





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    : Tuple    	=  getLogger(__name__)


lowercase    : str    	=  """cuda""" if torch.cuda.is_available() else """cpu"""


def     SCREAMING_SNAKE_CASE			(      lowerCAmelCase    ,       lowerCAmelCase    ,       lowerCAmelCase    ,       lowerCAmelCase = 8    ,       lowerCAmelCase = DEFAULT_DEVICE    ,       lowerCAmelCase=False    ,       lowerCAmelCase="summarization"    ,       lowerCAmelCase=None    ,       **lowerCAmelCase    ,       ):
       _UpperCamelCase              =						Path(lowerCAmelCase					).open('''w'''    ,       encoding='''utf-8'''					)
       _UpperCamelCase              =						str(lowerCAmelCase					)
       _UpperCamelCase              =						AutoModelForSeqaSeqLM.from_pretrained(lowerCAmelCase					).to(lowerCAmelCase					)
       if fpaa:
              _UpperCamelCase              =						model.half()

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

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


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


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

       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'''					)

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

       if args.reference_path is None:
              return {}

       # Compute scores
       _UpperCamelCase              =						calculate_bleu if '''translation''' in args.task else calculate_rouge
       _UpperCamelCase              =						[x.rstrip() for x in open(args.save_path					).readlines()]
       _UpperCamelCase              =						[x.rstrip() for x in open(args.reference_path					).readlines()][: len(lowerCAmelCase					)]
       _UpperCamelCase              =						score_fn(lowerCAmelCase    ,       lowerCAmelCase					)
       scores.update(lowerCAmelCase					)

       if args.dump_args:
              scores.update(lowerCAmelCase					)
       if args.info:
              _UpperCamelCase              =						args.info

       if verbose:
              print(lowerCAmelCase					)

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













import os
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path

from huggingface_hub import HfFolder, delete_repo
from huggingface_hub.file_download import http_get
from requests.exceptions import HTTPError

from transformers import (
    AlbertTokenizer,
    AutoTokenizer,
    BertTokenizer,
    BertTokenizerFast,
    GPTaTokenizerFast,
    is_tokenizers_available,
)
from transformers.testing_utils import TOKEN, USER, is_staging_test, require_tokenizers
from transformers.tokenization_utils import Trie


sys.path.append(str(Path(__file__).parent.parent / """utils"""))

from test_module.custom_tokenization import CustomTokenizer  # noqa E402


if is_tokenizers_available():
       from test_module.custom_tokenization_fast import CustomTokenizerFast

class  __A(						unittest.TestCase ):

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _UpperCamelCase              =						mock.Mock()
            _UpperCamelCase              =						500
            _UpperCamelCase              =						{}
            _UpperCamelCase              =						HTTPError
            _UpperCamelCase              =						{}

            # Download this model to make sure it's in the cache.
            _UpperCamelCase              =						BertTokenizer.from_pretrained('''hf-internal-testing/tiny-random-bert'''      )

            # Under the mock environment we get a 500 error when trying to reach the tokenizer.
            with mock.patch('''requests.Session.request''',       return_value=A      ) as mock_head:
                   _UpperCamelCase              =						BertTokenizer.from_pretrained('''hf-internal-testing/tiny-random-bert'''      )
                   # This check we did call the fake head request
                   mock_head.assert_called()

     @require_tokenizers
     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _UpperCamelCase              =						mock.Mock()
            _UpperCamelCase              =						500
            _UpperCamelCase              =						{}
            _UpperCamelCase              =						HTTPError
            _UpperCamelCase              =						{}

            # Download this model to make sure it's in the cache.
            _UpperCamelCase              =						GPTaTokenizerFast.from_pretrained('''gpt2'''      )

            # Under the mock environment we get a 500 error when trying to reach the tokenizer.
            with mock.patch('''requests.Session.request''',       return_value=A      ) as mock_head:
                   _UpperCamelCase              =						GPTaTokenizerFast.from_pretrained('''gpt2'''      )
                   # This check we did call the fake head request
                   mock_head.assert_called()

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            try:
                   _UpperCamelCase              =						tempfile.mktemp()
                   with open(A,       '''wb'''      ) as f:
                          http_get('''https://huggingface.co/albert-base-v1/resolve/main/spiece.model''',       A      )

                   _UpperCamelCase              =						AlbertTokenizer.from_pretrained(A      )
            finally:
                   os.remove(A      )

            # Supporting this legacy load introduced a weird bug where the tokenizer would load local files if they are in
            # the current folder and have the right name.
            if os.path.isfile('''tokenizer.json'''      ):
                   # We skip the test if the user has a `tokenizer.json` in this folder to avoid deleting it.
                   return
            try:
                   with open('''tokenizer.json''',       '''wb'''      ) as f:
                          http_get('''https://huggingface.co/hf-internal-testing/tiny-random-bert/blob/main/tokenizer.json''',       A      )
                   _UpperCamelCase              =						AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''      )
                   # The tiny random BERT has a vocab size of 1024, tiny gpt2 as a vocab size of 1000
                   self.assertEqual(tokenizer.vocab_size,       1000      )
                   # Tokenizer should depend on the remote checkpoint, not the local tokenizer.json file.

            finally:
                   os.remove('''tokenizer.json'''      )



     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _UpperCamelCase              =						AlbertTokenizer.from_pretrained('''https://huggingface.co/albert-base-v1/resolve/main/spiece.model'''      )






@is_staging_test
class  __A(						unittest.TestCase ):
     __A	  =       ["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]", "bla", "blou"]

     @classmethod
     def 	_UpperCamelCase     (      cls      ):





            """simple docstring"""

            _UpperCamelCase              =						TOKEN
            HfFolder.save_token(A      )

     @classmethod
     def 	_UpperCamelCase     (      cls      ):





            """simple docstring"""

            try:
                   delete_repo(token=cls._token,       repo_id='''test-tokenizer'''      )
            except HTTPError:
                   pass

            try:
                   delete_repo(token=cls._token,       repo_id='''valid_org/test-tokenizer-org'''      )
            except HTTPError:
                   pass

            try:
                   delete_repo(token=cls._token,       repo_id='''test-dynamic-tokenizer'''      )
            except HTTPError:
                   pass

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            with tempfile.TemporaryDirectory() as tmp_dir:
                   _UpperCamelCase              =						os.path.join(A,       '''vocab.txt'''      )
                   with open(A,       '''w''',       encoding='''utf-8'''      ) as vocab_writer:
                          vocab_writer.write(''''''.join([x + '''\n''' for x in self.vocab_tokens]      )      )
                   _UpperCamelCase              =						BertTokenizer(A      )

            tokenizer.push_to_hub('''test-tokenizer''',       use_auth_token=self._token      )
            _UpperCamelCase              =						BertTokenizer.from_pretrained(F'''{USER}/test-tokenizer'''      )
            self.assertDictEqual(new_tokenizer.vocab,       tokenizer.vocab      )

            # Reset repo
            delete_repo(token=self._token,       repo_id='''test-tokenizer'''      )

            # Push to hub via save_pretrained
            with tempfile.TemporaryDirectory() as tmp_dir:
                   tokenizer.save_pretrained(A,       repo_id='''test-tokenizer''',       push_to_hub=A,       use_auth_token=self._token      )

            _UpperCamelCase              =						BertTokenizer.from_pretrained(F'''{USER}/test-tokenizer'''      )
            self.assertDictEqual(new_tokenizer.vocab,       tokenizer.vocab      )

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            with tempfile.TemporaryDirectory() as tmp_dir:
                   _UpperCamelCase              =						os.path.join(A,       '''vocab.txt'''      )
                   with open(A,       '''w''',       encoding='''utf-8'''      ) as vocab_writer:
                          vocab_writer.write(''''''.join([x + '''\n''' for x in self.vocab_tokens]      )      )
                   _UpperCamelCase              =						BertTokenizer(A      )

            tokenizer.push_to_hub('''valid_org/test-tokenizer-org''',       use_auth_token=self._token      )
            _UpperCamelCase              =						BertTokenizer.from_pretrained('''valid_org/test-tokenizer-org'''      )
            self.assertDictEqual(new_tokenizer.vocab,       tokenizer.vocab      )

            # Reset repo
            delete_repo(token=self._token,       repo_id='''valid_org/test-tokenizer-org'''      )

            # Push to hub via save_pretrained
            with tempfile.TemporaryDirectory() as tmp_dir:
                   tokenizer.save_pretrained(
                       A,       repo_id='''valid_org/test-tokenizer-org''',       push_to_hub=A,       use_auth_token=self._token      )

            _UpperCamelCase              =						BertTokenizer.from_pretrained('''valid_org/test-tokenizer-org'''      )
            self.assertDictEqual(new_tokenizer.vocab,       tokenizer.vocab      )



     @require_tokenizers
     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            CustomTokenizer.register_for_auto_class()
            with tempfile.TemporaryDirectory() as tmp_dir:
                   _UpperCamelCase              =						os.path.join(A,       '''vocab.txt'''      )
                   with open(A,       '''w''',       encoding='''utf-8'''      ) as vocab_writer:
                          vocab_writer.write(''''''.join([x + '''\n''' for x in self.vocab_tokens]      )      )
                   _UpperCamelCase              =						CustomTokenizer(A      )

            # No fast custom tokenizer
            tokenizer.push_to_hub('''test-dynamic-tokenizer''',       use_auth_token=self._token      )

            _UpperCamelCase              =						AutoTokenizer.from_pretrained(F'''{USER}/test-dynamic-tokenizer''',       trust_remote_code=A      )
            # Can't make an isinstance check because the new_model.config is from the CustomTokenizer class of a dynamic module
            self.assertEqual(tokenizer.__class__.__name__,       '''CustomTokenizer'''      )

            # Fast and slow custom tokenizer
            CustomTokenizerFast.register_for_auto_class()
            with tempfile.TemporaryDirectory() as tmp_dir:
                   _UpperCamelCase              =						os.path.join(A,       '''vocab.txt'''      )
                   with open(A,       '''w''',       encoding='''utf-8'''      ) as vocab_writer:
                          vocab_writer.write(''''''.join([x + '''\n''' for x in self.vocab_tokens]      )      )

                   _UpperCamelCase              =						BertTokenizerFast.from_pretrained(A      )
                   bert_tokenizer.save_pretrained(A      )
                   _UpperCamelCase              =						CustomTokenizerFast.from_pretrained(A      )

            tokenizer.push_to_hub('''test-dynamic-tokenizer''',       use_auth_token=self._token      )

            _UpperCamelCase              =						AutoTokenizer.from_pretrained(F'''{USER}/test-dynamic-tokenizer''',       trust_remote_code=A      )
            # Can't make an isinstance check because the new_model.config is from the FakeConfig class of a dynamic module
            self.assertEqual(tokenizer.__class__.__name__,       '''CustomTokenizerFast'''      )
            _UpperCamelCase              =						AutoTokenizer.from_pretrained(
                F'''{USER}/test-dynamic-tokenizer''',       use_fast=A,       trust_remote_code=A      )
            # Can't make an isinstance check because the new_model.config is from the FakeConfig class of a dynamic module
            self.assertEqual(tokenizer.__class__.__name__,       '''CustomTokenizer'''      )






class  __A(						unittest.TestCase ):

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _UpperCamelCase              =						Trie()
            trie.add('''Hello 友達'''      )
            self.assertEqual(trie.data,       {'''H''': {'''e''': {'''l''': {'''l''': {'''o''': {''' ''': {'''友''': {'''達''': {'''''': 1}}}}}}}}}      )
            trie.add('''Hello'''      )
            trie.data
            self.assertEqual(trie.data,       {'''H''': {'''e''': {'''l''': {'''l''': {'''o''': {'''''': 1, ''' ''': {'''友''': {'''達''': {'''''': 1}}}}}}}}}      )

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _UpperCamelCase              =						Trie()
            self.assertEqual(trie.split('''[CLS] This is a extra_id_100'''      ),       ['''[CLS] This is a extra_id_100''']      )
            trie.add('''[CLS]'''      )
            trie.add('''extra_id_1'''      )
            trie.add('''extra_id_100'''      )
            self.assertEqual(trie.split('''[CLS] This is a extra_id_100'''      ),       ['''[CLS]''', ''' This is a ''', '''extra_id_100''']      )

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _UpperCamelCase              =						Trie()
            trie.add('''A'''      )
            self.assertEqual(trie.split('''ABC'''      ),       ['''A''', '''BC''']      )
            self.assertEqual(trie.split('''BCA'''      ),       ['''BC''', '''A''']      )

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _UpperCamelCase              =						Trie()
            trie.add('''TOKEN]'''      )
            trie.add('''[SPECIAL_TOKEN]'''      )
            self.assertEqual(trie.split('''This is something [SPECIAL_TOKEN]'''      ),       ['''This is something ''', '''[SPECIAL_TOKEN]''']      )

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _UpperCamelCase              =						Trie()
            trie.add('''A'''      )
            trie.add('''P'''      )
            trie.add('''[SPECIAL_TOKEN]'''      )
            self.assertEqual(trie.split('''This is something [SPECIAL_TOKEN]'''      ),       ['''This is something ''', '''[SPECIAL_TOKEN]''']      )

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _UpperCamelCase              =						Trie()
            trie.add('''AB'''      )
            trie.add('''B'''      )
            trie.add('''C'''      )
            self.assertEqual(trie.split('''ABC'''      ),       ['''AB''', '''C''']      )

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _UpperCamelCase              =						Trie()
            trie.add('''ABC'''      )
            trie.add('''B'''      )
            trie.add('''CD'''      )
            self.assertEqual(trie.split('''ABCD'''      ),       ['''ABC''', '''D''']      )



     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _UpperCamelCase              =						Trie()
            _UpperCamelCase              =						trie.cut_text('''ABC''',       [0, 0, 2, 1, 2, 3]      )
            self.assertEqual(A,       ['''AB''', '''C''']      )






