
import unittest

from transformers.models.xlm_prophetnet.tokenization_xlm_prophetnet import SPIECE_UNDERLINE, XLMProphetNetTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


_UpperCAmelCase					: Any      =       get_tests_dir("fixtures/test_sentencepiece.model")


@require_sentencepiece
class 				lowercase					(		_SCREAMING_SNAKE_CASE  ,   unittest.TestCase   ):
		__lowercase       :       Any	     =				XLMProphetNetTokenizer
		__lowercase       :       List[Any]	     =				False
		__lowercase       :       List[str]	     =				True



		def __UpperCamelCase ( self       )  ->					Any:
				"""simple docstring"""
				super().setUp()

				# We have a SentencePiece fixture for testing
				UpperCamelCase		=     XLMProphetNetTokenizer(A_				,      keep_accents=A_       )
				tokenizer.save_pretrained(self.tmpdirname       )



		def __UpperCamelCase ( self       )  ->					Optional[Any]:
				"""simple docstring"""
				UpperCamelCase		=     '[PAD]'
				UpperCamelCase		=     0

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



		def __UpperCamelCase ( self       )  ->					Any:
				"""simple docstring"""
				UpperCamelCase		=     list(self.get_tokenizer().get_vocab().keys()       )

				self.assertEqual(vocab_keys[0]				,      '[PAD]'       )
				self.assertEqual(vocab_keys[1]				,      '[CLS]'       )
				self.assertEqual(vocab_keys[-1]				,      'j'       )
				self.assertEqual(len(A_       )				,      1_012       )



		def __UpperCamelCase ( self       )  ->					Any:
				"""simple docstring"""
				self.assertEqual(self.get_tokenizer().vocab_size				,      1_012       )



		def __UpperCamelCase ( self       )  ->					int:
				"""simple docstring"""
				UpperCamelCase		=     XLMProphetNetTokenizer(A_				,      keep_accents=A_       )

				UpperCamelCase		=     tokenizer.tokenize('This is a test'       )
				self.assertListEqual(A_				,      ['▁This', '▁is', '▁a', '▁t', 'est']       )

				self.assertListEqual(
				    tokenizer.convert_tokens_to_ids(A_       )				,      [value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]]				,      )

				UpperCamelCase		=     tokenizer.tokenize('I was born in 92000, and this is falsé.'       )
				self.assertListEqual(
				    A_				,      [
				        SPIECE_UNDERLINE + 'I',
				        SPIECE_UNDERLINE + 'was',
				        SPIECE_UNDERLINE + 'b',
				        'or',
				        'n',
				        SPIECE_UNDERLINE + 'in',
				        SPIECE_UNDERLINE + '',
				        '9',
				        '2',
				        '0',
				        '0',
				        '0',
				        ',',
				        SPIECE_UNDERLINE + 'and',
				        SPIECE_UNDERLINE + 'this',
				        SPIECE_UNDERLINE + 'is',
				        SPIECE_UNDERLINE + 'f',
				        'al',
				        's',
				        'é',
				        '.',
				    ]				,      )
				UpperCamelCase		=     tokenizer.convert_tokens_to_ids(A_       )
				self.assertListEqual(
				    A_				,      [
				        value + tokenizer.fairseq_offset
				        for value in [8, 21, 84, 55, 24, 19, 7, -9, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, -9, 4]
				    ]				,      )

				UpperCamelCase		=     tokenizer.convert_ids_to_tokens(A_       )
				self.assertListEqual(
				    A_				,      [
				        SPIECE_UNDERLINE + 'I',
				        SPIECE_UNDERLINE + 'was',
				        SPIECE_UNDERLINE + 'b',
				        'or',
				        'n',
				        SPIECE_UNDERLINE + 'in',
				        SPIECE_UNDERLINE + '',
				        '[UNK]',
				        '2',
				        '0',
				        '0',
				        '0',
				        ',',
				        SPIECE_UNDERLINE + 'and',
				        SPIECE_UNDERLINE + 'this',
				        SPIECE_UNDERLINE + 'is',
				        SPIECE_UNDERLINE + 'f',
				        'al',
				        's',
				        '[UNK]',
				        '.',
				    ]				,      )



		@cached_property
		def __UpperCamelCase ( self       )  ->					Optional[int]:
				"""simple docstring"""
				return XLMProphetNetTokenizer.from_pretrained('microsoft/xprophetnet-large-wiki100-cased'       )



		@slow
		def __UpperCamelCase ( self       )  ->					List[Any]:
				"""simple docstring"""
				UpperCamelCase		=     'Hello World!'
				UpperCamelCase		=     [35_389, 6_672, 49, 2]
				self.assertListEqual(A_				,      self.big_tokenizer.encode(A_       )       )



		@slow
		def __UpperCamelCase ( self       )  ->					Optional[int]:
				"""simple docstring"""
				# fmt: off
				UpperCamelCase		=     {'input_ids': [[11_073, 82_783, 18, 26, 82_783, 549, 51_540, 248, 17_209, 1_301, 217, 20, 215_186, 1_325, 147, 17_209, 1_301, 217, 20, 56_370, 53, 122_020, 20, 16_477, 27, 87_355, 4_548, 20, 4_728, 78_392, 17, 159_969, 18, 26, 24_491, 629, 15, 538, 22_704, 5_439, 15, 2_788, 24_491, 9_885, 15, 43_534, 605, 15, 814, 18_403, 33_200, 29, 15, 43_534, 24_458, 12_410, 111, 24_966, 83_669, 9_637, 144_068, 26, 850, 22_346, 27, 147, 24_966, 83_669, 83_490, 26, 39_113, 735, 27, 689, 656, 2_800, 1_339, 4_600, 53, 122_020, 115_785, 34, 816, 1_339, 46_887, 18, 147, 53_905, 1_951, 42_238, 41_170, 17_732, 834, 436, 15, 27_523, 98_733, 217, 147, 5_542, 4_981, 930, 17_347, 16, 2], [20_091, 629, 94, 82_786, 58, 490, 20, 1_528, 84, 53_905, 344, 80_592, 110_128, 18_822, 5_267, 1_306, 62, 152_537, 308, 7_997, 401, 124_427, 549, 35_442, 225, 109, 15_055, 25_748, 147, 7_119, 43_712, 34, 767, 135_366, 18, 16, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [592, 63_784, 119_466, 17, 147_808, 88_214, 18, 656, 81, 32, 3_296, 10_280, 16, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 1, 1, 1, 1, 1, 1, 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, 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, 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, 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='microsoft/xprophetnet-large-wiki100-cased'				,      revision='1acad1643ddd54a44df6a1b797ada8373685d90e'				,      )


from ...configuration_utils import PretrainedConfig
from ...utils import logging


_UpperCAmelCase					: Union[str, Any]      =       logging.get_logger(__name__)

_UpperCAmelCase					: List[str]      =       {
    "EleutherAI/gpt-neox-20b": "https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/config.json",
    # See all GPTNeoX models at https://huggingface.co/models?filter=gpt_neox
}


class 				lowercase					(		_SCREAMING_SNAKE_CASE   ):
		__lowercase       :       List[str]	     =				"gpt_neox"



		def __init__( self				,      A_=50_432				,      A_=6_144				,      A_=44				,      A_=64				,      A_=24_576				,      A_="gelu"				,      A_=0.25				,      A_=10_000				,      A_=0.0				,      A_=0.0				,      A_=0.1				,      A_=2_048				,      A_=0.02				,      A_=1e-5				,      A_=True				,      A_=0				,      A_=2				,      A_=False				,      A_=True				,      A_=None				,      **A_				,      )  ->					Tuple:
				"""simple docstring"""
				super().__init__(bos_token_id=A_				,      eos_token_id=A_				,      **A_       )
				UpperCamelCase		=     vocab_size
				UpperCamelCase		=     max_position_embeddings
				UpperCamelCase		=     hidden_size
				UpperCamelCase		=     num_hidden_layers
				UpperCamelCase		=     num_attention_heads
				UpperCamelCase		=     intermediate_size
				UpperCamelCase		=     hidden_act
				UpperCamelCase		=     rotary_pct
				UpperCamelCase		=     rotary_emb_base
				UpperCamelCase		=     attention_dropout
				UpperCamelCase		=     hidden_dropout
				UpperCamelCase		=     classifier_dropout
				UpperCamelCase		=     initializer_range
				UpperCamelCase		=     layer_norm_eps
				UpperCamelCase		=     use_cache
				UpperCamelCase		=     tie_word_embeddings
				UpperCamelCase		=     use_parallel_residual
				UpperCamelCase		=     rope_scaling
				self._rope_scaling_validation()

				if self.hidden_size % self.num_attention_heads != 0:
						raise ValueError(
						    'The hidden size is not divisble by the number of attention heads! Make sure to update them!'       )



		def __UpperCamelCase ( self       )  ->					Tuple:
				"""simple docstring"""
				if self.rope_scaling is None:
						return

				if not isinstance(self.rope_scaling				,      A_       ) or len(self.rope_scaling       ) != 2:
						raise ValueError(
						    '`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, '
						    F'''got {self.rope_scaling}'''       )
				UpperCamelCase		=     self.rope_scaling.get('type'				,      A_       )
				UpperCamelCase		=     self.rope_scaling.get('factor'				,      A_       )
				if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
						raise ValueError(
						    F'''`rope_scaling`\'s name field must be one of [\'linear\', \'dynamic\'], got {rope_scaling_type}'''       )
				if rope_scaling_factor is None or not isinstance(A_				,      A_       ) or rope_scaling_factor <= 1.0:
						raise ValueError(F'''`rope_scaling`\'s factor field must be an float > 1, got {rope_scaling_factor}'''       )
