'''simple docstring'''





import copy
from typing import Any, Dict, List, Optional, Union

import numpy as np
import torch

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


SCREAMING_SNAKE_CASE :	int							     =		logging.get_logger(__name__)
class    A_ (       a_ ):
  _SCREAMING_SNAKE_CASE    				=			["""input_features""", """is_longer"""]

  def __init__(		self :      Optional[Any]     ,							__SCREAMING_SNAKE_CASE :      Optional[int]=64     ,							__SCREAMING_SNAKE_CASE :      str=4_80_00     ,							__SCREAMING_SNAKE_CASE :      int=4_80     ,							__SCREAMING_SNAKE_CASE :      Dict=10     ,							__SCREAMING_SNAKE_CASE :      Optional[int]=10_24     ,							__SCREAMING_SNAKE_CASE :      Optional[Any]=0.0     ,							__SCREAMING_SNAKE_CASE :      Union[str, Any]=False     ,							__SCREAMING_SNAKE_CASE :      float = 0     ,							__SCREAMING_SNAKE_CASE :      float = 1_40_00     ,							__SCREAMING_SNAKE_CASE :      int = None     ,							__SCREAMING_SNAKE_CASE :      str = "fusion"     ,							__SCREAMING_SNAKE_CASE :      str = "repeatpad"     ,							**__SCREAMING_SNAKE_CASE :      int     ,							):
   super().__init__(
       feature_size=__SCREAMING_SNAKE_CASE     ,							sampling_rate=__SCREAMING_SNAKE_CASE     ,							padding_value=__SCREAMING_SNAKE_CASE     ,							return_attention_mask=__SCREAMING_SNAKE_CASE     ,							**__SCREAMING_SNAKE_CASE     ,							)
   __a       =  top_db
   __a       =  truncation
   __a       =  padding
   __a       =  fft_window_size
   __a       =  (fft_window_size >> 1) + 1
   __a       =  hop_length
   __a       =  max_length_s
   __a       =  max_length_s * sampling_rate
   __a       =  sampling_rate
   __a       =  frequency_min
   __a       =  frequency_max
   __a       =  mel_filter_bank(
       num_frequency_bins=self.nb_frequency_bins     ,							num_mel_filters=__SCREAMING_SNAKE_CASE     ,							min_frequency=__SCREAMING_SNAKE_CASE     ,							max_frequency=__SCREAMING_SNAKE_CASE     ,							sampling_rate=__SCREAMING_SNAKE_CASE     ,							norm=__SCREAMING_SNAKE_CASE     ,							mel_scale="htk"     ,							)
   __a       =  mel_filter_bank(
       num_frequency_bins=self.nb_frequency_bins     ,							num_mel_filters=__SCREAMING_SNAKE_CASE     ,							min_frequency=__SCREAMING_SNAKE_CASE     ,							max_frequency=__SCREAMING_SNAKE_CASE     ,							sampling_rate=__SCREAMING_SNAKE_CASE     ,							norm="slaney"     ,							mel_scale="slaney"     ,							)

  def 		_UpperCAmelCase   (		self :      List[str]       ):
   __a       =  copy.deepcopy(self.__dict__       )
   __a       =  self.__class__.__name__
   if "mel_filters" in output:
    del output["mel_filters"]
   if "mel_filters_slaney" in output:
    del output["mel_filters_slaney"]
   return output

  def 		_UpperCAmelCase   (		self :      Tuple     ,							__SCREAMING_SNAKE_CASE :      np.array     ,							__SCREAMING_SNAKE_CASE :      Optional[np.array] = None       ):
   __a       =  spectrogram(
       __SCREAMING_SNAKE_CASE     ,							window_function(self.fft_window_size     ,							"hann"       )     ,							frame_length=self.fft_window_size     ,							hop_length=self.hop_length     ,							power=2.0     ,							mel_filters=__SCREAMING_SNAKE_CASE     ,							log_mel="dB"     ,							)
   return log_mel_spectrogram.T

  def 		_UpperCAmelCase   (		self :      Optional[int]     ,							__SCREAMING_SNAKE_CASE :      Optional[int]     ,							__SCREAMING_SNAKE_CASE :      Union[str, Any]     ,							__SCREAMING_SNAKE_CASE :      List[Any]       ):
   __a       =  np.array_split(list(range(0     ,							total_frames - chunk_frames + 1       )       )     ,							3       )
   if len(ranges[1]       ) == 0:
    # if the audio is too short, we just use the first chunk
    __a       =  [0]
   if len(ranges[2]       ) == 0:
    # if the audio is too short, we just use the first chunk
    __a       =  [0]
   # randomly choose index for each part
   __a       =  np.random.choice(ranges[0]       )
   __a       =  np.random.choice(ranges[1]       )
   __a       =  np.random.choice(ranges[2]       )

   __a       =  mel[idx_front : idx_front + chunk_frames, :]
   __a       =  mel[idx_middle : idx_middle + chunk_frames, :]
   __a       =  mel[idx_back : idx_back + chunk_frames, :]

   __a       =  torch.tensor(mel[None, None, :]       )
   __a       =  torch.nn.functional.interpolate(
       __SCREAMING_SNAKE_CASE     ,							size=[chunk_frames, 64]     ,							mode="bilinear"     ,							align_corners=__SCREAMING_SNAKE_CASE       )
   __a       =  mel_shrink[0][0].numpy()
   __a       =  np.stack([mel_shrink, mel_chunk_front, mel_chunk_middle, mel_chunk_back]     ,							axis=0       )
   return mel_fusion

  def 		_UpperCAmelCase   (		self :      Union[str, Any]     ,							__SCREAMING_SNAKE_CASE :      np.array     ,							__SCREAMING_SNAKE_CASE :      Optional[int]     ,							__SCREAMING_SNAKE_CASE :      List[Any]     ,							__SCREAMING_SNAKE_CASE :      str       ):

   if waveform.shape[0] > max_length:
    if truncation == "rand_trunc":
     __a       =  True
     # random crop to max_length (for compatibility) -> this should be handled by self.pad
     __a       =  len(__SCREAMING_SNAKE_CASE       ) - max_length
     __a       =  np.random.randint(0     ,							overflow + 1       )
     __a       =  waveform[idx : idx + max_length]
     __a       =  self._np_extract_fbank_features(__SCREAMING_SNAKE_CASE     ,							self.mel_filters_slaney       )[None, :]
    elif truncation == "fusion":
     __a       =  self._np_extract_fbank_features(__SCREAMING_SNAKE_CASE     ,							self.mel_filters       )
     __a       =  max_length // self.hop_length + 1  # the +1 related to how the spectrogram is computed
     __a       =  mel.shape[0]
     if chunk_frames == total_frames:
      # there is a corner case where the audio length is larger than max_length but smaller than max_length+hop_length.
      # In this case, we just use the whole audio.
      __a       =  np.stack([mel, mel, mel, mel]     ,							axis=0       )
      __a       =  False
     else:
      __a       =  self._random_mel_fusion(__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE       )
      __a       =  True
    else:
     raise NotImplementedError(f"""data_truncating {truncation} not implemented"""       )

   else:
    __a       =  False
    # only use repeat as a new possible value for padding. you repeat the audio before applying the usual max_length padding
    if waveform.shape[0] < max_length:
     if padding == "repeat":
      __a       =  int(max_length / len(__SCREAMING_SNAKE_CASE       )       )
      __a       =  np.stack(np.tile(__SCREAMING_SNAKE_CASE     ,							n_repeat + 1       )       )[:max_length]
     if padding == "repeatpad":
      __a       =  int(max_length / len(__SCREAMING_SNAKE_CASE       )       )
      __a       =  np.stack(np.tile(__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE       )       )
     __a       =  np.pad(__SCREAMING_SNAKE_CASE     ,							(0, max_length - waveform.shape[0])     ,							mode="constant"     ,							constant_values=0       )

    if truncation == "fusion":
     __a       =  self._np_extract_fbank_features(__SCREAMING_SNAKE_CASE     ,							self.mel_filters       )
     __a       =  np.stack([input_mel, input_mel, input_mel, input_mel]     ,							axis=0       )
    else:
     __a       =  self._np_extract_fbank_features(__SCREAMING_SNAKE_CASE     ,							self.mel_filters_slaney       )[None, :]

   return input_mel, longer

  def __call__(		self :      Tuple     ,							__SCREAMING_SNAKE_CASE :      Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]     ,							__SCREAMING_SNAKE_CASE :      str = None     ,							__SCREAMING_SNAKE_CASE :      Optional[str] = None     ,							__SCREAMING_SNAKE_CASE :      Optional[int] = None     ,							__SCREAMING_SNAKE_CASE :      Optional[int] = None     ,							__SCREAMING_SNAKE_CASE :      Optional[Union[str, TensorType]] = None     ,							**__SCREAMING_SNAKE_CASE :      int     ,							):
   __a       =  truncation if truncation is not None else self.truncation
   __a       =  padding if padding else self.padding

   if sampling_rate is not None:
    if sampling_rate != self.sampling_rate:
     raise ValueError(
         f"""The model corresponding to this feature extractor: {self.__class__.__name__} was trained using a"""
         f""" sampling rate of {self.sampling_rate}. Please make sure that the provided `raw_speech` input"""
         f""" was sampled with {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."       )

   __a       =  isinstance(__SCREAMING_SNAKE_CASE     ,							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}"""       )
   __a       =  is_batched_numpy or (
       isinstance(__SCREAMING_SNAKE_CASE     ,							(list, tuple)       ) and (isinstance(raw_speech[0]     ,							(np.ndarray, tuple, list)       ))
   )

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

   # always return batch
   if not is_batched:
    __a       =  [np.asarray(__SCREAMING_SNAKE_CASE       )]

   # convert to mel spectrogram, truncate and pad if needed.
   __a       =  [
       self._get_input_mel(__SCREAMING_SNAKE_CASE     ,							max_length if max_length else self.nb_max_samples     ,							__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE       )
       for waveform in raw_speech
   ]

   __a       =  []
   __a       =  []
   for mel, longer in padded_inputs:
    input_mel.append(__SCREAMING_SNAKE_CASE       )
    is_longer.append(__SCREAMING_SNAKE_CASE       )

   if truncation == "fusion" and sum(__SCREAMING_SNAKE_CASE       ) == 0:
    # if no audio is longer than 10s, then randomly select one audio to be longer
    __a       =  np.random.randint(0     ,							len(__SCREAMING_SNAKE_CASE       )       )
    __a       =  True

   if isinstance(input_mel[0]     ,							__SCREAMING_SNAKE_CASE       ):
    __a       =  [np.asarray(__SCREAMING_SNAKE_CASE     ,							dtype=np.floataa       ) for feature in input_mel]

   # is_longer is a list of bool
   __a       =  [[longer] for longer in is_longer]

   __a       =  {"input_features": input_mel, "is_longer": is_longer}
   __a       =  BatchFeature(__SCREAMING_SNAKE_CASE       )

   if return_tensors is not None:
    __a       =  input_features.convert_to_tensors(__SCREAMING_SNAKE_CASE       )

   return input_features



import argparse
import json
from pathlib import Path

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

from transformers import BeitConfig, BeitForImageClassification, BeitForMaskedImageModeling, BeitImageProcessor
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


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



def   __A		(			_A							,				_A=False							,				_A=False					):



 """simple docstring"""


 __a       =  "backbone." if is_semantic else ""

 __a       =  []
 for i in range(config.num_hidden_layers					):
  # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
  rename_keys.append((f"""{prefix}blocks.{i}.norm1.weight""", f"""beit.encoder.layer.{i}.layernorm_before.weight""")					)
  rename_keys.append((f"""{prefix}blocks.{i}.norm1.bias""", f"""beit.encoder.layer.{i}.layernorm_before.bias""")					)
  rename_keys.append(
      (f"""{prefix}blocks.{i}.attn.proj.weight""", f"""beit.encoder.layer.{i}.attention.output.dense.weight""")					)
  rename_keys.append(
      (f"""{prefix}blocks.{i}.attn.proj.bias""", f"""beit.encoder.layer.{i}.attention.output.dense.bias""")					)
  rename_keys.append((f"""{prefix}blocks.{i}.norm2.weight""", f"""beit.encoder.layer.{i}.layernorm_after.weight""")					)
  rename_keys.append((f"""{prefix}blocks.{i}.norm2.bias""", f"""beit.encoder.layer.{i}.layernorm_after.bias""")					)
  rename_keys.append((f"""{prefix}blocks.{i}.mlp.fc1.weight""", f"""beit.encoder.layer.{i}.intermediate.dense.weight""")					)
  rename_keys.append((f"""{prefix}blocks.{i}.mlp.fc1.bias""", f"""beit.encoder.layer.{i}.intermediate.dense.bias""")					)
  rename_keys.append((f"""{prefix}blocks.{i}.mlp.fc2.weight""", f"""beit.encoder.layer.{i}.output.dense.weight""")					)
  rename_keys.append((f"""{prefix}blocks.{i}.mlp.fc2.bias""", f"""beit.encoder.layer.{i}.output.dense.bias""")					)

 # projection layer + position embeddings
 rename_keys.extend(
     [
         (f"""{prefix}cls_token""", "beit.embeddings.cls_token"),
         (f"""{prefix}patch_embed.proj.weight""", "beit.embeddings.patch_embeddings.projection.weight"),
         (f"""{prefix}patch_embed.proj.bias""", "beit.embeddings.patch_embeddings.projection.bias"),
         (f"""{prefix}pos_embed""", "beit.embeddings.position_embeddings"),
     ]					)

 if has_lm_head:
  # mask token + layernorm
  rename_keys.extend(
      [
          ("mask_token", "beit.embeddings.mask_token"),
          ("norm.weight", "layernorm.weight"),
          ("norm.bias", "layernorm.bias"),
      ]					)
 else:
  # layernorm + classification head
  rename_keys.extend(
      [
          ("fc_norm.weight", "beit.pooler.layernorm.weight"),
          ("fc_norm.bias", "beit.pooler.layernorm.bias"),
          ("head.weight", "classifier.weight"),
          ("head.bias", "classifier.bias"),
      ]					)

 return rename_keys



def   __A		(			_A							,				_A							,				_A=False							,				_A=False					):



 """simple docstring"""


 for i in range(config.num_hidden_layers					):
  __a       =  "backbone." if is_semantic else ""
  # queries, keys and values
  __a       =  state_dict.pop(f"""{prefix}blocks.{i}.attn.qkv.weight"""					)
  __a       =  state_dict.pop(f"""{prefix}blocks.{i}.attn.q_bias"""					)
  __a       =  state_dict.pop(f"""{prefix}blocks.{i}.attn.v_bias"""					)

  __a       =  in_proj_weight[
      : config.hidden_size, :
  ]
  __a       =  q_bias
  __a       =  in_proj_weight[
      config.hidden_size : config.hidden_size * 2, :
  ]
  __a       =  in_proj_weight[
      -config.hidden_size :, :
  ]
  __a       =  v_bias

  # gamma_1 and gamma_2
  # we call them lambda because otherwise they are renamed when using .from_pretrained
  __a       =  state_dict.pop(f"""{prefix}blocks.{i}.gamma_1"""					)
  __a       =  state_dict.pop(f"""{prefix}blocks.{i}.gamma_2"""					)

  __a       =  gamma_a
  __a       =  gamma_a



def   __A		(			_A							,				_A							,				_A					):



 """simple docstring"""


 __a       =  dct.pop(_A					)
 __a       =  val



def   __A		(			):



 """simple docstring"""


 __a       =  "http://images.cocodataset.org/val2017/000000039769.jpg"
 __a       =  Image.open(requests.get(_A							,				stream=_A					).raw					)
 return im



@torch.no_grad()
def   __A		(			_A							,				_A							,				_A=False					):



 """simple docstring"""


 __a       =  False if "rvlcdip" in checkpoint_url else True
 __a       =  BeitConfig(use_absolute_position_embeddings=_A							,				use_mask_token=_A					)

 # size of the architecture
 if "large" in checkpoint_url or "dit-l" in checkpoint_url:
  __a       =  1024
  __a       =  4096
  __a       =  24
  __a       =  16

 # labels
 if "rvlcdip" in checkpoint_url:
  __a       =  16
  __a       =  "huggingface/label-files"
  __a       =  "rvlcdip-id2label.json"
  __a       =  json.load(open(hf_hub_download(_A							,				_A							,				repo_type="dataset"					)							,				"r"					)					)
  __a       =  {int(_A					): v for k, v in idalabel.items()}
  __a       =  idalabel
  __a       =  {v: k for k, v in idalabel.items()}

 # load state_dict of original model, remove and rename some keys
 __a       =  torch.hub.load_state_dict_from_url(_A							,				map_location="cpu"					)["model"]

 __a       =  create_rename_keys(_A							,				has_lm_head=_A					)
 for src, dest in rename_keys:
  rename_key(_A							,				_A							,				_A					)
 read_in_q_k_v(_A							,				_A							,				has_lm_head=_A					)

 # load HuggingFace model
 __a       =  BeitForMaskedImageModeling(_A					) if has_lm_head else BeitForImageClassification(_A					)
 model.eval()
 model.load_state_dict(_A					)

 # Check outputs on an image
 __a       =  BeitImageProcessor(
     size=config.image_size							,				resample=PILImageResampling.BILINEAR							,				do_center_crop=_A					)
 __a       =  prepare_img()

 __a       =  image_processor(images=_A							,				return_tensors="pt"					)
 __a       =  encoding["pixel_values"]

 __a       =  model(_A					)
 __a       =  outputs.logits

 # verify logits
 __a       =  [1, 16] if "rvlcdip" in checkpoint_url else [1, 196, 8192]
 assert logits.shape == torch.Size(_A					), "Shape of logits not as expected"

 Path(_A					).mkdir(exist_ok=_A					)
 print(f"""Saving model to {pytorch_dump_folder_path}"""					)
 model.save_pretrained(_A					)
 print(f"""Saving image processor to {pytorch_dump_folder_path}"""					)
 image_processor.save_pretrained(_A					)

 if push_to_hub:
  if has_lm_head:
   __a       =  "dit-base" if "base" in checkpoint_url else "dit-large"
  else:
   __a       =  "dit-base-finetuned-rvlcdip" if "dit-b" in checkpoint_url else "dit-large-finetuned-rvlcdip"
  image_processor.push_to_hub(
      repo_path_or_name=Path(_A							,				_A					)							,				organization="nielsr"							,				commit_message="Add image processor"							,				use_temp_dir=_A							,				)
  model.push_to_hub(
      repo_path_or_name=Path(_A							,				_A					)							,				organization="nielsr"							,				commit_message="Add model"							,				use_temp_dir=_A							,				)


if __name__ == "__main__":
  SCREAMING_SNAKE_CASE :	int							     =		argparse.ArgumentParser()

  parser.add_argument(
      """--checkpoint_url""",
      default="""https://layoutlm.blob.core.windows.net/dit/dit-pts/dit-base-224-p16-500k-62d53a.pth""",
      type=str,
      help="""URL to the original PyTorch checkpoint (.pth file).""",
  )
  parser.add_argument(
      """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the folder to output PyTorch model."""
  )
  parser.add_argument(
      """--push_to_hub""",
      action="""store_true""",
  )
  SCREAMING_SNAKE_CASE :	Union[str, Any]							     =		parser.parse_args()
  convert_dit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)


