





'''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       UpperCamelCase__  :
	lowerCAmelCase__					:			int								=						MBartConfig
	lowerCAmelCase__					:			List[Any]								=						{}
	lowerCAmelCase__					:			Dict								=						"gelu"







	def __init__(				self			:   Any , lowerCamelCase			:   Optional[int] , lowerCamelCase			:   Optional[int]=1_3 , lowerCamelCase			:   List[Any]=7 , lowerCamelCase			:   Any=True , lowerCamelCase			:   Optional[int]=False , lowerCamelCase			:   str=9_9 , lowerCamelCase			:   Union[str, Any]=3_2 , lowerCamelCase			:   Tuple=2 , lowerCamelCase			:   str=4 , lowerCamelCase			:   Any=3_7 , lowerCamelCase			:   Optional[int]=0.1 , lowerCamelCase			:   List[str]=0.1 , lowerCamelCase			:   List[Any]=2_0 , lowerCamelCase			:   Dict=2 , lowerCamelCase			:   Optional[Any]=1 , lowerCamelCase			:   Dict=0 , ):




				'''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			:   List[str]		):




				'''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(lowerCamelCase , lowerCamelCase , lowerCamelCase		)
				return config, inputs_dict







	def 						__a   (				self			:   Optional[int] , lowerCamelCase			:   str , lowerCamelCase			:   List[Any]		):




				'''simple docstring'''
				a__     = TFMBartModel(config=lowerCamelCase		).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(lowerCamelCase , attention_mask=lowerCamelCase , head_mask=lowerCamelCase , use_cache=lowerCamelCase		)

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







def    _lowerCamelCase  (__lowerCamelCase  :     Any					,  __lowerCamelCase  :     int					,  __lowerCamelCase  :     Any					,  __lowerCamelCase  :     List[Any]=None					,  __lowerCamelCase  :     Optional[Any]=None					,  __lowerCamelCase  :     Any=None					,  __lowerCamelCase  :     Dict=None					,  __lowerCamelCase  :     List[Any]=None					,  )   ->    Optional[Any]:
			if attention_mask is None:
						a__     = tf.cast(tf.math.not_equal(__lowerCamelCase					,  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       UpperCamelCase__  (		__lowerCAmelCase	,__lowerCAmelCase	,unittest.TestCase     ):
	lowerCAmelCase__					:			Any								=						(TFMBartForConditionalGeneration, TFMBartModel) if is_tf_available() else ()
	lowerCAmelCase__					:			str								=						(TFMBartForConditionalGeneration,) if is_tf_available() else ()
	lowerCAmelCase__					:			Tuple								=						(
	    {
	        "conversational": TFMBartForConditionalGeneration,
	        "feature-extraction": TFMBartModel,
	        "summarization": TFMBartForConditionalGeneration,
	        "text2text-generation": TFMBartForConditionalGeneration,
	        "translation": TFMBartForConditionalGeneration,
	    }
	    if is_tf_available()
	    else {}
	)
	lowerCAmelCase__					:			Optional[int]								=						True
	lowerCAmelCase__					:			Dict								=						False
	lowerCAmelCase__					:			List[Any]								=						False







	def 						__a   (				self			:   Union[str, Any] , lowerCamelCase			:   str , lowerCamelCase			:   Optional[int] , lowerCamelCase			:   str , lowerCamelCase			:   List[str] , lowerCamelCase			:   Tuple		):




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

				return False







	def 						__a   (				self			:   Optional[int]		):




				'''simple docstring'''
				a__     = TFMBartModelTester(self		)
				a__     = ConfigTester(self , config_class=lowerCamelCase		)







	def 						__a   (				self			:   Tuple		):




				'''simple docstring'''
				self.config_tester.run_common_tests()







	def 						__a   (				self			:   List[Any]		):




				'''simple docstring'''
				a__     = self.model_tester.prepare_config_and_inputs_for_common()
				self.model_tester.check_decoder_model_past_large_inputs(*lowerCamelCase		)






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







	@cached_property
	def 						__a   (				self			:   Optional[Any]		):




				'''simple docstring'''
				return AutoTokenizer.from_pretrained(self.model_name		)







	@cached_property
	def 						__a   (				self			:   int		):




				'''simple docstring'''
				a__     = TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name		)
				return model







	def 						__a   (				self			:   List[str] , **lowerCamelCase			:   int		):




				'''simple docstring'''
				a__     = self.translate_src_text(**lowerCamelCase		)
				self.assertListEqual(self.expected_text , lowerCamelCase		)







	def 						__a   (				self			:   str , **lowerCamelCase			:   List[str]		):




				'''simple docstring'''
				a__     = self.tokenizer(self.src_text , **lowerCamelCase , 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(lowerCamelCase , skip_special_tokens=lowerCamelCase		)
				return generated_words







	@slow
	def 						__a   (				self			:   Optional[int]		):




				'''simple docstring'''
				self._assert_generated_batch_equal_expected()










'''simple docstring'''


import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration







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







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







def    _lowerCamelCase  (__lowerCamelCase  :     List[Any]     )   ->    Any:
			a__     = torch.load(__lowerCamelCase					,  map_location="cpu"     )
			a__     = mam_aaa["args"] or mam_aaa["cfg"]["model"]
			a__     = mam_aaa["model"]
			remove_ignore_keys_(__lowerCamelCase     )
			a__     = state_dict["encoder.embed_tokens.weight"].shape[0]

			a__     = MaMaaaConfig(
			    vocab_size=__lowerCamelCase					,  max_position_embeddings=1024					,  encoder_layers=args.encoder_layers					,  decoder_layers=args.decoder_layers					,  encoder_attention_heads=args.encoder_attention_heads					,  decoder_attention_heads=args.decoder_attention_heads					,  encoder_ffn_dim=args.encoder_ffn_embed_dim					,  decoder_ffn_dim=args.decoder_ffn_embed_dim					,  d_model=args.encoder_embed_dim					,  encoder_layerdrop=args.encoder_layerdrop					,  decoder_layerdrop=args.decoder_layerdrop					,  dropout=args.dropout					,  attention_dropout=args.attention_dropout					,  activation_dropout=args.activation_dropout					,  activation_function="relu"					,  )

			a__     = state_dict["decoder.embed_tokens.weight"]
			a__     = MaMaaaForConditionalGeneration(__lowerCamelCase     )
			model.model.load_state_dict(__lowerCamelCase					,  strict=__lowerCamelCase     )
			a__     = make_linear_from_emb(model.model.shared     )

			return model


if __name__ == "__main__":
		lowerCAmelCase_   :   Tuple       =					argparse.ArgumentParser()
		# Required parameters
		parser.add_argument("fairseq_path", type=str, help="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.")
		lowerCAmelCase_   :   Tuple       =					parser.parse_args()
		lowerCAmelCase_   :   int       =					convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
		model.save_pretrained(args.pytorch_dump_folder_path)



