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_:   int   =      getLogger(__name__)


A_:   Union[str, Any]   =      '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			,):



							"""simple docstring"""


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

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

							_lowercase					=						time.time()
							# update config with task specific params
							use_task_specific_params(__UpperCAmelCase			,__UpperCAmelCase       )
							if prefix is None:
														_lowercase					=						prefix or getattr(model.config			,"""prefix"""			,""""""       ) or """"""
							for examples_chunk in tqdm(list(chunks(__UpperCAmelCase			,__UpperCAmelCase       )       )       ):
														_lowercase					=						[prefix + text for text in examples_chunk]
														_lowercase					=						tokenizer(__UpperCAmelCase			,return_tensors="""pt"""			,truncation=__UpperCAmelCase			,padding="""longest"""       ).to(__UpperCAmelCase       )
														_lowercase					=						model.generate(
														    input_ids=batch.input_ids			,attention_mask=batch.attention_mask			,**__UpperCAmelCase			,)
														_lowercase					=						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()
							_lowercase					=						int(time.time() - start_time       )  # seconds
							_lowercase					=						len(__UpperCAmelCase       )
							return {"n_obs": n_obs, "runtime": runtime, "seconds_per_sample": round(runtime / n_obs			,4       )}


def        __lowerCAmelCase      (	):



							"""simple docstring"""


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


def        __lowerCAmelCase      (	_A=True       ):



							"""simple docstring"""


							_lowercase					=						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
							_lowercase					,							_lowercase					=						parser.parse_known_args()
							_lowercase					=						parse_numeric_n_bool_cl_kwargs(__UpperCAmelCase       )
							if parsed_args and verbose:
														print(f'''parsed the following generate kwargs: {parsed_args}'''       )
							_lowercase					=						[""" """ + x.rstrip() if """t5""" in args.model_name else x.rstrip() for x in open(args.input_path       ).readlines()]
							if args.n_obs > 0:
														_lowercase					=						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.'''       )

							if args.device == "cpu" and args.fpaa:
														# this mix leads to RuntimeError: "threshold_cpu" not implemented for 'Half'
														raise ValueError("""Can\'t mix --fp16 and --device cpu"""       )

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

							if args.dump_args:
														scores.update(__UpperCAmelCase       )
							if args.info:
														_lowercase					=						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)








'''simple docstring'''

from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
							import numpy
							import tensorflow as tf

							from transformers import (
							    TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
							    TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
							    TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
							    BertConfig,
							    DPRConfig,
							    TFDPRContextEncoder,
							    TFDPRQuestionEncoder,
							    TFDPRReader,
							)






class 		a							:


				def __init__(						self       :   Any			,							lowercase_       :   int			,							lowercase_       :   str=13			,							lowercase_       :   Union[str, Any]=7			,							lowercase_       :   Any=True			,							lowercase_       :   List[str]=True			,							lowercase_       :   Tuple=True			,							lowercase_       :   List[Any]=True			,							lowercase_       :   str=99			,							lowercase_       :   int=32			,							lowercase_       :   int=2			,							lowercase_       :   Any=4			,							lowercase_       :   Union[str, Any]=37			,							lowercase_       :   Optional[int]="gelu"			,							lowercase_       :   Dict=0.1			,							lowercase_       :   Optional[Any]=0.1			,							lowercase_       :   Union[str, Any]=512			,							lowercase_       :   Dict=16			,							lowercase_       :   int=2			,							lowercase_       :   Any=0.02			,							lowercase_       :   int=3			,							lowercase_       :   Any=4			,							lowercase_       :   List[Any]=None			,							lowercase_       :   List[str]=0			,							):
									snake_case_		     =							parent
									snake_case_		     =							batch_size
									snake_case_		     =							seq_length
									snake_case_		     =							is_training
									snake_case_		     =							use_input_mask
									snake_case_		     =							use_token_type_ids
									snake_case_		     =							use_labels
									snake_case_		     =							vocab_size
									snake_case_		     =							hidden_size
									snake_case_		     =							num_hidden_layers
									snake_case_		     =							num_attention_heads
									snake_case_		     =							intermediate_size
									snake_case_		     =							hidden_act
									snake_case_		     =							hidden_dropout_prob
									snake_case_		     =							attention_probs_dropout_prob
									snake_case_		     =							max_position_embeddings
									snake_case_		     =							type_vocab_size
									snake_case_		     =							type_sequence_label_size
									snake_case_		     =							initializer_range
									snake_case_		     =							num_labels
									snake_case_		     =							num_choices
									snake_case_		     =							scope
									snake_case_		     =							projection_dim


				def     A_					(						self       :   int   ):
									snake_case_		     =							ids_tensor([self.batch_size, self.seq_length]			,							self.vocab_size   )

									snake_case_		     =							None
									if self.use_input_mask:
														# follow test_modeling_tf_ctrl.py
														snake_case_		     =							random_attention_mask([self.batch_size, self.seq_length]   )

									snake_case_		     =							None
									if self.use_token_type_ids:
														snake_case_		     =							ids_tensor([self.batch_size, self.seq_length]			,							self.type_vocab_size   )

									snake_case_		     =							None
									snake_case_		     =							None
									snake_case_		     =							None
									if self.use_labels:
														snake_case_		     =							ids_tensor([self.batch_size]			,							self.type_sequence_label_size   )
														snake_case_		     =							ids_tensor([self.batch_size, self.seq_length]			,							self.num_labels   )
														snake_case_		     =							ids_tensor([self.batch_size]			,							self.num_choices   )

									snake_case_		     =							BertConfig(
									    vocab_size=self.vocab_size			,							hidden_size=self.hidden_size			,							num_hidden_layers=self.num_hidden_layers			,							num_attention_heads=self.num_attention_heads			,							intermediate_size=self.intermediate_size			,							hidden_act=self.hidden_act			,							hidden_dropout_prob=self.hidden_dropout_prob			,							attention_probs_dropout_prob=self.attention_probs_dropout_prob			,							max_position_embeddings=self.max_position_embeddings			,							type_vocab_size=self.type_vocab_size			,							is_decoder=lowercase_			,							initializer_range=self.initializer_range			,							)
									snake_case_		     =							DPRConfig(projection_dim=self.projection_dim			,							**config.to_dict()   )

									return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels


				def     A_					(						self       :   Dict			,							lowercase_       :   Optional[Any]			,							lowercase_       :   int			,							lowercase_       :   Optional[int]			,							lowercase_       :   int			,							lowercase_       :   int			,							lowercase_       :   Union[str, Any]			,							lowercase_       :   str   ):
									snake_case_		     =							TFDPRContextEncoder(config=lowercase_   )
									snake_case_		     =							model(lowercase_			,							attention_mask=lowercase_			,							token_type_ids=lowercase_   )
									snake_case_		     =							model(lowercase_			,							token_type_ids=lowercase_   )
									snake_case_		     =							model(lowercase_   )
									self.parent.assertEqual(result.pooler_output.shape			,							(self.batch_size, self.projection_dim or self.hidden_size)   )


				def     A_					(						self       :   Any			,							lowercase_       :   Optional[Any]			,							lowercase_       :   List[str]			,							lowercase_       :   Union[str, Any]			,							lowercase_       :   Dict			,							lowercase_       :   Dict			,							lowercase_       :   Dict			,							lowercase_       :   Any   ):
									snake_case_		     =							TFDPRQuestionEncoder(config=lowercase_   )
									snake_case_		     =							model(lowercase_			,							attention_mask=lowercase_			,							token_type_ids=lowercase_   )
									snake_case_		     =							model(lowercase_			,							token_type_ids=lowercase_   )
									snake_case_		     =							model(lowercase_   )
									self.parent.assertEqual(result.pooler_output.shape			,							(self.batch_size, self.projection_dim or self.hidden_size)   )


				def     A_					(						self       :   str			,							lowercase_       :   Tuple			,							lowercase_       :   Tuple			,							lowercase_       :   Tuple			,							lowercase_       :   Tuple			,							lowercase_       :   Tuple			,							lowercase_       :   Dict			,							lowercase_       :   Dict   ):
									snake_case_		     =							TFDPRReader(config=lowercase_   )
									snake_case_		     =							model(lowercase_			,							attention_mask=lowercase_   )

									self.parent.assertEqual(result.start_logits.shape			,							(self.batch_size, self.seq_length)   )
									self.parent.assertEqual(result.end_logits.shape			,							(self.batch_size, self.seq_length)   )
									self.parent.assertEqual(result.relevance_logits.shape			,							(self.batch_size,)   )


				def     A_					(						self       :   Union[str, Any]   ):
									snake_case_		     =							self.prepare_config_and_inputs()
									(
									    (
									    snake_case_
									)     ,(
									    snake_case_
									)     ,(
									    snake_case_
									)     ,(
									    snake_case_
									)     ,(
									    snake_case_
									)     ,(
									    snake_case_
									)     ,(
									    snake_case_
									)     ,
									)		     =							config_and_inputs
									snake_case_		     =							{'''input_ids''': input_ids}
									return config, inputs_dict







@require_tf
class 		a							(  _lowerCamelCase  ,	_lowerCamelCase  ,	unittest.TestCase	):
				snake_case_           =						(
				    (
				        TFDPRContextEncoder,
				        TFDPRQuestionEncoder,
				        TFDPRReader,
				    )
				    if is_tf_available()
				    else ()
				)
				snake_case_           =						{"feature-extraction": TFDPRQuestionEncoder} if is_tf_available() else {}

				snake_case_           =						False
				snake_case_           =						False
				snake_case_           =						False
				snake_case_           =						False
				snake_case_           =						False


				def     A_					(						self       :   List[Any]   ):
									snake_case_		     =							TFDPRModelTester(self   )
									snake_case_		     =							ConfigTester(self			,							config_class=lowercase_			,							hidden_size=37   )


				def     A_					(						self       :   Any   ):
									self.config_tester.run_common_tests()


				def     A_					(						self       :   int   ):
									snake_case_		     =							self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_dpr_context_encoder(*lowercase_   )


				def     A_					(						self       :   List[Any]   ):
									snake_case_		     =							self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_dpr_question_encoder(*lowercase_   )


				def     A_					(						self       :   Any   ):
									snake_case_		     =							self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_dpr_reader(*lowercase_   )


				@slow
				def     A_					(						self       :   int   ):
									for model_name in TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
														snake_case_		     =							TFDPRContextEncoder.from_pretrained(lowercase_   )
														self.assertIsNotNone(lowercase_   )

									for model_name in TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
														snake_case_		     =							TFDPRContextEncoder.from_pretrained(lowercase_   )
														self.assertIsNotNone(lowercase_   )

									for model_name in TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
														snake_case_		     =							TFDPRQuestionEncoder.from_pretrained(lowercase_   )
														self.assertIsNotNone(lowercase_   )

									for model_name in TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
														snake_case_		     =							TFDPRReader.from_pretrained(lowercase_   )
														self.assertIsNotNone(lowercase_   )







@require_tf
class 		a							(  unittest.TestCase	):


				@slow
				def     A_					(						self       :   Dict   ):
									snake_case_		     =							TFDPRQuestionEncoder.from_pretrained('''facebook/dpr-question_encoder-single-nq-base'''   )

									snake_case_		     =							tf.constant(
									    [[101, 7592, 1010, 2003, 2026, 3899, 1_0140, 1029, 102]]   )  # [CLS] hello, is my dog cute? [SEP]
									snake_case_		     =							model(lowercase_   )[0]  # embedding shape = (1, 768)
									# compare the actual values for a slice.
									snake_case_		     =							tf.constant(
									    [
									        [
									            0.0323_6253,
									            0.1275_3335,
									            0.1681_8509,
									            0.0027_9786,
									            0.389_6933,
									            0.2426_4945,
									            0.217_8971,
									            -0.0233_5227,
									            -0.0848_1959,
									            -0.1432_4117,
									        ]
									    ]   )
									self.assertTrue(numpy.allclose(output[:, :10].numpy()			,							expected_slice.numpy()			,							atol=1e-4   )   )

