"""simple docstring"""

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      UpperCAmelCase_       (						_lowercase):






							def __init__(		self :      Optional[int]     ,						__UpperCamelCase :      int     ,						__UpperCamelCase :      List[Any]     ,						__UpperCamelCase :      Dict=None     ,						__UpperCamelCase :      Dict=1  )       ->       Dict:
												_UpperCamelCase      						=	tokenizer
												_UpperCamelCase      						=	dataset
												_UpperCamelCase      						=	len(__UpperCamelCase  ) if n_tasks is None else n_tasks
												_UpperCamelCase      						=	n_copies






							def __iter__(		self :      Any  )       ->       int:
												_UpperCamelCase      						=	[]
												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()  )
												_UpperCamelCase      						=	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      UpperCAmelCase_       (						_lowercase):






							def __init__(		self :      Optional[int]     ,						__UpperCamelCase :      Any     ,						__UpperCamelCase :      str     ,						__UpperCamelCase :      Any  )       ->       Optional[int]:
												_UpperCamelCase      						=	start_length
												_UpperCamelCase      						=	eof_strings
												_UpperCamelCase      						=	tokenizer






							def __call__(		self :      Dict     ,						__UpperCamelCase :      Dict     ,						__UpperCamelCase :      Optional[Any]     ,						**__UpperCamelCase :      str  )       ->       Union[str, Any]:
												_UpperCamelCase      						=	self.tokenizer.batch_decode(input_ids[:, self.start_length :]  )
												_UpperCamelCase      						=	[]
												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      lowercase		(						a__	:		Optional[Any]     )						->   Union[str, Any]:
					_UpperCamelCase      						=	re.split('''(%s)''' % '''|'''.join(UpperCAmelCase_     ) ,  UpperCAmelCase_     )
					# last string should be ""
					return "".join(string_list[:-2]     )






def      lowercase		(						a__	:		Dict ,  a__	:		int ,  a__	:		Any ,  a__	:		Dict ,  a__	:		int ,  a__	:		str=20 ,  **a__	:		Optional[Any]     )						->   Optional[int]:
					_UpperCamelCase      						=	defaultdict(UpperCAmelCase_     )  # dict of list of generated tokens
					for step, batch in tqdm(enumerate(UpperCAmelCase_     )     ):
										with torch.no_grad():
															_UpperCamelCase      						=	batch['''ids'''].shape[-1]
															_UpperCamelCase      						=	accelerator.unwrap_model(UpperCAmelCase_     ).generate(
															    input_ids=batch['''ids'''][:, : batch['''input_len''']] ,  num_return_sequences=UpperCAmelCase_ ,  **UpperCAmelCase_     )
															# each task is generated batch_size times
															_UpperCamelCase      						=	batch['''task_id'''].repeat(UpperCAmelCase_     )
															_UpperCamelCase      						=	accelerator.pad_across_processes(
															    UpperCAmelCase_ ,  dim=1 ,  pad_index=tokenizer.pad_token_id     )

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

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

					_UpperCamelCase      						=	[[] for _ in range(UpperCAmelCase_     )]
					for task, generated_tokens in gen_token_dict.items():
										for s in generated_tokens:
															_UpperCamelCase      						=	tokenizer.decode(UpperCAmelCase_ ,  skip_special_tokens=UpperCAmelCase_ ,  clean_up_tokenization_spaces=UpperCAmelCase_     )
															code_gens[task].append(remove_last_block(UpperCAmelCase_     )     )
					return code_gens






def      lowercase		(						)						->   int:
					# Setup configuration
					_UpperCamelCase      						=	HfArgumentParser(UpperCAmelCase_     )
					_UpperCamelCase      						=	parser.parse_args()

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

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

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

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

					# Generation settings
					_UpperCamelCase      						=	{
					    '''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 ,  UpperCAmelCase_ ,  UpperCAmelCase_     )]     ),
					}

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

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

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

					# Run a quick test to see if code evaluation is enabled
					try:
										_UpperCamelCase      						=	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

					_UpperCamelCase						,							_UpperCamelCase      						=	accelerator.prepare(UpperCAmelCase_ ,  UpperCAmelCase_     )

					_UpperCamelCase      						=	complete_code(
					    UpperCAmelCase_ ,  UpperCAmelCase_ ,  UpperCAmelCase_ ,  UpperCAmelCase_ ,  n_tasks=UpperCAmelCase_ ,  batch_size=args.batch_size ,  **UpperCAmelCase_ ,  )

					if accelerator.is_main_process:
										_UpperCamelCase      						=	[]

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

										# Evaluate completions with "code_eval" metric
										_UpperCamelCase						,							_UpperCamelCase      						=	code_eval_metric.compute(
										    references=UpperCAmelCase_ ,  predictions=UpperCAmelCase_ ,  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(UpperCAmelCase_ ,  UpperCAmelCase_     )


# 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()




'''simple docstring'''

from __future__ import annotations

import unittest

from transformers import EsmConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
     import numpy
     import tensorflow as tf

     from transformers.models.esm.modeling_tf_esm import (
         TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST,
         TFEsmForMaskedLM,
         TFEsmForSequenceClassification,
         TFEsmForTokenClassification,
         TFEsmModel,
     )





class      snake_case    :





  """simple docstring"""




  def __init__(    self			,  UpperCamelCase			,  ):







   """simple docstring"""
   lowerCamelCase_      =  parent
   lowerCamelCase_      =  13
   lowerCamelCase_      =  7
   lowerCamelCase_      =  True
   lowerCamelCase_      =  True
   lowerCamelCase_      =  True
   lowerCamelCase_      =  99
   lowerCamelCase_      =  32
   lowerCamelCase_      =  2
   lowerCamelCase_      =  4
   lowerCamelCase_      =  37
   lowerCamelCase_      =  "gelu"
   lowerCamelCase_      =  0.1
   lowerCamelCase_      =  0.1
   lowerCamelCase_      =  512
   lowerCamelCase_      =  16
   lowerCamelCase_      =  2
   lowerCamelCase_      =  0.02
   lowerCamelCase_      =  3
   lowerCamelCase_      =  4
   lowerCamelCase_      =  None






  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  ids_tensor([self.batch_size, self.seq_length]			,  self.vocab_size     )

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

   lowerCamelCase_      =  None
   lowerCamelCase_      =  None
   lowerCamelCase_      =  None
   if self.use_labels:
    lowerCamelCase_      =  ids_tensor([self.batch_size]			,  self.type_sequence_label_size     )
    lowerCamelCase_      =  ids_tensor([self.batch_size, self.seq_length]			,  self.num_labels     )
    lowerCamelCase_      =  ids_tensor([self.batch_size]			,  self.num_choices     )

   lowerCamelCase_      =  EsmConfig(
       vocab_size=self.vocab_size			,  hidden_size=self.hidden_size			,  num_hidden_layers=self.num_hidden_layers			,  pad_token_id=1			,  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			,  type_vocab_size=self.type_vocab_size			,  initializer_range=self.initializer_range			,  )

   return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels






  def   snake_case		(    self     ):







   """simple docstring"""
   (
       (
       lowerCamelCase_
   ) ,(
       lowerCamelCase_
   ) ,(
       lowerCamelCase_
   ) ,(
       lowerCamelCase_
   ) ,(
       lowerCamelCase_
   ) ,(
       lowerCamelCase_
   ) ,
   )      =  self.prepare_config_and_inputs()

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

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






  def   snake_case		(    self			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase     ):







   """simple docstring"""
   lowerCamelCase_      =  TFEsmModel(config=UpperCamelCase     )
   lowerCamelCase_      =  {"input_ids": input_ids, "attention_mask": input_mask}
   lowerCamelCase_      =  model(UpperCamelCase     )

   lowerCamelCase_      =  [input_ids, input_mask]
   lowerCamelCase_      =  model(UpperCamelCase     )

   lowerCamelCase_      =  model(UpperCamelCase     )

   self.parent.assertEqual(result.last_hidden_state.shape			,  (self.batch_size, self.seq_length, self.hidden_size)     )






  def   snake_case		(    self			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase			,  ):







   """simple docstring"""
   lowerCamelCase_      =  True

   lowerCamelCase_      =  TFEsmModel(config=UpperCamelCase     )
   lowerCamelCase_      =  {
       "input_ids": input_ids,
       "attention_mask": input_mask,
       "encoder_hidden_states": encoder_hidden_states,
       "encoder_attention_mask": encoder_attention_mask,
   }
   lowerCamelCase_      =  model(UpperCamelCase     )

   lowerCamelCase_      =  [input_ids, input_mask]
   lowerCamelCase_      =  model(UpperCamelCase			,  encoder_hidden_states=UpperCamelCase     )

   # Also check the case where encoder outputs are not passed
   lowerCamelCase_      =  model(UpperCamelCase			,  attention_mask=UpperCamelCase     )

   self.parent.assertEqual(result.last_hidden_state.shape			,  (self.batch_size, self.seq_length, self.hidden_size)     )






  def   snake_case		(    self			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase     ):







   """simple docstring"""
   lowerCamelCase_      =  TFEsmForMaskedLM(config=UpperCamelCase     )
   lowerCamelCase_      =  model([input_ids, input_mask]     )
   self.parent.assertEqual(result.logits.shape			,  (self.batch_size, self.seq_length, self.vocab_size)     )






  def   snake_case		(    self			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase     ):







   """simple docstring"""
   lowerCamelCase_      =  self.num_labels
   lowerCamelCase_      =  TFEsmForTokenClassification(config=UpperCamelCase     )
   lowerCamelCase_      =  {"input_ids": input_ids, "attention_mask": input_mask}
   lowerCamelCase_      =  model(UpperCamelCase     )
   self.parent.assertEqual(result.logits.shape			,  (self.batch_size, self.seq_length, self.num_labels)     )







  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  self.prepare_config_and_inputs()
   (
       (
       lowerCamelCase_
   ) ,(
       lowerCamelCase_
   ) ,(
       lowerCamelCase_
   ) ,(
       lowerCamelCase_
   ) ,(
       lowerCamelCase_
   ) ,(
       lowerCamelCase_
   ) ,
   )      =  config_and_inputs
   lowerCamelCase_      =  {"input_ids": input_ids, "attention_mask": input_mask}
   return config, inputs_dict





@require_tf
class      snake_case    (    lowercase			,						lowercase			,						unittest.TestCase			):





  """simple docstring"""




  _lowerCamelCase		    =  (
      (
          TFEsmModel,
          TFEsmForMaskedLM,
          TFEsmForSequenceClassification,
          TFEsmForTokenClassification,
      )
      if is_tf_available()
      else ()
  )
  _lowerCamelCase		    =  (
      {
          "feature-extraction": TFEsmModel,
          "fill-mask": TFEsmForMaskedLM,
          "text-classification": TFEsmForSequenceClassification,
          "token-classification": TFEsmForTokenClassification,
          "zero-shot": TFEsmForSequenceClassification,
      }
      if is_tf_available()
      else {}
  )
  _lowerCamelCase		    =  False
  _lowerCamelCase		    =  False






  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  TFEsmModelTester(self     )
   lowerCamelCase_      =  ConfigTester(self			,  config_class=UpperCamelCase			,  hidden_size=37     )






  def   snake_case		(    self     ):







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






  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_model(*UpperCamelCase     )






  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  self.model_tester.prepare_config_and_inputs_for_decoder()
   self.model_tester.create_and_check_model_as_decoder(*UpperCamelCase     )






  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_masked_lm(*UpperCamelCase     )






  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_token_classification(*UpperCamelCase     )






  @slow
  def   snake_case		(    self     ):







   """simple docstring"""
   for model_name in TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
    lowerCamelCase_      =  TFEsmModel.from_pretrained(UpperCamelCase     )
    self.assertIsNotNone(UpperCamelCase     )






  @unittest.skip("Protein models do not support embedding resizing."     )
  def   snake_case		(    self     ):







   """simple docstring"""
   pass






  @unittest.skip("Protein models do not support embedding resizing."     )
  def   snake_case		(    self     ):







   """simple docstring"""
   pass







  def   snake_case		(    self     ):







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

   for model_class in self.all_model_classes:
    lowerCamelCase_      =  model_class(UpperCamelCase     )
    assert isinstance(model.get_input_embeddings()			,  tf.keras.layers.Layer     )
    if model_class is TFEsmForMaskedLM:
     # Output embedding test differs from the main test because they're a matrix, not a layer
     lowerCamelCase_      =  model.get_bias()
     assert isinstance(UpperCamelCase			,  UpperCamelCase     )
     for k, v in name.items():
      assert isinstance(UpperCamelCase			,  tf.Variable     )
    else:
     lowerCamelCase_      =  model.get_output_embeddings()
     assert x is None
     lowerCamelCase_      =  model.get_bias()
     assert name is None





@require_tf
class      snake_case    (    unittest.TestCase			):





  """simple docstring"""




  @slow
  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  TFEsmForMaskedLM.from_pretrained("facebook/esm2_t6_8M_UR50D"     )

   lowerCamelCase_      =  tf.constant([[0, 1, 2, 3, 4, 5]]     )
   lowerCamelCase_      =  model(UpperCamelCase     )[0]
   lowerCamelCase_      =  [1, 6, 33]
   self.assertEqual(list(output.numpy().shape     )			,  UpperCamelCase     )
   # compare the actual values for a slice.
   lowerCamelCase_      =  tf.constant(
       [
           [
               [8.921_518, -10.589_814, -6.4_671_307],
               [-6.3_967_156, -13.911_377, -1.1_211_915],
               [-7.781_247, -13.951_557, -3.740_592],
           ]
       ]     )
   self.assertTrue(numpy.allclose(output[:, :3, :3].numpy()			,  expected_slice.numpy()			,  atol=1e-2     )     )







  @slow
  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  TFEsmModel.from_pretrained("facebook/esm2_t6_8M_UR50D"     )

   lowerCamelCase_      =  tf.constant([[0, 6, 4, 13, 5, 4, 16, 12, 11, 7, 2]]     )
   lowerCamelCase_      =  model(UpperCamelCase     )[0]
   # compare the actual values for a slice.
   lowerCamelCase_      =  tf.constant(
       [
           [
               [0.14_443_092, 0.54_125_327, 0.3_247_739],
               [0.30_340_484, 0.00_526_676, 0.31_077_722],
               [0.32_278_043, -0.24_987_096, 0.3_414_628],
           ]
       ]     )
   self.assertTrue(numpy.allclose(output[:, :3, :3].numpy()			,  expected_slice.numpy()			,  atol=1e-4     )     )



