"""simple docstring"""




import argparse

import torch

from transformers import (
    UniSpeechSatConfig,
    UniSpeechSatForAudioFrameClassification,
    UniSpeechSatForSequenceClassification,
    UniSpeechSatForXVector,
    WavaVecaFeatureExtractor,
    logging,
)


logging.set_verbosity_info()
__lowerCAmelCase     :				Any    				=    logging.get_logger(__name__)







def 				_UpperCAmelCase		(							lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__  ):





		"""simple docstring"""




		lowerCAmelCase__   = UniSpeechSatForSequenceClassification.from_pretrained(lowerCamelCase__      ,	config=lowerCamelCase__  )
		lowerCAmelCase__   = downstream_dict["""projector.weight"""]
		lowerCAmelCase__   = downstream_dict["""projector.bias"""]
		lowerCAmelCase__   = downstream_dict["""model.post_net.linear.weight"""]
		lowerCAmelCase__   = downstream_dict["""model.post_net.linear.bias"""]
		return model







def 				_UpperCAmelCase		(							lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__  ):





		"""simple docstring"""




		lowerCAmelCase__   = UniSpeechSatForAudioFrameClassification.from_pretrained(lowerCamelCase__      ,	config=lowerCamelCase__  )
		lowerCAmelCase__   = downstream_dict["""model.linear.weight"""]
		lowerCAmelCase__   = downstream_dict["""model.linear.bias"""]
		return model







def 				_UpperCAmelCase		(							lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__  ):





		"""simple docstring"""




		lowerCAmelCase__   = UniSpeechSatForXVector.from_pretrained(lowerCamelCase__      ,	config=lowerCamelCase__  )
		lowerCAmelCase__   = downstream_dict["""connector.weight"""]
		lowerCAmelCase__   = downstream_dict["""connector.bias"""]
		for i, kernel_size in enumerate(hf_config.tdnn_kernel  ):
				lowerCAmelCase__   = downstream_dict[
				    f"""model.framelevel_feature_extractor.module.{i}.kernel.weight"""
				]
				lowerCAmelCase__   = downstream_dict[f"""model.framelevel_feature_extractor.module.{i}.kernel.bias"""]

		lowerCAmelCase__   = downstream_dict["""model.utterancelevel_feature_extractor.linear1.weight"""]
		lowerCAmelCase__   = downstream_dict["""model.utterancelevel_feature_extractor.linear1.bias"""]
		lowerCAmelCase__   = downstream_dict["""model.utterancelevel_feature_extractor.linear2.weight"""]
		lowerCAmelCase__   = downstream_dict["""model.utterancelevel_feature_extractor.linear2.bias"""]
		lowerCAmelCase__   = downstream_dict["""objective.W"""]
		return model







@torch.no_grad()
def 				_UpperCAmelCase		(							lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__  ):





		"""simple docstring"""




		lowerCAmelCase__   = torch.load(lowerCamelCase__      ,	map_location="""cpu"""  )

		lowerCAmelCase__   = checkpoint["""Downstream"""]

		lowerCAmelCase__   = UniSpeechSatConfig.from_pretrained(lowerCamelCase__  )
		lowerCAmelCase__   = WavaVecaFeatureExtractor.from_pretrained(
		    lowerCamelCase__      ,	return_attention_mask=lowerCamelCase__      ,	do_normalize=lowerCamelCase__  )

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

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

		hf_feature_extractor.save_pretrained(lowerCamelCase__  )
		hf_model.save_pretrained(lowerCamelCase__  )


if __name__ == "__main__":
			__lowerCAmelCase     :				List[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.")
			__lowerCAmelCase     :				Tuple    				=    parser.parse_args()
			convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)





"""simple docstring"""




from typing import Optional, Tuple

import jax
import jax.numpy as jnp
from flax import linen as nn
from flax.core.frozen_dict import FrozenDict
from transformers import CLIPConfig, FlaxPreTrainedModel
from transformers.models.clip.modeling_flax_clip import FlaxCLIPVisionModule







def 				_UpperCAmelCase		(							lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__=1e-12  ):





		"""simple docstring"""




		lowerCAmelCase__   = jnp.divide(emb_a.T      ,	jnp.clip(jnp.linalg.norm(lowerCamelCase__      ,	axis=1  )      ,	a_min=lowerCamelCase__  )  ).T
		lowerCAmelCase__   = jnp.divide(emb_a.T      ,	jnp.clip(jnp.linalg.norm(lowerCamelCase__      ,	axis=1  )      ,	a_min=lowerCamelCase__  )  ).T
		return jnp.matmul(lowerCamelCase__      ,	norm_emb_a.T  )

class    a_   (  nn.Module      ):
					UpperCamelCase_			:						CLIPConfig
					UpperCamelCase_			:						jnp.dtype      =     jnp.floataa




					def     _SCREAMING_SNAKE_CASE    (						self    :  Tuple  ):
							lowerCAmelCase__   = FlaxCLIPVisionModule(self.config.vision_config  )
							lowerCAmelCase__   = nn.Dense(self.config.projection_dim	,    use_bias=snake_case__	,    dtype=self.dtype  )

							lowerCAmelCase__   = self.param("""concept_embeds"""	,    jax.nn.initializers.ones	,    (17, self.config.projection_dim)  )
							lowerCAmelCase__   = self.param(
							    """special_care_embeds"""	,    jax.nn.initializers.ones	,    (3, self.config.projection_dim)  )

							lowerCAmelCase__   = self.param("""concept_embeds_weights"""	,    jax.nn.initializers.ones	,    (17,)  )
							lowerCAmelCase__   = self.param("""special_care_embeds_weights"""	,    jax.nn.initializers.ones	,    (3,)  )




					def __call__(						self    :  Optional[int]	,    snake_case__    :  int  ):
							lowerCAmelCase__   = self.vision_model(snake_case__  )[1]
							lowerCAmelCase__   = self.visual_projection(snake_case__  )

							lowerCAmelCase__   = jax_cosine_distance(snake_case__	,    self.special_care_embeds  )
							lowerCAmelCase__   = jax_cosine_distance(snake_case__	,    self.concept_embeds  )

							# increase this value to create a stronger `nfsw` filter
							# at the cost of increasing the possibility of filtering benign image inputs
							lowerCAmelCase__   = 0.0

							lowerCAmelCase__   = special_cos_dist - self.special_care_embeds_weights[None, :] + adjustment
							lowerCAmelCase__   = jnp.round(snake_case__	,    3  )
							lowerCAmelCase__   = jnp.any(special_scores > 0	,    axis=1	,    keepdims=snake_case__  )
							# Use a lower threshold if an image has any special care concept
							lowerCAmelCase__   = is_special_care * 0.01

							lowerCAmelCase__   = cos_dist - self.concept_embeds_weights[None, :] + special_adjustment
							lowerCAmelCase__   = jnp.round(snake_case__	,    3  )
							lowerCAmelCase__   = jnp.any(concept_scores > 0	,    axis=1  )

							return has_nsfw_concepts

class    a_   (  __UpperCamelCase      ):
					UpperCamelCase_			:						Any      =     CLIPConfig
					UpperCamelCase_			:						str      =     "clip_input"
					UpperCamelCase_			:						str      =     FlaxStableDiffusionSafetyCheckerModule




					def __init__(						self    :  Optional[int]	,    snake_case__    :  CLIPConfig	,    snake_case__    :  Optional[Tuple] = None	,    snake_case__    :  int = 0	,    snake_case__    :  jnp.dtype = jnp.floataa	,    snake_case__    :  bool = True	,    **snake_case__    :  str	,    ):
							if input_shape is None:
									lowerCAmelCase__   = (1, 224, 224, 3)
							lowerCAmelCase__   = self.module_class(config=snake_case__	,    dtype=snake_case__	,    **snake_case__  )
							super().__init__(snake_case__	,    snake_case__	,    input_shape=snake_case__	,    seed=snake_case__	,    dtype=snake_case__	,    _do_init=_do_init  )




					def     _SCREAMING_SNAKE_CASE    (						self    :  Tuple	,    snake_case__    :  jax.random.KeyArray	,    snake_case__    :  Tuple	,    snake_case__    :  FrozenDict = None  ):
							# init input tensor
							lowerCAmelCase__   = jax.random.normal(snake_case__	,    snake_case__  )

							lowerCAmelCase__ ,       lowerCAmelCase__   = jax.random.split(snake_case__  )
							lowerCAmelCase__   = {"""params""": params_rng, """dropout""": dropout_rng}

							lowerCAmelCase__   = self.module.init(snake_case__	,    snake_case__  )["""params"""]

							return random_params




					def __call__(						self    :  Union[str, Any]	,    snake_case__    :  Union[str, Any]	,    snake_case__    :  dict = None	,    ):
							lowerCAmelCase__   = jnp.transpose(snake_case__	,    (0, 2, 3, 1)  )

							return self.module.apply(
							    {"""params""": params or self.params}	,    jnp.array(snake_case__	,    dtype=jnp.floataa  )	,    rngs={}	,    )




