


'''simple docstring'''






import argparse
import os

import torch

from transformers.utils import WEIGHTS_NAME


__UpperCAmelCase       							=				['''small''', '''medium''', '''large''']

__UpperCAmelCase       							=				'''lm_head.decoder.weight'''
__UpperCAmelCase       							=				'''lm_head.weight'''


def    _snake_case  (	A					,       A			)		->       Any:
							lowerCAmelCase__			  =      torch.load(A			)
							lowerCAmelCase__			  =      d.pop(A			)
							os.makedirs(A					,       exist_ok=A			)
							torch.save(A					,       os.path.join(A					,       A			)			)


if __name__ == "__main__":
		__UpperCAmelCase       							=				argparse.ArgumentParser()
		parser.add_argument('''--dialogpt_path''', default='''.''', type=str)
		__UpperCAmelCase       							=				parser.parse_args()
		for MODEL in DIALOGPT_MODELS:
				__UpperCAmelCase       							=				os.path.join(args.dialogpt_path, f"""{MODEL}_ft.pkl""")
				__UpperCAmelCase       							=				f"""./DialoGPT-{MODEL}"""
				convert_dialogpt_checkpoint(
				    checkpoint_path,
				    pytorch_dump_folder_path,
				)






'''simple docstring'''

import inspect
import unittest

from transformers import RegNetConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

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 RegNetForImageClassification, RegNetModel
    from transformers.models.regnet.modeling_regnet import REGNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
    from PIL import Image

    from transformers import AutoImageProcessor







class __a			:





   def __init__(       self					:      Any					,lowerCamelCase					:      Any					,lowerCamelCase					:      int=3					,lowerCamelCase					:      Optional[int]=32					,lowerCamelCase					:      Any=3					,lowerCamelCase					:      Dict=10					,lowerCamelCase					:      Union[str, Any]=[10, 20, 30, 40]					,lowerCamelCase					:      Any=[1, 1, 2, 1]					,lowerCamelCase					:      int=True					,lowerCamelCase					:      Optional[Any]=True					,lowerCamelCase					:      Dict="relu"					,lowerCamelCase					:      str=3					,lowerCamelCase					:      Union[str, Any]=None					,):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							parent
      __SCREAMING_SNAKE_CASE   				=							batch_size
      __SCREAMING_SNAKE_CASE   				=							image_size
      __SCREAMING_SNAKE_CASE   				=							num_channels
      __SCREAMING_SNAKE_CASE   				=							embeddings_size
      __SCREAMING_SNAKE_CASE   				=							hidden_sizes
      __SCREAMING_SNAKE_CASE   				=							depths
      __SCREAMING_SNAKE_CASE   				=							is_training
      __SCREAMING_SNAKE_CASE   				=							use_labels
      __SCREAMING_SNAKE_CASE   				=							hidden_act
      __SCREAMING_SNAKE_CASE   				=							num_labels
      __SCREAMING_SNAKE_CASE   				=							scope
      __SCREAMING_SNAKE_CASE   				=							len(lowerCamelCase     )





   def   UpperCAmelCase__  (       self					:      Union[str, Any]     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]     )

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

      __SCREAMING_SNAKE_CASE   				=							self.get_config()

      return config, pixel_values, labels





   def   UpperCAmelCase__  (       self					:      Any     ):

      '''simple docstring'''

      return RegNetConfig(
          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					,)





   def   UpperCAmelCase__  (       self					:      Tuple					,lowerCamelCase					:      int					,lowerCamelCase					:      List[str]					,lowerCamelCase					:      Optional[int]     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							RegNetModel(config=lowerCamelCase     )
      model.to(lowerCamelCase     )
      model.eval()
      __SCREAMING_SNAKE_CASE   				=							model(lowerCamelCase     )
      # expected last hidden states: B, C, H // 32, W // 32
      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					:      List[Any]					,lowerCamelCase					:      int					,lowerCamelCase					:      Dict					,lowerCamelCase					:      Any     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							self.num_labels
      __SCREAMING_SNAKE_CASE   				=							RegNetForImageClassification(lowerCamelCase     )
      model.to(lowerCamelCase     )
      model.eval()
      __SCREAMING_SNAKE_CASE   				=							model(lowerCamelCase					,labels=lowerCamelCase     )
      self.parent.assertEqual(result.logits.shape					,(self.batch_size, self.num_labels)     )





   def   UpperCAmelCase__  (       self					:      Dict     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							self.prepare_config_and_inputs()
      __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE   				=							config_and_inputs
      __SCREAMING_SNAKE_CASE   				=							{"""pixel_values""": pixel_values}
      return config, inputs_dict







@require_torch
class __a			( _snake_case, _snake_case, unittest.TestCase      ):
   __UpperCamelCase :			List[str]						   =						(RegNetModel, RegNetForImageClassification) if is_torch_available() else ()
   __UpperCamelCase :			Optional[int]						   =						(
       {'feature-extraction': RegNetModel, 'image-classification': RegNetForImageClassification}
       if is_torch_available()
       else {}
   )

   __UpperCamelCase :			Dict						   =						False
   __UpperCamelCase :			str						   =						False
   __UpperCamelCase :			Any						   =						False
   __UpperCamelCase :			Tuple						   =						False





   def   UpperCAmelCase__  (       self					:      List[str]     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							RegNetModelTester(self     )
      __SCREAMING_SNAKE_CASE   				=							ConfigTester(self					,config_class=lowerCamelCase					,has_text_modality=lowerCamelCase     )





   def   UpperCAmelCase__  (       self					:      Optional[int]     ):

      '''simple docstring'''

      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     ):

      '''simple docstring'''

      return





   @unittest.skip(reason="""RegNet does not use inputs_embeds"""     )
   def   UpperCAmelCase__  (       self					:      str     ):

      '''simple docstring'''

      pass





   @unittest.skip(reason="""RegNet does not support input and output embeddings"""     )
   def   UpperCAmelCase__  (       self					:      Tuple     ):

      '''simple docstring'''

      pass





   def   UpperCAmelCase__  (       self					:      Tuple     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE   				=							self.model_tester.prepare_config_and_inputs_for_common()

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

         __SCREAMING_SNAKE_CASE   				=							["""pixel_values"""]
         self.assertListEqual(arg_names[:1]					,lowerCamelCase     )





   def   UpperCAmelCase__  (       self					:      Any     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*lowerCamelCase     )





   def   UpperCAmelCase__  (       self					:      int     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE   				=							self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
         __SCREAMING_SNAKE_CASE   				=							model_class(config=lowerCamelCase     )
         for name, module in model.named_modules():
            if isinstance(lowerCamelCase					,(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     ):

      '''simple docstring'''

      def check_hidden_states_output(lowerCamelCase					:      Union[str, Any]					,lowerCamelCase					:      Tuple					,lowerCamelCase					:      Optional[int]     ):
         __SCREAMING_SNAKE_CASE   				=							model_class(lowerCamelCase     )
         model.to(lowerCamelCase     )
         model.eval()

         with torch.no_grad():
            __SCREAMING_SNAKE_CASE   				=							model(**self._prepare_for_class(lowerCamelCase					,lowerCamelCase     )     )

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

         __SCREAMING_SNAKE_CASE   				=							self.model_tester.num_stages
         self.assertEqual(len(lowerCamelCase     )					,expected_num_stages + 1     )

         # RegNet'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 // 2, self.model_tester.image_size // 2]					,)

      __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE   				=							self.model_tester.prepare_config_and_inputs_for_common()
      __SCREAMING_SNAKE_CASE   				=							["""basic""", """bottleneck"""]
      for model_class in self.all_model_classes:
         for layer_type in layers_type:
            __SCREAMING_SNAKE_CASE   				=							layer_type
            __SCREAMING_SNAKE_CASE   				=							True
            check_hidden_states_output(lowerCamelCase					,lowerCamelCase					,lowerCamelCase     )

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

            check_hidden_states_output(lowerCamelCase					,lowerCamelCase					,lowerCamelCase     )





   def   UpperCAmelCase__  (       self					:      Dict     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_image_classification(*lowerCamelCase     )





   @slow
   def   UpperCAmelCase__  (       self					:      Union[str, Any]     ):

      '''simple docstring'''

      for model_name in REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         __SCREAMING_SNAKE_CASE   				=							RegNetModel.from_pretrained(lowerCamelCase     )
         self.assertIsNotNone(lowerCamelCase     )





def     __magic_name__							(				)    ->       List[Any]:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE   				=							Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""   )
   return image







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





   @cached_property
   def   UpperCAmelCase__  (       self					:      List[str]     ):

      '''simple docstring'''

      return (
          AutoImageProcessor.from_pretrained(REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]     )
          if is_vision_available()
          else None
      )





   @slow
   def   UpperCAmelCase__  (       self					:      int     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							RegNetForImageClassification.from_pretrained(REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]     ).to(lowerCamelCase     )

      __SCREAMING_SNAKE_CASE   				=							self.default_image_processor
      __SCREAMING_SNAKE_CASE   				=							prepare_img()
      __SCREAMING_SNAKE_CASE   				=							image_processor(images=lowerCamelCase					,return_tensors="""pt"""     ).to(lowerCamelCase     )

      # forward pass
      with torch.no_grad():
         __SCREAMING_SNAKE_CASE   				=							model(**lowerCamelCase     )

      # verify the logits
      __SCREAMING_SNAKE_CASE   				=							torch.Size((1, 1000)     )
      self.assertEqual(outputs.logits.shape					,lowerCamelCase     )

      __SCREAMING_SNAKE_CASE   				=							torch.tensor([-0.4_180, -1.5_051, -3.4_836]     ).to(lowerCamelCase     )

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





