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


_SCREAMING_SNAKE_CASE:						Any    	=       getLogger(__name__)


_SCREAMING_SNAKE_CASE:						Tuple    	=       '''cuda''' if torch.cuda.is_available() else '''cpu'''






def    UpperCAmelCase__(						lowerCamelCase_							,	lowerCamelCase_							,	lowerCamelCase_							,	lowerCamelCase_ = 8							,	lowerCamelCase_ = DEFAULT_DEVICE							,	lowerCamelCase_=False							,	lowerCamelCase_="summarization"							,	lowerCamelCase_=None							,	**lowerCamelCase_							,	):



			"""simple docstring"""





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

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

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






def    UpperCAmelCase__(						):



			"""simple docstring"""





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






def    UpperCAmelCase__(						lowerCamelCase_=True  ):



			"""simple docstring"""





			_snake_case              =      argparse.ArgumentParser()
			parser.add_argument("model_name"							,	type=lowerCamelCase_							,	help="like facebook/bart-large-cnn,t5-base, etc."  )
			parser.add_argument("input_path"							,	type=lowerCamelCase_							,	help="like cnn_dm/test.source"  )
			parser.add_argument("save_path"							,	type=lowerCamelCase_							,	help="where to save summaries"  )
			parser.add_argument("--reference_path"							,	type=lowerCamelCase_							,	required=lowerCamelCase_							,	help="like cnn_dm/test.target"  )
			parser.add_argument("--score_path"							,	type=lowerCamelCase_							,	required=lowerCamelCase_							,	default="metrics.json"							,	help="where to save metrics"  )
			parser.add_argument("--device"							,	type=lowerCamelCase_							,	required=lowerCamelCase_							,	default=lowerCamelCase_							,	help="cuda, cuda:1, cpu etc."  )
			parser.add_argument(
			    "--prefix"							,	type=lowerCamelCase_							,	required=lowerCamelCase_							,	default=lowerCamelCase_							,	help="will be added to the begininng of src examples"  )
			parser.add_argument("--task"							,	type=lowerCamelCase_							,	default="summarization"							,	help="used for task_specific_params + metrics"  )
			parser.add_argument("--bs"							,	type=lowerCamelCase_							,	default=8							,	required=lowerCamelCase_							,	help="batch size"  )
			parser.add_argument(
			    "--n_obs"							,	type=lowerCamelCase_							,	default=-1							,	required=lowerCamelCase_							,	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=lowerCamelCase_							,	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
			_snake_case       , _snake_case              =      parser.parse_known_args()
			_snake_case              =      parse_numeric_n_bool_cl_kwargs(lowerCamelCase_  )
			if parsed_args and verbose:
						print(f'parsed the following generate kwargs: {parsed_args}'  )
			with open(args.input_path  ) as f:
						_snake_case              =      [" " + x.rstrip() if "t5" in args.model_name else x.rstrip() for x in f.readlines()]
			if args.n_obs > 0:
						_snake_case              =      examples[: args.n_obs]
			Path(args.save_path  ).parent.mkdir(exist_ok=lowerCamelCase_  )
			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.'  )
			_snake_case              =      generate_summaries_or_translations(
			    lowerCamelCase_							,	args.save_path							,	args.model_name							,	batch_size=args.bs							,	device=args.device							,	fpaa=args.fpaa							,	task=args.task							,	prefix=args.prefix							,	**lowerCamelCase_							,	)

			if args.reference_path is None:
						return {}

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

			if args.dump_args:
						scores.update(lowerCamelCase_  )
			if args.info:
						_snake_case              =      args.info

			if verbose:
						print(lowerCamelCase_  )

			if args.score_path is not None:
						json.dump(lowerCamelCase_							,	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)


import json
import os
import subprocess
import sys
import warnings
from argparse import ArgumentParser
from contextlib import AbstractContextManager
from typing import Dict, List, Optional

import requests

from ..utils import logging
from . import BaseTransformersCLICommand


_SCREAMING_SNAKE_CASE:						List[str]    	=       logging.get_logger(__name__)  # pylint: disable=invalid-name


_SCREAMING_SNAKE_CASE:						Dict    	=       '''lfs-multipart-upload'''

class 	_SCREAMING_SNAKE_CASE							(     _lowercase							):






		"""simple docstring"""







		@staticmethod
		def 				_a				(			__SCREAMING_SNAKE_CASE    :				ArgumentParser   ):






					"""simple docstring"""
					_snake_case              =      parser.add_parser(
					    "lfs-enable-largefiles"  ,					help=(
					        "Deprecated: use `huggingface-cli` instead. Configure your repository to enable upload of files > 5GB."
					    )  ,					)
					enable_parser.add_argument("path"  ,					type=__SCREAMING_SNAKE_CASE  ,					help="Local path to repository you want to configure."   )
					enable_parser.set_defaults(func=lambda __SCREAMING_SNAKE_CASE   : LfsEnableCommand(__SCREAMING_SNAKE_CASE   )   )

					_snake_case              =      parser.add_parser(
					    __SCREAMING_SNAKE_CASE  ,					help=(
					        "Deprecated: use `huggingface-cli` instead. "
					        "Command will get called by git-lfs, do not call it directly."
					    )  ,					)
					upload_parser.set_defaults(func=lambda __SCREAMING_SNAKE_CASE   : LfsUploadCommand(__SCREAMING_SNAKE_CASE   )   )

class 	_SCREAMING_SNAKE_CASE							:






		"""simple docstring"""







		def __init__(			self    :				Union[str, Any]  ,					__SCREAMING_SNAKE_CASE    :				List[str]   ):






					"""simple docstring"""
					_snake_case              =      args







		def 				_a				(			self    :				Optional[int]   ):






					"""simple docstring"""
					warnings.warn(
					    "Managing repositories through transformers-cli is deprecated. Please use `huggingface-cli` instead."   )
					_snake_case              =      os.path.abspath(self.args.path   )
					if not os.path.isdir(__SCREAMING_SNAKE_CASE   ):
								print("This does not look like a valid git repo."   )
								exit(1   )
					subprocess.run(
					    "git config lfs.customtransfer.multipart.path transformers-cli".split()  ,					check=__SCREAMING_SNAKE_CASE  ,					cwd=__SCREAMING_SNAKE_CASE   )
					subprocess.run(
					    F'git config lfs.customtransfer.multipart.args {LFS_MULTIPART_UPLOAD_COMMAND}'.split()  ,					check=__SCREAMING_SNAKE_CASE  ,					cwd=__SCREAMING_SNAKE_CASE  ,					)
					print("Local repo set up for largefiles"   )






def    UpperCAmelCase__(						lowerCamelCase_  ):



			"""simple docstring"""





			_snake_case              =      json.dumps(lowerCamelCase_  ) + "\n"
			sys.stdout.write(lowerCamelCase_  )
			sys.stdout.flush()






def    UpperCAmelCase__(						):



			"""simple docstring"""





			_snake_case              =      json.loads(sys.stdin.readline().strip()  )

			if "terminate" in (msg.get("type"  ), msg.get("event"  )):
						# terminate message received
						return None

			if msg.get("event"  ) not in ("download", "upload"):
						logger.critical("Received unexpected message"  )
						sys.exit(1  )

			return msg

class 	_SCREAMING_SNAKE_CASE							(     _lowercase							):






		"""simple docstring"""







		def __init__(			self    :				Optional[int]  ,					__SCREAMING_SNAKE_CASE    :				str  ,					__SCREAMING_SNAKE_CASE    :				int  ,					__SCREAMING_SNAKE_CASE    :				int   ):






					"""simple docstring"""
					_snake_case              =      filepath
					_snake_case              =      seek_from
					_snake_case              =      read_limit
					_snake_case              =      0



		def __enter__(			self    :				Optional[int]   ):






					"""simple docstring"""
					_snake_case              =      open(self.filepath  ,					"rb"   )
					self.f.seek(self.seek_from   )
					return self



		def __len__(			self    :				List[str]   ):






					"""simple docstring"""
					_snake_case              =      os.fstat(self.f.fileno()   ).st_size
					return min(self.read_limit  ,					total_length - self.seek_from   )



		def 				_a				(			self    :				Dict  ,					__SCREAMING_SNAKE_CASE    :				Any=-1   ):






					"""simple docstring"""
					if self.n_seen >= self.read_limit:
								return b""
					_snake_case              =      self.read_limit - self.n_seen
					_snake_case              =      self.f.read(remaining_amount if n < 0 else min(__SCREAMING_SNAKE_CASE  ,					__SCREAMING_SNAKE_CASE   )   )
					self.n_seen += len(__SCREAMING_SNAKE_CASE   )
					return data



		def __iter__(			self    :				str   ):






					"""simple docstring"""
					yield self.read(n=4 * 1_024 * 1_024   )







		def __exit__(			self    :				Dict  ,					*__SCREAMING_SNAKE_CASE    :				int   ):






					"""simple docstring"""
					self.f.close()

class 	_SCREAMING_SNAKE_CASE							:






		"""simple docstring"""







		def __init__(			self    :				str  ,					__SCREAMING_SNAKE_CASE    :				str   ):






					"""simple docstring"""
					_snake_case              =      args







		def 				_a				(			self    :				Optional[int]   ):






					"""simple docstring"""
					# Immediately after invoking a custom transfer process, git-lfs
					# sends initiation data to the process over stdin.
					# This tells the process useful information about the configuration.
					_snake_case              =      json.loads(sys.stdin.readline().strip()   )
					if not (init_msg.get("event"   ) == "init" and init_msg.get("operation"   ) == "upload"):
								write_msg({"error": {"code": 32, "message": "Wrong lfs init operation"}}   )
								sys.exit(1   )

					# The transfer process should use the information it needs from the
					# initiation structure, and also perform any one-off setup tasks it
					# needs to do. It should then respond on stdout with a simple empty
					# confirmation structure, as follows:
					write_msg({}   )

					# After the initiation exchange, git-lfs will send any number of
					# transfer requests to the stdin of the transfer process, in a serial sequence.
					while True:
								_snake_case              =      read_msg()
								if msg is None:
											# When all transfers have been processed, git-lfs will send
											# a terminate event to the stdin of the transfer process.
											# On receiving this message the transfer process should
											# clean up and terminate. No response is expected.
											sys.exit(0   )

								_snake_case              =      msg["oid"]
								_snake_case              =      msg["path"]
								_snake_case              =      msg["action"]["href"]
								_snake_case              =      msg["action"]["header"]
								_snake_case              =      int(header.pop("chunk_size"   )   )
								_snake_case              =      list(header.values()   )

								_snake_case              =      []
								for i, presigned_url in enumerate(__SCREAMING_SNAKE_CASE   ):
											with FileSlice(__SCREAMING_SNAKE_CASE  ,					seek_from=i * chunk_size  ,					read_limit=__SCREAMING_SNAKE_CASE   ) as data:
														_snake_case              =      requests.put(__SCREAMING_SNAKE_CASE  ,					data=__SCREAMING_SNAKE_CASE   )
														r.raise_for_status()
														parts.append(
														    {
														        "etag": r.headers.get("etag"   ),
														        "partNumber": i + 1,
														    }   )
														# In order to support progress reporting while data is uploading / downloading,
														# the transfer process should post messages to stdout
														write_msg(
														    {
														        "event": "progress",
														        "oid": oid,
														        "bytesSoFar": (i + 1) * chunk_size,
														        "bytesSinceLast": chunk_size,
														    }   )
														# Not precise but that's ok.

								_snake_case              =      requests.post(
								    __SCREAMING_SNAKE_CASE  ,					json={
								        "oid": oid,
								        "parts": parts,
								    }  ,					)
								r.raise_for_status()

								write_msg({"event": "complete", "oid": oid}   )

