





import json
import os
import unittest

from transformers import BatchEncoding, LEDTokenizer, LEDTokenizerFast
from transformers.models.led.tokenization_led import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin



@require_tokenizers
class       A__     ( __snake_case ,     unittest.TestCase    ):
      _UpperCAmelCase			:str							=     LEDTokenizer
      _UpperCAmelCase			:Union[str, Any]							=     LEDTokenizerFast
      _UpperCAmelCase			:Tuple							=     True





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       super().setUp()
       UpperCamelCase			:      Union[str, Any] 			=     [
           "l",
           "o",
           "w",
           "e",
           "r",
           "s",
           "t",
           "i",
           "d",
           "n",
           "\u0120",
           "\u0120l",
           "\u0120n",
           "\u0120lo",
           "\u0120low",
           "er",
           "\u0120lowest",
           "\u0120newer",
           "\u0120wider",
           "<unk>",
       ]
       UpperCamelCase			:      Union[str, Any] 			=     dict(zip(A_ ,							range(len(A_     )     )     )     )
       UpperCamelCase			:      Dict 			=     ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
       UpperCamelCase			:      Any 			=     {"unk_token": "<unk>"}

       UpperCamelCase			:      int 			=     os.path.join(self.tmpdirname ,							VOCAB_FILES_NAMES["vocab_file"]     )
       UpperCamelCase			:      Dict 			=     os.path.join(self.tmpdirname ,							VOCAB_FILES_NAMES["merges_file"]     )
       with open(self.vocab_file ,							"w" ,							encoding="utf-8"     ) as fp:
        fp.write(json.dumps(A_     ) + "\n"     )
       with open(self.merges_file ,							"w" ,							encoding="utf-8"     ) as fp:
        fp.write("\n".join(A_     )     )





      def  __UpperCamelCase(				self ,							**A_     ):
       '''simple docstring'''




       kwargs.update(self.special_tokens_map     )
       return self.tokenizer_class.from_pretrained(self.tmpdirname ,							**A_     )





      def  __UpperCamelCase(				self ,							**A_     ):
       '''simple docstring'''




       kwargs.update(self.special_tokens_map     )
       return self.rust_tokenizer_class.from_pretrained(self.tmpdirname ,							**A_     )





      def  __UpperCamelCase(				self ,							A_     ):
       '''simple docstring'''




       return "lower newer", "lower newer"





      @cached_property
      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       return LEDTokenizer.from_pretrained("allenai/led-base-16384"     )





      @cached_property
      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       return LEDTokenizerFast.from_pretrained("allenai/led-base-16384"     )





      @require_torch
      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       UpperCamelCase			:      List[Any] 			=     ["A long paragraph for summarization.", "Another paragraph for summarization."]
       UpperCamelCase			:      Optional[Any] 			=     [0, 250, 251, 1_7818, 13, 3_9186, 1938, 4, 2]

       for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
        UpperCamelCase			:      Optional[Any] 			=     tokenizer(A_ ,							max_length=len(A_     ) ,							padding=A_ ,							return_tensors="pt"     )
        self.assertIsInstance(A_ ,							A_     )

        self.assertEqual((2, 9) ,							batch.input_ids.shape     )
        self.assertEqual((2, 9) ,							batch.attention_mask.shape     )
        UpperCamelCase			:      List[Any] 			=     batch.input_ids.tolist()[0]
        self.assertListEqual(A_ ,							A_     )





      @require_torch
      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       UpperCamelCase			:      List[Any] 			=     ["A long paragraph for summarization.", "Another paragraph for summarization."]
       for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
        UpperCamelCase			:      Tuple 			=     tokenizer(A_ ,							padding=A_ ,							return_tensors="pt"     )
        self.assertIn("input_ids" ,							A_     )
        self.assertIn("attention_mask" ,							A_     )
        self.assertNotIn("labels" ,							A_     )
        self.assertNotIn("decoder_attention_mask" ,							A_     )





      @require_torch
      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       UpperCamelCase			:      List[Any] 			=     [
           "Summary of the text.",
           "Another summary.",
       ]
       for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
        UpperCamelCase			:      Union[str, Any] 			=     tokenizer(text_target=A_ ,							max_length=32 ,							padding="max_length" ,							return_tensors="pt"     )
        self.assertEqual(32 ,							targets["input_ids"].shape[1]     )





      @require_torch
      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
        UpperCamelCase			:      List[str] 			=     tokenizer(
            ["I am a small frog" * 1024, "I am a small frog"] ,							padding=A_ ,							truncation=A_ ,							return_tensors="pt"     )
        self.assertIsInstance(A_ ,							A_     )
        self.assertEqual(batch.input_ids.shape ,							(2, 5122)     )





      @require_torch
      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       UpperCamelCase			:      Tuple 			=     ["A long paragraph for summarization."]
       UpperCamelCase			:      List[str] 			=     [
           "Summary of the text.",
       ]
       for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
        UpperCamelCase			:      Optional[int] 			=     tokenizer(A_ ,							return_tensors="pt"     )
        UpperCamelCase			:      Tuple 			=     tokenizer(text_target=A_ ,							return_tensors="pt"     )
        UpperCamelCase			:      str 			=     inputs["input_ids"]
        UpperCamelCase			:      Any 			=     targets["input_ids"]
        self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item()     )
        self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item()     )
        self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item()     )
        self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item()     )





      @require_torch
      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
        UpperCamelCase			:      Any 			=     ["Summary of the text.", "Another summary."]
        UpperCamelCase			:      int 			=     [[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, -1, -1]]

        UpperCamelCase			:      Dict 			=     tokenizer(A_ ,							padding=A_     )
        UpperCamelCase			:      Union[str, Any] 			=     [[0] * len(A_     ) for x in encoded_output["input_ids"]]
        UpperCamelCase			:      Tuple 			=     tokenizer.pad(A_     )
        self.assertSequenceEqual(outputs["global_attention_mask"] ,							A_     )





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       pass





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
        with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""     ):
         UpperCamelCase			:      Dict 			=     self.rust_tokenizer_class.from_pretrained(A_ ,							**A_     )
         UpperCamelCase			:      Tuple 			=     self.tokenizer_class.from_pretrained(A_ ,							**A_     )
         UpperCamelCase			:      Optional[Any] 			=     "A, <mask> AllenNLP sentence."
         UpperCamelCase			:      Union[str, Any] 			=     tokenizer_r.encode_plus(A_ ,							add_special_tokens=A_ ,							return_token_type_ids=A_     )
         UpperCamelCase			:      Tuple 			=     tokenizer_p.encode_plus(A_ ,							add_special_tokens=A_ ,							return_token_type_ids=A_     )
         self.assertEqual(sum(tokens_r["token_type_ids"]     ) ,							sum(tokens_p["token_type_ids"]     )     )
         self.assertEqual(
             sum(tokens_r["attention_mask"]     ) / len(tokens_r["attention_mask"]     ) ,							sum(tokens_p["attention_mask"]     ) / len(tokens_p["attention_mask"]     ) ,							)

         UpperCamelCase			:      str 			=     tokenizer_r.convert_ids_to_tokens(tokens_r["input_ids"]     )
         UpperCamelCase			:      Optional[Any] 			=     tokenizer_p.convert_ids_to_tokens(tokens_p["input_ids"]     )
         self.assertSequenceEqual(tokens_p["input_ids"] ,							[0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2]     )
         self.assertSequenceEqual(tokens_r["input_ids"] ,							[0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2]     )

         self.assertSequenceEqual(
             A_ ,							["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"]     )
         self.assertSequenceEqual(
             A_ ,							["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"]     )







import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path

import torch_xla.distributed.xla_multiprocessing as xmp



def 	A_       (  )		->				Dict:
 UpperCamelCase			:      Tuple 			=     ArgumentParser(
     description=(
         "PyTorch TPU distributed training launch "
         "helper utility that will spawn up "
         "multiple distributed processes"
     )  )

 # Optional arguments for the launch helper
 parser.add_argument("--num_cores"							,     type=_lowerCAmelCase							,     default=1							,     help="Number of TPU cores to use (1 or 8)."  )

 # positional
 parser.add_argument(
     "training_script"							,     type=_lowerCAmelCase							,     help=(
         "The full path to the single TPU training "
         "program/script to be launched in parallel, "
         "followed by all the arguments for the "
         "training script"
     )							,     )

 # rest from the training program
 parser.add_argument("training_script_args"							,     nargs=_lowerCAmelCase  )

 return parser.parse_args()



def 	A_       (  )		->				Optional[int]:
 UpperCamelCase			:      Tuple 			=     parse_args()

 # Import training_script as a module.
 UpperCamelCase			:      Union[str, Any] 			=     Path(args.training_script  )
 sys.path.append(str(script_fpath.parent.resolve()  )  )
 UpperCamelCase			:      List[Any] 			=     script_fpath.stem
 UpperCamelCase			:      Optional[Any] 			=     importlib.import_module(_lowerCAmelCase  )

 # Patch sys.argv
 UpperCamelCase			:      List[Any] 			=     [args.training_script] + args.training_script_args + ["--tpu_num_cores", str(args.num_cores  )]
 xmp.spawn(mod._mp_fn							,     args=()							,     nprocs=args.num_cores  )


if __name__ == "__main__":
    main()
