


import unittest

from transformers import BertGenerationConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
  import torch

  from transformers import BertGenerationDecoder, BertGenerationEncoder







class       lowerCamelCase__   :





     def __init__(self		,     UpperCAmelCase		,     UpperCAmelCase=1_3		,     UpperCAmelCase=7		,     UpperCAmelCase=True		,     UpperCAmelCase=True		,     UpperCAmelCase=9_9		,     UpperCAmelCase=3_2		,     UpperCAmelCase=5		,     UpperCAmelCase=4		,     UpperCAmelCase=3_7		,     UpperCAmelCase="gelu"		,     UpperCAmelCase=0.1		,     UpperCAmelCase=0.1		,     UpperCAmelCase=5_0		,     UpperCAmelCase=0.02		,     UpperCAmelCase=True		,     UpperCAmelCase=None		,     )     ->   str:
      _lowercase							      =parent
      _lowercase							      =batch_size
      _lowercase							      =seq_length
      _lowercase							      =is_training
      _lowercase							      =use_input_mask
      _lowercase							      =vocab_size
      _lowercase							      =hidden_size
      _lowercase							      =num_hidden_layers
      _lowercase							      =num_attention_heads
      _lowercase							      =intermediate_size
      _lowercase							      =hidden_act
      _lowercase							      =hidden_dropout_prob
      _lowercase							      =attention_probs_dropout_prob
      _lowercase							      =max_position_embeddings
      _lowercase							      =initializer_range
      _lowercase							      =use_labels
      _lowercase							      =scope





     def      __A					(self       )     ->   int:
      _lowercase							      =ids_tensor([self.batch_size, self.seq_length]		,     self.vocab_size       )

      _lowercase							      =None
      if self.use_input_mask:
       _lowercase							      =random_attention_mask([self.batch_size, self.seq_length]       )

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

      _lowercase							      =self.get_config()

      return config, input_ids, input_mask, token_labels





     def      __A					(self       )     ->   Any:
      return BertGenerationConfig(
          vocab_size=self.vocab_size		,     hidden_size=self.hidden_size		,     num_hidden_layers=self.num_hidden_layers		,     num_attention_heads=self.num_attention_heads		,     intermediate_size=self.intermediate_size		,     hidden_act=self.hidden_act		,     hidden_dropout_prob=self.hidden_dropout_prob		,     attention_probs_dropout_prob=self.attention_probs_dropout_prob		,     max_position_embeddings=self.max_position_embeddings		,     is_decoder=UpperCAmelCase		,     initializer_range=self.initializer_range		,     )





     def      __A					(self       )     ->   Dict:
      (
          (
          _lowercase
      )      ,    (
          _lowercase
      )      ,    (
          _lowercase
      )      ,    (
          _lowercase
      )      ,    
      )							      =self.prepare_config_and_inputs()

      _lowercase							      =True
      _lowercase							      =floats_tensor([self.batch_size, self.seq_length, self.hidden_size]       )
      _lowercase							      =ids_tensor([self.batch_size, self.seq_length]		,     vocab_size=2       )

      return (
          config,
          input_ids,
          input_mask,
          token_labels,
          encoder_hidden_states,
          encoder_attention_mask,
      )





     def      __A					(self		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     **UpperCAmelCase		,     )     ->   Tuple:
      _lowercase							      =BertGenerationEncoder(config=UpperCAmelCase       )
      model.to(UpperCAmelCase       )
      model.eval()
      _lowercase							      =model(UpperCAmelCase		,     attention_mask=UpperCAmelCase       )
      _lowercase							      =model(UpperCAmelCase       )
      self.parent.assertEqual(result.last_hidden_state.shape		,     (self.batch_size, self.seq_length, self.hidden_size)       )





     def      __A					(self		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     **UpperCAmelCase		,     )     ->   List[str]:
      _lowercase							      =True
      _lowercase							      =BertGenerationEncoder(config=UpperCAmelCase       )
      model.to(UpperCAmelCase       )
      model.eval()
      _lowercase							      =model(
          UpperCAmelCase		,     attention_mask=UpperCAmelCase		,     encoder_hidden_states=UpperCAmelCase		,     encoder_attention_mask=UpperCAmelCase		,     )
      _lowercase							      =model(
          UpperCAmelCase		,     attention_mask=UpperCAmelCase		,     encoder_hidden_states=UpperCAmelCase		,     )
      self.parent.assertEqual(result.last_hidden_state.shape		,     (self.batch_size, self.seq_length, self.hidden_size)       )





     def      __A					(self		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     **UpperCAmelCase		,     )     ->   Dict:
      _lowercase							      =True
      _lowercase							      =True
      _lowercase							      =BertGenerationDecoder(config=UpperCAmelCase       ).to(UpperCAmelCase       ).eval()

      # first forward pass
      _lowercase							      =model(
          UpperCAmelCase		,     attention_mask=UpperCAmelCase		,     encoder_hidden_states=UpperCAmelCase		,     encoder_attention_mask=UpperCAmelCase		,     use_cache=UpperCAmelCase		,     )
      _lowercase							      =outputs.past_key_values

      # create hypothetical multiple next token and extent to next_input_ids
      _lowercase							      =ids_tensor((self.batch_size, 3)		,     config.vocab_size       )
      _lowercase							      =ids_tensor((self.batch_size, 3)		,     vocab_size=2       )

      # append to next input_ids and
      _lowercase							      =torch.cat([input_ids, next_tokens]		,     dim=-1       )
      _lowercase							      =torch.cat([input_mask, next_mask]		,     dim=-1       )

      _lowercase							      =model(
          UpperCAmelCase		,     attention_mask=UpperCAmelCase		,     encoder_hidden_states=UpperCAmelCase		,     encoder_attention_mask=UpperCAmelCase		,     output_hidden_states=UpperCAmelCase		,     )['''hidden_states'''][0]
      _lowercase							      =model(
          UpperCAmelCase		,     attention_mask=UpperCAmelCase		,     encoder_hidden_states=UpperCAmelCase		,     encoder_attention_mask=UpperCAmelCase		,     past_key_values=UpperCAmelCase		,     output_hidden_states=UpperCAmelCase		,     )['''hidden_states'''][0]

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

      self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]       )

      # test that outputs are equal for slice
      self.parent.assertTrue(torch.allclose(UpperCAmelCase		,     UpperCAmelCase		,     atol=1e-3       )       )





     def      __A					(self		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     *UpperCAmelCase		,     )     ->   Optional[int]:
      _lowercase							      =BertGenerationDecoder(UpperCAmelCase       )
      model.to(UpperCAmelCase       )
      model.eval()
      _lowercase							      =model(UpperCAmelCase		,     attention_mask=UpperCAmelCase		,     labels=UpperCAmelCase       )
      self.parent.assertEqual(result.logits.shape		,     (self.batch_size, self.seq_length, self.vocab_size)       )





     def      __A					(self       )     ->   Tuple:
      _lowercase      ,    _lowercase      ,    _lowercase      ,    _lowercase							      =self.prepare_config_and_inputs()
      _lowercase							      ={'''input_ids''': input_ids, '''attention_mask''': input_mask}
      return config, inputs_dict







@require_torch
class       lowerCamelCase__   (       lowerCAmelCase   ,							lowerCAmelCase   ,							lowerCAmelCase   ,							unittest.TestCase):
     SCREAMING_SNAKE_CASE__      			=			(BertGenerationEncoder, BertGenerationDecoder) if is_torch_available() else ()
     SCREAMING_SNAKE_CASE__      			=			(BertGenerationDecoder,) if is_torch_available() else ()
     SCREAMING_SNAKE_CASE__      			=			(
         {'''feature-extraction''': BertGenerationEncoder, '''text-generation''': BertGenerationDecoder}
         if is_torch_available()
         else {}
     )





     def      __A					(self       )     ->   Dict:
      _lowercase							      =BertGenerationEncoderTester(self       )
      _lowercase							      =ConfigTester(self		,     config_class=UpperCAmelCase		,     hidden_size=3_7       )





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





     def      __A					(self       )     ->   Optional[int]:
      _lowercase							      =self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*UpperCAmelCase       )





     def      __A					(self       )     ->   List[Any]:
      _lowercase      ,    _lowercase      ,    _lowercase      ,    _lowercase							      =self.model_tester.prepare_config_and_inputs()
      _lowercase							      ='''bert'''
      self.model_tester.create_and_check_model(UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase       )





     def      __A					(self       )     ->   Union[str, Any]:
      _lowercase							      =self.model_tester.prepare_config_and_inputs_for_decoder()
      self.model_tester.create_and_check_model_as_decoder(*UpperCAmelCase       )





     def      __A					(self       )     ->   List[str]:
      _lowercase							      =self.model_tester.prepare_config_and_inputs_for_decoder()
      self.model_tester.create_and_check_decoder_model_past_large_inputs(*UpperCAmelCase       )





     def      __A					(self       )     ->   int:
      # This regression test was failing with PyTorch < 1.3
      (
          (
          _lowercase
      )      ,    (
          _lowercase
      )      ,    (
          _lowercase
      )      ,    (
          _lowercase
      )      ,    (
          _lowercase
      )      ,    (
          _lowercase
      )      ,    
      )							      =self.model_tester.prepare_config_and_inputs_for_decoder()

      _lowercase							      =None

      self.model_tester.create_and_check_model_as_decoder(
          UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     )





     def      __A					(self       )     ->   str:
      _lowercase							      =self.model_tester.prepare_config_and_inputs_for_decoder()
      self.model_tester.create_and_check_for_causal_lm(*UpperCAmelCase       )





     @slow
     def      __A					(self       )     ->   Union[str, Any]:
      _lowercase							      =BertGenerationEncoder.from_pretrained('''google/bert_for_seq_generation_L-24_bbc_encoder'''       )
      self.assertIsNotNone(UpperCAmelCase       )







@require_torch
class       lowerCamelCase__   (       unittest.TestCase):





     @slow
     def      __A					(self       )     ->   List[str]:
      _lowercase							      =BertGenerationEncoder.from_pretrained('''google/bert_for_seq_generation_L-24_bbc_encoder'''       )
      _lowercase							      =torch.tensor([[1_0_1, 7_5_9_2, 1_0_1_0, 2_0_2_6, 3_8_9_9, 2_0_0_3, 1_0_1_4_0, 1_0_2]]       )
      with torch.no_grad():
       _lowercase							      =model(UpperCAmelCase       )[0]
      _lowercase							      =torch.Size([1, 8, 1_0_2_4]       )
      self.assertEqual(output.shape		,     UpperCAmelCase       )
      _lowercase							      =torch.tensor(
          [[[0.1775, 0.0083, -0.0321], [1.6002, 0.1287, 0.3912], [2.1473, 0.5791, 0.6066]]]       )
      self.assertTrue(torch.allclose(output[:, :3, :3]		,     UpperCAmelCase		,     atol=1e-4       )       )







@require_torch
class       lowerCamelCase__   (       unittest.TestCase):





     @slow
     def      __A					(self       )     ->   Dict:
      _lowercase							      =BertGenerationDecoder.from_pretrained('''google/bert_for_seq_generation_L-24_bbc_encoder'''       )
      _lowercase							      =torch.tensor([[1_0_1, 7_5_9_2, 1_0_1_0, 2_0_2_6, 3_8_9_9, 2_0_0_3, 1_0_1_4_0, 1_0_2]]       )
      with torch.no_grad():
       _lowercase							      =model(UpperCAmelCase       )[0]
      _lowercase							      =torch.Size([1, 8, 5_0_3_5_8]       )
      self.assertEqual(output.shape		,     UpperCAmelCase       )
      _lowercase							      =torch.tensor(
          [[[-0.5788, -2.5994, -3.7054], [0.0438, 4.7997, 1.8795], [1.5862, 6.6409, 4.4638]]]       )
      self.assertTrue(torch.allclose(output[:, :3, :3]		,     UpperCAmelCase		,     atol=1e-4       )       )





import json
import multiprocessing
import os
import re
from collections import defaultdict

import torch
from accelerate import Accelerator
from accelerate.utils import set_seed
from arguments import HumanEvalArguments
from datasets import load_dataset, load_metric
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader
from tqdm import tqdm

import transformers
from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser, StoppingCriteria, StoppingCriteriaList


UpperCAmelCase__   	=							['''\nclass''', '''\ndef''', '''\n#''', '''\n@''', '''\nprint''', '''\nif''']







class       lowerCamelCase__   (       lowerCAmelCase):





     def __init__(self		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase=None		,     UpperCAmelCase=1       )     ->   Dict:
      _lowercase							      =tokenizer
      _lowercase							      =dataset
      _lowercase							      =len(UpperCAmelCase       ) if n_tasks is None else n_tasks
      _lowercase							      =n_copies





     def __iter__(self       )     ->   Optional[Any]:
      _lowercase							      =[]
      for task in range(self.n_tasks       ):
       # without strip, the model generate commented codes ...
       prompts.append(self.tokenizer.eos_token + self.dataset[task]['''prompt'''].strip()       )
      _lowercase							      =self.tokenizer(UpperCAmelCase		,     padding=UpperCAmelCase		,     return_tensors='''pt'''       )
      for task in range(self.n_tasks       ):
       for _ in range(self.n_copies       ):
        yield {
            "ids": outputs.input_ids[task],
            "task_id": task,
            "input_len": outputs.attention_mask[task].sum(),
        }







class       lowerCamelCase__   (       lowerCAmelCase):





     def __init__(self		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase       )     ->   Optional[Any]:
      _lowercase							      =start_length
      _lowercase							      =eof_strings
      _lowercase							      =tokenizer





     def __call__(self		,     UpperCAmelCase		,     UpperCAmelCase		,     **UpperCAmelCase       )     ->   Dict:
      _lowercase							      =self.tokenizer.batch_decode(input_ids[:, self.start_length :]       )
      _lowercase							      =[]
      for decoded_generation in decoded_generations:
       done.append(any(stop_string in decoded_generation for stop_string in self.eof_strings       )       )
      return all(UpperCAmelCase       )



def 				UpperCAmelCase_					(							__snake_case						)     ->			Optional[Any]:







 """simple docstring"""


 _lowercase							      =re.split('''(%s)''' % '''|'''.join(__snake_case						)     ,						__snake_case						)
 # last string should be ""
 return "".join(string_list[:-2]						)
def 				UpperCAmelCase_					(							__snake_case     ,						__snake_case     ,						__snake_case     ,						__snake_case     ,						__snake_case     ,						__snake_case=20     ,						**__snake_case						)     ->			Tuple:







 """simple docstring"""


 _lowercase							      =defaultdict(__snake_case						)  # dict of list of generated tokens
 for step, batch in tqdm(enumerate(__snake_case						)						):
  with torch.no_grad():
   _lowercase							      =batch['''ids'''].shape[-1]
   _lowercase							      =accelerator.unwrap_model(__snake_case						).generate(
       input_ids=batch['''ids'''][:, : batch['''input_len''']]     ,						num_return_sequences=__snake_case     ,						**__snake_case						)
   # each task is generated batch_size times
   _lowercase							      =batch['''task_id'''].repeat(__snake_case						)
   _lowercase							      =accelerator.pad_across_processes(
       __snake_case     ,						dim=1     ,						pad_index=tokenizer.pad_token_id						)

   _lowercase      ,    _lowercase							      =accelerator.gather((generated_tokens, generated_tasks)						)
   _lowercase							      =generated_tokens.cpu().numpy()
   _lowercase							      =generated_tasks.cpu().numpy()

   for task, generated_tokens in zip(__snake_case     ,						__snake_case						):
    gen_token_dict[task].append(__snake_case						)

 _lowercase							      =[[] for _ in range(__snake_case						)]
 for task, generated_tokens in gen_token_dict.items():
  for s in generated_tokens:
   _lowercase							      =tokenizer.decode(__snake_case     ,						skip_special_tokens=__snake_case     ,						clean_up_tokenization_spaces=__snake_case						)
   code_gens[task].append(remove_last_block(__snake_case						)						)
 return code_gens
def 				UpperCAmelCase_					(							)     ->			str:







 """simple docstring"""


 _lowercase							      =HfArgumentParser(__snake_case						)
 _lowercase							      =parser.parse_args()

 transformers.logging.set_verbosity_error()
 # enables code execution in code_eval metric
 _lowercase							      =args.HF_ALLOW_CODE_EVAL
 # make sure tokenizer plays nice with multiprocessing
 _lowercase							      ='''false'''

 if args.num_workers is None:
  _lowercase							      =multiprocessing.cpu_count()

 # Use dataset load to feed to accelerate
 _lowercase							      =Accelerator()
 set_seed(args.seed     ,						device_specific=__snake_case						)

 # Load model and tokenizer
 _lowercase							      =AutoTokenizer.from_pretrained(args.model_ckpt						)
 _lowercase							      =tokenizer.eos_token
 _lowercase							      =AutoModelForCausalLM.from_pretrained(args.model_ckpt						)

 # Generation settings
 _lowercase							      ={
     '''do_sample''': args.do_sample,
     '''temperature''': args.temperature,
     '''max_new_tokens''': args.max_new_tokens,
     '''top_p''': args.top_p,
     '''top_k''': args.top_k,
     '''stopping_criteria''': StoppingCriteriaList([EndOfFunctionCriteria(0     ,						__snake_case     ,						__snake_case						)]						),
 }

 # Load evaluation dataset and metric
 _lowercase							      =load_dataset('''openai_humaneval'''						)
 _lowercase							      =load_metric('''code_eval'''						)

 _lowercase							      =args.num_tasks if args.num_tasks is not None else len(human_eval['''test''']						)
 _lowercase							      =args.n_samples // args.batch_size

 _lowercase							      =TokenizedDataset(__snake_case     ,						human_eval['''test''']     ,						n_copies=__snake_case     ,						n_tasks=__snake_case						)
 # do not confuse args.batch_size, which is actually the num_return_sequences
 _lowercase							      =DataLoader(__snake_case     ,						batch_size=1						)

 # Run a quick test to see if code evaluation is enabled
 try:
  _lowercase							      =code_eval_metric.compute(references=['''''']     ,						predictions=[['''''']]						)
 except ValueError as exception:
  print(
      '''Code evaluation not enabled. Read the warning below carefully and then use `--HF_ALLOW_CODE_EVAL="1"`'''
      ''' flag to enable code evaluation.'''						)
  raise exception

 _lowercase      ,    _lowercase							      =accelerator.prepare(__snake_case     ,						__snake_case						)

 _lowercase							      =complete_code(
     __snake_case     ,						__snake_case     ,						__snake_case     ,						__snake_case     ,						n_tasks=__snake_case     ,						batch_size=args.batch_size     ,						**__snake_case     ,						)

 if accelerator.is_main_process:
  _lowercase							      =[]

  for task in tqdm(range(__snake_case						)						):
   _lowercase							      =human_eval['''test'''][task]['''test''']
   _lowercase							      =F"check({human_eval['test'][task]['entry_point']})"
   references.append('''\n''' + test_func + '''\n''' + entry_point						)

  # Evaluate completions with "code_eval" metric
  _lowercase      ,    _lowercase							      =code_eval_metric.compute(
      references=__snake_case     ,						predictions=__snake_case     ,						num_workers=args.num_workers						)
  print(F"Results: {pass_at_k}"						)

  # Save results to json file
  with open(args.output_file     ,						'''w'''						) as fp:
   json.dump(__snake_case     ,						__snake_case						)


# For some reason the folliwng seems to be necessary sometimes for code_eval to work nice with multiprocessing
# https://stackoverflow.com/questions/60804599/python-multiprocessing-keeps-spawning-the-whole-script
if __name__ == "__main__":
  main()

