"""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  _snake_case     (					a__		):






						def       lowerCamelCase__     (       self      :			Union[str, Any]    ):
													__lowerCamelCase					:		Union[str, Any]		   =     self.config_class(**self.inputs_dict    )
													self.parent.assertTrue(hasattr(UpperCAmelCase							,					"width_multiplier"    )    )

class  _snake_case     :






						def __init__(       self      :			Any							,					UpperCAmelCase      :			str							,					UpperCAmelCase      :			int=13							,					UpperCAmelCase      :			Tuple=64							,					UpperCAmelCase      :			Any=2							,					UpperCAmelCase      :			List[str]=3							,					UpperCAmelCase      :			str="swish"							,					UpperCAmelCase      :			Optional[int]=3							,					UpperCAmelCase      :			List[Any]=32							,					UpperCAmelCase      :			str=0.1							,					UpperCAmelCase      :			Tuple=0.0_2							,					UpperCAmelCase      :			Any=True							,					UpperCAmelCase      :			List[Any]=True							,					UpperCAmelCase      :			Optional[Any]=10							,					UpperCAmelCase      :			int=None							,					UpperCAmelCase      :			str=0.2_5							,					UpperCAmelCase      :			Any=0.0							,					UpperCAmelCase      :			int=0.0							,					):
													__lowerCamelCase					:		int		   =     parent
													__lowerCamelCase					:		str		   =     batch_size
													__lowerCamelCase					:		Union[str, Any]		   =     image_size
													__lowerCamelCase					:		Dict		   =     patch_size
													__lowerCamelCase					:		Union[str, Any]		   =     num_channels
													__lowerCamelCase					:		List[str]		   =     make_divisible(512 * width_multiplier							,					divisor=8    )
													__lowerCamelCase					:		Union[str, Any]		   =     hidden_act
													__lowerCamelCase					:		List[str]		   =     conv_kernel_size
													__lowerCamelCase					:		int		   =     output_stride
													__lowerCamelCase					:		Union[str, Any]		   =     classifier_dropout_prob
													__lowerCamelCase					:		Tuple		   =     use_labels
													__lowerCamelCase					:		Tuple		   =     is_training
													__lowerCamelCase					:		Union[str, Any]		   =     num_labels
													__lowerCamelCase					:		Dict		   =     initializer_range
													__lowerCamelCase					:		Dict		   =     scope
													__lowerCamelCase					:		List[str]		   =     width_multiplier
													__lowerCamelCase					:		Optional[Any]		   =     ffn_dropout
													__lowerCamelCase					:		List[Any]		   =     attn_dropout






						def       lowerCamelCase__     (       self      :			Tuple    ):
													__lowerCamelCase					:		Union[str, Any]		   =     floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]    )

													__lowerCamelCase					:		Any		   =     None
													__lowerCamelCase					:		Union[str, Any]		   =     None
													if self.use_labels:
																				__lowerCamelCase					:		int		   =     ids_tensor([self.batch_size]							,					self.num_labels    )
																				__lowerCamelCase					:		Any		   =     ids_tensor([self.batch_size, self.image_size, self.image_size]							,					self.num_labels    )

													__lowerCamelCase					:		Dict		   =     self.get_config()

													return config, pixel_values, labels, pixel_labels






						def       lowerCamelCase__     (       self      :			Union[str, Any]    ):
													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      :			Any							,					UpperCAmelCase      :			str							,					UpperCAmelCase      :			int							,					UpperCAmelCase      :			List[str]							,					UpperCAmelCase      :			str    ):
													__lowerCamelCase					:		int		   =     MobileViTVaModel(config=UpperCAmelCase    )
													model.to(UpperCAmelCase    )
													model.eval()
													__lowerCamelCase					:		Tuple		   =     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      :			Tuple							,					UpperCAmelCase      :			Union[str, Any]							,					UpperCAmelCase      :			Optional[int]							,					UpperCAmelCase      :			Union[str, Any]							,					UpperCAmelCase      :			Tuple    ):
													__lowerCamelCase					:		str		   =     self.num_labels
													__lowerCamelCase					:		Tuple		   =     MobileViTVaForImageClassification(UpperCAmelCase    )
													model.to(UpperCAmelCase    )
													model.eval()
													__lowerCamelCase					:		Optional[int]		   =     model(UpperCAmelCase							,					labels=UpperCAmelCase    )
													self.parent.assertEqual(result.logits.shape							,					(self.batch_size, self.num_labels)    )






						def       lowerCamelCase__     (       self      :			str							,					UpperCAmelCase      :			int							,					UpperCAmelCase      :			Union[str, Any]							,					UpperCAmelCase      :			Dict							,					UpperCAmelCase      :			Union[str, Any]    ):
													__lowerCamelCase					:		str		   =     self.num_labels
													__lowerCamelCase					:		Any		   =     MobileViTVaForSemanticSegmentation(UpperCAmelCase    )
													model.to(UpperCAmelCase    )
													model.eval()
													__lowerCamelCase					:		List[str]		   =     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,
													    )							,					)
													__lowerCamelCase					:		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      :			int    ):
													__lowerCamelCase					:		Optional[Any]		   =     self.prepare_config_and_inputs()
													__lowerCamelCase   ,  __lowerCamelCase   ,  __lowerCamelCase   ,  __lowerCamelCase					:		int		   =     config_and_inputs
													__lowerCamelCase					:		List[Any]		   =     {"pixel_values": pixel_values}
													return config, inputs_dict

@require_torch
class  _snake_case     (					a__				, a__				, unittest.TestCase		):
						snake_case__			  =			(
						    (MobileViTVaModel, MobileViTVaForImageClassification, MobileViTVaForSemanticSegmentation)
						    if is_torch_available()
						    else ()
						)

						snake_case__			  =			(
						    {
						        "feature-extraction": MobileViTVaModel,
						        "image-classification": MobileViTVaForImageClassification,
						        "image-segmentation": MobileViTVaForSemanticSegmentation,
						    }
						    if is_torch_available()
						    else {}
						)

						snake_case__			  =			False
						snake_case__			  =			False
						snake_case__			  =			False
						snake_case__			  =			False






						def       lowerCamelCase__     (       self      :			Optional[Any]    ):
													__lowerCamelCase					:		List[str]		   =     MobileViTVaModelTester(self    )
													__lowerCamelCase					:		List[Any]		   =     MobileViTVaConfigTester(self							,					config_class=UpperCAmelCase							,					has_text_modality=UpperCAmelCase    )






						def       lowerCamelCase__     (       self      :			str    ):
													self.config_tester.run_common_tests()






						@unittest.skip(reason="MobileViTV2 does not use inputs_embeds"    )
						def       lowerCamelCase__     (       self      :			Any    ):
													pass






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






						@unittest.skip(reason="MobileViTV2 does not output attentions"    )
						def       lowerCamelCase__     (       self      :			Optional[int]    ):
													pass






						@require_torch_multi_gpu
						@unittest.skip(reason="Got `CUDA error: misaligned address` for tests after this one being run."    )
						def       lowerCamelCase__     (       self      :			List[Any]    ):
													pass






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






						def       lowerCamelCase__     (       self      :			Dict    ):
													__lowerCamelCase   ,  __lowerCamelCase					:		List[str]		   =     self.model_tester.prepare_config_and_inputs_for_common()

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

																				__lowerCamelCase					:		Any		   =     ["pixel_values"]
																				self.assertListEqual(arg_names[:1]							,					UpperCAmelCase    )






						def       lowerCamelCase__     (       self      :			List[Any]    ):
													__lowerCamelCase					:		int		   =     self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_model(*UpperCAmelCase    )






						def       lowerCamelCase__     (       self      :			Union[str, Any]    ):
													def check_hidden_states_output(UpperCAmelCase      :			Dict							,					UpperCAmelCase      :			str							,					UpperCAmelCase      :			Optional[Any]    ):
																				__lowerCamelCase					:		str		   =     model_class(UpperCAmelCase    )
																				model.to(UpperCAmelCase    )
																				model.eval()

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

																				__lowerCamelCase					:		Tuple		   =     outputs.hidden_states

																				__lowerCamelCase					:		Tuple		   =     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.
																				__lowerCamelCase					:		Union[str, Any]		   =     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    )

													__lowerCamelCase   ,  __lowerCamelCase					:		Optional[int]		   =     self.model_tester.prepare_config_and_inputs_for_common()

													for model_class in self.all_model_classes:
																				__lowerCamelCase					:		List[str]		   =     True
																				check_hidden_states_output(UpperCAmelCase							,					UpperCAmelCase							,					UpperCAmelCase    )

																				# check that output_hidden_states also work using config
																				del inputs_dict["output_hidden_states"]
																				__lowerCamelCase					:		Union[str, Any]		   =     True

																				check_hidden_states_output(UpperCAmelCase							,					UpperCAmelCase							,					UpperCAmelCase    )






						def       lowerCamelCase__     (       self      :			Union[str, Any]    ):
													__lowerCamelCase					:		Union[str, Any]		   =     self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_for_image_classification(*UpperCAmelCase    )






						def       lowerCamelCase__     (       self      :			Any    ):
													__lowerCamelCase					:		Union[str, Any]		   =     self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_for_semantic_segmentation(*UpperCAmelCase    )







						@slow
						def       lowerCamelCase__     (       self      :			Union[str, Any]    ):
													for model_name in MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																				__lowerCamelCase					:		Union[str, Any]		   =     MobileViTVaModel.from_pretrained(UpperCAmelCase    )
																				self.assertIsNotNone(UpperCAmelCase    )


def    lowercase_   (   ) ->      Any:



							'''simple docstring'''

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







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






						@cached_property
						def       lowerCamelCase__     (       self      :			Any    ):
													return (
													    MobileViTImageProcessor.from_pretrained("apple/mobilevitv2-1.0-imagenet1k-256"    )
													    if is_vision_available()
													    else None
													)






						@slow
						def       lowerCamelCase__     (       self      :			Optional[int]    ):
													__lowerCamelCase					:		Union[str, Any]		   =     MobileViTVaForImageClassification.from_pretrained("apple/mobilevitv2-1.0-imagenet1k-256"    ).to(
													    UpperCAmelCase    )

													__lowerCamelCase					:		List[str]		   =     self.default_image_processor
													__lowerCamelCase					:		Union[str, Any]		   =     prepare_img()
													__lowerCamelCase					:		str		   =     image_processor(images=UpperCAmelCase							,					return_tensors="pt"    ).to(UpperCAmelCase    )

													# forward pass
													with torch.no_grad():
																				__lowerCamelCase					:		str		   =     model(**UpperCAmelCase    )

													# verify the logits
													__lowerCamelCase					:		Tuple		   =     torch.Size((1, 1000)    )
													self.assertEqual(outputs.logits.shape							,					UpperCAmelCase    )

													__lowerCamelCase					:		str		   =     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      :			str    ):
													__lowerCamelCase					:		List[Any]		   =     MobileViTVaForSemanticSegmentation.from_pretrained("shehan97/mobilevitv2-1.0-voc-deeplabv3"    )
													__lowerCamelCase					:		Union[str, Any]		   =     model.to(UpperCAmelCase    )

													__lowerCamelCase					:		str		   =     MobileViTImageProcessor.from_pretrained("shehan97/mobilevitv2-1.0-voc-deeplabv3"    )

													__lowerCamelCase					:		Dict		   =     prepare_img()
													__lowerCamelCase					:		int		   =     image_processor(images=UpperCAmelCase							,					return_tensors="pt"    ).to(UpperCAmelCase    )

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

													# verify the logits
													__lowerCamelCase					:		Any		   =     torch.Size((1, 21, 32, 32)    )
													self.assertEqual(logits.shape							,					UpperCAmelCase    )

													__lowerCamelCase					:		Tuple		   =     torch.tensor(
													    [
													        [[7.0_8_6_3, 7.1_5_2_5, 6.8_2_0_1], [6.6_9_3_1, 6.8_7_7_0, 6.8_9_3_3], [6.2_9_7_8, 7.0_3_6_6, 6.9_6_3_6]],
													        [[-3.7_1_3_4, -3.6_7_1_2, -3.6_6_7_5], [-3.5_8_2_5, -3.3_5_4_9, -3.4_7_7_7], [-3.3_4_3_5, -3.3_9_7_9, -3.2_8_5_7]],
													        [[-2.9_3_2_9, -2.8_0_0_3, -2.7_3_6_9], [-3.0_5_6_4, -2.4_7_8_0, -2.0_2_0_7], [-2.6_8_8_9, -1.9_2_9_8, -1.7_6_4_0]],
													    ]							,					device=UpperCAmelCase							,					)

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







						@slow
						def       lowerCamelCase__     (       self      :			Optional[Any]    ):
													__lowerCamelCase					:		int		   =     MobileViTVaForSemanticSegmentation.from_pretrained("shehan97/mobilevitv2-1.0-voc-deeplabv3"    )
													__lowerCamelCase					:		Any		   =     model.to(UpperCAmelCase    )

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

													__lowerCamelCase					:		str		   =     prepare_img()
													__lowerCamelCase					:		Dict		   =     image_processor(images=UpperCAmelCase							,					return_tensors="pt"    ).to(UpperCAmelCase    )

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

													__lowerCamelCase					:		str		   =     outputs.logits.detach().cpu()

													__lowerCamelCase					:		List[Any]		   =     image_processor.post_process_semantic_segmentation(outputs=UpperCAmelCase							,					target_sizes=[(50, 60)]    )
													__lowerCamelCase					:		List[Any]		   =     torch.Size((50, 60)    )
													self.assertEqual(segmentation[0].shape							,					UpperCAmelCase    )

													__lowerCamelCase					:		Optional[Any]		   =     image_processor.post_process_semantic_segmentation(outputs=UpperCAmelCase    )
													__lowerCamelCase					:		List[Any]		   =     torch.Size((32, 32)    )
													self.assertEqual(segmentation[0].shape							,					UpperCAmelCase    )
"""simple docstring"""





import os
from argparse import ArgumentParser
from typing import List

import torch.utils.data

from datasets import Dataset, IterableDataset
from datasets.distributed import split_dataset_by_node


__A  =				4
__A  =				3
class  _snake_case     (					a__		):
						pass


def    lowercase_   (   _lowerCamelCase:	List[str]      ) ->      List[str]:



							'''simple docstring'''

							for shard in shards:
														for i in range(_lowerCamelCase      ):
																					yield {"i": i, "shard": shard}


def    lowercase_   (   ) ->      List[Any]:



							'''simple docstring'''

							__lowerCamelCase					:		List[Any]		   =     int(os.environ["RANK"]      )
							__lowerCamelCase					:		Optional[int]		   =     int(os.environ["WORLD_SIZE"]      )

							__lowerCamelCase					:		Any		   =     ArgumentParser()
							parser.add_argument("--streaming"						,   type=_lowerCamelCase      )
							parser.add_argument("--local_rank"						,   type=_lowerCamelCase      )
							parser.add_argument("--num_workers"						,   type=_lowerCamelCase						,   default=0      )
							__lowerCamelCase					:		Dict		   =     parser.parse_args()
							__lowerCamelCase					:		str		   =     args.streaming
							__lowerCamelCase					:		List[Any]		   =     args.num_workers

							__lowerCamelCase					:		Optional[Any]		   =     {"shards": [F"""shard_{shard_idx}""" for shard_idx in range(_lowerCamelCase      )]}
							__lowerCamelCase					:		int		   =     IterableDataset.from_generator(_lowerCamelCase						,   gen_kwargs=_lowerCamelCase      )
							if not streaming:
														__lowerCamelCase					:		Optional[int]		   =     Dataset.from_list(list(_lowerCamelCase      )      )

							__lowerCamelCase					:		Union[str, Any]		   =     split_dataset_by_node(_lowerCamelCase						,   rank=_lowerCamelCase						,   world_size=_lowerCamelCase      )
							__lowerCamelCase					:		Optional[Any]		   =     torch.utils.data.DataLoader(_lowerCamelCase						,   num_workers=_lowerCamelCase      )

							__lowerCamelCase					:		List[Any]		   =     NUM_SHARDS * NUM_ITEMS_PER_SHARD
							__lowerCamelCase					:		Optional[Any]		   =     full_size // world_size
							expected_local_size += int(rank < (full_size % world_size)      )

							__lowerCamelCase					:		Optional[Any]		   =     sum(1 for _ in dataloader      )
							if local_size != expected_local_size:
														raise FailedTestError(F"""local_size {local_size} != expected_local_size {expected_local_size}"""      )


if __name__ == "__main__":
				main()