





from pickle import UnpicklingError

import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict

from ..utils import logging


lowercase      :       str									=  logging.get_logger(__name__)



def A_	(		A__		,      A__				)  ->		List[str]:
       try:
              with open(A__		,      'rb'				) as flax_state_f:
                     a__						:	Union[str, Any] =				from_bytes(A__		,      flax_state_f.read()				)
       except UnpicklingError as e:
              try:
                     with open(A__				) as f:
                            if f.read().startswith('version'				):
                                   raise OSError(
                                       'You seem to have cloned a repository without having git-lfs installed. Please'
                                       ' install git-lfs and run `git lfs install` followed by `git lfs pull` in the'
                                       ' folder you cloned.'				)
                            else:
                                   raise ValueError from e
              except (UnicodeDecodeError, ValueError):
                     raise EnvironmentError(F'Unable to convert {model_file} to Flax deserializable object. '				)

       return load_flax_weights_in_pytorch_model(A__		,      A__				)



def A_	(		A__		,      A__				)  ->		str:

       try:
              import torch  # noqa: F401
       except ImportError:
              logger.error(
                  'Loading Flax weights in PyTorch requires both PyTorch and Flax to be installed. Please see'
                  ' https://pytorch.org/ and https://flax.readthedocs.io/en/latest/installation.html for installation'
                  ' instructions.'				)
              raise

       # check if we have bf16 weights
       a__						:	int =				flatten_dict(jax.tree_util.tree_map(lambda A__				: x.dtype == jnp.bfloataa		,      A__				)				).values()
       if any(A__				):
              # convert all weights to fp32 if they are bf16 since torch.from_numpy can-not handle bf16

              # and bf16 is not fully supported in PT yet.
              logger.warning(
                  'Found ``bfloat16`` weights in Flax model. Casting all ``bfloat16`` weights to ``float32`` '
                  'before loading those in PyTorch model.'				)
              a__						:	Optional[Any] =				jax.tree_util.tree_map(
                  lambda A__				: params.astype(np.floataa				) if params.dtype == jnp.bfloataa else params		,      A__				)

       a__						:	Dict =				''

       a__						:	Optional[int] =				flatten_dict(A__		,      sep='.'				)
       a__						:	Dict =				pt_model.state_dict()

       # keep track of unexpected & missing keys
       a__						:	Union[str, Any] =				[]
       a__						:	Optional[Any] =				set(pt_model_dict.keys()				)

       for flax_key_tuple, flax_tensor in flax_state_dict.items():
              a__						:	Union[str, Any] =				flax_key_tuple.split('.'				)

              if flax_key_tuple_array[-1] == "kernel" and flax_tensor.ndim == 4:
                     a__						:	Any =				flax_key_tuple_array[:-1] + ['weight']
                     a__						:	Union[str, Any] =				jnp.transpose(A__		,      (3, 2, 0, 1)				)
              elif flax_key_tuple_array[-1] == "kernel":
                     a__						:	List[str] =				flax_key_tuple_array[:-1] + ['weight']
                     a__						:	int =				flax_tensor.T
              elif flax_key_tuple_array[-1] == "scale":
                     a__						:	Dict =				flax_key_tuple_array[:-1] + ['weight']

              if "time_embedding" not in flax_key_tuple_array:
                     for i, flax_key_tuple_string in enumerate(A__				):
                            a__						:	Union[str, Any] =				(
                                flax_key_tuple_string.replace('_0'		,      '.0'				)
                                .replace('_1'		,      '.1'				)
                                .replace('_2'		,      '.2'				)
                                .replace('_3'		,      '.3'				)
                                .replace('_4'		,      '.4'				)
                                .replace('_5'		,      '.5'				)
                                .replace('_6'		,      '.6'				)
                                .replace('_7'		,      '.7'				)
                                .replace('_8'		,      '.8'				)
                                .replace('_9'		,      '.9'				)
                            )

              a__						:	Optional[int] =				'.'.join(A__				)

              if flax_key in pt_model_dict:
                     if flax_tensor.shape != pt_model_dict[flax_key].shape:
                            raise ValueError(
                                F'Flax checkpoint seems to be incorrect. Weight {flax_key_tuple} was expected '
                                F'to be of shape {pt_model_dict[flax_key].shape}, but is {flax_tensor.shape}.'				)
                     else:
                            # add weight to pytorch dict
                            a__						:	Optional[int] =				np.asarray(A__				) if not isinstance(A__		,      np.ndarray				) else flax_tensor
                            a__						:	Dict =				torch.from_numpy(A__				)
                            # remove from missing keys
                            missing_keys.remove(A__				)
              else:
                     # weight is not expected by PyTorch model
                     unexpected_keys.append(A__				)

       pt_model.load_state_dict(A__				)

       # re-transform missing_keys to list
       a__						:	Optional[Any] =				list(A__				)

       if len(A__				) > 0:
              logger.warning(
                  'Some weights of the Flax model were not used when initializing the PyTorch model'
                  F' {pt_model.__class__.__name__}: {unexpected_keys}\n- This IS expected if you are initializing'
                  F' {pt_model.__class__.__name__} from a Flax model trained on another task or with another architecture'
                  ' (e.g. initializing a BertForSequenceClassification model from a FlaxBertForPreTraining model).\n- This'
                  F' IS NOT expected if you are initializing {pt_model.__class__.__name__} from a Flax model that you expect'
                  ' to be exactly identical (e.g. initializing a BertForSequenceClassification model from a'
                  ' FlaxBertForSequenceClassification model).'				)
       if len(A__				) > 0:
              logger.warning(
                  F'Some weights of {pt_model.__class__.__name__} were not initialized from the Flax model and are newly'
                  F' initialized: {missing_keys}\nYou should probably TRAIN this model on a down-stream task to be able to'
                  ' use it for predictions and inference.'				)

       return pt_model









import inspect
import unittest

from transformers import ViTMSNConfig
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 ViTMSNForImageClassification, ViTMSNModel
    from transformers.models.vit_msn.modeling_vit_msn import VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
    from PIL import Image

    from transformers import ViTImageProcessor





class  A__    :




   """simple docstring"""



   def __init__(					self					,  lowercase					,  lowercase=13					,  lowercase=30					,  lowercase=2					,  lowercase=3					,  lowercase=True					,  lowercase=True					,  lowercase=32					,  lowercase=5					,  lowercase=4					,  lowercase=37					,  lowercase="gelu"					,  lowercase=0.1					,  lowercase=0.1					,  lowercase=10					,  lowercase=0.02					,  lowercase=None					,  )			->						Union[str, Any]:







          '''simple docstring'''

          a__						:	Optional[int] =				parent
          a__						:	List[str] =				batch_size
          a__						:	List[str] =				image_size
          a__						:	Dict =				patch_size
          a__						:	Optional[Any] =				num_channels
          a__						:	List[Any] =				is_training
          a__						:	str =				use_labels
          a__						:	Dict =				hidden_size
          a__						:	Tuple =				num_hidden_layers
          a__						:	Tuple =				num_attention_heads
          a__						:	Union[str, Any] =				intermediate_size
          a__						:	List[str] =				hidden_act
          a__						:	List[str] =				hidden_dropout_prob
          a__						:	Any =				attention_probs_dropout_prob
          a__						:	Dict =				type_sequence_label_size
          a__						:	Tuple =				initializer_range
          a__						:	Optional[int] =				scope

          # in ViT MSN, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
          a__						:	List[str] =				(image_size // patch_size) ** 2
          a__						:	Any =				num_patches + 1

   def 							__lowercase		(					self)			->						int:







          '''simple docstring'''

          a__						:	Tuple =				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

          a__						:	Tuple =				None
          if self.use_labels:
                 a__						:	List[Any] =				ids_tensor([self.batch_size]					,  self.type_sequence_label_size)

          a__						:	List[str] =				self.get_config()

          return config, pixel_values, labels

   def 							__lowercase		(					self)			->						Optional[int]:







          '''simple docstring'''

          return ViTMSNConfig(
              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					,  initializer_range=self.initializer_range					,  )

   def 							__lowercase		(					self					,  lowercase					,  lowercase					,  lowercase)			->						List[str]:







          '''simple docstring'''

          a__						:	int =				ViTMSNModel(config=lowercase)
          model.to(lowercase)
          model.eval()
          a__						:	int =				model(lowercase)
          self.parent.assertEqual(result.last_hidden_state.shape					,  (self.batch_size, self.seq_length, self.hidden_size))

   def 							__lowercase		(					self					,  lowercase					,  lowercase					,  lowercase)			->						Tuple:







          '''simple docstring'''

          a__						:	Optional[Any] =				self.type_sequence_label_size
          a__						:	List[str] =				ViTMSNForImageClassification(lowercase)
          model.to(lowercase)
          model.eval()
          a__						:	int =				model(lowercase					,  labels=lowercase)
          print('Pixel and labels shape: {pixel_values.shape}, {labels.shape}')
          print('Labels: {labels}')
          self.parent.assertEqual(result.logits.shape					,  (self.batch_size, self.type_sequence_label_size))

          # test greyscale images
          a__						:	List[str] =				1
          a__						:	Optional[int] =				ViTMSNForImageClassification(lowercase)
          model.to(lowercase)
          model.eval()

          a__						:	Dict =				floats_tensor([self.batch_size, 1, self.image_size, self.image_size])
          a__						:	Dict =				model(lowercase)
          self.parent.assertEqual(result.logits.shape					,  (self.batch_size, self.type_sequence_label_size))

   def 							__lowercase		(					self)			->						Tuple:







          '''simple docstring'''

          a__						:	List[Any] =				self.prepare_config_and_inputs()
          a__ ,							a__ ,							a__						:	Optional[Any] =				config_and_inputs
          a__						:	Dict =				{'pixel_values': pixel_values}
          return config, inputs_dict





@require_torch
class  A__    (							__UpperCAmelCase ,  __UpperCAmelCase ,  unittest.TestCase	):




   """simple docstring"""



   __A   :				Any         =  (ViTMSNModel, ViTMSNForImageClassification) if is_torch_available() else ()
   __A   :				Tuple         =  (
       {'''feature-extraction''': ViTMSNModel, '''image-classification''': ViTMSNForImageClassification}
       if is_torch_available()
       else {}
   )

   __A   :				List[str]         =  False
   __A   :				Optional[Any]         =  False
   __A   :				Union[str, Any]         =  False
   __A   :				Any         =  False

   def 							__lowercase		(					self)			->						List[str]:







          '''simple docstring'''

          a__						:	Optional[int] =				ViTMSNModelTester(self)
          a__						:	Union[str, Any] =				ConfigTester(self					,  config_class=lowercase					,  has_text_modality=lowercase					,  hidden_size=37)

   def 							__lowercase		(					self)			->						List[Any]:







          '''simple docstring'''

          self.config_tester.run_common_tests()

   @unittest.skip(reason='ViTMSN does not use inputs_embeds')
   def 							__lowercase		(					self)			->						Any:







          '''simple docstring'''

          pass

   def 							__lowercase		(					self)			->						Tuple:







          '''simple docstring'''

          a__ ,							a__						:	Tuple =				self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
                 a__						:	Union[str, Any] =				model_class(lowercase)
                 self.assertIsInstance(model.get_input_embeddings()					,  (nn.Module))
                 a__						:	str =				model.get_output_embeddings()
                 self.assertTrue(x is None or isinstance(lowercase					,  nn.Linear))

   def 							__lowercase		(					self)			->						Tuple:







          '''simple docstring'''

          a__ ,							a__						:	Optional[int] =				self.model_tester.prepare_config_and_inputs_for_common()

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

                 a__						:	Optional[Any] =				['pixel_values']
                 self.assertListEqual(arg_names[:1]					,  lowercase)

   def 							__lowercase		(					self)			->						str:







          '''simple docstring'''

          a__						:	List[str] =				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*lowercase)

   def 							__lowercase		(					self)			->						str:







          '''simple docstring'''

          a__						:	Any =				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_image_classification(*lowercase)

   @slow
   def 							__lowercase		(					self)			->						Union[str, Any]:







          '''simple docstring'''

          for model_name in VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                 a__						:	Tuple =				ViTMSNModel.from_pretrained(lowercase)
                 self.assertIsNotNone(lowercase)



def A_	(		)  ->		Dict:
       a__						:	List[str] =				Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'				)
       return image





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




   """simple docstring"""



   @cached_property
   def 							__lowercase		(					self)			->						List[Any]:







          '''simple docstring'''

          return ViTImageProcessor.from_pretrained('facebook/vit-msn-small') if is_vision_available() else None

   @slow
   def 							__lowercase		(					self)			->						List[Any]:







          '''simple docstring'''

          torch.manual_seed(2)
          a__						:	List[str] =				ViTMSNForImageClassification.from_pretrained('facebook/vit-msn-small').to(lowercase)

          a__						:	Any =				self.default_image_processor
          a__						:	List[Any] =				prepare_img()
          a__						:	Optional[Any] =				image_processor(images=lowercase					,  return_tensors='pt').to(lowercase)

          # forward pass
          with torch.no_grad():
                 a__						:	Tuple =				model(**lowercase)

          # verify the logits
          a__						:	Union[str, Any] =				torch.Size((1, 1000))
          self.assertEqual(outputs.logits.shape					,  lowercase)

          a__						:	Any =				torch.tensor([-0.08_03, -0.44_54, -0.23_75]).to(lowercase)

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


