






'''simple docstring'''

import argparse
import re

import torch
from CLAP import create_model

from transformers import AutoFeatureExtractor, ClapConfig, ClapModel


SCREAMING_SNAKE_CASE__											=     {
    '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',
}

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







def 					lowercase__				(   __UpperCamelCase	,		__UpperCamelCase=False					)->				List[Any]:
      UpperCamelCase						,UpperCamelCase       						=      create_model(
          """HTSAT-tiny"""	,		"""roberta"""	,		__UpperCamelCase	,		precision="""fp32"""	,		device="""cuda:0""" if torch.cuda.is_available() else """cpu"""	,		enable_fusion=__UpperCamelCase	,		fusion_type="""aff_2d""" if enable_fusion else None	,		)
      return model, model_cfg







def 					lowercase__				(   __UpperCamelCase					)->				Any:
      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(__UpperCamelCase	,		__UpperCamelCase					)

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

                  UpperCamelCase       						=      key.replace(F"sequential.{sequential_layer}."	,		F"layers.{int(__UpperCamelCase					)//3}.linear."					)
            elif re.match(__UpperCamelCase	,		__UpperCamelCase					):
                  UpperCamelCase       						=      int(re.match(__UpperCamelCase	,		__UpperCamelCase					).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 					lowercase__				(   __UpperCamelCase	,		__UpperCamelCase	,		__UpperCamelCase	,		__UpperCamelCase=False					)->				str:
      UpperCamelCase						,UpperCamelCase       						=      init_clap(__UpperCamelCase	,		enable_fusion=__UpperCamelCase					)

      clap_model.eval()
      UpperCamelCase       						=      clap_model.state_dict()
      UpperCamelCase       						=      rename_state_dict(__UpperCamelCase					)

      UpperCamelCase       						=      ClapConfig()
      UpperCamelCase       						=      enable_fusion
      UpperCamelCase       						=      ClapModel(__UpperCamelCase					)

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

      model.save_pretrained(__UpperCamelCase					)
      transformers_config.save_pretrained(__UpperCamelCase					)


if __name__ == "__main__":
     SCREAMING_SNAKE_CASE__											=     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')
     SCREAMING_SNAKE_CASE__											=     parser.parse_args()

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








'''simple docstring'''

# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny model through reduction of a normal pre-trained model, but keeping the
# full vocab, merges file, and thus also resulting in a larger model due to a large vocab size.
# This gives ~3MB in total for all files.
#
# If you want a 50 times smaller than this see `fsmt-make-super-tiny-model.py`, which is slightly more complicated
#
#
# It will be used then as "stas/tiny-wmt19-en-de"

# Build
from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration

SCREAMING_SNAKE_CASE__											=     'facebook/wmt19-en-de'
SCREAMING_SNAKE_CASE__											=     FSMTTokenizer.from_pretrained(mname)
# get the correct vocab sizes, etc. from the master model
SCREAMING_SNAKE_CASE__											=     FSMTConfig.from_pretrained(mname)
config.update(
    dict(
        d_model=4,
        encoder_layers=1,
        decoder_layers=1,
        encoder_ffn_dim=4,
        decoder_ffn_dim=4,
        encoder_attention_heads=1,
        decoder_attention_heads=1,
    )
)

SCREAMING_SNAKE_CASE__											=     FSMTForConditionalGeneration(config)
print(f'num of params {tiny_model.num_parameters()}')

# Test
SCREAMING_SNAKE_CASE__											=     tokenizer(['Making tiny model'], return_tensors='pt')
SCREAMING_SNAKE_CASE__											=     tiny_model(**batch)

print('test output:', len(outputs.logits[0]))

# Save
SCREAMING_SNAKE_CASE__											=     'tiny-wmt19-en-de'
tiny_model.half()  # makes it smaller
tiny_model.save_pretrained(mname_tiny)
tokenizer.save_pretrained(mname_tiny)

print(f'Generated {mname_tiny}')

# Upload
# transformers-cli upload tiny-wmt19-en-de
