




import argparse
import re

import torch
from CLAP import create_model

from transformers import AutoFeatureExtractor, ClapConfig, ClapModel


__a          =			{
    "text_branch": "text_model",
    "audio_branch": "audio_model.audio_encoder",
    "attn": "attention.self",
    "self.proj": "output.dense",
    "attention.self_mask": "attn_mask",
    "mlp.fc1": "intermediate.dense",
    "mlp.fc2": "output.dense",
    "norm1": "layernorm_before",
    "norm2": "layernorm_after",
    "bn0": "batch_norm",
}

__a          =			AutoFeatureExtractor.from_pretrained('laion/clap-htsat-unfused', truncation='rand_trunc')







def lowerCamelCase__       (      _lowercase			,						_lowercase=False				):

     '''simple docstring'''


     UpperCAmelCase_,   UpperCAmelCase_       =      create_model(
         '''HTSAT-tiny'''			,						'''roberta'''			,						lowerCamelCase__			,						precision='''fp32'''			,						device='''cuda:0''' if torch.cuda.is_available() else '''cpu'''			,						enable_fusion=lowerCamelCase__			,						fusion_type='''aff_2d''' if enable_fusion else None			,						)
     return model, model_cfg







def lowerCamelCase__       (      _lowercase				):

     '''simple docstring'''


     UpperCAmelCase_       =      {}

     UpperCAmelCase_       =      r'''.*sequential.(\d+).*'''
     UpperCAmelCase_       =      r'''.*_projection.(\d+).*'''

     for key, value in state_dict.items():
          # check if any key needs to be modified
          for key_to_modify, new_key in KEYS_TO_MODIFY_MAPPING.items():
               if key_to_modify in key:
                    UpperCAmelCase_       =      key.replace(lowerCamelCase__			,						lowerCamelCase__				)

          if re.match(lowerCamelCase__			,						lowerCamelCase__				):
               # replace sequential layers with list
               UpperCAmelCase_       =      re.match(lowerCamelCase__			,						lowerCamelCase__				).group(1				)

               UpperCAmelCase_       =      key.replace(f'''sequential.{sequential_layer}.'''			,						f'''layers.{int(lowerCamelCase__				)//3}.linear.'''				)
          elif re.match(lowerCamelCase__			,						lowerCamelCase__				):
               UpperCAmelCase_       =      int(re.match(lowerCamelCase__			,						lowerCamelCase__				).group(1				)				)

               # Because in CLAP they use `nn.Sequential`...
               UpperCAmelCase_       =      1 if projecton_layer == 0 else 2

               UpperCAmelCase_       =      key.replace(f'''_projection.{projecton_layer}.'''			,						f'''_projection.linear{transformers_projection_layer}.'''				)

          if "audio" and "qkv" in key:
               # split qkv into query key and value
               UpperCAmelCase_       =      value
               UpperCAmelCase_       =      mixed_qkv.size(0				) // 3

               UpperCAmelCase_       =      mixed_qkv[:qkv_dim]
               UpperCAmelCase_       =      mixed_qkv[qkv_dim : qkv_dim * 2]
               UpperCAmelCase_       =      mixed_qkv[qkv_dim * 2 :]

               UpperCAmelCase_       =      query_layer
               UpperCAmelCase_       =      key_layer
               UpperCAmelCase_       =      value_layer
          else:
               UpperCAmelCase_       =      value

     return model_state_dict







def lowerCamelCase__       (      _lowercase			,						_lowercase			,						_lowercase			,						_lowercase=False				):

     '''simple docstring'''


     UpperCAmelCase_,   UpperCAmelCase_       =      init_clap(lowerCamelCase__			,						enable_fusion=lowerCamelCase__				)

     clap_model.eval()
     UpperCAmelCase_       =      clap_model.state_dict()
     UpperCAmelCase_       =      rename_state_dict(lowerCamelCase__				)

     UpperCAmelCase_       =      ClapConfig()
     UpperCAmelCase_       =      enable_fusion
     UpperCAmelCase_       =      ClapModel(lowerCamelCase__				)

     # ignore the spectrogram embedding layer
     model.load_state_dict(lowerCamelCase__			,						strict=lowerCamelCase__				)

     model.save_pretrained(lowerCamelCase__				)
     transformers_config.save_pretrained(lowerCamelCase__				)


if __name__ == "__main__":
     __a          =			argparse.ArgumentParser()
     parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
     parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to fairseq checkpoint')
     parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
     parser.add_argument('--enable_fusion', action='store_true', help='Whether to enable fusion or not')
     __a          =			parser.parse_args()

     convert_clap_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.enable_fusion)





import argparse
import logging
import sys
from unittest.mock import patch

import run_glue_deebert

from transformers.testing_utils import TestCasePlus, get_gpu_count, require_torch_non_multi_gpu, slow


logging.basicConfig(level=logging.DEBUG)

__a          =			logging.getLogger()







def lowerCamelCase__       (      ):

     '''simple docstring'''


     UpperCAmelCase_      :							List[str]       =      argparse.ArgumentParser()
     parser.add_argument('''-f'''				)
     UpperCAmelCase_      :							Dict       =      parser.parse_args()
     return args.f



class   __a(					_a       ):






    """simple docstring"""





    def 							a__				(				self  )			->		None:
         UpperCAmelCase_      :							int       =      logging.StreamHandler(sys.stdout  )
         logger.addHandler(_SCREAMING_SNAKE_CASE  )





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE  )			->		str:
         UpperCAmelCase_      :							int       =      get_gpu_count()

         if n_gpu > 1:
              pass
              # XXX: doesn't quite work with n_gpu > 1 https://github.com/huggingface/transformers/issues/10560
              # script = f"{self.examples_dir_str}/research_projects/deebert/run_glue_deebert.py"
              # distributed_args = f"-m torch.distributed.launch --nproc_per_node={n_gpu} {script}".split()
              # cmd = [sys.executable] + distributed_args + args
              # execute_subprocess_async(cmd, env=self.get_env())
              # XXX: test the results - need to save them first into .json file
         else:
              args.insert(0       ,'''run_glue_deebert.py'''  )
              with patch.object(_SCREAMING_SNAKE_CASE       ,'''argv'''       ,_SCREAMING_SNAKE_CASE  ):
                   UpperCAmelCase_      :							List[str]       =      run_glue_deebert.main()
                   for value in result.values():
                        self.assertGreaterEqual(_SCREAMING_SNAKE_CASE       ,0.6_66  )





    @slow
    @require_torch_non_multi_gpu
    def 							a__				(				self  )			->		List[str]:
         UpperCAmelCase_      :							List[Any]       =      '''
            --model_type roberta
            --model_name_or_path roberta-base
            --task_name MRPC
            --do_train
            --do_eval
            --do_lower_case
            --data_dir ./tests/fixtures/tests_samples/MRPC/
            --max_seq_length 128
            --per_gpu_eval_batch_size=1
            --per_gpu_train_batch_size=8
            --learning_rate 2e-4
            --num_train_epochs 3
            --overwrite_output_dir
            --seed 42
            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --plot_data_dir ./examples/deebert/results/
            --save_steps 0
            --overwrite_cache
            --eval_after_first_stage
            '''.split()
         self.run_and_check(_SCREAMING_SNAKE_CASE  )

         UpperCAmelCase_      :							Union[str, Any]       =      '''
            --model_type roberta
            --model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --task_name MRPC
            --do_eval
            --do_lower_case
            --data_dir ./tests/fixtures/tests_samples/MRPC/
            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --plot_data_dir ./examples/deebert/results/
            --max_seq_length 128
            --eval_each_highway
            --eval_highway
            --overwrite_cache
            --per_gpu_eval_batch_size=1
            '''.split()
         self.run_and_check(_SCREAMING_SNAKE_CASE  )

         UpperCAmelCase_      :							Any       =      '''
            --model_type roberta
            --model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --task_name MRPC
            --do_eval
            --do_lower_case
            --data_dir ./tests/fixtures/tests_samples/MRPC/
            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --plot_data_dir ./examples/deebert/results/
            --max_seq_length 128
            --early_exit_entropy 0.1
            --eval_highway
            --overwrite_cache
            --per_gpu_eval_batch_size=1
            '''.split()
         self.run_and_check(_SCREAMING_SNAKE_CASE  )