




"""simple docstring"""

import collections
import inspect
import unittest

from transformers import DinatConfig
from transformers.testing_utils import require_natten, 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, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
		import torch
		from torch import nn

		from transformers import DinatBackbone, DinatForImageClassification, DinatModel
		from transformers.models.dinat.modeling_dinat import DINAT_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
		from PIL import Image

		from transformers import AutoImageProcessor
class      A__   :




	def __init__( self	:						int   ,     lowercase	:						Optional[Any]   ,     lowercase	:						Any=13   ,     lowercase	:						Tuple=64   ,     lowercase	:						List[str]=4   ,     lowercase	:						int=3   ,     lowercase	:						int=16   ,     lowercase	:						Optional[Any]=[1, 2, 1]   ,     lowercase	:						Tuple=[2, 4, 8]   ,     lowercase	:						Optional[int]=3   ,     lowercase	:						Optional[int]=[[3], [1, 2], [1]]   ,     lowercase	:						Tuple=2.0   ,     lowercase	:						Tuple=True   ,     lowercase	:						str=0.0   ,     lowercase	:						Union[str, Any]=0.0   ,     lowercase	:						Dict=0.1   ,     lowercase	:						str="gelu"   ,     lowercase	:						Optional[int]=True   ,     lowercase	:						List[str]=0.02   ,     lowercase	:						Optional[Any]=1e-5   ,     lowercase	:						Dict=True   ,     lowercase	:						Union[str, Any]=None   ,     lowercase	:						Tuple=True   ,     lowercase	:						Optional[Any]=10   ,     lowercase	:						Any=["stage1", "stage2"]   ,     lowercase	:						str=[1, 2]   ,     )       ->			Optional[Any]:
						__snake_case        =						parent
						__snake_case        =						batch_size
						__snake_case        =						image_size
						__snake_case        =						patch_size
						__snake_case        =						num_channels
						__snake_case        =						embed_dim
						__snake_case        =						depths
						__snake_case        =						num_heads
						__snake_case        =						kernel_size
						__snake_case        =						dilations
						__snake_case        =						mlp_ratio
						__snake_case        =						qkv_bias
						__snake_case        =						hidden_dropout_prob
						__snake_case        =						attention_probs_dropout_prob
						__snake_case        =						drop_path_rate
						__snake_case        =						hidden_act
						__snake_case        =						patch_norm
						__snake_case        =						layer_norm_eps
						__snake_case        =						initializer_range
						__snake_case        =						is_training
						__snake_case        =						scope
						__snake_case        =						use_labels
						__snake_case        =						num_labels
						__snake_case        =						out_features
						__snake_case        =						out_indices




	def 				a_		( self	:						Dict						)       ->			List[str]:
						__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	:						List[str]						)       ->			Dict:
						return DinatConfig(
						    num_labels=self.num_labels   ,     image_size=self.image_size   ,     patch_size=self.patch_size   ,     num_channels=self.num_channels   ,     embed_dim=self.embed_dim   ,     depths=self.depths   ,     num_heads=self.num_heads   ,     kernel_size=self.kernel_size   ,     dilations=self.dilations   ,     mlp_ratio=self.mlp_ratio   ,     qkv_bias=self.qkv_bias   ,     hidden_dropout_prob=self.hidden_dropout_prob   ,     attention_probs_dropout_prob=self.attention_probs_dropout_prob   ,     drop_path_rate=self.drop_path_rate   ,     hidden_act=self.hidden_act   ,     patch_norm=self.patch_norm   ,     layer_norm_eps=self.layer_norm_eps   ,     initializer_range=self.initializer_range   ,     out_features=self.out_features   ,     out_indices=self.out_indices   ,     )




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

						__snake_case        =						__snake_case        =						(config.image_size // config.patch_size) // (2 ** (len(config.depths						) - 1))
						__snake_case        =						int(config.embed_dim * 2 ** (len(config.depths						) - 1)						)

						self.parent.assertEqual(
						    result.last_hidden_state.shape   ,     (self.batch_size, expected_height, expected_width, expected_dim)						)




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

						# test greyscale images
						__snake_case        =						1
						__snake_case        =						DinatForImageClassification(lowercase						)
						model.to(lowercase						)
						model.eval()

						__snake_case        =						floats_tensor([self.batch_size, 1, self.image_size, self.image_size]						)
						__snake_case        =						model(lowercase						)
						self.parent.assertEqual(result.logits.shape   ,     (self.batch_size, self.num_labels)						)




	def 				a_		( self	:						List[Any]   ,     lowercase	:						Any   ,     lowercase	:						Any   ,     lowercase	:						List[str]						)       ->			Any:
						__snake_case        =						DinatBackbone(config=lowercase						)
						model.to(lowercase						)
						model.eval()
						__snake_case        =						model(lowercase						)

						# verify hidden states
						self.parent.assertEqual(len(result.feature_maps						)   ,     len(config.out_features						)						)
						self.parent.assertListEqual(list(result.feature_maps[0].shape						)   ,     [self.batch_size, model.channels[0], 16, 16]						)

						# verify channels
						self.parent.assertEqual(len(model.channels						)   ,     len(config.out_features						)						)

						# verify backbone works with out_features=None
						__snake_case        =						None
						__snake_case        =						DinatBackbone(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, model.channels[-1], 4, 4]						)

						# verify channels
						self.parent.assertEqual(len(model.channels						)   ,     1						)




	def 				a_		( self	:						Any						)       ->			Tuple:
						__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_natten
@require_torch
class      A__   (   lowercase_  ,						lowercase_  ,						unittest.TestCase  ):
	A_				:					Union[str, Any]      =   (
	    (
	        DinatModel,
	        DinatForImageClassification,
	        DinatBackbone,
	    )
	    if is_torch_available()
	    else ()
	)
	A_				:					Any      =   (
	    {"""feature-extraction""": DinatModel, """image-classification""": DinatForImageClassification}
	    if is_torch_available()
	    else {}
	)
	A_				:					Optional[int]      =   False

	A_				:					int      =   False
	A_				:					List[Any]      =   False
	A_				:					Optional[Any]      =   False
	A_				:					Tuple      =   False




	def 				a_		( self	:						List[str]						)       ->			int:
						__snake_case        =						DinatModelTester(self						)
						__snake_case        =						ConfigTester(self   ,     config_class=lowercase   ,     embed_dim=37						)




	def 				a_		( self	:						Optional[Any]						)       ->			Union[str, 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	:						Any						)       ->			Tuple:
						return




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




	def 				a_		( self	:						int						)       ->			Dict:
						__snake_case        =						self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_image_classification(*lowercase						)




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




	@unittest.skip(reason="""Dinat does not use inputs_embeds"""						)
	def 				a_		( self	:						List[Any]						)       ->			Tuple:
						pass




	@unittest.skip(reason="""Dinat does not use feedforward chunking"""						)
	def 				a_		( self	:						Any						)       ->			Optional[int]:
						pass




	def 				a_		( self	:						Any						)       ->			Optional[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(lowercase						)
											self.assertIsInstance(model.get_input_embeddings()   ,     (nn.Module)						)
											__snake_case        =						model.get_output_embeddings()
											self.assertTrue(x is None or isinstance(lowercase   ,     nn.Linear						)						)




	def 				a_		( self	:						Optional[int]						)       ->			List[str]:
						__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	:						Tuple						)       ->			List[Any]:
						self.skipTest("""Dinat's attention operation is handled entirely by NATTEN."""						)




	def 				a_		( self	:						Tuple   ,     lowercase	:						Any   ,     lowercase	:						Dict   ,     lowercase	:						Dict   ,     lowercase	:						Union[str, Any]						)       ->			Optional[Any]:
						__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.hidden_states

						__snake_case        =						getattr(
						    self.model_tester   ,     """expected_num_hidden_layers"""   ,     len(self.model_tester.depths						) + 1						)
						self.assertEqual(len(lowercase						)   ,     lowercase						)

						# Dinat has a different seq_length
						__snake_case        =						(
						    config.patch_size
						    if isinstance(config.patch_size   ,     collections.abc.Iterable						)
						    else (config.patch_size, config.patch_size)
						)

						__snake_case        =						image_size[0] // patch_size[0]
						__snake_case        =						image_size[1] // patch_size[1]

						self.assertListEqual(
						    list(hidden_states[0].shape[-3:]						)   ,     [height, width, self.model_tester.embed_dim]   ,     )

						if model_class.__name__ != "DinatBackbone":
											__snake_case        =						outputs.reshaped_hidden_states
											self.assertEqual(len(lowercase						)   ,     lowercase						)

											__snake_case   ,   __snake_case   ,   __snake_case   ,   __snake_case        =						reshaped_hidden_states[0].shape
											__snake_case        =						(
											    reshaped_hidden_states[0].view(lowercase   ,     lowercase   ,     lowercase   ,     lowercase						).permute(0   ,     2   ,     3   ,     1						)
											)
											self.assertListEqual(
											    list(reshaped_hidden_states.shape[-3:]						)   ,     [height, width, self.model_tester.embed_dim]   ,     )




	def 				a_		( self	:						List[str]						)       ->			str:
						__snake_case   ,   __snake_case        =						self.model_tester.prepare_config_and_inputs_for_common()

						__snake_case        =						(
						    self.model_tester.image_size
						    if isinstance(self.model_tester.image_size   ,     collections.abc.Iterable						)
						    else (self.model_tester.image_size, self.model_tester.image_size)
						)

						for model_class in self.all_model_classes:
											__snake_case        =						True
											self.check_hidden_states_output(lowercase   ,     lowercase   ,     lowercase   ,     lowercase						)

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

											self.check_hidden_states_output(lowercase   ,     lowercase   ,     lowercase   ,     lowercase						)




	@slow
	def 				a_		( self	:						Tuple						)       ->			List[Any]:
						for model_name in DINAT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											__snake_case        =						DinatModel.from_pretrained(lowercase						)
											self.assertIsNotNone(lowercase						)




	def 				a_		( self	:						Any						)       ->			List[str]:
						__snake_case   ,   __snake_case        =						self.model_tester.prepare_config_and_inputs_for_common()

						__snake_case        =						_config_zero_init(lowercase						)
						for model_class in self.all_model_classes:
											__snake_case        =						model_class(config=lowercase						)
											for name, param in model.named_parameters():
																if "embeddings" not in name and param.requires_grad:
																					self.assertIn(
																					    ((param.data.mean() * 1e9).round() / 1e9).item()   ,     [0.0, 1.0]   ,     msg=f'''Parameter {name} of model {model_class} seems not properly initialized'''   ,     )

@require_natten
@require_vision
@require_torch
class      A__   (   unittest.TestCase  ):




	@cached_property
	def 				a_		( self	:						Tuple						)       ->			Any:
						return AutoImageProcessor.from_pretrained("""shi-labs/dinat-mini-in1k-224"""						) if is_vision_available() else None




	@slow
	def 				a_		( self	:						Optional[Any]						)       ->			List[str]:
						__snake_case        =						DinatForImageClassification.from_pretrained("""shi-labs/dinat-mini-in1k-224"""						).to(lowercase						)
						__snake_case        =						self.default_image_processor

						__snake_case        =						Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""						)
						__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([-0.15_45, -0.76_67, 0.46_42]						).to(lowercase						)
						self.assertTrue(torch.allclose(outputs.logits[0, :3]   ,     lowercase   ,     atol=1e-4						)						)

@require_torch
@require_natten
class      A__   (   unittest.TestCase  ,						lowercase_  ):
	A_				:					Dict      =   (DinatBackbone,) if is_torch_available() else ()
	A_				:					List[Any]      =   DinatConfig




	def 				a_		( self	:						Tuple						)       ->			str:
						__snake_case        =						DinatModelTester(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)


