




import collections
import inspect
import unittest

from transformers import NatConfig
from transformers.testing_utils import require_natten, require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
      import torch
      from torch import nn

      from transformers import NatBackbone, NatForImageClassification, NatModel
      from transformers.models.nat.modeling_nat import NAT_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
      from PIL import Image

      from transformers import AutoImageProcessor






class 	A	:
     """simple docstring"""

     def __init__(			self     :      str		,     A__     :      Tuple		,     A__     :      Optional[int]=1_3		,     A__     :      Optional[int]=6_4		,     A__     :      Optional[Any]=4		,     A__     :      Optional[Any]=3		,     A__     :      List[str]=1_6		,     A__     :      Any=[1, 2, 1]		,     A__     :      Union[str, Any]=[2, 4, 8]		,     A__     :      List[str]=3		,     A__     :      List[str]=2.0		,     A__     :      List[str]=True		,     A__     :      List[Any]=0.0		,     A__     :      Dict=0.0		,     A__     :      Any=0.1		,     A__     :      Dict="gelu"		,     A__     :      Optional[Any]=True		,     A__     :      Optional[int]=0.02		,     A__     :      str=1E-5		,     A__     :      Dict=True		,     A__     :      Union[str, Any]=None		,     A__     :      Tuple=True		,     A__     :      int=1_0		,     A__     :      str=["stage1", "stage2"]		,     A__     :      int=[1, 2]		,     ) ->				List[str]:
           lowerCamelCase__					     = parent
           lowerCamelCase__					     = batch_size
           lowerCamelCase__					     = image_size
           lowerCamelCase__					     = patch_size
           lowerCamelCase__					     = num_channels
           lowerCamelCase__					     = embed_dim
           lowerCamelCase__					     = depths
           lowerCamelCase__					     = num_heads
           lowerCamelCase__					     = kernel_size
           lowerCamelCase__					     = mlp_ratio
           lowerCamelCase__					     = qkv_bias
           lowerCamelCase__					     = hidden_dropout_prob
           lowerCamelCase__					     = attention_probs_dropout_prob
           lowerCamelCase__					     = drop_path_rate
           lowerCamelCase__					     = hidden_act
           lowerCamelCase__					     = patch_norm
           lowerCamelCase__					     = layer_norm_eps
           lowerCamelCase__					     = initializer_range
           lowerCamelCase__					     = is_training
           lowerCamelCase__					     = scope
           lowerCamelCase__					     = use_labels
           lowerCamelCase__					     = num_labels
           lowerCamelCase__					     = out_features
           lowerCamelCase__					     = out_indices




     def        __snake_case      (			self     :      List[Any]	) ->				Optional[int]:
           lowerCamelCase__					     = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]	)

           lowerCamelCase__					     = None
           if self.use_labels:
                 lowerCamelCase__					     = ids_tensor([self.batch_size]		,     self.num_labels	)

           lowerCamelCase__					     = self.get_config()

           return config, pixel_values, labels




     def        __snake_case      (			self     :      List[str]	) ->				Optional[Any]:
           return NatConfig(
               num_labels=self.num_labels		,     image_size=self.image_size		,     patch_size=self.patch_size		,     num_channels=self.num_channels		,     embed_dim=self.embed_dim		,     depths=self.depths		,     num_heads=self.num_heads		,     kernel_size=self.kernel_size		,     mlp_ratio=self.mlp_ratio		,     qkv_bias=self.qkv_bias		,     hidden_dropout_prob=self.hidden_dropout_prob		,     attention_probs_dropout_prob=self.attention_probs_dropout_prob		,     drop_path_rate=self.drop_path_rate		,     hidden_act=self.hidden_act		,     patch_norm=self.patch_norm		,     layer_norm_eps=self.layer_norm_eps		,     initializer_range=self.initializer_range		,     out_features=self.out_features		,     out_indices=self.out_indices		,     )




     def        __snake_case      (			self     :      Optional[int]		,     A__     :      Optional[Any]		,     A__     :      List[str]		,     A__     :      Any	) ->				str:
           lowerCamelCase__					     = NatModel(config=A__	)
           model.to(A__	)
           model.eval()
           lowerCamelCase__					     = model(A__	)

           lowerCamelCase__					     = lowerCamelCase__					     = (config.image_size // config.patch_size) // (2 ** (len(config.depths	) - 1))
           lowerCamelCase__					     = int(config.embed_dim * 2 ** (len(config.depths	) - 1)	)

           self.parent.assertEqual(
               result.last_hidden_state.shape		,     (self.batch_size, expected_height, expected_width, expected_dim)	)




     def        __snake_case      (			self     :      List[Any]		,     A__     :      Any		,     A__     :      List[Any]		,     A__     :      Optional[Any]	) ->				Optional[int]:
           lowerCamelCase__					     = NatForImageClassification(A__	)
           model.to(A__	)
           model.eval()
           lowerCamelCase__					     = model(A__		,     labels=A__	)
           self.parent.assertEqual(result.logits.shape		,     (self.batch_size, self.num_labels)	)

           # test greyscale images
           lowerCamelCase__					     = 1
           lowerCamelCase__					     = NatForImageClassification(A__	)
           model.to(A__	)
           model.eval()

           lowerCamelCase__					     = floats_tensor([self.batch_size, 1, self.image_size, self.image_size]	)
           lowerCamelCase__					     = model(A__	)
           self.parent.assertEqual(result.logits.shape		,     (self.batch_size, self.num_labels)	)




     def        __snake_case      (			self     :      List[Any]		,     A__     :      List[Any]		,     A__     :      Dict		,     A__     :      Tuple	) ->				List[str]:
           lowerCamelCase__					     = NatBackbone(config=A__	)
           model.to(A__	)
           model.eval()
           lowerCamelCase__					     = model(A__	)

           # verify hidden states
           self.parent.assertEqual(len(result.feature_maps	)		,     len(config.out_features	)	)
           self.parent.assertListEqual(list(result.feature_maps[0].shape	)		,     [self.batch_size, model.channels[0], 1_6, 1_6]	)

           # verify channels
           self.parent.assertEqual(len(model.channels	)		,     len(config.out_features	)	)

           # verify backbone works with out_features=None
           lowerCamelCase__					     = None
           lowerCamelCase__					     = NatBackbone(config=A__	)
           model.to(A__	)
           model.eval()
           lowerCamelCase__					     = model(A__	)

           # verify feature maps
           self.parent.assertEqual(len(result.feature_maps	)		,     1	)
           self.parent.assertListEqual(list(result.feature_maps[0].shape	)		,     [self.batch_size, model.channels[-1], 4, 4]	)

           # verify channels
           self.parent.assertEqual(len(model.channels	)		,     1	)




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






@require_natten
@require_torch
class 	A	(						_lowerCAmelCase	,  _lowerCAmelCase	,  unittest.TestCase						):
     """simple docstring"""

     _snake_case:		str   =(
         (
             NatModel,
             NatForImageClassification,
             NatBackbone,
         )
         if is_torch_available()
         else ()
     )
     _snake_case:		List[Any]   =(
         {"feature-extraction": NatModel, "image-classification": NatForImageClassification}
         if is_torch_available()
         else {}
     )
     _snake_case:		Union[str, Any]   =False

     _snake_case:		List[str]   =False
     _snake_case:		Tuple   =False
     _snake_case:		Optional[int]   =False
     _snake_case:		List[Any]   =False




     def        __snake_case      (			self     :      Optional[int]	) ->				Dict:
           lowerCamelCase__					     = NatModelTester(self	)
           lowerCamelCase__					     = ConfigTester(self		,     config_class=A__		,     embed_dim=3_7	)




     def        __snake_case      (			self     :      List[Any]	) ->				List[Any]:
           self.create_and_test_config_common_properties()
           self.config_tester.create_and_test_config_to_json_string()
           self.config_tester.create_and_test_config_to_json_file()
           self.config_tester.create_and_test_config_from_and_save_pretrained()
           self.config_tester.create_and_test_config_with_num_labels()
           self.config_tester.check_config_can_be_init_without_params()
           self.config_tester.check_config_arguments_init()




     def        __snake_case      (			self     :      Dict	) ->				Optional[int]:
           return




     def        __snake_case      (			self     :      Union[str, Any]	) ->				Dict:
           lowerCamelCase__					     = self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*A__	)




     def        __snake_case      (			self     :      Union[str, Any]	) ->				int:
           lowerCamelCase__					     = self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_image_classification(*A__	)




     def        __snake_case      (			self     :      Dict	) ->				Dict:
           lowerCamelCase__					     = self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_backbone(*A__	)




     @unittest.skip(reason='Nat does not use inputs_embeds'	)
     def        __snake_case      (			self     :      Tuple	) ->				Optional[Any]:
           pass




     @unittest.skip(reason='Nat does not use feedforward chunking'	)
     def        __snake_case      (			self     :      List[Any]	) ->				Union[str, Any]:
           pass




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

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




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

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

                 lowerCamelCase__					     = ['pixel_values']
                 self.assertListEqual(arg_names[:1]		,     A__	)




     def        __snake_case      (			self     :      Dict	) ->				str:
           self.skipTest('Nat\'s attention operation is handled entirely by NATTEN.'	)




     def        __snake_case      (			self     :      List[str]		,     A__     :      List[str]		,     A__     :      str		,     A__     :      str		,     A__     :      Optional[int]	) ->				int:
           lowerCamelCase__					     = model_class(A__	)
           model.to(A__	)
           model.eval()

           with torch.no_grad():
                 lowerCamelCase__					     = model(**self._prepare_for_class(A__		,     A__	)	)

           lowerCamelCase__					     = outputs.hidden_states

           lowerCamelCase__					     = getattr(
               self.model_tester		,     'expected_num_hidden_layers'		,     len(self.model_tester.depths	) + 1	)
           self.assertEqual(len(A__	)		,     A__	)

           # Nat has a different seq_length
           lowerCamelCase__					     = (
               config.patch_size
               if isinstance(config.patch_size		,     collections.abc.Iterable	)
               else (config.patch_size, config.patch_size)
           )

           lowerCamelCase__					     = image_size[0] // patch_size[0]
           lowerCamelCase__					     = image_size[1] // patch_size[1]

           self.assertListEqual(
               list(hidden_states[0].shape[-3:]	)		,     [height, width, self.model_tester.embed_dim]		,     )

           if model_class.__name__ != "NatBackbone":
                 lowerCamelCase__					     = outputs.reshaped_hidden_states
                 self.assertEqual(len(A__	)		,     A__	)

                 lowerCamelCase__     ,						lowerCamelCase__     ,						lowerCamelCase__     ,						lowerCamelCase__					     = reshaped_hidden_states[0].shape
                 lowerCamelCase__					     = (
                     reshaped_hidden_states[0].view(A__		,     A__		,     A__		,     A__	).permute(0		,     2		,     3		,     1	)
                 )
                 self.assertListEqual(
                     list(reshaped_hidden_states.shape[-3:]	)		,     [height, width, self.model_tester.embed_dim]		,     )




     def        __snake_case      (			self     :      str	) ->				str:
           lowerCamelCase__     ,						lowerCamelCase__					     = self.model_tester.prepare_config_and_inputs_for_common()

           lowerCamelCase__					     = (
               self.model_tester.image_size
               if isinstance(self.model_tester.image_size		,     collections.abc.Iterable	)
               else (self.model_tester.image_size, self.model_tester.image_size)
           )

           for model_class in self.all_model_classes:
                 lowerCamelCase__					     = True
                 self.check_hidden_states_output(A__		,     A__		,     A__		,     A__	)

                 # check that output_hidden_states also work using config
                 del inputs_dict["output_hidden_states"]
                 lowerCamelCase__					     = True

                 self.check_hidden_states_output(A__		,     A__		,     A__		,     A__	)




     @slow
     def        __snake_case      (			self     :      Any	) ->				int:
           for model_name in NAT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                 lowerCamelCase__					     = NatModel.from_pretrained(A__	)
                 self.assertIsNotNone(A__	)




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

           lowerCamelCase__					     = _config_zero_init(A__	)
           for model_class in self.all_model_classes:
                 lowerCamelCase__					     = model_class(config=A__	)
                 for name, param in model.named_parameters():
                       if "embeddings" not in name and param.requires_grad:
                             self.assertIn(
                                 ((param.data.mean() * 1E9).round() / 1E9).item()		,     [0.0, 1.0]		,     msg=F"""Parameter {name} of model {model_class} seems not properly initialized"""		,     )






@require_natten
@require_vision
@require_torch
class 	A	(						unittest.TestCase						):
     """simple docstring"""

     @cached_property
     def        __snake_case      (			self     :      Tuple	) ->				str:
           return AutoImageProcessor.from_pretrained('shi-labs/nat-mini-in1k-224'	) if is_vision_available() else None




     @slow
     def        __snake_case      (			self     :      Any	) ->				Optional[int]:
           lowerCamelCase__					     = NatForImageClassification.from_pretrained('shi-labs/nat-mini-in1k-224'	).to(A__	)
           lowerCamelCase__					     = self.default_image_processor

           lowerCamelCase__					     = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'	)
           lowerCamelCase__					     = image_processor(images=A__		,     return_tensors='pt'	).to(A__	)

           # forward pass
           with torch.no_grad():
                 lowerCamelCase__					     = model(**A__	)

           # verify the logits
           lowerCamelCase__					     = torch.Size((1, 1_0_0_0)	)
           self.assertEqual(outputs.logits.shape		,     A__	)
           lowerCamelCase__					     = torch.tensor([0.3805, -0.8676, -0.3912]	).to(A__	)
           self.assertTrue(torch.allclose(outputs.logits[0, :3]		,     A__		,     atol=1E-4	)	)






@require_torch
@require_natten
class 	A	(						unittest.TestCase	,  _lowerCAmelCase						):
     """simple docstring"""

     _snake_case:		Union[str, Any]   =(NatBackbone,) if is_torch_available() else ()
     _snake_case:		Dict   =NatConfig




     def        __snake_case      (			self     :      Tuple	) ->				List[Any]:
           lowerCamelCase__					     = NatModelTester(self	)






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_tokenizers_available


_snake_case				= {}

try:
  if not is_sentencepiece_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _snake_case				= ["""MBart50Tokenizer"""]

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _snake_case				= ["""MBart50TokenizerFast"""]


if TYPE_CHECKING:
  try:
    if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_mbartaa import MBartaaTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_mbartaa_fast import MBartaaTokenizerFast

else:
  import sys

  _snake_case				= _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)






