


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 	a__       (							a__						):





							'''simple docstring'''






							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							str:
														lowerCAmelCase__      =			self.config_class(**self.inputs_dict     )
														self.parent.assertTrue(hasattr(lowerCamelCase_      ,					'''hidden_sizes'''     )     )
														self.parent.assertTrue(hasattr(lowerCamelCase_      ,					'''neck_hidden_sizes'''     )     )
														self.parent.assertTrue(hasattr(lowerCamelCase_      ,					'''num_attention_heads'''     )     )




class 	a__       :





							'''simple docstring'''






							def __init__(	self      ,					lowerCamelCase_      ,					lowerCamelCase_=13      ,					lowerCamelCase_=32      ,					lowerCamelCase_=2      ,					lowerCamelCase_=3      ,					lowerCamelCase_=6_40      ,					lowerCamelCase_=4      ,					lowerCamelCase_="silu"      ,					lowerCamelCase_=3      ,					lowerCamelCase_=32      ,					lowerCamelCase_=0.1      ,					lowerCamelCase_=0.1      ,					lowerCamelCase_=0.1      ,					lowerCamelCase_=0.02      ,					lowerCamelCase_=True      ,					lowerCamelCase_=True      ,					lowerCamelCase_=10      ,					lowerCamelCase_=None      ,					) ->							Tuple:
														lowerCAmelCase__      =			parent
														lowerCAmelCase__      =			batch_size
														lowerCAmelCase__      =			image_size
														lowerCAmelCase__      =			patch_size
														lowerCAmelCase__      =			num_channels
														lowerCAmelCase__      =			last_hidden_size
														lowerCAmelCase__      =			num_attention_heads
														lowerCAmelCase__      =			hidden_act
														lowerCAmelCase__      =			conv_kernel_size
														lowerCAmelCase__      =			output_stride
														lowerCAmelCase__      =			hidden_dropout_prob
														lowerCAmelCase__      =			attention_probs_dropout_prob
														lowerCAmelCase__      =			classifier_dropout_prob
														lowerCAmelCase__      =			use_labels
														lowerCAmelCase__      =			is_training
														lowerCAmelCase__      =			num_labels
														lowerCAmelCase__      =			initializer_range
														lowerCAmelCase__      =			scope







							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							List[str]:
														lowerCAmelCase__      =			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]     )

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

														lowerCAmelCase__      =			self.get_config()

														return config, pixel_values, labels, pixel_labels







							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							Dict:
														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 						__SCREAMING_SNAKE_CASE							(	self      ,					lowerCamelCase_      ,					lowerCamelCase_      ,					lowerCamelCase_      ,					lowerCamelCase_     ) ->							Union[str, Any]:
														lowerCAmelCase__      =			MobileViTModel(config=lowerCamelCase_     )
														model.to(lowerCamelCase_     )
														model.eval()
														lowerCAmelCase__      =			model(lowerCamelCase_     )
														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 						__SCREAMING_SNAKE_CASE							(	self      ,					lowerCamelCase_      ,					lowerCamelCase_      ,					lowerCamelCase_      ,					lowerCamelCase_     ) ->							Tuple:
														lowerCAmelCase__      =			self.num_labels
														lowerCAmelCase__      =			MobileViTForImageClassification(lowerCamelCase_     )
														model.to(lowerCamelCase_     )
														model.eval()
														lowerCAmelCase__      =			model(lowerCamelCase_      ,					labels=lowerCamelCase_     )
														self.parent.assertEqual(result.logits.shape      ,					(self.batch_size, self.num_labels)     )







							def 						__SCREAMING_SNAKE_CASE							(	self      ,					lowerCamelCase_      ,					lowerCamelCase_      ,					lowerCamelCase_      ,					lowerCamelCase_     ) ->							Optional[int]:
														lowerCAmelCase__      =			self.num_labels
														lowerCAmelCase__      =			MobileViTForSemanticSegmentation(lowerCamelCase_     )
														model.to(lowerCamelCase_     )
														model.eval()
														lowerCAmelCase__      =			model(lowerCamelCase_     )
														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__      =			model(lowerCamelCase_      ,					labels=lowerCamelCase_     )
														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 						__SCREAMING_SNAKE_CASE							(	self     ) ->							str:
														lowerCAmelCase__      =			self.prepare_config_and_inputs()
														lowerCAmelCase__     ,      lowerCAmelCase__     ,      lowerCAmelCase__     ,      lowerCAmelCase__      =			config_and_inputs
														lowerCAmelCase__      =			{'''pixel_values''': pixel_values}
														return config, inputs_dict




@require_torch
class 	a__       (							a__       ,       a__       ,       unittest.TestCase						):





							'''simple docstring'''






							lowercase__    :     Optional[Any]					=   (
							    (MobileViTModel, MobileViTForImageClassification, MobileViTForSemanticSegmentation)
							    if is_torch_available()
							    else ()
							)
							lowercase__    :     List[Any]					=   (
							    {
							        "feature-extraction": MobileViTModel,
							        "image-classification": MobileViTForImageClassification,
							        "image-segmentation": MobileViTForSemanticSegmentation,
							    }
							    if is_torch_available()
							    else {}
							)

							lowercase__    :     Tuple					=   False
							lowercase__    :     Any					=   False
							lowercase__    :     Optional[Any]					=   False
							lowercase__    :     Union[str, Any]					=   False







							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							Any:
														lowerCAmelCase__      =			MobileViTModelTester(self     )
														lowerCAmelCase__      =			MobileViTConfigTester(self      ,					config_class=lowerCamelCase_      ,					has_text_modality=lowerCamelCase_     )







							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							Tuple:
														self.config_tester.run_common_tests()







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







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







							@unittest.skip(reason='''MobileViT does not output attentions'''     )
							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							Union[str, Any]:
														pass







							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							int:
														lowerCAmelCase__     ,      lowerCAmelCase__      =			self.model_tester.prepare_config_and_inputs_for_common()

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

																					lowerCAmelCase__      =			['''pixel_values''']
																					self.assertListEqual(arg_names[:1]      ,					lowerCamelCase_     )







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







							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							Optional[Any]:
														lowerCAmelCase__      =			self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_model(*lowerCamelCase_     )







							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							Optional[Any]:
														def check_hidden_states_output(lowerCamelCase_      ,					lowerCamelCase_      ,					lowerCamelCase_     ):
																					lowerCAmelCase__      =			model_class(lowerCamelCase_     )
																					model.to(lowerCamelCase_     )
																					model.eval()

																					with torch.no_grad():
																												lowerCAmelCase__      =			model(**self._prepare_for_class(lowerCamelCase_      ,					lowerCamelCase_     )     )

																					lowerCAmelCase__      =			outputs.hidden_states

																					lowerCAmelCase__      =			5
																					self.assertEqual(len(lowerCamelCase_     )      ,					lowerCamelCase_     )

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

														for model_class in self.all_model_classes:
																					lowerCAmelCase__      =			True
																					check_hidden_states_output(lowerCamelCase_      ,					lowerCamelCase_      ,					lowerCamelCase_     )

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

																					check_hidden_states_output(lowerCamelCase_      ,					lowerCamelCase_      ,					lowerCamelCase_     )







							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							List[Any]:
														lowerCAmelCase__      =			self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_for_image_classification(*lowerCamelCase_     )







							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							Union[str, Any]:
														lowerCAmelCase__      =			self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_for_semantic_segmentation(*lowerCamelCase_     )







							@slow
							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							List[Any]:
														for model_name in MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																					lowerCAmelCase__      =			MobileViTModel.from_pretrained(lowerCamelCase_     )
																					self.assertIsNotNone(lowerCamelCase_     )


def       _snake_case     (  )		->					Any:
							lowerCAmelCase__      =			Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
							return image






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





							'''simple docstring'''






							@cached_property
							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							Dict:
														return MobileViTImageProcessor.from_pretrained('''apple/mobilevit-xx-small'''     ) if is_vision_available() else None







							@slow
							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							Optional[Any]:
														lowerCAmelCase__      =			MobileViTForImageClassification.from_pretrained('''apple/mobilevit-xx-small'''     ).to(lowerCamelCase_     )

														lowerCAmelCase__      =			self.default_image_processor
														lowerCAmelCase__      =			prepare_img()
														lowerCAmelCase__      =			image_processor(images=lowerCamelCase_      ,					return_tensors='''pt'''     ).to(lowerCamelCase_     )

														# forward pass
														with torch.no_grad():
																					lowerCAmelCase__      =			model(**lowerCamelCase_     )

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

														lowerCAmelCase__      =			torch.tensor([-1.9_364, -1.2_327, -0.4_653]     ).to(lowerCamelCase_     )

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







							@slow
							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							Any:
														lowerCAmelCase__      =			MobileViTForSemanticSegmentation.from_pretrained('''apple/deeplabv3-mobilevit-xx-small'''     )
														lowerCAmelCase__      =			model.to(lowerCamelCase_     )

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

														lowerCAmelCase__      =			prepare_img()
														lowerCAmelCase__      =			image_processor(images=lowerCamelCase_      ,					return_tensors='''pt'''     ).to(lowerCamelCase_     )

														# forward pass
														with torch.no_grad():
																					lowerCAmelCase__      =			model(**lowerCamelCase_     )
														lowerCAmelCase__      =			outputs.logits

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

														lowerCAmelCase__      =			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=lowerCamelCase_      ,					)

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







							@slow
							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							Optional[Any]:
														lowerCAmelCase__      =			MobileViTForSemanticSegmentation.from_pretrained('''apple/deeplabv3-mobilevit-xx-small'''     )
														lowerCAmelCase__      =			model.to(lowerCamelCase_     )

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

														lowerCAmelCase__      =			prepare_img()
														lowerCAmelCase__      =			image_processor(images=lowerCamelCase_      ,					return_tensors='''pt'''     ).to(lowerCamelCase_     )

														# forward pass
														with torch.no_grad():
																					lowerCAmelCase__      =			model(**lowerCamelCase_     )

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

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

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



'''simple docstring'''






import jax.numpy as jnp

from ...utils import logging
from ..ta.modeling_flax_ta import FlaxTaEncoderModel, FlaxTaForConditionalGeneration, FlaxTaModel
from .configuration_mta import MTaConfig


__UpperCAmelCase					       =							logging.get_logger(__name__)

__UpperCAmelCase					       =							'''T5Config'''


def       _snake_case     (  A				,					A				,					A )		->					jnp.ndarray:
							lowerCAmelCase__      =			jnp.zeros_like(A )
							lowerCAmelCase__      =			shifted_input_ids.at[:, 1:].set(input_ids[:, :-1] )
							lowerCAmelCase__      =			shifted_input_ids.at[:, 0].set(A )

							lowerCAmelCase__      =			jnp.where(shifted_input_ids == -100				,					A				,					A )
							return shifted_input_ids




class 	a__       (							a__						):





							'''simple docstring'''






							lowercase__    :     int					=   "mt5"
							lowercase__    :     Dict					=   MTaConfig




class 	a__       (							a__						):





							'''simple docstring'''






							lowercase__    :     int					=   "mt5"
							lowercase__    :     Any					=   MTaConfig




class 	a__       (							a__						):





							'''simple docstring'''






							lowercase__    :     Union[str, Any]					=   "mt5"
							lowercase__    :     Tuple					=   MTaConfig