





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 DetrConfig, DetrForObjectDetection, DetrForSegmentation, DetrImageProcessor, ResNetConfig
from transformers.utils import logging


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




def       __UpperCamelCase  (_SCREAMING_SNAKE_CASE		)    ->       Optional[int]:
  # initialize config
  if "resnet-50" in model_name:
    lowercase__           =  ResNetConfig.from_pretrained('microsoft/resnet-50'		)
  elif "resnet-101" in model_name:
    lowercase__           =  ResNetConfig.from_pretrained('microsoft/resnet-101'		)
  else:
    raise ValueError('Model name should include either resnet50 or resnet101'		)

  lowercase__           =  DetrConfig(use_timm_backbone=_SCREAMING_SNAKE_CASE		,						backbone_config=_SCREAMING_SNAKE_CASE		)

  # set label attributes
  lowercase__           =  'panoptic' in model_name
  if is_panoptic:
    lowercase__           =  250
  else:
    lowercase__           =  91
    lowercase__           =  'huggingface/label-files'
    lowercase__           =  'coco-detection-id2label.json'
    lowercase__           =  json.load(open(hf_hub_download(_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		,						repo_type='dataset'		)		,						'r'		)		)
    lowercase__           =  {int(_SCREAMING_SNAKE_CASE		): v for k, v in idalabel.items()}
    lowercase__           =  idalabel
    lowercase__           =  {v: k for k, v in idalabel.items()}

  return config, is_panoptic




def       __UpperCamelCase  (_SCREAMING_SNAKE_CASE		)    ->       List[Any]:
  # here we list all keys to be renamed (original name on the left, our name on the right)
  lowercase__           =  []

  # stem
  # fmt: off
  rename_keys.append(('backbone.0.body.conv1.weight', 'backbone.conv_encoder.model.embedder.embedder.convolution.weight')		)
  rename_keys.append(('backbone.0.body.bn1.weight', 'backbone.conv_encoder.model.embedder.embedder.normalization.weight')		)
  rename_keys.append(('backbone.0.body.bn1.bias', 'backbone.conv_encoder.model.embedder.embedder.normalization.bias')		)
  rename_keys.append(('backbone.0.body.bn1.running_mean', 'backbone.conv_encoder.model.embedder.embedder.normalization.running_mean')		)
  rename_keys.append(('backbone.0.body.bn1.running_var', 'backbone.conv_encoder.model.embedder.embedder.normalization.running_var')		)
  # stages
  for stage_idx in range(len(config.backbone_config.depths		)		):
    for layer_idx in range(config.backbone_config.depths[stage_idx]		):
      # shortcut
      if layer_idx == 0:
        rename_keys.append(
            (
                F"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.0.weight""",
                F"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.convolution.weight""",
            )		)
        rename_keys.append(
            (
                F"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.weight""",
                F"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.weight""",
            )		)
        rename_keys.append(
            (
                F"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.bias""",
                F"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.bias""",
            )		)
        rename_keys.append(
            (
                F"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_mean""",
                F"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_mean""",
            )		)
        rename_keys.append(
            (
                F"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_var""",
                F"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_var""",
            )		)
      # 3 convs
      for i in range(3		):
        rename_keys.append(
            (
                F"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.conv{i+1}.weight""",
                F"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.convolution.weight""",
            )		)
        rename_keys.append(
            (
                F"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.weight""",
                F"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.weight""",
            )		)
        rename_keys.append(
            (
                F"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.bias""",
                F"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.bias""",
            )		)
        rename_keys.append(
            (
                F"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_mean""",
                F"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_mean""",
            )		)
        rename_keys.append(
            (
                F"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_var""",
                F"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_var""",
            )		)
    # fmt: on

  for i in range(config.encoder_layers		):
    # 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}.multihead_attn.out_proj.weight""",
            F"""decoder.layers.{i}.encoder_attn.out_proj.weight""",
        )		)
    rename_keys.append(
        (
            F"""transformer.decoder.layers.{i}.multihead_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""")		)

  # convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
  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'),
      ]		)

  return rename_keys




def       __UpperCamelCase  (_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		)    ->       Tuple:
  lowercase__           =  state_dict.pop(_SCREAMING_SNAKE_CASE		)
  lowercase__           =  val




def       __UpperCamelCase  (_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE=False		)    ->       List[str]:
  lowercase__           =  ''
  if is_panoptic:
    lowercase__           =  '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)
    lowercase__           =  state_dict.pop(F"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"""		)
    lowercase__           =  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
    lowercase__           =  in_proj_weight[:256, :]
    lowercase__           =  in_proj_bias[:256]
    lowercase__           =  in_proj_weight[256:512, :]
    lowercase__           =  in_proj_bias[256:512]
    lowercase__           =  in_proj_weight[-256:, :]
    lowercase__           =  in_proj_bias[-256:]
  # next: transformer decoder (which is a bit more complex because it also includes cross-attention)
  for i in range(6		):
    # read in weights + bias of input projection layer of self-attention
    lowercase__           =  state_dict.pop(F"""{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight"""		)
    lowercase__           =  state_dict.pop(F"""{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_bias"""		)
    # next, add query, keys and values (in that order) to the state dict
    lowercase__           =  in_proj_weight[:256, :]
    lowercase__           =  in_proj_bias[:256]
    lowercase__           =  in_proj_weight[256:512, :]
    lowercase__           =  in_proj_bias[256:512]
    lowercase__           =  in_proj_weight[-256:, :]
    lowercase__           =  in_proj_bias[-256:]
    # read in weights + bias of input projection layer of cross-attention
    lowercase__           =  state_dict.pop(
        F"""{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight"""		)
    lowercase__           =  state_dict.pop(F"""{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_bias"""		)
    # next, add query, keys and values (in that order) of cross-attention to the state dict
    lowercase__           =  in_proj_weight_cross_attn[:256, :]
    lowercase__           =  in_proj_bias_cross_attn[:256]
    lowercase__           =  in_proj_weight_cross_attn[256:512, :]
    lowercase__           =  in_proj_bias_cross_attn[256:512]
    lowercase__           =  in_proj_weight_cross_attn[-256:, :]
    lowercase__           =  in_proj_bias_cross_attn[-256:]




def       __UpperCamelCase  ()    ->       int:
  lowercase__           =  'http://images.cocodataset.org/val2017/000000039769.jpg'
  lowercase__           =  Image.open(requests.get(_SCREAMING_SNAKE_CASE		,						stream=_SCREAMING_SNAKE_CASE		).raw		)

  return im




@torch.no_grad()
def       __UpperCamelCase  (_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE=None		,						_SCREAMING_SNAKE_CASE=False		)    ->       Dict:
  lowercase__      ,   lowercase__           =  get_detr_config(_SCREAMING_SNAKE_CASE		)

  # load original model from torch hub
  lowercase__           =  {
      'detr-resnet-50': 'detr_resnet50',
      'detr-resnet-101': 'detr_resnet101',
  }
  logger.info(F"""Converting model {model_name}..."""		)
  lowercase__           =  torch.hub.load('facebookresearch/detr'		,						model_name_to_original_name[model_name]		,						pretrained=_SCREAMING_SNAKE_CASE		).eval()
  lowercase__           =  detr.state_dict()
  # rename keys
  for src, dest in create_rename_keys(_SCREAMING_SNAKE_CASE		):
    if is_panoptic:
      lowercase__           =  'detr.' + src
    rename_key(_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		)
  # query, key and value matrices need special treatment
  read_in_q_k_v(_SCREAMING_SNAKE_CASE		,						is_panoptic=_SCREAMING_SNAKE_CASE		)
  # important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
  lowercase__           =  'detr.model.' if is_panoptic else 'model.'
  for key in state_dict.copy().keys():
    if is_panoptic:
      if (
          key.startswith('detr'		)
          and not key.startswith('class_labels_classifier'		)
          and not key.startswith('bbox_predictor'		)
      ):
        lowercase__           =  state_dict.pop(_SCREAMING_SNAKE_CASE		)
        lowercase__           =  val
      elif "class_labels_classifier" in key or "bbox_predictor" in key:
        lowercase__           =  state_dict.pop(_SCREAMING_SNAKE_CASE		)
        lowercase__           =  val
      elif key.startswith('bbox_attention'		) or key.startswith('mask_head'		):
        continue
      else:
        lowercase__           =  state_dict.pop(_SCREAMING_SNAKE_CASE		)
        lowercase__           =  val
    else:
      if not key.startswith('class_labels_classifier'		) and not key.startswith('bbox_predictor'		):
        lowercase__           =  state_dict.pop(_SCREAMING_SNAKE_CASE		)
        lowercase__           =  val

    # finally, create HuggingFace model and load state dict
  lowercase__           =  DetrForSegmentation(_SCREAMING_SNAKE_CASE		) if is_panoptic else DetrForObjectDetection(_SCREAMING_SNAKE_CASE		)
  model.load_state_dict(_SCREAMING_SNAKE_CASE		)
  model.eval()

  # verify our conversion on an image
  lowercase__           =  'coco_panoptic' if is_panoptic else 'coco_detection'
  lowercase__           =  DetrImageProcessor(format=_SCREAMING_SNAKE_CASE		)

  lowercase__           =  processor(images=prepare_img()		,						return_tensors='pt'		)
  lowercase__           =  encoding['pixel_values']

  lowercase__           =  detr(_SCREAMING_SNAKE_CASE		)
  lowercase__           =  model(_SCREAMING_SNAKE_CASE		)

  assert torch.allclose(outputs.logits		,						original_outputs['pred_logits']		,						atol=1E-3		)
  assert torch.allclose(outputs.pred_boxes		,						original_outputs['pred_boxes']		,						atol=1E-3		)
  if is_panoptic:
    assert torch.allclose(outputs.pred_masks		,						original_outputs['pred_masks']		,						atol=1E-4		)
  print('Looks ok!'		)

  if pytorch_dump_folder_path is not None:
    # Save model and image processor
    logger.info(F"""Saving PyTorch model and image processor to {pytorch_dump_folder_path}..."""		)
    Path(_SCREAMING_SNAKE_CASE		).mkdir(exist_ok=_SCREAMING_SNAKE_CASE		)
    model.save_pretrained(_SCREAMING_SNAKE_CASE		)
    processor.save_pretrained(_SCREAMING_SNAKE_CASE		)

  if push_to_hub:
    # Upload model and image processor to the hub
    logger.info('Uploading PyTorch model and image processor to the hub...'		)
    model.push_to_hub(F"""nielsr/{model_name}"""		)
    processor.push_to_hub(F"""nielsr/{model_name}"""		)


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

      parser.add_argument(
          """--model_name""",
          default="""detr-resnet-50""",
          type=str,
          choices=["""detr-resnet-50""", """detr-resnet-101"""],
          help="""Name of the 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."""
      )
      parser.add_argument("""--push_to_hub""", action="""store_true""", help="""Whether to push the model to the hub or not.""")
      lowercase_          =		parser.parse_args()
      convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)









import math
from typing import Dict, Iterable, List, Optional, Tuple, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    is_torch_available,
    is_torch_tensor,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_torch_available():
      import torch

if is_vision_available():
      import PIL


lowercase_          =		logging.get_logger(__name__)




def       __UpperCamelCase  (_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		)    ->       Tuple[int, int]:
  def constraint_to_multiple_of(_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE=0		,						_SCREAMING_SNAKE_CASE=None		):
    lowercase__           =  round(val / multiple		) * multiple

    if max_val is not None and x > max_val:
      lowercase__           =  math.floor(val / multiple		) * multiple

    if x < min_val:
      lowercase__           =  math.ceil(val / multiple		) * multiple

    return x

  lowercase__           =  (output_size, output_size) if isinstance(_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		) else output_size

  lowercase__      ,   lowercase__           =  get_image_size(_SCREAMING_SNAKE_CASE		)
  lowercase__      ,   lowercase__           =  output_size

  # determine new height and width
  lowercase__           =  output_height / input_height
  lowercase__           =  output_width / input_width

  if keep_aspect_ratio:
    # scale as little as possible
    if abs(1 - scale_width		) < abs(1 - scale_height		):
      # fit width
      lowercase__           =  scale_width
    else:
      # fit height
      lowercase__           =  scale_height

  lowercase__           =  constraint_to_multiple_of(scale_height * input_height		,						multiple=_SCREAMING_SNAKE_CASE		)
  lowercase__           =  constraint_to_multiple_of(scale_width * input_width		,						multiple=_SCREAMING_SNAKE_CASE		)

  return (new_height, new_width)


class     SCREAMING_SNAKE_CASE       (UpperCAmelCase				):
   _UpperCamelCase   :		Dict			 =	['pixel_values']


   def __init__(   self      :    Any      ,					a      :    bool = True      ,					a      :    Dict[str, int] = None      ,					a      :    PILImageResampling = PILImageResampling.BILINEAR      ,					a      :    bool = False      ,					a      :    int = 1      ,					a      :    bool = True      ,					a      :    Union[int, float] = 1 / 255      ,					a      :    bool = True      ,					a      :    Optional[Union[float, List[float]]] = None      ,					a      :    Optional[Union[float, List[float]]] = None      ,					**a      :    Tuple      ,					)->       None:





     """simple docstring"""







     super().__init__(**a	)
     lowercase__           =  size if size is not None else {'height': 384, 'width': 384}
     lowercase__           =  get_size_dict(a	)
     lowercase__           =  do_resize
     lowercase__           =  size
     lowercase__           =  keep_aspect_ratio
     lowercase__           =  ensure_multiple_of
     lowercase__           =  resample
     lowercase__           =  do_rescale
     lowercase__           =  rescale_factor
     lowercase__           =  do_normalize
     lowercase__           =  image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
     lowercase__           =  image_std if image_std is not None else IMAGENET_STANDARD_STD


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Dict      ,					a      :    np.ndarray      ,					a      :    Dict[str, int]      ,					a      :    bool = False      ,					a      :    int = 1      ,					a      :    PILImageResampling = PILImageResampling.BICUBIC      ,					a      :    Optional[Union[str, ChannelDimension]] = None      ,					**a      :    Optional[Any]      ,					)->       np.ndarray:





     """simple docstring"""







     lowercase__           =  get_size_dict(a	)
     if "height" not in size or "width" not in size:
       raise ValueError(f"""The size dictionary must contain the keys 'height' and 'width'. Got {size.keys()}"""	)
     lowercase__           =  get_resize_output_image_size(
         a      ,					output_size=(size['height'], size['width'])      ,					keep_aspect_ratio=a      ,					multiple=a      ,					)
     return resize(a      ,					size=a      ,					resample=a      ,					data_format=a      ,					**a	)


   def 	SCREAMING_SNAKE_CASE_							(   self      :    List[Any]      ,					a      :    np.ndarray      ,					a      :    Union[int, float]      ,					a      :    Optional[Union[str, ChannelDimension]] = None      ,					**a      :    Dict      ,					)->       str:





     """simple docstring"""







     return rescale(a      ,					scale=a      ,					data_format=a      ,					**a	)


   def 	SCREAMING_SNAKE_CASE_							(   self      :    List[str]      ,					a      :    np.ndarray      ,					a      :    Union[float, List[float]]      ,					a      :    Union[float, List[float]]      ,					a      :    Optional[Union[str, ChannelDimension]] = None      ,					**a      :    Optional[int]      ,					)->       np.ndarray:





     """simple docstring"""







     return normalize(a      ,					mean=a      ,					std=a      ,					data_format=a      ,					**a	)


   def 	SCREAMING_SNAKE_CASE_							(   self      :    int      ,					a      :    ImageInput      ,					a      :    bool = None      ,					a      :    int = None      ,					a      :    bool = None      ,					a      :    int = None      ,					a      :    PILImageResampling = None      ,					a      :    bool = None      ,					a      :    float = None      ,					a      :    bool = None      ,					a      :    Optional[Union[float, List[float]]] = None      ,					a      :    Optional[Union[float, List[float]]] = None      ,					a      :    Optional[Union[str, TensorType]] = None      ,					a      :    ChannelDimension = ChannelDimension.FIRST      ,					**a      :    str      ,					)->       PIL.Image.Image:





     """simple docstring"""







     lowercase__           =  do_resize if do_resize is not None else self.do_resize
     lowercase__           =  size if size is not None else self.size
     lowercase__           =  get_size_dict(a	)
     lowercase__           =  keep_aspect_ratio if keep_aspect_ratio is not None else self.keep_aspect_ratio
     lowercase__           =  ensure_multiple_of if ensure_multiple_of is not None else self.ensure_multiple_of
     lowercase__           =  resample if resample is not None else self.resample
     lowercase__           =  do_rescale if do_rescale is not None else self.do_rescale
     lowercase__           =  rescale_factor if rescale_factor is not None else self.rescale_factor
     lowercase__           =  do_normalize if do_normalize is not None else self.do_normalize
     lowercase__           =  image_mean if image_mean is not None else self.image_mean
     lowercase__           =  image_std if image_std is not None else self.image_std

     lowercase__           =  make_list_of_images(a	)

     if not valid_images(a	):
       raise ValueError(
           'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
           'torch.Tensor, tf.Tensor or jax.ndarray.'	)

     if do_resize and size is None or resample is None:
       raise ValueError('Size and resample must be specified if do_resize is True.'	)

     if do_rescale and rescale_factor is None:
       raise ValueError('Rescale factor must be specified if do_rescale is True.'	)

     if do_normalize and (image_mean is None or image_std is None):
       raise ValueError('Image mean and std must be specified if do_normalize is True.'	)

     # All transformations expect numpy arrays.
     lowercase__           =  [to_numpy_array(a	) for image in images]

     if do_resize:
       lowercase__           =  [self.resize(image=a      ,					size=a      ,					resample=a	) for image in images]

     if do_rescale:
       lowercase__           =  [self.rescale(image=a      ,					scale=a	) for image in images]

     if do_normalize:
       lowercase__           =  [self.normalize(image=a      ,					mean=a      ,					std=a	) for image in images]

     lowercase__           =  [to_channel_dimension_format(a      ,					a	) for image in images]

     lowercase__           =  {'pixel_values': images}
     return BatchFeature(data=a      ,					tensor_type=a	)


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Dict      ,					a      :    str      ,					a      :    List[Tuple] = None	)->       Optional[int]:





     """simple docstring"""







     lowercase__           =  outputs.logits

     # Resize logits and compute semantic segmentation maps
     if target_sizes is not None:
       if len(a	) != len(a	):
         raise ValueError(
             'Make sure that you pass in as many target sizes as the batch dimension of the logits'	)

       if is_torch_tensor(a	):
         lowercase__           =  target_sizes.numpy()

       lowercase__           =  []

       for idx in range(len(a	)	):
         lowercase__           =  torch.nn.functional.interpolate(
             logits[idx].unsqueeze(dim=0	)      ,					size=target_sizes[idx]      ,					mode='bilinear'      ,					align_corners=a	)
         lowercase__           =  resized_logits[0].argmax(dim=0	)
         semantic_segmentation.append(a	)
     else:
       lowercase__           =  logits.argmax(dim=1	)
       lowercase__           =  [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]	)]

     return semantic_segmentation


