



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


UpperCAmelCase      :							List[str]							       =get_tests_dir("""fixtures/test_sentencepiece_no_bos.model""")


@require_sentencepiece
@require_tokenizers
class 					_lowercase      (__lowercase					,      unittest.TestCase					):
				'''simple docstring'''







				lowercase__        =  PegasusTokenizer
				lowercase__        =  PegasusTokenizerFast
				lowercase__        =  True
				lowercase__        =  True

				def 					_lowerCamelCase    (       self			):

									'''simple docstring'''


									super().setUp()

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

				@cached_property
				def 					_lowerCamelCase    (       self			):

									'''simple docstring'''


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

				def 					_lowerCamelCase    (       self   ,       **snake_case__			):

									'''simple docstring'''


									return PegasusTokenizer.from_pretrained(self.tmpdirname   ,       **snake_case__			)

				def 					_lowerCamelCase    (       self   ,       snake_case__			):

									'''simple docstring'''


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

				def 					_lowerCamelCase    (       self			):

									'''simple docstring'''


									UpperCamelCase_						     =      "</s>"
									UpperCamelCase_						     =      1

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

				def 					_lowerCamelCase    (       self			):

									'''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(snake_case__			)   ,       1103			)

				def 					_lowerCamelCase    (       self			):

									'''simple docstring'''


									self.assertEqual(self.get_tokenizer().vocab_size   ,       1103			)

				def 					_lowerCamelCase    (       self			):

									'''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=snake_case__   ,       add_special_tokens=snake_case__			).input_ids[0]
									UpperCamelCase_						     =      py_tokenizer([raw_input_str]   ,       return_tensors=snake_case__   ,       add_special_tokens=snake_case__			).input_ids[0]
									self.assertListEqual(snake_case__   ,       snake_case__			)

				def 					_lowerCamelCase    (       self			):

									'''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, 1971, 113, 1679, 1_0710, 107, 1]
									UpperCamelCase_						     =      tokenizer([raw_input_str]   ,       return_tensors=snake_case__			).input_ids[0]
									self.assertListEqual(snake_case__   ,       snake_case__			)

				def 					_lowerCamelCase    (       self			):

									'''simple docstring'''


									UpperCamelCase_						     =      self._large_tokenizer
									# The tracebacks for the following asserts are **better** without messages or self.assertEqual
									assert tokenizer.vocab_size == 9_6103
									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 == 1024
									UpperCamelCase_						     =      "To ensure a smooth flow of bank resolutions."
									UpperCamelCase_						     =      [413, 615, 114, 2291, 1971, 113, 1679, 1_0710, 107, 1]
									UpperCamelCase_						     =      tokenizer([raw_input_str]   ,       return_tensors=snake_case__			).input_ids[0]
									self.assertListEqual(snake_case__   ,       snake_case__			)
									assert tokenizer.convert_ids_to_tokens([0, 1, 2, 3]			) == ["<pad>", "</s>", "<mask_1>", "<mask_2>"]

				@require_torch
				def 					_lowerCamelCase    (       self			):

									'''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(snake_case__   ,       padding=snake_case__   ,       truncation=snake_case__   ,       return_tensors="pt"			)
									UpperCamelCase_						     =      self._large_tokenizer(
									    text_target=snake_case__   ,       max_length=5   ,       padding=snake_case__   ,       truncation=snake_case__   ,       return_tensors="pt"			)

									assert batch.input_ids.shape == (2, 1024)
									assert batch.attention_mask.shape == (2, 1024)
									assert targets["input_ids"].shape == (2, 5)
									assert len(snake_case__			) == 2  # input_ids, attention_mask.

				@slow
				def 					_lowerCamelCase    (       self			):

									'''simple docstring'''


									UpperCamelCase_						     =      {"input_ids": [[3_8979, 143, 1_8485, 606, 130, 2_6669, 8_7686, 121, 5_4189, 1129, 111, 2_6669, 8_7686, 121, 9114, 1_4787, 121, 1_3249, 158, 592, 956, 121, 1_4621, 3_1576, 143, 6_2613, 108, 9688, 930, 4_3430, 1_1562, 6_2613, 304, 108, 1_1443, 897, 108, 9314, 1_7415, 6_3399, 108, 1_1443, 7614, 1_8316, 118, 4284, 7148, 1_2430, 143, 1400, 2_5703, 158, 111, 4284, 7148, 1_1772, 143, 2_1297, 1064, 158, 122, 204, 3506, 1754, 1133, 1_4787, 1581, 115, 3_3224, 4482, 111, 1355, 110, 2_9173, 317, 5_0833, 108, 2_0147, 9_4665, 111, 7_7198, 107, 1], [110, 6_2613, 117, 638, 112, 1133, 121, 2_0098, 1355, 7_9050, 1_3872, 135, 1596, 5_3541, 1352, 141, 1_3039, 5542, 124, 302, 518, 111, 268, 2956, 115, 149, 4427, 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, 1235, 2799, 1_8289, 1_7780, 204, 109, 9474, 1296, 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=snake_case__   ,       model_name="google/bigbird-pegasus-large-arxiv"   ,       revision="ba85d0851d708441f91440d509690f1ab6353415"   ,       )




@require_sentencepiece
@require_tokenizers
class 					_lowercase      (__lowercase					,      unittest.TestCase					):
				'''simple docstring'''







				lowercase__        =  PegasusTokenizer
				lowercase__        =  PegasusTokenizerFast
				lowercase__        =  True
				lowercase__        =  True

				def 					_lowerCamelCase    (       self			):

									'''simple docstring'''


									super().setUp()

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

				@cached_property
				def 					_lowerCamelCase    (       self			):

									'''simple docstring'''


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

				def 					_lowerCamelCase    (       self   ,       **snake_case__			):

									'''simple docstring'''


									return PegasusTokenizer.from_pretrained(self.tmpdirname   ,       **snake_case__			)

				def 					_lowerCamelCase    (       self   ,       snake_case__			):

									'''simple docstring'''


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

				def 					_lowerCamelCase    (       self			):

									'''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=snake_case__   ,       add_special_tokens=snake_case__			).input_ids[0]
									UpperCamelCase_						     =      py_tokenizer([raw_input_str]   ,       return_tensors=snake_case__   ,       add_special_tokens=snake_case__			).input_ids[0]
									self.assertListEqual(snake_case__   ,       snake_case__			)

				@require_torch
				def 					_lowerCamelCase    (       self			):

									'''simple docstring'''


									UpperCamelCase_						     =      ["This is going to be way too long." * 1000, "short example"]
									UpperCamelCase_						     =      ["not super long but more than 5 tokens", "tiny"]
									UpperCamelCase_						     =      self._large_tokenizer(snake_case__   ,       padding=snake_case__   ,       truncation=snake_case__   ,       return_tensors="pt"			)
									UpperCamelCase_						     =      self._large_tokenizer(
									    text_target=snake_case__   ,       max_length=5   ,       padding=snake_case__   ,       truncation=snake_case__   ,       return_tensors="pt"			)

									assert batch.input_ids.shape == (2, 4096)
									assert batch.attention_mask.shape == (2, 4096)
									assert targets["input_ids"].shape == (2, 5)
									assert len(snake_case__			) == 2  # input_ids, attention_mask.

				def 					_lowerCamelCase    (       self			):

									'''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(snake_case__			).input_ids

									self.assertListEqual(
									    snake_case__   ,       [182, 117, 142, 587, 4211, 120, 117, 263, 112, 804, 109, 856, 2_5016, 3137, 464, 109, 2_6955, 3137, 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						)