





"""simple docstring"""






import unittest

import numpy as np

from transformers import RobertaPreLayerNormConfig, 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():
		import jax.numpy as jnp

		from transformers.models.roberta_prelayernorm.modeling_flax_roberta_prelayernorm import (
		    FlaxRobertaPreLayerNormForCausalLM,
		    FlaxRobertaPreLayerNormForMaskedLM,
		    FlaxRobertaPreLayerNormForMultipleChoice,
		    FlaxRobertaPreLayerNormForQuestionAnswering,
		    FlaxRobertaPreLayerNormForSequenceClassification,
		    FlaxRobertaPreLayerNormForTokenClassification,
		    FlaxRobertaPreLayerNormModel,
		)




class 				__lowercase			(			unittest.TestCase							):







					'''simple docstring'''




					def __init__(						self	,			_UpperCAmelCase	,			_UpperCAmelCase=13	,			_UpperCAmelCase=7	,			_UpperCAmelCase=True	,			_UpperCAmelCase=True	,			_UpperCAmelCase=True	,			_UpperCAmelCase=True	,			_UpperCAmelCase=99	,			_UpperCAmelCase=32	,			_UpperCAmelCase=5	,			_UpperCAmelCase=4	,			_UpperCAmelCase=37	,			_UpperCAmelCase="gelu"	,			_UpperCAmelCase=0.1	,			_UpperCAmelCase=0.1	,			_UpperCAmelCase=512	,			_UpperCAmelCase=16	,			_UpperCAmelCase=2	,			_UpperCAmelCase=0.0_2	,			_UpperCAmelCase=4	,			):
									__a :						Union[str, Any]			       =  parent
									__a :						Union[str, Any]			       =  batch_size
									__a :						Optional[Any]			       =  seq_length
									__a :						str			       =  is_training
									__a :						Dict			       =  use_attention_mask
									__a :						Optional[Any]			       =  use_token_type_ids
									__a :						str			       =  use_labels
									__a :						List[Any]			       =  vocab_size
									__a :						Optional[Any]			       =  hidden_size
									__a :						List[str]			       =  num_hidden_layers
									__a :						Any			       =  num_attention_heads
									__a :						List[str]			       =  intermediate_size
									__a :						int			       =  hidden_act
									__a :						Any			       =  hidden_dropout_prob
									__a :						Any			       =  attention_probs_dropout_prob
									__a :						List[Any]			       =  max_position_embeddings
									__a :						str			       =  type_vocab_size
									__a :						Optional[int]			       =  type_sequence_label_size
									__a :						List[str]			       =  initializer_range
									__a :						Optional[Any]			       =  num_choices







					def  _lowerCamelCase		(						self     ):
									__a :						str			       =  ids_tensor([self.batch_size, self.seq_length]	,			self.vocab_size     )

									__a :						List[str]			       =  None
									if self.use_attention_mask:
													__a :						str			       =  random_attention_mask([self.batch_size, self.seq_length]     )

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

									__a :						int			       =  RobertaPreLayerNormConfig(
									    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=_UpperCAmelCase	,			initializer_range=self.initializer_range	,			)

									return config, input_ids, token_type_ids, attention_mask







					def  _lowerCamelCase		(						self     ):
									__a :						Union[str, Any]			       =  self.prepare_config_and_inputs()
									__a       ,						__a       ,						__a       ,						__a :						str			       =  config_and_inputs
									__a :						List[str]			       =  {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': attention_mask}
									return config, inputs_dict







					def  _lowerCamelCase		(						self     ):
									__a :						Any			       =  self.prepare_config_and_inputs()
									__a       ,						__a       ,						__a       ,						__a :						Optional[int]			       =  config_and_inputs

									__a :						List[str]			       =  True
									__a :						List[str]			       =  floats_tensor([self.batch_size, self.seq_length, self.hidden_size]     )
									__a :						Tuple			       =  ids_tensor([self.batch_size, self.seq_length]	,			vocab_size=2     )

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






@require_flax
# Copied from tests.models.roberta.test_modelling_flax_roberta.FlaxRobertaPreLayerNormModelTest with ROBERTA->ROBERTA_PRELAYERNORM,Roberta->RobertaPreLayerNorm,roberta-base->andreasmadsen/efficient_mlm_m0.40
class 				__lowercase			(			_UpperCamelCase   ,      unittest.TestCase							):







					'''simple docstring'''




					__lowerCAmelCase  =  True

					__lowerCAmelCase  =  (
					    (
					        FlaxRobertaPreLayerNormModel,
					        FlaxRobertaPreLayerNormForCausalLM,
					        FlaxRobertaPreLayerNormForMaskedLM,
					        FlaxRobertaPreLayerNormForSequenceClassification,
					        FlaxRobertaPreLayerNormForTokenClassification,
					        FlaxRobertaPreLayerNormForMultipleChoice,
					        FlaxRobertaPreLayerNormForQuestionAnswering,
					    )
					    if is_flax_available()
					    else ()
					)







					def  _lowerCamelCase		(						self     ):
									__a :						Dict			       =  FlaxRobertaPreLayerNormModelTester(self     )







					@slow
					def  _lowerCamelCase		(						self     ):
									for model_class_name in self.all_model_classes:
													__a :						Tuple			       =  model_class_name.from_pretrained('''andreasmadsen/efficient_mlm_m0.40'''	,			from_pt=_UpperCAmelCase     )
													__a :						List[Any]			       =  model(np.ones((1, 1)     )     )
													self.assertIsNotNone(_UpperCAmelCase     )






@require_flax
class 				__lowercase			(			unittest.TestCase							):







					'''simple docstring'''




					@slow
					def  _lowerCamelCase		(						self     ):
									__a :						List[str]			       =  FlaxRobertaPreLayerNormForMaskedLM.from_pretrained('''andreasmadsen/efficient_mlm_m0.40'''	,			from_pt=_UpperCAmelCase     )

									__a :						Optional[int]			       =  np.array([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]	,			dtype=jnp.intaa     )
									__a :						Dict			       =  model(_UpperCAmelCase     )[0]
									__a :						Any			       =  [1, 11, 50265]
									self.assertEqual(list(output.shape     )	,			_UpperCAmelCase     )
									# compare the actual values for a slice.
									__a :						int			       =  np.array(
									    [[[4_0.4_8_8_0, 1_8.0_1_9_9, -5.2_3_6_7], [-1.8_8_7_7, -4.0_8_8_5, 1_0.7_0_8_5], [-2.2_6_1_3, -5.6_1_1_0, 7.2_6_6_5]]]	,			dtype=np.floataa     )
									self.assertTrue(np.allclose(output[:, :3, :3]	,			_UpperCAmelCase	,			atol=1e-4     )     )







					@slow
					def  _lowerCamelCase		(						self     ):
									__a :						Optional[int]			       =  FlaxRobertaPreLayerNormModel.from_pretrained('''andreasmadsen/efficient_mlm_m0.40'''	,			from_pt=_UpperCAmelCase     )

									__a :						str			       =  np.array([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]	,			dtype=jnp.intaa     )
									__a :						Optional[Any]			       =  model(_UpperCAmelCase     )[0]
									# compare the actual values for a slice.
									__a :						Union[str, Any]			       =  np.array(
									    [[[0.0_2_0_8, -0.0_3_5_6, 0.0_2_3_7], [-0.1_5_6_9, -0.0_4_1_1, -0.2_6_2_6], [0.1_8_7_9, 0.0_1_2_5, -0.0_0_8_9]]]	,			dtype=np.floataa     )
									self.assertTrue(np.allclose(output[:, :3, :3]	,			_UpperCAmelCase	,			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


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

def     __A      (			a_						:str   ,       a_						:str)		->		List[Any]:
				if "xprophetnet" in prophetnet_checkpoint_path:
								__a :						List[str]			       =  XLMProphetNetForConditionalGenerationOld.from_pretrained(a_)
								__a       ,						__a :						int			       =  XLMProphetNetForConditionalGeneration.from_pretrained(
								    a_   ,       output_loading_info=a_)
				else:
								__a :						Optional[int]			       =  ProphetNetForConditionalGenerationOld.from_pretrained(a_)
								__a       ,						__a :						Optional[Any]			       =  ProphetNetForConditionalGeneration.from_pretrained(
								    a_   ,       output_loading_info=a_)

				__a :						Tuple			       =  ['''key_proj''', '''value_proj''', '''query_proj''']

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

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

								__a :						Optional[int]			       =  False
								for attribute in attributes:
												if attribute in mapping:
																__a :						str			       =  mapping[attribute]
																if not hasattr(a_   ,       a_) and len(a_) > 0:
																				__a :						List[str]			       =  attribute
												elif hasattr(a_   ,       a_):
																__a :						int			       =  attribute

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

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

												if attribute.isdigit():
																__a :						Optional[Any]			       =  model[int(a_)]
																__a :						str			       =  old_model[int(a_)]
												else:
																__a :						Optional[int]			       =  getattr(a_   ,       a_)

																if old_attribute == "":
																				__a :						str			       =  old_model
																else:
																				if not hasattr(a_   ,       a_):
																								raise ValueError(F"""{old_model} does not have {old_attribute}""")
																				__a :						Union[str, Any]			       =  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__":
		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)