




from __future__ import annotations

import unittest

from transformers import AutoTokenizer, PegasusConfig, 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, TFPegasusForConditionalGeneration, TFPegasusModel







@require_tf
class 						lowercase							:
     _a							 =						PegasusConfig
     _a							 =						{}
     _a							 =						"gelu"

     def __init__(  self		,     _a		,     _a=13		,     _a=7		,     _a=True		,     _a=False		,     _a=99		,     _a=32		,     _a=2		,     _a=4		,     _a=37		,     _a=0.1		,     _a=0.1		,     _a=40		,     _a=2		,     _a=1		,     _a=0		,     )						->							List[str]:
           _A							:     List[Any]			=						parent
           _A							:     Optional[Any]			=						batch_size
           _A							:     Tuple			=						seq_length
           _A							:     int			=						is_training
           _A							:     List[Any]			=						use_labels
           _A							:     int			=						vocab_size
           _A							:     Dict			=						hidden_size
           _A							:     Dict			=						num_hidden_layers
           _A							:     int			=						num_attention_heads
           _A							:     int			=						intermediate_size

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

     def 					a__	(  self				)						->							str:
           _A							:     str			=						ids_tensor([self.batch_size, self.seq_length - 1]		,     self.vocab_size				)
           _A							:     Optional[Any]			=						tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size				)		,     1				)
           _A							:     Any			=						tf.concat([input_ids, eos_tensor]		,     axis=1				)

           _A							:     Optional[int]			=						ids_tensor([self.batch_size, self.seq_length]		,     self.vocab_size				)

           _A							:     Optional[Any]			=						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							:     List[Any]			=						prepare_pegasus_inputs_dict(_a		,     _a		,     _a				)
           return config, inputs_dict



     def 					a__	(  self		,     _a		,     _a				)						->							Optional[Any]:
           _A							:     Any			=						TFPegasusModel(config=_a				).get_decoder()
           _A							:     Union[str, Any]			=						inputs_dict["""input_ids"""]

           _A							:     Union[str, Any]			=						input_ids[:1, :]
           _A							:     List[str]			=						inputs_dict["""attention_mask"""][:1, :]
           _A							:     Any			=						inputs_dict["""head_mask"""]
           _A							:     Any			=						1

           # first forward pass
           _A							:     int			=						model(_a		,     attention_mask=_a		,     head_mask=_a		,     use_cache=_a				)

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

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

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

           _A							:     Any			=						model(_a		,     attention_mask=_a				)[0]
           _A							:     List[Any]			=						model(_a		,     attention_mask=_a		,     past_key_values=_a				)[0]

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

           # select random slice
           _A							:     List[Any]			=						int(ids_tensor((1,)		,     output_from_past.shape[-1]				)				)
           _A							:     Optional[int]			=						output_from_no_past[:, -3:, random_slice_idx]
           _A							:     int			=						output_from_past[:, :, random_slice_idx]

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







def 				lowerCAmelCase_	(     snake_case_,snake_case_,snake_case_,snake_case_=None,snake_case_=None,snake_case_=None,snake_case_=None,snake_case_=None,):
      if attention_mask is None:
            _A							:     List[str]			=						tf.cast(tf.math.not_equal(snake_case_,config.pad_token_id     ),tf.inta     )
      if decoder_attention_mask is None:
            _A							:     int			=						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							:     List[Any]			=						tf.ones((config.encoder_layers, config.encoder_attention_heads)     )
      if decoder_head_mask is None:
            _A							:     Dict			=						tf.ones((config.decoder_layers, config.decoder_attention_heads)     )
      if cross_attn_head_mask is None:
            _A							:     Optional[int]			=						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 						lowercase							(   UpperCamelCase__,UpperCamelCase__,unittest.TestCase		):
     _a							 =						(TFPegasusForConditionalGeneration, TFPegasusModel) if is_tf_available() else ()
     _a							 =						(TFPegasusForConditionalGeneration,) if is_tf_available() else ()
     _a							 =						(
         {
             "conversational": TFPegasusForConditionalGeneration,
             "feature-extraction": TFPegasusModel,
             "summarization": TFPegasusForConditionalGeneration,
             "text2text-generation": TFPegasusForConditionalGeneration,
             "translation": TFPegasusForConditionalGeneration,
         }
         if is_tf_available()
         else {}
     )
     _a							 =						True
     _a							 =						False
     _a							 =						False

     def 					a__	(  self				)						->							Optional[Any]:
           _A							:     Tuple			=						TFPegasusModelTester(self				)
           _A							:     List[Any]			=						ConfigTester(self		,     config_class=_a				)

     def 					a__	(  self				)						->							Optional[int]:
           self.config_tester.run_common_tests()



     def 					a__	(  self				)						->							Any:
           _A							:     Union[str, Any]			=						self.model_tester.prepare_config_and_inputs_for_common()
           self.model_tester.check_decoder_model_past_large_inputs(*_a				)







@require_sentencepiece
@require_tokenizers
@require_tf
class 						lowercase							(   unittest.TestCase		):
     _a							 =						[
         " PG&E stated it scheduled the blackouts in response to forecasts for high winds amid dry conditions. The aim is to reduce the risk of wildfires. Nearly 800 thousand customers were scheduled to be affected by the shutoffs which were expected to last through at least midday tomorrow.",
         " The London trio are up for best UK act and best album, as well as getting two nominations in the best song category.\"We got told like this morning 'Oh I think you're nominated'\", said Dappy.\"And I was like 'Oh yeah, which one?' And now we've got nominated for four awards. I mean, wow!\"Bandmate Fazer added: \"We thought it's best of us to come down and mingle with everyone and say hello to the cameras. And now we find we've got four nominations.\"The band have two shots at the best song prize, getting the nod for their Tynchy Stryder collaboration Number One, and single Strong Again.Their album Uncle B will also go up against records by the likes of Beyonce and Kanye West.N-Dubz picked up the best newcomer Mobo in 2007, but female member Tulisa said they wouldn't be too disappointed if they didn't win this time around.\"At the end of the day we're grateful to be where we are in our careers.\"If it don't happen then it don't happen - live to fight another day and keep on making albums and hits for the fans.\"Dappy also revealed they could be performing live several times on the night.The group will be doing Number One and also a possible rendition of the War Child single, I Got Soul.The charity song is a  re-working of The Killers' All These Things That I've Done and is set to feature artists like Chipmunk, Ironik and Pixie Lott.This year's Mobos will be held outside of London for the first time, in Glasgow on 30 September.N-Dubz said they were looking forward to performing for their Scottish fans and boasted about their recent shows north of the border.\"We just done Edinburgh the other day,\" said Dappy.\"We smashed up an N-Dubz show over there. We done Aberdeen about three or four months ago - we smashed up that show over there! Everywhere we go we smash it up!\" ",
     ]
     _a							 =						[
         "California's largest electricity provider has cut power to hundreds of thousands of customers in an effort to"
         " reduce the risk of wildfires.",
         "N-Dubz have revealed they\'re \"grateful\" to have been nominated for four Mobo Awards.",
     ]  # differs slightly from pytorch, likely due to numerical differences in linear layers
     _a							 =						"google/pegasus-xsum"

     @cached_property
     def 					a__	(  self				)						->							List[Any]:
           return AutoTokenizer.from_pretrained(self.model_name				)

     @cached_property
     def 					a__	(  self				)						->							List[Any]:
           _A							:     str			=						TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name				)
           return model

     def 					a__	(  self		,     **_a				)						->							Tuple:
           _A							:     Dict			=						self.translate_src_text(**_a				)
           assert self.expected_text == generated_words

     def 					a__	(  self		,     **_a				)						->							Any:
           _A							:     str			=						self.tokenizer(self.src_text		,     **_a		,     padding=_a		,     return_tensors="""tf"""				)
           _A							:     Tuple			=						self.model.generate(
               model_inputs.input_ids		,     attention_mask=model_inputs.attention_mask		,     num_beams=2		,     use_cache=_a		,     )
           _A							:     List[str]			=						self.tokenizer.batch_decode(generated_ids.numpy()		,     skip_special_tokens=_a				)
           return generated_words



     @slow
     def 					a__	(  self				)						->							Optional[int]:
           self._assert_generated_batch_equal_expected()







import argparse
import shutil
import time
from json import JSONDecodeError
from logging import getLogger
from pathlib import Path
from typing import Dict, List

import torch
from torch.utils.data import DataLoader
from tqdm import tqdm

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import (
    SeqaSeqDataset,
    calculate_bleu,
    calculate_rouge,
    chunks,
    lmap,
    load_json,
    parse_numeric_n_bool_cl_kwargs,
    save_json,
    use_task_specific_params,
    write_txt_file,
)


_snake_case										=       getLogger(__name__)







def 				lowerCAmelCase_	(     snake_case_,snake_case_,snake_case_,snake_case_ = 8,snake_case_ = 1024,snake_case_="val",snake_case_=None,snake_case_=False,snake_case_="summarization",snake_case_=None,snake_case_=1,snake_case_ = None,snake_case_="",**snake_case_,):
      _A							:     Dict			=						str(snake_case_     )
      assert local_rank is not None
      torch.distributed.init_process_group(backend="""nccl""",rank=snake_case_     )

      _A							:     Tuple			=						Path(snake_case_     )
      _A							:     List[Any]			=						save_dir.joinpath(f'''rank_{local_rank}_output.json'''     )
      torch.cuda.set_device(snake_case_     )
      _A							:     Union[str, Any]			=						AutoModelForSeqaSeqLM.from_pretrained(snake_case_     ).cuda()
      if fpaa:
            _A							:     Any			=						model.half()
      # determine if we need to increase num_beams
      use_task_specific_params(snake_case_,snake_case_     )  # update config with task specific params
      _A							:     str			=						generate_kwargs.pop("""num_beams""",model.config.num_beams     )  # AttributeError risk?
      if num_return_sequences > num_beams:
            _A							:     int			=						num_return_sequences

      _A							:     Optional[Any]			=						AutoTokenizer.from_pretrained(snake_case_     )
      logger.info(f'''Inferred tokenizer type: {tokenizer.__class__}'''     )  # if this is wrong, check config.model_type.

      if max_source_length is None:
            _A							:     Optional[int]			=						tokenizer.model_max_length
      if prefix is None:
            _A							:     Tuple			=						prefix or getattr(model.config,"""prefix""",""""""     ) or """"""
      _A							:     Optional[int]			=						SeqaSeqDataset(
          snake_case_,snake_case_,snake_case_,max_target_length=1024,type_path=snake_case_,n_obs=snake_case_,prefix=snake_case_,**snake_case_,)
      # I set shuffle=True for a more accurate progress bar.
      # If all the longest samples are first, the prog bar estimate is too high at the beginning.
      _A							:     Optional[int]			=						ds.make_sortish_sampler(snake_case_,distributed=snake_case_,add_extra_examples=snake_case_,shuffle=snake_case_     )
      _A							:     Dict			=						DataLoader(snake_case_,sampler=snake_case_,batch_size=snake_case_,collate_fn=ds.collate_fn     )
      _A							:     Optional[Any]			=						[]
      for batch in tqdm(snake_case_     ):
            _A							:     Tuple			=						model.generate(
                input_ids=batch["""input_ids"""].to(model.device     ),attention_mask=batch["""attention_mask"""].to(model.device     ),num_return_sequences=snake_case_,num_beams=snake_case_,**snake_case_,)
            _A							:     Any			=						tokenizer.batch_decode(snake_case_,skip_special_tokens=snake_case_,clean_up_tokenization_spaces=snake_case_     )
            _A							:     Dict			=						batch["""ids"""]
            if num_return_sequences > 1:
                  _A							:     Any			=						chunks(snake_case_,snake_case_     )  # batch size chunks, each of size num_return_seq
            for i, pred in enumerate(snake_case_     ):
                  results.append({"""pred""": pred, """id""": ids[i].item()}     )
      save_json(snake_case_,snake_case_     )
      return results, sampler.num_replicas







def 				lowerCAmelCase_	(     ):
      _A							:     Tuple			=						argparse.ArgumentParser(
          epilog="""Unspecified args like --num_beams=2 --decoder_start_token_id=4 are passed to model.generate"""     )
      parser.add_argument("""--data_dir""",type=snake_case_,help="""like cnn_dm/test.source"""     )
      parser.add_argument(
          """--model_name""",type=snake_case_,help="""like facebook/bart-large-cnn,t5-base, etc.""",default="""sshleifer/distilbart-xsum-12-3""",)
      parser.add_argument("""--save_dir""",type=snake_case_,help="""where to save""",default="""tmp_gen"""     )
      parser.add_argument("""--max_source_length""",type=snake_case_,default=snake_case_     )
      parser.add_argument(
          """--type_path""",type=snake_case_,default="""test""",help="""which subset to evaluate typically train/val/test"""     )
      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(
          """--local_rank""",type=snake_case_,default=-1,required=snake_case_,help="""should be passed by distributed.launch"""     )

      parser.add_argument(
          """--n_obs""",type=snake_case_,default=snake_case_,required=snake_case_,help="""How many observations. Defaults to all."""     )
      parser.add_argument(
          """--num_return_sequences""",type=snake_case_,default=1,required=snake_case_,help="""How many sequences to return"""     )
      parser.add_argument(
          """--sync_timeout""",type=snake_case_,default=600,required=snake_case_,help="""How long should master process wait for other processes to finish.""",)
      parser.add_argument("""--src_lang""",type=snake_case_,default=snake_case_,required=snake_case_     )
      parser.add_argument("""--tgt_lang""",type=snake_case_,default=snake_case_,required=snake_case_     )
      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("""--fp16""",action="""store_true"""     )
      parser.add_argument("""--debug""",action="""store_true"""     )
      _A							:     Union[str, Any]			=						time.time()
      _A			,   _A							:     List[str]			=						parser.parse_known_args()
      _A							:     List[str]			=						parse_numeric_n_bool_cl_kwargs(snake_case_     )
      if generate_kwargs and args.local_rank <= 0:
            print(f'''parsed the following generate kwargs: {generate_kwargs}'''     )
      _A							:     Dict			=						Path(args.save_dir + """_tmp"""     )
      Path(snake_case_     ).mkdir(exist_ok=snake_case_     )  # this handles locking.
      _A							:     int			=						list(json_save_dir.glob("""rank_*.json"""     )     )
      if intermediate_files:
            raise ValueError(f'''Found files at {json_save_dir} please move or remove them.'''     )
            # In theory, a node could finish and save before another node hits this. If this happens, we can address later.
      _A							:     Any			=						{}
      if args.src_lang is not None:
            _A							:     int			=						args.src_lang
      if args.tgt_lang is not None:
            _A							:     Dict			=						args.tgt_lang

      Path(args.save_dir     ).mkdir(exist_ok=snake_case_     )
      _A			,   _A							:     str			=						eval_data_dir(
          args.data_dir,snake_case_,args.model_name,type_path=args.type_path,bs=args.bs,fpaa=args.fpaa,task=args.task,local_rank=args.local_rank,n_obs=args.n_obs,max_source_length=args.max_source_length,num_return_sequences=args.num_return_sequences,prefix=args.prefix,dataset_kwargs=snake_case_,**snake_case_,)

      if args.local_rank <= 0:
            _A							:     List[Any]			=						Path(args.save_dir     )
            save_dir.mkdir(exist_ok=snake_case_     )
            _A							:     Tuple			=						gather_results_from_each_node(snake_case_,snake_case_,args.sync_timeout     )
            _A							:     Optional[int]			=						combine_partial_results(snake_case_     )
            if args.num_return_sequences > 1:
                  _A							:     Optional[Any]			=						save_dir.joinpath("""pseudolabel_results.json"""     )
                  print(f'''Saving aggregated results at {save_path}, intermediate in {json_save_dir}/'''     )
                  save_json(snake_case_,snake_case_     )
                  return
            _A							:     List[str]			=						Path(args.data_dir     ).joinpath(args.type_path + """.target"""     )
            with open(snake_case_     ) as f:
                  _A							:     int			=						[x.rstrip() for x in f.readlines()][: len(snake_case_     )]

            # Calculate metrics, save metrics,  and save _generations.txt
            _A							:     Dict			=						"""translation""" in args.task
            _A							:     Optional[Any]			=						calculate_bleu if calc_bleu else calculate_rouge
            _A							:     Tuple			=						"""bleu""" if calc_bleu else """rouge"""
            _A							:     Dict			=						score_fn(snake_case_,snake_case_     )
            _A							:     List[Any]			=						len(snake_case_     )
            _A							:     Optional[int]			=						time.time() - start_time
            _A							:     Dict			=						round(runtime / metrics["""n_obs"""],4     )
            _A							:     Dict			=						num_replicas
            # TODO(@stas00): add whatever metadata to metrics
            _A							:     Any			=						save_dir.joinpath(f'''{args.type_path}_{metric_name}.json'''     )
            save_json(snake_case_,snake_case_,indent=snake_case_     )
            print(snake_case_     )
            write_txt_file(snake_case_,save_dir.joinpath(f'''{args.type_path}_generations.txt'''     )     )
            if args.debug:
                  write_txt_file(snake_case_,save_dir.joinpath(f'''{args.type_path}.target'''     )     )
            else:
                  shutil.rmtree(snake_case_     )







def 				lowerCAmelCase_	(     snake_case_     ):
      _A							:     Dict			=						[]
      for partial_result in partial_results:
            records.extend(snake_case_     )
      _A							:     Optional[Any]			=						sorted(snake_case_,key=lambda snake_case_     : x["id"]     )
      _A							:     List[str]			=						[x["""pred"""] for x in records]
      return preds







def 				lowerCAmelCase_	(     snake_case_,snake_case_,snake_case_     ):
      # WAIT FOR lots of .json files
      _A							:     Optional[Any]			=						time.time()
      logger.info("""waiting for all nodes to finish"""     )
      _A							:     List[str]			=						None
      while (time.time() - start_wait) < timeout:
            _A							:     str			=						list(save_dir.glob("""rank_*.json"""     )     )
            if len(snake_case_     ) < num_replicas:
                  continue
            try:
                  # make sure all json files are fully saved
                  _A							:     List[str]			=						lmap(snake_case_,snake_case_     )
                  return json_data
            except JSONDecodeError:
                  continue
      else:
            raise TimeoutError("""Rank 0 gave up on waiting for other processes"""     )
      # Unreachable


if __name__ == "__main__":
  # Usage for MT:
  run_generate()

