



"""simple docstring"""






import unittest

import numpy as np
import timeout_decorator  # noqa

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

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


if is_flax_available():
   import os

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

   import jax
   import jax.numpy as jnp

   from transformers import BlenderbotTokenizer
   from transformers.models.blenderbot.modeling_flax_blenderbot import (
       FlaxBlenderbotForConditionalGeneration,
       FlaxBlenderbotModel,
       shift_tokens_right,
   )






def 	snake_case_  (				A_ :		Tuple,    A_ :		List[Any],    A_ :		Dict=None,    A_ :		int=None,    A_ :		Any=None,    A_ :		List[Any]=None,    A_ :		Optional[int]=None,    A_ :		int=None,    ):


       '''simple docstring'''





       if attention_mask is None:
              _lowerCamelCase						:    Optional[int]         =			np.where(input_ids != config.pad_token_id,    1,    0						)
       if decoder_attention_mask is None:
              _lowerCamelCase						:    str         =			np.where(decoder_input_ids != config.pad_token_id,    1,    0						)
       if head_mask is None:
              _lowerCamelCase						:    str         =			np.ones((config.encoder_layers, config.encoder_attention_heads)						)
       if decoder_head_mask is None:
              _lowerCamelCase						:    str         =			np.ones((config.decoder_layers, config.decoder_attention_heads)						)
       if cross_attn_head_mask is None:
              _lowerCamelCase						:    List[Any]         =			np.ones((config.decoder_layers, config.decoder_attention_heads)						)
       return {
           "input_ids": input_ids,
           "decoder_input_ids": decoder_input_ids,
           "attention_mask": attention_mask,
           "decoder_attention_mask": attention_mask,
       }






class 	__snake_case	:




  def __init__(			self					:						Optional[int]						, __lowerCAmelCase					:						List[Any]						, __lowerCAmelCase					:						List[Any]=1_3						, __lowerCAmelCase					:						Dict=7						, __lowerCAmelCase					:						Any=True						, __lowerCAmelCase					:						Optional[Any]=False						, __lowerCAmelCase					:						Optional[Any]=9_9						, __lowerCAmelCase					:						Dict=1_6						, __lowerCAmelCase					:						Optional[int]=2						, __lowerCAmelCase					:						Tuple=4						, __lowerCAmelCase					:						List[str]=4						, __lowerCAmelCase					:						str="gelu"						, __lowerCAmelCase					:						Tuple=0.1						, __lowerCAmelCase					:						List[Any]=0.1						, __lowerCAmelCase					:						str=3_2						, __lowerCAmelCase					:						Any=2						, __lowerCAmelCase					:						Optional[Any]=1						, __lowerCAmelCase					:						List[str]=0						, __lowerCAmelCase					:						List[str]=0.02						, ):






         """simple docstring"""






         _lowerCamelCase						:    List[str]         =			parent
         _lowerCamelCase						:    Optional[int]         =			batch_size
         _lowerCamelCase						:    Any         =			seq_length
         _lowerCamelCase						:    List[Any]         =			is_training
         _lowerCamelCase						:    Optional[Any]         =			use_labels
         _lowerCamelCase						:    int         =			vocab_size
         _lowerCamelCase						:    Tuple         =			hidden_size
         _lowerCamelCase						:    List[str]         =			num_hidden_layers
         _lowerCamelCase						:    str         =			num_attention_heads
         _lowerCamelCase						:    List[str]         =			intermediate_size
         _lowerCamelCase						:    Optional[int]         =			hidden_act
         _lowerCamelCase						:    str         =			hidden_dropout_prob
         _lowerCamelCase						:    Tuple         =			attention_probs_dropout_prob
         _lowerCamelCase						:    List[str]         =			max_position_embeddings
         _lowerCamelCase						:    Tuple         =			eos_token_id
         _lowerCamelCase						:    Union[str, Any]         =			pad_token_id
         _lowerCamelCase						:    Union[str, Any]         =			bos_token_id
         _lowerCamelCase						:    Dict         =			initializer_range




  def 						SCREAMING_SNAKE_CASE    (			self					:						List[str]		):






         """simple docstring"""






         _lowerCamelCase						:    Union[str, Any]         =			np.clip(ids_tensor([self.batch_size, self.seq_length - 1]						, self.vocab_size		)						, 3						, self.vocab_size		)
         _lowerCamelCase						:    Tuple         =			np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)						, dtype=np.intaa		))						, -1		)

         _lowerCamelCase						:    int         =			shift_tokens_right(__lowerCAmelCase						, 1						, 2		)

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




  def 						SCREAMING_SNAKE_CASE    (			self					:						Dict		):






         """simple docstring"""






         _lowerCamelCase						:    Union[str, Any]         =			self.prepare_config_and_inputs()
         return config, inputs_dict




  def 						SCREAMING_SNAKE_CASE    (			self					:						str						, __lowerCAmelCase					:						Optional[Any]						, __lowerCAmelCase					:						Tuple						, __lowerCAmelCase					:						List[str]		):






         """simple docstring"""






         _lowerCamelCase						:    List[str]         =			2_0
         _lowerCamelCase						:    List[str]         =			model_class_name(__lowerCAmelCase		)

         _lowerCamelCase						:    List[str]         =			model.encode(inputs_dict['''input_ids''']		)

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

         _lowerCamelCase						:    Dict         =			model.init_cache(decoder_input_ids.shape[0]						, __lowerCAmelCase						, __lowerCAmelCase		)
         _lowerCamelCase						:    Optional[int]         =			jnp.ones((decoder_input_ids.shape[0], max_decoder_length)						, dtype='''i4'''		)

         _lowerCamelCase						:    int         =			jnp.broadcast_to(
             jnp.arange(decoder_input_ids.shape[-1] - 1		)[None, :]						, (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)						, )
         _lowerCamelCase						:    Optional[Any]         =			model.decode(
             decoder_input_ids[:, :-1]						, __lowerCAmelCase						, decoder_attention_mask=__lowerCAmelCase						, past_key_values=__lowerCAmelCase						, decoder_position_ids=__lowerCAmelCase						, )

         _lowerCamelCase						:    Tuple         =			jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]						, dtype='''i4'''		)
         _lowerCamelCase						:    Optional[Any]         =			model.decode(
             decoder_input_ids[:, -1:]						, __lowerCAmelCase						, decoder_attention_mask=__lowerCAmelCase						, past_key_values=outputs_cache.past_key_values						, decoder_position_ids=__lowerCAmelCase						, )

         _lowerCamelCase						:    Tuple         =			model.decode(__lowerCAmelCase						, __lowerCAmelCase		)

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




  def 						SCREAMING_SNAKE_CASE    (			self					:						Any						, __lowerCAmelCase					:						Any						, __lowerCAmelCase					:						str						, __lowerCAmelCase					:						str		):






         """simple docstring"""






         _lowerCamelCase						:    Any         =			2_0
         _lowerCamelCase						:    Dict         =			model_class_name(__lowerCAmelCase		)

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

         _lowerCamelCase						:    Dict         =			(
             inputs_dict['''decoder_input_ids'''],
             inputs_dict['''decoder_attention_mask'''],
         )

         _lowerCamelCase						:    Dict         =			jnp.concatenate(
             [
                 decoder_attention_mask,
                 jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])		),
             ]						, axis=-1						, )

         _lowerCamelCase						:    Optional[Any]         =			model.init_cache(decoder_input_ids.shape[0]						, __lowerCAmelCase						, __lowerCAmelCase		)
         _lowerCamelCase						:    Dict         =			jnp.broadcast_to(
             jnp.arange(decoder_input_ids.shape[-1] - 1		)[None, :]						, (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)						, )

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

         _lowerCamelCase						:    List[str]         =			model.decode(__lowerCAmelCase						, __lowerCAmelCase						, decoder_attention_mask=__lowerCAmelCase		)

         _lowerCamelCase						:    Dict         =			np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])		)		)
         self.parent.assertTrue(diff < 1E-3						, msg=f'''Max diff is {diff}'''		)






@require_flax
class 	__snake_case	(     unittest.TestCase):
  snake_case__    :       Dict        =						9_9




  def 						SCREAMING_SNAKE_CASE    (			self					:						Optional[Any]		):






         """simple docstring"""






         _lowerCamelCase						:    List[Any]         =			np.array(
             [
                 [7_1, 8_2, 1_8, 3_3, 4_6, 9_1, 2],
                 [6_8, 3_4, 2_6, 5_8, 3_0, 8_2, 2],
                 [5, 9_7, 1_7, 3_9, 9_4, 4_0, 2],
                 [7_6, 8_3, 9_4, 2_5, 7_0, 7_8, 2],
                 [8_7, 5_9, 4_1, 3_5, 4_8, 6_6, 2],
                 [5_5, 1_3, 1_6, 5_8, 5, 2, 1],  # note padding
                 [6_4, 2_7, 3_1, 5_1, 1_2, 7_5, 2],
                 [5_2, 6_4, 8_6, 1_7, 8_3, 3_9, 2],
                 [4_8, 6_1, 9, 2_4, 7_1, 8_2, 2],
                 [2_6, 1, 6_0, 4_8, 2_2, 1_3, 2],
                 [2_1, 5, 6_2, 2_8, 1_4, 7_6, 2],
                 [4_5, 9_8, 3_7, 8_6, 5_9, 4_8, 2],
                 [7_0, 7_0, 5_0, 9, 2_8, 0, 2],
             ]						, dtype=np.intaa						, )

         _lowerCamelCase						:    Dict         =			input_ids.shape[0]
         _lowerCamelCase						:    str         =			BlenderbotConfig(
             vocab_size=self.vocab_size						, d_model=2_4						, encoder_layers=2						, decoder_layers=2						, encoder_attention_heads=2						, decoder_attention_heads=2						, encoder_ffn_dim=3_2						, decoder_ffn_dim=3_2						, max_position_embeddings=4_8						, eos_token_id=2						, pad_token_id=1						, bos_token_id=0						, )
         return config, input_ids, batch_size




  def 						SCREAMING_SNAKE_CASE    (			self					:						List[Any]		):






         """simple docstring"""






         _lowerCamelCase						:    str         =			self._get_config_and_data()
         _lowerCamelCase						:    int         =			FlaxBlenderbotForConditionalGeneration(__lowerCAmelCase		)
         _lowerCamelCase						:    Dict         =			lm_model(input_ids=__lowerCAmelCase		)
         _lowerCamelCase						:    Union[str, Any]         =			(batch_size, input_ids.shape[1], config.vocab_size)
         self.assertEqual(outputs['''logits'''].shape						, __lowerCAmelCase		)




  def 						SCREAMING_SNAKE_CASE    (			self					:						List[Any]		):






         """simple docstring"""






         _lowerCamelCase						:    Optional[Any]         =			BlenderbotConfig(
             vocab_size=self.vocab_size						, d_model=1_4						, encoder_layers=2						, decoder_layers=2						, encoder_attention_heads=2						, decoder_attention_heads=2						, encoder_ffn_dim=8						, decoder_ffn_dim=8						, max_position_embeddings=4_8						, )
         _lowerCamelCase						:    Optional[Any]         =			FlaxBlenderbotForConditionalGeneration(__lowerCAmelCase		)
         _lowerCamelCase						:    Dict         =			np.array([[7_1, 8_2, 1_8, 3_3, 4_6, 9_1, 2], [6_8, 3_4, 2_6, 5_8, 3_0, 2, 1]]						, dtype=np.intaa		)
         _lowerCamelCase						:    Optional[int]         =			np.array([[8_2, 7_1, 8_2, 1_8, 2], [5_8, 6_8, 2, 1, 1]]						, dtype=np.intaa		)
         _lowerCamelCase						:    Dict         =			lm_model(input_ids=__lowerCAmelCase						, decoder_input_ids=__lowerCAmelCase		)
         _lowerCamelCase						:    Union[str, Any]         =			(*summary.shape, config.vocab_size)
         self.assertEqual(outputs['''logits'''].shape						, __lowerCAmelCase		)




  def 						SCREAMING_SNAKE_CASE    (			self					:						Optional[Any]		):






         """simple docstring"""






         _lowerCamelCase						:    List[str]         =			np.array([[7_1, 8_2, 1_8, 3_3, 2, 1, 1], [6_8, 3_4, 2_6, 5_8, 3_0, 8_2, 2]]						, dtype=np.intaa		)
         _lowerCamelCase						:    List[str]         =			shift_tokens_right(__lowerCAmelCase						, 1						, 2		)
         _lowerCamelCase						:    Dict         =			np.equal(__lowerCAmelCase						, 1		).astype(np.floataa		).sum()
         _lowerCamelCase						:    Union[str, Any]         =			np.equal(__lowerCAmelCase						, 1		).astype(np.floataa		).sum()
         self.assertEqual(shifted.shape						, input_ids.shape		)
         self.assertEqual(__lowerCAmelCase						, n_pad_before - 1		)
         self.assertTrue(np.equal(shifted[:, 0]						, 2		).all()		)






@require_flax
class 	__snake_case	(     _lowercase  ,					unittest.TestCase  ,					_lowercase):
  snake_case__    :       int        =						True
  snake_case__    :       List[Any]        =						(
      (
          FlaxBlenderbotModel,
          FlaxBlenderbotForConditionalGeneration,
      )
      if is_flax_available()
      else ()
  )
  snake_case__    :       Any        =						(FlaxBlenderbotForConditionalGeneration,) if is_flax_available() else ()




  def 						SCREAMING_SNAKE_CASE    (			self					:						Union[str, Any]		):






         """simple docstring"""






         _lowerCamelCase						:    Tuple         =			FlaxBlenderbotModelTester(self		)




  def 						SCREAMING_SNAKE_CASE    (			self					:						Optional[int]		):






         """simple docstring"""






         _lowerCamelCase						:    Tuple         =			self.model_tester.prepare_config_and_inputs()
         for model_class in self.all_model_classes:
                self.model_tester.check_use_cache_forward(__lowerCAmelCase						, __lowerCAmelCase						, __lowerCAmelCase		)




  def 						SCREAMING_SNAKE_CASE    (			self					:						Any		):






         """simple docstring"""






         _lowerCamelCase						:    Any         =			self.model_tester.prepare_config_and_inputs()
         for model_class in self.all_model_classes:
                self.model_tester.check_use_cache_forward_with_attn_mask(__lowerCAmelCase						, __lowerCAmelCase						, __lowerCAmelCase		)




  def 						SCREAMING_SNAKE_CASE    (			self					:						Optional[Any]		):






         """simple docstring"""






         _lowerCamelCase						:    List[str]         =			self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
                with self.subTest(model_class.__name__		):
                       _lowerCamelCase						:    List[Any]         =			self._prepare_for_class(__lowerCAmelCase						, __lowerCAmelCase		)
                       _lowerCamelCase						:    Optional[int]         =			model_class(__lowerCAmelCase		)

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

                       with self.subTest('''JIT Enabled'''		):
                              _lowerCamelCase						:    Optional[int]         =			encode_jitted(**__lowerCAmelCase		).to_tuple()

                       with self.subTest('''JIT Disabled'''		):
                              with jax.disable_jit():
                                     _lowerCamelCase						:    Any         =			encode_jitted(**__lowerCAmelCase		).to_tuple()

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




  def 						SCREAMING_SNAKE_CASE    (			self					:						Tuple		):






         """simple docstring"""






         _lowerCamelCase						:    List[Any]         =			self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
                with self.subTest(model_class.__name__		):
                       _lowerCamelCase						:    List[Any]         =			model_class(__lowerCAmelCase		)
                       _lowerCamelCase						:    str         =			model.encode(inputs_dict['''input_ids''']						, inputs_dict['''attention_mask''']		)

                       _lowerCamelCase						:    Any         =			{
                           '''decoder_input_ids''': inputs_dict['''decoder_input_ids'''],
                           '''decoder_attention_mask''': inputs_dict['''decoder_attention_mask'''],
                           '''encoder_outputs''': encoder_outputs,
                       }

                       @jax.jit
                       def decode_jitted(__lowerCAmelCase					:						Union[str, Any]						, __lowerCAmelCase					:						int						, __lowerCAmelCase					:						Any		):
                              return model.decode(
                                  decoder_input_ids=__lowerCAmelCase						, decoder_attention_mask=__lowerCAmelCase						, encoder_outputs=__lowerCAmelCase						, )

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

                       with self.subTest('''JIT Disabled'''		):
                              with jax.disable_jit():
                                     _lowerCamelCase						:    Union[str, Any]         =			decode_jitted(**__lowerCAmelCase		).to_tuple()

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




  @slow
  def 						SCREAMING_SNAKE_CASE    (			self					:						Dict		):






         """simple docstring"""






         for model_class_name in self.all_model_classes:
                _lowerCamelCase						:    Dict         =			model_class_name.from_pretrained('''facebook/blenderbot-400M-distill'''		)
                # FlaxBlenderbotForSequenceClassification expects eos token in input_ids
                _lowerCamelCase						:    Tuple         =			np.ones((1, 1)		) * model.config.eos_token_id
                _lowerCamelCase						:    Tuple         =			model(__lowerCAmelCase		)
                self.assertIsNotNone(__lowerCAmelCase		)




  @unittest.skipUnless(jax_device != '''cpu'''						, '''3B test too slow on CPU.'''		)
  @slow
  def 						SCREAMING_SNAKE_CASE    (			self					:						int		):






         """simple docstring"""






         _lowerCamelCase						:    Optional[Any]         =			{'''num_beams''': 1, '''early_stopping''': True, '''min_length''': 1_5, '''max_length''': 2_5}
         _lowerCamelCase						:    List[Any]         =			{'''skip_special_tokens''': True, '''clean_up_tokenization_spaces''': True}

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

         _lowerCamelCase						:    List[str]         =			['''Sam''']
         _lowerCamelCase						:    Union[str, Any]         =			tokenizer(__lowerCAmelCase						, return_tensors='''jax'''		)

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

         _lowerCamelCase						:    Dict         =			tokenizer.batch_decode(__lowerCAmelCase						, **__lowerCAmelCase		)
         assert generated_txt[0].strip() == tgt_text






"""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


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






def 	snake_case_  (				A_ :		str,    A_ :		str						):


       '''simple docstring'''





       if "xprophetnet" in prophetnet_checkpoint_path:
              _lowerCamelCase						:    Any         =			XLMProphetNetForConditionalGenerationOld.from_pretrained(A_						)
              _lowerCamelCase      ,   _lowerCamelCase						:    List[str]         =			XLMProphetNetForConditionalGeneration.from_pretrained(
                  A_,    output_loading_info=A_						)
       else:
              _lowerCamelCase						:    str         =			ProphetNetForConditionalGenerationOld.from_pretrained(A_						)
              _lowerCamelCase      ,   _lowerCamelCase						:    Any         =			ProphetNetForConditionalGeneration.from_pretrained(
                  A_,    output_loading_info=A_						)

       _lowerCamelCase						:    Optional[Any]         =			['''key_proj''', '''value_proj''', '''query_proj''']

       _lowerCamelCase						:    List[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						:    Union[str, Any]         =			key.split('''.'''						)

              if attributes[0] == "lm_head":
                     _lowerCamelCase						:    str         =			prophet
                     _lowerCamelCase						:    List[Any]         =			prophet_old
              else:
                     _lowerCamelCase						:    Optional[int]         =			prophet.prophetnet
                     _lowerCamelCase						:    Optional[Any]         =			prophet_old.model

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

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

                            elif attribute == "key_proj":
                                   _lowerCamelCase						:    int         =			nn.Parameter(old_model.in_proj_weight[embed_dim : 2 * embed_dim, :]						)
                                   _lowerCamelCase						:    str         =			nn.Parameter(old_model.in_proj_bias[embed_dim : 2 * embed_dim]						)
                            elif attribute == "value_proj":
                                   _lowerCamelCase						:    Tuple         =			nn.Parameter(old_model.in_proj_weight[2 * embed_dim :, :]						)
                                   _lowerCamelCase						:    str         =			nn.Parameter(old_model.in_proj_bias[2 * embed_dim :]						)
                            _lowerCamelCase						:    Dict         =			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] == 5_12, "We want 512 position_embeddings."
                            _lowerCamelCase						:    List[str]         =			nn.Parameter(old_model.embed_positions.weight[:5_12, :]						)
                            _lowerCamelCase						:    Optional[Any]         =			True
                            break

                     if attribute.isdigit():
                            _lowerCamelCase						:    Optional[int]         =			model[int(A_						)]
                            _lowerCamelCase						:    List[Any]         =			old_model[int(A_						)]
                     else:
                            _lowerCamelCase						:    List[str]         =			getattr(A_,    A_						)

                            if old_attribute == "":
                                   _lowerCamelCase						:    str         =			old_model
                            else:
                                   if not hasattr(A_,    A_						):
                                          raise ValueError(F'''{old_model} does not have {old_attribute}'''						)
                                   _lowerCamelCase						:    Optional[int]         =			getattr(A_,    A_						)

              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(A_						)


if __name__ == "__main__":
   lowerCAmelCase__      =    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.'''
   )
   lowerCAmelCase__      =    parser.parse_args()
   convert_prophetnet_checkpoint_to_pytorch(args.prophetnet_checkpoint_path, args.pytorch_dump_folder_path)


