

'''simple docstring'''

import tempfile
import unittest

import numpy as np
import torch

from diffusers import DiffusionPipeline
from diffusers.models.attention_processor import Attention, AttnAddedKVProcessor





class 							_snake_case	(    unittest.TestCase  ):
   def       __UpperCamelCase  (	self     : List[str]       ,SCREAMING_SNAKE_CASE__     : bool = False							):
     SCREAMING_SNAKE_CASE:Optional[int]   =   10

     if only_cross_attention:
       SCREAMING_SNAKE_CASE:int   =   12
     else:
       # when only cross attention is not set, the cross attention dim must be the same as the query dim
       SCREAMING_SNAKE_CASE:List[Any]   =   query_dim

     return {
         "query_dim": query_dim,
         "cross_attention_dim": cross_attention_dim,
         "heads": 2,
         "dim_head": 4,
         "added_kv_proj_dim": 6,
         "norm_num_groups": 1,
         "only_cross_attention": only_cross_attention,
         "processor": AttnAddedKVProcessor(),
     }
   def       __UpperCamelCase  (	self     : Tuple       ,SCREAMING_SNAKE_CASE__     : str       ,SCREAMING_SNAKE_CASE__     : Optional[int]							):
     SCREAMING_SNAKE_CASE:int   =   2

     SCREAMING_SNAKE_CASE:int   =   torch.rand(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__       ,3       ,2							)
     SCREAMING_SNAKE_CASE:Union[str, Any]   =   torch.rand(SCREAMING_SNAKE_CASE__       ,4       ,SCREAMING_SNAKE_CASE__							)
     SCREAMING_SNAKE_CASE:str   =   None

     return {
         "hidden_states": hidden_states,
         "encoder_hidden_states": encoder_hidden_states,
         "attention_mask": attention_mask,
     }







   def       __UpperCamelCase  (	self     : str							):
     # self and cross attention

     torch.manual_seed(0							)

     SCREAMING_SNAKE_CASE:Dict   =   self.get_constructor_arguments(only_cross_attention=SCREAMING_SNAKE_CASE__							)
     SCREAMING_SNAKE_CASE:List[Any]   =   Attention(**SCREAMING_SNAKE_CASE__							)

     self.assertTrue(attn.to_k is not None							)
     self.assertTrue(attn.to_v is not None							)

     SCREAMING_SNAKE_CASE:Union[str, Any]   =   self.get_forward_arguments(
         query_dim=constructor_args["query_dim"]       ,added_kv_proj_dim=constructor_args["added_kv_proj_dim"]							)

     SCREAMING_SNAKE_CASE:List[str]   =   attn(**SCREAMING_SNAKE_CASE__							)

     # only self attention

     torch.manual_seed(0							)

     SCREAMING_SNAKE_CASE:Tuple   =   self.get_constructor_arguments(only_cross_attention=SCREAMING_SNAKE_CASE__							)
     SCREAMING_SNAKE_CASE:str   =   Attention(**SCREAMING_SNAKE_CASE__							)

     self.assertTrue(attn.to_k is None							)
     self.assertTrue(attn.to_v is None							)

     SCREAMING_SNAKE_CASE:str   =   self.get_forward_arguments(
         query_dim=constructor_args["query_dim"]       ,added_kv_proj_dim=constructor_args["added_kv_proj_dim"]							)

     SCREAMING_SNAKE_CASE:str   =   attn(**SCREAMING_SNAKE_CASE__							)

     self.assertTrue((only_cross_attn_out != self_and_cross_attn_out).all()							)





class 							_snake_case	(    unittest.TestCase  ):
   def       __UpperCamelCase  (	self     : Dict							):
     SCREAMING_SNAKE_CASE:Optional[int]   =   DiffusionPipeline.from_pretrained("hf-internal-testing/tiny-stable-diffusion-pipe"       ,safety_checker=SCREAMING_SNAKE_CASE__							)

     SCREAMING_SNAKE_CASE:Tuple   =   pipe(
         "foo"       ,num_inference_steps=2       ,generator=torch.Generator("cpu"							).manual_seed(0							)       ,output_type="np"       ,).images

     # the initial conversion succeeds
     SCREAMING_SNAKE_CASE:Tuple   =   DiffusionPipeline.from_pretrained(
         "hf-internal-testing/tiny-stable-diffusion-pipe"       ,device_map="sequential"       ,safety_checker=SCREAMING_SNAKE_CASE__							)

     SCREAMING_SNAKE_CASE:List[Any]   =   pipe(
         "foo"       ,num_inference_steps=2       ,generator=torch.Generator("cpu"							).manual_seed(0							)       ,output_type="np"       ,).images

     with tempfile.TemporaryDirectory() as tmpdir:
       # save the converted model
       pipe.save_pretrained(SCREAMING_SNAKE_CASE__							)

       # can also load the converted weights
       SCREAMING_SNAKE_CASE:Tuple   =   DiffusionPipeline.from_pretrained(SCREAMING_SNAKE_CASE__       ,device_map="sequential"       ,safety_checker=SCREAMING_SNAKE_CASE__							)

     SCREAMING_SNAKE_CASE:List[Any]   =   pipe(
         "foo"       ,num_inference_steps=2       ,generator=torch.Generator("cpu"							).manual_seed(0							)       ,output_type="np"       ,).images

     self.assertTrue(np.allclose(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__       ,atol=1e-5							)							)
     self.assertTrue(np.allclose(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__       ,atol=1e-5							)							)









'''simple docstring'''

import argparse

import torch

from transformers import HubertConfig, HubertForSequenceClassification, WavaVecaFeatureExtractor, logging


logging.set_verbosity_info()
A_      =							logging.get_logger(__name__)

A_      =							["UtteranceLevel"]





@torch.no_grad()
def 				A_					(	snake_case ,    snake_case ,    snake_case ,    snake_case	):
  SCREAMING_SNAKE_CASE:Optional[int]   =   torch.load(snake_case ,    map_location="cpu"	)
  if checkpoint["Config"]["downstream_expert"]["modelrc"]["select"] not in SUPPORTED_MODELS:
    raise NotImplementedError(F'''The supported s3prl models are {SUPPORTED_MODELS}'''	)

  SCREAMING_SNAKE_CASE:Tuple   =   checkpoint["Downstream"]

  SCREAMING_SNAKE_CASE:Union[str, Any]   =   HubertConfig.from_pretrained(snake_case	)
  SCREAMING_SNAKE_CASE:int   =   HubertForSequenceClassification.from_pretrained(snake_case ,    config=snake_case	)
  SCREAMING_SNAKE_CASE:Union[str, Any]   =   WavaVecaFeatureExtractor.from_pretrained(
      snake_case ,    return_attention_mask=snake_case ,    do_normalize=snake_case	)

  if hf_congfig.use_weighted_layer_sum:
    SCREAMING_SNAKE_CASE:Tuple   =   checkpoint["Featurizer"]["weights"]

  SCREAMING_SNAKE_CASE:List[Any]   =   downstream_dict["projector.weight"]
  SCREAMING_SNAKE_CASE:Optional[Any]   =   downstream_dict["projector.bias"]
  SCREAMING_SNAKE_CASE:List[Any]   =   downstream_dict["model.post_net.linear.weight"]
  SCREAMING_SNAKE_CASE:Tuple   =   downstream_dict["model.post_net.linear.bias"]

  hf_feature_extractor.save_pretrained(snake_case	)
  hf_model.save_pretrained(snake_case	)


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)






