



import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import BlenderbotConfig, is_flax_available
from transformers.testing_utils import jax_device, require_flax, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
       import os

       # The slow tests are often failing with OOM error on GPU
       # This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
       # but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
       lowercase_	   =     'platform'

       import jax
       import jax.numpy as jnp

       from transformers import BlenderbotTokenizer
       from transformers.models.blenderbot.modeling_flax_blenderbot import (
           FlaxBlenderbotForConditionalGeneration,
           FlaxBlenderbotModel,
           shift_tokens_right,
       )
def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__=None      ,	SCREAMING_SNAKE_CASE__=None      ,	SCREAMING_SNAKE_CASE__=None      ,	SCREAMING_SNAKE_CASE__=None      ,	SCREAMING_SNAKE_CASE__=None      ,	SCREAMING_SNAKE_CASE__=None      ,	):
 if attention_mask is None:
  __lowerCamelCase		:							Optional[Any]		 =						np.where(input_ids != config.pad_token_id      ,	1      ,	0						)
 if decoder_attention_mask is None:
  __lowerCamelCase		:							List[Any]		 =						np.where(decoder_input_ids != config.pad_token_id      ,	1      ,	0						)
 if head_mask is None:
  __lowerCamelCase		:							Optional[Any]		 =						np.ones((config.encoder_layers, config.encoder_attention_heads)						)
 if decoder_head_mask is None:
  __lowerCamelCase		:							Union[str, Any]		 =						np.ones((config.decoder_layers, config.decoder_attention_heads)						)
 if cross_attn_head_mask is None:
  __lowerCamelCase		:							Optional[int]		 =						np.ones((config.decoder_layers, config.decoder_attention_heads)						)
 return {
     "input_ids": input_ids,
     "decoder_input_ids": decoder_input_ids,
     "attention_mask": attention_mask,
     "decoder_attention_mask": attention_mask,
 }


class 	A_ :




      '''simple docstring'''


      def __init__(    self:	List[Any]						,				a:	List[Any]						,				a:	str=13						,				a:	Dict=7						,				a:	Optional[Any]=True						,				a:	int=False						,				a:	int=99						,				a:	Dict=16						,				a:	Union[str, Any]=2						,				a:	Tuple=4						,				a:	Union[str, Any]=4						,				a:	List[str]="gelu"						,				a:	Union[str, Any]=0.1						,				a:	str=0.1						,				a:	List[Any]=32						,				a:	Optional[Any]=2						,				a:	str=1						,				a:	List[Any]=0						,				a:	Optional[int]=0.0_2						,				):
       __lowerCamelCase		:							Optional[int]		 =						parent
       __lowerCamelCase		:							Dict		 =						batch_size
       __lowerCamelCase		:							List[Any]		 =						seq_length
       __lowerCamelCase		:							Optional[Any]		 =						is_training
       __lowerCamelCase		:							Optional[Any]		 =						use_labels
       __lowerCamelCase		:							Tuple		 =						vocab_size
       __lowerCamelCase		:							Union[str, Any]		 =						hidden_size
       __lowerCamelCase		:							Dict		 =						num_hidden_layers
       __lowerCamelCase		:							Dict		 =						num_attention_heads
       __lowerCamelCase		:							Dict		 =						intermediate_size
       __lowerCamelCase		:							int		 =						hidden_act
       __lowerCamelCase		:							List[str]		 =						hidden_dropout_prob
       __lowerCamelCase		:							Tuple		 =						attention_probs_dropout_prob
       __lowerCamelCase		:							List[Any]		 =						max_position_embeddings
       __lowerCamelCase		:							Tuple		 =						eos_token_id
       __lowerCamelCase		:							Tuple		 =						pad_token_id
       __lowerCamelCase		:							Any		 =						bos_token_id
       __lowerCamelCase		:							Dict		 =						initializer_range




      def 					_snake_case     (    self:	Any    ):
       __lowerCamelCase		:							List[Any]		 =						np.clip(ids_tensor([self.batch_size, self.seq_length - 1]						,				self.vocab_size    )						,				3						,				self.vocab_size    )
       __lowerCamelCase		:							str		 =						np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)						,				dtype=np.intaa    ))						,				-1    )

       __lowerCamelCase		:							List[str]		 =						shift_tokens_right(a						,				1						,				2    )

       __lowerCamelCase		:							List[str]		 =						BlenderbotConfig(
           vocab_size=self.vocab_size						,				d_model=self.hidden_size						,				encoder_layers=self.num_hidden_layers						,				decoder_layers=self.num_hidden_layers						,				encoder_attention_heads=self.num_attention_heads						,				decoder_attention_heads=self.num_attention_heads						,				encoder_ffn_dim=self.intermediate_size						,				decoder_ffn_dim=self.intermediate_size						,				dropout=self.hidden_dropout_prob						,				attention_dropout=self.attention_probs_dropout_prob						,				max_position_embeddings=self.max_position_embeddings						,				eos_token_id=self.eos_token_id						,				bos_token_id=self.bos_token_id						,				pad_token_id=self.pad_token_id						,				initializer_range=self.initializer_range						,				use_cache=a						,				)
       __lowerCamelCase		:							List[str]		 =						prepare_blenderbot_inputs_dict(a						,				a						,				a    )
       return config, inputs_dict




      def 					_snake_case     (    self:	List[Any]    ):
       __lowerCamelCase		:							int		 =						self.prepare_config_and_inputs()
       return config, inputs_dict




      def 					_snake_case     (    self:	Optional[int]						,				a:	Any						,				a:	List[str]						,				a:	Tuple    ):
       __lowerCamelCase		:							Optional[int]		 =						20
       __lowerCamelCase		:							int		 =						model_class_name(a    )

       __lowerCamelCase		:							Optional[Any]		 =						model.encode(inputs_dict['input_ids']    )

       __lowerCamelCase		:							Any		 =						(
           inputs_dict['decoder_input_ids'],
           inputs_dict['decoder_attention_mask'],
       )

       __lowerCamelCase		:							List[str]		 =						model.init_cache(decoder_input_ids.shape[0]						,				a						,				a    )
       __lowerCamelCase		:							int		 =						jnp.ones((decoder_input_ids.shape[0], max_decoder_length)						,				dtype='i4'    )

       __lowerCamelCase		:							Union[str, Any]		 =						jnp.broadcast_to(
           jnp.arange(decoder_input_ids.shape[-1] - 1    )[None, :]						,				(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)						,				)
       __lowerCamelCase		:							List[str]		 =						model.decode(
           decoder_input_ids[:, :-1]						,				a						,				decoder_attention_mask=a						,				past_key_values=a						,				decoder_position_ids=a						,				)

       __lowerCamelCase		:							Optional[int]		 =						jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]						,				dtype='i4'    )
       __lowerCamelCase		:							str		 =						model.decode(
           decoder_input_ids[:, -1:]						,				a						,				decoder_attention_mask=a						,				past_key_values=outputs_cache.past_key_values						,				decoder_position_ids=a						,				)

       __lowerCamelCase		:							List[str]		 =						model.decode(a						,				a    )

       __lowerCamelCase		:							Optional[Any]		 =						np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])    )    )
       self.parent.assertTrue(diff < 1e-3						,				msg=F'Max diff is {diff}'    )




      def 					_snake_case     (    self:	Optional[int]						,				a:	List[Any]						,				a:	List[Any]						,				a:	List[Any]    ):
       __lowerCamelCase		:							Optional[Any]		 =						20
       __lowerCamelCase		:							Any		 =						model_class_name(a    )

       __lowerCamelCase		:							Tuple		 =						model.encode(inputs_dict['input_ids']    )

       __lowerCamelCase		:							List[Any]		 =						(
           inputs_dict['decoder_input_ids'],
           inputs_dict['decoder_attention_mask'],
       )

       __lowerCamelCase		:							Optional[int]		 =						jnp.concatenate(
           [
               decoder_attention_mask,
               jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])    ),
           ]						,				axis=-1						,				)

       __lowerCamelCase		:							Optional[Any]		 =						model.init_cache(decoder_input_ids.shape[0]						,				a						,				a    )
       __lowerCamelCase		:							Union[str, Any]		 =						jnp.broadcast_to(
           jnp.arange(decoder_input_ids.shape[-1] - 1    )[None, :]						,				(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)						,				)

       __lowerCamelCase		:							Any		 =						model.decode(
           decoder_input_ids[:, :-1]						,				a						,				decoder_attention_mask=a						,				past_key_values=a						,				decoder_position_ids=a						,				)
       __lowerCamelCase		:							List[Any]		 =						jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]						,				dtype='i4'    )
       __lowerCamelCase		:							Dict		 =						model.decode(
           decoder_input_ids[:, -1:]						,				a						,				past_key_values=outputs_cache.past_key_values						,				decoder_attention_mask=a						,				decoder_position_ids=a						,				)

       __lowerCamelCase		:							List[Any]		 =						model.decode(a						,				a						,				decoder_attention_mask=a    )

       __lowerCamelCase		:							List[Any]		 =						np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])    )    )
       self.parent.assertTrue(diff < 1e-3						,				msg=F'Max diff is {diff}'    )



@require_flax
class 	A_ (	unittest.TestCase   ):




      '''simple docstring'''


      __snake_case 	=       99




      def 					_snake_case     (    self:	Union[str, Any]    ):
       __lowerCamelCase		:							Dict		 =						np.array(
           [
               [71, 82, 18, 33, 46, 91, 2],
               [68, 34, 26, 58, 30, 82, 2],
               [5, 97, 17, 39, 94, 40, 2],
               [76, 83, 94, 25, 70, 78, 2],
               [87, 59, 41, 35, 48, 66, 2],
               [55, 13, 16, 58, 5, 2, 1],  # note padding
               [64, 27, 31, 51, 12, 75, 2],
               [52, 64, 86, 17, 83, 39, 2],
               [48, 61, 9, 24, 71, 82, 2],
               [26, 1, 60, 48, 22, 13, 2],
               [21, 5, 62, 28, 14, 76, 2],
               [45, 98, 37, 86, 59, 48, 2],
               [70, 70, 50, 9, 28, 0, 2],
           ]						,				dtype=np.intaa						,				)

       __lowerCamelCase		:							Tuple		 =						input_ids.shape[0]
       __lowerCamelCase		:							Optional[int]		 =						BlenderbotConfig(
           vocab_size=self.vocab_size						,				d_model=24						,				encoder_layers=2						,				decoder_layers=2						,				encoder_attention_heads=2						,				decoder_attention_heads=2						,				encoder_ffn_dim=32						,				decoder_ffn_dim=32						,				max_position_embeddings=48						,				eos_token_id=2						,				pad_token_id=1						,				bos_token_id=0						,				)
       return config, input_ids, batch_size




      def 					_snake_case     (    self:	Union[str, Any]    ):
       __lowerCamelCase		:							List[str]		 =						self._get_config_and_data()
       __lowerCamelCase		:							List[Any]		 =						FlaxBlenderbotForConditionalGeneration(a    )
       __lowerCamelCase		:							Optional[int]		 =						lm_model(input_ids=a    )
       __lowerCamelCase		:							Dict		 =						(batch_size, input_ids.shape[1], config.vocab_size)
       self.assertEqual(outputs['logits'].shape						,				a    )




      def 					_snake_case     (    self:	Union[str, Any]    ):
       __lowerCamelCase		:							Union[str, Any]		 =						BlenderbotConfig(
           vocab_size=self.vocab_size						,				d_model=14						,				encoder_layers=2						,				decoder_layers=2						,				encoder_attention_heads=2						,				decoder_attention_heads=2						,				encoder_ffn_dim=8						,				decoder_ffn_dim=8						,				max_position_embeddings=48						,				)
       __lowerCamelCase		:							str		 =						FlaxBlenderbotForConditionalGeneration(a    )
       __lowerCamelCase		:							Union[str, Any]		 =						np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]						,				dtype=np.intaa    )
       __lowerCamelCase		:							int		 =						np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]						,				dtype=np.intaa    )
       __lowerCamelCase		:							Any		 =						lm_model(input_ids=a						,				decoder_input_ids=a    )
       __lowerCamelCase		:							Tuple		 =						(*summary.shape, config.vocab_size)
       self.assertEqual(outputs['logits'].shape						,				a    )




      def 					_snake_case     (    self:	Union[str, Any]    ):
       __lowerCamelCase		:							Union[str, Any]		 =						np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]						,				dtype=np.intaa    )
       __lowerCamelCase		:							List[Any]		 =						shift_tokens_right(a						,				1						,				2    )
       __lowerCamelCase		:							List[str]		 =						np.equal(a						,				1    ).astype(np.floataa    ).sum()
       __lowerCamelCase		:							Dict		 =						np.equal(a						,				1    ).astype(np.floataa    ).sum()
       self.assertEqual(shifted.shape						,				input_ids.shape    )
       self.assertEqual(a						,				n_pad_before - 1    )
       self.assertTrue(np.equal(shifted[:, 0]						,				2    ).all()    )



@require_flax
class 	A_ (	__UpperCamelCase ,				unittest.TestCase ,				__UpperCamelCase   ):




      '''simple docstring'''


      __snake_case 	=       True
      __snake_case 	=       (
          (
              FlaxBlenderbotModel,
              FlaxBlenderbotForConditionalGeneration,
          )
          if is_flax_available()
          else ()
      )
      __snake_case 	=       (FlaxBlenderbotForConditionalGeneration,) if is_flax_available() else ()




      def 					_snake_case     (    self:	Optional[Any]    ):
       __lowerCamelCase		:							int		 =						FlaxBlenderbotModelTester(self    )




      def 					_snake_case     (    self:	Dict    ):
       __lowerCamelCase		:							List[str]		 =						self.model_tester.prepare_config_and_inputs()
       for model_class in self.all_model_classes:
        self.model_tester.check_use_cache_forward(a						,				a						,				a    )




      def 					_snake_case     (    self:	Optional[Any]    ):
       __lowerCamelCase		:							List[str]		 =						self.model_tester.prepare_config_and_inputs()
       for model_class in self.all_model_classes:
        self.model_tester.check_use_cache_forward_with_attn_mask(a						,				a						,				a    )




      def 					_snake_case     (    self:	Optional[Any]    ):
       __lowerCamelCase		:							Optional[int]		 =						self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
        with self.subTest(model_class.__name__    ):
         __lowerCamelCase		:							List[str]		 =						self._prepare_for_class(a						,				a    )
         __lowerCamelCase		:							str		 =						model_class(a    )

         @jax.jit
         def encode_jitted(a:	Any						,				a:	Dict=None						,				**a:	Union[str, Any]    ):
          return model.encode(input_ids=a						,				attention_mask=a    )

         with self.subTest('JIT Enabled'    ):
          __lowerCamelCase		:							Tuple		 =						encode_jitted(**a    ).to_tuple()

         with self.subTest('JIT Disabled'    ):
          with jax.disable_jit():
           __lowerCamelCase		:							int		 =						encode_jitted(**a    ).to_tuple()

         self.assertEqual(len(a    )						,				len(a    )    )
         for jitted_output, output in zip(a						,				a    ):
          self.assertEqual(jitted_output.shape						,				output.shape    )




      def 					_snake_case     (    self:	Union[str, Any]    ):
       __lowerCamelCase		:							List[str]		 =						self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
        with self.subTest(model_class.__name__    ):
         __lowerCamelCase		:							Tuple		 =						model_class(a    )
         __lowerCamelCase		:							Tuple		 =						model.encode(inputs_dict['input_ids']						,				inputs_dict['attention_mask']    )

         __lowerCamelCase		:							Tuple		 =						{
             'decoder_input_ids': inputs_dict['decoder_input_ids'],
             'decoder_attention_mask': inputs_dict['decoder_attention_mask'],
             'encoder_outputs': encoder_outputs,
         }

         @jax.jit
         def decode_jitted(a:	List[Any]						,				a:	List[str]						,				a:	Optional[Any]    ):
          return model.decode(
              decoder_input_ids=a						,				decoder_attention_mask=a						,				encoder_outputs=a						,				)

         with self.subTest('JIT Enabled'    ):
          __lowerCamelCase		:							Union[str, Any]		 =						decode_jitted(**a    ).to_tuple()

         with self.subTest('JIT Disabled'    ):
          with jax.disable_jit():
           __lowerCamelCase		:							List[Any]		 =						decode_jitted(**a    ).to_tuple()

         self.assertEqual(len(a    )						,				len(a    )    )
         for jitted_output, output in zip(a						,				a    ):
          self.assertEqual(jitted_output.shape						,				output.shape    )




      @slow
      def 					_snake_case     (    self:	int    ):
       for model_class_name in self.all_model_classes:
        __lowerCamelCase		:							List[str]		 =						model_class_name.from_pretrained('facebook/blenderbot-400M-distill'    )
        # FlaxBlenderbotForSequenceClassification expects eos token in input_ids
        __lowerCamelCase		:							str		 =						np.ones((1, 1)    ) * model.config.eos_token_id
        __lowerCamelCase		:							Optional[Any]		 =						model(a    )
        self.assertIsNotNone(a    )




      @unittest.skipUnless(jax_device != 'cpu'						,				'3B test too slow on CPU.'    )
      @slow
      def 					_snake_case     (    self:	Any    ):
       __lowerCamelCase		:							Tuple		 =						{'num_beams': 1, 'early_stopping': True, 'min_length': 15, 'max_length': 25}
       __lowerCamelCase		:							Tuple		 =						{'skip_special_tokens': True, 'clean_up_tokenization_spaces': True}

       __lowerCamelCase		:							List[Any]		 =						FlaxBlenderbotForConditionalGeneration.from_pretrained('facebook/blenderbot-3B'						,				from_pt=a    )
       __lowerCamelCase		:							Optional[int]		 =						BlenderbotTokenizer.from_pretrained('facebook/blenderbot-3B'    )

       __lowerCamelCase		:							Tuple		 =						['Sam']
       __lowerCamelCase		:							Union[str, Any]		 =						tokenizer(a						,				return_tensors='jax'    )

       __lowerCamelCase		:							Optional[Any]		 =						model.generate(**a						,				**a    )
       __lowerCamelCase		:							Tuple		 =						'Sam is a great name. It means "sun" in Gaelic.'

       __lowerCamelCase		:							Optional[Any]		 =						tokenizer.batch_decode(a						,				**a    )
       assert generated_txt[0].strip() == tgt_text





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 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__						):
 # Load configuration defined in the metadata file
 with open(SCREAMING_SNAKE_CASE__						) as metadata_file:
  __lowerCamelCase		:							List[str]		 =						json.load(SCREAMING_SNAKE_CASE__						)
 __lowerCamelCase		:							int		 =						LukeConfig(use_entity_aware_attention=SCREAMING_SNAKE_CASE__      ,	**metadata['model_config']						)

 # Load in the weights from the checkpoint_path
 __lowerCamelCase		:							Union[str, Any]		 =						torch.load(SCREAMING_SNAKE_CASE__      ,	map_location='cpu'						)

 # Load the entity vocab file
 __lowerCamelCase		:							List[Any]		 =						load_entity_vocab(SCREAMING_SNAKE_CASE__						)

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

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

 __lowerCamelCase		:							str		 =						LukeTokenizer.from_pretrained(SCREAMING_SNAKE_CASE__						)

 # Initialize the embeddings of the special tokens
 __lowerCamelCase		:							Union[str, Any]		 =						state_dict['embeddings.word_embeddings.weight']
 __lowerCamelCase		:							Tuple		 =						word_emb[tokenizer.convert_tokens_to_ids(['@']						)[0]].unsqueeze(0						)
 __lowerCamelCase		:							Any		 =						word_emb[tokenizer.convert_tokens_to_ids(['#']						)[0]].unsqueeze(0						)
 __lowerCamelCase		:							List[str]		 =						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"]:
   __lowerCamelCase		:							Optional[int]		 =						f'encoder.layer.{layer_index}.attention.self.'
   __lowerCamelCase		:							Dict		 =						state_dict[prefix + matrix_name]
   __lowerCamelCase		:							List[Any]		 =						state_dict[prefix + matrix_name]
   __lowerCamelCase		:							Union[str, Any]		 =						state_dict[prefix + matrix_name]

    # Initialize the embedding of the [MASK2] entity using that of the [MASK] entity for downstream tasks
 __lowerCamelCase		:							Optional[int]		 =						state_dict['entity_embeddings.entity_embeddings.weight']
 __lowerCamelCase		:							Union[str, Any]		 =						entity_emb[entity_vocab['[MASK]']]

 __lowerCamelCase		:							Optional[Any]		 =						LukeModel(config=SCREAMING_SNAKE_CASE__						).eval()

 __lowerCamelCase ,					__lowerCamelCase		:							List[Any]		 =						model.load_state_dict(SCREAMING_SNAKE_CASE__      ,	strict=SCREAMING_SNAKE_CASE__						)
 if not (len(SCREAMING_SNAKE_CASE__						) == 1 and missing_keys[0] == "embeddings.position_ids"):
  raise ValueError(f'Missing keys {", ".join(SCREAMING_SNAKE_CASE__						)}. 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
 __lowerCamelCase		:							Optional[Any]		 =						LukeTokenizer.from_pretrained(SCREAMING_SNAKE_CASE__      ,	task='entity_classification'						)

 __lowerCamelCase		:							Dict		 =						(
     '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 .'
 )
 __lowerCamelCase		:							Union[str, Any]		 =						(39, 42)
 __lowerCamelCase		:							Optional[Any]		 =						tokenizer(SCREAMING_SNAKE_CASE__      ,	entity_spans=[span]      ,	add_prefix_space=SCREAMING_SNAKE_CASE__      ,	return_tensors='pt'						)

 __lowerCamelCase		:							List[str]		 =						model(**SCREAMING_SNAKE_CASE__						)

 # Verify word hidden states
 if model_size == "large":
  __lowerCamelCase		:							Dict		 =						torch.Size((1, 42, 1_024)						)
  __lowerCamelCase		:							int		 =						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
  __lowerCamelCase		:							Union[str, Any]		 =						torch.Size((1, 42, 768)						)
  __lowerCamelCase		:							Tuple		 =						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]      ,	SCREAMING_SNAKE_CASE__      ,	atol=1e-4						):
  raise ValueError

 # Verify entity hidden states
 if model_size == "large":
  __lowerCamelCase		:							Union[str, Any]		 =						torch.Size((1, 1, 1_024)						)
  __lowerCamelCase		:							Dict		 =						torch.tensor([[0.0_466, -0.0_106, -0.0_179]]						)
 else:  # base
  __lowerCamelCase		:							int		 =						torch.Size((1, 1, 768)						)
  __lowerCamelCase		:							Dict		 =						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]      ,	SCREAMING_SNAKE_CASE__      ,	atol=1e-4						):
  raise ValueError

 # Finally, save our PyTorch model and tokenizer
 print('Saving PyTorch model to {}'.format(SCREAMING_SNAKE_CASE__						)						)
 model.save_pretrained(SCREAMING_SNAKE_CASE__						)
def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__						):
 __lowerCamelCase		:							Tuple		 =						{}
 with open(SCREAMING_SNAKE_CASE__      ,	'r'      ,	encoding='utf-8'						) as f:
  for index, line in enumerate(SCREAMING_SNAKE_CASE__						):
   __lowerCamelCase ,					__lowerCamelCase		:							List[Any]		 =						line.rstrip().split('\t'						)
   __lowerCamelCase		:							Any		 =						index

 return entity_vocab


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

