






import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
		from PIL import Image

		from transformers import CLIPImageProcessor, CLIPProcessor





@require_vision
class __lowerCamelCase (      unittest.TestCase):





	"""simple docstring"""






	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			tempfile.mkdtemp()

							# fmt: off
							_UpperCAmelCase									=			['l', 'o', 'w', 'e', 'r', 's', 't', 'i', 'd', 'n', 'lo', 'l</w>', 'w</w>', 'r</w>', 't</w>', 'low</w>', 'er</w>', 'lowest</w>', 'newer</w>', 'wider', '<unk>', '<|startoftext|>', '<|endoftext|>']
							# fmt: on
							_UpperCAmelCase									=			dict(zip(lowerCAmelCase__       ,    range(len(lowerCAmelCase__    )    )    )    )
							_UpperCAmelCase									=			['#version: 0.2', 'l o', 'lo w</w>', 'e r</w>', '']
							_UpperCAmelCase									=			{'unk_token': '<unk>'}

							_UpperCAmelCase									=			os.path.join(self.tmpdirname       ,    VOCAB_FILES_NAMES['vocab_file']    )
							_UpperCAmelCase									=			os.path.join(self.tmpdirname       ,    VOCAB_FILES_NAMES['merges_file']    )
							with open(self.vocab_file       ,    'w'       ,    encoding='utf-8'    ) as fp:
													fp.write(json.dumps(lowerCAmelCase__    ) + '\n'    )
							with open(self.merges_file       ,    'w'       ,    encoding='utf-8'    ) as fp:
													fp.write('\n'.join(lowerCAmelCase__    )    )

							_UpperCAmelCase									=			{
							    'do_resize': True,
							    'size': 20,
							    'do_center_crop': True,
							    'crop_size': 18,
							    'do_normalize': True,
							    'image_mean': [0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73],
							    'image_std': [0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11],
							}
							_UpperCAmelCase									=			os.path.join(self.tmpdirname       ,    lowerCAmelCase__    )
							with open(self.image_processor_file       ,    'w'       ,    encoding='utf-8'    ) as fp:
													json.dump(lowerCAmelCase__       ,    lowerCAmelCase__    )

	def        UpperCamelCase    (							self       ,    **UpperCAmelCase    ):
							"""simple docstring"""







							return CLIPTokenizer.from_pretrained(self.tmpdirname       ,    **lowerCAmelCase__    )

	def        UpperCamelCase    (							self       ,    **UpperCAmelCase    ):
							"""simple docstring"""







							return CLIPTokenizerFast.from_pretrained(self.tmpdirname       ,    **lowerCAmelCase__    )

	def        UpperCamelCase    (							self       ,    **UpperCAmelCase    ):
							"""simple docstring"""







							return CLIPImageProcessor.from_pretrained(self.tmpdirname       ,    **lowerCAmelCase__    )

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							shutil.rmtree(self.tmpdirname    )

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			[np.random.randint(255       ,    size=(3, 30, 400)       ,    dtype=np.uinta    )]

							_UpperCAmelCase									=			[Image.fromarray(np.moveaxis(lowerCAmelCase__       ,    0       ,    -1    )    ) for x in image_inputs]

							return image_inputs

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			self.get_tokenizer()
							_UpperCAmelCase									=			self.get_rust_tokenizer()
							_UpperCAmelCase									=			self.get_image_processor()

							_UpperCAmelCase									=			CLIPProcessor(tokenizer=lowerCAmelCase__       ,    image_processor=lowerCAmelCase__    )
							processor_slow.save_pretrained(self.tmpdirname    )
							_UpperCAmelCase									=			CLIPProcessor.from_pretrained(self.tmpdirname       ,    use_fast=lowerCAmelCase__    )

							_UpperCAmelCase									=			CLIPProcessor(tokenizer=lowerCAmelCase__       ,    image_processor=lowerCAmelCase__    )
							processor_fast.save_pretrained(self.tmpdirname    )
							_UpperCAmelCase									=			CLIPProcessor.from_pretrained(self.tmpdirname    )

							self.assertEqual(processor_slow.tokenizer.get_vocab()       ,    tokenizer_slow.get_vocab()    )
							self.assertEqual(processor_fast.tokenizer.get_vocab()       ,    tokenizer_fast.get_vocab()    )
							self.assertEqual(tokenizer_slow.get_vocab()       ,    tokenizer_fast.get_vocab()    )
							self.assertIsInstance(processor_slow.tokenizer       ,    lowerCAmelCase__    )
							self.assertIsInstance(processor_fast.tokenizer       ,    lowerCAmelCase__    )

							self.assertEqual(processor_slow.image_processor.to_json_string()       ,    image_processor.to_json_string()    )
							self.assertEqual(processor_fast.image_processor.to_json_string()       ,    image_processor.to_json_string()    )
							self.assertIsInstance(processor_slow.image_processor       ,    lowerCAmelCase__    )
							self.assertIsInstance(processor_fast.image_processor       ,    lowerCAmelCase__    )

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			CLIPProcessor(tokenizer=self.get_tokenizer()       ,    image_processor=self.get_image_processor()    )
							processor.save_pretrained(self.tmpdirname    )

							_UpperCAmelCase									=			self.get_tokenizer(bos_token='(BOS)'       ,    eos_token='(EOS)'    )
							_UpperCAmelCase									=			self.get_image_processor(do_normalize=lowerCAmelCase__       ,    padding_value=1.0    )

							_UpperCAmelCase									=			CLIPProcessor.from_pretrained(
							    self.tmpdirname       ,    bos_token='(BOS)'       ,    eos_token='(EOS)'       ,    do_normalize=lowerCAmelCase__       ,    padding_value=1.0    )

							self.assertEqual(processor.tokenizer.get_vocab()       ,    tokenizer_add_kwargs.get_vocab()    )
							self.assertIsInstance(processor.tokenizer       ,    lowerCAmelCase__    )

							self.assertEqual(processor.image_processor.to_json_string()       ,    image_processor_add_kwargs.to_json_string()    )
							self.assertIsInstance(processor.image_processor       ,    lowerCAmelCase__    )

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			self.get_image_processor()
							_UpperCAmelCase									=			self.get_tokenizer()

							_UpperCAmelCase									=			CLIPProcessor(tokenizer=lowerCAmelCase__       ,    image_processor=lowerCAmelCase__    )

							_UpperCAmelCase									=			self.prepare_image_inputs()

							_UpperCAmelCase									=			image_processor(lowerCAmelCase__       ,    return_tensors='np'    )
							_UpperCAmelCase									=			processor(images=lowerCAmelCase__       ,    return_tensors='np'    )

							for key in input_image_proc.keys():
													self.assertAlmostEqual(input_image_proc[key].sum()       ,    input_processor[key].sum()       ,    delta=1e-2    )

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			self.get_image_processor()
							_UpperCAmelCase									=			self.get_tokenizer()

							_UpperCAmelCase									=			CLIPProcessor(tokenizer=lowerCAmelCase__       ,    image_processor=lowerCAmelCase__    )

							_UpperCAmelCase									=			'lower newer'

							_UpperCAmelCase									=			processor(text=lowerCAmelCase__    )

							_UpperCAmelCase									=			tokenizer(lowerCAmelCase__    )

							for key in encoded_tok.keys():
													self.assertListEqual(encoded_tok[key]       ,    encoded_processor[key]    )

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			self.get_image_processor()
							_UpperCAmelCase									=			self.get_tokenizer()

							_UpperCAmelCase									=			CLIPProcessor(tokenizer=lowerCAmelCase__       ,    image_processor=lowerCAmelCase__    )

							_UpperCAmelCase									=			'lower newer'
							_UpperCAmelCase									=			self.prepare_image_inputs()

							_UpperCAmelCase									=			processor(text=lowerCAmelCase__       ,    images=lowerCAmelCase__    )

							self.assertListEqual(list(inputs.keys()    )       ,    ['input_ids', 'attention_mask', 'pixel_values']    )

							# test if it raises when no input is passed
							with pytest.raises(lowerCAmelCase__    ):
													processor()

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			self.get_image_processor()
							_UpperCAmelCase									=			self.get_tokenizer()

							_UpperCAmelCase									=			CLIPProcessor(tokenizer=lowerCAmelCase__       ,    image_processor=lowerCAmelCase__    )

							_UpperCAmelCase									=			[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

							_UpperCAmelCase									=			processor.batch_decode(lowerCAmelCase__    )
							_UpperCAmelCase									=			tokenizer.batch_decode(lowerCAmelCase__    )

							self.assertListEqual(lowerCAmelCase__       ,    lowerCAmelCase__    )

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			self.get_image_processor()
							_UpperCAmelCase									=			self.get_tokenizer()

							_UpperCAmelCase									=			CLIPProcessor(tokenizer=lowerCAmelCase__       ,    image_processor=lowerCAmelCase__    )

							_UpperCAmelCase									=			'lower newer'
							_UpperCAmelCase									=			self.prepare_image_inputs()

							_UpperCAmelCase									=			processor(text=lowerCAmelCase__       ,    images=lowerCAmelCase__    )

							self.assertListEqual(list(inputs.keys()    )       ,    processor.model_input_names    )




import argparse
import random

import joblib
import numpy as np
import torch
from igf.igf import (
    SecondaryLearner,
    collect_objective_set,
    compute_perplexity,
    generate_datasets,
    load_gpta,
    recopy_gpta,
    set_seed,
    train_secondary_learner,
)
from torch.utils.data import DataLoader, RandomSampler

from transformers import GPTaLMHeadModel




def    snake_case_     (  snake_case=32			,							snake_case=10			,							snake_case=1_00			,							snake_case=10_26			,							snake_case=True			,							snake_case="data/tokenized_stories_train_wikitext103.jbl"			,							snake_case="igf_context_pairs.jbl"			,							)  ->				Union[str, Any]:
       set_seed(3     )
       # generate train_data and objective_set
       lowercase__			,						lowercase__:   List[str]  			=   generate_datasets(
           snake_case			,							snake_case			,							number=snake_case			,							min_len=10_26			,							trim=snake_case     )
       # keeps model same across runs
       set_seed(4     )
       # model, lm_optimizer, lm_scheduler = recopy_gpt2(model, device, max_steps) # store original model weights
       # can we train on GPU?
       lowercase__:   Optional[Any]  			=   torch.device('cuda:0' if torch.cuda.is_available() else 'cpu'     )

       # load pretrained model
       lowercase__:   str  			=   load_gpta('gpt2'     ).to(snake_case     )
       print('computing perplexity on objective set'     )
       lowercase__:   int  			=   compute_perplexity(snake_case			,							snake_case			,							snake_case     ).item()
       print('perplexity on objective set:'			,							snake_case     )

       # collect igf pairs and save to file demo.jbl
       collect_objective_set(snake_case			,							snake_case			,							snake_case			,							snake_case			,							snake_case			,							snake_case			,							snake_case			,							snake_case     )

       # clean up, delete model and data we don't need anymore
       del model, train_data, objective_set
       torch.cuda.empty_cache()




def    snake_case_     (  snake_case			,							snake_case=15			,							snake_case=1_28			,							snake_case=1_00			,							snake_case="igf_model.pt"			,							)  ->				Optional[Any]:
       set_seed(42     )

       # Load pre-trained model
       lowercase__:   Any  			=   GPTaLMHeadModel.from_pretrained('gpt2'     )

       # Initialize secondary learner to use embedding weights of model
       lowercase__:   Any  			=   SecondaryLearner(snake_case     )

       # Train secondary learner
       lowercase__:   Tuple  			=   train_secondary_learner(
           snake_case			,							snake_case			,							max_epochs=snake_case			,							batch_size=snake_case			,							eval_freq=1_00			,							igf_model_path=snake_case			,							)

       del model, secondary_learner_train_data
       torch.cuda.empty_cache()

       return secondary_learner




def    snake_case_     (  snake_case			,							snake_case			,							snake_case			,							snake_case=32			,							snake_case=10_00			,							snake_case=16			,							snake_case=1.0			,							snake_case=recopy_gpta			,							snake_case=None			,							snake_case=10			,							snake_case="gpt2_finetuned.pt"			,							)  ->				Tuple:
       lowercase__:   Dict  			=   torch.device('cuda:0' if torch.cuda.is_available() else 'cpu'     )
       lowercase__:   Optional[int]  			=   RandomSampler(snake_case     )
       lowercase__:   Optional[int]  			=   DataLoader(snake_case			,							sampler=snake_case     )

       lowercase__:   int  			=   max_steps // (len(snake_case     )) + 1
       lowercase__:   Union[str, Any]  			=   0
       lowercase__:   Optional[Any]  			=   torch.zeros((1, context_len)			,							dtype=torch.long			,							device=snake_case     )
       lowercase__			,						lowercase__			,						lowercase__:   Union[str, Any]  			=   recopy_model(snake_case			,							snake_case			,							snake_case     )

       model.train()
       if secondary_learner is not None:
              secondary_learner.to(snake_case     )
              secondary_learner.eval()
       lowercase__:   List[Any]  			=   []
       lowercase__:   str  			=   0

       lowercase__:   Tuple  			=   []
       lowercase__:   Dict  			=   []

       # Compute the performance of the transformer model at the beginning
       lowercase__:   Optional[Any]  			=   compute_perplexity(snake_case			,							snake_case			,							snake_case     )
       test_perps.append(snake_case     )
       print('Test perplexity, step'			,							snake_case			,							':'			,							snake_case     )
       for epoch in range(int(snake_case     )     ):
              for step, example in enumerate(snake_case     ):
                     torch.cuda.empty_cache()
                     lowercase__:   Union[str, Any]  			=   random.randint(0			,							example.size(2     ) - context_len - 1     )
                     lowercase__:   Dict  			=   example[0, 0, start : start + context_len]
                     lm_optimizer.zero_grad()
                     lowercase__:   Union[str, Any]  			=   model(snake_case			,							labels=snake_case     )
                     lowercase__:   Tuple  			=   True

                     if secondary_learner is not None:
                            lowercase__:   Optional[Any]  			=   secondary_learner.forward(
                                torch.tensor(snake_case			,							dtype=torch.long			,							device=snake_case     ).unsqueeze(0     )     )[0].item()
                            observed_qs.append(float(snake_case     )     )

                            # Here we implement the simple non-constant threshold for the predicted IG(X) value
                            # We will decay the selectivity of our secondary learner filter from
                            # 1 standard deviation above average to 1 below average after 10 batches.

                            if global_step == 10:
                                   lowercase__:   Optional[Any]  			=   -1
                            if predicted_q < threshold:
                                   lowercase__:   str  			=   False

            # If we passed the filter, add the context to the batch!
                     if do_backprop:
                            contexts.append(np.array(context.cpu()     )     )
                            lowercase__:   List[Any]  			=   outputs[0]
                            lm_loss.backward()
                            examples += 1

                     del outputs

                     # Once the batch is filled with enough contexts, backprop on the batch.
                     if examples == batch_size:
                            torch.cuda.empty_cache()
                            lowercase__:   Any  			=   0
                            # Do LM backprop
                            torch.nn.utils.clip_grad_norm_(model.parameters()			,							3.0     )
                            lm_optimizer.step()
                            lm_scheduler.step()  # Update learning rate schedule
                            global_step += 1
                            # Compute the performance of the transformer model at this batch
                            if global_step % eval_interval == 0:
                                   lowercase__:   int  			=   compute_perplexity(snake_case			,							snake_case			,							snake_case     )
                                   test_perps.append(snake_case     )

                                   print('Test perplexity, step'			,							snake_case			,							':'			,							snake_case     )
            # Break out of the loop after 60 batches
                     if max_steps > 0 and global_step > 60:
                            break
              if max_steps > 0 and global_step > 60:
                     break

    # save finetuned transformer model
       torch.save(model.state_dict()			,							snake_case     )
       torch.cuda.empty_cache()
       # Do some cleaning up so we can reinitialize for the next run of this function
       del lm_optimizer
       del lm_scheduler
       return model




def    snake_case_     (  )  ->				str:
       lowercase__:   Tuple  			=   argparse.ArgumentParser(description='Fine-tune a transformer model with IGF on a language modeling task'     )

       # Required parameters
       parser.add_argument(
           '--data_dir'			,							default=snake_case			,							type=snake_case			,							required=snake_case			,							help='The input data dir. Should contain data files for WikiText.'			,							)
       parser.add_argument(
           '--model_name_or_path'			,							default=snake_case			,							type=snake_case			,							required=snake_case			,							help='Path to pretrained model or model identifier from huggingface.co/models'			,							)
       parser.add_argument(
           '--data_file'			,							type=snake_case			,							default=snake_case			,							help=(
               'A jbl file containing tokenized data which can be split as objective dataset, '
               'train_dataset and test_dataset.'
           )			,							)

       parser.add_argument(
           '--igf_data_file'			,							type=snake_case			,							default=snake_case			,							help='A jbl file containing the context and information gain pairs to train secondary learner.'			,							)

       parser.add_argument(
           '--output_dir'			,							default=snake_case			,							type=snake_case			,							required=snake_case			,							help='The output directory where the final fine-tuned model is stored.'			,							)

       parser.add_argument(
           '--tokenizer_name'			,							default=snake_case			,							type=snake_case			,							help='Pretrained tokenizer name or path if not the same as model_name'			,							)
       parser.add_argument('--seed'			,							type=snake_case			,							default=snake_case			,							help='A seed for reproducible training.'     )

       parser.add_argument(
           '--context_len'			,							default=32			,							type=snake_case			,							help=(
               'The maximum total input sequence length after tokenization. Sequences longer '
               'than this will be truncated, sequences shorter will be padded.'
           )			,							)

       parser.add_argument(
           '--size_objective_set'			,							default=1_00			,							type=snake_case			,							help='number of articles that are long enough to be used as our objective set'			,							)
       parser.add_argument(
           '--eval_freq'			,							default=1_00			,							type=snake_case			,							help='secondary model evaluation is triggered at eval_freq'     )

       parser.add_argument('--max_steps'			,							default=10_00			,							type=snake_case			,							help='To calculate training epochs'     )

       parser.add_argument(
           '--secondary_learner_batch_size'			,							default=1_28			,							type=snake_case			,							help='batch size of training data for secondary learner'			,							)

       parser.add_argument(
           '--batch_size'			,							default=16			,							type=snake_case			,							help='batch size of training data of language model(gpt2) '     )

       parser.add_argument(
           '--eval_interval'			,							default=10			,							type=snake_case			,							help=(
               'decay the selectivity of our secondary learner filter from'
               '1 standard deviation above average to 1 below average after 10 batches'
           )			,							)

       parser.add_argument(
           '--number'			,							default=1_00			,							type=snake_case			,							help='The number of examples split to be used as objective_set/test_data'     )

       parser.add_argument(
           '--min_len'			,							default=10_26			,							type=snake_case			,							help='The minimum length of the article to be used as objective set'     )

       parser.add_argument(
           '--secondary_learner_max_epochs'			,							default=15			,							type=snake_case			,							help='number of epochs to train secondary learner'     )

       parser.add_argument('--trim'			,							default=snake_case			,							type=snake_case			,							help='truncate the example if it exceeds context length'     )

       parser.add_argument(
           '--threshold'			,							default=1.0			,							type=snake_case			,							help=(
               'The threshold value used by secondary learner to filter the train_data and allow only'
               ' informative data as input to the model'
           )			,							)

       parser.add_argument('--finetuned_model_name'			,							default='gpt2_finetuned.pt'			,							type=snake_case			,							help='finetuned_model_name'     )

       parser.add_argument(
           '--recopy_model'			,							default=snake_case			,							type=snake_case			,							help='Reset the model to the original pretrained GPT-2 weights after each iteration'			,							)

       # function calls
       # Collecting *n* pairs of context and information gain(X, IG(X)) for training the secondary learner
       generate_n_pairs(
           context_len=32			,							max_steps=10			,							size_objective_set=1_00			,							min_len=10_26			,							trim=snake_case			,							data_file='data/tokenized_stories_train_wikitext103.jbl'			,							igf_data_file='igf_context_pairs.jbl'			,							)

       # Load train data for secondary learner
       lowercase__:   Tuple  			=   joblib.load('data/IGF_values.jbl'     )

       # Train secondary learner
       lowercase__:   List[str]  			=   training_secondary_learner(
           snake_case			,							secondary_learner_max_epochs=15			,							secondary_learner_batch_size=1_28			,							eval_freq=1_00			,							igf_model_path='igf_model.pt'			,							)

       # load pretrained gpt2 model
       lowercase__:   Dict  			=   GPTaLMHeadModel.from_pretrained('gpt2'     )
       set_seed(42     )

       # Generate train and test data to train and evaluate gpt2 model
       lowercase__			,						lowercase__:   Tuple  			=   generate_datasets(
           context_len=32			,							file='data/tokenized_stories_train_wikitext103.jbl'			,							number=1_00			,							min_len=10_26			,							trim=snake_case     )

       # fine-tuning of the gpt2 model using igf (Information Gain Filtration)
       finetune(
           snake_case			,							snake_case			,							snake_case			,							context_len=32			,							max_steps=10_00			,							batch_size=16			,							threshold=1.0			,							recopy_model=snake_case			,							secondary_learner=snake_case			,							eval_interval=10			,							finetuned_model_name='gpt2_finetuned.pt'			,							)


if __name__ == "__main__":
 main()


