






import inspect
import unittest

from transformers import ResNetConfig
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 ResNetBackbone, ResNetForImageClassification, ResNetModel
   from transformers.models.resnet.modeling_resnet import RESNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
   from PIL import Image

   from transformers import AutoImageProcessor

class 					snake_case_							:







   """simple docstring"""



   def __init__(    self:	Optional[Any]   ,       snake_case__:	int   ,       snake_case__:	List[str]=3   ,       snake_case__:	Dict=32   ,       snake_case__:	Dict=3   ,       snake_case__:	Optional[Any]=10   ,       snake_case__:	List[str]=[10, 20, 30, 40]   ,       snake_case__:	int=[1, 1, 2, 1]   ,       snake_case__:	Optional[int]=True   ,       snake_case__:	Optional[Any]=True   ,       snake_case__:	Dict="relu"   ,       snake_case__:	List[Any]=3   ,       snake_case__:	Any=None   ,       snake_case__:	Optional[Any]=["stage2", "stage3", "stage4"]   ,       snake_case__:	int=[2, 3, 4]   ,       )  ->		int:


       """simple docstring"""



       _lowercase     =	parent
       _lowercase     =	batch_size
       _lowercase     =	image_size
       _lowercase     =	num_channels
       _lowercase     =	embeddings_size
       _lowercase     =	hidden_sizes
       _lowercase     =	depths
       _lowercase     =	is_training
       _lowercase     =	use_labels
       _lowercase     =	hidden_act
       _lowercase     =	num_labels
       _lowercase     =	scope
       _lowercase     =	len(snake_case__	)
       _lowercase     =	out_features
       _lowercase     =	out_indices

   def 			SCREAMING_SNAKE_CASE__				(    self:	str	)  ->		Any:


       """simple docstring"""



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

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

       _lowercase     =	self.get_config()

       return config, pixel_values, labels

   def 			SCREAMING_SNAKE_CASE__				(    self:	Tuple	)  ->		Tuple:


       """simple docstring"""



       return ResNetConfig(
           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   ,       )

   def 			SCREAMING_SNAKE_CASE__				(    self:	Tuple   ,       snake_case__:	int   ,       snake_case__:	List[str]   ,       snake_case__:	Any	)  ->		Optional[int]:


       """simple docstring"""



       _lowercase     =	ResNetModel(config=snake_case__	)
       model.to(snake_case__	)
       model.eval()
       _lowercase     =	model(snake_case__	)
       # 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 			SCREAMING_SNAKE_CASE__				(    self:	Union[str, Any]   ,       snake_case__:	str   ,       snake_case__:	List[str]   ,       snake_case__:	Any	)  ->		str:


       """simple docstring"""



       _lowercase     =	self.num_labels
       _lowercase     =	ResNetForImageClassification(snake_case__	)
       model.to(snake_case__	)
       model.eval()
       _lowercase     =	model(snake_case__   ,       labels=snake_case__	)
       self.parent.assertEqual(result.logits.shape   ,       (self.batch_size, self.num_labels)	)

   def 			SCREAMING_SNAKE_CASE__				(    self:	str   ,       snake_case__:	str   ,       snake_case__:	str   ,       snake_case__:	Tuple	)  ->		List[Any]:


       """simple docstring"""



       _lowercase     =	ResNetBackbone(config=snake_case__	)
       model.to(snake_case__	)
       model.eval()
       _lowercase     =	model(snake_case__	)

       # 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
       _lowercase     =	None
       _lowercase     =	ResNetBackbone(config=snake_case__	)
       model.to(snake_case__	)
       model.eval()
       _lowercase     =	model(snake_case__	)

       # 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 			SCREAMING_SNAKE_CASE__				(    self:	Any	)  ->		Any:


       """simple docstring"""



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






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







   """simple docstring"""



   __lowerCAmelCase      =						(
       (
           ResNetModel,
           ResNetForImageClassification,
           ResNetBackbone,
       )
       if is_torch_available()
       else ()
   )
   __lowerCAmelCase      =						(
       {'''feature-extraction''': ResNetModel, '''image-classification''': ResNetForImageClassification}
       if is_torch_available()
       else {}
   )

   __lowerCAmelCase      =						True
   __lowerCAmelCase      =						False
   __lowerCAmelCase      =						False
   __lowerCAmelCase      =						False
   __lowerCAmelCase      =						False

   def 			SCREAMING_SNAKE_CASE__				(    self:	Optional[int]	)  ->		Optional[Any]:


       """simple docstring"""



       _lowercase     =	ResNetModelTester(self	)
       _lowercase     =	ConfigTester(self   ,       config_class=snake_case__   ,       has_text_modality=snake_case__	)

   def 			SCREAMING_SNAKE_CASE__				(    self:	Dict	)  ->		List[Any]:


       """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 			SCREAMING_SNAKE_CASE__				(    self:	int	)  ->		Tuple:


       """simple docstring"""



       return

   @unittest.skip(reason='ResNet does not use inputs_embeds'	)
   def 			SCREAMING_SNAKE_CASE__				(    self:	Tuple	)  ->		Tuple:


       """simple docstring"""



       pass

   @unittest.skip(reason='ResNet does not support input and output embeddings'	)
   def 			SCREAMING_SNAKE_CASE__				(    self:	int	)  ->		List[str]:


       """simple docstring"""



       pass

   def 			SCREAMING_SNAKE_CASE__				(    self:	Union[str, Any]	)  ->		Any:


       """simple docstring"""



       _lowercase     =	self.model_tester.prepare_config_and_inputs_for_common()

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

           _lowercase     =	["pixel_values"]
           self.assertListEqual(arg_names[:1]   ,       snake_case__	)

   def 			SCREAMING_SNAKE_CASE__				(    self:	List[str]	)  ->		Dict:


       """simple docstring"""



       _lowercase     =	self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*snake_case__	)

   def 			SCREAMING_SNAKE_CASE__				(    self:	List[Any]	)  ->		List[str]:


       """simple docstring"""



       _lowercase     =	self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_backbone(*snake_case__	)

   def 			SCREAMING_SNAKE_CASE__				(    self:	Any	)  ->		str:


       """simple docstring"""



       _lowercase     =	self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
           _lowercase     =	model_class(config=snake_case__	)
           for name, module in model.named_modules():
               if isinstance(snake_case__   ,       (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 			SCREAMING_SNAKE_CASE__				(    self:	int	)  ->		List[str]:


       """simple docstring"""



       def check_hidden_states_output(snake_case__:	List[Any]   ,       snake_case__:	Optional[int]   ,       snake_case__:	Optional[int]	):
           _lowercase     =	model_class(snake_case__	)
           model.to(snake_case__	)
           model.eval()

           with torch.no_grad():
               _lowercase     =	model(**self._prepare_for_class(snake_case__   ,       snake_case__	)	)

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

           _lowercase     =	self.model_tester.num_stages
           self.assertEqual(len(snake_case__	)   ,       expected_num_stages + 1	)

           # ResNet'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]   ,       )

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

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

               check_hidden_states_output(snake_case__   ,       snake_case__   ,       snake_case__	)

   @unittest.skip(reason='ResNet does not use feedforward chunking'	)
   def 			SCREAMING_SNAKE_CASE__				(    self:	Optional[int]	)  ->		Optional[Any]:


       """simple docstring"""



       pass

   def 			SCREAMING_SNAKE_CASE__				(    self:	int	)  ->		Any:


       """simple docstring"""



       _lowercase     =	self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*snake_case__	)

   @slow
   def 			SCREAMING_SNAKE_CASE__				(    self:	Dict	)  ->		Optional[int]:


       """simple docstring"""



       for model_name in RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           _lowercase     =	ResNetModel.from_pretrained(snake_case__	)
           self.assertIsNotNone(snake_case__	)






def        UpperCAmelCase      (   ):


    '''simple docstring'''



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

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







   """simple docstring"""



   @cached_property
   def 			SCREAMING_SNAKE_CASE__				(    self:	Optional[Any]	)  ->		List[str]:


       """simple docstring"""



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

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


       """simple docstring"""



       _lowercase     =	ResNetForImageClassification.from_pretrained(RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]	).to(snake_case__	)

       _lowercase     =	self.default_image_processor
       _lowercase     =	prepare_img()
       _lowercase     =	image_processor(images=snake_case__   ,       return_tensors='pt'	).to(snake_case__	)

       # forward pass
       with torch.no_grad():
           _lowercase     =	model(**snake_case__	)

       # verify the logits
       _lowercase     =	torch.Size((1, 1_000)	)
       self.assertEqual(outputs.logits.shape   ,       snake_case__	)

       _lowercase     =	torch.tensor([-11.1_069, -9.78_77, -8.37_77]	).to(snake_case__	)

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






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







   """simple docstring"""



   __lowerCAmelCase      =						(ResNetBackbone,) if is_torch_available() else ()
   __lowerCAmelCase      =						False
   __lowerCAmelCase      =						ResNetConfig

   def 			SCREAMING_SNAKE_CASE__				(    self:	Optional[int]	)  ->		List[str]:


       """simple docstring"""



       _lowercase     =	ResNetModelTester(self	)








import argparse

import torch

from transformers import HubertConfig, HubertForSequenceClassification, WavaVecaFeatureExtractor, logging


logging.set_verbosity_info()
lowerCamelCase										=  logging.get_logger(__name__)

lowerCamelCase										=  ['''UtteranceLevel''']
@torch.no_grad()
def        UpperCAmelCase      (   _lowercase,      _lowercase,      _lowercase,      _lowercase      ):


    '''simple docstring'''



    _lowercase     =	torch.load(_lowercase,      map_location='cpu'      )
    if checkpoint["Config"]["downstream_expert"]["modelrc"]["select"] not in SUPPORTED_MODELS:
        raise NotImplementedError(f'''The supported s3prl models are {SUPPORTED_MODELS}'''      )

    _lowercase     =	checkpoint['Downstream']

    _lowercase     =	HubertConfig.from_pretrained(_lowercase      )
    _lowercase     =	HubertForSequenceClassification.from_pretrained(_lowercase,      config=_lowercase      )
    _lowercase     =	WavaVecaFeatureExtractor.from_pretrained(
        _lowercase,      return_attention_mask=_lowercase,      do_normalize=_lowercase      )

    if hf_congfig.use_weighted_layer_sum:
        _lowercase     =	checkpoint['Featurizer']['weights']

    _lowercase     =	downstream_dict['projector.weight']
    _lowercase     =	downstream_dict['projector.bias']
    _lowercase     =	downstream_dict['model.post_net.linear.weight']
    _lowercase     =	downstream_dict['model.post_net.linear.bias']

    hf_feature_extractor.save_pretrained(_lowercase      )
    hf_model.save_pretrained(_lowercase      )


if __name__ == "__main__":
   lowerCamelCase										=  argparse.ArgumentParser()
   parser.add_argument(
       '''--base_model_name''', default=None, type=str, help='''Name of the huggingface pretrained base model.'''
   )
   parser.add_argument('''--config_path''', default=None, type=str, help='''Path to the huggingface classifier config.''')
   parser.add_argument('''--checkpoint_path''', default=None, type=str, help='''Path to the s3prl checkpoint.''')
   parser.add_argument('''--model_dump_path''', default=None, type=str, help='''Path to the final converted model.''')
   lowerCamelCase										=  parser.parse_args()
   convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)
