




"""simple docstring"""






import inspect
import unittest

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

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
			import torch

			from transformers import MobileViTVaForImageClassification, MobileViTVaForSemanticSegmentation, MobileViTVaModel
			from transformers.models.mobilevitva.modeling_mobilevitva import (
			    MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST,
			    make_divisible,
			)


if is_vision_available():
			from PIL import Image

			from transformers import MobileViTImageProcessor






class lowerCAmelCase__      (	lowercase		):






			'''simple docstring'''



			def    A_       (    self    ):
							_lowerCamelCase	:     str       =       self.config_class(**self.inputs_dict    )
							self.parent.assertTrue(hasattr(lowercase    , 'width_multiplier'    )    )






class lowerCAmelCase__      :






			'''simple docstring'''



			def __init__(    self    , lowercase    , lowercase=13    , lowercase=64    , lowercase=2    , lowercase=3    , lowercase="swish"    , lowercase=3    , lowercase=32    , lowercase=0.1    , lowercase=0.02    , lowercase=True    , lowercase=True    , lowercase=10    , lowercase=None    , lowercase=0.25    , lowercase=0.0    , lowercase=0.0    , ):
							_lowerCamelCase	:     int       =       parent
							_lowerCamelCase	:     List[Any]       =       batch_size
							_lowerCamelCase	:     Optional[int]       =       image_size
							_lowerCamelCase	:     int       =       patch_size
							_lowerCamelCase	:     Dict       =       num_channels
							_lowerCamelCase	:     Optional[Any]       =       make_divisible(512 * width_multiplier    , divisor=8    )
							_lowerCamelCase	:     Dict       =       hidden_act
							_lowerCamelCase	:     str       =       conv_kernel_size
							_lowerCamelCase	:     List[str]       =       output_stride
							_lowerCamelCase	:     List[Any]       =       classifier_dropout_prob
							_lowerCamelCase	:     Optional[Any]       =       use_labels
							_lowerCamelCase	:     int       =       is_training
							_lowerCamelCase	:     str       =       num_labels
							_lowerCamelCase	:     List[str]       =       initializer_range
							_lowerCamelCase	:     str       =       scope
							_lowerCamelCase	:     Optional[Any]       =       width_multiplier
							_lowerCamelCase	:     Union[str, Any]       =       ffn_dropout
							_lowerCamelCase	:     Optional[int]       =       attn_dropout
			def    A_       (    self    ):
							_lowerCamelCase	:     Optional[int]       =       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]    )

							_lowerCamelCase	:     Union[str, Any]       =       None
							_lowerCamelCase	:     Dict       =       None
							if self.use_labels:
											_lowerCamelCase	:     List[str]       =       ids_tensor([self.batch_size]    , self.num_labels    )
											_lowerCamelCase	:     int       =       ids_tensor([self.batch_size, self.image_size, self.image_size]    , self.num_labels    )

							_lowerCamelCase	:     Dict       =       self.get_config()

							return config, pixel_values, labels, pixel_labels
			def    A_       (    self    ):
							return MobileViTVaConfig(
							    image_size=self.image_size    , patch_size=self.patch_size    , num_channels=self.num_channels    , hidden_act=self.hidden_act    , conv_kernel_size=self.conv_kernel_size    , output_stride=self.output_stride    , classifier_dropout_prob=self.classifier_dropout_prob    , initializer_range=self.initializer_range    , width_multiplier=self.width_multiplier    , ffn_dropout=self.ffn_dropout_prob    , attn_dropout=self.attn_dropout_prob    , )
			def    A_       (    self    , lowercase    , lowercase    , lowercase    , lowercase    ):
							_lowerCamelCase	:     List[Any]       =       MobileViTVaModel(config=lowercase    )
							model.to(lowercase    )
							model.eval()
							_lowerCamelCase	:     List[Any]       =       model(lowercase    )
							self.parent.assertEqual(
							    result.last_hidden_state.shape    , (
							        self.batch_size,
							        self.last_hidden_size,
							        self.image_size // self.output_stride,
							        self.image_size // self.output_stride,
							    )    , )
			def    A_       (    self    , lowercase    , lowercase    , lowercase    , lowercase    ):
							_lowerCamelCase	:     Optional[int]       =       self.num_labels
							_lowerCamelCase	:     Tuple       =       MobileViTVaForImageClassification(lowercase    )
							model.to(lowercase    )
							model.eval()
							_lowerCamelCase	:     Optional[Any]       =       model(lowercase    , labels=lowercase    )
							self.parent.assertEqual(result.logits.shape    , (self.batch_size, self.num_labels)    )
			def    A_       (    self    , lowercase    , lowercase    , lowercase    , lowercase    ):
							_lowerCamelCase	:     Any       =       self.num_labels
							_lowerCamelCase	:     Any       =       MobileViTVaForSemanticSegmentation(lowercase    )
							model.to(lowercase    )
							model.eval()
							_lowerCamelCase	:     List[Any]       =       model(lowercase    )
							self.parent.assertEqual(
							    result.logits.shape    , (
							        self.batch_size,
							        self.num_labels,
							        self.image_size // self.output_stride,
							        self.image_size // self.output_stride,
							    )    , )
							_lowerCamelCase	:     int       =       model(lowercase    , labels=lowercase    )
							self.parent.assertEqual(
							    result.logits.shape    , (
							        self.batch_size,
							        self.num_labels,
							        self.image_size // self.output_stride,
							        self.image_size // self.output_stride,
							    )    , )






			def    A_       (    self    ):
							_lowerCamelCase	:     str       =       self.prepare_config_and_inputs()
							_lowerCamelCase,					_lowerCamelCase,					_lowerCamelCase,					_lowerCamelCase	:     List[Any]       =       config_and_inputs
							_lowerCamelCase	:     Optional[Any]       =       {'pixel_values': pixel_values}
							return config, inputs_dict






@require_torch
class lowerCAmelCase__      (	lowercase,      lowercase,      unittest.TestCase		):






			'''simple docstring'''



			lowerCamelCase__ =     (
			    (MobileViTVaModel, MobileViTVaForImageClassification, MobileViTVaForSemanticSegmentation)
			    if is_torch_available()
			    else ()
			)

			lowerCamelCase__ =     (
			    {
			        """feature-extraction""": MobileViTVaModel,
			        """image-classification""": MobileViTVaForImageClassification,
			        """image-segmentation""": MobileViTVaForSemanticSegmentation,
			    }
			    if is_torch_available()
			    else {}
			)

			lowerCamelCase__ =     False
			lowerCamelCase__ =     False
			lowerCamelCase__ =     False
			lowerCamelCase__ =     False
			def    A_       (    self    ):
							_lowerCamelCase	:     str       =       MobileViTVaModelTester(self    )
							_lowerCamelCase	:     Tuple       =       MobileViTVaConfigTester(self    , config_class=lowercase    , has_text_modality=lowercase    )
			def    A_       (    self    ):
							self.config_tester.run_common_tests()
			@unittest.skip(reason='MobileViTV2 does not use inputs_embeds'    )
			def    A_       (    self    ):
							pass
			@unittest.skip(reason='MobileViTV2 does not support input and output embeddings'    )
			def    A_       (    self    ):
							pass
			@unittest.skip(reason='MobileViTV2 does not output attentions'    )
			def    A_       (    self    ):
							pass
			@require_torch_multi_gpu
			@unittest.skip(reason='Got `CUDA error: misaligned address` for tests after this one being run.'    )
			def    A_       (    self    ):
							pass
			@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'    )
			def    A_       (    self    ):
							pass
			def    A_       (    self    ):
							_lowerCamelCase,					_lowerCamelCase	:     List[Any]       =       self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
											_lowerCamelCase	:     str       =       model_class(lowercase    )
											_lowerCamelCase	:     Optional[int]       =       inspect.signature(model.forward    )
											# signature.parameters is an OrderedDict => so arg_names order is deterministic
											_lowerCamelCase	:     Optional[Any]       =       [*signature.parameters.keys()]

											_lowerCamelCase	:     Dict       =       ['pixel_values']
											self.assertListEqual(arg_names[:1]    , lowercase    )
			def    A_       (    self    ):
							_lowerCamelCase	:     str       =       self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*lowercase    )
			def    A_       (    self    ):
							def check_hidden_states_output(lowercase    , lowercase    , lowercase    ):
											_lowerCamelCase	:     Any       =       model_class(lowercase    )
											model.to(lowercase    )
											model.eval()

											with torch.no_grad():
															_lowerCamelCase	:     Union[str, Any]       =       model(**self._prepare_for_class(lowercase    , lowercase    )    )

											_lowerCamelCase	:     Dict       =       outputs.hidden_states

											_lowerCamelCase	:     List[Any]       =       5
											self.assertEqual(len(lowercase    )    , lowercase    )

											# MobileViTV2's feature maps are of shape (batch_size, num_channels, height, width)
											# with the width and height being successively divided by 2.
											_lowerCamelCase	:     Any       =       2
											for i in range(len(lowercase    )    ):
															self.assertListEqual(
															    list(hidden_states[i].shape[-2:]    )    , [self.model_tester.image_size // divisor, self.model_tester.image_size // divisor]    , )
															divisor *= 2

											self.assertEqual(self.model_tester.output_stride    , divisor // 2    )

							_lowerCamelCase,					_lowerCamelCase	:     Tuple       =       self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
											_lowerCamelCase	:     List[str]       =       True
											check_hidden_states_output(lowercase    , lowercase    , lowercase    )

											# check that output_hidden_states also work using config
											del inputs_dict["output_hidden_states"]
											_lowerCamelCase	:     Any       =       True

											check_hidden_states_output(lowercase    , lowercase    , lowercase    )
			def    A_       (    self    ):
							_lowerCamelCase	:     Any       =       self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_image_classification(*lowercase    )
			def    A_       (    self    ):
							_lowerCamelCase	:     Union[str, Any]       =       self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_semantic_segmentation(*lowercase    )






			@slow
			def    A_       (    self    ):
							for model_name in MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											_lowerCamelCase	:     Optional[Any]       =       MobileViTVaModel.from_pretrained(lowercase    )
											self.assertIsNotNone(lowercase    )







def 	_snake_case     (							):
				_lowerCamelCase	:     List[Any]       =       Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'						)
				return image






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






			'''simple docstring'''



			@cached_property
			def    A_       (    self    ):
							return (
							    MobileViTImageProcessor.from_pretrained('apple/mobilevitv2-1.0-imagenet1k-256'    )
							    if is_vision_available()
							    else None
							)
			@slow
			def    A_       (    self    ):
							_lowerCamelCase	:     List[Any]       =       MobileViTVaForImageClassification.from_pretrained('apple/mobilevitv2-1.0-imagenet1k-256'    ).to(
							    lowercase    )

							_lowerCamelCase	:     Dict       =       self.default_image_processor
							_lowerCamelCase	:     Optional[Any]       =       prepare_img()
							_lowerCamelCase	:     Optional[Any]       =       image_processor(images=lowercase    , return_tensors='pt'    ).to(lowercase    )

							# forward pass
							with torch.no_grad():
											_lowerCamelCase	:     List[str]       =       model(**lowercase    )

							# verify the logits
							_lowerCamelCase	:     Optional[int]       =       torch.Size((1, 1000)    )
							self.assertEqual(outputs.logits.shape    , lowercase    )

							_lowerCamelCase	:     int       =       torch.tensor([-1.6336E00, -7.3204E-02, -5.1883E-01]    ).to(lowercase    )

							self.assertTrue(torch.allclose(outputs.logits[0, :3]    , lowercase    , atol=1E-4    )    )
			@slow
			def    A_       (    self    ):
							_lowerCamelCase	:     Optional[int]       =       MobileViTVaForSemanticSegmentation.from_pretrained('shehan97/mobilevitv2-1.0-voc-deeplabv3'    )
							_lowerCamelCase	:     int       =       model.to(lowercase    )

							_lowerCamelCase	:     Any       =       MobileViTImageProcessor.from_pretrained('shehan97/mobilevitv2-1.0-voc-deeplabv3'    )

							_lowerCamelCase	:     List[str]       =       prepare_img()
							_lowerCamelCase	:     Optional[int]       =       image_processor(images=lowercase    , return_tensors='pt'    ).to(lowercase    )

							# forward pass
							with torch.no_grad():
											_lowerCamelCase	:     Optional[int]       =       model(**lowercase    )
							_lowerCamelCase	:     Union[str, Any]       =       outputs.logits

							# verify the logits
							_lowerCamelCase	:     Optional[Any]       =       torch.Size((1, 21, 32, 32)    )
							self.assertEqual(logits.shape    , lowercase    )

							_lowerCamelCase	:     List[str]       =       torch.tensor(
							    [
							        [[7.08_63, 7.15_25, 6.82_01], [6.69_31, 6.87_70, 6.89_33], [6.29_78, 7.03_66, 6.96_36]],
							        [[-3.71_34, -3.67_12, -3.66_75], [-3.58_25, -3.35_49, -3.47_77], [-3.34_35, -3.39_79, -3.28_57]],
							        [[-2.93_29, -2.80_03, -2.73_69], [-3.05_64, -2.47_80, -2.02_07], [-2.68_89, -1.92_98, -1.76_40]],
							    ]    , device=lowercase    , )

							self.assertTrue(torch.allclose(logits[0, :3, :3, :3]    , lowercase    , atol=1E-4    )    )






			@slow
			def    A_       (    self    ):
							_lowerCamelCase	:     int       =       MobileViTVaForSemanticSegmentation.from_pretrained('shehan97/mobilevitv2-1.0-voc-deeplabv3'    )
							_lowerCamelCase	:     List[str]       =       model.to(lowercase    )

							_lowerCamelCase	:     List[str]       =       MobileViTImageProcessor.from_pretrained('shehan97/mobilevitv2-1.0-voc-deeplabv3'    )

							_lowerCamelCase	:     Union[str, Any]       =       prepare_img()
							_lowerCamelCase	:     Union[str, Any]       =       image_processor(images=lowercase    , return_tensors='pt'    ).to(lowercase    )

							# forward pass
							with torch.no_grad():
											_lowerCamelCase	:     str       =       model(**lowercase    )

							_lowerCamelCase	:     Any       =       outputs.logits.detach().cpu()

							_lowerCamelCase	:     Dict       =       image_processor.post_process_semantic_segmentation(outputs=lowercase    , target_sizes=[(50, 60)]    )
							_lowerCamelCase	:     Optional[int]       =       torch.Size((50, 60)    )
							self.assertEqual(segmentation[0].shape    , lowercase    )

							_lowerCamelCase	:     Any       =       image_processor.post_process_semantic_segmentation(outputs=lowercase    )
							_lowerCamelCase	:     Optional[Any]       =       torch.Size((32, 32)    )
							self.assertEqual(segmentation[0].shape    , lowercase    )





"""simple docstring"""






# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
# and https://github.com/hojonathanho/diffusion
import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers.schedulers.scheduling_utils import SchedulerMixin
from diffusers.utils import BaseOutput, deprecate






@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->DDIM
class lowerCAmelCase__      (	lowercase		):






			'''simple docstring'''



			lowerCamelCase__ =     42
			lowerCamelCase__ =     None







def 	_snake_case     (							lowercase__				,       lowercase__=0.9_9_9				,       lowercase__="cosine"				,       ):
				if alpha_transform_type == "cosine":

								def alpha_bar_fn(lowercase__						):
												return math.cos((t + 0.0_0_8) / 1.0_0_8 * math.pi / 2						) ** 2

				elif alpha_transform_type == "exp":

								def alpha_bar_fn(lowercase__						):
												return math.exp(t * -1_2.0						)

				else:
								raise ValueError(f'''Unsupported alpha_tranform_type: {alpha_transform_type}'''						)

				_lowerCamelCase	:     Optional[Any]       =       []
				for i in range(lowercase__						):
								_lowerCamelCase	:     Optional[Any]       =       i / num_diffusion_timesteps
								_lowerCamelCase	:     List[str]       =       (i + 1) / num_diffusion_timesteps
								betas.append(min(1 - alpha_bar_fn(lowercase__						) / alpha_bar_fn(lowercase__						)				,       lowercase__						)						)
				return torch.tensor(lowercase__				,       dtype=torch.floataa						)






class lowerCAmelCase__      (	lowercase,      lowercase		):






			'''simple docstring'''



			lowerCamelCase__ =     1
			@register_to_config
			def __init__(    self    , lowercase = 1000    , lowercase = 0.00_01    , lowercase = 0.02    , lowercase = "linear"    , lowercase = None    , lowercase = True    , lowercase = True    , lowercase = 0    , lowercase = "epsilon"    , lowercase = 1.0    , **lowercase    , ):
							if kwargs.get('set_alpha_to_one'    , lowercase    ) is not None:
											_lowerCamelCase	:     List[str]       =       (
											    'The `set_alpha_to_one` argument is deprecated. Please use `set_alpha_to_zero` instead.'
											)
											deprecate('set_alpha_to_one'    , '1.0.0'    , lowercase    , standard_warn=lowercase    )
											_lowerCamelCase	:     Union[str, Any]       =       kwargs['set_alpha_to_one']
							if trained_betas is not None:
											_lowerCamelCase	:     Tuple       =       torch.tensor(lowercase    , dtype=torch.floataa    )
							elif beta_schedule == "linear":
											_lowerCamelCase	:     List[str]       =       torch.linspace(lowercase    , lowercase    , lowercase    , dtype=torch.floataa    )
							elif beta_schedule == "scaled_linear":
											# this schedule is very specific to the latent diffusion model.
											_lowerCamelCase	:     Any       =       (
											    torch.linspace(beta_start**0.5    , beta_end**0.5    , lowercase    , dtype=torch.floataa    ) ** 2
											)
							elif beta_schedule == "squaredcos_cap_v2":
											# Glide cosine schedule
											_lowerCamelCase	:     Tuple       =       betas_for_alpha_bar(lowercase    )
							else:
											raise NotImplementedError(F'''{beta_schedule} does is not implemented for {self.__class__}'''    )

							_lowerCamelCase	:     str       =       1.0 - self.betas
							_lowerCamelCase	:     str       =       torch.cumprod(self.alphas    , dim=0    )

							# At every step in inverted ddim, we are looking into the next alphas_cumprod
							# For the final step, there is no next alphas_cumprod, and the index is out of bounds
							# `set_alpha_to_zero` decides whether we set this parameter simply to zero
							# in this case, self.step() just output the predicted noise
							# or whether we use the final alpha of the "non-previous" one.
							_lowerCamelCase	:     int       =       torch.tensor(0.0    ) if set_alpha_to_zero else self.alphas_cumprod[-1]

							# standard deviation of the initial noise distribution
							_lowerCamelCase	:     Optional[Any]       =       1.0

							# setable values
							_lowerCamelCase	:     Dict       =       None
							_lowerCamelCase	:     int       =       torch.from_numpy(np.arange(0    , lowercase    ).copy().astype(np.intaa    )    )
			def    A_       (    self    , lowercase    , lowercase = None    ):
							return sample
			def    A_       (    self    , lowercase    , lowercase = None    ):

							if num_inference_steps > self.config.num_train_timesteps:
											raise ValueError(
											    F'''`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:'''
											    F''' {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle'''
											    F''' maximal {self.config.num_train_timesteps} timesteps.'''    )

							_lowerCamelCase	:     int       =       num_inference_steps
							_lowerCamelCase	:     Optional[int]       =       self.config.num_train_timesteps // self.num_inference_steps
							# creates integer timesteps by multiplying by ratio
							# casting to int to avoid issues when num_inference_step is power of 3
							_lowerCamelCase	:     Any       =       (np.arange(0    , lowercase    ) * step_ratio).round().copy().astype(np.intaa    )
							_lowerCamelCase	:     Any       =       torch.from_numpy(lowercase    ).to(lowercase    )
							self.timesteps += self.config.steps_offset
			def    A_       (    self    , lowercase    , lowercase    , lowercase    , lowercase = 0.0    , lowercase = False    , lowercase = None    , lowercase = True    , ):
							# 1. get previous step value (=t+1)
							_lowerCamelCase	:     int       =       timestep + self.config.num_train_timesteps // self.num_inference_steps

							# 2. compute alphas, betas
							# change original implementation to exactly match noise levels for analogous forward process
							_lowerCamelCase	:     Dict       =       self.alphas_cumprod[timestep]
							_lowerCamelCase	:     str       =       (
							    self.alphas_cumprod[prev_timestep]
							    if prev_timestep < self.config.num_train_timesteps
							    else self.final_alpha_cumprod
							)

							_lowerCamelCase	:     Any       =       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	:     Optional[int]       =       (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
											_lowerCamelCase	:     Any       =       model_output
							elif self.config.prediction_type == "sample":
											_lowerCamelCase	:     Optional[Any]       =       model_output
											_lowerCamelCase	:     Tuple       =       (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
							elif self.config.prediction_type == "v_prediction":
											_lowerCamelCase	:     Any       =       (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
											_lowerCamelCase	:     Tuple       =       (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. Clip or threshold "predicted x_0"
							if self.config.clip_sample:
											_lowerCamelCase	:     Tuple       =       pred_original_sample.clamp(
											    -self.config.clip_sample_range    , self.config.clip_sample_range    )

							# 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
							_lowerCamelCase	:     Optional[int]       =       (1 - alpha_prod_t_prev) ** 0.5 * pred_epsilon

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

							if not return_dict:
											return (prev_sample, pred_original_sample)
							return DDIMSchedulerOutput(prev_sample=lowercase    , pred_original_sample=lowercase    )






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