






'''simple docstring'''



from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging


SCREAMING_SNAKE_CASE							=						logging.get_logger(__name__)

SCREAMING_SNAKE_CASE							=						{
    'EleutherAI/gpt-neo-1.3B': 'https://huggingface.co/EleutherAI/gpt-neo-1.3B/resolve/main/config.json',
    # See all GPTNeo models at https://huggingface.co/models?filter=gpt_neo
}
class UpperCAmelCase_       (     __A					):




      """simple docstring"""




      UpperCamelCase_             =      '''gpt_neo'''
      UpperCamelCase_             =      ['''past_key_values''']
      UpperCamelCase_             =      {'''num_attention_heads''': '''num_heads''', '''num_hidden_layers''': '''num_layers'''}






      def __init__(			self      :		Dict  ,   UpperCAmelCase      :		int=5_0257  ,   UpperCAmelCase      :		Optional[Any]=2048  ,   UpperCAmelCase      :		str=2048  ,   UpperCAmelCase      :		str=24  ,   UpperCAmelCase      :		Tuple=[[["global", "local"], 12]]  ,   UpperCAmelCase      :		int=16  ,   UpperCAmelCase      :		Union[str, Any]=None  ,   UpperCAmelCase      :		Optional[int]=256  ,   UpperCAmelCase      :		List[str]="gelu_new"  ,   UpperCAmelCase      :		Optional[int]=0.0  ,   UpperCAmelCase      :		Dict=0.0  ,   UpperCAmelCase      :		int=0.0  ,   UpperCAmelCase      :		Optional[Any]=0.1  ,   UpperCAmelCase      :		Optional[Any]=1e-5  ,   UpperCAmelCase      :		Dict=0.0_2  ,   UpperCAmelCase      :		Dict=True  ,   UpperCAmelCase      :		int=5_0256  ,   UpperCAmelCase      :		List[str]=5_0256  ,   **UpperCAmelCase      :		List[str]  ,   )	->	Union[str, Any]:


       '''simple docstring'''

       lowercase       : Tuple						=vocab_size
       lowercase       : Optional[int]						=max_position_embeddings
       lowercase       : Tuple						=hidden_size
       lowercase       : str						=num_layers
       lowercase       : Optional[Any]						=num_heads
       lowercase       : List[Any]						=intermediate_size
       lowercase       : Union[str, Any]						=window_size
       lowercase       : Optional[Any]						=activation_function
       lowercase       : Union[str, Any]						=resid_dropout
       lowercase       : List[str]						=embed_dropout
       lowercase       : int						=attention_dropout
       lowercase       : List[str]						=classifier_dropout
       lowercase       : List[Any]						=layer_norm_epsilon
       lowercase       : int						=initializer_range
       lowercase       : List[Any]						=use_cache

       lowercase       : Union[str, Any]						=bos_token_id
       lowercase       : Optional[int]						=eos_token_id

       lowercase       : Tuple						=attention_types
       lowercase       : List[str]						=self.expand_attention_types_params(UpperCAmelCase		)

       if len(self.attention_layers		) != self.num_layers:
        raise ValueError(
            '''Configuration for convolutional module is incorrect. '''
            '''It is required that `len(config.attention_layers)` == `config.num_layers` '''
            f'but is `len(config.attention_layers) = {len(self.attention_layers		)}`, '
            f'`config.num_layers = {self.num_layers}`. '
            '''`config.attention_layers` is prepared using `config.attention_types`. '''
            '''Please verify the value of `config.attention_types` argument.'''		)

       super().__init__(bos_token_id=UpperCAmelCase  ,   eos_token_id=UpperCAmelCase  ,   **UpperCAmelCase		)






      @staticmethod
      def 					A__   (			UpperCAmelCase      :		Dict		)	->	int:


       '''simple docstring'''

       lowercase       : int						=[]
       for item in attention_types:
        for _ in range(item[1]		):
         attentions.extend(item[0]		)
       return attentions





def 				lowercase_					(     __A			:							Any       ,   __A			:							Tuple       ,   __A			:							Tuple       ,   __A			:							Dict      )							->   List[str]:



 """simple docstring"""




 import torch

 lowercase       : str						=input.size()
 lowercase       : List[str]						=len(__A      )
 lowercase       : Optional[Any]						=shape[dimension]

 lowercase       : Optional[Any]						=torch.arange(0       ,   __A       ,   __A      )
 lowercase       : List[str]						=torch.div(sizedim - size       ,   __A       ,   rounding_mode='''floor'''      ) + 1
 lowercase       : Optional[int]						=torch.arange(__A      ) + low_indices[:min_length][:, None]

 lowercase       : List[Any]						=[slice(__A      )] * rank
 lowercase       : Dict						=indices
 lowercase       : str						=input[s]

 lowercase       : str						=list(range(0       ,   rank + 1      )      )
 perm.append(perm.pop(dimension + 1      )      )

 return sliced.permute(__A      )


def 				lowercase_					(     __A			:							Tuple       ,   __A			:							int      )							->   List[str]:



 """simple docstring"""




 import torch

 lowercase       : Any						=torch.arange(1       ,   __A      )
 lowercase       : Union[str, Any]						=torch.remainder(__A       ,   __A      )
 lowercase       : Any						=remainders == 0
 lowercase       : List[str]						=candidates[divisor_indices]
 lowercase       : Optional[int]						=torch.max(__A      )
 return largest_divisor, torch.div(__A       ,   __A       ,   rounding_mode='''floor'''      )





class UpperCAmelCase_       (     __A					):




      """simple docstring"""




      @property
      def 					A__   (			self      :		List[str]		)	->	Mapping[str, Mapping[int, str]]:


       '''simple docstring'''

       lowercase       : Union[str, Any]						=OrderedDict({'''input_ids''': {0: '''batch''', 1: '''sequence'''}}		)
       if self.use_past:
        self.fill_with_past_key_values_(UpperCAmelCase  ,   direction='''inputs'''		)
        lowercase       : List[str]						={0: '''batch''', 1: '''past_sequence + sequence'''}
       else:
        lowercase       : Dict						={0: '''batch''', 1: '''sequence'''}

       return common_inputs






      @property
      def 					A__   (			self      :		int		)	->	int:


       '''simple docstring'''

       return self._config.num_heads






      def 					A__   (			self      :		List[str]  ,   UpperCAmelCase      :		PreTrainedTokenizer  ,   UpperCAmelCase      :		int = -1  ,   UpperCAmelCase      :		int = -1  ,   UpperCAmelCase      :		bool = False  ,   UpperCAmelCase      :		Optional[TensorType] = None  ,   )	->	Mapping[str, Any]:


       '''simple docstring'''

       lowercase       : Union[str, Any]						=super(UpperCAmelCase  ,   self		).generate_dummy_inputs(
           UpperCAmelCase  ,   batch_size=UpperCAmelCase  ,   seq_length=UpperCAmelCase  ,   is_pair=UpperCAmelCase  ,   framework=UpperCAmelCase		)

       # We need to order the input in the way they appears in the forward()
       lowercase       : List[Any]						=OrderedDict({'''input_ids''': common_inputs['''input_ids''']}		)

       # Need to add the past_keys
       if self.use_past:
        if not is_torch_available():
         raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.'''		)
        else:
         import torch

         lowercase	,		lowercase       : Tuple						=common_inputs['''input_ids'''].shape
         # Not using the same length for past_key_values
         lowercase       : Optional[Any]						=seqlen + 2
         lowercase       : List[str]						=(
             batch,
             self.num_attention_heads,
             past_key_values_length,
             self._config.hidden_size // self.num_attention_heads,
         )
         lowercase       : Optional[Any]						=[
             (torch.zeros(UpperCAmelCase		), torch.zeros(UpperCAmelCase		)) for _ in range(self.num_layers		)
         ]

       lowercase       : Any						=common_inputs['''attention_mask''']
       if self.use_past:
        lowercase       : List[Any]						=ordered_inputs['''attention_mask'''].dtype
        lowercase       : List[str]						=torch.cat(
            [ordered_inputs['''attention_mask'''], torch.ones(UpperCAmelCase  ,   UpperCAmelCase  ,   dtype=UpperCAmelCase		)]  ,   dim=1		)

       return ordered_inputs






      @property
      def 					A__   (			self      :		str		)	->	int:


       '''simple docstring'''

       return 13









'''simple docstring'''


import torch

from diffusers import DDPMParallelScheduler

from .test_schedulers import SchedulerCommonTest

class      A       (						_a       ):
							lowercase_							     =							(DDPMParallelScheduler,)







							def   __lowerCAmelCase     (    self			:    Optional[Any]				,     **lowerCAmelCase_			:    Optional[int]   )     ->				List[Any]:


														"""simple docstring"""


														_a      	=							{
														    '''num_train_timesteps''': 10_00,
														    '''beta_start''': 0.0_0_0_1,
														    '''beta_end''': 0.0_2,
														    '''beta_schedule''': '''linear''',
														    '''variance_type''': '''fixed_small''',
														    '''clip_sample''': True,
														}

														config.update(**lowerCAmelCase_   )
														return config







							def   __lowerCAmelCase     (    self			:    Dict   )     ->				Any:


														"""simple docstring"""


														for timesteps in [1, 5, 1_00, 10_00]:
																					self.check_over_configs(num_train_timesteps=lowerCAmelCase_   )







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


														"""simple docstring"""


														for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1, 0.0_1, 0.1]				,     [0.0_0_2, 0.0_2, 0.2, 2]   ):
																					self.check_over_configs(beta_start=lowerCAmelCase_				,     beta_end=lowerCAmelCase_   )







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


														"""simple docstring"""


														for schedule in ["linear", "squaredcos_cap_v2"]:
																					self.check_over_configs(beta_schedule=lowerCAmelCase_   )







							def   __lowerCAmelCase     (    self			:    int   )     ->				Optional[Any]:


														"""simple docstring"""


														for variance in ["fixed_small", "fixed_large", "other"]:
																					self.check_over_configs(variance_type=lowerCAmelCase_   )







							def   __lowerCAmelCase     (    self			:    Any   )     ->				List[Any]:


														"""simple docstring"""


														for clip_sample in [True, False]:
																					self.check_over_configs(clip_sample=lowerCAmelCase_   )







							def   __lowerCAmelCase     (    self			:    Optional[Any]   )     ->				Optional[Any]:


														"""simple docstring"""


														self.check_over_configs(thresholding=lowerCAmelCase_   )
														for threshold in [0.5, 1.0, 2.0]:
																					for prediction_type in ["epsilon", "sample", "v_prediction"]:
																												self.check_over_configs(
																												    thresholding=lowerCAmelCase_				,     prediction_type=lowerCAmelCase_				,     sample_max_value=lowerCAmelCase_				,     )







							def   __lowerCAmelCase     (    self			:    Tuple   )     ->				str:


														"""simple docstring"""


														for prediction_type in ["epsilon", "sample", "v_prediction"]:
																					self.check_over_configs(prediction_type=lowerCAmelCase_   )







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


														"""simple docstring"""


														for t in [0, 5_00, 9_99]:
																					self.check_over_forward(time_step=lowerCAmelCase_   )







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


														"""simple docstring"""


														_a      	=							self.scheduler_classes[0]
														_a      	=							self.get_scheduler_config()
														_a      	=							scheduler_class(**lowerCAmelCase_   )

														assert torch.sum(torch.abs(scheduler._get_variance(0   ) - 0.0   )   ) < 1e-5
														assert torch.sum(torch.abs(scheduler._get_variance(4_87   ) - 0.0_0_9_7_9   )   ) < 1e-5
														assert torch.sum(torch.abs(scheduler._get_variance(9_99   ) - 0.0_2   )   ) < 1e-5







							def   __lowerCAmelCase     (    self			:    Dict   )     ->				str:


														"""simple docstring"""


														_a      	=							self.scheduler_classes[0]
														_a      	=							self.get_scheduler_config()
														_a      	=							scheduler_class(**lowerCAmelCase_   )

														_a      	=							len(lowerCAmelCase_   )

														_a      	=							self.dummy_model()
														_a      	=							self.dummy_sample_deter
														_a      	=							self.dummy_sample_deter + 0.1
														_a      	=							self.dummy_sample_deter - 0.1

														_a      	=							samplea.shape[0]
														_a      	=							torch.stack([samplea, samplea, samplea]				,     dim=0   )
														_a      	=							torch.arange(lowerCAmelCase_   )[0:3, None].repeat(1				,     lowerCAmelCase_   )

														_a      	=							model(samples.flatten(0				,     1   )				,     timesteps.flatten(0				,     1   )   )
														_a      	=							scheduler.batch_step_no_noise(lowerCAmelCase_				,     timesteps.flatten(0				,     1   )				,     samples.flatten(0				,     1   )   )

														_a      	=							torch.sum(torch.abs(lowerCAmelCase_   )   )
														_a      	=							torch.mean(torch.abs(lowerCAmelCase_   )   )

														assert abs(result_sum.item() - 1_1_5_3.1_8_3_3   ) < 1e-2
														assert abs(result_mean.item() - 0.5_0_0_5   ) < 1e-3







							def   __lowerCAmelCase     (    self			:    Optional[int]   )     ->				Dict:


														"""simple docstring"""


														_a      	=							self.scheduler_classes[0]
														_a      	=							self.get_scheduler_config()
														_a      	=							scheduler_class(**lowerCAmelCase_   )

														_a      	=							len(lowerCAmelCase_   )

														_a      	=							self.dummy_model()
														_a      	=							self.dummy_sample_deter
														_a      	=							torch.manual_seed(0   )

														for t in reversed(range(lowerCAmelCase_   )   ):
																					# 1. predict noise residual
																					_a      	=							model(lowerCAmelCase_				,     lowerCAmelCase_   )

																					# 2. predict previous mean of sample x_t-1
																					_a      	=							scheduler.step(lowerCAmelCase_				,     lowerCAmelCase_				,     lowerCAmelCase_				,     generator=lowerCAmelCase_   ).prev_sample

																					_a      	=							pred_prev_sample

														_a      	=							torch.sum(torch.abs(lowerCAmelCase_   )   )
														_a      	=							torch.mean(torch.abs(lowerCAmelCase_   )   )

														assert abs(result_sum.item() - 2_5_8.9_6_0_6   ) < 1e-2
														assert abs(result_mean.item() - 0.3_3_7_2   ) < 1e-3







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


														"""simple docstring"""


														_a      	=							self.scheduler_classes[0]
														_a      	=							self.get_scheduler_config(prediction_type='''v_prediction'''   )
														_a      	=							scheduler_class(**lowerCAmelCase_   )

														_a      	=							len(lowerCAmelCase_   )

														_a      	=							self.dummy_model()
														_a      	=							self.dummy_sample_deter
														_a      	=							torch.manual_seed(0   )

														for t in reversed(range(lowerCAmelCase_   )   ):
																					# 1. predict noise residual
																					_a      	=							model(lowerCAmelCase_				,     lowerCAmelCase_   )

																					# 2. predict previous mean of sample x_t-1
																					_a      	=							scheduler.step(lowerCAmelCase_				,     lowerCAmelCase_				,     lowerCAmelCase_				,     generator=lowerCAmelCase_   ).prev_sample

																					_a      	=							pred_prev_sample

														_a      	=							torch.sum(torch.abs(lowerCAmelCase_   )   )
														_a      	=							torch.mean(torch.abs(lowerCAmelCase_   )   )

														assert abs(result_sum.item() - 2_0_2.0_2_9_6   ) < 1e-2
														assert abs(result_mean.item() - 0.2_6_3_1   ) < 1e-3







							def   __lowerCAmelCase     (    self			:    int   )     ->				Dict:


														"""simple docstring"""


														_a      	=							self.scheduler_classes[0]
														_a      	=							self.get_scheduler_config()
														_a      	=							scheduler_class(**lowerCAmelCase_   )

														_a      	=							[1_00, 87, 50, 1, 0]

														scheduler.set_timesteps(timesteps=lowerCAmelCase_   )

														_a      	=							scheduler.timesteps

														for i, timestep in enumerate(lowerCAmelCase_   ):
																					if i == len(lowerCAmelCase_   ) - 1:
																												_a      	=							-1
																					else:
																												_a      	=							timesteps[i + 1]

																					_a      	=							scheduler.previous_timestep(lowerCAmelCase_   )
																					_a      	=							prev_t.item()

																					self.assertEqual(lowerCAmelCase_				,     lowerCAmelCase_   )







							def   __lowerCAmelCase     (    self			:    Dict   )     ->				List[Any]:


														"""simple docstring"""


														_a      	=							self.scheduler_classes[0]
														_a      	=							self.get_scheduler_config()
														_a      	=							scheduler_class(**lowerCAmelCase_   )

														_a      	=							[1_00, 87, 50, 51, 0]

														with self.assertRaises(lowerCAmelCase_				,     msg='''`custom_timesteps` must be in descending order.'''   ):
																					scheduler.set_timesteps(timesteps=lowerCAmelCase_   )







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


														"""simple docstring"""


														_a      	=							self.scheduler_classes[0]
														_a      	=							self.get_scheduler_config()
														_a      	=							scheduler_class(**lowerCAmelCase_   )

														_a      	=							[1_00, 87, 50, 1, 0]
														_a      	=							len(lowerCAmelCase_   )

														with self.assertRaises(lowerCAmelCase_				,     msg='''Can only pass one of `num_inference_steps` or `custom_timesteps`.'''   ):
																					scheduler.set_timesteps(num_inference_steps=lowerCAmelCase_				,     timesteps=lowerCAmelCase_   )







							def   __lowerCAmelCase     (    self			:    Dict   )     ->				Any:


														"""simple docstring"""


														_a      	=							self.scheduler_classes[0]
														_a      	=							self.get_scheduler_config()
														_a      	=							scheduler_class(**lowerCAmelCase_   )

														_a      	=							[scheduler.config.num_train_timesteps]

														with self.assertRaises(
														    lowerCAmelCase_				,     msg='''`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}'''				,     ):
																					scheduler.set_timesteps(timesteps=lowerCAmelCase_   )

