


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__			(      a__:		Union[str, Any]      ,  a__:		str      ,  a__:		List[Any]      ,  a__:		Any      ,  a__:		List[Any]	)  ->		List[str]:


							'''simple docstring'''





							with open(a__	) as metadata_file:
														_UpperCAmelCase					=      json.load(a__	)
							_UpperCAmelCase					=      LukeConfig(use_entity_aware_attention=a__      ,  **metadata['model_config']	)

							# Load in the weights from the checkpoint_path
							_UpperCAmelCase					=      torch.load(a__      ,  map_location='cpu'	)

							# Load the entity vocab file
							_UpperCAmelCase					=      load_entity_vocab(a__	)

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

							# Add special tokens to the token vocabulary for downstream tasks
							_UpperCAmelCase					=      AddedToken('<ent>'      ,  lstrip=a__      ,  rstrip=a__	)
							_UpperCAmelCase					=      AddedToken('<ent2>'      ,  lstrip=a__      ,  rstrip=a__	)
							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(a__	)
							with open(os.path.join(a__      ,  LukeTokenizer.vocab_files_names['entity_vocab_file']	)      ,  'w'	) as f:
														json.dump(a__      ,  a__	)

							_UpperCAmelCase					=      LukeTokenizer.from_pretrained(a__	)

							# Initialize the embeddings of the special tokens
							_UpperCAmelCase					=      state_dict['embeddings.word_embeddings.weight']
							_UpperCAmelCase					=      word_emb[tokenizer.convert_tokens_to_ids(['@']	)[0]].unsqueeze(0	)
							_UpperCAmelCase					=      word_emb[tokenizer.convert_tokens_to_ids(['#']	)[0]].unsqueeze(0	)
							_UpperCAmelCase					=      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					=      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]']]

							_UpperCAmelCase					=      LukeModel(config=a__	).eval()

							_UpperCAmelCase		,	_UpperCAmelCase					=      model.load_state_dict(a__      ,  strict=a__	)
							if not (len(a__	) == 1 and missing_keys[0] == "embeddings.position_ids"):
														raise ValueError(F'''Missing keys {", ".join(a__	)}. 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					=      LukeTokenizer.from_pretrained(a__      ,  task='entity_classification'	)

							_UpperCAmelCase					=      (
							    '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					=      (3_9, 4_2)
							_UpperCAmelCase					=      tokenizer(a__      ,  entity_spans=[span]      ,  add_prefix_space=a__      ,  return_tensors='pt'	)

							_UpperCAmelCase					=      model(**a__	)

							# Verify word hidden states
							if model_size == "large":
														_UpperCAmelCase					=      torch.Size((1, 4_2, 1_0_2_4)	)
														_UpperCAmelCase					=      torch.tensor(
														    [[0.0_133, 0.0_865, 0.0_095], [0.3_093, -0.2_576, -0.7_418], [-0.1_720, -0.2_117, -0.2_869]]	)
							else:  # base
														_UpperCAmelCase					=      torch.Size((1, 4_2, 7_6_8)	)
														_UpperCAmelCase					=      torch.tensor([[0.0_037, 0.1_368, -0.0_091], [0.1_099, 0.3_329, -0.1_095], [0.0_765, 0.5_335, 0.1_179]]	)

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

							# Verify entity hidden states
							if model_size == "large":
														_UpperCAmelCase					=      torch.Size((1, 1, 1_0_2_4)	)
														_UpperCAmelCase					=      torch.tensor([[0.0_466, -0.0_106, -0.0_179]]	)
							else:  # base
														_UpperCAmelCase					=      torch.Size((1, 1, 7_6_8)	)
														_UpperCAmelCase					=      torch.tensor([[0.1_457, 0.1_044, 0.0_174]]	)

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

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

def 	lowerCAmelCase__			(      a__:		str	)  ->		Optional[int]:


							'''simple docstring'''





							_UpperCAmelCase					=      {}
							with open(a__      ,  'r'      ,  encoding='utf-8'	) as f:
														for index, line in enumerate(a__	):
																					_UpperCAmelCase		,	_UpperCAmelCase					=      line.rstrip().split('\t'	)
																					_UpperCAmelCase					=      index

							return entity_vocab


if __name__ == "__main__":
		lowerCAmelCase__  :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.'''
		)
		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,
		)




from dataclasses import dataclass
from typing import List, Optional, Union

import numpy as np
import torch

from ...utils import BaseOutput, OptionalDependencyNotAvailable, is_torch_available, is_transformers_available

@dataclass
class 		__a				(    UpperCAmelCase      ):
		_a     :  Union[List[np.ndarray], torch.FloatTensor]


try:
		if not (is_transformers_available() and is_torch_available()):
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		from ...utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
		from .pipeline_text_to_video_synth import TextToVideoSDPipeline
		from .pipeline_text_to_video_synth_imgaimg import VideoToVideoSDPipeline  # noqa: F401
		from .pipeline_text_to_video_zero import TextToVideoZeroPipeline
