


"""simple docstring"""






import inspect
import unittest

from transformers import BitConfig
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_backbone_common import BackboneTesterMixin
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 BitBackbone, BitForImageClassification, BitImageProcessor, BitModel
   from transformers.models.bit.modeling_bit import BIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
   from PIL import Image


class     __lowerCamelCase  :

     '''simple docstring'''


     def __init__(      self    ,				__UpperCAmelCase    ,				__UpperCAmelCase=3    ,				__UpperCAmelCase=32    ,				__UpperCAmelCase=3    ,				__UpperCAmelCase=10    ,				__UpperCAmelCase=[8, 16, 32, 64]    ,				__UpperCAmelCase=[1, 1, 2, 1]    ,				__UpperCAmelCase=True    ,				__UpperCAmelCase=True    ,				__UpperCAmelCase="relu"    ,				__UpperCAmelCase=3    ,				__UpperCAmelCase=None    ,				__UpperCAmelCase=["stage2", "stage3", "stage4"]    ,				__UpperCAmelCase=[2, 3, 4]    ,				__UpperCAmelCase=1    ,				)       ->							Dict:
          _a    	=				parent
          _a    	=				batch_size
          _a    	=				image_size
          _a    	=				num_channels
          _a    	=				embeddings_size
          _a    	=				hidden_sizes
          _a    	=				depths
          _a    	=				is_training
          _a    	=				use_labels
          _a    	=				hidden_act
          _a    	=				num_labels
          _a    	=				scope
          _a    	=				len(__UpperCAmelCase							)
          _a    	=				out_features
          _a    	=				out_indices
          _a    	=				num_groups


     def 							_UpperCAmelCase      (      self							)       ->							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.num_labels							)

          _a    	=				self.get_config()

          return config, pixel_values, labels


     def 							_UpperCAmelCase      (      self							)       ->							Optional[Any]:
          return BitConfig(
              num_channels=self.num_channels    ,				embeddings_size=self.embeddings_size    ,				hidden_sizes=self.hidden_sizes    ,				depths=self.depths    ,				hidden_act=self.hidden_act    ,				num_labels=self.num_labels    ,				out_features=self.out_features    ,				out_indices=self.out_indices    ,				num_groups=self.num_groups    ,				)


     def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase							)       ->							Optional[int]:
          _a    	=				BitModel(config=__UpperCAmelCase							)
          model.to(__UpperCAmelCase							)
          model.eval()
          _a    	=				model(__UpperCAmelCase							)
          self.parent.assertEqual(
              result.last_hidden_state.shape    ,				(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)    ,				)


     def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase							)       ->							Dict:
          _a    	=				self.num_labels
          _a    	=				BitForImageClassification(__UpperCAmelCase							)
          model.to(__UpperCAmelCase							)
          model.eval()
          _a    	=				model(__UpperCAmelCase    ,				labels=__UpperCAmelCase							)
          self.parent.assertEqual(result.logits.shape    ,				(self.batch_size, self.num_labels)							)


     def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase							)       ->							List[Any]:
          _a    	=				BitBackbone(config=__UpperCAmelCase							)
          model.to(__UpperCAmelCase							)
          model.eval()
          _a    	=				model(__UpperCAmelCase							)

          # verify feature maps
          self.parent.assertEqual(len(result.feature_maps							)    ,				len(config.out_features							)							)
          self.parent.assertListEqual(list(result.feature_maps[0].shape							)    ,				[self.batch_size, self.hidden_sizes[1], 4, 4]							)

          # verify channels
          self.parent.assertEqual(len(model.channels							)    ,				len(config.out_features							)							)
          self.parent.assertListEqual(model.channels    ,				config.hidden_sizes[1:]							)

          # verify backbone works with out_features=None
          _a    	=				None
          _a    	=				BitBackbone(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, self.hidden_sizes[-1], 1, 1]							)

          # verify channels
          self.parent.assertEqual(len(model.channels							)    ,				1							)
          self.parent.assertListEqual(model.channels    ,				[config.hidden_sizes[-1]]							)





     def 							_UpperCAmelCase      (      self							)       ->							int:
          _a    	=				self.prepare_config_and_inputs()
          _a	,      _a	,      _a    	=				config_and_inputs
          _a    	=				{'''pixel_values''': pixel_values}
          return config, inputs_dict







@require_torch
class     __lowerCamelCase  (					a__  ,					a__  ,					unittest.TestCase				):

     '''simple docstring'''
     A_       :	Union[str, Any]    =		(BitModel, BitForImageClassification, BitBackbone) if is_torch_available() else ()
     A_       :	str    =		(
         {'feature-extraction': BitModel, 'image-classification': BitForImageClassification}
         if is_torch_available()
         else {}
     )

     A_       :	Dict    =		False
     A_       :	Union[str, Any]    =		False
     A_       :	Optional[Any]    =		False
     A_       :	Tuple    =		False
     A_       :	List[Any]    =		False


     def 							_UpperCAmelCase      (      self							)       ->							int:
          _a    	=				BitModelTester(self							)
          _a    	=				ConfigTester(self    ,				config_class=__UpperCAmelCase    ,				has_text_modality=__UpperCAmelCase							)


     def 							_UpperCAmelCase      (      self							)       ->							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 							_UpperCAmelCase      (      self							)       ->							Any:
          return


     @unittest.skip(reason='''Bit does not output attentions'''							)
     def 							_UpperCAmelCase      (      self							)       ->							Any:
          pass


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


     @unittest.skip(reason='''Bit does not support input and output embeddings'''							)
     def 							_UpperCAmelCase      (      self							)       ->							int:
          pass


     def 							_UpperCAmelCase      (      self							)       ->							List[str]:
          _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							)       ->							Optional[int]:
          _a    	=				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*__UpperCAmelCase							)


     def 							_UpperCAmelCase      (      self							)       ->							Tuple:
          _a    	=				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_backbone(*__UpperCAmelCase							)


     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(config=__UpperCAmelCase							)
               for name, module in model.named_modules():
                    if isinstance(__UpperCAmelCase    ,				(nn.BatchNormad, nn.GroupNorm)							):
                         self.assertTrue(
                             torch.all(module.weight == 1							)    ,				msg=F'Parameter {name} of model {model_class} seems not properly initialized'    ,				)
                         self.assertTrue(
                             torch.all(module.bias == 0							)    ,				msg=F'Parameter {name} of model {model_class} seems not properly initialized'    ,				)


     def 							_UpperCAmelCase      (      self							)       ->							Tuple:
          def check_hidden_states_output(__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase							):
               _a    	=				model_class(__UpperCAmelCase							)
               model.to(__UpperCAmelCase							)
               model.eval()

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

               _a    	=				outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

               _a    	=				self.model_tester.num_stages
               self.assertEqual(len(__UpperCAmelCase							)    ,				expected_num_stages + 1							)

               # Bit's feature maps are of shape (batch_size, num_channels, height, width)
               self.assertListEqual(
                   list(hidden_states[0].shape[-2:]							)    ,				[self.model_tester.image_size // 4, self.model_tester.image_size // 4]    ,				)

          _a	,      _a    	=				self.model_tester.prepare_config_and_inputs_for_common()
          _a    	=				['''preactivation''', '''bottleneck''']
          for model_class in self.all_model_classes:
               for layer_type in layers_type:
                    _a    	=				layer_type
                    _a    	=				True
                    check_hidden_states_output(__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase							)

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

                    check_hidden_states_output(__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase							)


     @unittest.skip(reason='''Bit does not use feedforward chunking'''							)
     def 							_UpperCAmelCase      (      self							)       ->							Optional[int]:
          pass


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





     @slow
     def 							_UpperCAmelCase      (      self							)       ->							List[Any]:
          for model_name in BIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               _a    	=				BitModel.from_pretrained(__UpperCAmelCase							)
               self.assertIsNotNone(__UpperCAmelCase							)







def       A_   (   ):






     """simple docstring"""

     _a    	=				Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''       )
     return image


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

     '''simple docstring'''


     @cached_property
     def 							_UpperCAmelCase      (      self							)       ->							Any:
          return (
              BitImageProcessor.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]							) if is_vision_available() else None
          )





     @slow
     def 							_UpperCAmelCase      (      self							)       ->							Optional[int]:
          _a    	=				BitForImageClassification.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]							).to(__UpperCAmelCase							)

          _a    	=				self.default_image_processor
          _a    	=				prepare_img()
          _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, 1000)							)
          self.assertEqual(outputs.logits.shape    ,				__UpperCAmelCase							)

          _a    	=				torch.tensor([[-0.6526, -0.5263, -1.4398]]							).to(__UpperCAmelCase							)

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







@require_torch
class     __lowerCamelCase  (					a__  ,					unittest.TestCase				):

     '''simple docstring'''
     A_       :	Dict    =		(BitBackbone,) if is_torch_available() else ()
     A_       :	Union[str, Any]    =		BitConfig

     A_       :	List[Any]    =		False


     def 							_UpperCAmelCase      (      self							)       ->							str:
          _a    	=				BitModelTester(self							)



"""simple docstring"""






from pathlib import Path

import fire
def       A_   (   _lowerCAmelCase     :					str,     _lowerCAmelCase     :					str,     _lowerCAmelCase     :					int       ):






     """simple docstring"""

     _a    	=				Path(_lowerCAmelCase       )
     _a    	=				Path(_lowerCAmelCase       )
     dest_dir.mkdir(exist_ok=_lowerCAmelCase       )
     for path in src_dir.iterdir():
          _a    	=				[x.rstrip() for x in list(path.open().readlines()       )][:n]
          _a    	=				dest_dir.joinpath(path.name       )
          print(_lowerCAmelCase       )
          dest_path.open('''w'''       ).write('''\n'''.join(_lowerCAmelCase       )       )


if __name__ == "__main__":
   fire.Fire(minify)