




from collections import OrderedDict
from typing import Any, List, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging


lowercase_      :							List[Any]    							=					logging.get_logger(__name__)

lowercase_      :							List[Any]    							=					{
    'gpt2': 'https://huggingface.co/gpt2/resolve/main/config.json',
    'gpt2-medium': 'https://huggingface.co/gpt2-medium/resolve/main/config.json',
    'gpt2-large': 'https://huggingface.co/gpt2-large/resolve/main/config.json',
    'gpt2-xl': 'https://huggingface.co/gpt2-xl/resolve/main/config.json',
    'distilgpt2': 'https://huggingface.co/distilgpt2/resolve/main/config.json',
}






class        __lowerCAmelCase					(			UpperCAmelCase__					):
			snake_case_ :					Optional[Any]						=	"gpt2"
			snake_case_ :					Tuple						=	["past_key_values"]
			snake_case_ :					List[str]						=	{
			    "hidden_size": "n_embd",
			    "max_position_embeddings": "n_positions",
			    "num_attention_heads": "n_head",
			    "num_hidden_layers": "n_layer",
			}







			def __init__(			self       :     Optional[Any]       ,       snake_case__       :     str=50_257       ,       snake_case__       :     Optional[Any]=1_024       ,       snake_case__       :     Union[str, Any]=768       ,       snake_case__       :     Optional[Any]=12       ,       snake_case__       :     int=12       ,       snake_case__       :     Any=None       ,       snake_case__       :     List[str]="gelu_new"       ,       snake_case__       :     Union[str, Any]=0.1       ,       snake_case__       :     List[str]=0.1       ,       snake_case__       :     List[Any]=0.1       ,       snake_case__       :     Optional[int]=1e-5       ,       snake_case__       :     Optional[int]=0.02       ,       snake_case__       :     int="cls_index"       ,       snake_case__       :     List[Any]=True       ,       snake_case__       :     str=None       ,       snake_case__       :     Optional[int]=True       ,       snake_case__       :     Tuple=0.1       ,       snake_case__       :     int=True       ,       snake_case__       :     Dict=True       ,       snake_case__       :     str=50_256       ,       snake_case__       :     List[Any]=50_256       ,       snake_case__       :     Tuple=False       ,       snake_case__       :     int=False       ,       **snake_case__       :     Union[str, Any]       ,       ):




									"""simple docstring"""


									_UpperCAmelCase						=	vocab_size
									_UpperCAmelCase						=	n_positions
									_UpperCAmelCase						=	n_embd
									_UpperCAmelCase						=	n_layer
									_UpperCAmelCase						=	n_head
									_UpperCAmelCase						=	n_inner
									_UpperCAmelCase						=	activation_function
									_UpperCAmelCase						=	resid_pdrop
									_UpperCAmelCase						=	embd_pdrop
									_UpperCAmelCase						=	attn_pdrop
									_UpperCAmelCase						=	layer_norm_epsilon
									_UpperCAmelCase						=	initializer_range
									_UpperCAmelCase						=	summary_type
									_UpperCAmelCase						=	summary_use_proj
									_UpperCAmelCase						=	summary_activation
									_UpperCAmelCase						=	summary_first_dropout
									_UpperCAmelCase						=	summary_proj_to_labels
									_UpperCAmelCase						=	scale_attn_weights
									_UpperCAmelCase						=	use_cache
									_UpperCAmelCase						=	scale_attn_by_inverse_layer_idx
									_UpperCAmelCase						=	reorder_and_upcast_attn

									_UpperCAmelCase						=	bos_token_id
									_UpperCAmelCase						=	eos_token_id

									super().__init__(bos_token_id=snake_case__       ,       eos_token_id=snake_case__       ,       **snake_case__ )






class        __lowerCAmelCase					(			UpperCAmelCase__					):







			def __init__(			self       :     int       ,       snake_case__       :     PretrainedConfig       ,       snake_case__       :     str = "default"       ,       snake_case__       :     List[PatchingSpec] = None       ,       snake_case__       :     bool = False       ,       ):




									"""simple docstring"""


									super().__init__(snake_case__       ,       task=snake_case__       ,       patching_specs=snake_case__       ,       use_past=snake_case__ )
									if not getattr(self._config       ,       "pad_token_id"       ,       snake_case__ ):
															# TODO: how to do that better?
															_UpperCAmelCase						=	0







			@property
			def UpperCamelCase     (			self       :     List[str] ):




									"""simple docstring"""


									_UpperCAmelCase						=	OrderedDict({"input_ids": {0: "batch", 1: "sequence"}} )
									if self.use_past:
															self.fill_with_past_key_values_(snake_case__       ,       direction="inputs" )
															_UpperCAmelCase						=	{0: "batch", 1: "past_sequence + sequence"}
									else:
															_UpperCAmelCase						=	{0: "batch", 1: "sequence"}

									return common_inputs







			@property
			def UpperCamelCase     (			self       :     Union[str, Any] ):




									"""simple docstring"""


									return self._config.n_layer







			@property
			def UpperCamelCase     (			self       :     Any ):




									"""simple docstring"""


									return self._config.n_head







			def UpperCamelCase     (			self       :     str       ,       snake_case__       :     PreTrainedTokenizer       ,       snake_case__       :     int = -1       ,       snake_case__       :     int = -1       ,       snake_case__       :     bool = False       ,       snake_case__       :     Optional[TensorType] = None       ,       ):




									"""simple docstring"""


									_UpperCAmelCase						=	super(snake_case__       ,       self ).generate_dummy_inputs(
									    snake_case__       ,       batch_size=snake_case__       ,       seq_length=snake_case__       ,       is_pair=snake_case__       ,       framework=snake_case__ )

									# We need to order the input in the way they appears in the forward()
									_UpperCAmelCase						=	OrderedDict({"input_ids": common_inputs["input_ids"]} )

									# Need to add the past_keys
									if self.use_past:
															if not is_torch_available():
																					raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed." )
															else:
																					import torch

																					_UpperCAmelCase				,					_UpperCAmelCase						=	common_inputs["input_ids"].shape
																					# Not using the same length for past_key_values
																					_UpperCAmelCase						=	seqlen + 2
																					_UpperCAmelCase						=	(
																					    batch,
																					    self.num_attention_heads,
																					    past_key_values_length,
																					    self._config.hidden_size // self.num_attention_heads,
																					)
																					_UpperCAmelCase						=	[
																					    (torch.zeros(snake_case__ ), torch.zeros(snake_case__ )) for _ in range(self.num_layers )
																					]

									_UpperCAmelCase						=	common_inputs["attention_mask"]
									if self.use_past:
															_UpperCAmelCase						=	ordered_inputs["attention_mask"].dtype
															_UpperCAmelCase						=	torch.cat(
															    [ordered_inputs["attention_mask"], torch.ones(snake_case__       ,       snake_case__       ,       dtype=snake_case__ )]       ,       dim=1 )

									return ordered_inputs







			@property
			def UpperCamelCase     (			self       :     int ):




									"""simple docstring"""


									return 13










import argparse
import datetime
import json
import time
import warnings
from logging import getLogger
from pathlib import Path
from typing import Dict, List

import torch
from tqdm import tqdm

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import calculate_bleu, calculate_rouge, chunks, parse_numeric_n_bool_cl_kwargs, use_task_specific_params


lowercase_      :							int    							=					getLogger(__name__)


lowercase_      :							str    							=					'cuda' if torch.cuda.is_available() else 'cpu'


def 				__SCREAMING_SNAKE_CASE    (     snake_case_      ,       snake_case_      ,       snake_case_      ,       snake_case_ = 8      ,       snake_case_ = DEFAULT_DEVICE      ,       snake_case_=False      ,       snake_case_="summarization"      ,       snake_case_=None      ,       **snake_case_      ,       ):

						'''simple docstring'''


						_UpperCAmelCase						=	Path(snake_case_		).open("w"      ,       encoding="utf-8"		)
						_UpperCAmelCase						=	str(snake_case_		)
						_UpperCAmelCase						=	AutoModelForSeqaSeqLM.from_pretrained(snake_case_		).to(snake_case_		)
						if fpaa:
												_UpperCAmelCase						=	model.half()

						_UpperCAmelCase						=	AutoTokenizer.from_pretrained(snake_case_		)
						logger.info(f"""Inferred tokenizer type: {tokenizer.__class__}"""		)  # if this is wrong, check config.model_type.

						_UpperCAmelCase						=	time.time()
						# update config with task specific params
						use_task_specific_params(snake_case_      ,       snake_case_		)
						if prefix is None:
												_UpperCAmelCase						=	prefix or getattr(model.config      ,       "prefix"      ,       ""		) or ""
						for examples_chunk in tqdm(list(chunks(snake_case_      ,       snake_case_		)		)		):
												_UpperCAmelCase						=	[prefix + text for text in examples_chunk]
												_UpperCAmelCase						=	tokenizer(snake_case_      ,       return_tensors="pt"      ,       truncation=snake_case_      ,       padding="longest"		).to(snake_case_		)
												_UpperCAmelCase						=	model.generate(
												    input_ids=batch.input_ids      ,       attention_mask=batch.attention_mask      ,       **snake_case_      ,       )
												_UpperCAmelCase						=	tokenizer.batch_decode(snake_case_      ,       skip_special_tokens=snake_case_      ,       clean_up_tokenization_spaces=snake_case_		)
												for hypothesis in dec:
																		fout.write(hypothesis + "\n"		)
																		fout.flush()
						fout.close()
						_UpperCAmelCase						=	int(time.time() - start_time		)  # seconds
						_UpperCAmelCase						=	len(snake_case_		)
						return {"n_obs": n_obs, "runtime": runtime, "seconds_per_sample": round(runtime / n_obs      ,       4		)}


def 				__SCREAMING_SNAKE_CASE    (     ):

						'''simple docstring'''


						return datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"		)


def 				__SCREAMING_SNAKE_CASE    (     snake_case_=True		):

						'''simple docstring'''


						_UpperCAmelCase						=	argparse.ArgumentParser()
						parser.add_argument("model_name"      ,       type=snake_case_      ,       help="like facebook/bart-large-cnn,t5-base, etc."		)
						parser.add_argument("input_path"      ,       type=snake_case_      ,       help="like cnn_dm/test.source"		)
						parser.add_argument("save_path"      ,       type=snake_case_      ,       help="where to save summaries"		)
						parser.add_argument("--reference_path"      ,       type=snake_case_      ,       required=snake_case_      ,       help="like cnn_dm/test.target"		)
						parser.add_argument("--score_path"      ,       type=snake_case_      ,       required=snake_case_      ,       default="metrics.json"      ,       help="where to save metrics"		)
						parser.add_argument("--device"      ,       type=snake_case_      ,       required=snake_case_      ,       default=snake_case_      ,       help="cuda, cuda:1, cpu etc."		)
						parser.add_argument(
						    "--prefix"      ,       type=snake_case_      ,       required=snake_case_      ,       default=snake_case_      ,       help="will be added to the begininng of src examples"		)
						parser.add_argument("--task"      ,       type=snake_case_      ,       default="summarization"      ,       help="used for task_specific_params + metrics"		)
						parser.add_argument("--bs"      ,       type=snake_case_      ,       default=8      ,       required=snake_case_      ,       help="batch size"		)
						parser.add_argument(
						    "--n_obs"      ,       type=snake_case_      ,       default=-1      ,       required=snake_case_      ,       help="How many observations. Defaults to all."		)
						parser.add_argument("--fp16"      ,       action="store_true"		)
						parser.add_argument("--dump-args"      ,       action="store_true"      ,       help="print the custom hparams with the results"		)
						parser.add_argument(
						    "--info"      ,       nargs="?"      ,       type=snake_case_      ,       const=datetime_now()      ,       help=(
						        "use in conjunction w/ --dump-args to print with the results whatever other info you'd like, e.g."
						        " lang=en-ru. If no value is passed, the current datetime string will be used."
						    )      ,       )
						# Unspecified args like --num_beams=2 --decoder_start_token_id=4 are passed to model.generate
						_UpperCAmelCase				,					_UpperCAmelCase						=	parser.parse_known_args()
						_UpperCAmelCase						=	parse_numeric_n_bool_cl_kwargs(snake_case_		)
						if parsed_args and verbose:
												print(f"""parsed the following generate kwargs: {parsed_args}"""		)
						with open(args.input_path		) as f:
												_UpperCAmelCase						=	[" " + x.rstrip() if "t5" in args.model_name else x.rstrip() for x in f.readlines()]
						if args.n_obs > 0:
												_UpperCAmelCase						=	examples[: args.n_obs]
						Path(args.save_path		).parent.mkdir(exist_ok=snake_case_		)
						if args.reference_path is None and Path(args.score_path		).exists():
												warnings.warn(f"""score_path {args.score_path} will be overwritten unless you type ctrl-c."""		)
						_UpperCAmelCase						=	generate_summaries_or_translations(
						    snake_case_      ,       args.save_path      ,       args.model_name      ,       batch_size=args.bs      ,       device=args.device      ,       fpaa=args.fpaa      ,       task=args.task      ,       prefix=args.prefix      ,       **snake_case_      ,       )

						if args.reference_path is None:
												return {}

						# Compute scores
						_UpperCAmelCase						=	calculate_bleu if "translation" in args.task else calculate_rouge
						_UpperCAmelCase						=	[x.rstrip() for x in open(args.save_path		).readlines()]
						_UpperCAmelCase						=	[x.rstrip() for x in open(args.reference_path		).readlines()][: len(snake_case_		)]
						_UpperCAmelCase						=	score_fn(snake_case_      ,       snake_case_		)
						scores.update(snake_case_		)

						if args.dump_args:
												scores.update(snake_case_		)
						if args.info:
												_UpperCAmelCase						=	args.info

						if verbose:
												print(snake_case_		)

						if args.score_path is not None:
												json.dump(snake_case_      ,       open(args.score_path      ,       "w"		)		)

						return scores


if __name__ == "__main__":
				# Usage for MT:
				# python run_eval.py MODEL_NAME $DATA_DIR/test.source $save_dir/test_translations.txt --reference_path $DATA_DIR/test.target --score_path $save_dir/test_bleu.json  --task translation $@
				run_generate(verbose=True)




