



import importlib
import math
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Tuple, Union

import flax
import jax.numpy as jnp

from ..utils import BaseOutput


UpperCAmelCase__       =	"scheduler_config.json"







class       __lowerCAmelCase   (      __lowerCAmelCase						):
			UpperCamelCase        =					1
			UpperCamelCase        =					2
			UpperCamelCase        =					3
			UpperCamelCase        =					4
			UpperCamelCase        =					5







@dataclass
class       __lowerCAmelCase   (      __lowerCAmelCase						):
			UpperCamelCase        =					42







class       __lowerCAmelCase   :
			UpperCamelCase        =					SCHEDULER_CONFIG_NAME
			UpperCamelCase        =					["dtype"]
			UpperCamelCase        =					[]
			UpperCamelCase        =					True



			@classmethod
			def    _lowerCamelCase	(						cls       :  int			,		A       :  Dict[str, Any] = None			,		A       :  Optional[str] = None			,		A       :  int=False			,		**A       :  Optional[int]			,		)       ->   Optional[int]:



						"""simple docstring"""






						_UpperCAmelCase			=       cls.load_config(
						    pretrained_model_name_or_path=lowerCamelCase__			,		subfolder=lowerCamelCase__			,		return_unused_kwargs=lowerCamelCase__			,		**lowerCamelCase__			,		)
						_UpperCAmelCase			=       cls.from_config(lowerCamelCase__			,		return_unused_kwargs=lowerCamelCase__			,		**lowerCamelCase__)

						if hasattr(lowerCamelCase__			,		'create_state') and getattr(lowerCamelCase__			,		'has_state'			,		lowerCamelCase__):
									_UpperCAmelCase			=       scheduler.create_state()

						if return_unused_kwargs:
									return scheduler, state, unused_kwargs

						return scheduler, state



			def    _lowerCamelCase	(						self       :  Optional[Any]			,		A       :  Union[str, os.PathLike]			,		A       :  bool = False			,		**A       :  Optional[Any])       ->   str:



						"""simple docstring"""






						self.save_config(save_directory=lowerCamelCase__			,		push_to_hub=lowerCamelCase__			,		**lowerCamelCase__)



			@property
			def    _lowerCamelCase	(						self       :  List[str])       ->   Tuple:



						"""simple docstring"""






						return self._get_compatibles()



			@classmethod
			def    _lowerCamelCase	(						cls       :  Optional[int])       ->   Any:



						"""simple docstring"""






						_UpperCAmelCase			=       list(set([cls.__name__] + cls._compatibles))
						_UpperCAmelCase			=       importlib.import_module(__name__.split('.')[0])
						_UpperCAmelCase			=       [
						    getattr(lowerCamelCase__			,		lowerCamelCase__) for c in compatible_classes_str if hasattr(lowerCamelCase__			,		lowerCamelCase__)
						]
						return compatible_classes





def 							A     (					_UpperCAmelCase    :       jnp.ndarray							,       _UpperCAmelCase    :       Tuple[int]   )    ->			Optional[int]:







			'''simple docstring'''
			assert len(_UpperCAmelCase   ) >= x.ndim
			return jnp.broadcast_to(x.reshape(x.shape + (1,) * (len(_UpperCAmelCase   ) - x.ndim)   )							,       _UpperCAmelCase   )





def 							A     (					_UpperCAmelCase    :       int							,       _UpperCAmelCase    :       Dict=0.999							,       _UpperCAmelCase    :       Tuple=jnp.floataa   )    ->			Tuple:







			'''simple docstring'''



			def alpha_bar(_UpperCAmelCase    :       Tuple   ):
						return math.cos((time_step + 0.008) / 1.008 * math.pi / 2   ) ** 2

			_UpperCAmelCase			=       []
			for i in range(_UpperCAmelCase   ):
						_UpperCAmelCase			=       i / num_diffusion_timesteps
						_UpperCAmelCase			=       (i + 1) / num_diffusion_timesteps
						betas.append(min(1 - alpha_bar(_UpperCAmelCase   ) / alpha_bar(_UpperCAmelCase   )							,       _UpperCAmelCase   )   )
			return jnp.array(_UpperCAmelCase							,       dtype=_UpperCAmelCase   )







@flax.struct.dataclass
class       __lowerCAmelCase   :
			UpperCamelCase        =					42
			UpperCamelCase        =					42
			UpperCamelCase        =					42



			@classmethod
			def    _lowerCamelCase	(						cls       :  List[Any]			,		A       :  Union[str, Any])       ->   Optional[Any]:



						"""simple docstring"""






						_UpperCAmelCase			=       scheduler.config

						if config.trained_betas is not None:
									_UpperCAmelCase			=       jnp.asarray(config.trained_betas			,		dtype=scheduler.dtype)
						elif config.beta_schedule == "linear":
									_UpperCAmelCase			=       jnp.linspace(config.beta_start			,		config.beta_end			,		config.num_train_timesteps			,		dtype=scheduler.dtype)
						elif config.beta_schedule == "scaled_linear":
									# this schedule is very specific to the latent diffusion model.
									_UpperCAmelCase			=       (
									    jnp.linspace(
									        config.beta_start**0.5			,		config.beta_end**0.5			,		config.num_train_timesteps			,		dtype=scheduler.dtype)
									    ** 2
									)
						elif config.beta_schedule == "squaredcos_cap_v2":
									# Glide cosine schedule
									_UpperCAmelCase			=       betas_for_alpha_bar(config.num_train_timesteps			,		dtype=scheduler.dtype)
						else:
									raise NotImplementedError(
									    F"beta_schedule {config.beta_schedule} is not implemented for scheduler {scheduler.__class__.__name__}")

						_UpperCAmelCase			=       1.0 - betas

						_UpperCAmelCase			=       jnp.cumprod(lowerCamelCase__			,		axis=0)

						return cls(
						    alphas=lowerCamelCase__			,		betas=lowerCamelCase__			,		alphas_cumprod=lowerCamelCase__			,		)





def 							A     (					_UpperCAmelCase    :       CommonSchedulerState							,       _UpperCAmelCase    :       jnp.ndarray							,       _UpperCAmelCase    :       jnp.ndarray							,       _UpperCAmelCase    :       jnp.ndarray   )    ->			Union[str, Any]:







			'''simple docstring'''
			_UpperCAmelCase			=       state.alphas_cumprod

			_UpperCAmelCase			=       alphas_cumprod[timesteps] ** 0.5
			_UpperCAmelCase			=       sqrt_alpha_prod.flatten()
			_UpperCAmelCase			=       broadcast_to_shape_from_left(_UpperCAmelCase							,       original_samples.shape   )

			_UpperCAmelCase			=       (1 - alphas_cumprod[timesteps]) ** 0.5
			_UpperCAmelCase			=       sqrt_one_minus_alpha_prod.flatten()
			_UpperCAmelCase			=       broadcast_to_shape_from_left(_UpperCAmelCase							,       original_samples.shape   )

			return sqrt_alpha_prod, sqrt_one_minus_alpha_prod





def 							A     (					_UpperCAmelCase    :       CommonSchedulerState							,       _UpperCAmelCase    :       jnp.ndarray							,       _UpperCAmelCase    :       jnp.ndarray							,       _UpperCAmelCase    :       jnp.ndarray   )    ->			Dict:







			'''simple docstring'''
			_UpperCAmelCase			=       get_sqrt_alpha_prod(_UpperCAmelCase							,       _UpperCAmelCase							,       _UpperCAmelCase							,       _UpperCAmelCase   )
			_UpperCAmelCase			=       sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
			return noisy_samples





def 							A     (					_UpperCAmelCase    :       CommonSchedulerState							,       _UpperCAmelCase    :       jnp.ndarray							,       _UpperCAmelCase    :       jnp.ndarray							,       _UpperCAmelCase    :       jnp.ndarray   )    ->			Dict:







			'''simple docstring'''
			_UpperCAmelCase			=       get_sqrt_alpha_prod(_UpperCAmelCase							,       _UpperCAmelCase							,       _UpperCAmelCase							,       _UpperCAmelCase   )
			_UpperCAmelCase			=       sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
			return velocity






import inspect
import unittest

from transformers import MobileViTConfig
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_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 MobileViTForImageClassification, MobileViTForSemanticSegmentation, MobileViTModel
							from transformers.models.mobilevit.modeling_mobilevit import MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
							from PIL import Image

							from transformers import MobileViTImageProcessor







class       __lowerCAmelCase   (      A						):



			def    _lowerCamelCase	(						self       :  List[str])       ->   int:



						"""simple docstring"""






						_UpperCAmelCase			=       self.config_class(**self.inputs_dict)
						self.parent.assertTrue(hasattr(A			,		'hidden_sizes'))
						self.parent.assertTrue(hasattr(A			,		'neck_hidden_sizes'))
						self.parent.assertTrue(hasattr(A			,		'num_attention_heads'))







class       __lowerCAmelCase   :



			def __init__(						self       :  int			,		A       :  Tuple			,		A       :  List[str]=13			,		A       :  List[str]=32			,		A       :  List[str]=2			,		A       :  List[str]=3			,		A       :  List[Any]=6_40			,		A       :  Any=4			,		A       :  int="silu"			,		A       :  int=3			,		A       :  Dict=32			,		A       :  List[Any]=0.1			,		A       :  Optional[Any]=0.1			,		A       :  Optional[int]=0.1			,		A       :  List[str]=0.0_2			,		A       :  int=True			,		A       :  Any=True			,		A       :  List[str]=10			,		A       :  Tuple=None			,		)       ->   Dict:



						"""simple docstring"""






						_UpperCAmelCase			=       parent
						_UpperCAmelCase			=       batch_size
						_UpperCAmelCase			=       image_size
						_UpperCAmelCase			=       patch_size
						_UpperCAmelCase			=       num_channels
						_UpperCAmelCase			=       last_hidden_size
						_UpperCAmelCase			=       num_attention_heads
						_UpperCAmelCase			=       hidden_act
						_UpperCAmelCase			=       conv_kernel_size
						_UpperCAmelCase			=       output_stride
						_UpperCAmelCase			=       hidden_dropout_prob
						_UpperCAmelCase			=       attention_probs_dropout_prob
						_UpperCAmelCase			=       classifier_dropout_prob
						_UpperCAmelCase			=       use_labels
						_UpperCAmelCase			=       is_training
						_UpperCAmelCase			=       num_labels
						_UpperCAmelCase			=       initializer_range
						_UpperCAmelCase			=       scope



			def    _lowerCamelCase	(						self       :  Union[str, Any])       ->   Any:



						"""simple docstring"""






						_UpperCAmelCase			=       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

						_UpperCAmelCase			=       None
						_UpperCAmelCase			=       None
						if self.use_labels:
									_UpperCAmelCase			=       ids_tensor([self.batch_size]			,		self.num_labels)
									_UpperCAmelCase			=       ids_tensor([self.batch_size, self.image_size, self.image_size]			,		self.num_labels)

						_UpperCAmelCase			=       self.get_config()

						return config, pixel_values, labels, pixel_labels



			def    _lowerCamelCase	(						self       :  str)       ->   int:



						"""simple docstring"""






						return MobileViTConfig(
						    image_size=self.image_size			,		patch_size=self.patch_size			,		num_channels=self.num_channels			,		num_attention_heads=self.num_attention_heads			,		hidden_act=self.hidden_act			,		conv_kernel_size=self.conv_kernel_size			,		output_stride=self.output_stride			,		hidden_dropout_prob=self.hidden_dropout_prob			,		attention_probs_dropout_prob=self.attention_probs_dropout_prob			,		classifier_dropout_prob=self.classifier_dropout_prob			,		initializer_range=self.initializer_range			,		)



			def    _lowerCamelCase	(						self       :  List[Any]			,		A       :  Dict			,		A       :  Tuple			,		A       :  int			,		A       :  Tuple)       ->   Union[str, Any]:



						"""simple docstring"""






						_UpperCAmelCase			=       MobileViTModel(config=A)
						model.to(A)
						model.eval()
						_UpperCAmelCase			=       model(A)
						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    _lowerCamelCase	(						self       :  int			,		A       :  Any			,		A       :  List[Any]			,		A       :  List[Any]			,		A       :  Optional[int])       ->   Union[str, Any]:



						"""simple docstring"""






						_UpperCAmelCase			=       self.num_labels
						_UpperCAmelCase			=       MobileViTForImageClassification(A)
						model.to(A)
						model.eval()
						_UpperCAmelCase			=       model(A			,		labels=A)
						self.parent.assertEqual(result.logits.shape			,		(self.batch_size, self.num_labels))



			def    _lowerCamelCase	(						self       :  int			,		A       :  Tuple			,		A       :  Optional[Any]			,		A       :  Union[str, Any]			,		A       :  List[Any])       ->   int:



						"""simple docstring"""






						_UpperCAmelCase			=       self.num_labels
						_UpperCAmelCase			=       MobileViTForSemanticSegmentation(A)
						model.to(A)
						model.eval()
						_UpperCAmelCase			=       model(A)
						self.parent.assertEqual(
						    result.logits.shape			,		(
						        self.batch_size,
						        self.num_labels,
						        self.image_size // self.output_stride,
						        self.image_size // self.output_stride,
						    )			,		)
						_UpperCAmelCase			=       model(A			,		labels=A)
						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    _lowerCamelCase	(						self       :  int)       ->   Any:



						"""simple docstring"""






						_UpperCAmelCase			=       self.prepare_config_and_inputs()
						_UpperCAmelCase	,					_UpperCAmelCase	,					_UpperCAmelCase	,					_UpperCAmelCase			=       config_and_inputs
						_UpperCAmelCase			=       {'pixel_values': pixel_values}
						return config, inputs_dict







@require_torch
class       __lowerCAmelCase   (      A    ,						A    ,						unittest.TestCase						):
			UpperCamelCase        =					(
			    (MobileViTModel, MobileViTForImageClassification, MobileViTForSemanticSegmentation)
			    if is_torch_available()
			    else ()
			)
			UpperCamelCase        =					(
			    {
			        '''feature-extraction''': MobileViTModel,
			        '''image-classification''': MobileViTForImageClassification,
			        '''image-segmentation''': MobileViTForSemanticSegmentation,
			    }
			    if is_torch_available()
			    else {}
			)

			UpperCamelCase        =					False
			UpperCamelCase        =					False
			UpperCamelCase        =					False
			UpperCamelCase        =					False



			def    _lowerCamelCase	(						self       :  str)       ->   Tuple:



						"""simple docstring"""






						_UpperCAmelCase			=       MobileViTModelTester(self)
						_UpperCAmelCase			=       MobileViTConfigTester(self			,		config_class=A			,		has_text_modality=A)



			def    _lowerCamelCase	(						self       :  Optional[int])       ->   str:



						"""simple docstring"""






						self.config_tester.run_common_tests()



			@unittest.skip(reason='MobileViT does not use inputs_embeds')
			def    _lowerCamelCase	(						self       :  Tuple)       ->   Dict:



						"""simple docstring"""






						pass



			@unittest.skip(reason='MobileViT does not support input and output embeddings')
			def    _lowerCamelCase	(						self       :  Optional[Any])       ->   List[Any]:



						"""simple docstring"""






						pass



			@unittest.skip(reason='MobileViT does not output attentions')
			def    _lowerCamelCase	(						self       :  Any)       ->   Optional[Any]:



						"""simple docstring"""






						pass



			def    _lowerCamelCase	(						self       :  List[Any])       ->   Optional[Any]:



						"""simple docstring"""






						_UpperCAmelCase	,					_UpperCAmelCase			=       self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
									_UpperCAmelCase			=       model_class(A)
									_UpperCAmelCase			=       inspect.signature(model.forward)
									# signature.parameters is an OrderedDict => so arg_names order is deterministic
									_UpperCAmelCase			=       [*signature.parameters.keys()]

									_UpperCAmelCase			=       ['pixel_values']
									self.assertListEqual(arg_names[:1]			,		A)



			@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.')
			def    _lowerCamelCase	(						self       :  Union[str, Any])       ->   List[str]:



						"""simple docstring"""






						pass



			def    _lowerCamelCase	(						self       :  Tuple)       ->   str:



						"""simple docstring"""






						_UpperCAmelCase			=       self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*A)



			def    _lowerCamelCase	(						self       :  Optional[int])       ->   Any:



						"""simple docstring"""






						def check_hidden_states_output(A       :  List[str]			,		A       :  Union[str, Any]			,		A       :  int):
									_UpperCAmelCase			=       model_class(A)
									model.to(A)
									model.eval()

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

									_UpperCAmelCase			=       outputs.hidden_states

									_UpperCAmelCase			=       5
									self.assertEqual(len(A)			,		A)

									# MobileViT's feature maps are of shape (batch_size, num_channels, height, width)
									# with the width and height being successively divided by 2.
									_UpperCAmelCase			=       2
									for i in range(len(A)):
												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)

						_UpperCAmelCase	,					_UpperCAmelCase			=       self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
									_UpperCAmelCase			=       True
									check_hidden_states_output(A			,		A			,		A)

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

									check_hidden_states_output(A			,		A			,		A)



			def    _lowerCamelCase	(						self       :  List[str])       ->   Union[str, Any]:



						"""simple docstring"""






						_UpperCAmelCase			=       self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_image_classification(*A)



			def    _lowerCamelCase	(						self       :  int)       ->   Tuple:



						"""simple docstring"""






						_UpperCAmelCase			=       self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_semantic_segmentation(*A)



			@slow
			def    _lowerCamelCase	(						self       :  Optional[int])       ->   Union[str, Any]:



						"""simple docstring"""






						for model_name in MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									_UpperCAmelCase			=       MobileViTModel.from_pretrained(A)
									self.assertIsNotNone(A)





def 							A     (					)    ->			List[str]:







			'''simple docstring'''
			_UpperCAmelCase			=       Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'   )
			return image







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



			@cached_property
			def    _lowerCamelCase	(						self       :  Tuple)       ->   Dict:



						"""simple docstring"""






						return MobileViTImageProcessor.from_pretrained('apple/mobilevit-xx-small') if is_vision_available() else None



			@slow
			def    _lowerCamelCase	(						self       :  List[Any])       ->   Optional[Any]:



						"""simple docstring"""






						_UpperCAmelCase			=       MobileViTForImageClassification.from_pretrained('apple/mobilevit-xx-small').to(A)

						_UpperCAmelCase			=       self.default_image_processor
						_UpperCAmelCase			=       prepare_img()
						_UpperCAmelCase			=       image_processor(images=A			,		return_tensors='pt').to(A)

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

						# verify the logits
						_UpperCAmelCase			=       torch.Size((1, 10_00))
						self.assertEqual(outputs.logits.shape			,		A)

						_UpperCAmelCase			=       torch.tensor([-1.9_3_6_4, -1.2_3_2_7, -0.4_6_5_3]).to(A)

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



			@slow
			def    _lowerCamelCase	(						self       :  List[Any])       ->   List[str]:



						"""simple docstring"""






						_UpperCAmelCase			=       MobileViTForSemanticSegmentation.from_pretrained('apple/deeplabv3-mobilevit-xx-small')
						_UpperCAmelCase			=       model.to(A)

						_UpperCAmelCase			=       MobileViTImageProcessor.from_pretrained('apple/deeplabv3-mobilevit-xx-small')

						_UpperCAmelCase			=       prepare_img()
						_UpperCAmelCase			=       image_processor(images=A			,		return_tensors='pt').to(A)

						# forward pass
						with torch.no_grad():
									_UpperCAmelCase			=       model(**A)
						_UpperCAmelCase			=       outputs.logits

						# verify the logits
						_UpperCAmelCase			=       torch.Size((1, 21, 32, 32))
						self.assertEqual(logits.shape			,		A)

						_UpperCAmelCase			=       torch.tensor(
						    [
						        [[6.9_7_1_3, 6.9_7_8_6, 7.2_4_2_2], [7.2_8_9_3, 7.2_8_2_5, 7.4_4_4_6], [7.6_5_8_0, 7.8_7_9_7, 7.9_4_2_0]],
						        [[-1_0.6_8_6_9, -1_0.3_2_5_0, -1_0.3_4_7_1], [-1_0.4_2_2_8, -9.9_8_6_8, -9.7_1_3_2], [-1_1.0_4_0_5, -1_1.0_2_2_1, -1_0.7_3_1_8]],
						        [[-3.3_0_8_9, -2.8_5_3_9, -2.6_7_4_0], [-3.2_7_0_6, -2.5_6_2_1, -2.5_1_0_8], [-3.2_5_3_4, -2.6_6_1_5, -2.6_6_5_1]],
						    ]			,		device=A			,		)

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



			@slow
			def    _lowerCamelCase	(						self       :  List[Any])       ->   List[str]:



						"""simple docstring"""






						_UpperCAmelCase			=       MobileViTForSemanticSegmentation.from_pretrained('apple/deeplabv3-mobilevit-xx-small')
						_UpperCAmelCase			=       model.to(A)

						_UpperCAmelCase			=       MobileViTImageProcessor.from_pretrained('apple/deeplabv3-mobilevit-xx-small')

						_UpperCAmelCase			=       prepare_img()
						_UpperCAmelCase			=       image_processor(images=A			,		return_tensors='pt').to(A)

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

						_UpperCAmelCase			=       outputs.logits.detach().cpu()

						_UpperCAmelCase			=       image_processor.post_process_semantic_segmentation(outputs=A			,		target_sizes=[(50, 60)])
						_UpperCAmelCase			=       torch.Size((50, 60))
						self.assertEqual(segmentation[0].shape			,		A)

						_UpperCAmelCase			=       image_processor.post_process_semantic_segmentation(outputs=A)
						_UpperCAmelCase			=       torch.Size((32, 32))
						self.assertEqual(segmentation[0].shape			,		A)
