





'''simple docstring'''







import argparse
import json
from collections import OrderedDict
from pathlib import Path

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

from transformers import (
    ConditionalDetrConfig,
    ConditionalDetrForObjectDetection,
    ConditionalDetrForSegmentation,
    ConditionalDetrImageProcessor,
)
from transformers.utils import logging


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

# here we list all keys to be renamed (original name on the left, our name on the right)
_lowercase       						=			[]
for i in range(6):
   # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
   rename_keys.append(
       (f'transformer.encoder.layers.{i}.self_attn.out_proj.weight', f'encoder.layers.{i}.self_attn.out_proj.weight')
   )
   rename_keys.append(
       (f'transformer.encoder.layers.{i}.self_attn.out_proj.bias', f'encoder.layers.{i}.self_attn.out_proj.bias')
   )
   rename_keys.append((f'transformer.encoder.layers.{i}.linear1.weight', f'encoder.layers.{i}.fc1.weight'))
   rename_keys.append((f'transformer.encoder.layers.{i}.linear1.bias', f'encoder.layers.{i}.fc1.bias'))
   rename_keys.append((f'transformer.encoder.layers.{i}.linear2.weight', f'encoder.layers.{i}.fc2.weight'))
   rename_keys.append((f'transformer.encoder.layers.{i}.linear2.bias', f'encoder.layers.{i}.fc2.bias'))
   rename_keys.append(
       (f'transformer.encoder.layers.{i}.norm1.weight', f'encoder.layers.{i}.self_attn_layer_norm.weight')
   )
   rename_keys.append((f'transformer.encoder.layers.{i}.norm1.bias', f'encoder.layers.{i}.self_attn_layer_norm.bias'))
   rename_keys.append((f'transformer.encoder.layers.{i}.norm2.weight', f'encoder.layers.{i}.final_layer_norm.weight'))
   rename_keys.append((f'transformer.encoder.layers.{i}.norm2.bias', f'encoder.layers.{i}.final_layer_norm.bias'))
   # decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
   rename_keys.append(
       (f'transformer.decoder.layers.{i}.self_attn.out_proj.weight', f'decoder.layers.{i}.self_attn.out_proj.weight')
   )
   rename_keys.append(
       (f'transformer.decoder.layers.{i}.self_attn.out_proj.bias', f'decoder.layers.{i}.self_attn.out_proj.bias')
   )
   rename_keys.append(
       (
           f'transformer.decoder.layers.{i}.cross_attn.out_proj.weight',
           f'decoder.layers.{i}.encoder_attn.out_proj.weight',
       )
   )
   rename_keys.append(
       (
           f'transformer.decoder.layers.{i}.cross_attn.out_proj.bias',
           f'decoder.layers.{i}.encoder_attn.out_proj.bias',
       )
   )
   rename_keys.append((f'transformer.decoder.layers.{i}.linear1.weight', f'decoder.layers.{i}.fc1.weight'))
   rename_keys.append((f'transformer.decoder.layers.{i}.linear1.bias', f'decoder.layers.{i}.fc1.bias'))
   rename_keys.append((f'transformer.decoder.layers.{i}.linear2.weight', f'decoder.layers.{i}.fc2.weight'))
   rename_keys.append((f'transformer.decoder.layers.{i}.linear2.bias', f'decoder.layers.{i}.fc2.bias'))
   rename_keys.append(
       (f'transformer.decoder.layers.{i}.norm1.weight', f'decoder.layers.{i}.self_attn_layer_norm.weight')
   )
   rename_keys.append((f'transformer.decoder.layers.{i}.norm1.bias', f'decoder.layers.{i}.self_attn_layer_norm.bias'))
   rename_keys.append(
       (f'transformer.decoder.layers.{i}.norm2.weight', f'decoder.layers.{i}.encoder_attn_layer_norm.weight')
   )
   rename_keys.append(
       (f'transformer.decoder.layers.{i}.norm2.bias', f'decoder.layers.{i}.encoder_attn_layer_norm.bias')
   )
   rename_keys.append((f'transformer.decoder.layers.{i}.norm3.weight', f'decoder.layers.{i}.final_layer_norm.weight'))
   rename_keys.append((f'transformer.decoder.layers.{i}.norm3.bias', f'decoder.layers.{i}.final_layer_norm.bias'))

   # q, k, v projections in self/cross-attention in decoder for conditional DETR
   rename_keys.append(
       (f'transformer.decoder.layers.{i}.sa_qcontent_proj.weight', f'decoder.layers.{i}.sa_qcontent_proj.weight')
   )
   rename_keys.append(
       (f'transformer.decoder.layers.{i}.sa_kcontent_proj.weight', f'decoder.layers.{i}.sa_kcontent_proj.weight')
   )
   rename_keys.append(
       (f'transformer.decoder.layers.{i}.sa_qpos_proj.weight', f'decoder.layers.{i}.sa_qpos_proj.weight')
   )
   rename_keys.append(
       (f'transformer.decoder.layers.{i}.sa_kpos_proj.weight', f'decoder.layers.{i}.sa_kpos_proj.weight')
   )
   rename_keys.append((f'transformer.decoder.layers.{i}.sa_v_proj.weight', f'decoder.layers.{i}.sa_v_proj.weight'))
   rename_keys.append(
       (f'transformer.decoder.layers.{i}.ca_qcontent_proj.weight', f'decoder.layers.{i}.ca_qcontent_proj.weight')
   )
   # rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.weight", f"decoder.layers.{i}.ca_qpos_proj.weight"))
   rename_keys.append(
       (f'transformer.decoder.layers.{i}.ca_kcontent_proj.weight', f'decoder.layers.{i}.ca_kcontent_proj.weight')
   )
   rename_keys.append(
       (f'transformer.decoder.layers.{i}.ca_kpos_proj.weight', f'decoder.layers.{i}.ca_kpos_proj.weight')
   )
   rename_keys.append((f'transformer.decoder.layers.{i}.ca_v_proj.weight', f'decoder.layers.{i}.ca_v_proj.weight'))
   rename_keys.append(
       (f'transformer.decoder.layers.{i}.ca_qpos_sine_proj.weight', f'decoder.layers.{i}.ca_qpos_sine_proj.weight')
   )

   rename_keys.append(
       (f'transformer.decoder.layers.{i}.sa_qcontent_proj.bias', f'decoder.layers.{i}.sa_qcontent_proj.bias')
   )
   rename_keys.append(
       (f'transformer.decoder.layers.{i}.sa_kcontent_proj.bias', f'decoder.layers.{i}.sa_kcontent_proj.bias')
   )
   rename_keys.append((f'transformer.decoder.layers.{i}.sa_qpos_proj.bias', f'decoder.layers.{i}.sa_qpos_proj.bias'))
   rename_keys.append((f'transformer.decoder.layers.{i}.sa_kpos_proj.bias', f'decoder.layers.{i}.sa_kpos_proj.bias'))
   rename_keys.append((f'transformer.decoder.layers.{i}.sa_v_proj.bias', f'decoder.layers.{i}.sa_v_proj.bias'))
   rename_keys.append(
       (f'transformer.decoder.layers.{i}.ca_qcontent_proj.bias', f'decoder.layers.{i}.ca_qcontent_proj.bias')
   )
   # rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.bias", f"decoder.layers.{i}.ca_qpos_proj.bias"))
   rename_keys.append(
       (f'transformer.decoder.layers.{i}.ca_kcontent_proj.bias', f'decoder.layers.{i}.ca_kcontent_proj.bias')
   )
   rename_keys.append((f'transformer.decoder.layers.{i}.ca_kpos_proj.bias', f'decoder.layers.{i}.ca_kpos_proj.bias'))
   rename_keys.append((f'transformer.decoder.layers.{i}.ca_v_proj.bias', f'decoder.layers.{i}.ca_v_proj.bias'))
   rename_keys.append(
       (f'transformer.decoder.layers.{i}.ca_qpos_sine_proj.bias', f'decoder.layers.{i}.ca_qpos_sine_proj.bias')
   )

# convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
# for conditional DETR, also convert reference point head and query scale MLP
rename_keys.extend(
    [
        ('input_proj.weight', 'input_projection.weight'),
        ('input_proj.bias', 'input_projection.bias'),
        ('query_embed.weight', 'query_position_embeddings.weight'),
        ('transformer.decoder.norm.weight', 'decoder.layernorm.weight'),
        ('transformer.decoder.norm.bias', 'decoder.layernorm.bias'),
        ('class_embed.weight', 'class_labels_classifier.weight'),
        ('class_embed.bias', 'class_labels_classifier.bias'),
        ('bbox_embed.layers.0.weight', 'bbox_predictor.layers.0.weight'),
        ('bbox_embed.layers.0.bias', 'bbox_predictor.layers.0.bias'),
        ('bbox_embed.layers.1.weight', 'bbox_predictor.layers.1.weight'),
        ('bbox_embed.layers.1.bias', 'bbox_predictor.layers.1.bias'),
        ('bbox_embed.layers.2.weight', 'bbox_predictor.layers.2.weight'),
        ('bbox_embed.layers.2.bias', 'bbox_predictor.layers.2.bias'),
        ('transformer.decoder.ref_point_head.layers.0.weight', 'decoder.ref_point_head.layers.0.weight'),
        ('transformer.decoder.ref_point_head.layers.0.bias', 'decoder.ref_point_head.layers.0.bias'),
        ('transformer.decoder.ref_point_head.layers.1.weight', 'decoder.ref_point_head.layers.1.weight'),
        ('transformer.decoder.ref_point_head.layers.1.bias', 'decoder.ref_point_head.layers.1.bias'),
        ('transformer.decoder.query_scale.layers.0.weight', 'decoder.query_scale.layers.0.weight'),
        ('transformer.decoder.query_scale.layers.0.bias', 'decoder.query_scale.layers.0.bias'),
        ('transformer.decoder.query_scale.layers.1.weight', 'decoder.query_scale.layers.1.weight'),
        ('transformer.decoder.query_scale.layers.1.bias', 'decoder.query_scale.layers.1.bias'),
        ('transformer.decoder.layers.0.ca_qpos_proj.weight', 'decoder.layers.0.ca_qpos_proj.weight'),
        ('transformer.decoder.layers.0.ca_qpos_proj.bias', 'decoder.layers.0.ca_qpos_proj.bias'),
    ]
)

def    __UpperCamelCase	(	a    : Any					,   a    : Optional[Any]					,   a    : List[Any]     ) ->Any:
 snake_case				=			state_dict.pop(a     )
 snake_case				=			val

def    __UpperCamelCase	(	a    : int     ) ->Tuple:
 snake_case				=			OrderedDict()
 for key, value in state_dict.items():
  if "backbone.0.body" in key:
   snake_case				=			key.replace('''backbone.0.body'''					,   '''backbone.conv_encoder.model'''     )
   snake_case				=			value
  else:
   snake_case				=			value

 return new_state_dict

def    __UpperCamelCase	(	a    : Tuple					,   a    : List[str]=False     ) ->Optional[Any]:
 snake_case				=			''''''
 if is_panoptic:
  snake_case				=			'''conditional_detr.'''

 # first: transformer encoder
 for i in range(6     ):
  # read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
  snake_case				=			state_dict.pop(f"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"""     )
  snake_case				=			state_dict.pop(f"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias"""     )
  # next, add query, keys and values (in that order) to the state dict
  snake_case				=			in_proj_weight[:256, :]
  snake_case				=			in_proj_bias[:256]
  snake_case				=			in_proj_weight[256:512, :]
  snake_case				=			in_proj_bias[256:512]
  snake_case				=			in_proj_weight[-256:, :]
  snake_case				=			in_proj_bias[-256:]

def    __UpperCamelCase	(	) ->List[str]:
 snake_case				=			'''http://images.cocodataset.org/val2017/000000039769.jpg'''
 snake_case				=			Image.open(requests.get(a					,   stream=a     ).raw     )

 return im

@torch.no_grad()
def    __UpperCamelCase	(	a    : int					,   a    : Any     ) ->Tuple:
 snake_case				=			ConditionalDetrConfig()
 # set backbone and dilation attributes
 if "resnet101" in model_name:
  snake_case				=			'''resnet101'''
 if "dc5" in model_name:
  snake_case				=			True
 snake_case				=			'''panoptic''' in model_name
 if is_panoptic:
  snake_case				=			250
 else:
  snake_case				=			91
  snake_case				=			'''huggingface/label-files'''
  snake_case				=			'''coco-detection-id2label.json'''
  snake_case				=			json.load(open(hf_hub_download(a					,   a					,   repo_type='''dataset'''     )					,   '''r'''     )     )
  snake_case				=			{int(a     ): v for k, v in idalabel.items()}
  snake_case				=			idalabel
  snake_case				=			{v: k for k, v in idalabel.items()}

 # load image processor
 snake_case				=			'''coco_panoptic''' if is_panoptic else '''coco_detection'''
 snake_case				=			ConditionalDetrImageProcessor(format=a     )

 # prepare image
 snake_case				=			prepare_img()
 snake_case				=			image_processor(images=a					,   return_tensors='''pt'''     )
 snake_case				=			encoding['''pixel_values''']

 logger.info(f"""Converting model {model_name}..."""     )

 # load original model from torch hub
 snake_case				=			torch.hub.load('''DeppMeng/ConditionalDETR'''					,   a					,   pretrained=a     ).eval()
 snake_case				=			conditional_detr.state_dict()
 # rename keys
 for src, dest in rename_keys:
  if is_panoptic:
   snake_case				=			'''conditional_detr.''' + src
  rename_key(a					,   a					,   a     )
 snake_case				=			rename_backbone_keys(a     )
 # query, key and value matrices need special treatment
 read_in_q_k_v(a					,   is_panoptic=a     )
 # important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
 snake_case				=			'''conditional_detr.model.''' if is_panoptic else '''model.'''
 for key in state_dict.copy().keys():
  if is_panoptic:
   if (
       key.startswith('''conditional_detr'''     )
       and not key.startswith('''class_labels_classifier'''     )
       and not key.startswith('''bbox_predictor'''     )
   ):
    snake_case				=			state_dict.pop(a     )
    snake_case				=			val
   elif "class_labels_classifier" in key or "bbox_predictor" in key:
    snake_case				=			state_dict.pop(a     )
    snake_case				=			val
   elif key.startswith('''bbox_attention'''     ) or key.startswith('''mask_head'''     ):
    continue
   else:
    snake_case				=			state_dict.pop(a     )
    snake_case				=			val
  else:
   if not key.startswith('''class_labels_classifier'''     ) and not key.startswith('''bbox_predictor'''     ):
    snake_case				=			state_dict.pop(a     )
    snake_case				=			val
    # finally, create HuggingFace model and load state dict
 snake_case				=			ConditionalDetrForSegmentation(a     ) if is_panoptic else ConditionalDetrForObjectDetection(a     )
 model.load_state_dict(a     )
 model.eval()
 model.push_to_hub(repo_id=a					,   organization='''DepuMeng'''					,   commit_message='''Add model'''     )
 # verify our conversion
 snake_case				=			conditional_detr(a     )
 snake_case				=			model(a     )
 assert torch.allclose(outputs.logits					,   original_outputs['''pred_logits''']					,   atol=1e-4     )
 assert torch.allclose(outputs.pred_boxes					,   original_outputs['''pred_boxes''']					,   atol=1e-4     )
 if is_panoptic:
  assert torch.allclose(outputs.pred_masks					,   original_outputs['''pred_masks''']					,   atol=1e-4     )

 # Save model and image processor
 logger.info(f"""Saving PyTorch model and image processor to {pytorch_dump_folder_path}..."""     )
 Path(a     ).mkdir(exist_ok=a     )
 model.save_pretrained(a     )
 image_processor.save_pretrained(a     )


if __name__ == "__main__":
   _lowercase       						=			argparse.ArgumentParser()

   parser.add_argument(
       '--model_name',
       default='conditional_detr_resnet50',
       type=str,
       help='Name of the CONDITIONAL_DETR model you\'d like to convert.',
   )
   parser.add_argument(
       '--pytorch_dump_folder_path', default=None, type=str, help='Path to the folder to output PyTorch model.'
   )
   _lowercase       						=			parser.parse_args()
   convert_conditional_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)








'''simple docstring'''







from typing import Dict, List, Optional, Union

import numpy as np

from .feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from .utils import PaddingStrategy, TensorType, is_tf_tensor, is_torch_tensor, logging, to_numpy


_lowercase       						=			logging.get_logger(__name__)
class 			_lowercase						(       __a					):







     def __init__( self    ,   A__    ,   A__    ,   A__    ,   **A__    ) ->	Union[str, Any]:
      snake_case				=			feature_size
      snake_case				=			sampling_rate
      snake_case				=			padding_value

      snake_case				=			kwargs.pop('''padding_side'''    ,   '''right'''    )
      snake_case				=			kwargs.pop('''return_attention_mask'''    ,   A__    )

      super().__init__(**A__    )







     def     UpperCamelCase			( self    ,   A__    ,   A__ = True    ,   A__ = None    ,   A__ = False    ,   A__ = None    ,   A__ = None    ,   A__ = None    ,   ) ->	BatchFeature:
      # If we have a list of dicts, let's convert it in a dict of lists
      # We do this to allow using this method as a collate_fn function in PyTorch Dataloader
      if isinstance(A__    ,   (list, tuple)    ) and isinstance(processed_features[0]    ,   (dict, BatchFeature)    ):
       snake_case				=			{
           key: [example[key] for example in processed_features] for key in processed_features[0].keys()
       }

      # The model's main input name, usually `input_values`, has be passed for padding
      if self.model_input_names[0] not in processed_features:
       raise ValueError(
           '''You should supply an instance of `transformers.BatchFeature` or list of `transformers.BatchFeature`'''
           F""" to this method that includes {self.model_input_names[0]}, but you provided"""
           F""" {list(processed_features.keys()    )}"""    )

      snake_case				=			processed_features[self.model_input_names[0]]
      snake_case				=			(
          return_attention_mask if return_attention_mask is not None else self.return_attention_mask
      )

      if len(A__    ) == 0:
       if return_attention_mask:
        snake_case				=			[]
       return processed_features

      # If we have PyTorch/TF tensors or lists as inputs, we cast them as Numpy arrays
      # and rebuild them afterwards if no return_tensors is specified
      # Note that we lose the specific device the tensor may be on for PyTorch

      snake_case				=			required_input[0]
      if isinstance(A__    ,   (list, tuple)    ):
       # first_element might be an empty list/tuple in some edge cases so we grab the first non empty element.
       snake_case				=			0
       while len(required_input[index]    ) == 0:
        index += 1
       if index < len(A__    ):
        snake_case				=			required_input[index][0]

      if return_tensors is None:
       if is_tf_tensor(A__    ):
        snake_case				=			'''tf'''
       elif is_torch_tensor(A__    ):
        snake_case				=			'''pt'''
       elif isinstance(A__    ,   (int, float, list, tuple, np.ndarray)    ):
        snake_case				=			'''np'''
       else:
        raise ValueError(
            F"""type of {first_element} unknown: {type(A__    )}. """
            '''Should be one of a python, numpy, pytorch or tensorflow object.'''    )

      for key, value in processed_features.items():
       if isinstance(value[0]    ,   (int, float)    ):
        snake_case				=			to_numpy(A__    )
       else:
        snake_case				=			[to_numpy(A__    ) for v in value]

        # Convert padding_strategy in PaddingStrategy
      snake_case				=			self._get_padding_strategies(padding=A__    ,   max_length=A__    )

      snake_case				=			processed_features[self.model_input_names[0]]

      snake_case				=			len(A__    )
      if not all(len(A__    ) == batch_size for v in processed_features.values()    ):
       raise ValueError('''Some items in the output dictionary have a different batch size than others.'''    )

      snake_case				=			[]
      for i in range(A__    ):
       snake_case				=			{k: v[i] for k, v in processed_features.items()}
       # truncation
       snake_case				=			self._truncate(
           A__    ,   max_length=A__    ,   pad_to_multiple_of=A__    ,   truncation=A__    ,   )
       truncated_inputs.append(A__    )

      if padding_strategy == PaddingStrategy.LONGEST:
       # make sure that `max_length` cannot be longer than the longest truncated length
       snake_case				=			max(len(input_slice[self.model_input_names[0]]    ) for input_slice in truncated_inputs    )
       snake_case				=			PaddingStrategy.MAX_LENGTH

      snake_case				=			{}
      for i in range(A__    ):
       # padding
       snake_case				=			self._pad(
           truncated_inputs[i]    ,   max_length=A__    ,   padding_strategy=A__    ,   pad_to_multiple_of=A__    ,   return_attention_mask=A__    ,   )

       for key, value in outputs.items():
        if key not in batch_outputs:
         snake_case				=			[]
        if value.dtype is np.dtype(np.floataa    ):
         snake_case				=			value.astype(np.floataa    )
        batch_outputs[key].append(A__    )

      return BatchFeature(A__    ,   tensor_type=A__    )







     def     UpperCamelCase			( self    ,   A__    ,   A__ = None    ,   A__ = PaddingStrategy.DO_NOT_PAD    ,   A__ = None    ,   A__ = None    ,   ) ->	dict:
      snake_case				=			processed_features[self.model_input_names[0]]

      if padding_strategy == PaddingStrategy.LONGEST:
       snake_case				=			len(A__    )

      if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
       snake_case				=			((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

      snake_case				=			padding_strategy != PaddingStrategy.DO_NOT_PAD and len(A__    ) < max_length

      if return_attention_mask and "attention_mask" not in processed_features:
       snake_case				=			np.ones(len(A__    )    ,   dtype=np.intaa    )

      if needs_to_be_padded:
       snake_case				=			max_length - len(A__    )
       if self.padding_side == "right":
        if return_attention_mask:
         snake_case				=			np.pad(
             processed_features['''attention_mask''']    ,   (0, difference)    )
        snake_case				=			((0, difference), (0, 0)) if self.feature_size > 1 else (0, difference)
        snake_case				=			np.pad(
            A__    ,   A__    ,   '''constant'''    ,   constant_values=self.padding_value    )
       elif self.padding_side == "left":
        if return_attention_mask:
         snake_case				=			np.pad(
             processed_features['''attention_mask''']    ,   (difference, 0)    )
        snake_case				=			((difference, 0), (0, 0)) if self.feature_size > 1 else (difference, 0)
        snake_case				=			np.pad(
            A__    ,   A__    ,   '''constant'''    ,   constant_values=self.padding_value    )
       else:
        raise ValueError('''Invalid padding strategy:''' + str(self.padding_side    )    )

      return processed_features







     def     UpperCamelCase			( self    ,   A__    ,   A__ = None    ,   A__ = None    ,   A__ = None    ,   ) ->	Union[str, Any]:
      if not truncation:
       return processed_features
      elif truncation and max_length is None:
       raise ValueError('''When setting ``truncation=True``, make sure that ``max_length`` is defined.'''    )

      snake_case				=			processed_features[self.model_input_names[0]]

      # find `max_length` that fits `pad_to_multiple_of`
      if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
       snake_case				=			((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

      snake_case				=			len(A__    ) > max_length

      if needs_to_be_truncated:
       snake_case				=			processed_features[self.model_input_names[0]][:max_length]
       if "attention_mask" in processed_features:
        snake_case				=			processed_features['''attention_mask'''][:max_length]

      return processed_features







     def     UpperCamelCase			( self    ,   A__=False    ,   A__=None    ) ->	Union[str, Any]:

      # Get padding strategy
      if padding is not False:
       if padding is True:
        snake_case				=			PaddingStrategy.LONGEST  # Default to pad to the longest sequence in the batch
       elif not isinstance(A__    ,   A__    ):
        snake_case				=			PaddingStrategy(A__    )
       elif isinstance(A__    ,   A__    ):
        snake_case				=			padding
      else:
       snake_case				=			PaddingStrategy.DO_NOT_PAD

      # Set max length if needed
      if max_length is None:
       if padding_strategy == PaddingStrategy.MAX_LENGTH:
        raise ValueError(
            F"""When setting ``padding={PaddingStrategy.MAX_LENGTH}``, make sure that max_length is defined"""    )

        # Test if we have a padding value
      if padding_strategy != PaddingStrategy.DO_NOT_PAD and (self.padding_value is None):
       raise ValueError(
           '''Asking to pad but the feature_extractor does not have a padding value. Please select a value to use'''
           ''' as `padding_value`. For example: `feature_extractor.padding_value = 0.0`.'''    )

      return padding_strategy

