"""simple docstring"""






import argparse

import torch

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


logging.set_verbosity_info()
__lowerCAmelCase    :       List[str]					     =logging.get_logger(__name__)


def 			UpperCAmelCase__       (				lowerCAmelCase__     :Optional[Any]					,		lowerCAmelCase__     :Any					,		lowerCAmelCase__     :Any		) ->    Dict:
      '''simple docstring'''


      lowercase														=	UniSpeechSatForSequenceClassification.from_pretrained(lowerCAmelCase__					,		config=lowerCAmelCase__		)
      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"""]
      return model


def 			UpperCAmelCase__       (				lowerCAmelCase__     :Dict					,		lowerCAmelCase__     :Optional[Any]					,		lowerCAmelCase__     :List[str]		) ->    Dict:
      '''simple docstring'''


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


def 			UpperCAmelCase__       (				lowerCAmelCase__     :Dict					,		lowerCAmelCase__     :Dict					,		lowerCAmelCase__     :Tuple		) ->    Optional[int]:
      '''simple docstring'''


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

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


@torch.no_grad()
def 			UpperCAmelCase__       (				lowerCAmelCase__     :Union[str, Any]					,		lowerCAmelCase__     :Any					,		lowerCAmelCase__     :Any					,		lowerCAmelCase__     :Dict		) ->    int:
      '''simple docstring'''


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

      lowercase														=	checkpoint["""Downstream"""]

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

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

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

      hf_feature_extractor.save_pretrained(lowerCAmelCase__		)
      hf_model.save_pretrained(lowerCAmelCase__		)


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




"""simple docstring"""






import tempfile

import numpy as np
import torch
from transformers import AutoTokenizer, TaEncoderModel

from diffusers import DDPMScheduler, UNetaDConditionModel
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.pipelines.deepfloyd_if import IFWatermarker
from diffusers.utils.testing_utils import torch_device

from ..test_pipelines_common import to_np





class  _A						:

      def 				A__       (						self						):







            """simple docstring"""



            torch.manual_seed(0						)
            lowercase														=	TaEncoderModel.from_pretrained("""hf-internal-testing/tiny-random-t5"""						)

            torch.manual_seed(0						)
            lowercase														=	AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-t5"""						)

            torch.manual_seed(0						)
            lowercase														=	UNetaDConditionModel(
                sample_size=32		,       layers_per_block=1		,       block_out_channels=[32, 64]		,       down_block_types=[
                    """ResnetDownsampleBlock2D""",
                    """SimpleCrossAttnDownBlock2D""",
                ]		,       mid_block_type="""UNetMidBlock2DSimpleCrossAttn"""		,       up_block_types=["""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""]		,       in_channels=3		,       out_channels=6		,       cross_attention_dim=32		,       encoder_hid_dim=32		,       attention_head_dim=8		,       addition_embed_type="""text"""		,       addition_embed_type_num_heads=2		,       cross_attention_norm="""group_norm"""		,       resnet_time_scale_shift="""scale_shift"""		,       act_fn="""gelu"""		,       )
            unet.set_attn_processor(AttnAddedKVProcessor()						)  # For reproducibility tests

            torch.manual_seed(0						)
            lowercase														=	DDPMScheduler(
                num_train_timesteps=1000		,       beta_schedule="""squaredcos_cap_v2"""		,       beta_start=0.0_0_0_1		,       beta_end=0.0_2		,       thresholding=__lowerCAmelCase		,       dynamic_thresholding_ratio=0.9_5		,       sample_max_value=1.0		,       prediction_type="""epsilon"""		,       variance_type="""learned_range"""		,       )

            torch.manual_seed(0						)
            lowercase														=	IFWatermarker()

            return {
                "text_encoder": text_encoder,
                "tokenizer": tokenizer,
                "unet": unet,
                "scheduler": scheduler,
                "watermarker": watermarker,
                "safety_checker": None,
                "feature_extractor": None,
            }

      def 				A__       (						self						):







            """simple docstring"""



            torch.manual_seed(0						)
            lowercase														=	TaEncoderModel.from_pretrained("""hf-internal-testing/tiny-random-t5"""						)

            torch.manual_seed(0						)
            lowercase														=	AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-t5"""						)

            torch.manual_seed(0						)
            lowercase														=	UNetaDConditionModel(
                sample_size=32		,       layers_per_block=[1, 2]		,       block_out_channels=[32, 64]		,       down_block_types=[
                    """ResnetDownsampleBlock2D""",
                    """SimpleCrossAttnDownBlock2D""",
                ]		,       mid_block_type="""UNetMidBlock2DSimpleCrossAttn"""		,       up_block_types=["""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""]		,       in_channels=6		,       out_channels=6		,       cross_attention_dim=32		,       encoder_hid_dim=32		,       attention_head_dim=8		,       addition_embed_type="""text"""		,       addition_embed_type_num_heads=2		,       cross_attention_norm="""group_norm"""		,       resnet_time_scale_shift="""scale_shift"""		,       act_fn="""gelu"""		,       class_embed_type="""timestep"""		,       mid_block_scale_factor=1.4_1_4		,       time_embedding_act_fn="""gelu"""		,       time_embedding_dim=32		,       )
            unet.set_attn_processor(AttnAddedKVProcessor()						)  # For reproducibility tests

            torch.manual_seed(0						)
            lowercase														=	DDPMScheduler(
                num_train_timesteps=1000		,       beta_schedule="""squaredcos_cap_v2"""		,       beta_start=0.0_0_0_1		,       beta_end=0.0_2		,       thresholding=__lowerCAmelCase		,       dynamic_thresholding_ratio=0.9_5		,       sample_max_value=1.0		,       prediction_type="""epsilon"""		,       variance_type="""learned_range"""		,       )

            torch.manual_seed(0						)
            lowercase														=	DDPMScheduler(
                num_train_timesteps=1000		,       beta_schedule="""squaredcos_cap_v2"""		,       beta_start=0.0_0_0_1		,       beta_end=0.0_2		,       )

            torch.manual_seed(0						)
            lowercase														=	IFWatermarker()

            return {
                "text_encoder": text_encoder,
                "tokenizer": tokenizer,
                "unet": unet,
                "scheduler": scheduler,
                "image_noising_scheduler": image_noising_scheduler,
                "watermarker": watermarker,
                "safety_checker": None,
                "feature_extractor": None,
            }

      def 				A__       (						self						):







            """simple docstring"""



            lowercase														=	self.get_dummy_components()
            lowercase														=	self.pipeline_class(**__lowerCAmelCase						)
            pipe.to(__lowerCAmelCase						)
            pipe.set_progress_bar_config(disable=__lowerCAmelCase						)

            lowercase														=	self.get_dummy_inputs(__lowerCAmelCase						)

            lowercase														=	inputs["""prompt"""]
            lowercase														=	inputs["""generator"""]
            lowercase														=	inputs["""num_inference_steps"""]
            lowercase														=	inputs["""output_type"""]

            if "image" in inputs:
                  lowercase														=	inputs["""image"""]
            else:
                  lowercase														=	None

            if "mask_image" in inputs:
                  lowercase														=	inputs["""mask_image"""]
            else:
                  lowercase														=	None

            if "original_image" in inputs:
                  lowercase														=	inputs["""original_image"""]
            else:
                  lowercase														=	None

            lowercase							,							lowercase														=	pipe.encode_prompt(__lowerCAmelCase						)

            # inputs with prompt converted to embeddings
            lowercase														=	{
                """prompt_embeds""": prompt_embeds,
                """negative_prompt_embeds""": negative_prompt_embeds,
                """generator""": generator,
                """num_inference_steps""": num_inference_steps,
                """output_type""": output_type,
            }

            if image is not None:
                  lowercase														=	image

            if mask_image is not None:
                  lowercase														=	mask_image

            if original_image is not None:
                  lowercase														=	original_image

            # set all optional components to None
            for optional_component in pipe._optional_components:
                  setattr(__lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase						)

            lowercase														=	pipe(**__lowerCAmelCase						)[0]

            with tempfile.TemporaryDirectory() as tmpdir:
                  pipe.save_pretrained(__lowerCAmelCase						)
                  lowercase														=	self.pipeline_class.from_pretrained(__lowerCAmelCase						)
                  pipe_loaded.to(__lowerCAmelCase						)
                  pipe_loaded.set_progress_bar_config(disable=__lowerCAmelCase						)

            pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor()						)  # For reproducibility tests

            for optional_component in pipe._optional_components:
                  self.assertTrue(
                      getattr(__lowerCAmelCase		,       __lowerCAmelCase						) is None		,       f'`{optional_component}` did not stay set to None after loading.'		,       )

            lowercase														=	self.get_dummy_inputs(__lowerCAmelCase						)

            lowercase														=	inputs["""generator"""]
            lowercase														=	inputs["""num_inference_steps"""]
            lowercase														=	inputs["""output_type"""]

            # inputs with prompt converted to embeddings
            lowercase														=	{
                """prompt_embeds""": prompt_embeds,
                """negative_prompt_embeds""": negative_prompt_embeds,
                """generator""": generator,
                """num_inference_steps""": num_inference_steps,
                """output_type""": output_type,
            }

            if image is not None:
                  lowercase														=	image

            if mask_image is not None:
                  lowercase														=	mask_image

            if original_image is not None:
                  lowercase														=	original_image

            lowercase														=	pipe_loaded(**__lowerCAmelCase						)[0]

            lowercase														=	np.abs(to_np(__lowerCAmelCase						) - to_np(__lowerCAmelCase						)						).max()
            self.assertLess(__lowerCAmelCase		,       1E-4						)







      def 				A__       (						self						):







            """simple docstring"""



            lowercase														=	self.get_dummy_components()
            lowercase														=	self.pipeline_class(**__lowerCAmelCase						)
            pipe.to(__lowerCAmelCase						)
            pipe.set_progress_bar_config(disable=__lowerCAmelCase						)

            lowercase														=	self.get_dummy_inputs(__lowerCAmelCase						)
            lowercase														=	pipe(**__lowerCAmelCase						)[0]

            with tempfile.TemporaryDirectory() as tmpdir:
                  pipe.save_pretrained(__lowerCAmelCase						)
                  lowercase														=	self.pipeline_class.from_pretrained(__lowerCAmelCase						)
                  pipe_loaded.to(__lowerCAmelCase						)
                  pipe_loaded.set_progress_bar_config(disable=__lowerCAmelCase						)

            pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor()						)  # For reproducibility tests

            lowercase														=	self.get_dummy_inputs(__lowerCAmelCase						)
            lowercase														=	pipe_loaded(**__lowerCAmelCase						)[0]

            lowercase														=	np.abs(to_np(__lowerCAmelCase						) - to_np(__lowerCAmelCase						)						).max()
            self.assertLess(__lowerCAmelCase		,       1E-4						)



