
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


lowercase  :					List[str]													=       get_tests_dir('fixtures/test_sentencepiece_no_bos.model')




@require_sentencepiece
@require_tokenizers
class  lowerCamelCase__							(				__lowercase     ,						unittest.TestCase):




	'''simple docstring'''




	_A								=     PegasusTokenizer
	_A								=     PegasusTokenizerFast
	_A								=     True
	_A								=     True







	def    _lowerCamelCase				(					self		:str						)		-> Any:
								super().setUp()

								# We have a SentencePiece fixture for testing
								__UpperCamelCase	:   Optional[Any]			   =					PegasusTokenizer(a						)
								tokenizer.save_pretrained(self.tmpdirname						)







	@cached_property
	def    _lowerCamelCase				(					self		:List[str]						)		-> Optional[Any]:
								return PegasusTokenizer.from_pretrained("google/pegasus-large"						)







	def    _lowerCamelCase				(					self		:Union[str, Any]     ,   **a		:List[str]						)		-> PegasusTokenizer:
								return PegasusTokenizer.from_pretrained(self.tmpdirname     ,   **a						)







	def    _lowerCamelCase				(					self		:Dict     ,   a		:List[Any]						)		-> Union[str, Any]:
								return ("This is a test", "This is a test")







	def    _lowerCamelCase				(					self		:Tuple						)		-> Tuple:
								__UpperCamelCase	:   Optional[Any]			   =					"</s>"
								__UpperCamelCase	:   Union[str, Any]			   =					1

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







	def    _lowerCamelCase				(					self		:Any						)		-> Optional[int]:
								__UpperCamelCase	:   List[str]			   =					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(a						)     ,   1_1_0_3						)







	def    _lowerCamelCase				(					self		:Union[str, Any]						)		-> Tuple:
								self.assertEqual(self.get_tokenizer().vocab_size     ,   1_1_0_3						)







	def    _lowerCamelCase				(					self		:Tuple						)		-> Union[str, Any]:
								__UpperCamelCase	:   int			   =					self.rust_tokenizer_class.from_pretrained(self.tmpdirname						)
								__UpperCamelCase	:   Tuple			   =					self.tokenizer_class.from_pretrained(self.tmpdirname						)
								__UpperCamelCase	:   str			   =					(
								    "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	:   Union[str, Any]			   =					rust_tokenizer([raw_input_str]     ,   return_tensors=a     ,   add_special_tokens=a						).input_ids[0]
								__UpperCamelCase	:   Tuple			   =					py_tokenizer([raw_input_str]     ,   return_tensors=a     ,   add_special_tokens=a						).input_ids[0]
								self.assertListEqual(a     ,   a						)







	def    _lowerCamelCase				(					self		:Optional[Any]						)		-> Any:
								__UpperCamelCase	:   Union[str, Any]			   =					self._large_tokenizer
								# <mask_1> masks whole sentence while <mask_2> masks single word
								__UpperCamelCase	:   Optional[Any]			   =					"<mask_1> To ensure a <mask_2> flow of bank resolutions."
								__UpperCamelCase	:   Union[str, Any]			   =					[2, 4_1_3, 6_1_5, 1_1_4, 3, 1_9_7_1, 1_1_3, 1_6_7_9, 1_0_7_1_0, 1_0_7, 1]
								__UpperCamelCase	:   int			   =					tokenizer([raw_input_str]     ,   return_tensors=a						).input_ids[0]
								self.assertListEqual(a     ,   a						)







	def    _lowerCamelCase				(					self		:str						)		-> Optional[int]:
								__UpperCamelCase	:   str			   =					self._large_tokenizer
								# The tracebacks for the following asserts are **better** without messages or self.assertEqual
								assert tokenizer.vocab_size == 9_6_1_0_3
								assert tokenizer.pad_token_id == 0
								assert tokenizer.eos_token_id == 1
								assert tokenizer.offset == 1_0_3
								assert tokenizer.unk_token_id == tokenizer.offset + 2 == 1_0_5
								assert tokenizer.unk_token == "<unk>"
								assert tokenizer.model_max_length == 1_0_2_4
								__UpperCamelCase	:   Tuple			   =					"To ensure a smooth flow of bank resolutions."
								__UpperCamelCase	:   int			   =					[4_1_3, 6_1_5, 1_1_4, 2_2_9_1, 1_9_7_1, 1_1_3, 1_6_7_9, 1_0_7_1_0, 1_0_7, 1]
								__UpperCamelCase	:   str			   =					tokenizer([raw_input_str]     ,   return_tensors=a						).input_ids[0]
								self.assertListEqual(a     ,   a						)
								assert tokenizer.convert_ids_to_tokens([0, 1, 2, 3]						) == ["<pad>", "</s>", "<mask_1>", "<mask_2>"]







	@require_torch
	def    _lowerCamelCase				(					self		:Any						)		-> int:
								__UpperCamelCase	:   Optional[int]			   =					["This is going to be way too long." * 1_5_0, "short example"]
								__UpperCamelCase	:   Optional[Any]			   =					["not super long but more than 5 tokens", "tiny"]
								__UpperCamelCase	:   str			   =					self._large_tokenizer(a     ,   padding=a     ,   truncation=a     ,   return_tensors="pt"						)
								__UpperCamelCase	:   Tuple			   =					self._large_tokenizer(
								    text_target=a     ,   max_length=5     ,   padding=a     ,   truncation=a     ,   return_tensors="pt"						)

								assert batch.input_ids.shape == (2, 1_0_2_4)
								assert batch.attention_mask.shape == (2, 1_0_2_4)
								assert targets["input_ids"].shape == (2, 5)
								assert len(a						) == 2  # input_ids, attention_mask.







	@slow
	def    _lowerCamelCase				(					self		:str						)		-> Optional[Any]:
								# fmt: off
								__UpperCamelCase	:   Optional[int]			   =					{"input_ids": [[3_8_9_7_9, 1_4_3, 1_8_4_8_5, 6_0_6, 1_3_0, 2_6_6_6_9, 8_7_6_8_6, 1_2_1, 5_4_1_8_9, 1_1_2_9, 1_1_1, 2_6_6_6_9, 8_7_6_8_6, 1_2_1, 9_1_1_4, 1_4_7_8_7, 1_2_1, 1_3_2_4_9, 1_5_8, 5_9_2, 9_5_6, 1_2_1, 1_4_6_2_1, 3_1_5_7_6, 1_4_3, 6_2_6_1_3, 1_0_8, 9_6_8_8, 9_3_0, 4_3_4_3_0, 1_1_5_6_2, 6_2_6_1_3, 3_0_4, 1_0_8, 1_1_4_4_3, 8_9_7, 1_0_8, 9_3_1_4, 1_7_4_1_5, 6_3_3_9_9, 1_0_8, 1_1_4_4_3, 7_6_1_4, 1_8_3_1_6, 1_1_8, 4_2_8_4, 7_1_4_8, 1_2_4_3_0, 1_4_3, 1_4_0_0, 2_5_7_0_3, 1_5_8, 1_1_1, 4_2_8_4, 7_1_4_8, 1_1_7_7_2, 1_4_3, 2_1_2_9_7, 1_0_6_4, 1_5_8, 1_2_2, 2_0_4, 3_5_0_6, 1_7_5_4, 1_1_3_3, 1_4_7_8_7, 1_5_8_1, 1_1_5, 3_3_2_2_4, 4_4_8_2, 1_1_1, 1_3_5_5, 1_1_0, 2_9_1_7_3, 3_1_7, 5_0_8_3_3, 1_0_8, 2_0_1_4_7, 9_4_6_6_5, 1_1_1, 7_7_1_9_8, 1_0_7, 1], [1_1_0, 6_2_6_1_3, 1_1_7, 6_3_8, 1_1_2, 1_1_3_3, 1_2_1, 2_0_0_9_8, 1_3_5_5, 7_9_0_5_0, 1_3_8_7_2, 1_3_5, 1_5_9_6, 5_3_5_4_1, 1_3_5_2, 1_4_1, 1_3_0_3_9, 5_5_4_2, 1_2_4, 3_0_2, 5_1_8, 1_1_1, 2_6_8, 2_9_5_6, 1_1_5, 1_4_9, 4_4_2_7, 1_0_7, 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_3_9, 1_2_3_5, 2_7_9_9, 1_8_2_8_9, 1_7_7_8_0, 2_0_4, 1_0_9, 9_4_7_4, 1_2_9_6, 1_0_7, 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=a     ,   model_name="google/bigbird-pegasus-large-arxiv"     ,   revision="ba85d0851d708441f91440d509690f1ab6353415"     ,   )




@require_sentencepiece
@require_tokenizers
class  lowerCamelCase__							(				__lowercase     ,						unittest.TestCase):




	'''simple docstring'''




	_A								=     PegasusTokenizer
	_A								=     PegasusTokenizerFast
	_A								=     True
	_A								=     True







	def    _lowerCamelCase				(					self		:int						)		-> Any:
								super().setUp()

								# We have a SentencePiece fixture for testing
								__UpperCamelCase	:   Union[str, Any]			   =					PegasusTokenizer(a     ,   offset=0     ,   mask_token_sent=a     ,   mask_token="[MASK]"						)
								tokenizer.save_pretrained(self.tmpdirname						)







	@cached_property
	def    _lowerCamelCase				(					self		:Dict						)		-> Tuple:
								return PegasusTokenizer.from_pretrained("google/bigbird-pegasus-large-arxiv"						)







	def    _lowerCamelCase				(					self		:List[str]     ,   **a		:Tuple						)		-> PegasusTokenizer:
								return PegasusTokenizer.from_pretrained(self.tmpdirname     ,   **a						)







	def    _lowerCamelCase				(					self		:Optional[int]     ,   a		:str						)		-> Optional[int]:
								return ("This is a test", "This is a test")







	def    _lowerCamelCase				(					self		:Tuple						)		-> Optional[int]:
								__UpperCamelCase	:   Dict			   =					self.rust_tokenizer_class.from_pretrained(self.tmpdirname						)
								__UpperCamelCase	:   List[Any]			   =					self.tokenizer_class.from_pretrained(self.tmpdirname						)
								__UpperCamelCase	:   int			   =					(
								    "Let's see which <unk> is the better <unk_token> one [MASK] It seems like this [MASK] was important </s>"
								    " <pad> <pad> <pad>"
								)
								__UpperCamelCase	:   int			   =					rust_tokenizer([raw_input_str]     ,   return_tensors=a     ,   add_special_tokens=a						).input_ids[0]
								__UpperCamelCase	:   Optional[int]			   =					py_tokenizer([raw_input_str]     ,   return_tensors=a     ,   add_special_tokens=a						).input_ids[0]
								self.assertListEqual(a     ,   a						)







	@require_torch
	def    _lowerCamelCase				(					self		:str						)		-> Any:
								__UpperCamelCase	:   List[str]			   =					["This is going to be way too long." * 1_0_0_0, "short example"]
								__UpperCamelCase	:   Union[str, Any]			   =					["not super long but more than 5 tokens", "tiny"]
								__UpperCamelCase	:   List[str]			   =					self._large_tokenizer(a     ,   padding=a     ,   truncation=a     ,   return_tensors="pt"						)
								__UpperCamelCase	:   str			   =					self._large_tokenizer(
								    text_target=a     ,   max_length=5     ,   padding=a     ,   truncation=a     ,   return_tensors="pt"						)

								assert batch.input_ids.shape == (2, 4_0_9_6)
								assert batch.attention_mask.shape == (2, 4_0_9_6)
								assert targets["input_ids"].shape == (2, 5)
								assert len(a						) == 2  # input_ids, attention_mask.







	def    _lowerCamelCase				(					self		:Union[str, Any]						)		-> Dict:
								__UpperCamelCase	:   Optional[Any]			   =					(
								    "This is an example string that is used to test the original TF implementation against the HF"
								    " implementation"
								)

								__UpperCamelCase	:   List[Any]			   =					self._large_tokenizer(a						).input_ids

								self.assertListEqual(
								    a     ,   [1_8_2, 1_1_7, 1_4_2, 5_8_7, 4_2_1_1, 1_2_0, 1_1_7, 2_6_3, 1_1_2, 8_0_4, 1_0_9, 8_5_6, 2_5_0_1_6, 3_1_3_7, 4_6_4, 1_0_9, 2_6_9_5_5, 3_1_3_7, 1]     ,   )

import unittest
from dataclasses import dataclass

import pytest

from accelerate.commands.config.config_args import SageMakerConfig
from accelerate.utils import ComputeEnvironment
from accelerate.utils.launch import _convert_nargs_to_dict




@dataclass
class  lowerCamelCase__							(				__lowercase):




	'''simple docstring'''




	_A								=     ComputeEnvironment.AMAZON_SAGEMAKER
	_A								=     True
	_A								=     'ml.p3.2xlarge'
	_A								=     'accelerate_sagemaker_execution_role'
	_A								=     'hf-sm'
	_A								=     'us-east-1'
	_A								=     1
	_A								=     'accelerate-sagemaker-1'
	_A								=     '1.6'
	_A								=     '4.4'
	_A								=     'train.py'
	_A								=     [
	    '--model_name_or_path',
	    'bert',
	    '--do_train',
	    'False',
	    '--epochs',
	    '3',
	    '--learning_rate',
	    '5e-5',
	    '--max_steps',
	    '50.5',
	]
	_A								=     [
	    '--model_name_or_path',
	    'bert',
	    '--do_train',
	    '--do_test',
	    'False',
	    '--do_predict',
	    '--epochs',
	    '3',
	    '--learning_rate',
	    '5e-5',
	    '--max_steps',
	    '50.5',
	]




class  lowerCamelCase__							(				unittest.TestCase):




	'''simple docstring'''




	def    _lowerCamelCase				(					self		:Any						)		-> Optional[int]:
								# If no defaults are changed, `to_kwargs` returns an empty dict.
								__UpperCamelCase	:   Optional[int]			   =					_convert_nargs_to_dict(MockLaunchConfig.success_training_script_args						)
								assert isinstance(converted_args["model_name_or_path"]     ,   a						)
								assert isinstance(converted_args["do_train"]     ,   a						)
								assert isinstance(converted_args["epochs"]     ,   a						)
								assert isinstance(converted_args["learning_rate"]     ,   a						)
								assert isinstance(converted_args["max_steps"]     ,   a						)

								with pytest.raises(a						):
															_convert_nargs_to_dict(MockLaunchConfig.fail_training_script_args						)