
"""simple docstring"""




import unittest

import numpy as np

from transformers import is_flax_available
from transformers.testing_utils import require_flax

from ..test_modeling_flax_common import ids_tensor


if is_flax_available():
			import jax
			import jax.numpy as jnp

			from transformers.generation import (
			    FlaxForcedBOSTokenLogitsProcessor,
			    FlaxForcedEOSTokenLogitsProcessor,
			    FlaxLogitsProcessorList,
			    FlaxMinLengthLogitsProcessor,
			    FlaxTemperatureLogitsWarper,
			    FlaxTopKLogitsWarper,
			    FlaxTopPLogitsWarper,
			)




@require_flax
class        SCREAMING_SNAKE_CASE__					(     unittest.TestCase ):





	'''simple docstring'''



	def 			A							(  self     :   List[str]      ,  lowercase     :   int      ,  lowercase     :   int     ):


				'''simple docstring'''


				_snake_case          =      jnp.ones((batch_size, length)     ) / length
				return scores



	def 			A							(  self     :   List[str]     ):


				'''simple docstring'''


				_snake_case          =      None
				_snake_case          =      20

				_snake_case          =      self._get_uniform_logits(batch_size=2      ,  length=lowerCAmelCase__     )

				# tweak scores to not be uniform anymore
				_snake_case          =      scores.at[1, 5].set((1 / length) + 0.1     )  # peak, 1st batch
				_snake_case          =      scores.at[1, 10].set((1 / length) - 0.4     )  # valley, 1st batch

				# compute softmax
				_snake_case          =      jax.nn.softmax(lowerCAmelCase__      ,  axis=-1     )

				_snake_case          =      FlaxTemperatureLogitsWarper(temperature=0.5     )
				_snake_case          =      FlaxTemperatureLogitsWarper(temperature=1.3     )

				_snake_case          =      jax.nn.softmax(temp_dist_warper_sharper(lowerCAmelCase__      ,  scores.copy()      ,  cur_len=lowerCAmelCase__     )      ,  axis=-1     )
				_snake_case          =      jax.nn.softmax(temp_dist_warper_smoother(lowerCAmelCase__      ,  scores.copy()      ,  cur_len=lowerCAmelCase__     )      ,  axis=-1     )

				# uniform distribution stays uniform
				self.assertTrue(jnp.allclose(probs[0, :]      ,  warped_prob_sharp[0, :]      ,  atol=1E-3     )     )
				self.assertTrue(jnp.allclose(probs[0, :]      ,  warped_prob_smooth[0, :]      ,  atol=1E-3     )     )

				# sharp peaks get higher, valleys get lower
				self.assertLess(probs[1, :].max()      ,  warped_prob_sharp[1, :].max()     )
				self.assertGreater(probs[1, :].min()      ,  warped_prob_sharp[1, :].min()     )

				# smooth peaks get lower, valleys get higher
				self.assertGreater(probs[1, :].max()      ,  warped_prob_smooth[1, :].max()     )
				self.assertLess(probs[1, :].min()      ,  warped_prob_smooth[1, :].min()     )



	def 			A							(  self     :   Union[str, Any]     ):


				'''simple docstring'''


				_snake_case          =      None
				_snake_case          =      10
				_snake_case          =      2

				# create ramp distribution
				_snake_case          =      np.broadcast_to(np.arange(lowerCAmelCase__     )[None, :]      ,  (batch_size, vocab_size)     ).copy()
				_snake_case          =      ramp_logits[1:, : vocab_size // 2] + vocab_size

				_snake_case          =      FlaxTopKLogitsWarper(3     )

				_snake_case          =      top_k_warp(lowerCAmelCase__      ,  lowerCAmelCase__      ,  cur_len=lowerCAmelCase__     )

				# check that correct tokens are filtered
				self.assertListEqual(jnp.isinf(scores[0]     ).tolist()      ,  7 * [True] + 3 * [False]     )
				self.assertListEqual(jnp.isinf(scores[1]     ).tolist()      ,  2 * [True] + 3 * [False] + 5 * [True]     )

				# check special case
				_snake_case          =      5
				_snake_case          =      FlaxTopKLogitsWarper(top_k=1      ,  filter_value=0.0      ,  min_tokens_to_keep=3     )

				_snake_case          =      np.broadcast_to(np.arange(lowerCAmelCase__     )[None, :]      ,  (batch_size, length)     ).copy()
				_snake_case          =      top_k_warp_safety_check(lowerCAmelCase__      ,  lowerCAmelCase__      ,  cur_len=lowerCAmelCase__     )

				# min_tokens overwrites k: 3 tokens are kept => 2 tokens are nullified
				self.assertListEqual((scores == 0.0).sum(axis=-1     ).tolist()      ,  [2, 2]     )



	def 			A							(  self     :   List[str]     ):


				'''simple docstring'''


				_snake_case          =      None
				_snake_case          =      10
				_snake_case          =      2

				# create distribution and take log (inverse to Softmax as taken in TopPLogitsWarper)
				_snake_case          =      np.log(np.array([[0.3, 0.1, 0.1, 0.5], [0.15, 0.3, 0.3, 0.25]]     )     )

				_snake_case          =      FlaxTopPLogitsWarper(0.8     )
				_snake_case          =      np.exp(top_p_warp(lowerCAmelCase__      ,  lowerCAmelCase__      ,  cur_len=lowerCAmelCase__     )     )

				# dist should be filtered to keep min num values so that sum is >= top_p
				# exp (-inf) => 0
				_snake_case          =      np.array([[0.3, 0.0, 0.0, 0.5], [0.0, 0.3, 0.3, 0.25]]     )
				self.assertTrue(np.allclose(lowerCAmelCase__      ,  lowerCAmelCase__      ,  atol=1E-3     )     )

				# check edge cases with negative and extreme logits
				_snake_case          =      np.broadcast_to(np.arange(lowerCAmelCase__     )[None, :]      ,  (batch_size, vocab_size)     ).copy() - (
				    vocab_size // 2
				)

				# make ramp_logits more extreme
				_snake_case          =      ramp_logits[1] * 100.0

				# make sure at least 2 tokens are kept
				_snake_case          =      FlaxTopPLogitsWarper(0.9      ,  min_tokens_to_keep=2      ,  filter_value=0.0     )
				_snake_case          =      top_p_warp(lowerCAmelCase__      ,  lowerCAmelCase__      ,  cur_len=lowerCAmelCase__     )

				# first batch should keep three tokens, second batch would keep only 1, but due to `min_tokens_to_keep=2` keeps 2.
				self.assertListEqual((filtered_dist != 0.0).sum(axis=-1     ).tolist()      ,  [3, 2]     )



	def 			A							(  self     :   Any     ):


				'''simple docstring'''


				_snake_case          =      20
				_snake_case          =      4
				_snake_case          =      0

				_snake_case          =      FlaxMinLengthLogitsProcessor(min_length=10      ,  eos_token_id=lowerCAmelCase__     )

				# check that min length is applied at length 5
				_snake_case          =      ids_tensor((batch_size, 20)      ,  vocab_size=20     )
				_snake_case          =      5
				_snake_case          =      self._get_uniform_logits(lowerCAmelCase__      ,  lowerCAmelCase__     )
				_snake_case          =      min_dist_processor(lowerCAmelCase__      ,  lowerCAmelCase__      ,  cur_len=lowerCAmelCase__     )
				self.assertListEqual(scores_before_min_length[:, eos_token_id].tolist()      ,  4 * [-float('inf'     )]     )

				# check that min length is not applied anymore at length 15
				_snake_case          =      self._get_uniform_logits(lowerCAmelCase__      ,  lowerCAmelCase__     )
				_snake_case          =      15
				_snake_case          =      min_dist_processor(lowerCAmelCase__      ,  lowerCAmelCase__      ,  cur_len=lowerCAmelCase__     )
				self.assertFalse(jnp.isinf(lowerCAmelCase__     ).any()     )



	def 			A							(  self     :   List[Any]     ):


				'''simple docstring'''


				_snake_case          =      20
				_snake_case          =      4
				_snake_case          =      0

				_snake_case          =      FlaxForcedBOSTokenLogitsProcessor(bos_token_id=lowerCAmelCase__     )

				# check that all scores are -inf except the bos_token_id score
				_snake_case          =      ids_tensor((batch_size, 1)      ,  vocab_size=20     )
				_snake_case          =      1
				_snake_case          =      self._get_uniform_logits(lowerCAmelCase__      ,  lowerCAmelCase__     )
				_snake_case          =      logits_processor(lowerCAmelCase__      ,  lowerCAmelCase__      ,  cur_len=lowerCAmelCase__     )
				self.assertTrue(jnp.isneginf(scores[:, bos_token_id + 1 :]     ).all()     )
				self.assertListEqual(scores[:, bos_token_id].tolist()      ,  4 * [0]     )  # score for bos_token_id shold be zero

				# check that bos_token_id is not forced if current length is greater than 1
				_snake_case          =      3
				_snake_case          =      self._get_uniform_logits(lowerCAmelCase__      ,  lowerCAmelCase__     )
				_snake_case          =      logits_processor(lowerCAmelCase__      ,  lowerCAmelCase__      ,  cur_len=lowerCAmelCase__     )
				self.assertFalse(jnp.isinf(lowerCAmelCase__     ).any()     )



	def 			A							(  self     :   int     ):


				'''simple docstring'''


				_snake_case          =      20
				_snake_case          =      4
				_snake_case          =      0
				_snake_case          =      5

				_snake_case          =      FlaxForcedEOSTokenLogitsProcessor(max_length=lowerCAmelCase__      ,  eos_token_id=lowerCAmelCase__     )

				# check that all scores are -inf except the eos_token_id when max_length is reached
				_snake_case          =      ids_tensor((batch_size, 4)      ,  vocab_size=20     )
				_snake_case          =      4
				_snake_case          =      self._get_uniform_logits(lowerCAmelCase__      ,  lowerCAmelCase__     )
				_snake_case          =      logits_processor(lowerCAmelCase__      ,  lowerCAmelCase__      ,  cur_len=lowerCAmelCase__     )
				self.assertTrue(jnp.isneginf(scores[:, eos_token_id + 1 :]     ).all()     )
				self.assertListEqual(scores[:, eos_token_id].tolist()      ,  4 * [0]     )  # score for eos_token_id should be zero

				# check that eos_token_id is not forced if max_length is not reached
				_snake_case          =      3
				_snake_case          =      self._get_uniform_logits(lowerCAmelCase__      ,  lowerCAmelCase__     )
				_snake_case          =      logits_processor(lowerCAmelCase__      ,  lowerCAmelCase__      ,  cur_len=lowerCAmelCase__     )
				self.assertFalse(jnp.isinf(lowerCAmelCase__     ).any()     )



	def 			A							(  self     :   Dict     ):


				'''simple docstring'''


				_snake_case          =      4
				_snake_case          =      10
				_snake_case          =      15
				_snake_case          =      2
				_snake_case          =      1
				_snake_case          =      15

				# dummy input_ids and scores
				_snake_case          =      ids_tensor((batch_size, sequence_length)      ,  lowerCAmelCase__     )
				_snake_case          =      input_ids.copy()

				_snake_case          =      self._get_uniform_logits(lowerCAmelCase__      ,  lowerCAmelCase__     )
				_snake_case          =      scores.copy()

				# instantiate all dist processors
				_snake_case          =      FlaxTemperatureLogitsWarper(temperature=0.5     )
				_snake_case          =      FlaxTopKLogitsWarper(3     )
				_snake_case          =      FlaxTopPLogitsWarper(0.8     )

				# instantiate all logits processors
				_snake_case          =      FlaxMinLengthLogitsProcessor(min_length=10      ,  eos_token_id=lowerCAmelCase__     )
				_snake_case          =      FlaxForcedBOSTokenLogitsProcessor(bos_token_id=lowerCAmelCase__     )
				_snake_case          =      FlaxForcedEOSTokenLogitsProcessor(max_length=lowerCAmelCase__      ,  eos_token_id=lowerCAmelCase__     )

				_snake_case          =      10

				# no processor list
				_snake_case          =      temp_dist_warp(lowerCAmelCase__      ,  lowerCAmelCase__      ,  cur_len=lowerCAmelCase__     )
				_snake_case          =      top_k_warp(lowerCAmelCase__      ,  lowerCAmelCase__      ,  cur_len=lowerCAmelCase__     )
				_snake_case          =      top_p_warp(lowerCAmelCase__      ,  lowerCAmelCase__      ,  cur_len=lowerCAmelCase__     )
				_snake_case          =      min_dist_proc(lowerCAmelCase__      ,  lowerCAmelCase__      ,  cur_len=lowerCAmelCase__     )
				_snake_case          =      bos_dist_proc(lowerCAmelCase__      ,  lowerCAmelCase__      ,  cur_len=lowerCAmelCase__     )
				_snake_case          =      eos_dist_proc(lowerCAmelCase__      ,  lowerCAmelCase__      ,  cur_len=lowerCAmelCase__     )

				# with processor list
				_snake_case          =      FlaxLogitsProcessorList(
				    [temp_dist_warp, top_k_warp, top_p_warp, min_dist_proc, bos_dist_proc, eos_dist_proc]     )
				_snake_case          =      processor(lowerCAmelCase__      ,  lowerCAmelCase__      ,  cur_len=lowerCAmelCase__     )

				# scores should be equal
				self.assertTrue(jnp.allclose(lowerCAmelCase__      ,  lowerCAmelCase__      ,  atol=1E-3     )     )

				# input_ids should never be changed
				self.assertListEqual(input_ids.tolist()      ,  input_ids_comp.tolist()     )





	def 			A							(  self     :   Optional[Any]     ):


				'''simple docstring'''


				_snake_case          =      4
				_snake_case          =      10
				_snake_case          =      15
				_snake_case          =      2
				_snake_case          =      1
				_snake_case          =      15

				# dummy input_ids and scores
				_snake_case          =      ids_tensor((batch_size, sequence_length)      ,  lowerCAmelCase__     )
				_snake_case          =      input_ids.copy()

				_snake_case          =      self._get_uniform_logits(lowerCAmelCase__      ,  lowerCAmelCase__     )
				_snake_case          =      scores.copy()

				# instantiate all dist processors
				_snake_case          =      FlaxTemperatureLogitsWarper(temperature=0.5     )
				_snake_case          =      FlaxTopKLogitsWarper(3     )
				_snake_case          =      FlaxTopPLogitsWarper(0.8     )

				# instantiate all logits processors
				_snake_case          =      FlaxMinLengthLogitsProcessor(min_length=10      ,  eos_token_id=lowerCAmelCase__     )
				_snake_case          =      FlaxForcedBOSTokenLogitsProcessor(bos_token_id=lowerCAmelCase__     )
				_snake_case          =      FlaxForcedEOSTokenLogitsProcessor(max_length=lowerCAmelCase__      ,  eos_token_id=lowerCAmelCase__     )

				_snake_case          =      10

				# no processor list
				def run_no_processor_list(lowercase     :   Dict      ,  lowercase     :   int      ,  lowercase     :   Optional[int]     ):
							_snake_case          =      temp_dist_warp(lowerCAmelCase__      ,  lowerCAmelCase__      ,  cur_len=lowerCAmelCase__     )
							_snake_case          =      top_k_warp(lowerCAmelCase__      ,  lowerCAmelCase__      ,  cur_len=lowerCAmelCase__     )
							_snake_case          =      top_p_warp(lowerCAmelCase__      ,  lowerCAmelCase__      ,  cur_len=lowerCAmelCase__     )
							_snake_case          =      min_dist_proc(lowerCAmelCase__      ,  lowerCAmelCase__      ,  cur_len=lowerCAmelCase__     )
							_snake_case          =      bos_dist_proc(lowerCAmelCase__      ,  lowerCAmelCase__      ,  cur_len=lowerCAmelCase__     )
							_snake_case          =      eos_dist_proc(lowerCAmelCase__      ,  lowerCAmelCase__      ,  cur_len=lowerCAmelCase__     )
							return scores

				# with processor list
				def run_processor_list(lowercase     :   str      ,  lowercase     :   str      ,  lowercase     :   List[str]     ):
							_snake_case          =      FlaxLogitsProcessorList(
							    [temp_dist_warp, top_k_warp, top_p_warp, min_dist_proc, bos_dist_proc, eos_dist_proc]     )
							_snake_case          =      processor(lowerCAmelCase__      ,  lowerCAmelCase__      ,  cur_len=lowerCAmelCase__     )
							return scores

				_snake_case          =      jax.jit(lowerCAmelCase__     )
				_snake_case          =      jax.jit(lowerCAmelCase__     )

				_snake_case          =      jitted_run_no_processor_list(lowerCAmelCase__      ,  lowerCAmelCase__      ,  lowerCAmelCase__     )
				_snake_case          =      jitted_run_processor_list(lowerCAmelCase__      ,  lowerCAmelCase__      ,  lowerCAmelCase__     )

				# scores should be equal
				self.assertTrue(jnp.allclose(lowerCAmelCase__      ,  lowerCAmelCase__      ,  atol=1E-3     )     )

				# input_ids should never be changed
				self.assertListEqual(input_ids.tolist()      ,  input_ids_comp.tolist()     )

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_		(	__lowercase       :     Optional[Any]	, __lowercase       :     Optional[int]	, __lowercase       :     str	, __lowercase       :     Optional[int]	, __lowercase       :     str			)			->  Union[str, Any]:
			# Load configuration defined in the metadata file
			with open(__lowercase			) as metadata_file:
						_snake_case          =      json.load(__lowercase			)
			_snake_case          =      LukeConfig(use_entity_aware_attention=__lowercase	, **metadata['model_config']			)

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

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

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

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

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

			_snake_case          =      MLukeTokenizer.from_pretrained(__lowercase			)

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

			_snake_case          =      state_dict['embeddings.word_embeddings.weight']
			_snake_case          =      word_emb[ent_init_index].unsqueeze(0			)
			_snake_case          =      word_emb[enta_init_index].unsqueeze(0			)
			_snake_case          =      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"]:
						_snake_case          =      state_dict[bias_name]
						_snake_case          =      decoder_bias[ent_init_index].unsqueeze(0			)
						_snake_case          =      decoder_bias[enta_init_index].unsqueeze(0			)
						_snake_case          =      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"]:
									_snake_case          =      f'''encoder.layer.{layer_index}.attention.self.'''
									_snake_case          =      state_dict[prefix + matrix_name]
									_snake_case          =      state_dict[prefix + matrix_name]
									_snake_case          =      state_dict[prefix + matrix_name]

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

			_snake_case          =      LukeForMaskedLM(config=__lowercase			).eval()

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

			_snake_case     , _snake_case          =      model.load_state_dict(__lowercase	, strict=__lowercase			)

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

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

			_snake_case          =      model(**__lowercase			)

			# Verify word hidden states
			if model_size == "large":
						raise NotImplementedError
			else:  # base
						_snake_case          =      torch.Size((1, 33, 768)			)
						_snake_case          =      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]	, __lowercase	, atol=1E-4			):
						raise ValueError

			# Verify entity hidden states
			if model_size == "large":
						raise NotImplementedError
			else:  # base
						_snake_case          =      torch.Size((1, 1, 768)			)
						_snake_case          =      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]	, __lowercase	, atol=1E-4			):
						raise ValueError

			# Verify masked word/entity prediction
			_snake_case          =      MLukeTokenizer.from_pretrained(__lowercase			)
			_snake_case          =      'Tokyo is the capital of <mask>.'
			_snake_case          =      (24, 30)
			_snake_case          =      tokenizer(__lowercase	, entity_spans=[span]	, return_tensors='pt'			)

			_snake_case          =      model(**__lowercase			)

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

			_snake_case          =      outputs.entity_logits[0][0].argmax().item()
			_snake_case          =      [
			    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(__lowercase			)			)
			model.save_pretrained(__lowercase			)






def 	a_		(	__lowercase       :     int			)			->  int:
			_snake_case          =      ['[MASK]', '[PAD]', '[UNK]']

			_snake_case          =      [json.loads(__lowercase			) for line in open(__lowercase			)]

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


if __name__ == "__main__":
			_lowerCamelCase	:    Union[str, 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	:    List[str]          =      parser.parse_args()
			convert_luke_checkpoint(
			    args.checkpoint_path,
			    args.metadata_path,
			    args.entity_vocab_path,
			    args.pytorch_dump_folder_path,
			    args.model_size,
			)