

"""simple docstring"""




import gzip
import hashlib
import json
import multiprocessing
import os
import re
import shutil
import time
from pathlib import Path

import numpy as np
from arguments import PreprocessingArguments
from datasets import load_dataset
from minhash_deduplication import deduplicate_dataset

from transformers import AutoTokenizer, HfArgumentParser


A_							:			Tuple											= re.compile(R"\s+")

def 	A       (     snake_case__    ):




			'''simple docstring'''


			return {"hash": hashlib.mda(re.sub(snake_case__		,				""""""		,				example["""content"""]    ).encode("""utf-8"""    )    ).hexdigest()}

def 	A       (     snake_case__    ):




			'''simple docstring'''


			SCREAMING_SNAKE_CASE__								=      [len(snake_case__    ) for line in example["""content"""].splitlines()]
			return {"line_mean": np.mean(snake_case__    ), "line_max": max(snake_case__    )}

def 	A       (     snake_case__    ):




			'''simple docstring'''


			SCREAMING_SNAKE_CASE__								=      np.mean([c.isalnum() for c in example["""content"""]]    )
			return {"alpha_frac": alpha_frac}

def 	A       (     snake_case__		,				snake_case__    ):




			'''simple docstring'''


			if example["hash"] in uniques:
						uniques.remove(example["""hash"""]    )
						return True
			else:
						return False

def 	A       (     snake_case__		,				snake_case__=5    ):




			'''simple docstring'''


			SCREAMING_SNAKE_CASE__								=      ["""auto-generated""", """autogenerated""", """automatically generated"""]
			SCREAMING_SNAKE_CASE__								=      example["""content"""].splitlines()
			for _, line in zip(range(snake_case__    )		,				snake_case__    ):
						for keyword in keywords:
									if keyword in line.lower():
												return {"autogenerated": True}
			else:
						return {"autogenerated": False}

def 	A       (     snake_case__		,				snake_case__=5		,				snake_case__=0.05    ):




			'''simple docstring'''


			SCREAMING_SNAKE_CASE__								=      ["""unit tests""", """test file""", """configuration file"""]
			SCREAMING_SNAKE_CASE__								=      example["""content"""].splitlines()
			SCREAMING_SNAKE_CASE__								=      0
			SCREAMING_SNAKE_CASE__								=      0
			# first test
			for _, line in zip(range(snake_case__    )		,				snake_case__    ):
						for keyword in keywords:
									if keyword in line.lower():
												return {"config_or_test": True}
    # second test
			SCREAMING_SNAKE_CASE__								=      example["""content"""].count("""\n"""    )
			SCREAMING_SNAKE_CASE__								=      int(coeff * nlines    )
			for line in lines:
						count_config += line.lower().count("""config"""    )
						count_test += line.lower().count("""test"""    )
						if count_config > threshold or count_test > threshold:
									return {"config_or_test": True}
			return {"config_or_test": False}

def 	A       (     snake_case__    ):




			'''simple docstring'''


			SCREAMING_SNAKE_CASE__								=      ["""def """, """class """, """for """, """while """]
			SCREAMING_SNAKE_CASE__								=      example["""content"""].splitlines()
			for line in lines:
						for keyword in keywords:
									if keyword in line.lower():
												return {"has_no_keywords": False}
			return {"has_no_keywords": True}

def 	A       (     snake_case__		,				snake_case__=4    ):




			'''simple docstring'''


			SCREAMING_SNAKE_CASE__								=      example["""content"""].splitlines()
			SCREAMING_SNAKE_CASE__								=      0
			for line in lines:
						counter += line.lower().count("""="""    )
						if counter > minimum:
									return {"has_few_assignments": False}
			return {"has_few_assignments": True}

def 	A       (     snake_case__    ):




			'''simple docstring'''


			SCREAMING_SNAKE_CASE__								=      tokenizer(example["""content"""]		,				truncation=snake_case__    )["""input_ids"""]
			SCREAMING_SNAKE_CASE__								=      len(example["""content"""]    ) / len(snake_case__    )
			return {"ratio": ratio}

def 	A       (     snake_case__    ):




			'''simple docstring'''


			SCREAMING_SNAKE_CASE__								=      {}
			results.update(get_hash(snake_case__    )    )
			results.update(line_stats(snake_case__    )    )
			results.update(alpha_stats(snake_case__    )    )
			results.update(char_token_ratio(snake_case__    )    )
			results.update(is_autogenerated(snake_case__    )    )
			results.update(is_config_or_test(snake_case__    )    )
			results.update(has_no_keywords(snake_case__    )    )
			results.update(has_few_assignments(snake_case__    )    )
			return results

def 	A       (     snake_case__		,				snake_case__		,				snake_case__    ):




			'''simple docstring'''


			if not check_uniques(snake_case__		,				snake_case__    ):
						return False
			elif example["autogenerated"]:
						return False
			elif example["line_max"] > args.line_max:
						return False
			elif example["line_mean"] > args.line_mean:
						return False
			elif example["alpha_frac"] < args.alpha_frac:
						return False
			elif example["ratio"] < args.min_token_ratio:
						return False
			elif example["config_or_test"] and np.random.rand() <= args.filter_proba:
						return False
			elif example["has_no_keywords"] and np.random.rand() <= args.filter_proba:
						return False
			elif example["has_few_assignments"]:
						return False
			else:
						return True

def 	A       (     snake_case__    ):




			'''simple docstring'''


			with open(snake_case__		,				"""rb"""    ) as f_in:
						with gzip.open(str(snake_case__    ) + """.gz"""		,				"""wb"""		,				compresslevel=6    ) as f_out:
									shutil.copyfileobj(snake_case__		,				snake_case__    )
			os.unlink(snake_case__    )


# Settings
A_							:			Any											= HfArgumentParser(PreprocessingArguments)
A_							:			int											= parser.parse_args()
if args.num_workers is None:
						A_							:			List[Any]											= multiprocessing.cpu_count()
A_							:			Union[str, Any]											= AutoTokenizer.from_pretrained(args.tokenizer_dir)

# Load dataset
A_							:			Any											= time.time()
A_							:			int											= load_dataset(args.dataset_name, split="train")
print(F'Time to load dataset: {time.time()-t_start:.2f}')

# Run preprocessing
A_							:			Tuple											= time.time()
A_							:			Union[str, Any]											= ds.map(preprocess, num_proc=args.num_workers)
print(F'Time to preprocess dataset: {time.time()-t_start:.2f}')

# Deduplicate hashes
A_							:			Optional[Any]											= set(ds.unique("hash"))
A_							:			Dict											= len(uniques) / len(ds)
print(F'Fraction of duplicates: {1-frac:.2%}')

# Deduplicate data and apply heuristics
A_							:			str											= time.time()
A_							:			List[Any]											= ds.filter(filter, fn_kwargs={"uniques": uniques, "args": args})
print(F'Time to filter dataset: {time.time()-t_start:.2f}')
print(F'Size of filtered dataset: {len(ds_filter)}')

# Deduplicate with minhash and jaccard similarity
if args.near_deduplication:
						A_							:			str											= time.time()
						A_					,	A_							:			List[Any]											= deduplicate_dataset(ds_filter, args.jaccard_threshold)
						print(F'Time to deduplicate dataset: {time.time()-t_start:.2f}')
						print(F'Size of deduplicate dataset: {len(ds_filter)}')

# Save data in batches of samples_per_file
A_							:			Dict											= Path(args.output_dir)
output_dir.mkdir(exist_ok=True)

# save duplicate_clusters in the output_dir as artifacts
# not sure it is the right place the save it
if args.near_deduplication:
						with open(output_dir / "duplicate_clusters.json", "w") as f:
												json.dump(duplicate_clusters, f)

A_							:			Optional[Any]											= output_dir / "data"
data_dir.mkdir(exist_ok=True)

A_							:			Tuple											= time.time()
for file_number, index in enumerate(range(0, len(ds_filter), args.samples_per_file)):
						A_							:			Dict											= str(data_dir / F'file-{file_number+1:012}.json')
						A_							:			Any											= min(len(ds_filter), index + args.samples_per_file)
						ds_filter.select(list(range(index, end_index))).to_json(file_path)
						compress_file(file_path)




print(F'Time to save dataset: {time.time()-t_start:.2f}')





"""simple docstring"""




import inspect
import unittest

import numpy as np

from tests.test_modeling_common import floats_tensor
from transformers import MaskaFormerConfig, 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 MaskaFormerForUniversalSegmentation, MaskaFormerModel

						if is_vision_available():
												from transformers import MaskaFormerImageProcessor

if is_vision_available():
						from PIL import Image





class    lowerCamelCase						:

			def __init__(					self				:						Optional[Any]    ,				__UpperCAmelCase				:						str    ,				__UpperCAmelCase				:						int=2    ,				__UpperCAmelCase				:						List[str]=True    ,				__UpperCAmelCase				:						List[str]=False    ,				__UpperCAmelCase				:						Dict=1_0    ,				__UpperCAmelCase				:						str=3    ,				__UpperCAmelCase				:						List[str]=3_2 * 8    ,				__UpperCAmelCase				:						List[Any]=3_2 * 8    ,				__UpperCAmelCase				:						Tuple=4    ,				__UpperCAmelCase				:						Tuple=6_4    ,				)   ->    Tuple:
						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__								=      hidden_dim
						SCREAMING_SNAKE_CASE__								=      hidden_dim

			def 					SCREAMING_SNAKE_CASE				(					self				:						List[Any]       )   ->    Any:
						SCREAMING_SNAKE_CASE__								=      floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size]       ).to(
						    __UpperCAmelCase       )

						SCREAMING_SNAKE_CASE__								=      torch.ones([self.batch_size, self.min_size, self.max_size]    ,				device=__UpperCAmelCase       )

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

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

			def 					SCREAMING_SNAKE_CASE				(					self				:						List[str]       )   ->    List[Any]:
						SCREAMING_SNAKE_CASE__								=      MaskaFormerConfig(
						    hidden_size=self.hidden_dim    ,				)
						SCREAMING_SNAKE_CASE__								=      self.num_queries
						SCREAMING_SNAKE_CASE__								=      self.num_labels

						SCREAMING_SNAKE_CASE__								=      [1, 1, 1, 1]
						SCREAMING_SNAKE_CASE__								=      self.num_channels

						SCREAMING_SNAKE_CASE__								=      6_4
						SCREAMING_SNAKE_CASE__								=      1_2_8
						SCREAMING_SNAKE_CASE__								=      self.hidden_dim
						SCREAMING_SNAKE_CASE__								=      self.hidden_dim
						SCREAMING_SNAKE_CASE__								=      self.hidden_dim
						return config

			def 					SCREAMING_SNAKE_CASE				(					self				:						Union[str, Any]       )   ->    Tuple:
						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 					SCREAMING_SNAKE_CASE				(					self				:						Tuple    ,				__UpperCAmelCase				:						str    ,				__UpperCAmelCase				:						Optional[int]       )   ->    int:
						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(__UpperCAmelCase       )    ,				len(config.backbone_config.depths       )       )
						self.parent.assertTrue(len(__UpperCAmelCase       )    ,				len(config.backbone_config.depths       )       )
						self.parent.assertTrue(len(__UpperCAmelCase       )    ,				config.decoder_layers       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						List[str]    ,				__UpperCAmelCase				:						Union[str, Any]    ,				__UpperCAmelCase				:						Dict    ,				__UpperCAmelCase				:						List[Any]    ,				__UpperCAmelCase				:						List[Any]=False       )   ->    Tuple:
						with torch.no_grad():
									SCREAMING_SNAKE_CASE__								=      MaskaFormerModel(config=__UpperCAmelCase       )
									model.to(__UpperCAmelCase       )
									model.eval()

									SCREAMING_SNAKE_CASE__								=      model(pixel_values=__UpperCAmelCase    ,				pixel_mask=__UpperCAmelCase       )
									SCREAMING_SNAKE_CASE__								=      model(__UpperCAmelCase    ,				output_hidden_states=__UpperCAmelCase       )

						self.parent.assertEqual(
						    output.transformer_decoder_last_hidden_state.shape    ,				(self.batch_size, self.num_queries, self.hidden_dim)    ,				)
						# 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(__UpperCAmelCase    ,				__UpperCAmelCase       )





			def 					SCREAMING_SNAKE_CASE				(					self				:						Union[str, Any]    ,				__UpperCAmelCase				:						str    ,				__UpperCAmelCase				:						str    ,				__UpperCAmelCase				:						Optional[Any]    ,				__UpperCAmelCase				:						List[Any]    ,				__UpperCAmelCase				:						Dict       )   ->    Dict:
						SCREAMING_SNAKE_CASE__								=      MaskaFormerForUniversalSegmentation(config=__UpperCAmelCase       )
						model.to(__UpperCAmelCase       )
						model.eval()

						def comm_check_on_output(__UpperCAmelCase				:						Optional[int]       ):
									# 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=__UpperCAmelCase    ,				pixel_mask=__UpperCAmelCase       )
									SCREAMING_SNAKE_CASE__								=      model(__UpperCAmelCase       )

									comm_check_on_output(__UpperCAmelCase       )

									SCREAMING_SNAKE_CASE__								=      model(
									    pixel_values=__UpperCAmelCase    ,				pixel_mask=__UpperCAmelCase    ,				mask_labels=__UpperCAmelCase    ,				class_labels=__UpperCAmelCase       )

						comm_check_on_output(__UpperCAmelCase       )

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





@require_torch
class    lowerCamelCase						(A__			,A__			,unittest.TestCase			):
			lowerCamelCase__   :		Union[str, Any]							=							(MaskaFormerModel, MaskaFormerForUniversalSegmentation) if is_torch_available() else ()
			lowerCamelCase__   :		Any							=							{'feature-extraction': MaskaFormerModel} if is_torch_available() else {}

			lowerCamelCase__   :		Tuple							=							False
			lowerCamelCase__   :		Dict							=							False
			lowerCamelCase__   :		Dict							=							False
			lowerCamelCase__   :		str							=							False

			def 					SCREAMING_SNAKE_CASE				(					self				:						Dict       )   ->    Tuple:
						SCREAMING_SNAKE_CASE__								=      MaskaFormerModelTester(self       )
						SCREAMING_SNAKE_CASE__								=      ConfigTester(self    ,				config_class=__UpperCAmelCase    ,				has_text_modality=__UpperCAmelCase       )

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

			def 					SCREAMING_SNAKE_CASE				(					self				:						List[Any]       )   ->    Tuple:
						SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__								=      self.model_tester.prepare_config_and_inputs_for_common()
						self.model_tester.create_and_check_maskaformer_model(__UpperCAmelCase    ,				**__UpperCAmelCase    ,				output_hidden_states=__UpperCAmelCase       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						Optional[int]       )   ->    int:
						SCREAMING_SNAKE_CASE__								=      self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_maskaformer_instance_segmentation_head_model(*__UpperCAmelCase       )

			@unittest.skip(reason="""Mask2Former does not use inputs_embeds"""       )
			def 					SCREAMING_SNAKE_CASE				(					self				:						int       )   ->    Any:
						pass

			@unittest.skip(reason="""Mask2Former does not have a get_input_embeddings method"""       )
			def 					SCREAMING_SNAKE_CASE				(					self				:						List[str]       )   ->    Any:
						pass

			@unittest.skip(reason="""Mask2Former is not a generative model"""       )
			def 					SCREAMING_SNAKE_CASE				(					self				:						Optional[int]       )   ->    Optional[Any]:
						pass

			@unittest.skip(reason="""Mask2Former does not use token embeddings"""       )
			def 					SCREAMING_SNAKE_CASE				(					self				:						Optional[Any]       )   ->    Tuple:
						pass

			@require_torch_multi_gpu
			@unittest.skip(
			    reason="""Mask2Former has some layers using `add_module` which doesn't work well with `nn.DataParallel`"""       )
			def 					SCREAMING_SNAKE_CASE				(					self				:						Dict       )   ->    int:
						pass

			@unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests."""       )
			def 					SCREAMING_SNAKE_CASE				(					self				:						Tuple       )   ->    int:
						pass

			def 					SCREAMING_SNAKE_CASE				(					self				:						Optional[int]       )   ->    Union[str, Any]:
						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(__UpperCAmelCase       )
									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]    ,				__UpperCAmelCase       )

			@slow
			def 					SCREAMING_SNAKE_CASE				(					self				:						List[str]       )   ->    Optional[Any]:
						for model_name in ["facebook/mask2former-swin-small-coco-instance"]:
									SCREAMING_SNAKE_CASE__								=      MaskaFormerModel.from_pretrained(__UpperCAmelCase       )
									self.assertIsNotNone(__UpperCAmelCase       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						Any       )   ->    Optional[int]:
						SCREAMING_SNAKE_CASE__								=      (self.model_tester.min_size,) * 2
						SCREAMING_SNAKE_CASE__								=      {
						    """pixel_values""": torch.randn((2, 3, *size)    ,				device=__UpperCAmelCase       ),
						    """mask_labels""": torch.randn((2, 1_0, *size)    ,				device=__UpperCAmelCase       ),
						    """class_labels""": torch.zeros(2    ,				1_0    ,				device=__UpperCAmelCase       ).long(),
						}
						SCREAMING_SNAKE_CASE__								=      self.model_tester.get_config()

						SCREAMING_SNAKE_CASE__								=      MaskaFormerForUniversalSegmentation(__UpperCAmelCase       ).to(__UpperCAmelCase       )
						SCREAMING_SNAKE_CASE__								=      model(**__UpperCAmelCase       )
						self.assertTrue(outputs.loss is not None       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						Any       )   ->    List[Any]:
						SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__								=      self.model_tester.prepare_config_and_inputs_for_common()
						self.model_tester.create_and_check_maskaformer_model(__UpperCAmelCase    ,				**__UpperCAmelCase    ,				output_hidden_states=__UpperCAmelCase       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						Optional[Any]       )   ->    Optional[int]:
						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(__UpperCAmelCase       ).to(__UpperCAmelCase       )
									SCREAMING_SNAKE_CASE__								=      model(**__UpperCAmelCase    ,				output_attentions=__UpperCAmelCase       )
									self.assertTrue(outputs.attentions is not None       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						Optional[Any]       )   ->    Optional[Any]:
						if not self.model_tester.is_training:
									return

						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(__UpperCAmelCase       )
						model.to(__UpperCAmelCase       )
						model.train()

						SCREAMING_SNAKE_CASE__								=      model(__UpperCAmelCase    ,				mask_labels=__UpperCAmelCase    ,				class_labels=__UpperCAmelCase       ).loss
						loss.backward()





			def 					SCREAMING_SNAKE_CASE				(					self				:						Optional[Any]       )   ->    List[str]:
						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(__UpperCAmelCase       ).to(__UpperCAmelCase       )
						model.train()

						SCREAMING_SNAKE_CASE__								=      model(__UpperCAmelCase    ,				mask_labels=__UpperCAmelCase    ,				class_labels=__UpperCAmelCase       )

						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()

						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=__UpperCAmelCase       )

						self.assertIsNotNone(encoder_hidden_states.grad       )
						self.assertIsNotNone(pixel_decoder_hidden_states.grad       )
						self.assertIsNotNone(transformer_decoder_hidden_states.grad       )
						self.assertIsNotNone(attentions.grad       )
A_							:			Optional[Any]											= 1E-4

def 	A       (     ):




			'''simple docstring'''


			SCREAMING_SNAKE_CASE__								=      Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""    )
			return image





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

			@cached_property
			def 					SCREAMING_SNAKE_CASE				(					self				:						Optional[Any]       )   ->    int:
						return "facebook/mask2former-swin-small-coco-instance"

			@cached_property
			def 					SCREAMING_SNAKE_CASE				(					self				:						Tuple       )   ->    Optional[int]:
						return MaskaFormerImageProcessor.from_pretrained(self.model_checkpoints       ) if is_vision_available() else None

			def 					SCREAMING_SNAKE_CASE				(					self				:						Optional[int]       )   ->    Any:
						SCREAMING_SNAKE_CASE__								=      MaskaFormerModel.from_pretrained(self.model_checkpoints       ).to(__UpperCAmelCase       )
						SCREAMING_SNAKE_CASE__								=      self.default_image_processor
						SCREAMING_SNAKE_CASE__								=      prepare_img()
						SCREAMING_SNAKE_CASE__								=      image_processor(__UpperCAmelCase    ,				return_tensors="""pt"""       ).to(__UpperCAmelCase       )
						SCREAMING_SNAKE_CASE__								=      inputs["""pixel_values"""].shape
						# check size is divisible by 32
						self.assertTrue((inputs_shape[-1] % 3_2) == 0 and (inputs_shape[-2] % 3_2) == 0       )
						# check size
						self.assertEqual(__UpperCAmelCase    ,				(1, 3, 3_8_4, 3_8_4)       )

						with torch.no_grad():
									SCREAMING_SNAKE_CASE__								=      model(**__UpperCAmelCase       )

						SCREAMING_SNAKE_CASE__								=      torch.tensor(
						    [[-0.2_790, -1.0_717, -1.1_668], [-0.5_128, -0.3_128, -0.4_987], [-0.5_832, 0.1_971, -0.0_197]]       ).to(__UpperCAmelCase       )
						self.assertTrue(
						    torch.allclose(
						        outputs.encoder_last_hidden_state[0, 0, :3, :3]    ,				__UpperCAmelCase    ,				atol=__UpperCAmelCase       )       )

						SCREAMING_SNAKE_CASE__								=      torch.tensor(
						    [[0.8_973, 1.1_847, 1.1_776], [1.1_934, 1.5_040, 1.5_128], [1.1_153, 1.4_486, 1.4_951]]       ).to(__UpperCAmelCase       )
						self.assertTrue(
						    torch.allclose(
						        outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3]    ,				__UpperCAmelCase    ,				atol=__UpperCAmelCase       )       )

						SCREAMING_SNAKE_CASE__								=      torch.tensor(
						    [[2.1_152, 1.7_000, -0.8_603], [1.5_808, 1.8_004, -0.9_353], [1.6_043, 1.7_495, -0.5_999]]       ).to(__UpperCAmelCase       )
						self.assertTrue(
						    torch.allclose(
						        outputs.transformer_decoder_last_hidden_state[0, :3, :3]    ,				__UpperCAmelCase    ,				atol=__UpperCAmelCase       )       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						Optional[Any]       )   ->    List[Any]:
						SCREAMING_SNAKE_CASE__								=      MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints       ).to(__UpperCAmelCase       ).eval()
						SCREAMING_SNAKE_CASE__								=      self.default_image_processor
						SCREAMING_SNAKE_CASE__								=      prepare_img()
						SCREAMING_SNAKE_CASE__								=      image_processor(__UpperCAmelCase    ,				return_tensors="""pt"""       ).to(__UpperCAmelCase       )
						SCREAMING_SNAKE_CASE__								=      inputs["""pixel_values"""].shape
						# check size is divisible by 32
						self.assertTrue((inputs_shape[-1] % 3_2) == 0 and (inputs_shape[-2] % 3_2) == 0       )
						# check size
						self.assertEqual(__UpperCAmelCase    ,				(1, 3, 3_8_4, 3_8_4)       )

						with torch.no_grad():
									SCREAMING_SNAKE_CASE__								=      model(**__UpperCAmelCase       )
						# masks_queries_logits
						SCREAMING_SNAKE_CASE__								=      outputs.masks_queries_logits
						self.assertEqual(
						    masks_queries_logits.shape    ,				(1, model.config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4)       )
						SCREAMING_SNAKE_CASE__								=      [
						    [-8.7_839, -9.0_056, -8.8_121],
						    [-7.4_104, -7.0_313, -6.5_401],
						    [-6.6_105, -6.3_427, -6.4_675],
						]
						SCREAMING_SNAKE_CASE__								=      torch.tensor(__UpperCAmelCase       ).to(__UpperCAmelCase       )
						self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]    ,				__UpperCAmelCase    ,				atol=__UpperCAmelCase       )       )
						# class_queries_logits
						SCREAMING_SNAKE_CASE__								=      outputs.class_queries_logits
						self.assertEqual(class_queries_logits.shape    ,				(1, model.config.num_queries, model.config.num_labels + 1)       )
						SCREAMING_SNAKE_CASE__								=      torch.tensor(
						    [
						        [1.8_324, -8.0_835, -4.1_922],
						        [0.8_450, -9.0_050, -3.6_053],
						        [0.3_045, -7.7_293, -3.0_275],
						    ]       ).to(__UpperCAmelCase       )
						self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]    ,				__UpperCAmelCase    ,				atol=__UpperCAmelCase       )       )





			def 					SCREAMING_SNAKE_CASE				(					self				:						int       )   ->    Any:
						SCREAMING_SNAKE_CASE__								=      MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints       ).to(__UpperCAmelCase       ).eval()
						SCREAMING_SNAKE_CASE__								=      self.default_image_processor

						SCREAMING_SNAKE_CASE__								=      image_processor(
						    [np.zeros((3, 8_0_0, 1_3_3_3)       ), np.zeros((3, 8_0_0, 1_3_3_3)       )]    ,				segmentation_maps=[np.zeros((3_8_4, 3_8_4)       ).astype(np.floataa       ), np.zeros((3_8_4, 3_8_4)       ).astype(np.floataa       )]    ,				return_tensors="""pt"""    ,				)

						SCREAMING_SNAKE_CASE__								=      inputs["""pixel_values"""].to(__UpperCAmelCase       )
						SCREAMING_SNAKE_CASE__								=      [el.to(__UpperCAmelCase       ) for el in inputs["""mask_labels"""]]
						SCREAMING_SNAKE_CASE__								=      [el.to(__UpperCAmelCase       ) for el in inputs["""class_labels"""]]

						with torch.no_grad():
									SCREAMING_SNAKE_CASE__								=      model(**__UpperCAmelCase       )

						self.assertTrue(outputs.loss is not None       )


