





'''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'				,	)










'''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



