"""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 lowerCamelCase		:






					'''simple docstring'''


					a   	=		MBartConfig
					a   	=		{}
					a   	=		"gelu"



					def __init__(						self			:				List[str]   , _snake_case			:				Any   , _snake_case			:				str=13   , _snake_case			:				str=7   , _snake_case			:				Optional[Any]=True   , _snake_case			:				int=False   , _snake_case			:				Tuple=99   , _snake_case			:				Tuple=32   , _snake_case			:				Optional[Any]=2   , _snake_case			:				str=4   , _snake_case			:				Dict=37   , _snake_case			:				int=0.1   , _snake_case			:				int=0.1   , _snake_case			:				List[Any]=20   , _snake_case			:				List[str]=2   , _snake_case			:				Tuple=1   , _snake_case			:				Dict=0   , )							->       List[str]:
									SCREAMING_SNAKE_CASE__		  =				parent
									SCREAMING_SNAKE_CASE__		  =				batch_size
									SCREAMING_SNAKE_CASE__		  =				seq_length
									SCREAMING_SNAKE_CASE__		  =				is_training
									SCREAMING_SNAKE_CASE__		  =				use_labels
									SCREAMING_SNAKE_CASE__		  =				vocab_size
									SCREAMING_SNAKE_CASE__		  =				hidden_size
									SCREAMING_SNAKE_CASE__		  =				num_hidden_layers
									SCREAMING_SNAKE_CASE__		  =				num_attention_heads
									SCREAMING_SNAKE_CASE__		  =				intermediate_size
									SCREAMING_SNAKE_CASE__		  =				hidden_dropout_prob
									SCREAMING_SNAKE_CASE__		  =				attention_probs_dropout_prob
									SCREAMING_SNAKE_CASE__		  =				max_position_embeddings
									SCREAMING_SNAKE_CASE__		  =				eos_token_id
									SCREAMING_SNAKE_CASE__		  =				pad_token_id
									SCREAMING_SNAKE_CASE__		  =				bos_token_id



					def 			lowerCAmelCase_					(						self			:				List[str]		)							->       Optional[Any]:
									SCREAMING_SNAKE_CASE__		  =				ids_tensor([self.batch_size, self.seq_length - 1]   , self.vocab_size		)
									SCREAMING_SNAKE_CASE__		  =				tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size		)   , 1		)
									SCREAMING_SNAKE_CASE__		  =				tf.concat([input_ids, eos_tensor]   , axis=1		)

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

									SCREAMING_SNAKE_CASE__		  =				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   , )
									SCREAMING_SNAKE_CASE__		  =				prepare_mbart_inputs_dict(_snake_case   , _snake_case   , _snake_case		)
									return config, inputs_dict



					def 			lowerCAmelCase_					(						self			:				int   , _snake_case			:				Any   , _snake_case			:				List[str]		)							->       Dict:
									SCREAMING_SNAKE_CASE__		  =				TFMBartModel(config=_snake_case		).get_decoder()
									SCREAMING_SNAKE_CASE__		  =				inputs_dict["input_ids"]

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

									# first forward pass
									SCREAMING_SNAKE_CASE__		  =				model(_snake_case   , attention_mask=_snake_case   , head_mask=_snake_case   , use_cache=_snake_case		)

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




def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase=None  ,					__UpperCAmelCase=None  ,					__UpperCAmelCase=None  ,					__UpperCAmelCase=None  ,					__UpperCAmelCase=None  ,					)							->   Optional[int]:
				if attention_mask is None:
								SCREAMING_SNAKE_CASE__		  =				tf.cast(tf.math.not_equal(__UpperCAmelCase  ,					config.pad_token_id       )  ,					tf.inta       )
				if decoder_attention_mask is None:
								SCREAMING_SNAKE_CASE__		  =				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:
								SCREAMING_SNAKE_CASE__		  =				tf.ones((config.encoder_layers, config.encoder_attention_heads)       )
				if decoder_head_mask is None:
								SCREAMING_SNAKE_CASE__		  =				tf.ones((config.decoder_layers, config.decoder_attention_heads)       )
				if cross_attn_head_mask is None:
								SCREAMING_SNAKE_CASE__		  =				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 lowerCamelCase		(_SCREAMING_SNAKE_CASE    ,    _SCREAMING_SNAKE_CASE    ,    unittest.TestCase      ):






					'''simple docstring'''


					a   	=		(TFMBartForConditionalGeneration, TFMBartModel) if is_tf_available() else ()
					a   	=		(TFMBartForConditionalGeneration,) if is_tf_available() else ()
					a   	=		(
					    {
					        "conversational": TFMBartForConditionalGeneration,
					        "feature-extraction": TFMBartModel,
					        "summarization": TFMBartForConditionalGeneration,
					        "text2text-generation": TFMBartForConditionalGeneration,
					        "translation": TFMBartForConditionalGeneration,
					    }
					    if is_tf_available()
					    else {}
					)
					a   	=		True
					a   	=		False
					a   	=		False



					def 			lowerCAmelCase_					(						self			:				int   , _snake_case			:				Tuple   , _snake_case			:				Optional[Any]   , _snake_case			:				Dict   , _snake_case			:				int   , _snake_case			:				Union[str, Any]		)							->       Tuple:
									if pipeline_test_casse_name != "FeatureExtractionPipelineTests":
													# Exception encountered when calling layer '...'
													return True

									return False



					def 			lowerCAmelCase_					(						self			:				Union[str, Any]		)							->       int:
									SCREAMING_SNAKE_CASE__		  =				TFMBartModelTester(self		)
									SCREAMING_SNAKE_CASE__		  =				ConfigTester(self   , config_class=_snake_case		)



					def 			lowerCAmelCase_					(						self			:				Dict		)							->       List[str]:
									self.config_tester.run_common_tests()



					def 			lowerCAmelCase_					(						self			:				List[str]		)							->       List[Any]:
									SCREAMING_SNAKE_CASE__		  =				self.model_tester.prepare_config_and_inputs_for_common()
									self.model_tester.check_decoder_model_past_large_inputs(*_snake_case		)



@require_sentencepiece
@require_tokenizers
@require_tf
class lowerCamelCase		(unittest.TestCase      ):






					'''simple docstring'''


					a   	=		[
					    " UN Chief Says There Is No Military Solution in Syria",
					]
					a   	=		[
					    "Şeful ONU declară că nu există o soluţie militară în Siria",
					]
					a   	=		"facebook/mbart-large-en-ro"



					@cached_property
					def 			lowerCAmelCase_					(						self			:				Union[str, Any]		)							->       int:
									return AutoTokenizer.from_pretrained(self.model_name		)



					@cached_property
					def 			lowerCAmelCase_					(						self			:				Tuple		)							->       Union[str, Any]:
									SCREAMING_SNAKE_CASE__		  =				TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name		)
									return model



					def 			lowerCAmelCase_					(						self			:				List[Any]   , **_snake_case			:				str		)							->       Any:
									SCREAMING_SNAKE_CASE__		  =				self.translate_src_text(**_snake_case		)
									self.assertListEqual(self.expected_text   , _snake_case		)



					def 			lowerCAmelCase_					(						self			:				Any   , **_snake_case			:				List[str]		)							->       List[Any]:
									SCREAMING_SNAKE_CASE__		  =				self.tokenizer(self.src_text   , **_snake_case   , return_tensors="tf"		)
									SCREAMING_SNAKE_CASE__		  =				self.model.generate(
									    model_inputs.input_ids   , attention_mask=model_inputs.attention_mask   , num_beams=2		)
									SCREAMING_SNAKE_CASE__		  =				self.tokenizer.batch_decode(_snake_case   , skip_special_tokens=_snake_case		)
									return generated_words



					@slow
					def 			lowerCAmelCase_					(						self			:				Optional[int]		)							->       Union[str, Any]:
									self._assert_generated_batch_equal_expected()





"""simple docstring"""






import argparse
import os

import torch

from transformers.utils import WEIGHTS_NAME


_A   					=    ['small', 'medium', 'large']

_A   					=    'lm_head.decoder.weight'
_A   					=    'lm_head.weight'




def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase  ,					__UpperCAmelCase       )							->   List[str]:
				SCREAMING_SNAKE_CASE__		  =				torch.load(__UpperCAmelCase       )
				SCREAMING_SNAKE_CASE__		  =				d.pop(__UpperCAmelCase       )
				os.makedirs(__UpperCAmelCase  ,					exist_ok=__UpperCAmelCase       )
				torch.save(__UpperCAmelCase  ,					os.path.join(__UpperCAmelCase  ,					__UpperCAmelCase       )       )


if __name__ == "__main__":
						_A   					=    argparse.ArgumentParser()
						parser.add_argument('--dialogpt_path', default='.', type=str)
						_A   					=    parser.parse_args()
						for MODEL in DIALOGPT_MODELS:
												_A   					=    os.path.join(args.dialogpt_path, F'{MODEL}_ft.pkl')
												_A   					=    F'./DialoGPT-{MODEL}'
												convert_dialogpt_checkpoint(
												    checkpoint_path,
												    pytorch_dump_folder_path,
												)




