



from pickle import UnpicklingError

import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict

from ..utils import logging


A__	      =       logging.get_logger(__name__)


def 		_UpperCAmelCase				(      snake_case	,						snake_case			):





					"""simple docstring"""

					try:
										with open(_a	,						"""rb"""			) as flax_state_f:
															_lowerCAmelCase						=    from_bytes(_a	,						flax_state_f.read()			)
					except UnpicklingError as e:
										try:
															with open(_a			) as f:
																				if f.read().startswith("""version"""			):
																									raise OSError(
																									    """You seem to have cloned a repository without having git-lfs installed. Please"""
																									    """ install git-lfs and run `git lfs install` followed by `git lfs pull` in the"""
																									    """ folder you cloned."""			)
																				else:
																									raise ValueError from e
										except (UnicodeDecodeError, ValueError):
															raise EnvironmentError(F'Unable to convert {model_file} to Flax deserializable object. '			)

					return load_flax_weights_in_pytorch_model(_a	,						_a			)


def 		_UpperCAmelCase				(      snake_case	,						snake_case			):





					"""simple docstring"""

					try:
										import torch  # noqa: F401
					except ImportError:
										logger.error(
										    """Loading Flax weights in PyTorch requires both PyTorch and Flax to be installed. Please see"""
										    """ https://pytorch.org/ and https://flax.readthedocs.io/en/latest/installation.html for installation"""
										    """ instructions."""			)
										raise

					# check if we have bf16 weights
					_lowerCAmelCase						=    flatten_dict(jax.tree_util.tree_map(lambda snake_case			: x.dtype == jnp.bfloataa	,						_a			)			).values()
					if any(_a			):
										# convert all weights to fp32 if they are bf16 since torch.from_numpy can-not handle bf16

										# and bf16 is not fully supported in PT yet.
										logger.warning(
										    """Found ``bfloat16`` weights in Flax model. Casting all ``bfloat16`` weights to ``float32`` """
										    """before loading those in PyTorch model."""			)
										_lowerCAmelCase						=    jax.tree_util.tree_map(
										    lambda snake_case			: params.astype(np.floataa			) if params.dtype == jnp.bfloataa else params	,						_a			)

					_lowerCAmelCase						=    """"""

					_lowerCAmelCase						=    flatten_dict(_a	,						sep="""."""			)
					_lowerCAmelCase						=    pt_model.state_dict()

					# keep track of unexpected & missing keys
					_lowerCAmelCase						=    []
					_lowerCAmelCase						=    set(pt_model_dict.keys()			)

					for flax_key_tuple, flax_tensor in flax_state_dict.items():
										_lowerCAmelCase						=    flax_key_tuple.split("""."""			)

										if flax_key_tuple_array[-1] == "kernel" and flax_tensor.ndim == 4:
															_lowerCAmelCase						=    flax_key_tuple_array[:-1] + ["""weight"""]
															_lowerCAmelCase						=    jnp.transpose(_a	,						(3, 2, 0, 1)			)
										elif flax_key_tuple_array[-1] == "kernel":
															_lowerCAmelCase						=    flax_key_tuple_array[:-1] + ["""weight"""]
															_lowerCAmelCase						=    flax_tensor.T
										elif flax_key_tuple_array[-1] == "scale":
															_lowerCAmelCase						=    flax_key_tuple_array[:-1] + ["""weight"""]

										if "time_embedding" not in flax_key_tuple_array:
															for i, flax_key_tuple_string in enumerate(_a			):
																				_lowerCAmelCase						=    (
																				    flax_key_tuple_string.replace("""_0"""	,						""".0"""			)
																				    .replace("""_1"""	,						""".1"""			)
																				    .replace("""_2"""	,						""".2"""			)
																				    .replace("""_3"""	,						""".3"""			)
																				    .replace("""_4"""	,						""".4"""			)
																				    .replace("""_5"""	,						""".5"""			)
																				    .replace("""_6"""	,						""".6"""			)
																				    .replace("""_7"""	,						""".7"""			)
																				    .replace("""_8"""	,						""".8"""			)
																				    .replace("""_9"""	,						""".9"""			)
																				)

										_lowerCAmelCase						=    """.""".join(_a			)

										if flax_key in pt_model_dict:
															if flax_tensor.shape != pt_model_dict[flax_key].shape:
																				raise ValueError(
																				    F'Flax checkpoint seems to be incorrect. Weight {flax_key_tuple} was expected '
																				    F'to be of shape {pt_model_dict[flax_key].shape}, but is {flax_tensor.shape}.'			)
															else:
																				# add weight to pytorch dict
																				_lowerCAmelCase						=    np.asarray(_a			) if not isinstance(_a	,						np.ndarray			) else flax_tensor
																				_lowerCAmelCase						=    torch.from_numpy(_a			)
																				# remove from missing keys
																				missing_keys.remove(_a			)
										else:
															# weight is not expected by PyTorch model
															unexpected_keys.append(_a			)

					pt_model.load_state_dict(_a			)

					# re-transform missing_keys to list
					_lowerCAmelCase						=    list(_a			)

					if len(_a			) > 0:
										logger.warning(
										    """Some weights of the Flax model were not used when initializing the PyTorch model"""
										    F' {pt_model.__class__.__name__}: {unexpected_keys}\n- This IS expected if you are initializing'
										    F' {pt_model.__class__.__name__} from a Flax model trained on another task or with another architecture'
										    """ (e.g. initializing a BertForSequenceClassification model from a FlaxBertForPreTraining model).\n- This"""
										    F' IS NOT expected if you are initializing {pt_model.__class__.__name__} from a Flax model that you expect'
										    """ to be exactly identical (e.g. initializing a BertForSequenceClassification model from a"""
										    """ FlaxBertForSequenceClassification model)."""			)
					if len(_a			) > 0:
										logger.warning(
										    F'Some weights of {pt_model.__class__.__name__} were not initialized from the Flax model and are newly'
										    F' initialized: {missing_keys}\nYou should probably TRAIN this model on a down-stream task to be able to'
										    """ use it for predictions and inference."""			)

					return pt_model









'''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()
A     =logging.get_logger(__name__)





def 						snake_case_							(_a	:							List[str]     ):
    UpperCAmelCase			       =		ASTConfig()

    if "10-10" in model_name:
        pass
    elif "speech-commands" in model_name:
        UpperCAmelCase			       =		1_2_8
    elif "12-12" in model_name:
        UpperCAmelCase			       =		1_2
        UpperCAmelCase			       =		1_2
    elif "14-14" in model_name:
        UpperCAmelCase			       =		1_4
        UpperCAmelCase			       =		1_4
    elif "16-16" in model_name:
        UpperCAmelCase			       =		1_6
        UpperCAmelCase			       =		1_6
    else:
        raise ValueError('''Model not supported'''     )

    UpperCAmelCase			       =		'''huggingface/label-files'''
    if "speech-commands" in model_name:
        UpperCAmelCase			       =		3_5
        UpperCAmelCase			       =		'''speech-commands-v2-id2label.json'''
    else:
        UpperCAmelCase			       =		5_2_7
        UpperCAmelCase			       =		'''audioset-id2label.json'''

    UpperCAmelCase			       =		json.load(open(hf_hub_download(_a    ,   _a    ,   repo_type='''dataset'''     )    ,   '''r'''     )     )
    UpperCAmelCase			       =		{int(_a     ): v for k, v in idalabel.items()}
    UpperCAmelCase			       =		idalabel
    UpperCAmelCase			       =		{v: k for k, v in idalabel.items()}

    return config





def 						snake_case_							(_a	:							Tuple     ):
    if "module.v" in name:
        UpperCAmelCase			       =		name.replace('''module.v'''    ,   '''audio_spectrogram_transformer'''     )
    if "cls_token" in name:
        UpperCAmelCase			       =		name.replace('''cls_token'''    ,   '''embeddings.cls_token'''     )
    if "dist_token" in name:
        UpperCAmelCase			       =		name.replace('''dist_token'''    ,   '''embeddings.distillation_token'''     )
    if "pos_embed" in name:
        UpperCAmelCase			       =		name.replace('''pos_embed'''    ,   '''embeddings.position_embeddings'''     )
    if "patch_embed.proj" in name:
        UpperCAmelCase			       =		name.replace('''patch_embed.proj'''    ,   '''embeddings.patch_embeddings.projection'''     )
    # transformer blocks
    if "blocks" in name:
        UpperCAmelCase			       =		name.replace('''blocks'''    ,   '''encoder.layer'''     )
    if "attn.proj" in name:
        UpperCAmelCase			       =		name.replace('''attn.proj'''    ,   '''attention.output.dense'''     )
    if "attn" in name:
        UpperCAmelCase			       =		name.replace('''attn'''    ,   '''attention.self'''     )
    if "norm1" in name:
        UpperCAmelCase			       =		name.replace('''norm1'''    ,   '''layernorm_before'''     )
    if "norm2" in name:
        UpperCAmelCase			       =		name.replace('''norm2'''    ,   '''layernorm_after'''     )
    if "mlp.fc1" in name:
        UpperCAmelCase			       =		name.replace('''mlp.fc1'''    ,   '''intermediate.dense'''     )
    if "mlp.fc2" in name:
        UpperCAmelCase			       =		name.replace('''mlp.fc2'''    ,   '''output.dense'''     )
    # final layernorm
    if "audio_spectrogram_transformer.norm" in name:
        UpperCAmelCase			       =		name.replace('''audio_spectrogram_transformer.norm'''    ,   '''audio_spectrogram_transformer.layernorm'''     )
    # classifier head
    if "module.mlp_head.0" in name:
        UpperCAmelCase			       =		name.replace('''module.mlp_head.0'''    ,   '''classifier.layernorm'''     )
    if "module.mlp_head.1" in name:
        UpperCAmelCase			       =		name.replace('''module.mlp_head.1'''    ,   '''classifier.dense'''     )

    return name





def 						snake_case_							(_a	:							Dict    ,   _a	:							List[Any]     ):
    for key in orig_state_dict.copy().keys():
        UpperCAmelCase			       =		orig_state_dict.pop(_a     )

        if "qkv" in key:
            UpperCAmelCase			       =		key.split('''.'''     )
            UpperCAmelCase			       =		int(key_split[3]     )
            UpperCAmelCase			       =		config.hidden_size
            if "weight" in key:
                UpperCAmelCase			       =		val[:dim, :]
                UpperCAmelCase			       =		val[dim : dim * 2, :]
                UpperCAmelCase			       =		val[-dim:, :]
            else:
                UpperCAmelCase			       =		val[:dim]
                UpperCAmelCase			       =		val[dim : dim * 2]
                UpperCAmelCase			       =		val[-dim:]
        else:
            UpperCAmelCase			       =		val

    return orig_state_dict





def 						snake_case_							(_a	:							Tuple     ):
    UpperCAmelCase			       =		[
        '''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 						snake_case_							(_a	:							int    ,   _a	:							Union[str, Any]    ,   _a	:							Dict=False     ):
    UpperCAmelCase			       =		get_audio_spectrogram_transformer_config(_a     )

    UpperCAmelCase			       =		{
        '''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			       =		model_name_to_url[model_name]
    UpperCAmelCase			       =		torch.hub.load_state_dict_from_url(_a    ,   map_location='''cpu'''     )
    # remove some keys
    remove_keys(_a     )
    # rename some keys
    UpperCAmelCase			       =		convert_state_dict(_a    ,   _a     )

    # load 🤗 model
    UpperCAmelCase			       =		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
    UpperCAmelCase			       =		-4.267_7393 if '''speech-commands''' not in model_name else -6.84_5978
    UpperCAmelCase			       =		4.568_9974 if '''speech-commands''' not in model_name else 5.565_4526
    UpperCAmelCase			       =		1_0_2_4 if '''speech-commands''' not in model_name else 1_2_8
    UpperCAmelCase			       =		ASTFeatureExtractor(mean=_a    ,   std=_a    ,   max_length=_a     )

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

        UpperCAmelCase       ,     UpperCAmelCase			       =		torchaudio.load(_a     )
        UpperCAmelCase			       =		waveform.squeeze().numpy()

    UpperCAmelCase			       =		feature_extractor(_a    ,   sampling_rate=1_6_0_0_0    ,   return_tensors='''pt'''     )

    # forward pass
    UpperCAmelCase			       =		model(**_a     )
    UpperCAmelCase			       =		outputs.logits

    if model_name == "ast-finetuned-audioset-10-10-0.4593":
        UpperCAmelCase			       =		torch.tensor([-0.8760, -7.0042, -8.6602]     )
    elif model_name == "ast-finetuned-audioset-10-10-0.450":
        UpperCAmelCase			       =		torch.tensor([-1.1986, -7.0903, -8.2718]     )
    elif model_name == "ast-finetuned-audioset-10-10-0.448":
        UpperCAmelCase			       =		torch.tensor([-2.6128, -8.0080, -9.4344]     )
    elif model_name == "ast-finetuned-audioset-10-10-0.448-v2":
        UpperCAmelCase			       =		torch.tensor([-1.5080, -7.4534, -8.8917]     )
    elif model_name == "ast-finetuned-audioset-12-12-0.447":
        UpperCAmelCase			       =		torch.tensor([-0.5050, -6.5833, -8.0843]     )
    elif model_name == "ast-finetuned-audioset-14-14-0.443":
        UpperCAmelCase			       =		torch.tensor([-0.3826, -7.0336, -8.2413]     )
    elif model_name == "ast-finetuned-audioset-16-16-0.442":
        UpperCAmelCase			       =		torch.tensor([-1.2113, -6.9101, -8.3470]     )
    elif model_name == "ast-finetuned-speech-commands-v2":
        UpperCAmelCase			       =		torch.tensor([6.1589, -8.0566, -8.7984]     )
    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__":
   A     =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.'
   )

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




