
"""simple docstring"""






import collections
import inspect
import unittest

from transformers import DinatConfig
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 DinatBackbone, DinatForImageClassification, DinatModel
      from transformers.models.dinat.modeling_dinat import DINAT_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
      from PIL import Image

      from transformers import AutoImageProcessor





class 							snake_case       :







   def __init__( self       ,							__UpperCAmelCase       ,							__UpperCAmelCase=13       ,							__UpperCAmelCase=64       ,							__UpperCAmelCase=4       ,							__UpperCAmelCase=3       ,							__UpperCAmelCase=16       ,							__UpperCAmelCase=[1, 2, 1]       ,							__UpperCAmelCase=[2, 4, 8]       ,							__UpperCAmelCase=3       ,							__UpperCAmelCase=[[3], [1, 2], [1]]       ,							__UpperCAmelCase=2.0       ,							__UpperCAmelCase=True       ,							__UpperCAmelCase=0.0       ,							__UpperCAmelCase=0.0       ,							__UpperCAmelCase=0.1       ,							__UpperCAmelCase="gelu"       ,							__UpperCAmelCase=True       ,							__UpperCAmelCase=0.02       ,							__UpperCAmelCase=1E-5       ,							__UpperCAmelCase=True       ,							__UpperCAmelCase=None       ,							__UpperCAmelCase=True       ,							__UpperCAmelCase=10       ,							__UpperCAmelCase=["stage1", "stage2"]       ,							__UpperCAmelCase=[1, 2]       ,							)   ->Tuple:
      a_						   =     parent
      a_						   =     batch_size
      a_						   =     image_size
      a_						   =     patch_size
      a_						   =     num_channels
      a_						   =     embed_dim
      a_						   =     depths
      a_						   =     num_heads
      a_						   =     kernel_size
      a_						   =     dilations
      a_						   =     mlp_ratio
      a_						   =     qkv_bias
      a_						   =     hidden_dropout_prob
      a_						   =     attention_probs_dropout_prob
      a_						   =     drop_path_rate
      a_						   =     hidden_act
      a_						   =     patch_norm
      a_						   =     layer_norm_eps
      a_						   =     initializer_range
      a_						   =     is_training
      a_						   =     scope
      a_						   =     use_labels
      a_						   =     num_labels
      a_						   =     out_features
      a_						   =     out_indices







   def    UpperCAmelCase__	( self)   ->str:
      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.num_labels)

      a_						   =     self.get_config()

      return config, pixel_values, labels







   def    UpperCAmelCase__	( self)   ->Dict:
      return DinatConfig(
          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       ,							dilations=self.dilations       ,							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    UpperCAmelCase__	( self       ,							__UpperCAmelCase       ,							__UpperCAmelCase       ,							__UpperCAmelCase)   ->Optional[Any]:
      a_						   =     DinatModel(config=__UpperCAmelCase)
      model.to(__UpperCAmelCase)
      model.eval()
      a_						   =     model(__UpperCAmelCase)

      a_						   =     a_						   =     (config.image_size // config.patch_size) // (2 ** (len(config.depths) - 1))
      a_						   =     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    UpperCAmelCase__	( self       ,							__UpperCAmelCase       ,							__UpperCAmelCase       ,							__UpperCAmelCase)   ->Optional[int]:
      a_						   =     DinatForImageClassification(__UpperCAmelCase)
      model.to(__UpperCAmelCase)
      model.eval()
      a_						   =     model(__UpperCAmelCase       ,							labels=__UpperCAmelCase)
      self.parent.assertEqual(result.logits.shape       ,							(self.batch_size, self.num_labels))

      # test greyscale images
      a_						   =     1
      a_						   =     DinatForImageClassification(__UpperCAmelCase)
      model.to(__UpperCAmelCase)
      model.eval()

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







   def    UpperCAmelCase__	( self       ,							__UpperCAmelCase       ,							__UpperCAmelCase       ,							__UpperCAmelCase)   ->int:
      a_						   =     DinatBackbone(config=__UpperCAmelCase)
      model.to(__UpperCAmelCase)
      model.eval()
      a_						   =     model(__UpperCAmelCase)

      # 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], 16, 16])

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

      # verify backbone works with out_features=None
      a_						   =     None
      a_						   =     DinatBackbone(config=__UpperCAmelCase)
      model.to(__UpperCAmelCase)
      model.eval()
      a_						   =     model(__UpperCAmelCase)

      # 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    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_natten
@require_torch
class 							snake_case       (     SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       unittest.TestCase		):
   a_					:		Union[str, Any]     					=     (
       (
           DinatModel,
           DinatForImageClassification,
           DinatBackbone,
       )
       if is_torch_available()
       else ()
   )
   a_					:		str     					=     (
       {"""feature-extraction""": DinatModel, """image-classification""": DinatForImageClassification}
       if is_torch_available()
       else {}
   )
   a_					:		Tuple     					=     False

   a_					:		int     					=     False
   a_					:		Optional[int]     					=     False
   a_					:		Optional[int]     					=     False
   a_					:		Optional[int]     					=     False







   def    UpperCAmelCase__	( self)   ->List[str]:
      a_						   =     DinatModelTester(self)
      a_						   =     ConfigTester(self       ,							config_class=__UpperCAmelCase       ,							embed_dim=37)







   def    UpperCAmelCase__	( self)   ->Tuple:
      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    UpperCAmelCase__	( self)   ->Dict:
      return







   def    UpperCAmelCase__	( self)   ->Any:
      a_						   =     self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*__UpperCAmelCase)







   def    UpperCAmelCase__	( self)   ->List[Any]:
      a_						   =     self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_image_classification(*__UpperCAmelCase)







   def    UpperCAmelCase__	( self)   ->Tuple:
      a_						   =     self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_backbone(*__UpperCAmelCase)







   @unittest.skip(reason="Dinat does not use inputs_embeds")
   def    UpperCAmelCase__	( self)   ->List[Any]:
      pass







   @unittest.skip(reason="Dinat does not use feedforward chunking")
   def    UpperCAmelCase__	( self)   ->Optional[int]:
      pass







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

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







   def    UpperCAmelCase__	( self)   ->Tuple:
      a_   ,			a_						   =     self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
         a_						   =     model_class(__UpperCAmelCase)
         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]       ,							__UpperCAmelCase)







   def    UpperCAmelCase__	( self)   ->int:
      self.skipTest("Dinat's attention operation is handled entirely by NATTEN.")







   def    UpperCAmelCase__	( self       ,							__UpperCAmelCase       ,							__UpperCAmelCase       ,							__UpperCAmelCase       ,							__UpperCAmelCase)   ->Optional[Any]:
      a_						   =     model_class(__UpperCAmelCase)
      model.to(__UpperCAmelCase)
      model.eval()

      with torch.no_grad():
         a_						   =     model(**self._prepare_for_class(__UpperCAmelCase       ,							__UpperCAmelCase))

      a_						   =     outputs.hidden_states

      a_						   =     getattr(
          self.model_tester       ,							"expected_num_hidden_layers"       ,							len(self.model_tester.depths) + 1)
      self.assertEqual(len(__UpperCAmelCase)       ,							__UpperCAmelCase)

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

      a_						   =     image_size[0] // patch_size[0]
      a_						   =     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__ != "DinatBackbone":
         a_						   =     outputs.reshaped_hidden_states
         self.assertEqual(len(__UpperCAmelCase)       ,							__UpperCAmelCase)

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







   def    UpperCAmelCase__	( self)   ->Dict:
      a_   ,			a_						   =     self.model_tester.prepare_config_and_inputs_for_common()

      a_						   =     (
          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:
         a_						   =     True
         self.check_hidden_states_output(__UpperCAmelCase       ,							__UpperCAmelCase       ,							__UpperCAmelCase       ,							__UpperCAmelCase)

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

         self.check_hidden_states_output(__UpperCAmelCase       ,							__UpperCAmelCase       ,							__UpperCAmelCase       ,							__UpperCAmelCase)







   @slow
   def    UpperCAmelCase__	( self)   ->List[str]:
      for model_name in DINAT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         a_						   =     DinatModel.from_pretrained(__UpperCAmelCase)
         self.assertIsNotNone(__UpperCAmelCase)







   def    UpperCAmelCase__	( self)   ->int:
      a_   ,			a_						   =     self.model_tester.prepare_config_and_inputs_for_common()

      a_						   =     _config_zero_init(__UpperCAmelCase)
      for model_class in self.all_model_classes:
         a_						   =     model_class(config=__UpperCAmelCase)
         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 							snake_case       (     unittest.TestCase		):







   @cached_property
   def    UpperCAmelCase__	( self)   ->Any:
      return AutoImageProcessor.from_pretrained("shi-labs/dinat-mini-in1k-224") if is_vision_available() else None







   @slow
   def    UpperCAmelCase__	( self)   ->str:
      a_						   =     DinatForImageClassification.from_pretrained("shi-labs/dinat-mini-in1k-224").to(__UpperCAmelCase)
      a_						   =     self.default_image_processor

      a_						   =     Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
      a_						   =     image_processor(images=__UpperCAmelCase       ,							return_tensors="pt").to(__UpperCAmelCase)

      # forward pass
      with torch.no_grad():
         a_						   =     model(**__UpperCAmelCase)

      # verify the logits
      a_						   =     torch.Size((1, 10_00))
      self.assertEqual(outputs.logits.shape       ,							__UpperCAmelCase)
      a_						   =     torch.tensor([-0.1_545, -0.7_667, 0.4_642]).to(__UpperCAmelCase)
      self.assertTrue(torch.allclose(outputs.logits[0, :3]       ,							__UpperCAmelCase       ,							atol=1E-4))






@require_torch
@require_natten
class 							snake_case       (     unittest.TestCase							,       SCREAMING_SNAKE_CASE_		):
   a_					:		Tuple     					=     (DinatBackbone,) if is_torch_available() else ()
   a_					:		int     					=     DinatConfig







   def    UpperCAmelCase__	( self)   ->Union[str, Any]:
      a_						   =     DinatModelTester(self)

"""simple docstring"""






import argparse

import torch

from transformers import RoFormerConfig, RoFormerForMaskedLM, load_tf_weights_in_roformer
from transformers.utils import logging


logging.set_verbosity_info()
def    UpperCamelCase						(					UpperCAmelCase ,   UpperCAmelCase ,   UpperCAmelCase     )						->Dict:

   """simple docstring"""



   a_						   =     RoFormerConfig.from_json_file(UpperCAmelCase     )
   print(F'''Building PyTorch model from configuration: {config}'''     )
   a_						   =     RoFormerForMaskedLM(UpperCAmelCase     )

   # Load weights from tf checkpoint
   load_tf_weights_in_roformer(UpperCAmelCase ,   UpperCAmelCase ,   UpperCAmelCase     )

   # Save pytorch-model
   print(F'''Save PyTorch model to {pytorch_dump_path}'''     )
   torch.save(model.state_dict() ,   UpperCAmelCase ,   _use_new_zipfile_serialization=UpperCAmelCase     )


if __name__ == "__main__":
      UpperCamelCase_						   =   argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          '--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
      )
      parser.add_argument(
          '--bert_config_file',
          default=None,
          type=str,
          required=True,
          help=(
              'The config json file corresponding to the pre-trained BERT model. \n'
              'This specifies the model architecture.'
          ),
      )
      parser.add_argument(
          '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
      )
      UpperCamelCase_						   =   parser.parse_args()
      convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)