


"""simple docstring"""

import argparse
import json
import os
from collections import OrderedDict

import torch

from transformers import LukeConfig, LukeForMaskedLM, MLukeTokenizer, XLMRobertaTokenizer
from transformers.tokenization_utils_base import AddedToken






@torch.no_grad()
def 							A		(       snake_case   :Any       ,      snake_case   :Tuple       ,      snake_case   :Optional[Any]       ,      snake_case   :Optional[Any]       ,      snake_case   :List[str]	)   ->			List[Any]:
 # Load configuration defined in the metadata file
 with open(snake_case	) as metadata_file:
  __UpperCamelCase       						= json.load(snake_case	)
 __UpperCamelCase       						= LukeConfig(use_entity_aware_attention=snake_case       ,      **metadata['model_config']	)

 # Load in the weights from the checkpoint_path
 __UpperCamelCase       						= torch.load(snake_case       ,      map_location='cpu'	)['module']

 # Load the entity vocab file
 __UpperCamelCase       						= load_original_entity_vocab(snake_case	)
 # add an entry for [MASK2]
 __UpperCamelCase       						= max(entity_vocab.values()	) + 1
 config.entity_vocab_size += 1

 __UpperCamelCase       						= XLMRobertaTokenizer.from_pretrained(metadata['model_config']['bert_model_name']	)

 # Add special tokens to the token vocabulary for downstream tasks
 __UpperCamelCase       						= AddedToken('<ent>'       ,      lstrip=snake_case       ,      rstrip=snake_case	)
 __UpperCamelCase       						= AddedToken('<ent2>'       ,      lstrip=snake_case       ,      rstrip=snake_case	)
 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(snake_case	)
 with open(os.path.join(snake_case       ,      'tokenizer_config.json'	)       ,      'r'	) as f:
  __UpperCamelCase       						= json.load(snake_case	)
 __UpperCamelCase       						= 'MLukeTokenizer'
 with open(os.path.join(snake_case       ,      'tokenizer_config.json'	)       ,      'w'	) as f:
  json.dump(snake_case       ,      snake_case	)

 with open(os.path.join(snake_case       ,      MLukeTokenizer.vocab_files_names['entity_vocab_file']	)       ,      'w'	) as f:
  json.dump(snake_case       ,      snake_case	)

 __UpperCamelCase       						= MLukeTokenizer.from_pretrained(snake_case	)

 # Initialize the embeddings of the special tokens
 __UpperCamelCase       						= tokenizer.convert_tokens_to_ids(['@']	)[0]
 __UpperCamelCase       						= tokenizer.convert_tokens_to_ids(['#']	)[0]

 __UpperCamelCase       						= state_dict['embeddings.word_embeddings.weight']
 __UpperCamelCase       						= word_emb[ent_init_index].unsqueeze(0	)
 __UpperCamelCase       						= word_emb[enta_init_index].unsqueeze(0	)
 __UpperCamelCase       						= torch.cat([word_emb, ent_emb, enta_emb]	)
 # add special tokens for 'entity_predictions.bias'
 for bias_name in ["lm_head.decoder.bias", "lm_head.bias"]:
  __UpperCamelCase       						= state_dict[bias_name]
  __UpperCamelCase       						= decoder_bias[ent_init_index].unsqueeze(0	)
  __UpperCamelCase       						= decoder_bias[enta_init_index].unsqueeze(0	)
  __UpperCamelCase       						= torch.cat([decoder_bias, ent_decoder_bias, enta_decoder_bias]	)

 # 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       						= f'encoder.layer.{layer_index}.attention.self.'
   __UpperCamelCase       						= state_dict[prefix + matrix_name]
   __UpperCamelCase       						= state_dict[prefix + matrix_name]
   __UpperCamelCase       						= state_dict[prefix + matrix_name]

    # Initialize the embedding of the [MASK2] entity using that of the [MASK] entity for downstream tasks
 __UpperCamelCase       						= state_dict['entity_embeddings.entity_embeddings.weight']
 __UpperCamelCase       						= entity_emb[entity_vocab['[MASK]']].unsqueeze(0	)
 __UpperCamelCase       						= torch.cat([entity_emb, entity_mask_emb]	)
 # add [MASK2] for 'entity_predictions.bias'
 __UpperCamelCase       						= state_dict['entity_predictions.bias']
 __UpperCamelCase       						= entity_prediction_bias[entity_vocab['[MASK]']].unsqueeze(0	)
 __UpperCamelCase       						= torch.cat([entity_prediction_bias, entity_mask_bias]	)

 __UpperCamelCase       						= LukeForMaskedLM(config=snake_case	).eval()

 state_dict.pop('entity_predictions.decoder.weight'	)
 state_dict.pop('lm_head.decoder.weight'	)
 state_dict.pop('lm_head.decoder.bias'	)
 __UpperCamelCase       						= OrderedDict()
 for key, value in state_dict.items():
  if not (key.startswith('lm_head'	) or key.startswith('entity_predictions'	)):
   __UpperCamelCase       						= state_dict[key]
  else:
   __UpperCamelCase       						= state_dict[key]

 __UpperCamelCase						,			__UpperCamelCase       						= model.load_state_dict(snake_case       ,      strict=snake_case	)

 if set(snake_case	) != {"luke.embeddings.position_ids"}:
  raise ValueError(f'Unexpected unexpected_keys: {unexpected_keys}'	)
 if set(snake_case	) != {
     "lm_head.decoder.weight",
     "lm_head.decoder.bias",
     "entity_predictions.decoder.weight",
 }:
  raise ValueError(f'Unexpected missing_keys: {missing_keys}'	)

 model.tie_weights()
 assert (model.luke.embeddings.word_embeddings.weight == model.lm_head.decoder.weight).all()
 assert (model.luke.entity_embeddings.entity_embeddings.weight == model.entity_predictions.decoder.weight).all()

 # Check outputs
 __UpperCamelCase       						= MLukeTokenizer.from_pretrained(snake_case       ,      task='entity_classification'	)

 __UpperCamelCase       						= 'ISO 639-3 uses the code fas for the dialects spoken across Iran and アフガニスタン (Afghanistan).'
 __UpperCamelCase       						= (0, 9)
 __UpperCamelCase       						= tokenizer(snake_case       ,      entity_spans=[span]       ,      return_tensors='pt'	)

 __UpperCamelCase       						= model(**snake_case	)

 # Verify word hidden states
 if model_size == "large":
  raise NotImplementedError
 else:  # base
  __UpperCamelCase       						= torch.Size((1, 3_3, 7_6_8)	)
  __UpperCamelCase       						= torch.tensor([[0.0_892, 0.0_596, -0.2_819], [0.0_134, 0.1_199, 0.0_573], [-0.0_169, 0.0_927, 0.0_644]]	)

 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]       ,      snake_case       ,      atol=1e-4	):
  raise ValueError

 # Verify entity hidden states
 if model_size == "large":
  raise NotImplementedError
 else:  # base
  __UpperCamelCase       						= torch.Size((1, 1, 7_6_8)	)
  __UpperCamelCase       						= torch.tensor([[-0.1_482, 0.0_609, 0.0_322]]	)

 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]       ,      snake_case       ,      atol=1e-4	):
  raise ValueError

 # Verify masked word/entity prediction
 __UpperCamelCase       						= MLukeTokenizer.from_pretrained(snake_case	)
 __UpperCamelCase       						= 'Tokyo is the capital of <mask>.'
 __UpperCamelCase       						= (2_4, 3_0)
 __UpperCamelCase       						= tokenizer(snake_case       ,      entity_spans=[span]       ,      return_tensors='pt'	)

 __UpperCamelCase       						= model(**snake_case	)

 __UpperCamelCase       						= encoding['input_ids'][0].tolist()
 __UpperCamelCase       						= input_ids.index(tokenizer.convert_tokens_to_ids('<mask>'	)	)
 __UpperCamelCase       						= outputs.logits[0][mask_position_id].argmax(dim=-1	)
 assert "Japan" == tokenizer.decode(snake_case	)

 __UpperCamelCase       						= outputs.entity_logits[0][0].argmax().item()
 __UpperCamelCase       						= [
     entity for entity, entity_id in tokenizer.entity_vocab.items() if entity_id == predicted_entity_id
 ]
 assert [e for e in multilingual_predicted_entities if e.startswith('en:'	)][0] == "en:Japan"

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






def 							A		(       snake_case   :str	)   ->			List[str]:
 __UpperCamelCase       						= ['[MASK]', '[PAD]', '[UNK]']

 __UpperCamelCase       						= [json.loads(snake_case	) for line in open(snake_case	)]

 __UpperCamelCase       						= {}
 for entry in data:
  __UpperCamelCase       						= entry['id']
  for entity_name, language in entry["entities"]:
   if entity_name in SPECIAL_TOKENS:
    __UpperCamelCase       						= entity_id
    break
   __UpperCamelCase       						= f'{language}:{entity_name}'
   __UpperCamelCase       						= entity_id
 return new_mapping


if __name__ == "__main__":
      UpperCamelCase			:       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."
      )
      UpperCamelCase			:       int									=				parser.parse_args()
      convert_luke_checkpoint(
          args.checkpoint_path,
          args.metadata_path,
          args.entity_vocab_path,
          args.pytorch_dump_folder_path,
          args.model_size,
      )





"""simple docstring"""

import unittest
from typing import Dict, List, Optional, Union

import numpy as np

from transformers.testing_utils import require_torch, require_vision
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 BridgeTowerImageProcessor

class __lowerCAmelCase	(   unittest.TestCase    ):





     def __init__(			self			,  __UpperCAmelCase			,  __UpperCAmelCase = True			,  __UpperCAmelCase = None			,  __UpperCAmelCase = 32			,  __UpperCAmelCase = True			,  __UpperCAmelCase = 1 / 255			,  __UpperCAmelCase = True			,  __UpperCAmelCase = True			,  __UpperCAmelCase = [0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3]			,  __UpperCAmelCase = [0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1]			,  __UpperCAmelCase = True			,  __UpperCAmelCase=7			,  __UpperCAmelCase=30			,  __UpperCAmelCase=400			,  __UpperCAmelCase=3			,  ):

      '''simple docstring'''




      __UpperCamelCase       						= parent
      __UpperCamelCase       						= do_resize
      __UpperCamelCase       						= size if size is not None else {'shortest_edge': 288}
      __UpperCamelCase       						= size_divisor
      __UpperCamelCase       						= do_rescale
      __UpperCamelCase       						= rescale_factor
      __UpperCamelCase       						= do_normalize
      __UpperCamelCase       						= do_center_crop
      __UpperCamelCase       						= image_mean
      __UpperCamelCase       						= image_std
      __UpperCamelCase       						= do_pad
      __UpperCamelCase       						= batch_size
      __UpperCamelCase       						= num_channels
      __UpperCamelCase       						= min_resolution
      __UpperCamelCase       						= max_resolution





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      return {
          "image_mean": self.image_mean,
          "image_std": self.image_std,
          "do_normalize": self.do_normalize,
          "do_resize": self.do_resize,
          "size": self.size,
          "size_divisor": self.size_divisor,
      }







     def 		UpperCAmelCase      (			self			,  __UpperCAmelCase			,  __UpperCAmelCase=False   ):

      '''simple docstring'''




      if not batched:
       __UpperCamelCase       						= self.size['shortest_edge']
       __UpperCamelCase       						= image_inputs[0]
       if isinstance(__UpperCAmelCase			,  Image.Image   ):
        __UpperCamelCase						,			__UpperCamelCase       						= image.size
       else:
        __UpperCamelCase						,			__UpperCamelCase       						= image.shape[1], image.shape[2]
       __UpperCamelCase       						= size / min(__UpperCAmelCase			,  __UpperCAmelCase   )
       if h < w:
        __UpperCamelCase						,			__UpperCamelCase       						= size, scale * w
       else:
        __UpperCamelCase						,			__UpperCamelCase       						= scale * h, size

       __UpperCamelCase       						= int((1333 / 800) * size   )
       if max(__UpperCAmelCase			,  __UpperCAmelCase   ) > max_size:
        __UpperCamelCase       						= max_size / max(__UpperCAmelCase			,  __UpperCAmelCase   )
        __UpperCamelCase       						= newh * scale
        __UpperCamelCase       						= neww * scale

       __UpperCamelCase						,			__UpperCamelCase       						= int(newh + 0.5   ), int(neww + 0.5   )
       __UpperCamelCase						,			__UpperCamelCase       						= (
           newh // self.size_divisor * self.size_divisor,
           neww // self.size_divisor * self.size_divisor,
       )

      else:
       __UpperCamelCase       						= []
       for image in image_inputs:
        __UpperCamelCase						,			__UpperCamelCase       						= self.get_expected_values([image]   )
        expected_values.append((expected_height, expected_width)   )
       __UpperCamelCase       						= max(__UpperCAmelCase			,  key=lambda __UpperCAmelCase   : item[0]   )[0]
       __UpperCamelCase       						= max(__UpperCAmelCase			,  key=lambda __UpperCAmelCase   : item[1]   )[1]

      return expected_height, expected_width


@require_torch
@require_vision
class __lowerCAmelCase	(   __SCREAMING_SNAKE_CASE    ,						unittest.TestCase    ):
     lowercase		     =		BridgeTowerImageProcessor if is_vision_available() else None





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= BridgeTowerImageProcessingTester(self   )





     @property
     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      return self.image_processor_tester.prepare_image_processor_dict()





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= self.image_processing_class(**self.image_processor_dict   )
      self.assertTrue(hasattr(__UpperCAmelCase			,  'image_mean'   )   )
      self.assertTrue(hasattr(__UpperCAmelCase			,  'image_std'   )   )
      self.assertTrue(hasattr(__UpperCAmelCase			,  'do_normalize'   )   )
      self.assertTrue(hasattr(__UpperCAmelCase			,  'do_resize'   )   )
      self.assertTrue(hasattr(__UpperCAmelCase			,  'size'   )   )
      self.assertTrue(hasattr(__UpperCAmelCase			,  'size_divisor'   )   )





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      pass





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= self.image_processing_class(**self.image_processor_dict   )
      # create random PIL images
      __UpperCamelCase       						= prepare_image_inputs(self.image_processor_tester			,  equal_resolution=__UpperCAmelCase   )
      for image in image_inputs:
       self.assertIsInstance(__UpperCAmelCase			,  Image.Image   )

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

      __UpperCamelCase						,			__UpperCamelCase       						= self.image_processor_tester.get_expected_values(__UpperCAmelCase   )
      self.assertEqual(
          encoded_images.shape			,  (1, self.image_processor_tester.num_channels, expected_height, expected_width)			,  )

      # Test batched
      __UpperCamelCase       						= image_processing(__UpperCAmelCase			,  return_tensors='pt'   ).pixel_values

      __UpperCamelCase						,			__UpperCamelCase       						= self.image_processor_tester.get_expected_values(__UpperCAmelCase			,  batched=__UpperCAmelCase   )
      self.assertEqual(
          encoded_images.shape			,  (
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              expected_height,
              expected_width,
          )			,  )





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= self.image_processing_class(**self.image_processor_dict   )
      # create random numpy tensors
      __UpperCamelCase       						= prepare_image_inputs(self.image_processor_tester			,  equal_resolution=__UpperCAmelCase			,  numpify=__UpperCAmelCase   )
      for image in image_inputs:
       self.assertIsInstance(__UpperCAmelCase			,  np.ndarray   )

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

      __UpperCamelCase						,			__UpperCamelCase       						= self.image_processor_tester.get_expected_values(__UpperCAmelCase   )
      self.assertEqual(
          encoded_images.shape			,  (1, self.image_processor_tester.num_channels, expected_height, expected_width)			,  )

      # Test batched
      __UpperCamelCase       						= image_processing(__UpperCAmelCase			,  return_tensors='pt'   ).pixel_values

      __UpperCamelCase						,			__UpperCamelCase       						= self.image_processor_tester.get_expected_values(__UpperCAmelCase			,  batched=__UpperCAmelCase   )
      self.assertEqual(
          encoded_images.shape			,  (
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              expected_height,
              expected_width,
          )			,  )







     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= self.image_processing_class(**self.image_processor_dict   )
      # create random PyTorch tensors
      __UpperCamelCase       						= prepare_image_inputs(self.image_processor_tester			,  equal_resolution=__UpperCAmelCase			,  torchify=__UpperCAmelCase   )
      for image in image_inputs:
       self.assertIsInstance(__UpperCAmelCase			,  torch.Tensor   )

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

      __UpperCamelCase						,			__UpperCamelCase       						= self.image_processor_tester.get_expected_values(__UpperCAmelCase   )
      self.assertEqual(
          encoded_images.shape			,  (1, self.image_processor_tester.num_channels, expected_height, expected_width)			,  )

      # Test batched
      __UpperCamelCase       						= image_processing(__UpperCAmelCase			,  return_tensors='pt'   ).pixel_values

      __UpperCamelCase						,			__UpperCamelCase       						= self.image_processor_tester.get_expected_values(__UpperCAmelCase			,  batched=__UpperCAmelCase   )
      self.assertEqual(
          encoded_images.shape			,  (
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              expected_height,
              expected_width,
          )			,  )



