


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


logging.set_verbosity_info()
__A :  Optional[int]    							=				logging.get_logger(__name__)







def     UpperCamelCase_      (   A__ :       Any ):
  '''simple docstring'''





  if "resnet-50" in model_name:
    lowerCAmelCase_      :				List[Any]      =       ResNetConfig.from_pretrained("""microsoft/resnet-50""" )
  elif "resnet-101" in model_name:
    lowerCAmelCase_      :				int      =       ResNetConfig.from_pretrained("""microsoft/resnet-101""" )
  else:
    raise ValueError("""Model name should include either resnet50 or resnet101""" )

  lowerCAmelCase_      :				List[str]      =       DetrConfig(use_timm_backbone=A__							,   backbone_config=A__ )

  # set label attributes
  lowerCAmelCase_      :				Tuple      =       """panoptic""" in model_name
  if is_panoptic:
    lowerCAmelCase_      :				Tuple      =       2_50
  else:
    lowerCAmelCase_      :				Dict      =       91
    lowerCAmelCase_      :				Union[str, Any]      =       """huggingface/label-files"""
    lowerCAmelCase_      :				Union[str, Any]      =       """coco-detection-id2label.json"""
    lowerCAmelCase_      :				Optional[Any]      =       json.load(open(hf_hub_download(A__							,   A__							,   repo_type="""dataset""" )							,   """r""" ) )
    lowerCAmelCase_      :				Union[str, Any]      =       {int(A__ ): v for k, v in idalabel.items()}
    lowerCAmelCase_      :				str      =       idalabel
    lowerCAmelCase_      :				int      =       {v: k for k, v in idalabel.items()}

  return config, is_panoptic







def     UpperCamelCase_      (   A__ :       List[str] ):
  '''simple docstring'''





  lowerCAmelCase_      :				Union[str, Any]      =       []

  # 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_      (   A__ :       Optional[int]							,   A__ :       Tuple							,   A__ :       Any ):
  '''simple docstring'''





  lowerCAmelCase_      :				Optional[int]      =       state_dict.pop(A__ )
  lowerCAmelCase_      :				Dict      =       val







def     UpperCamelCase_      (   A__ :       int							,   A__ :       Any=False ):
  '''simple docstring'''





  lowerCAmelCase_      :				str      =       """"""
  if is_panoptic:
    lowerCAmelCase_      :				List[str]      =       """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)
    lowerCAmelCase_      :				int      =       state_dict.pop(f'{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight' )
    lowerCAmelCase_      :				str      =       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
    lowerCAmelCase_      :				Any      =       in_proj_weight[:2_56, :]
    lowerCAmelCase_      :				str      =       in_proj_bias[:2_56]
    lowerCAmelCase_      :				str      =       in_proj_weight[2_56:5_12, :]
    lowerCAmelCase_      :				Optional[int]      =       in_proj_bias[2_56:5_12]
    lowerCAmelCase_      :				Any      =       in_proj_weight[-2_56:, :]
    lowerCAmelCase_      :				List[str]      =       in_proj_bias[-2_56:]
  # 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
    lowerCAmelCase_      :				Union[str, Any]      =       state_dict.pop(f'{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight' )
    lowerCAmelCase_      :				Optional[Any]      =       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
    lowerCAmelCase_      :				List[Any]      =       in_proj_weight[:2_56, :]
    lowerCAmelCase_      :				List[str]      =       in_proj_bias[:2_56]
    lowerCAmelCase_      :				Optional[int]      =       in_proj_weight[2_56:5_12, :]
    lowerCAmelCase_      :				List[Any]      =       in_proj_bias[2_56:5_12]
    lowerCAmelCase_      :				Dict      =       in_proj_weight[-2_56:, :]
    lowerCAmelCase_      :				Any      =       in_proj_bias[-2_56:]
    # read in weights + bias of input projection layer of cross-attention
    lowerCAmelCase_      :				str      =       state_dict.pop(
        f'{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight' )
    lowerCAmelCase_      :				Dict      =       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
    lowerCAmelCase_      :				List[str]      =       in_proj_weight_cross_attn[:2_56, :]
    lowerCAmelCase_      :				int      =       in_proj_bias_cross_attn[:2_56]
    lowerCAmelCase_      :				str      =       in_proj_weight_cross_attn[2_56:5_12, :]
    lowerCAmelCase_      :				str      =       in_proj_bias_cross_attn[2_56:5_12]
    lowerCAmelCase_      :				Optional[int]      =       in_proj_weight_cross_attn[-2_56:, :]
    lowerCAmelCase_      :				Tuple      =       in_proj_bias_cross_attn[-2_56:]







def     UpperCamelCase_      (   ):
  '''simple docstring'''





  lowerCAmelCase_      :				Any      =       """http://images.cocodataset.org/val2017/000000039769.jpg"""
  lowerCAmelCase_      :				Union[str, Any]      =       Image.open(requests.get(A__							,   stream=A__ ).raw )

  return im







@torch.no_grad()
def     UpperCamelCase_      (   A__ :       Dict							,   A__ :       int=None							,   A__ :       int=False ):
  '''simple docstring'''





  lowerCAmelCase_,							lowerCAmelCase_      :				List[str]      =       get_detr_config(A__ )

  # load original model from torch hub
  lowerCAmelCase_      :				Optional[Any]      =       {
      """detr-resnet-50""": """detr_resnet50""",
      """detr-resnet-101""": """detr_resnet101""",
  }
  logger.info(f'Converting model {model_name}...' )
  lowerCAmelCase_      :				Optional[int]      =       torch.hub.load("""facebookresearch/detr"""							,   model_name_to_original_name[model_name]							,   pretrained=A__ ).eval()
  lowerCAmelCase_      :				List[str]      =       detr.state_dict()
  # rename keys
  for src, dest in create_rename_keys(A__ ):
    if is_panoptic:
      lowerCAmelCase_      :				int      =       """detr.""" + src
    rename_key(A__							,   A__							,   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
  lowerCAmelCase_      :				Optional[Any]      =       """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""" )
      ):
        lowerCAmelCase_      :				Optional[Any]      =       state_dict.pop(A__ )
        lowerCAmelCase_      :				Tuple      =       val
      elif "class_labels_classifier" in key or "bbox_predictor" in key:
        lowerCAmelCase_      :				List[Any]      =       state_dict.pop(A__ )
        lowerCAmelCase_      :				str      =       val
      elif key.startswith("""bbox_attention""" ) or key.startswith("""mask_head""" ):
        continue
      else:
        lowerCAmelCase_      :				Dict      =       state_dict.pop(A__ )
        lowerCAmelCase_      :				Any      =       val
    else:
      if not key.startswith("""class_labels_classifier""" ) and not key.startswith("""bbox_predictor""" ):
        lowerCAmelCase_      :				str      =       state_dict.pop(A__ )
        lowerCAmelCase_      :				Any      =       val

    # finally, create HuggingFace model and load state dict
  lowerCAmelCase_      :				Union[str, Any]      =       DetrForSegmentation(A__ ) if is_panoptic else DetrForObjectDetection(A__ )
  model.load_state_dict(A__ )
  model.eval()

  # verify our conversion on an image
  lowerCAmelCase_      :				Optional[int]      =       """coco_panoptic""" if is_panoptic else """coco_detection"""
  lowerCAmelCase_      :				List[str]      =       DetrImageProcessor(format=A__ )

  lowerCAmelCase_      :				Optional[Any]      =       processor(images=prepare_img()							,   return_tensors="""pt""" )
  lowerCAmelCase_      :				List[str]      =       encoding["""pixel_values"""]

  lowerCAmelCase_      :				int      =       detr(A__ )
  lowerCAmelCase_      :				str      =       model(A__ )

  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(A__ ).mkdir(exist_ok=A__ )
    model.save_pretrained(A__ )
    processor.save_pretrained(A__ )

  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__":
  __A :  str    							=				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.")
  __A :  Union[str, Any]    							=				parser.parse_args()
  convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)







'''simple docstring'''






import inspect
import math
import tempfile
import unittest

import numpy as np

from transformers import ViTMAEConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
  import torch
  from torch import nn

  from transformers import ViTMAEForPreTraining, ViTMAEModel
  from transformers.models.vit.modeling_vit import VIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
  from PIL import Image

  from transformers import ViTImageProcessor






class        __snake_case	:







    """simple docstring"""



    def __init__(       self   :  Optional[Any]   ,						lowerCamelCase   :  Tuple   ,						lowerCamelCase   :  Tuple=13   ,						lowerCamelCase   :  Dict=30   ,						lowerCamelCase   :  Dict=2   ,						lowerCamelCase   :  Optional[int]=3   ,						lowerCamelCase   :  List[Any]=True   ,						lowerCamelCase   :  Any=True   ,						lowerCamelCase   :  str=32   ,						lowerCamelCase   :  Any=5   ,						lowerCamelCase   :  int=4   ,						lowerCamelCase   :  List[str]=37   ,						lowerCamelCase   :  Any="gelu"   ,						lowerCamelCase   :  Optional[Any]=0.1   ,						lowerCamelCase   :  List[str]=0.1   ,						lowerCamelCase   :  str=10   ,						lowerCamelCase   :  Optional[Any]=0.02   ,						lowerCamelCase   :  List[str]=3   ,						lowerCamelCase   :  Union[str, Any]=0.6   ,						lowerCamelCase   :  List[Any]=None   ,						)       ->  Optional[int]:
      lowerCAmelCase_      :				Optional[Any]      =       parent
      lowerCAmelCase_      :				Optional[int]      =       batch_size
      lowerCAmelCase_      :				int      =       image_size
      lowerCAmelCase_      :				List[Any]      =       patch_size
      lowerCAmelCase_      :				int      =       num_channels
      lowerCAmelCase_      :				Any      =       is_training
      lowerCAmelCase_      :				Tuple      =       use_labels
      lowerCAmelCase_      :				Optional[Any]      =       hidden_size
      lowerCAmelCase_      :				List[Any]      =       num_hidden_layers
      lowerCAmelCase_      :				Optional[Any]      =       num_attention_heads
      lowerCAmelCase_      :				Dict      =       intermediate_size
      lowerCAmelCase_      :				Union[str, Any]      =       hidden_act
      lowerCAmelCase_      :				Union[str, Any]      =       hidden_dropout_prob
      lowerCAmelCase_      :				Any      =       attention_probs_dropout_prob
      lowerCAmelCase_      :				List[Any]      =       type_sequence_label_size
      lowerCAmelCase_      :				Dict      =       initializer_range
      lowerCAmelCase_      :				List[str]      =       mask_ratio
      lowerCAmelCase_      :				Tuple      =       scope

      # in ViTMAE, the expected sequence length = (num_patches + 1) * (1 - config.mask_ratio), rounded above
      # (we add 1 for the [CLS] token)
      lowerCAmelCase_      :				Union[str, Any]      =       (image_size // patch_size) ** 2
      lowerCAmelCase_      :				Any      =       int(math.ceil((1 - mask_ratio) * (num_patches + 1)		)		)

    def __lowercase       (       self   :  Optional[int]		)       ->  str:
      lowerCAmelCase_      :				str      =       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

      lowerCAmelCase_      :				Optional[int]      =       None
      if self.use_labels:
        lowerCAmelCase_      :				int      =       ids_tensor([self.batch_size]   ,						self.type_sequence_label_size		)

      lowerCAmelCase_      :				str      =       self.get_config()

      return config, pixel_values, labels

    def __lowercase       (       self   :  Optional[int]		)       ->  Optional[int]:
      return ViTMAEConfig(
          image_size=self.image_size   ,						patch_size=self.patch_size   ,						num_channels=self.num_channels   ,						hidden_size=self.hidden_size   ,						num_hidden_layers=self.num_hidden_layers   ,						num_attention_heads=self.num_attention_heads   ,						intermediate_size=self.intermediate_size   ,						hidden_act=self.hidden_act   ,						hidden_dropout_prob=self.hidden_dropout_prob   ,						attention_probs_dropout_prob=self.attention_probs_dropout_prob   ,						is_decoder=lowerCamelCase   ,						initializer_range=self.initializer_range   ,						mask_ratio=self.mask_ratio   ,						)

    def __lowercase       (       self   :  Any   ,						lowerCamelCase   :  Any   ,						lowerCamelCase   :  Union[str, Any]   ,						lowerCamelCase   :  Dict		)       ->  Tuple:
      lowerCAmelCase_      :				Tuple      =       ViTMAEModel(config=lowerCamelCase		)
      model.to(lowerCamelCase		)
      model.eval()
      lowerCAmelCase_      :				Dict      =       model(lowerCamelCase		)
      self.parent.assertEqual(result.last_hidden_state.shape   ,						(self.batch_size, self.seq_length, self.hidden_size)		)

    def __lowercase       (       self   :  List[str]   ,						lowerCamelCase   :  Union[str, Any]   ,						lowerCamelCase   :  List[str]   ,						lowerCamelCase   :  Union[str, Any]		)       ->  Dict:
      lowerCAmelCase_      :				Tuple      =       ViTMAEForPreTraining(lowerCamelCase		)
      model.to(lowerCamelCase		)
      model.eval()
      lowerCAmelCase_      :				List[str]      =       model(lowerCamelCase		)
      lowerCAmelCase_      :				int      =       (self.image_size // self.patch_size) ** 2
      lowerCAmelCase_      :				int      =       self.patch_size**2 * self.num_channels
      self.parent.assertEqual(result.logits.shape   ,						(self.batch_size, num_patches, expected_num_channels)		)

      # test greyscale images
      lowerCAmelCase_      :				List[Any]      =       1
      lowerCAmelCase_      :				List[str]      =       ViTMAEForPreTraining(lowerCamelCase		)
      model.to(lowerCamelCase		)
      model.eval()
      lowerCAmelCase_      :				Optional[Any]      =       floats_tensor([self.batch_size, 1, self.image_size, self.image_size]		)
      lowerCAmelCase_      :				Tuple      =       model(lowerCamelCase		)
      lowerCAmelCase_      :				List[Any]      =       self.patch_size**2
      self.parent.assertEqual(result.logits.shape   ,						(self.batch_size, num_patches, expected_num_channels)		)





    def __lowercase       (       self   :  Optional[int]		)       ->  str:
      lowerCAmelCase_      :				Any      =       self.prepare_config_and_inputs()
      lowerCAmelCase_,							lowerCAmelCase_,							lowerCAmelCase_      :				List[Any]      =       config_and_inputs
      lowerCAmelCase_      :				Any      =       {"""pixel_values""": pixel_values}
      return config, inputs_dict







@require_torch
class        __snake_case	(       _SCREAMING_SNAKE_CASE     ,_SCREAMING_SNAKE_CASE     ,unittest.TestCase):







    """simple docstring"""



    lowercase       			= (ViTMAEModel, ViTMAEForPreTraining) if is_torch_available() else ()
    lowercase       			= {'feature-extraction': ViTMAEModel} if is_torch_available() else {}

    lowercase       			= False
    lowercase       			= False
    lowercase       			= False
    lowercase       			= False

    def __lowercase       (       self   :  Optional[Any]		)       ->  List[Any]:
      lowerCAmelCase_      :				Optional[int]      =       ViTMAEModelTester(self		)
      lowerCAmelCase_      :				Optional[int]      =       ConfigTester(self   ,						config_class=lowerCamelCase   ,						has_text_modality=lowerCamelCase   ,						hidden_size=37		)

    def __lowercase       (       self   :  Dict		)       ->  Tuple:
      self.config_tester.run_common_tests()

    @unittest.skip(reason="""ViTMAE does not use inputs_embeds"""		)
    def __lowercase       (       self   :  Optional[int]		)       ->  Optional[int]:
      pass

    def __lowercase       (       self   :  List[str]		)       ->  Tuple:
      lowerCAmelCase_,							lowerCAmelCase_      :				Optional[Any]      =       self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
        lowerCAmelCase_      :				List[str]      =       model_class(lowerCamelCase		)
        self.assertIsInstance(model.get_input_embeddings()   ,						(nn.Module)		)
        lowerCAmelCase_      :				Tuple      =       model.get_output_embeddings()
        self.assertTrue(x is None or isinstance(lowerCamelCase   ,						nn.Linear		)		)

    def __lowercase       (       self   :  Optional[Any]		)       ->  Any:
      lowerCAmelCase_,							lowerCAmelCase_      :				List[Any]      =       self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
        lowerCAmelCase_      :				Optional[int]      =       model_class(lowerCamelCase		)
        lowerCAmelCase_      :				Optional[Any]      =       inspect.signature(model.forward		)
        # signature.parameters is an OrderedDict => so arg_names order is deterministic
        lowerCAmelCase_      :				Any      =       [*signature.parameters.keys()]

        lowerCAmelCase_      :				Optional[Any]      =       ["""pixel_values"""]
        self.assertListEqual(arg_names[:1]   ,						lowerCamelCase		)

    def __lowercase       (       self   :  Tuple		)       ->  str:
      lowerCAmelCase_      :				Any      =       self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*lowerCamelCase		)

    def __lowercase       (       self   :  Optional[int]		)       ->  str:
      lowerCAmelCase_      :				Tuple      =       self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_pretraining(*lowerCamelCase		)

    def __lowercase       (       self   :  Optional[int]   ,						lowerCamelCase   :  Dict   ,						lowerCamelCase   :  List[Any]   ,						lowerCamelCase   :  Optional[Any]		)       ->  str:
      # make masks reproducible
      np.random.seed(2		)

      lowerCAmelCase_      :				Tuple      =       int((pt_model.config.image_size // pt_model.config.patch_size) ** 2		)
      lowerCAmelCase_      :				Optional[int]      =       np.random.uniform(size=(self.model_tester.batch_size, num_patches)		)
      lowerCAmelCase_      :				Optional[Any]      =       torch.from_numpy(lowerCamelCase		)

      # Add `noise` argument.
      # PT inputs will be prepared in `super().check_pt_tf_models()` with this added `noise` argument
      lowerCAmelCase_      :				int      =       pt_noise

      super().check_pt_tf_models(lowerCamelCase   ,						lowerCamelCase   ,						lowerCamelCase		)

    def __lowercase       (       self   :  int		)       ->  Dict:
      lowerCAmelCase_,							lowerCAmelCase_      :				Optional[Any]      =       self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
        lowerCAmelCase_      :				Optional[int]      =       model_class(lowerCamelCase		)
        model.to(lowerCamelCase		)
        model.eval()
        # make random mask reproducible
        torch.manual_seed(2		)
        with torch.no_grad():
          lowerCAmelCase_      :				Any      =       model(**self._prepare_for_class(lowerCamelCase   ,						lowerCamelCase		)		)

        lowerCAmelCase_      :				Any      =       outputs[0].cpu().numpy()
        lowerCAmelCase_      :				List[str]      =       0

        with tempfile.TemporaryDirectory() as tmpdirname:
          model.save_pretrained(lowerCamelCase		)
          lowerCAmelCase_      :				int      =       model_class.from_pretrained(lowerCamelCase		)
          model.to(lowerCamelCase		)
          # make random mask reproducible
          torch.manual_seed(2		)
          with torch.no_grad():
            lowerCAmelCase_      :				str      =       model(**self._prepare_for_class(lowerCamelCase   ,						lowerCamelCase		)		)

          # Make sure we don't have nans
          lowerCAmelCase_      :				Optional[Any]      =       after_outputs[0].cpu().numpy()
          lowerCAmelCase_      :				str      =       0
          lowerCAmelCase_      :				List[Any]      =       np.amax(np.abs(out_a - out_a		)		)
          self.assertLessEqual(lowerCamelCase   ,						1E-5		)

    @unittest.skip(
        reason="""ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load
    to get deterministic results."""		)
    def __lowercase       (       self   :  Optional[int]		)       ->  List[Any]:
      pass

    @unittest.skip(
        reason="""ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load
    to get deterministic results."""		)
    def __lowercase       (       self   :  Union[str, Any]		)       ->  str:
      pass

    @unittest.skip(
        reason="""ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load
    to get deterministic results."""		)
    def __lowercase       (       self   :  Optional[Any]		)       ->  Union[str, Any]:
      pass

    @unittest.skip(reason="""ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load"""		)
    def __lowercase       (       self   :  Tuple		)       ->  Optional[Any]:
      pass

    @unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests."""		)
    def __lowercase       (       self   :  List[Any]		)       ->  str:
      pass





    @slow
    def __lowercase       (       self   :  List[str]		)       ->  List[Any]:
      for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
        lowerCAmelCase_      :				List[Any]      =       ViTMAEModel.from_pretrained(lowerCamelCase		)
        self.assertIsNotNone(lowerCamelCase		)







def     UpperCamelCase_      (   ):
  '''simple docstring'''





  lowerCAmelCase_      :				List[str]      =       Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
  return image






@require_torch
@require_vision
class        __snake_case	(       unittest.TestCase):







    """simple docstring"""



    @cached_property
    def __lowercase       (       self   :  Union[str, Any]		)       ->  Union[str, Any]:
      return ViTImageProcessor.from_pretrained("""facebook/vit-mae-base"""		) if is_vision_available() else None





    @slow
    def __lowercase       (       self   :  int		)       ->  List[Any]:
      # make random mask reproducible across the PT and TF model
      np.random.seed(2		)

      lowerCAmelCase_      :				Dict      =       ViTMAEForPreTraining.from_pretrained("""facebook/vit-mae-base"""		).to(lowerCamelCase		)

      lowerCAmelCase_      :				Union[str, Any]      =       self.default_image_processor
      lowerCAmelCase_      :				Union[str, Any]      =       prepare_img()
      lowerCAmelCase_      :				Dict      =       image_processor(images=lowerCamelCase   ,						return_tensors="""pt"""		).to(lowerCamelCase		)

      # prepare a noise vector that will be also used for testing the TF model
      # (this way we can ensure that the PT and TF models operate on the same inputs)
      lowerCAmelCase_      :				Optional[int]      =       ViTMAEConfig()
      lowerCAmelCase_      :				Optional[Any]      =       int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2		)
      lowerCAmelCase_      :				Optional[int]      =       np.random.uniform(size=(1, num_patches)		)

      # forward pass
      with torch.no_grad():
        lowerCAmelCase_      :				str      =       model(**lowerCamelCase   ,						noise=torch.from_numpy(lowerCamelCase		).to(device=lowerCamelCase		)		)

      # verify the logits
      lowerCAmelCase_      :				str      =       torch.Size((1, 1_96, 7_68)		)
      self.assertEqual(outputs.logits.shape   ,						lowerCamelCase		)

      lowerCAmelCase_      :				str      =       torch.tensor(
          [[-0.0_548, -1.7_023, -0.9_325], [0.3_721, -0.5_670, -0.2_233], [0.8_235, -1.3_878, -0.3_524]]		)

      self.assertTrue(torch.allclose(outputs.logits[0, :3, :3]   ,						expected_slice.to(lowerCamelCase		)   ,						atol=1E-4		)		)



