
'''simple docstring'''







import collections
import inspect
import unittest

from transformers import SwinConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_backbone_common import BackboneTesterMixin
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 SwinBackbone, SwinForImageClassification, SwinForMaskedImageModeling, SwinModel
						from transformers.models.swin.modeling_swin import SWIN_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
						from PIL import Image

						from transformers import AutoImageProcessor


class       lowerCAmelCase			:




		def __init__(					self       ,    UpperCamelCase       ,    UpperCamelCase=13       ,    UpperCamelCase=32       ,    UpperCamelCase=2       ,    UpperCamelCase=3       ,    UpperCamelCase=16       ,    UpperCamelCase=[1, 2, 1]       ,    UpperCamelCase=[2, 2, 4]       ,    UpperCamelCase=2       ,    UpperCamelCase=2.0       ,    UpperCamelCase=True       ,    UpperCamelCase=0.0       ,    UpperCamelCase=0.0       ,    UpperCamelCase=0.1       ,    UpperCamelCase="gelu"       ,    UpperCamelCase=False       ,    UpperCamelCase=True       ,    UpperCamelCase=0.02       ,    UpperCamelCase=1e-5       ,    UpperCamelCase=True       ,    UpperCamelCase=None       ,    UpperCamelCase=True       ,    UpperCamelCase=10       ,    UpperCamelCase=8       ,    UpperCamelCase=["stage1", "stage2"]       ,    UpperCamelCase=[1, 2]       ,    ):
								_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
								_SCREAMING_SNAKE_CASE					=			out_features
								_SCREAMING_SNAKE_CASE					=			out_indices




		def      lowercase							(					self   ):
								_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      lowercase							(					self   ):
								return SwinConfig(
								    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       ,    out_features=self.out_features       ,    out_indices=self.out_indices       ,    )




		def      lowercase							(					self       ,    UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase   ):
								_SCREAMING_SNAKE_CASE					=			SwinModel(config=_UpperCAmelCase   )
								model.to(_UpperCAmelCase   )
								model.eval()
								_SCREAMING_SNAKE_CASE					=			model(_UpperCAmelCase   )

								_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      lowercase							(					self       ,    UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase   ):
								_SCREAMING_SNAKE_CASE					=			SwinBackbone(config=_UpperCAmelCase   )
								model.to(_UpperCAmelCase   )
								model.eval()
								_SCREAMING_SNAKE_CASE					=			model(_UpperCAmelCase   )

								# verify hidden states
								self.parent.assertEqual(len(result.feature_maps   )       ,    len(config.out_features   )   )
								self.parent.assertListEqual(list(result.feature_maps[0].shape   )       ,    [self.batch_size, model.channels[0], 16, 16]   )

								# verify channels
								self.parent.assertEqual(len(model.channels   )       ,    len(config.out_features   )   )

								# verify backbone works with out_features=None
								_SCREAMING_SNAKE_CASE					=			None
								_SCREAMING_SNAKE_CASE					=			SwinBackbone(config=_UpperCAmelCase   )
								model.to(_UpperCAmelCase   )
								model.eval()
								_SCREAMING_SNAKE_CASE					=			model(_UpperCAmelCase   )

								# verify feature maps
								self.parent.assertEqual(len(result.feature_maps   )       ,    1   )
								self.parent.assertListEqual(list(result.feature_maps[0].shape   )       ,    [self.batch_size, model.channels[-1], 4, 4]   )

								# verify channels
								self.parent.assertEqual(len(model.channels   )       ,    1   )




		def      lowercase							(					self       ,    UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase   ):
								_SCREAMING_SNAKE_CASE					=			SwinForMaskedImageModeling(config=_UpperCAmelCase   )
								model.to(_UpperCAmelCase   )
								model.eval()
								_SCREAMING_SNAKE_CASE					=			model(_UpperCAmelCase   )
								self.parent.assertEqual(
								    result.logits.shape       ,    (self.batch_size, self.num_channels, self.image_size, self.image_size)   )

								# test greyscale images
								_SCREAMING_SNAKE_CASE					=			1
								_SCREAMING_SNAKE_CASE					=			SwinForMaskedImageModeling(_UpperCAmelCase   )
								model.to(_UpperCAmelCase   )
								model.eval()

								_SCREAMING_SNAKE_CASE					=			floats_tensor([self.batch_size, 1, self.image_size, self.image_size]   )
								_SCREAMING_SNAKE_CASE					=			model(_UpperCAmelCase   )
								self.parent.assertEqual(result.logits.shape       ,    (self.batch_size, 1, self.image_size, self.image_size)   )




		def      lowercase							(					self       ,    UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase   ):
								_SCREAMING_SNAKE_CASE					=			self.type_sequence_label_size
								_SCREAMING_SNAKE_CASE					=			SwinForImageClassification(_UpperCAmelCase   )
								model.to(_UpperCAmelCase   )
								model.eval()
								_SCREAMING_SNAKE_CASE					=			model(_UpperCAmelCase       ,    labels=_UpperCAmelCase   )
								self.parent.assertEqual(result.logits.shape       ,    (self.batch_size, self.type_sequence_label_size)   )

								# test greyscale images
								_SCREAMING_SNAKE_CASE					=			1
								_SCREAMING_SNAKE_CASE					=			SwinForImageClassification(_UpperCAmelCase   )
								model.to(_UpperCAmelCase   )
								model.eval()

								_SCREAMING_SNAKE_CASE					=			floats_tensor([self.batch_size, 1, self.image_size, self.image_size]   )
								_SCREAMING_SNAKE_CASE					=			model(_UpperCAmelCase   )
								self.parent.assertEqual(result.logits.shape       ,    (self.batch_size, self.type_sequence_label_size)   )




		def      lowercase							(					self   ):
								_SCREAMING_SNAKE_CASE					=			self.prepare_config_and_inputs()
								(
								    (
								    _SCREAMING_SNAKE_CASE		
								),				(
								    _SCREAMING_SNAKE_CASE		
								),				(
								    _SCREAMING_SNAKE_CASE		
								),						
								)					=			config_and_inputs
								_SCREAMING_SNAKE_CASE					=			{"pixel_values": pixel_values}
								return config, inputs_dict


@require_torch
class       lowerCAmelCase			(					__UpperCAmelCase		,  __UpperCAmelCase		,  unittest.TestCase      ):
		a				:       str							=       (
		    (
		        SwinModel,
		        SwinBackbone,
		        SwinForImageClassification,
		        SwinForMaskedImageModeling,
		    )
		    if is_torch_available()
		    else ()
		)
		a				:       Optional[Any]							=       (
		    {'feature-extraction': SwinModel, 'image-classification': SwinForImageClassification}
		    if is_torch_available()
		    else {}
		)
		a				:       Optional[int]							=       True

		a				:       Dict							=       False
		a				:       Dict							=       False
		a				:       int							=       False




		def      lowercase							(					self   ):
								_SCREAMING_SNAKE_CASE					=			SwinModelTester(self   )
								_SCREAMING_SNAKE_CASE					=			ConfigTester(self       ,    config_class=_UpperCAmelCase       ,    embed_dim=37   )




		def      lowercase							(					self   ):
								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      lowercase							(					self   ):
								return




		def      lowercase							(					self   ):
								_SCREAMING_SNAKE_CASE					=			self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*_UpperCAmelCase   )




		@unittest.skip(reason="Got `CUDA error: misaligned address` with PyTorch 2.0.0."   )
		def      lowercase							(					self   ):
								pass




		def      lowercase							(					self   ):
								super().test_training_gradient_checkpointing()




		def      lowercase							(					self   ):
								_SCREAMING_SNAKE_CASE					=			self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_backbone(*_UpperCAmelCase   )




		def      lowercase							(					self   ):
								_SCREAMING_SNAKE_CASE					=			self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_masked_image_modeling(*_UpperCAmelCase   )




		def      lowercase							(					self   ):
								_SCREAMING_SNAKE_CASE					=			self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_image_classification(*_UpperCAmelCase   )




		@unittest.skip(reason="Swin does not use inputs_embeds"   )
		def      lowercase							(					self   ):
								pass




		@unittest.skip(reason="Swin Transformer does not use feedforward chunking"   )
		def      lowercase							(					self   ):
								pass




		def      lowercase							(					self   ):
								_SCREAMING_SNAKE_CASE,				_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(_UpperCAmelCase   )
														self.assertIsInstance(model.get_input_embeddings()       ,    (nn.Module)   )
														_SCREAMING_SNAKE_CASE					=			model.get_output_embeddings()
														self.assertTrue(x is None or isinstance(_UpperCAmelCase       ,    nn.Linear   )   )




		def      lowercase							(					self   ):
								_SCREAMING_SNAKE_CASE,				_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(_UpperCAmelCase   )
														_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]       ,    _UpperCAmelCase   )




		def      lowercase							(					self   ):
								_SCREAMING_SNAKE_CASE,				_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(_UpperCAmelCase   )
														model.to(_UpperCAmelCase   )
														model.eval()
														with torch.no_grad():
																				_SCREAMING_SNAKE_CASE					=			model(**self._prepare_for_class(_UpperCAmelCase       ,    _UpperCAmelCase   )   )
														_SCREAMING_SNAKE_CASE					=			outputs.attentions
														_SCREAMING_SNAKE_CASE					=			len(self.model_tester.depths   )
														self.assertEqual(len(_UpperCAmelCase   )       ,    _UpperCAmelCase   )

														# 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(_UpperCAmelCase   )
														model.to(_UpperCAmelCase   )
														model.eval()
														with torch.no_grad():
																				_SCREAMING_SNAKE_CASE					=			model(**self._prepare_for_class(_UpperCAmelCase       ,    _UpperCAmelCase   )   )
														_SCREAMING_SNAKE_CASE					=			outputs.attentions
														self.assertEqual(len(_UpperCAmelCase   )       ,    _UpperCAmelCase   )

														self.assertListEqual(
														    list(attentions[0].shape[-3:]   )       ,    [self.model_tester.num_heads[0], window_size_squared, window_size_squared]       ,    )
														_SCREAMING_SNAKE_CASE					=			len(_UpperCAmelCase   )

														# Check attention is always last and order is fine
														_SCREAMING_SNAKE_CASE					=			True
														_SCREAMING_SNAKE_CASE					=			True
														_SCREAMING_SNAKE_CASE					=			model_class(_UpperCAmelCase   )
														model.to(_UpperCAmelCase   )
														model.eval()
														with torch.no_grad():
																				_SCREAMING_SNAKE_CASE					=			model(**self._prepare_for_class(_UpperCAmelCase       ,    _UpperCAmelCase   )   )

														# also another +1 for reshaped_hidden_states
														_SCREAMING_SNAKE_CASE					=			1 if model_class.__name__ == "SwinBackbone" else 2
														self.assertEqual(out_len + added_hidden_states       ,    len(_UpperCAmelCase   )   )

														_SCREAMING_SNAKE_CASE					=			outputs.attentions

														self.assertEqual(len(_UpperCAmelCase   )       ,    _UpperCAmelCase   )

														self.assertListEqual(
														    list(self_attentions[0].shape[-3:]   )       ,    [self.model_tester.num_heads[0], window_size_squared, window_size_squared]       ,    )




		def      lowercase							(					self       ,    UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase   ):
								_SCREAMING_SNAKE_CASE					=			model_class(_UpperCAmelCase   )
								model.to(_UpperCAmelCase   )
								model.eval()

								with torch.no_grad():
														_SCREAMING_SNAKE_CASE					=			model(**self._prepare_for_class(_UpperCAmelCase       ,    _UpperCAmelCase   )   )

								_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(_UpperCAmelCase   )       ,    _UpperCAmelCase   )

								# Swin 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]       ,    )

								if not model_class.__name__ == "SwinBackbone":
														_SCREAMING_SNAKE_CASE					=			outputs.reshaped_hidden_states
														self.assertEqual(len(_UpperCAmelCase   )       ,    _UpperCAmelCase   )

														_SCREAMING_SNAKE_CASE,				_SCREAMING_SNAKE_CASE,				_SCREAMING_SNAKE_CASE,				_SCREAMING_SNAKE_CASE					=			reshaped_hidden_states[0].shape
														_SCREAMING_SNAKE_CASE					=			(
														    reshaped_hidden_states[0].view(_UpperCAmelCase       ,    _UpperCAmelCase       ,    height * width   ).permute(0       ,    2       ,    1   )
														)
														self.assertListEqual(
														    list(reshaped_hidden_states.shape[-2:]   )       ,    [num_patches, self.model_tester.embed_dim]       ,    )




		def      lowercase							(					self   ):
								_SCREAMING_SNAKE_CASE,				_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(_UpperCAmelCase       ,    _UpperCAmelCase       ,    _UpperCAmelCase       ,    _UpperCAmelCase   )

														# check that output_hidden_states also work using config
														del inputs_dict["output_hidden_states"]
														_SCREAMING_SNAKE_CASE					=			True

														self.check_hidden_states_output(_UpperCAmelCase       ,    _UpperCAmelCase       ,    _UpperCAmelCase       ,    _UpperCAmelCase   )




		def      lowercase							(					self   ):
								_SCREAMING_SNAKE_CASE,				_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(_UpperCAmelCase       ,    _UpperCAmelCase       ,    _UpperCAmelCase       ,    (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(_UpperCAmelCase       ,    _UpperCAmelCase       ,    _UpperCAmelCase       ,    (padded_height, padded_width)   )




		@slow
		def      lowercase							(					self   ):
								for model_name in SWIN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
														_SCREAMING_SNAKE_CASE					=			SwinModel.from_pretrained(_UpperCAmelCase   )
														self.assertIsNotNone(_UpperCAmelCase   )




		def      lowercase							(					self   ):
								_SCREAMING_SNAKE_CASE,				_SCREAMING_SNAKE_CASE					=			self.model_tester.prepare_config_and_inputs_for_common()

								_SCREAMING_SNAKE_CASE					=			_config_zero_init(_UpperCAmelCase   )
								for model_class in self.all_model_classes:
														_SCREAMING_SNAKE_CASE					=			model_class(config=_UpperCAmelCase   )
														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'       ,    )


@require_vision
@require_torch
class       lowerCAmelCase			(					unittest.TestCase      ):




		@cached_property
		def      lowercase							(					self   ):
								return (
								    AutoImageProcessor.from_pretrained("microsoft/swin-tiny-patch4-window7-224"   )
								    if is_vision_available()
								    else None
								)




		@slow
		def      lowercase							(					self   ):
								_SCREAMING_SNAKE_CASE					=			SwinForImageClassification.from_pretrained("microsoft/swin-tiny-patch4-window7-224"   ).to(_UpperCAmelCase   )
								_SCREAMING_SNAKE_CASE					=			self.default_image_processor

								_SCREAMING_SNAKE_CASE					=			Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"   )
								_SCREAMING_SNAKE_CASE					=			image_processor(images=_UpperCAmelCase       ,    return_tensors="pt"   ).to(_UpperCAmelCase   )

								# forward pass
								with torch.no_grad():
														_SCREAMING_SNAKE_CASE					=			model(**_UpperCAmelCase   )

								# verify the logits
								_SCREAMING_SNAKE_CASE					=			torch.Size((1, 1_000)   )
								self.assertEqual(outputs.logits.shape       ,    _UpperCAmelCase   )
								_SCREAMING_SNAKE_CASE					=			torch.tensor([-0.09_48, -0.64_54, -0.09_21]   ).to(_UpperCAmelCase   )
								self.assertTrue(torch.allclose(outputs.logits[0, :3]       ,    _UpperCAmelCase       ,    atol=1e-4   )   )


@require_torch
class       lowerCAmelCase			(					unittest.TestCase		,  __UpperCAmelCase      ):
		a				:       Optional[Any]							=       (SwinBackbone,) if is_torch_available() else ()
		a				:       List[str]							=       SwinConfig




		def      lowercase							(					self   ):
								_SCREAMING_SNAKE_CASE					=			SwinModelTester(self   )

# 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 			UpperCamelCase_				:




      '''simple docstring'''







      a		:CommonSchedulerState
      a		:jnp.ndarray

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







      @classmethod
      def lowercase__		( cls					,					_UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase					,					):
            return cls(
                common=_UpperCAmelCase					,					final_alpha_cumprod=_UpperCAmelCase					,					init_noise_sigma=_UpperCAmelCase					,					timesteps=_UpperCAmelCase					,					)


@dataclass
class 			UpperCamelCase_				(  A     ):




      '''simple docstring'''







      a		:DDIMSchedulerState


class 			UpperCamelCase_				(  A					,       A     ):




      '''simple docstring'''







      a		:int						 =      [e.name for e in FlaxKarrasDiffusionSchedulers]

      a		:jnp.dtype







      @property
      def lowercase__		( self):
            return True







      @register_to_config
      def __init__( self					,					_UpperCAmelCase = 1_000					,					_UpperCAmelCase = 0.0001					,					_UpperCAmelCase = 0.02					,					_UpperCAmelCase = "linear"					,					_UpperCAmelCase = None					,					_UpperCAmelCase = True					,					_UpperCAmelCase = 0					,					_UpperCAmelCase = "epsilon"					,					_UpperCAmelCase = jnp.floataa					,					):
            lowerCAmelCase_						=      dtype







      def lowercase__		( self					,					_UpperCAmelCase = None):
            if common is None:
                  lowerCAmelCase_						=      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.
            lowerCAmelCase_						=      (
                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
            lowerCAmelCase_						=      jnp.array(1.0					,					dtype=self.dtype)

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

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







      def lowercase__		( self					,					_UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase = None):
            return sample







      def lowercase__		( self					,					_UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase = ()):
            lowerCAmelCase_						=      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
            lowerCAmelCase_						=      (jnp.arange(0					,					_UpperCAmelCase) * step_ratio).round()[::-1] + self.config.steps_offset

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







      def lowercase__		( self					,					_UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase):
            lowerCAmelCase_						=      state.common.alphas_cumprod[timestep]
            lowerCAmelCase_						=      jnp.where(
                prev_timestep >= 0					,					state.common.alphas_cumprod[prev_timestep]					,					state.final_alpha_cumprod)
            lowerCAmelCase_						=      1 - alpha_prod_t
            lowerCAmelCase_						=      1 - alpha_prod_t_prev

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

            return variance







      def lowercase__		( self					,					_UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase = 0.0					,					_UpperCAmelCase = True					,					):
            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)
            lowerCAmelCase_						=      timestep - self.config.num_train_timesteps // state.num_inference_steps

            lowerCAmelCase_						=      state.common.alphas_cumprod
            lowerCAmelCase_						=      state.final_alpha_cumprod

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

            lowerCAmelCase_						=      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":
                  lowerCAmelCase_						=      (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
                  lowerCAmelCase_						=      model_output
            elif self.config.prediction_type == "sample":
                  lowerCAmelCase_						=      model_output
                  lowerCAmelCase_						=      (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
            elif self.config.prediction_type == "v_prediction":
                  lowerCAmelCase_						=      (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
                  lowerCAmelCase_						=      (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)
            lowerCAmelCase_						=      self._get_variance(_UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase)
            lowerCAmelCase_						=      eta * variance ** 0.5

            # 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
            lowerCAmelCase_						=      (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
            lowerCAmelCase_						=      alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

            if not return_dict:
                  return (prev_sample, state)

            return FlaxDDIMSchedulerOutput(prev_sample=_UpperCAmelCase					,					state=_UpperCAmelCase)







      def lowercase__		( self					,					_UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase					,					):
            return add_noise_common(state.common					,					_UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase)







      def lowercase__		( self					,					_UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase					,					):
            return get_velocity_common(state.common					,					_UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase)







      def __len__( self):
            return self.config.num_train_timesteps



