






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


lowerCamelCase      :       Union[str, Any]    =						'''scheduler_config.json'''



class 				_UpperCamelCase      (a_      ):
      snake_case_	=				1
      snake_case_	=				2
      snake_case_	=				3
      snake_case_	=				4
      snake_case_	=				5



@dataclass
class 				_UpperCamelCase      (a_      ):
      snake_case_	=				42



class 				_UpperCamelCase      :
      snake_case_	=				SCHEDULER_CONFIG_NAME
      snake_case_	=				["""dtype"""]
      snake_case_	=				[]
      snake_case_	=				True


      @classmethod
      def 			__UpperCAmelCase		(						cls       ,    __UpperCamelCase = None       ,    __UpperCamelCase = None       ,    __UpperCamelCase=False       ,    **__UpperCamelCase       ,    )->						Optional[Any]:
           __lowerCAmelCase      ,			__lowerCAmelCase             =       cls.load_config(
               pretrained_model_name_or_path=__UpperCamelCase       ,    subfolder=__UpperCamelCase       ,    return_unused_kwargs=__UpperCamelCase       ,    **__UpperCamelCase       ,    )
           __lowerCAmelCase      ,			__lowerCAmelCase             =       cls.from_config(__UpperCamelCase       ,    return_unused_kwargs=__UpperCamelCase       ,    **__UpperCamelCase						)

           if hasattr(__UpperCamelCase       ,    "create_state"						) and getattr(__UpperCamelCase       ,    "has_state"       ,    __UpperCamelCase						):
                __lowerCAmelCase             =       scheduler.create_state()

           if return_unused_kwargs:
                return scheduler, state, unused_kwargs

           return scheduler, state


      def 			__UpperCAmelCase		(						self       ,    __UpperCamelCase       ,    __UpperCamelCase = False       ,    **__UpperCamelCase						)->						Optional[Any]:
           self.save_config(save_directory=__UpperCamelCase       ,    push_to_hub=__UpperCamelCase       ,    **__UpperCamelCase						)


      @property
      def 			__UpperCAmelCase		(						self						)->						Dict:
           return self._get_compatibles()


      @classmethod
      def 			__UpperCAmelCase		(						cls						)->						Tuple:
           __lowerCAmelCase             =       list(set([cls.__name__] + cls._compatibles						)						)
           __lowerCAmelCase             =       importlib.import_module(__name__.split("."						)[0]						)
           __lowerCAmelCase             =       [
               getattr(__UpperCamelCase       ,    __UpperCamelCase						) for c in compatible_classes_str if hasattr(__UpperCamelCase       ,    __UpperCamelCase						)
           ]
           return compatible_classes





def    __lowerCAmelCase   (    __snake_case       ,			__snake_case							):
     assert len(__snake_case							) >= x.ndim
     return jnp.broadcast_to(x.reshape(x.shape + (1,) * (len(__snake_case							) - x.ndim)							)       ,			__snake_case							)





def    __lowerCAmelCase   (    __snake_case       ,			__snake_case=0.999       ,			__snake_case=jnp.floataa							):

     def alpha_bar(__snake_case							):
          return math.cos((time_step + 0.008) / 1.008 * math.pi / 2							) ** 2

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



@flax.struct.dataclass
class 				_UpperCamelCase      :
      snake_case_	=				42
      snake_case_	=				42
      snake_case_	=				42


      @classmethod
      def 			__UpperCAmelCase		(						cls       ,    __UpperCamelCase						)->						int:
           __lowerCAmelCase             =       scheduler.config

           if config.trained_betas is not None:
                __lowerCAmelCase             =       jnp.asarray(config.trained_betas       ,    dtype=scheduler.dtype						)
           elif config.beta_schedule == "linear":
                __lowerCAmelCase             =       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.
                __lowerCAmelCase             =       (
                    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
                __lowerCAmelCase             =       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__}"""						)

           __lowerCAmelCase             =       1.0 - betas

           __lowerCAmelCase             =       jnp.cumprod(__UpperCamelCase       ,    axis=0						)

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





def    __lowerCAmelCase   (    __snake_case       ,			__snake_case       ,			__snake_case       ,			__snake_case							):
     __lowerCAmelCase             =       state.alphas_cumprod

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

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

     return sqrt_alpha_prod, sqrt_one_minus_alpha_prod





def    __lowerCAmelCase   (    __snake_case       ,			__snake_case       ,			__snake_case       ,			__snake_case							):
     __lowerCAmelCase      ,			__lowerCAmelCase             =       get_sqrt_alpha_prod(__snake_case       ,			__snake_case       ,			__snake_case       ,			__snake_case							)
     __lowerCAmelCase             =       sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
     return noisy_samples





def    __lowerCAmelCase   (    __snake_case       ,			__snake_case       ,			__snake_case       ,			__snake_case							):
     __lowerCAmelCase      ,			__lowerCAmelCase             =       get_sqrt_alpha_prod(__snake_case       ,			__snake_case       ,			__snake_case       ,			__snake_case							)
     __lowerCAmelCase             =       sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
     return velocity














import warnings
from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType



class 				_UpperCamelCase      (a_      ):
      snake_case_	=				["""image_processor""", """tokenizer"""]
      snake_case_	=				"""FlavaImageProcessor"""
      snake_case_	=				("""BertTokenizer""", """BertTokenizerFast""")


      def __init__(						self       ,    __UpperCamelCase=None       ,    __UpperCamelCase=None       ,    **__UpperCamelCase						)->						Tuple:
           __lowerCAmelCase             =       None
           if "feature_extractor" in kwargs:
                warnings.warn(
                    "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
                    " instead."       ,    __UpperCamelCase       ,    )
                __lowerCAmelCase             =       kwargs.pop("feature_extractor"						)

           __lowerCAmelCase             =       image_processor if image_processor is not None else feature_extractor
           if image_processor is None:
                raise ValueError("You need to specify an `image_processor`."						)
           if tokenizer is None:
                raise ValueError("You need to specify a `tokenizer`."						)

           super().__init__(__UpperCamelCase       ,    __UpperCamelCase						)
           __lowerCAmelCase             =       self.image_processor


      def __call__(						self       ,    __UpperCamelCase = None       ,    __UpperCamelCase = None       ,    __UpperCamelCase = True       ,    __UpperCamelCase = False       ,    __UpperCamelCase = False       ,    __UpperCamelCase = None       ,    __UpperCamelCase = 0       ,    __UpperCamelCase = None       ,    __UpperCamelCase = None       ,    __UpperCamelCase = None       ,    __UpperCamelCase = None       ,    __UpperCamelCase = None       ,    __UpperCamelCase = False       ,    __UpperCamelCase = False       ,    __UpperCamelCase = False       ,    __UpperCamelCase = False       ,    __UpperCamelCase = True       ,    __UpperCamelCase = None       ,    **__UpperCamelCase       ,    )->						int:

           if text is None and images is None:
                raise ValueError("You have to specify either text or images. Both cannot be none."						)

           if text is not None:
                __lowerCAmelCase             =       self.tokenizer(
                    text=__UpperCamelCase       ,    add_special_tokens=__UpperCamelCase       ,    padding=__UpperCamelCase       ,    truncation=__UpperCamelCase       ,    max_length=__UpperCamelCase       ,    stride=__UpperCamelCase       ,    pad_to_multiple_of=__UpperCamelCase       ,    return_token_type_ids=__UpperCamelCase       ,    return_attention_mask=__UpperCamelCase       ,    return_overflowing_tokens=__UpperCamelCase       ,    return_special_tokens_mask=__UpperCamelCase       ,    return_offsets_mapping=__UpperCamelCase       ,    return_length=__UpperCamelCase       ,    verbose=__UpperCamelCase       ,    return_tensors=__UpperCamelCase       ,    **__UpperCamelCase       ,    )
           if images is not None:
                __lowerCAmelCase             =       self.image_processor(
                    __UpperCamelCase       ,    return_image_mask=__UpperCamelCase       ,    return_codebook_pixels=__UpperCamelCase       ,    return_tensors=__UpperCamelCase       ,    **__UpperCamelCase       ,    )

           if text is not None and images is not None:
                encoding.update(__UpperCamelCase						)
                return encoding
           elif text is not None:
                return encoding
           else:
                return BatchEncoding(data=dict(**__UpperCamelCase						)       ,    tensor_type=__UpperCamelCase						)


      def 			__UpperCAmelCase		(						self       ,    *__UpperCamelCase       ,    **__UpperCamelCase						)->						int:
           return self.tokenizer.batch_decode(*__UpperCamelCase       ,    **__UpperCamelCase						)


      def 			__UpperCAmelCase		(						self       ,    *__UpperCamelCase       ,    **__UpperCamelCase						)->						Optional[int]:
           return self.tokenizer.decode(*__UpperCamelCase       ,    **__UpperCamelCase						)


      @property
      def 			__UpperCAmelCase		(						self						)->						Any:
           __lowerCAmelCase             =       self.tokenizer.model_input_names
           __lowerCAmelCase             =       self.image_processor.model_input_names
           return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names						)						)


      @property
      def 			__UpperCAmelCase		(						self						)->						Dict:
           warnings.warn(
               "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."       ,    __UpperCamelCase       ,    )
           return self.image_processor_class


      @property
      def 			__UpperCAmelCase		(						self						)->						Union[str, Any]:
           warnings.warn(
               "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."       ,    __UpperCamelCase       ,    )
           return self.image_processor






