





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

import numpy as np
import torch

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


UpperCAmelCase__  :	Optional[Any]     =logging.get_logger(__name__)  # pylint: disable=invalid-name

@dataclass
class 					__A   (  a			):
				__A            = 42




class 					__A   (  a			,    a			):
				__A            = 1


				@register_to_config
				def __init__(						self      ,       UpperCAmelCase_ = 40      ,       UpperCAmelCase_ = 0.0_0_2      ,       UpperCAmelCase_ = 8_0.0      ,       UpperCAmelCase_ = 0.5      ,       UpperCAmelCase_ = 1.0      ,       UpperCAmelCase_ = 7.0      ,       UpperCAmelCase_ = True      ,       ):
								# standard deviation of the initial noise distribution
								lowerCamelCase		       =sigma_max

								lowerCamelCase		       =np.linspace(0      ,       1      ,       UpperCAmelCase_							)
								lowerCamelCase		       =self._convert_to_karras(UpperCAmelCase_							)
								lowerCamelCase		       =self.sigma_to_t(UpperCAmelCase_							)

								# setable values
								lowerCamelCase		       =None
								lowerCamelCase		       =torch.from_numpy(UpperCAmelCase_							)
								lowerCamelCase		       =torch.from_numpy(UpperCAmelCase_							)
								lowerCamelCase		       =False
								lowerCamelCase		       =False


				def _snake_case				(						self      ,       UpperCAmelCase_      ,       UpperCAmelCase_=None							):
								if schedule_timesteps is None:
												lowerCamelCase		       =self.timesteps

								lowerCamelCase		       =(schedule_timesteps == timestep).nonzero()
								return indices.item()


				def _snake_case				(						self      ,       UpperCAmelCase_      ,       UpperCAmelCase_							):
								# Get sigma corresponding to timestep
								if isinstance(UpperCAmelCase_      ,       torch.Tensor							):
												lowerCamelCase		       =timestep.to(self.timesteps.device							)
								lowerCamelCase		       =self.index_for_timestep(UpperCAmelCase_							)
								lowerCamelCase		       =self.sigmas[step_idx]

								lowerCamelCase		       =sample / ((sigma**2 + self.config.sigma_data**2) ** 0.5)

								lowerCamelCase		       =True
								return sample


				def _snake_case				(						self      ,       UpperCAmelCase_							):
								if not isinstance(UpperCAmelCase_      ,       np.ndarray							):
												lowerCamelCase		       =np.array(UpperCAmelCase_      ,       dtype=np.floataa							)

								lowerCamelCase		       =1000 * 0.2_5 * np.log(sigmas + 1E-44							)

								return timesteps


				def _snake_case				(						self      ,       UpperCAmelCase_ = None      ,       UpperCAmelCase_ = None      ,       UpperCAmelCase_ = None      ,       ):
								if num_inference_steps is None and timesteps is None:
												raise ValueError("""Exactly one of `num_inference_steps` or `timesteps` must be supplied."""							)

								if num_inference_steps is not None and timesteps is not None:
												raise ValueError("""Can only pass one of `num_inference_steps` or `timesteps`."""							)

								# Follow DDPMScheduler custom timesteps logic
								if timesteps is not None:
												for i in range(1      ,       len(UpperCAmelCase_							)							):
																if timesteps[i] >= timesteps[i - 1]:
																				raise ValueError("""`timesteps` must be in descending order."""							)

												if timesteps[0] >= self.config.num_train_timesteps:
																raise ValueError(
																    f"""`timesteps` must start before `self.config.train_timesteps`:"""
																    f""" {self.config.num_train_timesteps}."""							)

												lowerCamelCase		       =np.array(UpperCAmelCase_      ,       dtype=np.intaa							)
												lowerCamelCase		       =True
								else:
												if num_inference_steps > self.config.num_train_timesteps:
																raise ValueError(
																    f"""`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:"""
																    f""" {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle"""
																    f""" maximal {self.config.num_train_timesteps} timesteps."""							)

												lowerCamelCase		       =num_inference_steps

												lowerCamelCase		       =self.config.num_train_timesteps // self.num_inference_steps
												lowerCamelCase		       =(np.arange(0      ,       UpperCAmelCase_							) * step_ratio).round()[::-1].copy().astype(np.intaa							)
												lowerCamelCase		       =False

								# Map timesteps to Karras sigmas directly for multistep sampling
								# See https://github.com/openai/consistency_models/blob/main/cm/karras_diffusion.py#L675
								lowerCamelCase		       =self.config.num_train_timesteps
								lowerCamelCase		       =timesteps[::-1].copy()
								lowerCamelCase		       =ramp / (num_train_timesteps - 1)
								lowerCamelCase		       =self._convert_to_karras(UpperCAmelCase_							)
								lowerCamelCase		       =self.sigma_to_t(UpperCAmelCase_							)

								lowerCamelCase		       =np.concatenate([sigmas, [self.sigma_min]]							).astype(np.floataa							)
								lowerCamelCase		       =torch.from_numpy(UpperCAmelCase_							).to(device=UpperCAmelCase_							)

								if str(UpperCAmelCase_							).startswith("""mps"""							):
												# mps does not support float64
												lowerCamelCase		       =torch.from_numpy(UpperCAmelCase_							).to(UpperCAmelCase_      ,       dtype=torch.floataa							)
								else:
												lowerCamelCase		       =torch.from_numpy(UpperCAmelCase_							).to(device=UpperCAmelCase_							)


				def _snake_case				(						self      ,       UpperCAmelCase_							):
								lowerCamelCase		       =self.config.sigma_min
								lowerCamelCase		       =self.config.sigma_max

								lowerCamelCase		       =self.config.rho
								lowerCamelCase		       =sigma_min ** (1 / rho)
								lowerCamelCase		       =sigma_max ** (1 / rho)
								lowerCamelCase		       =(max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
								return sigmas


				def _snake_case				(						self      ,       UpperCAmelCase_							):
								lowerCamelCase		       =self.config.sigma_data

								lowerCamelCase		       =sigma_data**2 / (sigma**2 + sigma_data**2)
								lowerCamelCase		       =sigma * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
								return c_skip, c_out


				def _snake_case				(						self      ,       UpperCAmelCase_							):
								lowerCamelCase		       =self.config.sigma_min
								lowerCamelCase		       =self.config.sigma_data

								lowerCamelCase		       =sigma_data**2 / ((sigma - sigma_min) ** 2 + sigma_data**2)
								lowerCamelCase		       =(sigma - sigma_min) * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
								return c_skip, c_out


				def _snake_case				(						self      ,       UpperCAmelCase_      ,       UpperCAmelCase_      ,       UpperCAmelCase_      ,       UpperCAmelCase_ = None      ,       UpperCAmelCase_ = True      ,       ):

								if (
								    isinstance(UpperCAmelCase_      ,       UpperCAmelCase_							)
								    or isinstance(UpperCAmelCase_      ,       torch.IntTensor							)
								    or isinstance(UpperCAmelCase_      ,       torch.LongTensor							)
								):
												raise ValueError(
												    (
												        """Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to"""
												        f""" `{self.__class__}.step()` is not supported. Make sure to pass"""
												        """ one of the `scheduler.timesteps` as a timestep."""
												    )      ,       )

								if not self.is_scale_input_called:
												logger.warning(
												    """The `scale_model_input` function should be called before `step` to ensure correct denoising. """
												    """See `StableDiffusionPipeline` for a usage example."""							)

								if isinstance(UpperCAmelCase_      ,       torch.Tensor							):
												lowerCamelCase		       =timestep.to(self.timesteps.device							)

								lowerCamelCase		       =self.config.sigma_min
								lowerCamelCase		       =self.config.sigma_max

								lowerCamelCase		       =self.index_for_timestep(UpperCAmelCase_							)

								# sigma_next corresponds to next_t in original implementation
								lowerCamelCase		       =self.sigmas[step_index]
								if step_index + 1 < self.config.num_train_timesteps:
												lowerCamelCase		       =self.sigmas[step_index + 1]
								else:
												# Set sigma_next to sigma_min
												lowerCamelCase		       =self.sigmas[-1]

								# Get scalings for boundary conditions
								lowerCamelCase       ,     lowerCamelCase		       =self.get_scalings_for_boundary_condition(UpperCAmelCase_							)

								# 1. Denoise model output using boundary conditions
								lowerCamelCase		       =c_out * model_output + c_skip * sample
								if self.config.clip_denoised:
												lowerCamelCase		       =denoised.clamp(-1      ,       1							)

								# 2. Sample z ~ N(0, s_noise^2 * I)
								# Noise is not used for onestep sampling.
								if len(self.timesteps							) > 1:
												lowerCamelCase		       =randn_tensor(
												    model_output.shape      ,       dtype=model_output.dtype      ,       device=model_output.device      ,       generator=UpperCAmelCase_							)
								else:
												lowerCamelCase		       =torch.zeros_like(UpperCAmelCase_							)
								lowerCamelCase		       =noise * self.config.s_noise

								lowerCamelCase		       =sigma_next.clamp(min=UpperCAmelCase_      ,       max=UpperCAmelCase_							)

								# 3. Return noisy sample
								# tau = sigma_hat, eps = sigma_min
								lowerCamelCase		       =denoised + z * (sigma_hat**2 - sigma_min**2) ** 0.5

								if not return_dict:
												return (prev_sample,)

								return CMStochasticIterativeSchedulerOutput(prev_sample=UpperCAmelCase_							)


				def _snake_case				(						self      ,       UpperCAmelCase_      ,       UpperCAmelCase_      ,       UpperCAmelCase_      ,       ):
								# Make sure sigmas and timesteps have the same device and dtype as original_samples
								lowerCamelCase		       =self.sigmas.to(device=original_samples.device      ,       dtype=original_samples.dtype							)
								if original_samples.device.type == "mps" and torch.is_floating_point(UpperCAmelCase_							):
												# mps does not support float64
												lowerCamelCase		       =self.timesteps.to(original_samples.device      ,       dtype=torch.floataa							)
												lowerCamelCase		       =timesteps.to(original_samples.device      ,       dtype=torch.floataa							)
								else:
												lowerCamelCase		       =self.timesteps.to(original_samples.device							)
												lowerCamelCase		       =timesteps.to(original_samples.device							)

								lowerCamelCase		       =[(schedule_timesteps == t).nonzero().item() for t in timesteps]

								lowerCamelCase		       =sigmas[step_indices].flatten()
								while len(sigma.shape							) < len(original_samples.shape							):
												lowerCamelCase		       =sigma.unsqueeze(-1							)

								lowerCamelCase		       =original_samples + noise * sigma
								return noisy_samples


				def __len__(						self							):
								return self.config.num_train_timesteps













import unittest

from transformers import SpeechaTextaConfig
from transformers.testing_utils import is_torch_available, require_torch, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
   import torch

   from transformers.models.speech_to_text_a.modeling_speech_to_text_a import (
       SpeechaTextaDecoder,
       SpeechaTextaForCausalLM,
   )


@require_torch
class        SCREAMING_SNAKE_CASE	:
     '''simple docstring'''


     def __init__( self				:		List[str]    ,       UpperCAmelCase_				:		Any    ,       UpperCAmelCase_				:		List[Any]=99    ,       UpperCAmelCase_				:		Tuple=13    ,       UpperCAmelCase_				:		Dict=16    ,       UpperCAmelCase_				:		int=7    ,       UpperCAmelCase_				:		Optional[Any]=True    ,       UpperCAmelCase_				:		List[Any]=True    ,       UpperCAmelCase_				:		Any=True    ,       UpperCAmelCase_				:		List[str]=False    ,       UpperCAmelCase_				:		Tuple=True    ,       UpperCAmelCase_				:		Tuple=2    ,       UpperCAmelCase_				:		List[str]=32    ,       UpperCAmelCase_				:		Union[str, Any]=4    ,       UpperCAmelCase_				:		Any=4    ,       UpperCAmelCase_				:		Optional[int]=30    ,       UpperCAmelCase_				:		Union[str, Any]=0    ,       UpperCAmelCase_				:		Union[str, Any]=1    ,       UpperCAmelCase_				:		Any=2    ,       UpperCAmelCase_				:		Dict=None    ,       ):
       SCREAMING_SNAKE_CASE				:					Any		  =			parent
       SCREAMING_SNAKE_CASE				:					Tuple		  =			batch_size
       SCREAMING_SNAKE_CASE				:					str		  =			decoder_seq_length
       # For common tests
       SCREAMING_SNAKE_CASE				:					Tuple		  =			self.decoder_seq_length
       SCREAMING_SNAKE_CASE				:					str		  =			is_training
       SCREAMING_SNAKE_CASE				:					Dict		  =			use_attention_mask
       SCREAMING_SNAKE_CASE				:					int		  =			use_labels

       SCREAMING_SNAKE_CASE				:					Tuple		  =			vocab_size
       SCREAMING_SNAKE_CASE				:					int		  =			d_model
       SCREAMING_SNAKE_CASE				:					List[str]		  =			d_model
       SCREAMING_SNAKE_CASE				:					int		  =			decoder_layers
       SCREAMING_SNAKE_CASE				:					List[Any]		  =			decoder_layers
       SCREAMING_SNAKE_CASE				:					List[Any]		  =			decoder_ffn_dim
       SCREAMING_SNAKE_CASE				:					Tuple		  =			decoder_attention_heads
       SCREAMING_SNAKE_CASE				:					Dict		  =			decoder_attention_heads
       SCREAMING_SNAKE_CASE				:					Optional[int]		  =			eos_token_id
       SCREAMING_SNAKE_CASE				:					List[Any]		  =			bos_token_id
       SCREAMING_SNAKE_CASE				:					List[Any]		  =			pad_token_id
       SCREAMING_SNAKE_CASE				:					List[str]		  =			decoder_start_token_id
       SCREAMING_SNAKE_CASE				:					List[Any]		  =			use_cache
       SCREAMING_SNAKE_CASE				:					Tuple		  =			max_position_embeddings

       SCREAMING_SNAKE_CASE				:					Any		  =			None
       SCREAMING_SNAKE_CASE				:					Tuple		  =			decoder_seq_length
       SCREAMING_SNAKE_CASE				:					int		  =			2
       SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			1


     def  _A				( self				:		Optional[int]					):
       SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			ids_tensor([self.batch_size, self.decoder_seq_length]    ,       self.vocab_size					)

       SCREAMING_SNAKE_CASE				:					str		  =			None
       if self.use_attention_mask:
         SCREAMING_SNAKE_CASE				:					Tuple		  =			ids_tensor([self.batch_size, self.decoder_seq_length]    ,       vocab_size=2					)

       SCREAMING_SNAKE_CASE				:					str		  =			None
       if self.use_labels:
         SCREAMING_SNAKE_CASE				:					List[str]		  =			ids_tensor([self.batch_size, self.decoder_seq_length]    ,       self.vocab_size					)

       SCREAMING_SNAKE_CASE				:					str		  =			SpeechaTextaConfig(
           vocab_size=self.vocab_size    ,       d_model=self.d_model    ,       decoder_layers=self.decoder_layers    ,       decoder_ffn_dim=self.decoder_ffn_dim    ,       decoder_attention_heads=self.decoder_attention_heads    ,       eos_token_id=self.eos_token_id    ,       bos_token_id=self.bos_token_id    ,       use_cache=self.use_cache    ,       pad_token_id=self.pad_token_id    ,       decoder_start_token_id=self.decoder_start_token_id    ,       max_position_embeddings=self.max_position_embeddings    ,       )

       return (
           config,
           input_ids,
           attention_mask,
           lm_labels,
       )


     def  _A				( self				:		Optional[Any]    ,       UpperCAmelCase_				:		int    ,       UpperCAmelCase_				:		Dict    ,       UpperCAmelCase_				:		int    ,       UpperCAmelCase_				:		Any    ,       ):
       SCREAMING_SNAKE_CASE				:					str		  =			True
       SCREAMING_SNAKE_CASE				:					Optional[int]		  =			SpeechaTextaDecoder(config=UpperCAmelCase_					).to(UpperCAmelCase_					).eval()
       SCREAMING_SNAKE_CASE				:					Tuple		  =			input_ids[:2]

       input_ids[input_ids == 0] += 1
       # first forward pass
       SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			model(UpperCAmelCase_    ,       use_cache=UpperCAmelCase_					)
       SCREAMING_SNAKE_CASE				:					List[str]		  =			model(UpperCAmelCase_					)
       SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			model(UpperCAmelCase_    ,       use_cache=UpperCAmelCase_					)

       self.parent.assertTrue(len(UpperCAmelCase_					) == len(UpperCAmelCase_					)					)
       self.parent.assertTrue(len(UpperCAmelCase_					) == len(UpperCAmelCase_					) + 1					)

       SCREAMING_SNAKE_CASE				:					List[Any]		  =			outputs["past_key_values"]

       # create hypothetical next token and extent to next_input_ids
       SCREAMING_SNAKE_CASE				:					str		  =			ids_tensor((2, 1)    ,       config.vocab_size - 1					) + 1

       # append to next input_ids and
       SCREAMING_SNAKE_CASE				:					str		  =			torch.cat([input_ids, next_tokens]    ,       dim=-1					)

       print(UpperCAmelCase_					)

       SCREAMING_SNAKE_CASE				:					List[str]		  =			model(UpperCAmelCase_					)["last_hidden_state"]
       SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			model(UpperCAmelCase_    ,       past_key_values=UpperCAmelCase_					)["last_hidden_state"]

       # select random slice
       SCREAMING_SNAKE_CASE				:					List[Any]		  =			ids_tensor((1,)    ,       output_from_past.shape[-1]					).item()
       SCREAMING_SNAKE_CASE				:					str		  =			output_from_no_past[:, next_input_ids.shape[-1] - 1, random_slice_idx].detach()
       SCREAMING_SNAKE_CASE				:					int		  =			output_from_past[:, 0, random_slice_idx].detach()

       # test that outputs are equal for slice
       assert torch.allclose(UpperCAmelCase_    ,       UpperCAmelCase_    ,       atol=1E-3					)






     def  _A				( self				:		str					):
       SCREAMING_SNAKE_CASE				:					int		  =			self.prepare_config_and_inputs()
       (
           (
           SCREAMING_SNAKE_CASE
       )  ,     (
           SCREAMING_SNAKE_CASE
       )  ,     (
           SCREAMING_SNAKE_CASE
       )  ,     (
           SCREAMING_SNAKE_CASE
       )  ,     
       )				:					Optional[Any]		  =			config_and_inputs

       SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			{
           "input_ids": input_ids,
           "attention_mask": attention_mask,
       }
       return config, inputs_dict





@require_torch
class        SCREAMING_SNAKE_CASE	(	lowerCAmelCase					,						lowerCAmelCase					,						lowerCAmelCase					,						unittest.TestCase    ):
     '''simple docstring'''


     UpperCamelCase_							:						int								=		(SpeechaTextaDecoder, SpeechaTextaForCausalLM) if is_torch_available() else ()
     UpperCamelCase_							:						Tuple								=		(SpeechaTextaForCausalLM,) if is_torch_available() else ()
     UpperCamelCase_							:						int								=		{'''text-generation''': SpeechaTextaForCausalLM} if is_torch_available() else {}
     UpperCamelCase_							:						Tuple								=		True
     UpperCamelCase_							:						Union[str, Any]								=		False


     def  _A				( self				:		Tuple    ,       ):
       SCREAMING_SNAKE_CASE				:					Any		  =			SpeechaTextaStandaloneDecoderModelTester(self    ,       is_training=UpperCAmelCase_					)
       SCREAMING_SNAKE_CASE				:					Optional[int]		  =			ConfigTester(self    ,       config_class=UpperCAmelCase_					)


     def  _A				( self				:		Optional[Any]					):
       pass


     def  _A				( self				:		str					):
       pass


     def  _A				( self				:		Dict					):
       pass


     def  _A				( self				:		Optional[Any]					):
       self.config_tester.run_common_tests()


     def  _A				( self				:		Optional[int]					):
       SCREAMING_SNAKE_CASE				:					List[Any]		  =			self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_decoder_model_past(*UpperCAmelCase_					)






     def  _A				( self				:		Optional[int]					):
       return






