import unittest

from transformers import PegasusConfig, PegasusTokenizer, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
    import os

    # The slow tests are often failing with OOM error on GPU
    # This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
    # but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
    _SCREAMING_SNAKE_CASE										=						'platform'
    import jax
    import jax.numpy as jnp
    import numpy as np

    from transformers import FlaxPegasusForConditionalGeneration, FlaxPegasusModel



@require_flax
class 				a							:







 """simple docstring"""






 lowerCamelCase	:Optional[Any]    			=		PegasusConfig
 lowerCamelCase	:Dict    			=		{}
 lowerCamelCase	:Any    			=		'''gelu'''



 def __init__(					self		,				lowerCAmelCase_		,				lowerCAmelCase_=13		,				lowerCAmelCase_=7		,				lowerCAmelCase_=True		,				lowerCAmelCase_=False		,				lowerCAmelCase_=99		,				lowerCAmelCase_=32		,				lowerCAmelCase_=5		,				lowerCAmelCase_=4		,				lowerCAmelCase_=37		,				lowerCAmelCase_=0.1		,				lowerCAmelCase_=0.1		,				lowerCAmelCase_=20		,				lowerCAmelCase_=2		,				lowerCAmelCase_=1		,				lowerCAmelCase_=0		,				)  ->					List[str]:
        _A        =  parent
        _A        =  batch_size
        _A        =  seq_length
        _A        =  is_training
        _A        =  use_labels
        _A        =  vocab_size
        _A        =  hidden_size
        _A        =  num_hidden_layers
        _A        =  num_attention_heads
        _A        =  intermediate_size

        _A        =  hidden_dropout_prob
        _A        =  attention_probs_dropout_prob
        _A        =  max_position_embeddings
        _A        =  eos_token_id
        _A        =  pad_token_id
        _A        =  bos_token_id



 def      UpperCAmelCase							(					self							)  ->					List[str]:
        _A        =  ids_tensor([self.batch_size, self.seq_length - 1]		,				self.vocab_size							).clip(3		,				self.vocab_size							)
        _A        =  np.expand_dims(np.array([self.eos_token_id] * self.batch_size							)		,				1							)
        _A        =  np.concatenate([input_ids, eos_tensor]		,				axis=1							)

        _A        =  ids_tensor([self.batch_size, self.seq_length]		,				self.vocab_size							)

        _A        =  self.config_cls(
            vocab_size=self.vocab_size		,				d_model=self.hidden_size		,				encoder_layers=self.num_hidden_layers		,				decoder_layers=self.num_hidden_layers		,				encoder_attention_heads=self.num_attention_heads		,				decoder_attention_heads=self.num_attention_heads		,				encoder_ffn_dim=self.intermediate_size		,				decoder_ffn_dim=self.intermediate_size		,				dropout=self.hidden_dropout_prob		,				attention_dropout=self.attention_probs_dropout_prob		,				max_position_embeddings=self.max_position_embeddings		,				eos_token_ids=[2]		,				bos_token_id=self.bos_token_id		,				pad_token_id=self.pad_token_id		,				decoder_start_token_id=self.pad_token_id		,				**self.config_updates		,				)
        _A        =  prepare_pegasus_inputs_dict(lowerCAmelCase_		,				lowerCAmelCase_		,				lowerCAmelCase_							)
        return config, inputs_dict



 def      UpperCAmelCase							(					self		,				lowerCAmelCase_		,				lowerCAmelCase_		,				lowerCAmelCase_							)  ->					Any:
        _A        =  20
        _A        =  model_class_name(lowerCAmelCase_							)

        _A        =  model.encode(inputs_dict["""input_ids"""]							)

        _A   ,  _A        =  (
            inputs_dict["""decoder_input_ids"""],
            inputs_dict["""decoder_attention_mask"""],
        )

        _A        =  model.init_cache(decoder_input_ids.shape[0]		,				lowerCAmelCase_		,				lowerCAmelCase_							)
        _A        =  jnp.ones((decoder_input_ids.shape[0], max_decoder_length)		,				dtype="""i4"""							)

        _A        =  jnp.broadcast_to(
            jnp.arange(decoder_input_ids.shape[-1] - 1							)[None, :]		,				(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)		,				)
        _A        =  model.decode(
            decoder_input_ids[:, :-1]		,				lowerCAmelCase_		,				decoder_attention_mask=lowerCAmelCase_		,				past_key_values=lowerCAmelCase_		,				decoder_position_ids=lowerCAmelCase_		,				)

        _A        =  jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]		,				dtype="""i4"""							)
        _A        =  model.decode(
            decoder_input_ids[:, -1:]		,				lowerCAmelCase_		,				decoder_attention_mask=lowerCAmelCase_		,				past_key_values=outputs_cache.past_key_values		,				decoder_position_ids=lowerCAmelCase_		,				)

        _A        =  model.decode(lowerCAmelCase_		,				lowerCAmelCase_							)

        _A        =  np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])							)							)
        self.parent.assertTrue(diff < 1E-3		,				msg=F'''Max diff is {diff}'''							)



 def      UpperCAmelCase							(					self		,				lowerCAmelCase_		,				lowerCAmelCase_		,				lowerCAmelCase_							)  ->					int:
        _A        =  20
        _A        =  model_class_name(lowerCAmelCase_							)

        _A        =  model.encode(inputs_dict["""input_ids"""]							)

        _A   ,  _A        =  (
            inputs_dict["""decoder_input_ids"""],
            inputs_dict["""decoder_attention_mask"""],
        )

        _A        =  jnp.concatenate(
            [
                decoder_attention_mask,
                jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])							),
            ]		,				axis=-1		,				)

        _A        =  model.init_cache(decoder_input_ids.shape[0]		,				lowerCAmelCase_		,				lowerCAmelCase_							)
        _A        =  jnp.broadcast_to(
            jnp.arange(decoder_input_ids.shape[-1] - 1							)[None, :]		,				(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)		,				)

        _A        =  model.decode(
            decoder_input_ids[:, :-1]		,				lowerCAmelCase_		,				decoder_attention_mask=lowerCAmelCase_		,				past_key_values=lowerCAmelCase_		,				decoder_position_ids=lowerCAmelCase_		,				)
        _A        =  jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]		,				dtype="""i4"""							)
        _A        =  model.decode(
            decoder_input_ids[:, -1:]		,				lowerCAmelCase_		,				past_key_values=outputs_cache.past_key_values		,				decoder_attention_mask=lowerCAmelCase_		,				decoder_position_ids=lowerCAmelCase_		,				)

        _A        =  model.decode(lowerCAmelCase_		,				lowerCAmelCase_		,				decoder_attention_mask=lowerCAmelCase_							)

        _A        =  np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])							)							)
        self.parent.assertTrue(diff < 1E-3		,				msg=F'''Max diff is {diff}'''							)






def  snake_case  (       snake_case__			:Optional[int]   ,	snake_case__			:str   ,	snake_case__			:Any   ,	snake_case__			:int=None   ,	snake_case__			:List[str]=None   ,	)						->   Union[str, Any]:
       if attention_mask is None:
              _A        =  np.not_equal(snake_case__   ,	config.pad_token_id).astype(np.inta)
       if decoder_attention_mask is None:
              _A        =  np.concatenate(
                  [
                      np.ones(decoder_input_ids[:, :1].shape   ,	dtype=np.inta),
                      np.not_equal(decoder_input_ids[:, 1:]   ,	config.pad_token_id).astype(np.inta),
                  ]   ,	axis=-1   ,	)
       return {
           "input_ids": input_ids,
           "decoder_input_ids": decoder_input_ids,
           "attention_mask": attention_mask,
           "decoder_attention_mask": decoder_attention_mask,
       }







@require_flax
class 				a							(			__lowerCAmelCase       ,			unittest.TestCase       ):







 """simple docstring"""






 lowerCamelCase	:int    			=		(
     (
         FlaxPegasusForConditionalGeneration,
         FlaxPegasusModel,
     )
     if is_flax_available()
     else ()
 )
 lowerCamelCase	:Optional[int]    			=		(FlaxPegasusForConditionalGeneration,) if is_flax_available() else ()
 lowerCamelCase	:Dict    			=		True
 lowerCamelCase	:List[str]    			=		False
 lowerCamelCase	:Dict    			=		False
 lowerCamelCase	:str    			=		False



 def      UpperCAmelCase							(					self							)  ->					List[Any]:
        _A        =  FlaxPegasusModelTester(self							)
        _A        =  ConfigTester(self		,				config_class=lowerCAmelCase_							)



 def      UpperCAmelCase							(					self							)  ->					Optional[Any]:
        self.config_tester.run_common_tests()



 def      UpperCAmelCase							(					self							)  ->					Any:
        _A   ,  _A        =  self.model_tester.prepare_config_and_inputs_for_common()
        for model_class in self.all_model_classes:
               self.model_tester.check_use_cache_forward(lowerCAmelCase_		,				lowerCAmelCase_		,				lowerCAmelCase_							)



 def      UpperCAmelCase							(					self							)  ->					Any:
        _A   ,  _A        =  self.model_tester.prepare_config_and_inputs_for_common()
        for model_class in self.all_model_classes:
               self.model_tester.check_use_cache_forward_with_attn_mask(lowerCAmelCase_		,				lowerCAmelCase_		,				lowerCAmelCase_							)



 def      UpperCAmelCase							(					self							)  ->					Optional[Any]:
        _A   ,  _A        =  self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
               with self.subTest(model_class.__name__							):
                      _A        =  self._prepare_for_class(lowerCAmelCase_		,				lowerCAmelCase_							)
                      _A        =  model_class(lowerCAmelCase_							)

                      @jax.jit
                      def encode_jitted(lowerCAmelCase_		,				lowerCAmelCase_=None		,				**lowerCAmelCase_							):
                             return model.encode(input_ids=lowerCAmelCase_		,				attention_mask=lowerCAmelCase_							)

                      with self.subTest("""JIT Enabled"""							):
                             _A        =  encode_jitted(**lowerCAmelCase_							).to_tuple()

                      with self.subTest("""JIT Disabled"""							):
                             with jax.disable_jit():
                                    _A        =  encode_jitted(**lowerCAmelCase_							).to_tuple()

                      self.assertEqual(len(lowerCAmelCase_							)		,				len(lowerCAmelCase_							)							)
                      for jitted_output, output in zip(lowerCAmelCase_		,				lowerCAmelCase_							):
                             self.assertEqual(jitted_output.shape		,				output.shape							)



 def      UpperCAmelCase							(					self							)  ->					str:
        _A   ,  _A        =  self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
               with self.subTest(model_class.__name__							):
                      _A        =  model_class(lowerCAmelCase_							)
                      _A        =  model.encode(inputs_dict["""input_ids"""]		,				inputs_dict["""attention_mask"""]							)

                      _A        =  {
                          """decoder_input_ids""": inputs_dict["""decoder_input_ids"""],
                          """decoder_attention_mask""": inputs_dict["""decoder_attention_mask"""],
                          """encoder_outputs""": encoder_outputs,
                      }

                      @jax.jit
                      def decode_jitted(lowerCAmelCase_		,				lowerCAmelCase_		,				lowerCAmelCase_							):
                             return model.decode(
                                 decoder_input_ids=lowerCAmelCase_		,				decoder_attention_mask=lowerCAmelCase_		,				encoder_outputs=lowerCAmelCase_		,				)

                      with self.subTest("""JIT Enabled"""							):
                             _A        =  decode_jitted(**lowerCAmelCase_							).to_tuple()

                      with self.subTest("""JIT Disabled"""							):
                             with jax.disable_jit():
                                    _A        =  decode_jitted(**lowerCAmelCase_							).to_tuple()

                      self.assertEqual(len(lowerCAmelCase_							)		,				len(lowerCAmelCase_							)							)
                      for jitted_output, output in zip(lowerCAmelCase_		,				lowerCAmelCase_							):
                             self.assertEqual(jitted_output.shape		,				output.shape							)



 @slow
 def      UpperCAmelCase							(					self							)  ->					List[Any]:
        for model_class_name in self.all_model_classes:
               _A        =  model_class_name.from_pretrained("""google/pegasus-large"""		,				from_pt=lowerCAmelCase_							)
               _A        =  np.ones((1, 1)							)
               _A        =  model(lowerCAmelCase_							)
               self.assertIsNotNone(lowerCAmelCase_							)



 @slow
 def      UpperCAmelCase							(					self							)  ->					Any:
        _A        =  FlaxPegasusForConditionalGeneration.from_pretrained("""google/pegasus-xsum"""							)
        _A        =  PegasusTokenizer.from_pretrained("""google/pegasus-xsum"""							)

        _A        =  [
            """ PG&E stated it scheduled the blackouts in response to forecasts for high winds amid dry conditions. The aim is to reduce the risk of wildfires. Nearly 800 thousand customers were scheduled to be affected by the shutoffs which were expected to last through at least midday tomorrow.""",
            """ The London trio are up for best UK act and best album, as well as getting two nominations in the best song category.\"We got told like this morning 'Oh I think you're nominated'\", said Dappy.\"And I was like 'Oh yeah, which one?' And now we've got nominated for four awards. I mean, wow!\"Bandmate Fazer added: \"We thought it's best of us to come down and mingle with everyone and say hello to the cameras. And now we find we've got four nominations.\"The band have two shots at the best song prize, getting the nod for their Tynchy Stryder collaboration Number One, and single Strong Again.Their album Uncle B will also go up against records by the likes of Beyonce and Kanye West.N-Dubz picked up the best newcomer Mobo in 2007, but female member Tulisa said they wouldn't be too disappointed if they didn't win this time around.\"At the end of the day we're grateful to be where we are in our careers.\"If it don't happen then it don't happen - live to fight another day and keep on making albums and hits for the fans.\"Dappy also revealed they could be performing live several times on the night.The group will be doing Number One and also a possible rendition of the War Child single, I Got Soul.The charity song is a  re-working of The Killers' All These Things That I've Done and is set to feature artists like Chipmunk, Ironik and Pixie Lott.This year's Mobos will be held outside of London for the first time, in Glasgow on 30 September.N-Dubz said they were looking forward to performing for their Scottish fans and boasted about their recent shows north of the border.\"We just done Edinburgh the other day,\" said Dappy.\"We smashed up an N-Dubz show over there. We done Aberdeen about three or four months ago - we smashed up that show over there! Everywhere we go we smash it up!\" """,
        ]

        _A        =  [
            """California's largest electricity provider has turned off power to hundreds of thousands of customers.""",
            """Pop group N-Dubz have revealed they were surprised to get four nominations for this year's Mobo Awards.""",
        ]

        _A        =  tokenizer(lowerCAmelCase_		,				return_tensors="""np"""		,				truncation=lowerCAmelCase_		,				max_length=5_12		,				padding=lowerCAmelCase_							)
        _A        =  model.generate(**lowerCAmelCase_		,				num_beams=2							).sequences
        _A        =  tokenizer.batch_decode(lowerCAmelCase_		,				skip_special_tokens=lowerCAmelCase_							)
        assert tgt_text == decoded





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 				a							:







 """simple docstring"""






 def __init__(					self		,				lowerCAmelCase_		,				lowerCAmelCase_=13		,				lowerCAmelCase_=30		,				lowerCAmelCase_=2		,				lowerCAmelCase_=3		,				lowerCAmelCase_=True		,				lowerCAmelCase_=True		,				lowerCAmelCase_=32		,				lowerCAmelCase_=5		,				lowerCAmelCase_=4		,				lowerCAmelCase_=37		,				lowerCAmelCase_="gelu"		,				lowerCAmelCase_=0.1		,				lowerCAmelCase_=0.1		,				lowerCAmelCase_=10		,				lowerCAmelCase_=0.02		,				lowerCAmelCase_=3		,				lowerCAmelCase_=0.6		,				lowerCAmelCase_=None		,				)  ->					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      UpperCAmelCase							(					self							)  ->					Union[str, Any]:
        _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      UpperCAmelCase							(					self							)  ->					str:
        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=lowerCAmelCase_		,				initializer_range=self.initializer_range		,				mask_ratio=self.mask_ratio		,				)



 def      UpperCAmelCase							(					self		,				lowerCAmelCase_		,				lowerCAmelCase_		,				lowerCAmelCase_							)  ->					Any:
        _A        =  ViTMAEModel(config=lowerCAmelCase_							)
        model.to(lowerCAmelCase_							)
        model.eval()
        _A        =  model(lowerCAmelCase_							)
        self.parent.assertEqual(result.last_hidden_state.shape		,				(self.batch_size, self.seq_length, self.hidden_size)							)



 def      UpperCAmelCase							(					self		,				lowerCAmelCase_		,				lowerCAmelCase_		,				lowerCAmelCase_							)  ->					List[Any]:
        _A        =  ViTMAEForPreTraining(lowerCAmelCase_							)
        model.to(lowerCAmelCase_							)
        model.eval()
        _A        =  model(lowerCAmelCase_							)
        _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(lowerCAmelCase_							)
        model.to(lowerCAmelCase_							)
        model.eval()
        _A        =  floats_tensor([self.batch_size, 1, self.image_size, self.image_size]							)
        _A        =  model(lowerCAmelCase_							)
        _A        =  self.patch_size**2
        self.parent.assertEqual(result.logits.shape		,				(self.batch_size, num_patches, expected_num_channels)							)



 def      UpperCAmelCase							(					self							)  ->					Dict:
        _A        =  self.prepare_config_and_inputs()
        _A   ,  _A   ,  _A        =  config_and_inputs
        _A        =  {"""pixel_values""": pixel_values}
        return config, inputs_dict



@require_torch
class 				a							(			__lowerCAmelCase       ,			__lowerCAmelCase       ,			unittest.TestCase       ):







 """simple docstring"""






 lowerCamelCase	:Union[str, Any]    			=		(ViTMAEModel, ViTMAEForPreTraining) if is_torch_available() else ()
 lowerCamelCase	:List[Any]    			=		{'''feature-extraction''': ViTMAEModel} if is_torch_available() else {}

 lowerCamelCase	:List[Any]    			=		False
 lowerCamelCase	:Tuple    			=		False
 lowerCamelCase	:int    			=		False
 lowerCamelCase	:Any    			=		False



 def      UpperCAmelCase							(					self							)  ->					str:
        _A        =  ViTMAEModelTester(self							)
        _A        =  ConfigTester(self		,				config_class=lowerCAmelCase_		,				has_text_modality=lowerCAmelCase_		,				hidden_size=37							)



 def      UpperCAmelCase							(					self							)  ->					str:
        self.config_tester.run_common_tests()



 @unittest.skip(reason="""ViTMAE does not use inputs_embeds"""							)
 def      UpperCAmelCase							(					self							)  ->					Optional[Any]:
        pass



 def      UpperCAmelCase							(					self							)  ->					str:
        _A   ,  _A        =  self.model_tester.prepare_config_and_inputs_for_common()

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



 def      UpperCAmelCase							(					self							)  ->					Union[str, Any]:
        _A   ,  _A        =  self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
               _A        =  model_class(lowerCAmelCase_							)
               _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]		,				lowerCAmelCase_							)



 def      UpperCAmelCase							(					self							)  ->					Union[str, Any]:
        _A        =  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*lowerCAmelCase_							)



 def      UpperCAmelCase							(					self							)  ->					Dict:
        _A        =  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_pretraining(*lowerCAmelCase_							)



 def      UpperCAmelCase							(					self		,				lowerCAmelCase_		,				lowerCAmelCase_		,				lowerCAmelCase_							)  ->					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(lowerCAmelCase_							)

        # 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(lowerCAmelCase_		,				lowerCAmelCase_		,				lowerCAmelCase_							)



 def      UpperCAmelCase							(					self							)  ->					Any:
        _A   ,  _A        =  self.model_tester.prepare_config_and_inputs_for_common()

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

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

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

                      # 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(lowerCAmelCase_		,				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      UpperCAmelCase							(					self							)  ->					Tuple:
        pass



 @unittest.skip(
     reason="""ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load
    to get deterministic results."""							)
 def      UpperCAmelCase							(					self							)  ->					str:
        pass



 @unittest.skip(
     reason="""ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load
    to get deterministic results."""							)
 def      UpperCAmelCase							(					self							)  ->					str:
        pass



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



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



 @slow
 def      UpperCAmelCase							(					self							)  ->					Optional[Any]:
        for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               _A        =  ViTMAEModel.from_pretrained(lowerCAmelCase_							)
               self.assertIsNotNone(lowerCAmelCase_							)






def  snake_case  (       )						->   List[str]:
       _A        =  Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""")
       return image







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







 """simple docstring"""






 @cached_property
 def      UpperCAmelCase							(					self							)  ->					int:
        return ViTImageProcessor.from_pretrained("""facebook/vit-mae-base"""							) if is_vision_available() else None



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

        _A        =  ViTMAEForPreTraining.from_pretrained("""facebook/vit-mae-base"""							).to(lowerCAmelCase_							)

        _A        =  self.default_image_processor
        _A        =  prepare_img()
        _A        =  image_processor(images=lowerCAmelCase_		,				return_tensors="""pt"""							).to(lowerCAmelCase_							)

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

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

        _A        =  torch.tensor(
            [[-0.0548, -1.7023, -0.9325], [0.3721, -0.5670, -0.2233], [0.8235, -1.3878, -0.3524]]							)

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




