



'''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


_UpperCamelCase											=						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 					lowerCamelCase_    :
  """simple docstring"""




  def __init__( self      :	List[Any]			,	_a      :	Optional[int]			,	_a      :	Optional[int]=16			,	_a      :	Dict=13			,	_a      :	Any=7			,	_a      :	List[str]=14			,	_a      :	Tuple=10			,	_a      :	Dict=19			,	_a      :	Union[str, Any]=5			,	_a      :	Tuple=4			,	_a      :	Dict=True			,	_a      :	Optional[int]=16			,	_a      :	Optional[Any]=2			,	_a      :	Union[str, Any]=4			,	_a      :	Union[str, Any]=4			,	_a      :	List[Any]="gelu"			,	_a      :	List[str]=0.1			,	_a      :	Union[str, Any]=0.1			,	_a      :	Tuple=[1, 2, 3, 4, 5]			,	_a      :	Tuple=25			,	_a      :	List[str]=5			,	)					->					List[str]:
    __lowerCamelCase       :       str           = d_model
    __lowerCamelCase       :       int           = parent
    __lowerCamelCase       :       List[Any]           = batch_size
    __lowerCamelCase       :       int           = prediction_length
    __lowerCamelCase       :       List[str]           = context_length
    __lowerCamelCase       :       Optional[int]           = cardinality
    __lowerCamelCase       :       str           = num_time_features
    __lowerCamelCase       :       List[str]           = lags_sequence
    __lowerCamelCase       :       Dict           = embedding_dimension
    __lowerCamelCase       :       Dict           = is_training
    __lowerCamelCase       :       int           = hidden_size
    __lowerCamelCase       :       str           = num_hidden_layers
    __lowerCamelCase       :       Union[str, Any]           = num_attention_heads
    __lowerCamelCase       :       str           = intermediate_size
    __lowerCamelCase       :       Dict           = hidden_act
    __lowerCamelCase       :       Optional[int]           = hidden_dropout_prob
    __lowerCamelCase       :       Dict           = attention_probs_dropout_prob

    __lowerCamelCase       :       str           = context_length
    __lowerCamelCase       :       Union[str, Any]           = prediction_length + label_length
    __lowerCamelCase       :       Optional[Any]           = label_length

    __lowerCamelCase       :       List[Any]           = moving_average
    __lowerCamelCase       :       Any           = 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			,	_a      :	str   )					->					List[Any]:
    __lowerCamelCase       :       Tuple           = config.context_length + max(config.lags_sequence   )

    __lowerCamelCase       :       Union[str, Any]           = ids_tensor([self.batch_size, 1]			,	config.cardinality[0]   )
    __lowerCamelCase       :       List[Any]           = floats_tensor([self.batch_size, _past_length, config.num_time_features]   )
    __lowerCamelCase       :       Union[str, Any]           = floats_tensor([self.batch_size, _past_length]   )
    __lowerCamelCase       :       Optional[Any]           = floats_tensor([self.batch_size, _past_length]   ) > 0.5

    # decoder inputs
    __lowerCamelCase       :       List[str]           = floats_tensor([self.batch_size, config.prediction_length, config.num_time_features]   )
    __lowerCamelCase       :       List[Any]           = floats_tensor([self.batch_size, config.prediction_length]   )

    __lowerCamelCase       :       Dict           = {
        '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       :       Optional[Any]           = self.get_config()
    __lowerCamelCase       :       Tuple           = self.prepare_autoformer_inputs_dict(_a   )
    return config, inputs_dict




  def 						_lowercase     ( self      :	Dict   )					->					Any:
    __lowerCamelCase       ,__lowerCamelCase       :       Union[str, Any]           = self.prepare_config_and_inputs()
    return config, inputs_dict




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

    __lowerCamelCase       :       Union[str, Any]           = outputs.encoder_last_hidden_state
    __lowerCamelCase       :       List[str]           = outputs.last_hidden_state

    with tempfile.TemporaryDirectory() as tmpdirname:
      __lowerCamelCase       :       Union[str, Any]           = model.get_encoder()
      encoder.save_pretrained(_a   )
      __lowerCamelCase       :       int           = AutoformerEncoder.from_pretrained(_a   ).to(_a   )

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

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

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

    __lowerCamelCase       :       Optional[int]           = torch.cat(
        (
            torch.cat((seasonal_input[:, -config.label_length :, ...], zeros)			,	dim=1   ),
            feature[:, config.context_length - config.label_length :, ...],
        )			,	dim=-1			,	)
    __lowerCamelCase       :       Tuple           = 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       :       Tuple           = model.get_decoder()
      decoder.save_pretrained(_a   )
      __lowerCamelCase       :       Union[str, Any]           = AutoformerDecoder.from_pretrained(_a   ).to(_a   )

    __lowerCamelCase       :       Optional[Any]           = 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 					lowerCamelCase_    (				SCREAMING_SNAKE_CASE_   ,   SCREAMING_SNAKE_CASE_   ,   unittest.TestCase     ):
  """simple docstring"""




  a_												=(AutoformerModel, AutoformerForPrediction) if is_torch_available() else ()
  a_												=(AutoformerForPrediction,) if is_torch_available() else ()
  a_												={"""feature-extraction""": AutoformerModel} if is_torch_available() else {}
  a_												=False
  a_												=False
  a_												=False
  a_												=False
  a_												=False
  a_												=False




  def 						_lowercase     ( self      :	Tuple   )					->					Optional[int]:
    __lowerCamelCase       :       List[str]           = AutoformerModelTester(self   )
    __lowerCamelCase       :       int           = 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       ,__lowerCamelCase       :       Union[str, Any]           = self.model_tester.prepare_config_and_inputs()
    for model_class in self.all_model_classes:
      __lowerCamelCase       :       List[Any]           = model_class(_a   )

      with tempfile.TemporaryDirectory() as tmpdirname:
        model.save_pretrained(_a   )
        __lowerCamelCase       ,__lowerCamelCase       :       Optional[int]           = model_class.from_pretrained(_a			,	output_loading_info=_a   )
      self.assertEqual(info['missing_keys']			,	[]   )




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




  def 						_lowercase     ( self      :	Any   )					->					int:
    __lowerCamelCase       ,__lowerCamelCase       :       Any           = self.model_tester.prepare_config_and_inputs_for_common()

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

      __lowerCamelCase       :       Any           = [
          '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       ,__lowerCamelCase       :       Optional[Any]           = self.model_tester.prepare_config_and_inputs_for_common()
    __lowerCamelCase       :       int           = True

    __lowerCamelCase       :       str           = getattr(self.model_tester			,	'seq_length'			,	_a   )
    __lowerCamelCase       :       Union[str, Any]           = getattr(self.model_tester			,	'decoder_seq_length'			,	_a   )
    __lowerCamelCase       :       Optional[int]           = getattr(self.model_tester			,	'encoder_seq_length'			,	_a   )
    __lowerCamelCase       :       str           = getattr(self.model_tester			,	'd_model'			,	_a   )
    __lowerCamelCase       :       Union[str, Any]           = getattr(self.model_tester			,	'num_attention_heads'			,	_a   )
    __lowerCamelCase       :       Dict           = d_model // num_attention_heads

    for model_class in self.all_model_classes:
      __lowerCamelCase       :       Tuple           = True
      __lowerCamelCase       :       List[str]           = False
      __lowerCamelCase       :       Optional[Any]           = True
      __lowerCamelCase       :       Optional[Any]           = model_class(_a   )
      model.to(_a   )
      model.eval()
      with torch.no_grad():
        __lowerCamelCase       :       Dict           = model(**self._prepare_for_class(_a			,	_a   )   )
      __lowerCamelCase       :       Tuple           = 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       :       int           = True
      __lowerCamelCase       :       Any           = model_class(_a   )
      model.to(_a   )
      model.eval()
      with torch.no_grad():
        __lowerCamelCase       :       Tuple           = model(**self._prepare_for_class(_a			,	_a   )   )
      __lowerCamelCase       :       Optional[int]           = 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       :       Optional[Any]           = len(_a   )

      __lowerCamelCase       :       List[str]           = 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       :       Union[str, Any]           = 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       :       Dict           = 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       :       Any           = True
    __lowerCamelCase       :       Dict           = True
    __lowerCamelCase       :       Tuple           = model_class(_a   )
    model.to(_a   )
    model.eval()
    with torch.no_grad():
      __lowerCamelCase       :       Dict           = model(**self._prepare_for_class(_a			,	_a   )   )

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

    __lowerCamelCase       :       Tuple           = 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        a_     (							_lowerCAmelCase="train-batch.pt"       )      ->      str:
  __lowerCamelCase       :       Dict           = hf_hub_download(repo_id='hf-internal-testing/tourism-monthly-batch'       ,filename=_lowerCAmelCase       ,repo_type='dataset'       )
  __lowerCamelCase       :       List[Any]           = torch.load(_lowerCAmelCase       ,map_location=_lowerCAmelCase       )
  return batch






@require_torch
@slow
class 					lowerCamelCase_    (				unittest.TestCase     ):
  """simple docstring"""




  def 						_lowercase     ( self      :	List[str]   )					->					str:
    __lowerCamelCase       :       int           = AutoformerModel.from_pretrained('huggingface/autoformer-tourism-monthly'   ).to(_a   )
    __lowerCamelCase       :       Optional[Any]           = prepare_batch()

    with torch.no_grad():
      __lowerCamelCase       :       List[str]           = 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       :       Optional[Any]           = torch.Size(
        (64, model.config.prediction_length + model.config.label_length, model.config.feature_size)   )
    self.assertEqual(output.shape			,	_a   )

    __lowerCamelCase       :       Optional[int]           = torch.tensor(
        [[0.3593, -1.3398, 0.6330], [0.2279, 1.5396, -0.1792], [0.0450, 1.3225, -0.2335]]			,	device=_a   )
    self.assertTrue(torch.allclose(output[0, :3, :3]			,	_a			,	atol=_a   )   )




  def 						_lowercase     ( self      :	int   )					->					Optional[int]:
    __lowerCamelCase       :       int           = AutoformerForPrediction.from_pretrained('huggingface/autoformer-tourism-monthly'   ).to(_a   )
    __lowerCamelCase       :       Optional[int]           = prepare_batch('val-batch.pt'   )
    with torch.no_grad():
      __lowerCamelCase       :       Dict           = 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       :       List[str]           = torch.Size((64, model.config.context_length, model.config.d_model)   )
    self.assertEqual(output.shape			,	_a   )

    __lowerCamelCase       :       List[Any]           = torch.tensor(
        [[-0.0734, -0.9036, 0.8358], [4.7186, 2.4113, 1.9581], [1.7953, 2.3558, 1.2970]]			,	device=_a   )
    self.assertTrue(torch.allclose(output[0, :3, :3]			,	_a			,	atol=_a   )   )




  def 						_lowercase     ( self      :	Optional[Any]   )					->					List[Any]:
    __lowerCamelCase       :       str           = AutoformerForPrediction.from_pretrained('huggingface/autoformer-tourism-monthly'   ).to(_a   )
    __lowerCamelCase       :       str           = prepare_batch('val-batch.pt'   )
    with torch.no_grad():
      __lowerCamelCase       :       List[Any]           = 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       :       List[str]           = torch.Size((64, model.config.num_parallel_samples, model.config.prediction_length)   )
    self.assertEqual(outputs.sequences.shape			,	_a   )

    __lowerCamelCase       :       int           = torch.tensor([3130.6763, 4056.5293, 7053.0786]			,	device=_a   )
    __lowerCamelCase       :       List[str]           = 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)






