
'''simple docstring'''





import inspect
import unittest

from transformers import ResNetConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from transformers.utils import cached_property, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor


if is_flax_available():
     import jax
     import jax.numpy as jnp

     from transformers.models.resnet.modeling_flax_resnet import FlaxResNetForImageClassification, FlaxResNetModel

if is_vision_available():
     from PIL import Image

     from transformers import AutoImageProcessor



class 			lowerCAmelCase__ (							unittest.TestCase							):



  '''simple docstring'''






  def __init__(self	:			List[Any]						,			lowerCAmelCase_	:			Union[str, Any]						,			lowerCAmelCase_	:			int=3						,			lowerCAmelCase_	:			List[Any]=32						,			lowerCAmelCase_	:			str=3						,			lowerCAmelCase_	:			Dict=10						,			lowerCAmelCase_	:			Dict=[10, 20, 30, 40]						,			lowerCAmelCase_	:			Any=[1, 1, 2, 1]						,			lowerCAmelCase_	:			List[Any]=True						,			lowerCAmelCase_	:			str=True						,			lowerCAmelCase_	:			List[Any]="relu"						,			lowerCAmelCase_	:			List[str]=3						,			lowerCAmelCase_	:			List[Any]=None						,			) ->							Dict:
        snake_case_    							=				parent
        snake_case_    							=				batch_size
        snake_case_    							=				image_size
        snake_case_    							=				num_channels
        snake_case_    							=				embeddings_size
        snake_case_    							=				hidden_sizes
        snake_case_    							=				depths
        snake_case_    							=				is_training
        snake_case_    							=				use_labels
        snake_case_    							=				hidden_act
        snake_case_    							=				num_labels
        snake_case_    							=				scope
        snake_case_    							=				len(lowerCAmelCase_     )

  def 		_lowerCamelCase						(self	:			Optional[Any]     ) ->							int:
        snake_case_    							=				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]     )

        snake_case_    							=				self.get_config()

        return config, pixel_values

  def 		_lowerCamelCase						(self	:			List[str]     ) ->							Tuple:
        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						,			image_size=self.image_size						,			)

  def 		_lowerCamelCase						(self	:			int						,			lowerCAmelCase_	:			List[str]						,			lowerCAmelCase_	:			Dict     ) ->							Optional[int]:
        snake_case_    							=				FlaxResNetModel(config=lowerCAmelCase_     )
        snake_case_    							=				model(lowerCAmelCase_     )

        # Output shape (b, c, h, w)
        self.parent.assertEqual(
            result.last_hidden_state.shape						,			(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)						,			)

  def 		_lowerCamelCase						(self	:			str						,			lowerCAmelCase_	:			List[str]						,			lowerCAmelCase_	:			Any     ) ->							List[Any]:
        snake_case_    							=				self.num_labels
        snake_case_    							=				FlaxResNetForImageClassification(config=lowerCAmelCase_     )
        snake_case_    							=				model(lowerCAmelCase_     )
        self.parent.assertEqual(result.logits.shape						,			(self.batch_size, self.num_labels)     )


  def 		_lowerCamelCase						(self	:			int     ) ->							List[str]:
        snake_case_    							=				self.prepare_config_and_inputs()
        snake_case_							,						snake_case_    							=				config_and_inputs
        snake_case_    							=				{"pixel_values": pixel_values}
        return config, inputs_dict



@require_flax
class 			lowerCAmelCase__ (							UpperCamelCase       ,				unittest.TestCase							):



  '''simple docstring'''






  _UpperCamelCase							=			(FlaxResNetModel, FlaxResNetForImageClassification) if is_flax_available() else ()

  _UpperCamelCase							=			False
  _UpperCamelCase							=			False
  _UpperCamelCase							=			False

  def 		_lowerCamelCase						(self	:			Optional[Any]     ) ->							None:
        snake_case_    							=				FlaxResNetModelTester(self     )
        snake_case_    							=				ConfigTester(self						,			config_class=lowerCAmelCase_						,			has_text_modality=lowerCAmelCase_     )

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

  def 		_lowerCamelCase						(self	:			List[Any]     ) ->							Dict:
        snake_case_    							=				self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*lowerCAmelCase_     )

  def 		_lowerCamelCase						(self	:			List[Any]     ) ->							str:
        snake_case_    							=				self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase_     )

  @unittest.skip(reason="ResNet does not use inputs_embeds"     )
  def 		_lowerCamelCase						(self	:			int     ) ->							Optional[int]:
        pass

  @unittest.skip(reason="ResNet does not support input and output embeddings"     )
  def 		_lowerCamelCase						(self	:			str     ) ->							Any:
        pass

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

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

              snake_case_    							=				["pixel_values"]
              self.assertListEqual(arg_names[:1]						,			lowerCAmelCase_     )

  def 		_lowerCamelCase						(self	:			Dict     ) ->							List[Any]:
        def check_hidden_states_output(lowerCAmelCase_	:			List[str]						,			lowerCAmelCase_	:			Optional[Any]						,			lowerCAmelCase_	:			Optional[int]     ):
              snake_case_    							=				model_class(lowerCAmelCase_     )

              snake_case_    							=				model(**self._prepare_for_class(lowerCAmelCase_						,			lowerCAmelCase_     )     )

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

              snake_case_    							=				self.model_tester.num_stages
              self.assertEqual(len(lowerCAmelCase_     )						,			expected_num_stages + 1     )

  @unittest.skip(reason="ResNet does not use feedforward chunking"     )
  def 		_lowerCamelCase						(self	:			Tuple     ) ->							Tuple:
        pass


  def 		_lowerCamelCase						(self	:			Any     ) ->							Optional[int]:
        snake_case_							,						snake_case_    							=				self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
              with self.subTest(model_class.__name__     ):
                    snake_case_    							=				self._prepare_for_class(lowerCAmelCase_						,			lowerCAmelCase_     )
                    snake_case_    							=				model_class(lowerCAmelCase_     )

                    @jax.jit
                    def model_jitted(lowerCAmelCase_	:			int						,			**lowerCAmelCase_	:			Any     ):
                          return model(pixel_values=lowerCAmelCase_						,			**lowerCAmelCase_     )

                    with self.subTest("JIT Enabled"     ):
                          snake_case_    							=				model_jitted(**lowerCAmelCase_     ).to_tuple()

                    with self.subTest("JIT Disabled"     ):
                          with jax.disable_jit():
                                snake_case_    							=				model_jitted(**lowerCAmelCase_     ).to_tuple()

                    self.assertEqual(len(lowerCAmelCase_     )						,			len(lowerCAmelCase_     )     )
                    for jitted_output, output in zip(lowerCAmelCase_						,			lowerCAmelCase_     ):
                          self.assertEqual(jitted_output.shape						,			output.shape     )

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




@require_flax
class 			lowerCAmelCase__ (							unittest.TestCase							):



  '''simple docstring'''






  @cached_property
  def 		_lowerCamelCase						(self	:			int     ) ->							str:
        return AutoImageProcessor.from_pretrained("microsoft/resnet-50"     ) if is_vision_available() else None


  @slow
  def 		_lowerCamelCase						(self	:			List[Any]     ) ->							Union[str, Any]:
        snake_case_    							=				FlaxResNetForImageClassification.from_pretrained("microsoft/resnet-50"     )

        snake_case_    							=				self.default_image_processor
        snake_case_    							=				prepare_img()
        snake_case_    							=				image_processor(images=lowerCAmelCase_						,			return_tensors="np"     )

        snake_case_    							=				model(**lowerCAmelCase_     )

        # verify the logits
        snake_case_    							=				(1, 10_00)
        self.assertEqual(outputs.logits.shape						,			lowerCAmelCase_     )

        snake_case_    							=				jnp.array([-11.1_069, -9.7_877, -8.3_777]     )

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



'''simple docstring'''





import argparse

import torch

from transformers import (
    WavaVecaFeatureExtractor,
    WavLMConfig,
    WavLMForAudioFrameClassification,
    WavLMForSequenceClassification,
    WavLMForXVector,
    logging,
)


logging.set_verbosity_info()
A							     = logging.get_logger(__name__)
def 	snake_case_							(				_a  :				Optional[Any]			,       _a  :				Tuple			,       _a  :				int      ):
      snake_case_    							=				WavLMForSequenceClassification.from_pretrained(_a			,       config=_a      )
      snake_case_    							=				downstream_dict["projector.weight"]
      snake_case_    							=				downstream_dict["projector.bias"]
      snake_case_    							=				downstream_dict["model.post_net.linear.weight"]
      snake_case_    							=				downstream_dict["model.post_net.linear.bias"]
      return model
def 	snake_case_							(				_a  :				List[str]			,       _a  :				Optional[Any]			,       _a  :				List[str]      ):
      snake_case_    							=				WavLMForAudioFrameClassification.from_pretrained(_a			,       config=_a      )
      snake_case_    							=				downstream_dict["model.linear.weight"]
      snake_case_    							=				downstream_dict["model.linear.bias"]
      return model
def 	snake_case_							(				_a  :				Dict			,       _a  :				Tuple			,       _a  :				Any      ):
      snake_case_    							=				WavLMForXVector.from_pretrained(_a			,       config=_a      )
      snake_case_    							=				downstream_dict["connector.weight"]
      snake_case_    							=				downstream_dict["connector.bias"]
      for i, kernel_size in enumerate(hf_config.tdnn_kernel      ):
            snake_case_    							=				downstream_dict[
                F'''model.framelevel_feature_extractor.module.{i}.kernel.weight'''
            ]
            snake_case_    							=				downstream_dict[F'''model.framelevel_feature_extractor.module.{i}.kernel.bias''']

      snake_case_    							=				downstream_dict["model.utterancelevel_feature_extractor.linear1.weight"]
      snake_case_    							=				downstream_dict["model.utterancelevel_feature_extractor.linear1.bias"]
      snake_case_    							=				downstream_dict["model.utterancelevel_feature_extractor.linear2.weight"]
      snake_case_    							=				downstream_dict["model.utterancelevel_feature_extractor.linear2.bias"]
      snake_case_    							=				downstream_dict["objective.W"]
      return model
@torch.no_grad()
def 	snake_case_							(				_a  :				Union[str, Any]			,       _a  :				int			,       _a  :				str			,       _a  :				List[Any]      ):
      snake_case_    							=				torch.load(_a			,       map_location="cpu"      )

      snake_case_    							=				checkpoint["Downstream"]

      snake_case_    							=				WavLMConfig.from_pretrained(_a      )
      snake_case_    							=				WavaVecaFeatureExtractor.from_pretrained(
          _a			,       return_attention_mask=_a			,       do_normalize=_a      )

      snake_case_    							=				hf_config.architectures[0]
      if arch.endswith("ForSequenceClassification"      ):
            snake_case_    							=				convert_classification(_a			,       _a			,       _a      )
      elif arch.endswith("ForAudioFrameClassification"      ):
            snake_case_    							=				convert_diarization(_a			,       _a			,       _a      )
      elif arch.endswith("ForXVector"      ):
            snake_case_    							=				convert_xvector(_a			,       _a			,       _a      )
      else:
            raise NotImplementedError(F'''S3PRL weights conversion is not supported for {arch}'''      )

      if hf_config.use_weighted_layer_sum:
            snake_case_    							=				checkpoint["Featurizer"]["weights"]

      hf_feature_extractor.save_pretrained(_a      )
      hf_model.save_pretrained(_a      )


if __name__ == "__main__":
     A							     = 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.")
     A							     = parser.parse_args()
     convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)

