




'''simple docstring'''





import argparse
from collections import OrderedDict
from pathlib import Path

import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision.transforms import functional as F

from transformers import DetrImageProcessor, TableTransformerConfig, TableTransformerForObjectDetection
from transformers.utils import logging


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

# here we list all keys to be renamed (original name on the left, our name on the right)
_lowerCamelCase			:	int							 =     []
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}.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 encoder + 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.encoder.norm.weight""", """encoder.layernorm.weight"""),
        ("""transformer.encoder.norm.bias""", """encoder.layernorm.bias"""),
        ("""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"""),
    ]
)







def SCREAMING_SNAKE_CASE       ( lowercase_				,       lowercase_				,       lowercase_		)     -> Optional[Any]:



       """simple docstring"""

       A__   				=					state_dict.pop(lowercase_		)
       A__   				=					val







def SCREAMING_SNAKE_CASE       ( lowercase_		)     -> int:



       """simple docstring"""

       A__   				=					OrderedDict()
       for key, value in state_dict.items():
              if "backbone.0.body" in key:
                     A__   				=					key.replace('''backbone.0.body'''				,       '''backbone.conv_encoder.model'''		)
                     A__   				=					value
              else:
                     A__   				=					value

       return new_state_dict







def SCREAMING_SNAKE_CASE       ( lowercase_		)     -> Optional[Any]:



       """simple docstring"""

       A__   				=					''''''

       # 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)
              A__   				=					state_dict.pop(f"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"""		)
              A__   				=					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
              A__   				=					in_proj_weight[:256, :]
              A__   				=					in_proj_bias[:256]
              A__   				=					in_proj_weight[256:512, :]
              A__   				=					in_proj_bias[256:512]
              A__   				=					in_proj_weight[-256:, :]
              A__   				=					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
              A__   				=					state_dict.pop(f"""{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight"""		)
              A__   				=					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
              A__   				=					in_proj_weight[:256, :]
              A__   				=					in_proj_bias[:256]
              A__   				=					in_proj_weight[256:512, :]
              A__   				=					in_proj_bias[256:512]
              A__   				=					in_proj_weight[-256:, :]
              A__   				=					in_proj_bias[-256:]
              # read in weights + bias of input projection layer of cross-attention
              A__   				=					state_dict.pop(
                  f"""{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight"""		)
              A__   				=					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
              A__   				=					in_proj_weight_cross_attn[:256, :]
              A__   				=					in_proj_bias_cross_attn[:256]
              A__   				=					in_proj_weight_cross_attn[256:512, :]
              A__   				=					in_proj_bias_cross_attn[256:512]
              A__   				=					in_proj_weight_cross_attn[-256:, :]
              A__   				=					in_proj_bias_cross_attn[-256:]







def SCREAMING_SNAKE_CASE       ( lowercase_				,       lowercase_		)     -> Dict:



       """simple docstring"""

       A__				,							A__   				=					image.size
       A__   				=					max(lowercase_				,       lowercase_		)
       A__   				=					800 if '''detection''' in checkpoint_url else 1_000
       A__   				=					target_max_size / current_max_size
       A__   				=					image.resize((int(round(scale * width		)		), int(round(scale * height		)		))		)

       return resized_image







def SCREAMING_SNAKE_CASE       ( lowercase_		)     -> Tuple:



       """simple docstring"""

       A__   				=					F.to_tensor(lowercase_		)
       A__   				=					F.normalize(lowercase_				,       mean=[0.4_85, 0.4_56, 0.4_06]				,       std=[0.2_29, 0.2_24, 0.2_25]		)
       return image







@torch.no_grad()
def SCREAMING_SNAKE_CASE       ( lowercase_				,       lowercase_				,       lowercase_		)     -> Optional[int]:



       """simple docstring"""

       logger.info('''Converting model...'''		)

       # load original state dict
       A__   				=					torch.hub.load_state_dict_from_url(lowercase_				,       map_location='''cpu'''		)
       # rename keys
       for src, dest in rename_keys:
              rename_key(lowercase_				,       lowercase_				,       lowercase_		)
       A__   				=					rename_backbone_keys(lowercase_		)
       # query, key and value matrices need special treatment
       read_in_q_k_v(lowercase_		)
       # important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
       A__   				=					'''model.'''
       for key in state_dict.copy().keys():
              if not key.startswith('''class_labels_classifier'''		) and not key.startswith('''bbox_predictor'''		):
                     A__   				=					state_dict.pop(lowercase_		)
                     A__   				=					val
    # create HuggingFace model and load state dict
       A__   				=					TableTransformerConfig(
           backbone='''resnet18'''				,       mask_loss_coefficient=1				,       dice_loss_coefficient=1				,       ce_loss_coefficient=1				,       bbox_loss_coefficient=5				,       giou_loss_coefficient=2				,       eos_coefficient=0.4				,       class_cost=1				,       bbox_cost=5				,       giou_cost=2				,       )

       if "detection" in checkpoint_url:
              A__   				=					15
              A__   				=					2
              A__   				=					{0: '''table''', 1: '''table rotated'''}
              A__   				=					idalabel
              A__   				=					{v: k for k, v in idalabel.items()}
       else:
              A__   				=					125
              A__   				=					6
              A__   				=					{
                  0: '''table''',
                  1: '''table column''',
                  2: '''table row''',
                  3: '''table column header''',
                  4: '''table projected row header''',
                  5: '''table spanning cell''',
              }
              A__   				=					idalabel
              A__   				=					{v: k for k, v in idalabel.items()}

       A__   				=					DetrImageProcessor(
           format='''coco_detection'''				,       max_size=800 if '''detection''' in checkpoint_url else 1_000		)
       A__   				=					TableTransformerForObjectDetection(lowercase_		)
       model.load_state_dict(lowercase_		)
       model.eval()

       # verify our conversion
       A__   				=					'''example_pdf.png''' if '''detection''' in checkpoint_url else '''example_table.png'''
       A__   				=					hf_hub_download(repo_id='''nielsr/example-pdf'''				,       repo_type='''dataset'''				,       filename=lowercase_		)
       A__   				=					Image.open(lowercase_		).convert('''RGB'''		)
       A__   				=					normalize(resize(lowercase_				,       lowercase_		)		).unsqueeze(0		)

       A__   				=					model(lowercase_		)

       if "detection" in checkpoint_url:
              A__   				=					(1, 15, 3)
              A__   				=					torch.tensor(
                  [[-6.78_97, -16.99_85, 6.79_37], [-8.01_86, -22.21_92, 6.96_77], [-7.31_17, -21.07_08, 7.40_55]]		)
              A__   				=					torch.tensor([[0.48_67, 0.17_67, 0.67_32], [0.67_18, 0.44_79, 0.38_30], [0.47_16, 0.17_60, 0.63_64]]		)

       else:
              A__   				=					(1, 125, 7)
              A__   				=					torch.tensor(
                  [[-18.14_30, -8.32_14, 4.82_74], [-18.46_85, -7.13_61, -4.26_67], [-26.36_93, -9.34_29, -4.99_62]]		)
              A__   				=					torch.tensor([[0.49_83, 0.55_95, 0.94_40], [0.49_16, 0.63_15, 0.59_54], [0.61_08, 0.86_37, 0.11_35]]		)

       assert outputs.logits.shape == expected_shape
       assert torch.allclose(outputs.logits[0, :3, :3]				,       lowercase_				,       atol=1E-4		)
       assert torch.allclose(outputs.pred_boxes[0, :3, :3]				,       lowercase_				,       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(lowercase_		).mkdir(exist_ok=lowercase_		)
              model.save_pretrained(lowercase_		)
              image_processor.save_pretrained(lowercase_		)

       if push_to_hub:
              # Push model to HF hub
              logger.info('''Pushing model to the hub...'''		)
              A__   				=					(
                  '''microsoft/table-transformer-detection'''
                  if '''detection''' in checkpoint_url
                  else '''microsoft/table-transformer-structure-recognition'''
              )
              model.push_to_hub(lowercase_		)
              image_processor.push_to_hub(lowercase_		)


if __name__ == "__main__":
  _lowerCamelCase			:	str							 =     argparse.ArgumentParser()

  parser.add_argument(
      """--checkpoint_url""",
      default="""https://pubtables1m.blob.core.windows.net/model/pubtables1m_detection_detr_r18.pth""",
      type=str,
      choices=[
          """https://pubtables1m.blob.core.windows.net/model/pubtables1m_detection_detr_r18.pth""",
          """https://pubtables1m.blob.core.windows.net/model/pubtables1m_structure_detr_r18.pth""",
      ],
      help="""URL of the Table Transformer checkpoint 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 or not to push the converted model to the 🤗 hub."""
  )
  _lowerCamelCase			:	str							 =     parser.parse_args()
  convert_table_transformer_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)








import inspect
import unittest

import numpy as np

from tests.test_modeling_common import floats_tensor
from transformers import DetrConfig, MaskFormerConfig, SwinConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
  import torch

  from transformers import MaskFormerForInstanceSegmentation, MaskFormerModel

  if is_vision_available():
    from transformers import MaskFormerImageProcessor

if is_vision_available():
  from PIL import Image


class        UpperCamelCase_					:

    '''simple docstring'''






    def __init__(    self   :			Tuple      ,				UpperCAmelCase__   :			Dict      ,				UpperCAmelCase__   :			Any=2      ,				UpperCAmelCase__   :			List[str]=True      ,				UpperCAmelCase__   :			Any=False      ,				UpperCAmelCase__   :			Union[str, Any]=10      ,				UpperCAmelCase__   :			Any=3      ,				UpperCAmelCase__   :			Optional[int]=32 * 4      ,				UpperCAmelCase__   :			int=32 * 6      ,				UpperCAmelCase__   :			List[str]=4      ,				UpperCAmelCase__   :			Dict=32      ,				)		->Optional[int]:





           '''simple docstring'''

           A__   				=					parent
           A__   				=					batch_size
           A__   				=					is_training
           A__   				=					use_auxiliary_loss
           A__   				=					num_queries
           A__   				=					num_channels
           A__   				=					min_size
           A__   				=					max_size
           A__   				=					num_labels
           A__   				=					mask_feature_size


    def  SCREAMING_SNAKE_CASE							(    self   :			Dict)		->int:





           '''simple docstring'''

           A__   				=					floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size]).to(
               UpperCAmelCase__)

           A__   				=					torch.ones([self.batch_size, self.min_size, self.max_size]      ,				device=UpperCAmelCase__)

           A__   				=					(
               torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size]      ,				device=UpperCAmelCase__) > 0.5
           ).float()
           A__   				=					(torch.rand((self.batch_size, self.num_labels)      ,				device=UpperCAmelCase__) > 0.5).long()

           A__   				=					self.get_config()
           return config, pixel_values, pixel_mask, mask_labels, class_labels


    def  SCREAMING_SNAKE_CASE							(    self   :			str)		->List[Any]:





           '''simple docstring'''

           return MaskFormerConfig.from_backbone_and_decoder_configs(
               backbone_config=SwinConfig(
                   depths=[1, 1, 1, 1]      ,				)      ,				decoder_config=DetrConfig(
                   decoder_ffn_dim=128      ,				num_queries=self.num_queries      ,				decoder_attention_heads=2      ,				d_model=self.mask_feature_size      ,				)      ,				mask_feature_size=self.mask_feature_size      ,				fpn_feature_size=self.mask_feature_size      ,				num_channels=self.num_channels      ,				num_labels=self.num_labels      ,				)


    def  SCREAMING_SNAKE_CASE							(    self   :			Union[str, Any])		->List[Any]:





           '''simple docstring'''

           A__				,							A__				,							A__				,							A__				,							A__   				=					self.prepare_config_and_inputs()
           A__   				=					{'''pixel_values''': pixel_values, '''pixel_mask''': pixel_mask}
           return config, inputs_dict


    def  SCREAMING_SNAKE_CASE							(    self   :			Tuple      ,				UpperCAmelCase__   :			Optional[int]      ,				UpperCAmelCase__   :			List[Any])		->int:





           '''simple docstring'''

           A__   				=					output.encoder_hidden_states
           A__   				=					output.pixel_decoder_hidden_states
           A__   				=					output.transformer_decoder_hidden_states

           self.parent.assertTrue(len(UpperCAmelCase__)      ,				len(config.backbone_config.depths))
           self.parent.assertTrue(len(UpperCAmelCase__)      ,				len(config.backbone_config.depths))
           self.parent.assertTrue(len(UpperCAmelCase__)      ,				config.decoder_config.decoder_layers)


    def  SCREAMING_SNAKE_CASE							(    self   :			Optional[Any]      ,				UpperCAmelCase__   :			Optional[Any]      ,				UpperCAmelCase__   :			Union[str, Any]      ,				UpperCAmelCase__   :			Union[str, Any]      ,				UpperCAmelCase__   :			Optional[Any]=False)		->Optional[int]:





           '''simple docstring'''

           with torch.no_grad():
                  A__   				=					MaskFormerModel(config=UpperCAmelCase__)
                  model.to(UpperCAmelCase__)
                  model.eval()

                  A__   				=					model(pixel_values=UpperCAmelCase__      ,				pixel_mask=UpperCAmelCase__)
                  A__   				=					model(UpperCAmelCase__      ,				output_hidden_states=UpperCAmelCase__)
           # the correct shape of output.transformer_decoder_hidden_states ensure the correcteness of the
           # encoder and pixel decoder
           self.parent.assertEqual(
               output.transformer_decoder_last_hidden_state.shape      ,				(self.batch_size, self.num_queries, self.mask_feature_size)      ,				)
           # let's ensure the other two hidden state exists
           self.parent.assertTrue(output.pixel_decoder_last_hidden_state is not None)
           self.parent.assertTrue(output.encoder_last_hidden_state is not None)

           if output_hidden_states:
                  self.check_output_hidden_state(UpperCAmelCase__      ,				UpperCAmelCase__)





    def  SCREAMING_SNAKE_CASE							(    self   :			str      ,				UpperCAmelCase__   :			Tuple      ,				UpperCAmelCase__   :			Optional[int]      ,				UpperCAmelCase__   :			List[str]      ,				UpperCAmelCase__   :			int      ,				UpperCAmelCase__   :			int)		->List[Any]:





           '''simple docstring'''

           A__   				=					MaskFormerForInstanceSegmentation(config=UpperCAmelCase__)
           model.to(UpperCAmelCase__)
           model.eval()

           def comm_check_on_output(UpperCAmelCase__   :			str):
                  # let's still check that all the required stuff is there
                  self.parent.assertTrue(result.transformer_decoder_last_hidden_state is not None)
                  self.parent.assertTrue(result.pixel_decoder_last_hidden_state is not None)
                  self.parent.assertTrue(result.encoder_last_hidden_state is not None)
                  # okay, now we need to check the logits shape
                  # due to the encoder compression, masks have a //4 spatial size
                  self.parent.assertEqual(
                      result.masks_queries_logits.shape      ,				(self.batch_size, self.num_queries, self.min_size // 4, self.max_size // 4)      ,				)
                  # + 1 for null class
                  self.parent.assertEqual(
                      result.class_queries_logits.shape      ,				(self.batch_size, self.num_queries, self.num_labels + 1))

           with torch.no_grad():
                  A__   				=					model(pixel_values=UpperCAmelCase__      ,				pixel_mask=UpperCAmelCase__)
                  A__   				=					model(UpperCAmelCase__)

                  comm_check_on_output(UpperCAmelCase__)

                  A__   				=					model(
                      pixel_values=UpperCAmelCase__      ,				pixel_mask=UpperCAmelCase__      ,				mask_labels=UpperCAmelCase__      ,				class_labels=UpperCAmelCase__)

           comm_check_on_output(UpperCAmelCase__)

           self.parent.assertTrue(result.loss is not None)
           self.parent.assertEqual(result.loss.shape      ,				torch.Size([1]))


@require_torch
class        UpperCamelCase_					(				UpperCAmelCase__	,      UpperCAmelCase__	,      unittest.TestCase  ):

    '''simple docstring'''






    UpperCAmelCase__													=   (MaskFormerModel, MaskFormerForInstanceSegmentation) if is_torch_available() else ()
    UpperCAmelCase__													=   (
        {'''feature-extraction''': MaskFormerModel, '''image-segmentation''': MaskFormerForInstanceSegmentation}
        if is_torch_available()
        else {}
    )

    UpperCAmelCase__													=   False
    UpperCAmelCase__													=   False
    UpperCAmelCase__													=   False
    UpperCAmelCase__													=   False


    def  SCREAMING_SNAKE_CASE							(    self   :			Union[str, Any])		->Optional[int]:





           '''simple docstring'''

           A__   				=					MaskFormerModelTester(self)
           A__   				=					ConfigTester(self      ,				config_class=UpperCAmelCase__      ,				has_text_modality=UpperCAmelCase__)


    def  SCREAMING_SNAKE_CASE							(    self   :			List[Any])		->Tuple:





           '''simple docstring'''

           self.config_tester.run_common_tests()


    def  SCREAMING_SNAKE_CASE							(    self   :			Dict)		->List[str]:





           '''simple docstring'''

           A__				,							A__   				=					self.model_tester.prepare_config_and_inputs_for_common()
           self.model_tester.create_and_check_maskformer_model(UpperCAmelCase__      ,				**UpperCAmelCase__      ,				output_hidden_states=UpperCAmelCase__)


    def  SCREAMING_SNAKE_CASE							(    self   :			str)		->Any:





           '''simple docstring'''

           A__   				=					self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_maskformer_instance_segmentation_head_model(*UpperCAmelCase__)


    @unittest.skip(reason='''MaskFormer does not use inputs_embeds''')
    def  SCREAMING_SNAKE_CASE							(    self   :			Dict)		->Tuple:





           '''simple docstring'''

           pass


    @unittest.skip(reason='''MaskFormer does not have a get_input_embeddings method''')
    def  SCREAMING_SNAKE_CASE							(    self   :			Optional[int])		->Union[str, Any]:





           '''simple docstring'''

           pass


    @unittest.skip(reason='''MaskFormer is not a generative model''')
    def  SCREAMING_SNAKE_CASE							(    self   :			str)		->Union[str, Any]:





           '''simple docstring'''

           pass


    @unittest.skip(reason='''MaskFormer does not use token embeddings''')
    def  SCREAMING_SNAKE_CASE							(    self   :			List[Any])		->Union[str, Any]:





           '''simple docstring'''

           pass


    @require_torch_multi_gpu
    @unittest.skip(
        reason='''MaskFormer has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`''')
    def  SCREAMING_SNAKE_CASE							(    self   :			Optional[int])		->Optional[int]:





           '''simple docstring'''

           pass


    @unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.''')
    def  SCREAMING_SNAKE_CASE							(    self   :			Tuple)		->Dict:





           '''simple docstring'''

           pass


    def  SCREAMING_SNAKE_CASE							(    self   :			Dict)		->Dict:





           '''simple docstring'''

           A__				,							A__   				=					self.model_tester.prepare_config_and_inputs_for_common()

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

                  A__   				=					['''pixel_values''']
                  self.assertListEqual(arg_names[:1]      ,				UpperCAmelCase__)


    @slow
    def  SCREAMING_SNAKE_CASE							(    self   :			Any)		->Optional[Any]:





           '''simple docstring'''

           for model_name in ["facebook/maskformer-swin-small-coco"]:
                  A__   				=					MaskFormerModel.from_pretrained(UpperCAmelCase__)
                  self.assertIsNotNone(UpperCAmelCase__)


    def  SCREAMING_SNAKE_CASE							(    self   :			Union[str, Any])		->Optional[int]:





           '''simple docstring'''

           A__   				=					(self.model_tester.min_size,) * 2
           A__   				=					{
               '''pixel_values''': torch.randn((2, 3, *size)      ,				device=UpperCAmelCase__),
               '''mask_labels''': torch.randn((2, 10, *size)      ,				device=UpperCAmelCase__),
               '''class_labels''': torch.zeros(2      ,				10      ,				device=UpperCAmelCase__).long(),
           }

           A__   				=					MaskFormerForInstanceSegmentation(MaskFormerConfig()).to(UpperCAmelCase__)
           A__   				=					model(**UpperCAmelCase__)
           self.assertTrue(outputs.loss is not None)


    def  SCREAMING_SNAKE_CASE							(    self   :			Union[str, Any])		->Union[str, Any]:





           '''simple docstring'''

           A__				,							A__   				=					self.model_tester.prepare_config_and_inputs_for_common()
           self.model_tester.create_and_check_maskformer_model(UpperCAmelCase__      ,				**UpperCAmelCase__      ,				output_hidden_states=UpperCAmelCase__)


    def  SCREAMING_SNAKE_CASE							(    self   :			str)		->Union[str, Any]:





           '''simple docstring'''

           A__				,							A__   				=					self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                  A__   				=					model_class(UpperCAmelCase__).to(UpperCAmelCase__)
                  A__   				=					model(**UpperCAmelCase__      ,				output_attentions=UpperCAmelCase__)
                  self.assertTrue(outputs.attentions is not None)


    def  SCREAMING_SNAKE_CASE							(    self   :			List[Any])		->Union[str, Any]:





           '''simple docstring'''

           if not self.model_tester.is_training:
                  return
           # only MaskFormerForInstanceSegmentation has the loss
           A__   				=					self.all_model_classes[1]
           A__				,							A__				,							A__				,							A__				,							A__   				=					self.model_tester.prepare_config_and_inputs()

           A__   				=					model_class(UpperCAmelCase__)
           model.to(UpperCAmelCase__)
           model.train()

           A__   				=					model(UpperCAmelCase__      ,				mask_labels=UpperCAmelCase__      ,				class_labels=UpperCAmelCase__).loss
           loss.backward()





    def  SCREAMING_SNAKE_CASE							(    self   :			int)		->str:





           '''simple docstring'''

           A__   				=					self.all_model_classes[1]
           A__				,							A__				,							A__				,							A__				,							A__   				=					self.model_tester.prepare_config_and_inputs()
           A__   				=					True
           A__   				=					True

           A__   				=					model_class(UpperCAmelCase__)
           model.to(UpperCAmelCase__)
           model.train()

           A__   				=					model(UpperCAmelCase__      ,				mask_labels=UpperCAmelCase__      ,				class_labels=UpperCAmelCase__)

           A__   				=					outputs.encoder_hidden_states[0]
           encoder_hidden_states.retain_grad()

           A__   				=					outputs.pixel_decoder_hidden_states[0]
           pixel_decoder_hidden_states.retain_grad()
           # we requires_grad=True in inputs_embeds (line 2152), the original implementation don't
           A__   				=					outputs.transformer_decoder_hidden_states[0]
           transformer_decoder_hidden_states.retain_grad()

           A__   				=					outputs.attentions[0]
           attentions.retain_grad()

           outputs.loss.backward(retain_graph=UpperCAmelCase__)

           self.assertIsNotNone(encoder_hidden_states.grad)
           self.assertIsNotNone(pixel_decoder_hidden_states.grad)
           self.assertIsNotNone(transformer_decoder_hidden_states.grad)
           self.assertIsNotNone(attentions.grad)
_lowerCamelCase			:	Tuple							 =     1E-4







def SCREAMING_SNAKE_CASE       ( )     -> int:



       """simple docstring"""

       A__   				=					Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''		)
       return image


@require_vision
@slow
class        UpperCamelCase_					(				unittest.TestCase  ):

    '''simple docstring'''






    @cached_property
    def  SCREAMING_SNAKE_CASE							(    self   :			Tuple)		->List[str]:





           '''simple docstring'''

           return (
               MaskFormerImageProcessor.from_pretrained('''facebook/maskformer-swin-small-coco''')
               if is_vision_available()
               else None
           )


    def  SCREAMING_SNAKE_CASE							(    self   :			Union[str, Any])		->Optional[int]:





           '''simple docstring'''

           A__   				=					MaskFormerModel.from_pretrained('''facebook/maskformer-swin-small-coco''').to(UpperCAmelCase__)
           A__   				=					self.default_image_processor
           A__   				=					prepare_img()
           A__   				=					image_processor(UpperCAmelCase__      ,				return_tensors='''pt''').to(UpperCAmelCase__)
           A__   				=					inputs['''pixel_values'''].shape
           # check size is divisible by 32
           self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0)
           # check size
           self.assertEqual(UpperCAmelCase__      ,				(1, 3, 800, 1_088))

           with torch.no_grad():
                  A__   				=					model(**UpperCAmelCase__)

           A__   				=					torch.tensor(
               [[-0.0482, 0.9228, 0.4951], [-0.2547, 0.8017, 0.8527], [-0.0069, 0.3385, -0.0089]]).to(UpperCAmelCase__)
           self.assertTrue(
               torch.allclose(
                   outputs.encoder_last_hidden_state[0, 0, :3, :3]      ,				UpperCAmelCase__      ,				atol=UpperCAmelCase__))

           A__   				=					torch.tensor(
               [[-0.8422, -0.8434, -0.9718], [-1.0144, -0.5565, -0.4195], [-1.0038, -0.4484, -0.1961]]).to(UpperCAmelCase__)
           self.assertTrue(
               torch.allclose(
                   outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3]      ,				UpperCAmelCase__      ,				atol=UpperCAmelCase__))

           A__   				=					torch.tensor(
               [[0.2852, -0.0159, 0.9735], [0.6254, 0.1858, 0.8529], [-0.0680, -0.4116, 1.8413]]).to(UpperCAmelCase__)
           self.assertTrue(
               torch.allclose(
                   outputs.transformer_decoder_last_hidden_state[0, :3, :3]      ,				UpperCAmelCase__      ,				atol=UpperCAmelCase__))


    def  SCREAMING_SNAKE_CASE							(    self   :			Any)		->Optional[Any]:





           '''simple docstring'''

           A__   				=					(
               MaskFormerForInstanceSegmentation.from_pretrained('''facebook/maskformer-swin-small-coco''')
               .to(UpperCAmelCase__)
               .eval()
           )
           A__   				=					self.default_image_processor
           A__   				=					prepare_img()
           A__   				=					image_processor(UpperCAmelCase__      ,				return_tensors='''pt''').to(UpperCAmelCase__)
           A__   				=					inputs['''pixel_values'''].shape
           # check size is divisible by 32
           self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0)
           # check size
           self.assertEqual(UpperCAmelCase__      ,				(1, 3, 800, 1_088))

           with torch.no_grad():
                  A__   				=					model(**UpperCAmelCase__)
           # masks_queries_logits
           A__   				=					outputs.masks_queries_logits
           self.assertEqual(
               masks_queries_logits.shape      ,				(1, model.config.decoder_config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4)      ,				)
           A__   				=					[
               [-1.3737124, -1.7724937, -1.9364233],
               [-1.5977281, -1.9867939, -2.1523695],
               [-1.5795398, -1.9269832, -2.093942],
           ]
           A__   				=					torch.tensor(UpperCAmelCase__).to(UpperCAmelCase__)
           self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]      ,				UpperCAmelCase__      ,				atol=UpperCAmelCase__))
           # class_queries_logits
           A__   				=					outputs.class_queries_logits
           self.assertEqual(
               class_queries_logits.shape      ,				(1, model.config.decoder_config.num_queries, model.config.num_labels + 1))
           A__   				=					torch.tensor(
               [
                   [1.65_12e00, -5.25_72e00, -3.35_19e00],
                   [3.61_69e-02, -5.90_25e00, -2.93_13e00],
                   [1.07_66e-04, -7.76_30e00, -5.12_63e00],
               ]).to(UpperCAmelCase__)
           self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]      ,				UpperCAmelCase__      ,				atol=UpperCAmelCase__))


    def  SCREAMING_SNAKE_CASE							(    self   :			str)		->Optional[Any]:





           '''simple docstring'''

           A__   				=					(
               MaskFormerForInstanceSegmentation.from_pretrained('''facebook/maskformer-resnet101-coco-stuff''')
               .to(UpperCAmelCase__)
               .eval()
           )
           A__   				=					self.default_image_processor
           A__   				=					prepare_img()
           A__   				=					image_processor(UpperCAmelCase__      ,				return_tensors='''pt''').to(UpperCAmelCase__)
           A__   				=					inputs['''pixel_values'''].shape
           # check size is divisible by 32
           self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0)
           # check size
           self.assertEqual(UpperCAmelCase__      ,				(1, 3, 800, 1_088))

           with torch.no_grad():
                  A__   				=					model(**UpperCAmelCase__)
           # masks_queries_logits
           A__   				=					outputs.masks_queries_logits
           self.assertEqual(
               masks_queries_logits.shape      ,				(1, model.config.decoder_config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4)      ,				)
           A__   				=					[[-0.9046, -2.6366, -4.6062], [-3.4179, -5.7890, -8.8057], [-4.9179, -7.6560, -10.7711]]
           A__   				=					torch.tensor(UpperCAmelCase__).to(UpperCAmelCase__)
           self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]      ,				UpperCAmelCase__      ,				atol=UpperCAmelCase__))
           # class_queries_logits
           A__   				=					outputs.class_queries_logits
           self.assertEqual(
               class_queries_logits.shape      ,				(1, model.config.decoder_config.num_queries, model.config.num_labels + 1))
           A__   				=					torch.tensor(
               [[4.7188, -3.2585, -2.8857], [6.6871, -2.9181, -1.2487], [7.2449, -2.2764, -2.1874]]).to(UpperCAmelCase__)
           self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]      ,				UpperCAmelCase__      ,				atol=UpperCAmelCase__))





    def  SCREAMING_SNAKE_CASE							(    self   :			Dict)		->Dict:





           '''simple docstring'''

           A__   				=					(
               MaskFormerForInstanceSegmentation.from_pretrained('''facebook/maskformer-swin-small-coco''')
               .to(UpperCAmelCase__)
               .eval()
           )
           A__   				=					self.default_image_processor

           A__   				=					image_processor(
               [np.zeros((3, 800, 1_333)), np.zeros((3, 800, 1_333))]      ,				segmentation_maps=[np.zeros((384, 384)).astype(np.floataa), np.zeros((384, 384)).astype(np.floataa)]      ,				return_tensors='''pt'''      ,				)

           A__   				=					inputs['''pixel_values'''].to(UpperCAmelCase__)
           A__   				=					[el.to(UpperCAmelCase__) for el in inputs['''mask_labels''']]
           A__   				=					[el.to(UpperCAmelCase__) for el in inputs['''class_labels''']]

           with torch.no_grad():
                  A__   				=					model(**UpperCAmelCase__)

           self.assertTrue(outputs.loss is not None)


