



import os
import re
import warnings
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer


if TYPE_CHECKING:
	from ...tokenization_utils_base import TextInput
from ...utils import logging


__A       =						logging.get_logger(__name__)

__A       =						{'''vocab_file''': '''spiece.model'''}

__A       =						{
    '''vocab_file''': {
        '''t5-small''': '''https://huggingface.co/t5-small/resolve/main/spiece.model''',
        '''t5-base''': '''https://huggingface.co/t5-base/resolve/main/spiece.model''',
        '''t5-large''': '''https://huggingface.co/t5-large/resolve/main/spiece.model''',
        '''t5-3b''': '''https://huggingface.co/t5-3b/resolve/main/spiece.model''',
        '''t5-11b''': '''https://huggingface.co/t5-11b/resolve/main/spiece.model''',
    }
}


# TODO(PVP) - this should be removed in Transformers v5
__A       =						{
    '''t5-small''': 512,
    '''t5-base''': 512,
    '''t5-large''': 512,
    '''t5-3b''': 512,
    '''t5-11b''': 512,
}

__A       =						'''▁'''






class 		lowercase						(			snake_case__):






	"""simple docstring"""


	a__				: Optional[Any]		       =							VOCAB_FILES_NAMES
	a__				: Dict		       =							PRETRAINED_VOCAB_FILES_MAP
	a__				: Dict		       =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	a__				: Union[str, Any]		       =							["input_ids", "attention_mask"]




	def __init__(     self     :						Optional[Any]						,  __UpperCAmelCase     :						Union[str, Any]						,  __UpperCAmelCase     :						Optional[int]="</s>"						,  __UpperCAmelCase     :						Any="<unk>"						,  __UpperCAmelCase     :						List[str]="<pad>"						,  __UpperCAmelCase     :						int=100						,  __UpperCAmelCase     :						str=None						,  __UpperCAmelCase     :						Optional[Dict[str, Any]] = None						,  __UpperCAmelCase     :						List[Any]=True						,  **__UpperCAmelCase     :						Tuple						,  )      ->			None:
							# Add extra_ids to the special token list
							if extra_ids > 0 and additional_special_tokens is None:
													UpperCAmelCase_=       [F"""<extra_id_{i}>""" for i in range(__UpperCAmelCase					)]
							elif extra_ids > 0 and additional_special_tokens is not None:
													# Check that we have the right number of extra_id special tokens
													UpperCAmelCase_=       len(set(filter(lambda __UpperCAmelCase					: bool("""extra_id""" in str(__UpperCAmelCase					)					)						,  __UpperCAmelCase					)					)					)
													if extra_tokens != extra_ids:
																			raise ValueError(
																			    F"""Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are"""
																			    """ provided to T5Tokenizer. In this case the additional_special_tokens must include the extra_ids"""
																			    """ tokens"""					)
							if legacy:
													logger.warning_once(
													    F"""You are using the legacy behaviour of the {self.__class__}. This means that tokens that come after special tokens will not be properly handled. We recommend you to"""
													    """ read the related pull request available at https://github.com/huggingface/transformers/pull/24565"""					)

							UpperCAmelCase_=       legacy
							UpperCAmelCase_=       {} if sp_model_kwargs is None else sp_model_kwargs

							super().__init__(
							    eos_token=__UpperCAmelCase						,  unk_token=__UpperCAmelCase						,  pad_token=__UpperCAmelCase						,  extra_ids=__UpperCAmelCase						,  additional_special_tokens=__UpperCAmelCase						,  sp_model_kwargs=self.sp_model_kwargs						,  legacy=__UpperCAmelCase						,  **__UpperCAmelCase						,  )

							UpperCAmelCase_=       vocab_file
							UpperCAmelCase_=       extra_ids

							UpperCAmelCase_=       spm.SentencePieceProcessor(**self.sp_model_kwargs					)
							self.sp_model.Load(__UpperCAmelCase					)




	@staticmethod
	def   _SCREAMING_SNAKE_CASE		(     __UpperCAmelCase     :						int						,  __UpperCAmelCase     :						Union[str, Any]						,  __UpperCAmelCase     :						Optional[int]					)      ->			str:
							if pretrained_model_name_or_path in TaTokenizer.max_model_input_sizes:
													UpperCAmelCase_=       TaTokenizer.max_model_input_sizes[pretrained_model_name_or_path]
													if init_max_model_length is not None and init_max_model_length != max_model_length:
																			return init_max_model_length
													elif init_max_model_length is None:
																			warnings.warn(
																			    """This tokenizer was incorrectly instantiated with a model max length of"""
																			    F""" {deprecated_max_model_length} which will be corrected in Transformers v5.\nFor now, this"""
																			    """ behavior is kept to avoid breaking backwards compatibility when padding/encoding with"""
																			    """ `truncation is True`.\n- Be aware that you SHOULD NOT rely on"""
																			    F""" {pretrained_model_name_or_path} automatically truncating your input to"""
																			    F""" {deprecated_max_model_length} when padding/encoding.\n- If you want to encode/pad to sequences"""
																			    F""" longer than {deprecated_max_model_length} you can either instantiate this tokenizer with"""
																			    """ `model_max_length` or pass `max_length` when encoding/padding.\n- To avoid this warning, please"""
																			    """ instantiate this tokenizer with `model_max_length` set to your preferred value."""						,  __UpperCAmelCase						,  )

							return max_model_length




	@property
	def   _SCREAMING_SNAKE_CASE		(     self     :						Optional[Any]					)      ->			str:
							return self.sp_model.get_piece_size() + self._extra_ids




	def   _SCREAMING_SNAKE_CASE		(     self     :						Optional[Any]					)      ->			Any:
							UpperCAmelCase_=       {self.convert_ids_to_tokens(__UpperCAmelCase					): i for i in range(self.vocab_size					)}
							vocab.update(self.added_tokens_encoder					)
							return vocab




	def   _SCREAMING_SNAKE_CASE		(     self     :						int						,  __UpperCAmelCase     :						List[int]						,  __UpperCAmelCase     :						Optional[List[int]] = None						,  __UpperCAmelCase     :						bool = False					)      ->			List[int]:
							if already_has_special_tokens:
													return super().get_special_tokens_mask(
													    token_ids_a=__UpperCAmelCase						,  token_ids_a=__UpperCAmelCase						,  already_has_special_tokens=__UpperCAmelCase					)

							# normal case: some special tokens
							if token_ids_a is None:
													return ([0] * len(__UpperCAmelCase					)) + [1]
							return ([0] * len(__UpperCAmelCase					)) + [1] + ([0] * len(__UpperCAmelCase					)) + [1]




	def   _SCREAMING_SNAKE_CASE		(     self     :						int					)      ->			int:
							return list(
							    set(filter(lambda __UpperCAmelCase					: bool(re.search(r"""<extra_id_\d+>"""						,  __UpperCAmelCase					)					) is not None						,  self.additional_special_tokens					)					)					)




	def   _SCREAMING_SNAKE_CASE		(     self     :						Dict					)      ->			int:
							return [self._convert_token_to_id(__UpperCAmelCase					) for token in self.get_sentinel_tokens()]




	def   _SCREAMING_SNAKE_CASE		(     self     :						Optional[int]						,  __UpperCAmelCase     :						List[int]					)      ->			List[int]:
							if len(__UpperCAmelCase					) > 0 and token_ids[-1] == self.eos_token_id:
													warnings.warn(
													    F"""This sequence already has {self.eos_token}. In future versions this behavior may lead to duplicated"""
													    """ eos tokens being added."""					)
													return token_ids
							else:
													return token_ids + [self.eos_token_id]




	def   _SCREAMING_SNAKE_CASE		(     self     :						Tuple						,  __UpperCAmelCase     :						List[int]						,  __UpperCAmelCase     :						Optional[List[int]] = None					)      ->			List[int]:
							UpperCAmelCase_=       [self.eos_token_id]

							if token_ids_a is None:
													return len(token_ids_a + eos					) * [0]
							return len(token_ids_a + eos + token_ids_a + eos					) * [0]




	def   _SCREAMING_SNAKE_CASE		(     self     :						Optional[int]						,  __UpperCAmelCase     :						List[int]						,  __UpperCAmelCase     :						Optional[List[int]] = None					)      ->			List[int]:
							UpperCAmelCase_=       self._add_eos_if_not_present(__UpperCAmelCase					)
							if token_ids_a is None:
													return token_ids_a
							else:
													UpperCAmelCase_=       self._add_eos_if_not_present(__UpperCAmelCase					)
													return token_ids_a + token_ids_a




	def __getstate__(     self     :						int					)      ->			List[Any]:
							UpperCAmelCase_=       self.__dict__.copy()
							UpperCAmelCase_=       None
							return state




	def __setstate__(     self     :						Any						,  __UpperCAmelCase     :						Any					)      ->			int:
							UpperCAmelCase_=       d

							# for backward compatibility
							if not hasattr(self						,  """sp_model_kwargs"""					):
													UpperCAmelCase_=       {}

							UpperCAmelCase_=       spm.SentencePieceProcessor(**self.sp_model_kwargs					)
							self.sp_model.Load(self.vocab_file					)




	def   _SCREAMING_SNAKE_CASE		(     self     :						Any						,  __UpperCAmelCase     :						"TextInput"						,  **__UpperCAmelCase     :						str					)      ->			List[str]:
							# Replace the SPIECE_UNDERLINE with a space to make sure SPIECE_UNDERLINE is only used at
							# the beginning of the text
							if not self.legacy:
													UpperCAmelCase_=       SPIECE_UNDERLINE + text.replace(__UpperCAmelCase						,  """ """					)
							return super().tokenize(__UpperCAmelCase						,  **__UpperCAmelCase					)




	def   _SCREAMING_SNAKE_CASE		(     self     :						List[str]						,  __UpperCAmelCase     :						Dict						,  **__UpperCAmelCase     :						List[str]					)      ->			Optional[int]:
							if not self.legacy:
													UpperCAmelCase_=       text.startswith(__UpperCAmelCase					)
													if is_first:
																			UpperCAmelCase_=       text[1:]

							UpperCAmelCase_=       self.sp_model.encode(__UpperCAmelCase						,  out_type=__UpperCAmelCase					)

							if not self.legacy and not is_first and not text.startswith(""" """					) and tokens[0].startswith(__UpperCAmelCase					):
													UpperCAmelCase_=       ([tokens[0][1:]] if len(tokens[0]					) > 1 else []) + tokens[1:]
							return tokens




	def   _SCREAMING_SNAKE_CASE		(     self     :						Union[str, Any]						,  __UpperCAmelCase     :						Tuple					)      ->			List[str]:
							if token.startswith("""<extra_id_"""					):
													UpperCAmelCase_=       re.match(r"""<extra_id_(\d+)>"""						,  __UpperCAmelCase					)
													UpperCAmelCase_=       int(match.group(1					)					)
													return self.vocab_size - num - 1
							return self.sp_model.piece_to_id(__UpperCAmelCase					)




	def   _SCREAMING_SNAKE_CASE		(     self     :						int						,  __UpperCAmelCase     :						List[Any]					)      ->			Union[str, Any]:
							if index < self.sp_model.get_piece_size():
													UpperCAmelCase_=       self.sp_model.IdToPiece(__UpperCAmelCase					)
							else:
													UpperCAmelCase_=       F"""<extra_id_{self.vocab_size - 1 - index}>"""
							return token




	def   _SCREAMING_SNAKE_CASE		(     self     :						Dict						,  __UpperCAmelCase     :						List[str]					)      ->			str:
							UpperCAmelCase_=       []
							UpperCAmelCase_=       """"""
							UpperCAmelCase_=       False
							for token in tokens:
													# make sure that special tokens are not decoded using sentencepiece model
													if token in self.all_special_tokens:
																			if not prev_is_special:
																									out_string += " "
																			out_string += self.sp_model.decode(__UpperCAmelCase					) + token
																			UpperCAmelCase_=       True
																			UpperCAmelCase_=       []
													else:
																			current_sub_tokens.append(__UpperCAmelCase					)
																			UpperCAmelCase_=       False
							out_string += self.sp_model.decode(__UpperCAmelCase					)
							return out_string.strip()




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

							if os.path.abspath(self.vocab_file					) != os.path.abspath(__UpperCAmelCase					) and os.path.isfile(self.vocab_file					):
													copyfile(self.vocab_file						,  __UpperCAmelCase					)
							elif not os.path.isfile(self.vocab_file					):
													with open(__UpperCAmelCase						,  """wb"""					) as fi:
																			UpperCAmelCase_=       self.sp_model.serialized_model_proto()
																			fi.write(__UpperCAmelCase					)

							return (out_vocab_file,)









import unittest

from parameterized import parameterized

from transformers import AutoTokenizer, GPTNeoXConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, 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 (
	    GPTNeoXForCausalLM,
	    GPTNeoXForQuestionAnswering,
	    GPTNeoXForSequenceClassification,
	    GPTNeoXForTokenClassification,
	    GPTNeoXModel,
	)






class 		lowercase						:






	"""simple docstring"""


	def __init__(     self     :						Any						,  __UpperCAmelCase     :						str						,  __UpperCAmelCase     :						List[Any]=13						,  __UpperCAmelCase     :						Dict=7						,  __UpperCAmelCase     :						Optional[Any]=True						,  __UpperCAmelCase     :						Union[str, Any]=True						,  __UpperCAmelCase     :						Any=True						,  __UpperCAmelCase     :						Dict=True						,  __UpperCAmelCase     :						Dict=99						,  __UpperCAmelCase     :						Union[str, Any]=64						,  __UpperCAmelCase     :						Dict=5						,  __UpperCAmelCase     :						int=4						,  __UpperCAmelCase     :						int=37						,  __UpperCAmelCase     :						Dict="gelu"						,  __UpperCAmelCase     :						List[Any]=0.1						,  __UpperCAmelCase     :						int=0.1						,  __UpperCAmelCase     :						Union[str, Any]=512						,  __UpperCAmelCase     :						Any=16						,  __UpperCAmelCase     :						Union[str, Any]=2						,  __UpperCAmelCase     :						List[str]=0.02						,  __UpperCAmelCase     :						Union[str, Any]=3						,  __UpperCAmelCase     :						Tuple=4						,  __UpperCAmelCase     :						str=None						,  )      ->			str:
							UpperCAmelCase_=       parent
							UpperCAmelCase_=       batch_size
							UpperCAmelCase_=       seq_length
							UpperCAmelCase_=       is_training
							UpperCAmelCase_=       use_input_mask
							UpperCAmelCase_=       use_token_type_ids
							UpperCAmelCase_=       use_labels
							UpperCAmelCase_=       vocab_size
							UpperCAmelCase_=       hidden_size
							UpperCAmelCase_=       num_hidden_layers
							UpperCAmelCase_=       num_attention_heads
							UpperCAmelCase_=       intermediate_size
							UpperCAmelCase_=       hidden_act
							UpperCAmelCase_=       hidden_dropout_prob
							UpperCAmelCase_=       attention_probs_dropout_prob
							UpperCAmelCase_=       max_position_embeddings
							UpperCAmelCase_=       type_vocab_size
							UpperCAmelCase_=       type_sequence_label_size
							UpperCAmelCase_=       initializer_range
							UpperCAmelCase_=       num_labels
							UpperCAmelCase_=       num_choices
							UpperCAmelCase_=       scope
							UpperCAmelCase_=       vocab_size - 1




	def   _SCREAMING_SNAKE_CASE		(     self     :						Union[str, Any]					)      ->			List[Any]:
							UpperCAmelCase_=       ids_tensor([self.batch_size, self.seq_length]						,  self.vocab_size					)

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

							UpperCAmelCase_=       None
							if self.use_labels:
													UpperCAmelCase_=       ids_tensor([self.batch_size, self.seq_length]						,  self.num_labels					)

							UpperCAmelCase_=       self.get_config()

							return config, input_ids, input_mask, token_labels




	def   _SCREAMING_SNAKE_CASE		(     self     :						str					)      ->			Union[str, Any]:
							return GPTNeoXConfig(
							    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=__UpperCAmelCase						,  initializer_range=self.initializer_range						,  pad_token_id=self.pad_token_id						,  )




	def   _SCREAMING_SNAKE_CASE		(     self     :						Tuple					)      ->			Optional[int]:
							UpperCAmelCase_,       UpperCAmelCase_,       UpperCAmelCase_,       UpperCAmelCase_=       self.prepare_config_and_inputs()

							UpperCAmelCase_=       True

							return config, input_ids, input_mask, token_labels




	def   _SCREAMING_SNAKE_CASE		(     self     :						Tuple						,  __UpperCAmelCase     :						List[Any]						,  __UpperCAmelCase     :						Dict						,  __UpperCAmelCase     :						Union[str, Any]					)      ->			Optional[int]:
							UpperCAmelCase_=       GPTNeoXModel(config=__UpperCAmelCase					)
							model.to(__UpperCAmelCase					)
							model.eval()
							UpperCAmelCase_=       model(__UpperCAmelCase						,  attention_mask=__UpperCAmelCase					)
							UpperCAmelCase_=       model(__UpperCAmelCase					)
							self.parent.assertEqual(result.last_hidden_state.shape						,  (self.batch_size, self.seq_length, self.hidden_size)					)




	def   _SCREAMING_SNAKE_CASE		(     self     :						Any						,  __UpperCAmelCase     :						Optional[Any]						,  __UpperCAmelCase     :						List[str]						,  __UpperCAmelCase     :						Tuple					)      ->			Dict:
							UpperCAmelCase_=       True
							UpperCAmelCase_=       GPTNeoXModel(__UpperCAmelCase					)
							model.to(__UpperCAmelCase					)
							model.eval()
							UpperCAmelCase_=       model(__UpperCAmelCase						,  attention_mask=__UpperCAmelCase					)
							self.parent.assertEqual(result.last_hidden_state.shape						,  (self.batch_size, self.seq_length, self.hidden_size)					)




	def   _SCREAMING_SNAKE_CASE		(     self     :						Tuple						,  __UpperCAmelCase     :						List[Any]						,  __UpperCAmelCase     :						str						,  __UpperCAmelCase     :						Optional[Any]						,  __UpperCAmelCase     :						Dict					)      ->			int:
							UpperCAmelCase_=       GPTNeoXForCausalLM(config=__UpperCAmelCase					)
							model.to(__UpperCAmelCase					)
							model.eval()
							UpperCAmelCase_=       model(__UpperCAmelCase						,  attention_mask=__UpperCAmelCase						,  labels=__UpperCAmelCase					)
							self.parent.assertEqual(result.logits.shape						,  (self.batch_size, self.seq_length, self.vocab_size)					)




	def   _SCREAMING_SNAKE_CASE		(     self     :						Union[str, Any]						,  __UpperCAmelCase     :						Tuple						,  __UpperCAmelCase     :						List[str]						,  __UpperCAmelCase     :						str						,  __UpperCAmelCase     :						List[Any]					)      ->			Union[str, Any]:
							UpperCAmelCase_=       self.num_labels
							UpperCAmelCase_=       GPTNeoXForQuestionAnswering(__UpperCAmelCase					)
							model.to(__UpperCAmelCase					)
							model.eval()
							UpperCAmelCase_=       model(__UpperCAmelCase						,  attention_mask=__UpperCAmelCase					)
							self.parent.assertEqual(result.start_logits.shape						,  (self.batch_size, self.seq_length)					)
							self.parent.assertEqual(result.end_logits.shape						,  (self.batch_size, self.seq_length)					)




	def   _SCREAMING_SNAKE_CASE		(     self     :						Optional[int]						,  __UpperCAmelCase     :						List[Any]						,  __UpperCAmelCase     :						Tuple						,  __UpperCAmelCase     :						Optional[Any]						,  __UpperCAmelCase     :						List[str]					)      ->			Union[str, Any]:
							UpperCAmelCase_=       self.num_labels
							UpperCAmelCase_=       GPTNeoXForSequenceClassification(__UpperCAmelCase					)
							model.to(__UpperCAmelCase					)
							model.eval()
							UpperCAmelCase_=       ids_tensor([self.batch_size]						,  self.type_sequence_label_size					)
							UpperCAmelCase_=       model(__UpperCAmelCase						,  attention_mask=__UpperCAmelCase						,  labels=__UpperCAmelCase					)
							self.parent.assertEqual(result.logits.shape						,  (self.batch_size, self.num_labels)					)




	def   _SCREAMING_SNAKE_CASE		(     self     :						Optional[int]						,  __UpperCAmelCase     :						Dict						,  __UpperCAmelCase     :						List[Any]						,  __UpperCAmelCase     :						str						,  __UpperCAmelCase     :						Dict					)      ->			Dict:
							UpperCAmelCase_=       self.num_labels
							UpperCAmelCase_=       GPTNeoXForTokenClassification(__UpperCAmelCase					)
							model.to(__UpperCAmelCase					)
							model.eval()
							UpperCAmelCase_=       model(__UpperCAmelCase						,  attention_mask=__UpperCAmelCase						,  labels=__UpperCAmelCase					)
							self.parent.assertEqual(result.logits.shape						,  (self.batch_size, self.seq_length, self.num_labels)					)




	def   _SCREAMING_SNAKE_CASE		(     self     :						Union[str, Any]						,  __UpperCAmelCase     :						Union[str, Any]						,  __UpperCAmelCase     :						List[str]						,  __UpperCAmelCase     :						int					)      ->			Optional[int]:
							UpperCAmelCase_=       True
							UpperCAmelCase_=       GPTNeoXForCausalLM(config=__UpperCAmelCase					)
							model.to(__UpperCAmelCase					)
							model.eval()

							# first forward pass
							UpperCAmelCase_=       model(__UpperCAmelCase						,  attention_mask=__UpperCAmelCase						,  use_cache=__UpperCAmelCase					)
							UpperCAmelCase_=       outputs.past_key_values

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

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

							UpperCAmelCase_=       model(__UpperCAmelCase						,  attention_mask=__UpperCAmelCase						,  output_hidden_states=__UpperCAmelCase					)
							UpperCAmelCase_=       output_from_no_past["""hidden_states"""][0]
							UpperCAmelCase_=       model(
							    __UpperCAmelCase						,  attention_mask=__UpperCAmelCase						,  past_key_values=__UpperCAmelCase						,  output_hidden_states=__UpperCAmelCase						,  )["""hidden_states"""][0]

							# select random slice
							UpperCAmelCase_=       ids_tensor((1,)						,  output_from_past.shape[-1]					).item()
							UpperCAmelCase_=       output_from_no_past[:, -3:, random_slice_idx].detach()
							UpperCAmelCase_=       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(__UpperCAmelCase						,  __UpperCAmelCase						,  atol=1E-3					)					)




	def   _SCREAMING_SNAKE_CASE		(     self     :						str					)      ->			List[str]:
							UpperCAmelCase_=       self.prepare_config_and_inputs()
							UpperCAmelCase_,       UpperCAmelCase_,       UpperCAmelCase_,       UpperCAmelCase_=       config_and_inputs
							UpperCAmelCase_=       {"""input_ids""": input_ids, """attention_mask""": input_mask}
							return config, inputs_dict






@require_torch
class 		lowercase						(			snake_case__			,   snake_case__			,   snake_case__			,   unittest.TestCase):






	"""simple docstring"""


	a__				: Union[str, Any]		       =							(
	    (
	        GPTNeoXModel,
	        GPTNeoXForCausalLM,
	        GPTNeoXForQuestionAnswering,
	        GPTNeoXForSequenceClassification,
	        GPTNeoXForTokenClassification,
	    )
	    if is_torch_available()
	    else ()
	)
	a__				: Any		       =							(GPTNeoXForCausalLM,) if is_torch_available() else ()
	a__				: str		       =							(
	    {
	        "feature-extraction": GPTNeoXModel,
	        "question-answering": GPTNeoXForQuestionAnswering,
	        "text-classification": GPTNeoXForSequenceClassification,
	        "text-generation": GPTNeoXForCausalLM,
	        "token-classification": GPTNeoXForTokenClassification,
	        "zero-shot": GPTNeoXForSequenceClassification,
	    }
	    if is_torch_available()
	    else {}
	)
	a__				: Optional[int]		       =							False
	a__				: Tuple		       =							False
	a__				: int		       =							False
	a__				: List[Any]		       =							False




	def   _SCREAMING_SNAKE_CASE		(     self     :						Optional[Any]					)      ->			Dict:
							UpperCAmelCase_=       GPTNeoXModelTester(self					)
							UpperCAmelCase_=       ConfigTester(self						,  config_class=__UpperCAmelCase						,  hidden_size=64						,  num_attention_heads=8					)




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




	def   _SCREAMING_SNAKE_CASE		(     self     :						List[str]					)      ->			List[Any]:
							UpperCAmelCase_,       UpperCAmelCase_,       UpperCAmelCase_,       UpperCAmelCase_=       self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(__UpperCAmelCase						,  __UpperCAmelCase						,  __UpperCAmelCase					)




	def   _SCREAMING_SNAKE_CASE		(     self     :						List[str]					)      ->			Union[str, Any]:
							UpperCAmelCase_,       UpperCAmelCase_,       UpperCAmelCase_,       UpperCAmelCase_=       self.model_tester.prepare_config_and_inputs_for_decoder()
							self.model_tester.create_and_check_model_as_decoder(__UpperCAmelCase						,  __UpperCAmelCase						,  __UpperCAmelCase					)




	def   _SCREAMING_SNAKE_CASE		(     self     :						Tuple					)      ->			Tuple:
							# This regression test was failing with PyTorch < 1.3
							UpperCAmelCase_,       UpperCAmelCase_,       UpperCAmelCase_,       UpperCAmelCase_=       self.model_tester.prepare_config_and_inputs_for_decoder()

							UpperCAmelCase_=       None

							self.model_tester.create_and_check_model_as_decoder(__UpperCAmelCase						,  __UpperCAmelCase						,  __UpperCAmelCase					)




	def   _SCREAMING_SNAKE_CASE		(     self     :						Dict					)      ->			Union[str, Any]:
							UpperCAmelCase_,       UpperCAmelCase_,       UpperCAmelCase_,       UpperCAmelCase_=       self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_decoder_model_past_large_inputs(__UpperCAmelCase						,  __UpperCAmelCase						,  __UpperCAmelCase					)




	def   _SCREAMING_SNAKE_CASE		(     self     :						Any					)      ->			Any:
							UpperCAmelCase_=       self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_causal_lm(*__UpperCAmelCase					)




	def   _SCREAMING_SNAKE_CASE		(     self     :						Optional[Any]					)      ->			int:
							UpperCAmelCase_=       self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_question_answering(*__UpperCAmelCase					)




	def   _SCREAMING_SNAKE_CASE		(     self     :						Tuple					)      ->			Union[str, Any]:
							UpperCAmelCase_=       self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_sequence_classification(*__UpperCAmelCase					)




	def   _SCREAMING_SNAKE_CASE		(     self     :						List[Any]					)      ->			List[Any]:
							UpperCAmelCase_=       self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_token_classification(*__UpperCAmelCase					)




	@unittest.skip(reason="""Feed forward chunking is not implemented"""					)
	def   _SCREAMING_SNAKE_CASE		(     self     :						Tuple					)      ->			Union[str, Any]:
							pass




	@parameterized.expand([("""linear""",), ("""dynamic""",)]					)
	def   _SCREAMING_SNAKE_CASE		(     self     :						str						,  __UpperCAmelCase     :						Any					)      ->			Dict:
							UpperCAmelCase_,       UpperCAmelCase_=       self.model_tester.prepare_config_and_inputs_for_common()
							UpperCAmelCase_=       ids_tensor([1, 10]						,  config.vocab_size					)
							UpperCAmelCase_=       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_=       GPTNeoXModel(__UpperCAmelCase					)
							original_model.to(__UpperCAmelCase					)
							original_model.eval()
							UpperCAmelCase_=       original_model(__UpperCAmelCase					).last_hidden_state
							UpperCAmelCase_=       original_model(__UpperCAmelCase					).last_hidden_state

							set_seed(42					)  # Fixed seed at init time so the two models get the same random weights
							UpperCAmelCase_=       {"""type""": scaling_type, """factor""": 10.0}
							UpperCAmelCase_=       GPTNeoXModel(__UpperCAmelCase					)
							scaled_model.to(__UpperCAmelCase					)
							scaled_model.eval()
							UpperCAmelCase_=       scaled_model(__UpperCAmelCase					).last_hidden_state
							UpperCAmelCase_=       scaled_model(__UpperCAmelCase					).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(__UpperCAmelCase						,  __UpperCAmelCase						,  atol=1E-5					)					)
							else:
													self.assertFalse(torch.allclose(__UpperCAmelCase						,  __UpperCAmelCase						,  atol=1E-5					)					)

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






@require_torch
class 		lowercase						(			unittest.TestCase):






	"""simple docstring"""


	@slow
	def   _SCREAMING_SNAKE_CASE		(     self     :						int					)      ->			Tuple:
							UpperCAmelCase_=       AutoTokenizer.from_pretrained("""EleutherAI/pythia-410m-deduped"""					)
							for checkpointing in [True, False]:
													UpperCAmelCase_=       GPTNeoXForCausalLM.from_pretrained("""EleutherAI/pythia-410m-deduped"""					)

													if checkpointing:
																			model.gradient_checkpointing_enable()
													else:
																			model.gradient_checkpointing_disable()
													model.to(__UpperCAmelCase					)

													UpperCAmelCase_=       tokenizer("""My favorite food is"""						,  return_tensors="""pt"""					).to(__UpperCAmelCase					)
													# The hub repo. is updated on 2023-04-04, resulting in poor outputs.
													# See: https://github.com/huggingface/transformers/pull/24193
													UpperCAmelCase_=       """My favorite food is a good old-fashioned, old-fashioned, old-fashioned.\n\nI'm not sure"""

													UpperCAmelCase_=       model.generate(**__UpperCAmelCase						,  do_sample=__UpperCAmelCase						,  max_new_tokens=20					)
													UpperCAmelCase_=       tokenizer.batch_decode(__UpperCAmelCase					)[0]

													self.assertEqual(__UpperCAmelCase						,  __UpperCAmelCase					)




