

import inspect
import unittest

from transformers import DPTConfig
from transformers.file_utils import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

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


if is_torch_available():
  import torch
  from torch import nn

  from transformers import MODEL_MAPPING, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTModel
  from transformers.models.dpt.modeling_dpt import DPT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
  from PIL import Image

  from transformers import DPTImageProcessor






class 							A_ :


     def __init__(							self			:		str			,  UpperCAmelCase			:		List[Any]			,  UpperCAmelCase			:		int=2			,  UpperCAmelCase			:		Dict=3_2			,  UpperCAmelCase			:		str=1_6			,  UpperCAmelCase			:		Dict=3			,  UpperCAmelCase			:		Optional[Any]=True			,  UpperCAmelCase			:		str=True			,  UpperCAmelCase			:		Tuple=3_2			,  UpperCAmelCase			:		Dict=4			,  UpperCAmelCase			:		Dict=[0, 1, 2, 3]			,  UpperCAmelCase			:		List[Any]=4			,  UpperCAmelCase			:		int=3_7			,  UpperCAmelCase			:		List[str]="gelu"			,  UpperCAmelCase			:		Any=0.1			,  UpperCAmelCase			:		List[str]=0.1			,  UpperCAmelCase			:		List[str]=0.02			,  UpperCAmelCase			:		str=3			,  UpperCAmelCase			:		str=[1, 3_8_4, 2_4, 2_4]			,  UpperCAmelCase			:		List[Any]=True			,  UpperCAmelCase			:		List[str]=None			,  )							->	Tuple:
      __lowerCAmelCase:   List[Any]   	=      parent
      __lowerCAmelCase:   int   	=      batch_size
      __lowerCAmelCase:   Union[str, Any]   	=      image_size
      __lowerCAmelCase:   Optional[Any]   	=      patch_size
      __lowerCAmelCase:   Any   	=      num_channels
      __lowerCAmelCase:   Optional[int]   	=      is_training
      __lowerCAmelCase:   Optional[int]   	=      use_labels
      __lowerCAmelCase:   Any   	=      hidden_size
      __lowerCAmelCase:   Optional[int]   	=      num_hidden_layers
      __lowerCAmelCase:   Tuple   	=      backbone_out_indices
      __lowerCAmelCase:   Union[str, Any]   	=      num_attention_heads
      __lowerCAmelCase:   List[Any]   	=      intermediate_size
      __lowerCAmelCase:   Any   	=      hidden_act
      __lowerCAmelCase:   int   	=      hidden_dropout_prob
      __lowerCAmelCase:   Dict   	=      attention_probs_dropout_prob
      __lowerCAmelCase:   List[Any]   	=      initializer_range
      __lowerCAmelCase:   Dict   	=      num_labels
      __lowerCAmelCase:   Optional[Any]   	=      backbone_featmap_shape
      __lowerCAmelCase:   Union[str, Any]   	=      scope
      __lowerCAmelCase:   Dict   	=      is_hybrid
      # sequence length of DPT = num_patches + 1 (we add 1 for the [CLS] token)
      __lowerCAmelCase:   Any   	=      (image_size // patch_size) ** 2
      __lowerCAmelCase:   Optional[int]   	=      num_patches + 1


     def   UpperCAmelCase  (							self			:		Any					)							->	Optional[Any]:
      __lowerCAmelCase:   Tuple   	=      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]					)

      __lowerCAmelCase:   List[str]   	=      None
      if self.use_labels:
       __lowerCAmelCase:   int   	=      ids_tensor([self.batch_size, self.image_size, self.image_size]			,  self.num_labels					)

      __lowerCAmelCase:   Optional[Any]   	=      self.get_config()

      return config, pixel_values, labels


     def   UpperCAmelCase  (							self			:		Optional[int]					)							->	int:
      __lowerCAmelCase:   Any   	=      {
          'global_padding': 'same',
          'layer_type': 'bottleneck',
          'depths': [3, 4, 9],
          'out_features': ['stage1', 'stage2', 'stage3'],
          'embedding_dynamic_padding': True,
          'hidden_sizes': [9_6, 1_9_2, 3_8_4, 7_6_8],
          'num_groups': 2,
      }

      return DPTConfig(
          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			,  backbone_out_indices=self.backbone_out_indices			,  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			,  is_hybrid=self.is_hybrid			,  backbone_config=__lowerCAmelCase			,  backbone_featmap_shape=self.backbone_featmap_shape			,  )


     def   UpperCAmelCase  (							self			:		List[str]			,  UpperCAmelCase			:		Any			,  UpperCAmelCase			:		Any			,  UpperCAmelCase			:		Union[str, Any]					)							->	Dict:
      __lowerCAmelCase:   int   	=      DPTModel(config=__lowerCAmelCase					)
      model.to(__lowerCAmelCase					)
      model.eval()
      __lowerCAmelCase:   List[Any]   	=      model(__lowerCAmelCase					)
      self.parent.assertEqual(result.last_hidden_state.shape			,  (self.batch_size, self.seq_length, self.hidden_size)					)


     def   UpperCAmelCase  (							self			:		int			,  UpperCAmelCase			:		List[Any]			,  UpperCAmelCase			:		str			,  UpperCAmelCase			:		Optional[int]					)							->	Tuple:
      __lowerCAmelCase:   int   	=      self.num_labels
      __lowerCAmelCase:   Union[str, Any]   	=      DPTForDepthEstimation(__lowerCAmelCase					)
      model.to(__lowerCAmelCase					)
      model.eval()
      __lowerCAmelCase:   List[Any]   	=      model(__lowerCAmelCase					)
      self.parent.assertEqual(result.predicted_depth.shape			,  (self.batch_size, self.image_size, self.image_size)					)


     def   UpperCAmelCase  (							self			:		List[str]			,  UpperCAmelCase			:		Dict			,  UpperCAmelCase			:		List[str]			,  UpperCAmelCase			:		Optional[int]					)							->	List[Any]:
      __lowerCAmelCase:   int   	=      self.num_labels
      __lowerCAmelCase:   List[str]   	=      DPTForSemanticSegmentation(__lowerCAmelCase					)
      model.to(__lowerCAmelCase					)
      model.eval()
      __lowerCAmelCase:   Optional[int]   	=      model(__lowerCAmelCase			,  labels=__lowerCAmelCase					)
      self.parent.assertEqual(
          result.logits.shape			,  (self.batch_size, self.num_labels, self.image_size, self.image_size)					)


     def   UpperCAmelCase  (							self			:		Any					)							->	Any:
      __lowerCAmelCase:   int   	=      self.prepare_config_and_inputs()
      __lowerCAmelCase	,							__lowerCAmelCase	,							__lowerCAmelCase:   Union[str, Any]   	=      config_and_inputs
      __lowerCAmelCase:   Any   	=      {'pixel_values': pixel_values}
      return config, inputs_dict







@require_torch
class 							A_ (		lowerCAmelCase_							,	lowerCAmelCase_							,	unittest.TestCase					):
     _lowercase			:     Optional[int]								=	(DPTModel, DPTForDepthEstimation, DPTForSemanticSegmentation) if is_torch_available() else ()
     _lowercase			:     Any								=	(
         {
             'depth-estimation': DPTForDepthEstimation,
             'feature-extraction': DPTModel,
             'image-segmentation': DPTForSemanticSegmentation,
         }
         if is_torch_available()
         else {}
     )

     _lowercase			:     Dict								=	False
     _lowercase			:     str								=	False
     _lowercase			:     int								=	False


     def   UpperCAmelCase  (							self			:		int					)							->	List[str]:
      __lowerCAmelCase:   str   	=      DPTModelTester(self					)
      __lowerCAmelCase:   int   	=      ConfigTester(self			,  config_class=__lowerCAmelCase			,  has_text_modality=__lowerCAmelCase			,  hidden_size=3_7					)


     def   UpperCAmelCase  (							self			:		List[str]					)							->	int:
      self.config_tester.run_common_tests()


     @unittest.skip(reason='DPT does not use inputs_embeds'					)
     def   UpperCAmelCase  (							self			:		str					)							->	Optional[Any]:
      pass


     def   UpperCAmelCase  (							self			:		str					)							->	List[str]:
      __lowerCAmelCase	,							__lowerCAmelCase:   Tuple   	=      self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
       __lowerCAmelCase:   str   	=      model_class(__lowerCAmelCase					)
       self.assertIsInstance(model.get_input_embeddings()			,  (nn.Module)					)
       __lowerCAmelCase:   Union[str, Any]   	=      model.get_output_embeddings()
       self.assertTrue(x is None or isinstance(__lowerCAmelCase			,  nn.Linear					)					)


     def   UpperCAmelCase  (							self			:		str					)							->	Union[str, Any]:
      __lowerCAmelCase	,							__lowerCAmelCase:   List[str]   	=      self.model_tester.prepare_config_and_inputs_for_common()

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

       __lowerCAmelCase:   Optional[int]   	=      ['pixel_values']
       self.assertListEqual(arg_names[:1]			,  __lowerCAmelCase					)


     def   UpperCAmelCase  (							self			:		Optional[Any]					)							->	Tuple:
      __lowerCAmelCase:   Dict   	=      self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*__lowerCAmelCase					)


     def   UpperCAmelCase  (							self			:		str					)							->	str:
      __lowerCAmelCase:   Optional[int]   	=      self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_depth_estimation(*__lowerCAmelCase					)


     def   UpperCAmelCase  (							self			:		Union[str, Any]					)							->	Optional[Any]:
      __lowerCAmelCase:   int   	=      self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_semantic_segmentation(*__lowerCAmelCase					)


     def   UpperCAmelCase  (							self			:		Dict					)							->	List[str]:
      for model_class in self.all_model_classes:
       if model_class.__name__ == "DPTForDepthEstimation":
        continue

       __lowerCAmelCase	,							__lowerCAmelCase:   int   	=      self.model_tester.prepare_config_and_inputs_for_common()
       __lowerCAmelCase:   str   	=      True

       if model_class in get_values(__lowerCAmelCase					):
        continue

       __lowerCAmelCase:   Dict   	=      model_class(__lowerCAmelCase					)
       model.to(__lowerCAmelCase					)
       model.train()
       __lowerCAmelCase:   Optional[int]   	=      self._prepare_for_class(__lowerCAmelCase			,  __lowerCAmelCase			,  return_labels=__lowerCAmelCase					)
       __lowerCAmelCase:   List[Any]   	=      model(**__lowerCAmelCase					).loss
       loss.backward()


     def   UpperCAmelCase  (							self			:		Any					)							->	str:
      for model_class in self.all_model_classes:
       if model_class.__name__ == "DPTForDepthEstimation":
        continue

       __lowerCAmelCase	,							__lowerCAmelCase:   Any   	=      self.model_tester.prepare_config_and_inputs_for_common()
       __lowerCAmelCase:   Dict   	=      False
       __lowerCAmelCase:   Optional[Any]   	=      True

       if model_class in get_values(__lowerCAmelCase					) or not model_class.supports_gradient_checkpointing:
        continue
       __lowerCAmelCase:   Tuple   	=      model_class(__lowerCAmelCase					)
       model.to(__lowerCAmelCase					)
       model.gradient_checkpointing_enable()
       model.train()
       __lowerCAmelCase:   Optional[Any]   	=      self._prepare_for_class(__lowerCAmelCase			,  __lowerCAmelCase			,  return_labels=__lowerCAmelCase					)
       __lowerCAmelCase:   List[Any]   	=      model(**__lowerCAmelCase					).loss
       loss.backward()


     def   UpperCAmelCase  (							self			:		Any					)							->	str:
      __lowerCAmelCase	,							__lowerCAmelCase:   Optional[int]   	=      self.model_tester.prepare_config_and_inputs_for_common()

      __lowerCAmelCase:   Dict   	=      _config_zero_init(__lowerCAmelCase					)
      for model_class in self.all_model_classes:
       __lowerCAmelCase:   Tuple   	=      model_class(config=__lowerCAmelCase					)
       # Skip the check for the backbone
       __lowerCAmelCase:   Dict   	=      []
       for name, module in model.named_modules():
        if module.__class__.__name__ == "DPTViTHybridEmbeddings":
         __lowerCAmelCase:   Optional[Any]   	=      [F'''{name}.{key}''' for key in module.state_dict().keys()]
         break

       for name, param in model.named_parameters():
        if param.requires_grad:
         if name in backbone_params:
          continue
         self.assertIn(
             ((param.data.mean() * 1E9).round() / 1E9).item()			,  [0.0, 1.0]			,  msg=F'''Parameter {name} of model {model_class} seems not properly initialized'''			,  )


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


     @slow
     def   UpperCAmelCase  (							self			:		List[Any]					)							->	List[Any]:
      for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[1:]:
       __lowerCAmelCase:   Dict   	=      DPTModel.from_pretrained(__lowerCAmelCase					)
       self.assertIsNotNone(__lowerCAmelCase					)


     def   UpperCAmelCase  (							self			:		Optional[Any]					)							->	Any:
      # We do this test only for DPTForDepthEstimation since it is the only model that uses readout_type
      __lowerCAmelCase	,							__lowerCAmelCase:   Dict   	=      self.model_tester.prepare_config_and_inputs_for_common()
      __lowerCAmelCase:   Any   	=      'add'
      with self.assertRaises(__lowerCAmelCase					):
       __lowerCAmelCase:   List[Any]   	=      DPTForDepthEstimation(__lowerCAmelCase					)







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

 """simple docstring"""






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






@require_torch
@require_vision
@slow
class 							A_ (		unittest.TestCase					):


     def   UpperCAmelCase  (							self			:		Union[str, Any]					)							->	List[str]:
      __lowerCAmelCase:   List[Any]   	=      DPTImageProcessor.from_pretrained('Intel/dpt-hybrid-midas'					)
      __lowerCAmelCase:   Tuple   	=      DPTForDepthEstimation.from_pretrained('Intel/dpt-hybrid-midas'					).to(__lowerCAmelCase					)

      __lowerCAmelCase:   Dict   	=      prepare_img()
      __lowerCAmelCase:   Optional[int]   	=      image_processor(images=__lowerCAmelCase			,  return_tensors='pt'					).to(__lowerCAmelCase					)

      # forward pass
      with torch.no_grad():
       __lowerCAmelCase:   Optional[int]   	=      model(**__lowerCAmelCase					)
       __lowerCAmelCase:   List[str]   	=      outputs.predicted_depth

      # verify the predicted depth
      __lowerCAmelCase:   Optional[Any]   	=      torch.Size((1, 3_8_4, 3_8_4)					)
      self.assertEqual(predicted_depth.shape			,  __lowerCAmelCase					)

      __lowerCAmelCase:   Optional[int]   	=      torch.tensor(
          [[[5.6437, 5.6146, 5.6511], [5.4371, 5.5649, 5.5958], [5.5215, 5.5184, 5.5293]]]					).to(__lowerCAmelCase					)

      self.assertTrue(torch.allclose(outputs.predicted_depth[:3, :3, :3] / 1_0_0			,  __lowerCAmelCase			,  atol=1E-4					)					)






"""simple docstring"""


import argparse

from transformers import (
    TapasConfig,
    TapasForMaskedLM,
    TapasForQuestionAnswering,
    TapasForSequenceClassification,
    TapasModel,
    TapasTokenizer,
    load_tf_weights_in_tapas,
)
from transformers.utils import logging


logging.set_verbosity_info()






def 			__UpperCAmelCase    (	lowercase      ,lowercase      ,lowercase      ,lowercase      ,lowercase						):







  """simple docstring"""
  # Initialise PyTorch model.
  # If you want to convert a checkpoint that uses absolute position embeddings, make sure to set reset_position_index_per_cell of
  # TapasConfig to False.

  # initialize configuration from json file
  _UpperCAmelCase         =					TapasConfig.from_json_file(lowercase						)
  # set absolute/relative position embeddings parameter
  _UpperCAmelCase         =					reset_position_index_per_cell

  # set remaining parameters of TapasConfig as well as the model based on the task
  if task == "SQA":
    _UpperCAmelCase         =					TapasForQuestionAnswering(config=lowercase						)
  elif task == "WTQ":
    # run_task_main.py hparams
    _UpperCAmelCase         =					4
    _UpperCAmelCase         =					True
    # hparam_utils.py hparams
    _UpperCAmelCase         =					0.66_46_94
    _UpperCAmelCase         =					0.20_79_51
    _UpperCAmelCase         =					0.12_11_94
    _UpperCAmelCase         =					True
    _UpperCAmelCase         =					True
    _UpperCAmelCase         =					False
    _UpperCAmelCase         =					0.0_35_25_13

    _UpperCAmelCase         =					TapasForQuestionAnswering(config=lowercase						)
  elif task == "WIKISQL_SUPERVISED":
    # run_task_main.py hparams
    _UpperCAmelCase         =					4
    _UpperCAmelCase         =					False
    # hparam_utils.py hparams
    _UpperCAmelCase         =					36.45_19
    _UpperCAmelCase         =					0.90_34_21
    _UpperCAmelCase         =					2_22.0_88
    _UpperCAmelCase         =					True
    _UpperCAmelCase         =					True
    _UpperCAmelCase         =					True
    _UpperCAmelCase         =					0.76_31_41

    _UpperCAmelCase         =					TapasForQuestionAnswering(config=lowercase						)
  elif task == "TABFACT":
    _UpperCAmelCase         =					TapasForSequenceClassification(config=lowercase						)
  elif task == "MLM":
    _UpperCAmelCase         =					TapasForMaskedLM(config=lowercase						)
  elif task == "INTERMEDIATE_PRETRAINING":
    _UpperCAmelCase         =					TapasModel(config=lowercase						)
  else:
    raise ValueError(f'''Task {task} not supported.'''						)

  print(f'''Building PyTorch model from configuration: {config}'''						)
  # Load weights from tf checkpoint
  load_tf_weights_in_tapas(lowercase      ,lowercase      ,lowercase						)

  # Save pytorch-model (weights and configuration)
  print(f'''Save PyTorch model to {pytorch_dump_path}'''						)
  model.save_pretrained(lowercase						)

  # Save tokenizer files
  print(f'''Save tokenizer files to {pytorch_dump_path}'''						)
  _UpperCAmelCase         =					TapasTokenizer(vocab_file=tf_checkpoint_path[:-10] + """vocab.txt"""      ,model_max_length=5_12						)
  tokenizer.save_pretrained(lowercase						)

  print("""Used relative position embeddings:"""      ,model.config.reset_position_index_per_cell						)


if __name__ == "__main__":
    UpperCAmelCase__     						=  argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        """--task""", default="""SQA""", type=str, help="""Model task for which to convert a checkpoint. Defaults to SQA."""
    )
    parser.add_argument(
        """--reset_position_index_per_cell""",
        default=False,
        action="""store_true""",
        help="""Whether to use relative position embeddings or not. Defaults to True.""",
    )
    parser.add_argument(
        """--tf_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
    )
    parser.add_argument(
        """--tapas_config_file""",
        default=None,
        type=str,
        required=True,
        help=(
            """The config json file corresponding to the pre-trained TAPAS model. \n"""
            """This specifies the model architecture."""
        ),
    )
    parser.add_argument(
        """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
    )
    UpperCAmelCase__     						=  parser.parse_args()
    convert_tf_checkpoint_to_pytorch(
        args.task,
        args.reset_position_index_per_cell,
        args.tf_checkpoint_path,
        args.tapas_config_file,
        args.pytorch_dump_path,
    )
