



"""simple docstring"""





from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging


lowerCamelCase      =		logging.get_logger(__name__)

lowerCamelCase      =		{
    '''Helsinki-NLP/opus-mt-en-de''': '''https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/config.json''',
    # See all Marian models at https://huggingface.co/models?filter=marian
}
class    lowercase__     (					__a   ):







       '''simple docstring'''


       UpperCamelCase		=  '''marian'''
       UpperCamelCase		=  ['''past_key_values''']
       UpperCamelCase		=  {'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}



       def __init__(							self    :						List[Any]	,       _UpperCAmelCase    :						Tuple=58101	,       _UpperCAmelCase    :						str=None	,       _UpperCAmelCase    :						List[Any]=1024	,       _UpperCAmelCase    :						List[str]=12	,       _UpperCAmelCase    :						Any=4096	,       _UpperCAmelCase    :						Any=16	,       _UpperCAmelCase    :						Optional[Any]=12	,       _UpperCAmelCase    :						Union[str, Any]=4096	,       _UpperCAmelCase    :						int=16	,       _UpperCAmelCase    :						Any=0.0	,       _UpperCAmelCase    :						Union[str, Any]=0.0	,       _UpperCAmelCase    :						Dict=True	,       _UpperCAmelCase    :						List[Any]=True	,       _UpperCAmelCase    :						Dict="gelu"	,       _UpperCAmelCase    :						str=1024	,       _UpperCAmelCase    :						Tuple=0.1	,       _UpperCAmelCase    :						List[str]=0.0	,       _UpperCAmelCase    :						Any=0.0	,       _UpperCAmelCase    :						int=0.02	,       _UpperCAmelCase    :						Tuple=58100	,       _UpperCAmelCase    :						List[Any]=False	,       _UpperCAmelCase    :						Dict=58100	,       _UpperCAmelCase    :						List[Any]=0	,       _UpperCAmelCase    :						int=0	,       _UpperCAmelCase    :						Union[str, Any]=True	,       **_UpperCAmelCase    :						str	,       )				->		List[Any]:


             '''simple docstring'''




             UpperCAmelCase_    				=							vocab_size
             UpperCAmelCase_    				=							decoder_vocab_size or vocab_size
             UpperCAmelCase_    				=							max_position_embeddings
             UpperCAmelCase_    				=							d_model
             UpperCAmelCase_    				=							encoder_ffn_dim
             UpperCAmelCase_    				=							encoder_layers
             UpperCAmelCase_    				=							encoder_attention_heads
             UpperCAmelCase_    				=							decoder_ffn_dim
             UpperCAmelCase_    				=							decoder_layers
             UpperCAmelCase_    				=							decoder_attention_heads
             UpperCAmelCase_    				=							dropout
             UpperCAmelCase_    				=							attention_dropout
             UpperCAmelCase_    				=							activation_dropout
             UpperCAmelCase_    				=							activation_function
             UpperCAmelCase_    				=							init_std
             UpperCAmelCase_    				=							encoder_layerdrop
             UpperCAmelCase_    				=							decoder_layerdrop
             UpperCAmelCase_    				=							use_cache
             UpperCAmelCase_    				=							encoder_layers
             UpperCAmelCase_    				=							scale_embedding  # scale factor will be sqrt(d_model) if True
             UpperCAmelCase_    				=							share_encoder_decoder_embeddings
             super().__init__(
                 pad_token_id=snake_case__	,       eos_token_id=snake_case__	,       is_encoder_decoder=snake_case__	,       decoder_start_token_id=snake_case__	,       forced_eos_token_id=snake_case__	,       **snake_case__	,       )



class    lowercase__     (					__a   ):







       '''simple docstring'''


       @property
       # Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.inputs
       def   lowercase__       (							self    :						Union[str, Any]					)				->		Dict:


             '''simple docstring'''




             if self.task in ["default", "seq2seq-lm"]:
                   UpperCAmelCase_    				=							OrderedDict(
                       [
                           ("input_ids", {0: "batch", 1: "encoder_sequence"}),
                           ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
                       ]					)

                   if self.use_past:
                         UpperCAmelCase_    				=							{0: "batch"}
                         UpperCAmelCase_    				=							{0: "batch", 1: "past_decoder_sequence + sequence"}
                   else:
                         UpperCAmelCase_    				=							{0: "batch", 1: "decoder_sequence"}
                         UpperCAmelCase_    				=							{0: "batch", 1: "decoder_sequence"}

                   if self.use_past:
                         self.fill_with_past_key_values_(snake_case__	,       direction="inputs"					)
             elif self.task == "causal-lm":
                   # TODO: figure this case out.
                   UpperCAmelCase_    				=							OrderedDict(
                       [
                           ("input_ids", {0: "batch", 1: "encoder_sequence"}),
                           ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
                       ]					)
                   if self.use_past:
                         UpperCAmelCase_				,					UpperCAmelCase_    				=							self.num_layers
                         for i in range(snake_case__					):
                               UpperCAmelCase_    				=							{0: "batch", 2: "past_sequence + sequence"}
                               UpperCAmelCase_    				=							{0: "batch", 2: "past_sequence + sequence"}
             else:
                   UpperCAmelCase_    				=							OrderedDict(
                       [
                           ("input_ids", {0: "batch", 1: "encoder_sequence"}),
                           ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
                           ("decoder_input_ids", {0: "batch", 1: "decoder_sequence"}),
                           ("decoder_attention_mask", {0: "batch", 1: "decoder_sequence"}),
                       ]					)

             return common_inputs



       @property
       # Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.outputs
       def   lowercase__       (							self    :						Union[str, Any]					)				->		Tuple:


             '''simple docstring'''




             if self.task in ["default", "seq2seq-lm"]:
                   UpperCAmelCase_    				=							super().outputs
             else:
                   UpperCAmelCase_    				=							super(snake_case__	,       self					).outputs
                   if self.use_past:
                         UpperCAmelCase_				,					UpperCAmelCase_    				=							self.num_layers
                         for i in range(snake_case__					):
                               UpperCAmelCase_    				=							{0: "batch", 2: "past_sequence + sequence"}
                               UpperCAmelCase_    				=							{0: "batch", 2: "past_sequence + sequence"}
             return common_outputs



       def   lowercase__       (							self    :						int	,       _UpperCAmelCase    :						PreTrainedTokenizer	,       _UpperCAmelCase    :						int = -1	,       _UpperCAmelCase    :						int = -1	,       _UpperCAmelCase    :						bool = False	,       _UpperCAmelCase    :						Optional[TensorType] = None	,       )				->		Optional[int]:


             '''simple docstring'''




             UpperCAmelCase_    				=							self._generate_dummy_inputs_for_encoder_and_decoder(
                 snake_case__	,       snake_case__	,       snake_case__	,       snake_case__	,       snake_case__					)

             # Generate decoder inputs
             UpperCAmelCase_    				=							seq_length if not self.use_past else 1
             UpperCAmelCase_    				=							self._generate_dummy_inputs_for_encoder_and_decoder(
                 snake_case__	,       snake_case__	,       snake_case__	,       snake_case__	,       snake_case__					)
             UpperCAmelCase_    				=							{F"""decoder_{name}""": tensor for name, tensor in decoder_inputs.items()}
             UpperCAmelCase_    				=							dict(**snake_case__	,       **snake_case__					)

             if self.use_past:
                   if not is_torch_available():
                         raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed."					)
                   else:
                         import torch
                   UpperCAmelCase_				,					UpperCAmelCase_    				=							common_inputs["input_ids"].shape
                   UpperCAmelCase_    				=							common_inputs["decoder_input_ids"].shape[1]
                   UpperCAmelCase_				,					UpperCAmelCase_    				=							self.num_attention_heads
                   UpperCAmelCase_    				=							(
                       batch,
                       num_encoder_attention_heads,
                       encoder_seq_length,
                       self._config.hidden_size // num_encoder_attention_heads,
                   )
                   UpperCAmelCase_    				=							decoder_seq_length + 3
                   UpperCAmelCase_    				=							(
                       batch,
                       num_decoder_attention_heads,
                       decoder_past_length,
                       self._config.hidden_size // num_decoder_attention_heads,
                   )

                   UpperCAmelCase_    				=							torch.cat(
                       [common_inputs["decoder_attention_mask"], torch.ones(snake_case__	,       snake_case__					)]	,       dim=1					)

                   UpperCAmelCase_    				=							[]
                   # If the number of encoder and decoder layers are present in the model configuration, both are considered
                   UpperCAmelCase_				,					UpperCAmelCase_    				=							self.num_layers
                   UpperCAmelCase_    				=							min(snake_case__	,       snake_case__					)
                   UpperCAmelCase_    				=							max(snake_case__	,       snake_case__					) - min_num_layers
                   UpperCAmelCase_    				=							"encoder" if num_encoder_layers > num_decoder_layers else "decoder"

                   for _ in range(snake_case__					):
                         common_inputs["past_key_values"].append(
                             (
                                 torch.zeros(snake_case__					),
                                 torch.zeros(snake_case__					),
                                 torch.zeros(snake_case__					),
                                 torch.zeros(snake_case__					),
                             )					)
                   # TODO: test this.
                   UpperCAmelCase_    				=							encoder_shape if remaining_side_name == "encoder" else decoder_shape
                   for _ in range(snake_case__	,       snake_case__					):
                         common_inputs["past_key_values"].append((torch.zeros(snake_case__					), torch.zeros(snake_case__					))					)
             return common_inputs



       def   lowercase__       (							self    :						Optional[int]	,       _UpperCAmelCase    :						PreTrainedTokenizer	,       _UpperCAmelCase    :						int = -1	,       _UpperCAmelCase    :						int = -1	,       _UpperCAmelCase    :						bool = False	,       _UpperCAmelCase    :						Optional[TensorType] = None	,       )				->		Any:


             '''simple docstring'''




             UpperCAmelCase_    				=							self._generate_dummy_inputs_for_encoder_and_decoder(
                 snake_case__	,       snake_case__	,       snake_case__	,       snake_case__	,       snake_case__					)

             if self.use_past:
                   if not is_torch_available():
                         raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed."					)
                   else:
                         import torch
                   UpperCAmelCase_				,					UpperCAmelCase_    				=							common_inputs["input_ids"].shape
                   # Not using the same length for past_key_values
                   UpperCAmelCase_    				=							seqlen + 2
                   UpperCAmelCase_				,					UpperCAmelCase_    				=							self.num_layers
                   UpperCAmelCase_				,					UpperCAmelCase_    				=							self.num_attention_heads
                   UpperCAmelCase_    				=							(
                       batch,
                       num_encoder_attention_heads,
                       past_key_values_length,
                       self._config.hidden_size // num_encoder_attention_heads,
                   )

                   UpperCAmelCase_    				=							common_inputs["attention_mask"].dtype
                   UpperCAmelCase_    				=							torch.cat(
                       [common_inputs["attention_mask"], torch.ones(snake_case__	,       snake_case__	,       dtype=snake_case__					)]	,       dim=1					)
                   UpperCAmelCase_    				=							[
                       (torch.zeros(snake_case__					), torch.zeros(snake_case__					)) for _ in range(snake_case__					)
                   ]
             return common_inputs



       def   lowercase__       (							self    :						List[Any]	,       _UpperCAmelCase    :						PreTrainedTokenizer	,       _UpperCAmelCase    :						int = -1	,       _UpperCAmelCase    :						int = -1	,       _UpperCAmelCase    :						bool = False	,       _UpperCAmelCase    :						Optional[TensorType] = None	,       )				->		Tuple:


             '''simple docstring'''




             UpperCAmelCase_    				=							compute_effective_axis_dimension(
                 snake_case__	,       fixed_dimension=OnnxConfig.default_fixed_batch	,       num_token_to_add=0					)

             # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
             UpperCAmelCase_    				=							tokenizer.num_special_tokens_to_add(snake_case__					)
             UpperCAmelCase_    				=							compute_effective_axis_dimension(
                 snake_case__	,       fixed_dimension=OnnxConfig.default_fixed_sequence	,       num_token_to_add=snake_case__					)

             # Generate dummy inputs according to compute batch and sequence
             UpperCAmelCase_    				=							[" ".join([tokenizer.unk_token]					) * seq_length] * batch_size
             UpperCAmelCase_    				=							dict(tokenizer(snake_case__	,       return_tensors=snake_case__					)					)
             return common_inputs



       def   lowercase__       (							self    :						int	,       _UpperCAmelCase    :						PreTrainedTokenizer	,       _UpperCAmelCase    :						int = -1	,       _UpperCAmelCase    :						int = -1	,       _UpperCAmelCase    :						bool = False	,       _UpperCAmelCase    :						Optional[TensorType] = None	,       )				->		str:


             '''simple docstring'''




             if self.task in ["default", "seq2seq-lm"]:
                   UpperCAmelCase_    				=							self._generate_dummy_inputs_for_default_and_seqaseq_lm(
                       snake_case__	,       batch_size=snake_case__	,       seq_length=snake_case__	,       is_pair=snake_case__	,       framework=snake_case__					)

             else:
                   UpperCAmelCase_    				=							self._generate_dummy_inputs_for_causal_lm(
                       snake_case__	,       batch_size=snake_case__	,       seq_length=snake_case__	,       is_pair=snake_case__	,       framework=snake_case__					)

             return common_inputs



       def   lowercase__       (							self    :						Optional[Any]	,       _UpperCAmelCase    :						Tuple	,       _UpperCAmelCase    :						List[str]	,       _UpperCAmelCase    :						List[Any]	,       _UpperCAmelCase    :						Tuple					)				->		str:


             '''simple docstring'''




             if self.task in ["default", "seq2seq-lm"]:
                   UpperCAmelCase_    				=							super()._flatten_past_key_values_(snake_case__	,       snake_case__	,       snake_case__	,       snake_case__					)
             else:
                   UpperCAmelCase_    				=							super(snake_case__	,       self					)._flatten_past_key_values_(
                       snake_case__	,       snake_case__	,       snake_case__	,       snake_case__					)







       @property
       def   lowercase__       (							self    :						Any					)				->		List[Any]:


             '''simple docstring'''




             return 1e-4









"""simple docstring"""





import argparse
import re

import torch
from CLAP import create_model

from transformers import AutoFeatureExtractor, ClapConfig, ClapModel


lowerCamelCase      =		{
    """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""",
}

lowerCamelCase      =		AutoFeatureExtractor.from_pretrained("""laion/clap-htsat-unfused""", truncation="""rand_trunc""")
def      a__			(   lowerCAmelCase__			,    lowerCAmelCase__=False   ):
      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      a__			(   lowerCAmelCase__   ):
      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      a__			(   lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__=False   ):
      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__":
  lowerCamelCase      =		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""")
  lowerCamelCase      =		parser.parse_args()

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




