
'''simple docstring'''







import argparse
import logging
import os

import datasets
import tensorflow as tf

from transformers import AutoTokenizer


_lowercase			:  Dict   							=	logging.getLogger(__name__)
def  snake_case_		(				):



						"""simple docstring"""





						lowercase_     :					Optional[int]													=	argparse.ArgumentParser(
						    description='''Prepare TFRecord shards from pre-tokenized samples of the wikitext dataset.'''							)
						parser.add_argument(
						    '''--dataset_name'''				,				type=snake_case__				,				default='''wikitext'''				,				help='''Name of the training. Explore datasets at: hf.co/datasets.'''				,				)
						parser.add_argument(
						    '''--dataset_config'''				,				type=snake_case__				,				default='''wikitext-103-raw-v1'''				,				help='''Configuration name of the dataset.'''							)
						parser.add_argument(
						    '''--tokenizer_name_or_path'''				,				type=snake_case__				,				default='''sayakpaul/unigram-tokenizer-wikitext'''				,				help='''Tokenizer identifier. Can be a local filepath or a Hub identifier.'''				,				)
						parser.add_argument(
						    '''--shard_size'''				,				type=snake_case__				,				default=1000				,				help='''Number of entries to go in a single shard.'''				,				)
						parser.add_argument('''--split'''				,				type=snake_case__				,				default='''train'''				,				choices=['''train''', '''test''', '''validation''']							)
						parser.add_argument(
						    '''--limit'''				,				default=snake_case__				,				type=snake_case__				,				help='''Limit the number of shards (used for debugging).'''				,				)
						parser.add_argument(
						    '''--max_length'''				,				type=snake_case__				,				default=512				,				help='''Maximum sequence length. For training on TPUs, it helps to have a maximum'''
						    ''' sequence length that is a multiple of 8.'''				,				)
						parser.add_argument(
						    '''--output_dir'''				,				default='''tf-tpu'''				,				type=snake_case__				,				help='''Output directory where the TFRecord shards will be saved. If the'''
						    ''' path is appended with `gs://` (\'gs://tf-tpu\', for example) then the TFRecord'''
						    ''' shards will be directly saved to a Google Cloud Storage bucket.'''				,				)

						lowercase_     :					Tuple													=	parser.parse_args()
						return args
def  snake_case_		(				__SCREAMING_SNAKE_CASE :			Optional[Any]							):



						"""simple docstring"""





						def fn(__SCREAMING_SNAKE_CASE :			Tuple							):
												return tokenizer(examples['''text''']							)

						return fn
def  snake_case_		(				__SCREAMING_SNAKE_CASE :			List[Any]							):



						"""simple docstring"""





						lowercase_     :					Any													=	[]
						for i in range(len(tokenized_data['''input_ids''']							)							):
												lowercase_     :					Optional[Any]													=	{
												    '''input_ids''': tf.train.Feature(intaa_list=tf.train.IntaaList(value=tokenized_data['''input_ids'''][i]							)							),
												    '''attention_mask''': tf.train.Feature(
												        intaa_list=tf.train.IntaaList(value=tokenized_data['''attention_mask'''][i]							)							),
												}
												lowercase_     :					Tuple													=	tf.train.Features(feature=snake_case__							)
												lowercase_     :					Union[str, Any]													=	tf.train.Example(features=snake_case__							)
												lowercase_     :					Tuple													=	example.SerializeToString()
												records.append(snake_case__							)
						return records
def  snake_case_		(				__SCREAMING_SNAKE_CASE :			Dict							):



						"""simple docstring"""





						lowercase_     :					Tuple													=	datasets.load_dataset(args.dataset_name				,				args.dataset_config				,				split=args.split							)

						if args.limit is not None:
												lowercase_     :					Tuple													=	min(len(snake_case__							)				,				args.limit							)
												lowercase_     :					Tuple													=	dataset.select(range(snake_case__							)							)
												print(F'''Limiting the dataset to {args.limit} entries.'''							)

						lowercase_     :					Union[str, Any]													=	AutoTokenizer.from_pretrained(args.tokenizer_name_or_path							)

						# Handle output directory creation.
						# For serializing into a Google Cloud Storage Bucket, one needs to first
						# create a bucket.
						if "gs" not in args.output_dir:
												if not os.path.exists(args.output_dir							):
																		os.makedirs(args.output_dir							)
												lowercase_     :					Optional[Any]													=	os.path.join(args.output_dir				,				args.split							)
												if not os.path.exists(snake_case__							):
																		os.makedirs(snake_case__							)
						else:
												lowercase_     :					Union[str, Any]													=	os.path.join(args.output_dir				,				args.split							)

						# Tokenize the whole dataset at once.
						lowercase_     :					List[str]													=	tokenize_function(snake_case__							)
						lowercase_     :					List[str]													=	dataset.map(snake_case__				,				batched=snake_case__				,				num_proc=4				,				remove_columns=['''text''']							)

						# We need to concatenate all our texts together, and then split the result
						# into chunks of a fixed size, which we will call block_size. To do this, we
						# will use the map method again, with the option batched=True. When we use batched=True,
						# the function we pass to map() will be passed multiple inputs at once, allowing us
						# to group them into more or fewer examples than we had in the input.
						# This allows us to create our new fixed-length samples. The advantage of this
						# method is that we don't lose a whole lot of content from the dataset compared to the
						# case where we simply tokenize with a pre-defined max_length.

						def group_texts(__SCREAMING_SNAKE_CASE :			List[Any]							):
												# Concatenate all texts.
												lowercase_     :					List[str]													=	{k: sum(examples[k]				,				[]							) for k in examples.keys()}
												lowercase_     :					Tuple													=	len(concatenated_examples[list(examples.keys()							)[0]]							)
												# We drop the small remainder, though you could add padding instead if the model supports it
												# In this, as in all things, we advise you to follow your heart 🫀
												lowercase_     :					int													=	(total_length // args.max_length) * args.max_length
												# Split by chunks of max_len.
												lowercase_     :					List[Any]													=	{
												    k: [t[i : i + args.max_length] for i in range(0				,				snake_case__				,				args.max_length							)]
												    for k, t in concatenated_examples.items()
												}
												return result

						lowercase_     :					int													=	dataset_tokenized.map(snake_case__				,				batched=snake_case__				,				batch_size=1000				,				num_proc=4							)

						lowercase_     :					str													=	0
						lowercase_     :					Union[str, Any]													=	0
						for shard in range(0				,				len(snake_case__							)				,				args.shard_size							):
												lowercase_     :					List[str]													=	grouped_dataset[shard : shard + args.shard_size]
												lowercase_     :					str													=	len(dataset_snapshot['''input_ids''']							)
												lowercase_     :					Any													=	os.path.join(snake_case__				,				F'''dataset-{shard_count}-{records_containing}.tfrecord'''							)
												lowercase_     :					Union[str, Any]													=	get_serialized_examples(snake_case__							)

												with tf.io.TFRecordWriter(snake_case__							) as out_file:
																		for i in range(len(snake_case__							)							):
																								lowercase_     :					Optional[Any]													=	serialized_examples[i]
																								out_file.write(snake_case__							)
																		print('''Wrote file {} containing {} records'''.format(snake_case__				,				snake_case__							)							)

												shard_count += 1
												total_records += records_containing

						with open(F'''split-{args.split}-records-count.txt'''				,				'''w'''							) as f:
												print(F'''Total {args.split} records: {total_records}'''				,				file=snake_case__							)


if __name__ == "__main__":
							_lowercase			:  List[str]   							=	parse_args()
							main(args)





'''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__       :






					def __init__(       self    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE=2    ,							__SCREAMING_SNAKE_CASE=True    ,							__SCREAMING_SNAKE_CASE=False    ,							__SCREAMING_SNAKE_CASE=10    ,							__SCREAMING_SNAKE_CASE=3    ,							__SCREAMING_SNAKE_CASE=32 * 4    ,							__SCREAMING_SNAKE_CASE=32 * 6    ,							__SCREAMING_SNAKE_CASE=4    ,							__SCREAMING_SNAKE_CASE=32    ,							):


											"""simple docstring"""





											lowercase_     :					Tuple													=	parent
											lowercase_     :					Optional[int]													=	batch_size
											lowercase_     :					Dict													=	is_training
											lowercase_     :					Optional[Any]													=	use_auxiliary_loss
											lowercase_     :					Optional[Any]													=	num_queries
											lowercase_     :					Any													=	num_channels
											lowercase_     :					str													=	min_size
											lowercase_     :					str													=	max_size
											lowercase_     :					Optional[Any]													=	num_labels
											lowercase_     :					List[str]													=	mask_feature_size






					def      _snake_case   (       self		):


											"""simple docstring"""





											lowercase_     :					Dict													=	floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size]		).to(
											    __SCREAMING_SNAKE_CASE		)

											lowercase_     :					str													=	torch.ones([self.batch_size, self.min_size, self.max_size]    ,							device=__SCREAMING_SNAKE_CASE		)

											lowercase_     :					str													=	(
											    torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size]    ,							device=__SCREAMING_SNAKE_CASE		) > 0.5
											).float()
											lowercase_     :					str													=	(torch.rand((self.batch_size, self.num_labels)    ,							device=__SCREAMING_SNAKE_CASE		) > 0.5).long()

											lowercase_     :					Optional[int]													=	self.get_config()
											return config, pixel_values, pixel_mask, mask_labels, class_labels






					def      _snake_case   (       self		):


											"""simple docstring"""





											return MaskFormerConfig.from_backbone_and_decoder_configs(
											    backbone_config=SwinConfig(
											        depths=[1, 1, 1, 1]    ,							)    ,							decoder_config=DetrConfig(
											        decoder_ffn_dim=1_28    ,							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      _snake_case   (       self		):


											"""simple docstring"""





											lowercase_						,			lowercase_						,			lowercase_						,			lowercase_						,			lowercase_     :					Optional[Any]													=	self.prepare_config_and_inputs()
											lowercase_     :					Any													=	{'''pixel_values''': pixel_values, '''pixel_mask''': pixel_mask}
											return config, inputs_dict






					def      _snake_case   (       self    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											lowercase_     :					List[str]													=	output.encoder_hidden_states
											lowercase_     :					List[Any]													=	output.pixel_decoder_hidden_states
											lowercase_     :					int													=	output.transformer_decoder_hidden_states

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






					def      _snake_case   (       self    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE=False		):


											"""simple docstring"""





											with torch.no_grad():
																	lowercase_     :					Any													=	MaskFormerModel(config=__SCREAMING_SNAKE_CASE		)
																	model.to(__SCREAMING_SNAKE_CASE		)
																	model.eval()

																	lowercase_     :					Dict													=	model(pixel_values=__SCREAMING_SNAKE_CASE    ,							pixel_mask=__SCREAMING_SNAKE_CASE		)
																	lowercase_     :					Union[str, Any]													=	model(__SCREAMING_SNAKE_CASE    ,							output_hidden_states=__SCREAMING_SNAKE_CASE		)
											# 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(__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE		)






					def      _snake_case   (       self    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											lowercase_     :					Dict													=	MaskFormerForInstanceSegmentation(config=__SCREAMING_SNAKE_CASE		)
											model.to(__SCREAMING_SNAKE_CASE		)
											model.eval()

											def comm_check_on_output(__SCREAMING_SNAKE_CASE		):
																	# 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():
																	lowercase_     :					Tuple													=	model(pixel_values=__SCREAMING_SNAKE_CASE    ,							pixel_mask=__SCREAMING_SNAKE_CASE		)
																	lowercase_     :					str													=	model(__SCREAMING_SNAKE_CASE		)

																	comm_check_on_output(__SCREAMING_SNAKE_CASE		)

																	lowercase_     :					Tuple													=	model(
																	    pixel_values=__SCREAMING_SNAKE_CASE    ,							pixel_mask=__SCREAMING_SNAKE_CASE    ,							mask_labels=__SCREAMING_SNAKE_CASE    ,							class_labels=__SCREAMING_SNAKE_CASE		)

											comm_check_on_output(__SCREAMING_SNAKE_CASE		)

											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					):
					lowerCAmelCase_					       =					(MaskFormerModel, MaskFormerForInstanceSegmentation) if is_torch_available() else ()
					lowerCAmelCase_					       =					(
					    {'''feature-extraction''': MaskFormerModel, '''image-segmentation''': MaskFormerForInstanceSegmentation}
					    if is_torch_available()
					    else {}
					)

					lowerCAmelCase_					       =					False
					lowerCAmelCase_					       =					False
					lowerCAmelCase_					       =					False
					lowerCAmelCase_					       =					False






					def      _snake_case   (       self		):


											"""simple docstring"""





											lowercase_     :					int													=	MaskFormerModelTester(self		)
											lowercase_     :					Tuple													=	ConfigTester(self    ,							config_class=__SCREAMING_SNAKE_CASE    ,							has_text_modality=__SCREAMING_SNAKE_CASE		)






					def      _snake_case   (       self		):


											"""simple docstring"""





											self.config_tester.run_common_tests()






					def      _snake_case   (       self		):


											"""simple docstring"""





											lowercase_						,			lowercase_     :					List[Any]													=	self.model_tester.prepare_config_and_inputs_for_common()
											self.model_tester.create_and_check_maskformer_model(__SCREAMING_SNAKE_CASE    ,							**__SCREAMING_SNAKE_CASE    ,							output_hidden_states=__SCREAMING_SNAKE_CASE		)






					def      _snake_case   (       self		):


											"""simple docstring"""





											lowercase_     :					List[Any]													=	self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_maskformer_instance_segmentation_head_model(*__SCREAMING_SNAKE_CASE		)






					@unittest.skip(reason='''MaskFormer does not use inputs_embeds'''		)
					def      _snake_case   (       self		):


											"""simple docstring"""





											pass






					@unittest.skip(reason='''MaskFormer does not have a get_input_embeddings method'''		)
					def      _snake_case   (       self		):


											"""simple docstring"""





											pass






					@unittest.skip(reason='''MaskFormer is not a generative model'''		)
					def      _snake_case   (       self		):


											"""simple docstring"""





											pass






					@unittest.skip(reason='''MaskFormer does not use token embeddings'''		)
					def      _snake_case   (       self		):


											"""simple docstring"""





											pass






					@require_torch_multi_gpu
					@unittest.skip(
					    reason='''MaskFormer has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`'''		)
					def      _snake_case   (       self		):


											"""simple docstring"""





											pass






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


											"""simple docstring"""





											pass






					def      _snake_case   (       self		):


											"""simple docstring"""





											lowercase_						,			lowercase_     :					Tuple													=	self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
																	lowercase_     :					Union[str, Any]													=	model_class(__SCREAMING_SNAKE_CASE		)
																	lowercase_     :					Any													=	inspect.signature(model.forward		)
																	# signature.parameters is an OrderedDict => so arg_names order is deterministic
																	lowercase_     :					Dict													=	[*signature.parameters.keys()]

																	lowercase_     :					str													=	['''pixel_values''']
																	self.assertListEqual(arg_names[:1]    ,							__SCREAMING_SNAKE_CASE		)






					@slow
					def      _snake_case   (       self		):


											"""simple docstring"""





											for model_name in ["facebook/maskformer-swin-small-coco"]:
																	lowercase_     :					Tuple													=	MaskFormerModel.from_pretrained(__SCREAMING_SNAKE_CASE		)
																	self.assertIsNotNone(__SCREAMING_SNAKE_CASE		)






					def      _snake_case   (       self		):


											"""simple docstring"""





											lowercase_     :					Union[str, Any]													=	(self.model_tester.min_size,) * 2
											lowercase_     :					int													=	{
											    '''pixel_values''': torch.randn((2, 3, *size)    ,							device=__SCREAMING_SNAKE_CASE		),
											    '''mask_labels''': torch.randn((2, 10, *size)    ,							device=__SCREAMING_SNAKE_CASE		),
											    '''class_labels''': torch.zeros(2    ,							10    ,							device=__SCREAMING_SNAKE_CASE		).long(),
											}

											lowercase_     :					Any													=	MaskFormerForInstanceSegmentation(MaskFormerConfig()		).to(__SCREAMING_SNAKE_CASE		)
											lowercase_     :					Union[str, Any]													=	model(**__SCREAMING_SNAKE_CASE		)
											self.assertTrue(outputs.loss is not None		)






					def      _snake_case   (       self		):


											"""simple docstring"""





											lowercase_						,			lowercase_     :					int													=	self.model_tester.prepare_config_and_inputs_for_common()
											self.model_tester.create_and_check_maskformer_model(__SCREAMING_SNAKE_CASE    ,							**__SCREAMING_SNAKE_CASE    ,							output_hidden_states=__SCREAMING_SNAKE_CASE		)






					def      _snake_case   (       self		):


											"""simple docstring"""





											lowercase_						,			lowercase_     :					str													=	self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
																	lowercase_     :					List[str]													=	model_class(__SCREAMING_SNAKE_CASE		).to(__SCREAMING_SNAKE_CASE		)
																	lowercase_     :					Tuple													=	model(**__SCREAMING_SNAKE_CASE    ,							output_attentions=__SCREAMING_SNAKE_CASE		)
																	self.assertTrue(outputs.attentions is not None		)






					def      _snake_case   (       self		):


											"""simple docstring"""





											if not self.model_tester.is_training:
																	return
											# only MaskFormerForInstanceSegmentation has the loss
											lowercase_     :					Optional[Any]													=	self.all_model_classes[1]
											lowercase_						,			lowercase_						,			lowercase_						,			lowercase_						,			lowercase_     :					Tuple													=	self.model_tester.prepare_config_and_inputs()

											lowercase_     :					Optional[int]													=	model_class(__SCREAMING_SNAKE_CASE		)
											model.to(__SCREAMING_SNAKE_CASE		)
											model.train()

											lowercase_     :					Any													=	model(__SCREAMING_SNAKE_CASE    ,							mask_labels=__SCREAMING_SNAKE_CASE    ,							class_labels=__SCREAMING_SNAKE_CASE		).loss
											loss.backward()






					def      _snake_case   (       self		):


											"""simple docstring"""





											lowercase_     :					Any													=	self.all_model_classes[1]
											lowercase_						,			lowercase_						,			lowercase_						,			lowercase_						,			lowercase_     :					Any													=	self.model_tester.prepare_config_and_inputs()
											lowercase_     :					Tuple													=	True
											lowercase_     :					Optional[Any]													=	True

											lowercase_     :					Tuple													=	model_class(__SCREAMING_SNAKE_CASE		)
											model.to(__SCREAMING_SNAKE_CASE		)
											model.train()

											lowercase_     :					List[Any]													=	model(__SCREAMING_SNAKE_CASE    ,							mask_labels=__SCREAMING_SNAKE_CASE    ,							class_labels=__SCREAMING_SNAKE_CASE		)

											lowercase_     :					Any													=	outputs.encoder_hidden_states[0]
											encoder_hidden_states.retain_grad()

											lowercase_     :					Any													=	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
											lowercase_     :					List[Any]													=	outputs.transformer_decoder_hidden_states[0]
											transformer_decoder_hidden_states.retain_grad()

											lowercase_     :					Tuple													=	outputs.attentions[0]
											attentions.retain_grad()

											outputs.loss.backward(retain_graph=__SCREAMING_SNAKE_CASE		)

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







_lowercase			:  int   							=	1E-4
def  snake_case_		(    ):



						"""simple docstring"""





						lowercase_     :					Optional[int]													=	Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''							)
						return image






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






					@cached_property
					def      _snake_case   (       self		):


											"""simple docstring"""





											return (
											    MaskFormerImageProcessor.from_pretrained('''facebook/maskformer-swin-small-coco'''		)
											    if is_vision_available()
											    else None
											)






					def      _snake_case   (       self		):


											"""simple docstring"""





											lowercase_     :					Any													=	MaskFormerModel.from_pretrained('''facebook/maskformer-swin-small-coco'''		).to(__SCREAMING_SNAKE_CASE		)
											lowercase_     :					List[str]													=	self.default_image_processor
											lowercase_     :					Dict													=	prepare_img()
											lowercase_     :					Any													=	image_processor(__SCREAMING_SNAKE_CASE    ,							return_tensors='''pt'''		).to(__SCREAMING_SNAKE_CASE		)
											lowercase_     :					Optional[Any]													=	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(__SCREAMING_SNAKE_CASE    ,							(1, 3, 8_00, 10_88)		)

											with torch.no_grad():
																	lowercase_     :					Any													=	model(**__SCREAMING_SNAKE_CASE		)

											lowercase_     :					List[str]													=	torch.tensor(
											    [[-0.0_482, 0.9_228, 0.4_951], [-0.2_547, 0.8_017, 0.8_527], [-0.0_069, 0.3_385, -0.0_089]]		).to(__SCREAMING_SNAKE_CASE		)
											self.assertTrue(
											    torch.allclose(
											        outputs.encoder_last_hidden_state[0, 0, :3, :3]    ,							__SCREAMING_SNAKE_CASE    ,							atol=__SCREAMING_SNAKE_CASE		)		)

											lowercase_     :					List[Any]													=	torch.tensor(
											    [[-0.8_422, -0.8_434, -0.9_718], [-1.0_144, -0.5_565, -0.4_195], [-1.0_038, -0.4_484, -0.1_961]]		).to(__SCREAMING_SNAKE_CASE		)
											self.assertTrue(
											    torch.allclose(
											        outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3]    ,							__SCREAMING_SNAKE_CASE    ,							atol=__SCREAMING_SNAKE_CASE		)		)

											lowercase_     :					Dict													=	torch.tensor(
											    [[0.2_852, -0.0_159, 0.9_735], [0.6_254, 0.1_858, 0.8_529], [-0.0_680, -0.4_116, 1.8_413]]		).to(__SCREAMING_SNAKE_CASE		)
											self.assertTrue(
											    torch.allclose(
											        outputs.transformer_decoder_last_hidden_state[0, :3, :3]    ,							__SCREAMING_SNAKE_CASE    ,							atol=__SCREAMING_SNAKE_CASE		)		)






					def      _snake_case   (       self		):


											"""simple docstring"""





											lowercase_     :					List[Any]													=	(
											    MaskFormerForInstanceSegmentation.from_pretrained('''facebook/maskformer-swin-small-coco'''		)
											    .to(__SCREAMING_SNAKE_CASE		)
											    .eval()
											)
											lowercase_     :					List[str]													=	self.default_image_processor
											lowercase_     :					Union[str, Any]													=	prepare_img()
											lowercase_     :					List[str]													=	image_processor(__SCREAMING_SNAKE_CASE    ,							return_tensors='''pt'''		).to(__SCREAMING_SNAKE_CASE		)
											lowercase_     :					Dict													=	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(__SCREAMING_SNAKE_CASE    ,							(1, 3, 8_00, 10_88)		)

											with torch.no_grad():
																	lowercase_     :					Tuple													=	model(**__SCREAMING_SNAKE_CASE		)
											# masks_queries_logits
											lowercase_     :					Union[str, Any]													=	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)    ,							)
											lowercase_     :					Any													=	[
											    [-1.3_737_124, -1.7_724_937, -1.9_364_233],
											    [-1.5_977_281, -1.9_867_939, -2.1_523_695],
											    [-1.5_795_398, -1.9_269_832, -2.093_942],
											]
											lowercase_     :					List[str]													=	torch.tensor(__SCREAMING_SNAKE_CASE		).to(__SCREAMING_SNAKE_CASE		)
											self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]    ,							__SCREAMING_SNAKE_CASE    ,							atol=__SCREAMING_SNAKE_CASE		)		)
											# class_queries_logits
											lowercase_     :					Optional[int]													=	outputs.class_queries_logits
											self.assertEqual(
											    class_queries_logits.shape    ,							(1, model.config.decoder_config.num_queries, model.config.num_labels + 1)		)
											lowercase_     :					Any													=	torch.tensor(
											    [
											        [1.6_5_1_2E0_0, -5.2_5_7_2E0_0, -3.3_5_1_9E0_0],
											        [3.6_1_6_9E-0_2, -5.9_0_2_5E0_0, -2.9_3_1_3E0_0],
											        [1.0_7_6_6E-0_4, -7.7_6_3_0E0_0, -5.1_2_6_3E0_0],
											    ]		).to(__SCREAMING_SNAKE_CASE		)
											self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]    ,							__SCREAMING_SNAKE_CASE    ,							atol=__SCREAMING_SNAKE_CASE		)		)






					def      _snake_case   (       self		):


											"""simple docstring"""





											lowercase_     :					int													=	(
											    MaskFormerForInstanceSegmentation.from_pretrained('''facebook/maskformer-resnet101-coco-stuff'''		)
											    .to(__SCREAMING_SNAKE_CASE		)
											    .eval()
											)
											lowercase_     :					Tuple													=	self.default_image_processor
											lowercase_     :					Any													=	prepare_img()
											lowercase_     :					Union[str, Any]													=	image_processor(__SCREAMING_SNAKE_CASE    ,							return_tensors='''pt'''		).to(__SCREAMING_SNAKE_CASE		)
											lowercase_     :					Union[str, Any]													=	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(__SCREAMING_SNAKE_CASE    ,							(1, 3, 8_00, 10_88)		)

											with torch.no_grad():
																	lowercase_     :					Union[str, Any]													=	model(**__SCREAMING_SNAKE_CASE		)
											# masks_queries_logits
											lowercase_     :					Union[str, Any]													=	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)    ,							)
											lowercase_     :					Optional[int]													=	[[-0.9_046, -2.6_366, -4.6_062], [-3.4_179, -5.7_890, -8.8_057], [-4.9_179, -7.6_560, -10.7_711]]
											lowercase_     :					Dict													=	torch.tensor(__SCREAMING_SNAKE_CASE		).to(__SCREAMING_SNAKE_CASE		)
											self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]    ,							__SCREAMING_SNAKE_CASE    ,							atol=__SCREAMING_SNAKE_CASE		)		)
											# class_queries_logits
											lowercase_     :					Any													=	outputs.class_queries_logits
											self.assertEqual(
											    class_queries_logits.shape    ,							(1, model.config.decoder_config.num_queries, model.config.num_labels + 1)		)
											lowercase_     :					Union[str, Any]													=	torch.tensor(
											    [[4.7_188, -3.2_585, -2.8_857], [6.6_871, -2.9_181, -1.2_487], [7.2_449, -2.2_764, -2.1_874]]		).to(__SCREAMING_SNAKE_CASE		)
											self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]    ,							__SCREAMING_SNAKE_CASE    ,							atol=__SCREAMING_SNAKE_CASE		)		)






					def      _snake_case   (       self		):


											"""simple docstring"""





											lowercase_     :					int													=	(
											    MaskFormerForInstanceSegmentation.from_pretrained('''facebook/maskformer-swin-small-coco'''		)
											    .to(__SCREAMING_SNAKE_CASE		)
											    .eval()
											)
											lowercase_     :					int													=	self.default_image_processor

											lowercase_     :					Optional[Any]													=	image_processor(
											    [np.zeros((3, 8_00, 13_33)		), np.zeros((3, 8_00, 13_33)		)]    ,							segmentation_maps=[np.zeros((3_84, 3_84)		).astype(np.floataa		), np.zeros((3_84, 3_84)		).astype(np.floataa		)]    ,							return_tensors='''pt'''    ,							)

											lowercase_     :					Optional[int]													=	inputs['''pixel_values'''].to(__SCREAMING_SNAKE_CASE		)
											lowercase_     :					List[str]													=	[el.to(__SCREAMING_SNAKE_CASE		) for el in inputs['''mask_labels''']]
											lowercase_     :					int													=	[el.to(__SCREAMING_SNAKE_CASE		) for el in inputs['''class_labels''']]

											with torch.no_grad():
																	lowercase_     :					List[str]													=	model(**__SCREAMING_SNAKE_CASE		)

											self.assertTrue(outputs.loss is not None		)



