).to(SCREAMING_SNAKE_CASE_       )

          # 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)
          UpperCamelCase    :  Tuple							=	ViTMAEConfig()
          UpperCamelCase    :  Dict							=	int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2       )
          UpperCamelCase    :  List[str]							=	np.random.uniform(size=(1, num_patches)       )

          # forward pass
          with torch.no_grad():
             UpperCamelCase    :  Tuple							=	model(**SCREAMING_SNAKE_CASE_,			noise=torch.from_numpy(SCREAMING_SNAKE_CASE_       ).to(device=SCREAMING_SNAKE_CASE_       )       )

          # verify the logits
          UpperCamelCase    :  List[Any]							=	torch.Size((1, 196, 768)       )
          self.assertEqual(outputs.logits.shape,			SCREAMING_SNAKE_CASE_       )

          UpperCamelCase    :  int							=	torch.tensor(
              [[-0.05_48, -1.70_23, -0.93_25], [0.37_21, -0.56_70, -0.22_33], [0.82_35, -1.38_78, -0.35_24]]       )

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








import io
import json
import unittest

from parameterized import parameterized

from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu


__UpperCAmelCase          =       get_tests_dir() + '''/test_data/fsmt/fsmt_val_data.json'''
with io.open(filename, '''r''', encoding='''utf-8''') as f:
    __UpperCAmelCase          =       json.load(f)



@require_torch
class 				lowerCAmelCase_		(  unittest.TestCase							):
       def 					snake_case_       (			self,			SCREAMING_SNAKE_CASE_       )     ->		List[str]:
          return FSMTTokenizer.from_pretrained(SCREAMING_SNAKE_CASE_       )
       def 					snake_case_       (			self,			SCREAMING_SNAKE_CASE_       )     ->		List[str]:
          UpperCamelCase    :  List[Any]							=	FSMTForConditionalGeneration.from_pretrained(SCREAMING_SNAKE_CASE_       ).to(SCREAMING_SNAKE_CASE_       )
          if torch_device == "cuda":
             model.half()
          return model






       @parameterized.expand(
           [
               ['en-ru', 26.0],
               ['ru-en', 22.0],
               ['en-de', 22.0],
               ['de-en', 29.0],
           ]       )
       @slow
       def 					snake_case_       (			self,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_       )     ->		Any:
          # note: this test is not testing the best performance since it only evals a small batch
          # but it should be enough to detect a regression in the output quality
          UpperCamelCase    :  int							=	F"""facebook/wmt19-{pair}"""
          UpperCamelCase    :  Optional[int]							=	self.get_tokenizer(SCREAMING_SNAKE_CASE_       )
          UpperCamelCase    :  str							=	self.get_model(SCREAMING_SNAKE_CASE_       )

          UpperCamelCase    :  Dict							=	bleu_data[pair]['src']
          UpperCamelCase    :  Tuple							=	bleu_data[pair]['tgt']

          UpperCamelCase    :  List[Any]							=	tokenizer(SCREAMING_SNAKE_CASE_,			return_tensors='pt',			truncation=SCREAMING_SNAKE_CASE_,			padding='longest'       ).to(SCREAMING_SNAKE_CASE_       )
          UpperCamelCase    :  Any							=	model.generate(
              input_ids=batch.input_ids,			num_beams=8,			)
          UpperCamelCase    :  Tuple							=	tokenizer.batch_decode(
              SCREAMING_SNAKE_CASE_,			skip_special_tokens=SCREAMING_SNAKE_CASE_,			clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE_       )
          UpperCamelCase    :  Optional[Any]							=	calculate_bleu(SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_       )
          print(SCREAMING_SNAKE_CASE_       )
          self.assertGreaterEqual(scores['bleu'],			SCREAMING_SNAKE_CASE_       )



