
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   UpperCamelCase_      (      __a		,    unittest.TestCase		):







						'''simple docstring'''



						UpperCamelCase      :  List[str]				   =					LEDTokenizer
						UpperCamelCase      :  List[Any]				   =					LEDTokenizerFast
						UpperCamelCase      :  Optional[Any]				   =					True





						def        SCREAMING_SNAKE_CASE(			self							:int     )  ->Optional[Any]:
										super().setUp()
										lowercase         =      [
										    "l",
										    "o",
										    "w",
										    "e",
										    "r",
										    "s",
										    "t",
										    "i",
										    "d",
										    "n",
										    "\u0120",
										    "\u0120l",
										    "\u0120n",
										    "\u0120lo",
										    "\u0120low",
										    "er",
										    "\u0120lowest",
										    "\u0120newer",
										    "\u0120wider",
										    "<unk>",
										]
										lowercase         =      dict(zip(lowerCAmelCase__		,    range(len(lowerCAmelCase__     )     )     )     )
										lowercase         =      ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
										lowercase         =      {"unk_token": "<unk>"}

										lowercase         =      os.path.join(self.tmpdirname		,    VOCAB_FILES_NAMES["vocab_file"]     )
										lowercase         =      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(lowerCAmelCase__     ) + "\n"     )
										with open(self.merges_file		,    "w"		,    encoding="utf-8"     ) as fp:
														fp.write("\n".join(lowerCAmelCase__     )     )





						def        SCREAMING_SNAKE_CASE(			self							:List[Any]		,    **lowerCAmelCase__							:List[Any]     )  ->Dict:
										kwargs.update(self.special_tokens_map     )
										return self.tokenizer_class.from_pretrained(self.tmpdirname		,    **lowerCAmelCase__     )





						def        SCREAMING_SNAKE_CASE(			self							:List[Any]		,    **lowerCAmelCase__							:List[str]     )  ->int:
										kwargs.update(self.special_tokens_map     )
										return self.rust_tokenizer_class.from_pretrained(self.tmpdirname		,    **lowerCAmelCase__     )





						def        SCREAMING_SNAKE_CASE(			self							:Dict		,    lowerCAmelCase__							:str     )  ->List[str]:
										return "lower newer", "lower newer"





						@cached_property
						def        SCREAMING_SNAKE_CASE(			self							:int     )  ->str:
										return LEDTokenizer.from_pretrained("allenai/led-base-16384"     )





						@cached_property
						def        SCREAMING_SNAKE_CASE(			self							:str     )  ->str:
										return LEDTokenizerFast.from_pretrained("allenai/led-base-16384"     )





						@require_torch
						def        SCREAMING_SNAKE_CASE(			self							:Union[str, Any]     )  ->Any:
										lowercase         =      ["A long paragraph for summarization.", "Another paragraph for summarization."]
										lowercase         =      [0, 250, 251, 17818, 13, 39186, 1938, 4, 2]

										for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
														lowercase         =      tokenizer(lowerCAmelCase__		,    max_length=len(lowerCAmelCase__     )		,    padding=lowerCAmelCase__		,    return_tensors="pt"     )
														self.assertIsInstance(lowerCAmelCase__		,    lowerCAmelCase__     )

														self.assertEqual((2, 9)		,    batch.input_ids.shape     )
														self.assertEqual((2, 9)		,    batch.attention_mask.shape     )
														lowercase         =      batch.input_ids.tolist()[0]
														self.assertListEqual(lowerCAmelCase__		,    lowerCAmelCase__     )





						@require_torch
						def        SCREAMING_SNAKE_CASE(			self							:Union[str, Any]     )  ->List[Any]:
										lowercase         =      ["A long paragraph for summarization.", "Another paragraph for summarization."]
										for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
														lowercase         =      tokenizer(lowerCAmelCase__		,    padding=lowerCAmelCase__		,    return_tensors="pt"     )
														self.assertIn("input_ids"		,    lowerCAmelCase__     )
														self.assertIn("attention_mask"		,    lowerCAmelCase__     )
														self.assertNotIn("labels"		,    lowerCAmelCase__     )
														self.assertNotIn("decoder_attention_mask"		,    lowerCAmelCase__     )





						@require_torch
						def        SCREAMING_SNAKE_CASE(			self							:List[str]     )  ->Optional[int]:
										lowercase         =      [
										    "Summary of the text.",
										    "Another summary.",
										]
										for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
														lowercase         =      tokenizer(text_target=lowerCAmelCase__		,    max_length=32		,    padding="max_length"		,    return_tensors="pt"     )
														self.assertEqual(32		,    targets["input_ids"].shape[1]     )





						@require_torch
						def        SCREAMING_SNAKE_CASE(			self							:Any     )  ->Optional[Any]:
										for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
														lowercase         =      tokenizer(
														    ["I am a small frog" * 1024, "I am a small frog"]		,    padding=lowerCAmelCase__		,    truncation=lowerCAmelCase__		,    return_tensors="pt"     )
														self.assertIsInstance(lowerCAmelCase__		,    lowerCAmelCase__     )
														self.assertEqual(batch.input_ids.shape		,    (2, 5122)     )





						@require_torch
						def        SCREAMING_SNAKE_CASE(			self							:List[Any]     )  ->int:
										lowercase         =      ["A long paragraph for summarization."]
										lowercase         =      [
										    "Summary of the text.",
										]
										for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
														lowercase         =      tokenizer(lowerCAmelCase__		,    return_tensors="pt"     )
														lowercase         =      tokenizer(text_target=lowerCAmelCase__		,    return_tensors="pt"     )
														lowercase         =      inputs["input_ids"]
														lowercase         =      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        SCREAMING_SNAKE_CASE(			self							:Union[str, Any]     )  ->str:
										for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
														lowercase         =      ["Summary of the text.", "Another summary."]
														lowercase         =      [[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, -1, -1]]

														lowercase         =      tokenizer(lowerCAmelCase__		,    padding=lowerCAmelCase__     )
														lowercase         =      [[0] * len(lowerCAmelCase__     ) for x in encoded_output["input_ids"]]
														lowercase         =      tokenizer.pad(lowerCAmelCase__     )
														self.assertSequenceEqual(outputs["global_attention_mask"]		,    lowerCAmelCase__     )





						def        SCREAMING_SNAKE_CASE(			self							:int     )  ->int:
										pass





						def        SCREAMING_SNAKE_CASE(			self							:str     )  ->Any:
										for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
														with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''     ):
																		lowercase         =      self.rust_tokenizer_class.from_pretrained(lowerCAmelCase__		,    **lowerCAmelCase__     )
																		lowercase         =      self.tokenizer_class.from_pretrained(lowerCAmelCase__		,    **lowerCAmelCase__     )
																		lowercase         =      "A, <mask> AllenNLP sentence."
																		lowercase         =      tokenizer_r.encode_plus(lowerCAmelCase__		,    add_special_tokens=lowerCAmelCase__		,    return_token_type_ids=lowerCAmelCase__     )
																		lowercase         =      tokenizer_p.encode_plus(lowerCAmelCase__		,    add_special_tokens=lowerCAmelCase__		,    return_token_type_ids=lowerCAmelCase__     )
																		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"]     )		,    )

																		lowercase         =      tokenizer_r.convert_ids_to_tokens(tokens_r["input_ids"]     )
																		lowercase         =      tokenizer_p.convert_ids_to_tokens(tokens_p["input_ids"]     )
																		self.assertSequenceEqual(tokens_p["input_ids"]		,    [0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2]     )
																		self.assertSequenceEqual(tokens_r["input_ids"]		,    [0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2]     )

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







				'''simple docstring'''


				lowercase         =      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=__magic_name__		,    default=1		,    help="Number of TPU cores to use (1 or 8)."						)

				# positional
				parser.add_argument(
				    "training_script"		,    type=__magic_name__		,    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=__magic_name__						)

				return parser.parse_args()

def  __snake_case							(	):







				'''simple docstring'''


				lowercase         =      parse_args()

				# Import training_script as a module.
				lowercase         =      Path(args.training_script						)
				sys.path.append(str(script_fpath.parent.resolve()						)						)
				lowercase         =      script_fpath.stem
				lowercase         =      importlib.import_module(__magic_name__						)

				# Patch sys.argv
				lowercase         =      [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()
