





import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput




def 				_lowerCAmelCase  (			lowerCamelCase_     :					Optional[int]						,    lowerCamelCase_     :					Tuple=0.9_99						,    lowerCamelCase_     :					Tuple="cosine"						,    ):
			if alpha_transform_type == "cosine":

						def alpha_bar_fn(lowerCamelCase_     :					List[Any]			):
									return math.cos((t + 0.0_08) / 1.0_08 * math.pi / 2			) ** 2

			elif alpha_transform_type == "exp":

						def alpha_bar_fn(lowerCamelCase_     :					int			):
									return math.exp(t * -12.0			)

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

			__lowercase			  =							[]
			for i in range(A_			):
						__lowercase			  =							i / num_diffusion_timesteps
						__lowercase			  =							(i + 1) / num_diffusion_timesteps
						betas.append(min(1 - alpha_bar_fn(A_			) / alpha_bar_fn(A_			)						,    A_			)			)
			return torch.tensor(A_						,    dtype=torch.floataa			)






class 							__lowercase    ( a_	, a_  ):







	'''simple docstring'''

	a      :			Tuple     =    [e.name for e in KarrasDiffusionSchedulers]
	a      :			List[Any]     =    2


	@register_to_config
	def __init__(self       ,_lowerCamelCase = 1000       ,_lowerCamelCase = 0.0_0_0_8_5       ,_lowerCamelCase = 0.0_1_2       ,_lowerCamelCase = "linear"       ,_lowerCamelCase = None       ,_lowerCamelCase = "epsilon"       ,_lowerCamelCase = False       ,_lowerCamelCase = False       ,_lowerCamelCase = 1.0       ,_lowerCamelCase = "linspace"       ,_lowerCamelCase = 0       ,)							->							Dict:







				'''simple docstring'''





				if trained_betas is not None:
							__lowercase			  =							torch.tensor(lowercase_       ,dtype=torch.floataa	)
				elif beta_schedule == "linear":
							__lowercase			  =							torch.linspace(lowercase_       ,lowercase_       ,lowercase_       ,dtype=torch.floataa	)
				elif beta_schedule == "scaled_linear":
							# this schedule is very specific to the latent diffusion model.
							__lowercase			  =							(
							    torch.linspace(beta_start**0.5       ,beta_end**0.5       ,lowercase_       ,dtype=torch.floataa	) ** 2
							)
				elif beta_schedule == "squaredcos_cap_v2":
							# Glide cosine schedule
							__lowercase			  =							betas_for_alpha_bar(lowercase_       ,alpha_transform_type='''cosine'''	)
				elif beta_schedule == "exp":
							__lowercase			  =							betas_for_alpha_bar(lowercase_       ,alpha_transform_type='''exp'''	)
				else:
							raise NotImplementedError(f"{beta_schedule} does is not implemented for {self.__class__}"	)

				__lowercase			  =							1.0 - self.betas
				__lowercase			  =							torch.cumprod(self.alphas       ,dim=0	)

				#  set all values
				self.set_timesteps(lowercase_       ,lowercase_       ,lowercase_	)
				__lowercase			  =							use_karras_sigmas


	def   _UpperCAmelCase       (self       ,_lowerCamelCase       ,_lowerCamelCase=None	)							->							List[Any]:







				'''simple docstring'''





				if schedule_timesteps is None:
							__lowercase			  =							self.timesteps

				__lowercase			  =							(schedule_timesteps == timestep).nonzero()

				# The sigma index that is taken for the **very** first `step`
				# is always the second index (or the last index if there is only 1)
				# This way we can ensure we don't accidentally skip a sigma in
				# case we start in the middle of the denoising schedule (e.g. for image-to-image)
				if len(self._index_counter	) == 0:
							__lowercase			  =							1 if len(lowercase_	) > 1 else 0
				else:
							__lowercase			  =							timestep.cpu().item() if torch.is_tensor(lowercase_	) else timestep
							__lowercase			  =							self._index_counter[timestep_int]

				return indices[pos].item()


	@property
	def   _UpperCAmelCase       (self	)							->							int:







				'''simple docstring'''





				if self.config.timestep_spacing in ["linspace", "trailing"]:
							return self.sigmas.max()

				return (self.sigmas.max() ** 2 + 1) ** 0.5


	def   _UpperCAmelCase       (self       ,_lowerCamelCase       ,_lowerCamelCase       ,)							->							Any:







				'''simple docstring'''





				__lowercase			  =							self.index_for_timestep(lowercase_	)

				__lowercase			  =							self.sigmas[step_index]
				__lowercase			  =							sample / ((sigma**2 + 1) ** 0.5)
				return sample


	def   _UpperCAmelCase       (self       ,_lowerCamelCase       ,_lowerCamelCase = None       ,_lowerCamelCase = None       ,)							->							Union[str, Any]:







				'''simple docstring'''





				__lowercase			  =							num_inference_steps

				__lowercase			  =							num_train_timesteps or self.config.num_train_timesteps

				# "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
				if self.config.timestep_spacing == "linspace":
							__lowercase			  =							np.linspace(0       ,num_train_timesteps - 1       ,lowercase_       ,dtype=lowercase_	)[::-1].copy()
				elif self.config.timestep_spacing == "leading":
							__lowercase			  =							num_train_timesteps // self.num_inference_steps
							# creates integer timesteps by multiplying by ratio
							# casting to int to avoid issues when num_inference_step is power of 3
							__lowercase			  =							(np.arange(0       ,lowercase_	) * step_ratio).round()[::-1].copy().astype(lowercase_	)
							timesteps += self.config.steps_offset
				elif self.config.timestep_spacing == "trailing":
							__lowercase			  =							num_train_timesteps / self.num_inference_steps
							# creates integer timesteps by multiplying by ratio
							# casting to int to avoid issues when num_inference_step is power of 3
							__lowercase			  =							(np.arange(lowercase_       ,0       ,-step_ratio	)).round().copy().astype(lowercase_	)
							timesteps -= 1
				else:
							raise ValueError(
							    f"{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'."	)

				__lowercase			  =							np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5	)
				__lowercase			  =							np.log(lowercase_	)
				__lowercase			  =							np.interp(lowercase_       ,np.arange(0       ,len(lowercase_	)	)       ,lowercase_	)

				if self.config.use_karras_sigmas:
							__lowercase			  =							self._convert_to_karras(in_sigmas=lowercase_       ,num_inference_steps=self.num_inference_steps	)
							__lowercase			  =							np.array([self._sigma_to_t(lowercase_       ,lowercase_	) for sigma in sigmas]	)

				__lowercase			  =							np.concatenate([sigmas, [0.0]]	).astype(np.floataa	)
				__lowercase			  =							torch.from_numpy(lowercase_	).to(device=lowercase_	)
				__lowercase			  =							torch.cat([sigmas[:1], sigmas[1:-1].repeat_interleave(2	), sigmas[-1:]]	)

				__lowercase			  =							torch.from_numpy(lowercase_	)
				__lowercase			  =							torch.cat([timesteps[:1], timesteps[1:].repeat_interleave(2	)]	)

				if str(lowercase_	).startswith('''mps'''	):
							# mps does not support float64
							__lowercase			  =							timesteps.to(lowercase_       ,dtype=torch.floataa	)
				else:
							__lowercase			  =							timesteps.to(device=lowercase_	)

				# empty dt and derivative
				__lowercase			  =							None
				__lowercase			  =							None

				# for exp beta schedules, such as the one for `pipeline_shap_e.py`
				# we need an index counter
				__lowercase			  =							defaultdict(lowercase_	)


	def   _UpperCAmelCase       (self       ,_lowerCamelCase       ,_lowerCamelCase	)							->							List[Any]:







				'''simple docstring'''





				__lowercase			  =							np.log(lowercase_	)

				# get distribution
				__lowercase			  =							log_sigma - log_sigmas[:, np.newaxis]

				# get sigmas range
				__lowercase			  =							np.cumsum((dists >= 0)       ,axis=0	).argmax(axis=0	).clip(max=log_sigmas.shape[0] - 2	)
				__lowercase			  =							low_idx + 1

				__lowercase			  =							log_sigmas[low_idx]
				__lowercase			  =							log_sigmas[high_idx]

				# interpolate sigmas
				__lowercase			  =							(low - log_sigma) / (low - high)
				__lowercase			  =							np.clip(lowercase_       ,0       ,1	)

				# transform interpolation to time range
				__lowercase			  =							(1 - w) * low_idx + w * high_idx
				__lowercase			  =							t.reshape(sigma.shape	)
				return t


	def   _UpperCAmelCase       (self       ,_lowerCamelCase       ,_lowerCamelCase	)							->							int:







				'''simple docstring'''





				__lowercase			  =							in_sigmas[-1].item()
				__lowercase			  =							in_sigmas[0].item()

				__lowercase			  =							7.0  # 7.0 is the value used in the paper
				__lowercase			  =							np.linspace(0       ,1       ,lowercase_	)
				__lowercase			  =							sigma_min ** (1 / rho)
				__lowercase			  =							sigma_max ** (1 / rho)
				__lowercase			  =							(max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
				return sigmas


	@property
	def   _UpperCAmelCase       (self	)							->							int:







				'''simple docstring'''





				return self.dt is None


	def   _UpperCAmelCase       (self       ,_lowerCamelCase       ,_lowerCamelCase       ,_lowerCamelCase       ,_lowerCamelCase = True       ,)							->							List[str]:







				'''simple docstring'''





				__lowercase			  =							self.index_for_timestep(lowercase_	)

				# advance index counter by 1
				__lowercase			  =							timestep.cpu().item() if torch.is_tensor(lowercase_	) else timestep
				self._index_counter[timestep_int] += 1

				if self.state_in_first_order:
							__lowercase			  =							self.sigmas[step_index]
							__lowercase			  =							self.sigmas[step_index + 1]
				else:
							# 2nd order / Heun's method
							__lowercase			  =							self.sigmas[step_index - 1]
							__lowercase			  =							self.sigmas[step_index]

				# currently only gamma=0 is supported. This usually works best anyways.
				# We can support gamma in the future but then need to scale the timestep before
				# passing it to the model which requires a change in API
				__lowercase			  =							0
				__lowercase			  =							sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

				# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
				if self.config.prediction_type == "epsilon":
							__lowercase			  =							sigma_hat if self.state_in_first_order else sigma_next
							__lowercase			  =							sample - sigma_input * model_output
				elif self.config.prediction_type == "v_prediction":
							__lowercase			  =							sigma_hat if self.state_in_first_order else sigma_next
							__lowercase			  =							model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
							    sample / (sigma_input**2 + 1)
							)
				elif self.config.prediction_type == "sample":
							__lowercase			  =							model_output
				else:
							raise ValueError(
							    f"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`"	)

				if self.config.clip_sample:
							__lowercase			  =							pred_original_sample.clamp(
							    -self.config.clip_sample_range       ,self.config.clip_sample_range	)

				if self.state_in_first_order:
							# 2. Convert to an ODE derivative for 1st order
							__lowercase			  =							(sample - pred_original_sample) / sigma_hat
							# 3. delta timestep
							__lowercase			  =							sigma_next - sigma_hat

							# store for 2nd order step
							__lowercase			  =							derivative
							__lowercase			  =							dt
							__lowercase			  =							sample
				else:
							# 2. 2nd order / Heun's method
							__lowercase			  =							(sample - pred_original_sample) / sigma_next
							__lowercase			  =							(self.prev_derivative + derivative) / 2

							# 3. take prev timestep & sample
							__lowercase			  =							self.dt
							__lowercase			  =							self.sample

							# free dt and derivative
							# Note, this puts the scheduler in "first order mode"
							__lowercase			  =							None
							__lowercase			  =							None
							__lowercase			  =							None

				__lowercase			  =							sample + derivative * dt

				if not return_dict:
							return (prev_sample,)

				return SchedulerOutput(prev_sample=lowercase_	)


	def   _UpperCAmelCase       (self       ,_lowerCamelCase       ,_lowerCamelCase       ,_lowerCamelCase       ,)							->							List[str]:







				'''simple docstring'''





				__lowercase			  =							self.sigmas.to(device=original_samples.device       ,dtype=original_samples.dtype	)
				if original_samples.device.type == "mps" and torch.is_floating_point(lowercase_	):
							# mps does not support float64
							__lowercase			  =							self.timesteps.to(original_samples.device       ,dtype=torch.floataa	)
							__lowercase			  =							timesteps.to(original_samples.device       ,dtype=torch.floataa	)
				else:
							__lowercase			  =							self.timesteps.to(original_samples.device	)
							__lowercase			  =							timesteps.to(original_samples.device	)

				__lowercase			  =							[self.index_for_timestep(lowercase_       ,lowercase_	) for t in timesteps]

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

				__lowercase			  =							original_samples + noise * sigma
				return noisy_samples


	def __len__(self	)							->							Dict:







				'''simple docstring'''





				return self.config.num_train_timesteps












'''simple docstring'''



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_SCREAMING_SNAKE_CASE   =      {
    '''configuration_blenderbot''': [
        '''BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''BlenderbotConfig''',
        '''BlenderbotOnnxConfig''',
    ],
    '''tokenization_blenderbot''': ['''BlenderbotTokenizer'''],
}

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_SCREAMING_SNAKE_CASE   =      ['''BlenderbotTokenizerFast''']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_SCREAMING_SNAKE_CASE   =      [
	    '''BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''BlenderbotForCausalLM''',
	    '''BlenderbotForConditionalGeneration''',
	    '''BlenderbotModel''',
	    '''BlenderbotPreTrainedModel''',
	]


try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_SCREAMING_SNAKE_CASE   =      [
	    '''TFBlenderbotForConditionalGeneration''',
	    '''TFBlenderbotModel''',
	    '''TFBlenderbotPreTrainedModel''',
	]


try:
	if not is_flax_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_SCREAMING_SNAKE_CASE   =      [
	    '''FlaxBlenderbotForConditionalGeneration''',
	    '''FlaxBlenderbotModel''',
	    '''FlaxBlenderbotPreTrainedModel''',
	]


if TYPE_CHECKING:
	from .configuration_blenderbot import (
	    BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    BlenderbotConfig,
	    BlenderbotOnnxConfig,
	)
	from .tokenization_blenderbot import BlenderbotTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_blenderbot_fast import BlenderbotTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_blenderbot import (
		    BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    BlenderbotForCausalLM,
		    BlenderbotForConditionalGeneration,
		    BlenderbotModel,
		    BlenderbotPreTrainedModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_blenderbot import (
		    TFBlenderbotForConditionalGeneration,
		    TFBlenderbotModel,
		    TFBlenderbotPreTrainedModel,
		)

	try:
		if not is_flax_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_flax_blenderbot import (
		    FlaxBlenderbotForConditionalGeneration,
		    FlaxBlenderbotModel,
		    FlaxBlenderbotPreTrainedModel,
		)

else:
	import sys

	_SCREAMING_SNAKE_CASE   =      _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





