
"""simple docstring"""





from __future__ import annotations

import os
import tempfile
import unittest

from transformers import ConvBertConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
    import tensorflow as tf

    from transformers import (
        TFConvBertForMaskedLM,
        TFConvBertForMultipleChoice,
        TFConvBertForQuestionAnswering,
        TFConvBertForSequenceClassification,
        TFConvBertForTokenClassification,
        TFConvBertModel,
    )


class A_	:




  '''simple docstring'''







  def __init__(		self				,   lowercase_				,   lowercase_=13				,   lowercase_=7				,   lowercase_=True				,   lowercase_=True				,   lowercase_=True				,   lowercase_=True				,   lowercase_=99				,   lowercase_=32				,   lowercase_=2				,   lowercase_=4				,   lowercase_=37				,   lowercase_="gelu"				,   lowercase_=0.1				,   lowercase_=0.1				,   lowercase_=512				,   lowercase_=16				,   lowercase_=2				,   lowercase_=0.02				,   lowercase_=3				,   lowercase_=4				,   lowercase_=None				,   ):

     """simple docstring"""
     UpperCAmelCase_					:					List[Any]			     =    parent
     UpperCAmelCase_					:					Dict			     =    13
     UpperCAmelCase_					:					List[str]			     =    7
     UpperCAmelCase_					:					Dict			     =    True
     UpperCAmelCase_					:					Optional[Any]			     =    True
     UpperCAmelCase_					:					Union[str, Any]			     =    True
     UpperCAmelCase_					:					Dict			     =    True
     UpperCAmelCase_					:					Union[str, Any]			     =    99
     UpperCAmelCase_					:					Tuple			     =    384
     UpperCAmelCase_					:					Optional[Any]			     =    2
     UpperCAmelCase_					:					Any			     =    4
     UpperCAmelCase_					:					int			     =    37
     UpperCAmelCase_					:					Dict			     =    "gelu"
     UpperCAmelCase_					:					Optional[int]			     =    0.1
     UpperCAmelCase_					:					Tuple			     =    0.1
     UpperCAmelCase_					:					Optional[Any]			     =    512
     UpperCAmelCase_					:					Dict			     =    16
     UpperCAmelCase_					:					int			     =    2
     UpperCAmelCase_					:					Union[str, Any]			     =    0.02
     UpperCAmelCase_					:					int			     =    3
     UpperCAmelCase_					:					Optional[Any]			     =    4
     UpperCAmelCase_					:					Any			     =    128
     UpperCAmelCase_					:					List[str]			     =    2
     UpperCAmelCase_					:					List[Any]			     =    9
     UpperCAmelCase_					:					List[Any]			     =    1
     UpperCAmelCase_					:					List[Any]			     =    None







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					Any			     =    ids_tensor([self.batch_size, self.seq_length]				,   self.vocab_size  )

     UpperCAmelCase_					:					List[str]			     =    None
     if self.use_input_mask:
        UpperCAmelCase_					:					Tuple			     =    random_attention_mask([self.batch_size, self.seq_length]  )

     UpperCAmelCase_					:					List[Any]			     =    None
     if self.use_token_type_ids:
        UpperCAmelCase_					:					List[str]			     =    ids_tensor([self.batch_size, self.seq_length]				,   self.type_vocab_size  )

     UpperCAmelCase_					:					Optional[Any]			     =    None
     UpperCAmelCase_					:					Dict			     =    None
     UpperCAmelCase_					:					Tuple			     =    None
     if self.use_labels:
        UpperCAmelCase_					:					List[str]			     =    ids_tensor([self.batch_size]				,   self.type_sequence_label_size  )
        UpperCAmelCase_					:					List[str]			     =    ids_tensor([self.batch_size, self.seq_length]				,   self.num_labels  )
        UpperCAmelCase_					:					Optional[Any]			     =    ids_tensor([self.batch_size]				,   self.num_choices  )

     UpperCAmelCase_					:					str			     =    ConvBertConfig(
         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				,   initializer_range=self.initializer_range				,   return_dict=lowercase_				,   )

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







  def        UpperCamelCase__					(		self				,   lowercase_				,   lowercase_				,   lowercase_				,   lowercase_				,   lowercase_				,   lowercase_				,   lowercase_  ):

     """simple docstring"""
     UpperCAmelCase_					:					Optional[int]			     =    TFConvBertModel(config=lowercase_  )
     UpperCAmelCase_					:					int			     =    {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}

     UpperCAmelCase_					:					List[str]			     =    [input_ids, input_mask]
     UpperCAmelCase_					:					Union[str, Any]			     =    model(lowercase_  )

     UpperCAmelCase_					:					str			     =    model(lowercase_  )

     self.parent.assertEqual(result.last_hidden_state.shape				,   (self.batch_size, self.seq_length, self.hidden_size)  )







  def        UpperCamelCase__					(		self				,   lowercase_				,   lowercase_				,   lowercase_				,   lowercase_				,   lowercase_				,   lowercase_				,   lowercase_  ):

     """simple docstring"""
     UpperCAmelCase_					:					Optional[Any]			     =    TFConvBertForMaskedLM(config=lowercase_  )
     UpperCAmelCase_					:					Tuple			     =    {
         "input_ids": input_ids,
         "attention_mask": input_mask,
         "token_type_ids": token_type_ids,
     }
     UpperCAmelCase_					:					Tuple			     =    model(lowercase_  )
     self.parent.assertEqual(result.logits.shape				,   (self.batch_size, self.seq_length, self.vocab_size)  )







  def        UpperCamelCase__					(		self				,   lowercase_				,   lowercase_				,   lowercase_				,   lowercase_				,   lowercase_				,   lowercase_				,   lowercase_  ):

     """simple docstring"""
     UpperCAmelCase_					:					List[Any]			     =    self.num_labels
     UpperCAmelCase_					:					List[str]			     =    TFConvBertForSequenceClassification(config=lowercase_  )
     UpperCAmelCase_					:					List[str]			     =    {
         "input_ids": input_ids,
         "attention_mask": input_mask,
         "token_type_ids": token_type_ids,
     }

     UpperCAmelCase_					:					List[Any]			     =    model(lowercase_  )
     self.parent.assertEqual(result.logits.shape				,   (self.batch_size, self.num_labels)  )







  def        UpperCamelCase__					(		self				,   lowercase_				,   lowercase_				,   lowercase_				,   lowercase_				,   lowercase_				,   lowercase_				,   lowercase_  ):

     """simple docstring"""
     UpperCAmelCase_					:					str			     =    self.num_choices
     UpperCAmelCase_					:					Tuple			     =    TFConvBertForMultipleChoice(config=lowercase_  )
     UpperCAmelCase_					:					str			     =    tf.tile(tf.expand_dims(lowercase_				,   1  )				,   (1, self.num_choices, 1)  )
     UpperCAmelCase_					:					Tuple			     =    tf.tile(tf.expand_dims(lowercase_				,   1  )				,   (1, self.num_choices, 1)  )
     UpperCAmelCase_					:					Optional[Any]			     =    tf.tile(tf.expand_dims(lowercase_				,   1  )				,   (1, self.num_choices, 1)  )
     UpperCAmelCase_					:					Any			     =    {
         "input_ids": multiple_choice_inputs_ids,
         "attention_mask": multiple_choice_input_mask,
         "token_type_ids": multiple_choice_token_type_ids,
     }
     UpperCAmelCase_					:					Union[str, Any]			     =    model(lowercase_  )
     self.parent.assertEqual(result.logits.shape				,   (self.batch_size, self.num_choices)  )







  def        UpperCamelCase__					(		self				,   lowercase_				,   lowercase_				,   lowercase_				,   lowercase_				,   lowercase_				,   lowercase_				,   lowercase_  ):

     """simple docstring"""
     UpperCAmelCase_					:					Dict			     =    self.num_labels
     UpperCAmelCase_					:					int			     =    TFConvBertForTokenClassification(config=lowercase_  )
     UpperCAmelCase_					:					Optional[Any]			     =    {
         "input_ids": input_ids,
         "attention_mask": input_mask,
         "token_type_ids": token_type_ids,
     }
     UpperCAmelCase_					:					Any			     =    model(lowercase_  )
     self.parent.assertEqual(result.logits.shape				,   (self.batch_size, self.seq_length, self.num_labels)  )







  def        UpperCamelCase__					(		self				,   lowercase_				,   lowercase_				,   lowercase_				,   lowercase_				,   lowercase_				,   lowercase_				,   lowercase_  ):

     """simple docstring"""
     UpperCAmelCase_					:					Tuple			     =    TFConvBertForQuestionAnswering(config=lowercase_  )
     UpperCAmelCase_					:					str			     =    {
         "input_ids": input_ids,
         "attention_mask": input_mask,
         "token_type_ids": token_type_ids,
     }

     UpperCAmelCase_					:					Optional[int]			     =    model(lowercase_  )
     self.parent.assertEqual(result.start_logits.shape				,   (self.batch_size, self.seq_length)  )
     self.parent.assertEqual(result.end_logits.shape				,   (self.batch_size, self.seq_length)  )







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					Dict			     =    self.prepare_config_and_inputs()
     (
         (
         UpperCAmelCase_
     )     ,   (
         UpperCAmelCase_
     )     ,   (
         UpperCAmelCase_
     )     ,   (
         UpperCAmelCase_
     )     ,   (
         UpperCAmelCase_
     )     ,   (
         UpperCAmelCase_
     )     ,   (
         UpperCAmelCase_
     )     ,   
     )					:					Optional[int]			     =    config_and_inputs
     UpperCAmelCase_					:					Tuple			     =    {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
     return config, inputs_dict




@require_tf
class A_	(lowercase__				,lowercase__				,unittest.TestCase      ):




  '''simple docstring'''
  SCREAMING_SNAKE_CASE__			:						Union[str, Any]       =		(
      (
          TFConvBertModel,
          TFConvBertForMaskedLM,
          TFConvBertForQuestionAnswering,
          TFConvBertForSequenceClassification,
          TFConvBertForTokenClassification,
          TFConvBertForMultipleChoice,
      )
      if is_tf_available()
      else ()
  )
  SCREAMING_SNAKE_CASE__			:						Optional[int]       =		(
      {
          """feature-extraction""": TFConvBertModel,
          """fill-mask""": TFConvBertForMaskedLM,
          """question-answering""": TFConvBertForQuestionAnswering,
          """text-classification""": TFConvBertForSequenceClassification,
          """token-classification""": TFConvBertForTokenClassification,
          """zero-shot""": TFConvBertForSequenceClassification,
      }
      if is_tf_available()
      else {}
  )
  SCREAMING_SNAKE_CASE__			:						Optional[int]       =		False
  SCREAMING_SNAKE_CASE__			:						List[str]       =		False
  SCREAMING_SNAKE_CASE__			:						Dict       =		False







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					str			     =    TFConvBertModelTester(self  )
     UpperCAmelCase_					:					int			     =    ConfigTester(self				,   config_class=lowercase_				,   hidden_size=37  )







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     self.config_tester.run_common_tests()







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					Union[str, Any]			     =    self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*lowercase_  )







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					Union[str, Any]			     =    self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_masked_lm(*lowercase_  )







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					Tuple			     =    self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_multiple_choice(*lowercase_  )







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					Tuple			     =    self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_question_answering(*lowercase_  )







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					Optional[int]			     =    self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_sequence_classification(*lowercase_  )







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					Tuple			     =    self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_token_classification(*lowercase_  )







  @slow
  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_     ,   UpperCAmelCase_					:					Optional[Any]			     =    self.model_tester.prepare_config_and_inputs_for_common()
     UpperCAmelCase_					:					Optional[int]			     =    True
     UpperCAmelCase_					:					Tuple			     =    True

     if hasattr(lowercase_				,   "use_cache"  ):
        UpperCAmelCase_					:					Optional[int]			     =    True

     UpperCAmelCase_					:					Any			     =    getattr(self.model_tester				,   "encoder_seq_length"				,   self.model_tester.seq_length  )
     UpperCAmelCase_					:					List[Any]			     =    getattr(self.model_tester				,   "key_length"				,   lowercase_  )

     for model_class in self.all_model_classes:
        UpperCAmelCase_					:					Dict			     =    self._prepare_for_class(lowercase_				,   lowercase_  )
        UpperCAmelCase_					:					Dict			     =    model_class(lowercase_  )
        UpperCAmelCase_					:					Dict			     =    len(model(lowercase_  )  )

        with tempfile.TemporaryDirectory() as tmpdirname:
           model.save_pretrained(lowercase_				,   saved_model=lowercase_  )
           UpperCAmelCase_					:					Union[str, Any]			     =    os.path.join(lowercase_				,   "saved_model"				,   "1"  )
           UpperCAmelCase_					:					str			     =    tf.keras.models.load_model(lowercase_  )
           UpperCAmelCase_					:					Optional[int]			     =    model(lowercase_  )

           if self.is_encoder_decoder:
              UpperCAmelCase_					:					List[Any]			     =    outputs["encoder_hidden_states"]
              UpperCAmelCase_					:					Union[str, Any]			     =    outputs["encoder_attentions"]
           else:
              UpperCAmelCase_					:					List[Any]			     =    outputs["hidden_states"]
              UpperCAmelCase_					:					int			     =    outputs["attentions"]

           self.assertEqual(len(lowercase_  )				,   lowercase_  )

           UpperCAmelCase_					:					str			     =    getattr(
               self.model_tester				,   "expected_num_hidden_layers"				,   self.model_tester.num_hidden_layers + 1  )

           self.assertEqual(len(lowercase_  )				,   lowercase_  )
           self.assertListEqual(
               list(output_hidden_states[0].shape[-2:]  )				,   [self.model_tester.seq_length, self.model_tester.hidden_size]				,   )

           self.assertEqual(len(lowercase_  )				,   self.model_tester.num_hidden_layers  )
           self.assertListEqual(
               list(output_attentions[0].shape[-3:]  )				,   [self.model_tester.num_attention_heads / 2, encoder_seq_length, encoder_key_length]				,   )







  @slow
  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					Tuple			     =    TFConvBertModel.from_pretrained("YituTech/conv-bert-base"  )
     self.assertIsNotNone(lowercase_  )







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_     ,   UpperCAmelCase_					:					str			     =    self.model_tester.prepare_config_and_inputs_for_common()
     UpperCAmelCase_					:					Any			     =    True
     UpperCAmelCase_					:					List[Any]			     =    getattr(self.model_tester				,   "decoder_seq_length"				,   self.model_tester.seq_length  )
     UpperCAmelCase_					:					Tuple			     =    getattr(self.model_tester				,   "encoder_seq_length"				,   self.model_tester.seq_length  )
     UpperCAmelCase_					:					Optional[Any]			     =    getattr(self.model_tester				,   "key_length"				,   lowercase_  )
     UpperCAmelCase_					:					List[Any]			     =    getattr(self.model_tester				,   "key_length"				,   lowercase_  )

     def check_decoder_attentions_output(lowercase_  ):
        UpperCAmelCase_					:					Tuple			     =    len(lowercase_  )
        self.assertEqual(out_len % 2				,   0  )
        UpperCAmelCase_					:					Any			     =    outputs.decoder_attentions
        self.assertEqual(len(lowercase_  )				,   self.model_tester.num_hidden_layers  )
        self.assertListEqual(
            list(decoder_attentions[0].shape[-3:]  )				,   [self.model_tester.num_attention_heads / 2, decoder_seq_length, decoder_key_length]				,   )

     def check_encoder_attentions_output(lowercase_  ):
        UpperCAmelCase_					:					Union[str, Any]			     =    [
            t.numpy() for t in (outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions)
        ]
        self.assertEqual(len(lowercase_  )				,   self.model_tester.num_hidden_layers  )
        self.assertListEqual(
            list(attentions[0].shape[-3:]  )				,   [self.model_tester.num_attention_heads / 2, encoder_seq_length, encoder_key_length]				,   )

     for model_class in self.all_model_classes:
        UpperCAmelCase_					:					str			     =    True
        UpperCAmelCase_					:					Any			     =    False
        UpperCAmelCase_					:					List[str]			     =    model_class(lowercase_  )
        UpperCAmelCase_					:					int			     =    model(self._prepare_for_class(lowercase_				,   lowercase_  )  )
        UpperCAmelCase_					:					Any			     =    len(lowercase_  )
        self.assertEqual(config.output_hidden_states				,   lowercase_  )
        check_encoder_attentions_output(lowercase_  )

        if self.is_encoder_decoder:
           UpperCAmelCase_					:					List[Any]			     =    model_class(lowercase_  )
           UpperCAmelCase_					:					Optional[Any]			     =    model(self._prepare_for_class(lowercase_				,   lowercase_  )  )
           self.assertEqual(config.output_hidden_states				,   lowercase_  )
           check_decoder_attentions_output(lowercase_  )

        # Check that output attentions can also be changed via the config
        del inputs_dict["output_attentions"]
        UpperCAmelCase_					:					Tuple			     =    True
        UpperCAmelCase_					:					List[Any]			     =    model_class(lowercase_  )
        UpperCAmelCase_					:					Optional[Any]			     =    model(self._prepare_for_class(lowercase_				,   lowercase_  )  )
        self.assertEqual(config.output_hidden_states				,   lowercase_  )
        check_encoder_attentions_output(lowercase_  )

        # Check attention is always last and order is fine
        UpperCAmelCase_					:					Union[str, Any]			     =    True
        UpperCAmelCase_					:					List[Any]			     =    True
        UpperCAmelCase_					:					Optional[Any]			     =    model_class(lowercase_  )
        UpperCAmelCase_					:					List[str]			     =    model(self._prepare_for_class(lowercase_				,   lowercase_  )  )

        self.assertEqual(out_len + (2 if self.is_encoder_decoder else 1)				,   len(lowercase_  )  )
        self.assertEqual(model.config.output_hidden_states				,   lowercase_  )
        check_encoder_attentions_output(lowercase_  )




@require_tf
class A_	(unittest.TestCase      ):




  '''simple docstring'''







  @slow
  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					Union[str, Any]			     =    TFConvBertModel.from_pretrained("YituTech/conv-bert-base"  )
     UpperCAmelCase_					:					Tuple			     =    tf.constant([[0, 1, 2, 3, 4, 5]]  )
     UpperCAmelCase_					:					str			     =    model(lowercase_  )[0]

     UpperCAmelCase_					:					Any			     =    [1, 6, 768]
     self.assertEqual(output.shape				,   lowercase_  )

     UpperCAmelCase_					:					str			     =    tf.constant(
         [
             [
                 [-0.03_47_54_93, -0.4_68_60_34, -0.30_63_88_32],
                 [0.22_63_72_48, -0.26_98_86_46, -0.7_42_34_24],
                 [0.10_32_48_68, -0.45_01_35_08, -0.58_28_07_84],
             ]
         ]  )
     tf.debugging.assert_near(output[:, :3, :3]				,   lowercase_				,   atol=1E-4  )







'''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)






