


"""simple docstring"""







import argparse
import json
from pathlib import Path

import torch
import torchaudio
from datasets import load_dataset
from huggingface_hub import hf_hub_download

from transformers import ASTConfig, ASTFeatureExtractor, ASTForAudioClassification
from transformers.utils import logging


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


def      lowercase			(				A_       )->      Dict:
  '''simple docstring'''







  a						:		str      							=			ASTConfig()

  if "10-10" in model_name:
    pass
  elif "speech-commands" in model_name:
    a						:		Union[str, Any]      							=			128
  elif "12-12" in model_name:
    a						:		List[Any]      							=			12
    a						:		str      							=			12
  elif "14-14" in model_name:
    a						:		List[Any]      							=			14
    a						:		Optional[int]      							=			14
  elif "16-16" in model_name:
    a						:		Any      							=			16
    a						:		List[Any]      							=			16
  else:
    raise ValueError("Model not supported"       )

  a						:		Optional[int]      							=			"huggingface/label-files"
  if "speech-commands" in model_name:
    a						:		Optional[int]      							=			35
    a						:		List[str]      							=			"speech-commands-v2-id2label.json"
  else:
    a						:		Optional[Any]      							=			527
    a						:		Tuple      							=			"audioset-id2label.json"

  a						:		List[str]      							=			json.load(open(hf_hub_download(A_  ,   A_  ,   repo_type="dataset"       )  ,   "r"       )       )
  a						:		Union[str, Any]      							=			{int(A_       ): v for k, v in idalabel.items()}
  a						:		Any      							=			idalabel
  a						:		str      							=			{v: k for k, v in idalabel.items()}

  return config


def      lowercase			(				A_       )->      Tuple:
  '''simple docstring'''







  if "module.v" in name:
    a						:		Union[str, Any]      							=			name.replace("module.v"  ,   "audio_spectrogram_transformer"       )
  if "cls_token" in name:
    a						:		List[Any]      							=			name.replace("cls_token"  ,   "embeddings.cls_token"       )
  if "dist_token" in name:
    a						:		Union[str, Any]      							=			name.replace("dist_token"  ,   "embeddings.distillation_token"       )
  if "pos_embed" in name:
    a						:		str      							=			name.replace("pos_embed"  ,   "embeddings.position_embeddings"       )
  if "patch_embed.proj" in name:
    a						:		Union[str, Any]      							=			name.replace("patch_embed.proj"  ,   "embeddings.patch_embeddings.projection"       )
  # transformer blocks
  if "blocks" in name:
    a						:		Union[str, Any]      							=			name.replace("blocks"  ,   "encoder.layer"       )
  if "attn.proj" in name:
    a						:		str      							=			name.replace("attn.proj"  ,   "attention.output.dense"       )
  if "attn" in name:
    a						:		Tuple      							=			name.replace("attn"  ,   "attention.self"       )
  if "norm1" in name:
    a						:		int      							=			name.replace("norm1"  ,   "layernorm_before"       )
  if "norm2" in name:
    a						:		Union[str, Any]      							=			name.replace("norm2"  ,   "layernorm_after"       )
  if "mlp.fc1" in name:
    a						:		Union[str, Any]      							=			name.replace("mlp.fc1"  ,   "intermediate.dense"       )
  if "mlp.fc2" in name:
    a						:		Optional[Any]      							=			name.replace("mlp.fc2"  ,   "output.dense"       )
  # final layernorm
  if "audio_spectrogram_transformer.norm" in name:
    a						:		Tuple      							=			name.replace("audio_spectrogram_transformer.norm"  ,   "audio_spectrogram_transformer.layernorm"       )
  # classifier head
  if "module.mlp_head.0" in name:
    a						:		List[str]      							=			name.replace("module.mlp_head.0"  ,   "classifier.layernorm"       )
  if "module.mlp_head.1" in name:
    a						:		Optional[int]      							=			name.replace("module.mlp_head.1"  ,   "classifier.dense"       )

  return name


def      lowercase			(				A_  ,   A_       )->      Any:
  '''simple docstring'''







  for key in orig_state_dict.copy().keys():
    a						:		str      							=			orig_state_dict.pop(A_       )

    if "qkv" in key:
      a						:		int      							=			key.split("."       )
      a						:		Optional[int]      							=			int(key_split[3]       )
      a						:		int      							=			config.hidden_size
      if "weight" in key:
        a						:		List[str]      							=			val[:dim, :]
        a						:		Any      							=			val[dim : dim * 2, :]
        a						:		int      							=			val[-dim:, :]
      else:
        a						:		Optional[Any]      							=			val[:dim]
        a						:		Union[str, Any]      							=			val[dim : dim * 2]
        a						:		str      							=			val[-dim:]
    else:
      a						:		str      							=			val

  return orig_state_dict


def      lowercase			(				A_       )->      Dict:
  '''simple docstring'''







  a						:		Union[str, Any]      							=			[
      "module.v.head.weight",
      "module.v.head.bias",
      "module.v.head_dist.weight",
      "module.v.head_dist.bias",
  ]
  for k in ignore_keys:
    state_dict.pop(A_  ,   A_       )


@torch.no_grad()
def      lowercase			(				A_  ,   A_  ,   A_=False       )->      Optional[int]:
  '''simple docstring'''







  a						:		Optional[int]      							=			get_audio_spectrogram_transformer_config(A_       )

  a						:		Dict      							=			{
      "ast-finetuned-audioset-10-10-0.4593": (
          "https://www.dropbox.com/s/ca0b1v2nlxzyeb4/audioset_10_10_0.4593.pth?dl=1"
      ),
      "ast-finetuned-audioset-10-10-0.450": (
          "https://www.dropbox.com/s/1tv0hovue1bxupk/audioset_10_10_0.4495.pth?dl=1"
      ),
      "ast-finetuned-audioset-10-10-0.448": (
          "https://www.dropbox.com/s/6u5sikl4b9wo4u5/audioset_10_10_0.4483.pth?dl=1"
      ),
      "ast-finetuned-audioset-10-10-0.448-v2": (
          "https://www.dropbox.com/s/kt6i0v9fvfm1mbq/audioset_10_10_0.4475.pth?dl=1"
      ),
      "ast-finetuned-audioset-12-12-0.447": (
          "https://www.dropbox.com/s/snfhx3tizr4nuc8/audioset_12_12_0.4467.pth?dl=1"
      ),
      "ast-finetuned-audioset-14-14-0.443": (
          "https://www.dropbox.com/s/z18s6pemtnxm4k7/audioset_14_14_0.4431.pth?dl=1"
      ),
      "ast-finetuned-audioset-16-16-0.442": (
          "https://www.dropbox.com/s/mdsa4t1xmcimia6/audioset_16_16_0.4422.pth?dl=1"
      ),
      "ast-finetuned-speech-commands-v2": (
          "https://www.dropbox.com/s/q0tbqpwv44pquwy/speechcommands_10_10_0.9812.pth?dl=1"
      ),
  }

  # load original state_dict
  a						:		Any      							=			model_name_to_url[model_name]
  a						:		List[Any]      							=			torch.hub.load_state_dict_from_url(A_  ,   map_location="cpu"       )
  # remove some keys
  remove_keys(A_       )
  # rename some keys
  a						:		Union[str, Any]      							=			convert_state_dict(A_  ,   A_       )

  # load 🤗 model
  a						:		List[str]      							=			ASTForAudioClassification(A_       )
  model.eval()

  model.load_state_dict(A_       )

  # verify outputs on dummy input
  # source: https://github.com/YuanGongND/ast/blob/79e873b8a54d0a3b330dd522584ff2b9926cd581/src/run.py#L62
  a						:		Tuple      							=			-4.2_6_7_7_3_9_3 if "speech-commands" not in model_name else -6.8_4_5_9_7_8
  a						:		Union[str, Any]      							=			4.5_6_8_9_9_7_4 if "speech-commands" not in model_name else 5.5_6_5_4_5_2_6
  a						:		str      							=			1_024 if "speech-commands" not in model_name else 128
  a						:		List[Any]      							=			ASTFeatureExtractor(mean=A_  ,   std=A_  ,   max_length=A_       )

  if "speech-commands" in model_name:
    a						:		List[str]      							=			load_dataset("speech_commands"  ,   "v0.02"  ,   split="validation"       )
    a						:		int      							=			dataset[0]["audio"]["array"]
  else:
    a						:		Tuple      							=			hf_hub_download(
        repo_id="nielsr/audio-spectogram-transformer-checkpoint"  ,   filename="sample_audio.flac"  ,   repo_type="dataset"  ,   )

    a							,		a						:		Tuple      							=			torchaudio.load(A_       )
    a						:		Optional[Any]      							=			waveform.squeeze().numpy()

  a						:		Union[str, Any]      							=			feature_extractor(A_  ,   sampling_rate=16_000  ,   return_tensors="pt"       )

  # forward pass
  a						:		Optional[Any]      							=			model(**A_       )
  a						:		List[str]      							=			outputs.logits

  if model_name == "ast-finetuned-audioset-10-10-0.4593":
    a						:		Any      							=			torch.tensor([-0.8_7_6_0, -7.0_0_4_2, -8.6_6_0_2]       )
  elif model_name == "ast-finetuned-audioset-10-10-0.450":
    a						:		Optional[int]      							=			torch.tensor([-1.1_9_8_6, -7.0_9_0_3, -8.2_7_1_8]       )
  elif model_name == "ast-finetuned-audioset-10-10-0.448":
    a						:		List[str]      							=			torch.tensor([-2.6_1_2_8, -8.0_0_8_0, -9.4_3_4_4]       )
  elif model_name == "ast-finetuned-audioset-10-10-0.448-v2":
    a						:		Tuple      							=			torch.tensor([-1.5_0_8_0, -7.4_5_3_4, -8.8_9_1_7]       )
  elif model_name == "ast-finetuned-audioset-12-12-0.447":
    a						:		int      							=			torch.tensor([-0.5_0_5_0, -6.5_8_3_3, -8.0_8_4_3]       )
  elif model_name == "ast-finetuned-audioset-14-14-0.443":
    a						:		Any      							=			torch.tensor([-0.3_8_2_6, -7.0_3_3_6, -8.2_4_1_3]       )
  elif model_name == "ast-finetuned-audioset-16-16-0.442":
    a						:		Dict      							=			torch.tensor([-1.2_1_1_3, -6.9_1_0_1, -8.3_4_7_0]       )
  elif model_name == "ast-finetuned-speech-commands-v2":
    a						:		Union[str, Any]      							=			torch.tensor([6.1_5_8_9, -8.0_5_6_6, -8.7_9_8_4]       )
  else:
    raise ValueError("Unknown model name"       )
  if not torch.allclose(logits[0, :3]  ,   A_  ,   atol=1e-4       ):
    raise ValueError("Logits don't match"       )
  print("Looks ok!"       )

  if pytorch_dump_folder_path is not None:
    Path(A_       ).mkdir(exist_ok=A_       )
    print(F'''Saving model {model_name} to {pytorch_dump_folder_path}'''       )
    model.save_pretrained(A_       )
    print(F'''Saving feature extractor to {pytorch_dump_folder_path}'''       )
    feature_extractor.save_pretrained(A_       )

  if push_to_hub:
    print("Pushing model and feature extractor to the hub..."       )
    model.push_to_hub(F'''MIT/{model_name}'''       )
    feature_extractor.push_to_hub(F'''MIT/{model_name}'''       )


if __name__ == "__main__":
 __lowercase    					=      argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     """--model_name""",
     default="""ast-finetuned-audioset-10-10-0.4593""",
     type=str,
     help="""Name of the Audio Spectrogram Transformer model you'd like to convert.""",
 )
 parser.add_argument(
     """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
 )
 parser.add_argument(
     """--push_to_hub""", action="""store_true""", help="""Whether or not to push the converted model to the 🤗 hub."""
 )

 __lowercase    					=      parser.parse_args()
 convert_audio_spectrogram_transformer_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)












# 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 vocab first, and then a tiny model - so the outcome is truly tiny -
# all files ~60KB. As compared to taking a full-size model, reducing to the minimum its layers and
# emb dimensions, but keeping the full vocab + merges files, leading to ~3MB in total for all files.
# The latter is done by `fsmt-make-super-tiny-model.py`.
#
# It will be used then as "stas/tiny-wmt19-en-ru"

from pathlib import Path
import json
import tempfile

from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration
from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES

_A     =			'''tiny-wmt19-en-ru'''

# Build

# borrowed from a test
_A     =			[
    '''l''',
    '''o''',
    '''w''',
    '''e''',
    '''r''',
    '''s''',
    '''t''',
    '''i''',
    '''d''',
    '''n''',
    '''w</w>''',
    '''r</w>''',
    '''t</w>''',
    '''lo''',
    '''low''',
    '''er</w>''',
    '''low</w>''',
    '''lowest</w>''',
    '''newer</w>''',
    '''wider</w>''',
    '''<unk>''',
]
_A     =			dict(zip(vocab, range(len(vocab))))
_A     =			['''l o 123''', '''lo w 1456''', '''e r</w> 1789''', '''''']

with tempfile.TemporaryDirectory() as tmpdirname:
						_A     =			Path(tmpdirname)
						_A     =			build_dir / VOCAB_FILES_NAMES['''src_vocab_file''']
						_A     =			build_dir / VOCAB_FILES_NAMES['''tgt_vocab_file''']
						_A     =			build_dir / VOCAB_FILES_NAMES['''merges_file''']
						with open(src_vocab_file, '''w''') as fp:
												fp.write(json.dumps(vocab_tokens))
						with open(tgt_vocab_file, '''w''') as fp:
												fp.write(json.dumps(vocab_tokens))
						with open(merges_file, '''w''') as fp:
												fp.write('''\n'''.join(merges))

						_A     =			FSMTTokenizer(
						    langs=['''en''', '''ru'''],
						    src_vocab_size=len(vocab),
						    tgt_vocab_size=len(vocab),
						    src_vocab_file=src_vocab_file,
						    tgt_vocab_file=tgt_vocab_file,
						    merges_file=merges_file,
						)

_A     =			FSMTConfig(
    langs=['''ru''', '''en'''],
    src_vocab_size=1_000,
    tgt_vocab_size=1_000,
    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,
)

_A     =			FSMTForConditionalGeneration(config)
print(f"num of params {tiny_model.num_parameters()}")

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

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

# Save
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-ru




