



'''simple docstring'''

from __future__ import annotations

import unittest

from transformers import BlenderbotSmallConfig, BlenderbotSmallTokenizer, is_tf_available
from transformers.testing_utils import 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, TFBlenderbotSmallForConditionalGeneration, TFBlenderbotSmallModel



@require_tf
class 		lowerCamelCase__      :




					'''simple docstring'''



					A_						    =						BlenderbotSmallConfig
					A_						    =						{}
					A_						    =						"""gelu"""




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



						'''simple docstring'''





						_lowercase					:       Tuple       =		parent
						_lowercase					:       Optional[Any]       =		batch_size
						_lowercase					:       Union[str, Any]       =		seq_length
						_lowercase					:       Dict       =		is_training
						_lowercase					:       List[str]       =		use_labels
						_lowercase					:       int       =		vocab_size
						_lowercase					:       Any       =		hidden_size
						_lowercase					:       Union[str, Any]       =		num_hidden_layers
						_lowercase					:       Optional[int]       =		num_attention_heads
						_lowercase					:       Any       =		intermediate_size

						_lowercase					:       str       =		hidden_dropout_prob
						_lowercase					:       Tuple       =		attention_probs_dropout_prob
						_lowercase					:       Optional[Any]       =		max_position_embeddings
						_lowercase					:       str       =		eos_token_id
						_lowercase					:       List[str]       =		pad_token_id
						_lowercase					:       str       =		bos_token_id




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



						'''simple docstring'''





						_lowercase					:       Optional[int]       =		ids_tensor([self.batch_size, self.seq_length - 1]     ,  self.vocab_size					)
						_lowercase					:       List[Any]       =		tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size					)     ,  1					)
						_lowercase					:       Dict       =		tf.concat([input_ids, eos_tensor]     ,  axis=1					)

						_lowercase					:       str       =		ids_tensor([self.batch_size, self.seq_length]     ,  self.vocab_size					)

						_lowercase					:       Any       =		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     ,  )
						_lowercase					:       int       =		prepare_blenderbot_small_inputs_dict(UpperCamelCase_     ,  UpperCamelCase_     ,  UpperCamelCase_					)
						return config, inputs_dict




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



						'''simple docstring'''





						_lowercase					:       List[Any]       =		TFBlenderbotSmallModel(config=UpperCamelCase_					).get_decoder()
						_lowercase					:       str       =		inputs_dict['input_ids']

						_lowercase					:       Union[str, Any]       =		input_ids[:1, :]
						_lowercase					:       List[str]       =		inputs_dict['attention_mask'][:1, :]
						_lowercase					:       Optional[Any]       =		inputs_dict['head_mask']
						_lowercase					:       Optional[int]       =		1

						# first forward pass
						_lowercase					:       Any       =		model(UpperCamelCase_     ,  attention_mask=UpperCamelCase_     ,  head_mask=UpperCamelCase_     ,  use_cache=UpperCamelCase_					)

						_lowercase  ,    _lowercase					:       Tuple       =		outputs.to_tuple()

						# create hypothetical next token and extent to next_input_ids
						_lowercase					:       Tuple       =		ids_tensor((self.batch_size, 3)     ,  config.vocab_size					)
						_lowercase					:       List[Any]       =		tf.cast(ids_tensor((self.batch_size, 3)     ,  2					)     ,  tf.inta					)

						# append to next input_ids and
						_lowercase					:       str       =		tf.concat([input_ids, next_tokens]     ,  axis=-1					)
						_lowercase					:       Dict       =		tf.concat([attention_mask, next_attn_mask]     ,  axis=-1					)

						_lowercase					:       Optional[int]       =		model(UpperCamelCase_     ,  attention_mask=UpperCamelCase_					)[0]
						_lowercase					:       Optional[int]       =		model(UpperCamelCase_     ,  attention_mask=UpperCamelCase_     ,  past_key_values=UpperCamelCase_					)[0]

						self.parent.assertEqual(next_tokens.shape[1]     ,  output_from_past.shape[1]					)

						# select random slice
						_lowercase					:       Union[str, Any]       =		int(ids_tensor((1,)     ,  output_from_past.shape[-1]					)					)
						_lowercase					:       Union[str, Any]       =		output_from_no_past[:, -3:, random_slice_idx]
						_lowercase					:       Any       =		output_from_past[:, :, random_slice_idx]

						# test that outputs are equal for slice
						tf.debugging.assert_near(UpperCamelCase_     ,  UpperCamelCase_     ,  rtol=1E-3					)







def 						__UpperCamelCase				(							_lowercase,				_lowercase,				_lowercase,				_lowercase=None,				_lowercase=None,				_lowercase=None,				_lowercase=None,				_lowercase=None,				) ->			Tuple:
	if attention_mask is None:
		_lowercase					:       Any       =		tf.cast(tf.math.not_equal(_lowercase,				config.pad_token_id					),				tf.inta					)
	if decoder_attention_mask is None:
		_lowercase					:       Optional[Any]       =		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:
		_lowercase					:       str       =		tf.ones((config.encoder_layers, config.encoder_attention_heads)					)
	if decoder_head_mask is None:
		_lowercase					:       Optional[int]       =		tf.ones((config.decoder_layers, config.decoder_attention_heads)					)
	if cross_attn_head_mask is None:
		_lowercase					:       Dict       =		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__      (						A	,   A	,   unittest.TestCase     ):




					'''simple docstring'''



					A_						    =						(
					    (TFBlenderbotSmallForConditionalGeneration, TFBlenderbotSmallModel) if is_tf_available() else ()
					)
					A_						    =						(TFBlenderbotSmallForConditionalGeneration,) if is_tf_available() else ()
					A_						    =						(
					    {
					        """conversational""": TFBlenderbotSmallForConditionalGeneration,
					        """feature-extraction""": TFBlenderbotSmallModel,
					        """summarization""": TFBlenderbotSmallForConditionalGeneration,
					        """text2text-generation""": TFBlenderbotSmallForConditionalGeneration,
					        """translation""": TFBlenderbotSmallForConditionalGeneration,
					    }
					    if is_tf_available()
					    else {}
					)
					A_						    =						True
					A_						    =						False
					A_						    =						False




					def 						__UpperCAmelCase     (  self				:	int					)  ->       List[str]:



						'''simple docstring'''





						_lowercase					:       Union[str, Any]       =		TFBlenderbotSmallModelTester(self					)
						_lowercase					:       Tuple       =		ConfigTester(self     ,  config_class=UpperCamelCase_					)




					def 						__UpperCAmelCase     (  self				:	Optional[int]					)  ->       Dict:



						'''simple docstring'''





						self.config_tester.run_common_tests()




					def 						__UpperCAmelCase     (  self				:	Optional[int]					)  ->       Union[str, Any]:



						'''simple docstring'''





						_lowercase					:       str       =		self.model_tester.prepare_config_and_inputs_for_common()
						self.model_tester.check_decoder_model_past_large_inputs(*UpperCamelCase_					)






@require_tokenizers
@require_tf
class 		lowerCamelCase__      (						unittest.TestCase     ):




					'''simple docstring'''



					A_						    =						[
					    """Social anxiety\nWow, I am never shy. Do you have anxiety?\nYes. I end up sweating and blushing and feel like """
					    """  i'm going to throw up.\nand why is that?"""
					]
					A_						    =						"""facebook/blenderbot_small-90M"""




					@cached_property
					def 						__UpperCAmelCase     (  self				:	Optional[int]					)  ->       Any:



						'''simple docstring'''





						return BlenderbotSmallTokenizer.from_pretrained('facebook/blenderbot-90M'					)




					@cached_property
					def 						__UpperCAmelCase     (  self				:	Union[str, Any]					)  ->       List[str]:



						'''simple docstring'''





						_lowercase					:       Tuple       =		TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name					)
						return model




					@slow
					def 						__UpperCAmelCase     (  self				:	Union[str, Any]					)  ->       List[Any]:



						'''simple docstring'''





						_lowercase					:       Optional[Any]       =		self.tokenizer(self.src_text     ,  return_tensors='tf'					)
						_lowercase					:       List[Any]       =		self.model.generate(
						    model_inputs.input_ids     ,  attention_mask=model_inputs.attention_mask     ,  num_beams=2     ,  use_cache=UpperCamelCase_     ,  )
						_lowercase					:       Optional[Any]       =		self.tokenizer.batch_decode(generated_ids.numpy()     ,  skip_special_tokens=UpperCamelCase_					)[0]
						assert generated_words in (
						    "i don't know. i just feel like i'm going to throw up. it's not fun.",
						    "i'm not sure. i just feel like i've been feeling like i have to be in a certain place",
						    "i'm not sure. i just feel like i've been in a bad situation.",
						)





'''simple docstring'''

import argparse
import os
import shutil

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







def 						__UpperCamelCase				(							_lowercase					) ->			List[Any]:
	_lowercase					:       Tuple       =		args.pruning_method
	_lowercase					:       int       =		args.threshold

	_lowercase					:       str       =		args.model_name_or_path.rstrip('/'					)
	_lowercase					:       Dict       =		args.target_model_path

	print(f'''Load fine-pruned model from {model_name_or_path}'''					)
	_lowercase					:       str       =		torch.load(os.path.join(_lowercase,				'pytorch_model.bin'					)					)
	_lowercase					:       List[Any]       =		{}

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

	if target_model_path is None:
		_lowercase					:       List[Any]       =		os.path.join(
		    os.path.dirname(_lowercase					),				f'''bertarized_{os.path.basename(_lowercase					)}'''					)

	if not os.path.isdir(_lowercase					):
		shutil.copytree(_lowercase,				_lowercase					)
		print(f'''\nCreated folder {target_model_path}'''					)

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


if __name__ == "__main__":
		_A				:    Union[str, Any]     						=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''',
		)

		_A				:    List[Any]     						=parser.parse_args()

		main(args)
