






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 		lowerCAmelCase     :

					'''simple docstring'''

					def __init__(  self				:	Any		,     __a				:	Optional[Any]		,     __a				:	List[str]=13		,     __a				:	Dict=7		,     __a				:	Optional[Any]=True		,     __a				:	Tuple=True		,     __a				:	Optional[Any]=True		,     __a				:	Dict=True		,     __a				:	Optional[int]=99		,     __a				:	Tuple=64		,     __a				:	Any=5		,     __a				:	Dict=4		,     __a				:	Union[str, Any]=37		,     __a				:	List[str]="gelu"		,     __a				:	int=0.1		,     __a				:	Any=0.1		,     __a				:	int=512		,     __a				:	Union[str, Any]=16		,     __a				:	Optional[Any]=2		,     __a				:	Optional[int]=0.02		,     __a				:	Dict=3		,     __a				:	List[str]=4		,     __a				:	Any=None		,     )			->  int:


												"""simple docstring"""

												__lowercase      :							str				=     parent
												__lowercase      :							Tuple				=     batch_size
												__lowercase      :							Union[str, Any]				=     seq_length
												__lowercase      :							Optional[Any]				=     is_training
												__lowercase      :							List[Any]				=     use_input_mask
												__lowercase      :							Union[str, Any]				=     use_token_type_ids
												__lowercase      :							List[str]				=     use_labels
												__lowercase      :							Optional[Any]				=     vocab_size
												__lowercase      :							Optional[int]				=     hidden_size
												__lowercase      :							List[str]				=     num_hidden_layers
												__lowercase      :							Optional[Any]				=     num_attention_heads
												__lowercase      :							Dict				=     intermediate_size
												__lowercase      :							List[Any]				=     hidden_act
												__lowercase      :							Dict				=     hidden_dropout_prob
												__lowercase      :							Optional[Any]				=     attention_probs_dropout_prob
												__lowercase      :							List[str]				=     max_position_embeddings
												__lowercase      :							int				=     type_vocab_size
												__lowercase      :							Dict				=     type_sequence_label_size
												__lowercase      :							int				=     initializer_range
												__lowercase      :							Dict				=     num_labels
												__lowercase      :							Dict				=     num_choices
												__lowercase      :							Optional[int]				=     scope
												__lowercase      :							Optional[Any]				=     vocab_size - 1



					def       lowerCAmelCase				(  self				:	str							)			->  Union[str, Any]:


												"""simple docstring"""

												__lowercase      :							List[Any]				=     ids_tensor([self.batch_size, self.seq_length]		,     self.vocab_size							)

												__lowercase      :							Union[str, Any]				=     None
												if self.use_input_mask:
																			__lowercase      :							Any				=     random_attention_mask([self.batch_size, self.seq_length]							)

												__lowercase      :							List[Any]				=     None
												if self.use_labels:
																			__lowercase      :							int				=     ids_tensor([self.batch_size, self.seq_length]		,     self.num_labels							)

												__lowercase      :							List[Any]				=     self.get_config()

												return config, input_ids, input_mask, token_labels



					def       lowerCAmelCase				(  self				:	str							)			->  List[str]:


												"""simple docstring"""

												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=__a		,     initializer_range=self.initializer_range		,     pad_token_id=self.pad_token_id		,     )



					def       lowerCAmelCase				(  self				:	List[str]							)			->  Any:


												"""simple docstring"""

												__lowercase			,			__lowercase			,			__lowercase			,			__lowercase      :							Union[str, Any]				=     self.prepare_config_and_inputs()

												__lowercase      :							Tuple				=     True

												return config, input_ids, input_mask, token_labels



					def       lowerCAmelCase				(  self				:	Optional[int]		,     __a				:	int		,     __a				:	List[Any]		,     __a				:	Dict							)			->  int:


												"""simple docstring"""

												__lowercase      :							Tuple				=     GPTNeoXModel(config=__a							)
												model.to(__a							)
												model.eval()
												__lowercase      :							Optional[Any]				=     model(__a		,     attention_mask=__a							)
												__lowercase      :							Any				=     model(__a							)
												self.parent.assertEqual(result.last_hidden_state.shape		,     (self.batch_size, self.seq_length, self.hidden_size)							)



					def       lowerCAmelCase				(  self				:	int		,     __a				:	Any		,     __a				:	Optional[int]		,     __a				:	str							)			->  int:


												"""simple docstring"""

												__lowercase      :							List[str]				=     True
												__lowercase      :							List[str]				=     GPTNeoXModel(__a							)
												model.to(__a							)
												model.eval()
												__lowercase      :							Union[str, Any]				=     model(__a		,     attention_mask=__a							)
												self.parent.assertEqual(result.last_hidden_state.shape		,     (self.batch_size, self.seq_length, self.hidden_size)							)



					def       lowerCAmelCase				(  self				:	Optional[int]		,     __a				:	str		,     __a				:	Tuple		,     __a				:	Any		,     __a				:	Optional[Any]							)			->  List[Any]:


												"""simple docstring"""

												__lowercase      :							Any				=     GPTNeoXForCausalLM(config=__a							)
												model.to(__a							)
												model.eval()
												__lowercase      :							List[Any]				=     model(__a		,     attention_mask=__a		,     labels=__a							)
												self.parent.assertEqual(result.logits.shape		,     (self.batch_size, self.seq_length, self.vocab_size)							)



					def       lowerCAmelCase				(  self				:	Any		,     __a				:	Optional[Any]		,     __a				:	List[str]		,     __a				:	str		,     __a				:	Optional[Any]							)			->  Union[str, Any]:


												"""simple docstring"""

												__lowercase      :							List[str]				=     self.num_labels
												__lowercase      :							List[str]				=     GPTNeoXForQuestionAnswering(__a							)
												model.to(__a							)
												model.eval()
												__lowercase      :							List[str]				=     model(__a		,     attention_mask=__a							)
												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       lowerCAmelCase				(  self				:	List[str]		,     __a				:	Any		,     __a				:	List[Any]		,     __a				:	Union[str, Any]		,     __a				:	Dict							)			->  Dict:


												"""simple docstring"""

												__lowercase      :							Optional[Any]				=     self.num_labels
												__lowercase      :							Optional[Any]				=     GPTNeoXForSequenceClassification(__a							)
												model.to(__a							)
												model.eval()
												__lowercase      :							Optional[Any]				=     ids_tensor([self.batch_size]		,     self.type_sequence_label_size							)
												__lowercase      :							Dict				=     model(__a		,     attention_mask=__a		,     labels=__a							)
												self.parent.assertEqual(result.logits.shape		,     (self.batch_size, self.num_labels)							)



					def       lowerCAmelCase				(  self				:	List[str]		,     __a				:	Union[str, Any]		,     __a				:	Any		,     __a				:	List[str]		,     __a				:	Any							)			->  Any:


												"""simple docstring"""

												__lowercase      :							Union[str, Any]				=     self.num_labels
												__lowercase      :							Optional[int]				=     GPTNeoXForTokenClassification(__a							)
												model.to(__a							)
												model.eval()
												__lowercase      :							Union[str, Any]				=     model(__a		,     attention_mask=__a		,     labels=__a							)
												self.parent.assertEqual(result.logits.shape		,     (self.batch_size, self.seq_length, self.num_labels)							)



					def       lowerCAmelCase				(  self				:	Tuple		,     __a				:	str		,     __a				:	Optional[int]		,     __a				:	Any							)			->  Any:


												"""simple docstring"""

												__lowercase      :							Any				=     True
												__lowercase      :							Tuple				=     GPTNeoXForCausalLM(config=__a							)
												model.to(__a							)
												model.eval()

												# first forward pass
												__lowercase      :							int				=     model(__a		,     attention_mask=__a		,     use_cache=__a							)
												__lowercase      :							int				=     outputs.past_key_values

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

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

												__lowercase      :							Union[str, Any]				=     model(__a		,     attention_mask=__a		,     output_hidden_states=__a							)
												__lowercase      :							Optional[int]				=     output_from_no_past["""hidden_states"""][0]
												__lowercase      :							List[str]				=     model(
												    __a		,     attention_mask=__a		,     past_key_values=__a		,     output_hidden_states=__a		,     )["""hidden_states"""][0]

												# select random slice
												__lowercase      :							Union[str, Any]				=     ids_tensor((1,)		,     output_from_past.shape[-1]							).item()
												__lowercase      :							Tuple				=     output_from_no_past[:, -3:, random_slice_idx].detach()
												__lowercase      :							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(__a		,     __a		,     atol=1E-3							)							)



					def       lowerCAmelCase				(  self				:	str							)			->  Optional[Any]:


												"""simple docstring"""

												__lowercase      :							Dict				=     self.prepare_config_and_inputs()
												__lowercase			,			__lowercase			,			__lowercase			,			__lowercase      :							Optional[Any]				=     config_and_inputs
												__lowercase      :							Any				=     {"""input_ids""": input_ids, """attention_mask""": input_mask}
												return config, inputs_dict







@require_torch
class 		lowerCAmelCase     ( __a      ,       __a      ,       __a      ,       unittest.TestCase			):

					'''simple docstring'''

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



					def       lowerCAmelCase				(  self				:	List[str]							)			->  Optional[int]:


												"""simple docstring"""

												__lowercase      :							Optional[Any]				=     GPTNeoXModelTester(self							)
												__lowercase      :							int				=     ConfigTester(self		,     config_class=__a		,     hidden_size=64		,     num_attention_heads=8							)



					def       lowerCAmelCase				(  self				:	List[str]							)			->  Union[str, Any]:


												"""simple docstring"""

												self.config_tester.run_common_tests()



					def       lowerCAmelCase				(  self				:	Tuple							)			->  Tuple:


												"""simple docstring"""

												__lowercase			,			__lowercase			,			__lowercase			,			__lowercase      :							Dict				=     self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_model(__a		,     __a		,     __a							)



					def       lowerCAmelCase				(  self				:	Union[str, Any]							)			->  Optional[Any]:


												"""simple docstring"""

												__lowercase			,			__lowercase			,			__lowercase			,			__lowercase      :							Optional[Any]				=     self.model_tester.prepare_config_and_inputs_for_decoder()
												self.model_tester.create_and_check_model_as_decoder(__a		,     __a		,     __a							)



					def       lowerCAmelCase				(  self				:	List[str]							)			->  int:


												"""simple docstring"""

												__lowercase			,			__lowercase			,			__lowercase			,			__lowercase      :							Optional[int]				=     self.model_tester.prepare_config_and_inputs_for_decoder()

												__lowercase      :							Dict				=     None

												self.model_tester.create_and_check_model_as_decoder(__a		,     __a		,     __a							)



					def       lowerCAmelCase				(  self				:	Union[str, Any]							)			->  Union[str, Any]:


												"""simple docstring"""

												__lowercase			,			__lowercase			,			__lowercase			,			__lowercase      :							Tuple				=     self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_decoder_model_past_large_inputs(__a		,     __a		,     __a							)



					def       lowerCAmelCase				(  self				:	Dict							)			->  Union[str, Any]:


												"""simple docstring"""

												__lowercase      :							Optional[int]				=     self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_causal_lm(*__a							)



					def       lowerCAmelCase				(  self				:	List[str]							)			->  Tuple:


												"""simple docstring"""

												__lowercase      :							Optional[Any]				=     self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_question_answering(*__a							)



					def       lowerCAmelCase				(  self				:	List[Any]							)			->  Optional[Any]:


												"""simple docstring"""

												__lowercase      :							Any				=     self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_sequence_classification(*__a							)



					def       lowerCAmelCase				(  self				:	Tuple							)			->  Any:


												"""simple docstring"""

												__lowercase      :							List[str]				=     self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_token_classification(*__a							)



					@unittest.skip(reason="""Feed forward chunking is not implemented"""							)
					def       lowerCAmelCase				(  self				:	Any							)			->  Optional[Any]:


												"""simple docstring"""

												pass



					@parameterized.expand([("""linear""",), ("""dynamic""",)]							)
					def       lowerCAmelCase				(  self				:	int		,     __a				:	int							)			->  Any:


												"""simple docstring"""

												__lowercase			,			__lowercase      :							List[str]				=     self.model_tester.prepare_config_and_inputs_for_common()
												__lowercase      :							Tuple				=     ids_tensor([1, 10]		,     config.vocab_size							)
												__lowercase      :							Dict				=     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
												__lowercase      :							List[Any]				=     GPTNeoXModel(__a							)
												original_model.to(__a							)
												original_model.eval()
												__lowercase      :							Union[str, Any]				=     original_model(__a							).last_hidden_state
												__lowercase      :							Tuple				=     original_model(__a							).last_hidden_state

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

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







@require_torch
class 		lowerCAmelCase     ( unittest.TestCase			):

					'''simple docstring'''

					@slow
					def       lowerCAmelCase				(  self				:	Optional[Any]							)			->  str:


												"""simple docstring"""

												__lowercase      :							int				=     AutoTokenizer.from_pretrained("""EleutherAI/pythia-410m-deduped"""							)
												for checkpointing in [True, False]:
																			__lowercase      :							List[Any]				=     GPTNeoXForCausalLM.from_pretrained("""EleutherAI/pythia-410m-deduped"""							)

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

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

																			__lowercase      :							Tuple				=     model.generate(**__a		,     do_sample=__a		,     max_new_tokens=20							)
																			__lowercase      :							List[Any]				=     tokenizer.batch_decode(__a							)[0]

																			self.assertEqual(__a		,     __a							)







# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

import math
from typing import Union

import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import randn_tensor
from .scheduling_utils import SchedulerMixin

class 		lowerCAmelCase     ( __a      ,       __a			):

					'''simple docstring'''

					_A						:			str									=      1



					@register_to_config
					def __init__(  self				:	Optional[int]		,     __a				:	Tuple=2000		,     __a				:	List[str]=0.1		,     __a				:	str=20		,     __a				:	Optional[int]=1E-3							)			->  int:


												"""simple docstring"""

												__lowercase      :							Tuple				=     None
												__lowercase      :							Union[str, Any]				=     None
												__lowercase      :							int				=     None



					def       lowerCAmelCase				(  self				:	List[Any]		,     __a				:	Any		,     __a				:	Union[str, torch.device] = None							)			->  str:


												"""simple docstring"""

												__lowercase      :							List[str]				=     torch.linspace(1		,     self.config.sampling_eps		,     __a		,     device=__a							)



					def       lowerCAmelCase				(  self				:	Tuple		,     __a				:	List[Any]		,     __a				:	Tuple		,     __a				:	int		,     __a				:	Optional[int]=None							)			->  str:


												"""simple docstring"""

												if self.timesteps is None:
																			raise ValueError(
																			    """`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"""							)

												# TODO(Patrick) better comments + non-PyTorch
												# postprocess model score
												__lowercase      :							Dict				=     (
												    -0.25 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min
												)
												__lowercase      :							int				=     torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff							)							)
												__lowercase      :							Union[str, Any]				=     std.flatten()
												while len(std.shape							) < len(score.shape							):
																			__lowercase      :							Optional[Any]				=     std.unsqueeze(-1							)
												__lowercase      :							List[Any]				=     -score / std

												# compute
												__lowercase      :							Dict				=     -1.0 / len(self.timesteps							)

												__lowercase      :							int				=     self.config.beta_min + t * (self.config.beta_max - self.config.beta_min)
												__lowercase      :							List[Any]				=     beta_t.flatten()
												while len(beta_t.shape							) < len(x.shape							):
																			__lowercase      :							Union[str, Any]				=     beta_t.unsqueeze(-1							)
												__lowercase      :							List[str]				=     -0.5 * beta_t * x

												__lowercase      :							int				=     torch.sqrt(__a							)
												__lowercase      :							Union[str, Any]				=     drift - diffusion**2 * score
												__lowercase      :							Optional[Any]				=     x + drift * dt

												# add noise
												__lowercase      :							List[str]				=     randn_tensor(x.shape		,     layout=x.layout		,     generator=__a		,     device=x.device		,     dtype=x.dtype							)
												__lowercase      :							str				=     x_mean + diffusion * math.sqrt(-dt							) * noise

												return x, x_mean



					def __len__(  self				:	Tuple							)			->  Optional[int]:


												"""simple docstring"""

												return self.config.num_train_timesteps