code: 
  
  
  
  
  
  '''simple docstring'''
  
  import collections
  import inspect
  import unittest
  
  from transformers import DonutSwinConfig
  from transformers.testing_utils import require_torch, slow, torch_device
  from transformers.utils import is_torch_available
  
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
  from ...test_pipeline_mixin import PipelineTesterMixin
  
  
  if is_torch_available():
        import torch
        from torch import nn
  
        from transformers import DonutSwinModel
        from transformers.models.donut.modeling_donut_swin import DONUT_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST
  
  
  class       a_  :
  
      '''simple docstring'''
  
  
      def __init__(	self				,	A				,	A=13				,	A=32				,	A=2				,	A=3				,	A=16				,	A=[1, 2, 1]				,	A=[2, 2, 4]				,	A=2				,	A=2.0				,	A=True				,	A=0.0				,	A=0.0				,	A=0.1				,	A="gelu"				,	A=False				,	A=True				,	A=0.02				,	A=1e-5				,	A=True				,	A=None				,	A=True				,	A=10				,	A=8				,	)	->     Tuple:
          _SCREAMING_SNAKE_CASE             =    parent
          _SCREAMING_SNAKE_CASE             =    batch_size
          _SCREAMING_SNAKE_CASE             =    image_size
          _SCREAMING_SNAKE_CASE             =    patch_size
          _SCREAMING_SNAKE_CASE             =    num_channels
          _SCREAMING_SNAKE_CASE             =    embed_dim
          _SCREAMING_SNAKE_CASE             =    depths
          _SCREAMING_SNAKE_CASE             =    num_heads
          _SCREAMING_SNAKE_CASE             =    window_size
          _SCREAMING_SNAKE_CASE             =    mlp_ratio
          _SCREAMING_SNAKE_CASE             =    qkv_bias
          _SCREAMING_SNAKE_CASE             =    hidden_dropout_prob
          _SCREAMING_SNAKE_CASE             =    attention_probs_dropout_prob
          _SCREAMING_SNAKE_CASE             =    drop_path_rate
          _SCREAMING_SNAKE_CASE             =    hidden_act
          _SCREAMING_SNAKE_CASE             =    use_absolute_embeddings
          _SCREAMING_SNAKE_CASE             =    patch_norm
          _SCREAMING_SNAKE_CASE             =    layer_norm_eps
          _SCREAMING_SNAKE_CASE             =    initializer_range
          _SCREAMING_SNAKE_CASE             =    is_training
          _SCREAMING_SNAKE_CASE             =    scope
          _SCREAMING_SNAKE_CASE             =    use_labels
          _SCREAMING_SNAKE_CASE             =    type_sequence_label_size
          _SCREAMING_SNAKE_CASE             =    encoder_stride
  
  
      def     snake_case_(	self )	->     int:
          _SCREAMING_SNAKE_CASE             =    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
  
          _SCREAMING_SNAKE_CASE             =    None
          if self.use_labels:
              _SCREAMING_SNAKE_CASE             =    ids_tensor([self.batch_size]				,	self.type_sequence_label_size )
  
          _SCREAMING_SNAKE_CASE             =    self.get_config()
  
          return config, pixel_values, labels
  
  
      def     snake_case_(	self )	->     List[str]:
          return DonutSwinConfig(
              image_size=self.image_size				,	patch_size=self.patch_size				,	num_channels=self.num_channels				,	embed_dim=self.embed_dim				,	depths=self.depths				,	num_heads=self.num_heads				,	window_size=self.window_size				,	mlp_ratio=self.mlp_ratio				,	qkv_bias=self.qkv_bias				,	hidden_dropout_prob=self.hidden_dropout_prob				,	attention_probs_dropout_prob=self.attention_probs_dropout_prob				,	drop_path_rate=self.drop_path_rate				,	hidden_act=self.hidden_act				,	use_absolute_embeddings=self.use_absolute_embeddings				,	path_norm=self.patch_norm				,	layer_norm_eps=self.layer_norm_eps				,	initializer_range=self.initializer_range				,	encoder_stride=self.encoder_stride				,	)
  
  
      def     snake_case_(	self				,	A				,	A				,	A )	->     str:
          _SCREAMING_SNAKE_CASE             =    DonutSwinModel(config=A )
          model.to(A )
          model.eval()
          _SCREAMING_SNAKE_CASE             =    model(A )
  
          _SCREAMING_SNAKE_CASE             =    ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths ) - 1))
          _SCREAMING_SNAKE_CASE             =    int(config.embed_dim * 2 ** (len(config.depths ) - 1) )
  
          self.parent.assertEqual(result.last_hidden_state.shape				,	(self.batch_size, expected_seq_len, expected_dim) )
  
  
  
  
  
      def     snake_case_(	self )	->     Optional[Any]:
          _SCREAMING_SNAKE_CASE             =    self.prepare_config_and_inputs()
          (
              _SCREAMING_SNAKE_CASE       
          )             =    config_and_inputs
          _SCREAMING_SNAKE_CASE             =    {'''pixel_values''': pixel_values}
          return config, inputs_dict
  
  
  
  
  @require_torch
  class       a_  (       snake_case_   ,					snake_case_   ,					unittest.TestCase ):
  
      '''simple docstring'''
      UpperCamelCase									=				(DonutSwinModel,) if is_torch_available() else ()
      UpperCamelCase									=				{'''feature-extraction''': DonutSwinModel} if is_torch_available() else {}
      UpperCamelCase									=				True
  
      UpperCamelCase									=				False
      UpperCamelCase									=				False
      UpperCamelCase									=				False
  
  
      def     snake_case_(	self )	->     Union[str, Any]:
          _SCREAMING_SNAKE_CASE             =    DonutSwinModelTester(self )
          _SCREAMING_SNAKE_CASE             =    ConfigTester(self				,	config_class=A				,	embed_dim=37 )
  
  
      def     snake_case_(	self )	->     Any:
          self.create_and_test_config_common_properties()
          self.config_tester.create_and_test_config_to_json_string()
          self.config_tester.create_and_test_config_to_json_file()
          self.config_tester.create_and_test_config_from_and_save_pretrained()
          self.config_tester.create_and_test_config_with_num_labels()
          self.config_tester.check_config_can_be_init_without_params()
          self.config_tester.check_config_arguments_init()
  
  
      def     snake_case_(	self )	->     str:
          return
  
  
      def     snake_case_(	self )	->     Any:
          _SCREAMING_SNAKE_CASE             =    self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*A )
  
  
      def     snake_case_(	self )	->     Tuple:
          # DonutSwin does not use inputs_embeds
          pass
  
  
      def     snake_case_(	self )	->     Optional[Any]:
          _SCREAMING_SNAKE_CASE             =    self.model_tester.prepare_config_and_inputs_for_common()
  
          for model_class in self.all_model_classes:
              _SCREAMING_SNAKE_CASE             =    model_class(A )
              self.assertIsInstance(model.get_input_embeddings()				,	(nn.Module) )
              _SCREAMING_SNAKE_CASE             =    model.get_output_embeddings()
              self.assertTrue(x is None or isinstance(A				,	nn.Linear ) )
  
  
      def     snake_case_(	self )	->     Union[str, Any]:
          _SCREAMING_SNAKE_CASE             =    self.model_tester.prepare_config_and_inputs_for_common()
  
          for model_class in self.all_model_classes:
              _SCREAMING_SNAKE_CASE             =    model_class(A )
              _SCREAMING_SNAKE_CASE             =    inspect.signature(model.forward )
              # signature.parameters is an OrderedDict => so arg_names order is deterministic
              _SCREAMING_SNAKE_CASE             =    [*signature.parameters.keys()]
  
              _SCREAMING_SNAKE_CASE             =    ['''pixel_values''']
              self.assertListEqual(arg_names[:1]				,	A )
  
  
      def     snake_case_(	self )	->     str:
          _SCREAMING_SNAKE_CASE             =    self.model_tester.prepare_config_and_inputs_for_common()
          _SCREAMING_SNAKE_CASE             =    True
  
          for model_class in self.all_model_classes:
              _SCREAMING_SNAKE_CASE             =    True
              _SCREAMING_SNAKE_CASE             =    False
              _SCREAMING_SNAKE_CASE             =    True
              _SCREAMING_SNAKE_CASE             =    model_class(A )
              model.to(A )
              model.eval()
              with torch.no_grad():
                  _SCREAMING_SNAKE_CASE             =    model(**self._prepare_for_class(A				,	A ) )
              _SCREAMING_SNAKE_CASE             =    outputs.attentions
              _SCREAMING_SNAKE_CASE             =    len(self.model_tester.depths )
              self.assertEqual(len(A )				,	A )
  
              # check that output_attentions also work using config
              del inputs_dict["output_attentions"]
              _SCREAMING_SNAKE_CASE             =    True
              _SCREAMING_SNAKE_CASE             =    config.window_size**2
              _SCREAMING_SNAKE_CASE             =    model_class(A )
              model.to(A )
              model.eval()
              with torch.no_grad():
                  _SCREAMING_SNAKE_CASE             =    model(**self._prepare_for_class(A				,	A ) )
              _SCREAMING_SNAKE_CASE             =    outputs.attentions
              self.assertEqual(len(A )				,	A )
  
              self.assertListEqual(
                  list(attentions[0].shape[-3:] )				,	[self.model_tester.num_heads[0], window_size_squared, window_size_squared]				,	)
              _SCREAMING_SNAKE_CASE             =    len(A )
  
              # Check attention is always last and order is fine
              _SCREAMING_SNAKE_CASE             =    True
              _SCREAMING_SNAKE_CASE             =    True
              _SCREAMING_SNAKE_CASE             =    model_class(A )
              model.to(A )
              model.eval()
              with torch.no_grad():
                  _SCREAMING_SNAKE_CASE             =    model(**self._prepare_for_class(A				,	A ) )
  
              if hasattr(self.model_tester				,	"""num_hidden_states_types""" ):
                  _SCREAMING_SNAKE_CASE             =    self.model_tester.num_hidden_states_types
              else:
                  # also another +1 for reshaped_hidden_states
                  _SCREAMING_SNAKE_CASE             =    2
              self.assertEqual(out_len + added_hidden_states				,	len(A ) )
  
              _SCREAMING_SNAKE_CASE             =    outputs.attentions
  
              self.assertEqual(len(A )				,	A )
  
              self.assertListEqual(
                  list(self_attentions[0].shape[-3:] )				,	[self.model_tester.num_heads[0], window_size_squared, window_size_squared]				,	)
  
  
      def     snake_case_(	self				,	A				,	A				,	A				,	A )	->     Dict:
          _SCREAMING_SNAKE_CASE             =    model_class(A )
          model.to(A )
          model.eval()
  
          with torch.no_grad():
              _SCREAMING_SNAKE_CASE             =    model(**self._prepare_for_class(A				,	A ) )
  
          _SCREAMING_SNAKE_CASE             =    outputs.hidden_states
  
          _SCREAMING_SNAKE_CASE             =    getattr(
              self.model_tester				,	"""expected_num_hidden_layers"""				,	len(self.model_tester.depths ) + 1 )
          self.assertEqual(len(A )				,	A )
  
          # DonutSwin has a different seq_length
          _SCREAMING_SNAKE_CASE             =    (
              config.patch_size
              if isinstance(config.patch_size				,	collections.abc.Iterable )
              else (config.patch_size, config.patch_size)
          )
  
          _SCREAMING_SNAKE_CASE             =    (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
  
          self.assertListEqual(
              list(hidden_states[0].shape[-2:] )				,	[num_patches, self.model_tester.embed_dim]				,	)
  
          _SCREAMING_SNAKE_CASE             =    outputs.reshaped_hidden_states
          self.assertEqual(len(A )				,	A )
  
          _SCREAMING_SNAKE_CASE             =    reshaped_hidden_states[0].shape
          _SCREAMING_SNAKE_CASE             =    (
              reshaped_hidden_states[0].view(A				,	A				,	height * width ).permute(0				,	2				,	1 )
          )
          self.assertListEqual(
              list(reshaped_hidden_states.shape[-2:] )				,	[num_patches, self.model_tester.embed_dim]				,	)
  
  
      def     snake_case_(	self )	->     Optional[int]:
          _SCREAMING_SNAKE_CASE             =    self.model_tester.prepare_config_and_inputs_for_common()
  
          _SCREAMING_SNAKE_CASE             =    (
              self.model_tester.image_size
              if isinstance(self.model_tester.image_size				,	collections.abc.Iterable )
              else (self.model_tester.image_size, self.model_tester.image_size)
          )
  
          for model_class in self.all_model_classes:
              _SCREAMING_SNAKE_CASE             =    True
              self.check_hidden_states_output(A				,	A				,	A				,	A )
  
              # check that output_hidden_states also work using config
              del inputs_dict["output_hidden_states"]
              _SCREAMING_SNAKE_CASE             =    True
  
              self.check_hidden_states_output(A				,	A				,	A				,	A )
  
  
      def     snake_case_(	self )	->     List[Any]:
          _SCREAMING_SNAKE_CASE             =    self.model_tester.prepare_config_and_inputs_for_common()
          _SCREAMING_SNAKE_CASE             =    3
  
          _SCREAMING_SNAKE_CASE             =    (
              self.model_tester.image_size
              if isinstance(self.model_tester.image_size				,	collections.abc.Iterable )
              else (self.model_tester.image_size, self.model_tester.image_size)
          )
          _SCREAMING_SNAKE_CASE             =    (
              config.patch_size
              if isinstance(config.patch_size				,	collections.abc.Iterable )
              else (config.patch_size, config.patch_size)
          )
  
          _SCREAMING_SNAKE_CASE             =    image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
          _SCREAMING_SNAKE_CASE             =    image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])
  
          for model_class in self.all_model_classes:
              _SCREAMING_SNAKE_CASE             =    True
              self.check_hidden_states_output(A				,	A				,	A				,	(padded_height, padded_width) )
  
              # check that output_hidden_states also work using config
              del inputs_dict["output_hidden_states"]
              _SCREAMING_SNAKE_CASE             =    True
              self.check_hidden_states_output(A				,	A				,	A				,	(padded_height, padded_width) )
  
  
      @slow
      def     snake_case_(	self )	->     int:
          for model_name in DONUT_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              _SCREAMING_SNAKE_CASE             =    DonutSwinModel.from_pretrained(A )
              self.assertIsNotNone(A )
  
  
  
  
  
      def     snake_case_(	self )	->     Any:
          _SCREAMING_SNAKE_CASE             =    self.model_tester.prepare_config_and_inputs_for_common()
  
          _SCREAMING_SNAKE_CASE             =    _config_zero_init(A )
          for model_class in self.all_model_classes:
              _SCREAMING_SNAKE_CASE             =    model_class(config=A )
              for name, param in model.named_parameters():
                  if "embeddings" not in name and param.requires_grad:
                      self.assertIn(
                          ((param.data.mean() * 1e9).round() / 1e9).item()				,	[0.0, 1.0]				,	msg=f'Parameter {name} of model {model_class} seems not properly initialized'				,	)
  
  
  
  
style_context: 
  
  
  
  
  
  '''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_  :
  
      '''simple docstring'''
      UpperCamelCase									=				42
      UpperCamelCase									=				42
  
      # setable values
      UpperCamelCase									=				42
      UpperCamelCase									=				42
      UpperCamelCase									=				None
  
  
      @classmethod
      def     snake_case_(	cls				,	A				,	A				,	A				,	A				,	)	->     List[Any]:
          return cls(
              common=A				,	final_alpha_cumprod=A				,	init_noise_sigma=A				,	timesteps=A				,	)
  
  
  
  
  @dataclass
  class       a_  (       snake_case_ ):
  
      '''simple docstring'''
      UpperCamelCase									=				42
  
  
  
  
  class       a_  (       snake_case_   ,					snake_case_ ):
  
      '''simple docstring'''
      UpperCamelCase									=				[e.name for e in FlaxKarrasDiffusionSchedulers]
  
      UpperCamelCase									=				42
  
  
      @property
      def     snake_case_(	self )	->     List[Any]:
          return True
  
  
      @register_to_config
      def __init__(	self				,	A = 1000				,	A = 0.0001				,	A = 0.02				,	A = "linear"				,	A = None				,	A = True				,	A = 0				,	A = "epsilon"				,	A = jnp.floataa				,	)	->     int:
          _SCREAMING_SNAKE_CASE             =    dtype
  
  
      def     snake_case_(	self				,	A = None )	->     DDIMSchedulerState:
          if common is None:
              _SCREAMING_SNAKE_CASE             =    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.
          _SCREAMING_SNAKE_CASE             =    (
              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
          _SCREAMING_SNAKE_CASE             =    jnp.array(1.0				,	dtype=self.dtype )
  
          _SCREAMING_SNAKE_CASE             =    jnp.arange(0				,	self.config.num_train_timesteps ).round()[::-1]
  
          return DDIMSchedulerState.create(
              common=A				,	final_alpha_cumprod=A				,	init_noise_sigma=A				,	timesteps=A				,	)
  
  
      def     snake_case_(	self				,	A				,	A				,	A = None )	->     jnp.ndarray:
          return sample
  
  
      def     snake_case_(	self				,	A				,	A				,	A = () )	->     DDIMSchedulerState:
          _SCREAMING_SNAKE_CASE             =    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
          _SCREAMING_SNAKE_CASE             =    (jnp.arange(0				,	A ) * step_ratio).round()[::-1] + self.config.steps_offset
  
          return state.replace(
              num_inference_steps=A				,	timesteps=A				,	)
  
  
      def     snake_case_(	self				,	A				,	A				,	A )	->     int:
          _SCREAMING_SNAKE_CASE             =    state.common.alphas_cumprod[timestep]
          _SCREAMING_SNAKE_CASE             =    jnp.where(
              prev_timestep >= 0				,	state.common.alphas_cumprod[prev_timestep]				,	state.final_alpha_cumprod )
          _SCREAMING_SNAKE_CASE             =    1 - alpha_prod_t
          _SCREAMING_SNAKE_CASE             =    1 - alpha_prod_t_prev
  
          _SCREAMING_SNAKE_CASE             =    (beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)
  
          return variance
  
  
      def     snake_case_(	self				,	A				,	A				,	A				,	A				,	A = 0.0				,	A = 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)
          _SCREAMING_SNAKE_CASE             =    timestep - self.config.num_train_timesteps // state.num_inference_steps
  
          _SCREAMING_SNAKE_CASE             =    state.common.alphas_cumprod
          _SCREAMING_SNAKE_CASE             =    state.final_alpha_cumprod
  
          # 2. compute alphas, betas
          _SCREAMING_SNAKE_CASE             =    alphas_cumprod[timestep]
          _SCREAMING_SNAKE_CASE             =    jnp.where(prev_timestep >= 0				,	alphas_cumprod[prev_timestep]				,	A )
  
          _SCREAMING_SNAKE_CASE             =    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":
              _SCREAMING_SNAKE_CASE             =    (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
              _SCREAMING_SNAKE_CASE             =    model_output
          elif self.config.prediction_type == "sample":
              _SCREAMING_SNAKE_CASE             =    model_output
              _SCREAMING_SNAKE_CASE             =    (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
          elif self.config.prediction_type == "v_prediction":
              _SCREAMING_SNAKE_CASE             =    (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
              _SCREAMING_SNAKE_CASE             =    (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)
          _SCREAMING_SNAKE_CASE             =    self._get_variance(A				,	A				,	A )
          _SCREAMING_SNAKE_CASE             =    eta * variance ** 0.5
  
          # 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
          _SCREAMING_SNAKE_CASE             =    (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
          _SCREAMING_SNAKE_CASE             =    alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction
  
          if not return_dict:
              return (prev_sample, state)
  
          return FlaxDDIMSchedulerOutput(prev_sample=A				,	state=A )
  
  
      def     snake_case_(	self				,	A				,	A				,	A				,	A				,	)	->     jnp.ndarray:
          return add_noise_common(state.common				,	A				,	A				,	A )
  
  
      def     snake_case_(	self				,	A				,	A				,	A				,	A				,	)	->     jnp.ndarray:
          return get_velocity_common(state.common				,	A				,	A				,	A )
  
  
  
  
  
      def __len__(	self )	->     Any:
          return self.config.num_train_timesteps
  
  
  
  