
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


A  :			str     				=				getLogger(__name__)


A  :			int     				=				"cuda" if torch.cuda.is_available() else "cpu"


def 					__lowerCAmelCase    (    a__		,					a__		,					a__		,					a__ = 8		,					a__ = DEFAULT_DEVICE		,					a__=False		,					a__="summarization"		,					a__=None		,					**a__		,					) ->						Dict:
				__a    							=					Path(UpperCamelCase_						).open('''w'''		,					encoding='''utf-8'''						)
				__a    							=					str(UpperCamelCase_						)
				__a    							=					AutoModelForSeqaSeqLM.from_pretrained(UpperCamelCase_						).to(UpperCamelCase_						)
				if fpaa:
								__a    							=					model.half()

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

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


def 					__lowerCAmelCase    (    ) ->						Optional[Any]:
				return datetime.datetime.now().strftime('''%Y-%m-%d %H:%M:%S'''						)


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

				if args.reference_path is None:
								return {}

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

				if args.dump_args:
								scores.update(UpperCamelCase_						)
				if args.info:
								__a    							=					args.info

				if verbose:
								print(UpperCamelCase_						)

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








# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING

import numpy as np
import pyarrow as pa

from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter


if TYPE_CHECKING:
					import tensorflow as tf







class     __A(				TensorFormatter[Mapping, '''tf.Tensor''', Mapping]    ):




				def __init__(  self	,  _snake_case=None	,  **_snake_case       )					->   Dict:


								'''simple docstring'''




								super().__init__(features=_snake_case       )
								__a    							=					tf_tensor_kwargs
								import tensorflow as tf  # noqa: import tf at initialization




				def        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case       )					->   Optional[Any]:


								'''simple docstring'''




								import tensorflow as tf

								if isinstance(_snake_case	,  _snake_case       ) and column:
												if all(
												    isinstance(_snake_case	,  tf.Tensor       ) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column       ):
																return tf.stack(_snake_case       )
												elif all(
												    isinstance(_snake_case	,  (tf.Tensor, tf.RaggedTensor)       ) and x.ndim == 1 and x.dtype == column[0].dtype
												    for x in column       ):
																# only rag 1-D tensors, otherwise some dimensions become ragged even though they were consolidated
																return tf.ragged.stack(_snake_case       )

								return column




				def        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case       )					->   Optional[Any]:


								'''simple docstring'''




								import tensorflow as tf

								if value is None:
												return value

								__a    							=					{}

								if isinstance(_snake_case	,  (np.number, np.ndarray)       ) and np.issubdtype(value.dtype	,  np.integer       ):
												__a    							=					{'''dtype''': tf.intaa}
								elif isinstance(_snake_case	,  (np.number, np.ndarray)       ) and np.issubdtype(value.dtype	,  np.floating       ):
												__a    							=					{'''dtype''': tf.floataa}
								elif config.PIL_AVAILABLE and "PIL" in sys.modules:
												import PIL.Image

												if isinstance(_snake_case	,  PIL.Image.Image       ):
																__a    							=					np.asarray(_snake_case       )

								return tf.convert_to_tensor(_snake_case	,  **{**default_dtype, **self.tf_tensor_kwargs}       )




				def        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case       )					->   str:


								'''simple docstring'''




								import tensorflow as tf

								# support for torch, tf, jax etc.
								if config.TORCH_AVAILABLE and "torch" in sys.modules:
												import torch

												if isinstance(_snake_case	,  torch.Tensor       ):
																return self._tensorize(data_struct.detach().cpu().numpy()[()]       )
								if hasattr(_snake_case	,  '''__array__'''       ) and not isinstance(_snake_case	,  tf.Tensor       ):
												__a    							=					data_struct.__array__()
								# support for nested types like struct of list of struct
								if isinstance(_snake_case	,  np.ndarray       ):
												if data_struct.dtype == object:  # tf tensors cannot be instantied from an array of objects
																return self._consolidate([self.recursive_tensorize(_snake_case       ) for substruct in data_struct]       )
								elif isinstance(_snake_case	,  (list, tuple)       ):
												return self._consolidate([self.recursive_tensorize(_snake_case       ) for substruct in data_struct]       )
								return self._tensorize(_snake_case       )




				def        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case       )					->   Tuple:


								'''simple docstring'''




								return map_nested(self._recursive_tensorize	,  _snake_case	,  map_list=_snake_case       )




				def        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case       )					->   Mapping:


								'''simple docstring'''




								__a    							=					self.numpy_arrow_extractor().extract_row(_snake_case       )
								__a    							=					self.python_features_decoder.decode_row(_snake_case       )
								return self.recursive_tensorize(_snake_case       )




				def        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case       )					->   "tf.Tensor":


								'''simple docstring'''




								__a    							=					self.numpy_arrow_extractor().extract_column(_snake_case       )
								__a    							=					self.python_features_decoder.decode_column(_snake_case	,  pa_table.column_names[0]       )
								__a    							=					self.recursive_tensorize(_snake_case       )
								__a    							=					self._consolidate(_snake_case       )
								return column




				def        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case       )					->   Mapping:


								'''simple docstring'''




								__a    							=					self.numpy_arrow_extractor().extract_batch(_snake_case       )
								__a    							=					self.python_features_decoder.decode_batch(_snake_case       )
								__a    							=					self.recursive_tensorize(_snake_case       )
								for column_name in batch:
												__a    							=					self._consolidate(batch[column_name]       )
								return batch