




import argparse
from pathlib import Path

import fairseq
import torch
from fairseq.models.xmod import XMODModel as FairseqXmodModel
from packaging import version

from transformers import XmodConfig, XmodForMaskedLM, XmodForSequenceClassification
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse('0.12.2'):
		raise Exception('requires fairseq >= 0.12.2')
if version.parse(fairseq.__version__) > version.parse('2'):
		raise Exception('requires fairseq < v2')

logging.set_verbosity_info()
__lowerCAmelCase	:       Union[str, Any]     =	logging.get_logger(__name__)

__lowerCAmelCase	:       Optional[Any]     =	'Hello, World!'
__lowerCAmelCase	:       Any     =	'en_XX'




def     __magic_name__       (				A	:      str,						A	:      str,						A	:      bool     ):






			'''simple docstring'''







			a					=    Path("data_bin"     )
			a					=    FairseqXmodModel.from_pretrained(
			    model_name_or_path=str(Path(A     ).parent     ),						checkpoint_file=Path(A     ).name,						_name="xmod_base",						arch="xmod_base",						task="multilingual_masked_lm",						data_name_or_path=str(A     ),						bpe="sentencepiece",						sentencepiece_model=str(Path(A     ).parent / "sentencepiece.bpe.model"     ),						src_dict=str(data_dir / "dict.txt"     ),						)
			xmod.eval()  # disable dropout
			print(A     )

			a					=    xmod.model.encoder.sentence_encoder
			a					=    XmodConfig(
			    vocab_size=xmod_sent_encoder.embed_tokens.num_embeddings,						hidden_size=xmod.cfg.model.encoder_embed_dim,						num_hidden_layers=xmod.cfg.model.encoder_layers,						num_attention_heads=xmod.cfg.model.encoder_attention_heads,						intermediate_size=xmod.cfg.model.encoder_ffn_embed_dim,						max_position_embeddings=514,						type_vocab_size=1,						layer_norm_eps=1E-5,						pre_norm=xmod.cfg.model.encoder_normalize_before,						adapter_reduction_factor=getattr(xmod.cfg.model,						"bottleneck",						2     ),						adapter_layer_norm=xmod.cfg.model.adapter_layer_norm,						adapter_reuse_layer_norm=xmod.cfg.model.adapter_reuse_layer_norm,						ln_before_adapter=xmod.cfg.model.ln_before_adapter,						languages=xmod.cfg.model.languages,						)
			if classification_head:
						a					=    xmod.model.classification_heads["mnli"].out_proj.weight.shape[0]

			print("Our X-MOD config:",						A     )

			a					=    XmodForSequenceClassification(A     ) if classification_head else XmodForMaskedLM(A     )
			model.eval()

			# Now let's copy all the weights.
			# Embeddings
			a					=    xmod_sent_encoder.embed_tokens.weight
			a					=    xmod_sent_encoder.embed_positions.weight
			a					=    torch.zeros_like(
			    model.roberta.embeddings.token_type_embeddings.weight     )  # just zero them out b/c xmod doesn't use them.

			a					=    xmod_sent_encoder.layernorm_embedding.weight
			a					=    xmod_sent_encoder.layernorm_embedding.bias

			for i in range(config.num_hidden_layers     ):
						# Encoder: start of layer
						a					=    model.roberta.encoder.layer[i]
						a					=    xmod_sent_encoder.layers[i]

						# self attention
						a					=    layer.attention.self
						if not (
						    xmod_layer.self_attn.k_proj.weight.data.shape
						    == xmod_layer.self_attn.q_proj.weight.data.shape
						    == xmod_layer.self_attn.v_proj.weight.data.shape
						    == torch.Size((config.hidden_size, config.hidden_size)     )
						):
									raise AssertionError("Dimensions of self-attention weights do not match."     )

						a					=    xmod_layer.self_attn.q_proj.weight
						a					=    xmod_layer.self_attn.q_proj.bias
						a					=    xmod_layer.self_attn.k_proj.weight
						a					=    xmod_layer.self_attn.k_proj.bias
						a					=    xmod_layer.self_attn.v_proj.weight
						a					=    xmod_layer.self_attn.v_proj.bias

						# self-attention output
						a					=    layer.attention.output
						if self_output.dense.weight.shape != xmod_layer.self_attn.out_proj.weight.shape:
									raise AssertionError("Dimensions of self-attention output weights do not match."     )
						a					=    xmod_layer.self_attn.out_proj.weight
						a					=    xmod_layer.self_attn.out_proj.bias
						a					=    xmod_layer.self_attn_layer_norm.weight
						a					=    xmod_layer.self_attn_layer_norm.bias

						# intermediate
						a					=    layer.intermediate
						if intermediate.dense.weight.shape != xmod_layer.fca.weight.shape:
									raise AssertionError("Dimensions of intermediate weights do not match."     )
						a					=    xmod_layer.fca.weight
						a					=    xmod_layer.fca.bias

						# output
						a					=    layer.output
						if bert_output.dense.weight.shape != xmod_layer.fca.weight.shape:
									raise AssertionError("Dimensions of feed-forward weights do not match."     )
						a					=    xmod_layer.fca.weight
						a					=    xmod_layer.fca.bias
						a					=    xmod_layer.final_layer_norm.weight
						a					=    xmod_layer.final_layer_norm.bias
						if bert_output.adapter_layer_norm is not None:
									a					=    xmod_layer.adapter_layer_norm.weight
									a					=    xmod_layer.adapter_layer_norm.bias

						if sorted(bert_output.adapter_modules.keys()     ) != sorted(xmod_layer.adapter_modules.keys()     ):
									raise AssertionError("Lists of language adapters do not match."     )
						for lang_code, adapter in xmod_layer.adapter_modules.items():
									a					=    bert_output.adapter_modules[lang_code]
									a					=    xmod_layer.adapter_modules[lang_code]
									a					=    from_adapter.fca.weight
									a					=    from_adapter.fca.bias
									a					=    from_adapter.fca.weight
									a					=    from_adapter.fca.bias

						# end of layer

			if xmod_sent_encoder.layer_norm is not None:
						a					=    xmod_sent_encoder.layer_norm.weight
						a					=    xmod_sent_encoder.layer_norm.bias

			if classification_head:
						a					=    xmod.model.classification_heads["mnli"].dense.weight
						a					=    xmod.model.classification_heads["mnli"].dense.bias
						a					=    xmod.model.classification_heads["mnli"].out_proj.weight
						a					=    xmod.model.classification_heads["mnli"].out_proj.bias
			else:
						# LM Head
						a					=    xmod.model.encoder.lm_head.dense.weight
						a					=    xmod.model.encoder.lm_head.dense.bias
						a					=    xmod.model.encoder.lm_head.layer_norm.weight
						a					=    xmod.model.encoder.lm_head.layer_norm.bias
						a					=    xmod.model.encoder.lm_head.weight
						a					=    xmod.model.encoder.lm_head.bias

			# Let's check that we get the same results.
			a					=    xmod.encode(A     ).unsqueeze(0     )  # batch of size 1
			model.roberta.set_default_language(A     )

			a					=    model(A     )[0]
			if classification_head:
						a					=    xmod.model.classification_heads["mnli"](xmod.extract_features(A     )     )
			else:
						a					=    xmod.model(A,						lang_id=[SAMPLE_LANGUAGE]     )[0]
			print(our_output.shape,						their_output.shape     )
			a					=    torch.max(torch.abs(our_output - their_output     )     ).item()
			print(F"""max_absolute_diff = {max_absolute_diff}"""     )  # ~ 1e-7
			a					=    torch.allclose(A,						A,						atol=1E-3     )
			print("Do both models output the same tensors?",						"🔥" if success else "💩"     )
			if not success:
						raise Exception("Something went wRoNg"     )

			Path(A     ).mkdir(parents=A,						exist_ok=A     )
			print(F"""Saving model to {pytorch_dump_folder_path}"""     )
			model.save_pretrained(A     )


if __name__ == "__main__":
		__lowerCAmelCase	:       List[str]     =	argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '--xmod_checkpoint_path', default=None, type=str, required=True, help='Path the official PyTorch dump.'
		)
		parser.add_argument(
		    '--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
		)
		parser.add_argument(
		    '--classification_head', action='store_true', help='Whether to convert a final classification head.'
		)
		__lowerCAmelCase	:       int     =	parser.parse_args()
		convert_xmod_checkpoint_to_pytorch(
		    args.xmod_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
		)












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	:       Union[str, Any]     =	['\nclass', '\ndef', '\n#', '\n@', '\nprint', '\nif']




class 					snake_case__							(_UpperCamelCase     ):
				"""simple docstring"""



				def __init__(   self       :  Any      ,   __lowerCamelCase       :  List[str]      ,   __lowerCamelCase       :  Union[str, Any]      ,   __lowerCamelCase       :  Union[str, Any]=None      ,   __lowerCamelCase       :  Any=1							)      ->  Union[str, Any]:
							a					=    tokenizer
							a					=    dataset
							a					=    len(__lowerCamelCase							) if n_tasks is None else n_tasks
							a					=    n_copies





				def __iter__(   self       :  Tuple							)      ->  str:
							a					=    []
							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()							)
							a					=    self.tokenizer(__lowerCamelCase      ,   padding=__lowerCamelCase      ,   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__							(_UpperCamelCase     ):
				"""simple docstring"""



				def __init__(   self       :  int      ,   __lowerCamelCase       :  Dict      ,   __lowerCamelCase       :  Dict      ,   __lowerCamelCase       :  Tuple							)      ->  Optional[Any]:
							a					=    start_length
							a					=    eof_strings
							a					=    tokenizer





				def __call__(   self       :  int      ,   __lowerCamelCase       :  Dict      ,   __lowerCamelCase       :  List[str]      ,   **__lowerCamelCase       :  Optional[int]							)      ->  Optional[Any]:
							a					=    self.tokenizer.batch_decode(input_ids[:, self.start_length :]							)
							a					=    []
							for decoded_generation in decoded_generations:
										done.append(any(stop_string in decoded_generation for stop_string in self.eof_strings							)							)
							return all(__lowerCamelCase							)




def     __magic_name__       (				A	:      List[Any]     ):






			'''simple docstring'''







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




def     __magic_name__       (				A	:      Union[str, Any],						A	:      Optional[Any],						A	:      List[Any],						A	:      Optional[Any],						A	:      List[str],						A	:      List[Any]=20,						**A	:      Union[str, Any]     ):






			'''simple docstring'''







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

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

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

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




def     __magic_name__       (				):






			'''simple docstring'''







			a					=    HfArgumentParser(A     )
			a					=    parser.parse_args()

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

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

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

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

			# Generation settings
			a					=    {
			    "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,						A,						A     )]     ),
			}

			# Load evaluation dataset and metric
			a					=    load_dataset("openai_humaneval"     )
			a					=    load_metric("code_eval"     )

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

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

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

			a					,	a					=    accelerator.prepare(A,						A     )

			a					=    complete_code(
			    A,						A,						A,						A,						n_tasks=A,						batch_size=args.batch_size,						**A,						)

			if accelerator.is_main_process:
						a					=    []

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

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


# 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()






