


from __future__ import annotations

import unittest

import numpy as np

from transformers import OPTConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, slow

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 GPTaTokenizer, TFOPTForCausalLM, TFOPTModel



def 	_lowerCAmelCase  (							lowerCAmelCase_							:List[str]						,  lowerCAmelCase_							:Dict						,  lowerCAmelCase_							:str=None						,  lowerCAmelCase_							:List[Any]=None							)->Any:
   '''simple docstring'''






   if attention_mask is None:
      snake_case_    			=    tf.cast(tf.math.not_equal(a_						,  config.pad_token_id							)						,  tf.inta							)
   return {"input_ids": input_ids, "attention_mask": attention_mask}


@require_tf
class 			__lowerCAmelCase   :






  """simple docstring"""





  _SCREAMING_SNAKE_CASE									=				OPTConfig
  _SCREAMING_SNAKE_CASE									=				{}
  _SCREAMING_SNAKE_CASE									=				'gelu'



  def __init__(   self				:				Tuple			, _lowerCAmelCase				:				Tuple			, _lowerCAmelCase				:				Optional[Any]=1_3			, _lowerCAmelCase				:				Union[str, Any]=7			, _lowerCAmelCase				:				Tuple=True			, _lowerCAmelCase				:				Optional[int]=False			, _lowerCAmelCase				:				List[str]=9_9			, _lowerCAmelCase				:				str=1_6			, _lowerCAmelCase				:				Optional[Any]=2			, _lowerCAmelCase				:				List[str]=4			, _lowerCAmelCase				:				Optional[int]=4			, _lowerCAmelCase				:				str="gelu"			, _lowerCAmelCase				:				Any=0.1			, _lowerCAmelCase				:				int=0.1			, _lowerCAmelCase				:				int=2_0			, _lowerCAmelCase				:				Tuple=2			, _lowerCAmelCase				:				Optional[int]=1			, _lowerCAmelCase				:				Union[str, Any]=0			, _lowerCAmelCase				:				str=1_6			, _lowerCAmelCase				:				Dict=1_6			, )      ->       Optional[Any]:




     """simple docstring"""
     snake_case_    			=    parent
     snake_case_    			=    batch_size
     snake_case_    			=    seq_length
     snake_case_    			=    is_training
     snake_case_    			=    use_labels
     snake_case_    			=    vocab_size
     snake_case_    			=    hidden_size
     snake_case_    			=    num_hidden_layers
     snake_case_    			=    num_attention_heads
     snake_case_    			=    intermediate_size
     snake_case_    			=    hidden_act
     snake_case_    			=    hidden_dropout_prob
     snake_case_    			=    attention_probs_dropout_prob
     snake_case_    			=    max_position_embeddings
     snake_case_    			=    eos_token_id
     snake_case_    			=    pad_token_id
     snake_case_    			=    bos_token_id
     snake_case_    			=    embed_dim
     snake_case_    			=    word_embed_proj_dim
     snake_case_    			=    False



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




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

     snake_case_    			=    self.config_cls(
         vocab_size=self.vocab_size			, hidden_size=self.hidden_size			, num_hidden_layers=self.num_hidden_layers			, num_attention_heads=self.num_attention_heads			, 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_id=self.eos_token_id			, bos_token_id=self.bos_token_id			, pad_token_id=self.pad_token_id			, embed_dim=self.embed_dim			, word_embed_proj_dim=self.word_embed_proj_dim			, is_encoder_decoder=_lowerCAmelCase			, **self.config_updates			, )
     snake_case_    			=    prepare_opt_inputs_dict(_lowerCAmelCase			, _lowerCAmelCase     )
     return config, inputs_dict



  def     lowerCAmelCase__     (   self				:				Dict			, _lowerCAmelCase				:				int			, _lowerCAmelCase				:				Optional[int]     )      ->       Any:




     """simple docstring"""
     snake_case_    			=    TFOPTModel(config=_lowerCAmelCase     )
     snake_case_    			=    inputs_dict["input_ids"]

     snake_case_    			=    input_ids[:1, :]
     snake_case_    			=    inputs_dict["attention_mask"][:1, :]
     snake_case_    			=    1

     # first forward pass
     snake_case_    			=    model(_lowerCAmelCase			, attention_mask=_lowerCAmelCase			, use_cache=_lowerCAmelCase     )

     snake_case_			,			snake_case_    			=    outputs.to_tuple()

     # create hypothetical next token and extent to next_input_ids
     snake_case_    			=    ids_tensor((self.batch_size, 3)			, config.vocab_size     )
     snake_case_    			=    tf.cast(ids_tensor((self.batch_size, 3)			, 2     )			, tf.inta     )

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

     snake_case_    			=    model(_lowerCAmelCase			, attention_mask=_lowerCAmelCase     )[0]
     snake_case_    			=    model(_lowerCAmelCase			, attention_mask=_lowerCAmelCase			, past_key_values=_lowerCAmelCase     )[0]

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

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

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






@require_tf
class 			__lowerCAmelCase   (       __SCREAMING_SNAKE_CASE       ,     __SCREAMING_SNAKE_CASE       ,     unittest.TestCase    ):






  """simple docstring"""





  _SCREAMING_SNAKE_CASE									=				(TFOPTModel, TFOPTForCausalLM) if is_tf_available() else ()
  _SCREAMING_SNAKE_CASE									=				(TFOPTForCausalLM,) if is_tf_available() else ()
  _SCREAMING_SNAKE_CASE									=				(
      {'feature-extraction': TFOPTModel, 'text-generation': TFOPTForCausalLM} if is_tf_available() else {}
  )
  _SCREAMING_SNAKE_CASE									=				False
  _SCREAMING_SNAKE_CASE									=				False
  _SCREAMING_SNAKE_CASE									=				False
  _SCREAMING_SNAKE_CASE									=				10



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




     """simple docstring"""
     snake_case_    			=    TFOPTModelTester(self     )
     snake_case_    			=    ConfigTester(self			, config_class=_lowerCAmelCase     )



  def     lowerCAmelCase__     (   self				:				Dict     )      ->       int:




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



  def     lowerCAmelCase__     (   self				:				Optional[int]     )      ->       str:




     """simple docstring"""
     snake_case_    			=    self.model_tester.prepare_config_and_inputs_for_common()
     self.model_tester.check_decoder_model_past_large_inputs(*_lowerCAmelCase     )



  def     lowerCAmelCase__     (   self				:				Any     )      ->       Dict:




     """simple docstring"""
     snake_case_			,			snake_case_    			=    self.model_tester.prepare_config_and_inputs_for_common()

     def _get_word_embedding_weight(_lowerCAmelCase				:				Optional[int]			, _lowerCAmelCase				:				Optional[Any]     ):
        if hasattr(_lowerCAmelCase			, "weight"     ):
           return embedding_layer.weight
        else:
           # Here we build the word embeddings weights if not exists.
           # And then we retry to get the attribute once built.
           model.build()
           if hasattr(_lowerCAmelCase			, "weight"     ):
              return embedding_layer.weight
           else:
              return None

     for model_class in self.all_model_classes:
        for size in [config.vocab_size - 1_0, config.vocab_size + 1_0]:
           # build the embeddings
           snake_case_    			=    model_class(config=_lowerCAmelCase     )
           snake_case_    			=    _get_word_embedding_weight(_lowerCAmelCase			, model.get_input_embeddings()     )
           snake_case_    			=    _get_word_embedding_weight(_lowerCAmelCase			, model.get_output_embeddings()     )

           # reshape the embeddings
           model.resize_token_embeddings(_lowerCAmelCase     )
           snake_case_    			=    _get_word_embedding_weight(_lowerCAmelCase			, model.get_input_embeddings()     )
           snake_case_    			=    _get_word_embedding_weight(_lowerCAmelCase			, model.get_output_embeddings()     )

           # check that the resized embeddings size matches the desired size.
           snake_case_    			=    size if size is not None else config.vocab_size

           self.assertEqual(new_input_embeddings.shape[0]			, _lowerCAmelCase     )

           # check that weights remain the same after resizing
           snake_case_    			=    True
           for pa, pa in zip(old_input_embeddings.value()			, new_input_embeddings.value()     ):
              if tf.math.reduce_sum(tf.math.abs(pa - pa     )     ) > 0:
                 snake_case_    			=    False
           self.assertTrue(_lowerCAmelCase     )

           if old_output_embeddings is not None and new_output_embeddings is not None:
              self.assertEqual(new_output_embeddings.shape[0]			, _lowerCAmelCase     )

              snake_case_    			=    True
              for pa, pa in zip(old_output_embeddings.value()			, new_output_embeddings.value()     ):
                 if tf.math.reduce_sum(tf.math.abs(pa - pa     )     ) > 0:
                    snake_case_    			=    False
              self.assertTrue(_lowerCAmelCase     )






def 	_lowerCAmelCase  (							lowerCAmelCase_							:Optional[Any]							)->List[str]:
   '''simple docstring'''






   return tf.constant(a_						,  dtype=tf.intaa							)


@require_tf
class 			__lowerCAmelCase   (       unittest.TestCase    ):






  """simple docstring"""





  _SCREAMING_SNAKE_CASE									=				99



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




     """simple docstring"""
     snake_case_    			=    tf.ones((4, 1)			, dtype=tf.intaa     ) * 2
     snake_case_    			=    tf.concat([ids_tensor((4, 6)			, self.vocab_size - 3     ) + 3, eos_column_vector]			, axis=1     )
     snake_case_    			=    input_ids.shape[0]
     snake_case_    			=    OPTConfig(
         vocab_size=self.vocab_size			, hidden_size=2_4			, num_hidden_layers=2			, num_attention_heads=2			, ffn_dim=3_2			, max_position_embeddings=4_8			, eos_token_id=2			, pad_token_id=1			, bos_token_id=0			, )
     return config, input_ids, batch_size






@require_sentencepiece
@require_tf
class 			__lowerCAmelCase   (       unittest.TestCase    ):






  """simple docstring"""





  @slow
  def     lowerCAmelCase__     (   self				:				List[str]     )      ->       Dict:




     """simple docstring"""
     snake_case_    			=    TFOPTModel.from_pretrained("facebook/opt-350m"     )
     snake_case_    			=    _long_tensor([[0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 4_6_0_7_8, 1_5_8_8, 2]]     )
     snake_case_    			=    tf.not_equal(_lowerCAmelCase			, model.config.pad_token_id     )
     with tf.GradientTape():
        snake_case_    			=    model(input_ids=_lowerCAmelCase			, attention_mask=_lowerCAmelCase     ).last_hidden_state
     snake_case_    			=    (1, 1_1, 5_1_2)
     self.assertEqual(output.shape			, _lowerCAmelCase     )
     snake_case_    			=    tf.constant(
         [[-0.2_873, -1.9_218, -0.3_033], [-1.2_710, -0.1_338, -0.1_902], [0.4_095, 0.1_214, -1.3_121]]     )
     self.assertTrue(np.allclose(output[:, :3, :3]			, _lowerCAmelCase			, atol=4e-3     )     )

     snake_case_    			=    tf.function(_lowerCAmelCase			, jit_compile=_lowerCAmelCase     )
     snake_case_    			=    xla_generate(_lowerCAmelCase			, _lowerCAmelCase     )[0]
     self.assertTrue(np.allclose(output[:, :3, :3]			, _lowerCAmelCase			, atol=4e-2     )     )






@require_tf
@slow
class 			__lowerCAmelCase   (       unittest.TestCase    ):






  """simple docstring"""





  def     lowerCAmelCase__     (   self				:				Optional[Any]     )      ->       Optional[int]:




     """simple docstring"""
     super().setUp()
     snake_case_    			=    "facebook/opt-350m"



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




     """simple docstring"""
     snake_case_    			=    TFOPTForCausalLM.from_pretrained(self.path_model     )
     snake_case_    			=    GPTaTokenizer.from_pretrained(self.path_model     )

     snake_case_    			=    [
         "Today is a beautiful day and I want to",
         "In the city of",
         "Paris is the capital of France and",
         "Computers and mobile phones have taken",
     ]
     # verify that prompt without BOS token is identical to Metaseq -> add_special_tokens=False
     snake_case_    			=    tokenizer(_lowerCAmelCase			, return_tensors="tf"			, padding=_lowerCAmelCase			, add_special_tokens=_lowerCAmelCase     )
     snake_case_    			=    tf.math.reduce_mean(model(inputs.input_ids			, attention_mask=inputs.attention_mask     )[0]			, axis=-1     )
     snake_case_    			=    tf.constant(
         [
             [1.3_851, -13.8_923, -10.5_229, -10.7_533, -0.2_309, -10.2_384, -0.5_365, -9.0_947, -5.1_670],
             [-4.7_073, -10.6_276, -3.9_415, -21.5_242, -0.2_822, -0.2_822, -0.2_822, -0.2_822, -0.2_822],
             [0.6_247, -3.4_229, -8.9_179, -1.4_297, -14.1_650, 1.4_146, -9.0_218, -0.2_703, -0.2_703],
             [6.4_783, -1.9_913, -10.7_926, -2.3_336, 1.5_092, -0.9_974, -6.8_213, 1.3_477, 1.3_477],
         ]     )
     self.assertTrue(np.allclose(_lowerCAmelCase			, _lowerCAmelCase			, atol=1e-4     )     )

     snake_case_    			=    tf.function(_lowerCAmelCase			, jit_compile=_lowerCAmelCase     )
     snake_case_    			=    tf.math.reduce_mean(xla_generate(inputs.input_ids			, attention_mask=inputs.attention_mask     )[0]			, axis=-1     )
     self.assertTrue(np.allclose(_lowerCAmelCase			, _lowerCAmelCase			, atol=1e-4     )     )






@require_tf
@slow
class 			__lowerCAmelCase   (       unittest.TestCase    ):






  """simple docstring"""





  @property
  def     lowerCAmelCase__     (   self				:				int     )      ->       Tuple:




     """simple docstring"""
     return [
         "Today is a beautiful day and I want",
         "In the city of",
         "Paris is the capital of France and",
         "Computers and mobile phones have taken",
     ]



  def     lowerCAmelCase__     (   self				:				Optional[int]     )      ->       Tuple:




     """simple docstring"""
     snake_case_    			=    "facebook/opt-125m"

     snake_case_    			=    [
         "Today is a beautiful day and I want to",
         "In the city of New York, the city",
         "Paris is the capital of France and the capital",
         "Computers and mobile phones have taken over the",
     ]

     snake_case_    			=    []
     snake_case_    			=    GPTaTokenizer.from_pretrained(_lowerCAmelCase     )
     snake_case_    			=    TFOPTForCausalLM.from_pretrained(_lowerCAmelCase     )

     for prompt in self.prompts:
        snake_case_    			=    tokenizer(_lowerCAmelCase			, return_tensors="tf"     ).input_ids

        snake_case_    			=    model.generate(_lowerCAmelCase			, max_length=1_0     )

        snake_case_    			=    tokenizer.batch_decode(_lowerCAmelCase			, skip_special_tokens=_lowerCAmelCase     )
        predicted_outputs += generated_string

     self.assertListEqual(_lowerCAmelCase			, _lowerCAmelCase     )



  def     lowerCAmelCase__     (   self				:				int     )      ->       Optional[Any]:




     """simple docstring"""
     snake_case_    			=    "facebook/opt-350m"

     snake_case_    			=    GPTaTokenizer.from_pretrained(_lowerCAmelCase     )
     snake_case_    			=    TFOPTForCausalLM.from_pretrained(_lowerCAmelCase     )

     snake_case_    			=    "left"

     # use different length sentences to test batching
     snake_case_    			=    [
         "Hello, my dog is a little",
         "Today, I",
     ]

     snake_case_    			=    tokenizer(_lowerCAmelCase			, return_tensors="tf"			, padding=_lowerCAmelCase     )
     snake_case_    			=    inputs["input_ids"]

     snake_case_    			=    model.generate(input_ids=_lowerCAmelCase			, attention_mask=inputs["attention_mask"]     )

     snake_case_    			=    tokenizer(sentences[0]			, return_tensors="tf"     ).input_ids
     snake_case_    			=    model.generate(input_ids=_lowerCAmelCase     )

     snake_case_    			=    inputs_non_padded.shape[-1] - tf.math.reduce_sum(
         tf.cast(inputs["attention_mask"][-1]			, tf.intaa     )     )
     snake_case_    			=    tokenizer(sentences[1]			, return_tensors="tf"     ).input_ids
     snake_case_    			=    model.generate(input_ids=_lowerCAmelCase			, max_length=model.config.max_length - num_paddings     )

     snake_case_    			=    tokenizer.batch_decode(_lowerCAmelCase			, skip_special_tokens=_lowerCAmelCase     )
     snake_case_    			=    tokenizer.decode(output_non_padded[0]			, skip_special_tokens=_lowerCAmelCase     )
     snake_case_    			=    tokenizer.decode(output_padded[0]			, skip_special_tokens=_lowerCAmelCase     )

     snake_case_    			=    [
         "Hello, my dog is a little bit of a dork.\nI'm a little bit",
         "Today, I was in the middle of a conversation with a friend about the",
     ]
     self.assertListEqual(_lowerCAmelCase			, _lowerCAmelCase     )
     self.assertListEqual(_lowerCAmelCase			, [non_padded_sentence, padded_sentence]     )



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




     """simple docstring"""
     snake_case_    			=    "facebook/opt-350m"

     snake_case_    			=    [
         "Today is a beautiful day and I want to",
         "In the city of San Francisco, the city",
         "Paris is the capital of France and the capital",
         "Computers and mobile phones have taken over the",
     ]

     snake_case_    			=    []
     snake_case_    			=    GPTaTokenizer.from_pretrained(_lowerCAmelCase     )
     snake_case_    			=    TFOPTForCausalLM.from_pretrained(_lowerCAmelCase     )

     for prompt in self.prompts:
        snake_case_    			=    tokenizer(_lowerCAmelCase			, return_tensors="tf"     ).input_ids

        snake_case_    			=    model.generate(_lowerCAmelCase			, max_length=1_0     )

        snake_case_    			=    tokenizer.batch_decode(_lowerCAmelCase			, skip_special_tokens=_lowerCAmelCase     )
        predicted_outputs += generated_string

     self.assertListEqual(_lowerCAmelCase			, _lowerCAmelCase     )






import argparse
import os
import shutil

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


def 						UpperCAmelCase			(	a_						)		->	List[str]:







				"""simple docstring"""



				__A   			= args.pruning_method
				__A   			= args.threshold

				__A   			= args.model_name_or_path.rstrip("/"						)
				__A   			= args.target_model_path

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

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

				if target_model_path is None:
								__A   			= os.path.join(
								    os.path.dirname(a_						)		,   F'''bertarized_{os.path.basename(a_						)}'''						)

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

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


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE							:Tuple							 =				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',
					)

					SCREAMING_SNAKE_CASE							:str							 =				parser.parse_args()

					main(args)
