





'''simple docstring'''






import inspect
import tempfile
import unittest

from huggingface_hub import hf_hub_download

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

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


A__				:   Optional[int]      =1e-4

if is_torch_available():
      import torch

      from transformers import AutoformerConfig, AutoformerForPrediction, AutoformerModel
      from transformers.models.autoformer.modeling_autoformer import AutoformerDecoder, AutoformerEncoder




@require_torch
class  UpperCAmelCase     :




      def __init__(			self			: List[Any]			,			__snake_case			: Optional[int]			,			__snake_case			: Optional[int]=16			,			__snake_case			: Dict=13			,			__snake_case			: Any=7			,			__snake_case			: List[str]=14			,			__snake_case			: Tuple=10			,			__snake_case			: Dict=19			,			__snake_case			: Union[str, Any]=5			,			__snake_case			: Tuple=4			,			__snake_case			: Dict=True			,			__snake_case			: Optional[int]=16			,			__snake_case			: Optional[Any]=2			,			__snake_case			: Union[str, Any]=4			,			__snake_case			: Union[str, Any]=4			,			__snake_case			: List[Any]="gelu"			,			__snake_case			: List[str]=0.1			,			__snake_case			: Union[str, Any]=0.1			,			__snake_case			: Tuple=[1, 2, 3, 4, 5]			,			__snake_case			: Tuple=25			,			__snake_case			: List[str]=5			,			)				->      List[str]:
            _lowerCAmelCase							     =							d_model
            _lowerCAmelCase							     =							parent
            _lowerCAmelCase							     =							batch_size
            _lowerCAmelCase							     =							prediction_length
            _lowerCAmelCase							     =							context_length
            _lowerCAmelCase							     =							cardinality
            _lowerCAmelCase							     =							num_time_features
            _lowerCAmelCase							     =							lags_sequence
            _lowerCAmelCase							     =							embedding_dimension
            _lowerCAmelCase							     =							is_training
            _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							     =							context_length
            _lowerCAmelCase							     =							prediction_length + label_length
            _lowerCAmelCase							     =							label_length

            _lowerCAmelCase							     =							moving_average
            _lowerCAmelCase							     =							autocorrelation_factor




      def 				lowercase__    (			self			: str  )				->      int:
            return AutoformerConfig(
                d_model=self.d_model			,			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			,			prediction_length=self.prediction_length			,			context_length=self.context_length			,			label_length=self.label_length			,			lags_sequence=self.lags_sequence			,			num_time_features=self.num_time_features			,			num_static_categorical_features=1			,			cardinality=[self.cardinality]			,			embedding_dimension=[self.embedding_dimension]			,			moving_average=self.moving_average			,			)




      def 				lowercase__    (			self			: int			,			__snake_case			: str  )				->      List[Any]:
            _lowerCAmelCase							     =							config.context_length + max(config.lags_sequence  )

            _lowerCAmelCase							     =							ids_tensor([self.batch_size, 1]			,			config.cardinality[0]  )
            _lowerCAmelCase							     =							floats_tensor([self.batch_size, _past_length, config.num_time_features]  )
            _lowerCAmelCase							     =							floats_tensor([self.batch_size, _past_length]  )
            _lowerCAmelCase							     =							floats_tensor([self.batch_size, _past_length]  ) > 0.5

            # decoder inputs
            _lowerCAmelCase							     =							floats_tensor([self.batch_size, config.prediction_length, config.num_time_features]  )
            _lowerCAmelCase							     =							floats_tensor([self.batch_size, config.prediction_length]  )

            _lowerCAmelCase							     =							{
                'past_values': past_values,
                'static_categorical_features': static_categorical_features,
                'past_time_features': past_time_features,
                'past_observed_mask': past_observed_mask,
                'future_time_features': future_time_features,
                'future_values': future_values,
            }
            return inputs_dict




      def 				lowercase__    (			self			: str  )				->      Tuple:
            _lowerCAmelCase							     =							self.get_config()
            _lowerCAmelCase							     =							self.prepare_autoformer_inputs_dict(_a  )
            return config, inputs_dict




      def 				lowercase__    (			self			: Dict  )				->      Any:
            _lowerCAmelCase							     =							self.prepare_config_and_inputs()
            return config, inputs_dict




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

            _lowerCAmelCase							     =							outputs.encoder_last_hidden_state
            _lowerCAmelCase							     =							outputs.last_hidden_state

            with tempfile.TemporaryDirectory() as tmpdirname:
                  _lowerCAmelCase							     =							model.get_encoder()
                  encoder.save_pretrained(_a  )
                  _lowerCAmelCase							     =							AutoformerEncoder.from_pretrained(_a  ).to(_a  )

            _lowerCAmelCase							     =							model.create_network_inputs(**_a  )
            _lowerCAmelCase							     =							model.decomposition_layer(transformer_inputs[:, : config.context_length, ...]  )

            _lowerCAmelCase							     =							torch.cat(
                (transformer_inputs[:, : config.context_length, ...], feature[:, : config.context_length, ...])			,			dim=-1			,			)
            _lowerCAmelCase							     =							encoder(inputs_embeds=_a  )[0]
            self.parent.assertTrue((encoder_last_hidden_state_a - encoder_last_hidden_state).abs().max().item() < 1E-3  )

            _lowerCAmelCase							     =							(
                torch.mean(transformer_inputs[:, : config.context_length, ...]			,			dim=1  )
                .unsqueeze(1  )
                .repeat(1			,			config.prediction_length			,			1  )
            )
            _lowerCAmelCase							     =							torch.zeros(
                [transformer_inputs.shape[0], config.prediction_length, transformer_inputs.shape[2]]			,			device=enc_input.device			,			)

            _lowerCAmelCase							     =							torch.cat(
                (
                    torch.cat((seasonal_input[:, -config.label_length :, ...], zeros)			,			dim=1  ),
                    feature[:, config.context_length - config.label_length :, ...],
                )			,			dim=-1			,			)
            _lowerCAmelCase							     =							torch.cat(
                (
                    torch.cat((trend_input[:, -config.label_length :, ...], mean)			,			dim=1  ),
                    feature[:, config.context_length - config.label_length :, ...],
                )			,			dim=-1			,			)

            with tempfile.TemporaryDirectory() as tmpdirname:
                  _lowerCAmelCase							     =							model.get_decoder()
                  decoder.save_pretrained(_a  )
                  _lowerCAmelCase							     =							AutoformerDecoder.from_pretrained(_a  ).to(_a  )

            _lowerCAmelCase							     =							decoder(
                trend=_a			,			inputs_embeds=_a			,			encoder_hidden_states=_a			,			)[0]

            self.parent.assertTrue((last_hidden_state_a - last_hidden_state).abs().max().item() < 1E-3  )




@require_torch
class  UpperCAmelCase     (			SCREAMING_SNAKE_CASE_							,		SCREAMING_SNAKE_CASE_							,		unittest.TestCase   ):
      _lowercase:			Union[str, Any]						     =   (AutoformerModel, AutoformerForPrediction) if is_torch_available() else ()
      _lowercase:			List[str]						     =   (AutoformerForPrediction,) if is_torch_available() else ()
      _lowercase:			Optional[Any]						     =   {'''feature-extraction''': AutoformerModel} if is_torch_available() else {}
      _lowercase:			Optional[int]						     =   False
      _lowercase:			Union[str, Any]						     =   False
      _lowercase:			Optional[int]						     =   False
      _lowercase:			List[str]						     =   False
      _lowercase:			List[Any]						     =   False
      _lowercase:			Dict						     =   False




      def 				lowercase__    (			self			: Tuple  )				->      Optional[int]:
            _lowerCAmelCase							     =							AutoformerModelTester(self  )
            _lowerCAmelCase							     =							ConfigTester(self			,			config_class=_a			,			has_text_modality=_a  )




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




      def 				lowercase__    (			self			: Tuple  )				->      Optional[Any]:
            _lowerCAmelCase							     =							self.model_tester.prepare_config_and_inputs()
            for model_class in self.all_model_classes:
                  _lowerCAmelCase							     =							model_class(_a  )

                  with tempfile.TemporaryDirectory() as tmpdirname:
                        model.save_pretrained(_a  )
                        _lowerCAmelCase							     =							model_class.from_pretrained(_a			,			output_loading_info=_a  )
                  self.assertEqual(info["""missing_keys"""]			,			[]  )




      def 				lowercase__    (			self			: Optional[Any]  )				->      Tuple:
            _lowerCAmelCase							     =							self.model_tester.prepare_config_and_inputs_for_common()
            self.model_tester.check_encoder_decoder_model_standalone(*_a  )




      @unittest.skip(reason="""Model has no tokens embeddings"""  )
      def 				lowercase__    (			self			: Optional[int]  )				->      Optional[int]:
            pass




      def 				lowercase__    (			self			: Optional[Any]  )				->      str:
            _lowerCAmelCase							     =							inspect.signature(getattr(_a			,			"""forward"""  )  )
            # The main input is the name of the argument after `self`
            _lowerCAmelCase							     =							list(model_signature.parameters.keys()  )[1]
            self.assertEqual(AutoformerModel.main_input_name			,			_a  )




      def 				lowercase__    (			self			: Any  )				->      int:
            _lowerCAmelCase							     =							self.model_tester.prepare_config_and_inputs_for_common()

            for model_class in self.all_model_classes:
                  _lowerCAmelCase							     =							model_class(_a  )
                  _lowerCAmelCase							     =							inspect.signature(model.forward  )
                  # signature.parameters is an OrderedDict => so arg_names order is deterministic
                  _lowerCAmelCase							     =							[*signature.parameters.keys()]

                  _lowerCAmelCase							     =							[
                      'past_values',
                      'past_time_features',
                      'past_observed_mask',
                      'static_categorical_features',
                      'static_real_features',
                      'future_values',
                      'future_time_features',
                  ]

                  if model.__class__.__name__ in ["AutoformerForPrediction"]:
                        expected_arg_names.append("""future_observed_mask"""  )

                  expected_arg_names.extend(
                      [
                          """decoder_attention_mask""",
                          """head_mask""",
                          """decoder_head_mask""",
                          """cross_attn_head_mask""",
                          """encoder_outputs""",
                          """past_key_values""",
                          """output_hidden_states""",
                          """output_attentions""",
                          """use_cache""",
                          """return_dict""",
                      ]  )

                  self.assertListEqual(arg_names[: len(_a  )]			,			_a  )




      def 				lowercase__    (			self			: Tuple  )				->      List[Any]:
            _lowerCAmelCase							     =							self.model_tester.prepare_config_and_inputs_for_common()
            _lowerCAmelCase							     =							True

            _lowerCAmelCase							     =							getattr(self.model_tester			,			"""seq_length"""			,			_a  )
            _lowerCAmelCase							     =							getattr(self.model_tester			,			"""decoder_seq_length"""			,			_a  )
            _lowerCAmelCase							     =							getattr(self.model_tester			,			"""encoder_seq_length"""			,			_a  )
            _lowerCAmelCase							     =							getattr(self.model_tester			,			"""d_model"""			,			_a  )
            _lowerCAmelCase							     =							getattr(self.model_tester			,			"""num_attention_heads"""			,			_a  )
            _lowerCAmelCase							     =							d_model // num_attention_heads

            for model_class in self.all_model_classes:
                  _lowerCAmelCase							     =							True
                  _lowerCAmelCase							     =							False
                  _lowerCAmelCase							     =							True
                  _lowerCAmelCase							     =							model_class(_a  )
                  model.to(_a  )
                  model.eval()
                  with torch.no_grad():
                        _lowerCAmelCase							     =							model(**self._prepare_for_class(_a			,			_a  )  )
                  _lowerCAmelCase							     =							outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
                  self.assertEqual(len(_a  )			,			self.model_tester.num_hidden_layers  )

                  # check that output_attentions also work using config
                  del inputs_dict["output_attentions"]
                  _lowerCAmelCase							     =							True
                  _lowerCAmelCase							     =							model_class(_a  )
                  model.to(_a  )
                  model.eval()
                  with torch.no_grad():
                        _lowerCAmelCase							     =							model(**self._prepare_for_class(_a			,			_a  )  )
                  _lowerCAmelCase							     =							outputs.encoder_attentions
                  self.assertEqual(len(_a  )			,			self.model_tester.num_hidden_layers  )

                  self.assertListEqual(
                      list(attentions[0].shape[-3:]  )			,			[self.model_tester.num_attention_heads, encoder_seq_length, dim]			,			)
                  _lowerCAmelCase							     =							len(_a  )

                  _lowerCAmelCase							     =							7

                  if "last_hidden_state" in outputs:
                        correct_outlen += 1

                  if "trend" in outputs:
                        correct_outlen += 1

                  if "past_key_values" in outputs:
                        correct_outlen += 1  # past_key_values have been returned

                  if "loss" in outputs:
                        correct_outlen += 1

                  if "params" in outputs:
                        correct_outlen += 1

                  self.assertEqual(_a			,			_a  )

                  # decoder attentions
                  _lowerCAmelCase							     =							outputs.decoder_attentions
                  self.assertIsInstance(_a			,			(list, tuple)  )
                  self.assertEqual(len(_a  )			,			self.model_tester.num_hidden_layers  )
                  self.assertListEqual(
                      list(decoder_attentions[0].shape[-3:]  )			,			[self.model_tester.num_attention_heads, decoder_seq_length, dim]			,			)

                  # cross attentions
                  _lowerCAmelCase							     =							outputs.cross_attentions
                  self.assertIsInstance(_a			,			(list, tuple)  )
                  self.assertEqual(len(_a  )			,			self.model_tester.num_hidden_layers  )
                  self.assertListEqual(
                      list(cross_attentions[0].shape[-3:]  )			,			[self.model_tester.num_attention_heads, decoder_seq_length, dim]			,			)

            # Check attention is always last and order is fine
            _lowerCAmelCase							     =							True
            _lowerCAmelCase							     =							True
            _lowerCAmelCase							     =							model_class(_a  )
            model.to(_a  )
            model.eval()
            with torch.no_grad():
                  _lowerCAmelCase							     =							model(**self._prepare_for_class(_a			,			_a  )  )

            self.assertEqual(out_len + 2			,			len(_a  )  )

            _lowerCAmelCase							     =							outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions

            self.assertEqual(len(_a  )			,			self.model_tester.num_hidden_layers  )
            self.assertListEqual(
                list(self_attentions[0].shape[-3:]  )			,			[self.model_tester.num_attention_heads, encoder_seq_length, dim]			,			)




      @is_flaky()
      def 				lowercase__    (			self			: str  )				->      Any:
            super().test_retain_grad_hidden_states_attentions()




def    UpperCamelCase__      (		lowerCAmelCase="train-batch.pt"  ):





      """simple docstring"""


      _lowerCAmelCase							     =							hf_hub_download(repo_id="""hf-internal-testing/tourism-monthly-batch"""							,	filename=_lowerCAmelCase							,	repo_type="""dataset"""  )
      _lowerCAmelCase							     =							torch.load(_lowerCAmelCase							,	map_location=_lowerCAmelCase  )
      return batch





@require_torch
@slow
class  UpperCAmelCase     (			unittest.TestCase   ):




      def 				lowercase__    (			self			: List[str]  )				->      str:
            _lowerCAmelCase							     =							AutoformerModel.from_pretrained("""huggingface/autoformer-tourism-monthly"""  ).to(_a  )
            _lowerCAmelCase							     =							prepare_batch()

            with torch.no_grad():
                  _lowerCAmelCase							     =							model(
                      past_values=batch["""past_values"""]			,			past_time_features=batch["""past_time_features"""]			,			past_observed_mask=batch["""past_observed_mask"""]			,			static_categorical_features=batch["""static_categorical_features"""]			,			future_values=batch["""future_values"""]			,			future_time_features=batch["""future_time_features"""]			,			)[0]

            _lowerCAmelCase							     =							torch.Size(
                (64, model.config.prediction_length + model.config.label_length, model.config.feature_size)  )
            self.assertEqual(output.shape			,			_a  )

            _lowerCAmelCase							     =							torch.tensor(
                [[0.35_93, -1.33_98, 0.63_30], [0.22_79, 1.53_96, -0.17_92], [0.04_50, 1.32_25, -0.23_35]]			,			device=_a  )
            self.assertTrue(torch.allclose(output[0, :3, :3]			,			_a			,			atol=_a  )  )




      def 				lowercase__    (			self			: int  )				->      Optional[int]:
            _lowerCAmelCase							     =							AutoformerForPrediction.from_pretrained("""huggingface/autoformer-tourism-monthly"""  ).to(_a  )
            _lowerCAmelCase							     =							prepare_batch("""val-batch.pt"""  )
            with torch.no_grad():
                  _lowerCAmelCase							     =							model(
                      past_values=batch["""past_values"""]			,			past_time_features=batch["""past_time_features"""]			,			past_observed_mask=batch["""past_observed_mask"""]			,			static_categorical_features=batch["""static_categorical_features"""]			,			).encoder_last_hidden_state
            _lowerCAmelCase							     =							torch.Size((64, model.config.context_length, model.config.d_model)  )
            self.assertEqual(output.shape			,			_a  )

            _lowerCAmelCase							     =							torch.tensor(
                [[-0.07_34, -0.90_36, 0.83_58], [4.71_86, 2.41_13, 1.95_81], [1.79_53, 2.35_58, 1.29_70]]			,			device=_a  )
            self.assertTrue(torch.allclose(output[0, :3, :3]			,			_a			,			atol=_a  )  )




      def 				lowercase__    (			self			: Optional[Any]  )				->      List[Any]:
            _lowerCAmelCase							     =							AutoformerForPrediction.from_pretrained("""huggingface/autoformer-tourism-monthly"""  ).to(_a  )
            _lowerCAmelCase							     =							prepare_batch("""val-batch.pt"""  )
            with torch.no_grad():
                  _lowerCAmelCase							     =							model.generate(
                      static_categorical_features=batch["""static_categorical_features"""]			,			past_time_features=batch["""past_time_features"""]			,			past_values=batch["""past_values"""]			,			future_time_features=batch["""future_time_features"""]			,			past_observed_mask=batch["""past_observed_mask"""]			,			)
            _lowerCAmelCase							     =							torch.Size((64, model.config.num_parallel_samples, model.config.prediction_length)  )
            self.assertEqual(outputs.sequences.shape			,			_a  )

            _lowerCAmelCase							     =							torch.tensor([31_30.67_63, 40_56.52_93, 70_53.07_86]			,			device=_a  )
            _lowerCAmelCase							     =							outputs.sequences.mean(dim=1  )
            self.assertTrue(torch.allclose(mean_prediction[0, -3:]			,			_a			,			rtol=1E-1  )  )











'''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)






