






import collections
import inspect
import unittest
from typing import Dict, List, Tuple

from transformers import MaskFormerSwinConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, torch_device
from transformers.utils import is_torch_available

from ...test_backbone_common import BackboneTesterMixin
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 torch import nn

		from transformers import MaskFormerSwinBackbone
		from transformers.models.maskformer import MaskFormerSwinModel





class __lowerCamelCase :





	"""simple docstring"""






	def __init__(							self       ,    UpperCAmelCase       ,    UpperCAmelCase=13       ,    UpperCAmelCase=32       ,    UpperCAmelCase=2       ,    UpperCAmelCase=3       ,    UpperCAmelCase=16       ,    UpperCAmelCase=[1, 2, 1]       ,    UpperCAmelCase=[2, 2, 4]       ,    UpperCAmelCase=2       ,    UpperCAmelCase=2.0       ,    UpperCAmelCase=True       ,    UpperCAmelCase=0.0       ,    UpperCAmelCase=0.0       ,    UpperCAmelCase=0.1       ,    UpperCAmelCase="gelu"       ,    UpperCAmelCase=False       ,    UpperCAmelCase=True       ,    UpperCAmelCase=0.02       ,    UpperCAmelCase=1e-5       ,    UpperCAmelCase=True       ,    UpperCAmelCase=None       ,    UpperCAmelCase=True       ,    UpperCAmelCase=10       ,    UpperCAmelCase=8       ,    UpperCAmelCase=["stage1", "stage2", "stage3"]       ,    UpperCAmelCase=[1, 2, 3]       ,    ):
							"""simple docstring"""







							_UpperCAmelCase									=			parent
							_UpperCAmelCase									=			batch_size
							_UpperCAmelCase									=			image_size
							_UpperCAmelCase									=			patch_size
							_UpperCAmelCase									=			num_channels
							_UpperCAmelCase									=			embed_dim
							_UpperCAmelCase									=			depths
							_UpperCAmelCase									=			num_heads
							_UpperCAmelCase									=			window_size
							_UpperCAmelCase									=			mlp_ratio
							_UpperCAmelCase									=			qkv_bias
							_UpperCAmelCase									=			hidden_dropout_prob
							_UpperCAmelCase									=			attention_probs_dropout_prob
							_UpperCAmelCase									=			drop_path_rate
							_UpperCAmelCase									=			hidden_act
							_UpperCAmelCase									=			use_absolute_embeddings
							_UpperCAmelCase									=			patch_norm
							_UpperCAmelCase									=			layer_norm_eps
							_UpperCAmelCase									=			initializer_range
							_UpperCAmelCase									=			is_training
							_UpperCAmelCase									=			scope
							_UpperCAmelCase									=			use_labels
							_UpperCAmelCase									=			type_sequence_label_size
							_UpperCAmelCase									=			encoder_stride
							_UpperCAmelCase									=			out_features
							_UpperCAmelCase									=			out_indices

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]    )

							_UpperCAmelCase									=			None
							if self.use_labels:
													_UpperCAmelCase									=			ids_tensor([self.batch_size]       ,    self.type_sequence_label_size    )

							_UpperCAmelCase									=			self.get_config()

							return config, pixel_values, labels

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							return MaskFormerSwinConfig(
							    image_size=self.image_size       ,    patch_size=self.patch_size       ,    num_channels=self.num_channels       ,    embed_dim=self.embed_dim       ,    depths=self.depths       ,    num_heads=self.num_heads       ,    window_size=self.window_size       ,    mlp_ratio=self.mlp_ratio       ,    qkv_bias=self.qkv_bias       ,    hidden_dropout_prob=self.hidden_dropout_prob       ,    attention_probs_dropout_prob=self.attention_probs_dropout_prob       ,    drop_path_rate=self.drop_path_rate       ,    hidden_act=self.hidden_act       ,    use_absolute_embeddings=self.use_absolute_embeddings       ,    path_norm=self.patch_norm       ,    layer_norm_eps=self.layer_norm_eps       ,    initializer_range=self.initializer_range       ,    encoder_stride=self.encoder_stride       ,    out_features=self.out_features       ,    out_indices=self.out_indices       ,    )

	def        UpperCamelCase    (							self       ,    UpperCAmelCase       ,    UpperCAmelCase       ,    UpperCAmelCase    ):
							"""simple docstring"""







							_UpperCAmelCase									=			MaskFormerSwinModel(config=UpperCAmelCase    )
							model.to(UpperCAmelCase    )
							model.eval()
							_UpperCAmelCase									=			model(UpperCAmelCase    )

							_UpperCAmelCase									=			((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths    ) - 1))
							_UpperCAmelCase									=			int(config.embed_dim * 2 ** (len(config.depths    ) - 1)    )

							self.parent.assertEqual(result.last_hidden_state.shape       ,    (self.batch_size, expected_seq_len, expected_dim)    )

	def        UpperCamelCase    (							self       ,    UpperCAmelCase       ,    UpperCAmelCase       ,    UpperCAmelCase    ):
							"""simple docstring"""







							_UpperCAmelCase									=			MaskFormerSwinBackbone(config=UpperCAmelCase    )
							model.to(UpperCAmelCase    )
							model.eval()
							_UpperCAmelCase									=			model(UpperCAmelCase    )

							# verify feature maps
							self.parent.assertEqual(len(result.feature_maps    )       ,    len(config.out_features    )    )
							self.parent.assertListEqual(list(result.feature_maps[0].shape    )       ,    [13, 16, 16, 16]    )

							# verify channels
							self.parent.assertEqual(len(model.channels    )       ,    len(config.out_features    )    )
							self.parent.assertListEqual(model.channels       ,    [16, 32, 64]    )

							# verify ValueError
							with self.parent.assertRaises(UpperCAmelCase    ):
													_UpperCAmelCase									=			['stem']
													_UpperCAmelCase									=			MaskFormerSwinBackbone(config=UpperCAmelCase    )

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			self.prepare_config_and_inputs()
							_UpperCAmelCase					, _UpperCAmelCase					, _UpperCAmelCase									=			config_and_inputs
							_UpperCAmelCase									=			{'pixel_values': pixel_values}
							return config, inputs_dict





@require_torch
class __lowerCamelCase (      snake_case__     ,       snake_case__     ,       unittest.TestCase):





	"""simple docstring"""






	UpperCamelCase__								=      (
	    (
	        MaskFormerSwinModel,
	        MaskFormerSwinBackbone,
	    )
	    if is_torch_available()
	    else ()
	)
	UpperCamelCase__								=      {"feature-extraction": MaskFormerSwinModel} if is_torch_available() else {}
	UpperCamelCase__								=      False
	UpperCamelCase__								=      False
	UpperCamelCase__								=      False
	UpperCamelCase__								=      False
	UpperCamelCase__								=      False

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			MaskFormerSwinModelTester(self    )
							_UpperCAmelCase									=			ConfigTester(self       ,    config_class=UpperCAmelCase       ,    embed_dim=37    )

	@require_torch_multi_gpu
	@unittest.skip(
	    reason=(
	        '`MaskFormerSwinModel` outputs `hidden_states_spatial_dimensions` which doesn\'t work well with'
	        ' `nn.DataParallel`'
	    )    )
	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							pass

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							self.create_and_test_config_common_properties()
							self.config_tester.create_and_test_config_to_json_string()
							self.config_tester.create_and_test_config_to_json_file()
							self.config_tester.create_and_test_config_from_and_save_pretrained()
							self.config_tester.create_and_test_config_with_num_labels()
							self.config_tester.check_config_can_be_init_without_params()
							self.config_tester.check_config_arguments_init()

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							return

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*UpperCAmelCase    )

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_backbone(*UpperCAmelCase    )

	@unittest.skip('Swin does not use inputs_embeds'    )
	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							pass

	@unittest.skip('Swin does not support feedforward chunking'    )
	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							pass

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase					, _UpperCAmelCase									=			self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
													_UpperCAmelCase									=			model_class(UpperCAmelCase    )
													self.assertIsInstance(model.get_input_embeddings()       ,    (nn.Module)    )
													_UpperCAmelCase									=			model.get_output_embeddings()
													self.assertTrue(x is None or isinstance(UpperCAmelCase       ,    nn.Linear    )    )

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase					, _UpperCAmelCase									=			self.model_tester.prepare_config_and_inputs_for_common()

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

													_UpperCAmelCase									=			['pixel_values']
													self.assertListEqual(arg_names[:1]       ,    UpperCAmelCase    )

	@unittest.skip(reason='MaskFormerSwin is only used as backbone and doesn\'t support output_attentions'    )
	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							pass

	@unittest.skip(reason='MaskFormerSwin is only used as an internal backbone'    )
	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							pass

	def        UpperCamelCase    (							self       ,    UpperCAmelCase       ,    UpperCAmelCase       ,    UpperCAmelCase       ,    UpperCAmelCase    ):
							"""simple docstring"""







							_UpperCAmelCase									=			model_class(UpperCAmelCase    )
							model.to(UpperCAmelCase    )
							model.eval()

							with torch.no_grad():
													_UpperCAmelCase									=			model(**self._prepare_for_class(UpperCAmelCase       ,    UpperCAmelCase    )    )

							_UpperCAmelCase									=			outputs.hidden_states

							_UpperCAmelCase									=			getattr(
							    self.model_tester       ,    'expected_num_hidden_layers'       ,    len(self.model_tester.depths    ) + 1    )
							self.assertEqual(len(UpperCAmelCase    )       ,    UpperCAmelCase    )

							# Swin has a different seq_length
							_UpperCAmelCase									=			(
							    config.patch_size
							    if isinstance(config.patch_size       ,    collections.abc.Iterable    )
							    else (config.patch_size, config.patch_size)
							)

							_UpperCAmelCase									=			(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

							self.assertListEqual(
							    list(hidden_states[0].shape[-2:]    )       ,    [num_patches, self.model_tester.embed_dim]       ,    )

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase					, _UpperCAmelCase									=			self.model_tester.prepare_config_and_inputs_for_common()

							_UpperCAmelCase									=			(
							    self.model_tester.image_size
							    if isinstance(self.model_tester.image_size       ,    collections.abc.Iterable    )
							    else (self.model_tester.image_size, self.model_tester.image_size)
							)

							for model_class in self.all_model_classes:
													_UpperCAmelCase									=			True
													self.check_hidden_states_output(UpperCAmelCase       ,    UpperCAmelCase       ,    UpperCAmelCase       ,    UpperCAmelCase    )

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

													self.check_hidden_states_output(UpperCAmelCase       ,    UpperCAmelCase       ,    UpperCAmelCase       ,    UpperCAmelCase    )

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase					, _UpperCAmelCase									=			self.model_tester.prepare_config_and_inputs_for_common()
							_UpperCAmelCase									=			3

							_UpperCAmelCase									=			(
							    self.model_tester.image_size
							    if isinstance(self.model_tester.image_size       ,    collections.abc.Iterable    )
							    else (self.model_tester.image_size, self.model_tester.image_size)
							)
							_UpperCAmelCase									=			(
							    config.patch_size
							    if isinstance(config.patch_size       ,    collections.abc.Iterable    )
							    else (config.patch_size, config.patch_size)
							)

							_UpperCAmelCase									=			image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
							_UpperCAmelCase									=			image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

							for model_class in self.all_model_classes:
													_UpperCAmelCase									=			True
													self.check_hidden_states_output(UpperCAmelCase       ,    UpperCAmelCase       ,    UpperCAmelCase       ,    (padded_height, padded_width)    )

													# check that output_hidden_states also work using config
													del inputs_dict["output_hidden_states"]
													_UpperCAmelCase									=			True
													self.check_hidden_states_output(UpperCAmelCase       ,    UpperCAmelCase       ,    UpperCAmelCase       ,    (padded_height, padded_width)    )

	@unittest.skip(reason='MaskFormerSwin doesn\'t have pretrained checkpoints'    )
	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							pass

	@unittest.skip(reason='This will be fixed once MaskFormerSwin is replaced by native Swin'    )
	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							pass

	@unittest.skip(reason='This will be fixed once MaskFormerSwin is replaced by native Swin'    )
	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							pass

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase					, _UpperCAmelCase									=			self.model_tester.prepare_config_and_inputs_for_common()

							def set_nan_tensor_to_zero(UpperCAmelCase    ):
													_UpperCAmelCase									=			0
													return t

							def check_equivalence(UpperCAmelCase       ,    UpperCAmelCase       ,    UpperCAmelCase       ,    UpperCAmelCase={}    ):
													with torch.no_grad():
																			_UpperCAmelCase									=			model(**UpperCAmelCase       ,    return_dict=UpperCAmelCase       ,    **UpperCAmelCase    )
																			_UpperCAmelCase									=			model(**UpperCAmelCase       ,    return_dict=UpperCAmelCase       ,    **UpperCAmelCase    ).to_tuple()

																			def recursive_check(UpperCAmelCase       ,    UpperCAmelCase    ):
																									if isinstance(UpperCAmelCase       ,    (List, Tuple)    ):
																															for tuple_iterable_value, dict_iterable_value in zip(UpperCAmelCase       ,    UpperCAmelCase    ):
																																					recursive_check(UpperCAmelCase       ,    UpperCAmelCase    )
																									elif isinstance(UpperCAmelCase       ,    UpperCAmelCase    ):
																															for tuple_iterable_value, dict_iterable_value in zip(
																															    tuple_object.values()       ,    dict_object.values()    ):
																																					recursive_check(UpperCAmelCase       ,    UpperCAmelCase    )
																									elif tuple_object is None:
																															return
																									else:
																															self.assertTrue(
																															    torch.allclose(
																															        set_nan_tensor_to_zero(UpperCAmelCase    )       ,    set_nan_tensor_to_zero(UpperCAmelCase    )       ,    atol=1e-5    )       ,    msg=(
																															        'Tuple and dict output are not equal. Difference:'
																															        F""" {torch.max(torch.abs(tuple_object - dict_object    )    )}. Tuple has `nan`:"""
																															        F""" {torch.isnan(UpperCAmelCase    ).any()} and `inf`: {torch.isinf(UpperCAmelCase    )}. Dict has"""
																															        F""" `nan`: {torch.isnan(UpperCAmelCase    ).any()} and `inf`: {torch.isinf(UpperCAmelCase    )}."""
																															    )       ,    )

																			recursive_check(UpperCAmelCase       ,    UpperCAmelCase    )

							for model_class in self.all_model_classes:
													_UpperCAmelCase									=			model_class(UpperCAmelCase    )
													model.to(UpperCAmelCase    )
													model.eval()

													_UpperCAmelCase									=			self._prepare_for_class(UpperCAmelCase       ,    UpperCAmelCase    )
													_UpperCAmelCase									=			self._prepare_for_class(UpperCAmelCase       ,    UpperCAmelCase    )
													check_equivalence(UpperCAmelCase       ,    UpperCAmelCase       ,    UpperCAmelCase    )

													_UpperCAmelCase									=			self._prepare_for_class(UpperCAmelCase       ,    UpperCAmelCase       ,    return_labels=UpperCAmelCase    )
													_UpperCAmelCase									=			self._prepare_for_class(UpperCAmelCase       ,    UpperCAmelCase       ,    return_labels=UpperCAmelCase    )
													check_equivalence(UpperCAmelCase       ,    UpperCAmelCase       ,    UpperCAmelCase    )

													_UpperCAmelCase									=			self._prepare_for_class(UpperCAmelCase       ,    UpperCAmelCase    )
													_UpperCAmelCase									=			self._prepare_for_class(UpperCAmelCase       ,    UpperCAmelCase    )
													check_equivalence(UpperCAmelCase       ,    UpperCAmelCase       ,    UpperCAmelCase       ,    {'output_hidden_states': True}    )

													_UpperCAmelCase									=			self._prepare_for_class(UpperCAmelCase       ,    UpperCAmelCase       ,    return_labels=UpperCAmelCase    )
													_UpperCAmelCase									=			self._prepare_for_class(UpperCAmelCase       ,    UpperCAmelCase       ,    return_labels=UpperCAmelCase    )
													check_equivalence(UpperCAmelCase       ,    UpperCAmelCase       ,    UpperCAmelCase       ,    {'output_hidden_states': True}    )





@require_torch
class __lowerCamelCase (      unittest.TestCase     ,       snake_case__):





	"""simple docstring"""






	UpperCamelCase__								=      (MaskFormerSwinBackbone,) if is_torch_available() else ()
	UpperCamelCase__								=      MaskFormerSwinConfig

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			MaskFormerSwinModelTester(self    )

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase					, _UpperCAmelCase									=			self.model_tester.prepare_config_and_inputs_for_common()
							_UpperCAmelCase									=			inputs_dict['pixel_values'].shape[0]

							for backbone_class in self.all_model_classes:
													_UpperCAmelCase									=			backbone_class(UpperCAmelCase    )
													backbone.to(UpperCAmelCase    )
													backbone.eval()

													_UpperCAmelCase									=			backbone(**UpperCAmelCase    )

													# Test default outputs and verify feature maps
													self.assertIsInstance(outputs.feature_maps       ,    UpperCAmelCase    )
													self.assertTrue(len(outputs.feature_maps    ) == len(backbone.channels    )    )
													for feature_map, n_channels in zip(outputs.feature_maps       ,    backbone.channels    ):
																			self.assertTrue(feature_map.shape[:2]       ,    (batch_size, n_channels)    )
													self.assertIsNone(outputs.hidden_states    )
													self.assertIsNone(outputs.attentions    )

													# Test output_hidden_states=True
													_UpperCAmelCase									=			backbone(**UpperCAmelCase       ,    output_hidden_states=UpperCAmelCase    )
													self.assertIsNotNone(outputs.hidden_states    )
													self.assertTrue(len(outputs.hidden_states    )       ,    len(backbone.stage_names    )    )
													# We skip the stem layer
													for hidden_states, n_channels in zip(outputs.hidden_states[1:]       ,    backbone.channels    ):
																			for hidden_state in hidden_states:
																									# Hidden states are in the format (batch_size, (height * width), n_channels)
																									_UpperCAmelCase					, _UpperCAmelCase					, _UpperCAmelCase									=			hidden_state.shape
																									self.assertTrue((h_batch_size, h_n_channels)       ,    (batch_size, n_channels)    )

            # Test output_attentions=True
													if self.has_attentions:
																			_UpperCAmelCase									=			backbone(**UpperCAmelCase       ,    output_attentions=UpperCAmelCase    )
																			self.assertIsNotNone(outputs.attentions    )


import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path

import torch_xla.distributed.xla_multiprocessing as xmp






def 							UpperCAmelCase_		(							):
	lowercase       =    ArgumentParser(
	    description=(
	        'PyTorch TPU distributed training launch '
	        'helper utility that will spawn up '
	        'multiple distributed processes'
	    )							)

	# Optional arguments for the launch helper
	parser.add_argument('--num_cores' ,							type=__SCREAMING_SNAKE_CASE ,							default=1 ,							help='Number of TPU cores to use (1 or 8).'							)

	# positional
	parser.add_argument(
	    'training_script' ,							type=__SCREAMING_SNAKE_CASE ,							help=(
	        'The full path to the single TPU training '
	        'program/script to be launched in parallel, '
	        'followed by all the arguments for the '
	        'training script'
	    ) ,							)

	# rest from the training program
	parser.add_argument('training_script_args' ,							nargs=__SCREAMING_SNAKE_CASE							)

	return parser.parse_args()






def 							UpperCAmelCase_		(							):
	lowercase       =    parse_args()

	# Import training_script as a module.
	lowercase       =    Path(args.training_script							)
	sys.path.append(str(script_fpath.parent.resolve()							)							)
	lowercase       =    script_fpath.stem
	lowercase       =    importlib.import_module(__SCREAMING_SNAKE_CASE							)

	# Patch sys.argv
	lowercase       =    [args.training_script] + args.training_script_args + ['--tpu_num_cores', str(args.num_cores							)]
	xmp.spawn(mod._mp_fn ,							args=() ,							nprocs=args.num_cores							)


if __name__ == "__main__":
	main()

