

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


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

class 			a					(		UpperCAmelCase			):


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







										'''simple docstring'''


										_UpperCAmelCase    :						Dict							=      tokenizer
										_UpperCAmelCase    :						Optional[Any]							=      dataset
										_UpperCAmelCase    :						List[str]							=      len(A_		) if n_tasks is None else n_tasks
										_UpperCAmelCase    :						List[str]							=      n_copies





						def __iter__(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						Optional[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    :						Optional[int]							=      self.tokenizer(A_  ,   padding=A_  ,   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 			a					(		UpperCAmelCase			):


						def __init__(    self  ,   A_  ,   A_  ,   A_		):







										'''simple docstring'''


										_UpperCAmelCase    :						int							=      start_length
										_UpperCAmelCase    :						Tuple							=      eof_strings
										_UpperCAmelCase    :						Optional[Any]							=      tokenizer





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







										'''simple docstring'''


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







def 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					Union[str, Any]	)				->    Optional[Any]:
				_UpperCAmelCase    :						List[str]							=      re.split("(%s)" % "|".join(lowerCAmelCase	)					, lowerCAmelCase	)
				# last string should be ""
				return "".join(string_list[:-2]	)

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

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

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

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

def 							__SCREAMING_SNAKE_CASE							(			)				->    int:
				# Setup configuration
				_UpperCAmelCase    :						Dict							=      HfArgumentParser(lowerCAmelCase	)
				_UpperCAmelCase    :						int							=      parser.parse_args()

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

				if args.num_workers is None:
								_UpperCAmelCase    :						Union[str, Any]							=      multiprocessing.cpu_count()

				# Use dataset load to feed to accelerate
				_UpperCAmelCase    :						str							=      Accelerator()
				set_seed(args.seed					, device_specific=lowerCAmelCase	)

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

				# Generation settings
				_UpperCAmelCase    :						List[str]							=      {
				    "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					, lowerCAmelCase					, lowerCAmelCase	)]	),
				}

				# Load evaluation dataset and metric
				_UpperCAmelCase    :						int							=      load_dataset("openai_humaneval"	)
				_UpperCAmelCase    :						str							=      load_metric("code_eval"	)

				_UpperCAmelCase    :						Tuple							=      args.num_tasks if args.num_tasks is not None else len(human_eval["test"]	)
				_UpperCAmelCase    :						List[str]							=      args.n_samples // args.batch_size

				_UpperCAmelCase    :						int							=      TokenizedDataset(lowerCAmelCase					, human_eval["test"]					, n_copies=lowerCAmelCase					, n_tasks=lowerCAmelCase	)
				# do not confuse args.batch_size, which is actually the num_return_sequences
				_UpperCAmelCase    :						List[str]							=      DataLoader(lowerCAmelCase					, batch_size=1	)

				# Run a quick test to see if code evaluation is enabled
				try:
								_UpperCAmelCase    :						Dict							=      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    :						str							=      accelerator.prepare(lowerCAmelCase					, lowerCAmelCase	)

				_UpperCAmelCase    :						Dict							=      complete_code(
				    lowerCAmelCase					, lowerCAmelCase					, lowerCAmelCase					, lowerCAmelCase					, n_tasks=lowerCAmelCase					, batch_size=args.batch_size					, **lowerCAmelCase					, )

				if accelerator.is_main_process:
								_UpperCAmelCase    :						Any							=      []

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

								# Evaluate completions with "code_eval" metric
								_UpperCAmelCase						,							_UpperCAmelCase    :						Optional[int]							=      code_eval_metric.compute(
								    references=lowerCAmelCase					, predictions=lowerCAmelCase					, 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(lowerCAmelCase					, lowerCAmelCase	)


# 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 argparse

import torch

from transformers import BlenderbotConfig, BlenderbotForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE_            = logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_            = [
    ['attention', 'attn'],
    ['encoder_attention', 'encoder_attn'],
    ['q_lin', 'q_proj'],
    ['k_lin', 'k_proj'],
    ['v_lin', 'v_proj'],
    ['out_lin', 'out_proj'],
    ['norm_embeddings', 'layernorm_embedding'],
    ['position_embeddings', 'embed_positions'],
    ['embeddings', 'embed_tokens'],
    ['ffn.lin', 'fc'],
]

def 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					List[str]	)				->    Tuple:
				if k == "embeddings.weight":
								return "shared.weight"

				for parlai_name, hf_name in PATTERNS:
								_UpperCAmelCase    :						Optional[int]							=      k.replace(lowerCAmelCase					, lowerCAmelCase	)

				if k.startswith("encoder"	):
								_UpperCAmelCase    :						Tuple							=      k.replace(".attn"					, ".self_attn"	)
								_UpperCAmelCase    :						str							=      k.replace("norm1"					, "self_attn_layer_norm"	)
								_UpperCAmelCase    :						Optional[Any]							=      k.replace("norm2"					, "final_layer_norm"	)
				elif k.startswith("decoder"	):
								_UpperCAmelCase    :						Union[str, Any]							=      k.replace("norm1"					, "self_attn_layer_norm"	)
								_UpperCAmelCase    :						Tuple							=      k.replace("norm2"					, "encoder_attn_layer_norm"	)
								_UpperCAmelCase    :						Any							=      k.replace("norm3"					, "final_layer_norm"	)
				return k

def 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					Optional[int]	)				->    List[Any]:
				_UpperCAmelCase    :						int							=      [
				    "model.encoder.layernorm_embedding.weight",
				    "model.encoder.layernorm_embedding.bias",
				    "model.decoder.layernorm_embedding.weight",
				    "model.decoder.layernorm_embedding.bias",
				]
				for k in keys:
								_UpperCAmelCase    :						Dict							=      sd.pop(lowerCAmelCase	)
								_UpperCAmelCase    :						str							=      k.replace("layernorm_embedding"					, "layer_norm"	)
								assert new_k not in sd
								_UpperCAmelCase    :						Dict							=      v


SCREAMING_SNAKE_CASE_            = ['START']

@torch.no_grad()
def 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					Optional[Any]					, lowerCAmelCase:					int					, lowerCAmelCase:					int	)				->    int:
				_UpperCAmelCase    :						Union[str, Any]							=      torch.load(lowerCAmelCase					, map_location="cpu"	)
				_UpperCAmelCase    :						List[str]							=      model["model"]
				_UpperCAmelCase    :						List[str]							=      BlenderbotConfig.from_json_file(lowerCAmelCase	)
				_UpperCAmelCase    :						str							=      BlenderbotForConditionalGeneration(lowerCAmelCase	)
				_UpperCAmelCase    :						List[str]							=      m.model.state_dict().keys()
				_UpperCAmelCase    :						List[Any]							=      []
				_UpperCAmelCase    :						List[str]							=      {}
				for k, v in sd.items():
								if k in IGNORE_KEYS:
												continue

								_UpperCAmelCase    :						Tuple							=      rename_state_dict_key(lowerCAmelCase	)
								if new_k not in valid_keys:
												failures.append([k, new_k]	)
								else:
												_UpperCAmelCase    :						Tuple							=      v
				if cfg.normalize_before:  # Blenderbot-3B checkpoints. Rename layernorm_embedding -> layer_norm
								rename_layernorm_keys(lowerCAmelCase	)
				m.model.load_state_dict(lowerCAmelCase					, strict=lowerCAmelCase	)
				m.half()
				m.save_pretrained(lowerCAmelCase	)


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE_            = argparse.ArgumentParser()
					# Required parameters
					parser.add_argument('--src_path', type=str, help='like blenderbot-model.bin')
					parser.add_argument('--save_dir', default='hf_blenderbot', type=str, help='Where to save converted model.')
					parser.add_argument(
					    '--hf_config_json', default='blenderbot-3b-config.json', type=str, help='Path to config to use'
					)
					SCREAMING_SNAKE_CASE_            = parser.parse_args()
					convert_parlai_checkpoint(args.src_path, args.save_dir, args.hf_config_json)


