



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


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


class   snake_case_ (lowercase__  ):




       """simple docstring"""






       def __init__(     self     ,lowercase     ,lowercase     ,lowercase=None     ,lowercase=1):

             """simple docstring"""





             UpperCAmelCase_  :	Any				      =		tokenizer
             UpperCAmelCase_  :	Optional[int]				      =		dataset
             UpperCAmelCase_  :	List[str]				      =		len(lowercase) if n_tasks is None else n_tasks
             UpperCAmelCase_  :	int				      =		n_copies



       def __iter__(     self):

             """simple docstring"""





             UpperCAmelCase_  :	Union[str, Any]				      =		[]
             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_  :	List[str]				      =		self.tokenizer(lowercase     ,padding=lowercase     ,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   snake_case_ (lowercase__  ):




       """simple docstring"""






       def __init__(     self     ,lowercase     ,lowercase     ,lowercase):

             """simple docstring"""





             UpperCAmelCase_  :	Dict				      =		start_length
             UpperCAmelCase_  :	int				      =		eof_strings
             UpperCAmelCase_  :	Union[str, Any]				      =		tokenizer



       def __call__(     self     ,lowercase     ,lowercase     ,**lowercase):

             """simple docstring"""





             UpperCAmelCase_  :	Optional[Any]				      =		self.tokenizer.batch_decode(input_ids[:, self.start_length :])
             UpperCAmelCase_  :	Any				      =		[]
             for decoded_generation in decoded_generations:
                   done.append(any(stop_string in decoded_generation for stop_string in self.eof_strings))
             return all(lowercase)







def    _snake_case (       __snake_case    )     ->     Dict:






      '''simple docstring'''







      UpperCAmelCase_  :	List[str]				      =		re.split("(%s)" % "|".join(__snake_case    )					,  __snake_case    )
      # last string should be ""
      return "".join(string_list[:-2]    )







def    _snake_case (       __snake_case					,  __snake_case					,  __snake_case					,  __snake_case					,  __snake_case					,  __snake_case=2_0					,  **__snake_case    )     ->     Dict:






      '''simple docstring'''







      UpperCAmelCase_  :	str				      =		defaultdict(__snake_case    )  # dict of list of generated tokens
      for step, batch in tqdm(enumerate(__snake_case    )    ):
            with torch.no_grad():
                  UpperCAmelCase_  :	Any				      =		batch["ids"].shape[-1]
                  UpperCAmelCase_  :	str				      =		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
                  UpperCAmelCase_  :	Optional[int]				      =		batch["task_id"].repeat(__snake_case    )
                  UpperCAmelCase_  :	str				      =		accelerator.pad_across_processes(
                      __snake_case					,  dim=1					,  pad_index=tokenizer.pad_token_id    )

                  UpperCAmelCase_      ,      UpperCAmelCase_  :	List[Any]				      =		accelerator.gather((generated_tokens, generated_tasks)    )
                  UpperCAmelCase_  :	Union[str, Any]				      =		generated_tokens.cpu().numpy()
                  UpperCAmelCase_  :	List[str]				      =		generated_tasks.cpu().numpy()

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

      UpperCAmelCase_  :	int				      =		[[] for _ in range(__snake_case    )]
      for task, generated_tokens in gen_token_dict.items():
            for s in generated_tokens:
                  UpperCAmelCase_  :	int				      =		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    _snake_case (       )     ->     Union[str, Any]:






      '''simple docstring'''







      UpperCAmelCase_  :	Optional[Any]				      =		HfArgumentParser(__snake_case    )
      UpperCAmelCase_  :	Tuple				      =		parser.parse_args()

      transformers.logging.set_verbosity_error()
      # enables code execution in code_eval metric
      UpperCAmelCase_  :	Tuple				      =		args.HF_ALLOW_CODE_EVAL
      # make sure tokenizer plays nice with multiprocessing
      UpperCAmelCase_  :	Union[str, Any]				      =		"false"

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

      # Use dataset load to feed to accelerate
      UpperCAmelCase_  :	Optional[Any]				      =		Accelerator()
      set_seed(args.seed					,  device_specific=__snake_case    )

      # Load model and tokenizer
      UpperCAmelCase_  :	List[Any]				      =		AutoTokenizer.from_pretrained(args.model_ckpt    )
      UpperCAmelCase_  :	Union[str, Any]				      =		tokenizer.eos_token
      UpperCAmelCase_  :	List[str]				      =		AutoModelForCausalLM.from_pretrained(args.model_ckpt    )

      # Generation settings
      UpperCAmelCase_  :	List[Any]				      =		{
          "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
      UpperCAmelCase_  :	Dict				      =		load_dataset("openai_humaneval"    )
      UpperCAmelCase_  :	List[str]				      =		load_metric("code_eval"    )

      UpperCAmelCase_  :	Optional[Any]				      =		args.num_tasks if args.num_tasks is not None else len(human_eval["test"]    )
      UpperCAmelCase_  :	str				      =		args.n_samples // args.batch_size

      UpperCAmelCase_  :	Tuple				      =		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
      UpperCAmelCase_  :	Any				      =		DataLoader(__snake_case					,  batch_size=1    )

      # Run a quick test to see if code evaluation is enabled
      try:
            UpperCAmelCase_  :	List[str]				      =		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_  :	Optional[Any]				      =		accelerator.prepare(__snake_case					,  __snake_case    )

      UpperCAmelCase_  :	int				      =		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:
            UpperCAmelCase_  :	Tuple				      =		[]

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

            # Evaluate completions with "code_eval" metric
            UpperCAmelCase_      ,      UpperCAmelCase_  :	Tuple				      =		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()





import copy
import inspect
import unittest

from transformers import AutoBackbone
from transformers.configuration_utils import PretrainedConfig
from transformers.testing_utils import require_timm, require_torch, torch_device
from transformers.utils.import_utils import is_torch_available

from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor


if is_torch_available():
   import torch

   from transformers import TimmBackbone, TimmBackboneConfig

from ...test_pipeline_mixin import PipelineTesterMixin


class   snake_case_ :




       """simple docstring"""






       def __init__(     self     ,lowercase     ,lowercase=None     ,lowercase=None     ,lowercase=None     ,lowercase="resnet50"     ,lowercase=3     ,lowercase=32     ,lowercase=3     ,lowercase=True     ,lowercase=True     ,):

             """simple docstring"""





             UpperCAmelCase_  :	Union[str, Any]				      =		parent
             UpperCAmelCase_  :	int				      =		out_indices if out_indices is not None else [4]
             UpperCAmelCase_  :	Any				      =		stage_names
             UpperCAmelCase_  :	List[Any]				      =		out_features
             UpperCAmelCase_  :	List[str]				      =		backbone
             UpperCAmelCase_  :	Union[str, Any]				      =		batch_size
             UpperCAmelCase_  :	int				      =		image_size
             UpperCAmelCase_  :	str				      =		num_channels
             UpperCAmelCase_  :	Optional[Any]				      =		use_pretrained_backbone
             UpperCAmelCase_  :	Optional[int]				      =		is_training



       def  A_   (     self):

             """simple docstring"""





             UpperCAmelCase_  :	int				      =		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
             UpperCAmelCase_  :	Dict				      =		self.get_config()

             return config, pixel_values



       def  A_   (     self):

             """simple docstring"""





             return TimmBackboneConfig(
                 image_size=self.image_size     ,num_channels=self.num_channels     ,out_features=self.out_features     ,out_indices=self.out_indices     ,stage_names=self.stage_names     ,use_pretrained_backbone=self.use_pretrained_backbone     ,backbone=self.backbone     ,)



       def  A_   (     self     ,lowercase     ,lowercase):

             """simple docstring"""





             UpperCAmelCase_  :	List[Any]				      =		TimmBackbone(config=lowercase)
             model.to(lowercase)
             model.eval()
             with torch.no_grad():
                   UpperCAmelCase_  :	Union[str, Any]				      =		model(lowercase)
             self.parent.assertEqual(
                 result.feature_map[-1].shape     ,(self.batch_size, model.channels[-1], 14, 14)     ,)



       def  A_   (     self):

             """simple docstring"""





             UpperCAmelCase_  :	Tuple				      =		self.prepare_config_and_inputs()
             UpperCAmelCase_      ,      UpperCAmelCase_  :	Optional[int]				      =		config_and_inputs
             UpperCAmelCase_  :	Union[str, Any]				      =		{"pixel_values": pixel_values}
             return config, inputs_dict


@require_torch
@require_timm
class   snake_case_ (lowercase__ , lowercase__ , lowercase__ , unittest.TestCase  ):




       """simple docstring"""






       _lowerCamelCase			    =				(TimmBackbone,) if is_torch_available() else ()
       _lowerCamelCase			    =				{"""feature-extraction""": TimmBackbone} if is_torch_available() else {}
       _lowerCamelCase			    =				False
       _lowerCamelCase			    =				False
       _lowerCamelCase			    =				False
       _lowerCamelCase			    =				False



       def  A_   (     self):

             """simple docstring"""





             UpperCAmelCase_  :	Union[str, Any]				      =		TimmBackboneModelTester(self)
             UpperCAmelCase_  :	Tuple				      =		ConfigTester(self     ,config_class=lowercase     ,has_text_modality=lowercase)



       def  A_   (     self):

             """simple docstring"""





             self.config_tester.create_and_test_config_to_json_string()
             self.config_tester.create_and_test_config_to_json_file()
             self.config_tester.create_and_test_config_from_and_save_pretrained()
             self.config_tester.create_and_test_config_with_num_labels()
             self.config_tester.check_config_can_be_init_without_params()
             self.config_tester.check_config_arguments_init()



       def  A_   (     self):

             """simple docstring"""





             UpperCAmelCase_  :	int				      =		"resnet18"
             UpperCAmelCase_  :	Tuple				      =		"microsoft/resnet-18"

             UpperCAmelCase_  :	int				      =		AutoBackbone.from_pretrained(lowercase     ,use_timm_backbone=lowercase)
             UpperCAmelCase_  :	Dict				      =		AutoBackbone.from_pretrained(lowercase)

             self.assertEqual(len(timm_model.out_features)     ,len(transformers_model.out_features))
             self.assertEqual(len(timm_model.stage_names)     ,len(transformers_model.stage_names))
             self.assertEqual(timm_model.channels     ,transformers_model.channels)
             # Out indices are set to the last layer by default. For timm models, we don't know
             # the number of layers in advance, so we set it to (-1,), whereas for transformers
             # models, we set it to [len(stage_names) - 1] (kept for backward compatibility).
             self.assertEqual(timm_model.out_indices     ,(-1,))
             self.assertEqual(transformers_model.out_indices     ,[len(timm_model.stage_names) - 1])

             UpperCAmelCase_  :	Dict				      =		AutoBackbone.from_pretrained(lowercase     ,use_timm_backbone=lowercase     ,out_indices=[1, 2, 3])
             UpperCAmelCase_  :	List[str]				      =		AutoBackbone.from_pretrained(lowercase     ,out_indices=[1, 2, 3])

             self.assertEqual(timm_model.out_indices     ,transformers_model.out_indices)
             self.assertEqual(len(timm_model.out_features)     ,len(transformers_model.out_features))
             self.assertEqual(timm_model.channels     ,transformers_model.channels)



       @unittest.skip("TimmBackbone doesn't support feed forward chunking")
       def  A_   (     self):

             """simple docstring"""





             pass



       @unittest.skip("TimmBackbone doesn't have num_hidden_layers attribute")
       def  A_   (     self):

             """simple docstring"""





             pass



       @unittest.skip("TimmBackbone initialization is managed on the timm side")
       def  A_   (     self):

             """simple docstring"""





             pass



       @unittest.skip("TimmBackbone models doesn't have inputs_embeds")
       def  A_   (     self):

             """simple docstring"""





             pass



       @unittest.skip("TimmBackbone models doesn't have inputs_embeds")
       def  A_   (     self):

             """simple docstring"""





             pass



       @unittest.skip("TimmBackbone model cannot be created without specifying a backbone checkpoint")
       def  A_   (     self):

             """simple docstring"""





             pass



       @unittest.skip("Only checkpoints on timm can be loaded into TimmBackbone")
       def  A_   (     self):

             """simple docstring"""





             pass



       @unittest.skip("model weights aren't tied in TimmBackbone.")
       def  A_   (     self):

             """simple docstring"""





             pass



       @unittest.skip("model weights aren't tied in TimmBackbone.")
       def  A_   (     self):

             """simple docstring"""





             pass



       @unittest.skip("Only checkpoints on timm can be loaded into TimmBackbone")
       def  A_   (     self):

             """simple docstring"""





             pass



       @unittest.skip("Only checkpoints on timm can be loaded into TimmBackbone")
       def  A_   (     self):

             """simple docstring"""





             pass



       @unittest.skip("TimmBackbone doesn't have hidden size info in its configuration.")
       def  A_   (     self):

             """simple docstring"""





             pass



       @unittest.skip("TimmBackbone doesn't support output_attentions.")
       def  A_   (     self):

             """simple docstring"""





             pass



       @unittest.skip("Safetensors is not supported by timm.")
       def  A_   (     self):

             """simple docstring"""





             pass



       @unittest.skip("Will be fixed soon by reducing the size of the model used for common tests.")
       def  A_   (     self):

             """simple docstring"""





             pass



       def  A_   (     self):

             """simple docstring"""





             UpperCAmelCase_      ,      UpperCAmelCase_  :	int				      =		self.model_tester.prepare_config_and_inputs_for_common()

             for model_class in self.all_model_classes:
                   UpperCAmelCase_  :	Union[str, Any]				      =		model_class(lowercase)
                   UpperCAmelCase_  :	Optional[Any]				      =		inspect.signature(model.forward)
                   # signature.parameters is an OrderedDict => so arg_names order is deterministic
                   UpperCAmelCase_  :	str				      =		[*signature.parameters.keys()]

                   UpperCAmelCase_  :	Any				      =		["pixel_values"]
                   self.assertListEqual(arg_names[:1]     ,lowercase)



       def  A_   (     self):

             """simple docstring"""





             UpperCAmelCase_      ,      UpperCAmelCase_  :	Tuple				      =		self.model_tester.prepare_config_and_inputs_for_common()
             UpperCAmelCase_  :	Dict				      =		True
             UpperCAmelCase_  :	str				      =		self.has_attentions

             # no need to test all models as different heads yield the same functionality
             UpperCAmelCase_  :	str				      =		self.all_model_classes[0]
             UpperCAmelCase_  :	List[Any]				      =		model_class(lowercase)
             model.to(lowercase)

             UpperCAmelCase_  :	Any				      =		self._prepare_for_class(lowercase     ,lowercase)
             UpperCAmelCase_  :	Optional[int]				      =		model(**lowercase)
             UpperCAmelCase_  :	Optional[Any]				      =		outputs[0][-1]

             # Encoder-/Decoder-only models
             UpperCAmelCase_  :	List[Any]				      =		outputs.hidden_states[0]
             hidden_states.retain_grad()

             if self.has_attentions:
                   UpperCAmelCase_  :	int				      =		outputs.attentions[0]
                   attentions.retain_grad()

             output.flatten()[0].backward(retain_graph=lowercase)

             self.assertIsNotNone(hidden_states.grad)

             if self.has_attentions:
                   self.assertIsNotNone(attentions.grad)



       def  A_   (     self):

             """simple docstring"""





             UpperCAmelCase_      ,      UpperCAmelCase_  :	List[Any]				      =		self.model_tester.prepare_config_and_inputs_for_common()

             for model_class in self.all_model_classes:
                   UpperCAmelCase_  :	Any				      =		model_class(lowercase)
                   model.to(lowercase)
                   model.eval()
                   UpperCAmelCase_  :	Tuple				      =		model(**lowercase)

                   self.assertEqual(len(result.feature_maps)     ,len(config.out_indices))
                   self.assertEqual(len(model.channels)     ,len(config.out_indices))

                   # Check output of last stage is taken if out_features=None, out_indices=None
                   UpperCAmelCase_  :	Tuple				      =		copy.deepcopy(lowercase)
                   UpperCAmelCase_  :	List[Any]				      =		None
                   UpperCAmelCase_  :	Optional[Any]				      =		model_class(lowercase)
                   model.to(lowercase)
                   model.eval()
                   UpperCAmelCase_  :	Tuple				      =		model(**lowercase)

                   self.assertEqual(len(result.feature_maps)     ,1)
                   self.assertEqual(len(model.channels)     ,1)

                   # Check backbone can be initialized with fresh weights
                   UpperCAmelCase_  :	str				      =		copy.deepcopy(lowercase)
                   UpperCAmelCase_  :	Optional[Any]				      =		False
                   UpperCAmelCase_  :	List[str]				      =		model_class(lowercase)
                   model.to(lowercase)
                   model.eval()
                   UpperCAmelCase_  :	Union[str, Any]				      =		model(**lowercase)
