





'''simple docstring'''






import math
import unittest

from transformers import BioGptConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
      import torch

      from transformers import (
          BioGptForCausalLM,
          BioGptForSequenceClassification,
          BioGptForTokenClassification,
          BioGptModel,
          BioGptTokenizer,
      )
      from transformers.models.biogpt.modeling_biogpt import BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST




class  UpperCAmelCase     :




      def __init__(			self			: Tuple			,			__snake_case			: Optional[Any]			,			__snake_case			: str=13			,			__snake_case			: Any=7			,			__snake_case			: str=True			,			__snake_case			: List[Any]=True			,			__snake_case			: str=False			,			__snake_case			: Optional[int]=True			,			__snake_case			: str=99			,			__snake_case			: Optional[int]=32			,			__snake_case			: List[Any]=5			,			__snake_case			: str=4			,			__snake_case			: Any=37			,			__snake_case			: Tuple="gelu"			,			__snake_case			: Union[str, Any]=0.1			,			__snake_case			: Tuple=0.1			,			__snake_case			: Tuple=5_12			,			__snake_case			: Union[str, Any]=16			,			__snake_case			: Union[str, Any]=2			,			__snake_case			: str=0.02			,			__snake_case			: List[str]=3			,			__snake_case			: List[Any]=4			,			__snake_case			: Optional[int]=None			,			)				->      int:
            _lowerCAmelCase							     =							parent
            _lowerCAmelCase							     =							batch_size
            _lowerCAmelCase							     =							seq_length
            _lowerCAmelCase							     =							is_training
            _lowerCAmelCase							     =							use_input_mask
            _lowerCAmelCase							     =							use_token_type_ids
            _lowerCAmelCase							     =							use_labels
            _lowerCAmelCase							     =							vocab_size
            _lowerCAmelCase							     =							hidden_size
            _lowerCAmelCase							     =							num_hidden_layers
            _lowerCAmelCase							     =							num_attention_heads
            _lowerCAmelCase							     =							intermediate_size
            _lowerCAmelCase							     =							hidden_act
            _lowerCAmelCase							     =							hidden_dropout_prob
            _lowerCAmelCase							     =							attention_probs_dropout_prob
            _lowerCAmelCase							     =							max_position_embeddings
            _lowerCAmelCase							     =							type_vocab_size
            _lowerCAmelCase							     =							type_sequence_label_size
            _lowerCAmelCase							     =							initializer_range
            _lowerCAmelCase							     =							num_labels
            _lowerCAmelCase							     =							num_choices
            _lowerCAmelCase							     =							scope




      def 				lowercase__    (			self			: Tuple  )				->      Union[str, Any]:
            _lowerCAmelCase							     =							ids_tensor([self.batch_size, self.seq_length]			,			self.vocab_size  )

            _lowerCAmelCase							     =							None
            if self.use_input_mask:
                  _lowerCAmelCase							     =							random_attention_mask([self.batch_size, self.seq_length]  )

            _lowerCAmelCase							     =							None
            if self.use_token_type_ids:
                  _lowerCAmelCase							     =							ids_tensor([self.batch_size, self.seq_length]			,			self.type_vocab_size  )

            _lowerCAmelCase							     =							None
            _lowerCAmelCase							     =							None
            _lowerCAmelCase							     =							None
            if self.use_labels:
                  _lowerCAmelCase							     =							ids_tensor([self.batch_size]			,			self.type_sequence_label_size  )
                  _lowerCAmelCase							     =							ids_tensor([self.batch_size, self.seq_length]			,			self.num_labels  )
                  _lowerCAmelCase							     =							ids_tensor([self.batch_size]			,			self.num_choices  )

            _lowerCAmelCase							     =							self.get_config()

            return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels




      def 				lowercase__    (			self			: Optional[Any]  )				->      List[str]:
            return BioGptConfig(
                vocab_size=self.vocab_size			,			hidden_size=self.hidden_size			,			num_hidden_layers=self.num_hidden_layers			,			num_attention_heads=self.num_attention_heads			,			intermediate_size=self.intermediate_size			,			hidden_act=self.hidden_act			,			hidden_dropout_prob=self.hidden_dropout_prob			,			attention_probs_dropout_prob=self.attention_probs_dropout_prob			,			max_position_embeddings=self.max_position_embeddings			,			type_vocab_size=self.type_vocab_size			,			is_decoder=_a			,			initializer_range=self.initializer_range			,			)




      def 				lowercase__    (			self			: Dict			,			__snake_case			: Any			,			__snake_case			: int			,			__snake_case			: int			,			__snake_case			: Union[str, Any]			,			__snake_case			: Any			,			__snake_case			: Optional[Any]			,			__snake_case			: Any  )				->      List[str]:
            _lowerCAmelCase							     =							BioGptModel(config=_a  )
            model.to(_a  )
            model.eval()
            _lowerCAmelCase							     =							model(_a			,			attention_mask=_a  )
            _lowerCAmelCase							     =							model(_a  )
            self.parent.assertEqual(result.last_hidden_state.shape			,			(self.batch_size, self.seq_length, self.hidden_size)  )




      def 				lowercase__    (			self			: str			,			__snake_case			: Union[str, Any]			,			__snake_case			: Optional[Any]			,			__snake_case			: Optional[Any]			,			__snake_case			: Tuple			,			__snake_case			: Union[str, Any]			,			__snake_case			: int			,			__snake_case			: Any			,			__snake_case			: List[str]			,			__snake_case			: Tuple			,			)				->      List[str]:
            _lowerCAmelCase							     =							BioGptForCausalLM(config=_a  )
            model.to(_a  )
            model.eval()
            _lowerCAmelCase							     =							model(_a			,			attention_mask=_a			,			token_type_ids=_a			,			labels=_a  )
            self.parent.assertEqual(result.logits.shape			,			(self.batch_size, self.seq_length, self.vocab_size)  )




      def 				lowercase__    (			self			: int			,			__snake_case			: int			,			__snake_case			: Tuple			,			__snake_case			: Union[str, Any]			,			__snake_case			: List[str]			,			__snake_case			: Optional[Any]			,			*__snake_case			: List[Any]  )				->      str:
            _lowerCAmelCase							     =							BioGptModel(config=_a  )
            model.to(_a  )
            model.eval()

            # create attention mask
            _lowerCAmelCase							     =							torch.ones(input_ids.shape			,			dtype=torch.long			,			device=_a  )
            _lowerCAmelCase							     =							self.seq_length // 2
            _lowerCAmelCase							     =							0

            # first forward pass
            _lowerCAmelCase							     =							model(_a			,			attention_mask=_a  ).to_tuple()

            # create hypothetical next token and extent to next_input_ids
            _lowerCAmelCase							     =							ids_tensor((self.batch_size, 1)			,			config.vocab_size  )

            # change a random masked slice from input_ids
            _lowerCAmelCase							     =							ids_tensor((1,)			,			_a  ).item() + 1
            _lowerCAmelCase							     =							ids_tensor((self.batch_size, 1)			,			config.vocab_size  ).squeeze(-1  )
            _lowerCAmelCase							     =							random_other_next_tokens

            # append to next input_ids and attn_mask
            _lowerCAmelCase							     =							torch.cat([input_ids, next_tokens]			,			dim=-1  )
            _lowerCAmelCase							     =							torch.cat(
                [attn_mask, torch.ones((attn_mask.shape[0], 1)			,			dtype=torch.long			,			device=_a  )]			,			dim=1			,			)

            # get two different outputs
            _lowerCAmelCase							     =							model(_a			,			attention_mask=_a  )['last_hidden_state']
            _lowerCAmelCase							     =							model(_a			,			past_key_values=_a			,			attention_mask=_a  )['last_hidden_state']

            # select random slice
            _lowerCAmelCase							     =							ids_tensor((1,)			,			output_from_past.shape[-1]  ).item()
            _lowerCAmelCase							     =							output_from_no_past[:, -1, random_slice_idx].detach()
            _lowerCAmelCase							     =							output_from_past[:, 0, random_slice_idx].detach()

            # test that outputs are equal for slice
            self.parent.assertTrue(torch.allclose(_a			,			_a			,			atol=1E-3  )  )




      def 				lowercase__    (			self			: Union[str, Any]			,			__snake_case			: Optional[Any]			,			__snake_case			: Optional[int]			,			__snake_case			: str			,			__snake_case			: List[str]			,			__snake_case			: Any			,			*__snake_case			: Dict  )				->      Optional[Any]:
            _lowerCAmelCase							     =							BioGptModel(config=_a  ).to(_a  ).eval()

            _lowerCAmelCase							     =							torch.ones(input_ids.shape			,			dtype=torch.long			,			device=_a  )

            # first forward pass
            _lowerCAmelCase							     =							model(_a			,			attention_mask=_a			,			use_cache=_a  )

            _lowerCAmelCase							     =							outputs.to_tuple()

            # create hypothetical multiple next token and extent to next_input_ids
            _lowerCAmelCase							     =							ids_tensor((self.batch_size, 3)			,			config.vocab_size  )
            _lowerCAmelCase							     =							ids_tensor((self.batch_size, 3)			,			2  )

            # append to next input_ids and
            _lowerCAmelCase							     =							torch.cat([input_ids, next_tokens]			,			dim=-1  )
            _lowerCAmelCase							     =							torch.cat([attention_mask, next_attn_mask]			,			dim=-1  )

            _lowerCAmelCase							     =							model(_a			,			attention_mask=_a  )['last_hidden_state']
            _lowerCAmelCase							     =							model(_a			,			attention_mask=_a			,			past_key_values=_a  )[
                'last_hidden_state'
            ]

            # select random slice
            _lowerCAmelCase							     =							ids_tensor((1,)			,			output_from_past.shape[-1]  ).item()
            _lowerCAmelCase							     =							output_from_no_past[:, -3:, random_slice_idx].detach()
            _lowerCAmelCase							     =							output_from_past[:, :, random_slice_idx].detach()

            self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]  )

            # test that outputs are equal for slice
            self.parent.assertTrue(torch.allclose(_a			,			_a			,			atol=1E-3  )  )




      def 				lowercase__    (			self			: List[str]			,			__snake_case			: List[Any]			,			__snake_case			: Any			,			__snake_case			: Dict			,			__snake_case			: int			,			__snake_case			: List[Any]			,			*__snake_case			: List[Any]			,			__snake_case			: Optional[Any]=False  )				->      Optional[Any]:
            _lowerCAmelCase							     =							BioGptForCausalLM(_a  )
            model.to(_a  )
            if gradient_checkpointing:
                  model.gradient_checkpointing_enable()

            _lowerCAmelCase							     =							model(_a			,			labels=_a  )
            self.parent.assertEqual(result.loss.shape			,			()  )
            self.parent.assertEqual(result.logits.shape			,			(self.batch_size, self.seq_length, self.vocab_size)  )
            result.loss.backward()




      def 				lowercase__    (			self			: Any			,			__snake_case			: str			,			*__snake_case			: List[Any]  )				->      str:
            _lowerCAmelCase							     =							BioGptModel(_a  )
            _lowerCAmelCase							     =							model.config.initializer_range / math.sqrt(2 * model.config.num_hidden_layers  )
            for key in model.state_dict().keys():
                  if "c_proj" in key and "weight" in key:
                        self.parent.assertLessEqual(abs(torch.std(model.state_dict()[key]  ) - model_std  )			,			0.0_01  )
                        self.parent.assertLessEqual(abs(torch.mean(model.state_dict()[key]  ) - 0.0  )			,			0.01  )




      def 				lowercase__    (			self			: Any			,			__snake_case			: List[Any]			,			__snake_case			: List[Any]			,			__snake_case			: List[str]			,			__snake_case			: List[str]			,			__snake_case			: str			,			*__snake_case			: Optional[Any]  )				->      Dict:
            _lowerCAmelCase							     =							self.num_labels
            _lowerCAmelCase							     =							BioGptForTokenClassification(_a  )
            model.to(_a  )
            model.eval()
            _lowerCAmelCase							     =							model(_a			,			attention_mask=_a			,			token_type_ids=_a  )
            self.parent.assertEqual(result.logits.shape			,			(self.batch_size, self.seq_length, self.num_labels)  )




      def 				lowercase__    (			self			: Optional[Any]  )				->      int:
            _lowerCAmelCase							     =							self.prepare_config_and_inputs()
            (
                _lowerCAmelCase
            )							     =							config_and_inputs
            _lowerCAmelCase							     =							{'input_ids': input_ids, 'attention_mask': input_mask}
            return config, inputs_dict




@require_torch
class  UpperCAmelCase     (			SCREAMING_SNAKE_CASE_							,		SCREAMING_SNAKE_CASE_							,		SCREAMING_SNAKE_CASE_							,		unittest.TestCase   ):
      _lowercase:			Tuple						     =   (
          (BioGptModel, BioGptForCausalLM, BioGptForSequenceClassification, BioGptForTokenClassification)
          if is_torch_available()
          else ()
      )
      _lowercase:			Optional[Any]						     =   (BioGptForCausalLM,) if is_torch_available() else ()
      _lowercase:			Any						     =   (
          {
              '''feature-extraction''': BioGptModel,
              '''text-classification''': BioGptForSequenceClassification,
              '''text-generation''': BioGptForCausalLM,
              '''token-classification''': BioGptForTokenClassification,
              '''zero-shot''': BioGptForSequenceClassification,
          }
          if is_torch_available()
          else {}
      )
      _lowercase:			str						     =   False




      def 				lowercase__    (			self			: Dict  )				->      List[Any]:
            _lowerCAmelCase							     =							BioGptModelTester(self  )
            _lowerCAmelCase							     =							ConfigTester(self			,			config_class=_a			,			hidden_size=37  )




      def 				lowercase__    (			self			: Optional[int]  )				->      str:
            self.config_tester.run_common_tests()




      def 				lowercase__    (			self			: Optional[Any]  )				->      List[str]:
            _lowerCAmelCase							     =							self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_model(*_a  )




      def 				lowercase__    (			self			: Tuple  )				->      Dict:
            _lowerCAmelCase							     =							self.model_tester.prepare_config_and_inputs()
            for type in ["absolute", "relative_key", "relative_key_query"]:
                  _lowerCAmelCase							     =							type
                  self.model_tester.create_and_check_model(*_a  )




      def 				lowercase__    (			self			: Optional[Any]  )				->      int:
            _lowerCAmelCase							     =							self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_biogpt_model_attention_mask_past(*_a  )




      def 				lowercase__    (			self			: str  )				->      Optional[int]:
            _lowerCAmelCase							     =							self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_forward_and_backwards(*_a			,			gradient_checkpointing=_a  )




      def 				lowercase__    (			self			: Tuple  )				->      Dict:
            _lowerCAmelCase							     =							self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_biogpt_model_past_large_inputs(*_a  )




      def 				lowercase__    (			self			: str  )				->      int:
            _lowerCAmelCase							     =							self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_biogpt_weight_initialization(*_a  )




      def 				lowercase__    (			self			: Optional[int]  )				->      int:
            _lowerCAmelCase							     =							self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_biogpt_for_token_classification(*_a  )




      @slow
      def 				lowercase__    (			self			: Optional[int]  )				->      int:
            _lowerCAmelCase							     =							BioGptForCausalLM.from_pretrained("""microsoft/biogpt"""  )
            model.to(_a  )
            _lowerCAmelCase							     =							BioGptTokenizer.from_pretrained("""microsoft/biogpt"""  )

            _lowerCAmelCase							     =							'left'

            # Define PAD Token = EOS Token = 50256
            _lowerCAmelCase							     =							tokenizer.eos_token
            _lowerCAmelCase							     =							model.config.eos_token_id

            # use different length sentences to test batching
            _lowerCAmelCase							     =							[
                'Hello, my dog is a little',
                'Today, I',
            ]

            _lowerCAmelCase							     =							tokenizer(_a			,			return_tensors="""pt"""			,			padding=_a  )
            _lowerCAmelCase							     =							inputs['input_ids'].to(_a  )

            _lowerCAmelCase							     =							model.generate(
                input_ids=_a			,			attention_mask=inputs["""attention_mask"""].to(_a  )			,			)

            _lowerCAmelCase							     =							tokenizer(sentences[0]			,			return_tensors="""pt"""  ).input_ids.to(_a  )
            _lowerCAmelCase							     =							model.generate(input_ids=_a  )

            _lowerCAmelCase							     =							inputs_non_padded.shape[-1] - inputs['attention_mask'][-1].long().sum().cpu().item()
            _lowerCAmelCase							     =							tokenizer(sentences[1]			,			return_tensors="""pt"""  ).input_ids.to(_a  )
            _lowerCAmelCase							     =							model.generate(input_ids=_a			,			max_length=model.config.max_length - num_paddings  )

            _lowerCAmelCase							     =							tokenizer.batch_decode(_a			,			skip_special_tokens=_a  )
            _lowerCAmelCase							     =							tokenizer.decode(output_non_padded[0]			,			skip_special_tokens=_a  )
            _lowerCAmelCase							     =							tokenizer.decode(output_padded[0]			,			skip_special_tokens=_a  )

            _lowerCAmelCase							     =							[
                'Hello, my dog is a little bit bigger than a little bit.',
                'Today, I have a good idea of how to use the information',
            ]
            self.assertListEqual(_a			,			_a  )
            self.assertListEqual(_a			,			[non_padded_sentence, padded_sentence]  )




      @slow
      def 				lowercase__    (			self			: Tuple  )				->      int:
            for model_name in BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                  _lowerCAmelCase							     =							BioGptModel.from_pretrained(_a  )
                  self.assertIsNotNone(_a  )




      def 				lowercase__    (			self			: Union[str, Any]  )				->      List[str]:
            _lowerCAmelCase							     =							self.model_tester.prepare_config_and_inputs_for_common()
            _lowerCAmelCase							     =							3
            _lowerCAmelCase							     =							input_dict['input_ids']
            _lowerCAmelCase							     =							input_ids.ne(1  ).to(_a  )
            _lowerCAmelCase							     =							ids_tensor([self.model_tester.batch_size]			,			self.model_tester.type_sequence_label_size  )
            _lowerCAmelCase							     =							BioGptForSequenceClassification(_a  )
            model.to(_a  )
            model.eval()
            _lowerCAmelCase							     =							model(_a			,			attention_mask=_a			,			labels=_a  )
            self.assertEqual(result.logits.shape			,			(self.model_tester.batch_size, self.model_tester.num_labels)  )




      def 				lowercase__    (			self			: List[Any]  )				->      Optional[Any]:
            _lowerCAmelCase							     =							self.model_tester.prepare_config_and_inputs_for_common()
            _lowerCAmelCase							     =							3
            _lowerCAmelCase							     =							'multi_label_classification'
            _lowerCAmelCase							     =							input_dict['input_ids']
            _lowerCAmelCase							     =							input_ids.ne(1  ).to(_a  )
            _lowerCAmelCase							     =							ids_tensor(
                [self.model_tester.batch_size, config.num_labels]			,			self.model_tester.type_sequence_label_size  ).to(torch.float  )
            _lowerCAmelCase							     =							BioGptForSequenceClassification(_a  )
            model.to(_a  )
            model.eval()
            _lowerCAmelCase							     =							model(_a			,			attention_mask=_a			,			labels=_a  )
            self.assertEqual(result.logits.shape			,			(self.model_tester.batch_size, self.model_tester.num_labels)  )




@require_torch
class  UpperCAmelCase     (			unittest.TestCase   ):




      @slow
      def 				lowercase__    (			self			: int  )				->      Dict:
            _lowerCAmelCase							     =							BioGptForCausalLM.from_pretrained("""microsoft/biogpt"""  )
            _lowerCAmelCase							     =							torch.tensor([[2, 48_05, 9, 6_56, 21]]  )
            _lowerCAmelCase							     =							model(_a  )[0]

            _lowerCAmelCase							     =							4_23_84

            _lowerCAmelCase							     =							torch.Size((1, 5, vocab_size)  )
            self.assertEqual(output.shape			,			_a  )

            _lowerCAmelCase							     =							torch.tensor(
                [[[-9.52_36, -9.89_18, 10.45_57], [-11.04_69, -9.64_23, 8.10_22], [-8.86_64, -7.88_26, 5.53_25]]]  )

            self.assertTrue(torch.allclose(output[:, :3, :3]			,			_a			,			atol=1E-4  )  )




      @slow
      def 				lowercase__    (			self			: Dict  )				->      Dict:
            _lowerCAmelCase							     =							BioGptTokenizer.from_pretrained("""microsoft/biogpt"""  )
            _lowerCAmelCase							     =							BioGptForCausalLM.from_pretrained("""microsoft/biogpt"""  )
            model.to(_a  )

            torch.manual_seed(0  )
            _lowerCAmelCase							     =							tokenizer("""COVID-19 is"""			,			return_tensors="""pt"""  ).to(_a  )
            _lowerCAmelCase							     =							model.generate(
                **_a			,			min_length=1_00			,			max_length=10_24			,			num_beams=5			,			early_stopping=_a			,			)
            _lowerCAmelCase							     =							tokenizer.decode(output_ids[0]			,			skip_special_tokens=_a  )

            _lowerCAmelCase							     =							(
                'COVID-19 is a global pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the'
                ' causative agent of coronavirus disease 2019 (COVID-19), which has spread to more than 200 countries and'
                ' territories, including the United States (US), Canada, Australia, New Zealand, the United Kingdom (UK),'
                ' and the United States of America (USA), as of March 11, 2020, with more than 800,000 confirmed cases and'
                ' more than 800,000 deaths.'
            )
            self.assertEqual(_a			,			_a  )











'''simple docstring'''





import argparse

from torch import nn

# transformers_old should correspond to branch `save_old_prophetnet_model_structure` here
# original prophetnet_checkpoints are saved under `patrickvonplaten/..._old` respectively
from transformers_old.modeling_prophetnet import (
    ProphetNetForConditionalGeneration as ProphetNetForConditionalGenerationOld,
)
from transformers_old.modeling_xlm_prophetnet import (
    XLMProphetNetForConditionalGeneration as XLMProphetNetForConditionalGenerationOld,
)

from transformers import ProphetNetForConditionalGeneration, XLMProphetNetForConditionalGeneration, logging


_UpperCamelCase											=						logging.get_logger(__name__)
logging.set_verbosity_info()

def        a_     (							_lowerCAmelCase       ,_lowerCAmelCase       )      ->      Optional[int]:
  if "xprophetnet" in prophetnet_checkpoint_path:
    __lowerCamelCase       :       Union[str, Any]           = XLMProphetNetForConditionalGenerationOld.from_pretrained(_lowerCAmelCase       )
    __lowerCamelCase       ,__lowerCamelCase       :       List[str]           = XLMProphetNetForConditionalGeneration.from_pretrained(
        _lowerCAmelCase       ,output_loading_info=_lowerCAmelCase       )
  else:
    __lowerCamelCase       :       Optional[int]           = ProphetNetForConditionalGenerationOld.from_pretrained(_lowerCAmelCase       )
    __lowerCamelCase       ,__lowerCamelCase       :       List[str]           = ProphetNetForConditionalGeneration.from_pretrained(
        _lowerCAmelCase       ,output_loading_info=_lowerCAmelCase       )

  __lowerCamelCase       :       Union[str, Any]           = ['key_proj', 'value_proj', 'query_proj']

  __lowerCamelCase       :       Optional[Any]           = {
      'self_attn': 'ngram_self_attn',
      'cross_attn': 'encoder_attn',
      'cross_attn_layer_norm': 'encoder_attn_layer_norm',
      'feed_forward_layer_norm': 'final_layer_norm',
      'feed_forward': '',
      'intermediate': 'fc1',
      'output': 'fc2',
      'key_proj': 'k_proj',
      'query_proj': 'q_proj',
      'value_proj': 'v_proj',
      'word_embeddings': 'embed_tokens',
      'embeddings_layer_norm': 'emb_layer_norm',
      'relative_pos_embeddings': 'relative_linear',
      'ngram_embeddings': 'ngram_input_embed',
      'position_embeddings': 'embed_positions',
  }

  for key in loading_info["missing_keys"]:
    __lowerCamelCase       :       Optional[int]           = key.split('.'       )

    if attributes[0] == "lm_head":
      __lowerCamelCase       :       Dict           = prophet
      __lowerCamelCase       :       List[Any]           = prophet_old
    else:
      __lowerCamelCase       :       Any           = prophet.prophetnet
      __lowerCamelCase       :       Any           = prophet_old.model

    __lowerCamelCase       :       Optional[Any]           = False
    for attribute in attributes:
      if attribute in mapping:
        __lowerCamelCase       :       Any           = mapping[attribute]
        if not hasattr(_lowerCAmelCase       ,_lowerCAmelCase       ) and len(_lowerCAmelCase       ) > 0:
          __lowerCamelCase       :       int           = attribute
      elif hasattr(_lowerCAmelCase       ,_lowerCAmelCase       ):
        __lowerCamelCase       :       Optional[int]           = attribute

      if attribute == "weight":
        assert old_model.weight.shape == model.weight.shape, "Shapes have to match!"
        __lowerCamelCase       :       List[Any]           = old_model.weight
        logger.info(F'{attribute} is initialized.'       )
        __lowerCamelCase       :       List[Any]           = True
        break
      elif attribute == "bias":
        assert old_model.bias.shape == model.bias.shape, "Shapes have to match!"
        __lowerCamelCase       :       List[Any]           = old_model.bias
        logger.info(F'{attribute} is initialized'       )
        __lowerCamelCase       :       Dict           = True
        break
      elif attribute in special_keys and hasattr(_lowerCAmelCase       ,'in_proj_weight'       ):
        __lowerCamelCase       :       Optional[Any]           = old_model.in_proj_weight.shape[0] // 3
        __lowerCamelCase       :       Optional[Any]           = getattr(_lowerCAmelCase       ,_lowerCAmelCase       )
        param.weight.shape == old_model.in_proj_weight[:embed_dim, :].shape, "Shapes have to match"
        param.bias.shape == old_model.in_proj_bias[:embed_dim].shape, "Shapes have to match"
        if attribute == "query_proj":
          __lowerCamelCase       :       Optional[int]           = nn.Parameter(old_model.in_proj_weight[:embed_dim, :]       )
          __lowerCamelCase       :       Dict           = nn.Parameter(old_model.in_proj_bias[:embed_dim]       )

        elif attribute == "key_proj":
          __lowerCamelCase       :       List[str]           = nn.Parameter(old_model.in_proj_weight[embed_dim : 2 * embed_dim, :]       )
          __lowerCamelCase       :       Dict           = nn.Parameter(old_model.in_proj_bias[embed_dim : 2 * embed_dim]       )
        elif attribute == "value_proj":
          __lowerCamelCase       :       str           = nn.Parameter(old_model.in_proj_weight[2 * embed_dim :, :]       )
          __lowerCamelCase       :       Optional[int]           = nn.Parameter(old_model.in_proj_bias[2 * embed_dim :]       )
        __lowerCamelCase       :       Optional[int]           = True
        break
      elif attribute == "position_embeddings":
        assert (
            model.position_embeddings.weight.shape[-1] == old_model.embed_positions.weight.shape[-1]
        ), "Hidden size has to match"
        assert model.position_embeddings.weight.shape[0] == 512, "We want 512 position_embeddings."
        __lowerCamelCase       :       Optional[int]           = nn.Parameter(old_model.embed_positions.weight[:512, :]       )
        __lowerCamelCase       :       Dict           = True
        break

      if attribute.isdigit():
        __lowerCamelCase       :       List[str]           = model[int(_lowerCAmelCase       )]
        __lowerCamelCase       :       Union[str, Any]           = old_model[int(_lowerCAmelCase       )]
      else:
        __lowerCamelCase       :       Union[str, Any]           = getattr(_lowerCAmelCase       ,_lowerCAmelCase       )

        if old_attribute == "":
          __lowerCamelCase       :       str           = old_model
        else:
          if not hasattr(_lowerCAmelCase       ,_lowerCAmelCase       ):
            raise ValueError(F'{old_model} does not have {old_attribute}'       )
          __lowerCamelCase       :       str           = getattr(_lowerCAmelCase       ,_lowerCAmelCase       )

    if not is_key_init:
      raise ValueError(F'{key} was not correctly initialized!'       )

  print(F'Saving model to {pytorch_dump_folder_path}'       )
  prophet.save_pretrained(_lowerCAmelCase       )


if __name__ == "__main__":
   _UpperCamelCase											=						argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       '--prophetnet_checkpoint_path', default=None, type=str, required=True, help='Path the official PyTorch dump.'
   )
   parser.add_argument(
       '--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
   )
   _UpperCamelCase											=						parser.parse_args()
   convert_prophetnet_checkpoint_to_pytorch(args.prophetnet_checkpoint_path, args.pytorch_dump_folder_path)






