




import warnings
from typing import Any, Dict, 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 ...utils import PaddingStrategy, TensorType, logging


__A        =    logging.get_logger(__name__)







class     __lowerCAmelCase (       lowerCAmelCase__ ):

			"""simple docstring"""







			snake_case_   		=       ["""input_values""", """attention_mask"""]


			def __init__(      self			,						lowerCamelCase__ = 1			,						lowerCamelCase__ = 16_000			,						lowerCamelCase__ = 0.0			,						lowerCamelCase__ = False			,						lowerCamelCase__ = 80			,						lowerCamelCase__ = 16			,						lowerCamelCase__ = 64			,						lowerCamelCase__ = "hann_window"			,						lowerCamelCase__ = 1.0			,						lowerCamelCase__ = 80			,						lowerCamelCase__ = 7_600			,						lowerCamelCase__ = 1e-10			,						lowerCamelCase__ = 2			,						lowerCamelCase__ = True			,						**lowerCamelCase__			,						)    ->      Dict:



							'''simple docstring'''
							super().__init__(feature_size=UpperCamelCase__			,						sampling_rate=UpperCamelCase__			,						padding_value=UpperCamelCase__			,						**UpperCamelCase__							)
							__lowerCamelCase		       =    do_normalize
							__lowerCamelCase		       =    return_attention_mask

							__lowerCamelCase		       =    num_mel_bins
							__lowerCamelCase		       =    hop_length
							__lowerCamelCase		       =    win_length
							__lowerCamelCase		       =    win_function
							__lowerCamelCase		       =    frame_signal_scale
							__lowerCamelCase		       =    fmin
							__lowerCamelCase		       =    fmax
							__lowerCamelCase		       =    mel_floor
							__lowerCamelCase		       =    reduction_factor

							__lowerCamelCase		       =    win_length * sampling_rate // 1_000
							__lowerCamelCase		       =    hop_length * sampling_rate // 1_000
							__lowerCamelCase		       =    optimal_fft_length(self.sample_size							)
							__lowerCamelCase		       =    (self.n_fft // 2) + 1

							__lowerCamelCase		       =    window_function(window_length=self.sample_size			,						name=self.win_function			,						periodic=UpperCamelCase__							)

							__lowerCamelCase		       =    mel_filter_bank(
							    num_frequency_bins=self.n_freqs			,						num_mel_filters=self.num_mel_bins			,						min_frequency=self.fmin			,						max_frequency=self.fmax			,						sampling_rate=self.sampling_rate			,						norm='slaney'			,						mel_scale='slaney'			,						)

							if frame_signal_scale != 1.0:
											warnings.warn(
											    'The argument `frame_signal_scale` is deprecated and will be removed in version 4.30.0 of Transformers'			,						UpperCamelCase__			,						)
							if reduction_factor != 2.0:
											warnings.warn(
											    'The argument `reduction_factor` is deprecated and will be removed in version 4.30.0 of Transformers'			,						UpperCamelCase__			,						)


			@staticmethod
			# Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm
			def       lowercase_    (      lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__ = 0.0							)    ->      List[np.ndarray]:



							'''simple docstring'''
							if attention_mask is not None:
											__lowerCamelCase		       =    np.array(UpperCamelCase__			,						np.intaa							)
											__lowerCamelCase		       =    []

											for vector, length in zip(UpperCamelCase__			,						attention_mask.sum(-1							)							):
															__lowerCamelCase		       =    (vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1e-7							)
															if length < normed_slice.shape[0]:
																			__lowerCamelCase		       =    padding_value

															normed_input_values.append(UpperCamelCase__							)
							else:
											__lowerCamelCase		       =    [(x - x.mean()) / np.sqrt(x.var() + 1e-7							) for x in input_values]

							return normed_input_values


			def       lowercase_    (      self			,						lowerCamelCase__			,						)    ->      np.ndarray:



							'''simple docstring'''
							__lowerCamelCase		       =    spectrogram(
							    UpperCamelCase__			,						window=self.window			,						frame_length=self.sample_size			,						hop_length=self.sample_stride			,						fft_length=self.n_fft			,						mel_filters=self.mel_filters			,						mel_floor=self.mel_floor			,						log_mel='log10'			,						)
							return log_mel_spec.T


			def __call__(      self			,						lowerCamelCase__ = None			,						lowerCamelCase__ = None			,						lowerCamelCase__ = False			,						lowerCamelCase__ = None			,						lowerCamelCase__ = False			,						lowerCamelCase__ = None			,						lowerCamelCase__ = None			,						lowerCamelCase__ = None			,						lowerCamelCase__ = None			,						**lowerCamelCase__			,						)    ->      BatchFeature:



							'''simple docstring'''
							if audio is None and audio_target is None:
											raise ValueError('You must provide either `audio` or `audio_target` values.'							)

							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 audio 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.'							)

							if audio is not None:
											__lowerCamelCase		       =    self._process_audio(
											    UpperCamelCase__			,						UpperCamelCase__			,						UpperCamelCase__			,						UpperCamelCase__			,						UpperCamelCase__			,						UpperCamelCase__			,						UpperCamelCase__			,						UpperCamelCase__			,						**UpperCamelCase__			,						)
							else:
											__lowerCamelCase		       =    None

							if audio_target is not None:
											__lowerCamelCase		       =    self._process_audio(
											    UpperCamelCase__			,						UpperCamelCase__			,						UpperCamelCase__			,						UpperCamelCase__			,						UpperCamelCase__			,						UpperCamelCase__			,						UpperCamelCase__			,						UpperCamelCase__			,						**UpperCamelCase__			,						)

											if inputs is None:
															return inputs_target
											else:
															__lowerCamelCase		       =    inputs_target["input_values"]
															__lowerCamelCase		       =    inputs_target.get('attention_mask'							)
															if decoder_attention_mask is not None:
																			__lowerCamelCase		       =    decoder_attention_mask

							return inputs


			def       lowercase_    (      self			,						lowerCamelCase__			,						lowerCamelCase__ = False			,						lowerCamelCase__ = False			,						lowerCamelCase__ = None			,						lowerCamelCase__ = False			,						lowerCamelCase__ = None			,						lowerCamelCase__ = None			,						lowerCamelCase__ = None			,						**lowerCamelCase__			,						)    ->      BatchFeature:



							'''simple docstring'''
							__lowerCamelCase		       =    isinstance(UpperCamelCase__			,						np.ndarray							) and len(speech.shape							) > 1
							if is_batched_numpy and len(speech.shape							) > 2:
											raise ValueError(f"""Only mono-channel audio is supported for input to {self}"""							)
							__lowerCamelCase		       =    is_batched_numpy or (
							    isinstance(UpperCamelCase__			,						(list, tuple)							) and (isinstance(speech[0]			,						(np.ndarray, tuple, list)							))
							)

							if is_batched:
											__lowerCamelCase		       =    [np.asarray(UpperCamelCase__			,						dtype=np.floataa							) for speech in speech]
							elif not is_batched and not isinstance(UpperCamelCase__			,						np.ndarray							):
											__lowerCamelCase		       =    np.asarray(UpperCamelCase__			,						dtype=np.floataa							)
							elif isinstance(UpperCamelCase__			,						np.ndarray							) and speech.dtype is np.dtype(np.floataa							):
											__lowerCamelCase		       =    speech.astype(np.floataa							)

							# always return batch
							if not is_batched:
											__lowerCamelCase		       =    [speech]

							# needed to make pad() work on spectrogram inputs
							__lowerCamelCase		       =    self.feature_size

							# convert into correct format for padding
							if is_target:
											__lowerCamelCase		       =    [self._extract_mel_features(UpperCamelCase__							) for waveform in speech]
											__lowerCamelCase		       =    BatchFeature({'input_values': features}							)
											__lowerCamelCase		       =    self.num_mel_bins
							else:
											__lowerCamelCase		       =    BatchFeature({'input_values': speech}							)

							__lowerCamelCase		       =    self.pad(
							    UpperCamelCase__			,						padding=UpperCamelCase__			,						max_length=UpperCamelCase__			,						truncation=UpperCamelCase__			,						pad_to_multiple_of=UpperCamelCase__			,						return_attention_mask=UpperCamelCase__			,						**UpperCamelCase__			,						)

							__lowerCamelCase		       =    feature_size_hack

							# convert input values to correct format
							__lowerCamelCase		       =    padded_inputs["input_values"]
							if not isinstance(input_values[0]			,						np.ndarray							):
											__lowerCamelCase		       =    [np.asarray(UpperCamelCase__			,						dtype=np.floataa							) for array in input_values]
							elif (
							    not isinstance(UpperCamelCase__			,						np.ndarray							)
							    and isinstance(input_values[0]			,						np.ndarray							)
							    and input_values[0].dtype is np.dtype(np.floataa							)
							):
											__lowerCamelCase		       =    [array.astype(np.floataa							) for array in input_values]
							elif isinstance(UpperCamelCase__			,						np.ndarray							) and input_values.dtype is np.dtype(np.floataa							):
											__lowerCamelCase		       =    input_values.astype(np.floataa							)

							# convert attention_mask to correct format
							__lowerCamelCase		       =    padded_inputs.get('attention_mask'							)
							if attention_mask is not None:
											__lowerCamelCase		       =    [np.asarray(UpperCamelCase__			,						dtype=np.intaa							) for array in attention_mask]

							# zero-mean and unit-variance normalization
							if not is_target and self.do_normalize:
											__lowerCamelCase		       =    (
											    attention_mask
											    if self._get_padding_strategies(UpperCamelCase__			,						max_length=UpperCamelCase__							) is not PaddingStrategy.DO_NOT_PAD
											    else None
											)
											__lowerCamelCase		       =    self.zero_mean_unit_var_norm(
											    padded_inputs['input_values']			,						attention_mask=UpperCamelCase__			,						padding_value=self.padding_value							)

							if return_tensors is not None:
											__lowerCamelCase		       =    padded_inputs.convert_to_tensors(UpperCamelCase__							)

							return padded_inputs






			def       lowercase_    (      self							)    ->      Dict[str, Any]:



							'''simple docstring'''
							__lowerCamelCase		       =    super().to_dict()

							# Don't serialize these as they are derived from the other properties.
							__lowerCamelCase		       =    ["window", "mel_filters", "sample_size", "sample_stride", "n_fft", "n_freqs"]
							for name in names:
											if name in output:
															del output[name]

							return output








import argparse
import json
from pathlib import Path

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import DeiTConfig, DeiTForImageClassificationWithTeacher, DeiTImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE__       :      str  =   logging.get_logger(__name__)

def      A      (      _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE=False    )	-> Any:
       lowerCamelCase   :      Any			 =					[]
       for i in range(config.num_hidden_layers    ):
              # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
              rename_keys.append((f'''blocks.{i}.norm1.weight''', f'''deit.encoder.layer.{i}.layernorm_before.weight''')    )
              rename_keys.append((f'''blocks.{i}.norm1.bias''', f'''deit.encoder.layer.{i}.layernorm_before.bias''')    )
              rename_keys.append((f'''blocks.{i}.attn.proj.weight''', f'''deit.encoder.layer.{i}.attention.output.dense.weight''')    )
              rename_keys.append((f'''blocks.{i}.attn.proj.bias''', f'''deit.encoder.layer.{i}.attention.output.dense.bias''')    )
              rename_keys.append((f'''blocks.{i}.norm2.weight''', f'''deit.encoder.layer.{i}.layernorm_after.weight''')    )
              rename_keys.append((f'''blocks.{i}.norm2.bias''', f'''deit.encoder.layer.{i}.layernorm_after.bias''')    )
              rename_keys.append((f'''blocks.{i}.mlp.fc1.weight''', f'''deit.encoder.layer.{i}.intermediate.dense.weight''')    )
              rename_keys.append((f'''blocks.{i}.mlp.fc1.bias''', f'''deit.encoder.layer.{i}.intermediate.dense.bias''')    )
              rename_keys.append((f'''blocks.{i}.mlp.fc2.weight''', f'''deit.encoder.layer.{i}.output.dense.weight''')    )
              rename_keys.append((f'''blocks.{i}.mlp.fc2.bias''', f'''deit.encoder.layer.{i}.output.dense.bias''')    )

       # projection layer + position embeddings
       rename_keys.extend(
           [
               ("cls_token", "deit.embeddings.cls_token"),
               ("dist_token", "deit.embeddings.distillation_token"),
               ("patch_embed.proj.weight", "deit.embeddings.patch_embeddings.projection.weight"),
               ("patch_embed.proj.bias", "deit.embeddings.patch_embeddings.projection.bias"),
               ("pos_embed", "deit.embeddings.position_embeddings"),
           ]    )

       if base_model:
              # layernorm + pooler
              rename_keys.extend(
                  [
                      ("norm.weight", "layernorm.weight"),
                      ("norm.bias", "layernorm.bias"),
                      ("pre_logits.fc.weight", "pooler.dense.weight"),
                      ("pre_logits.fc.bias", "pooler.dense.bias"),
                  ]    )

              # if just the base model, we should remove "deit" from all keys that start with "deit"
              lowerCamelCase   :      Union[str, Any]			 =					[(pair[0], pair[1][4:]) if pair[1].startswith("deit"    ) else pair for pair in rename_keys]
       else:
              # layernorm + classification heads
              rename_keys.extend(
                  [
                      ("norm.weight", "deit.layernorm.weight"),
                      ("norm.bias", "deit.layernorm.bias"),
                      ("head.weight", "cls_classifier.weight"),
                      ("head.bias", "cls_classifier.bias"),
                      ("head_dist.weight", "distillation_classifier.weight"),
                      ("head_dist.bias", "distillation_classifier.bias"),
                  ]    )

       return rename_keys

def      A      (      _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE=False    )	-> str:
       for i in range(config.num_hidden_layers    ):
              if base_model:
                     lowerCamelCase   :      Optional[int]			 =					""
              else:
                     lowerCamelCase   :      List[str]			 =					"deit."
              # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
              lowerCamelCase   :      List[str]			 =					state_dict.pop(f'''blocks.{i}.attn.qkv.weight'''    )
              lowerCamelCase   :      Optional[int]			 =					state_dict.pop(f'''blocks.{i}.attn.qkv.bias'''    )
              # next, add query, keys and values (in that order) to the state dict
              lowerCamelCase   :      List[Any]			 =					in_proj_weight[
                  : config.hidden_size, :
              ]
              lowerCamelCase   :      Any			 =					in_proj_bias[: config.hidden_size]
              lowerCamelCase   :      List[str]			 =					in_proj_weight[
                  config.hidden_size : config.hidden_size * 2, :
              ]
              lowerCamelCase   :      Optional[Any]			 =					in_proj_bias[
                  config.hidden_size : config.hidden_size * 2
              ]
              lowerCamelCase   :      List[str]			 =					in_proj_weight[
                  -config.hidden_size :, :
              ]
              lowerCamelCase   :      List[Any]			 =					in_proj_bias[-config.hidden_size :]

def      A      (      _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    )	-> str:
       lowerCamelCase   :      List[str]			 =					dct.pop(_SCREAMING_SNAKE_CASE    )
       lowerCamelCase   :      Any			 =					val

def      A      (      )	-> List[str]:
       lowerCamelCase   :      Union[str, Any]			 =					"http://images.cocodataset.org/val2017/000000039769.jpg"
       lowerCamelCase   :      str			 =					Image.open(requests.get(_SCREAMING_SNAKE_CASE ,stream=_SCREAMING_SNAKE_CASE    ).raw    )
       return im

@torch.no_grad()
def      A      (      _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    )	-> Optional[Any]:
       lowerCamelCase   :      Union[str, Any]			 =					DeiTConfig()
       # all deit models have fine-tuned heads
       lowerCamelCase   :      Optional[int]			 =					False
       # dataset (fine-tuned on ImageNet 2012), patch_size and image_size
       lowerCamelCase   :      Dict			 =					1000
       lowerCamelCase   :      Tuple			 =					"huggingface/label-files"
       lowerCamelCase   :      List[str]			 =					"imagenet-1k-id2label.json"
       lowerCamelCase   :      List[Any]			 =					json.load(open(hf_hub_download(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,repo_type="dataset"    ) ,"r"    )    )
       lowerCamelCase   :      Optional[int]			 =					{int(_SCREAMING_SNAKE_CASE    ): v for k, v in idalabel.items()}
       lowerCamelCase   :      Tuple			 =					idalabel
       lowerCamelCase   :      str			 =					{v: k for k, v in idalabel.items()}
       lowerCamelCase   :      Dict			 =					int(deit_name[-6:-4]    )
       lowerCamelCase   :      Optional[Any]			 =					int(deit_name[-3:]    )
       # size of the architecture
       if deit_name[9:].startswith("tiny"    ):
              lowerCamelCase   :      Optional[Any]			 =					192
              lowerCamelCase   :      List[str]			 =					768
              lowerCamelCase   :      Tuple			 =					12
              lowerCamelCase   :      Optional[Any]			 =					3
       elif deit_name[9:].startswith("small"    ):
              lowerCamelCase   :      str			 =					384
              lowerCamelCase   :      Optional[Any]			 =					1536
              lowerCamelCase   :      Dict			 =					12
              lowerCamelCase   :      Optional[int]			 =					6
       if deit_name[9:].startswith("base"    ):
              pass
       elif deit_name[4:].startswith("large"    ):
              lowerCamelCase   :      str			 =					1024
              lowerCamelCase   :      List[str]			 =					4096
              lowerCamelCase   :      Any			 =					24
              lowerCamelCase   :      Dict			 =					16

       # load original model from timm
       lowerCamelCase   :      List[Any]			 =					timm.create_model(_SCREAMING_SNAKE_CASE ,pretrained=_SCREAMING_SNAKE_CASE    )
       timm_model.eval()

       # load state_dict of original model, remove and rename some keys
       lowerCamelCase   :      Dict			 =					timm_model.state_dict()
       lowerCamelCase   :      Dict			 =					create_rename_keys(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    )
       for src, dest in rename_keys:
              rename_key(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    )
       read_in_q_k_v(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    )

       # load HuggingFace model
       lowerCamelCase   :      Optional[Any]			 =					DeiTForImageClassificationWithTeacher(_SCREAMING_SNAKE_CASE    ).eval()
       model.load_state_dict(_SCREAMING_SNAKE_CASE    )

       # Check outputs on an image, prepared by DeiTImageProcessor
       lowerCamelCase   :      Any			 =					int(
           (256 / 224) * config.image_size    )  # to maintain same ratio w.r.t. 224 images, see https://github.com/facebookresearch/deit/blob/ab5715372db8c6cad5740714b2216d55aeae052e/datasets.py#L103
       lowerCamelCase   :      Union[str, Any]			 =					DeiTImageProcessor(size=_SCREAMING_SNAKE_CASE ,crop_size=config.image_size    )
       lowerCamelCase   :      str			 =					image_processor(images=prepare_img() ,return_tensors="pt"    )
       lowerCamelCase   :      int			 =					encoding["pixel_values"]
       lowerCamelCase   :      Optional[Any]			 =					model(_SCREAMING_SNAKE_CASE    )

       lowerCamelCase   :      Union[str, Any]			 =					timm_model(_SCREAMING_SNAKE_CASE    )
       assert timm_logits.shape == outputs.logits.shape
       assert torch.allclose(_SCREAMING_SNAKE_CASE ,outputs.logits ,atol=1e-3    )

       Path(_SCREAMING_SNAKE_CASE    ).mkdir(exist_ok=_SCREAMING_SNAKE_CASE    )
       print(f'''Saving model {deit_name} to {pytorch_dump_folder_path}'''    )
       model.save_pretrained(_SCREAMING_SNAKE_CASE    )
       print(f'''Saving image processor to {pytorch_dump_folder_path}'''    )
       image_processor.save_pretrained(_SCREAMING_SNAKE_CASE    )


if __name__ == "__main__":
       SCREAMING_SNAKE_CASE__       :      Optional[Any]  =   argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           '--deit_name',
           default='vit_deit_base_distilled_patch16_224',
           type=str,
           help='Name of the DeiT 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.'
       )

       SCREAMING_SNAKE_CASE__       :      List[str]  =   parser.parse_args()
       convert_deit_checkpoint(args.deit_name, args.pytorch_dump_folder_path)






