
"""simple docstring"""




import argparse
from pathlib import Path

import fairseq
import torch
from fairseq.models.xmod import XMODModel as FairseqXmodModel
from packaging import version

from transformers import XmodConfig, XmodForMaskedLM, XmodForSequenceClassification
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse("""0.12.2"""):
    raise Exception("""requires fairseq >= 0.12.2""")
if version.parse(fairseq.__version__) > version.parse("""2"""):
    raise Exception("""requires fairseq < v2""")

logging.set_verbosity_info()
A__      :	Union[str, Any]          =    logging.get_logger(__name__)

A__      :	int          =    """Hello, World!"""
A__      :	Any          =    """en_XX"""



def 							a__ (				lowerCAmelCase	:       str       ,    lowerCAmelCase	:       str       ,    lowerCAmelCase	:       bool		):






     '''simple docstring'''
     UpperCAmelCase__							: Any 							=						Path("data_bin"		)
     UpperCAmelCase__							: Optional[Any] 							=						FairseqXmodModel.from_pretrained(
         model_name_or_path=str(Path(lowerCAmelCase		).parent		)       ,    checkpoint_file=Path(lowerCAmelCase		).name       ,    _name="xmod_base"       ,    arch="xmod_base"       ,    task="multilingual_masked_lm"       ,    data_name_or_path=str(lowerCAmelCase		)       ,    bpe="sentencepiece"       ,    sentencepiece_model=str(Path(lowerCAmelCase		).parent / "sentencepiece.bpe.model"		)       ,    src_dict=str(data_dir / "dict.txt"		)       ,    )
     xmod.eval()  # disable dropout
     print(lowerCAmelCase		)

     UpperCAmelCase__							: Dict 							=						xmod.model.encoder.sentence_encoder
     UpperCAmelCase__							: int 							=						XmodConfig(
         vocab_size=xmod_sent_encoder.embed_tokens.num_embeddings       ,    hidden_size=xmod.cfg.model.encoder_embed_dim       ,    num_hidden_layers=xmod.cfg.model.encoder_layers       ,    num_attention_heads=xmod.cfg.model.encoder_attention_heads       ,    intermediate_size=xmod.cfg.model.encoder_ffn_embed_dim       ,    max_position_embeddings=514       ,    type_vocab_size=1       ,    layer_norm_eps=1E-5       ,    pre_norm=xmod.cfg.model.encoder_normalize_before       ,    adapter_reduction_factor=getattr(xmod.cfg.model       ,    "bottleneck"       ,    2		)       ,    adapter_layer_norm=xmod.cfg.model.adapter_layer_norm       ,    adapter_reuse_layer_norm=xmod.cfg.model.adapter_reuse_layer_norm       ,    ln_before_adapter=xmod.cfg.model.ln_before_adapter       ,    languages=xmod.cfg.model.languages       ,    )
     if classification_head:
          UpperCAmelCase__							: str 							=						xmod.model.classification_heads["mnli"].out_proj.weight.shape[0]

     print("Our X-MOD config:"       ,    lowerCAmelCase		)

     UpperCAmelCase__							: Optional[int] 							=						XmodForSequenceClassification(lowerCAmelCase		) if classification_head else XmodForMaskedLM(lowerCAmelCase		)
     model.eval()

     # Now let's copy all the weights.
     # Embeddings
     UpperCAmelCase__							: List[Any] 							=						xmod_sent_encoder.embed_tokens.weight
     UpperCAmelCase__							: Optional[int] 							=						xmod_sent_encoder.embed_positions.weight
     UpperCAmelCase__							: Union[str, Any] 							=						torch.zeros_like(
         model.roberta.embeddings.token_type_embeddings.weight		)  # just zero them out b/c xmod doesn't use them.

     UpperCAmelCase__							: Any 							=						xmod_sent_encoder.layernorm_embedding.weight
     UpperCAmelCase__							: Tuple 							=						xmod_sent_encoder.layernorm_embedding.bias

     for i in range(config.num_hidden_layers		):
          # Encoder: start of layer
          UpperCAmelCase__							: int 							=						model.roberta.encoder.layer[i]
          UpperCAmelCase__							: Tuple 							=						xmod_sent_encoder.layers[i]

          # self attention
          UpperCAmelCase__							: Union[str, Any] 							=						layer.attention.self
          if not (
              xmod_layer.self_attn.k_proj.weight.data.shape
              == xmod_layer.self_attn.q_proj.weight.data.shape
              == xmod_layer.self_attn.v_proj.weight.data.shape
              == torch.Size((config.hidden_size, config.hidden_size)		)
          ):
               raise AssertionError("Dimensions of self-attention weights do not match."		)

          UpperCAmelCase__							: Any 							=						xmod_layer.self_attn.q_proj.weight
          UpperCAmelCase__							: Tuple 							=						xmod_layer.self_attn.q_proj.bias
          UpperCAmelCase__							: List[Any] 							=						xmod_layer.self_attn.k_proj.weight
          UpperCAmelCase__							: Any 							=						xmod_layer.self_attn.k_proj.bias
          UpperCAmelCase__							: List[Any] 							=						xmod_layer.self_attn.v_proj.weight
          UpperCAmelCase__							: List[Any] 							=						xmod_layer.self_attn.v_proj.bias

          # self-attention output
          UpperCAmelCase__							: Optional[Any] 							=						layer.attention.output
          if self_output.dense.weight.shape != xmod_layer.self_attn.out_proj.weight.shape:
               raise AssertionError("Dimensions of self-attention output weights do not match."		)
          UpperCAmelCase__							: List[Any] 							=						xmod_layer.self_attn.out_proj.weight
          UpperCAmelCase__							: Any 							=						xmod_layer.self_attn.out_proj.bias
          UpperCAmelCase__							: Union[str, Any] 							=						xmod_layer.self_attn_layer_norm.weight
          UpperCAmelCase__							: Optional[Any] 							=						xmod_layer.self_attn_layer_norm.bias

          # intermediate
          UpperCAmelCase__							: List[str] 							=						layer.intermediate
          if intermediate.dense.weight.shape != xmod_layer.fca.weight.shape:
               raise AssertionError("Dimensions of intermediate weights do not match."		)
          UpperCAmelCase__							: Optional[int] 							=						xmod_layer.fca.weight
          UpperCAmelCase__							: str 							=						xmod_layer.fca.bias

          # output
          UpperCAmelCase__							: Union[str, Any] 							=						layer.output
          if bert_output.dense.weight.shape != xmod_layer.fca.weight.shape:
               raise AssertionError("Dimensions of feed-forward weights do not match."		)
          UpperCAmelCase__							: str 							=						xmod_layer.fca.weight
          UpperCAmelCase__							: List[Any] 							=						xmod_layer.fca.bias
          UpperCAmelCase__							: Any 							=						xmod_layer.final_layer_norm.weight
          UpperCAmelCase__							: Tuple 							=						xmod_layer.final_layer_norm.bias
          if bert_output.adapter_layer_norm is not None:
               UpperCAmelCase__							: List[str] 							=						xmod_layer.adapter_layer_norm.weight
               UpperCAmelCase__							: Any 							=						xmod_layer.adapter_layer_norm.bias

          if sorted(bert_output.adapter_modules.keys()		) != sorted(xmod_layer.adapter_modules.keys()		):
               raise AssertionError("Lists of language adapters do not match."		)
          for lang_code, adapter in xmod_layer.adapter_modules.items():
               UpperCAmelCase__							: Any 							=						bert_output.adapter_modules[lang_code]
               UpperCAmelCase__							: Dict 							=						xmod_layer.adapter_modules[lang_code]
               UpperCAmelCase__							: Optional[Any] 							=						from_adapter.fca.weight
               UpperCAmelCase__							: Dict 							=						from_adapter.fca.bias
               UpperCAmelCase__							: Optional[int] 							=						from_adapter.fca.weight
               UpperCAmelCase__							: str 							=						from_adapter.fca.bias

          # end of layer

     if xmod_sent_encoder.layer_norm is not None:
          UpperCAmelCase__							: str 							=						xmod_sent_encoder.layer_norm.weight
          UpperCAmelCase__							: List[Any] 							=						xmod_sent_encoder.layer_norm.bias

     if classification_head:
          UpperCAmelCase__							: Dict 							=						xmod.model.classification_heads["mnli"].dense.weight
          UpperCAmelCase__							: Optional[int] 							=						xmod.model.classification_heads["mnli"].dense.bias
          UpperCAmelCase__							: Optional[Any] 							=						xmod.model.classification_heads["mnli"].out_proj.weight
          UpperCAmelCase__							: Optional[int] 							=						xmod.model.classification_heads["mnli"].out_proj.bias
     else:
          # LM Head
          UpperCAmelCase__							: List[Any] 							=						xmod.model.encoder.lm_head.dense.weight
          UpperCAmelCase__							: Dict 							=						xmod.model.encoder.lm_head.dense.bias
          UpperCAmelCase__							: Union[str, Any] 							=						xmod.model.encoder.lm_head.layer_norm.weight
          UpperCAmelCase__							: Dict 							=						xmod.model.encoder.lm_head.layer_norm.bias
          UpperCAmelCase__							: Dict 							=						xmod.model.encoder.lm_head.weight
          UpperCAmelCase__							: Any 							=						xmod.model.encoder.lm_head.bias

     # Let's check that we get the same results.
     UpperCAmelCase__							: Dict 							=						xmod.encode(lowerCAmelCase		).unsqueeze(0		)  # batch of size 1
     model.roberta.set_default_language(lowerCAmelCase		)

     UpperCAmelCase__							: Dict 							=						model(lowerCAmelCase		)[0]
     if classification_head:
          UpperCAmelCase__							: Any 							=						xmod.model.classification_heads["mnli"](xmod.extract_features(lowerCAmelCase		)		)
     else:
          UpperCAmelCase__							: Dict 							=						xmod.model(lowerCAmelCase       ,    lang_id=[SAMPLE_LANGUAGE]		)[0]
     print(our_output.shape       ,    their_output.shape		)
     UpperCAmelCase__							: str 							=						torch.max(torch.abs(our_output - their_output		)		).item()
     print(F"max_absolute_diff = {max_absolute_diff}"		)  # ~ 1e-7
     UpperCAmelCase__							: Optional[int] 							=						torch.allclose(lowerCAmelCase       ,    lowerCAmelCase       ,    atol=1E-3		)
     print("Do both models output the same tensors?"       ,    "🔥" if success else "💩"		)
     if not success:
          raise Exception("Something went wRoNg"		)

     Path(lowerCAmelCase		).mkdir(parents=lowerCAmelCase       ,    exist_ok=lowerCAmelCase		)
     print(F"Saving model to {pytorch_dump_folder_path}"		)
     model.save_pretrained(lowerCAmelCase		)


if __name__ == "__main__":
    A__      :	int          =    argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        """--xmod_checkpoint_path""", default=None, type=str, required=True, help="""Path the official PyTorch dump."""
    )
    parser.add_argument(
        """--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
    )
    parser.add_argument(
        """--classification_head""", action="""store_true""", help="""Whether to convert a final classification head."""
    )
    A__      :	int          =    parser.parse_args()
    convert_xmod_checkpoint_to_pytorch(
        args.xmod_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
    )





"""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   _lowercase						:


     '''simple docstring'''





     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=0.02 ,			__UpperCamelCase=3 ,			__UpperCamelCase=0.6 ,			__UpperCamelCase=None ,			)->					List[Any]:
          UpperCAmelCase__							: str 							=						parent
          UpperCAmelCase__							: Optional[Any] 							=						batch_size
          UpperCAmelCase__							: Any 							=						image_size
          UpperCAmelCase__							: Dict 							=						patch_size
          UpperCAmelCase__							: Optional[Any] 							=						num_channels
          UpperCAmelCase__							: Union[str, Any] 							=						is_training
          UpperCAmelCase__							: Any 							=						use_labels
          UpperCAmelCase__							: List[Any] 							=						hidden_size
          UpperCAmelCase__							: Dict 							=						num_hidden_layers
          UpperCAmelCase__							: List[str] 							=						num_attention_heads
          UpperCAmelCase__							: List[Any] 							=						intermediate_size
          UpperCAmelCase__							: Dict 							=						hidden_act
          UpperCAmelCase__							: List[Any] 							=						hidden_dropout_prob
          UpperCAmelCase__							: Optional[int] 							=						attention_probs_dropout_prob
          UpperCAmelCase__							: Optional[Any] 							=						type_sequence_label_size
          UpperCAmelCase__							: Union[str, Any] 							=						initializer_range
          UpperCAmelCase__							: int 							=						mask_ratio
          UpperCAmelCase__							: Tuple 							=						scope

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



     def  lowerCAmelCase__       (      self  )->					List[Any]:
          UpperCAmelCase__							: List[Any] 							=						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]  )

          UpperCAmelCase__							: Optional[Any] 							=						None
          if self.use_labels:
               UpperCAmelCase__							: Dict 							=						ids_tensor([self.batch_size] ,			self.type_sequence_label_size  )

          UpperCAmelCase__							: Optional[int] 							=						self.get_config()

          return config, pixel_values, labels



     def  lowerCAmelCase__       (      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  lowerCAmelCase__       (      self ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase  )->					Union[str, Any]:
          UpperCAmelCase__							: Optional[Any] 							=						ViTMAEModel(config=__UpperCamelCase  )
          model.to(__UpperCamelCase  )
          model.eval()
          UpperCAmelCase__							: List[str] 							=						model(__UpperCamelCase  )
          self.parent.assertEqual(result.last_hidden_state.shape ,			(self.batch_size, self.seq_length, self.hidden_size)  )



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase  )->					Optional[int]:
          UpperCAmelCase__							: List[Any] 							=						ViTMAEForPreTraining(__UpperCamelCase  )
          model.to(__UpperCamelCase  )
          model.eval()
          UpperCAmelCase__							: Optional[int] 							=						model(__UpperCamelCase  )
          UpperCAmelCase__							: List[str] 							=						(self.image_size // self.patch_size) ** 2
          UpperCAmelCase__							: List[str] 							=						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__							: Dict 							=						1
          UpperCAmelCase__							: str 							=						ViTMAEForPreTraining(__UpperCamelCase  )
          model.to(__UpperCamelCase  )
          model.eval()
          UpperCAmelCase__							: int 							=						floats_tensor([self.batch_size, 1, self.image_size, self.image_size]  )
          UpperCAmelCase__							: List[str] 							=						model(__UpperCamelCase  )
          UpperCAmelCase__							: List[str] 							=						self.patch_size**2
          self.parent.assertEqual(result.logits.shape ,			(self.batch_size, num_patches, expected_num_channels)  )



     def  lowerCAmelCase__       (      self  )->					Dict:
          UpperCAmelCase__							: Tuple 							=						self.prepare_config_and_inputs()
          UpperCAmelCase__							,	UpperCAmelCase__							,	UpperCAmelCase__							: str 							=						config_and_inputs
          UpperCAmelCase__							: int 							=						{"pixel_values": pixel_values}
          return config, inputs_dict







@require_torch
class   _lowercase						(       lowerCAmelCase_	,   lowerCAmelCase_	,   unittest.TestCase				):


     '''simple docstring'''





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

     _A								=							False
     _A								=							False
     _A								=							False
     _A								=							False



     def  lowerCAmelCase__       (      self  )->					List[Any]:
          UpperCAmelCase__							: Any 							=						ViTMAEModelTester(self  )
          UpperCAmelCase__							: List[str] 							=						ConfigTester(self ,			config_class=__UpperCamelCase ,			has_text_modality=__UpperCamelCase ,			hidden_size=37  )



     def  lowerCAmelCase__       (      self  )->					int:
          self.config_tester.run_common_tests()



     @unittest.skip(reason="ViTMAE does not use inputs_embeds"  )
     def  lowerCAmelCase__       (      self  )->					Dict:
          pass



     def  lowerCAmelCase__       (      self  )->					List[str]:
          UpperCAmelCase__							,	UpperCAmelCase__							: Tuple 							=						self.model_tester.prepare_config_and_inputs_for_common()

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



     def  lowerCAmelCase__       (      self  )->					Optional[Any]:
          UpperCAmelCase__							,	UpperCAmelCase__							: int 							=						self.model_tester.prepare_config_and_inputs_for_common()

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

               UpperCAmelCase__							: Tuple 							=						["pixel_values"]
               self.assertListEqual(arg_names[:1] ,			__UpperCamelCase  )



     def  lowerCAmelCase__       (      self  )->					Any:
          UpperCAmelCase__							: int 							=						self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*__UpperCamelCase  )



     def  lowerCAmelCase__       (      self  )->					Dict:
          UpperCAmelCase__							: int 							=						self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_pretraining(*__UpperCamelCase  )



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase  )->					Dict:
          # make masks reproducible
          np.random.seed(2  )

          UpperCAmelCase__							: Tuple 							=						int((pt_model.config.image_size // pt_model.config.patch_size) ** 2  )
          UpperCAmelCase__							: Union[str, Any] 							=						np.random.uniform(size=(self.model_tester.batch_size, num_patches)  )
          UpperCAmelCase__							: str 							=						torch.from_numpy(__UpperCamelCase  )

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

          super().check_pt_tf_models(__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase  )



     def  lowerCAmelCase__       (      self  )->					List[Any]:
          UpperCAmelCase__							,	UpperCAmelCase__							: Optional[int] 							=						self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
               UpperCAmelCase__							: List[Any] 							=						model_class(__UpperCamelCase  )
               model.to(__UpperCamelCase  )
               model.eval()
               # make random mask reproducible
               torch.manual_seed(2  )
               with torch.no_grad():
                    UpperCAmelCase__							: Optional[int] 							=						model(**self._prepare_for_class(__UpperCamelCase ,			__UpperCamelCase  )  )

               UpperCAmelCase__							: Optional[Any] 							=						outputs[0].cpu().numpy()
               UpperCAmelCase__							: Union[str, Any] 							=						0

               with tempfile.TemporaryDirectory() as tmpdirname:
                    model.save_pretrained(__UpperCamelCase  )
                    UpperCAmelCase__							: Union[str, Any] 							=						model_class.from_pretrained(__UpperCamelCase  )
                    model.to(__UpperCamelCase  )
                    # make random mask reproducible
                    torch.manual_seed(2  )
                    with torch.no_grad():
                         UpperCAmelCase__							: List[Any] 							=						model(**self._prepare_for_class(__UpperCamelCase ,			__UpperCamelCase  )  )

                    # Make sure we don't have nans
                    UpperCAmelCase__							: Tuple 							=						after_outputs[0].cpu().numpy()
                    UpperCAmelCase__							: int 							=						0
                    UpperCAmelCase__							: str 							=						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  lowerCAmelCase__       (      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  lowerCAmelCase__       (      self  )->					Any:
          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  lowerCAmelCase__       (      self  )->					Optional[Any]:
          pass



     @unittest.skip(reason="ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load"  )
     def  lowerCAmelCase__       (      self  )->					List[Any]:
          pass



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



     @slow
     def  lowerCAmelCase__       (      self  )->					Union[str, Any]:
          for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               UpperCAmelCase__							: Tuple 							=						ViTMAEModel.from_pretrained(__UpperCamelCase  )
               self.assertIsNotNone(__UpperCamelCase  )





def 							a__ (				):






     '''simple docstring'''
     UpperCAmelCase__							: Union[str, Any] 							=						Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"		)
     return image







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


     '''simple docstring'''





     @cached_property
     def  lowerCAmelCase__       (      self  )->					List[Any]:
          return ViTImageProcessor.from_pretrained("facebook/vit-mae-base"  ) if is_vision_available() else None



     @slow
     def  lowerCAmelCase__       (      self  )->					Optional[int]:
          # make random mask reproducible across the PT and TF model
          np.random.seed(2  )

          UpperCAmelCase__							: Any 							=						ViTMAEForPreTraining.from_pretrained("facebook/vit-mae-base"  ).to(__UpperCamelCase  )

          UpperCAmelCase__							: Tuple 							=						self.default_image_processor
          UpperCAmelCase__							: List[Any] 							=						prepare_img()
          UpperCAmelCase__							: Optional[Any] 							=						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)
          UpperCAmelCase__							: List[Any] 							=						ViTMAEConfig()
          UpperCAmelCase__							: str 							=						int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2  )
          UpperCAmelCase__							: Optional[int] 							=						np.random.uniform(size=(1, num_patches)  )

          # forward pass
          with torch.no_grad():
               UpperCAmelCase__							: List[str] 							=						model(**__UpperCamelCase ,			noise=torch.from_numpy(__UpperCamelCase  ).to(device=__UpperCamelCase  )  )

          # verify the logits
          UpperCAmelCase__							: str 							=						torch.Size((1, 1_96, 7_68)  )
          self.assertEqual(outputs.logits.shape ,			__UpperCamelCase  )

          UpperCAmelCase__							: List[Any] 							=						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(__UpperCamelCase  ) ,			atol=1E-4  )  )



