

'''simple docstring'''


import json
import os
from typing import Optional, Tuple

import regex as re

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


lowerCAmelCase__										= logging.get_logger(__name__)

lowerCAmelCase__										= {
    "vocab_file": "vocab.json",
    "merges_file": "merges.txt",
}

lowerCAmelCase__										= {
    "vocab_file": {"ctrl": "https://raw.githubusercontent.com/salesforce/ctrl/master/ctrl-vocab.json"},
    "merges_file": {"ctrl": "https://raw.githubusercontent.com/salesforce/ctrl/master/ctrl-merges.txt"},
}

lowerCAmelCase__										= {
    "ctrl": 256,
}

lowerCAmelCase__										= {
    "Pregnancy": 168629,
    "Christianity": 7675,
    "Explain": 106423,
    "Fitness": 63440,
    "Saving": 63163,
    "Ask": 27171,
    "Ass": 95985,
    "Joke": 163509,
    "Questions": 45622,
    "Thoughts": 49605,
    "Retail": 52342,
    "Feminism": 164338,
    "Writing": 11992,
    "Atheism": 192263,
    "Netflix": 48616,
    "Computing": 39639,
    "Opinion": 43213,
    "Alone": 44967,
    "Funny": 58917,
    "Gaming": 40358,
    "Human": 4088,
    "India": 1331,
    "Joker": 77138,
    "Diet": 36206,
    "Legal": 11859,
    "Norman": 4939,
    "Tip": 72689,
    "Weight": 52343,
    "Movies": 46273,
    "Running": 23425,
    "Science": 2090,
    "Horror": 37793,
    "Confession": 60572,
    "Finance": 12250,
    "Politics": 16360,
    "Scary": 191985,
    "Support": 12654,
    "Technologies": 32516,
    "Teenage": 66160,
    "Event": 32769,
    "Learned": 67460,
    "Notion": 182770,
    "Wikipedia": 37583,
    "Books": 6665,
    "Extract": 76050,
    "Confessions": 102701,
    "Conspiracy": 75932,
    "Links": 63674,
    "Narcissus": 150425,
    "Relationship": 54766,
    "Relationships": 134796,
    "Reviews": 41671,
    "News": 4256,
    "Translation": 26820,
    "multilingual": 128406,
}







def 						SCREAMING_SNAKE_CASE(  UpperCamelCase  )    ->    Union[str, Any]:
	UpperCAmelCase_		:      Optional[int]       	=					set()
	UpperCAmelCase_		:      str       	=					word[0]
	for char in word[1:]:
		pairs.add((prev_char, char)  )
		UpperCAmelCase_		:      Any       	=					char

	UpperCAmelCase_		:      Optional[int]       	=					set(UpperCamelCase  )
	return pairs







class 					lowercase						(  a_ ):
							_lowerCamelCase		:      str=   VOCAB_FILES_NAMES
							_lowerCamelCase		:      Optional[int]=   PRETRAINED_VOCAB_FILES_MAP
							_lowerCamelCase		:      Tuple=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							_lowerCamelCase		:      Optional[Any]=   CONTROL_CODES


							def __init__(			self    ,			_snake_case    ,			_snake_case    ,			_snake_case="<unk>"    ,			**_snake_case)							->				Optional[int]:
								super().__init__(unk_token=_snake_case    ,			**_snake_case)

								with open(_snake_case    ,			encoding='utf-8') as vocab_handle:
									UpperCAmelCase_		:      int       	=					json.load(_snake_case)
								UpperCAmelCase_		:      List[Any]       	=					{v: k for k, v in self.encoder.items()}
								with open(_snake_case    ,			encoding='utf-8') as merges_handle:
									UpperCAmelCase_		:      Optional[int]       	=					merges_handle.read().split('\n')[1:-1]
								UpperCAmelCase_		:      List[str]       	=					[tuple(merge.split()) for merge in merges]
								UpperCAmelCase_		:      Optional[int]       	=					dict(zip(_snake_case    ,			range(len(_snake_case))))
								UpperCAmelCase_		:      List[Any]       	=					{}


							@property
							def 						_snake_case							(			self)							->				Optional[Any]:
								return len(self.encoder)


							def 						_snake_case							(			self)							->				Tuple:
								return dict(self.encoder    ,			**self.added_tokens_encoder)


							def 						_snake_case							(			self    ,			_snake_case)							->				str:
								if token in self.cache:
									return self.cache[token]
								UpperCAmelCase_		:      Optional[int]       	=					tuple(_snake_case)
								UpperCAmelCase_		:      Optional[int]       	=					tuple(list(word[:-1]) + [word[-1] + '</w>'])
								UpperCAmelCase_		:      Optional[Any]       	=					get_pairs(_snake_case)

								if not pairs:
									return token

								while True:
									UpperCAmelCase_		:      Union[str, Any]       	=					min(_snake_case    ,			key=lambda _snake_case: self.bpe_ranks.get(_snake_case    ,			float('inf')))
									if bigram not in self.bpe_ranks:
										break
									UpperCAmelCase_		:      List[Any]       	=					bigram
									UpperCAmelCase_		:      Optional[Any]       	=					[]
									UpperCAmelCase_		:      List[Any]       	=					0
									while i < len(_snake_case):
										try:
											UpperCAmelCase_		:      List[Any]       	=					word.index(_snake_case    ,			_snake_case)
										except ValueError:
											new_word.extend(word[i:])
											break
										else:
											new_word.extend(word[i:j])
											UpperCAmelCase_		:      int       	=					j

										if word[i] == first and i < len(_snake_case) - 1 and word[i + 1] == second:
											new_word.append(first + second)
											i += 2
										else:
											new_word.append(word[i])
											i += 1
									UpperCAmelCase_		:      Union[str, Any]       	=					tuple(_snake_case)
									UpperCAmelCase_		:      int       	=					new_word
									if len(_snake_case) == 1:
										break
									else:
										UpperCAmelCase_		:      List[Any]       	=					get_pairs(_snake_case)
								UpperCAmelCase_		:      Dict       	=					'@@ '.join(_snake_case)
								UpperCAmelCase_		:      Optional[Any]       	=					word[:-4]
								UpperCAmelCase_		:      int       	=					word
								return word


							def 						_snake_case							(			self    ,			_snake_case)							->				Tuple:
								UpperCAmelCase_		:      Tuple       	=					[]

								UpperCAmelCase_		:      Tuple       	=					re.findall(r'\S+\n?'    ,			_snake_case)

								for token in words:
									split_tokens.extend(list(self.bpe(_snake_case).split(' ')))
								return split_tokens


							def 						_snake_case							(			self    ,			_snake_case)							->				List[str]:
								return self.encoder.get(_snake_case    ,			self.encoder.get(self.unk_token))


							def 						_snake_case							(			self    ,			_snake_case)							->				List[str]:
								return self.decoder.get(_snake_case    ,			self.unk_token)


							def 						_snake_case							(			self    ,			_snake_case)							->				str:
								UpperCAmelCase_		:      int       	=					' '.join(_snake_case).replace('@@ '    ,			'').strip()
								return out_string





							def 						_snake_case							(			self    ,			_snake_case    ,			_snake_case = None)							->				Tuple[str]:
								if not os.path.isdir(_snake_case):
									logger.error(F"""Vocabulary path ({save_directory}) should be a directory""")
									return
								UpperCAmelCase_		:      List[str]       	=					os.path.join(
								    _snake_case    ,			(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'])
								UpperCAmelCase_		:      Optional[int]       	=					os.path.join(
								    _snake_case    ,			(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['merges_file'])

								with open(_snake_case    ,			'w'    ,			encoding='utf-8') as f:
									f.write(json.dumps(self.encoder    ,			indent=2    ,			sort_keys=_snake_case    ,			ensure_ascii=_snake_case) + '\n')

								UpperCAmelCase_		:      Optional[int]       	=					0
								with open(_snake_case    ,			'w'    ,			encoding='utf-8') as writer:
									writer.write('#version: 0.2\n')
									for bpe_tokens, token_index in sorted(self.bpe_ranks.items()    ,			key=lambda _snake_case: kv[1]):
										if index != token_index:
											logger.warning(
											    F"""Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."""
											    ' Please check that the tokenizer is not corrupted!')
											UpperCAmelCase_		:      Dict       	=					token_index
										writer.write(' '.join(_snake_case) + '\n')
										index += 1

								return vocab_file, merge_file

							# def decode(self, token_ids, skip_special_tokens=False, clean_up_tokenization_spaces=True):
							#     filtered_tokens = ' '.join(self.convert_ids_to_tokens(token_ids, skip_special_tokens=skip_special_tokens))
							#     tokens_generated_so_far = re.sub('(@@ )', '', string=filtered_tokens)
							#     tokens_generated_so_far = re.sub('(@@ ?$)', '', string=tokens_generated_so_far)
							#     return ''.join(tokens_generated_so_far)









'''simple docstring'''


import unittest

from parameterized import parameterized

from transformers import OpenLlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
						import torch

						from transformers import OpenLlamaForCausalLM, OpenLlamaForSequenceClassification, OpenLlamaModel



class 					lowercase						:


							def __init__(			self    ,			_snake_case    ,			_snake_case=13    ,			_snake_case=7    ,			_snake_case=True    ,			_snake_case=True    ,			_snake_case=False    ,			_snake_case=True    ,			_snake_case=99    ,			_snake_case=32    ,			_snake_case=5    ,			_snake_case=4    ,			_snake_case=37    ,			_snake_case="gelu"    ,			_snake_case=0.1    ,			_snake_case=0.1    ,			_snake_case=512    ,			_snake_case=16    ,			_snake_case=2    ,			_snake_case=0.02    ,			_snake_case=3    ,			_snake_case=4    ,			_snake_case=None    ,			)							->				str:
								UpperCAmelCase_		:      Dict       	=					parent
								UpperCAmelCase_		:      Tuple       	=					batch_size
								UpperCAmelCase_		:      Tuple       	=					seq_length
								UpperCAmelCase_		:      Dict       	=					is_training
								UpperCAmelCase_		:      Tuple       	=					use_input_mask
								UpperCAmelCase_		:      List[Any]       	=					use_token_type_ids
								UpperCAmelCase_		:      Optional[int]       	=					use_labels
								UpperCAmelCase_		:      List[str]       	=					vocab_size
								UpperCAmelCase_		:      int       	=					hidden_size
								UpperCAmelCase_		:      Any       	=					num_hidden_layers
								UpperCAmelCase_		:      Optional[int]       	=					num_attention_heads
								UpperCAmelCase_		:      List[str]       	=					intermediate_size
								UpperCAmelCase_		:      Union[str, Any]       	=					hidden_act
								UpperCAmelCase_		:      Optional[int]       	=					hidden_dropout_prob
								UpperCAmelCase_		:      Any       	=					attention_probs_dropout_prob
								UpperCAmelCase_		:      Dict       	=					max_position_embeddings
								UpperCAmelCase_		:      Union[str, Any]       	=					type_vocab_size
								UpperCAmelCase_		:      int       	=					type_sequence_label_size
								UpperCAmelCase_		:      List[str]       	=					initializer_range
								UpperCAmelCase_		:      Any       	=					num_labels
								UpperCAmelCase_		:      Optional[Any]       	=					num_choices
								UpperCAmelCase_		:      str       	=					scope


							def 						_snake_case							(			self)							->				Tuple:
								UpperCAmelCase_		:      Any       	=					ids_tensor([self.batch_size, self.seq_length]    ,			self.vocab_size)

								UpperCAmelCase_		:      Any       	=					None
								if self.use_input_mask:
									UpperCAmelCase_		:      Optional[Any]       	=					random_attention_mask([self.batch_size, self.seq_length])

								UpperCAmelCase_		:      Union[str, Any]       	=					None
								if self.use_token_type_ids:
									UpperCAmelCase_		:      Tuple       	=					ids_tensor([self.batch_size, self.seq_length]    ,			self.type_vocab_size)

								UpperCAmelCase_		:      Dict       	=					None
								UpperCAmelCase_		:      List[str]       	=					None
								UpperCAmelCase_		:      Optional[Any]       	=					None
								if self.use_labels:
									UpperCAmelCase_		:      Any       	=					ids_tensor([self.batch_size]    ,			self.type_sequence_label_size)
									UpperCAmelCase_		:      List[str]       	=					ids_tensor([self.batch_size, self.seq_length]    ,			self.num_labels)
									UpperCAmelCase_		:      Tuple       	=					ids_tensor([self.batch_size]    ,			self.num_choices)

								UpperCAmelCase_		:      Tuple       	=					self.get_config()

								return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels


							def 						_snake_case							(			self)							->				Optional[Any]:
								return OpenLlamaConfig(
								    vocab_size=self.vocab_size    ,			hidden_size=self.hidden_size    ,			num_hidden_layers=self.num_hidden_layers    ,			num_attention_heads=self.num_attention_heads    ,			intermediate_size=self.intermediate_size    ,			hidden_act=self.hidden_act    ,			hidden_dropout_prob=self.hidden_dropout_prob    ,			attention_probs_dropout_prob=self.attention_probs_dropout_prob    ,			max_position_embeddings=self.max_position_embeddings    ,			type_vocab_size=self.type_vocab_size    ,			is_decoder=_snake_case    ,			initializer_range=self.initializer_range    ,			use_stable_embedding=_snake_case    ,			)


							def 						_snake_case							(			self    ,			_snake_case    ,			_snake_case    ,			_snake_case    ,			_snake_case    ,			_snake_case    ,			_snake_case    ,			_snake_case)							->				Union[str, Any]:
								UpperCAmelCase_		:      str       	=					OpenLlamaModel(config=_snake_case)
								model.to(_snake_case)
								model.eval()
								UpperCAmelCase_		:      Dict       	=					model(_snake_case    ,			attention_mask=_snake_case)
								UpperCAmelCase_		:      List[str]       	=					model(_snake_case)
								self.parent.assertEqual(result.last_hidden_state.shape    ,			(self.batch_size, self.seq_length, self.hidden_size))


							def 						_snake_case							(			self    ,			_snake_case    ,			_snake_case    ,			_snake_case    ,			_snake_case    ,			_snake_case    ,			_snake_case    ,			_snake_case    ,			_snake_case    ,			_snake_case    ,			)							->				int:
								UpperCAmelCase_		:      Union[str, Any]       	=					True
								UpperCAmelCase_		:      List[Any]       	=					OpenLlamaModel(_snake_case)
								model.to(_snake_case)
								model.eval()
								UpperCAmelCase_		:      Optional[int]       	=					model(
								    _snake_case    ,			attention_mask=_snake_case    ,			encoder_hidden_states=_snake_case    ,			encoder_attention_mask=_snake_case    ,			)
								UpperCAmelCase_		:      Any       	=					model(
								    _snake_case    ,			attention_mask=_snake_case    ,			encoder_hidden_states=_snake_case    ,			)
								UpperCAmelCase_		:      Optional[int]       	=					model(_snake_case    ,			attention_mask=_snake_case)
								self.parent.assertEqual(result.last_hidden_state.shape    ,			(self.batch_size, self.seq_length, self.hidden_size))


							def 						_snake_case							(			self    ,			_snake_case    ,			_snake_case    ,			_snake_case    ,			_snake_case    ,			_snake_case    ,			_snake_case    ,			_snake_case    ,			_snake_case    ,			_snake_case    ,			)							->				Any:
								UpperCAmelCase_		:      Optional[int]       	=					OpenLlamaForCausalLM(config=_snake_case)
								model.to(_snake_case)
								model.eval()
								UpperCAmelCase_		:      Optional[int]       	=					model(_snake_case    ,			attention_mask=_snake_case    ,			labels=_snake_case)
								self.parent.assertEqual(result.logits.shape    ,			(self.batch_size, self.seq_length, self.vocab_size))


							def 						_snake_case							(			self    ,			_snake_case    ,			_snake_case    ,			_snake_case    ,			_snake_case    ,			_snake_case    ,			_snake_case    ,			_snake_case    ,			_snake_case    ,			_snake_case    ,			)							->				Union[str, Any]:
								UpperCAmelCase_		:      Any       	=					True
								UpperCAmelCase_		:      Optional[Any]       	=					True
								UpperCAmelCase_		:      int       	=					OpenLlamaForCausalLM(config=_snake_case)
								model.to(_snake_case)
								model.eval()

								# first forward pass
								UpperCAmelCase_		:      List[str]       	=					model(
								    _snake_case    ,			attention_mask=_snake_case    ,			encoder_hidden_states=_snake_case    ,			encoder_attention_mask=_snake_case    ,			use_cache=_snake_case    ,			)
								UpperCAmelCase_		:      Dict       	=					outputs.past_key_values

								# create hypothetical multiple next token and extent to next_input_ids
								UpperCAmelCase_		:      str       	=					ids_tensor((self.batch_size, 3)    ,			config.vocab_size)
								UpperCAmelCase_		:      int       	=					ids_tensor((self.batch_size, 3)    ,			vocab_size=2)

								# append to next input_ids and
								UpperCAmelCase_		:      Tuple       	=					torch.cat([input_ids, next_tokens]    ,			dim=-1)
								UpperCAmelCase_		:      List[str]       	=					torch.cat([input_mask, next_mask]    ,			dim=-1)

								UpperCAmelCase_		:      Optional[Any]       	=					model(
								    _snake_case    ,			attention_mask=_snake_case    ,			encoder_hidden_states=_snake_case    ,			encoder_attention_mask=_snake_case    ,			output_hidden_states=_snake_case    ,			)['hidden_states'][0]
								UpperCAmelCase_		:      List[str]       	=					model(
								    _snake_case    ,			attention_mask=_snake_case    ,			encoder_hidden_states=_snake_case    ,			encoder_attention_mask=_snake_case    ,			past_key_values=_snake_case    ,			output_hidden_states=_snake_case    ,			)['hidden_states'][0]

								# select random slice
								UpperCAmelCase_		:      Optional[Any]       	=					ids_tensor((1,)    ,			output_from_past.shape[-1]).item()
								UpperCAmelCase_		:      Optional[int]       	=					output_from_no_past[:, -3:, random_slice_idx].detach()
								UpperCAmelCase_		:      Union[str, Any]       	=					output_from_past[:, :, random_slice_idx].detach()

								self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1])

								# test that outputs are equal for slice
								self.parent.assertTrue(torch.allclose(_snake_case    ,			_snake_case    ,			atol=1e-3))





							def 						_snake_case							(			self)							->				List[Any]:
								UpperCAmelCase_		:      Optional[Any]       	=					self.prepare_config_and_inputs()
								(
								    (
								    UpperCAmelCase_
								)	,    (
								    UpperCAmelCase_
								)	,    (
								    UpperCAmelCase_
								)	,    (
								    UpperCAmelCase_
								)	,    (
								    UpperCAmelCase_
								)	,    (
								    UpperCAmelCase_
								)	,    (
								    UpperCAmelCase_
								)	,    
								)		:      Union[str, Any]       	=					config_and_inputs
								UpperCAmelCase_		:      List[str]       	=					{'input_ids': input_ids, 'attention_mask': input_mask}
								return config, inputs_dict



@require_torch
class 					lowercase						(  a_,					a_,					a_,					unittest.TestCase ):
							_lowerCamelCase		:      Dict=   (
							    (OpenLlamaModel, OpenLlamaForCausalLM, OpenLlamaForSequenceClassification) if is_torch_available() else ()
							)
							_lowerCamelCase		:      str=   (OpenLlamaForCausalLM,) if is_torch_available() else ()
							_lowerCamelCase		:      Optional[int]=   (
							    {
							        "feature-extraction": OpenLlamaModel,
							        "text-classification": OpenLlamaForSequenceClassification,
							        "text-generation": OpenLlamaForCausalLM,
							        "zero-shot": OpenLlamaForSequenceClassification,
							    }
							    if is_torch_available()
							    else {}
							)
							_lowerCamelCase		:      Tuple=   False
							_lowerCamelCase		:      int=   False


							def 						_snake_case							(			self)							->				int:
								UpperCAmelCase_		:      Any       	=					OpenLlamaModelTester(self)
								UpperCAmelCase_		:      Union[str, Any]       	=					ConfigTester(self    ,			config_class=_snake_case    ,			hidden_size=37)


							def 						_snake_case							(			self)							->				Optional[Any]:
								self.config_tester.run_common_tests()


							def 						_snake_case							(			self)							->				str:
								UpperCAmelCase_		:      Tuple       	=					self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*_snake_case)


							def 						_snake_case							(			self)							->				Union[str, Any]:
								UpperCAmelCase_		:      str       	=					self.model_tester.prepare_config_and_inputs()
								for type in ["absolute", "relative_key", "relative_key_query"]:
									UpperCAmelCase_		:      List[Any]       	=					type
									self.model_tester.create_and_check_model(*_snake_case)


							def 						_snake_case							(			self)							->				Union[str, Any]:
								UpperCAmelCase_	,    UpperCAmelCase_		:      List[Any]       	=					self.model_tester.prepare_config_and_inputs_for_common()
								UpperCAmelCase_		:      List[str]       	=					3
								UpperCAmelCase_		:      List[str]       	=					input_dict['input_ids']
								UpperCAmelCase_		:      Optional[Any]       	=					input_ids.ne(1).to(_snake_case)
								UpperCAmelCase_		:      Any       	=					ids_tensor([self.model_tester.batch_size]    ,			self.model_tester.type_sequence_label_size)
								UpperCAmelCase_		:      List[Any]       	=					OpenLlamaForSequenceClassification(_snake_case)
								model.to(_snake_case)
								model.eval()
								UpperCAmelCase_		:      Tuple       	=					model(_snake_case    ,			attention_mask=_snake_case    ,			labels=_snake_case)
								self.assertEqual(result.logits.shape    ,			(self.model_tester.batch_size, self.model_tester.num_labels))


							def 						_snake_case							(			self)							->				Any:
								UpperCAmelCase_	,    UpperCAmelCase_		:      Dict       	=					self.model_tester.prepare_config_and_inputs_for_common()
								UpperCAmelCase_		:      Optional[Any]       	=					3
								UpperCAmelCase_		:      int       	=					'single_label_classification'
								UpperCAmelCase_		:      int       	=					input_dict['input_ids']
								UpperCAmelCase_		:      Any       	=					input_ids.ne(1).to(_snake_case)
								UpperCAmelCase_		:      Any       	=					ids_tensor([self.model_tester.batch_size]    ,			self.model_tester.type_sequence_label_size)
								UpperCAmelCase_		:      int       	=					OpenLlamaForSequenceClassification(_snake_case)
								model.to(_snake_case)
								model.eval()
								UpperCAmelCase_		:      List[str]       	=					model(_snake_case    ,			attention_mask=_snake_case    ,			labels=_snake_case)
								self.assertEqual(result.logits.shape    ,			(self.model_tester.batch_size, self.model_tester.num_labels))


							def 						_snake_case							(			self)							->				Optional[int]:
								UpperCAmelCase_	,    UpperCAmelCase_		:      List[Any]       	=					self.model_tester.prepare_config_and_inputs_for_common()
								UpperCAmelCase_		:      int       	=					3
								UpperCAmelCase_		:      Any       	=					'multi_label_classification'
								UpperCAmelCase_		:      Tuple       	=					input_dict['input_ids']
								UpperCAmelCase_		:      str       	=					input_ids.ne(1).to(_snake_case)
								UpperCAmelCase_		:      Any       	=					ids_tensor(
								    [self.model_tester.batch_size, config.num_labels]    ,			self.model_tester.type_sequence_label_size).to(torch.float)
								UpperCAmelCase_		:      Optional[Any]       	=					OpenLlamaForSequenceClassification(_snake_case)
								model.to(_snake_case)
								model.eval()
								UpperCAmelCase_		:      List[Any]       	=					model(_snake_case    ,			attention_mask=_snake_case    ,			labels=_snake_case)
								self.assertEqual(result.logits.shape    ,			(self.model_tester.batch_size, self.model_tester.num_labels))


							@unittest.skip('Open-Llama buffers include complex numbers, which breaks this test')
							def 						_snake_case							(			self)							->				Optional[int]:
								pass





							@parameterized.expand([('linear',), ('dynamic',)])
							def 						_snake_case							(			self    ,			_snake_case)							->				List[str]:
								UpperCAmelCase_	,    UpperCAmelCase_		:      Any       	=					self.model_tester.prepare_config_and_inputs_for_common()
								UpperCAmelCase_		:      List[Any]       	=					ids_tensor([1, 10]    ,			config.vocab_size)
								UpperCAmelCase_		:      List[str]       	=					ids_tensor([1, int(config.max_position_embeddings * 1.5)]    ,			config.vocab_size)

								set_seed(42)  # Fixed seed at init time so the two models get the same random weights
								UpperCAmelCase_		:      int       	=					OpenLlamaModel(_snake_case)
								original_model.to(_snake_case)
								original_model.eval()
								UpperCAmelCase_		:      Optional[Any]       	=					original_model(_snake_case).last_hidden_state
								UpperCAmelCase_		:      Tuple       	=					original_model(_snake_case).last_hidden_state

								set_seed(42)  # Fixed seed at init time so the two models get the same random weights
								UpperCAmelCase_		:      Dict       	=					{'type': scaling_type, 'factor': 10.0}
								UpperCAmelCase_		:      Union[str, Any]       	=					OpenLlamaModel(_snake_case)
								scaled_model.to(_snake_case)
								scaled_model.eval()
								UpperCAmelCase_		:      Dict       	=					scaled_model(_snake_case).last_hidden_state
								UpperCAmelCase_		:      Optional[int]       	=					scaled_model(_snake_case).last_hidden_state

								# Dynamic scaling does not change the RoPE embeddings until it receives an input longer than the original
								# maximum sequence length, so the outputs for the short input should match.
								if scaling_type == "dynamic":
									self.assertTrue(torch.allclose(_snake_case    ,			_snake_case    ,			atol=1e-5))
								else:
									self.assertFalse(torch.allclose(_snake_case    ,			_snake_case    ,			atol=1e-5))

								# The output should be different for long inputs
								self.assertFalse(torch.allclose(_snake_case    ,			_snake_case    ,			atol=1e-5))






