


"""simple docstring"""


from __future__ import annotations

import unittest

from transformers import AutoTokenizer, MBartConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
		import tensorflow as tf

		from transformers import TFAutoModelForSeqaSeqLM, TFMBartForConditionalGeneration, TFMBartModel
@require_tf
class snake_case      :
		__UpperCamelCase           =			MBartConfig
		__UpperCamelCase           =			{}
		__UpperCamelCase           =			'gelu'







		def __init__(					self      :       Tuple    ,   a__      :       Optional[int]    ,   a__      :       int=13    ,   a__      :       Dict=7    ,   a__      :       List[Any]=True    ,   a__      :       Optional[Any]=False    ,   a__      :       Dict=99    ,   a__      :       Union[str, Any]=32    ,   a__      :       Tuple=2    ,   a__      :       Union[str, Any]=4    ,   a__      :       Tuple=37    ,   a__      :       List[str]=0.1    ,   a__      :       Dict=0.1    ,   a__      :       List[str]=20    ,   a__      :       Dict=2    ,   a__      :       Tuple=1    ,   a__      :       Any=0    ,   )       ->						Tuple:





						'''simple docstring'''







						_A					  =			parent
						_A					  =			batch_size
						_A					  =			seq_length
						_A					  =			is_training
						_A					  =			use_labels
						_A					  =			vocab_size
						_A					  =			hidden_size
						_A					  =			num_hidden_layers
						_A					  =			num_attention_heads
						_A					  =			intermediate_size
						_A					  =			hidden_dropout_prob
						_A					  =			attention_probs_dropout_prob
						_A					  =			max_position_embeddings
						_A					  =			eos_token_id
						_A					  =			pad_token_id
						_A					  =			bos_token_id







		def  a_ (					self      :       Tuple		)       ->						Union[str, Any]:





						'''simple docstring'''







						_A					  =			ids_tensor([self.batch_size, self.seq_length - 1]    ,   self.vocab_size		)
						_A					  =			tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size		)    ,   1		)
						_A					  =			tf.concat([input_ids, eos_tensor]    ,   axis=1		)

						_A					  =			ids_tensor([self.batch_size, self.seq_length]    ,   self.vocab_size		)

						_A					  =			self.config_cls(
						    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_ids=[2]    ,   bos_token_id=self.bos_token_id    ,   pad_token_id=self.pad_token_id    ,   decoder_start_token_id=self.pad_token_id    ,   **self.config_updates    ,   )
						_A					  =			prepare_mbart_inputs_dict(a__    ,   a__    ,   a__		)
						return config, inputs_dict







		def  a_ (					self      :       Tuple    ,   a__      :       List[Any]    ,   a__      :       Dict		)       ->						Any:





						'''simple docstring'''







						_A					  =			TFMBartModel(config=a__		).get_decoder()
						_A					  =			inputs_dict["input_ids"]

						_A					  =			input_ids[:1, :]
						_A					  =			inputs_dict["attention_mask"][:1, :]
						_A					  =			inputs_dict["head_mask"]
						_A					  =			1

						# first forward pass
						_A					  =			model(a__    ,   attention_mask=a__    ,   head_mask=a__    ,   use_cache=a__		)

						_A  ,		_A					  =			outputs.to_tuple()
						_A					  =			past_key_values[1]






def      a__ (				__lowercase     ,			__lowercase     ,			__lowercase     ,			__lowercase=None     ,			__lowercase=None     ,			__lowercase=None     ,			__lowercase=None     ,			__lowercase=None     ,			)	->	Union[str, Any]:
				if attention_mask is None:
								_A					  =			tf.cast(tf.math.not_equal(__lowercase     ,			config.pad_token_id   )     ,			tf.inta   )
				if decoder_attention_mask is None:
								_A					  =			tf.concat(
								    [
								        tf.ones(decoder_input_ids[:, :1].shape     ,			dtype=tf.inta   ),
								        tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:]     ,			config.pad_token_id   )     ,			tf.inta   ),
								    ]     ,			axis=-1     ,			)
				if head_mask is None:
								_A					  =			tf.ones((config.encoder_layers, config.encoder_attention_heads)   )
				if decoder_head_mask is None:
								_A					  =			tf.ones((config.decoder_layers, config.decoder_attention_heads)   )
				if cross_attn_head_mask is None:
								_A					  =			tf.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": decoder_attention_mask,
				    "head_mask": head_mask,
				    "decoder_head_mask": decoder_head_mask,
				    "cross_attn_head_mask": cross_attn_head_mask,
				}
@require_tf
class snake_case      (  _UpperCamelCase     ,				_UpperCamelCase     ,				unittest.TestCase):
		__UpperCamelCase           =			(TFMBartForConditionalGeneration, TFMBartModel) if is_tf_available() else ()
		__UpperCamelCase           =			(TFMBartForConditionalGeneration,) if is_tf_available() else ()
		__UpperCamelCase           =			(
		    {
		        'conversational': TFMBartForConditionalGeneration,
		        'feature-extraction': TFMBartModel,
		        'summarization': TFMBartForConditionalGeneration,
		        'text2text-generation': TFMBartForConditionalGeneration,
		        'translation': TFMBartForConditionalGeneration,
		    }
		    if is_tf_available()
		    else {}
		)
		__UpperCamelCase           =			True
		__UpperCamelCase           =			False
		__UpperCamelCase           =			False







		def  a_ (					self      :       Any    ,   a__      :       List[str]    ,   a__      :       str    ,   a__      :       Tuple    ,   a__      :       List[Any]    ,   a__      :       int		)       ->						Optional[Any]:





						'''simple docstring'''







						if pipeline_test_casse_name != "FeatureExtractionPipelineTests":
										# Exception encountered when calling layer '...'
										return True

						return False







		def  a_ (					self      :       Tuple		)       ->						Optional[Any]:





						'''simple docstring'''







						_A					  =			TFMBartModelTester(self		)
						_A					  =			ConfigTester(self    ,   config_class=a__		)







		def  a_ (					self      :       int		)       ->						Tuple:





						'''simple docstring'''







						self.config_tester.run_common_tests()







		def  a_ (					self      :       List[Any]		)       ->						int:





						'''simple docstring'''







						_A					  =			self.model_tester.prepare_config_and_inputs_for_common()
						self.model_tester.check_decoder_model_past_large_inputs(*a__		)





@require_sentencepiece
@require_tokenizers
@require_tf
class snake_case      (  unittest.TestCase):
		__UpperCamelCase           =			[
		    ' UN Chief Says There Is No Military Solution in Syria',
		]
		__UpperCamelCase           =			[
		    'Şeful ONU declară că nu există o soluţie militară în Siria',
		]
		__UpperCamelCase           =			'facebook/mbart-large-en-ro'







		@cached_property
		def  a_ (					self      :       int		)       ->						Dict:





						'''simple docstring'''







						return AutoTokenizer.from_pretrained(self.model_name		)







		@cached_property
		def  a_ (					self      :       Union[str, Any]		)       ->						Tuple:





						'''simple docstring'''







						_A					  =			TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name		)
						return model







		def  a_ (					self      :       str    ,   **a__      :       Any		)       ->						Union[str, Any]:





						'''simple docstring'''







						_A					  =			self.translate_src_text(**a__		)
						self.assertListEqual(self.expected_text    ,   a__		)







		def  a_ (					self      :       Dict    ,   **a__      :       List[Any]		)       ->						Optional[int]:





						'''simple docstring'''







						_A					  =			self.tokenizer(self.src_text    ,   **a__    ,   return_tensors="tf"		)
						_A					  =			self.model.generate(
						    model_inputs.input_ids    ,   attention_mask=model_inputs.attention_mask    ,   num_beams=2		)
						_A					  =			self.tokenizer.batch_decode(a__    ,   skip_special_tokens=a__		)
						return generated_words







		@slow
		def  a_ (					self      :       Any		)       ->						Optional[Any]:





						'''simple docstring'''







						self._assert_generated_batch_equal_expected()



"""simple docstring"""


import argparse

import torch
from torch import nn

from transformers import MBartConfig, MBartForConditionalGeneration






def      a__ (				__lowercase   )	->	Optional[int]:
				_A					  =			[
				    "encoder.version",
				    "decoder.version",
				    "model.encoder.version",
				    "model.decoder.version",
				    "_float_tensor",
				    "decoder.output_projection.weight",
				]
				for k in ignore_keys:
								state_dict.pop(__lowercase     ,			__lowercase   )






def      a__ (				__lowercase   )	->	List[Any]:
				_A  ,		_A					  =			emb.weight.shape
				_A					  =			nn.Linear(__lowercase     ,			__lowercase     ,			bias=__lowercase   )
				_A					  =			emb.weight.data
				return lin_layer






def      a__ (				__lowercase     ,			__lowercase="facebook/mbart-large-en-ro"     ,			__lowercase=False     ,			__lowercase=False   )	->	List[str]:
				_A					  =			torch.load(__lowercase     ,			map_location="cpu"   )["model"]
				remove_ignore_keys_(__lowercase   )
				_A					  =			state_dict["encoder.embed_tokens.weight"].shape[0]

				_A					  =			MBartConfig.from_pretrained(__lowercase     ,			vocab_size=__lowercase   )
				if mbart_aa and finetuned:
								_A					  =			"relu"

				_A					  =			state_dict["decoder.embed_tokens.weight"]
				_A					  =			MBartForConditionalGeneration(__lowercase   )
				model.model.load_state_dict(__lowercase   )

				if finetuned:
								_A					  =			make_linear_from_emb(model.model.shared   )

				return model


if __name__ == "__main__":
		a_       							=    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")
		a_       							=    parser.parse_args()
		a_       							=    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)