






import json
import pathlib
import unittest

import numpy as np

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

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
 import torch

if is_vision_available():
 from PIL import Image

 from transformers import YolosImageProcessor







class 			UpperCAmelCase_						(					unittest.TestCase ):



      """simple docstring"""
      def __init__(				self    ,				SCREAMING_SNAKE_CASE_    ,				SCREAMING_SNAKE_CASE_=7    ,				SCREAMING_SNAKE_CASE_=3    ,				SCREAMING_SNAKE_CASE_=30    ,				SCREAMING_SNAKE_CASE_=400    ,				SCREAMING_SNAKE_CASE_=True    ,				SCREAMING_SNAKE_CASE_=None    ,				SCREAMING_SNAKE_CASE_=True    ,				SCREAMING_SNAKE_CASE_=[0.5, 0.5, 0.5]    ,				SCREAMING_SNAKE_CASE_=[0.5, 0.5, 0.5]    ,				SCREAMING_SNAKE_CASE_=True    ,				SCREAMING_SNAKE_CASE_=1 / 255    ,				SCREAMING_SNAKE_CASE_=True    ,				)				->      List[str]:
          # by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
          UpperCamelCase       :List[str]								=   size if size is not None else {'''shortest_edge''': 18, '''longest_edge''': 1333}
          UpperCamelCase       :List[str]								=   parent
          UpperCamelCase       :Dict								=   batch_size
          UpperCamelCase       :Optional[Any]								=   num_channels
          UpperCamelCase       :str								=   min_resolution
          UpperCamelCase       :Any								=   max_resolution
          UpperCamelCase       :List[Any]								=   do_resize
          UpperCamelCase       :Optional[int]								=   size
          UpperCamelCase       :List[str]								=   do_normalize
          UpperCamelCase       :Any								=   image_mean
          UpperCamelCase       :Union[str, Any]								=   image_std
          UpperCamelCase       :int								=   do_rescale
          UpperCamelCase       :str								=   rescale_factor
          UpperCamelCase       :str								=   do_pad
      def     UpperCAmelCase						(				self )				->      Dict:
          return {
              "do_resize": self.do_resize,
              "size": self.size,
              "do_normalize": self.do_normalize,
              "image_mean": self.image_mean,
              "image_std": self.image_std,
              "do_rescale": self.do_rescale,
              "rescale_factor": self.rescale_factor,
              "do_pad": self.do_pad,
          }




      def     UpperCAmelCase						(				self    ,				SCREAMING_SNAKE_CASE_    ,				SCREAMING_SNAKE_CASE_=False )				->      List[str]:
          if not batched:
              UpperCamelCase       :List[Any]								=   image_inputs[0]
              if isinstance(SCREAMING_SNAKE_CASE_    ,				Image.Image ):
                  UpperCamelCase       :str								=   image.size
              else:
                  UpperCamelCase       :int								=   image.shape[1], image.shape[2]
              if w < h:
                  UpperCamelCase       :List[str]								=   int(self.size['''shortest_edge'''] * h / w )
                  UpperCamelCase       :Optional[int]								=   self.size['''shortest_edge''']
              elif w > h:
                  UpperCamelCase       :Dict								=   self.size['''shortest_edge''']
                  UpperCamelCase       :List[Any]								=   int(self.size['''shortest_edge'''] * w / h )
              else:
                  UpperCamelCase       :Union[str, Any]								=   self.size['''shortest_edge''']
                  UpperCamelCase       :Optional[int]								=   self.size['''shortest_edge''']

          else:
              UpperCamelCase       :Any								=   []
              for image in image_inputs:
                  UpperCamelCase       :List[Any]								=   self.get_expected_values([image] )
                  expected_values.append((expected_height, expected_width) )
              UpperCamelCase       :List[str]								=   max(SCREAMING_SNAKE_CASE_    ,				key=lambda SCREAMING_SNAKE_CASE_ : item[0] )[0]
              UpperCamelCase       :Optional[int]								=   max(SCREAMING_SNAKE_CASE_    ,				key=lambda SCREAMING_SNAKE_CASE_ : item[1] )[1]

          return expected_height, expected_width







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



      """simple docstring"""
      UpperCamelCase_				:					List[Any]           =YolosImageProcessor if is_vision_available() else None
      def     UpperCAmelCase						(				self )				->      Dict:
          UpperCamelCase       :Optional[Any]								=   YolosImageProcessingTester(self )
      @property
      def     UpperCAmelCase						(				self )				->      Dict:
          return self.image_processor_tester.prepare_image_processor_dict()
      def     UpperCAmelCase						(				self )				->      Union[str, Any]:
          UpperCamelCase       :str								=   self.image_processing_class(**self.image_processor_dict )
          self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_    ,				'''image_mean''' ) )
          self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_    ,				'''image_std''' ) )
          self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_    ,				'''do_normalize''' ) )
          self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_    ,				'''do_resize''' ) )
          self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_    ,				'''size''' ) )
      def     UpperCAmelCase						(				self )				->      List[Any]:
          UpperCamelCase       :Optional[Any]								=   self.image_processing_class.from_dict(self.image_processor_dict )
          self.assertEqual(image_processor.size    ,				{'''shortest_edge''': 18, '''longest_edge''': 1333} )
          self.assertEqual(image_processor.do_pad    ,				SCREAMING_SNAKE_CASE_ )

          UpperCamelCase       :Any								=   self.image_processing_class.from_dict(
              self.image_processor_dict    ,				size=42    ,				max_size=84    ,				pad_and_return_pixel_mask=SCREAMING_SNAKE_CASE_ )
          self.assertEqual(image_processor.size    ,				{'''shortest_edge''': 42, '''longest_edge''': 84} )
          self.assertEqual(image_processor.do_pad    ,				SCREAMING_SNAKE_CASE_ )
      def     UpperCAmelCase						(				self )				->      Any:
          pass
      def     UpperCAmelCase						(				self )				->      Tuple:
          # Initialize image_processing
          UpperCamelCase       :Union[str, Any]								=   self.image_processing_class(**self.image_processor_dict )
          # create random PIL images
          UpperCamelCase       :Union[str, Any]								=   prepare_image_inputs(self.image_processor_tester    ,				equal_resolution=SCREAMING_SNAKE_CASE_ )
          for image in image_inputs:
              self.assertIsInstance(SCREAMING_SNAKE_CASE_    ,				Image.Image )

          # Test not batched input
          UpperCamelCase       :str								=   image_processing(image_inputs[0]    ,				return_tensors='''pt''' ).pixel_values

          UpperCamelCase       :Union[str, Any]								=   self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE_ )

          self.assertEqual(
              encoded_images.shape    ,				(1, self.image_processor_tester.num_channels, expected_height, expected_width)    ,				)

          # Test batched
          UpperCamelCase       :Tuple								=   self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE_    ,				batched=SCREAMING_SNAKE_CASE_ )

          UpperCamelCase       :Tuple								=   image_processing(SCREAMING_SNAKE_CASE_    ,				return_tensors='''pt''' ).pixel_values
          self.assertEqual(
              encoded_images.shape    ,				(
                  self.image_processor_tester.batch_size,
                  self.image_processor_tester.num_channels,
                  expected_height,
                  expected_width,
              )    ,				)
      def     UpperCAmelCase						(				self )				->      Any:
          # Initialize image_processing
          UpperCamelCase       :Dict								=   self.image_processing_class(**self.image_processor_dict )
          # create random numpy tensors
          UpperCamelCase       :Dict								=   prepare_image_inputs(self.image_processor_tester    ,				equal_resolution=SCREAMING_SNAKE_CASE_    ,				numpify=SCREAMING_SNAKE_CASE_ )
          for image in image_inputs:
              self.assertIsInstance(SCREAMING_SNAKE_CASE_    ,				np.ndarray )

          # Test not batched input
          UpperCamelCase       :Tuple								=   image_processing(image_inputs[0]    ,				return_tensors='''pt''' ).pixel_values

          UpperCamelCase       :int								=   self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE_ )

          self.assertEqual(
              encoded_images.shape    ,				(1, self.image_processor_tester.num_channels, expected_height, expected_width)    ,				)

          # Test batched
          UpperCamelCase       :Optional[int]								=   image_processing(SCREAMING_SNAKE_CASE_    ,				return_tensors='''pt''' ).pixel_values

          UpperCamelCase       :Any								=   self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE_    ,				batched=SCREAMING_SNAKE_CASE_ )

          self.assertEqual(
              encoded_images.shape    ,				(
                  self.image_processor_tester.batch_size,
                  self.image_processor_tester.num_channels,
                  expected_height,
                  expected_width,
              )    ,				)
      def     UpperCAmelCase						(				self )				->      List[Any]:
          # Initialize image_processing
          UpperCamelCase       :Any								=   self.image_processing_class(**self.image_processor_dict )
          # create random PyTorch tensors
          UpperCamelCase       :int								=   prepare_image_inputs(self.image_processor_tester    ,				equal_resolution=SCREAMING_SNAKE_CASE_    ,				torchify=SCREAMING_SNAKE_CASE_ )
          for image in image_inputs:
              self.assertIsInstance(SCREAMING_SNAKE_CASE_    ,				torch.Tensor )

          # Test not batched input
          UpperCamelCase       :List[str]								=   image_processing(image_inputs[0]    ,				return_tensors='''pt''' ).pixel_values

          UpperCamelCase       :Tuple								=   self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE_ )

          self.assertEqual(
              encoded_images.shape    ,				(1, self.image_processor_tester.num_channels, expected_height, expected_width)    ,				)

          # Test batched
          UpperCamelCase       :Tuple								=   image_processing(SCREAMING_SNAKE_CASE_    ,				return_tensors='''pt''' ).pixel_values

          UpperCamelCase       :Any								=   self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE_    ,				batched=SCREAMING_SNAKE_CASE_ )

          self.assertEqual(
              encoded_images.shape    ,				(
                  self.image_processor_tester.batch_size,
                  self.image_processor_tester.num_channels,
                  expected_height,
                  expected_width,
              )    ,				)
      def     UpperCAmelCase						(				self )				->      List[str]:
          # Initialize image_processings
          UpperCamelCase       :Dict								=   self.image_processing_class(**self.image_processor_dict )
          UpperCamelCase       :Dict								=   self.image_processing_class(do_resize=SCREAMING_SNAKE_CASE_    ,				do_normalize=SCREAMING_SNAKE_CASE_    ,				do_rescale=SCREAMING_SNAKE_CASE_ )
          # create random PyTorch tensors
          UpperCamelCase       :List[Any]								=   prepare_image_inputs(self.image_processor_tester    ,				equal_resolution=SCREAMING_SNAKE_CASE_    ,				torchify=SCREAMING_SNAKE_CASE_ )
          for image in image_inputs:
              self.assertIsInstance(SCREAMING_SNAKE_CASE_    ,				torch.Tensor )

          # Test whether the method "pad" and calling the image processor return the same tensors
          UpperCamelCase       :int								=   image_processing_a.pad(SCREAMING_SNAKE_CASE_    ,				return_tensors='''pt''' )
          UpperCamelCase       :Optional[int]								=   image_processing_a(SCREAMING_SNAKE_CASE_    ,				return_tensors='''pt''' )

          self.assertTrue(
              torch.allclose(encoded_images_with_method['''pixel_values''']    ,				encoded_images['''pixel_values''']    ,				atol=1e-4 ) )
      @slow
      def     UpperCAmelCase						(				self )				->      Tuple:
          # prepare image and target
          UpperCamelCase       :Any								=   Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
          with open('''./tests/fixtures/tests_samples/COCO/coco_annotations.txt'''    ,				'''r''' ) as f:
              UpperCamelCase       :Optional[int]								=   json.loads(f.read() )

          UpperCamelCase       :List[str]								=   {'''image_id''': 3_9769, '''annotations''': target}

          # encode them
          UpperCamelCase       :Tuple								=   YolosImageProcessor.from_pretrained('''hustvl/yolos-small''' )
          UpperCamelCase       :Optional[Any]								=   image_processing(images=SCREAMING_SNAKE_CASE_    ,				annotations=SCREAMING_SNAKE_CASE_    ,				return_tensors='''pt''' )

          # verify pixel values
          UpperCamelCase       :Optional[Any]								=   torch.Size([1, 3, 800, 1066] )
          self.assertEqual(encoding['''pixel_values'''].shape    ,				SCREAMING_SNAKE_CASE_ )

          UpperCamelCase       :List[str]								=   torch.tensor([0.2796, 0.3138, 0.3481] )
          self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]    ,				SCREAMING_SNAKE_CASE_    ,				atol=1e-4 ) )

          # verify area
          UpperCamelCase       :Dict								=   torch.tensor([5887.9600, 1_1250.2061, 48_9353.8438, 83_7122.7500, 14_7967.5156, 16_5732.3438] )
          self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']    ,				SCREAMING_SNAKE_CASE_ ) )
          # verify boxes
          UpperCamelCase       :Tuple								=   torch.Size([6, 4] )
          self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape    ,				SCREAMING_SNAKE_CASE_ )
          UpperCamelCase       :Dict								=   torch.tensor([0.5503, 0.2765, 0.0604, 0.2215] )
          self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]    ,				SCREAMING_SNAKE_CASE_    ,				atol=1e-3 ) )
          # verify image_id
          UpperCamelCase       :str								=   torch.tensor([3_9769] )
          self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']    ,				SCREAMING_SNAKE_CASE_ ) )
          # verify is_crowd
          UpperCamelCase       :Optional[Any]								=   torch.tensor([0, 0, 0, 0, 0, 0] )
          self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']    ,				SCREAMING_SNAKE_CASE_ ) )
          # verify class_labels
          UpperCamelCase       :Any								=   torch.tensor([75, 75, 63, 65, 17, 17] )
          self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']    ,				SCREAMING_SNAKE_CASE_ ) )
          # verify orig_size
          UpperCamelCase       :Tuple								=   torch.tensor([480, 640] )
          self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']    ,				SCREAMING_SNAKE_CASE_ ) )
          # verify size
          UpperCamelCase       :Optional[int]								=   torch.tensor([800, 1066] )
          self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']    ,				SCREAMING_SNAKE_CASE_ ) )




      @slow
      def     UpperCAmelCase						(				self )				->      Optional[int]:
          # prepare image, target and masks_path
          UpperCamelCase       :Tuple								=   Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
          with open('''./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'''    ,				'''r''' ) as f:
              UpperCamelCase       :Any								=   json.loads(f.read() )

          UpperCamelCase       :Optional[Any]								=   {'''file_name''': '''000000039769.png''', '''image_id''': 3_9769, '''segments_info''': target}

          UpperCamelCase       :Optional[Any]								=   pathlib.Path('''./tests/fixtures/tests_samples/COCO/coco_panoptic''' )

          # encode them
          UpperCamelCase       :Dict								=   YolosImageProcessor(format='''coco_panoptic''' )
          UpperCamelCase       :Any								=   image_processing(images=SCREAMING_SNAKE_CASE_    ,				annotations=SCREAMING_SNAKE_CASE_    ,				masks_path=SCREAMING_SNAKE_CASE_    ,				return_tensors='''pt''' )

          # verify pixel values
          UpperCamelCase       :Tuple								=   torch.Size([1, 3, 800, 1066] )
          self.assertEqual(encoding['''pixel_values'''].shape    ,				SCREAMING_SNAKE_CASE_ )

          UpperCamelCase       :List[Any]								=   torch.tensor([0.2796, 0.3138, 0.3481] )
          self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]    ,				SCREAMING_SNAKE_CASE_    ,				atol=1e-4 ) )

          # verify area
          UpperCamelCase       :List[Any]								=   torch.tensor([14_7979.6875, 16_5527.0469, 48_4638.5938, 1_1292.9375, 5879.6562, 7634.1147] )
          self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']    ,				SCREAMING_SNAKE_CASE_ ) )
          # verify boxes
          UpperCamelCase       :Optional[Any]								=   torch.Size([6, 4] )
          self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape    ,				SCREAMING_SNAKE_CASE_ )
          UpperCamelCase       :Dict								=   torch.tensor([0.2625, 0.5437, 0.4688, 0.8625] )
          self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]    ,				SCREAMING_SNAKE_CASE_    ,				atol=1e-3 ) )
          # verify image_id
          UpperCamelCase       :List[str]								=   torch.tensor([3_9769] )
          self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']    ,				SCREAMING_SNAKE_CASE_ ) )
          # verify is_crowd
          UpperCamelCase       :str								=   torch.tensor([0, 0, 0, 0, 0, 0] )
          self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']    ,				SCREAMING_SNAKE_CASE_ ) )
          # verify class_labels
          UpperCamelCase       :List[str]								=   torch.tensor([17, 17, 63, 75, 75, 93] )
          self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']    ,				SCREAMING_SNAKE_CASE_ ) )
          # verify masks
          UpperCamelCase       :Optional[Any]								=   82_2873
          self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item()    ,				SCREAMING_SNAKE_CASE_ )
          # verify orig_size
          UpperCamelCase       :Dict								=   torch.tensor([480, 640] )
          self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']    ,				SCREAMING_SNAKE_CASE_ ) )
          # verify size
          UpperCamelCase       :Optional[Any]								=   torch.tensor([800, 1066] )
          self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']    ,				SCREAMING_SNAKE_CASE_ ) )










"""simple docstring"""
import argparse
import json
import os

import torch

from transformers import LukeConfig, LukeModel, LukeTokenizer, RobertaTokenizer
from transformers.tokenization_utils_base import AddedToken





@torch.no_grad()
def  lowerCamelCase_						(UpperCamelCase__		:		Optional[Any]     ,       UpperCamelCase__		:		Union[str, Any]     ,       UpperCamelCase__		:		int     ,       UpperCamelCase__		:		Dict     ,       UpperCamelCase__		:		Optional[Any]							):
    # Load configuration defined in the metadata file
    with open(UpperCamelCase__							) as metadata_file:
        _UpperCAmelCase							:  Dict											=    json.load(UpperCamelCase__							)
    _UpperCAmelCase							:  List[Any]											=    LukeConfig(use_entity_aware_attention=UpperCamelCase__     ,       **metadata['''model_config''']							)

    # Load in the weights from the checkpoint_path
    _UpperCAmelCase							:  List[Any]											=    torch.load(UpperCamelCase__     ,       map_location='''cpu'''							)

    # Load the entity vocab file
    _UpperCAmelCase							:  Optional[int]											=    load_entity_vocab(UpperCamelCase__							)

    _UpperCAmelCase							:  Optional[int]											=    RobertaTokenizer.from_pretrained(metadata['''model_config''']['''bert_model_name''']							)

    # Add special tokens to the token vocabulary for downstream tasks
    _UpperCAmelCase							:  int											=    AddedToken('''<ent>'''     ,       lstrip=UpperCamelCase__     ,       rstrip=UpperCamelCase__							)
    _UpperCAmelCase							:  Optional[Any]											=    AddedToken('''<ent2>'''     ,       lstrip=UpperCamelCase__     ,       rstrip=UpperCamelCase__							)
    tokenizer.add_special_tokens({'''additional_special_tokens''': [entity_token_a, entity_token_a]}							)
    config.vocab_size += 2

    print(F'Saving tokenizer to {pytorch_dump_folder_path}'							)
    tokenizer.save_pretrained(UpperCamelCase__							)
    with open(os.path.join(UpperCamelCase__     ,       LukeTokenizer.vocab_files_names['''entity_vocab_file''']							)     ,       '''w'''							) as f:
        json.dump(UpperCamelCase__     ,       UpperCamelCase__							)

    _UpperCAmelCase							:  Any											=    LukeTokenizer.from_pretrained(UpperCamelCase__							)

    # Initialize the embeddings of the special tokens
    _UpperCAmelCase							:  str											=    state_dict['''embeddings.word_embeddings.weight''']
    _UpperCAmelCase							:  Dict											=    word_emb[tokenizer.convert_tokens_to_ids(['''@''']							)[0]].unsqueeze(0							)
    _UpperCAmelCase							:  Union[str, Any]											=    word_emb[tokenizer.convert_tokens_to_ids(['''#''']							)[0]].unsqueeze(0							)
    _UpperCAmelCase							:  Tuple											=    torch.cat([word_emb, ent_emb, enta_emb]							)

    # Initialize the query layers of the entity-aware self-attention mechanism
    for layer_index in range(config.num_hidden_layers							):
        for matrix_name in ["query.weight", "query.bias"]:
            _UpperCAmelCase							:  List[Any]											=    F'encoder.layer.{layer_index}.attention.self.'
            _UpperCAmelCase							:  Optional[Any]											=    state_dict[prefix + matrix_name]
            _UpperCAmelCase							:  Tuple											=    state_dict[prefix + matrix_name]
            _UpperCAmelCase							:  str											=    state_dict[prefix + matrix_name]

    # Initialize the embedding of the [MASK2] entity using that of the [MASK] entity for downstream tasks
    _UpperCAmelCase							:  Any											=    state_dict['''entity_embeddings.entity_embeddings.weight''']
    _UpperCAmelCase							:  Dict											=    entity_emb[entity_vocab['''[MASK]''']]

    _UpperCAmelCase							:  Optional[int]											=    LukeModel(config=UpperCamelCase__							).eval()

    _UpperCAmelCase							,				_UpperCAmelCase							:  int											=    model.load_state_dict(UpperCamelCase__     ,       strict=UpperCamelCase__							)
    if not (len(UpperCamelCase__							) == 1 and missing_keys[0] == "embeddings.position_ids"):
        raise ValueError(F'Missing keys {", ".join(UpperCamelCase__							)}. Expected only missing embeddings.position_ids'							)
    if not (all(key.startswith('''entity_predictions'''							) or key.startswith('''lm_head'''							) for key in unexpected_keys							)):
        raise ValueError(
            '''Unexpected keys'''
            F' {", ".join([key for key in unexpected_keys if not (key.startswith("entity_predictions"							) or key.startswith("lm_head"							))]							)}'							)

    # Check outputs
    _UpperCAmelCase							:  Optional[int]											=    LukeTokenizer.from_pretrained(UpperCamelCase__     ,       task='''entity_classification'''							)

    _UpperCAmelCase							:  List[str]											=    (
        '''Top seed Ana Ivanovic said on Thursday she could hardly believe her luck as a fortuitous netcord helped the'''
        ''' new world number one avoid a humiliating second- round exit at Wimbledon .'''
    )
    _UpperCAmelCase							:  Dict											=    (39, 42)
    _UpperCAmelCase							:  Any											=    tokenizer(UpperCamelCase__     ,       entity_spans=[span]     ,       add_prefix_space=UpperCamelCase__     ,       return_tensors='''pt'''							)

    _UpperCAmelCase							:  List[Any]											=    model(**UpperCamelCase__							)

    # Verify word hidden states
    if model_size == "large":
        _UpperCAmelCase							:  str											=    torch.Size((1, 42, 1024)							)
        _UpperCAmelCase							:  Union[str, Any]											=    torch.tensor(
            [[0.0133, 0.0865, 0.0095], [0.3093, -0.2576, -0.7418], [-0.1720, -0.2117, -0.2869]]							)
    else:  # base
        _UpperCAmelCase							:  Optional[Any]											=    torch.Size((1, 42, 768)							)
        _UpperCAmelCase							:  str											=    torch.tensor([[0.0037, 0.1368, -0.0091], [0.1099, 0.3329, -0.1095], [0.0765, 0.5335, 0.1179]]							)

    if not (outputs.last_hidden_state.shape == expected_shape):
        raise ValueError(
            F'Outputs.last_hidden_state.shape is {outputs.last_hidden_state.shape}, Expected shape is {expected_shape}'							)
    if not torch.allclose(outputs.last_hidden_state[0, :3, :3]     ,       UpperCamelCase__     ,       atol=1E-4							):
        raise ValueError

    # Verify entity hidden states
    if model_size == "large":
        _UpperCAmelCase							:  int											=    torch.Size((1, 1, 1024)							)
        _UpperCAmelCase							:  str											=    torch.tensor([[0.0466, -0.0106, -0.0179]]							)
    else:  # base
        _UpperCAmelCase							:  List[str]											=    torch.Size((1, 1, 768)							)
        _UpperCAmelCase							:  List[Any]											=    torch.tensor([[0.1457, 0.1044, 0.0174]]							)

    if not (outputs.entity_last_hidden_state.shape != expected_shape):
        raise ValueError(
            F'Outputs.entity_last_hidden_state.shape is {outputs.entity_last_hidden_state.shape}, Expected shape is'
            F' {expected_shape}'							)
    if not torch.allclose(outputs.entity_last_hidden_state[0, :3, :3]     ,       UpperCamelCase__     ,       atol=1E-4							):
        raise ValueError

    # Finally, save our PyTorch model and tokenizer
    print('''Saving PyTorch model to {}'''.format(UpperCamelCase__							)							)
    model.save_pretrained(UpperCamelCase__							)





def  lowerCamelCase_						(UpperCamelCase__		:		Union[str, Any]							):
    _UpperCAmelCase							:  Any											=    {}
    with open(UpperCamelCase__     ,       '''r'''     ,       encoding='''utf-8'''							) as f:
        for index, line in enumerate(UpperCamelCase__							):
            _UpperCAmelCase							,				_UpperCAmelCase							:  Any											=    line.rstrip().split('''\t'''							)
            _UpperCAmelCase							:  Tuple											=    index

    return entity_vocab


if __name__ == "__main__":
  _lowerCAmelCase    :List[Any]     =					argparse.ArgumentParser()
  # Required parameters
  parser.add_argument('--checkpoint_path', type=str, help='Path to a pytorch_model.bin file.')
  parser.add_argument(
      '--metadata_path', default=None, type=str, help='Path to a metadata.json file, defining the configuration.'
  )
  parser.add_argument(
      '--entity_vocab_path',
      default=None,
      type=str,
      help='Path to an entity_vocab.tsv file, containing the entity vocabulary.',
  )
  parser.add_argument(
      '--pytorch_dump_folder_path', default=None, type=str, help='Path to where to dump the output PyTorch model.'
  )
  parser.add_argument(
      '--model_size', default='base', type=str, choices=['base', 'large'], help='Size of the model to be converted.'
  )
  _lowerCAmelCase    :Any     =					parser.parse_args()
  convert_luke_checkpoint(
      args.checkpoint_path,
      args.metadata_path,
      args.entity_vocab_path,
      args.pytorch_dump_folder_path,
      args.model_size,
  )


