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        lowercase_   (					__snake_case		):



							def 	UpperCamelCase	(						self    ):
													_snake_case						:					int    =   self.config_class(**self.inputs_dict    )
													self.parent.assertTrue(hasattr(lowercase_				,    "hidden_sizes"    )    )
													self.parent.assertTrue(hasattr(lowercase_				,    "neck_hidden_sizes"    )    )
													self.parent.assertTrue(hasattr(lowercase_				,    "num_attention_heads"    )    )




class        lowercase_   :



							def __init__(						self				,    lowercase_				,    lowercase_=13				,    lowercase_=32				,    lowercase_=2				,    lowercase_=3				,    lowercase_=640				,    lowercase_=4				,    lowercase_="silu"				,    lowercase_=3				,    lowercase_=32				,    lowercase_=0.1				,    lowercase_=0.1				,    lowercase_=0.1				,    lowercase_=0.02				,    lowercase_=True				,    lowercase_=True				,    lowercase_=10				,    lowercase_=None				,    ):
													_snake_case						:					str    =   parent
													_snake_case						:					Union[str, Any]    =   batch_size
													_snake_case						:					List[str]    =   image_size
													_snake_case						:					Union[str, Any]    =   patch_size
													_snake_case						:					Dict    =   num_channels
													_snake_case						:					Union[str, Any]    =   last_hidden_size
													_snake_case						:					Dict    =   num_attention_heads
													_snake_case						:					str    =   hidden_act
													_snake_case						:					Union[str, Any]    =   conv_kernel_size
													_snake_case						:					int    =   output_stride
													_snake_case						:					Any    =   hidden_dropout_prob
													_snake_case						:					List[str]    =   attention_probs_dropout_prob
													_snake_case						:					Tuple    =   classifier_dropout_prob
													_snake_case						:					Dict    =   use_labels
													_snake_case						:					Union[str, Any]    =   is_training
													_snake_case						:					Dict    =   num_labels
													_snake_case						:					str    =   initializer_range
													_snake_case						:					Union[str, Any]    =   scope



							def 	UpperCamelCase	(						self    ):
													_snake_case						:					Optional[Any]    =   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]    )

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

													_snake_case						:					List[Any]    =   self.get_config()

													return config, pixel_values, labels, pixel_labels



							def 	UpperCamelCase	(						self    ):
													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 	UpperCamelCase	(						self				,    lowercase_				,    lowercase_				,    lowercase_				,    lowercase_    ):
													_snake_case						:					List[str]    =   MobileViTModel(config=lowercase_    )
													model.to(lowercase_    )
													model.eval()
													_snake_case						:					int    =   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 	UpperCamelCase	(						self				,    lowercase_				,    lowercase_				,    lowercase_				,    lowercase_    ):
													_snake_case						:					List[str]    =   self.num_labels
													_snake_case						:					str    =   MobileViTForImageClassification(lowercase_    )
													model.to(lowercase_    )
													model.eval()
													_snake_case						:					str    =   model(lowercase_				,    labels=lowercase_    )
													self.parent.assertEqual(result.logits.shape				,    (self.batch_size, self.num_labels)    )



							def 	UpperCamelCase	(						self				,    lowercase_				,    lowercase_				,    lowercase_				,    lowercase_    ):
													_snake_case						:					Dict    =   self.num_labels
													_snake_case						:					Dict    =   MobileViTForSemanticSegmentation(lowercase_    )
													model.to(lowercase_    )
													model.eval()
													_snake_case						:					Tuple    =   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,
													    )				,    )
													_snake_case						:					Optional[Any]    =   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 	UpperCamelCase	(						self    ):
													_snake_case						:					Union[str, Any]    =   self.prepare_config_and_inputs()
													_snake_case    ,_snake_case    ,_snake_case    ,_snake_case						:					List[str]    =   config_and_inputs
													_snake_case						:					Optional[Any]    =   {"pixel_values": pixel_values}
													return config, inputs_dict




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

							_lowerCamelCase						=		False
							_lowerCamelCase						=		False
							_lowerCamelCase						=		False
							_lowerCamelCase						=		False



							def 	UpperCamelCase	(						self    ):
													_snake_case						:					Dict    =   MobileViTModelTester(self    )
													_snake_case						:					List[Any]    =   MobileViTConfigTester(self				,    config_class=lowercase_				,    has_text_modality=lowercase_    )



							def 	UpperCamelCase	(						self    ):
													self.config_tester.run_common_tests()



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



							@unittest.skip(reason="MobileViT does not support input and output embeddings"    )
							def 	UpperCamelCase	(						self    ):
													pass



							@unittest.skip(reason="MobileViT does not output attentions"    )
							def 	UpperCamelCase	(						self    ):
													pass



							def 	UpperCamelCase	(						self    ):
													_snake_case    ,_snake_case						:					Union[str, Any]    =   self.model_tester.prepare_config_and_inputs_for_common()

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

																			_snake_case						:					Any    =   ["pixel_values"]
																			self.assertListEqual(arg_names[:1]				,    lowercase_    )



							@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests."    )
							def 	UpperCamelCase	(						self    ):
													pass



							def 	UpperCamelCase	(						self    ):
													_snake_case						:					List[str]    =   self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_model(*lowercase_    )



							def 	UpperCamelCase	(						self    ):
													def check_hidden_states_output(lowercase_				,    lowercase_				,    lowercase_    ):
																			_snake_case						:					Any    =   model_class(lowercase_    )
																			model.to(lowercase_    )
																			model.eval()

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

																			_snake_case						:					List[str]    =   outputs.hidden_states

																			_snake_case						:					List[Any]    =   5
																			self.assertEqual(len(lowercase_    )				,    lowercase_    )

																			# MobileViT's feature maps are of shape (batch_size, num_channels, height, width)
																			# with the width and height being successively divided by 2.
																			_snake_case						:					Dict    =   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    )

													_snake_case    ,_snake_case						:					str    =   self.model_tester.prepare_config_and_inputs_for_common()

													for model_class in self.all_model_classes:
																			_snake_case						:					str    =   True
																			check_hidden_states_output(lowercase_				,    lowercase_				,    lowercase_    )

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

																			check_hidden_states_output(lowercase_				,    lowercase_				,    lowercase_    )



							def 	UpperCamelCase	(						self    ):
													_snake_case						:					Union[str, Any]    =   self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_for_image_classification(*lowercase_    )



							def 	UpperCamelCase	(						self    ):
													_snake_case						:					Dict    =   self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_for_semantic_segmentation(*lowercase_    )



							@slow
							def 	UpperCamelCase	(						self    ):
													for model_name in MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																			_snake_case						:					Dict    =   MobileViTModel.from_pretrained(lowercase_    )
																			self.assertIsNotNone(lowercase_    )




def  snake_case						()	->	Optional[Any]:

						'''simple docstring'''



						_snake_case						:					Optional[int]    =   Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"						)
						return image



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



							@cached_property
							def 	UpperCamelCase	(						self    ):
													return MobileViTImageProcessor.from_pretrained("apple/mobilevit-xx-small"    ) if is_vision_available() else None



							@slow
							def 	UpperCamelCase	(						self    ):
													_snake_case						:					Union[str, Any]    =   MobileViTForImageClassification.from_pretrained("apple/mobilevit-xx-small"    ).to(lowercase_    )

													_snake_case						:					Tuple    =   self.default_image_processor
													_snake_case						:					Tuple    =   prepare_img()
													_snake_case						:					Optional[int]    =   image_processor(images=lowercase_				,    return_tensors="pt"    ).to(lowercase_    )

													# forward pass
													with torch.no_grad():
																			_snake_case						:					Optional[int]    =   model(**lowercase_    )

													# verify the logits
													_snake_case						:					Any    =   torch.Size((1, 1_000)    )
													self.assertEqual(outputs.logits.shape				,    lowercase_    )

													_snake_case						:					str    =   torch.tensor([-1.9_364, -1.2_327, -0.4_653]    ).to(lowercase_    )

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



							@slow
							def 	UpperCamelCase	(						self    ):
													_snake_case						:					Union[str, Any]    =   MobileViTForSemanticSegmentation.from_pretrained("apple/deeplabv3-mobilevit-xx-small"    )
													_snake_case						:					Tuple    =   model.to(lowercase_    )

													_snake_case						:					List[str]    =   MobileViTImageProcessor.from_pretrained("apple/deeplabv3-mobilevit-xx-small"    )

													_snake_case						:					Tuple    =   prepare_img()
													_snake_case						:					Optional[Any]    =   image_processor(images=lowercase_				,    return_tensors="pt"    ).to(lowercase_    )

													# forward pass
													with torch.no_grad():
																			_snake_case						:					Tuple    =   model(**lowercase_    )
													_snake_case						:					Tuple    =   outputs.logits

													# verify the logits
													_snake_case						:					Dict    =   torch.Size((1, 21, 32, 32)    )
													self.assertEqual(logits.shape				,    lowercase_    )

													_snake_case						:					Dict    =   torch.tensor(
													    [
													        [[6.9_713, 6.9_786, 7.2_422], [7.2_893, 7.2_825, 7.4_446], [7.6_580, 7.8_797, 7.9_420]],
													        [[-10.6_869, -10.3_250, -10.3_471], [-10.4_228, -9.9_868, -9.7_132], [-11.0_405, -11.0_221, -10.7_318]],
													        [[-3.3_089, -2.8_539, -2.6_740], [-3.2_706, -2.5_621, -2.5_108], [-3.2_534, -2.6_615, -2.6_651]],
													    ]				,    device=lowercase_				,    )

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



							@slow
							def 	UpperCamelCase	(						self    ):
													_snake_case						:					Dict    =   MobileViTForSemanticSegmentation.from_pretrained("apple/deeplabv3-mobilevit-xx-small"    )
													_snake_case						:					Optional[Any]    =   model.to(lowercase_    )

													_snake_case						:					List[str]    =   MobileViTImageProcessor.from_pretrained("apple/deeplabv3-mobilevit-xx-small"    )

													_snake_case						:					int    =   prepare_img()
													_snake_case						:					Optional[int]    =   image_processor(images=lowercase_				,    return_tensors="pt"    ).to(lowercase_    )

													# forward pass
													with torch.no_grad():
																			_snake_case						:					List[Any]    =   model(**lowercase_    )

													_snake_case						:					Union[str, Any]    =   outputs.logits.detach().cpu()

													_snake_case						:					Optional[Any]    =   image_processor.post_process_semantic_segmentation(outputs=lowercase_				,    target_sizes=[(50, 60)]    )
													_snake_case						:					Any    =   torch.Size((50, 60)    )
													self.assertEqual(segmentation[0].shape				,    lowercase_    )

													_snake_case						:					str    =   image_processor.post_process_semantic_segmentation(outputs=lowercase_    )
													_snake_case						:					Dict    =   torch.Size((32, 32)    )
													self.assertEqual(segmentation[0].shape				,    lowercase_    )
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


__SCREAMING_SNAKE_CASE						:   Any         =			{'configuration_mra': ['MRA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MraConfig']}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__SCREAMING_SNAKE_CASE						:   Optional[Any]         =			[
				    'MRA_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'MraForMaskedLM',
				    'MraForMultipleChoice',
				    'MraForQuestionAnswering',
				    'MraForSequenceClassification',
				    'MraForTokenClassification',
				    'MraLayer',
				    'MraModel',
				    'MraPreTrainedModel',
				]


if TYPE_CHECKING:
				from .configuration_mra import MRA_PRETRAINED_CONFIG_ARCHIVE_MAP, MraConfig

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_mra import (
								    MRA_PRETRAINED_MODEL_ARCHIVE_LIST,
								    MraForMaskedLM,
								    MraForMultipleChoice,
								    MraForQuestionAnswering,
								    MraForSequenceClassification,
								    MraForTokenClassification,
								    MraLayer,
								    MraModel,
								    MraPreTrainedModel,
								)
else:
				import sys

				__SCREAMING_SNAKE_CASE						:   str         =			_LazyModule(__name__, globals()['__file__'], _import_structure)