import os
from pathlib import Path

import numpy as np
import pytest
from pack_dataset import pack_data_dir
from parameterized import parameterized
from save_len_file import save_len_file
from torch.utils.data import DataLoader

from transformers import AutoTokenizer
from transformers.models.mbart.modeling_mbart import shift_tokens_right
from transformers.testing_utils import TestCasePlus, slow
from utils import FAIRSEQ_AVAILABLE, DistributedSortishSampler, LegacySeqaSeqDataset, SeqaSeqDataset


__lowerCamelCase					:	int       =  "bert-base-cased"
__lowerCamelCase					:	List[Any]       =  "google/pegasus-xsum"
__lowerCamelCase					:	Optional[Any]       =  [" Sam ate lunch today.", "Sams lunch ingredients."]
__lowerCamelCase					:	Any       =  ["A very interesting story about what I ate for lunch.", "Avocado, celery, turkey, coffee"]
__lowerCamelCase					:	List[str]       =  "patrickvonplaten/t5-tiny-random"
__lowerCamelCase					:	Tuple       =  "sshleifer/bart-tiny-random"
__lowerCamelCase					:	List[str]       =  "sshleifer/tiny-mbart"
__lowerCamelCase					:	List[Any]       =  "sshleifer/tiny-marian-en-de"




def       _snake_case	(   lowerCAmelCase							:   Path   , lowerCAmelCase							:   list	):
		"""simple docstring"""







		SCREAMING_SNAKE_CASE_  :		List[str]				      =    "\n".join(__A	)
		Path(__A	).open("w"	).writelines(__A	)




def       _snake_case	(   lowerCAmelCase							:   str	):
		"""simple docstring"""







		for split in ["train", "val", "test"]:
				_dump_articles(os.path.join(__A   , f'{split}.source'	)   , __A	)
				_dump_articles(os.path.join(__A   , f'{split}.target'	)   , __A	)
		return tmp_dir







class 		a__						(					A__					):

					@parameterized.expand(
					    [
					        MBART_TINY,
					        MARIAN_TINY,
					        T5_TINY,
					        BART_TINY,
					        PEGASUS_XSUM,
					    ],)
					@slow
					def 				__UpperCamelCase   (    self      :       List[str],_A      :       str    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		List[str]				      =    AutoTokenizer.from_pretrained(_snake_case    )
							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()    )
							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    max(len(tokenizer.encode(_snake_case    )    ) for a in ARTICLES    )
							SCREAMING_SNAKE_CASE_  :		Any				      =    max(len(tokenizer.encode(_snake_case    )    ) for a in SUMMARIES    )
							SCREAMING_SNAKE_CASE_  :		str				      =    4
							SCREAMING_SNAKE_CASE_  :		List[str]				      =    8
							assert max_len_target > max_src_len  # Will be truncated
							assert max_len_source > max_src_len  # Will be truncated
							SCREAMING_SNAKE_CASE_      ,    SCREAMING_SNAKE_CASE_  :		int				      =    "ro_RO", "de_DE"  # ignored for all but mbart, but never causes error.
							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    SeqaSeqDataset(
							    _snake_case,data_dir=_snake_case,type_path="train",max_source_length=_snake_case,max_target_length=_snake_case,src_lang=_snake_case,tgt_lang=_snake_case,)
							SCREAMING_SNAKE_CASE_  :		str				      =    DataLoader(_snake_case,batch_size=2,collate_fn=train_dataset.collate_fn    )
							for batch in dataloader:
									assert isinstance(_snake_case,_snake_case    )
									assert batch["attention_mask"].shape == batch["input_ids"].shape
									# show that articles were trimmed.
									assert batch["input_ids"].shape[1] == max_src_len
									# show that targets are the same len
									assert batch["labels"].shape[1] == max_tgt_len
									if tok_name != MBART_TINY:
											continue
									# check language codes in correct place
									SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    shift_tokens_right(batch["labels"],tokenizer.pad_token_id    )
									assert batch["decoder_input_ids"][0, 0].item() == tokenizer.lang_code_to_id[tgt_lang]
									assert batch["decoder_input_ids"][0, -1].item() == tokenizer.eos_token_id
									assert batch["input_ids"][0, -2].item() == tokenizer.eos_token_id
									assert batch["input_ids"][0, -1].item() == tokenizer.lang_code_to_id[src_lang]

									break  # No need to test every batch

					@parameterized.expand([BART_TINY, BERT_BASE_CASED]    )
					def 				__UpperCamelCase   (    self      :       Optional[int],_A      :       int    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		int				      =    AutoTokenizer.from_pretrained(_snake_case    )
							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()    )
							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    max(len(tokenizer.encode(_snake_case    )    ) for a in ARTICLES    )
							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    max(len(tokenizer.encode(_snake_case    )    ) for a in SUMMARIES    )
							SCREAMING_SNAKE_CASE_  :		List[str]				      =    4
							SCREAMING_SNAKE_CASE_  :		Tuple				      =    LegacySeqaSeqDataset(
							    _snake_case,data_dir=_snake_case,type_path="train",max_source_length=20,max_target_length=_snake_case,)
							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    DataLoader(_snake_case,batch_size=2,collate_fn=train_dataset.collate_fn    )
							for batch in dataloader:
									assert batch["attention_mask"].shape == batch["input_ids"].shape
									# show that articles were trimmed.
									assert batch["input_ids"].shape[1] == max_len_source
									assert 20 >= batch["input_ids"].shape[1]  # trimmed significantly
									# show that targets were truncated
									assert batch["labels"].shape[1] == trunc_target  # Truncated
									assert max_len_target > trunc_target  # Truncated
									break  # No need to test every batch

					def 				__UpperCamelCase   (    self      :       Optional[int]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		str				      =    AutoTokenizer.from_pretrained("facebook/mbart-large-cc25"    )

							SCREAMING_SNAKE_CASE_  :		List[str]				      =    Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()    )    )
							SCREAMING_SNAKE_CASE_  :		Dict				      =    tmp_dir.joinpath("train.source"    ).open().readlines()
							SCREAMING_SNAKE_CASE_  :		List[str]				      =    Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()    )    )
							pack_data_dir(_snake_case,_snake_case,128,_snake_case    )
							SCREAMING_SNAKE_CASE_  :		Dict				      =    {x.name for x in tmp_dir.iterdir()}
							SCREAMING_SNAKE_CASE_  :		Tuple				      =    {x.name for x in save_dir.iterdir()}
							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    save_dir.joinpath("train.source"    ).open().readlines()
							# orig: [' Sam ate lunch today.\n', 'Sams lunch ingredients.']
							# desired_packed: [' Sam ate lunch today.\n Sams lunch ingredients.']
							assert len(_snake_case    ) < len(_snake_case    )
							assert len(_snake_case    ) == 1
							assert len(packed_examples[0]    ) == sum(len(_snake_case    ) for x in orig_examples    )
							assert orig_paths == new_paths

					@pytest.mark.skipif(not FAIRSEQ_AVAILABLE,reason="This test requires fairseq"    )
					def 				__UpperCamelCase   (    self      :       List[Any]    ):







							"""simple docstring"""






							if not FAIRSEQ_AVAILABLE:
									return
							SCREAMING_SNAKE_CASE_      ,    SCREAMING_SNAKE_CASE_      ,    SCREAMING_SNAKE_CASE_  :		Any				      =    self._get_dataset(max_len=64    )
							SCREAMING_SNAKE_CASE_  :		Dict				      =    64
							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    ds.make_dynamic_sampler(_snake_case,required_batch_size_multiple=_snake_case    )
							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    [len(_snake_case    ) for x in batch_sampler]
							assert len(set(_snake_case    )    ) > 1  # it's not dynamic batch size if every batch is the same length
							assert sum(_snake_case    ) == len(_snake_case    )  # no dropped or added examples
							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    DataLoader(_snake_case,batch_sampler=_snake_case,collate_fn=ds.collate_fn,num_workers=2    )
							SCREAMING_SNAKE_CASE_  :		Tuple				      =    []
							SCREAMING_SNAKE_CASE_  :		str				      =    []
							for batch in data_loader:
									SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    batch["input_ids"].shape
									SCREAMING_SNAKE_CASE_  :		List[str]				      =    src_shape[0]
									assert bs % required_batch_size_multiple == 0 or bs < required_batch_size_multiple
									SCREAMING_SNAKE_CASE_  :		int				      =    np.product(batch["input_ids"].shape    )
									num_src_per_batch.append(_snake_case    )
									if num_src_tokens > (max_tokens * 1.1):
											failures.append(_snake_case    )
							assert num_src_per_batch[0] == max(_snake_case    )
							if failures:
									raise AssertionError(F'too many tokens in {len(_snake_case    )} batches'    )

					def 				__UpperCamelCase   (    self      :       int    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_      ,    SCREAMING_SNAKE_CASE_      ,    SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    self._get_dataset(max_len=512    )
							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    2
							SCREAMING_SNAKE_CASE_  :		int				      =    ds.make_sortish_sampler(_snake_case,shuffle=_snake_case    )

							SCREAMING_SNAKE_CASE_  :		List[str]				      =    DataLoader(_snake_case,batch_size=_snake_case,collate_fn=ds.collate_fn,num_workers=2    )
							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    DataLoader(_snake_case,batch_size=_snake_case,collate_fn=ds.collate_fn,num_workers=2,sampler=_snake_case    )

							SCREAMING_SNAKE_CASE_  :		Any				      =    tokenizer.pad_token_id

							def count_pad_tokens(_A      :       List[Any],_A      :       Tuple="input_ids"    ):
									return [batch[k].eq(_snake_case    ).sum().item() for batch in data_loader]

							assert sum(count_pad_tokens(_snake_case,k="labels"    )    ) < sum(count_pad_tokens(_snake_case,k="labels"    )    )
							assert sum(count_pad_tokens(_snake_case    )    ) < sum(count_pad_tokens(_snake_case    )    )
							assert len(_snake_case    ) == len(_snake_case    )

					def 				__UpperCamelCase   (    self      :       Optional[Any],_A      :       Dict=1000,_A      :       str=128    ):







							"""simple docstring"""






							if os.getenv("USE_REAL_DATA",_snake_case    ):
									SCREAMING_SNAKE_CASE_  :		int				      =    "examples/seq2seq/wmt_en_ro"
									SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    max_len * 2 * 64
									if not Path(_snake_case    ).joinpath("train.len"    ).exists():
											save_len_file(_snake_case,_snake_case    )
							else:
									SCREAMING_SNAKE_CASE_  :		Any				      =    "examples/seq2seq/test_data/wmt_en_ro"
									SCREAMING_SNAKE_CASE_  :		int				      =    max_len * 4
									save_len_file(_snake_case,_snake_case    )

							SCREAMING_SNAKE_CASE_  :		Dict				      =    AutoTokenizer.from_pretrained(_snake_case    )
							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    SeqaSeqDataset(
							    _snake_case,data_dir=_snake_case,type_path="train",max_source_length=_snake_case,max_target_length=_snake_case,n_obs=_snake_case,)
							return ds, max_tokens, tokenizer

					def 				__UpperCamelCase   (    self      :       List[str]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_      ,    SCREAMING_SNAKE_CASE_      ,    SCREAMING_SNAKE_CASE_  :		Tuple				      =    self._get_dataset()
							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    set(DistributedSortishSampler(_snake_case,256,num_replicas=2,rank=0,add_extra_examples=_snake_case    )    )
							SCREAMING_SNAKE_CASE_  :		Dict				      =    set(DistributedSortishSampler(_snake_case,256,num_replicas=2,rank=1,add_extra_examples=_snake_case    )    )
							assert idsa.intersection(_snake_case    ) == set()






					@parameterized.expand(
					    [
					        MBART_TINY,
					        MARIAN_TINY,
					        T5_TINY,
					        BART_TINY,
					        PEGASUS_XSUM,
					    ],)
					def 				__UpperCamelCase   (    self      :       List[str],_A      :       List[str]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		str				      =    AutoTokenizer.from_pretrained(_snake_case,use_fast=_snake_case    )
							if tok_name == MBART_TINY:
									SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    SeqaSeqDataset(
									    _snake_case,data_dir=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()    ),type_path="train",max_source_length=4,max_target_length=8,src_lang="EN",tgt_lang="FR",)
									SCREAMING_SNAKE_CASE_  :		int				      =    train_dataset.dataset_kwargs
									assert "src_lang" in kwargs and "tgt_lang" in kwargs
							else:
									SCREAMING_SNAKE_CASE_  :		Dict				      =    SeqaSeqDataset(
									    _snake_case,data_dir=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()    ),type_path="train",max_source_length=4,max_target_length=8,)
									SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    train_dataset.dataset_kwargs
									assert "add_prefix_space" not in kwargs if tok_name != BART_TINY else "add_prefix_space" in kwargs
									assert len(_snake_case    ) == 1 if tok_name == BART_TINY else len(_snake_case    ) == 0






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 __snake_case			:







   def __init__(      self		:						List[str]					,						_snake_case		:						Union[str, Any]					,						_snake_case		:						List[str]=2					,						_snake_case		:						Any=True					,						_snake_case		:						Any=False					,						_snake_case		:						List[str]=10					,						_snake_case		:						Any=3					,						_snake_case		:						Union[str, Any]=32 * 4					,						_snake_case		:						List[Any]=32 * 6					,						_snake_case		:						Tuple=4					,						_snake_case		:						Dict=32					,						):

        """simple docstring"""


        UpperCAmelCase_          =							parent
        UpperCAmelCase_          =							batch_size
        UpperCAmelCase_          =							is_training
        UpperCAmelCase_          =							use_auxiliary_loss
        UpperCAmelCase_          =							num_queries
        UpperCAmelCase_          =							num_channels
        UpperCAmelCase_          =							min_size
        UpperCAmelCase_          =							max_size
        UpperCAmelCase_          =							num_labels
        UpperCAmelCase_          =							mask_feature_size







   def lowerCamelCase						(      self		:						Any):

        """simple docstring"""


        UpperCAmelCase_          =							floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size]).to(
            _snake_case)

        UpperCAmelCase_          =							torch.ones([self.batch_size, self.min_size, self.max_size]					,						device=_snake_case)

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

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







   def lowerCamelCase						(      self		:						Any):

        """simple docstring"""


        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 lowerCamelCase						(      self		:						int):

        """simple docstring"""


        UpperCAmelCase_     ,       UpperCAmelCase_     ,       UpperCAmelCase_     ,       UpperCAmelCase_     ,       UpperCAmelCase_          =							self.prepare_config_and_inputs()
        UpperCAmelCase_          =							{'''pixel_values''': pixel_values, '''pixel_mask''': pixel_mask}
        return config, inputs_dict







   def lowerCamelCase						(      self		:						str					,						_snake_case		:						List[Any]					,						_snake_case		:						List[str]):

        """simple docstring"""


        UpperCAmelCase_          =							output.encoder_hidden_states
        UpperCAmelCase_          =							output.pixel_decoder_hidden_states
        UpperCAmelCase_          =							output.transformer_decoder_hidden_states

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







   def lowerCamelCase						(      self		:						Union[str, Any]					,						_snake_case		:						List[str]					,						_snake_case		:						int					,						_snake_case		:						Optional[Any]					,						_snake_case		:						str=False):

        """simple docstring"""


        with torch.no_grad():
             UpperCAmelCase_          =							MaskFormerModel(config=_snake_case)
             model.to(_snake_case)
             model.eval()

             UpperCAmelCase_          =							model(pixel_values=_snake_case					,						pixel_mask=_snake_case)
             UpperCAmelCase_          =							model(_snake_case					,						output_hidden_states=_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(_snake_case					,						_snake_case)







   def lowerCamelCase						(      self		:						List[Any]					,						_snake_case		:						List[Any]					,						_snake_case		:						List[Any]					,						_snake_case		:						str					,						_snake_case		:						Optional[int]					,						_snake_case		:						Union[str, Any]):

        """simple docstring"""


        UpperCAmelCase_          =							MaskFormerForInstanceSegmentation(config=_snake_case)
        model.to(_snake_case)
        model.eval()

        def comm_check_on_output(_snake_case		:						Tuple):
             # 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():
             UpperCAmelCase_          =							model(pixel_values=_snake_case					,						pixel_mask=_snake_case)
             UpperCAmelCase_          =							model(_snake_case)

             comm_check_on_output(_snake_case)

             UpperCAmelCase_          =							model(
                 pixel_values=_snake_case					,						pixel_mask=_snake_case					,						mask_labels=_snake_case					,						class_labels=_snake_case)

        comm_check_on_output(_snake_case)

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







@require_torch
class __snake_case			(			a ,  a ,  unittest.TestCase		):
   UpperCAmelCase__  :    Union[str, Any]          =  (MaskFormerModel, MaskFormerForInstanceSegmentation) if is_torch_available() else ()
   UpperCAmelCase__  :    Optional[Any]          =  (
       {'''feature-extraction''': MaskFormerModel, '''image-segmentation''': MaskFormerForInstanceSegmentation}
       if is_torch_available()
       else {}
   )

   UpperCAmelCase__  :    Dict          =  False
   UpperCAmelCase__  :    List[str]          =  False
   UpperCAmelCase__  :    Optional[Any]          =  False
   UpperCAmelCase__  :    Union[str, Any]          =  False







   def lowerCamelCase						(      self		:						List[Any]):

        """simple docstring"""


        UpperCAmelCase_          =							MaskFormerModelTester(self)
        UpperCAmelCase_          =							ConfigTester(self					,						config_class=_snake_case					,						has_text_modality=_snake_case)







   def lowerCamelCase						(      self		:						List[Any]):

        """simple docstring"""


        self.config_tester.run_common_tests()







   def lowerCamelCase						(      self		:						str):

        """simple docstring"""


        UpperCAmelCase_     ,       UpperCAmelCase_          =							self.model_tester.prepare_config_and_inputs_for_common()
        self.model_tester.create_and_check_maskformer_model(_snake_case					,						**_snake_case					,						output_hidden_states=_snake_case)







   def lowerCamelCase						(      self		:						str):

        """simple docstring"""


        UpperCAmelCase_          =							self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_maskformer_instance_segmentation_head_model(*_snake_case)







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

        """simple docstring"""


        pass







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

        """simple docstring"""


        pass







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

        """simple docstring"""


        pass







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

        """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 lowerCamelCase						(      self		:						Any):

        """simple docstring"""


        pass







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

        """simple docstring"""


        pass







   def lowerCamelCase						(      self		:						List[str]):

        """simple docstring"""


        UpperCAmelCase_     ,       UpperCAmelCase_          =							self.model_tester.prepare_config_and_inputs_for_common()

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

             UpperCAmelCase_          =							['''pixel_values''']
             self.assertListEqual(arg_names[:1]					,						_snake_case)







   @slow
   def lowerCamelCase						(      self		:						Union[str, Any]):

        """simple docstring"""


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







   def lowerCamelCase						(      self		:						str):

        """simple docstring"""


        UpperCAmelCase_          =							(self.model_tester.min_size,) * 2
        UpperCAmelCase_          =							{
            '''pixel_values''': torch.randn((2, 3, *size)					,						device=_snake_case),
            '''mask_labels''': torch.randn((2, 10, *size)					,						device=_snake_case),
            '''class_labels''': torch.zeros(2					,						10					,						device=_snake_case).long(),
        }

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







   def lowerCamelCase						(      self		:						Optional[Any]):

        """simple docstring"""


        UpperCAmelCase_     ,       UpperCAmelCase_          =							self.model_tester.prepare_config_and_inputs_for_common()
        self.model_tester.create_and_check_maskformer_model(_snake_case					,						**_snake_case					,						output_hidden_states=_snake_case)







   def lowerCamelCase						(      self		:						str):

        """simple docstring"""


        UpperCAmelCase_     ,       UpperCAmelCase_          =							self.model_tester.prepare_config_and_inputs_for_common()

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







   def lowerCamelCase						(      self		:						int):

        """simple docstring"""


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

        UpperCAmelCase_          =							model_class(_snake_case)
        model.to(_snake_case)
        model.train()

        UpperCAmelCase_          =							model(_snake_case					,						mask_labels=_snake_case					,						class_labels=_snake_case).loss
        loss.backward()







   def lowerCamelCase						(      self		:						Optional[int]):

        """simple docstring"""


        UpperCAmelCase_          =							self.all_model_classes[1]
        UpperCAmelCase_     ,       UpperCAmelCase_     ,       UpperCAmelCase_     ,       UpperCAmelCase_     ,       UpperCAmelCase_          =							self.model_tester.prepare_config_and_inputs()
        UpperCAmelCase_          =							True
        UpperCAmelCase_          =							True

        UpperCAmelCase_          =							model_class(_snake_case)
        model.to(_snake_case)
        model.train()

        UpperCAmelCase_          =							model(_snake_case					,						mask_labels=_snake_case					,						class_labels=_snake_case)

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

        UpperCAmelCase_          =							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
        UpperCAmelCase_          =							outputs.transformer_decoder_hidden_states[0]
        transformer_decoder_hidden_states.retain_grad()

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

        outputs.loss.backward(retain_graph=_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)







snake_case_ : Dict    		=       1e-4


def    A						()   ->      Union[str, Any]:







     """simple docstring"""


     UpperCAmelCase_          =							Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''       )
     return image







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







   @cached_property
   def lowerCamelCase						(      self		:						List[str]):

        """simple docstring"""


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







   def lowerCamelCase						(      self		:						List[Any]):

        """simple docstring"""


        UpperCAmelCase_          =							MaskFormerModel.from_pretrained('''facebook/maskformer-swin-small-coco''').to(_snake_case)
        UpperCAmelCase_          =							self.default_image_processor
        UpperCAmelCase_          =							prepare_img()
        UpperCAmelCase_          =							image_processor(_snake_case					,						return_tensors='''pt''').to(_snake_case)
        UpperCAmelCase_          =							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(_snake_case					,						(1, 3, 800, 1088))

        with torch.no_grad():
             UpperCAmelCase_          =							model(**_snake_case)

        UpperCAmelCase_          =							torch.tensor(
            [[-0.0_4_8_2, 0.9_2_2_8, 0.4_9_5_1], [-0.2_5_4_7, 0.8_0_1_7, 0.8_5_2_7], [-0.0_0_6_9, 0.3_3_8_5, -0.0_0_8_9]]).to(_snake_case)
        self.assertTrue(
            torch.allclose(
                outputs.encoder_last_hidden_state[0, 0, :3, :3]					,						_snake_case					,						atol=_snake_case))

        UpperCAmelCase_          =							torch.tensor(
            [[-0.8_4_2_2, -0.8_4_3_4, -0.9_7_1_8], [-1.0_1_4_4, -0.5_5_6_5, -0.4_1_9_5], [-1.0_0_3_8, -0.4_4_8_4, -0.1_9_6_1]]).to(_snake_case)
        self.assertTrue(
            torch.allclose(
                outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3]					,						_snake_case					,						atol=_snake_case))

        UpperCAmelCase_          =							torch.tensor(
            [[0.2_8_5_2, -0.0_1_5_9, 0.9_7_3_5], [0.6_2_5_4, 0.1_8_5_8, 0.8_5_2_9], [-0.0_6_8_0, -0.4_1_1_6, 1.8_4_1_3]]).to(_snake_case)
        self.assertTrue(
            torch.allclose(
                outputs.transformer_decoder_last_hidden_state[0, :3, :3]					,						_snake_case					,						atol=_snake_case))







   def lowerCamelCase						(      self		:						List[str]):

        """simple docstring"""


        UpperCAmelCase_          =							(
            MaskFormerForInstanceSegmentation.from_pretrained('''facebook/maskformer-swin-small-coco''')
            .to(_snake_case)
            .eval()
        )
        UpperCAmelCase_          =							self.default_image_processor
        UpperCAmelCase_          =							prepare_img()
        UpperCAmelCase_          =							image_processor(_snake_case					,						return_tensors='''pt''').to(_snake_case)
        UpperCAmelCase_          =							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(_snake_case					,						(1, 3, 800, 1088))

        with torch.no_grad():
             UpperCAmelCase_          =							model(**_snake_case)
        # masks_queries_logits
        UpperCAmelCase_          =							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)					,						)
        UpperCAmelCase_          =							[
            [-1.3_7_3_7_1_2_4, -1.7_7_2_4_9_3_7, -1.9_3_6_4_2_3_3],
            [-1.5_9_7_7_2_8_1, -1.9_8_6_7_9_3_9, -2.1_5_2_3_6_9_5],
            [-1.5_7_9_5_3_9_8, -1.9_2_6_9_8_3_2, -2.0_9_3_9_4_2],
        ]
        UpperCAmelCase_          =							torch.tensor(_snake_case).to(_snake_case)
        self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]					,						_snake_case					,						atol=_snake_case))
        # class_queries_logits
        UpperCAmelCase_          =							outputs.class_queries_logits
        self.assertEqual(
            class_queries_logits.shape					,						(1, model.config.decoder_config.num_queries, model.config.num_labels + 1))
        UpperCAmelCase_          =							torch.tensor(
            [
                [1.65_12e00, -5.25_72e00, -3.35_19e00],
                [3.61_69e-02, -5.90_25e00, -2.93_13e00],
                [1.07_66e-04, -7.76_30e00, -5.12_63e00],
            ]).to(_snake_case)
        self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]					,						_snake_case					,						atol=_snake_case))







   def lowerCamelCase						(      self		:						Optional[Any]):

        """simple docstring"""


        UpperCAmelCase_          =							(
            MaskFormerForInstanceSegmentation.from_pretrained('''facebook/maskformer-resnet101-coco-stuff''')
            .to(_snake_case)
            .eval()
        )
        UpperCAmelCase_          =							self.default_image_processor
        UpperCAmelCase_          =							prepare_img()
        UpperCAmelCase_          =							image_processor(_snake_case					,						return_tensors='''pt''').to(_snake_case)
        UpperCAmelCase_          =							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(_snake_case					,						(1, 3, 800, 1088))

        with torch.no_grad():
             UpperCAmelCase_          =							model(**_snake_case)
        # masks_queries_logits
        UpperCAmelCase_          =							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)					,						)
        UpperCAmelCase_          =							[[-0.9_0_4_6, -2.6_3_6_6, -4.6_0_6_2], [-3.4_1_7_9, -5.7_8_9_0, -8.8_0_5_7], [-4.9_1_7_9, -7.6_5_6_0, -1_0.7_7_1_1]]
        UpperCAmelCase_          =							torch.tensor(_snake_case).to(_snake_case)
        self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]					,						_snake_case					,						atol=_snake_case))
        # class_queries_logits
        UpperCAmelCase_          =							outputs.class_queries_logits
        self.assertEqual(
            class_queries_logits.shape					,						(1, model.config.decoder_config.num_queries, model.config.num_labels + 1))
        UpperCAmelCase_          =							torch.tensor(
            [[4.7_1_8_8, -3.2_5_8_5, -2.8_8_5_7], [6.6_8_7_1, -2.9_1_8_1, -1.2_4_8_7], [7.2_4_4_9, -2.2_7_6_4, -2.1_8_7_4]]).to(_snake_case)
        self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]					,						_snake_case					,						atol=_snake_case))







   def lowerCamelCase						(      self		:						Tuple):

        """simple docstring"""


        UpperCAmelCase_          =							(
            MaskFormerForInstanceSegmentation.from_pretrained('''facebook/maskformer-swin-small-coco''')
            .to(_snake_case)
            .eval()
        )
        UpperCAmelCase_          =							self.default_image_processor

        UpperCAmelCase_          =							image_processor(
            [np.zeros((3, 800, 1333)), np.zeros((3, 800, 1333))]					,						segmentation_maps=[np.zeros((384, 384)).astype(np.floataa), np.zeros((384, 384)).astype(np.floataa)]					,						return_tensors='''pt'''					,						)

        UpperCAmelCase_          =							inputs['''pixel_values'''].to(_snake_case)
        UpperCAmelCase_          =							[el.to(_snake_case) for el in inputs['''mask_labels''']]
        UpperCAmelCase_          =							[el.to(_snake_case) for el in inputs['''class_labels''']]

        with torch.no_grad():
             UpperCAmelCase_          =							model(**_snake_case)

        self.assertTrue(outputs.loss is not None)






