
"""simple docstring"""

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 YolosConfig, YolosForObjectDetection, YolosImageProcessor
from transformers.utils import logging


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






def 							A						(    snake_case				:str		)				->     YolosConfig:
 __UpperCamelCase      =	YolosConfig()

 # size of the architecture
 if "yolos_ti" in yolos_name:
  __UpperCamelCase      =	1_9_2
  __UpperCamelCase      =	7_6_8
  __UpperCamelCase      =	1_2
  __UpperCamelCase      =	3
  __UpperCamelCase      =	[8_0_0, 1_3_3_3]
  __UpperCamelCase      =	False
 elif yolos_name == "yolos_s_dWr":
  __UpperCamelCase      =	3_3_0
  __UpperCamelCase      =	1_4
  __UpperCamelCase      =	6
  __UpperCamelCase      =	1_3_2_0
 elif "yolos_s" in yolos_name:
  __UpperCamelCase      =	3_8_4
  __UpperCamelCase      =	1_5_3_6
  __UpperCamelCase      =	1_2
  __UpperCamelCase      =	6
 elif "yolos_b" in yolos_name:
  __UpperCamelCase      =	[8_0_0, 1_3_4_4]

 __UpperCamelCase      =	9_1
 __UpperCamelCase      =	'huggingface/label-files'
 __UpperCamelCase      =	'coco-detection-id2label.json'
 __UpperCamelCase      =	json.load(open(hf_hub_download(snake_case    , snake_case    , repo_type='dataset'		)    , 'r'		)		)
 __UpperCamelCase      =	{int(snake_case		): v for k, v in idalabel.items()}
 __UpperCamelCase      =	idalabel
 __UpperCamelCase      =	{v: k for k, v in idalabel.items()}

 return config






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






def 							A						(    snake_case				:str		)				->     str:
 if "backbone" in name:
  __UpperCamelCase      =	name.replace('backbone'    , 'vit'		)
 if "cls_token" in name:
  __UpperCamelCase      =	name.replace('cls_token'    , 'embeddings.cls_token'		)
 if "det_token" in name:
  __UpperCamelCase      =	name.replace('det_token'    , 'embeddings.detection_tokens'		)
 if "mid_pos_embed" in name:
  __UpperCamelCase      =	name.replace('mid_pos_embed'    , 'encoder.mid_position_embeddings'		)
 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'		)
 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'		)
 if "class_embed" in name:
  __UpperCamelCase      =	name.replace('class_embed'    , 'class_labels_classifier'		)
 if "bbox_embed" in name:
  __UpperCamelCase      =	name.replace('bbox_embed'    , 'bbox_predictor'		)
 if "vit.norm" in name:
  __UpperCamelCase      =	name.replace('vit.norm'    , 'vit.layernorm'		)

 return name






def 							A						(    snake_case				:dict    , snake_case				:YolosForObjectDetection		)				->     dict:
 for key in orig_state_dict.copy().keys():
  __UpperCamelCase      =	orig_state_dict.pop(snake_case		)

  if "qkv" in key:
   __UpperCamelCase      =	key.split('.'		)
   __UpperCamelCase      =	int(key_split[2]		)
   __UpperCamelCase      =	model.vit.encoder.layer[layer_num].attention.attention.all_head_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 							A						(    )				->     torch.Tensor:
 __UpperCamelCase      =	'http://images.cocodataset.org/val2017/000000039769.jpg'
 __UpperCamelCase      =	Image.open(requests.get(snake_case    , stream=snake_case		).raw		)
 return im






@torch.no_grad()
def 							A						(    snake_case				:str    , snake_case				:str    , snake_case				:str    , snake_case				:bool = False		)				->     str:
 __UpperCamelCase      =	get_yolos_config(snake_case		)

 # load original state_dict
 __UpperCamelCase      =	torch.load(snake_case    , map_location='cpu'		)['model']

 # load 🤗 model
 __UpperCamelCase      =	YolosForObjectDetection(snake_case		)
 model.eval()
 __UpperCamelCase      =	convert_state_dict(snake_case    , snake_case		)
 model.load_state_dict(snake_case		)

 # Check outputs on an image, prepared by YolosImageProcessor
 __UpperCamelCase      =	8_0_0 if yolos_name != 'yolos_ti' else 5_1_2
 __UpperCamelCase      =	YolosImageProcessor(format='coco_detection'    , size=snake_case		)
 __UpperCamelCase      =	image_processor(images=prepare_img()    , return_tensors='pt'		)
 __UpperCamelCase      =	model(**snake_case		)
 __UpperCamelCase  ,     __UpperCamelCase      =	outputs.logits, outputs.pred_boxes

 __UpperCamelCase  ,     __UpperCamelCase      =	None, None
 if yolos_name == "yolos_ti":
  __UpperCamelCase      =	torch.tensor(
      [[-39.5_022, -11.9_820, -17.6_888], [-29.9_574, -9.9_769, -17.7_691], [-42.3_281, -20.7_200, -30.6_294]]		)
  __UpperCamelCase      =	torch.tensor(
      [[0.4_021, 0.0_836, 0.7_979], [0.0_184, 0.2_609, 0.0_364], [0.1_781, 0.2_004, 0.2_095]]		)
 elif yolos_name == "yolos_s_200_pre":
  __UpperCamelCase      =	torch.tensor(
      [[-24.0_248, -10.3_024, -14.8_290], [-42.0_392, -16.8_200, -27.4_334], [-27.2_743, -11.8_154, -18.7_148]]		)
  __UpperCamelCase      =	torch.tensor(
      [[0.2_559, 0.5_455, 0.4_706], [0.2_989, 0.7_279, 0.1_875], [0.7_732, 0.4_017, 0.4_462]]		)
 elif yolos_name == "yolos_s_300_pre":
  __UpperCamelCase      =	torch.tensor(
      [[-36.2_220, -14.4_385, -23.5_457], [-35.6_970, -14.7_583, -21.3_935], [-31.5_939, -13.6_042, -16.8_049]]		)
  __UpperCamelCase      =	torch.tensor(
      [[0.7_614, 0.2_316, 0.4_728], [0.7_168, 0.4_495, 0.3_855], [0.4_996, 0.1_466, 0.9_996]]		)
 elif yolos_name == "yolos_s_dWr":
  __UpperCamelCase      =	torch.tensor(
      [[-42.8_668, -24.1_049, -41.1_690], [-34.7_456, -14.1_274, -24.9_194], [-33.7_898, -12.1_946, -25.6_495]]		)
  __UpperCamelCase      =	torch.tensor(
      [[0.5_587, 0.2_773, 0.0_605], [0.5_004, 0.3_014, 0.9_994], [0.4_999, 0.1_548, 0.9_994]]		)
 elif yolos_name == "yolos_base":
  __UpperCamelCase      =	torch.tensor(
      [[-40.6_064, -24.3_084, -32.6_447], [-55.1_990, -30.7_719, -35.5_877], [-51.4_311, -33.3_507, -35.6_462]]		)
  __UpperCamelCase      =	torch.tensor(
      [[0.5_555, 0.2_794, 0.0_655], [0.9_049, 0.2_664, 0.1_894], [0.9_183, 0.1_984, 0.1_635]]		)
 else:
  raise ValueError(f'Unknown yolos_name: {yolos_name}'		)

 assert torch.allclose(logits[0, :3, :3]    , snake_case    , atol=1e-4		)
 assert torch.allclose(pred_boxes[0, :3, :3]    , snake_case    , atol=1e-4		)

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

 if push_to_hub:
  __UpperCamelCase      =	{
      'yolos_ti': 'yolos-tiny',
      'yolos_s_200_pre': 'yolos-small',
      'yolos_s_300_pre': 'yolos-small-300',
      'yolos_s_dWr': 'yolos-small-dwr',
      'yolos_base': 'yolos-base',
  }

  print('Pushing to the hub...'		)
  __UpperCamelCase      =	model_mapping[yolos_name]
  image_processor.push_to_hub(snake_case    , organization='hustvl'		)
  model.push_to_hub(snake_case    , organization='hustvl'		)


if __name__ == "__main__":
      UpperCamelCase     :    Tuple       =			argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          "--yolos_name",
          default="yolos_s_200_pre",
          type=str,
          help=(
              "Name of the YOLOS model you'd like to convert. Should be one of 'yolos_ti', 'yolos_s_200_pre',"
              " 'yolos_s_300_pre', 'yolos_s_dWr', 'yolos_base'."
          ),
      )
      parser.add_argument(
          "--checkpoint_path", default=None, type=str, help="Path to the original state dict (.pth file)."
      )
      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."
      )

      UpperCamelCase     :    List[str]       =			parser.parse_args()
      convert_yolos_checkpoint(args.yolos_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)





"""simple docstring"""

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


UpperCamelCase     :    int       =			logging.get_logger(__name__)

class __lowerCAmelCase		(				__SCREAMING_SNAKE_CASE      ):
     lowercase						       =						["input_values", "attention_mask"]





     def __init__(     self     ,					__UpperCAmelCase = 1     ,					__UpperCAmelCase = 1_6000     ,					__UpperCAmelCase = 0.0     ,					__UpperCAmelCase = False     ,					__UpperCAmelCase = 80     ,					__UpperCAmelCase = 16     ,					__UpperCAmelCase = 64     ,					__UpperCAmelCase = "hann_window"     ,					__UpperCAmelCase = 1.0     ,					__UpperCAmelCase = 80     ,					__UpperCAmelCase = 7600     ,					__UpperCAmelCase = 1E-10     ,					__UpperCAmelCase = 2     ,					__UpperCAmelCase = True     ,					**__UpperCAmelCase     ,					):

      '''simple docstring'''




      super().__init__(feature_size=__UpperCAmelCase     ,					sampling_rate=__UpperCAmelCase     ,					padding_value=__UpperCAmelCase     ,					**__UpperCAmelCase				)
      __UpperCamelCase      =	do_normalize
      __UpperCamelCase      =	return_attention_mask

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

      __UpperCamelCase      =	win_length * sampling_rate // 1000
      __UpperCamelCase      =	hop_length * sampling_rate // 1000
      __UpperCamelCase      =	optimal_fft_length(self.sample_size				)
      __UpperCamelCase      =	(self.n_fft // 2) + 1

      __UpperCamelCase      =	window_function(window_length=self.sample_size     ,					name=self.win_function     ,					periodic=__UpperCAmelCase				)

      __UpperCamelCase      =	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 						UpperCAmelCase (     __UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase = 0.0				):

      '''simple docstring'''




      if attention_mask is not None:
       __UpperCamelCase      =	np.array(__UpperCAmelCase     ,					np.intaa				)
       __UpperCamelCase      =	[]

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

        normed_input_values.append(__UpperCAmelCase				)
      else:
       __UpperCamelCase      =	[(x - x.mean()) / np.sqrt(x.var() + 1E-7				) for x in input_values]

      return normed_input_values





     def 						UpperCAmelCase (     self     ,					__UpperCAmelCase     ,					):

      '''simple docstring'''




      __UpperCamelCase      =	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     ,					__UpperCAmelCase = None     ,					__UpperCAmelCase = None     ,					__UpperCAmelCase = False     ,					__UpperCAmelCase = None     ,					__UpperCAmelCase = False     ,					__UpperCAmelCase = None     ,					__UpperCAmelCase = None     ,					__UpperCAmelCase = None     ,					__UpperCAmelCase = None     ,					**__UpperCAmelCase     ,					):

      '''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:
       __UpperCamelCase      =	self._process_audio(
           __UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					**__UpperCAmelCase     ,					)
      else:
       __UpperCamelCase      =	None

      if audio_target is not None:
       __UpperCamelCase      =	self._process_audio(
           __UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					**__UpperCAmelCase     ,					)

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

      return inputs





     def 						UpperCAmelCase (     self     ,					__UpperCAmelCase     ,					__UpperCAmelCase = False     ,					__UpperCAmelCase = False     ,					__UpperCAmelCase = None     ,					__UpperCAmelCase = False     ,					__UpperCAmelCase = None     ,					__UpperCAmelCase = None     ,					__UpperCAmelCase = None     ,					**__UpperCAmelCase     ,					):

      '''simple docstring'''




      __UpperCamelCase      =	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}'				)
      __UpperCamelCase      =	is_batched_numpy or (
          isinstance(__UpperCAmelCase     ,					(list, tuple)				) and (isinstance(speech[0]     ,					(np.ndarray, tuple, list)				))
      )

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

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

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

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

      __UpperCamelCase      =	self.pad(
          __UpperCAmelCase     ,					padding=__UpperCAmelCase     ,					max_length=__UpperCAmelCase     ,					truncation=__UpperCAmelCase     ,					pad_to_multiple_of=__UpperCAmelCase     ,					return_attention_mask=__UpperCAmelCase     ,					**__UpperCAmelCase     ,					)

      __UpperCamelCase      =	feature_size_hack

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

      # convert attention_mask to correct format
      __UpperCamelCase      =	padded_inputs.get('attention_mask'				)
      if attention_mask is not None:
       __UpperCamelCase      =	[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:
       __UpperCamelCase      =	(
           attention_mask
           if self._get_padding_strategies(__UpperCAmelCase     ,					max_length=__UpperCAmelCase				) is not PaddingStrategy.DO_NOT_PAD
           else None
       )
       __UpperCamelCase      =	self.zero_mean_unit_var_norm(
           padded_inputs['input_values']     ,					attention_mask=__UpperCAmelCase     ,					padding_value=self.padding_value				)

      if return_tensors is not None:
       __UpperCamelCase      =	padded_inputs.convert_to_tensors(__UpperCAmelCase				)

      return padded_inputs







     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      __UpperCamelCase      =	super().to_dict()

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

      return output



