





"""simple docstring"""




from __future__ import annotations

import unittest
import warnings

from transformers import AutoTokenizer, MarianConfig, MarianTokenizer, TranslationPipeline, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property

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


if is_tf_available():
   import tensorflow as tf

   from transformers import TFAutoModelForSeqaSeqLM, TFMarianModel, TFMarianMTModel

@require_tf
class 			_UpperCAmelCase  :



      '''simple docstring'''
      lowercase_				:			int        =       MarianConfig
      lowercase_				:			List[Any]        =       {}
      lowercase_				:			Optional[int]        =       """gelu"""

      def __init__(					self ,				snake_case_ ,				snake_case_=1_3 ,				snake_case_=7 ,				snake_case_=True ,				snake_case_=False ,				snake_case_=9_9 ,				snake_case_=3_2 ,				snake_case_=2 ,				snake_case_=4 ,				snake_case_=3_7 ,				snake_case_=0.1 ,				snake_case_=0.1 ,				snake_case_=2_0 ,				snake_case_=2 ,				snake_case_=1 ,				snake_case_=0 ,				):

        """simple docstring"""

        A_  :	int    						=  parent
        A_  :	Dict    						=  batch_size
        A_  :	Optional[int]    						=  seq_length
        A_  :	Dict    						=  is_training
        A_  :	Tuple    						=  use_labels
        A_  :	List[Any]    						=  vocab_size
        A_  :	Dict    						=  hidden_size
        A_  :	Tuple    						=  num_hidden_layers
        A_  :	Union[str, Any]    						=  num_attention_heads
        A_  :	Dict    						=  intermediate_size

        A_  :	int    						=  hidden_dropout_prob
        A_  :	Union[str, Any]    						=  attention_probs_dropout_prob
        A_  :	List[str]    						=  max_position_embeddings
        A_  :	Optional[Any]    						=  eos_token_id
        A_  :	str    						=  pad_token_id
        A_  :	int    						=  bos_token_id

      def  lowerCamelCase_      (					self  ):

        """simple docstring"""

        A_  :	Dict    						=  ids_tensor([self.batch_size, self.seq_length - 1] ,				self.vocab_size  )
        A_  :	str    						=  tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size  ) ,				1  )
        A_  :	List[Any]    						=  tf.concat([input_ids, eos_tensor] ,				axis=1  )

        A_  :	Union[str, Any]    						=  ids_tensor([self.batch_size, self.seq_length] ,				self.vocab_size  )

        A_  :	str    						=  self.config_cls(
            vocab_size=self.vocab_size ,				d_model=self.hidden_size ,				encoder_layers=self.num_hidden_layers ,				decoder_layers=self.num_hidden_layers ,				encoder_attention_heads=self.num_attention_heads ,				decoder_attention_heads=self.num_attention_heads ,				encoder_ffn_dim=self.intermediate_size ,				decoder_ffn_dim=self.intermediate_size ,				dropout=self.hidden_dropout_prob ,				attention_dropout=self.attention_probs_dropout_prob ,				max_position_embeddings=self.max_position_embeddings ,				eos_token_ids=[2] ,				bos_token_id=self.bos_token_id ,				pad_token_id=self.pad_token_id ,				decoder_start_token_id=self.pad_token_id ,				**self.config_updates ,				)
        A_  :	Dict    						=  prepare_marian_inputs_dict(snake_case_ ,				snake_case_ ,				snake_case_  )
        return config, inputs_dict

      def  lowerCamelCase_      (					self ,				snake_case_ ,				snake_case_  ):

        """simple docstring"""

        A_  :	List[Any]    						=  TFMarianModel(config=snake_case_  ).get_decoder()
        A_  :	List[Any]    						=  inputs_dict['input_ids']

        A_  :	Tuple    						=  input_ids[:1, :]
        A_  :	Any    						=  inputs_dict['attention_mask'][:1, :]
        A_  :	List[Any]    						=  inputs_dict['head_mask']
        A_  :	Dict    						=  1

        # first forward pass
        A_  :	List[Any]    						=  model(snake_case_ ,				attention_mask=snake_case_ ,				head_mask=snake_case_ ,				use_cache=snake_case_  )

        A_						,	A_  :	Union[str, Any]    						=  outputs.to_tuple()

        # create hypothetical next token and extent to next_input_ids
        A_  :	List[Any]    						=  ids_tensor((self.batch_size, 3) ,				config.vocab_size  )
        A_  :	int    						=  tf.cast(ids_tensor((self.batch_size, 3) ,				2  ) ,				tf.inta  )

        # append to next input_ids and
        A_  :	int    						=  tf.concat([input_ids, next_tokens] ,				axis=-1  )
        A_  :	int    						=  tf.concat([attention_mask, next_attn_mask] ,				axis=-1  )

        A_  :	Optional[Any]    						=  model(snake_case_ ,				attention_mask=snake_case_  )[0]
        A_  :	Union[str, Any]    						=  model(snake_case_ ,				attention_mask=snake_case_ ,				past_key_values=snake_case_  )[0]

        self.parent.assertEqual(next_tokens.shape[1] ,				output_from_past.shape[1]  )

        # select random slice
        A_  :	Dict    						=  int(ids_tensor((1,) ,				output_from_past.shape[-1]  )  )
        A_  :	str    						=  output_from_no_past[:, -3:, random_slice_idx]
        A_  :	Tuple    						=  output_from_past[:, :, random_slice_idx]

        # test that outputs are equal for slice
        tf.debugging.assert_near(snake_case_ ,				snake_case_ ,				rtol=1E-3  )





def 						UpperCAmelCase__		(					_UpperCAmelCase			, _UpperCAmelCase			, _UpperCAmelCase			, _UpperCAmelCase=None			, _UpperCAmelCase=None			, _UpperCAmelCase=None			, _UpperCAmelCase=None			, _UpperCAmelCase=None			, ):






  """simple docstring"""





  if attention_mask is None:
    A_  :	List[str]    						=  tf.cast(tf.math.not_equal(_UpperCAmelCase			, config.pad_token_id )			, tf.inta )
  if decoder_attention_mask is None:
    A_  :	Any    						=  tf.concat(
        [
            tf.ones(decoder_input_ids[:, :1].shape			, dtype=tf.inta ),
            tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:]			, config.pad_token_id )			, tf.inta ),
        ]			, axis=-1			, )
  if head_mask is None:
    A_  :	Optional[int]    						=  tf.ones((config.encoder_layers, config.encoder_attention_heads) )
  if decoder_head_mask is None:
    A_  :	str    						=  tf.ones((config.decoder_layers, config.decoder_attention_heads) )
  if cross_attn_head_mask is None:
    A_  :	Any    						=  tf.ones((config.decoder_layers, config.decoder_attention_heads) )
  return {
      "input_ids": input_ids,
      "decoder_input_ids": decoder_input_ids,
      "attention_mask": attention_mask,
      "decoder_attention_mask": decoder_attention_mask,
      "head_mask": head_mask,
      "decoder_head_mask": decoder_head_mask,
      "cross_attn_head_mask": cross_attn_head_mask,
  }

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



      '''simple docstring'''
      lowercase_				:			Union[str, Any]        =       (TFMarianMTModel, TFMarianModel) if is_tf_available() else ()
      lowercase_				:			List[Any]        =       (TFMarianMTModel,) if is_tf_available() else ()
      lowercase_				:			str        =       (
          {
              """conversational""": TFMarianMTModel,
              """feature-extraction""": TFMarianModel,
              """summarization""": TFMarianMTModel,
              """text2text-generation""": TFMarianMTModel,
              """translation""": TFMarianMTModel,
          }
          if is_tf_available()
          else {}
      )
      lowercase_				:			Union[str, Any]        =       True
      lowercase_				:			List[Any]        =       False
      lowercase_				:			List[str]        =       False

      def  lowerCamelCase_      (					self  ):

        """simple docstring"""

        A_  :	Optional[Any]    						=  TFMarianModelTester(self  )
        A_  :	List[str]    						=  ConfigTester(self ,				config_class=snake_case_  )

      def  lowerCamelCase_      (					self  ):

        """simple docstring"""

        self.config_tester.run_common_tests()

      def  lowerCamelCase_      (					self  ):

        """simple docstring"""

        A_  :	str    						=  self.model_tester.prepare_config_and_inputs_for_common()
        self.model_tester.check_decoder_model_past_large_inputs(*snake_case_  )


@require_tf
class 			_UpperCAmelCase  (       unittest.TestCase		):



      '''simple docstring'''
      lowercase_				:			Union[str, Any]        =       1_000  # show more chars for failing integration tests

      @classmethod
      def  lowerCamelCase_      (					cls  ):

        """simple docstring"""

        A_  :	Any    						=  F"""Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}"""
        return cls

      @cached_property
      def  lowerCamelCase_      (					self  ):

        """simple docstring"""

        return AutoTokenizer.from_pretrained(self.model_name  )

      @property
      def  lowerCamelCase_      (					self  ):

        """simple docstring"""

        return self.tokenizer.eos_token_id

      @cached_property
      def  lowerCamelCase_      (					self  ):

        """simple docstring"""

        warnings.simplefilter('error'  )
        A_  :	TFMarianMTModel    						=  TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name  )
        assert isinstance(snake_case_ ,				snake_case_  )
        A_  :	List[Any]    						=  model.config
        self.assertListEqual(c.bad_words_ids ,				[[c.pad_token_id]]  )
        self.assertEqual(c.max_length ,				5_1_2  )
        self.assertEqual(c.decoder_start_token_id ,				c.pad_token_id  )
        return model

      def  lowerCamelCase_      (					self ,				**snake_case_  ):

        """simple docstring"""

        A_  :	int    						=  self.translate_src_text(**snake_case_  )
        self.assertListEqual(self.expected_text ,				snake_case_  )

      def  lowerCamelCase_      (					self ,				**snake_case_  ):

        """simple docstring"""

        A_  :	Any    						=  self.tokenizer(self.src_text ,				**snake_case_ ,				padding=snake_case_ ,				return_tensors='tf'  )
        A_  :	Optional[Any]    						=  self.model.generate(
            model_inputs.input_ids ,				attention_mask=model_inputs.attention_mask ,				num_beams=2 ,				max_length=1_2_8  )
        A_  :	Optional[int]    						=  self.tokenizer.batch_decode(generated_ids.numpy() ,				skip_special_tokens=snake_case_  )
        return generated_words


@require_sentencepiece
@require_tokenizers
@require_tf
class 			_UpperCAmelCase  (       UpperCAmelCase__		):



      '''simple docstring'''
      lowercase_				:			str        =       """mt"""
      lowercase_				:			List[Any]        =       """en"""
      lowercase_				:			List[Any]        =       ["""Billi messu b'mod ġentili, Ġesù fejjaq raġel li kien milqut bil - marda kerha tal - ġdiem."""]
      lowercase_				:			Tuple        =       ["""Touching gently, Jesus healed a man who was affected by the sad disease of leprosy."""]

      @unittest.skip('Skipping until #12647 is resolved.'  )
      @slow
      def  lowerCamelCase_      (					self  ):

        """simple docstring"""

        self._assert_generated_batch_equal_expected()


@require_sentencepiece
@require_tokenizers
@require_tf
class 			_UpperCAmelCase  (       UpperCAmelCase__		):



      '''simple docstring'''
      lowercase_				:			List[str]        =       """en"""
      lowercase_				:			List[Any]        =       """zh"""
      lowercase_				:			List[Any]        =       ["""My name is Wolfgang and I live in Berlin"""]
      lowercase_				:			Dict        =       ["""我叫沃尔夫冈 我住在柏林"""]

      @unittest.skip('Skipping until #12647 is resolved.'  )
      @slow
      def  lowerCamelCase_      (					self  ):

        """simple docstring"""

        self._assert_generated_batch_equal_expected()


@require_sentencepiece
@require_tokenizers
@require_tf
class 			_UpperCAmelCase  (       UpperCAmelCase__		):



      '''simple docstring'''
      lowercase_				:			int        =       """en"""
      lowercase_				:			List[str]        =       """ROMANCE"""
      lowercase_				:			str        =       [
          """>>fr<< Don't spend so much time watching TV.""",
          """>>pt<< Your message has been sent.""",
          """>>es<< He's two years older than me.""",
      ]
      lowercase_				:			Dict        =       [
          """Ne passez pas autant de temps à regarder la télé.""",
          """A sua mensagem foi enviada.""",
          """Es dos años más viejo que yo.""",
      ]

      @unittest.skip('Skipping until #12647 is resolved.'  )
      @slow
      def  lowerCamelCase_      (					self  ):

        """simple docstring"""

        self._assert_generated_batch_equal_expected()

      @unittest.skip('Skipping until #12647 is resolved.'  )
      @slow
      def  lowerCamelCase_      (					self  ):

        """simple docstring"""

        A_  :	Dict    						=  TranslationPipeline(self.model ,				self.tokenizer ,				framework='tf'  )
        A_  :	int    						=  pipeline(self.src_text  )
        self.assertEqual(self.expected_text ,				[x['translation_text'] for x in output]  )






"""simple docstring"""




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_					:						Optional[Any]      = 'cuda' if torch.cuda.is_available() else 'cpu'





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






  """simple docstring"""





  A_  :	Tuple    						=  Path(_UpperCAmelCase ).open('w'			, encoding='utf-8' )
  A_  :	List[str]    						=  str(_UpperCAmelCase )
  A_  :	List[str]    						=  AutoModelForSeqaSeqLM.from_pretrained(_UpperCAmelCase ).to(_UpperCAmelCase )
  if fpaa:
    A_  :	List[str]    						=  model.half()

  A_  :	int    						=  AutoTokenizer.from_pretrained(_UpperCAmelCase )
  logger.info(f"""Inferred tokenizer type: {tokenizer.__class__}""" )  # if this is wrong, check config.model_type.

  A_  :	Optional[int]    						=  time.time()
  # update config with task specific params
  use_task_specific_params(_UpperCAmelCase			, _UpperCAmelCase )
  if prefix is None:
    A_  :	Optional[int]    						=  prefix or getattr(model.config			, 'prefix'			, '' ) or ''
  for examples_chunk in tqdm(list(chunks(_UpperCAmelCase			, _UpperCAmelCase ) ) ):
    A_  :	Any    						=  [prefix + text for text in examples_chunk]
    A_  :	List[Any]    						=  tokenizer(_UpperCAmelCase			, return_tensors='pt'			, truncation=_UpperCAmelCase			, padding='longest' ).to(_UpperCAmelCase )
    A_  :	Dict    						=  model.generate(
        input_ids=batch.input_ids			, attention_mask=batch.attention_mask			, **_UpperCAmelCase			, )
    A_  :	Dict    						=  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_  :	Dict    						=  int(time.time() - start_time )  # seconds
  A_  :	Any    						=  len(_UpperCAmelCase )
  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__		(					_UpperCAmelCase=True ):






  """simple docstring"""





  A_  :	int    						=  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_  :	int    						=  parser.parse_known_args()
  A_  :	List[Any]    						=  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_  :	List[str]    						=  [' ' + x.rstrip() if 't5' in args.model_name else x.rstrip() for x in f.readlines()]
  if args.n_obs > 0:
    A_  :	int    						=  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_  :	Optional[Any]    						=  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_  :	Union[str, Any]    						=  calculate_bleu if 'translation' in args.task else calculate_rouge
  A_  :	List[Any]    						=  [x.rstrip() for x in open(args.save_path ).readlines()]
  A_  :	Dict    						=  [x.rstrip() for x in open(args.reference_path ).readlines()][: len(_UpperCAmelCase )]
  A_  :	dict    						=  score_fn(_UpperCAmelCase			, _UpperCAmelCase )
  scores.update(_UpperCAmelCase )

  if args.dump_args:
    scores.update(_UpperCAmelCase )
  if args.info:
    A_  :	Any    						=  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)