



'''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     snake_case_					(     lowerCAmelCase_       ,   lowerCAmelCase_       ,   lowerCAmelCase_       ,   lowerCAmelCase_       ,   lowerCAmelCase_		)->		List[str]:





       '''simple docstring'''


       with open(lowerCAmelCase_		) as metadata_file:
              _UpperCAmelCase  :						Tuple			     = json.load(lowerCAmelCase_		)
       _UpperCAmelCase  :						Optional[Any]			     = LukeConfig(use_entity_aware_attention=lowerCAmelCase_       ,   **metadata["""model_config"""]		)

       # Load in the weights from the checkpoint_path
       _UpperCAmelCase  :						Optional[int]			     = torch.load(lowerCAmelCase_       ,   map_location="""cpu"""		)["module"]

       # Load the entity vocab file
       _UpperCAmelCase  :						List[str]			     = load_original_entity_vocab(lowerCAmelCase_		)
       # add an entry for [MASK2]
       _UpperCAmelCase  :						Dict			     = max(entity_vocab.values()		) + 1
       config.entity_vocab_size += 1

       _UpperCAmelCase  :						List[Any]			     = XLMRobertaTokenizer.from_pretrained(metadata["""model_config"""]["""bert_model_name"""]		)

       # Add special tokens to the token vocabulary for downstream tasks
       _UpperCAmelCase  :						Union[str, Any]			     = AddedToken("""<ent>"""       ,   lstrip=lowerCAmelCase_       ,   rstrip=lowerCAmelCase_		)
       _UpperCAmelCase  :						List[str]			     = AddedToken("""<ent2>"""       ,   lstrip=lowerCAmelCase_       ,   rstrip=lowerCAmelCase_		)
       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(lowerCAmelCase_		)
       with open(os.path.join(lowerCAmelCase_       ,   """tokenizer_config.json"""		)       ,   """r"""		) as f:
              _UpperCAmelCase  :						int			     = json.load(lowerCAmelCase_		)
       _UpperCAmelCase  :						Optional[int]			     = "MLukeTokenizer"
       with open(os.path.join(lowerCAmelCase_       ,   """tokenizer_config.json"""		)       ,   """w"""		) as f:
              json.dump(lowerCAmelCase_       ,   lowerCAmelCase_		)

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

       _UpperCAmelCase  :						List[Any]			     = MLukeTokenizer.from_pretrained(lowerCAmelCase_		)

       # Initialize the embeddings of the special tokens
       _UpperCAmelCase  :						Union[str, Any]			     = tokenizer.convert_tokens_to_ids(["""@"""]		)[0]
       _UpperCAmelCase  :						Tuple			     = tokenizer.convert_tokens_to_ids(["""#"""]		)[0]

       _UpperCAmelCase  :						Optional[int]			     = state_dict["embeddings.word_embeddings.weight"]
       _UpperCAmelCase  :						str			     = word_emb[ent_init_index].unsqueeze(0		)
       _UpperCAmelCase  :						int			     = word_emb[enta_init_index].unsqueeze(0		)
       _UpperCAmelCase  :						Optional[Any]			     = 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  :						Optional[Any]			     = state_dict[bias_name]
              _UpperCAmelCase  :						List[str]			     = decoder_bias[ent_init_index].unsqueeze(0		)
              _UpperCAmelCase  :						Tuple			     = decoder_bias[enta_init_index].unsqueeze(0		)
              _UpperCAmelCase  :						Tuple			     = 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  :						int			     = F'''encoder.layer.{layer_index}.attention.self.'''
                     _UpperCAmelCase  :						List[str]			     = state_dict[prefix + matrix_name]
                     _UpperCAmelCase  :						Optional[int]			     = state_dict[prefix + matrix_name]
                     _UpperCAmelCase  :						Dict			     = 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  :						List[str]			     = entity_emb[entity_vocab["[MASK]"]].unsqueeze(0		)
       _UpperCAmelCase  :						Optional[int]			     = torch.cat([entity_emb, entity_mask_emb]		)
       # add [MASK2] for 'entity_predictions.bias'
       _UpperCAmelCase  :						Optional[Any]			     = state_dict["entity_predictions.bias"]
       _UpperCAmelCase  :						Any			     = entity_prediction_bias[entity_vocab["[MASK]"]].unsqueeze(0		)
       _UpperCAmelCase  :						List[Any]			     = torch.cat([entity_prediction_bias, entity_mask_bias]		)

       _UpperCAmelCase  :						Dict			     = LukeForMaskedLM(config=lowerCAmelCase_		).eval()

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

       _UpperCAmelCase  :						Optional[int]			     = model.load_state_dict(lowerCAmelCase_       ,   strict=lowerCAmelCase_		)

       if set(lowerCAmelCase_		) != {"luke.embeddings.position_ids"}:
              raise ValueError(F'''Unexpected unexpected_keys: {unexpected_keys}'''		)
       if set(lowerCAmelCase_		) != {
           "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  :						str			     = MLukeTokenizer.from_pretrained(lowerCAmelCase_       ,   task="""entity_classification"""		)

       _UpperCAmelCase  :						str			     = "ISO 639-3 uses the code fas for the dialects spoken across Iran and アフガニスタン (Afghanistan)."
       _UpperCAmelCase  :						Dict			     = (0, 9)
       _UpperCAmelCase  :						Optional[int]			     = tokenizer(lowerCAmelCase_       ,   entity_spans=[span]       ,   return_tensors="""pt"""		)

       _UpperCAmelCase  :						Optional[Any]			     = model(**lowerCAmelCase_		)

       # Verify word hidden states
       if model_size == "large":
              raise NotImplementedError
       else:  # base
              _UpperCAmelCase  :						Tuple			     = torch.Size((1, 33, 768)		)
              _UpperCAmelCase  :						Dict			     = torch.tensor([[0.0_8_9_2, 0.0_5_9_6, -0.2_8_1_9], [0.0_1_3_4, 0.1_1_9_9, 0.0_5_7_3], [-0.0_1_6_9, 0.0_9_2_7, 0.0_6_4_4]]		)

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

       # Verify entity hidden states
       if model_size == "large":
              raise NotImplementedError
       else:  # base
              _UpperCAmelCase  :						Tuple			     = torch.Size((1, 1, 768)		)
              _UpperCAmelCase  :						Optional[int]			     = torch.tensor([[-0.1_4_8_2, 0.0_6_0_9, 0.0_3_2_2]]		)

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

       # Verify masked word/entity prediction
       _UpperCAmelCase  :						Tuple			     = MLukeTokenizer.from_pretrained(lowerCAmelCase_		)
       _UpperCAmelCase  :						List[str]			     = "Tokyo is the capital of <mask>."
       _UpperCAmelCase  :						Optional[int]			     = (24, 30)
       _UpperCAmelCase  :						Any			     = tokenizer(lowerCAmelCase_       ,   entity_spans=[span]       ,   return_tensors="""pt"""		)

       _UpperCAmelCase  :						Any			     = model(**lowerCAmelCase_		)

       _UpperCAmelCase  :						Union[str, Any]			     = encoding["input_ids"][0].tolist()
       _UpperCAmelCase  :						str			     = input_ids.index(tokenizer.convert_tokens_to_ids("""<mask>"""		)		)
       _UpperCAmelCase  :						Tuple			     = outputs.logits[0][mask_position_id].argmax(dim=-1		)
       assert "Japan" == tokenizer.decode(lowerCAmelCase_		)

       _UpperCAmelCase  :						Any			     = outputs.entity_logits[0][0].argmax().item()
       _UpperCAmelCase  :						List[str]			     = [
           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(lowerCAmelCase_		)		)
       model.save_pretrained(lowerCAmelCase_		)




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





       '''simple docstring'''


       _UpperCAmelCase  :						Optional[Any]			     = ["[MASK]", "[PAD]", "[UNK]"]

       _UpperCAmelCase  :						Tuple			     = [json.loads(lowerCAmelCase_		) for line in open(lowerCAmelCase_		)]

       _UpperCAmelCase  :						Any			     = {}
       for entry in data:
              _UpperCAmelCase  :						Tuple			     = entry["id"]
              for entity_name, language in entry["entities"]:
                     if entity_name in SPECIAL_TOKENS:
                            _UpperCAmelCase  :						Tuple			     = entity_id
                            break
                     _UpperCAmelCase  :						Tuple			     = F'''{language}:{entity_name}'''
                     _UpperCAmelCase  :						Tuple			     = entity_id
       return new_mapping


if __name__ == "__main__":
   A_							: int						 =	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."""
   )
   A_							: Optional[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 huggingface_hub import hf_hub_download

from transformers import MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING, VideoMAEFeatureExtractor
from transformers.pipelines import VideoClassificationPipeline, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_decord,
    require_tf,
    require_torch,
    require_torch_or_tf,
    require_vision,
)

from .test_pipelines_common import ANY


@is_pipeline_test
@require_torch_or_tf
@require_vision
@require_decord
class       SCREAMING_SNAKE_CASE__				(		unittest.TestCase				):
       """simple docstring"""


       a  :				int   =MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING

       def        lowercase__							(						self		,     snake_case__		,     snake_case__		,     snake_case__						):






         """simple docstring"""





         lowerCAmelCase     :     Tuple								=							hf_hub_download(
             repo_id="nateraw/video-demo"		,     filename="archery.mp4"		,     repo_type="dataset"						)
         lowerCAmelCase     :     Dict								=							VideoClassificationPipeline(model=snake_case__		,     image_processor=snake_case__		,     top_k=2						)
         lowerCAmelCase     :     Any								=							[
             example_video_filepath,
             "https://huggingface.co/datasets/nateraw/video-demo/resolve/main/archery.mp4",
         ]
         return video_classifier, examples

       def        lowercase__							(						self		,     snake_case__		,     snake_case__						):






         """simple docstring"""





         for example in examples:
           lowerCAmelCase     :     str								=							video_classifier(snake_case__						)

           self.assertEqual(
               snake_case__		,     [
                   {"score": ANY(snake_case__						), "label": ANY(snake_case__						)},
                   {"score": ANY(snake_case__						), "label": ANY(snake_case__						)},
               ]		,     )

       @require_torch
       def        lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase     :     Union[str, Any]								=							"hf-internal-testing/tiny-random-VideoMAEForVideoClassification"
         lowerCAmelCase     :     str								=							VideoMAEFeatureExtractor(
             size={"shortest_edge": 10}		,     crop_size={"height": 10, "width": 10}						)
         lowerCAmelCase     :     int								=							pipeline(
             "video-classification"		,     model=snake_case__		,     feature_extractor=snake_case__		,     frame_sampling_rate=4						)

         lowerCAmelCase     :     Optional[int]								=							hf_hub_download(repo_id="nateraw/video-demo"		,     filename="archery.mp4"		,     repo_type="dataset"						)
         lowerCAmelCase     :     Union[str, Any]								=							video_classifier(snake_case__		,     top_k=2						)
         self.assertEqual(
             nested_simplify(snake_case__		,     decimals=4						)		,     [{"score": 0.5199, "label": "LABEL_0"}, {"score": 0.4801, "label": "LABEL_1"}]		,     )

         lowerCAmelCase     :     Tuple								=							video_classifier(
             [
                 video_file_path,
                 video_file_path,
             ]		,     top_k=2		,     )
         self.assertEqual(
             nested_simplify(snake_case__		,     decimals=4						)		,     [
                 [{"score": 0.5199, "label": "LABEL_0"}, {"score": 0.4801, "label": "LABEL_1"}],
                 [{"score": 0.5199, "label": "LABEL_0"}, {"score": 0.4801, "label": "LABEL_1"}],
             ]		,     )







       @require_tf
       def        lowercase__							(						self						):






         """simple docstring"""





         pass





