




"""simple docstring"""

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      A__   :




	def __init__( self	:						Dict   ,     lowercase	:						Tuple   ,     lowercase	:						Union[str, Any]=3   ,     lowercase	:						str=32   ,     lowercase	:						str=3   ,     lowercase	:						str=10   ,     lowercase	:						Tuple=[10, 20, 30, 40]   ,     lowercase	:						int=[1, 1, 2, 1]   ,     lowercase	:						str=True   ,     lowercase	:						Any=True   ,     lowercase	:						Dict="relu"   ,     lowercase	:						Union[str, Any]=3   ,     lowercase	:						List[Any]=None   ,     lowercase	:						int=["stage2", "stage3", "stage4"]   ,     lowercase	:						Union[str, Any]=[2, 3, 4]   ,     )       ->			Tuple:
						__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(lowercase						)
						__snake_case        =						out_features
						__snake_case        =						out_indices




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

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

						__snake_case        =						self.get_config()

						return config, pixel_values, labels




	def 				a_		( self	:						Union[str, Any]						)       ->			Union[str, Any]:
						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 				a_		( self	:						Dict   ,     lowercase	:						Dict   ,     lowercase	:						List[Any]   ,     lowercase	:						Union[str, Any]						)       ->			int:
						__snake_case        =						ResNetModel(config=lowercase						)
						model.to(lowercase						)
						model.eval()
						__snake_case        =						model(lowercase						)
						# 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 				a_		( self	:						List[Any]   ,     lowercase	:						Optional[Any]   ,     lowercase	:						Optional[Any]   ,     lowercase	:						Union[str, Any]						)       ->			Optional[int]:
						__snake_case        =						self.num_labels
						__snake_case        =						ResNetForImageClassification(lowercase						)
						model.to(lowercase						)
						model.eval()
						__snake_case        =						model(lowercase   ,     labels=lowercase						)
						self.parent.assertEqual(result.logits.shape   ,     (self.batch_size, self.num_labels)						)




	def 				a_		( self	:						Union[str, Any]   ,     lowercase	:						Optional[Any]   ,     lowercase	:						Any   ,     lowercase	:						str						)       ->			Dict:
						__snake_case        =						ResNetBackbone(config=lowercase						)
						model.to(lowercase						)
						model.eval()
						__snake_case        =						model(lowercase						)

						# 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
						__snake_case        =						None
						__snake_case        =						ResNetBackbone(config=lowercase						)
						model.to(lowercase						)
						model.eval()
						__snake_case        =						model(lowercase						)

						# 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 				a_		( self	:						Tuple						)       ->			List[str]:
						__snake_case        =						self.prepare_config_and_inputs()
						__snake_case   ,   __snake_case   ,   __snake_case        =						config_and_inputs
						__snake_case        =						{"""pixel_values""": pixel_values}
						return config, inputs_dict

@require_torch
class      A__   (   lowercase_  ,						lowercase_  ,						unittest.TestCase  ):
	A_				:					Optional[int]      =   (
	    (
	        ResNetModel,
	        ResNetForImageClassification,
	        ResNetBackbone,
	    )
	    if is_torch_available()
	    else ()
	)
	A_				:					Optional[int]      =   (
	    {"""feature-extraction""": ResNetModel, """image-classification""": ResNetForImageClassification}
	    if is_torch_available()
	    else {}
	)

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




	def 				a_		( self	:						int						)       ->			Tuple:
						__snake_case        =						ResNetModelTester(self						)
						__snake_case        =						ConfigTester(self   ,     config_class=lowercase   ,     has_text_modality=lowercase						)




	def 				a_		( self	:						Optional[Any]						)       ->			List[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 				a_		( self	:						Optional[int]						)       ->			str:
						return




	@unittest.skip(reason="""ResNet does not use inputs_embeds"""						)
	def 				a_		( self	:						Tuple						)       ->			int:
						pass




	@unittest.skip(reason="""ResNet does not support input and output embeddings"""						)
	def 				a_		( self	:						Dict						)       ->			Union[str, Any]:
						pass




	def 				a_		( self	:						Optional[Any]						)       ->			Tuple:
						__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(lowercase						)
											__snake_case        =						inspect.signature(model.forward						)
											# 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]   ,     lowercase						)




	def 				a_		( self	:						List[str]						)       ->			List[Any]:
						__snake_case        =						self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*lowercase						)




	def 				a_		( self	:						Dict						)       ->			Union[str, Any]:
						__snake_case        =						self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_backbone(*lowercase						)




	def 				a_		( self	:						Dict						)       ->			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(config=lowercase						)
											for name, module in model.named_modules():
																if isinstance(lowercase   ,     (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 				a_		( self	:						List[Any]						)       ->			str:
						def check_hidden_states_output(lowercase	:						Union[str, Any]   ,     lowercase	:						Any   ,     lowercase	:						str						):
											__snake_case        =						model_class(lowercase						)
											model.to(lowercase						)
											model.eval()

											with torch.no_grad():
																__snake_case        =						model(**self._prepare_for_class(lowercase   ,     lowercase						)						)

											__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(lowercase						)   ,     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]   ,     )

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

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

																check_hidden_states_output(lowercase   ,     lowercase   ,     lowercase						)




	@unittest.skip(reason="""ResNet does not use feedforward chunking"""						)
	def 				a_		( self	:						List[Any]						)       ->			Union[str, Any]:
						pass




	def 				a_		( self	:						Union[str, Any]						)       ->			Any:
						__snake_case        =						self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_image_classification(*lowercase						)




	@slow
	def 				a_		( self	:						Optional[int]						)       ->			List[str]:
						for model_name in RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											__snake_case        =						ResNetModel.from_pretrained(lowercase						)
											self.assertIsNotNone(lowercase						)




def    snake_case__						(							):
					__snake_case        =						Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""		)
					return image
@require_torch
@require_vision
class      A__   (   unittest.TestCase  ):




	@cached_property
	def 				a_		( self	:						Any						)       ->			Optional[Any]:
						return (
						    AutoImageProcessor.from_pretrained(RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]						)
						    if is_vision_available()
						    else None
						)




	@slow
	def 				a_		( self	:						Optional[Any]						)       ->			int:
						__snake_case        =						ResNetForImageClassification.from_pretrained(RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]						).to(lowercase						)

						__snake_case        =						self.default_image_processor
						__snake_case        =						prepare_img()
						__snake_case        =						image_processor(images=lowercase   ,     return_tensors="""pt"""						).to(lowercase						)

						# forward pass
						with torch.no_grad():
											__snake_case        =						model(**lowercase						)

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

						__snake_case        =						torch.tensor([-11.10_69, -9.78_77, -8.37_77]						).to(lowercase						)

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

@require_torch
class      A__   (   lowercase_  ,						unittest.TestCase  ):
	A_				:					Any      =   (ResNetBackbone,) if is_torch_available() else ()
	A_				:					List[str]      =   False
	A_				:					Optional[int]      =   ResNetConfig




	def 				a_		( self	:						Optional[int]						)       ->			str:
						__snake_case        =						ResNetModelTester(self						)








"""simple docstring"""

import argparse

import torch

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


logging.set_verbosity_info()
UpperCamelCase__			:     Optional[Any]			       =				logging.get_logger(__name__)




def    snake_case__						(							lowerCAmelCase :		str					,       lowerCAmelCase :		Optional[Any]					,       lowerCAmelCase :		Optional[int]		):
					__snake_case        =						WavLMForSequenceClassification.from_pretrained(lowerCAmelCase					,       config=lowerCAmelCase		)
					__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__						(							lowerCAmelCase :		List[Any]					,       lowerCAmelCase :		Dict					,       lowerCAmelCase :		Dict		):
					__snake_case        =						WavLMForAudioFrameClassification.from_pretrained(lowerCAmelCase					,       config=lowerCAmelCase		)
					__snake_case        =						downstream_dict["""model.linear.weight"""]
					__snake_case        =						downstream_dict["""model.linear.bias"""]
					return model




def    snake_case__						(							lowerCAmelCase :		List[Any]					,       lowerCAmelCase :		Dict					,       lowerCAmelCase :		Dict		):
					__snake_case        =						WavLMForXVector.from_pretrained(lowerCAmelCase					,       config=lowerCAmelCase		)
					__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__						(							lowerCAmelCase :		str					,       lowerCAmelCase :		Tuple					,       lowerCAmelCase :		List[str]					,       lowerCAmelCase :		Optional[int]		):
					__snake_case        =						torch.load(lowerCAmelCase					,       map_location="""cpu"""		)

					__snake_case        =						checkpoint["""Downstream"""]

					__snake_case        =						WavLMConfig.from_pretrained(lowerCAmelCase		)
					__snake_case        =						WavaVecaFeatureExtractor.from_pretrained(
					    lowerCAmelCase					,       return_attention_mask=lowerCAmelCase					,       do_normalize=lowerCAmelCase		)

					__snake_case        =						hf_config.architectures[0]
					if arch.endswith("""ForSequenceClassification"""		):
										__snake_case        =						convert_classification(lowerCAmelCase					,       lowerCAmelCase					,       lowerCAmelCase		)
					elif arch.endswith("""ForAudioFrameClassification"""		):
										__snake_case        =						convert_diarization(lowerCAmelCase					,       lowerCAmelCase					,       lowerCAmelCase		)
					elif arch.endswith("""ForXVector"""		):
										__snake_case        =						convert_xvector(lowerCAmelCase					,       lowerCAmelCase					,       lowerCAmelCase		)
					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(lowerCAmelCase		)
					hf_model.save_pretrained(lowerCAmelCase		)


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


