"""simple docstring"""




import os
from pathlib import Path

import numpy as np
import pytest
from pack_dataset import pack_data_dir
from parameterized import parameterized
from save_len_file import save_len_file
from torch.utils.data import DataLoader

from transformers import AutoTokenizer
from transformers.models.mbart.modeling_mbart import shift_tokens_right
from transformers.testing_utils import TestCasePlus, slow
from utils import FAIRSEQ_AVAILABLE, DistributedSortishSampler, LegacySeqaSeqDataset, SeqaSeqDataset


__lowerCamelCase							 =     "bert-base-cased"
__lowerCamelCase							 =     "google/pegasus-xsum"
__lowerCamelCase							 =     [" Sam ate lunch today.", "Sams lunch ingredients."]
__lowerCamelCase							 =     ["A very interesting story about what I ate for lunch.", "Avocado, celery, turkey, coffee"]
__lowerCamelCase							 =     "patrickvonplaten/t5-tiny-random"
__lowerCamelCase							 =     "sshleifer/bart-tiny-random"
__lowerCamelCase							 =     "sshleifer/tiny-mbart"
__lowerCamelCase							 =     "sshleifer/tiny-marian-en-de"






def      UpperCAmelCase    (		UpperCamelCase__				,			UpperCamelCase__ ):






							"""simple docstring"""





							A__		   =							'\n'.join(UpperCamelCase__ )
							Path(UpperCamelCase__ ).open('w' ).writelines(UpperCamelCase__ )






def      UpperCAmelCase    (		UpperCamelCase__ ):






							"""simple docstring"""





							for split in ["train", "val", "test"]:
														_dump_articles(os.path.join(UpperCamelCase__				,			F'''{split}.source''' )				,			UpperCamelCase__ )
														_dump_articles(os.path.join(UpperCamelCase__				,			F'''{split}.target''' )				,			UpperCamelCase__ )
							return tmp_dir


class 			UpperCamelCase__(	__A       ):





						@parameterized.expand(
						    [
						        MBART_TINY,
						        MARIAN_TINY,
						        T5_TINY,
						        BART_TINY,
						        PEGASUS_XSUM,
						    ]						,)
						@slow
						def  snake_case__		(				self						,__UpperCAmelCase   )    -> Any:
													A__		   =							AutoTokenizer.from_pretrained(__UpperCAmelCase   )
													A__		   =							make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()   )
													A__		   =							max(len(tokenizer.encode(__UpperCAmelCase   )   ) for a in ARTICLES   )
													A__		   =							max(len(tokenizer.encode(__UpperCAmelCase   )   ) for a in SUMMARIES   )
													A__		   =							4
													A__		   =							8
													assert max_len_target > max_src_len  # Will be truncated
													assert max_len_source > max_src_len  # Will be truncated
													A__   ,     A__		   =							'ro_RO', 'de_DE'  # ignored for all but mbart, but never causes error.
													A__		   =							SeqaSeqDataset(
													    __UpperCAmelCase						,data_dir=__UpperCAmelCase						,type_path='train'						,max_source_length=__UpperCAmelCase						,max_target_length=__UpperCAmelCase						,src_lang=__UpperCAmelCase						,tgt_lang=__UpperCAmelCase						,)
													A__		   =							DataLoader(__UpperCAmelCase						,batch_size=2						,collate_fn=train_dataset.collate_fn   )
													for batch in dataloader:
																				assert isinstance(__UpperCAmelCase						,__UpperCAmelCase   )
																				assert batch["attention_mask"].shape == batch["input_ids"].shape
																				# show that articles were trimmed.
																				assert batch["input_ids"].shape[1] == max_src_len
																				# show that targets are the same len
																				assert batch["labels"].shape[1] == max_tgt_len
																				if tok_name != MBART_TINY:
																											continue
																				# check language codes in correct place
																				A__		   =							shift_tokens_right(batch['labels']						,tokenizer.pad_token_id   )
																				assert batch["decoder_input_ids"][0, 0].item() == tokenizer.lang_code_to_id[tgt_lang]
																				assert batch["decoder_input_ids"][0, -1].item() == tokenizer.eos_token_id
																				assert batch["input_ids"][0, -2].item() == tokenizer.eos_token_id
																				assert batch["input_ids"][0, -1].item() == tokenizer.lang_code_to_id[src_lang]

																				break  # No need to test every batch





						@parameterized.expand([BART_TINY, BERT_BASE_CASED]   )
						def  snake_case__		(				self						,__UpperCAmelCase   )    -> Union[str, Any]:
													A__		   =							AutoTokenizer.from_pretrained(__UpperCAmelCase   )
													A__		   =							make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()   )
													A__		   =							max(len(tokenizer.encode(__UpperCAmelCase   )   ) for a in ARTICLES   )
													A__		   =							max(len(tokenizer.encode(__UpperCAmelCase   )   ) for a in SUMMARIES   )
													A__		   =							4
													A__		   =							LegacySeqaSeqDataset(
													    __UpperCAmelCase						,data_dir=__UpperCAmelCase						,type_path='train'						,max_source_length=20						,max_target_length=__UpperCAmelCase						,)
													A__		   =							DataLoader(__UpperCAmelCase						,batch_size=2						,collate_fn=train_dataset.collate_fn   )
													for batch in dataloader:
																				assert batch["attention_mask"].shape == batch["input_ids"].shape
																				# show that articles were trimmed.
																				assert batch["input_ids"].shape[1] == max_len_source
																				assert 20 >= batch["input_ids"].shape[1]  # trimmed significantly
																				# show that targets were truncated
																				assert batch["labels"].shape[1] == trunc_target  # Truncated
																				assert max_len_target > trunc_target  # Truncated
																				break  # No need to test every batch





						def  snake_case__		(				self   )    -> Tuple:
													A__		   =							AutoTokenizer.from_pretrained('facebook/mbart-large-cc25'   )

													A__		   =							Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()   )   )
													A__		   =							tmp_dir.joinpath('train.source'   ).open().readlines()
													A__		   =							Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()   )   )
													pack_data_dir(__UpperCAmelCase						,__UpperCAmelCase						,1_28						,__UpperCAmelCase   )
													A__		   =							{x.name for x in tmp_dir.iterdir()}
													A__		   =							{x.name for x in save_dir.iterdir()}
													A__		   =							save_dir.joinpath('train.source'   ).open().readlines()
													# orig: [' Sam ate lunch today.\n', 'Sams lunch ingredients.']
													# desired_packed: [' Sam ate lunch today.\n Sams lunch ingredients.']
													assert len(__UpperCAmelCase   ) < len(__UpperCAmelCase   )
													assert len(__UpperCAmelCase   ) == 1
													assert len(packed_examples[0]   ) == sum(len(__UpperCAmelCase   ) for x in orig_examples   )
													assert orig_paths == new_paths





						@pytest.mark.skipif(not FAIRSEQ_AVAILABLE						,reason='This test requires fairseq'   )
						def  snake_case__		(				self   )    -> List[str]:
													if not FAIRSEQ_AVAILABLE:
																				return
													A__   ,     A__   ,     A__		   =							self._get_dataset(max_len=64   )
													A__		   =							64
													A__		   =							ds.make_dynamic_sampler(__UpperCAmelCase						,required_batch_size_multiple=__UpperCAmelCase   )
													A__		   =							[len(__UpperCAmelCase   ) for x in batch_sampler]
													assert len(set(__UpperCAmelCase   )   ) > 1  # it's not dynamic batch size if every batch is the same length
													assert sum(__UpperCAmelCase   ) == len(__UpperCAmelCase   )  # no dropped or added examples
													A__		   =							DataLoader(__UpperCAmelCase						,batch_sampler=__UpperCAmelCase						,collate_fn=ds.collate_fn						,num_workers=2   )
													A__		   =							[]
													A__		   =							[]
													for batch in data_loader:
																				A__		   =							batch['input_ids'].shape
																				A__		   =							src_shape[0]
																				assert bs % required_batch_size_multiple == 0 or bs < required_batch_size_multiple
																				A__		   =							np.product(batch['input_ids'].shape   )
																				num_src_per_batch.append(__UpperCAmelCase   )
																				if num_src_tokens > (max_tokens * 1.1):
																											failures.append(__UpperCAmelCase   )
													assert num_src_per_batch[0] == max(__UpperCAmelCase   )
													if failures:
																				raise AssertionError(f'''too many tokens in {len(__UpperCAmelCase   )} batches'''   )





						def  snake_case__		(				self   )    -> List[str]:
													A__   ,     A__   ,     A__		   =							self._get_dataset(max_len=5_12   )
													A__		   =							2
													A__		   =							ds.make_sortish_sampler(__UpperCAmelCase						,shuffle=__UpperCAmelCase   )

													A__		   =							DataLoader(__UpperCAmelCase						,batch_size=__UpperCAmelCase						,collate_fn=ds.collate_fn						,num_workers=2   )
													A__		   =							DataLoader(__UpperCAmelCase						,batch_size=__UpperCAmelCase						,collate_fn=ds.collate_fn						,num_workers=2						,sampler=__UpperCAmelCase   )

													A__		   =							tokenizer.pad_token_id

													def count_pad_tokens(__UpperCAmelCase						,__UpperCAmelCase="input_ids"   ):
																				return [batch[k].eq(__UpperCAmelCase   ).sum().item() for batch in data_loader]

													assert sum(count_pad_tokens(__UpperCAmelCase						,k='labels'   )   ) < sum(count_pad_tokens(__UpperCAmelCase						,k='labels'   )   )
													assert sum(count_pad_tokens(__UpperCAmelCase   )   ) < sum(count_pad_tokens(__UpperCAmelCase   )   )
													assert len(__UpperCAmelCase   ) == len(__UpperCAmelCase   )





						def  snake_case__		(				self						,__UpperCAmelCase=10_00						,__UpperCAmelCase=1_28   )    -> List[str]:
													if os.getenv('USE_REAL_DATA'						,__UpperCAmelCase   ):
																				A__		   =							'examples/seq2seq/wmt_en_ro'
																				A__		   =							max_len * 2 * 64
																				if not Path(__UpperCAmelCase   ).joinpath('train.len'   ).exists():
																											save_len_file(__UpperCAmelCase						,__UpperCAmelCase   )
													else:
																				A__		   =							'examples/seq2seq/test_data/wmt_en_ro'
																				A__		   =							max_len * 4
																				save_len_file(__UpperCAmelCase						,__UpperCAmelCase   )

													A__		   =							AutoTokenizer.from_pretrained(__UpperCAmelCase   )
													A__		   =							SeqaSeqDataset(
													    __UpperCAmelCase						,data_dir=__UpperCAmelCase						,type_path='train'						,max_source_length=__UpperCAmelCase						,max_target_length=__UpperCAmelCase						,n_obs=__UpperCAmelCase						,)
													return ds, max_tokens, tokenizer





						def  snake_case__		(				self   )    -> Tuple:
													A__   ,     A__   ,     A__		   =							self._get_dataset()
													A__		   =							set(DistributedSortishSampler(__UpperCAmelCase						,2_56						,num_replicas=2						,rank=0						,add_extra_examples=__UpperCAmelCase   )   )
													A__		   =							set(DistributedSortishSampler(__UpperCAmelCase						,2_56						,num_replicas=2						,rank=1						,add_extra_examples=__UpperCAmelCase   )   )
													assert idsa.intersection(__UpperCAmelCase   ) == set()





						@parameterized.expand(
						    [
						        MBART_TINY,
						        MARIAN_TINY,
						        T5_TINY,
						        BART_TINY,
						        PEGASUS_XSUM,
						    ]						,)
						def  snake_case__		(				self						,__UpperCAmelCase   )    -> Optional[Any]:
													A__		   =							AutoTokenizer.from_pretrained(__UpperCAmelCase						,use_fast=__UpperCAmelCase   )
													if tok_name == MBART_TINY:
																				A__		   =							SeqaSeqDataset(
																				    __UpperCAmelCase						,data_dir=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()   )						,type_path='train'						,max_source_length=4						,max_target_length=8						,src_lang='EN'						,tgt_lang='FR'						,)
																				A__		   =							train_dataset.dataset_kwargs
																				assert "src_lang" in kwargs and "tgt_lang" in kwargs
													else:
																				A__		   =							SeqaSeqDataset(
																				    __UpperCAmelCase						,data_dir=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()   )						,type_path='train'						,max_source_length=4						,max_target_length=8						,)
																				A__		   =							train_dataset.dataset_kwargs
																				assert "add_prefix_space" not in kwargs if tok_name != BART_TINY else "add_prefix_space" in kwargs
																				assert len(__UpperCAmelCase   ) == 1 if tok_name == BART_TINY else len(__UpperCAmelCase   ) == 0






"""simple docstring"""




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 			UpperCamelCase__:





						def __init__(				self						,__UpperCAmelCase						,__UpperCAmelCase=13						,__UpperCAmelCase=7						,__UpperCAmelCase=True						,__UpperCAmelCase=True						,__UpperCAmelCase=True						,__UpperCAmelCase=True						,__UpperCAmelCase=99						,__UpperCAmelCase=64						,__UpperCAmelCase=5						,__UpperCAmelCase=4						,__UpperCAmelCase=37						,__UpperCAmelCase="gelu"						,__UpperCAmelCase=0.1						,__UpperCAmelCase=0.1						,__UpperCAmelCase=5_12						,__UpperCAmelCase=16						,__UpperCAmelCase=2						,__UpperCAmelCase=0.0_2						,__UpperCAmelCase=3						,__UpperCAmelCase=4						,__UpperCAmelCase=None						,)    -> List[Any]:
													A__		   =							parent
													A__		   =							batch_size
													A__		   =							seq_length
													A__		   =							is_training
													A__		   =							use_input_mask
													A__		   =							use_token_type_ids
													A__		   =							use_labels
													A__		   =							vocab_size
													A__		   =							hidden_size
													A__		   =							num_hidden_layers
													A__		   =							num_attention_heads
													A__		   =							intermediate_size
													A__		   =							hidden_act
													A__		   =							hidden_dropout_prob
													A__		   =							attention_probs_dropout_prob
													A__		   =							max_position_embeddings
													A__		   =							type_vocab_size
													A__		   =							type_sequence_label_size
													A__		   =							initializer_range
													A__		   =							num_labels
													A__		   =							num_choices
													A__		   =							scope
													A__		   =							vocab_size - 1





						def  snake_case__		(				self   )    -> str:
													A__		   =							ids_tensor([self.batch_size, self.seq_length]						,self.vocab_size   )

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

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

													A__		   =							self.get_config()

													return config, input_ids, input_mask, token_labels





						def  snake_case__		(				self   )    -> List[str]:
													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  snake_case__		(				self   )    -> List[str]:
													A__   ,     A__   ,     A__   ,     A__		   =							self.prepare_config_and_inputs()

													A__		   =							True

													return config, input_ids, input_mask, token_labels





						def  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase   )    -> Union[str, Any]:
													A__		   =							GPTNeoXModel(config=__UpperCAmelCase   )
													model.to(__UpperCAmelCase   )
													model.eval()
													A__		   =							model(__UpperCAmelCase						,attention_mask=__UpperCAmelCase   )
													A__		   =							model(__UpperCAmelCase   )
													self.parent.assertEqual(result.last_hidden_state.shape						,(self.batch_size, self.seq_length, self.hidden_size)   )





						def  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase   )    -> Optional[Any]:
													A__		   =							True
													A__		   =							GPTNeoXModel(__UpperCAmelCase   )
													model.to(__UpperCAmelCase   )
													model.eval()
													A__		   =							model(__UpperCAmelCase						,attention_mask=__UpperCAmelCase   )
													self.parent.assertEqual(result.last_hidden_state.shape						,(self.batch_size, self.seq_length, self.hidden_size)   )





						def  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase   )    -> Dict:
													A__		   =							GPTNeoXForCausalLM(config=__UpperCAmelCase   )
													model.to(__UpperCAmelCase   )
													model.eval()
													A__		   =							model(__UpperCAmelCase						,attention_mask=__UpperCAmelCase						,labels=__UpperCAmelCase   )
													self.parent.assertEqual(result.logits.shape						,(self.batch_size, self.seq_length, self.vocab_size)   )





						def  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase   )    -> Dict:
													A__		   =							self.num_labels
													A__		   =							GPTNeoXForQuestionAnswering(__UpperCAmelCase   )
													model.to(__UpperCAmelCase   )
													model.eval()
													A__		   =							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  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase   )    -> str:
													A__		   =							self.num_labels
													A__		   =							GPTNeoXForSequenceClassification(__UpperCAmelCase   )
													model.to(__UpperCAmelCase   )
													model.eval()
													A__		   =							ids_tensor([self.batch_size]						,self.type_sequence_label_size   )
													A__		   =							model(__UpperCAmelCase						,attention_mask=__UpperCAmelCase						,labels=__UpperCAmelCase   )
													self.parent.assertEqual(result.logits.shape						,(self.batch_size, self.num_labels)   )





						def  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase   )    -> Dict:
													A__		   =							self.num_labels
													A__		   =							GPTNeoXForTokenClassification(__UpperCAmelCase   )
													model.to(__UpperCAmelCase   )
													model.eval()
													A__		   =							model(__UpperCAmelCase						,attention_mask=__UpperCAmelCase						,labels=__UpperCAmelCase   )
													self.parent.assertEqual(result.logits.shape						,(self.batch_size, self.seq_length, self.num_labels)   )





						def  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase   )    -> Dict:
													A__		   =							True
													A__		   =							GPTNeoXForCausalLM(config=__UpperCAmelCase   )
													model.to(__UpperCAmelCase   )
													model.eval()

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

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

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

													A__		   =							model(__UpperCAmelCase						,attention_mask=__UpperCAmelCase						,output_hidden_states=__UpperCAmelCase   )
													A__		   =							output_from_no_past['hidden_states'][0]
													A__		   =							model(
													    __UpperCAmelCase						,attention_mask=__UpperCAmelCase						,past_key_values=__UpperCAmelCase						,output_hidden_states=__UpperCAmelCase						,)['hidden_states'][0]

													# select random slice
													A__		   =							ids_tensor((1,)						,output_from_past.shape[-1]   ).item()
													A__		   =							output_from_no_past[:, -3:, random_slice_idx].detach()
													A__		   =							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  snake_case__		(				self   )    -> Dict:
													A__		   =							self.prepare_config_and_inputs()
													A__   ,     A__   ,     A__   ,     A__		   =							config_and_inputs
													A__		   =							{'input_ids': input_ids, 'attention_mask': input_mask}
													return config, inputs_dict






@require_torch
class 			UpperCamelCase__(	__A						,  __A						,  __A						,  unittest.TestCase       ):
						lowerCAmelCase__     :  Optional[int]  							=						(
						    (
						        GPTNeoXModel,
						        GPTNeoXForCausalLM,
						        GPTNeoXForQuestionAnswering,
						        GPTNeoXForSequenceClassification,
						        GPTNeoXForTokenClassification,
						    )
						    if is_torch_available()
						    else ()
						)
						lowerCAmelCase__     :  List[Any]  							=						(GPTNeoXForCausalLM,) if is_torch_available() else ()
						lowerCAmelCase__     :  List[str]  							=						(
						    {
						        'feature-extraction': GPTNeoXModel,
						        'question-answering': GPTNeoXForQuestionAnswering,
						        'text-classification': GPTNeoXForSequenceClassification,
						        'text-generation': GPTNeoXForCausalLM,
						        'token-classification': GPTNeoXForTokenClassification,
						        'zero-shot': GPTNeoXForSequenceClassification,
						    }
						    if is_torch_available()
						    else {}
						)
						lowerCAmelCase__     :  Union[str, Any]  							=						False
						lowerCAmelCase__     :  str  							=						False
						lowerCAmelCase__     :  Any  							=						False
						lowerCAmelCase__     :  str  							=						False





						def  snake_case__		(				self   )    -> Tuple:
													A__		   =							GPTNeoXModelTester(self   )
													A__		   =							ConfigTester(self						,config_class=__UpperCAmelCase						,hidden_size=64						,num_attention_heads=8   )





						def  snake_case__		(				self   )    -> str:
													self.config_tester.run_common_tests()





						def  snake_case__		(				self   )    -> List[str]:
													A__   ,     A__   ,     A__   ,     A__		   =							self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_model(__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase   )





						def  snake_case__		(				self   )    -> Dict:
													A__   ,     A__   ,     A__   ,     A__		   =							self.model_tester.prepare_config_and_inputs_for_decoder()
													self.model_tester.create_and_check_model_as_decoder(__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase   )





						def  snake_case__		(				self   )    -> Optional[int]:
													# This regression test was failing with PyTorch < 1.3
													A__   ,     A__   ,     A__   ,     A__		   =							self.model_tester.prepare_config_and_inputs_for_decoder()

													A__		   =							None

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





						def  snake_case__		(				self   )    -> str:
													A__   ,     A__   ,     A__   ,     A__		   =							self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_decoder_model_past_large_inputs(__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase   )





						def  snake_case__		(				self   )    -> Optional[int]:
													A__		   =							self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_for_causal_lm(*__UpperCAmelCase   )





						def  snake_case__		(				self   )    -> List[str]:
													A__		   =							self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_for_question_answering(*__UpperCAmelCase   )





						def  snake_case__		(				self   )    -> Any:
													A__		   =							self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_for_sequence_classification(*__UpperCAmelCase   )





						def  snake_case__		(				self   )    -> List[Any]:
													A__		   =							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  snake_case__		(				self   )    -> str:
													pass





						@parameterized.expand([('linear',), ('dynamic',)]   )
						def  snake_case__		(				self						,__UpperCAmelCase   )    -> Tuple:
													A__   ,     A__		   =							self.model_tester.prepare_config_and_inputs_for_common()
													A__		   =							ids_tensor([1, 10]						,config.vocab_size   )
													A__		   =							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
													A__		   =							GPTNeoXModel(__UpperCAmelCase   )
													original_model.to(__UpperCAmelCase   )
													original_model.eval()
													A__		   =							original_model(__UpperCAmelCase   ).last_hidden_state
													A__		   =							original_model(__UpperCAmelCase   ).last_hidden_state

													set_seed(42   )  # Fixed seed at init time so the two models get the same random weights
													A__		   =							{'type': scaling_type, 'factor': 1_0.0}
													A__		   =							GPTNeoXModel(__UpperCAmelCase   )
													scaled_model.to(__UpperCAmelCase   )
													scaled_model.eval()
													A__		   =							scaled_model(__UpperCAmelCase   ).last_hidden_state
													A__		   =							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 			UpperCamelCase__(	unittest.TestCase       ):





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

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

																				A__		   =							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
																				A__		   =							'My favorite food is a good old-fashioned, old-fashioned, old-fashioned.\n\nI\'m not sure'

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

																				self.assertEqual(__UpperCAmelCase						,__UpperCAmelCase   )





