
'''simple docstring'''





# DISCLAIMER: This file is strongly influenced by https://github.com/ermongroup/ddim

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

import flax
import jax
import jax.numpy as jnp

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
    CommonSchedulerState,
    FlaxKarrasDiffusionSchedulers,
    FlaxSchedulerMixin,
    FlaxSchedulerOutput,
    add_noise_common,
    get_velocity_common,
)






@flax.struct.dataclass
class 							UpperCAmelCase_							:

 '''simple docstring'''
 lowercase_					:    Optional[int]				=							42

 # setable values
 lowercase_					:    List[Any]				=							42
 lowercase_					:    List[Any]				=							42
 lowercase_					:    int				=							None







 @classmethod
 def 							UpperCamelCase  (					cls		:  List[str]			,				snake_case__		:  List[Any]			,				snake_case__		:  Any			,				snake_case__		:  int ):



   '''simple docstring'''




   return cls(common=lowercase__			,				init_noise_sigma=lowercase__			,				timesteps=lowercase__ )






@dataclass
class 							UpperCAmelCase_							(    A				):

 '''simple docstring'''
 lowercase_					:    Any				=							42






class 							UpperCAmelCase_							(    A					,				A				):

 '''simple docstring'''
 lowercase_					:    Tuple				=							[e.name for e in FlaxKarrasDiffusionSchedulers]

 lowercase_					:    List[str]				=							42







 @property
 def 							UpperCamelCase  (					self		:  Optional[Any] ):



   '''simple docstring'''




   return True







 @register_to_config
 def __init__(					self		:  Dict			,				snake_case__		:  Dict = 10_00			,				snake_case__		:  Dict = 0.0001			,				snake_case__		:  Tuple = 0.02			,				snake_case__		:  Union[str, Any] = "linear"			,				snake_case__		:  Dict = None			,				snake_case__		:  Dict = "fixed_small"			,				snake_case__		:  int = True			,				snake_case__		:  List[Any] = "epsilon"			,				snake_case__		:  List[Any] = jnp.floataa			,				):



   '''simple docstring'''




   UpperCAmelCase__      :					Any				       =							dtype







 def 							UpperCamelCase  (					self		:  Tuple			,				snake_case__		:  Tuple = None ):



   '''simple docstring'''




   if common is None:
     UpperCAmelCase__      :					str				       =							CommonSchedulerState.create(self )

   # standard deviation of the initial noise distribution
   UpperCAmelCase__      :					List[str]				       =							jnp.array(1.0			,				dtype=self.dtype )

   UpperCAmelCase__      :					Tuple				       =							jnp.arange(0			,				self.config.num_train_timesteps ).round()[::-1]

   return DDPMSchedulerState.create(
       common=lowercase__			,				init_noise_sigma=lowercase__			,				timesteps=lowercase__			,				)







 def 							UpperCamelCase  (					self		:  List[str]			,				snake_case__		:  str			,				snake_case__		:  Dict			,				snake_case__		:  Union[str, Any] = None ):



   '''simple docstring'''




   return sample







 def 							UpperCamelCase  (					self		:  Union[str, Any]			,				snake_case__		:  Tuple			,				snake_case__		:  Optional[int]			,				snake_case__		:  List[str] = () ):



   '''simple docstring'''




   UpperCAmelCase__      :					Tuple				       =							self.config.num_train_timesteps // num_inference_steps
   # creates integer timesteps by multiplying by ratio
   # rounding to avoid issues when num_inference_step is power of 3
   UpperCAmelCase__      :					Dict				       =							(jnp.arange(0			,				lowercase__ ) * step_ratio).round()[::-1]

   return state.replace(
       num_inference_steps=lowercase__			,				timesteps=lowercase__			,				)







 def 							UpperCamelCase  (					self		:  Tuple			,				snake_case__		:  List[str]			,				snake_case__		:  Tuple			,				snake_case__		:  Optional[int]=None			,				snake_case__		:  Optional[int]=None ):



   '''simple docstring'''




   UpperCAmelCase__      :					Any				       =							state.common.alphas_cumprod[t]
   UpperCAmelCase__      :					List[Any]				       =							jnp.where(t > 0			,				state.common.alphas_cumprod[t - 1]			,				jnp.array(1.0			,				dtype=self.dtype ) )

   # For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
   # and sample from it to get previous sample
   # x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
   UpperCAmelCase__      :					List[Any]				       =							(1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]

   if variance_type is None:
     UpperCAmelCase__      :					List[str]				       =							self.config.variance_type

   # hacks - were probably added for training stability
   if variance_type == "fixed_small":
     UpperCAmelCase__      :					Dict				       =							jnp.clip(lowercase__			,				a_min=1e-20 )
   # for rl-diffuser https://arxiv.org/abs/2205.09991
   elif variance_type == "fixed_small_log":
     UpperCAmelCase__      :					List[str]				       =							jnp.log(jnp.clip(lowercase__			,				a_min=1e-20 ) )
   elif variance_type == "fixed_large":
     UpperCAmelCase__      :					Optional[int]				       =							state.common.betas[t]
   elif variance_type == "fixed_large_log":
     # Glide max_log
     UpperCAmelCase__      :					Dict				       =							jnp.log(state.common.betas[t] )
   elif variance_type == "learned":
     return predicted_variance
   elif variance_type == "learned_range":
     UpperCAmelCase__      :					Optional[int]				       =							variance
     UpperCAmelCase__      :					Optional[int]				       =							state.common.betas[t]
     UpperCAmelCase__      :					List[str]				       =							(predicted_variance + 1) / 2
     UpperCAmelCase__      :					Union[str, Any]				       =							frac * max_log + (1 - frac) * min_log

   return variance







 def 							UpperCamelCase  (					self		:  Union[str, Any]			,				snake_case__		:  List[str]			,				snake_case__		:  Any			,				snake_case__		:  int			,				snake_case__		:  List[str]			,				snake_case__		:  Optional[Any] = None			,				snake_case__		:  Any = True			,				):



   '''simple docstring'''




   UpperCAmelCase__      :					Optional[int]				       =							timestep

   if key is None:
     UpperCAmelCase__      :					List[str]				       =							jax.random.PRNGKey(0 )

   if model_output.shape[1] == sample.shape[1] * 2 and self.config.variance_type in ["learned", "learned_range"]:
     UpperCAmelCase__       ,			UpperCAmelCase__      :					Optional[Any]				       =							jnp.split(lowercase__			,				sample.shape[1]			,				axis=1 )
   else:
     UpperCAmelCase__      :					Optional[int]				       =							None

   # 1. compute alphas, betas
   UpperCAmelCase__      :					Any				       =							state.common.alphas_cumprod[t]
   UpperCAmelCase__      :					List[Any]				       =							jnp.where(t > 0			,				state.common.alphas_cumprod[t - 1]			,				jnp.array(1.0			,				dtype=self.dtype ) )
   UpperCAmelCase__      :					Any				       =							1 - alpha_prod_t
   UpperCAmelCase__      :					List[str]				       =							1 - alpha_prod_t_prev

   # 2. compute predicted original sample from predicted noise also called
   # "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
   if self.config.prediction_type == "epsilon":
     UpperCAmelCase__      :					Any				       =							(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
   elif self.config.prediction_type == "sample":
     UpperCAmelCase__      :					Optional[int]				       =							model_output
   elif self.config.prediction_type == "v_prediction":
     UpperCAmelCase__      :					List[Any]				       =							(alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
   else:
     raise ValueError(
         F"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample` """
         " for the FlaxDDPMScheduler." )

   # 3. Clip "predicted x_0"
   if self.config.clip_sample:
     UpperCAmelCase__      :					Tuple				       =							jnp.clip(lowercase__			,				-1			,				1 )

   # 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
   # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
   UpperCAmelCase__      :					Any				       =							(alpha_prod_t_prev ** 0.5 * state.common.betas[t]) / beta_prod_t
   UpperCAmelCase__      :					Optional[Any]				       =							state.common.alphas[t] ** 0.5 * beta_prod_t_prev / beta_prod_t

   # 5. Compute predicted previous sample µ_t
   # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
   UpperCAmelCase__      :					Any				       =							pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

   # 6. Add noise
   def random_variance():
     UpperCAmelCase__      :					Dict				       =							jax.random.split(lowercase__			,				num=1 )
     UpperCAmelCase__      :					int				       =							jax.random.normal(lowercase__			,				shape=model_output.shape			,				dtype=self.dtype )
     return (self._get_variance(lowercase__			,				lowercase__			,				predicted_variance=lowercase__ ) ** 0.5) * noise

   UpperCAmelCase__      :					List[Any]				       =							jnp.where(t > 0			,				random_variance()			,				jnp.zeros(model_output.shape			,				dtype=self.dtype ) )

   UpperCAmelCase__      :					int				       =							pred_prev_sample + variance

   if not return_dict:
     return (pred_prev_sample, state)

   return FlaxDDPMSchedulerOutput(prev_sample=lowercase__			,				state=lowercase__ )







 def 							UpperCamelCase  (					self		:  str			,				snake_case__		:  int			,				snake_case__		:  Optional[int]			,				snake_case__		:  Optional[Any]			,				snake_case__		:  Optional[int]			,				):



   '''simple docstring'''




   return add_noise_common(state.common			,				lowercase__			,				lowercase__			,				lowercase__ )







 def 							UpperCamelCase  (					self		:  Tuple			,				snake_case__		:  int			,				snake_case__		:  str			,				snake_case__		:  Union[str, Any]			,				snake_case__		:  List[Any]			,				):



   '''simple docstring'''




   return get_velocity_common(state.common			,				lowercase__			,				lowercase__			,				lowercase__ )







 def __len__(					self		:  Tuple ):



   '''simple docstring'''




   return self.config.num_train_timesteps






import warnings
from typing import List, Optional, Union

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





class        lowerCAmelCase__  (					__magic_name__			):



       '''simple docstring'''







       lowercase_						=    ["""image_processor""", """tokenizer"""]
       lowercase_						=    """LayoutLMv2ImageProcessor"""
       lowercase_						=    ("""LayoutXLMTokenizer""", """LayoutXLMTokenizerFast""")

       def __init__(							self ,   lowercase__=None ,   lowercase__=None ,   **lowercase__     ):







             '''simple docstring'''



             if "feature_extractor" in kwargs:
                   warnings.warn(
                       '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
                       ''' instead.''' ,   lowercase__ ,   )
                   __A    =kwargs.pop('''feature_extractor'''     )

             __A    =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__(lowercase__ ,   lowercase__     )

       def __call__(							self ,   lowercase__ ,   lowercase__ = None ,   lowercase__ = None ,   lowercase__ = None ,   lowercase__ = None ,   lowercase__ = True ,   lowercase__ = False ,   lowercase__ = None ,   lowercase__ = None ,   lowercase__ = 0 ,   lowercase__ = None ,   lowercase__ = None ,   lowercase__ = None ,   lowercase__ = False ,   lowercase__ = False ,   lowercase__ = False ,   lowercase__ = False ,   lowercase__ = True ,   lowercase__ = None ,   **lowercase__ ,   ):







             '''simple docstring'''



             if self.image_processor.apply_ocr and (boxes is not None):
                   raise ValueError(
                       '''You cannot provide bounding boxes '''
                       '''if you initialized the image processor with apply_ocr set to True.'''     )

             if self.image_processor.apply_ocr and (word_labels is not None):
                   raise ValueError(
                       '''You cannot provide word labels if you initialized the image processor with apply_ocr set to True.'''     )

             if return_overflowing_tokens is True and return_offsets_mapping is False:
                   raise ValueError('''You cannot return overflowing tokens without returning the offsets mapping.'''     )

             # first, apply the image processor
             __A    =self.image_processor(images=lowercase__ ,   return_tensors=lowercase__     )

             # second, apply the tokenizer
             if text is not None and self.image_processor.apply_ocr and text_pair is None:
                   if isinstance(lowercase__ ,   lowercase__     ):
                         __A    =[text]  # add batch dimension (as the image processor always adds a batch dimension)
                   __A    =features['''words''']

             __A    =self.tokenizer(
                 text=text if text is not None else features['''words'''] ,   text_pair=text_pair if text_pair is not None else None ,   boxes=boxes if boxes is not None else features['''boxes'''] ,   word_labels=lowercase__ ,   add_special_tokens=lowercase__ ,   padding=lowercase__ ,   truncation=lowercase__ ,   max_length=lowercase__ ,   stride=lowercase__ ,   pad_to_multiple_of=lowercase__ ,   return_token_type_ids=lowercase__ ,   return_attention_mask=lowercase__ ,   return_overflowing_tokens=lowercase__ ,   return_special_tokens_mask=lowercase__ ,   return_offsets_mapping=lowercase__ ,   return_length=lowercase__ ,   verbose=lowercase__ ,   return_tensors=lowercase__ ,   **lowercase__ ,   )

             # add pixel values
             __A    =features.pop('''pixel_values'''     )
             if return_overflowing_tokens is True:
                   __A    =self.get_overflowing_images(lowercase__ ,   encoded_inputs['''overflow_to_sample_mapping''']     )
             __A    =images

             return encoded_inputs

       def     __UpperCamelCase     (							self ,   lowercase__ ,   lowercase__     ):







             '''simple docstring'''



             __A    =[]
             for sample_idx in overflow_to_sample_mapping:
                   images_with_overflow.append(images[sample_idx]     )

             if len(lowercase__     ) != len(lowercase__     ):
                   raise ValueError(
                       '''Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got'''
                       f''' {len(lowercase__     )} and {len(lowercase__     )}'''     )

             return images_with_overflow

       def     __UpperCamelCase     (							self ,   *lowercase__ ,   **lowercase__     ):







             '''simple docstring'''



             return self.tokenizer.batch_decode(*lowercase__ ,   **lowercase__     )

       def     __UpperCamelCase     (							self ,   *lowercase__ ,   **lowercase__     ):







             '''simple docstring'''



             return self.tokenizer.decode(*lowercase__ ,   **lowercase__     )

       @property
       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             return ["input_ids", "bbox", "attention_mask", "image"]

       @property
       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             warnings.warn(
                 '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.''' ,   lowercase__ ,   )
             return self.image_processor_class


       @property
       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             warnings.warn(
                 '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.''' ,   lowercase__ ,   )
             return self.image_processor


