




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			:





				'''simple docstring'''



				__a							: List[Any]										=MBartConfig
				__a							: List[Any]										={}
				__a							: str										="""gelu"""



				def __init__( self       , UpperCAmelCase_       , UpperCAmelCase_=13       , UpperCAmelCase_=7       , UpperCAmelCase_=True       , UpperCAmelCase_=False       , UpperCAmelCase_=99       , UpperCAmelCase_=32       , UpperCAmelCase_=2       , UpperCAmelCase_=4       , UpperCAmelCase_=37       , UpperCAmelCase_=0.1       , UpperCAmelCase_=0.1       , UpperCAmelCase_=20       , UpperCAmelCase_=2       , UpperCAmelCase_=1       , UpperCAmelCase_=0       , ):
											lowerCAmelCase   =		parent
											lowerCAmelCase   =		batch_size
											lowerCAmelCase   =		seq_length
											lowerCAmelCase   =		is_training
											lowerCAmelCase   =		use_labels
											lowerCAmelCase   =		vocab_size
											lowerCAmelCase   =		hidden_size
											lowerCAmelCase   =		num_hidden_layers
											lowerCAmelCase   =		num_attention_heads
											lowerCAmelCase   =		intermediate_size
											lowerCAmelCase   =		hidden_dropout_prob
											lowerCAmelCase   =		attention_probs_dropout_prob
											lowerCAmelCase   =		max_position_embeddings
											lowerCAmelCase   =		eos_token_id
											lowerCAmelCase   =		pad_token_id
											lowerCAmelCase   =		bos_token_id



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

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

											lowerCAmelCase   =		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       , )
											lowerCAmelCase   =		prepare_mbart_inputs_dict(UpperCAmelCase_       , UpperCAmelCase_       , UpperCAmelCase_						)
											return config, inputs_dict






				def       __snake_case		( self       , UpperCAmelCase_       , UpperCAmelCase_						):
											lowerCAmelCase   =		TFMBartModel(config=UpperCAmelCase_						).get_decoder()
											lowerCAmelCase   =		inputs_dict['''input_ids''']

											lowerCAmelCase   =		input_ids[:1, :]
											lowerCAmelCase   =		inputs_dict['''attention_mask'''][:1, :]
											lowerCAmelCase   =		inputs_dict['''head_mask''']
											lowerCAmelCase   =		1

											# first forward pass
											lowerCAmelCase   =		model(UpperCAmelCase_       , attention_mask=UpperCAmelCase_       , head_mask=UpperCAmelCase_       , use_cache=UpperCAmelCase_						)

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




def       UpperCAmelCase       (     _snake_case						, _snake_case						, _snake_case						, _snake_case=None						, _snake_case=None						, _snake_case=None						, _snake_case=None						, _snake_case=None						, ):
							if attention_mask is None:
														lowerCAmelCase   =		tf.cast(tf.math.not_equal(_snake_case						, config.pad_token_id							)						, tf.inta							)
							if decoder_attention_mask is None:
														lowerCAmelCase   =		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:
														lowerCAmelCase   =		tf.ones((config.encoder_layers, config.encoder_attention_heads)							)
							if decoder_head_mask is None:
														lowerCAmelCase   =		tf.ones((config.decoder_layers, config.decoder_attention_heads)							)
							if cross_attn_head_mask is None:
														lowerCAmelCase   =		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			( __UpperCAmelCase     ,					__UpperCAmelCase     ,					unittest.TestCase  ):





				'''simple docstring'''



				__a							: Tuple										=(TFMBartForConditionalGeneration, TFMBartModel) if is_tf_available() else ()
				__a							: Dict										=(TFMBartForConditionalGeneration,) if is_tf_available() else ()
				__a							: List[Any]										=(
				    {
				        """conversational""": TFMBartForConditionalGeneration,
				        """feature-extraction""": TFMBartModel,
				        """summarization""": TFMBartForConditionalGeneration,
				        """text2text-generation""": TFMBartForConditionalGeneration,
				        """translation""": TFMBartForConditionalGeneration,
				    }
				    if is_tf_available()
				    else {}
				)
				__a							: Optional[Any]										=True
				__a							: int										=False
				__a							: Optional[Any]										=False



				def       __snake_case		( self       , UpperCAmelCase_       , UpperCAmelCase_       , UpperCAmelCase_       , UpperCAmelCase_       , UpperCAmelCase_						):
											if pipeline_test_casse_name != "FeatureExtractionPipelineTests":
																		# Exception encountered when calling layer '...'
																		return True

											return False



				def       __snake_case		( self						):
											lowerCAmelCase   =		TFMBartModelTester(self						)
											lowerCAmelCase   =		ConfigTester(self       , config_class=UpperCAmelCase_						)



				def       __snake_case		( self						):
											self.config_tester.run_common_tests()






				def       __snake_case		( self						):
											lowerCAmelCase   =		self.model_tester.prepare_config_and_inputs_for_common()
											self.model_tester.check_decoder_model_past_large_inputs(*UpperCAmelCase_						)




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





				'''simple docstring'''



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



				@cached_property
				def       __snake_case		( self						):
											return AutoTokenizer.from_pretrained(self.model_name						)



				@cached_property
				def       __snake_case		( self						):
											lowerCAmelCase   =		TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name						)
											return model



				def       __snake_case		( self       , **UpperCAmelCase_						):
											lowerCAmelCase   =		self.translate_src_text(**UpperCAmelCase_						)
											self.assertListEqual(self.expected_text       , UpperCAmelCase_						)



				def       __snake_case		( self       , **UpperCAmelCase_						):
											lowerCAmelCase   =		self.tokenizer(self.src_text       , **UpperCAmelCase_       , return_tensors='''tf'''						)
											lowerCAmelCase   =		self.model.generate(
											    model_inputs.input_ids       , attention_mask=model_inputs.attention_mask       , num_beams=2						)
											lowerCAmelCase   =		self.tokenizer.batch_decode(UpperCAmelCase_       , skip_special_tokens=UpperCAmelCase_						)
											return generated_words






				@slow
				def       __snake_case		( self						):
											self._assert_generated_batch_equal_expected()









import argparse
import os
import shutil

import torch
from emmental.modules import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer



def       UpperCAmelCase       (     _snake_case							):
							lowerCAmelCase   =		args.pruning_method
							lowerCAmelCase   =		args.threshold

							lowerCAmelCase   =		args.model_name_or_path.rstrip('''/'''							)
							lowerCAmelCase   =		args.target_model_path

							print(F"""Load fine-pruned model from {model_name_or_path}"""							)
							lowerCAmelCase   =		torch.load(os.path.join(_snake_case						, '''pytorch_model.bin'''							)							)
							lowerCAmelCase   =		{}

							for name, tensor in model.items():
														if "embeddings" in name or "LayerNorm" in name or "pooler" in name:
																					lowerCAmelCase   =		tensor
																					print(F"""Copied layer {name}"""							)
														elif "classifier" in name or "qa_output" in name:
																					lowerCAmelCase   =		tensor
																					print(F"""Copied layer {name}"""							)
														elif "bias" in name:
																					lowerCAmelCase   =		tensor
																					print(F"""Copied layer {name}"""							)
														else:
																					if pruning_method == "magnitude":
																												lowerCAmelCase   =		MagnitudeBinarizer.apply(inputs=_snake_case						, threshold=_snake_case							)
																												lowerCAmelCase   =		tensor * mask
																												print(F"""Pruned layer {name}"""							)
																					elif pruning_method == "topK":
																												if "mask_scores" in name:
																																			continue
																												lowerCAmelCase   =		name[:-6]
																												lowerCAmelCase   =		model[F"""{prefix_}mask_scores"""]
																												lowerCAmelCase   =		TopKBinarizer.apply(_snake_case						, _snake_case							)
																												lowerCAmelCase   =		tensor * mask
																												print(F"""Pruned layer {name}"""							)
																					elif pruning_method == "sigmoied_threshold":
																												if "mask_scores" in name:
																																			continue
																												lowerCAmelCase   =		name[:-6]
																												lowerCAmelCase   =		model[F"""{prefix_}mask_scores"""]
																												lowerCAmelCase   =		ThresholdBinarizer.apply(_snake_case						, _snake_case						, _snake_case							)
																												lowerCAmelCase   =		tensor * mask
																												print(F"""Pruned layer {name}"""							)
																					elif pruning_method == "l0":
																												if "mask_scores" in name:
																																			continue
																												lowerCAmelCase   =		name[:-6]
																												lowerCAmelCase   =		model[F"""{prefix_}mask_scores"""]
																												lowerCAmelCase   ,     lowerCAmelCase   =		-0.1, 1.1
																												lowerCAmelCase   =		torch.sigmoid(_snake_case							)
																												lowerCAmelCase   =		s * (r - l) + l
																												lowerCAmelCase   =		s_bar.clamp(min=0.0						, max=1.0							)
																												lowerCAmelCase   =		tensor * mask
																												print(F"""Pruned layer {name}"""							)
																					else:
																												raise ValueError('''Unknown pruning method'''							)

							if target_model_path is None:
														lowerCAmelCase   =		os.path.join(
														    os.path.dirname(_snake_case							)						, F"""bertarized_{os.path.basename(_snake_case							)}"""							)

							if not os.path.isdir(_snake_case							):
														shutil.copytree(_snake_case						, _snake_case							)
														print(F"""\nCreated folder {target_model_path}"""							)

							torch.save(_snake_case						, os.path.join(_snake_case						, '''pytorch_model.bin'''							)							)
							print('''\nPruned model saved! See you later!'''							)


if __name__ == "__main__":
		UpperCAmelCase_						=argparse.ArgumentParser()

		parser.add_argument(
		    """--pruning_method""",
		    choices=["""l0""", """magnitude""", """topK""", """sigmoied_threshold"""],
		    type=str,
		    required=True,
		    help=(
		        """Pruning Method (l0 = L0 regularization, magnitude = Magnitude pruning, topK = Movement pruning,"""
		        """ sigmoied_threshold = Soft movement pruning)"""
		    ),
		)
		parser.add_argument(
		    """--threshold""",
		    type=float,
		    required=False,
		    help=(
		        """For `magnitude` and `topK`, it is the level of remaining weights (in %) in the fine-pruned model."""
		        """For `sigmoied_threshold`, it is the threshold \tau against which the (sigmoied) scores are compared."""
		        """Not needed for `l0`"""
		    ),
		)
		parser.add_argument(
		    """--model_name_or_path""",
		    type=str,
		    required=True,
		    help="""Folder containing the model that was previously fine-pruned""",
		)
		parser.add_argument(
		    """--target_model_path""",
		    default=None,
		    type=str,
		    required=False,
		    help="""Folder containing the model that was previously fine-pruned""",
		)

		UpperCAmelCase_						=parser.parse_args()

		main(args)



