


"""simple docstring"""







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

import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch

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






@dataclass
class       __A				(       _SCREAMING_SNAKE_CASE			):






 """simple docstring"""




 __lowerCAmelCase 					=  42
 __lowerCAmelCase 					=  42






class       __A				(       _SCREAMING_SNAKE_CASE,    _SCREAMING_SNAKE_CASE			):






 """simple docstring"""




 __lowerCAmelCase 					=  1


 @register_to_config
 def __init__(		self ,    __A = 2000 ,    __A = 0.15 ,    __A = 0.01 ,    __A = 1_348.0 ,    __A = 1E-5 ,    __A = 1 ,    )					->     str:
     # standard deviation of the initial noise distribution
     a 		=sigma_max

     # setable values
     a 		=None

     self.set_sigmas(__A ,    __A ,    __A ,    __A						)


 def       SCREAMING_SNAKE_CASE							(		self ,    __A ,    __A = None						)					->     torch.FloatTensor:
     return sample


 def       SCREAMING_SNAKE_CASE							(		self ,    __A ,    __A = None ,    __A = None						)					->     Union[str, Any]:
     a 		=sampling_eps if sampling_eps is not None else self.config.sampling_eps

     a 		=torch.linspace(1 ,    __A ,    __A ,    device=__A						)


 def       SCREAMING_SNAKE_CASE							(		self ,    __A ,    __A = None ,    __A = None ,    __A = None						)					->     Union[str, Any]:
     a 		=sigma_min if sigma_min is not None else self.config.sigma_min
     a 		=sigma_max if sigma_max is not None else self.config.sigma_max
     a 		=sampling_eps if sampling_eps is not None else self.config.sampling_eps
     if self.timesteps is None:
         self.set_timesteps(__A ,    __A						)

     a 		=sigma_min * (sigma_max / sigma_min) ** (self.timesteps / sampling_eps)
     a 		=torch.exp(torch.linspace(math.log(__A						) ,    math.log(__A						) ,    __A						)						)
     a 		=torch.tensor([sigma_min * (sigma_max / sigma_min) ** t for t in self.timesteps]						)


 def       SCREAMING_SNAKE_CASE							(		self ,    __A ,    __A						)					->     Any:
     return torch.where(
         timesteps == 0 ,    torch.zeros_like(t.to(timesteps.device						)						) ,    self.discrete_sigmas[timesteps - 1].to(timesteps.device						) ,    )


 def       SCREAMING_SNAKE_CASE							(		self ,    __A ,    __A ,    __A ,    __A = None ,    __A = True ,    )					->     Union[SdeVeOutput, Tuple]:
     if self.timesteps is None:
         raise ValueError(
             '''`self.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler'''						)

     a 		=timestep * torch.ones(
         sample.shape[0] ,    device=sample.device						)  # torch.repeat_interleave(timestep, sample.shape[0])
     a 		=(timestep * (len(self.timesteps						) - 1)).long()

     # mps requires indices to be in the same device, so we use cpu as is the default with cuda
     a 		=timesteps.to(self.discrete_sigmas.device						)

     a 		=self.discrete_sigmas[timesteps].to(sample.device						)
     a 		=self.get_adjacent_sigma(__A ,    __A						).to(sample.device						)
     a 		=torch.zeros_like(__A						)
     a 		=(sigma**2 - adjacent_sigma**2) ** 0.5

     # equation 6 in the paper: the model_output modeled by the network is grad_x log pt(x)
     # also equation 47 shows the analog from SDE models to ancestral sampling methods
     a 		=diffusion.flatten()
     while len(diffusion.shape						) < len(sample.shape						):
         a 		=diffusion.unsqueeze(-1						)
     a 		=drift - diffusion**2 * model_output

     #  equation 6: sample noise for the diffusion term of
     a 		=randn_tensor(
         sample.shape ,    layout=sample.layout ,    generator=__A ,    device=sample.device ,    dtype=sample.dtype						)
     a 		=sample - drift  # subtract because `dt` is a small negative timestep
     # TODO is the variable diffusion the correct scaling term for the noise?
     a 		=prev_sample_mean + diffusion * noise  # add impact of diffusion field g

     if not return_dict:
         return (prev_sample, prev_sample_mean)

     return SdeVeOutput(prev_sample=__A ,    prev_sample_mean=__A						)


 def       SCREAMING_SNAKE_CASE							(		self ,    __A ,    __A ,    __A = None ,    __A = True ,    )					->     Union[SchedulerOutput, Tuple]:
     if self.timesteps is None:
         raise ValueError(
             '''`self.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler'''						)

     # For small batch sizes, the paper "suggest replacing norm(z) with sqrt(d), where d is the dim. of z"
     # sample noise for correction
     a 		=randn_tensor(sample.shape ,    layout=sample.layout ,    generator=__A						).to(sample.device						)

     # compute step size from the model_output, the noise, and the snr
     a 		=torch.norm(model_output.reshape(model_output.shape[0] ,    -1						) ,    dim=-1						).mean()
     a 		=torch.norm(noise.reshape(noise.shape[0] ,    -1						) ,    dim=-1						).mean()
     a 		=(self.config.snr * noise_norm / grad_norm) ** 2 * 2
     a 		=step_size * torch.ones(sample.shape[0]						).to(sample.device						)
     # self.repeat_scalar(step_size, sample.shape[0])

     # compute corrected sample: model_output term and noise term
     a 		=step_size.flatten()
     while len(step_size.shape						) < len(sample.shape						):
         a 		=step_size.unsqueeze(-1						)
     a 		=sample + step_size * model_output
     a 		=prev_sample_mean + ((step_size * 2) ** 0.5) * noise

     if not return_dict:
         return (prev_sample,)

     return SchedulerOutput(prev_sample=__A						)


 def       SCREAMING_SNAKE_CASE							(		self ,    __A ,    __A ,    __A ,    )					->     torch.FloatTensor:
     # Make sure sigmas and timesteps have the same device and dtype as original_samples
     a 		=timesteps.to(original_samples.device						)
     a 		=self.discrete_sigmas.to(original_samples.device						)[timesteps]
     a 		=(
         noise * sigmas[:, None, None, None]
         if noise is not None
         else torch.randn_like(__A						) * sigmas[:, None, None, None]
     )
     a 		=noise + original_samples
     return noisy_samples





 def __len__(		self						)					->     Optional[int]:
     return self.config.num_train_timesteps
"""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   )





