
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()
lowerCamelCase      =logging.get_logger(__name__)


def 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__		):
						UpperCamelCase__    :   List[str]					=				ASTConfig()

						if "10-10" in model_name:
												pass
						elif "speech-commands" in model_name:
												UpperCamelCase__    :   List[Any]					=				1_2_8
						elif "12-12" in model_name:
												UpperCamelCase__    :   str					=				1_2
												UpperCamelCase__    :   Tuple					=				1_2
						elif "14-14" in model_name:
												UpperCamelCase__    :   Any					=				1_4
												UpperCamelCase__    :   Tuple					=				1_4
						elif "16-16" in model_name:
												UpperCamelCase__    :   Any					=				1_6
												UpperCamelCase__    :   Optional[Any]					=				1_6
						else:
												raise ValueError('''Model not supported'''		)

						UpperCamelCase__    :   Tuple					=				"""huggingface/label-files"""
						if "speech-commands" in model_name:
												UpperCamelCase__    :   Optional[Any]					=				3_5
												UpperCamelCase__    :   Optional[Any]					=				"""speech-commands-v2-id2label.json"""
						else:
												UpperCamelCase__    :   Optional[int]					=				5_2_7
												UpperCamelCase__    :   int					=				"""audioset-id2label.json"""

						UpperCamelCase__    :   str					=				json.load(open(hf_hub_download(snake_case_ ,		snake_case_ ,		repo_type='''dataset'''		) ,		'''r'''		)		)
						UpperCamelCase__    :   List[str]					=				{int(snake_case_		): v for k, v in idalabel.items()}
						UpperCamelCase__    :   Optional[int]					=				idalabel
						UpperCamelCase__    :   Tuple					=				{v: k for k, v in idalabel.items()}

						return config


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

						return name


def 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__ ,		UpperCamelCase__		):
						for key in orig_state_dict.copy().keys():
												UpperCamelCase__    :   int					=				orig_state_dict.pop(snake_case_		)

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

						return orig_state_dict


def 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__		):
						UpperCamelCase__    :   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(snake_case_ ,		snake_case_		)


@torch.no_grad()
def 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__=False		):
						UpperCamelCase__    :   Any					=				get_audio_spectrogram_transformer_config(snake_case_		)

						UpperCamelCase__    :   str					=				{
						    """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
						UpperCamelCase__    :   Optional[Any]					=				model_name_to_url[model_name]
						UpperCamelCase__    :   Dict					=				torch.hub.load_state_dict_from_url(snake_case_ ,		map_location='''cpu'''		)
						# remove some keys
						remove_keys(snake_case_		)
						# rename some keys
						UpperCamelCase__    :   Tuple					=				convert_state_dict(snake_case_ ,		snake_case_		)

						# load 🤗 model
						UpperCamelCase__    :   str					=				ASTForAudioClassification(snake_case_		)
						model.eval()

						model.load_state_dict(snake_case_		)

						# verify outputs on dummy input
						# source: https://github.com/YuanGongND/ast/blob/79e873b8a54d0a3b330dd522584ff2b9926cd581/src/run.py#L62
						UpperCamelCase__    :   Any					=				-4.267_7393 if """speech-commands""" not in model_name else -6.84_5978
						UpperCamelCase__    :   Tuple					=				4.568_9974 if """speech-commands""" not in model_name else 5.565_4526
						UpperCamelCase__    :   Any					=				1_0_2_4 if """speech-commands""" not in model_name else 1_2_8
						UpperCamelCase__    :   Union[str, Any]					=				ASTFeatureExtractor(mean=snake_case_ ,		std=snake_case_ ,		max_length=snake_case_		)

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

												UpperCamelCase__    :   List[Any]					=				torchaudio.load(snake_case_		)
												UpperCamelCase__    :   Union[str, Any]					=				waveform.squeeze().numpy()

						UpperCamelCase__    :   List[str]					=				feature_extractor(snake_case_ ,		sampling_rate=1_6_0_0_0 ,		return_tensors='''pt'''		)

						# forward pass
						UpperCamelCase__    :   str					=				model(**snake_case_		)
						UpperCamelCase__    :   Optional[Any]					=				outputs.logits

						if model_name == "ast-finetuned-audioset-10-10-0.4593":
												UpperCamelCase__    :   Optional[int]					=				torch.tensor([-0.8760, -7.0042, -8.6602]		)
						elif model_name == "ast-finetuned-audioset-10-10-0.450":
												UpperCamelCase__    :   Any					=				torch.tensor([-1.1986, -7.0903, -8.2718]		)
						elif model_name == "ast-finetuned-audioset-10-10-0.448":
												UpperCamelCase__    :   Any					=				torch.tensor([-2.6128, -8.0080, -9.4344]		)
						elif model_name == "ast-finetuned-audioset-10-10-0.448-v2":
												UpperCamelCase__    :   Any					=				torch.tensor([-1.5080, -7.4534, -8.8917]		)
						elif model_name == "ast-finetuned-audioset-12-12-0.447":
												UpperCamelCase__    :   str					=				torch.tensor([-0.5050, -6.5833, -8.0843]		)
						elif model_name == "ast-finetuned-audioset-14-14-0.443":
												UpperCamelCase__    :   int					=				torch.tensor([-0.3826, -7.0336, -8.2413]		)
						elif model_name == "ast-finetuned-audioset-16-16-0.442":
												UpperCamelCase__    :   Union[str, Any]					=				torch.tensor([-1.2113, -6.9101, -8.3470]		)
						elif model_name == "ast-finetuned-speech-commands-v2":
												UpperCamelCase__    :   int					=				torch.tensor([6.1589, -8.0566, -8.7984]		)
						else:
												raise ValueError('''Unknown model name'''		)
						if not torch.allclose(logits[0, :3] ,		snake_case_ ,		atol=1e-4		):
												raise ValueError('''Logits don\'t match'''		)
						print('''Looks ok!'''		)

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

						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__":
				lowerCamelCase      =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."
				)

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




import argparse

from torch import nn

# transformers_old should correspond to branch `save_old_prophetnet_model_structure` here
# original prophetnet_checkpoints are saved under `patrickvonplaten/..._old` respectively
from transformers_old.modeling_prophetnet import (
    ProphetNetForConditionalGeneration as ProphetNetForConditionalGenerationOld,
)
from transformers_old.modeling_xlm_prophetnet import (
    XLMProphetNetForConditionalGeneration as XLMProphetNetForConditionalGenerationOld,
)

from transformers import ProphetNetForConditionalGeneration, XLMProphetNetForConditionalGeneration, logging


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


def 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__ ,		UpperCamelCase__		):
      if "xprophetnet" in prophetnet_checkpoint_path:
            UpperCamelCase__    :   int					=				XLMProphetNetForConditionalGenerationOld.from_pretrained(UpperCamelCase__		)
            UpperCamelCase__				,UpperCamelCase__    :   Union[str, Any]					=				XLMProphetNetForConditionalGeneration.from_pretrained(
                UpperCamelCase__ ,		output_loading_info=UpperCamelCase__		)
      else:
            UpperCamelCase__    :   Tuple					=				ProphetNetForConditionalGenerationOld.from_pretrained(UpperCamelCase__		)
            UpperCamelCase__				,UpperCamelCase__    :   Union[str, Any]					=				ProphetNetForConditionalGeneration.from_pretrained(
                UpperCamelCase__ ,		output_loading_info=UpperCamelCase__		)

      UpperCamelCase__    :   Optional[Any]					=				['''key_proj''', '''value_proj''', '''query_proj''']

      UpperCamelCase__    :   Tuple					=				{
          '''self_attn''': '''ngram_self_attn''',
          '''cross_attn''': '''encoder_attn''',
          '''cross_attn_layer_norm''': '''encoder_attn_layer_norm''',
          '''feed_forward_layer_norm''': '''final_layer_norm''',
          '''feed_forward''': '''''',
          '''intermediate''': '''fc1''',
          '''output''': '''fc2''',
          '''key_proj''': '''k_proj''',
          '''query_proj''': '''q_proj''',
          '''value_proj''': '''v_proj''',
          '''word_embeddings''': '''embed_tokens''',
          '''embeddings_layer_norm''': '''emb_layer_norm''',
          '''relative_pos_embeddings''': '''relative_linear''',
          '''ngram_embeddings''': '''ngram_input_embed''',
          '''position_embeddings''': '''embed_positions''',
      }

      for key in loading_info["missing_keys"]:
            UpperCamelCase__    :   Tuple					=				key.split('''.'''		)

            if attributes[0] == "lm_head":
                  UpperCamelCase__    :   Union[str, Any]					=				prophet
                  UpperCamelCase__    :   int					=				prophet_old
            else:
                  UpperCamelCase__    :   int					=				prophet.prophetnet
                  UpperCamelCase__    :   str					=				prophet_old.model

            UpperCamelCase__    :   Tuple					=				False
            for attribute in attributes:
                  if attribute in mapping:
                        UpperCamelCase__    :   List[str]					=				mapping[attribute]
                        if not hasattr(UpperCamelCase__ ,		UpperCamelCase__		) and len(UpperCamelCase__		) > 0:
                              UpperCamelCase__    :   Tuple					=				attribute
                  elif hasattr(UpperCamelCase__ ,		UpperCamelCase__		):
                        UpperCamelCase__    :   Tuple					=				attribute

                  if attribute == "weight":
                        assert old_model.weight.shape == model.weight.shape, "Shapes have to match!"
                        UpperCamelCase__    :   Dict					=				old_model.weight
                        logger.info(f'''{attribute} is initialized.'''		)
                        UpperCamelCase__    :   Optional[Any]					=				True
                        break
                  elif attribute == "bias":
                        assert old_model.bias.shape == model.bias.shape, "Shapes have to match!"
                        UpperCamelCase__    :   Any					=				old_model.bias
                        logger.info(f'''{attribute} is initialized'''		)
                        UpperCamelCase__    :   Any					=				True
                        break
                  elif attribute in special_keys and hasattr(UpperCamelCase__ ,		'''in_proj_weight'''		):
                        UpperCamelCase__    :   Optional[int]					=				old_model.in_proj_weight.shape[0] // 3
                        UpperCamelCase__    :   Optional[Any]					=				getattr(UpperCamelCase__ ,		UpperCamelCase__		)
                        param.weight.shape == old_model.in_proj_weight[:embed_dim, :].shape, "Shapes have to match"
                        param.bias.shape == old_model.in_proj_bias[:embed_dim].shape, "Shapes have to match"
                        if attribute == "query_proj":
                              UpperCamelCase__    :   Optional[Any]					=				nn.Parameter(old_model.in_proj_weight[:embed_dim, :]		)
                              UpperCamelCase__    :   str					=				nn.Parameter(old_model.in_proj_bias[:embed_dim]		)

                        elif attribute == "key_proj":
                              UpperCamelCase__    :   Union[str, Any]					=				nn.Parameter(old_model.in_proj_weight[embed_dim : 2 * embed_dim, :]		)
                              UpperCamelCase__    :   Optional[int]					=				nn.Parameter(old_model.in_proj_bias[embed_dim : 2 * embed_dim]		)
                        elif attribute == "value_proj":
                              UpperCamelCase__    :   List[str]					=				nn.Parameter(old_model.in_proj_weight[2 * embed_dim :, :]		)
                              UpperCamelCase__    :   Tuple					=				nn.Parameter(old_model.in_proj_bias[2 * embed_dim :]		)
                        UpperCamelCase__    :   Tuple					=				True
                        break
                  elif attribute == "position_embeddings":
                        assert (
                            model.position_embeddings.weight.shape[-1] == old_model.embed_positions.weight.shape[-1]
                        ), "Hidden size has to match"
                        assert model.position_embeddings.weight.shape[0] == 5_1_2, "We want 512 position_embeddings."
                        UpperCamelCase__    :   Optional[Any]					=				nn.Parameter(old_model.embed_positions.weight[:5_1_2, :]		)
                        UpperCamelCase__    :   List[Any]					=				True
                        break

                  if attribute.isdigit():
                        UpperCamelCase__    :   int					=				model[int(UpperCamelCase__		)]
                        UpperCamelCase__    :   int					=				old_model[int(UpperCamelCase__		)]
                  else:
                        UpperCamelCase__    :   str					=				getattr(UpperCamelCase__ ,		UpperCamelCase__		)

                        if old_attribute == "":
                              UpperCamelCase__    :   List[str]					=				old_model
                        else:
                              if not hasattr(UpperCamelCase__ ,		UpperCamelCase__		):
                                    raise ValueError(f'''{old_model} does not have {old_attribute}'''		)
                              UpperCamelCase__    :   List[Any]					=				getattr(UpperCamelCase__ ,		UpperCamelCase__		)

            if not is_key_init:
                  raise ValueError(f'''{key} was not correctly initialized!'''		)

      print(f'''Saving model to {pytorch_dump_folder_path}'''		)
      prophet.save_pretrained(UpperCamelCase__		)


if __name__ == "__main__":
    lowerCamelCase      =argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        "--prophetnet_checkpoint_path", default=None, type=str, required=True, help="Path the official PyTorch dump."
    )
    parser.add_argument(
        "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
    )
    lowerCamelCase      =parser.parse_args()
    convert_prophetnet_checkpoint_to_pytorch(args.prophetnet_checkpoint_path, args.pytorch_dump_folder_path)


