




"""simple docstring"""


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

import numpy as np
import torch

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






@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->UnCLIP
class 					lowercase(     __a ):







 '''simple docstring'''





 lowercase__ 					=					42
 lowercase__ 					=					None







def 					UpperCAmelCase__       (snake_case__							:					str		,   snake_case__							:					List[Any]=0.9_99		,   snake_case__							:					Dict="cosine"		,   ):
  """simple docstring"""

  if alpha_transform_type == "cosine":

    def alpha_bar_fn(snake_case__							:					Dict    ):
      return math.cos((t + 0.0_08) / 1.0_08 * math.pi / 2    ) ** 2

  elif alpha_transform_type == "exp":

    def alpha_bar_fn(snake_case__							:					Union[str, Any]    ):
      return math.exp(t * -12.0    )

  else:
    raise ValueError(F"Unsupported alpha_tranform_type: {alpha_transform_type}"    )

  _snake_case							:		int								=  []
  for i in range(snake_case__    ):
    _snake_case							:		Tuple								=  i / num_diffusion_timesteps
    _snake_case							:		Union[str, Any]								=  (i + 1) / num_diffusion_timesteps
    betas.append(min(1 - alpha_bar_fn(snake_case__    ) / alpha_bar_fn(snake_case__    )		,   snake_case__    )    )
  return torch.tensor(snake_case__		,   dtype=torch.floataa    )






class 					lowercase(     __a       ,			__a ):







 '''simple docstring'''





 @register_to_config
 def __init__(				self:		List[str],      a_:		int = 1_000,      a_:		str = "fixed_small_log",      a_:		bool = True,      a_:		Optional[float] = 1.0,      a_:		str = "epsilon",      a_:		str = "squaredcos_cap_v2",      ):




   '''simple docstring'''



   if beta_schedule != "squaredcos_cap_v2":
     raise ValueError("""UnCLIPScheduler only supports `beta_schedule`: 'squaredcos_cap_v2'"""		)

   _snake_case							:		Tuple								=  betas_for_alpha_bar(a_		)

   _snake_case							:		List[str]								=  1.0 - self.betas
   _snake_case							:		Optional[int]								=  torch.cumprod(self.alphas,      dim=0		)
   _snake_case							:		Tuple								=  torch.tensor(1.0		)

   # standard deviation of the initial noise distribution
   _snake_case							:		Optional[Any]								=  1.0

   # setable values
   _snake_case							:		str								=  None
   _snake_case							:		Tuple								=  torch.from_numpy(np.arange(0,      a_		)[::-1].copy()		)

   _snake_case							:		int								=  variance_type


 def  UpperCamelCase_   (				self:		Dict,      a_:		torch.FloatTensor,      a_:		Optional[int] = None		):




   '''simple docstring'''



   return sample


 def  UpperCamelCase_   (				self:		Any,      a_:		int,      a_:		Union[str, torch.device] = None		):




   '''simple docstring'''



   _snake_case							:		List[Any]								=  num_inference_steps
   _snake_case							:		Any								=  (self.config.num_train_timesteps - 1) / (self.num_inference_steps - 1)
   _snake_case							:		Optional[int]								=  (np.arange(0,      a_		) * step_ratio).round()[::-1].copy().astype(np.intaa		)
   _snake_case							:		Optional[Any]								=  torch.from_numpy(a_		).to(a_		)


 def  UpperCamelCase_   (				self:		List[Any],      a_:		Tuple,      a_:		Dict=None,      a_:		Union[str, Any]=None,      a_:		Tuple=None		):




   '''simple docstring'''



   if prev_timestep is None:
     _snake_case							:		Optional[Any]								=  t - 1

   _snake_case							:		Dict								=  self.alphas_cumprod[t]
   _snake_case							:		List[Any]								=  self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
   _snake_case							:		List[str]								=  1 - alpha_prod_t
   _snake_case							:		List[Any]								=  1 - alpha_prod_t_prev

   if prev_timestep == t - 1:
     _snake_case							:		int								=  self.betas[t]
   else:
     _snake_case							:		Union[str, Any]								=  1 - alpha_prod_t / alpha_prod_t_prev

   # For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
   # and sample from it to get previous sample
   # x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
   _snake_case							:		int								=  beta_prod_t_prev / beta_prod_t * beta

   if variance_type is None:
     _snake_case							:		Any								=  self.config.variance_type

   # hacks - were probably added for training stability
   if variance_type == "fixed_small_log":
     _snake_case							:		Optional[int]								=  torch.log(torch.clamp(a_,      min=1E-20		)		)
     _snake_case							:		int								=  torch.exp(0.5 * variance		)
   elif variance_type == "learned_range":
     # NOTE difference with DDPM scheduler
     _snake_case							:		Optional[Any]								=  variance.log()
     _snake_case							:		List[str]								=  beta.log()

     _snake_case							:		Optional[int]								=  (predicted_variance + 1) / 2
     _snake_case							:		Dict								=  frac * max_log + (1 - frac) * min_log

   return variance


 def  UpperCamelCase_   (				self:		Optional[Any],      a_:		torch.FloatTensor,      a_:		int,      a_:		torch.FloatTensor,      a_:		Optional[int] = None,      a_:		Tuple=None,      a_:		bool = True,      ):




   '''simple docstring'''



   _snake_case							:		List[Any]								=  timestep

   if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type == "learned_range":
     _snake_case		,					_snake_case							:		str								=  torch.split(a_,      sample.shape[1],      dim=1		)
   else:
     _snake_case							:		Dict								=  None

   # 1. compute alphas, betas
   if prev_timestep is None:
     _snake_case							:		Optional[Any]								=  t - 1

   _snake_case							:		Optional[int]								=  self.alphas_cumprod[t]
   _snake_case							:		Optional[int]								=  self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
   _snake_case							:		Optional[Any]								=  1 - alpha_prod_t
   _snake_case							:		int								=  1 - alpha_prod_t_prev

   if prev_timestep == t - 1:
     _snake_case							:		str								=  self.betas[t]
     _snake_case							:		int								=  self.alphas[t]
   else:
     _snake_case							:		Dict								=  1 - alpha_prod_t / alpha_prod_t_prev
     _snake_case							:		str								=  1 - beta

   # 2. compute predicted original sample from predicted noise also called
   # "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
   if self.config.prediction_type == "epsilon":
     _snake_case							:		List[str]								=  (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
   elif self.config.prediction_type == "sample":
     _snake_case							:		Tuple								=  model_output
   else:
     raise ValueError(
         f"prediction_type given as {self.config.prediction_type} must be one of `epsilon` or `sample`"
         """ for the UnCLIPScheduler."""		)

   # 3. Clip "predicted x_0"
   if self.config.clip_sample:
     _snake_case							:		int								=  torch.clamp(
         a_,      -self.config.clip_sample_range,      self.config.clip_sample_range		)

   # 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
   # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
   _snake_case							:		List[Any]								=  (alpha_prod_t_prev ** 0.5 * beta) / beta_prod_t
   _snake_case							:		Any								=  alpha ** 0.5 * beta_prod_t_prev / beta_prod_t

   # 5. Compute predicted previous sample µ_t
   # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
   _snake_case							:		Dict								=  pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

   # 6. Add noise
   _snake_case							:		Any								=  0
   if t > 0:
     _snake_case							:		str								=  randn_tensor(
         model_output.shape,      dtype=model_output.dtype,      generator=a_,      device=model_output.device		)

     _snake_case							:		Union[str, Any]								=  self._get_variance(
         a_,      predicted_variance=a_,      prev_timestep=a_,      )

     if self.variance_type == "fixed_small_log":
       _snake_case							:		Tuple								=  variance
     elif self.variance_type == "learned_range":
       _snake_case							:		int								=  (0.5 * variance).exp()
     else:
       raise ValueError(
           f"variance_type given as {self.variance_type} must be one of `fixed_small_log` or `learned_range`"
           """ for the UnCLIPScheduler."""		)

     _snake_case							:		List[Any]								=  variance * variance_noise

   _snake_case							:		Any								=  pred_prev_sample + variance

   if not return_dict:
     return (pred_prev_sample,)

   return UnCLIPSchedulerOutput(prev_sample=a_,      pred_original_sample=a_		)


 def  UpperCamelCase_   (				self:		Union[str, Any],      a_:		torch.FloatTensor,      a_:		torch.FloatTensor,      a_:		torch.IntTensor,      ):




   '''simple docstring'''



   _snake_case							:		List[Any]								=  self.alphas_cumprod.to(device=original_samples.device,      dtype=original_samples.dtype		)
   _snake_case							:		str								=  timesteps.to(original_samples.device		)

   _snake_case							:		Union[str, Any]								=  alphas_cumprod[timesteps] ** 0.5
   _snake_case							:		List[str]								=  sqrt_alpha_prod.flatten()
   while len(sqrt_alpha_prod.shape		) < len(original_samples.shape		):
     _snake_case							:		Optional[Any]								=  sqrt_alpha_prod.unsqueeze(-1		)

   _snake_case							:		Tuple								=  (1 - alphas_cumprod[timesteps]) ** 0.5
   _snake_case							:		Optional[Any]								=  sqrt_one_minus_alpha_prod.flatten()
   while len(sqrt_one_minus_alpha_prod.shape		) < len(original_samples.shape		):
     _snake_case							:		str								=  sqrt_one_minus_alpha_prod.unsqueeze(-1		)

   _snake_case							:		Any								=  sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
   return noisy_samples










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_      :



	'''simple docstring'''




	def __init__(       self       ,			a       ,			a=13       ,			a=7       ,			a=True       ,			a=True       ,			a=True       ,			a=True       ,			a=99       ,			a=64       ,			a=5       ,			a=4       ,			a=37       ,			a="gelu"       ,			a=0.1       ,			a=0.1       ,			a=5_12       ,			a=16       ,			a=2       ,			a=0.02       ,			a=3       ,			a=4       ,			a=None       ,			)       ->    str:
								snake_case_				=							parent
								snake_case_				=							batch_size
								snake_case_				=							seq_length
								snake_case_				=							is_training
								snake_case_				=							use_input_mask
								snake_case_				=							use_token_type_ids
								snake_case_				=							use_labels
								snake_case_				=							vocab_size
								snake_case_				=							hidden_size
								snake_case_				=							num_hidden_layers
								snake_case_				=							num_attention_heads
								snake_case_				=							intermediate_size
								snake_case_				=							hidden_act
								snake_case_				=							hidden_dropout_prob
								snake_case_				=							attention_probs_dropout_prob
								snake_case_				=							max_position_embeddings
								snake_case_				=							type_vocab_size
								snake_case_				=							type_sequence_label_size
								snake_case_				=							initializer_range
								snake_case_				=							num_labels
								snake_case_				=							num_choices
								snake_case_				=							scope
								snake_case_				=							vocab_size - 1




	def 							_UpperCamelCase			(       self					)       ->    Tuple:
								snake_case_				=							ids_tensor([self.batch_size, self.seq_length]       ,			self.vocab_size					)

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

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

								snake_case_				=							self.get_config()

								return config, input_ids, input_mask, token_labels




	def 							_UpperCamelCase			(       self					)       ->    int:
								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 							_UpperCamelCase			(       self					)       ->    int:
								snake_case_			,		snake_case_			,		snake_case_			,		snake_case_				=							self.prepare_config_and_inputs()

								snake_case_				=							True

								return config, input_ids, input_mask, token_labels




	def 							_UpperCamelCase			(       self       ,			a       ,			a       ,			a					)       ->    Optional[int]:
								snake_case_				=							GPTNeoXModel(config=a					)
								model.to(a					)
								model.eval()
								snake_case_				=							model(a       ,			attention_mask=a					)
								snake_case_				=							model(a					)
								self.parent.assertEqual(result.last_hidden_state.shape       ,			(self.batch_size, self.seq_length, self.hidden_size)					)




	def 							_UpperCamelCase			(       self       ,			a       ,			a       ,			a					)       ->    List[str]:
								snake_case_				=							True
								snake_case_				=							GPTNeoXModel(a					)
								model.to(a					)
								model.eval()
								snake_case_				=							model(a       ,			attention_mask=a					)
								self.parent.assertEqual(result.last_hidden_state.shape       ,			(self.batch_size, self.seq_length, self.hidden_size)					)




	def 							_UpperCamelCase			(       self       ,			a       ,			a       ,			a       ,			a					)       ->    int:
								snake_case_				=							GPTNeoXForCausalLM(config=a					)
								model.to(a					)
								model.eval()
								snake_case_				=							model(a       ,			attention_mask=a       ,			labels=a					)
								self.parent.assertEqual(result.logits.shape       ,			(self.batch_size, self.seq_length, self.vocab_size)					)




	def 							_UpperCamelCase			(       self       ,			a       ,			a       ,			a       ,			a					)       ->    Optional[int]:
								snake_case_				=							self.num_labels
								snake_case_				=							GPTNeoXForQuestionAnswering(a					)
								model.to(a					)
								model.eval()
								snake_case_				=							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 							_UpperCamelCase			(       self       ,			a       ,			a       ,			a       ,			a					)       ->    List[str]:
								snake_case_				=							self.num_labels
								snake_case_				=							GPTNeoXForSequenceClassification(a					)
								model.to(a					)
								model.eval()
								snake_case_				=							ids_tensor([self.batch_size]       ,			self.type_sequence_label_size					)
								snake_case_				=							model(a       ,			attention_mask=a       ,			labels=a					)
								self.parent.assertEqual(result.logits.shape       ,			(self.batch_size, self.num_labels)					)




	def 							_UpperCamelCase			(       self       ,			a       ,			a       ,			a       ,			a					)       ->    str:
								snake_case_				=							self.num_labels
								snake_case_				=							GPTNeoXForTokenClassification(a					)
								model.to(a					)
								model.eval()
								snake_case_				=							model(a       ,			attention_mask=a       ,			labels=a					)
								self.parent.assertEqual(result.logits.shape       ,			(self.batch_size, self.seq_length, self.num_labels)					)




	def 							_UpperCamelCase			(       self       ,			a       ,			a       ,			a					)       ->    List[Any]:
								snake_case_				=							True
								snake_case_				=							GPTNeoXForCausalLM(config=a					)
								model.to(a					)
								model.eval()

								# first forward pass
								snake_case_				=							model(a       ,			attention_mask=a       ,			use_cache=a					)
								snake_case_				=							outputs.past_key_values

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

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

								snake_case_				=							model(a       ,			attention_mask=a       ,			output_hidden_states=a					)
								snake_case_				=							output_from_no_past['hidden_states'][0]
								snake_case_				=							model(
								    a       ,			attention_mask=a       ,			past_key_values=a       ,			output_hidden_states=a       ,			)['hidden_states'][0]

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






@require_torch
class  UpperCamelCase_      (			snake_case_       ,					snake_case_       ,					snake_case_       ,					unittest.TestCase				):



	'''simple docstring'''




	lowerCAmelCase													= (
	    (
	        GPTNeoXModel,
	        GPTNeoXForCausalLM,
	        GPTNeoXForQuestionAnswering,
	        GPTNeoXForSequenceClassification,
	        GPTNeoXForTokenClassification,
	    )
	    if is_torch_available()
	    else ()
	)
	lowerCAmelCase													= (GPTNeoXForCausalLM,) if is_torch_available() else ()
	lowerCAmelCase													= (
	    {
	        '''feature-extraction''': GPTNeoXModel,
	        '''question-answering''': GPTNeoXForQuestionAnswering,
	        '''text-classification''': GPTNeoXForSequenceClassification,
	        '''text-generation''': GPTNeoXForCausalLM,
	        '''token-classification''': GPTNeoXForTokenClassification,
	        '''zero-shot''': GPTNeoXForSequenceClassification,
	    }
	    if is_torch_available()
	    else {}
	)
	lowerCAmelCase													= False
	lowerCAmelCase													= False
	lowerCAmelCase													= False
	lowerCAmelCase													= False




	def 							_UpperCamelCase			(       self					)       ->    str:
								snake_case_				=							GPTNeoXModelTester(self					)
								snake_case_				=							ConfigTester(self       ,			config_class=a       ,			hidden_size=64       ,			num_attention_heads=8					)




	def 							_UpperCamelCase			(       self					)       ->    Any:
								self.config_tester.run_common_tests()




	def 							_UpperCamelCase			(       self					)       ->    Optional[int]:
								snake_case_			,		snake_case_			,		snake_case_			,		snake_case_				=							self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(a       ,			a       ,			a					)




	def 							_UpperCamelCase			(       self					)       ->    int:
								snake_case_			,		snake_case_			,		snake_case_			,		snake_case_				=							self.model_tester.prepare_config_and_inputs_for_decoder()
								self.model_tester.create_and_check_model_as_decoder(a       ,			a       ,			a					)




	def 							_UpperCamelCase			(       self					)       ->    Dict:
								# This regression test was failing with PyTorch < 1.3
								snake_case_			,		snake_case_			,		snake_case_			,		snake_case_				=							self.model_tester.prepare_config_and_inputs_for_decoder()

								snake_case_				=							None

								self.model_tester.create_and_check_model_as_decoder(a       ,			a       ,			a					)




	def 							_UpperCamelCase			(       self					)       ->    str:
								snake_case_			,		snake_case_			,		snake_case_			,		snake_case_				=							self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_decoder_model_past_large_inputs(a       ,			a       ,			a					)




	def 							_UpperCamelCase			(       self					)       ->    List[Any]:
								snake_case_				=							self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_causal_lm(*a					)




	def 							_UpperCamelCase			(       self					)       ->    Any:
								snake_case_				=							self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_question_answering(*a					)




	def 							_UpperCamelCase			(       self					)       ->    List[str]:
								snake_case_				=							self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_sequence_classification(*a					)




	def 							_UpperCamelCase			(       self					)       ->    Any:
								snake_case_				=							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 							_UpperCamelCase			(       self					)       ->    List[str]:
								pass






	@parameterized.expand([('linear',), ('dynamic',)]					)
	def 							_UpperCamelCase			(       self       ,			a					)       ->    int:
								snake_case_			,		snake_case_				=							self.model_tester.prepare_config_and_inputs_for_common()
								snake_case_				=							ids_tensor([1, 10]       ,			config.vocab_size					)
								snake_case_				=							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
								snake_case_				=							GPTNeoXModel(a					)
								original_model.to(a					)
								original_model.eval()
								snake_case_				=							original_model(a					).last_hidden_state
								snake_case_				=							original_model(a					).last_hidden_state

								set_seed(42					)  # Fixed seed at init time so the two models get the same random weights
								snake_case_				=							{'type': scaling_type, 'factor': 10.0}
								snake_case_				=							GPTNeoXModel(a					)
								scaled_model.to(a					)
								scaled_model.eval()
								snake_case_				=							scaled_model(a					).last_hidden_state
								snake_case_				=							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  UpperCamelCase_      (			unittest.TestCase				):



	'''simple docstring'''




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

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

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

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

															self.assertEqual(a       ,			a					)





