


"""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    (				snake_case_     ):







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

							'''simple docstring'''




							snake_case   :				Dict				 =   self.config_class(**self.inputs_dict						)
							self.parent.assertTrue(hasattr(UpperCamelCase__						,     "width_multiplier"						)						)







class _lowerCAmelCase    :







			def __init__(     self						,     UpperCamelCase__						,     UpperCamelCase__=13						,     UpperCamelCase__=64						,     UpperCamelCase__=2						,     UpperCamelCase__=3						,     UpperCamelCase__="swish"						,     UpperCamelCase__=3						,     UpperCamelCase__=32						,     UpperCamelCase__=0.1						,     UpperCamelCase__=0.02						,     UpperCamelCase__=True						,     UpperCamelCase__=True						,     UpperCamelCase__=10						,     UpperCamelCase__=None						,     UpperCamelCase__=0.25						,     UpperCamelCase__=0.0						,     UpperCamelCase__=0.0						,     )     ->		Any:

							'''simple docstring'''




							snake_case   :				str				 =   parent
							snake_case   :				Dict				 =   batch_size
							snake_case   :				Union[str, Any]				 =   image_size
							snake_case   :				Union[str, Any]				 =   patch_size
							snake_case   :				Tuple				 =   num_channels
							snake_case   :				Union[str, Any]				 =   make_divisible(512 * width_multiplier						,     divisor=8						)
							snake_case   :				int				 =   hidden_act
							snake_case   :				List[Any]				 =   conv_kernel_size
							snake_case   :				Optional[Any]				 =   output_stride
							snake_case   :				str				 =   classifier_dropout_prob
							snake_case   :				int				 =   use_labels
							snake_case   :				int				 =   is_training
							snake_case   :				str				 =   num_labels
							snake_case   :				List[Any]				 =   initializer_range
							snake_case   :				List[Any]				 =   scope
							snake_case   :				List[str]				 =   width_multiplier
							snake_case   :				str				 =   ffn_dropout
							snake_case   :				Dict				 =   attn_dropout







			def 			lowerCamelCase		(     self						)     ->		str:

							'''simple docstring'''




							snake_case   :				int				 =   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

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

							snake_case   :				Any				 =   self.get_config()

							return config, pixel_values, labels, pixel_labels







			def 			lowerCamelCase		(     self						)     ->		Dict:

							'''simple docstring'''




							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 			lowerCamelCase		(     self						,     UpperCamelCase__						,     UpperCamelCase__						,     UpperCamelCase__						,     UpperCamelCase__						)     ->		Dict:

							'''simple docstring'''




							snake_case   :				Tuple				 =   MobileViTVaModel(config=UpperCamelCase__						)
							model.to(UpperCamelCase__						)
							model.eval()
							snake_case   :				Dict				 =   model(UpperCamelCase__						)
							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						,     UpperCamelCase__						,     UpperCamelCase__						,     UpperCamelCase__						,     UpperCamelCase__						)     ->		str:

							'''simple docstring'''




							snake_case   :				Any				 =   self.num_labels
							snake_case   :				List[Any]				 =   MobileViTVaForImageClassification(UpperCamelCase__						)
							model.to(UpperCamelCase__						)
							model.eval()
							snake_case   :				Any				 =   model(UpperCamelCase__						,     labels=UpperCamelCase__						)
							self.parent.assertEqual(result.logits.shape						,     (self.batch_size, self.num_labels)						)







			def 			lowerCamelCase		(     self						,     UpperCamelCase__						,     UpperCamelCase__						,     UpperCamelCase__						,     UpperCamelCase__						)     ->		Optional[Any]:

							'''simple docstring'''




							snake_case   :				Any				 =   self.num_labels
							snake_case   :				Optional[Any]				 =   MobileViTVaForSemanticSegmentation(UpperCamelCase__						)
							model.to(UpperCamelCase__						)
							model.eval()
							snake_case   :				Dict				 =   model(UpperCamelCase__						)
							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   :				List[Any]				 =   model(UpperCamelCase__						,     labels=UpperCamelCase__						)
							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						)     ->		Optional[Any]:

							'''simple docstring'''




							snake_case   :				Optional[Any]				 =   self.prepare_config_and_inputs()
							snake_case   :				Optional[int]				 =   config_and_inputs
							snake_case   :				str				 =   {"pixel_values": pixel_values}
							return config, inputs_dict







@require_torch
class _lowerCAmelCase    (				snake_case_   ,							snake_case_   ,							unittest.TestCase     ):
			__UpperCAmelCase			:	Tuple							  =  (
			    (MobileViTVaModel, MobileViTVaForImageClassification, MobileViTVaForSemanticSegmentation)
			    if is_torch_available()
			    else ()
			)

			__UpperCAmelCase			:	Dict							  =  (
			    {
			        '''feature-extraction''': MobileViTVaModel,
			        '''image-classification''': MobileViTVaForImageClassification,
			        '''image-segmentation''': MobileViTVaForSemanticSegmentation,
			    }
			    if is_torch_available()
			    else {}
			)

			__UpperCAmelCase			:	str							  =  False
			__UpperCAmelCase			:	Dict							  =  False
			__UpperCAmelCase			:	Optional[Any]							  =  False
			__UpperCAmelCase			:	List[str]							  =  False







			def 			lowerCamelCase		(     self						)     ->		List[Any]:

							'''simple docstring'''




							snake_case   :				Tuple				 =   MobileViTVaModelTester(self						)
							snake_case   :				Optional[int]				 =   MobileViTVaConfigTester(self						,     config_class=UpperCamelCase__						,     has_text_modality=UpperCamelCase__						)







			def 			lowerCamelCase		(     self						)     ->		Dict:

							'''simple docstring'''




							self.config_tester.run_common_tests()







			@unittest.skip(reason="MobileViTV2 does not use inputs_embeds"						)
			def 			lowerCamelCase		(     self						)     ->		Dict:

							'''simple docstring'''




							pass







			@unittest.skip(reason="MobileViTV2 does not support input and output embeddings"						)
			def 			lowerCamelCase		(     self						)     ->		Any:

							'''simple docstring'''




							pass







			@unittest.skip(reason="MobileViTV2 does not output attentions"						)
			def 			lowerCamelCase		(     self						)     ->		int:

							'''simple docstring'''




							pass







			@require_torch_multi_gpu
			@unittest.skip(reason="Got `CUDA error: misaligned address` for tests after this one being run."						)
			def 			lowerCamelCase		(     self						)     ->		Union[str, Any]:

							'''simple docstring'''




							pass







			@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests."						)
			def 			lowerCamelCase		(     self						)     ->		Optional[int]:

							'''simple docstring'''




							pass







			def 			lowerCamelCase		(     self						)     ->		Optional[int]:

							'''simple docstring'''




							snake_case   :				Union[str, Any]				 =   self.model_tester.prepare_config_and_inputs_for_common()

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

											snake_case   :				Tuple				 =   ["pixel_values"]
											self.assertListEqual(arg_names[:1]						,     UpperCamelCase__						)







			def 			lowerCamelCase		(     self						)     ->		Optional[Any]:

							'''simple docstring'''




							snake_case   :				List[str]				 =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*UpperCamelCase__						)







			def 			lowerCamelCase		(     self						)     ->		List[str]:

							'''simple docstring'''




							def check_hidden_states_output(UpperCamelCase__						,     UpperCamelCase__						,     UpperCamelCase__						):
											snake_case   :				List[Any]				 =   model_class(UpperCamelCase__						)
											model.to(UpperCamelCase__						)
											model.eval()

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

											snake_case   :				str				 =   outputs.hidden_states

											snake_case   :				List[Any]				 =   5
											self.assertEqual(len(UpperCamelCase__						)						,     UpperCamelCase__						)

											# MobileViTV2's feature maps are of shape (batch_size, num_channels, height, width)
											# with the width and height being successively divided by 2.
											snake_case   :				str				 =   2
											for i in range(len(UpperCamelCase__						)						):
															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   :				Dict				 =   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(UpperCamelCase__						,     UpperCamelCase__						,     UpperCamelCase__						)

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

											check_hidden_states_output(UpperCamelCase__						,     UpperCamelCase__						,     UpperCamelCase__						)







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

							'''simple docstring'''




							snake_case   :				Dict				 =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_image_classification(*UpperCamelCase__						)







			def 			lowerCamelCase		(     self						)     ->		List[str]:

							'''simple docstring'''




							snake_case   :				Any				 =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_semantic_segmentation(*UpperCamelCase__						)







			@slow
			def 			lowerCamelCase		(     self						)     ->		Optional[int]:

							'''simple docstring'''




							for model_name in MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											snake_case   :				Tuple				 =   MobileViTVaModel.from_pretrained(UpperCamelCase__						)
											self.assertIsNotNone(UpperCamelCase__						)







def   __lowerCAmelCase						(						)     ->						Dict:


				"""simple docstring"""





				snake_case   :				List[Any]				 =   Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"			)
				return image







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







			@cached_property
			def 			lowerCamelCase		(     self						)     ->		str:

							'''simple docstring'''




							return (
							    MobileViTImageProcessor.from_pretrained("apple/mobilevitv2-1.0-imagenet1k-256"						)
							    if is_vision_available()
							    else None
							)







			@slow
			def 			lowerCamelCase		(     self						)     ->		Any:

							'''simple docstring'''




							snake_case   :				List[Any]				 =   MobileViTVaForImageClassification.from_pretrained("apple/mobilevitv2-1.0-imagenet1k-256"						).to(
							    UpperCamelCase__						)

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

							# forward pass
							with torch.no_grad():
											snake_case   :				str				 =   model(**UpperCamelCase__						)

							# verify the logits
							snake_case   :				int				 =   torch.Size((1, 1000)						)
							self.assertEqual(outputs.logits.shape						,     UpperCamelCase__						)

							snake_case   :				Union[str, Any]				 =   torch.tensor([-1.6336e00, -7.3204e-02, -5.1883e-01]						).to(UpperCamelCase__						)

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







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

							'''simple docstring'''




							snake_case   :				Optional[Any]				 =   MobileViTVaForSemanticSegmentation.from_pretrained("shehan97/mobilevitv2-1.0-voc-deeplabv3"						)
							snake_case   :				Dict				 =   model.to(UpperCamelCase__						)

							snake_case   :				Optional[Any]				 =   MobileViTImageProcessor.from_pretrained("shehan97/mobilevitv2-1.0-voc-deeplabv3"						)

							snake_case   :				Optional[int]				 =   prepare_img()
							snake_case   :				str				 =   image_processor(images=UpperCamelCase__						,     return_tensors="pt"						).to(UpperCamelCase__						)

							# forward pass
							with torch.no_grad():
											snake_case   :				Union[str, Any]				 =   model(**UpperCamelCase__						)
							snake_case   :				str				 =   outputs.logits

							# verify the logits
							snake_case   :				int				 =   torch.Size((1, 21, 32, 32)						)
							self.assertEqual(logits.shape						,     UpperCamelCase__						)

							snake_case   :				Optional[Any]				 =   torch.tensor(
							    [
							        [[7.0863, 7.1525, 6.8201], [6.6931, 6.8770, 6.8933], [6.2978, 7.0366, 6.9636]],
							        [[-3.7134, -3.6712, -3.6675], [-3.5825, -3.3549, -3.4777], [-3.3435, -3.3979, -3.2857]],
							        [[-2.9329, -2.8003, -2.7369], [-3.0564, -2.4780, -2.0207], [-2.6889, -1.9298, -1.7640]],
							    ]						,     device=UpperCamelCase__						,     )

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







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

							'''simple docstring'''




							snake_case   :				str				 =   MobileViTVaForSemanticSegmentation.from_pretrained("shehan97/mobilevitv2-1.0-voc-deeplabv3"						)
							snake_case   :				int				 =   model.to(UpperCamelCase__						)

							snake_case   :				int				 =   MobileViTImageProcessor.from_pretrained("shehan97/mobilevitv2-1.0-voc-deeplabv3"						)

							snake_case   :				Tuple				 =   prepare_img()
							snake_case   :				List[str]				 =   image_processor(images=UpperCamelCase__						,     return_tensors="pt"						).to(UpperCamelCase__						)

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

							snake_case   :				List[str]				 =   outputs.logits.detach().cpu()

							snake_case   :				Tuple				 =   image_processor.post_process_semantic_segmentation(outputs=UpperCamelCase__						,     target_sizes=[(50, 60)]						)
							snake_case   :				Dict				 =   torch.Size((50, 60)						)
							self.assertEqual(segmentation[0].shape						,     UpperCamelCase__						)

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









"""simple docstring"""



from pathlib import Path

import fire
from tqdm import tqdm







def   __lowerCAmelCase						(						lowercase  :  Optional[Any]="ro"						,							lowercase  :  Union[str, Any]="en"						,							lowercase  :  str="wmt16"						,							lowercase  :  Any=None			)     ->						None:


    """simple docstring"""





    try:
        import datasets
    except (ModuleNotFoundError, ImportError):
        raise ImportError("run pip install datasets"			)
    snake_case   :				Any				 =   F'{src_lang}-{tgt_lang}'
    print(F'Converting {dataset}-{pair}'			)
    snake_case   :				Union[str, Any]				 =   datasets.load_dataset(lowercase						,							lowercase			)
    if save_dir is None:
        snake_case   :				int				 =   F'{dataset}-{pair}'
    snake_case   :				Optional[Any]				 =   Path(lowercase			)
    save_dir.mkdir(exist_ok=lowercase			)

    for split in ds.keys():
        print(F'Splitting {split} with {ds[split].num_rows} records'			)

        # to save to val.source, val.target like summary datasets
        snake_case   :				Any				 =   "val" if split == "validation" else split
        snake_case   :				List[str]				 =   save_dir.joinpath(F'{fn}.source'			)
        snake_case   :				int				 =   save_dir.joinpath(F'{fn}.target'			)
        snake_case   :				str				 =   src_path.open("w+"			)
        snake_case   :				Optional[int]				 =   tgt_path.open("w+"			)

        # reader is the bottleneck so writing one record at a time doesn't slow things down
        for x in tqdm(ds[split]			):
            snake_case   :				int				 =   x["translation"]
            src_fp.write(ex[src_lang] + "\n"			)
            tgt_fp.write(ex[tgt_lang] + "\n"			)

    print(F'Saved {dataset} dataset to {save_dir}'			)


if __name__ == "__main__":
  fire.Fire(download_wmt_dataset)





