"""simple docstring"""


# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
# and https://github.com/hojonathanho/diffusion

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

import flax
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 					a					:
  A_ :				CommonSchedulerState
  A_ :				jnp.ndarray

  # setable values
  A_ :				jnp.ndarray
  A_ :				jnp.ndarray
  A_ :				Optional[int] 			=							None



  @classmethod
  def        lowerCAmelCase_				(		cls   :    Optional[int]						,  lowerCamelCase_   :    CommonSchedulerState						,  lowerCamelCase_   :    jnp.ndarray						,  lowerCamelCase_   :    jnp.ndarray						,  lowerCamelCase_   :    jnp.ndarray						,  )      -> Optional[Any]:
      return cls(
          common=lowerCamelCase_						,  final_alpha_cumprod=lowerCamelCase_						,  init_noise_sigma=lowerCamelCase_						,  timesteps=lowerCamelCase_						,  )






@dataclass
class 					a					(				A_							):
  A_ :				DDIMSchedulerState






class 					a					(				A_   ,		A_							):
  A_ :				Optional[int] 			=							[e.name for e in FlaxKarrasDiffusionSchedulers]

  A_ :				jnp.dtype



  @property
  def        lowerCAmelCase_				(		self   :    Optional[int]     )      -> Optional[Any]:
      return True



  @register_to_config
  def __init__(		self   :    Optional[int]						,  lowerCamelCase_   :    int = 10_00						,  lowerCamelCase_   :    float = 0.00_01						,  lowerCamelCase_   :    float = 0.02						,  lowerCamelCase_   :    str = "linear"						,  lowerCamelCase_   :    Optional[jnp.ndarray] = None						,  lowerCamelCase_   :    bool = True						,  lowerCamelCase_   :    int = 0						,  lowerCamelCase_   :    str = "epsilon"						,  lowerCamelCase_   :    jnp.dtype = jnp.floataa						,  )      -> Any:
      __a			      =						dtype



  def        lowerCAmelCase_				(		self   :    Dict						,  lowerCamelCase_   :    Optional[CommonSchedulerState] = None     )      -> DDIMSchedulerState:
      if common is None:
          __a			      =						CommonSchedulerState.create(self     )

      # At every step in ddim, we are looking into the previous alphas_cumprod
      # For the final step, there is no previous alphas_cumprod because we are already at 0
      # `set_alpha_to_one` decides whether we set this parameter simply to one or
      # whether we use the final alpha of the "non-previous" one.
      __a			      =						(
          jnp.array(1.0						,  dtype=self.dtype     ) if self.config.set_alpha_to_one else common.alphas_cumprod[0]
      )

      # standard deviation of the initial noise distribution
      __a			      =						jnp.array(1.0						,  dtype=self.dtype     )

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

      return DDIMSchedulerState.create(
          common=lowerCamelCase_						,  final_alpha_cumprod=lowerCamelCase_						,  init_noise_sigma=lowerCamelCase_						,  timesteps=lowerCamelCase_						,  )



  def        lowerCAmelCase_				(		self   :    str						,  lowerCamelCase_   :    DDIMSchedulerState						,  lowerCamelCase_   :    jnp.ndarray						,  lowerCamelCase_   :    Optional[int] = None     )      -> jnp.ndarray:
      return sample



  def        lowerCAmelCase_				(		self   :    List[str]						,  lowerCamelCase_   :    DDIMSchedulerState						,  lowerCamelCase_   :    int						,  lowerCamelCase_   :    Tuple = ()     )      -> DDIMSchedulerState:
      __a			      =						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
      __a			      =						(jnp.arange(0						,  lowerCamelCase_     ) * step_ratio).round()[::-1] + self.config.steps_offset

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



  def        lowerCAmelCase_				(		self   :    int						,  lowerCamelCase_   :    DDIMSchedulerState						,  lowerCamelCase_   :    int						,  lowerCamelCase_   :    Any     )      -> str:
      __a			      =						state.common.alphas_cumprod[timestep]
      __a			      =						jnp.where(
          prev_timestep >= 0						,  state.common.alphas_cumprod[prev_timestep]						,  state.final_alpha_cumprod     )
      __a			      =						1 - alpha_prod_t
      __a			      =						1 - alpha_prod_t_prev

      __a			      =						(beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)

      return variance



  def        lowerCAmelCase_				(		self   :    List[str]						,  lowerCamelCase_   :    DDIMSchedulerState						,  lowerCamelCase_   :    jnp.ndarray						,  lowerCamelCase_   :    int						,  lowerCamelCase_   :    jnp.ndarray						,  lowerCamelCase_   :    float = 0.0						,  lowerCamelCase_   :    bool = True						,  )      -> Union[FlaxDDIMSchedulerOutput, Tuple]:
      if state.num_inference_steps is None:
          raise ValueError(
              """Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler"""     )

      # See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf
      # Ideally, read DDIM paper in-detail understanding

      # Notation (<variable name> -> <name in paper>
      # - pred_noise_t -> e_theta(x_t, t)
      # - pred_original_sample -> f_theta(x_t, t) or x_0
      # - std_dev_t -> sigma_t
      # - eta -> η
      # - pred_sample_direction -> "direction pointing to x_t"
      # - pred_prev_sample -> "x_t-1"

      # 1. get previous step value (=t-1)
      __a			      =						timestep - self.config.num_train_timesteps // state.num_inference_steps

      __a			      =						state.common.alphas_cumprod
      __a			      =						state.final_alpha_cumprod

      # 2. compute alphas, betas
      __a			      =						alphas_cumprod[timestep]
      __a			      =						jnp.where(prev_timestep >= 0						,  alphas_cumprod[prev_timestep]						,  lowerCamelCase_     )

      __a			      =						1 - alpha_prod_t

      # 3. compute predicted original sample from predicted noise also called
      # "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
      if self.config.prediction_type == "epsilon":
          __a			      =						(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
          __a			      =						model_output
      elif self.config.prediction_type == "sample":
          __a			      =						model_output
          __a			      =						(sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
      elif self.config.prediction_type == "v_prediction":
          __a			      =						(alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
          __a			      =						(alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample
      else:
          raise ValueError(
              F"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or"""
              """ `v_prediction`"""     )

      # 4. compute variance: "sigma_t(η)" -> see formula (16)
      # σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)
      __a			      =						self._get_variance(lowerCamelCase_						,  lowerCamelCase_						,  lowerCamelCase_     )
      __a			      =						eta * variance ** 0.5

      # 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
      __a			      =						(1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * pred_epsilon

      # 6. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
      __a			      =						alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

      if not return_dict:
          return (prev_sample, state)

      return FlaxDDIMSchedulerOutput(prev_sample=lowerCamelCase_						,  state=lowerCamelCase_     )



  def        lowerCAmelCase_				(		self   :    str						,  lowerCamelCase_   :    DDIMSchedulerState						,  lowerCamelCase_   :    jnp.ndarray						,  lowerCamelCase_   :    jnp.ndarray						,  lowerCamelCase_   :    jnp.ndarray						,  )      -> jnp.ndarray:
      return add_noise_common(state.common						,  lowerCamelCase_						,  lowerCamelCase_						,  lowerCamelCase_     )



  def        lowerCAmelCase_				(		self   :    Any						,  lowerCamelCase_   :    DDIMSchedulerState						,  lowerCamelCase_   :    jnp.ndarray						,  lowerCamelCase_   :    jnp.ndarray						,  lowerCamelCase_   :    jnp.ndarray						,  )      -> jnp.ndarray:
      return get_velocity_common(state.common						,  lowerCamelCase_						,  lowerCamelCase_						,  lowerCamelCase_     )



  def __len__(		self   :    Tuple     )      -> Union[str, Any]:
      return self.config.num_train_timesteps

"""simple docstring"""


import unittest

from transformers import MarkupLMConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import cached_property

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 import (
     MarkupLMForQuestionAnswering,
     MarkupLMForSequenceClassification,
     MarkupLMForTokenClassification,
     MarkupLMModel,
 )

# TODO check dependencies
from transformers import MarkupLMFeatureExtractor, MarkupLMProcessor, MarkupLMTokenizer






class 					a					:



  def __init__(		self   :    str						,  lowerCamelCase_   :    List[str]						,  lowerCamelCase_   :    Tuple=13						,  lowerCamelCase_   :    List[Any]=7						,  lowerCamelCase_   :    List[str]=True						,  lowerCamelCase_   :    str=True						,  lowerCamelCase_   :    Tuple=True						,  lowerCamelCase_   :    Optional[Any]=True						,  lowerCamelCase_   :    str=99						,  lowerCamelCase_   :    str=32						,  lowerCamelCase_   :    List[Any]=5						,  lowerCamelCase_   :    Dict=4						,  lowerCamelCase_   :    str=37						,  lowerCamelCase_   :    List[Any]="gelu"						,  lowerCamelCase_   :    List[str]=0.1						,  lowerCamelCase_   :    Optional[int]=0.1						,  lowerCamelCase_   :    List[str]=5_12						,  lowerCamelCase_   :    Union[str, Any]=16						,  lowerCamelCase_   :    Any=2						,  lowerCamelCase_   :    List[str]=0.02						,  lowerCamelCase_   :    List[Any]=3						,  lowerCamelCase_   :    Any=None						,  lowerCamelCase_   :    List[Any]=20						,  lowerCamelCase_   :    Tuple=30						,  lowerCamelCase_   :    Optional[Any]=2						,  lowerCamelCase_   :    List[Any]=2						,  lowerCamelCase_   :    Union[str, Any]=10						,  )      -> Any:
      __a			      =						parent
      __a			      =						batch_size
      __a			      =						seq_length
      __a			      =						is_training
      __a			      =						use_input_mask
      __a			      =						use_token_type_ids
      __a			      =						use_labels
      __a			      =						vocab_size
      __a			      =						hidden_size
      __a			      =						num_hidden_layers
      __a			      =						num_attention_heads
      __a			      =						intermediate_size
      __a			      =						hidden_act
      __a			      =						hidden_dropout_prob
      __a			      =						attention_probs_dropout_prob
      __a			      =						max_position_embeddings
      __a			      =						type_vocab_size
      __a			      =						type_sequence_label_size
      __a			      =						initializer_range
      __a			      =						num_labels
      __a			      =						scope
      __a			      =						max_xpath_tag_unit_embeddings
      __a			      =						max_xpath_subs_unit_embeddings
      __a			      =						tag_pad_id
      __a			      =						subs_pad_id
      __a			      =						max_depth



  def        lowerCAmelCase_				(		self   :    Dict     )      -> Dict:
      __a			      =						ids_tensor([self.batch_size, self.seq_length]						,  self.vocab_size     )

      __a			      =						ids_tensor(
          [self.batch_size, self.seq_length, self.max_depth]						,  self.max_xpath_tag_unit_embeddings     )

      __a			      =						ids_tensor(
          [self.batch_size, self.seq_length, self.max_depth]						,  self.max_xpath_subs_unit_embeddings     )

      __a			      =						None
      if self.use_input_mask:
          __a			      =						ids_tensor([self.batch_size, self.seq_length]						,  vocab_size=2     )

      __a			      =						None
      if self.use_token_type_ids:
          __a			      =						ids_tensor([self.batch_size, self.seq_length]						,  self.type_vocab_size     )

      __a			      =						None
      __a			      =						None
      if self.use_labels:
          __a			      =						ids_tensor([self.batch_size]						,  self.type_sequence_label_size     )
          __a			      =						ids_tensor([self.batch_size, self.seq_length]						,  self.num_labels     )

      __a			      =						self.get_config()

      return (
          config,
          input_ids,
          xpath_tags_seq,
          xpath_subs_seq,
          token_type_ids,
          input_mask,
          sequence_labels,
          token_labels,
      )



  def        lowerCAmelCase_				(		self   :    List[Any]     )      -> Optional[Any]:
      return MarkupLMConfig(
          vocab_size=self.vocab_size						,  hidden_size=self.hidden_size						,  num_hidden_layers=self.num_hidden_layers						,  num_attention_heads=self.num_attention_heads						,  intermediate_size=self.intermediate_size						,  hidden_act=self.hidden_act						,  hidden_dropout_prob=self.hidden_dropout_prob						,  attention_probs_dropout_prob=self.attention_probs_dropout_prob						,  max_position_embeddings=self.max_position_embeddings						,  type_vocab_size=self.type_vocab_size						,  initializer_range=self.initializer_range						,  max_xpath_tag_unit_embeddings=self.max_xpath_tag_unit_embeddings						,  max_xpath_subs_unit_embeddings=self.max_xpath_subs_unit_embeddings						,  tag_pad_id=self.tag_pad_id						,  subs_pad_id=self.subs_pad_id						,  max_depth=self.max_depth						,  )



  def        lowerCAmelCase_				(		self   :    Tuple						,  lowerCamelCase_   :    int						,  lowerCamelCase_   :    int						,  lowerCamelCase_   :    List[str]						,  lowerCamelCase_   :    int						,  lowerCamelCase_   :    Any						,  lowerCamelCase_   :    Optional[Any]						,  lowerCamelCase_   :    Any						,  lowerCamelCase_   :    List[Any]						,  )      -> int:
      __a			      =						MarkupLMModel(config=lowerCamelCase_     )
      model.to(lowerCamelCase_     )
      model.eval()
      print("""Configs:"""						,  model.config.tag_pad_id						,  model.config.subs_pad_id     )
      __a			      =						model(lowerCamelCase_						,  attention_mask=lowerCamelCase_						,  token_type_ids=lowerCamelCase_     )
      __a			      =						model(lowerCamelCase_						,  token_type_ids=lowerCamelCase_     )
      __a			      =						model(lowerCamelCase_     )
      self.parent.assertEqual(result.last_hidden_state.shape						,  (self.batch_size, self.seq_length, self.hidden_size)     )
      self.parent.assertEqual(result.pooler_output.shape						,  (self.batch_size, self.hidden_size)     )



  def        lowerCAmelCase_				(		self   :    Optional[int]						,  lowerCamelCase_   :    List[Any]						,  lowerCamelCase_   :    Optional[int]						,  lowerCamelCase_   :    str						,  lowerCamelCase_   :    Optional[int]						,  lowerCamelCase_   :    List[Any]						,  lowerCamelCase_   :    Tuple						,  lowerCamelCase_   :    List[str]						,  lowerCamelCase_   :    int						,  )      -> Optional[Any]:
      __a			      =						self.num_labels
      __a			      =						MarkupLMForSequenceClassification(lowerCamelCase_     )
      model.to(lowerCamelCase_     )
      model.eval()
      __a			      =						model(
          lowerCamelCase_						,  xpath_tags_seq=lowerCamelCase_						,  xpath_subs_seq=lowerCamelCase_						,  attention_mask=lowerCamelCase_						,  token_type_ids=lowerCamelCase_						,  labels=lowerCamelCase_						,  )
      self.parent.assertEqual(result.logits.shape						,  (self.batch_size, self.num_labels)     )



  def        lowerCAmelCase_				(		self   :    int						,  lowerCamelCase_   :    Any						,  lowerCamelCase_   :    Any						,  lowerCamelCase_   :    List[Any]						,  lowerCamelCase_   :    Dict						,  lowerCamelCase_   :    Union[str, Any]						,  lowerCamelCase_   :    Union[str, Any]						,  lowerCamelCase_   :    str						,  lowerCamelCase_   :    Union[str, Any]						,  )      -> int:
      __a			      =						self.num_labels
      __a			      =						MarkupLMForTokenClassification(config=lowerCamelCase_     )
      model.to(lowerCamelCase_     )
      model.eval()
      __a			      =						model(
          lowerCamelCase_						,  xpath_tags_seq=lowerCamelCase_						,  xpath_subs_seq=lowerCamelCase_						,  attention_mask=lowerCamelCase_						,  token_type_ids=lowerCamelCase_						,  labels=lowerCamelCase_						,  )
      self.parent.assertEqual(result.logits.shape						,  (self.batch_size, self.seq_length, self.num_labels)     )



  def        lowerCAmelCase_				(		self   :    List[Any]						,  lowerCamelCase_   :    Tuple						,  lowerCamelCase_   :    Dict						,  lowerCamelCase_   :    Optional[int]						,  lowerCamelCase_   :    Optional[Any]						,  lowerCamelCase_   :    Optional[Any]						,  lowerCamelCase_   :    Union[str, Any]						,  lowerCamelCase_   :    Dict						,  lowerCamelCase_   :    List[Any]						,  )      -> Tuple:
      __a			      =						MarkupLMForQuestionAnswering(config=lowerCamelCase_     )
      model.to(lowerCamelCase_     )
      model.eval()
      __a			      =						model(
          lowerCamelCase_						,  xpath_tags_seq=lowerCamelCase_						,  xpath_subs_seq=lowerCamelCase_						,  attention_mask=lowerCamelCase_						,  token_type_ids=lowerCamelCase_						,  start_positions=lowerCamelCase_						,  end_positions=lowerCamelCase_						,  )
      self.parent.assertEqual(result.start_logits.shape						,  (self.batch_size, self.seq_length)     )
      self.parent.assertEqual(result.end_logits.shape						,  (self.batch_size, self.seq_length)     )



  def        lowerCAmelCase_				(		self   :    str     )      -> str:
      __a			      =						self.prepare_config_and_inputs()
      (
          (
          __a
      )      ,  (
          __a
      )      ,  (
          __a
      )      ,  (
          __a
      )      ,  (
          __a
      )      ,  (
          __a
      )      ,  (
          __a
      )      ,  (
          __a
      )      ,  
      )			      =						config_and_inputs
      __a			      =						{
          """input_ids""": input_ids,
          """xpath_tags_seq""": xpath_tags_seq,
          """xpath_subs_seq""": xpath_subs_seq,
          """token_type_ids""": token_type_ids,
          """attention_mask""": input_mask,
      }
      return config, inputs_dict






@require_torch
class 					a					(				A_   ,		A_   ,		unittest.TestCase							):
  A_ :				int 			=							(
      (
          MarkupLMModel,
          MarkupLMForSequenceClassification,
          MarkupLMForTokenClassification,
          MarkupLMForQuestionAnswering,
      )
      if is_torch_available()
      else None
  )
  A_ :				Any 			=							(
      {
          '''feature-extraction''': MarkupLMModel,
          '''question-answering''': MarkupLMForQuestionAnswering,
          '''text-classification''': MarkupLMForSequenceClassification,
          '''token-classification''': MarkupLMForTokenClassification,
          '''zero-shot''': MarkupLMForSequenceClassification,
      }
      if is_torch_available()
      else {}
  )



  def        lowerCAmelCase_				(		self   :    Union[str, Any]						,  lowerCamelCase_   :    Optional[Any]						,  lowerCamelCase_   :    Union[str, Any]						,  lowerCamelCase_   :    Optional[int]						,  lowerCamelCase_   :    List[str]						,  lowerCamelCase_   :    Union[str, Any]     )      -> List[Any]:
      # ValueError: Nodes must be of type `List[str]` (single pretokenized example), or `List[List[str]]`
      # (batch of pretokenized examples).
      return True



  def        lowerCAmelCase_				(		self   :    Tuple     )      -> str:
      __a			      =						MarkupLMModelTester(self     )
      __a			      =						ConfigTester(self						,  config_class=lowerCamelCase_						,  hidden_size=37     )



  def        lowerCAmelCase_				(		self   :    Union[str, Any]     )      -> List[str]:
      self.config_tester.run_common_tests()



  def        lowerCAmelCase_				(		self   :    str     )      -> Dict:
      __a			      =						self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*lowerCamelCase_     )



  def        lowerCAmelCase_				(		self   :    Dict     )      -> Optional[int]:
      __a			      =						self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_sequence_classification(*lowerCamelCase_     )



  def        lowerCAmelCase_				(		self   :    Tuple     )      -> Tuple:
      __a			      =						self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_token_classification(*lowerCamelCase_     )



  def        lowerCAmelCase_				(		self   :    Union[str, Any]     )      -> int:
      __a			      =						self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_question_answering(*lowerCamelCase_     )

def 						UpperCamelCase				(	):
    __a			      =						"""
    <!DOCTYPE html>
    <html>
    <head>
    <title>Page Title</title>
    </head>
    <body>

    <h1>This is a Heading</h1>
    <p>This is a paragraph.</p>

    </body>
    </html>
    """

    return html_string






@require_torch
class 					a					(				unittest.TestCase							):



  @cached_property
  def        lowerCAmelCase_				(		self   :    int     )      -> Any:
      # TODO use from_pretrained here
      __a			      =						MarkupLMFeatureExtractor()
      __a			      =						MarkupLMTokenizer.from_pretrained("""microsoft/markuplm-base"""     )

      return MarkupLMProcessor(lowerCamelCase_						,  lowerCamelCase_     )



  @slow
  def        lowerCAmelCase_				(		self   :    Optional[int]     )      -> Dict:
      __a			      =						MarkupLMModel.from_pretrained("""microsoft/markuplm-base"""     ).to(lowerCamelCase_     )

      __a			      =						self.default_processor

      __a			      =						processor(prepare_html_string()						,  return_tensors="""pt"""     )
      __a			      =						inputs.to(lowerCamelCase_     )

      # forward pass
      with torch.no_grad():
          __a			      =						model(**lowerCamelCase_     )

      # verify the last hidden states
      __a			      =						torch.Size([1, 14, 7_68]     )
      self.assertEqual(outputs.last_hidden_state.shape						,  lowerCamelCase_     )

      __a			      =						torch.tensor(
          [[0.06_75, -0.00_52, 0.50_01], [-0.22_81, 0.08_02, 0.21_92], [-0.05_83, -0.33_11, 0.11_85]]     ).to(lowerCamelCase_     )

      self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3]						,  lowerCamelCase_						,  atol=1E-4     )     )
