






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 							SCREAMING_SNAKE_CASE__						(    unittest.TestCase						):





					"""simple docstring"""






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



					def 	_lowercase					( self					:  int	):
						snake_case__			:				List[str]       =  ids_tensor([self.batch_size, self.seq_length]							,		self.vocab_size	)

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

						snake_case__			:				Tuple       =  None
						if self.use_token_type_ids:
							snake_case__			:				Union[str, Any]       =  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 	_lowercase					( self					:  Union[str, Any]	):
						snake_case__			:				str       =  self.prepare_config_and_inputs()
						snake_case__,						snake_case__,						snake_case__,						snake_case__			:				Optional[int]       =  config_and_inputs
						snake_case__			:				Union[str, Any]       =  {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": attention_mask}
						return config, inputs_dict






					def 	_lowercase					( self					:  List[Any]	):
						snake_case__			:				int       =  self.prepare_config_and_inputs()
						snake_case__,						snake_case__,						snake_case__,						snake_case__			:				Any       =  config_and_inputs

						snake_case__			:				str       =  True
						snake_case__			:				int       =  floats_tensor([self.batch_size, self.seq_length, self.hidden_size]	)
						snake_case__			:				List[str]       =  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 							SCREAMING_SNAKE_CASE__						(    lowerCamelCase__			,       unittest.TestCase						):





					"""simple docstring"""






					a_     =		True

					a_     =		(
					    (
					        FlaxBertModel,
					        FlaxBertForPreTraining,
					        FlaxBertForMaskedLM,
					        FlaxBertForMultipleChoice,
					        FlaxBertForQuestionAnswering,
					        FlaxBertForNextSentencePrediction,
					        FlaxBertForSequenceClassification,
					        FlaxBertForTokenClassification,
					        FlaxBertForQuestionAnswering,
					    )
					    if is_flax_available()
					    else ()
					)



					def 	_lowercase					( self					:  List[Any]	):
						snake_case__			:				int       =  FlaxBertModelTester(self	)






					@slow
					def 	_lowercase					( self					:  str	):
						snake_case__			:				Optional[Any]       =  FlaxBertModel.from_pretrained("bert-base-cased"	)
						snake_case__			:				Any       =  model(np.ones((1, 1)	)	)
						self.assertIsNotNone(__A	)









import argparse

import torch
from torch import nn

from transformers import MBartConfig, MBartForConditionalGeneration







def 					SCREAMING_SNAKE_CASE    (			snake_case_ :							str       ):
	snake_case__			:				Optional[Any]       =  [
	    "encoder.version",
	    "decoder.version",
	    "model.encoder.version",
	    "model.decoder.version",
	    "_float_tensor",
	    "decoder.output_projection.weight",
	]
	for k in ignore_keys:
		state_dict.pop(snake_case_					,  snake_case_       )







def 					SCREAMING_SNAKE_CASE    (			snake_case_ :							int       ):
	snake_case__,						snake_case__			:				Optional[Any]       =  emb.weight.shape
	snake_case__			:				Tuple       =  nn.Linear(snake_case_					,  snake_case_					,  bias=snake_case_       )
	snake_case__			:				Optional[int]       =  emb.weight.data
	return lin_layer







def 					SCREAMING_SNAKE_CASE    (			snake_case_ :							List[Any]					,  snake_case_ :							Optional[Any]="facebook/mbart-large-en-ro"					,  snake_case_ :							Optional[int]=False					,  snake_case_ :							List[Any]=False       ):
	snake_case__			:				Tuple       =  torch.load(snake_case_					,  map_location="cpu"       )["model"]
	remove_ignore_keys_(snake_case_       )
	snake_case__			:				Any       =  state_dict["encoder.embed_tokens.weight"].shape[0]

	snake_case__			:				List[Any]       =  MBartConfig.from_pretrained(snake_case_					,  vocab_size=snake_case_       )
	if mbart_aa and finetuned:
		snake_case__			:				int       =  "relu"

	snake_case__			:				List[str]       =  state_dict["decoder.embed_tokens.weight"]
	snake_case__			:				Tuple       =  MBartForConditionalGeneration(snake_case_       )
	model.model.load_state_dict(snake_case_       )

	if finetuned:
		snake_case__			:				Any       =  make_linear_from_emb(model.model.shared       )

	return model


if __name__ == "__main__":
	__lowerCamelCase :      int										=				argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    """fairseq_path""", type=str, help="""bart.large, bart.large.cnn or a path to a model.pt on local filesystem."""
	)
	parser.add_argument("""pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
	parser.add_argument(
	    """--hf_config""",
	    default="""facebook/mbart-large-cc25""",
	    type=str,
	    help="""Which huggingface architecture to use: mbart-large""",
	)
	parser.add_argument("""--mbart_50""", action="""store_true""", help="""whether the model is mMART-50 checkpoint""")
	parser.add_argument("""--finetuned""", action="""store_true""", help="""whether the model is a fine-tuned checkpoint""")
	__lowerCamelCase :      Optional[Any]										=				parser.parse_args()
	__lowerCamelCase :      str										=				convert_fairseq_mbart_checkpoint_from_disk(
	    args.fairseq_path, hf_config_path=args.hf_config, finetuned=args.finetuned, mbart_aa=args.mbart_aa
	)
	model.save_pretrained(args.pytorch_dump_folder_path)



