






'''simple docstring'''


from typing import Optional

import numpy as np
import torch
from torch import nn
from transformers import GPTaConfig, GPTaLMHeadModel
from transformers.modeling_utils import ModuleUtilsMixin

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class    _SCREAMING_SNAKE_CASE							(		SCREAMING_SNAKE_CASE       ,SCREAMING_SNAKE_CASE       ,SCREAMING_SNAKE_CASE	):


			'''simple docstring'''





			__a							:							Tuple					     =					[R"h\.\d+\.attn\.bias", R"h\.\d+\.attn\.masked_bias"]






			@register_to_config
			def __init__(   self							:						int ,      lowercase							:						int ,      lowercase							:						int ,      lowercase							:						Optional[int] = None ,      lowercase							:						int = 5_0_2_5_7 ,      lowercase							:						int = 1_0_2_4 ,      lowercase							:						int = 7_6_8 ,      lowercase							:						int = 1_2 ,      lowercase							:						int = 1_2 ,      lowercase							:						Optional[int] = None ,      lowercase							:						str = "gelu_new" ,      lowercase							:						float = 0.1 ,      lowercase							:						float = 0.1 ,      lowercase							:						float = 0.1 ,      lowercase							:						float = 1e-5 ,      lowercase							:						float = 0.0_2 ,      lowercase							:						bool = True ,      lowercase							:						bool = True ,      lowercase							:						bool = False ,      lowercase							:						bool = False ,      )							->    Union[str, Any]:

									'''simple docstring'''







									super().__init__()

									UpperCamelCase__         = prefix_length

									if prefix_inner_dim != n_embd and prefix_hidden_dim is None:
															raise ValueError(
															    f"`prefix_hidden_dim` cannot be `None` when `prefix_inner_dim`: {prefix_hidden_dim} and"
															    f" `n_embd`: {n_embd} are not equal."			)

									UpperCamelCase__         = prefix_inner_dim
									UpperCamelCase__         = prefix_hidden_dim

									UpperCamelCase__         = (
									    nn.Linear(self.prefix_inner_dim ,      self.prefix_hidden_dim			)
									    if self.prefix_hidden_dim is not None
									    else nn.Identity()
									)
									UpperCamelCase__         = (
									    nn.Linear(self.prefix_hidden_dim ,      lowercase			) if self.prefix_hidden_dim is not None else nn.Identity()
									)

									UpperCamelCase__         = GPTaConfig(
									    vocab_size=lowercase ,      n_positions=lowercase ,      n_embd=lowercase ,      n_layer=lowercase ,      n_head=lowercase ,      n_inner=lowercase ,      activation_function=lowercase ,      resid_pdrop=lowercase ,      embd_pdrop=lowercase ,      attn_pdrop=lowercase ,      layer_norm_epsilon=lowercase ,      initializer_range=lowercase ,      scale_attn_weights=lowercase ,      use_cache=lowercase ,      scale_attn_by_inverse_layer_idx=lowercase ,      reorder_and_upcast_attn=lowercase ,      )
									UpperCamelCase__         = GPTaLMHeadModel(lowercase			)






			def 				A      (   self							:						List[str] ,      lowercase							:						torch.Tensor ,      lowercase							:						torch.Tensor ,      lowercase							:						Optional[torch.Tensor] = None ,      lowercase							:						Optional[torch.Tensor] = None ,      )							->    Tuple:

									'''simple docstring'''







									UpperCamelCase__         = self.transformer.transformer.wte(lowercase			)
									UpperCamelCase__         = self.encode_prefix(lowercase			)
									UpperCamelCase__         = self.decode_prefix(lowercase			)
									UpperCamelCase__         = torch.cat((prefix_embeds, embedding_text) ,      dim=1			)

									if labels is not None:
															UpperCamelCase__         = self.get_dummy_token(input_ids.shape[0] ,      input_ids.device			)
															UpperCamelCase__         = torch.cat((dummy_token, input_ids) ,      dim=1			)
									UpperCamelCase__         = self.transformer(inputs_embeds=lowercase ,      labels=lowercase ,      attention_mask=lowercase			)
									if self.prefix_hidden_dim is not None:
															return out, hidden
									else:
															return out






			def 				A      (   self							:						Optional[Any] ,      lowercase							:						int ,      lowercase							:						torch.device			)							->    torch.Tensor:

									'''simple docstring'''







									return torch.zeros(lowercase ,      self.prefix_length ,      dtype=torch.intaa ,      device=lowercase			)






			def 				A      (   self							:						Tuple ,      lowercase							:						str			)							->    List[Any]:

									'''simple docstring'''







									return self.encode_prefix(lowercase			)






			@torch.no_grad()
			def 				A      (   self							:						int ,      lowercase							:						Tuple ,      lowercase							:						List[Any] ,      lowercase							:						Any			)							->    Tuple:

									'''simple docstring'''







									UpperCamelCase__         = torch.split(lowercase ,      1 ,      dim=0			)
									UpperCamelCase__         = []
									UpperCamelCase__         = []
									for feature in features:
															UpperCamelCase__         = self.decode_prefix(feature.to(lowercase			)			)  # back to the clip feature
															# Only support beam search for now
															UpperCamelCase__    ,   UpperCamelCase__         = self.generate_beam(
															    input_embeds=lowercase ,      device=lowercase ,      eos_token_id=lowercase			)
															generated_tokens.append(output_tokens[0]			)
															generated_seq_lengths.append(seq_lengths[0]			)
									UpperCamelCase__         = torch.stack(lowercase			)
									UpperCamelCase__         = torch.stack(lowercase			)
									return generated_tokens, generated_seq_lengths







			@torch.no_grad()
			def 				A      (   self							:						Dict ,      lowercase							:						str=None ,      lowercase							:						Tuple=None ,      lowercase							:						str=None ,      lowercase							:						int = 5 ,      lowercase							:						int = 6_7 ,      lowercase							:						float = 1.0 ,      lowercase							:						Optional[int] = None ,      )							->    str:

									'''simple docstring'''







									UpperCamelCase__         = eos_token_id
									UpperCamelCase__         = None
									UpperCamelCase__         = None
									UpperCamelCase__         = torch.ones(lowercase ,      device=lowercase ,      dtype=torch.int			)
									UpperCamelCase__         = torch.zeros(lowercase ,      device=lowercase ,      dtype=torch.bool			)

									if input_embeds is not None:
															UpperCamelCase__         = input_embeds
									else:
															UpperCamelCase__         = self.transformer.transformer.wte(lowercase			)

									for i in range(lowercase			):
															UpperCamelCase__         = self.transformer(inputs_embeds=lowercase			)
															UpperCamelCase__         = outputs.logits
															UpperCamelCase__         = logits[:, -1, :] / (temperature if temperature > 0 else 1.0)
															UpperCamelCase__         = logits.softmax(-1			).log()

															if scores is None:
																					UpperCamelCase__    ,   UpperCamelCase__         = logits.topk(lowercase ,      -1			)
																					UpperCamelCase__         = generated.expand(lowercase ,      *generated.shape[1:]			)
																					UpperCamelCase__    ,   UpperCamelCase__         = next_tokens.permute(1 ,      0			), scores.squeeze(0			)
																					if tokens is None:
																											UpperCamelCase__         = next_tokens
																					else:
																											UpperCamelCase__         = tokens.expand(lowercase ,      *tokens.shape[1:]			)
																											UpperCamelCase__         = torch.cat((tokens, next_tokens) ,      dim=1			)
															else:
																					UpperCamelCase__         = -float(np.inf			)
																					UpperCamelCase__         = 0
																					UpperCamelCase__         = scores[:, None] + logits
																					seq_lengths[~is_stopped] += 1
																					UpperCamelCase__         = scores_sum / seq_lengths[:, None]
																					UpperCamelCase__    ,   UpperCamelCase__         = scores_sum_average.view(-1			).topk(lowercase ,      -1			)
																					UpperCamelCase__         = next_tokens // scores_sum.shape[1]
																					UpperCamelCase__         = seq_lengths[next_tokens_source]
																					UpperCamelCase__         = next_tokens % scores_sum.shape[1]
																					UpperCamelCase__         = next_tokens.unsqueeze(1			)
																					UpperCamelCase__         = tokens[next_tokens_source]
																					UpperCamelCase__         = torch.cat((tokens, next_tokens) ,      dim=1			)
																					UpperCamelCase__         = generated[next_tokens_source]
																					UpperCamelCase__         = scores_sum_average * seq_lengths
																					UpperCamelCase__         = is_stopped[next_tokens_source]

															UpperCamelCase__         = self.transformer.transformer.wte(next_tokens.squeeze()			).view(generated.shape[0] ,      1 ,      -1			)
															UpperCamelCase__         = torch.cat((generated, next_token_embed) ,      dim=1			)
															UpperCamelCase__         = is_stopped + next_tokens.eq(lowercase			).squeeze()
															if is_stopped.all():
																					break

									UpperCamelCase__         = scores / seq_lengths
									UpperCamelCase__         = scores.argsort(descending=lowercase			)
									# tokens tensors are already padded to max_seq_length
									UpperCamelCase__         = [tokens[i] for i in order]
									UpperCamelCase__         = torch.stack(lowercase ,      dim=0			)
									UpperCamelCase__         = torch.tensor([seq_lengths[i] for i in order] ,      dtype=seq_lengths.dtype			)
									return output_texts, seq_lengths








'''simple docstring'''


import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest
class    _SCREAMING_SNAKE_CASE							(		SCREAMING_SNAKE_CASE	):


			'''simple docstring'''





			__a							:							Dict					     =					(DPMSolverSinglestepScheduler,)
			__a							:							List[Any]					     =					(("num_inference_steps", 25),)






			def 				A      (   self							:						List[Any] ,      **lowercase							:						Any			)							->    Optional[int]:

									'''simple docstring'''







									UpperCamelCase__         = {
									    """num_train_timesteps""": 1_0_0_0,
									    """beta_start""": 0.0_0_0_1,
									    """beta_end""": 0.0_2,
									    """beta_schedule""": """linear""",
									    """solver_order""": 2,
									    """prediction_type""": """epsilon""",
									    """thresholding""": False,
									    """sample_max_value""": 1.0,
									    """algorithm_type""": """dpmsolver++""",
									    """solver_type""": """midpoint""",
									    """lambda_min_clipped""": -float("""inf"""			),
									    """variance_type""": None,
									}

									config.update(**lowercase			)
									return config






			def 				A      (   self							:						List[Any] ,      lowercase							:						Tuple=0 ,      **lowercase							:						Optional[Any]			)							->    Any:

									'''simple docstring'''







									UpperCamelCase__         = dict(self.forward_default_kwargs			)
									UpperCamelCase__         = kwargs.pop("""num_inference_steps""" ,      lowercase			)
									UpperCamelCase__         = self.dummy_sample
									UpperCamelCase__         = 0.1 * sample
									UpperCamelCase__         = [residual + 0.2, residual + 0.1_5, residual + 0.1_0]

									for scheduler_class in self.scheduler_classes:
															UpperCamelCase__         = self.get_scheduler_config(**lowercase			)
															UpperCamelCase__         = scheduler_class(**lowercase			)
															scheduler.set_timesteps(lowercase			)
															# copy over dummy past residuals
															UpperCamelCase__         = dummy_past_residuals[: scheduler.config.solver_order]

															with tempfile.TemporaryDirectory() as tmpdirname:
																					scheduler.save_config(lowercase			)
																					UpperCamelCase__         = scheduler_class.from_pretrained(lowercase			)
																					new_scheduler.set_timesteps(lowercase			)
																					# copy over dummy past residuals
																					UpperCamelCase__         = dummy_past_residuals[: new_scheduler.config.solver_order]

															UpperCamelCase__    ,   UpperCamelCase__         = sample, sample
															for t in range(lowercase ,      time_step + scheduler.config.solver_order + 1			):
																					UpperCamelCase__         = scheduler.step(lowercase ,      lowercase ,      lowercase ,      **lowercase			).prev_sample
																					UpperCamelCase__         = new_scheduler.step(lowercase ,      lowercase ,      lowercase ,      **lowercase			).prev_sample

																					assert torch.sum(torch.abs(output - new_output			)			) < 1e-5, "Scheduler outputs are not identical"






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

									'''simple docstring'''







									pass






			def 				A      (   self							:						Any ,      lowercase							:						List[Any]=0 ,      **lowercase							:						Optional[int]			)							->    Optional[int]:

									'''simple docstring'''







									UpperCamelCase__         = dict(self.forward_default_kwargs			)
									UpperCamelCase__         = kwargs.pop("""num_inference_steps""" ,      lowercase			)
									UpperCamelCase__         = self.dummy_sample
									UpperCamelCase__         = 0.1 * sample
									UpperCamelCase__         = [residual + 0.2, residual + 0.1_5, residual + 0.1_0]

									for scheduler_class in self.scheduler_classes:
															UpperCamelCase__         = self.get_scheduler_config()
															UpperCamelCase__         = scheduler_class(**lowercase			)
															scheduler.set_timesteps(lowercase			)

															# copy over dummy past residuals (must be after setting timesteps)
															UpperCamelCase__         = dummy_past_residuals[: scheduler.config.solver_order]

															with tempfile.TemporaryDirectory() as tmpdirname:
																					scheduler.save_config(lowercase			)
																					UpperCamelCase__         = scheduler_class.from_pretrained(lowercase			)
																					# copy over dummy past residuals
																					new_scheduler.set_timesteps(lowercase			)

																					# copy over dummy past residual (must be after setting timesteps)
																					UpperCamelCase__         = dummy_past_residuals[: new_scheduler.config.solver_order]

															UpperCamelCase__         = scheduler.step(lowercase ,      lowercase ,      lowercase ,      **lowercase			).prev_sample
															UpperCamelCase__         = new_scheduler.step(lowercase ,      lowercase ,      lowercase ,      **lowercase			).prev_sample

															assert torch.sum(torch.abs(output - new_output			)			) < 1e-5, "Scheduler outputs are not identical"






			def 				A      (   self							:						Union[str, Any] ,      lowercase							:						Any=None ,      **lowercase							:						List[Any]			)							->    Dict:

									'''simple docstring'''







									if scheduler is None:
															UpperCamelCase__         = self.scheduler_classes[0]
															UpperCamelCase__         = self.get_scheduler_config(**lowercase			)
															UpperCamelCase__         = scheduler_class(**lowercase			)

									UpperCamelCase__         = self.scheduler_classes[0]
									UpperCamelCase__         = self.get_scheduler_config(**lowercase			)
									UpperCamelCase__         = scheduler_class(**lowercase			)

									UpperCamelCase__         = 1_0
									UpperCamelCase__         = self.dummy_model()
									UpperCamelCase__         = self.dummy_sample_deter
									scheduler.set_timesteps(lowercase			)

									for i, t in enumerate(scheduler.timesteps			):
															UpperCamelCase__         = model(lowercase ,      lowercase			)
															UpperCamelCase__         = scheduler.step(lowercase ,      lowercase ,      lowercase			).prev_sample

									return sample






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

									'''simple docstring'''







									UpperCamelCase__         = DPMSolverSinglestepScheduler(**self.get_scheduler_config()			)
									UpperCamelCase__         = 5_0
									UpperCamelCase__         = self.dummy_model()
									UpperCamelCase__         = self.dummy_sample_deter
									scheduler.set_timesteps(lowercase			)

									# make sure that the first t is uneven
									for i, t in enumerate(scheduler.timesteps[3:]			):
															UpperCamelCase__         = model(lowercase ,      lowercase			)
															UpperCamelCase__         = scheduler.step(lowercase ,      lowercase ,      lowercase			).prev_sample

									UpperCamelCase__         = torch.mean(torch.abs(lowercase			)			)

									assert abs(result_mean.item() - 0.2_5_7_4			) < 1e-3






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

									'''simple docstring'''







									for timesteps in [2_5, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
															self.check_over_configs(num_train_timesteps=lowercase			)






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

									'''simple docstring'''







									UpperCamelCase__         = DPMSolverSinglestepScheduler(**self.get_scheduler_config()			)
									UpperCamelCase__         = self.full_loop(scheduler=lowercase			)
									UpperCamelCase__         = torch.mean(torch.abs(lowercase			)			)

									assert abs(result_mean.item() - 0.2_7_9_1			) < 1e-3

									UpperCamelCase__         = DEISMultistepScheduler.from_config(scheduler.config			)
									UpperCamelCase__         = DPMSolverMultistepScheduler.from_config(scheduler.config			)
									UpperCamelCase__         = UniPCMultistepScheduler.from_config(scheduler.config			)
									UpperCamelCase__         = DPMSolverSinglestepScheduler.from_config(scheduler.config			)

									UpperCamelCase__         = self.full_loop(scheduler=lowercase			)
									UpperCamelCase__         = torch.mean(torch.abs(lowercase			)			)

									assert abs(result_mean.item() - 0.2_7_9_1			) < 1e-3






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

									'''simple docstring'''







									self.check_over_configs(thresholding=lowercase			)
									for order in [1, 2, 3]:
															for solver_type in ["midpoint", "heun"]:
																					for threshold in [0.5, 1.0, 2.0]:
																											for prediction_type in ["epsilon", "sample"]:
																																	self.check_over_configs(
																																	    thresholding=lowercase ,      prediction_type=lowercase ,      sample_max_value=lowercase ,      algorithm_type="""dpmsolver++""" ,      solver_order=lowercase ,      solver_type=lowercase ,      )






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

									'''simple docstring'''







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






			def 				A      (   self							:						Optional[int]			)							->    int:

									'''simple docstring'''







									for algorithm_type in ["dpmsolver", "dpmsolver++"]:
															for solver_type in ["midpoint", "heun"]:
																					for order in [1, 2, 3]:
																											for prediction_type in ["epsilon", "sample"]:
																																	self.check_over_configs(
																																	    solver_order=lowercase ,      solver_type=lowercase ,      prediction_type=lowercase ,      algorithm_type=lowercase ,      )
																																	UpperCamelCase__         = self.full_loop(
																																	    solver_order=lowercase ,      solver_type=lowercase ,      prediction_type=lowercase ,      algorithm_type=lowercase ,      )
																																	assert not torch.isnan(lowercase			).any(), "Samples have nan numbers"






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

									'''simple docstring'''







									self.check_over_configs(lower_order_final=lowercase			)
									self.check_over_configs(lower_order_final=lowercase			)






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

									'''simple docstring'''







									self.check_over_configs(lambda_min_clipped=-float("""inf"""			)			)
									self.check_over_configs(lambda_min_clipped=-5.1			)






			def 				A      (   self							:						List[Any]			)							->    int:

									'''simple docstring'''







									self.check_over_configs(variance_type=lowercase			)
									self.check_over_configs(variance_type="""learned_range"""			)






			def 				A      (   self							:						Tuple			)							->    int:

									'''simple docstring'''







									for num_inference_steps in [1, 2, 3, 5, 1_0, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
															self.check_over_forward(num_inference_steps=lowercase ,      time_step=0			)






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

									'''simple docstring'''







									UpperCamelCase__         = self.full_loop()
									UpperCamelCase__         = torch.mean(torch.abs(lowercase			)			)

									assert abs(result_mean.item() - 0.2_7_9_1			) < 1e-3






			def 				A      (   self							:						str			)							->    int:

									'''simple docstring'''







									UpperCamelCase__         = self.full_loop(use_karras_sigmas=lowercase			)
									UpperCamelCase__         = torch.mean(torch.abs(lowercase			)			)

									assert abs(result_mean.item() - 0.2_2_4_8			) < 1e-3






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

									'''simple docstring'''







									UpperCamelCase__         = self.full_loop(prediction_type="""v_prediction"""			)
									UpperCamelCase__         = torch.mean(torch.abs(lowercase			)			)

									assert abs(result_mean.item() - 0.1_4_5_3			) < 1e-3






			def 				A      (   self							:						int			)							->    int:

									'''simple docstring'''







									UpperCamelCase__         = self.full_loop(prediction_type="""v_prediction""" ,      use_karras_sigmas=lowercase			)
									UpperCamelCase__         = torch.mean(torch.abs(lowercase			)			)

									assert abs(result_mean.item() - 0.0_6_4_9			) < 1e-3







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

									'''simple docstring'''







									UpperCamelCase__         = self.scheduler_classes[0]
									UpperCamelCase__         = self.get_scheduler_config(thresholding=lowercase ,      dynamic_thresholding_ratio=0			)
									UpperCamelCase__         = scheduler_class(**lowercase			)

									UpperCamelCase__         = 1_0
									UpperCamelCase__         = self.dummy_model()
									UpperCamelCase__         = self.dummy_sample_deter.half()
									scheduler.set_timesteps(lowercase			)

									for i, t in enumerate(scheduler.timesteps			):
															UpperCamelCase__         = model(lowercase ,      lowercase			)
															UpperCamelCase__         = scheduler.step(lowercase ,      lowercase ,      lowercase			).prev_sample

									assert sample.dtype == torch.floataa
