




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()
__magic_name__  :  str					=       logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
__magic_name__  :  str					=       []
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 		a_							( __lowerCAmelCase   , __lowerCAmelCase   , __lowerCAmelCase				):
  lowerCAmelCase__										=      state_dict.pop(SCREAMING_SNAKE_CASE__				)
  lowerCAmelCase__										=      val
def 		a_							( __lowerCAmelCase				):
  lowerCAmelCase__										=      OrderedDict()
  for key, value in state_dict.items():
    if "backbone.0.body" in key:
      lowerCAmelCase__										=      key.replace('''backbone.0.body'''   , '''backbone.conv_encoder.model'''				)
      lowerCAmelCase__										=      value
    else:
      lowerCAmelCase__										=      value

  return new_state_dict
def 		a_							( __lowerCAmelCase   , __lowerCAmelCase=False				):
  lowerCAmelCase__										=      ""
  if is_panoptic:
    lowerCAmelCase__										=      "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)
    lowerCAmelCase__										=      state_dict.pop(F"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"""				)
    lowerCAmelCase__										=      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__										=      in_proj_weight[:2_56, :]
    lowerCAmelCase__										=      in_proj_bias[:2_56]
    lowerCAmelCase__										=      in_proj_weight[2_56:5_12, :]
    lowerCAmelCase__										=      in_proj_bias[2_56:5_12]
    lowerCAmelCase__										=      in_proj_weight[-2_56:, :]
    lowerCAmelCase__										=      in_proj_bias[-2_56:]
def 		a_							( ):
  lowerCAmelCase__										=      "http://images.cocodataset.org/val2017/000000039769.jpg"
  lowerCAmelCase__										=      Image.open(requests.get(SCREAMING_SNAKE_CASE__   , stream=SCREAMING_SNAKE_CASE__				).raw				)

  return im
@torch.no_grad()
def 		a_							( __lowerCAmelCase   , __lowerCAmelCase				):
  lowerCAmelCase__										=      ConditionalDetrConfig()
  # set backbone and dilation attributes
  if "resnet101" in model_name:
    lowerCAmelCase__										=      "resnet101"
  if "dc5" in model_name:
    lowerCAmelCase__										=      True
  lowerCAmelCase__										=      "panoptic" in model_name
  if is_panoptic:
    lowerCAmelCase__										=      2_50
  else:
    lowerCAmelCase__										=      91
    lowerCAmelCase__										=      "huggingface/label-files"
    lowerCAmelCase__										=      "coco-detection-id2label.json"
    lowerCAmelCase__										=      json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE__   , SCREAMING_SNAKE_CASE__   , repo_type='''dataset'''				)   , '''r'''				)				)
    lowerCAmelCase__										=      {int(SCREAMING_SNAKE_CASE__				): v for k, v in idalabel.items()}
    lowerCAmelCase__										=      idalabel
    lowerCAmelCase__										=      {v: k for k, v in idalabel.items()}

  # load image processor
  lowerCAmelCase__										=      "coco_panoptic" if is_panoptic else "coco_detection"
  lowerCAmelCase__										=      ConditionalDetrImageProcessor(format=SCREAMING_SNAKE_CASE__				)

  # prepare image
  lowerCAmelCase__										=      prepare_img()
  lowerCAmelCase__										=      image_processor(images=SCREAMING_SNAKE_CASE__   , return_tensors='''pt'''				)
  lowerCAmelCase__										=      encoding["pixel_values"]

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

  # load original model from torch hub
  lowerCAmelCase__										=      torch.hub.load('''DeppMeng/ConditionalDETR'''   , SCREAMING_SNAKE_CASE__   , pretrained=SCREAMING_SNAKE_CASE__				).eval()
  lowerCAmelCase__										=      conditional_detr.state_dict()
  # rename keys
  for src, dest in rename_keys:
    if is_panoptic:
      lowerCAmelCase__										=      "conditional_detr." + src
    rename_key(SCREAMING_SNAKE_CASE__   , SCREAMING_SNAKE_CASE__   , SCREAMING_SNAKE_CASE__				)
  lowerCAmelCase__										=      rename_backbone_keys(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
  lowerCAmelCase__										=      "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'''				)
      ):
        lowerCAmelCase__										=      state_dict.pop(SCREAMING_SNAKE_CASE__				)
        lowerCAmelCase__										=      val
      elif "class_labels_classifier" in key or "bbox_predictor" in key:
        lowerCAmelCase__										=      state_dict.pop(SCREAMING_SNAKE_CASE__				)
        lowerCAmelCase__										=      val
      elif key.startswith('''bbox_attention'''				) or key.startswith('''mask_head'''				):
        continue
      else:
        lowerCAmelCase__										=      state_dict.pop(SCREAMING_SNAKE_CASE__				)
        lowerCAmelCase__										=      val
    else:
      if not key.startswith('''class_labels_classifier'''				) and not key.startswith('''bbox_predictor'''				):
        lowerCAmelCase__										=      state_dict.pop(SCREAMING_SNAKE_CASE__				)
        lowerCAmelCase__										=      val
    # finally, create HuggingFace model and load state dict
  lowerCAmelCase__										=      ConditionalDetrForSegmentation(SCREAMING_SNAKE_CASE__				) if is_panoptic else ConditionalDetrForObjectDetection(SCREAMING_SNAKE_CASE__				)
  model.load_state_dict(SCREAMING_SNAKE_CASE__				)
  model.eval()
  model.push_to_hub(repo_id=SCREAMING_SNAKE_CASE__   , organization='''DepuMeng'''   , commit_message='''Add model'''				)
  # verify our conversion
  lowerCAmelCase__										=      conditional_detr(SCREAMING_SNAKE_CASE__				)
  lowerCAmelCase__										=      model(SCREAMING_SNAKE_CASE__				)
  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(SCREAMING_SNAKE_CASE__				).mkdir(exist_ok=SCREAMING_SNAKE_CASE__				)
  model.save_pretrained(SCREAMING_SNAKE_CASE__				)
  image_processor.save_pretrained(SCREAMING_SNAKE_CASE__				)


if __name__ == "__main__":
      __magic_name__  :  Any					=       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."""
      )
      __magic_name__  :  Optional[int]					=       parser.parse_args()
      convert_conditional_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)






"""simple docstring"""




import inspect
import unittest

from transformers import MobileViTVaConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, 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 transformers import MobileViTVaForImageClassification, MobileViTVaForSemanticSegmentation, MobileViTVaModel
 from transformers.models.mobilevitva.modeling_mobilevitva import (
     MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST,
     make_divisible,
 )


if is_vision_available():
 from PIL import Image

 from transformers import MobileViTImageProcessor






class       UpperCAmelCase_    (							_UpperCamelCase		):



      def 		snake_case_   (	self	:      str		):
           _UpperCAmelCase     :      Dict														=       self.config_class(**self.inputs_dict		)
           self.parent.assertTrue(hasattr(A	,      "width_multiplier"		)		)







class       UpperCAmelCase_    :



      def __init__(	self	:      Dict	,      A	:      Union[str, Any]	,      A	:      Dict=1_3	,      A	:      Union[str, Any]=6_4	,      A	:      int=2	,      A	:      Tuple=3	,      A	:      int="swish"	,      A	:      Optional[int]=3	,      A	:      Tuple=3_2	,      A	:      Tuple=0.1	,      A	:      List[Any]=0.02	,      A	:      Dict=True	,      A	:      str=True	,      A	:      Any=1_0	,      A	:      Optional[int]=None	,      A	:      int=0.25	,      A	:      int=0.0	,      A	:      Dict=0.0	,      ):
           _UpperCAmelCase     :      List[Any]														=       parent
           _UpperCAmelCase     :      Optional[Any]														=       batch_size
           _UpperCAmelCase     :      Dict														=       image_size
           _UpperCAmelCase     :      int														=       patch_size
           _UpperCAmelCase     :      List[str]														=       num_channels
           _UpperCAmelCase     :      Optional[int]														=       make_divisible(5_1_2 * width_multiplier	,      divisor=8		)
           _UpperCAmelCase     :      Dict														=       hidden_act
           _UpperCAmelCase     :      List[str]														=       conv_kernel_size
           _UpperCAmelCase     :      Optional[int]														=       output_stride
           _UpperCAmelCase     :      Union[str, Any]														=       classifier_dropout_prob
           _UpperCAmelCase     :      str														=       use_labels
           _UpperCAmelCase     :      Dict														=       is_training
           _UpperCAmelCase     :      Optional[int]														=       num_labels
           _UpperCAmelCase     :      Tuple														=       initializer_range
           _UpperCAmelCase     :      Tuple														=       scope
           _UpperCAmelCase     :      List[Any]														=       width_multiplier
           _UpperCAmelCase     :      List[str]														=       ffn_dropout
           _UpperCAmelCase     :      List[Any]														=       attn_dropout



      def 		snake_case_   (	self	:      Union[str, Any]		):
           _UpperCAmelCase     :      Optional[Any]														=       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

           _UpperCAmelCase     :      str														=       None
           _UpperCAmelCase     :      List[Any]														=       None
           if self.use_labels:
                _UpperCAmelCase     :      Dict														=       ids_tensor([self.batch_size]	,      self.num_labels		)
                _UpperCAmelCase     :      List[Any]														=       ids_tensor([self.batch_size, self.image_size, self.image_size]	,      self.num_labels		)

           _UpperCAmelCase     :      Optional[int]														=       self.get_config()

           return config, pixel_values, labels, pixel_labels



      def 		snake_case_   (	self	:      List[Any]		):
           return MobileViTVaConfig(
               image_size=self.image_size	,      patch_size=self.patch_size	,      num_channels=self.num_channels	,      hidden_act=self.hidden_act	,      conv_kernel_size=self.conv_kernel_size	,      output_stride=self.output_stride	,      classifier_dropout_prob=self.classifier_dropout_prob	,      initializer_range=self.initializer_range	,      width_multiplier=self.width_multiplier	,      ffn_dropout=self.ffn_dropout_prob	,      attn_dropout=self.attn_dropout_prob	,      )



      def 		snake_case_   (	self	:      int	,      A	:      Optional[Any]	,      A	:      Optional[int]	,      A	:      Any	,      A	:      int		):
           _UpperCAmelCase     :      List[str]														=       MobileViTVaModel(config=A		)
           model.to(A		)
           model.eval()
           _UpperCAmelCase     :      str														=       model(A		)
           self.parent.assertEqual(
               result.last_hidden_state.shape	,      (
                   self.batch_size,
                   self.last_hidden_size,
                   self.image_size // self.output_stride,
                   self.image_size // self.output_stride,
               )	,      )



      def 		snake_case_   (	self	:      Any	,      A	:      Optional[int]	,      A	:      Optional[Any]	,      A	:      str	,      A	:      List[str]		):
           _UpperCAmelCase     :      List[str]														=       self.num_labels
           _UpperCAmelCase     :      int														=       MobileViTVaForImageClassification(A		)
           model.to(A		)
           model.eval()
           _UpperCAmelCase     :      str														=       model(A	,      labels=A		)
           self.parent.assertEqual(result.logits.shape	,      (self.batch_size, self.num_labels)		)



      def 		snake_case_   (	self	:      Optional[int]	,      A	:      Tuple	,      A	:      Any	,      A	:      Any	,      A	:      Optional[Any]		):
           _UpperCAmelCase     :      Union[str, Any]														=       self.num_labels
           _UpperCAmelCase     :      Optional[int]														=       MobileViTVaForSemanticSegmentation(A		)
           model.to(A		)
           model.eval()
           _UpperCAmelCase     :      List[str]														=       model(A		)
           self.parent.assertEqual(
               result.logits.shape	,      (
                   self.batch_size,
                   self.num_labels,
                   self.image_size // self.output_stride,
                   self.image_size // self.output_stride,
               )	,      )
           _UpperCAmelCase     :      Optional[int]														=       model(A	,      labels=A		)
           self.parent.assertEqual(
               result.logits.shape	,      (
                   self.batch_size,
                   self.num_labels,
                   self.image_size // self.output_stride,
                   self.image_size // self.output_stride,
               )	,      )



      def 		snake_case_   (	self	:      Any		):
           _UpperCAmelCase     :      Any														=       self.prepare_config_and_inputs()
           _UpperCAmelCase							,			_UpperCAmelCase							,			_UpperCAmelCase							,			_UpperCAmelCase     :      str														=       config_and_inputs
           _UpperCAmelCase     :      str														=       {"pixel_values": pixel_values}
           return config, inputs_dict







@require_torch
class       UpperCAmelCase_    (							_UpperCamelCase			,     _UpperCamelCase			,     unittest.TestCase		):
      __SCREAMING_SNAKE_CASE		:  Tuple						=					(
          (MobileViTVaModel, MobileViTVaForImageClassification, MobileViTVaForSemanticSegmentation)
          if is_torch_available()
          else ()
      )

      __SCREAMING_SNAKE_CASE		:  Optional[Any]						=					(
          {
              'feature-extraction': MobileViTVaModel,
              'image-classification': MobileViTVaForImageClassification,
              'image-segmentation': MobileViTVaForSemanticSegmentation,
          }
          if is_torch_available()
          else {}
      )

      __SCREAMING_SNAKE_CASE		:  str						=					False
      __SCREAMING_SNAKE_CASE		:  List[Any]						=					False
      __SCREAMING_SNAKE_CASE		:  Dict						=					False
      __SCREAMING_SNAKE_CASE		:  Union[str, Any]						=					False



      def 		snake_case_   (	self	:      Union[str, Any]		):
           _UpperCAmelCase     :      Dict														=       MobileViTVaModelTester(self		)
           _UpperCAmelCase     :      Optional[Any]														=       MobileViTVaConfigTester(self	,      config_class=A	,      has_text_modality=A		)



      def 		snake_case_   (	self	:      int		):
           self.config_tester.run_common_tests()



      @unittest.skip(reason="MobileViTV2 does not use inputs_embeds"		)
      def 		snake_case_   (	self	:      Optional[int]		):
           pass



      @unittest.skip(reason="MobileViTV2 does not support input and output embeddings"		)
      def 		snake_case_   (	self	:      Optional[Any]		):
           pass



      @unittest.skip(reason="MobileViTV2 does not output attentions"		)
      def 		snake_case_   (	self	:      int		):
           pass



      @require_torch_multi_gpu
      @unittest.skip(reason="Got `CUDA error: misaligned address` for tests after this one being run."		)
      def 		snake_case_   (	self	:      Any		):
           pass



      @unittest.skip("Will be fixed soon by reducing the size of the model used for common tests."		)
      def 		snake_case_   (	self	:      str		):
           pass



      def 		snake_case_   (	self	:      Union[str, Any]		):
           _UpperCAmelCase							,			_UpperCAmelCase     :      Optional[Any]														=       self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                _UpperCAmelCase     :      Union[str, Any]														=       model_class(A		)
                _UpperCAmelCase     :      str														=       inspect.signature(model.forward		)
                # signature.parameters is an OrderedDict => so arg_names order is deterministic
                _UpperCAmelCase     :      int														=       [*signature.parameters.keys()]

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



      def 		snake_case_   (	self	:      int		):
           _UpperCAmelCase     :      Union[str, Any]														=       self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*A		)



      def 		snake_case_   (	self	:      Optional[Any]		):
           def check_hidden_states_output(A	:      Union[str, Any]	,      A	:      Dict	,      A	:      str		):
                _UpperCAmelCase     :      int														=       model_class(A		)
                model.to(A		)
                model.eval()

                with torch.no_grad():
                     _UpperCAmelCase     :      Tuple														=       model(**self._prepare_for_class(A	,      A		)		)

                _UpperCAmelCase     :      List[str]														=       outputs.hidden_states

                _UpperCAmelCase     :      int														=       5
                self.assertEqual(len(A		)	,      A		)

                # MobileViTV2's feature maps are of shape (batch_size, num_channels, height, width)
                # with the width and height being successively divided by 2.
                _UpperCAmelCase     :      Optional[int]														=       2
                for i in range(len(A		)		):
                     self.assertListEqual(
                         list(hidden_states[i].shape[-2:]		)	,      [self.model_tester.image_size // divisor, self.model_tester.image_size // divisor]	,      )
                     divisor *= 2

                self.assertEqual(self.model_tester.output_stride	,      divisor // 2		)

           _UpperCAmelCase							,			_UpperCAmelCase     :      List[Any]														=       self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                _UpperCAmelCase     :      Optional[Any]														=       True
                check_hidden_states_output(A	,      A	,      A		)

                # check that output_hidden_states also work using config
                del inputs_dict["output_hidden_states"]
                _UpperCAmelCase     :      str														=       True

                check_hidden_states_output(A	,      A	,      A		)



      def 		snake_case_   (	self	:      Union[str, Any]		):
           _UpperCAmelCase     :      List[str]														=       self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_image_classification(*A		)



      def 		snake_case_   (	self	:      Union[str, Any]		):
           _UpperCAmelCase     :      int														=       self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_semantic_segmentation(*A		)



      @slow
      def 		snake_case_   (	self	:      int		):
           for model_name in MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                _UpperCAmelCase     :      Union[str, Any]														=       MobileViTVaModel.from_pretrained(A		)
                self.assertIsNotNone(A		)







def 		__snake_case		( )    ->  Optional[Any]:





     '''simple docstring'''

     _UpperCAmelCase     :      Any														=       Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"							)
     return image






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



      @cached_property
      def 		snake_case_   (	self	:      Any		):
           return (
               MobileViTImageProcessor.from_pretrained("apple/mobilevitv2-1.0-imagenet1k-256"		)
               if is_vision_available()
               else None
           )



      @slow
      def 		snake_case_   (	self	:      Dict		):
           _UpperCAmelCase     :      Dict														=       MobileViTVaForImageClassification.from_pretrained("apple/mobilevitv2-1.0-imagenet1k-256"		).to(
               A		)

           _UpperCAmelCase     :      Optional[Any]														=       self.default_image_processor
           _UpperCAmelCase     :      Tuple														=       prepare_img()
           _UpperCAmelCase     :      Union[str, Any]														=       image_processor(images=A	,      return_tensors="pt"		).to(A		)

           # forward pass
           with torch.no_grad():
                _UpperCAmelCase     :      Tuple														=       model(**A		)

           # verify the logits
           _UpperCAmelCase     :      Tuple														=       torch.Size((1, 1_0_0_0)		)
           self.assertEqual(outputs.logits.shape	,      A		)

           _UpperCAmelCase     :      Optional[Any]														=       torch.tensor([-1.6336e00, -7.3204e-02, -5.1883e-01]		).to(A		)

           self.assertTrue(torch.allclose(outputs.logits[0, :3]	,      A	,      atol=1e-4		)		)



      @slow
      def 		snake_case_   (	self	:      Optional[int]		):
           _UpperCAmelCase     :      List[Any]														=       MobileViTVaForSemanticSegmentation.from_pretrained("shehan97/mobilevitv2-1.0-voc-deeplabv3"		)
           _UpperCAmelCase     :      Any														=       model.to(A		)

           _UpperCAmelCase     :      Optional[Any]														=       MobileViTImageProcessor.from_pretrained("shehan97/mobilevitv2-1.0-voc-deeplabv3"		)

           _UpperCAmelCase     :      Dict														=       prepare_img()
           _UpperCAmelCase     :      Union[str, Any]														=       image_processor(images=A	,      return_tensors="pt"		).to(A		)

           # forward pass
           with torch.no_grad():
                _UpperCAmelCase     :      int														=       model(**A		)
           _UpperCAmelCase     :      Any														=       outputs.logits

           # verify the logits
           _UpperCAmelCase     :      Optional[int]														=       torch.Size((1, 2_1, 3_2, 3_2)		)
           self.assertEqual(logits.shape	,      A		)

           _UpperCAmelCase     :      Union[str, Any]														=       torch.tensor(
               [
                   [[7.0_863, 7.1_525, 6.8_201], [6.6_931, 6.8_770, 6.8_933], [6.2_978, 7.0_366, 6.9_636]],
                   [[-3.7_134, -3.6_712, -3.6_675], [-3.5_825, -3.3_549, -3.4_777], [-3.3_435, -3.3_979, -3.2_857]],
                   [[-2.9_329, -2.8_003, -2.7_369], [-3.0_564, -2.4_780, -2.0_207], [-2.6_889, -1.9_298, -1.7_640]],
               ]	,      device=A	,      )

           self.assertTrue(torch.allclose(logits[0, :3, :3, :3]	,      A	,      atol=1e-4		)		)



      @slow
      def 		snake_case_   (	self	:      List[str]		):
           _UpperCAmelCase     :      List[str]														=       MobileViTVaForSemanticSegmentation.from_pretrained("shehan97/mobilevitv2-1.0-voc-deeplabv3"		)
           _UpperCAmelCase     :      Tuple														=       model.to(A		)

           _UpperCAmelCase     :      Dict														=       MobileViTImageProcessor.from_pretrained("shehan97/mobilevitv2-1.0-voc-deeplabv3"		)

           _UpperCAmelCase     :      Optional[int]														=       prepare_img()
           _UpperCAmelCase     :      Optional[int]														=       image_processor(images=A	,      return_tensors="pt"		).to(A		)

           # forward pass
           with torch.no_grad():
                _UpperCAmelCase     :      Tuple														=       model(**A		)

           _UpperCAmelCase     :      Optional[int]														=       outputs.logits.detach().cpu()

           _UpperCAmelCase     :      List[Any]														=       image_processor.post_process_semantic_segmentation(outputs=A	,      target_sizes=[(5_0, 6_0)]		)
           _UpperCAmelCase     :      Dict														=       torch.Size((5_0, 6_0)		)
           self.assertEqual(segmentation[0].shape	,      A		)

           _UpperCAmelCase     :      Dict														=       image_processor.post_process_semantic_segmentation(outputs=A		)
           _UpperCAmelCase     :      Union[str, Any]														=       torch.Size((3_2, 3_2)		)
           self.assertEqual(segmentation[0].shape	,      A		)
