
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


_UpperCAmelCase      :				Dict          = {
    "configuration_altclip": [
        "ALTCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "AltCLIPConfig",
        "AltCLIPTextConfig",
        "AltCLIPVisionConfig",
    ],
    "processing_altclip": ["AltCLIPProcessor"],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_UpperCAmelCase      :				List[Any]          = [
		    "ALTCLIP_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "AltCLIPPreTrainedModel",
		    "AltCLIPModel",
		    "AltCLIPTextModel",
		    "AltCLIPVisionModel",
		]


if TYPE_CHECKING:
		from .configuration_altclip import (
		    ALTCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    AltCLIPConfig,
		    AltCLIPTextConfig,
		    AltCLIPVisionConfig,
		)
		from .processing_altclip import AltCLIPProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_altclip import (
				    ALTCLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
				    AltCLIPModel,
				    AltCLIPPreTrainedModel,
				    AltCLIPTextModel,
				    AltCLIPVisionModel,
				)


else:
		import sys

		_UpperCAmelCase      :				List[str]          = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


import importlib
import math
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Tuple, Union

import flax
import jax.numpy as jnp

from ..utils import BaseOutput


_UpperCAmelCase      :				str          = "scheduler_config.json"


class        lowercase      (	_SCREAMING_SNAKE_CASE     ):
		__lowercase : Tuple			  =       1
		__lowercase : int			  =       2
		__lowercase : List[Any]			  =       3
		__lowercase : str			  =       4
		__lowercase : Optional[Any]			  =       5






@dataclass
class        lowercase      (	_SCREAMING_SNAKE_CASE     ):
		__lowercase : jnp.ndarray






class        lowercase      :
		__lowercase : Union[str, Any]			  =       SCHEDULER_CONFIG_NAME
		__lowercase : Dict			  =       ["dtype"]
		__lowercase : List[Any]			  =       []
		__lowercase : Dict			  =       True



		@classmethod
		def __UpperCamelCase				(				cls       ,   A_ = None       ,   A_ = None       ,   A_=False       ,   **A_       ,   )						->      Optional[Any]:
				"""simple docstring"""
				UpperCamelCase			,						UpperCamelCase						=  cls.load_config(
				    pretrained_model_name_or_path=A_       ,   subfolder=A_       ,   return_unused_kwargs=A_       ,   **A_       ,   )
				UpperCamelCase			,						UpperCamelCase						=  cls.from_config(A_       ,   return_unused_kwargs=A_       ,   **A_ )

				if hasattr(A_       ,   'create_state' ) and getattr(A_       ,   'has_state'       ,   A_ ):
						UpperCamelCase						=  scheduler.create_state()

				if return_unused_kwargs:
						return scheduler, state, unused_kwargs

				return scheduler, state



		def __UpperCamelCase				(				self       ,   A_       ,   A_ = False       ,   **A_ )						->      str:
				"""simple docstring"""
				self.save_config(save_directory=A_       ,   push_to_hub=A_       ,   **A_ )



		@property
		def __UpperCamelCase				(				self )						->      int:
				"""simple docstring"""
				return self._get_compatibles()



		@classmethod
		def __UpperCamelCase				(				cls )						->      int:
				"""simple docstring"""
				UpperCamelCase						=  list(set([cls.__name__] + cls._compatibles ) )
				UpperCamelCase						=  importlib.import_module(__name__.split('.' )[0] )
				UpperCamelCase						=  [
				    getattr(A_       ,   A_ ) for c in compatible_classes_str if hasattr(A_       ,   A_ )
				]
				return compatible_classes






def    A (      lowercase					, lowercase				)     ->						jnp.ndarray:
		'''simple docstring'''

		assert len(lowercase				) >= x.ndim
		return jnp.broadcast_to(x.reshape(x.shape + (1,) * (len(lowercase				) - x.ndim)				)					, lowercase				)


def    A (      lowercase					, lowercase=0.9_9_9					, lowercase=jnp.floataa				)     ->						jnp.ndarray:
		'''simple docstring'''

		def alpha_bar(lowercase				):
				return math.cos((time_step + 0.0_0_8) / 1.0_0_8 * math.pi / 2				) ** 2

		UpperCamelCase						=  []
		for i in range(lowercase				):
				UpperCamelCase						=  i / num_diffusion_timesteps
				UpperCamelCase						=  (i + 1) / num_diffusion_timesteps
				betas.append(min(1 - alpha_bar(lowercase				) / alpha_bar(lowercase				)					, lowercase				)				)
		return jnp.array(lowercase					, dtype=lowercase				)


@flax.struct.dataclass
class        lowercase      :
		__lowercase : jnp.ndarray
		__lowercase : jnp.ndarray
		__lowercase : jnp.ndarray



		@classmethod
		def __UpperCamelCase				(				cls       ,   A_ )						->      Optional[int]:
				"""simple docstring"""
				UpperCamelCase						=  scheduler.config

				if config.trained_betas is not None:
						UpperCamelCase						=  jnp.asarray(config.trained_betas       ,   dtype=scheduler.dtype )
				elif config.beta_schedule == "linear":
						UpperCamelCase						=  jnp.linspace(config.beta_start       ,   config.beta_end       ,   config.num_train_timesteps       ,   dtype=scheduler.dtype )
				elif config.beta_schedule == "scaled_linear":
						# this schedule is very specific to the latent diffusion model.
						UpperCamelCase						=  (
						    jnp.linspace(
						        config.beta_start**0.5       ,   config.beta_end**0.5       ,   config.num_train_timesteps       ,   dtype=scheduler.dtype )
						    ** 2
						)
				elif config.beta_schedule == "squaredcos_cap_v2":
						# Glide cosine schedule
						UpperCamelCase						=  betas_for_alpha_bar(config.num_train_timesteps       ,   dtype=scheduler.dtype )
				else:
						raise NotImplementedError(
						    F'''beta_schedule {config.beta_schedule} is not implemented for scheduler {scheduler.__class__.__name__}''' )

				UpperCamelCase						=  1.0 - betas

				UpperCamelCase						=  jnp.cumprod(A_       ,   axis=0 )

				return cls(
				    alphas=A_       ,   betas=A_       ,   alphas_cumprod=A_       ,   )






def    A (      lowercase					, lowercase					, lowercase					, lowercase				)     ->						List[Any]:
		'''simple docstring'''

		UpperCamelCase						=  state.alphas_cumprod

		UpperCamelCase						=  alphas_cumprod[timesteps] ** 0.5
		UpperCamelCase						=  sqrt_alpha_prod.flatten()
		UpperCamelCase						=  broadcast_to_shape_from_left(lowercase					, original_samples.shape				)

		UpperCamelCase						=  (1 - alphas_cumprod[timesteps]) ** 0.5
		UpperCamelCase						=  sqrt_one_minus_alpha_prod.flatten()
		UpperCamelCase						=  broadcast_to_shape_from_left(lowercase					, original_samples.shape				)

		return sqrt_alpha_prod, sqrt_one_minus_alpha_prod


def    A (      lowercase					, lowercase					, lowercase					, lowercase				)     ->						Dict:
		'''simple docstring'''

		UpperCamelCase			,						UpperCamelCase						=  get_sqrt_alpha_prod(lowercase					, lowercase					, lowercase					, lowercase				)
		UpperCamelCase						=  sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
		return noisy_samples


def    A (      lowercase					, lowercase					, lowercase					, lowercase				)     ->						int:
		'''simple docstring'''

		UpperCamelCase			,						UpperCamelCase						=  get_sqrt_alpha_prod(lowercase					, lowercase					, lowercase					, lowercase				)
		UpperCamelCase						=  sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
		return velocity
