


"""simple docstring"""




import inspect
import unittest

import numpy as np

from tests.test_modeling_common import floats_tensor
from transformers import DetrConfig, MaskFormerConfig, SwinConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
	import torch

	from transformers import MaskFormerForInstanceSegmentation, MaskFormerModel

	if is_vision_available():
		from transformers import MaskFormerImageProcessor

if is_vision_available():
	from PIL import Image
class 					lowerCAmelCase	:




		'''simple docstring'''







		def __init__(	self						,       lowerCAmelCase__						,       lowerCAmelCase__=2						,       lowerCAmelCase__=True						,       lowerCAmelCase__=False						,       lowerCAmelCase__=10						,       lowerCAmelCase__=3						,       lowerCAmelCase__=32 * 4						,       lowerCAmelCase__=32 * 6						,       lowerCAmelCase__=4						,       lowerCAmelCase__=32						,       )     ->							Optional[Any]:
								SCREAMING_SNAKE_CASE													=					parent
								SCREAMING_SNAKE_CASE													=					batch_size
								SCREAMING_SNAKE_CASE													=					is_training
								SCREAMING_SNAKE_CASE													=					use_auxiliary_loss
								SCREAMING_SNAKE_CASE													=					num_queries
								SCREAMING_SNAKE_CASE													=					num_channels
								SCREAMING_SNAKE_CASE													=					min_size
								SCREAMING_SNAKE_CASE													=					max_size
								SCREAMING_SNAKE_CASE													=					num_labels
								SCREAMING_SNAKE_CASE													=					mask_feature_size







		def 							__A						(	self				)     ->							List[str]:
								SCREAMING_SNAKE_CASE													=					floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size]				).to(
								    lowerCAmelCase__				)

								SCREAMING_SNAKE_CASE													=					torch.ones([self.batch_size, self.min_size, self.max_size]						,       device=lowerCAmelCase__				)

								SCREAMING_SNAKE_CASE													=					(
								    torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size]						,       device=lowerCAmelCase__				) > 0.5
								).float()
								SCREAMING_SNAKE_CASE													=					(torch.rand((self.batch_size, self.num_labels)						,       device=lowerCAmelCase__				) > 0.5).long()

								SCREAMING_SNAKE_CASE													=					self.get_config()
								return config, pixel_values, pixel_mask, mask_labels, class_labels







		def 							__A						(	self				)     ->							List[str]:
								return MaskFormerConfig.from_backbone_and_decoder_configs(
								    backbone_config=SwinConfig(
								        depths=[1, 1, 1, 1]						,       )						,       decoder_config=DetrConfig(
								        decoder_ffn_dim=128						,       num_queries=self.num_queries						,       decoder_attention_heads=2						,       d_model=self.mask_feature_size						,       )						,       mask_feature_size=self.mask_feature_size						,       fpn_feature_size=self.mask_feature_size						,       num_channels=self.num_channels						,       num_labels=self.num_labels						,       )







		def 							__A						(	self				)     ->							Dict:
								SCREAMING_SNAKE_CASE							,				SCREAMING_SNAKE_CASE							,				SCREAMING_SNAKE_CASE							,				SCREAMING_SNAKE_CASE							,				SCREAMING_SNAKE_CASE													=					self.prepare_config_and_inputs()
								SCREAMING_SNAKE_CASE													=					{'pixel_values': pixel_values, 'pixel_mask': pixel_mask}
								return config, inputs_dict







		def 							__A						(	self						,       lowerCAmelCase__						,       lowerCAmelCase__				)     ->							Optional[Any]:
								SCREAMING_SNAKE_CASE													=					output.encoder_hidden_states
								SCREAMING_SNAKE_CASE													=					output.pixel_decoder_hidden_states
								SCREAMING_SNAKE_CASE													=					output.transformer_decoder_hidden_states

								self.parent.assertTrue(len(lowerCAmelCase__				)						,       len(config.backbone_config.depths				)				)
								self.parent.assertTrue(len(lowerCAmelCase__				)						,       len(config.backbone_config.depths				)				)
								self.parent.assertTrue(len(lowerCAmelCase__				)						,       config.decoder_config.decoder_layers				)







		def 							__A						(	self						,       lowerCAmelCase__						,       lowerCAmelCase__						,       lowerCAmelCase__						,       lowerCAmelCase__=False				)     ->							str:
								with torch.no_grad():
														SCREAMING_SNAKE_CASE													=					MaskFormerModel(config=lowerCAmelCase__				)
														model.to(lowerCAmelCase__				)
														model.eval()

														SCREAMING_SNAKE_CASE													=					model(pixel_values=lowerCAmelCase__						,       pixel_mask=lowerCAmelCase__				)
														SCREAMING_SNAKE_CASE													=					model(lowerCAmelCase__						,       output_hidden_states=lowerCAmelCase__				)
								# the correct shape of output.transformer_decoder_hidden_states ensure the correcteness of the
								# encoder and pixel decoder
								self.parent.assertEqual(
								    output.transformer_decoder_last_hidden_state.shape						,       (self.batch_size, self.num_queries, self.mask_feature_size)						,       )
								# let's ensure the other two hidden state exists
								self.parent.assertTrue(output.pixel_decoder_last_hidden_state is not None				)
								self.parent.assertTrue(output.encoder_last_hidden_state is not None				)

								if output_hidden_states:
														self.check_output_hidden_state(lowerCAmelCase__						,       lowerCAmelCase__				)







		def 							__A						(	self						,       lowerCAmelCase__						,       lowerCAmelCase__						,       lowerCAmelCase__						,       lowerCAmelCase__						,       lowerCAmelCase__				)     ->							Any:
								SCREAMING_SNAKE_CASE													=					MaskFormerForInstanceSegmentation(config=lowerCAmelCase__				)
								model.to(lowerCAmelCase__				)
								model.eval()

								def comm_check_on_output(lowerCAmelCase__				):
														# let's still check that all the required stuff is there
														self.parent.assertTrue(result.transformer_decoder_last_hidden_state is not None				)
														self.parent.assertTrue(result.pixel_decoder_last_hidden_state is not None				)
														self.parent.assertTrue(result.encoder_last_hidden_state is not None				)
														# okay, now we need to check the logits shape
														# due to the encoder compression, masks have a //4 spatial size
														self.parent.assertEqual(
														    result.masks_queries_logits.shape						,       (self.batch_size, self.num_queries, self.min_size // 4, self.max_size // 4)						,       )
														# + 1 for null class
														self.parent.assertEqual(
														    result.class_queries_logits.shape						,       (self.batch_size, self.num_queries, self.num_labels + 1)				)

								with torch.no_grad():
														SCREAMING_SNAKE_CASE													=					model(pixel_values=lowerCAmelCase__						,       pixel_mask=lowerCAmelCase__				)
														SCREAMING_SNAKE_CASE													=					model(lowerCAmelCase__				)

														comm_check_on_output(lowerCAmelCase__				)

														SCREAMING_SNAKE_CASE													=					model(
														    pixel_values=lowerCAmelCase__						,       pixel_mask=lowerCAmelCase__						,       mask_labels=lowerCAmelCase__						,       class_labels=lowerCAmelCase__				)

								comm_check_on_output(lowerCAmelCase__				)

								self.parent.assertTrue(result.loss is not None				)
								self.parent.assertEqual(result.loss.shape						,       torch.Size([1]				)				)





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




		'''simple docstring'''







		SCREAMING_SNAKE_CASE_						:     Optional[int]								=     (MaskFormerModel, MaskFormerForInstanceSegmentation) if is_torch_available() else ()
		SCREAMING_SNAKE_CASE_						:     str								=     (
		    {"""feature-extraction""": MaskFormerModel, """image-segmentation""": MaskFormerForInstanceSegmentation}
		    if is_torch_available()
		    else {}
		)

		SCREAMING_SNAKE_CASE_						:     Union[str, Any]								=     False
		SCREAMING_SNAKE_CASE_						:     Optional[Any]								=     False
		SCREAMING_SNAKE_CASE_						:     Optional[Any]								=     False
		SCREAMING_SNAKE_CASE_						:     str								=     False







		def 							__A						(	self				)     ->							Union[str, Any]:
								SCREAMING_SNAKE_CASE													=					MaskFormerModelTester(self				)
								SCREAMING_SNAKE_CASE													=					ConfigTester(self						,       config_class=lowerCAmelCase__						,       has_text_modality=lowerCAmelCase__				)







		def 							__A						(	self				)     ->							List[str]:
								self.config_tester.run_common_tests()







		def 							__A						(	self				)     ->							List[str]:
								SCREAMING_SNAKE_CASE							,				SCREAMING_SNAKE_CASE													=					self.model_tester.prepare_config_and_inputs_for_common()
								self.model_tester.create_and_check_maskformer_model(lowerCAmelCase__						,       **lowerCAmelCase__						,       output_hidden_states=lowerCAmelCase__				)







		def 							__A						(	self				)     ->							Tuple:
								SCREAMING_SNAKE_CASE													=					self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_maskformer_instance_segmentation_head_model(*lowerCAmelCase__				)







		@unittest.skip(reason='MaskFormer does not use inputs_embeds'				)
		def 							__A						(	self				)     ->							Any:
								pass







		@unittest.skip(reason='MaskFormer does not have a get_input_embeddings method'				)
		def 							__A						(	self				)     ->							List[str]:
								pass







		@unittest.skip(reason='MaskFormer is not a generative model'				)
		def 							__A						(	self				)     ->							Tuple:
								pass







		@unittest.skip(reason='MaskFormer does not use token embeddings'				)
		def 							__A						(	self				)     ->							Tuple:
								pass







		@require_torch_multi_gpu
		@unittest.skip(
		    reason='MaskFormer has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`'				)
		def 							__A						(	self				)     ->							List[Any]:
								pass







		@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'				)
		def 							__A						(	self				)     ->							str:
								pass







		def 							__A						(	self				)     ->							Tuple:
								SCREAMING_SNAKE_CASE							,				SCREAMING_SNAKE_CASE													=					self.model_tester.prepare_config_and_inputs_for_common()

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

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







		@slow
		def 							__A						(	self				)     ->							Optional[int]:
								for model_name in ["facebook/maskformer-swin-small-coco"]:
														SCREAMING_SNAKE_CASE													=					MaskFormerModel.from_pretrained(lowerCAmelCase__				)
														self.assertIsNotNone(lowerCAmelCase__				)







		def 							__A						(	self				)     ->							int:
								SCREAMING_SNAKE_CASE													=					(self.model_tester.min_size,) * 2
								SCREAMING_SNAKE_CASE													=					{
								    'pixel_values': torch.randn((2, 3, *size)						,       device=lowerCAmelCase__				),
								    'mask_labels': torch.randn((2, 10, *size)						,       device=lowerCAmelCase__				),
								    'class_labels': torch.zeros(2						,       10						,       device=lowerCAmelCase__				).long(),
								}

								SCREAMING_SNAKE_CASE													=					MaskFormerForInstanceSegmentation(MaskFormerConfig()				).to(lowerCAmelCase__				)
								SCREAMING_SNAKE_CASE													=					model(**lowerCAmelCase__				)
								self.assertTrue(outputs.loss is not None				)







		def 							__A						(	self				)     ->							Any:
								SCREAMING_SNAKE_CASE							,				SCREAMING_SNAKE_CASE													=					self.model_tester.prepare_config_and_inputs_for_common()
								self.model_tester.create_and_check_maskformer_model(lowerCAmelCase__						,       **lowerCAmelCase__						,       output_hidden_states=lowerCAmelCase__				)







		def 							__A						(	self				)     ->							List[str]:
								SCREAMING_SNAKE_CASE							,				SCREAMING_SNAKE_CASE													=					self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
														SCREAMING_SNAKE_CASE													=					model_class(lowerCAmelCase__				).to(lowerCAmelCase__				)
														SCREAMING_SNAKE_CASE													=					model(**lowerCAmelCase__						,       output_attentions=lowerCAmelCase__				)
														self.assertTrue(outputs.attentions is not None				)







		def 							__A						(	self				)     ->							List[str]:
								if not self.model_tester.is_training:
														return
								# only MaskFormerForInstanceSegmentation has the loss
								SCREAMING_SNAKE_CASE													=					self.all_model_classes[1]
								SCREAMING_SNAKE_CASE							,				SCREAMING_SNAKE_CASE							,				SCREAMING_SNAKE_CASE							,				SCREAMING_SNAKE_CASE							,				SCREAMING_SNAKE_CASE													=					self.model_tester.prepare_config_and_inputs()

								SCREAMING_SNAKE_CASE													=					model_class(lowerCAmelCase__				)
								model.to(lowerCAmelCase__				)
								model.train()

								SCREAMING_SNAKE_CASE													=					model(lowerCAmelCase__						,       mask_labels=lowerCAmelCase__						,       class_labels=lowerCAmelCase__				).loss
								loss.backward()







		def 							__A						(	self				)     ->							Any:
								# only MaskFormerForInstanceSegmentation has the loss
								SCREAMING_SNAKE_CASE													=					self.all_model_classes[1]
								SCREAMING_SNAKE_CASE							,				SCREAMING_SNAKE_CASE							,				SCREAMING_SNAKE_CASE							,				SCREAMING_SNAKE_CASE							,				SCREAMING_SNAKE_CASE													=					self.model_tester.prepare_config_and_inputs()
								SCREAMING_SNAKE_CASE													=					True
								SCREAMING_SNAKE_CASE													=					True

								SCREAMING_SNAKE_CASE													=					model_class(lowerCAmelCase__				)
								model.to(lowerCAmelCase__				)
								model.train()

								SCREAMING_SNAKE_CASE													=					model(lowerCAmelCase__						,       mask_labels=lowerCAmelCase__						,       class_labels=lowerCAmelCase__				)

								SCREAMING_SNAKE_CASE													=					outputs.encoder_hidden_states[0]
								encoder_hidden_states.retain_grad()

								SCREAMING_SNAKE_CASE													=					outputs.pixel_decoder_hidden_states[0]
								pixel_decoder_hidden_states.retain_grad()
								# we requires_grad=True in inputs_embeds (line 2152), the original implementation don't
								SCREAMING_SNAKE_CASE													=					outputs.transformer_decoder_hidden_states[0]
								transformer_decoder_hidden_states.retain_grad()

								SCREAMING_SNAKE_CASE													=					outputs.attentions[0]
								attentions.retain_grad()

								outputs.loss.backward(retain_graph=lowerCAmelCase__				)

								self.assertIsNotNone(encoder_hidden_states.grad				)
								self.assertIsNotNone(pixel_decoder_hidden_states.grad				)
								self.assertIsNotNone(transformer_decoder_hidden_states.grad				)
								self.assertIsNotNone(attentions.grad				)





__UpperCamelCase						=      1E-4


def     lowercase			()   ->						Optional[Any]:
						SCREAMING_SNAKE_CASE													=					Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'  )
						return image


@require_vision
@slow
class 					lowerCAmelCase	(					unittest.TestCase    ):




		'''simple docstring'''







		@cached_property
		def 							__A						(	self				)     ->							List[Any]:
								return (
								    MaskFormerImageProcessor.from_pretrained('facebook/maskformer-swin-small-coco'				)
								    if is_vision_available()
								    else None
								)







		def 							__A						(	self				)     ->							List[Any]:
								SCREAMING_SNAKE_CASE													=					MaskFormerModel.from_pretrained('facebook/maskformer-swin-small-coco'				).to(lowerCAmelCase__				)
								SCREAMING_SNAKE_CASE													=					self.default_image_processor
								SCREAMING_SNAKE_CASE													=					prepare_img()
								SCREAMING_SNAKE_CASE													=					image_processor(lowerCAmelCase__						,       return_tensors='pt'				).to(lowerCAmelCase__				)
								SCREAMING_SNAKE_CASE													=					inputs['pixel_values'].shape
								# check size is divisible by 32
								self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0				)
								# check size
								self.assertEqual(lowerCAmelCase__						,       (1, 3, 800, 1_088)				)

								with torch.no_grad():
														SCREAMING_SNAKE_CASE													=					model(**lowerCAmelCase__				)

								SCREAMING_SNAKE_CASE													=					torch.tensor(
								    [[-0.04_82, 0.92_28, 0.49_51], [-0.25_47, 0.80_17, 0.85_27], [-0.00_69, 0.33_85, -0.00_89]]				).to(lowerCAmelCase__				)
								self.assertTrue(
								    torch.allclose(
								        outputs.encoder_last_hidden_state[0, 0, :3, :3]						,       lowerCAmelCase__						,       atol=lowerCAmelCase__				)				)

								SCREAMING_SNAKE_CASE													=					torch.tensor(
								    [[-0.84_22, -0.84_34, -0.97_18], [-1.01_44, -0.55_65, -0.41_95], [-1.00_38, -0.44_84, -0.19_61]]				).to(lowerCAmelCase__				)
								self.assertTrue(
								    torch.allclose(
								        outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3]						,       lowerCAmelCase__						,       atol=lowerCAmelCase__				)				)

								SCREAMING_SNAKE_CASE													=					torch.tensor(
								    [[0.28_52, -0.01_59, 0.97_35], [0.62_54, 0.18_58, 0.85_29], [-0.06_80, -0.41_16, 1.84_13]]				).to(lowerCAmelCase__				)
								self.assertTrue(
								    torch.allclose(
								        outputs.transformer_decoder_last_hidden_state[0, :3, :3]						,       lowerCAmelCase__						,       atol=lowerCAmelCase__				)				)







		def 							__A						(	self				)     ->							Optional[Any]:
								SCREAMING_SNAKE_CASE													=					(
								    MaskFormerForInstanceSegmentation.from_pretrained('facebook/maskformer-swin-small-coco'				)
								    .to(lowerCAmelCase__				)
								    .eval()
								)
								SCREAMING_SNAKE_CASE													=					self.default_image_processor
								SCREAMING_SNAKE_CASE													=					prepare_img()
								SCREAMING_SNAKE_CASE													=					image_processor(lowerCAmelCase__						,       return_tensors='pt'				).to(lowerCAmelCase__				)
								SCREAMING_SNAKE_CASE													=					inputs['pixel_values'].shape
								# check size is divisible by 32
								self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0				)
								# check size
								self.assertEqual(lowerCAmelCase__						,       (1, 3, 800, 1_088)				)

								with torch.no_grad():
														SCREAMING_SNAKE_CASE													=					model(**lowerCAmelCase__				)
								# masks_queries_logits
								SCREAMING_SNAKE_CASE													=					outputs.masks_queries_logits
								self.assertEqual(
								    masks_queries_logits.shape						,       (1, model.config.decoder_config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4)						,       )
								SCREAMING_SNAKE_CASE													=					[
								    [-1.3_73_71_24, -1.7_72_49_37, -1.9_36_42_33],
								    [-1.5_97_72_81, -1.9_86_79_39, -2.1_52_36_95],
								    [-1.5_79_53_98, -1.9_26_98_32, -2.09_39_42],
								]
								SCREAMING_SNAKE_CASE													=					torch.tensor(lowerCAmelCase__				).to(lowerCAmelCase__				)
								self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]						,       lowerCAmelCase__						,       atol=lowerCAmelCase__				)				)
								# class_queries_logits
								SCREAMING_SNAKE_CASE													=					outputs.class_queries_logits
								self.assertEqual(
								    class_queries_logits.shape						,       (1, model.config.decoder_config.num_queries, model.config.num_labels + 1)				)
								SCREAMING_SNAKE_CASE													=					torch.tensor(
								    [
								        [1.6512e00, -5.2572e00, -3.3519e00],
								        [3.6169e-02, -5.9025e00, -2.9313e00],
								        [1.0766e-04, -7.7630e00, -5.1263e00],
								    ]				).to(lowerCAmelCase__				)
								self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]						,       lowerCAmelCase__						,       atol=lowerCAmelCase__				)				)







		def 							__A						(	self				)     ->							Tuple:
								SCREAMING_SNAKE_CASE													=					(
								    MaskFormerForInstanceSegmentation.from_pretrained('facebook/maskformer-resnet101-coco-stuff'				)
								    .to(lowerCAmelCase__				)
								    .eval()
								)
								SCREAMING_SNAKE_CASE													=					self.default_image_processor
								SCREAMING_SNAKE_CASE													=					prepare_img()
								SCREAMING_SNAKE_CASE													=					image_processor(lowerCAmelCase__						,       return_tensors='pt'				).to(lowerCAmelCase__				)
								SCREAMING_SNAKE_CASE													=					inputs['pixel_values'].shape
								# check size is divisible by 32
								self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0				)
								# check size
								self.assertEqual(lowerCAmelCase__						,       (1, 3, 800, 1_088)				)

								with torch.no_grad():
														SCREAMING_SNAKE_CASE													=					model(**lowerCAmelCase__				)
								# masks_queries_logits
								SCREAMING_SNAKE_CASE													=					outputs.masks_queries_logits
								self.assertEqual(
								    masks_queries_logits.shape						,       (1, model.config.decoder_config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4)						,       )
								SCREAMING_SNAKE_CASE													=					[[-0.90_46, -2.63_66, -4.60_62], [-3.41_79, -5.78_90, -8.80_57], [-4.91_79, -7.65_60, -10.77_11]]
								SCREAMING_SNAKE_CASE													=					torch.tensor(lowerCAmelCase__				).to(lowerCAmelCase__				)
								self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]						,       lowerCAmelCase__						,       atol=lowerCAmelCase__				)				)
								# class_queries_logits
								SCREAMING_SNAKE_CASE													=					outputs.class_queries_logits
								self.assertEqual(
								    class_queries_logits.shape						,       (1, model.config.decoder_config.num_queries, model.config.num_labels + 1)				)
								SCREAMING_SNAKE_CASE													=					torch.tensor(
								    [[4.71_88, -3.25_85, -2.88_57], [6.68_71, -2.91_81, -1.24_87], [7.24_49, -2.27_64, -2.18_74]]				).to(lowerCAmelCase__				)
								self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]						,       lowerCAmelCase__						,       atol=lowerCAmelCase__				)				)







		def 							__A						(	self				)     ->							Any:
								SCREAMING_SNAKE_CASE													=					(
								    MaskFormerForInstanceSegmentation.from_pretrained('facebook/maskformer-swin-small-coco'				)
								    .to(lowerCAmelCase__				)
								    .eval()
								)
								SCREAMING_SNAKE_CASE													=					self.default_image_processor

								SCREAMING_SNAKE_CASE													=					image_processor(
								    [np.zeros((3, 800, 1_333)				), np.zeros((3, 800, 1_333)				)]						,       segmentation_maps=[np.zeros((384, 384)				).astype(np.floataa				), np.zeros((384, 384)				).astype(np.floataa				)]						,       return_tensors='pt'						,       )

								SCREAMING_SNAKE_CASE													=					inputs['pixel_values'].to(lowerCAmelCase__				)
								SCREAMING_SNAKE_CASE													=					[el.to(lowerCAmelCase__				) for el in inputs['mask_labels']]
								SCREAMING_SNAKE_CASE													=					[el.to(lowerCAmelCase__				) for el in inputs['class_labels']]

								with torch.no_grad():
														SCREAMING_SNAKE_CASE													=					model(**lowerCAmelCase__				)

								self.assertTrue(outputs.loss is not None				)






"""simple docstring"""




import argparse
import os

import numpy as np
import tensorflow as tf
import torch

from transformers import BertModel


def     lowercase			(SCREAMING_SNAKE_CASE_		:				BertModel ,			SCREAMING_SNAKE_CASE_		:				str ,			SCREAMING_SNAKE_CASE_		:				str  )   ->						Dict:
						SCREAMING_SNAKE_CASE													=					('dense.weight', 'attention.self.query', 'attention.self.key', 'attention.self.value')

						SCREAMING_SNAKE_CASE													=					(
						    ('layer.', 'layer_'),
						    ('word_embeddings.weight', 'word_embeddings'),
						    ('position_embeddings.weight', 'position_embeddings'),
						    ('token_type_embeddings.weight', 'token_type_embeddings'),
						    ('.', '/'),
						    ('LayerNorm/weight', 'LayerNorm/gamma'),
						    ('LayerNorm/bias', 'LayerNorm/beta'),
						    ('weight', 'kernel'),
						)

						if not os.path.isdir(SCREAMING_SNAKE_CASE_  ):
												os.makedirs(SCREAMING_SNAKE_CASE_  )

						SCREAMING_SNAKE_CASE													=					model.state_dict()

						def to_tf_var_name(SCREAMING_SNAKE_CASE_		:				str  ):
												for patt, repl in iter(SCREAMING_SNAKE_CASE_  ):
																		SCREAMING_SNAKE_CASE													=					name.replace(SCREAMING_SNAKE_CASE_ ,			SCREAMING_SNAKE_CASE_  )
												return F'bert/{name}'

						def create_tf_var(SCREAMING_SNAKE_CASE_		:				np.ndarray ,			SCREAMING_SNAKE_CASE_		:				str ,			SCREAMING_SNAKE_CASE_		:				tf.Session  ):
												SCREAMING_SNAKE_CASE													=					tf.dtypes.as_dtype(tensor.dtype  )
												SCREAMING_SNAKE_CASE													=					tf.get_variable(dtype=SCREAMING_SNAKE_CASE_ ,			shape=tensor.shape ,			name=SCREAMING_SNAKE_CASE_ ,			initializer=tf.zeros_initializer()  )
												session.run(tf.variables_initializer([tf_var]  )  )
												session.run(SCREAMING_SNAKE_CASE_  )
												return tf_var

						tf.reset_default_graph()
						with tf.Session() as session:
												for var_name in state_dict:
																		SCREAMING_SNAKE_CASE													=					to_tf_var_name(SCREAMING_SNAKE_CASE_  )
																		SCREAMING_SNAKE_CASE													=					state_dict[var_name].numpy()
																		if any(x in var_name for x in tensors_to_transpose  ):
																								SCREAMING_SNAKE_CASE													=					torch_tensor.T
																		SCREAMING_SNAKE_CASE													=					create_tf_var(tensor=SCREAMING_SNAKE_CASE_ ,			name=SCREAMING_SNAKE_CASE_ ,			session=SCREAMING_SNAKE_CASE_  )
																		tf.keras.backend.set_value(SCREAMING_SNAKE_CASE_ ,			SCREAMING_SNAKE_CASE_  )
																		SCREAMING_SNAKE_CASE													=					session.run(SCREAMING_SNAKE_CASE_  )
																		print(F'Successfully created {tf_name}: {np.allclose(SCREAMING_SNAKE_CASE_ ,			SCREAMING_SNAKE_CASE_  )}'  )

												SCREAMING_SNAKE_CASE													=					tf.train.Saver(tf.trainable_variables()  )
												saver.save(SCREAMING_SNAKE_CASE_ ,			os.path.join(SCREAMING_SNAKE_CASE_ ,			model_name.replace('-' ,			'_'  ) + '.ckpt'  )  )


def     lowercase			(SCREAMING_SNAKE_CASE_		:				Union[str, Any]=None  )   ->						Any:
						SCREAMING_SNAKE_CASE													=					argparse.ArgumentParser()
						parser.add_argument('--model_name' ,			type=SCREAMING_SNAKE_CASE_ ,			required=SCREAMING_SNAKE_CASE_ ,			help='model name e.g. bert-base-uncased'  )
						parser.add_argument(
						    '--cache_dir' ,			type=SCREAMING_SNAKE_CASE_ ,			default=SCREAMING_SNAKE_CASE_ ,			required=SCREAMING_SNAKE_CASE_ ,			help='Directory containing pytorch model'  )
						parser.add_argument('--pytorch_model_path' ,			type=SCREAMING_SNAKE_CASE_ ,			required=SCREAMING_SNAKE_CASE_ ,			help='/path/to/<pytorch-model-name>.bin'  )
						parser.add_argument('--tf_cache_dir' ,			type=SCREAMING_SNAKE_CASE_ ,			required=SCREAMING_SNAKE_CASE_ ,			help='Directory in which to save tensorflow model'  )
						SCREAMING_SNAKE_CASE													=					parser.parse_args(SCREAMING_SNAKE_CASE_  )

						SCREAMING_SNAKE_CASE													=					BertModel.from_pretrained(
						    pretrained_model_name_or_path=args.model_name ,			state_dict=torch.load(args.pytorch_model_path  ) ,			cache_dir=args.cache_dir ,			)

						convert_pytorch_checkpoint_to_tf(model=SCREAMING_SNAKE_CASE_ ,			ckpt_dir=args.tf_cache_dir ,			model_name=args.model_name  )


if __name__ == "__main__":
	main()


