
import unittest

from transformers import ErnieMConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
 import torch

 from transformers import (
     ErnieMForInformationExtraction,
     ErnieMForMultipleChoice,
     ErnieMForQuestionAnswering,
     ErnieMForSequenceClassification,
     ErnieMForTokenClassification,
     ErnieMModel,
 )
 from transformers.models.ernie_m.modeling_ernie_m import ERNIE_M_PRETRAINED_MODEL_ARCHIVE_LIST


class 			UpperCamelCase_				:




      '''simple docstring'''







      def __init__( self					,					_UpperCAmelCase					,					_UpperCAmelCase=13					,					_UpperCAmelCase=7					,					_UpperCAmelCase=True					,					_UpperCAmelCase=True					,					_UpperCAmelCase=True					,					_UpperCAmelCase=99					,					_UpperCAmelCase=32					,					_UpperCAmelCase=5					,					_UpperCAmelCase=4					,					_UpperCAmelCase=37					,					_UpperCAmelCase="gelu"					,					_UpperCAmelCase=0.1					,					_UpperCAmelCase=0.1					,					_UpperCAmelCase=512					,					_UpperCAmelCase=16					,					_UpperCAmelCase=2					,					_UpperCAmelCase=0.02					,					_UpperCAmelCase=3					,					_UpperCAmelCase=4					,					_UpperCAmelCase=None					,					):
            lowerCAmelCase_						=      parent
            lowerCAmelCase_						=      batch_size
            lowerCAmelCase_						=      seq_length
            lowerCAmelCase_						=      is_training
            lowerCAmelCase_						=      use_input_mask
            lowerCAmelCase_						=      use_labels
            lowerCAmelCase_						=      vocab_size
            lowerCAmelCase_						=      hidden_size
            lowerCAmelCase_						=      num_hidden_layers
            lowerCAmelCase_						=      num_attention_heads
            lowerCAmelCase_						=      intermediate_size
            lowerCAmelCase_						=      hidden_act
            lowerCAmelCase_						=      hidden_dropout_prob
            lowerCAmelCase_						=      attention_probs_dropout_prob
            lowerCAmelCase_						=      max_position_embeddings
            lowerCAmelCase_						=      type_vocab_size
            lowerCAmelCase_						=      type_sequence_label_size
            lowerCAmelCase_						=      initializer_range
            lowerCAmelCase_						=      num_labels
            lowerCAmelCase_						=      num_choices
            lowerCAmelCase_						=      scope







      def lowercase__		( self):
            lowerCAmelCase_						=      ids_tensor([self.batch_size, self.seq_length]					,					self.vocab_size)

            lowerCAmelCase_						=      None
            if self.use_input_mask:
                  lowerCAmelCase_						=      random_attention_mask([self.batch_size, self.seq_length])

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

            lowerCAmelCase_						=      self.get_config()

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







      def lowercase__		( self):
            lowerCAmelCase_						=      ids_tensor([self.batch_size, self.seq_length]					,					self.vocab_size)

            lowerCAmelCase_						=      None
            if self.use_input_mask:
                  lowerCAmelCase_						=      random_attention_mask([self.batch_size, self.seq_length])
            lowerCAmelCase_						=      self.get_config()

            return config, input_ids, input_mask







      def lowercase__		( self):
            return ErnieMConfig(
                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					,					initializer_range=self.initializer_range					,					)







      def lowercase__		( self					,					_UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase):
            lowerCAmelCase_						=      ErnieMModel(config=SCREAMING_SNAKE_CASE_)
            model.to(SCREAMING_SNAKE_CASE_)
            model.eval()
            lowerCAmelCase_						=      model(SCREAMING_SNAKE_CASE_					,					return_dict=SCREAMING_SNAKE_CASE_)
            self.parent.assertEqual(result.last_hidden_state.shape					,					(self.batch_size, self.seq_length, self.hidden_size))







      def lowercase__		( self					,					_UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase):
            lowerCAmelCase_						=      ErnieMForQuestionAnswering(config=SCREAMING_SNAKE_CASE_)
            model.to(SCREAMING_SNAKE_CASE_)
            model.eval()
            lowerCAmelCase_						=      model(
                SCREAMING_SNAKE_CASE_					,					attention_mask=SCREAMING_SNAKE_CASE_					,					start_positions=SCREAMING_SNAKE_CASE_					,					end_positions=SCREAMING_SNAKE_CASE_					,					)
            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))







      def lowercase__		( self					,					_UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase):
            lowerCAmelCase_						=      ErnieMForInformationExtraction(config=SCREAMING_SNAKE_CASE_)
            model.to(SCREAMING_SNAKE_CASE_)
            model.eval()
            lowerCAmelCase_						=      torch.ones_like(SCREAMING_SNAKE_CASE_					,					dtype=torch.floataa)
            lowerCAmelCase_						=      model(
                SCREAMING_SNAKE_CASE_					,					attention_mask=SCREAMING_SNAKE_CASE_					,					start_positions=SCREAMING_SNAKE_CASE_					,					end_positions=SCREAMING_SNAKE_CASE_					,					)
            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))







      def lowercase__		( self					,					_UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase):
            lowerCAmelCase_						=      self.num_labels
            lowerCAmelCase_						=      ErnieMForSequenceClassification(SCREAMING_SNAKE_CASE_)
            model.to(SCREAMING_SNAKE_CASE_)
            model.eval()
            lowerCAmelCase_						=      model(SCREAMING_SNAKE_CASE_					,					attention_mask=SCREAMING_SNAKE_CASE_					,					labels=SCREAMING_SNAKE_CASE_)
            self.parent.assertEqual(result.logits.shape					,					(self.batch_size, self.num_labels))







      def lowercase__		( self					,					_UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase):
            lowerCAmelCase_						=      self.num_labels
            lowerCAmelCase_						=      ErnieMForTokenClassification(config=SCREAMING_SNAKE_CASE_)
            model.to(SCREAMING_SNAKE_CASE_)
            model.eval()
            input_ids.to(SCREAMING_SNAKE_CASE_)
            input_mask.to(SCREAMING_SNAKE_CASE_)
            token_labels.to(SCREAMING_SNAKE_CASE_)

            lowerCAmelCase_						=      model(SCREAMING_SNAKE_CASE_					,					attention_mask=SCREAMING_SNAKE_CASE_					,					labels=SCREAMING_SNAKE_CASE_)

            self.parent.assertEqual(result.logits.shape					,					(self.batch_size, self.seq_length, self.num_labels))







      def lowercase__		( self					,					_UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase):
            lowerCAmelCase_						=      self.num_choices
            lowerCAmelCase_						=      ErnieMForMultipleChoice(config=SCREAMING_SNAKE_CASE_)
            model.to(SCREAMING_SNAKE_CASE_)
            model.eval()
            lowerCAmelCase_						=      input_ids.unsqueeze(1).expand(-1					,					self.num_choices					,					-1).contiguous()
            lowerCAmelCase_						=      input_mask.unsqueeze(1).expand(-1					,					self.num_choices					,					-1).contiguous()
            lowerCAmelCase_						=      model(
                SCREAMING_SNAKE_CASE_					,					attention_mask=SCREAMING_SNAKE_CASE_					,					labels=SCREAMING_SNAKE_CASE_					,					)
            self.parent.assertEqual(result.logits.shape					,					(self.batch_size, self.num_choices))







      def lowercase__		( self):
            lowerCAmelCase_						=      self.prepare_config_and_inputs()
            (
                (
                lowerCAmelCase_       
            )			,		(
                lowerCAmelCase_       
            )			,		(
                lowerCAmelCase_       
            )			,		(
                lowerCAmelCase_       
            )			,		(
                lowerCAmelCase_       
            )			,		(
                lowerCAmelCase_       
            )			,		       
            )						=      config_and_inputs
            lowerCAmelCase_						=      {'''input_ids''': input_ids, '''attention_mask''': input_mask}
            return config, inputs_dict


@require_torch
class 			UpperCamelCase_				(  A					,       A					,       unittest.TestCase     ):




      '''simple docstring'''







      a		:Union[str, Any]						 =      (
          (
              ErnieMModel,
              ErnieMForMultipleChoice,
              ErnieMForQuestionAnswering,
              ErnieMForSequenceClassification,
              ErnieMForTokenClassification,
          )
          if is_torch_available()
          else ()
      )
      a		:Optional[int]						 =      ()
      a		:Optional[int]						 =      (
          {
              'feature-extraction': ErnieMModel,
              'question-answering': ErnieMForQuestionAnswering,
              'text-classification': ErnieMForSequenceClassification,
              'token-classification': ErnieMForTokenClassification,
              'zero-shot': ErnieMForSequenceClassification,
          }
          if is_torch_available()
          else {}
      )
      a		:List[str]						 =      False







      def lowercase__		( self					,					_UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase):
            if pipeline_test_casse_name == "QAPipelineTests":
                  return True

            return False







      def lowercase__		( self):
            lowerCAmelCase_						=      ErnieMModelTester(self)
            lowerCAmelCase_						=      ConfigTester(self					,					config_class=SCREAMING_SNAKE_CASE_					,					hidden_size=37)







      def lowercase__		( self):
            self.config_tester.run_common_tests()







      def lowercase__		( self):
            lowerCAmelCase_						=      self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_)







      def lowercase__		( self):
            lowerCAmelCase_						=      self.model_tester.prepare_config_and_inputs()
            for type in ["absolute", "relative_key", "relative_key_query"]:
                  lowerCAmelCase_						=      type
                  self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_)







      def lowercase__		( self):
            lowerCAmelCase_						=      self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_multiple_choice(*SCREAMING_SNAKE_CASE_)







      def lowercase__		( self):
            lowerCAmelCase_						=      self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_question_answering(*SCREAMING_SNAKE_CASE_)







      def lowercase__		( self):
            lowerCAmelCase_						=      self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_sequence_classification(*SCREAMING_SNAKE_CASE_)







      def lowercase__		( self):
            lowerCAmelCase_						=      self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_information_extraction(*SCREAMING_SNAKE_CASE_)







      def lowercase__		( self):
            lowerCAmelCase_						=      self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_token_classification(*SCREAMING_SNAKE_CASE_)







      @slow
      def lowercase__		( self):
            for model_name in ERNIE_M_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                  lowerCAmelCase_						=      ErnieMModel.from_pretrained(SCREAMING_SNAKE_CASE_)
                  self.assertIsNotNone(SCREAMING_SNAKE_CASE_)


@require_torch
class 			UpperCamelCase_				(  unittest.TestCase     ):




      '''simple docstring'''







      @slow
      def lowercase__		( self):
            lowerCAmelCase_						=      ErnieMModel.from_pretrained('''susnato/ernie-m-base_pytorch''')
            model.eval()
            lowerCAmelCase_						=      torch.tensor([[0, 1, 2, 3, 4, 5]])
            lowerCAmelCase_						=      model(SCREAMING_SNAKE_CASE_)[0]

            # TODO Replace vocab size
            lowerCAmelCase_						=      768

            lowerCAmelCase_						=      torch.Size((1, 6, hidden_size))
            self.assertEqual(output.shape					,					SCREAMING_SNAKE_CASE_)

            lowerCAmelCase_						=      torch.tensor(
                [[[-0.0012, 0.1245, -0.0214], [-0.0742, 0.0244, -0.0771], [-0.0333, 0.1164, -0.1554]]])

            self.assertTrue(torch.allclose(output[:, :3, :3]					,					SCREAMING_SNAKE_CASE_					,					atol=1E-3))





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


lowerCamelCase_				:							List[str]       =    getLogger(__name__)


lowerCamelCase_				:							Dict       =    "cuda" if torch.cuda.is_available() else "cpu"
def        __lowercase(							__snake_case				:		List[str]    ,__snake_case				:		str    ,__snake_case				:		str    ,__snake_case				:		int = 8    ,__snake_case				:		str = DEFAULT_DEVICE    ,__snake_case				:		str=False    ,__snake_case				:		Tuple="summarization"    ,__snake_case				:		List[str]=None    ,**__snake_case				:		Dict    ,)       ->   Dict:
       __snake_case						       =   Path(__snake_case					).open('w'    ,encoding='utf-8'					)
       __snake_case						       =   str(__snake_case					)
       __snake_case						       =   AutoModelForSeqaSeqLM.from_pretrained(__snake_case					).to(__snake_case					)
       if fpaa:
              __snake_case						       =   model.half()

       __snake_case						       =   AutoTokenizer.from_pretrained(__snake_case					)
       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(__snake_case    ,__snake_case					)
       if prefix is None:
              __snake_case						       =   prefix or getattr(model.config    ,'prefix'    ,''					) or ''
       for examples_chunk in tqdm(list(chunks(__snake_case    ,__snake_case					)					)					):
              __snake_case						       =   [prefix + text for text in examples_chunk]
              __snake_case						       =   tokenizer(__snake_case    ,return_tensors='pt'    ,truncation=__snake_case    ,padding='longest'					).to(__snake_case					)
              __snake_case						       =   model.generate(
                  input_ids=batch.input_ids    ,attention_mask=batch.attention_mask    ,**__snake_case    ,)
              __snake_case						       =   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()
       __snake_case						       =   int(time.time() - start_time					)  # seconds
       __snake_case						       =   len(__snake_case					)
       return {"n_obs": n_obs, "runtime": runtime, "seconds_per_sample": round(runtime / n_obs    ,4					)}
def        __lowercase(							)       ->   Optional[int]:
       return datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S'					)
def        __lowercase(							__snake_case				:		Optional[int]=True					)       ->   Dict:
       __snake_case						       =   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
       __snake_case       ,       __snake_case						       =   parser.parse_known_args()
       __snake_case						       =   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:
              __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=__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.'''					)
       __snake_case						       =   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
       __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(__snake_case					)]
       __snake_case						       =   score_fn(__snake_case    ,__snake_case					)
       scores.update(__snake_case					)

       if args.dump_args:
              scores.update(__snake_case					)
       if args.info:
              __snake_case						       =   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)
