




'''simple docstring'''
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   __UpperCAmelCase				(     lowerCAmelCase				):



					'''simple docstring'''





					def     __snake_case						(	self     :						int)    ->							List[str]:
								A_									= 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   __UpperCAmelCase				:



					'''simple docstring'''





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



					def     __snake_case						(	self     :						Union[str, Any])    ->							str:
								A_									= floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

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

								A_									= self.get_config()

								return config, pixel_values, labels, pixel_labels



					def     __snake_case						(	self     :						Union[str, Any])    ->							List[str]:
								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     __snake_case						(	self     :						Dict      ,       _lowercase     :						Any      ,       _lowercase     :						Union[str, Any]      ,       _lowercase     :						Optional[int]      ,       _lowercase     :						str)    ->							int:
								A_									= MobileViTModel(config=_lowercase)
								model.to(_lowercase)
								model.eval()
								A_									= 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     __snake_case						(	self     :						Optional[int]      ,       _lowercase     :						Optional[Any]      ,       _lowercase     :						str      ,       _lowercase     :						Tuple      ,       _lowercase     :						Any)    ->							Any:
								A_									= self.num_labels
								A_									= MobileViTForImageClassification(_lowercase)
								model.to(_lowercase)
								model.eval()
								A_									= model(_lowercase      ,       labels=_lowercase)
								self.parent.assertEqual(result.logits.shape      ,       (self.batch_size, self.num_labels))



					def     __snake_case						(	self     :						str      ,       _lowercase     :						Union[str, Any]      ,       _lowercase     :						Union[str, Any]      ,       _lowercase     :						Tuple      ,       _lowercase     :						int)    ->							List[str]:
								A_									= self.num_labels
								A_									= MobileViTForSemanticSegmentation(_lowercase)
								model.to(_lowercase)
								model.eval()
								A_									= 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,
								    )      ,       )
								A_									= 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     __snake_case						(	self     :						Optional[int])    ->							Any:
								A_									= self.prepare_config_and_inputs()
								A_		, A_		, A_		, A_									= config_and_inputs
								A_									= {'pixel_values': pixel_values}
								return config, inputs_dict





@require_torch
class   __UpperCAmelCase				(     lowerCAmelCase			,lowerCAmelCase			,unittest.TestCase				):



					'''simple docstring'''





					_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     __snake_case						(	self     :						Dict)    ->							List[str]:
								A_									= MobileViTModelTester(self)
								A_									= MobileViTConfigTester(self      ,       config_class=_lowercase      ,       has_text_modality=_lowercase)



					def     __snake_case						(	self     :						str)    ->							Optional[int]:
								self.config_tester.run_common_tests()



					@unittest.skip(reason='MobileViT does not use inputs_embeds')
					def     __snake_case						(	self     :						Dict)    ->							List[str]:
								pass



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



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



					def     __snake_case						(	self     :						int)    ->							int:
								A_		, A_									= self.model_tester.prepare_config_and_inputs_for_common()

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

											A_									= ['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     __snake_case						(	self     :						Optional[Any])    ->							Any:
								pass



					def     __snake_case						(	self     :						List[str])    ->							List[str]:
								A_									= self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*_lowercase)



					def     __snake_case						(	self     :						int)    ->							List[Any]:
								def check_hidden_states_output(_lowercase     :						str      ,       _lowercase     :						List[str]      ,       _lowercase     :						Any):
											A_									= model_class(_lowercase)
											model.to(_lowercase)
											model.eval()

											with torch.no_grad():
														A_									= model(**self._prepare_for_class(_lowercase      ,       _lowercase))

											A_									= outputs.hidden_states

											A_									= 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.
											A_									= 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)

								A_		, A_									= self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
											A_									= True
											check_hidden_states_output(_lowercase      ,       _lowercase      ,       _lowercase)

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

											check_hidden_states_output(_lowercase      ,       _lowercase      ,       _lowercase)



					def     __snake_case						(	self     :						List[Any])    ->							Dict:
								A_									= self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_image_classification(*_lowercase)



					def     __snake_case						(	self     :						Dict)    ->							List[Any]:
								A_									= self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_semantic_segmentation(*_lowercase)



					@slow
					def     __snake_case						(	self     :						int)    ->							Optional[int]:
								for model_name in MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											A_									= MobileViTModel.from_pretrained(_lowercase)
											self.assertIsNotNone(_lowercase)





def 			lowerCamelCase(							)		->		Tuple:
			A_									= Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'     )
			return image






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



					'''simple docstring'''





					@cached_property
					def     __snake_case						(	self     :						List[str])    ->							Optional[int]:
								return MobileViTImageProcessor.from_pretrained('apple/mobilevit-xx-small') if is_vision_available() else None



					@slow
					def     __snake_case						(	self     :						Optional[int])    ->							str:
								A_									= MobileViTForImageClassification.from_pretrained('apple/mobilevit-xx-small').to(_lowercase)

								A_									= self.default_image_processor
								A_									= prepare_img()
								A_									= image_processor(images=_lowercase      ,       return_tensors='pt').to(_lowercase)

								# forward pass
								with torch.no_grad():
											A_									= model(**_lowercase)

								# verify the logits
								A_									= torch.Size((1, 1_000))
								self.assertEqual(outputs.logits.shape      ,       _lowercase)

								A_									= torch.tensor([-1.93_64, -1.23_27, -0.46_53]).to(_lowercase)

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



					@slow
					def     __snake_case						(	self     :						Optional[int])    ->							List[Any]:
								A_									= MobileViTForSemanticSegmentation.from_pretrained('apple/deeplabv3-mobilevit-xx-small')
								A_									= model.to(_lowercase)

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

								A_									= prepare_img()
								A_									= image_processor(images=_lowercase      ,       return_tensors='pt').to(_lowercase)

								# forward pass
								with torch.no_grad():
											A_									= model(**_lowercase)
								A_									= outputs.logits

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

								A_									= torch.tensor(
								    [
								        [[6.97_13, 6.97_86, 7.24_22], [7.28_93, 7.28_25, 7.44_46], [7.65_80, 7.87_97, 7.94_20]],
								        [[-10.68_69, -10.32_50, -10.34_71], [-10.42_28, -9.98_68, -9.71_32], [-11.04_05, -11.02_21, -10.73_18]],
								        [[-3.30_89, -2.85_39, -2.67_40], [-3.27_06, -2.56_21, -2.51_08], [-3.25_34, -2.66_15, -2.66_51]],
								    ]      ,       device=_lowercase      ,       )

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



					@slow
					def     __snake_case						(	self     :						Union[str, Any])    ->							Optional[int]:
								A_									= MobileViTForSemanticSegmentation.from_pretrained('apple/deeplabv3-mobilevit-xx-small')
								A_									= model.to(_lowercase)

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

								A_									= prepare_img()
								A_									= image_processor(images=_lowercase      ,       return_tensors='pt').to(_lowercase)

								# forward pass
								with torch.no_grad():
											A_									= model(**_lowercase)

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

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

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










'''simple docstring'''
import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_torch_available, is_vision_available


if is_torch_available():
							import torch


if is_vision_available():
							from PIL import Image

							from transformers import MgpstrProcessor, ViTImageProcessor
@require_torch
@require_vision
class   __UpperCAmelCase				(     unittest.TestCase				):



					'''simple docstring'''





					_UpperCamelCase							=		ViTImageProcessor if is_vision_available() else None



					@property
					def     __snake_case						(	self     :						List[str])    ->							Optional[int]:
								return self.image_processor_tester.prepare_image_processor_dict()



					def     __snake_case						(	self     :						Optional[Any])    ->							List[str]:
								A_									= (3, 32, 128)
								A_									= tempfile.mkdtemp()

								# fmt: off
								A_									= ['[GO]', '[s]', '0', '1', '2', '3', '4', '5', '6', '7', '8', '9', 'a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 't', 'u', 'v', 'w', 'x', 'y', 'z']
								# fmt: on
								A_									= dict(zip(_lowercase      ,       range(len(_lowercase))))

								A_									= os.path.join(self.tmpdirname      ,       VOCAB_FILES_NAMES['vocab_file'])
								with open(self.vocab_file      ,       'w'      ,       encoding='utf-8') as fp:
											fp.write(json.dumps(_lowercase) + '\n')

								A_									= {
								    'do_normalize': False,
								    'do_resize': True,
								    'image_processor_type': 'ViTImageProcessor',
								    'resample': 3,
								    'size': {'height': 32, 'width': 128},
								}
								A_									= os.path.join(self.tmpdirname      ,       _lowercase)
								with open(self.image_processor_file      ,       'w'      ,       encoding='utf-8') as fp:
											json.dump(_lowercase      ,       _lowercase)



					def     __snake_case						(	self     :						int      ,       **_lowercase     :						Optional[int])    ->							int:
								return MgpstrTokenizer.from_pretrained(self.tmpdirname      ,       **_lowercase)



					def     __snake_case						(	self     :						Optional[int]      ,       **_lowercase     :						Optional[int])    ->							Union[str, Any]:
								return ViTImageProcessor.from_pretrained(self.tmpdirname      ,       **_lowercase)



					def     __snake_case						(	self     :						Dict)    ->							str:
								shutil.rmtree(self.tmpdirname)



					def     __snake_case						(	self     :						Union[str, Any])    ->							Any:
								A_									= np.random.randint(255      ,       size=(3, 30, 400)      ,       dtype=np.uinta)

								A_									= Image.fromarray(np.moveaxis(_lowercase      ,       0      ,       -1))

								return image_input



					def     __snake_case						(	self     :						Optional[Any])    ->							List[Any]:
								A_									= self.get_tokenizer()
								A_									= self.get_image_processor()

								A_									= MgpstrProcessor(tokenizer=_lowercase      ,       image_processor=_lowercase)
								processor.save_pretrained(self.tmpdirname)
								A_									= MgpstrProcessor.from_pretrained(self.tmpdirname      ,       use_fast=_lowercase)

								self.assertEqual(processor.char_tokenizer.get_vocab()      ,       tokenizer.get_vocab())
								self.assertIsInstance(processor.char_tokenizer      ,       _lowercase)

								self.assertEqual(processor.image_processor.to_json_string()      ,       image_processor.to_json_string())
								self.assertIsInstance(processor.image_processor      ,       _lowercase)



					def     __snake_case						(	self     :						Union[str, Any])    ->							Optional[Any]:
								A_									= self.get_tokenizer()
								A_									= self.get_image_processor()

								A_									= MgpstrProcessor(tokenizer=_lowercase      ,       image_processor=_lowercase)
								processor.save_pretrained(self.tmpdirname)

								A_									= self.get_tokenizer(bos_token='(BOS)'      ,       eos_token='(EOS)')
								A_									= self.get_image_processor(do_normalize=_lowercase      ,       padding_value=1.0)

								A_									= MgpstrProcessor.from_pretrained(
								    self.tmpdirname      ,       bos_token='(BOS)'      ,       eos_token='(EOS)'      ,       do_normalize=_lowercase      ,       padding_value=1.0)

								self.assertEqual(processor.char_tokenizer.get_vocab()      ,       tokenizer_add_kwargs.get_vocab())
								self.assertIsInstance(processor.char_tokenizer      ,       _lowercase)

								self.assertEqual(processor.image_processor.to_json_string()      ,       image_processor_add_kwargs.to_json_string())
								self.assertIsInstance(processor.image_processor      ,       _lowercase)



					def     __snake_case						(	self     :						List[Any])    ->							str:
								A_									= self.get_image_processor()
								A_									= self.get_tokenizer()

								A_									= MgpstrProcessor(tokenizer=_lowercase      ,       image_processor=_lowercase)

								A_									= self.prepare_image_inputs()

								A_									= image_processor(_lowercase      ,       return_tensors='np')
								A_									= processor(images=_lowercase      ,       return_tensors='np')

								for key in input_image_proc.keys():
											self.assertAlmostEqual(input_image_proc[key].sum()      ,       input_processor[key].sum()      ,       delta=1E-2)



					def     __snake_case						(	self     :						Any)    ->							str:
								A_									= self.get_image_processor()
								A_									= self.get_tokenizer()

								A_									= MgpstrProcessor(tokenizer=_lowercase      ,       image_processor=_lowercase)

								A_									= 'test'

								A_									= processor(text=_lowercase)

								A_									= tokenizer(_lowercase)
								for key in encoded_tok.keys():
											self.assertListEqual(encoded_tok[key]      ,       encoded_processor[key])



					def     __snake_case						(	self     :						str)    ->							Dict:
								A_									= self.get_image_processor()
								A_									= self.get_tokenizer()

								A_									= MgpstrProcessor(tokenizer=_lowercase      ,       image_processor=_lowercase)

								A_									= 'test'
								A_									= self.prepare_image_inputs()

								A_									= processor(text=_lowercase      ,       images=_lowercase)

								self.assertListEqual(list(inputs.keys())      ,       ['pixel_values', 'labels'])

								# test if it raises when no input is passed
								with pytest.raises(_lowercase):
											processor()



					def     __snake_case						(	self     :						Union[str, Any])    ->							Optional[int]:
								A_									= self.get_image_processor()
								A_									= self.get_tokenizer()

								A_									= MgpstrProcessor(tokenizer=_lowercase      ,       image_processor=_lowercase)

								A_									= [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9], [3, 4, 3, 1, 1, 8, 9]]

								A_									= processor.char_decode(_lowercase)
								A_									= tokenizer.batch_decode(_lowercase)
								A_									= [seq.replace(' '      ,       '') for seq in decoded_tok]

								self.assertListEqual(_lowercase      ,       _lowercase)



					def     __snake_case						(	self     :						List[str])    ->							str:
								A_									= self.get_image_processor()
								A_									= self.get_tokenizer()

								A_									= MgpstrProcessor(tokenizer=_lowercase      ,       image_processor=_lowercase)

								A_									= None
								A_									= self.prepare_image_inputs()

								A_									= processor(text=_lowercase      ,       images=_lowercase)

								self.assertListEqual(list(inputs.keys())      ,       processor.model_input_names)



					def     __snake_case						(	self     :						List[str])    ->							Any:
								A_									= self.get_image_processor()
								A_									= self.get_tokenizer()

								A_									= MgpstrProcessor(tokenizer=_lowercase      ,       image_processor=_lowercase)

								A_									= torch.randn(1      ,       27      ,       38)
								A_									= torch.randn(1      ,       27      ,       50_257)
								A_									= torch.randn(1      ,       27      ,       30_522)

								A_									= processor.batch_decode([char_input, bpe_input, wp_input])

								self.assertListEqual(list(results.keys())      ,       ['generated_text', 'scores', 'char_preds', 'bpe_preds', 'wp_preds'])




