



'''simple docstring'''

import unittest

import numpy as np

from transformers import BertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
	from transformers.models.bert.modeling_flax_bert import (
	    FlaxBertForMaskedLM,
	    FlaxBertForMultipleChoice,
	    FlaxBertForNextSentencePrediction,
	    FlaxBertForPreTraining,
	    FlaxBertForQuestionAnswering,
	    FlaxBertForSequenceClassification,
	    FlaxBertForTokenClassification,
	    FlaxBertModel,
	)






class 						a_		(     unittest.TestCase    ):


							def __init__(				self   :						Any      ,						a_   :						Union[str, Any]      ,						a_   :						Optional[int]=1_3      ,						a_   :						Tuple=7      ,						a_   :						Optional[int]=True      ,						a_   :						List[Any]=True      ,						a_   :						str=True      ,						a_   :						List[str]=True      ,						a_   :						List[str]=9_9      ,						a_   :						Optional[int]=3_2      ,						a_   :						str=5      ,						a_   :						List[str]=4      ,						a_   :						Tuple=3_7      ,						a_   :						List[Any]="gelu"      ,						a_   :						Union[str, Any]=0.1      ,						a_   :						Dict=0.1      ,						a_   :						Tuple=5_1_2      ,						a_   :						Union[str, Any]=1_6      ,						a_   :						Union[str, Any]=2      ,						a_   :						str=0.0_2      ,						a_   :						Optional[int]=4      ,						)			->					Optional[Any]:
										snake_case:    str      							=parent
										snake_case:    Optional[int]      							=batch_size
										snake_case:    List[Any]      							=seq_length
										snake_case:    Tuple      							=is_training
										snake_case:    Union[str, Any]      							=use_attention_mask
										snake_case:    int      							=use_token_type_ids
										snake_case:    Union[str, Any]      							=use_labels
										snake_case:    Optional[int]      							=vocab_size
										snake_case:    int      							=hidden_size
										snake_case:    Union[str, Any]      							=num_hidden_layers
										snake_case:    List[Any]      							=num_attention_heads
										snake_case:    Any      							=intermediate_size
										snake_case:    int      							=hidden_act
										snake_case:    Tuple      							=hidden_dropout_prob
										snake_case:    List[str]      							=attention_probs_dropout_prob
										snake_case:    Dict      							=max_position_embeddings
										snake_case:    Union[str, Any]      							=type_vocab_size
										snake_case:    Union[str, Any]      							=type_sequence_label_size
										snake_case:    str      							=initializer_range
										snake_case:    Tuple      							=num_choices


							def    UpperCamelCase     (				self   :						Optional[Any]			)			->					Dict:
										snake_case:    List[Any]      							=ids_tensor([self.batch_size, self.seq_length]      ,						self.vocab_size			)

										snake_case:    Any      							=None
										if self.use_attention_mask:
													snake_case:    Union[str, Any]      							=random_attention_mask([self.batch_size, self.seq_length]			)

										snake_case:    Any      							=None
										if self.use_token_type_ids:
													snake_case:    int      							=ids_tensor([self.batch_size, self.seq_length]      ,						self.type_vocab_size			)

										snake_case:    Optional[Any]      							=BertConfig(
										    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      ,						is_decoder=a_      ,						initializer_range=self.initializer_range      ,						)

										return config, input_ids, token_type_ids, attention_mask


							def    UpperCamelCase     (				self   :						List[Any]			)			->					str:
										snake_case:    Optional[int]      							=self.prepare_config_and_inputs()
										snake_case							,  snake_case							,  snake_case							,  snake_case:    int      							=config_and_inputs
										snake_case:    Optional[int]      							={'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': attention_mask}
										return config, inputs_dict




							def    UpperCamelCase     (				self   :						Optional[Any]			)			->					Union[str, Any]:
										snake_case:    Dict      							=self.prepare_config_and_inputs()
										snake_case							,  snake_case							,  snake_case							,  snake_case:    Dict      							=config_and_inputs

										snake_case:    str      							=True
										snake_case:    Tuple      							=floats_tensor([self.batch_size, self.seq_length, self.hidden_size]			)
										snake_case:    Union[str, Any]      							=ids_tensor([self.batch_size, self.seq_length]      ,						vocab_size=2			)

										return (
										    config,
										    input_ids,
										    attention_mask,
										    encoder_hidden_states,
										    encoder_attention_mask,
										)






@require_flax
class 						a_		(     snake_case    ,   unittest.TestCase    ):
							UpperCAmelCase			:		List[Any]        =				True

							UpperCAmelCase			:		Union[str, Any]        =				(
							    (
							        FlaxBertModel,
							        FlaxBertForPreTraining,
							        FlaxBertForMaskedLM,
							        FlaxBertForMultipleChoice,
							        FlaxBertForQuestionAnswering,
							        FlaxBertForNextSentencePrediction,
							        FlaxBertForSequenceClassification,
							        FlaxBertForTokenClassification,
							        FlaxBertForQuestionAnswering,
							    )
							    if is_flax_available()
							    else ()
							)


							def    UpperCamelCase     (				self   :						Optional[Any]			)			->					Dict:
										snake_case:    Optional[Any]      							=FlaxBertModelTester(self			)




							@slow
							def    UpperCamelCase     (				self   :						Tuple			)			->					Tuple:
										# Only check this for base model, not necessary for all model classes.
										# This will also help speed-up tests.
										snake_case:    Dict      							=FlaxBertModel.from_pretrained('bert-base-cased'			)
										snake_case:    str      							=model(np.ones((1, 1)			)			)
										self.assertIsNotNone(a_			)









'''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


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







def a_     (    __UpperCAmelCase				,							__UpperCAmelCase					)				->					Dict:





			"""simple docstring"""







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

			snake_case:    str      							=['key_proj', 'value_proj', 'query_proj']

			snake_case:    str      							={
			    '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"]:
						snake_case:    List[str]      							=key.split('.'					)

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

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

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

												elif attribute == "key_proj":
															snake_case:    Union[str, Any]      							=nn.Parameter(old_model.in_proj_weight[embed_dim : 2 * embed_dim, :]					)
															snake_case:    List[str]      							=nn.Parameter(old_model.in_proj_bias[embed_dim : 2 * embed_dim]					)
												elif attribute == "value_proj":
															snake_case:    List[str]      							=nn.Parameter(old_model.in_proj_weight[2 * embed_dim :, :]					)
															snake_case:    Union[str, Any]      							=nn.Parameter(old_model.in_proj_bias[2 * embed_dim :]					)
												snake_case:    Any      							=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."
												snake_case:    Dict      							=nn.Parameter(old_model.embed_positions.weight[:5_12, :]					)
												snake_case:    Dict      							=True
												break

									if attribute.isdigit():
												snake_case:    int      							=model[int(__UpperCAmelCase					)]
												snake_case:    Optional[int]      							=old_model[int(__UpperCAmelCase					)]
									else:
												snake_case:    List[str]      							=getattr(__UpperCAmelCase				,							__UpperCAmelCase					)

												if old_attribute == "":
															snake_case:    Union[str, Any]      							=old_model
												else:
															if not hasattr(__UpperCAmelCase				,							__UpperCAmelCase					):
																		raise ValueError(f'''{old_model} does not have {old_attribute}'''					)
															snake_case:    List[Any]      							=getattr(__UpperCAmelCase				,							__UpperCAmelCase					)

						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(__UpperCAmelCase					)


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




