

'''simple docstring'''
from collections import OrderedDict
from typing import Any, List, Mapping, Optional

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


_lowerCamelCase	 =			logging.get_logger(__name__)

_lowerCamelCase	 =			{
    """gpt2""": """https://huggingface.co/gpt2/resolve/main/config.json""",
    """gpt2-medium""": """https://huggingface.co/gpt2-medium/resolve/main/config.json""",
    """gpt2-large""": """https://huggingface.co/gpt2-large/resolve/main/config.json""",
    """gpt2-xl""": """https://huggingface.co/gpt2-xl/resolve/main/config.json""",
    """distilgpt2""": """https://huggingface.co/distilgpt2/resolve/main/config.json""",
}
class  _snake_case						(__SCREAMING_SNAKE_CASE):
			__A   :     Dict          ="gpt2"
			__A   :     Any          =["past_key_values"]
			__A   :     Any          ={
			    "hidden_size": "n_embd",
			    "max_position_embeddings": "n_positions",
			    "num_attention_heads": "n_head",
			    "num_hidden_layers": "n_layer",
			}






			def __init__(   self       ,_snake_case=5_02_57       ,_snake_case=10_24       ,_snake_case=7_68       ,_snake_case=12       ,_snake_case=12       ,_snake_case=None       ,_snake_case="gelu_new"       ,_snake_case=0.1       ,_snake_case=0.1       ,_snake_case=0.1       ,_snake_case=1E-5       ,_snake_case=0.02       ,_snake_case="cls_index"       ,_snake_case=True       ,_snake_case=None       ,_snake_case=True       ,_snake_case=0.1       ,_snake_case=True       ,_snake_case=True       ,_snake_case=5_02_56       ,_snake_case=5_02_56       ,_snake_case=False       ,_snake_case=False       ,**_snake_case       ,):
				UpperCAmelCase_			:	Any      =   vocab_size
				UpperCAmelCase_			:	List[str]      =   n_positions
				UpperCAmelCase_			:	Optional[int]      =   n_embd
				UpperCAmelCase_			:	Optional[Any]      =   n_layer
				UpperCAmelCase_			:	Union[str, Any]      =   n_head
				UpperCAmelCase_			:	Union[str, Any]      =   n_inner
				UpperCAmelCase_			:	Union[str, Any]      =   activation_function
				UpperCAmelCase_			:	int      =   resid_pdrop
				UpperCAmelCase_			:	str      =   embd_pdrop
				UpperCAmelCase_			:	List[str]      =   attn_pdrop
				UpperCAmelCase_			:	Any      =   layer_norm_epsilon
				UpperCAmelCase_			:	Tuple      =   initializer_range
				UpperCAmelCase_			:	Tuple      =   summary_type
				UpperCAmelCase_			:	List[str]      =   summary_use_proj
				UpperCAmelCase_			:	Any      =   summary_activation
				UpperCAmelCase_			:	str      =   summary_first_dropout
				UpperCAmelCase_			:	Optional[int]      =   summary_proj_to_labels
				UpperCAmelCase_			:	int      =   scale_attn_weights
				UpperCAmelCase_			:	Dict      =   use_cache
				UpperCAmelCase_			:	Optional[int]      =   scale_attn_by_inverse_layer_idx
				UpperCAmelCase_			:	Optional[int]      =   reorder_and_upcast_attn

				UpperCAmelCase_			:	Optional[int]      =   bos_token_id
				UpperCAmelCase_			:	List[str]      =   eos_token_id

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




class  _snake_case						(__SCREAMING_SNAKE_CASE):






			def __init__(   self       ,_snake_case       ,_snake_case = "default"       ,_snake_case = None       ,_snake_case = False       ,):
				super().__init__(_snake_case       ,task=_snake_case       ,patching_specs=_snake_case       ,use_past=_snake_case			)
				if not getattr(self._config       ,"pad_token_id"       ,_snake_case			):
					# TODO: how to do that better?
					UpperCAmelCase_			:	Optional[int]      =   0






			@property
			def 						UpperCamelCase__				(   self			):
				UpperCAmelCase_			:	Any      =   OrderedDict({"input_ids": {0: "batch", 1: "sequence"}}			)
				if self.use_past:
					self.fill_with_past_key_values_(_snake_case       ,direction="inputs"			)
					UpperCAmelCase_			:	str      =   {0: "batch", 1: "past_sequence + sequence"}
				else:
					UpperCAmelCase_			:	List[str]      =   {0: "batch", 1: "sequence"}

				return common_inputs






			@property
			def 						UpperCamelCase__				(   self			):
				return self._config.n_layer






			@property
			def 						UpperCamelCase__				(   self			):
				return self._config.n_head






			def 						UpperCamelCase__				(   self       ,_snake_case       ,_snake_case = -1       ,_snake_case = -1       ,_snake_case = False       ,_snake_case = None       ,):
				UpperCAmelCase_			:	str      =   super(_snake_case       ,self			).generate_dummy_inputs(
				    _snake_case       ,batch_size=_snake_case       ,seq_length=_snake_case       ,is_pair=_snake_case       ,framework=_snake_case			)

				# We need to order the input in the way they appears in the forward()
				UpperCAmelCase_			:	Optional[int]      =   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

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

				UpperCAmelCase_			:	Union[str, Any]      =   common_inputs["attention_mask"]
				if self.use_past:
					UpperCAmelCase_			:	Union[str, Any]      =   ordered_inputs["attention_mask"].dtype
					UpperCAmelCase_			:	Tuple      =   torch.cat(
					    [ordered_inputs["attention_mask"], torch.ones(_snake_case       ,_snake_case       ,dtype=_snake_case			)]       ,dim=1			)

				return ordered_inputs






			@property
			def 						UpperCamelCase__				(   self			):
				return 13







'''simple docstring'''
import torch

from diffusers import KDPMaAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest
class  _snake_case						(__SCREAMING_SNAKE_CASE):
			__A   :     Optional[int]          =(KDPMaAncestralDiscreteScheduler,)
			__A   :     List[Any]          =10






			def 						UpperCamelCase__				(   self       ,**_snake_case			):
				UpperCAmelCase_			:	List[Any]      =   {
				    "num_train_timesteps": 11_00,
				    "beta_start": 0.0001,
				    "beta_end": 0.02,
				    "beta_schedule": "linear",
				}

				config.update(**_snake_case			)
				return config






			def 						UpperCamelCase__				(   self			):
				for timesteps in [10, 50, 1_00, 10_00]:
					self.check_over_configs(num_train_timesteps=_snake_case			)






			def 						UpperCamelCase__				(   self			):
				for beta_start, beta_end in zip([0.00001, 0.0001, 0.001]       ,[0.0002, 0.002, 0.02]			):
					self.check_over_configs(beta_start=_snake_case       ,beta_end=_snake_case			)






			def 						UpperCamelCase__				(   self			):
				for schedule in ["linear", "scaled_linear"]:
					self.check_over_configs(beta_schedule=_snake_case			)






			def 						UpperCamelCase__				(   self			):
				if torch_device == "mps":
					return
				UpperCAmelCase_			:	str      =   self.scheduler_classes[0]
				UpperCAmelCase_			:	str      =   self.get_scheduler_config()
				UpperCAmelCase_			:	str      =   scheduler_class(**_snake_case			)

				scheduler.set_timesteps(self.num_inference_steps			)

				UpperCAmelCase_			:	Dict      =   torch.manual_seed(0			)

				UpperCAmelCase_			:	str      =   self.dummy_model()
				UpperCAmelCase_			:	str      =   self.dummy_sample_deter * scheduler.init_noise_sigma
				UpperCAmelCase_			:	Optional[Any]      =   sample.to(_snake_case			)

				for i, t in enumerate(scheduler.timesteps			):
					UpperCAmelCase_			:	Tuple      =   scheduler.scale_model_input(_snake_case       ,_snake_case			)

					UpperCAmelCase_			:	Union[str, Any]      =   model(_snake_case       ,_snake_case			)

					UpperCAmelCase_			:	Optional[Any]      =   scheduler.step(_snake_case       ,_snake_case       ,_snake_case       ,generator=_snake_case			)
					UpperCAmelCase_			:	Optional[Any]      =   output.prev_sample

				UpperCAmelCase_			:	Dict      =   torch.sum(torch.abs(_snake_case			)			)
				UpperCAmelCase_			:	List[Any]      =   torch.mean(torch.abs(_snake_case			)			)

				assert abs(result_sum.item() - 13849.3877			) < 1E-2
				assert abs(result_mean.item() - 18.0331			) < 5E-3






			def 						UpperCamelCase__				(   self			):
				for prediction_type in ["epsilon", "v_prediction"]:
					self.check_over_configs(prediction_type=_snake_case			)






			def 						UpperCamelCase__				(   self			):
				if torch_device == "mps":
					return
				UpperCAmelCase_			:	Any      =   self.scheduler_classes[0]
				UpperCAmelCase_			:	Any      =   self.get_scheduler_config(prediction_type="v_prediction"			)
				UpperCAmelCase_			:	Dict      =   scheduler_class(**_snake_case			)

				scheduler.set_timesteps(self.num_inference_steps			)

				UpperCAmelCase_			:	Union[str, Any]      =   self.dummy_model()
				UpperCAmelCase_			:	List[str]      =   self.dummy_sample_deter * scheduler.init_noise_sigma
				UpperCAmelCase_			:	int      =   sample.to(_snake_case			)

				UpperCAmelCase_			:	str      =   torch.manual_seed(0			)

				for i, t in enumerate(scheduler.timesteps			):
					UpperCAmelCase_			:	Dict      =   scheduler.scale_model_input(_snake_case       ,_snake_case			)

					UpperCAmelCase_			:	str      =   model(_snake_case       ,_snake_case			)

					UpperCAmelCase_			:	Optional[Any]      =   scheduler.step(_snake_case       ,_snake_case       ,_snake_case       ,generator=_snake_case			)
					UpperCAmelCase_			:	Any      =   output.prev_sample

				UpperCAmelCase_			:	Any      =   torch.sum(torch.abs(_snake_case			)			)
				UpperCAmelCase_			:	Tuple      =   torch.mean(torch.abs(_snake_case			)			)

				assert abs(result_sum.item() - 328.9970			) < 1E-2
				assert abs(result_mean.item() - 0.4284			) < 1E-3






			def 						UpperCamelCase__				(   self			):
				if torch_device == "mps":
					return
				UpperCAmelCase_			:	Optional[Any]      =   self.scheduler_classes[0]
				UpperCAmelCase_			:	Union[str, Any]      =   self.get_scheduler_config()
				UpperCAmelCase_			:	str      =   scheduler_class(**_snake_case			)

				scheduler.set_timesteps(self.num_inference_steps       ,device=_snake_case			)
				UpperCAmelCase_			:	Dict      =   torch.manual_seed(0			)

				UpperCAmelCase_			:	List[str]      =   self.dummy_model()
				UpperCAmelCase_			:	str      =   self.dummy_sample_deter.to(_snake_case			) * scheduler.init_noise_sigma

				for t in scheduler.timesteps:
					UpperCAmelCase_			:	Union[str, Any]      =   scheduler.scale_model_input(_snake_case       ,_snake_case			)

					UpperCAmelCase_			:	Tuple      =   model(_snake_case       ,_snake_case			)

					UpperCAmelCase_			:	int      =   scheduler.step(_snake_case       ,_snake_case       ,_snake_case       ,generator=_snake_case			)
					UpperCAmelCase_			:	List[str]      =   output.prev_sample

				UpperCAmelCase_			:	Any      =   torch.sum(torch.abs(_snake_case			)			)
				UpperCAmelCase_			:	Optional[int]      =   torch.mean(torch.abs(_snake_case			)			)

				assert abs(result_sum.item() - 13849.3818			) < 1E-1
				assert abs(result_mean.item() - 18.0331			) < 1E-3




