





"""simple docstring"""





import tempfile
import unittest

import numpy as np

import transformers
from transformers import GPTaTokenizer, GPTJConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, tooslow

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


if is_flax_available():
							import jax
							import jax.numpy as jnp

							from transformers.modeling_flax_pytorch_utils import (
							    convert_pytorch_state_dict_to_flax,
							    load_flax_weights_in_pytorch_model,
							)
							from transformers.models.gptj.modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel

if is_torch_available():
							import torch
class 		SCREAMING_SNAKE_CASE_				:
				"""simple docstring"""




				def __init__(  self	:Optional[Any]    ,     __lowercase	:Optional[int]    ,     __lowercase	:Any=14    ,     __lowercase	:List[str]=7    ,     __lowercase	:Union[str, Any]=True    ,     __lowercase	:str=True    ,     __lowercase	:int=False    ,     __lowercase	:Union[str, Any]=True    ,     __lowercase	:Optional[int]=99    ,     __lowercase	:Any=32    ,     __lowercase	:Union[str, Any]=4    ,     __lowercase	:List[Any]=4    ,     __lowercase	:Any=4    ,     __lowercase	:Dict=37    ,     __lowercase	:Optional[Any]="gelu"    ,     __lowercase	:Dict=0.1    ,     __lowercase	:Optional[Any]=0.1    ,     __lowercase	:Optional[int]=512    ,     __lowercase	:int=0.02    ,     ):
											__lowerCamelCase   :   Union[str, Any]       =parent
											__lowerCamelCase   :   Union[str, Any]       =batch_size
											__lowerCamelCase   :   Any       =seq_length
											__lowerCamelCase   :   List[Any]       =is_training
											__lowerCamelCase   :   List[str]       =use_input_mask
											__lowerCamelCase   :   Any       =use_token_type_ids
											__lowerCamelCase   :   int       =use_labels
											__lowerCamelCase   :   str       =vocab_size
											__lowerCamelCase   :   Any       =hidden_size
											__lowerCamelCase   :   Tuple       =rotary_dim
											__lowerCamelCase   :   str       =num_hidden_layers
											__lowerCamelCase   :   List[str]       =num_attention_heads
											__lowerCamelCase   :   Any       =intermediate_size
											__lowerCamelCase   :   Optional[Any]       =hidden_act
											__lowerCamelCase   :   Any       =hidden_dropout_prob
											__lowerCamelCase   :   Any       =attention_probs_dropout_prob
											__lowerCamelCase   :   List[Any]       =max_position_embeddings
											__lowerCamelCase   :   Union[str, Any]       =initializer_range
											__lowerCamelCase   :   Optional[int]       =None
											__lowerCamelCase   :   Optional[Any]       =vocab_size - 1
											__lowerCamelCase   :   Optional[int]       =vocab_size - 1
											__lowerCamelCase   :   List[str]       =vocab_size - 1




				def        __lowercase	(  self	:int      ):
											__lowerCamelCase   :   str       =ids_tensor([self.batch_size, self.seq_length]    ,     self.vocab_size      )

											__lowerCamelCase   :   int       =None
											if self.use_input_mask:
																		__lowerCamelCase   :   List[Any]       =random_attention_mask([self.batch_size, self.seq_length]      )

											__lowerCamelCase   :   str       =GPTJConfig(
											    vocab_size=self.vocab_size    ,     n_embd=self.hidden_size    ,     n_layer=self.num_hidden_layers    ,     n_head=self.num_attention_heads    ,     n_positions=self.max_position_embeddings    ,     use_cache=__lowercase    ,     bos_token_id=self.bos_token_id    ,     eos_token_id=self.eos_token_id    ,     pad_token_id=self.pad_token_id    ,     rotary_dim=self.rotary_dim    ,     )

											return (config, input_ids, input_mask)




				def        __lowercase	(  self	:Optional[Any]      ):
											__lowerCamelCase   :   Optional[Any]       =self.prepare_config_and_inputs()
											__lowerCamelCase     ,    __lowerCamelCase     ,    __lowerCamelCase   :   str       =config_and_inputs
											__lowerCamelCase   :   Optional[int]       ={'''input_ids''': input_ids, '''attention_mask''': attention_mask}
											return config, inputs_dict




				def        __lowercase	(  self	:List[str]    ,     __lowercase	:Optional[int]    ,     __lowercase	:int    ,     __lowercase	:List[str]    ,     __lowercase	:str      ):
											__lowerCamelCase   :   List[Any]       =20
											__lowerCamelCase   :   str       =model_class_name(__lowercase      )

											__lowerCamelCase   :   List[str]       =model.init_cache(input_ids.shape[0]    ,     __lowercase      )
											__lowerCamelCase   :   str       =jnp.ones((input_ids.shape[0], max_decoder_length)    ,     dtype='''i4'''      )

											__lowerCamelCase   :   Dict       =jnp.broadcast_to(
											    jnp.arange(input_ids.shape[-1] - 1      )[None, :]    ,     (input_ids.shape[0], input_ids.shape[-1] - 1)      )
											__lowerCamelCase   :   Tuple       =model(
											    input_ids[:, :-1]    ,     attention_mask=__lowercase    ,     past_key_values=__lowercase    ,     position_ids=__lowercase    ,     )

											__lowerCamelCase   :   Dict       =jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]    ,     dtype='''i4'''      )
											__lowerCamelCase   :   Dict       =model(
											    input_ids[:, -1:]    ,     attention_mask=__lowercase    ,     past_key_values=outputs_cache.past_key_values    ,     position_ids=__lowercase    ,     )

											__lowerCamelCase   :   Optional[int]       =model(__lowercase      )

											__lowerCamelCase   :   Tuple       =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        __lowercase	(  self	:Dict    ,     __lowercase	:Dict    ,     __lowercase	:List[Any]    ,     __lowercase	:List[Any]    ,     __lowercase	:List[str]      ):
											__lowerCamelCase   :   Optional[int]       =20
											__lowerCamelCase   :   List[Any]       =model_class_name(__lowercase      )

											__lowerCamelCase   :   Any       =jnp.concatenate(
											    [attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1])      )]    ,     axis=-1    ,     )

											__lowerCamelCase   :   List[Any]       =model.init_cache(input_ids.shape[0]    ,     __lowercase      )
											__lowerCamelCase   :   Optional[int]       =jnp.broadcast_to(
											    jnp.arange(input_ids.shape[-1] - 1      )[None, :]    ,     (input_ids.shape[0], input_ids.shape[-1] - 1)      )

											__lowerCamelCase   :   str       =model(
											    input_ids[:, :-1]    ,     attention_mask=__lowercase    ,     past_key_values=__lowercase    ,     position_ids=__lowercase    ,     )
											__lowerCamelCase   :   Dict       =jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]    ,     dtype='''i4'''      )
											__lowerCamelCase   :   Tuple       =model(
											    input_ids[:, -1:]    ,     past_key_values=outputs_cache.past_key_values    ,     attention_mask=__lowercase    ,     position_ids=__lowercase    ,     )

											__lowerCamelCase   :   Dict       =model(__lowercase    ,     attention_mask=__lowercase      )

											__lowerCamelCase   :   Optional[int]       =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 		SCREAMING_SNAKE_CASE_				(     snake_case__							,	snake_case__							,	unittest.TestCase ):
				"""simple docstring"""
				__snake_case :							Dict									=		(FlaxGPTJModel, FlaxGPTJForCausalLM) if is_flax_available() else ()
				__snake_case :							int									=		(FlaxGPTJForCausalLM,) if is_flax_available() else ()




				def        __lowercase	(  self	:Optional[Any]      ):
											__lowerCamelCase   :   int       =FlaxGPTJModelTester(self      )




				def        __lowercase	(  self	:Optional[int]      ):
											for model_class_name in self.all_model_classes:
																		__lowerCamelCase     ,    __lowerCamelCase     ,    __lowerCamelCase   :   str       =self.model_tester.prepare_config_and_inputs()
																		self.model_tester.check_use_cache_forward(__lowercase    ,     __lowercase    ,     __lowercase    ,     __lowercase      )




				def        __lowercase	(  self	:Union[str, Any]      ):
											for model_class_name in self.all_model_classes:
																		__lowerCamelCase     ,    __lowerCamelCase     ,    __lowerCamelCase   :   List[Any]       =self.model_tester.prepare_config_and_inputs()
																		self.model_tester.check_use_cache_forward_with_attn_mask(
																		    __lowercase    ,     __lowercase    ,     __lowercase    ,     __lowercase      )




				@tooslow
				def        __lowercase	(  self	:List[Any]      ):
											__lowerCamelCase   :   int       =GPTaTokenizer.from_pretrained('''gpt2'''    ,     pad_token='''<|endoftext|>'''    ,     padding_side='''left'''      )
											__lowerCamelCase   :   Dict       =tokenizer(['''Hello this is a long string''', '''Hey''']    ,     return_tensors='''np'''    ,     padding=__lowercase    ,     truncation=__lowercase      )

											__lowerCamelCase   :   List[str]       =FlaxGPTJForCausalLM.from_pretrained('''EleutherAI/gpt-j-6B'''      )
											__lowerCamelCase   :   Any       =False
											__lowerCamelCase   :   Optional[int]       =model.config.eos_token_id

											__lowerCamelCase   :   Union[str, Any]       =jax.jit(model.generate      )

											__lowerCamelCase   :   Optional[int]       =jit_generate(
											    inputs['''input_ids''']    ,     attention_mask=inputs['''attention_mask''']    ,     pad_token_id=tokenizer.pad_token_id      ).sequences

											__lowerCamelCase   :   Any       =tokenizer.batch_decode(__lowercase    ,     skip_special_tokens=__lowercase      )

											__lowerCamelCase   :   Tuple       =[
											    '''Hello this is a long string of text.\n\nI\'m trying to get the text of the''',
											    '''Hey, I\'m a little late to the party. I\'m going to''',
											]

											self.assertListEqual(__lowercase    ,     __lowercase      )




				@is_pt_flax_cross_test
				def        __lowercase	(  self	:Tuple      ):
											__lowerCamelCase     ,    __lowerCamelCase   :   Any       =self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
																		with self.subTest(model_class.__name__      ):
																									# prepare inputs
																									__lowerCamelCase   :   int       =self._prepare_for_class(__lowercase    ,     __lowercase      )
																									__lowerCamelCase   :   List[str]       ={k: torch.tensor(v.tolist()      ) for k, v in prepared_inputs_dict.items()}

																									# load corresponding PyTorch class
																									__lowerCamelCase   :   Any       =model_class.__name__[4:]  # Skip the "Flax" at the beginning
																									__lowerCamelCase   :   int       =getattr(__lowercase    ,     __lowercase      )

																									__lowerCamelCase     ,    __lowerCamelCase   :   Union[str, Any]       =pt_inputs['''input_ids'''].shape
																									__lowerCamelCase   :   Any       =np.random.randint(0    ,     seq_length - 1    ,     size=(batch_size,)      )
																									for batch_idx, start_index in enumerate(__lowercase      ):
																																__lowerCamelCase   :   Any       =0
																																__lowerCamelCase   :   Dict       =1
																																__lowerCamelCase   :   Dict       =0
																																__lowerCamelCase   :   int       =1
																									__lowerCamelCase   :   Optional[Any]       =pt_model_class(__lowercase      ).eval()
																									__lowerCamelCase   :   Tuple       =model_class(__lowercase    ,     dtype=jnp.floataa      )

																									__lowerCamelCase   :   List[str]       =convert_pytorch_state_dict_to_flax(pt_model.state_dict()    ,     __lowercase      )
																									__lowerCamelCase   :   Union[str, Any]       =fx_state

																									with torch.no_grad():
																																__lowerCamelCase   :   Optional[Any]       =pt_model(**__lowercase      ).to_tuple()

																									__lowerCamelCase   :   str       =fx_model(**__lowercase      ).to_tuple()
																									self.assertEqual(len(__lowercase      )    ,     len(__lowercase      )    ,     '''Output lengths differ between Flax and PyTorch'''      )
																									for fx_output, pt_output in zip(__lowercase    ,     __lowercase      ):
																																self.assert_almost_equals(fx_output[:, -1]    ,     pt_output[:, -1].numpy()    ,     4e-2      )

																									with tempfile.TemporaryDirectory() as tmpdirname:
																																pt_model.save_pretrained(__lowercase      )
																																__lowerCamelCase   :   Optional[Any]       =model_class.from_pretrained(__lowercase    ,     from_pt=__lowercase      )

																									__lowerCamelCase   :   Dict       =fx_model_loaded(**__lowercase      ).to_tuple()
																									self.assertEqual(
																									    len(__lowercase      )    ,     len(__lowercase      )    ,     '''Output lengths differ between Flax and PyTorch'''      )
																									for fx_output_loaded, pt_output in zip(__lowercase    ,     __lowercase      ):
																																self.assert_almost_equals(fx_output_loaded[:, -1]    ,     pt_output[:, -1].numpy()    ,     4e-2      )




				@is_pt_flax_cross_test
				def        __lowercase	(  self	:int      ):
											__lowerCamelCase     ,    __lowerCamelCase   :   Optional[Any]       =self.model_tester.prepare_config_and_inputs_for_common()
											for model_class in self.all_model_classes:
																		with self.subTest(model_class.__name__      ):
																									# prepare inputs
																									__lowerCamelCase   :   List[str]       =self._prepare_for_class(__lowercase    ,     __lowercase      )
																									__lowerCamelCase   :   Tuple       ={k: torch.tensor(v.tolist()      ) for k, v in prepared_inputs_dict.items()}

																									# load corresponding PyTorch class
																									__lowerCamelCase   :   Optional[Any]       =model_class.__name__[4:]  # Skip the "Flax" at the beginning
																									__lowerCamelCase   :   Any       =getattr(__lowercase    ,     __lowercase      )

																									__lowerCamelCase   :   int       =pt_model_class(__lowercase      ).eval()
																									__lowerCamelCase   :   Union[str, Any]       =model_class(__lowercase    ,     dtype=jnp.floataa      )

																									__lowerCamelCase   :   Optional[int]       =load_flax_weights_in_pytorch_model(__lowercase    ,     fx_model.params      )
																									__lowerCamelCase     ,    __lowerCamelCase   :   List[str]       =pt_inputs['''input_ids'''].shape
																									__lowerCamelCase   :   str       =np.random.randint(0    ,     seq_length - 1    ,     size=(batch_size,)      )
																									for batch_idx, start_index in enumerate(__lowercase      ):
																																__lowerCamelCase   :   Any       =0
																																__lowerCamelCase   :   Optional[int]       =1
																																__lowerCamelCase   :   List[Any]       =0
																																__lowerCamelCase   :   Dict       =1

																									# make sure weights are tied in PyTorch
																									pt_model.tie_weights()

																									with torch.no_grad():
																																__lowerCamelCase   :   Optional[int]       =pt_model(**__lowercase      ).to_tuple()

																									__lowerCamelCase   :   Any       =fx_model(**__lowercase      ).to_tuple()
																									self.assertEqual(len(__lowercase      )    ,     len(__lowercase      )    ,     '''Output lengths differ between Flax and PyTorch'''      )
																									for fx_output, pt_output in zip(__lowercase    ,     __lowercase      ):
																																self.assert_almost_equals(fx_output[:, -1]    ,     pt_output[:, -1].numpy()    ,     4e-2      )

																									with tempfile.TemporaryDirectory() as tmpdirname:
																																fx_model.save_pretrained(__lowercase      )
																																__lowerCamelCase   :   Any       =pt_model_class.from_pretrained(__lowercase    ,     from_flax=__lowercase      )

																									with torch.no_grad():
																																__lowerCamelCase   :   Union[str, Any]       =pt_model_loaded(**__lowercase      ).to_tuple()

																									self.assertEqual(
																									    len(__lowercase      )    ,     len(__lowercase      )    ,     '''Output lengths differ between Flax and PyTorch'''      )
																									for fx_output, pt_output in zip(__lowercase    ,     __lowercase      ):
																																self.assert_almost_equals(fx_output[:, -1]    ,     pt_output[:, -1].numpy()    ,     4e-2      )




				@tooslow
				def        __lowercase	(  self	:List[str]      ):
											for model_class_name in self.all_model_classes:
																		__lowerCamelCase   :   List[str]       =model_class_name.from_pretrained('''EleutherAI/gpt-j-6B'''      )
																		__lowerCamelCase   :   Tuple       =model(np.ones((1, 1)      )      )
																		self.assertIsNotNone(__lowercase      )











"""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 					lowerCAmelCase_      (   SCREAMING_SNAKE_CASE							:				str     ,    SCREAMING_SNAKE_CASE							:				str ):


							'''simple docstring'''







							if "xprophetnet" in prophetnet_checkpoint_path:
														__lowerCamelCase   :   Optional[Any]       =XLMProphetNetForConditionalGenerationOld.from_pretrained(SCREAMING_SNAKE_CASE )
														__lowerCamelCase     ,    __lowerCamelCase   :   List[Any]       =XLMProphetNetForConditionalGeneration.from_pretrained(
														    SCREAMING_SNAKE_CASE     ,    output_loading_info=SCREAMING_SNAKE_CASE )
							else:
														__lowerCamelCase   :   int       =ProphetNetForConditionalGenerationOld.from_pretrained(SCREAMING_SNAKE_CASE )
														__lowerCamelCase     ,    __lowerCamelCase   :   int       =ProphetNetForConditionalGeneration.from_pretrained(
														    SCREAMING_SNAKE_CASE     ,    output_loading_info=SCREAMING_SNAKE_CASE )

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

							__lowerCamelCase   :   Tuple       ={
							    '''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   :   int       =key.split('''.''' )

														if attributes[0] == "lm_head":
																					__lowerCamelCase   :   int       =prophet
																					__lowerCamelCase   :   Optional[int]       =prophet_old
														else:
																					__lowerCamelCase   :   Any       =prophet.prophetnet
																					__lowerCamelCase   :   Union[str, Any]       =prophet_old.model

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

																					if attribute == "weight":
																												assert old_model.weight.shape == model.weight.shape, "Shapes have to match!"
																												__lowerCamelCase   :   str       =old_model.weight
																												logger.info(F'{attribute} is initialized.' )
																												__lowerCamelCase   :   Any       =True
																												break
																					elif attribute == "bias":
																												assert old_model.bias.shape == model.bias.shape, "Shapes have to match!"
																												__lowerCamelCase   :   Union[str, Any]       =old_model.bias
																												logger.info(F'{attribute} is initialized' )
																												__lowerCamelCase   :   str       =True
																												break
																					elif attribute in special_keys and hasattr(SCREAMING_SNAKE_CASE     ,    '''in_proj_weight''' ):
																												__lowerCamelCase   :   int       =old_model.in_proj_weight.shape[0] // 3
																												__lowerCamelCase   :   Union[str, Any]       =getattr(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE )
																												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   :   List[str]       =nn.Parameter(old_model.in_proj_weight[:embed_dim, :] )
																																			__lowerCamelCase   :   str       =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   :   Tuple       =nn.Parameter(old_model.in_proj_bias[embed_dim : 2 * embed_dim] )
																												elif attribute == "value_proj":
																																			__lowerCamelCase   :   Optional[Any]       =nn.Parameter(old_model.in_proj_weight[2 * embed_dim :, :] )
																																			__lowerCamelCase   :   int       =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] == 512, "We want 512 position_embeddings."
																												__lowerCamelCase   :   str       =nn.Parameter(old_model.embed_positions.weight[:512, :] )
																												__lowerCamelCase   :   Dict       =True
																												break

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

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

														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(SCREAMING_SNAKE_CASE )


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)




