



from typing import List, Optional, Union

import numpy as np

from ....audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ....feature_extraction_sequence_utils import SequenceFeatureExtractor
from ....feature_extraction_utils import BatchFeature
from ....file_utils import PaddingStrategy, TensorType
from ....utils import logging


_snake_case		=  logging.get_logger(__name__)



class   UpperCAmelCase_    (       a):
							lowerCamelCase__   			=		['input_features', 'attention_mask']







							def __init__(   self,			__a=80,			__a=1_6000,			__a=0.0,			__a=10,			__a=25,			__a="hamming_window",			__a=32_768.0,			__a=0.97,			__a=1.0,			__a=True,			__a=True,			__a=False,			**__a,			):

														'''simple docstring'''

														super().__init__(feature_size=__a,			sampling_rate=__a,			padding_value=__a,			**__a)

														_lowerCAmelCase     :							int    =    feature_size
														_lowerCAmelCase     :							Optional[Any]    =    sampling_rate
														_lowerCAmelCase     :							Tuple    =    padding_value
														_lowerCAmelCase     :							int    =    hop_length
														_lowerCAmelCase     :							str    =    win_length
														_lowerCAmelCase     :							str    =    frame_signal_scale
														_lowerCAmelCase     :							Union[str, Any]    =    preemphasis_coeff
														_lowerCAmelCase     :							Optional[Any]    =    mel_floor
														_lowerCAmelCase     :							str    =    normalize_means
														_lowerCAmelCase     :							Union[str, Any]    =    normalize_vars
														_lowerCAmelCase     :							List[str]    =    win_function
														_lowerCAmelCase     :							str    =    return_attention_mask

														_lowerCAmelCase     :							str    =    win_length * sampling_rate // 1000
														_lowerCAmelCase     :							Union[str, Any]    =    hop_length * sampling_rate // 1000

														_lowerCAmelCase     :							Tuple    =    optimal_fft_length(self.sample_size)
														_lowerCAmelCase     :							Tuple    =    (self.n_fft // 2) + 1







							def    snake_case__      (   self,			__a):

														'''simple docstring'''

														if self.win_function == "hamming_window":
																					_lowerCAmelCase     :							str    =    window_function(window_length=self.sample_size,			name=self.win_function,			periodic=__a)
														else:
																					_lowerCAmelCase     :							Union[str, Any]    =    window_function(window_length=self.sample_size,			name=self.win_function)

														_lowerCAmelCase     :							List[Any]    =    mel_filter_bank(
														    num_frequency_bins=self.n_freqs,			num_mel_filters=self.feature_size,			min_frequency=0.0,			max_frequency=self.sampling_rate / 2.0,			sampling_rate=self.sampling_rate,			)

														_lowerCAmelCase     :							Optional[Any]    =    spectrogram(
														    one_waveform * self.frame_signal_scale,			window=__a,			frame_length=self.sample_size,			hop_length=self.sample_stride,			fft_length=self.n_fft,			center=__a,			preemphasis=self.preemphasis_coeff,			mel_filters=__a,			mel_floor=self.mel_floor,			log_mel="log",			)
														return msfc_features.T







							def    snake_case__      (   self,			__a,			__a,			__a):

														'''simple docstring'''

														if self.normalize_means:
																					_lowerCAmelCase     :							Dict    =    x[:input_length].mean(axis=0)
																					_lowerCAmelCase     :							Optional[Any]    =    np.subtract(__a,			__a)
														if self.normalize_vars:
																					_lowerCAmelCase     :							List[str]    =    x[:input_length].std(axis=0)
																					_lowerCAmelCase     :							Optional[int]    =    np.divide(__a,			__a)

														if input_length < x.shape[0]:
																					_lowerCAmelCase     :							Union[str, Any]    =    padding_value

														# make sure array is in float32
														_lowerCAmelCase     :							Optional[int]    =    x.astype(np.floataa)

														return x







							def    snake_case__      (   self,			__a,			__a = None):

														'''simple docstring'''

														_lowerCAmelCase     :							int    =    attention_mask.sum(-1) if attention_mask is not None else [x.shape[0] for x in input_features]
														return [self._normalize_one(__a,			__a,			self.padding_value) for x, n in zip(__a,			__a)]







							def __call__(   self,			__a,			__a = False,			__a = None,			__a = False,			__a = None,			__a = None,			__a = None,			__a = None,			**__a,			):

														'''simple docstring'''

														if sampling_rate is not None:
																					if sampling_rate != self.sampling_rate:
																												raise ValueError(
																												    f"The model corresponding to this feature extractor: {self} was trained using a sampling rate of"
																												    f" {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled with"
																												    f" {self.sampling_rate} and not {sampling_rate}.")
														else:
																					logger.warning(
																					    "It is strongly recommended to pass the ``sampling_rate`` argument to this function. "
																					    "Failing to do so can result in silent errors that might be hard to debug.")

														_lowerCAmelCase     :							List[Any]    =    isinstance(__a,			np.ndarray) and len(raw_speech.shape) > 1
														if is_batched_numpy and len(raw_speech.shape) > 2:
																					raise ValueError(f"Only mono-channel audio is supported for input to {self}")
														_lowerCAmelCase     :							int    =    is_batched_numpy or (
														    isinstance(__a,			(list, tuple)) and (isinstance(raw_speech[0],			(np.ndarray, tuple, list)))
														)

														if is_batched:
																					_lowerCAmelCase     :							Any    =    [np.asarray(__a,			dtype=np.floataa) for speech in raw_speech]
														elif not is_batched and not isinstance(__a,			np.ndarray):
																					_lowerCAmelCase     :							Tuple    =    np.asarray(__a,			dtype=np.floataa)
														elif isinstance(__a,			np.ndarray) and raw_speech.dtype is np.dtype(np.floataa):
																					_lowerCAmelCase     :							Union[str, Any]    =    raw_speech.astype(np.floataa)

														# always return batch
														if not is_batched:
																					_lowerCAmelCase     :							Optional[Any]    =    [raw_speech]

														# extract fbank features
														_lowerCAmelCase     :							List[str]    =    [self._extract_mfsc_features(__a) for one_waveform in raw_speech]

														# convert into correct format for padding
														_lowerCAmelCase     :							int    =    BatchFeature({"input_features": features})

														_lowerCAmelCase     :							str    =    self.pad(
														    __a,			padding=__a,			max_length=__a,			truncation=__a,			pad_to_multiple_of=__a,			return_attention_mask=__a,			**__a,			)
														# make sure list is in array format
														_lowerCAmelCase     :							Optional[Any]    =    padded_inputs.get("input_features")
														if isinstance(input_features[0],			__a):
																					_lowerCAmelCase     :							int    =    [np.asarray(__a,			dtype=np.floataa) for feature in input_features]

														_lowerCAmelCase     :							str    =    padded_inputs.get("attention_mask")
														if attention_mask is not None:
																					_lowerCAmelCase     :							Optional[Any]    =    [np.asarray(__a,			dtype=np.intaa) for array in attention_mask]

														if self.normalize_means or self.normalize_vars:
																					_lowerCAmelCase     :							int    =    (
																					    np.array(__a,			dtype=np.intaa)
																					    if self._get_padding_strategies(__a,			max_length=__a) is not PaddingStrategy.DO_NOT_PAD
																					    and padding
																					    else None
																					)
																					_lowerCAmelCase     :							List[Any]    =    self.normalize(
																					    padded_inputs["input_features"],			attention_mask=__a)

														if return_tensors is not None:
																					_lowerCAmelCase     :							Tuple    =    padded_inputs.convert_to_tensors(__a)

														return padded_inputs





import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm import create_model
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform

from transformers import BitConfig, BitForImageClassification, BitImageProcessor
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


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






def 				A				(					_lowerCamelCase  ):





							'''simple docstring'''




							_lowerCAmelCase     :							List[Any]    =    "huggingface/label-files"
							_lowerCAmelCase     :							int    =    "imagenet-1k-id2label.json"
							_lowerCAmelCase     :							Tuple    =    json.load(open(hf_hub_download(_lowerCamelCase				, _lowerCamelCase				, repo_type="dataset"  )				, "r"  )  )
							_lowerCAmelCase     :							Tuple    =    {int(_lowerCamelCase  ): v for k, v in idalabel.items()}
							_lowerCAmelCase     :							Union[str, Any]    =    {v: k for k, v in idalabel.items()}

							_lowerCAmelCase     :							Tuple    =    "std_conv" if "bit" in model_name else False

							# note that when using BiT as backbone for ViT-hybrid checkpoints,
							# one needs to additionally set config.layer_type = "bottleneck", config.stem_type = "same",
							# config.conv_layer = "std_conv_same"
							_lowerCAmelCase     :							Optional[int]    =    BitConfig(
							    conv_layer=_lowerCamelCase				, num_labels=1_000				, idalabel=_lowerCamelCase				, labelaid=_lowerCamelCase				, )

							return config






def 				A				(					_lowerCamelCase  ):





							'''simple docstring'''




							if "stem.conv" in name:
														_lowerCAmelCase     :							List[str]    =    name.replace("stem.conv"				, "bit.embedder.convolution"  )
							if "blocks" in name:
														_lowerCAmelCase     :							Any    =    name.replace("blocks"				, "layers"  )
							if "head.fc" in name:
														_lowerCAmelCase     :							Optional[Any]    =    name.replace("head.fc"				, "classifier.1"  )
							if name.startswith("norm"  ):
														_lowerCAmelCase     :							Any    =    "bit." + name
							if "bit" not in name and "classifier" not in name:
														_lowerCAmelCase     :							Dict    =    "bit.encoder." + name

							return name






def 				A				(					):





							'''simple docstring'''




							_lowerCAmelCase     :							Tuple    =    "http://images.cocodataset.org/val2017/000000039769.jpg"
							_lowerCAmelCase     :							Optional[int]    =    Image.open(requests.get(_lowerCamelCase				, stream=_lowerCamelCase  ).raw  )
							return im






@torch.no_grad()
def 				A				(					_lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase=False  ):





							'''simple docstring'''




							_lowerCAmelCase     :							Dict    =    get_config(_lowerCamelCase  )

							# load original model from timm
							_lowerCAmelCase     :							int    =    create_model(_lowerCamelCase				, pretrained=_lowerCamelCase  )
							timm_model.eval()

							# load state_dict of original model
							_lowerCAmelCase     :							Any    =    timm_model.state_dict()
							for key in state_dict.copy().keys():
														_lowerCAmelCase     :							Dict    =    state_dict.pop(_lowerCamelCase  )
														_lowerCAmelCase     :							Tuple    =    val.squeeze() if "head" in key else val

							# load HuggingFace model
							_lowerCAmelCase     :							Optional[Any]    =    BitForImageClassification(_lowerCamelCase  )
							model.eval()
							model.load_state_dict(_lowerCamelCase  )

							# create image processor
							_lowerCAmelCase     :							Dict    =    create_transform(**resolve_data_config({}				, model=_lowerCamelCase  )  )
							_lowerCAmelCase     :							Optional[int]    =    transform.transforms

							_lowerCAmelCase     :							Tuple    =    {
							    "bilinear": PILImageResampling.BILINEAR,
							    "bicubic": PILImageResampling.BICUBIC,
							    "nearest": PILImageResampling.NEAREST,
							}

							_lowerCAmelCase     :							Tuple    =    BitImageProcessor(
							    do_resize=_lowerCamelCase				, size={"shortest_edge": timm_transforms[0].size}				, resample=pillow_resamplings[timm_transforms[0].interpolation.value]				, do_center_crop=_lowerCamelCase				, crop_size={"height": timm_transforms[1].size[0], "width": timm_transforms[1].size[1]}				, do_normalize=_lowerCamelCase				, image_mean=timm_transforms[-1].mean.tolist()				, image_std=timm_transforms[-1].std.tolist()				, )

							_lowerCAmelCase     :							Optional[int]    =    prepare_img()
							_lowerCAmelCase     :							Any    =    transform(_lowerCamelCase  ).unsqueeze(0  )
							_lowerCAmelCase     :							Optional[int]    =    processor(_lowerCamelCase				, return_tensors="pt"  ).pixel_values

							# verify pixel values
							assert torch.allclose(_lowerCamelCase				, _lowerCamelCase  )

							# verify logits
							with torch.no_grad():
														_lowerCAmelCase     :							Tuple    =    model(_lowerCamelCase  )
														_lowerCAmelCase     :							str    =    outputs.logits

							print("Logits:"				, logits[0, :3]  )
							print("Predicted class:"				, model.config.idalabel[logits.argmax(-1  ).item()]  )
							_lowerCAmelCase     :							Union[str, Any]    =    timm_model(_lowerCamelCase  )
							assert timm_logits.shape == outputs.logits.shape
							assert torch.allclose(_lowerCamelCase				, outputs.logits				, atol=1e-3  )
							print("Looks ok!"  )

							if pytorch_dump_folder_path is not None:
														Path(_lowerCamelCase  ).mkdir(exist_ok=_lowerCamelCase  )
														print(F"Saving model {model_name} and processor to {pytorch_dump_folder_path}"  )
														model.save_pretrained(_lowerCamelCase  )
														processor.save_pretrained(_lowerCamelCase  )

							if push_to_hub:
														print(F"Pushing model {model_name} and processor to the hub"  )
														model.push_to_hub(F"ybelkada/{model_name}"  )
														processor.push_to_hub(F"ybelkada/{model_name}"  )


if __name__ == "__main__":
						_snake_case		=  argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    "--model_name",
						    default="resnetv2_50x1_bitm",
						    type=str,
						    help="Name of the BiT timm 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 to push the model to the hub.",
						)

						_snake_case		=  parser.parse_args()
						convert_bit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
