
"""simple docstring"""


import unittest

from transformers import PegasusTokenizer, PegasusTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


SCREAMING_SNAKE_CASE      =     get_tests_dir("""fixtures/test_sentencepiece_no_bos.model""")



@require_sentencepiece
@require_tokenizers
class   __a  (      _lowerCAmelCase    ,     unittest.TestCase	):
       UpperCamelCase_		:					int   =			PegasusTokenizer
       UpperCamelCase_		:					List[Any]   =			PegasusTokenizerFast
       UpperCamelCase_		:					Any   =			True
       UpperCamelCase_		:					Optional[Any]   =			True






       def 	_SCREAMING_SNAKE_CASE       (  self :							Optional[int]			)->  List[str]:







              """simple docstring"""
              super().setUp()

              # We have a SentencePiece fixture for testing
              UpperCamelCase					 =		PegasusTokenizer(UpperCAmelCase_			)
              tokenizer.save_pretrained(self.tmpdirname			)






       @cached_property
       def 	_SCREAMING_SNAKE_CASE       (  self :							str			)->  Any:







              """simple docstring"""
              return PegasusTokenizer.from_pretrained("google/pegasus-large"			)






       def 	_SCREAMING_SNAKE_CASE       (  self :							Union[str, Any]       ,      **UpperCAmelCase_ :							Union[str, Any]			)->  PegasusTokenizer:







              """simple docstring"""
              return PegasusTokenizer.from_pretrained(self.tmpdirname       ,      **UpperCAmelCase_			)






       def 	_SCREAMING_SNAKE_CASE       (  self :							Dict       ,      UpperCAmelCase_ :							List[Any]			)->  Tuple:







              """simple docstring"""
              return ("This is a test", "This is a test")






       def 	_SCREAMING_SNAKE_CASE       (  self :							Union[str, Any]			)->  int:







              """simple docstring"""
              UpperCamelCase					 =		"</s>"
              UpperCamelCase					 =		1

              self.assertEqual(self.get_tokenizer()._convert_token_to_id(UpperCAmelCase_			)       ,      UpperCAmelCase_			)
              self.assertEqual(self.get_tokenizer()._convert_id_to_token(UpperCAmelCase_			)       ,      UpperCAmelCase_			)






       def 	_SCREAMING_SNAKE_CASE       (  self :							Optional[int]			)->  Optional[int]:







              """simple docstring"""
              UpperCamelCase					 =		list(self.get_tokenizer().get_vocab().keys()			)

              self.assertEqual(vocab_keys[0]       ,      "<pad>"			)
              self.assertEqual(vocab_keys[1]       ,      "</s>"			)
              self.assertEqual(vocab_keys[-1]       ,      "v"			)
              self.assertEqual(len(UpperCAmelCase_			)       ,      1_103			)






       def 	_SCREAMING_SNAKE_CASE       (  self :							Optional[int]			)->  List[str]:







              """simple docstring"""
              self.assertEqual(self.get_tokenizer().vocab_size       ,      1_103			)






       def 	_SCREAMING_SNAKE_CASE       (  self :							Tuple			)->  Optional[int]:







              """simple docstring"""
              UpperCamelCase					 =		self.rust_tokenizer_class.from_pretrained(self.tmpdirname			)
              UpperCamelCase					 =		self.tokenizer_class.from_pretrained(self.tmpdirname			)
              UpperCamelCase					 =		(
                  "Let's see which <unk> is the better <unk_token_11> one <mask_1> It seems like this <mask_2> was important"
                  " </s> <pad> <pad> <pad>"
              )
              UpperCamelCase					 =		rust_tokenizer([raw_input_str]       ,      return_tensors=UpperCAmelCase_       ,      add_special_tokens=UpperCAmelCase_			).input_ids[0]
              UpperCamelCase					 =		py_tokenizer([raw_input_str]       ,      return_tensors=UpperCAmelCase_       ,      add_special_tokens=UpperCAmelCase_			).input_ids[0]
              self.assertListEqual(UpperCAmelCase_       ,      UpperCAmelCase_			)






       def 	_SCREAMING_SNAKE_CASE       (  self :							List[Any]			)->  Optional[Any]:







              """simple docstring"""
              UpperCamelCase					 =		self._large_tokenizer
              # <mask_1> masks whole sentence while <mask_2> masks single word
              UpperCamelCase					 =		"<mask_1> To ensure a <mask_2> flow of bank resolutions."
              UpperCamelCase					 =		[2, 413, 615, 114, 3, 1_971, 113, 1_679, 10_710, 107, 1]
              UpperCamelCase					 =		tokenizer([raw_input_str]       ,      return_tensors=UpperCAmelCase_			).input_ids[0]
              self.assertListEqual(UpperCAmelCase_       ,      UpperCAmelCase_			)






       def 	_SCREAMING_SNAKE_CASE       (  self :							Union[str, Any]			)->  Optional[Any]:







              """simple docstring"""
              UpperCamelCase					 =		self._large_tokenizer
              # The tracebacks for the following asserts are **better** without messages or self.assertEqual
              assert tokenizer.vocab_size == 96_103
              assert tokenizer.pad_token_id == 0
              assert tokenizer.eos_token_id == 1
              assert tokenizer.offset == 103
              assert tokenizer.unk_token_id == tokenizer.offset + 2 == 105
              assert tokenizer.unk_token == "<unk>"
              assert tokenizer.model_max_length == 1_024
              UpperCamelCase					 =		"To ensure a smooth flow of bank resolutions."
              UpperCamelCase					 =		[413, 615, 114, 2_291, 1_971, 113, 1_679, 10_710, 107, 1]
              UpperCamelCase					 =		tokenizer([raw_input_str]       ,      return_tensors=UpperCAmelCase_			).input_ids[0]
              self.assertListEqual(UpperCAmelCase_       ,      UpperCAmelCase_			)
              assert tokenizer.convert_ids_to_tokens([0, 1, 2, 3]			) == ["<pad>", "</s>", "<mask_1>", "<mask_2>"]






       @require_torch
       def 	_SCREAMING_SNAKE_CASE       (  self :							Optional[int]			)->  Tuple:







              """simple docstring"""
              UpperCamelCase					 =		["This is going to be way too long." * 150, "short example"]
              UpperCamelCase					 =		["not super long but more than 5 tokens", "tiny"]
              UpperCamelCase					 =		self._large_tokenizer(UpperCAmelCase_       ,      padding=UpperCAmelCase_       ,      truncation=UpperCAmelCase_       ,      return_tensors="pt"			)
              UpperCamelCase					 =		self._large_tokenizer(
                  text_target=UpperCAmelCase_       ,      max_length=5       ,      padding=UpperCAmelCase_       ,      truncation=UpperCAmelCase_       ,      return_tensors="pt"			)

              assert batch.input_ids.shape == (2, 1_024)
              assert batch.attention_mask.shape == (2, 1_024)
              assert targets["input_ids"].shape == (2, 5)
              assert len(UpperCAmelCase_			) == 2  # input_ids, attention_mask.






       @slow
       def 	_SCREAMING_SNAKE_CASE       (  self :							Optional[int]			)->  List[str]:







              """simple docstring"""
              # fmt: off
              UpperCamelCase					 =		{"input_ids": [[38_979, 143, 18_485, 606, 130, 26_669, 87_686, 121, 54_189, 1_129, 111, 26_669, 87_686, 121, 9_114, 14_787, 121, 13_249, 158, 592, 956, 121, 14_621, 31_576, 143, 62_613, 108, 9_688, 930, 43_430, 11_562, 62_613, 304, 108, 11_443, 897, 108, 9_314, 17_415, 63_399, 108, 11_443, 7_614, 18_316, 118, 4_284, 7_148, 12_430, 143, 1_400, 25_703, 158, 111, 4_284, 7_148, 11_772, 143, 21_297, 1_064, 158, 122, 204, 3_506, 1_754, 1_133, 14_787, 1_581, 115, 33_224, 4_482, 111, 1_355, 110, 29_173, 317, 50_833, 108, 20_147, 94_665, 111, 77_198, 107, 1], [110, 62_613, 117, 638, 112, 1_133, 121, 20_098, 1_355, 79_050, 13_872, 135, 1_596, 53_541, 1_352, 141, 13_039, 5_542, 124, 302, 518, 111, 268, 2_956, 115, 149, 4_427, 107, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [139, 1_235, 2_799, 18_289, 17_780, 204, 109, 9_474, 1_296, 107, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
              # fmt: on

              self.tokenizer_integration_test_util(
                  expected_encoding=UpperCAmelCase_       ,      model_name="google/bigbird-pegasus-large-arxiv"       ,      revision="ba85d0851d708441f91440d509690f1ab6353415"       ,      )





@require_sentencepiece
@require_tokenizers
class   __a  (      _lowerCAmelCase    ,     unittest.TestCase	):
       UpperCamelCase_		:					int   =			PegasusTokenizer
       UpperCamelCase_		:					Tuple   =			PegasusTokenizerFast
       UpperCamelCase_		:					Any   =			True
       UpperCamelCase_		:					Optional[Any]   =			True






       def 	_SCREAMING_SNAKE_CASE       (  self :							Dict			)->  List[str]:







              """simple docstring"""
              super().setUp()

              # We have a SentencePiece fixture for testing
              UpperCamelCase					 =		PegasusTokenizer(UpperCAmelCase_       ,      offset=0       ,      mask_token_sent=UpperCAmelCase_       ,      mask_token="[MASK]"			)
              tokenizer.save_pretrained(self.tmpdirname			)






       @cached_property
       def 	_SCREAMING_SNAKE_CASE       (  self :							int			)->  List[str]:







              """simple docstring"""
              return PegasusTokenizer.from_pretrained("google/bigbird-pegasus-large-arxiv"			)






       def 	_SCREAMING_SNAKE_CASE       (  self :							Any       ,      **UpperCAmelCase_ :							Union[str, Any]			)->  PegasusTokenizer:







              """simple docstring"""
              return PegasusTokenizer.from_pretrained(self.tmpdirname       ,      **UpperCAmelCase_			)






       def 	_SCREAMING_SNAKE_CASE       (  self :							Dict       ,      UpperCAmelCase_ :							Tuple			)->  Optional[Any]:







              """simple docstring"""
              return ("This is a test", "This is a test")






       def 	_SCREAMING_SNAKE_CASE       (  self :							Tuple			)->  str:







              """simple docstring"""
              UpperCamelCase					 =		self.rust_tokenizer_class.from_pretrained(self.tmpdirname			)
              UpperCamelCase					 =		self.tokenizer_class.from_pretrained(self.tmpdirname			)
              UpperCamelCase					 =		(
                  "Let's see which <unk> is the better <unk_token> one [MASK] It seems like this [MASK] was important </s>"
                  " <pad> <pad> <pad>"
              )
              UpperCamelCase					 =		rust_tokenizer([raw_input_str]       ,      return_tensors=UpperCAmelCase_       ,      add_special_tokens=UpperCAmelCase_			).input_ids[0]
              UpperCamelCase					 =		py_tokenizer([raw_input_str]       ,      return_tensors=UpperCAmelCase_       ,      add_special_tokens=UpperCAmelCase_			).input_ids[0]
              self.assertListEqual(UpperCAmelCase_       ,      UpperCAmelCase_			)






       @require_torch
       def 	_SCREAMING_SNAKE_CASE       (  self :							Optional[int]			)->  int:







              """simple docstring"""
              UpperCamelCase					 =		["This is going to be way too long." * 1_000, "short example"]
              UpperCamelCase					 =		["not super long but more than 5 tokens", "tiny"]
              UpperCamelCase					 =		self._large_tokenizer(UpperCAmelCase_       ,      padding=UpperCAmelCase_       ,      truncation=UpperCAmelCase_       ,      return_tensors="pt"			)
              UpperCamelCase					 =		self._large_tokenizer(
                  text_target=UpperCAmelCase_       ,      max_length=5       ,      padding=UpperCAmelCase_       ,      truncation=UpperCAmelCase_       ,      return_tensors="pt"			)

              assert batch.input_ids.shape == (2, 4_096)
              assert batch.attention_mask.shape == (2, 4_096)
              assert targets["input_ids"].shape == (2, 5)
              assert len(UpperCAmelCase_			) == 2  # input_ids, attention_mask.






       def 	_SCREAMING_SNAKE_CASE       (  self :							List[str]			)->  str:







              """simple docstring"""
              UpperCamelCase					 =		(
                  "This is an example string that is used to test the original TF implementation against the HF"
                  " implementation"
              )

              UpperCamelCase					 =		self._large_tokenizer(UpperCAmelCase_			).input_ids

              self.assertListEqual(
                  UpperCAmelCase_       ,      [182, 117, 142, 587, 4_211, 120, 117, 263, 112, 804, 109, 856, 25_016, 3_137, 464, 109, 26_955, 3_137, 1]       ,      )







"""simple docstring"""


import inspect
import os
import unittest

import torch

import accelerate
from accelerate import debug_launcher
from accelerate.test_utils import (
    execute_subprocess_async,
    require_cpu,
    require_huggingface_suite,
    require_multi_gpu,
    require_single_gpu,
)
from accelerate.utils import patch_environment



@require_huggingface_suite
class   __a  (      unittest.TestCase	):






       def 	_SCREAMING_SNAKE_CASE       (  self :							Optional[int]			)->  Dict:







              """simple docstring"""
              UpperCamelCase					 =		inspect.getfile(accelerate.test_utils			)
              UpperCamelCase					 =		os.path.sep.join(
                  mod_file.split(os.path.sep			)[:-1] + ["scripts", "external_deps", "test_metrics.py"]			)

              from accelerate.test_utils.scripts.external_deps import test_metrics  # noqa: F401

              UpperCamelCase					 =		test_metrics






       @require_cpu
       def 	_SCREAMING_SNAKE_CASE       (  self :							Dict			)->  Union[str, Any]:







              """simple docstring"""
              debug_launcher(self.test_metrics.main       ,      num_processes=1			)






       @require_cpu
       def 	_SCREAMING_SNAKE_CASE       (  self :							Tuple			)->  List[str]:







              """simple docstring"""
              debug_launcher(self.test_metrics.main			)






       @require_single_gpu
       def 	_SCREAMING_SNAKE_CASE       (  self :							List[Any]			)->  str:







              """simple docstring"""
              self.test_metrics.main()






       @require_multi_gpu
       def 	_SCREAMING_SNAKE_CASE       (  self :							Optional[Any]			)->  Optional[Any]:







              """simple docstring"""
              print(f"Found {torch.cuda.device_count()} devices."			)
              UpperCamelCase					 =		["torchrun", f"--nproc_per_node={torch.cuda.device_count()}", self.test_file_path]
              with patch_environment(omp_num_threads=1			):
                     execute_subprocess_async(UpperCAmelCase_       ,      env=os.environ.copy()			)





