

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						:







    """simple docstring"""





    def __init__(				self			:	str,lowercase_			:	int,lowercase_			:	Optional[Any]=1_3,lowercase_			:	Optional[int]=7,lowercase_			:	str=True,lowercase_			:	Optional[int]=True,lowercase_			:	Dict=True,lowercase_			:	str=True,lowercase_			:	Union[str, Any]=9_9,lowercase_			:	Union[str, Any]=3_2,lowercase_			:	Optional[Any]=2,lowercase_			:	int=4,lowercase_			:	str=3_7,lowercase_			:	int="gelu",lowercase_			:	Dict=0.1,lowercase_			:	Union[str, Any]=0.1,lowercase_			:	Optional[int]=5_1_2,lowercase_			:	Union[str, Any]=1_6,lowercase_			:	Any=2,lowercase_			:	Dict=0.02,lowercase_			:	int=3,lowercase_			:	Dict=4,lowercase_			:	str=None,lowercase_			:	Optional[int]=0,)->   Optional[int]:





           '''simple docstring'''







           A__   				=       parent
           A__   				=       batch_size
           A__   				=       seq_length
           A__   				=       is_training
           A__   				=       use_input_mask
           A__   				=       use_token_type_ids
           A__   				=       use_labels
           A__   				=       vocab_size
           A__   				=       hidden_size
           A__   				=       num_hidden_layers
           A__   				=       num_attention_heads
           A__   				=       intermediate_size
           A__   				=       hidden_act
           A__   				=       hidden_dropout_prob
           A__   				=       attention_probs_dropout_prob
           A__   				=       max_position_embeddings
           A__   				=       type_vocab_size
           A__   				=       type_sequence_label_size
           A__   				=       initializer_range
           A__   				=       num_labels
           A__   				=       num_choices
           A__   				=       scope
           A__   				=       projection_dim


    def 						snake_case__					(				self			:	List[str]		)->   Tuple:





           '''simple docstring'''







           A__   				=       ids_tensor([self.batch_size, self.seq_length],self.vocab_size		)

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

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

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

           A__   				=       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,)
           A__   				=       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 						snake_case__					(				self			:	List[Any],lowercase_			:	Optional[int],lowercase_			:	Optional[Any],lowercase_			:	str,lowercase_			:	int,lowercase_			:	Tuple,lowercase_			:	List[Any],lowercase_			:	Dict		)->   List[str]:





           '''simple docstring'''







           A__   				=       TFDPRContextEncoder(config=lowercase_		)
           A__   				=       model(lowercase_,attention_mask=lowercase_,token_type_ids=lowercase_		)
           A__   				=       model(lowercase_,token_type_ids=lowercase_		)
           A__   				=       model(lowercase_		)
           self.parent.assertEqual(result.pooler_output.shape,(self.batch_size, self.projection_dim or self.hidden_size)		)


    def 						snake_case__					(				self			:	Optional[int],lowercase_			:	Optional[Any],lowercase_			:	Tuple,lowercase_			:	Any,lowercase_			:	Any,lowercase_			:	Union[str, Any],lowercase_			:	Union[str, Any],lowercase_			:	str		)->   str:





           '''simple docstring'''







           A__   				=       TFDPRQuestionEncoder(config=lowercase_		)
           A__   				=       model(lowercase_,attention_mask=lowercase_,token_type_ids=lowercase_		)
           A__   				=       model(lowercase_,token_type_ids=lowercase_		)
           A__   				=       model(lowercase_		)
           self.parent.assertEqual(result.pooler_output.shape,(self.batch_size, self.projection_dim or self.hidden_size)		)


    def 						snake_case__					(				self			:	int,lowercase_			:	List[Any],lowercase_			:	Optional[Any],lowercase_			:	Any,lowercase_			:	Any,lowercase_			:	List[Any],lowercase_			:	Union[str, Any],lowercase_			:	Tuple		)->   List[Any]:





           '''simple docstring'''







           A__   				=       TFDPRReader(config=lowercase_		)
           A__   				=       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 						snake_case__					(				self			:	int		)->   Union[str, Any]:





           '''simple docstring'''







           A__   				=       self.prepare_config_and_inputs()
           (
               (
               A__						
           )				,						(
               A__						
           )				,						(
               A__						
           )				,						(
               A__						
           )				,						(
               A__						
           )				,						(
               A__						
           )				,						(
               A__						
           )				,												
           )   				=       config_and_inputs
           A__   				=       {'input_ids': input_ids}
           return config, inputs_dict




@require_tf
class 			A						(							_UpperCAmelCase   ,							_UpperCAmelCase   ,							unittest.TestCase			):







    """simple docstring"""





    lowerCamelCase		     =							(
        (
            TFDPRContextEncoder,
            TFDPRQuestionEncoder,
            TFDPRReader,
        )
        if is_tf_available()
        else ()
    )
    lowerCamelCase		     =							{'feature-extraction': TFDPRQuestionEncoder} if is_tf_available() else {}

    lowerCamelCase		     =							False
    lowerCamelCase		     =							False
    lowerCamelCase		     =							False
    lowerCamelCase		     =							False
    lowerCamelCase		     =							False


    def 						snake_case__					(				self			:	str		)->   List[Any]:





           '''simple docstring'''







           A__   				=       TFDPRModelTester(self		)
           A__   				=       ConfigTester(self,config_class=lowercase_,hidden_size=3_7		)


    def 						snake_case__					(				self			:	Dict		)->   Any:





           '''simple docstring'''







           self.config_tester.run_common_tests()


    def 						snake_case__					(				self			:	Tuple		)->   Tuple:





           '''simple docstring'''







           A__   				=       self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_dpr_context_encoder(*lowercase_		)


    def 						snake_case__					(				self			:	Optional[int]		)->   Union[str, Any]:





           '''simple docstring'''







           A__   				=       self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_dpr_question_encoder(*lowercase_		)


    def 						snake_case__					(				self			:	List[Any]		)->   int:





           '''simple docstring'''







           A__   				=       self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_dpr_reader(*lowercase_		)


    @slow
    def 						snake_case__					(				self			:	Any		)->   Any:





           '''simple docstring'''







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

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

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

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




@require_tf
class 			A						(							unittest.TestCase			):







    """simple docstring"""





    @slow
    def 						snake_case__					(				self			:	int		)->   Dict:





           '''simple docstring'''







           A__   				=       TFDPRQuestionEncoder.from_pretrained('facebook/dpr-question_encoder-single-nq-base'		)

           A__   				=       tf.constant(
               [[1_0_1, 7_5_9_2, 1_0_1_0, 2_0_0_3, 2_0_2_6, 3_8_9_9, 1_0_1_4_0, 1_0_2_9, 1_0_2]]		)  # [CLS] hello, is my dog cute? [SEP]
           A__   				=       model(lowercase_		)[0]  # embedding shape = (1, 768)
           # compare the actual values for a slice.
           A__   				=       tf.constant(
               [
                   [
                       0.03_236_253,
                       0.12_753_335,
                       0.16_818_509,
                       0.00_279_786,
                       0.3_896_933,
                       0.24_264_945,
                       0.2_178_971,
                       -0.02_335_227,
                       -0.08_481_959,
                       -0.14_324_117,
                   ]
               ]		)
           self.assertTrue(numpy.allclose(output[:, :1_0].numpy(),expected_slice.numpy(),atol=1E-4		)		)




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_			=  getLogger(__name__)


lowercase_			=  "cuda" if torch.cuda.is_available() else "cpu"
def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     List[str]   ,      SCREAMING_SNAKE_CASE__      :     str   ,      SCREAMING_SNAKE_CASE__      :     str   ,      SCREAMING_SNAKE_CASE__      :     int = 8   ,      SCREAMING_SNAKE_CASE__      :     str = DEFAULT_DEVICE   ,      SCREAMING_SNAKE_CASE__      :     Optional[Any]=False   ,      SCREAMING_SNAKE_CASE__      :     Dict="summarization"   ,      SCREAMING_SNAKE_CASE__      :     Any=None   ,      **SCREAMING_SNAKE_CASE__      :     str   ,      )							->				Dict:





    '''simple docstring'''






    A__   				=       Path(SCREAMING_SNAKE_CASE__				).open('w'   ,      encoding='utf-8'				)
    A__   				=       str(SCREAMING_SNAKE_CASE__				)
    A__   				=       AutoModelForSeqaSeqLM.from_pretrained(SCREAMING_SNAKE_CASE__				).to(SCREAMING_SNAKE_CASE__				)
    if fpaa:
        A__   				=       model.half()

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





    '''simple docstring'''






    return datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S'				)
def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     Optional[int]=True				)							->				Dict:





    '''simple docstring'''






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

    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'				)

    A__   				=       generate_summaries_or_translations(
        SCREAMING_SNAKE_CASE__   ,      args.save_path   ,      args.model_name   ,      batch_size=args.bs   ,      device=args.device   ,      fpaa=args.fpaa   ,      task=args.task   ,      prefix=args.prefix   ,      **SCREAMING_SNAKE_CASE__   ,      )

    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(SCREAMING_SNAKE_CASE__				)]
    A__   				=       score_fn(SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__				)
    scores.update(SCREAMING_SNAKE_CASE__				)

    if args.dump_args:
        scores.update(SCREAMING_SNAKE_CASE__				)
    if args.info:
        A__   				=       args.info

    if verbose:
        print(SCREAMING_SNAKE_CASE__				)

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

