




import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional

import datasets
import evaluate
import numpy as np
from datasets import load_dataset

import transformers
from transformers import (
    AutoConfig,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    DataCollatorWithPadding,
    EvalPrediction,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    default_data_collator,
    set_seed,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version


# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version('4.31.0')

require_version('datasets>=1.8.0', 'To fix: pip install -r examples/pytorch/text-classification/requirements.txt')

SCREAMING_SNAKE_CASE__       :      Tuple  =   logging.getLogger(__name__)




@dataclass
class 		UpperCamelCase__	:

    '''simple docstring'''



    lowerCamelCase_      :	Tuple	     =      field(
        default=1_2_8    ,				metadata={
            """help""": (
                """The maximum total input sequence length after tokenization. Sequences longer """
                """than this will be truncated, sequences shorter will be padded."""
            )
        }    ,				)
    lowerCamelCase_      :	List[Any]	     =      field(
        default=UpperCamelCase__    ,				metadata={"""help""": """Overwrite the cached preprocessed datasets or not."""}     )
    lowerCamelCase_      :	str	     =      field(
        default=UpperCamelCase__    ,				metadata={
            """help""": (
                """Whether to pad all samples to `max_seq_length`. """
                """If False, will pad the samples dynamically when batching to the maximum length in the batch."""
            )
        }    ,				)
    lowerCamelCase_      :	List[str]	     =      field(
        default=UpperCamelCase__    ,				metadata={
            """help""": (
                """For debugging purposes or quicker training, truncate the number of training examples to this """
                """value if set."""
            )
        }    ,				)
    lowerCamelCase_      :	Any	     =      field(
        default=UpperCamelCase__    ,				metadata={
            """help""": (
                """For debugging purposes or quicker training, truncate the number of evaluation examples to this """
                """value if set."""
            )
        }    ,				)
    lowerCamelCase_      :	List[str]	     =      field(
        default=UpperCamelCase__    ,				metadata={
            """help""": (
                """For debugging purposes or quicker training, truncate the number of prediction examples to this """
                """value if set."""
            )
        }    ,				)




@dataclass
class 		UpperCamelCase__	:

    '''simple docstring'''



    lowerCamelCase_      :	int	     =      field(
        default=UpperCamelCase__    ,				metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""}     )
    lowerCamelCase_      :	Dict	     =      field(
        default=UpperCamelCase__    ,				metadata={"""help""": """Evaluation language. Also train language if `train_language` is set to None."""}     )
    lowerCamelCase_      :	Any	     =      field(
        default=UpperCamelCase__    ,				metadata={"""help""": """Train language if it is different from the evaluation language."""}     )
    lowerCamelCase_      :	Dict	     =      field(
        default=UpperCamelCase__    ,				metadata={"""help""": """Pretrained config name or path if not the same as model_name"""}     )
    lowerCamelCase_      :	Dict	     =      field(
        default=UpperCamelCase__    ,				metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""}     )
    lowerCamelCase_      :	Any	     =      field(
        default=UpperCamelCase__    ,				metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""}    ,				)
    lowerCamelCase_      :	List[Any]	     =      field(
        default=UpperCamelCase__    ,				metadata={"""help""": """arg to indicate if tokenizer should do lower case in AutoTokenizer.from_pretrained()"""}    ,				)
    lowerCamelCase_      :	str	     =      field(
        default=UpperCamelCase__    ,				metadata={"""help""": """Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."""}    ,				)
    lowerCamelCase_      :	Optional[int]	     =      field(
        default="""main"""    ,				metadata={"""help""": """The specific model version to use (can be a branch name, tag name or commit id)."""}    ,				)
    lowerCamelCase_      :	Union[str, Any]	     =      field(
        default=UpperCamelCase__    ,				metadata={
            """help""": (
                """Will use the token generated when running `huggingface-cli login` (necessary to use this script """
                """with private models)."""
            )
        }    ,				)
    lowerCamelCase_      :	Any	     =      field(
        default=UpperCamelCase__    ,				metadata={"""help""": """Will enable to load a pretrained model whose head dimensions are different."""}    ,				)




def      A      (      )	-> str:
       # See all possible arguments in src/transformers/training_args.py
       # or by passing the --help flag to this script.
       # We now keep distinct sets of args, for a cleaner separation of concerns.

       lowerCamelCase   :      List[Any]			 =					HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)    )
       lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase   :      str			 =					parser.parse_args_into_dataclasses()

       # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
       # information sent is the one passed as arguments along with your Python/PyTorch versions.
       send_example_telemetry("run_xnli" ,UpperCamelCase__    )

       # Setup logging
       logging.basicConfig(
           format="%(asctime)s - %(levelname)s - %(name)s - %(message)s" ,datefmt="%m/%d/%Y %H:%M:%S" ,handlers=[logging.StreamHandler(sys.stdout    )] ,)

       if training_args.should_log:
              # The default of training_args.log_level is passive, so we set log level at info here to have that default.
              transformers.utils.logging.set_verbosity_info()

       lowerCamelCase   :      List[Any]			 =					training_args.get_process_log_level()
       logger.setLevel(UpperCamelCase__    )
       datasets.utils.logging.set_verbosity(UpperCamelCase__    )
       transformers.utils.logging.set_verbosity(UpperCamelCase__    )
       transformers.utils.logging.enable_default_handler()
       transformers.utils.logging.enable_explicit_format()

       # Log on each process the small summary:
       logger.warning(
           f'''Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}'''
           + f'''distributed training: {bool(training_args.local_rank != -1    )}, 16-bits training: {training_args.fpaa}'''    )
       logger.info(f'''Training/evaluation parameters {training_args}'''    )

       # Detecting last checkpoint.
       lowerCamelCase   :      List[Any]			 =					None
       if os.path.isdir(training_args.output_dir    ) and training_args.do_train and not training_args.overwrite_output_dir:
              lowerCamelCase   :      str			 =					get_last_checkpoint(training_args.output_dir    )
              if last_checkpoint is None and len(os.listdir(training_args.output_dir    )    ) > 0:
                     raise ValueError(
                         f'''Output directory ({training_args.output_dir}) already exists and is not empty. '''
                         "Use --overwrite_output_dir to overcome."    )
              elif last_checkpoint is not None:
                     logger.info(
                         f'''Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '''
                         "the `--output_dir` or add `--overwrite_output_dir` to train from scratch."    )

    # Set seed before initializing model.
       set_seed(training_args.seed    )

       # In distributed training, the load_dataset function guarantees that only one local process can concurrently
       # download the dataset.
       # Downloading and loading xnli dataset from the hub.
       if training_args.do_train:
              if model_args.train_language is None:
                     lowerCamelCase   :      str			 =					load_dataset(
                         "xnli" ,model_args.language ,split="train" ,cache_dir=model_args.cache_dir ,use_auth_token=True if model_args.use_auth_token else None ,)
              else:
                     lowerCamelCase   :      Dict			 =					load_dataset(
                         "xnli" ,model_args.train_language ,split="train" ,cache_dir=model_args.cache_dir ,use_auth_token=True if model_args.use_auth_token else None ,)
              lowerCamelCase   :      Optional[Any]			 =					train_dataset.features["label"].names

       if training_args.do_eval:
              lowerCamelCase   :      List[str]			 =					load_dataset(
                  "xnli" ,model_args.language ,split="validation" ,cache_dir=model_args.cache_dir ,use_auth_token=True if model_args.use_auth_token else None ,)
              lowerCamelCase   :      Union[str, Any]			 =					eval_dataset.features["label"].names

       if training_args.do_predict:
              lowerCamelCase   :      Optional[int]			 =					load_dataset(
                  "xnli" ,model_args.language ,split="test" ,cache_dir=model_args.cache_dir ,use_auth_token=True if model_args.use_auth_token else None ,)
              lowerCamelCase   :      int			 =					predict_dataset.features["label"].names

       # Labels
       lowerCamelCase   :      str			 =					len(UpperCamelCase__    )

       # Load pretrained model and tokenizer
       # In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently
       # download model & vocab.
       lowerCamelCase   :      Optional[int]			 =					AutoConfig.from_pretrained(
           model_args.config_name if model_args.config_name else model_args.model_name_or_path ,num_labels=UpperCamelCase__ ,idalabel={str(UpperCamelCase__    ): label for i, label in enumerate(UpperCamelCase__    )} ,labelaid={label: i for i, label in enumerate(UpperCamelCase__    )} ,finetuning_task="xnli" ,cache_dir=model_args.cache_dir ,revision=model_args.model_revision ,use_auth_token=True if model_args.use_auth_token else None ,)
       lowerCamelCase   :      List[str]			 =					AutoTokenizer.from_pretrained(
           model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path ,do_lower_case=model_args.do_lower_case ,cache_dir=model_args.cache_dir ,use_fast=model_args.use_fast_tokenizer ,revision=model_args.model_revision ,use_auth_token=True if model_args.use_auth_token else None ,)
       lowerCamelCase   :      Optional[Any]			 =					AutoModelForSequenceClassification.from_pretrained(
           model_args.model_name_or_path ,from_tf=bool(".ckpt" in model_args.model_name_or_path    ) ,config=UpperCamelCase__ ,cache_dir=model_args.cache_dir ,revision=model_args.model_revision ,use_auth_token=True if model_args.use_auth_token else None ,ignore_mismatched_sizes=model_args.ignore_mismatched_sizes ,)

       # Preprocessing the datasets
       # Padding strategy
       if data_args.pad_to_max_length:
              lowerCamelCase   :      Optional[Any]			 =					"max_length"
       else:
              # We will pad later, dynamically at batch creation, to the max sequence length in each batch
              lowerCamelCase   :      int			 =					False

       def preprocess_function(_SCREAMING_SNAKE_CASE    ):
              # Tokenize the texts
              return tokenizer(
                  examples["premise"] ,examples["hypothesis"] ,padding=UpperCamelCase__ ,max_length=data_args.max_seq_length ,truncation=UpperCamelCase__ ,)

       if training_args.do_train:
              if data_args.max_train_samples is not None:
                     lowerCamelCase   :      str			 =					min(len(UpperCamelCase__    ) ,data_args.max_train_samples    )
                     lowerCamelCase   :      int			 =					train_dataset.select(range(UpperCamelCase__    )    )
              with training_args.main_process_first(desc="train dataset map pre-processing"    ):
                     lowerCamelCase   :      Union[str, Any]			 =					train_dataset.map(
                         UpperCamelCase__ ,batched=UpperCamelCase__ ,load_from_cache_file=not data_args.overwrite_cache ,desc="Running tokenizer on train dataset" ,)
              # Log a few random samples from the training set:
              for index in random.sample(range(len(UpperCamelCase__    )    ) ,3    ):
                     logger.info(f'''Sample {index} of the training set: {train_dataset[index]}.'''    )

       if training_args.do_eval:
              if data_args.max_eval_samples is not None:
                     lowerCamelCase   :      Any			 =					min(len(UpperCamelCase__    ) ,data_args.max_eval_samples    )
                     lowerCamelCase   :      List[Any]			 =					eval_dataset.select(range(UpperCamelCase__    )    )
              with training_args.main_process_first(desc="validation dataset map pre-processing"    ):
                     lowerCamelCase   :      str			 =					eval_dataset.map(
                         UpperCamelCase__ ,batched=UpperCamelCase__ ,load_from_cache_file=not data_args.overwrite_cache ,desc="Running tokenizer on validation dataset" ,)

       if training_args.do_predict:
              if data_args.max_predict_samples is not None:
                     lowerCamelCase   :      Union[str, Any]			 =					min(len(UpperCamelCase__    ) ,data_args.max_predict_samples    )
                     lowerCamelCase   :      int			 =					predict_dataset.select(range(UpperCamelCase__    )    )
              with training_args.main_process_first(desc="prediction dataset map pre-processing"    ):
                     lowerCamelCase   :      List[str]			 =					predict_dataset.map(
                         UpperCamelCase__ ,batched=UpperCamelCase__ ,load_from_cache_file=not data_args.overwrite_cache ,desc="Running tokenizer on prediction dataset" ,)

    # Get the metric function
       lowerCamelCase   :      Dict			 =					evaluate.load("xnli"    )

       # You can define your custom compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
       # predictions and label_ids field) and has to return a dictionary string to float.
       def compute_metrics(_SCREAMING_SNAKE_CASE    ):
              lowerCamelCase   :      List[Any]			 =					p.predictions[0] if isinstance(p.predictions ,UpperCamelCase__    ) else p.predictions
              lowerCamelCase   :      List[str]			 =					np.argmax(UpperCamelCase__ ,axis=1    )
              return metric.compute(predictions=UpperCamelCase__ ,references=p.label_ids    )

       # Data collator will default to DataCollatorWithPadding, so we change it if we already did the padding.
       if data_args.pad_to_max_length:
              lowerCamelCase   :      Union[str, Any]			 =					default_data_collator
       elif training_args.fpaa:
              lowerCamelCase   :      Tuple			 =					DataCollatorWithPadding(UpperCamelCase__ ,pad_to_multiple_of=8    )
       else:
              lowerCamelCase   :      int			 =					None

       # Initialize our Trainer
       lowerCamelCase   :      List[str]			 =					Trainer(
           model=UpperCamelCase__ ,args=UpperCamelCase__ ,train_dataset=train_dataset if training_args.do_train else None ,eval_dataset=eval_dataset if training_args.do_eval else None ,compute_metrics=UpperCamelCase__ ,tokenizer=UpperCamelCase__ ,data_collator=UpperCamelCase__ ,)

       # Training
       if training_args.do_train:
              lowerCamelCase   :      List[str]			 =					None
              if training_args.resume_from_checkpoint is not None:
                     lowerCamelCase   :      Any			 =					training_args.resume_from_checkpoint
              elif last_checkpoint is not None:
                     lowerCamelCase   :      int			 =					last_checkpoint
              lowerCamelCase   :      Union[str, Any]			 =					trainer.train(resume_from_checkpoint=UpperCamelCase__    )
              lowerCamelCase   :      Union[str, Any]			 =					train_result.metrics
              lowerCamelCase   :      List[Any]			 =					(
                  data_args.max_train_samples if data_args.max_train_samples is not None else len(UpperCamelCase__    )
              )
              lowerCamelCase   :      List[str]			 =					min(UpperCamelCase__ ,len(UpperCamelCase__    )    )

              trainer.save_model()  # Saves the tokenizer too for easy upload

              trainer.log_metrics("train" ,UpperCamelCase__    )
              trainer.save_metrics("train" ,UpperCamelCase__    )
              trainer.save_state()

       # Evaluation
       if training_args.do_eval:
              logger.info("*** Evaluate ***"    )
              lowerCamelCase   :      Optional[Any]			 =					trainer.evaluate(eval_dataset=UpperCamelCase__    )

              lowerCamelCase   :      Optional[int]			 =					data_args.max_eval_samples if data_args.max_eval_samples is not None else len(UpperCamelCase__    )
              lowerCamelCase   :      Tuple			 =					min(UpperCamelCase__ ,len(UpperCamelCase__    )    )

              trainer.log_metrics("eval" ,UpperCamelCase__    )
              trainer.save_metrics("eval" ,UpperCamelCase__    )

       # Prediction
       if training_args.do_predict:
              logger.info("*** Predict ***"    )
              lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase   :      str			 =					trainer.predict(UpperCamelCase__ ,metric_key_prefix="predict"    )

              lowerCamelCase   :      Optional[int]			 =					(
                  data_args.max_predict_samples if data_args.max_predict_samples is not None else len(UpperCamelCase__    )
              )
              lowerCamelCase   :      List[Any]			 =					min(UpperCamelCase__ ,len(UpperCamelCase__    )    )

              trainer.log_metrics("predict" ,UpperCamelCase__    )
              trainer.save_metrics("predict" ,UpperCamelCase__    )

              lowerCamelCase   :      str			 =					np.argmax(UpperCamelCase__ ,axis=1    )
              lowerCamelCase   :      List[Any]			 =					os.path.join(training_args.output_dir ,"predictions.txt"    )
              if trainer.is_world_process_zero():
                     with open(UpperCamelCase__ ,"w"    ) as writer:
                            writer.write("index\tprediction\n"    )
                            for index, item in enumerate(UpperCamelCase__    ):
                                   lowerCamelCase   :      Optional[Any]			 =					label_list[item]
                                   writer.write(f'''{index}\t{item}\n'''    )


if __name__ == "__main__":
       main()













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     SCREAMING_SNAKE_CASE__ :



 def __init__(							self,__lowerCamelCase,__lowerCamelCase=2,__lowerCamelCase=True,__lowerCamelCase=False,__lowerCamelCase=10,__lowerCamelCase=3,__lowerCamelCase=32 * 8,__lowerCamelCase=32 * 8,__lowerCamelCase=4,__lowerCamelCase=64,):
        A__											=						parent
        A__											=						batch_size
        A__											=						is_training
        A__											=						use_auxiliary_loss
        A__											=						num_queries
        A__											=						num_channels
        A__											=						min_size
        A__											=						max_size
        A__											=						num_labels
        A__											=						hidden_dim
        A__											=						hidden_dim



 def 				UpperCamelCase			(							self ):
        A__											=						floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size] ).to(
            __lowerCamelCase )

        A__											=						torch.ones([self.batch_size, self.min_size, self.max_size],device=__lowerCamelCase )

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

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



 def 				UpperCamelCase			(							self ):
        A__											=						MaskaFormerConfig(
            hidden_size=self.hidden_dim,)
        A__											=						self.num_queries
        A__											=						self.num_labels

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

        A__											=						64
        A__											=						128
        A__											=						self.hidden_dim
        A__											=						self.hidden_dim
        A__											=						self.hidden_dim
        return config



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



 def 				UpperCamelCase			(							self,__lowerCamelCase,__lowerCamelCase ):
        A__											=						output.encoder_hidden_states
        A__											=						output.pixel_decoder_hidden_states
        A__											=						output.transformer_decoder_hidden_states

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



 def 				UpperCamelCase			(							self,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase=False ):
        with torch.no_grad():
               A__											=						MaskaFormerModel(config=__lowerCamelCase )
               model.to(__lowerCamelCase )
               model.eval()

               A__											=						model(pixel_values=__lowerCamelCase,pixel_mask=__lowerCamelCase )
               A__											=						model(__lowerCamelCase,output_hidden_states=__lowerCamelCase )

        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(__lowerCamelCase,__lowerCamelCase )




 def 				UpperCamelCase			(							self,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase ):
        A__											=						MaskaFormerForUniversalSegmentation(config=__lowerCamelCase )
        model.to(__lowerCamelCase )
        model.eval()

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

        with torch.no_grad():
               A__											=						model(pixel_values=__lowerCamelCase,pixel_mask=__lowerCamelCase )
               A__											=						model(__lowerCamelCase )

               comm_check_on_output(__lowerCamelCase )

               A__											=						model(
                   pixel_values=__lowerCamelCase,pixel_mask=__lowerCamelCase,mask_labels=__lowerCamelCase,class_labels=__lowerCamelCase )

        comm_check_on_output(__lowerCamelCase )

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




@require_torch
class     SCREAMING_SNAKE_CASE__ (						UpperCamelCase__				,					UpperCamelCase__				,					unittest.TestCase      ):
 __SCREAMING_SNAKE_CASE							       =	(MaskaFormerModel, MaskaFormerForUniversalSegmentation) if is_torch_available() else ()
 __SCREAMING_SNAKE_CASE							       =	{'''feature-extraction''': MaskaFormerModel} if is_torch_available() else {}

 __SCREAMING_SNAKE_CASE							       =	False
 __SCREAMING_SNAKE_CASE							       =	False
 __SCREAMING_SNAKE_CASE							       =	False
 __SCREAMING_SNAKE_CASE							       =	False



 def 				UpperCamelCase			(							self ):
        A__											=						MaskaFormerModelTester(self )
        A__											=						ConfigTester(self,config_class=__lowerCamelCase,has_text_modality=__lowerCamelCase )



 def 				UpperCamelCase			(							self ):
        self.config_tester.run_common_tests()



 def 				UpperCamelCase			(							self ):
        A__					,	A__											=						self.model_tester.prepare_config_and_inputs_for_common()
        self.model_tester.create_and_check_maskaformer_model(__lowerCamelCase,**__lowerCamelCase,output_hidden_states=__lowerCamelCase )



 def 				UpperCamelCase			(							self ):
        A__											=						self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_maskaformer_instance_segmentation_head_model(*__lowerCamelCase )



 @unittest.skip(reason='''Mask2Former does not use inputs_embeds''' )
 def 				UpperCamelCase			(							self ):
        pass



 @unittest.skip(reason='''Mask2Former does not have a get_input_embeddings method''' )
 def 				UpperCamelCase			(							self ):
        pass



 @unittest.skip(reason='''Mask2Former is not a generative model''' )
 def 				UpperCamelCase			(							self ):
        pass



 @unittest.skip(reason='''Mask2Former does not use token embeddings''' )
 def 				UpperCamelCase			(							self ):
        pass



 @require_torch_multi_gpu
 @unittest.skip(
     reason='''Mask2Former has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`''' )
 def 				UpperCamelCase			(							self ):
        pass



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



 def 				UpperCamelCase			(							self ):
        A__					,	A__											=						self.model_tester.prepare_config_and_inputs_for_common()

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

               A__											=						['''pixel_values''']
               self.assertListEqual(arg_names[:1],__lowerCamelCase )



 @slow
 def 				UpperCamelCase			(							self ):
        for model_name in ["facebook/mask2former-swin-small-coco-instance"]:
               A__											=						MaskaFormerModel.from_pretrained(__lowerCamelCase )
               self.assertIsNotNone(__lowerCamelCase )



 def 				UpperCamelCase			(							self ):
        A__											=						(self.model_tester.min_size,) * 2
        A__											=						{
            '''pixel_values''': torch.randn((2, 3, *size),device=__lowerCamelCase ),
            '''mask_labels''': torch.randn((2, 10, *size),device=__lowerCamelCase ),
            '''class_labels''': torch.zeros(2,10,device=__lowerCamelCase ).long(),
        }
        A__											=						self.model_tester.get_config()

        A__											=						MaskaFormerForUniversalSegmentation(__lowerCamelCase ).to(__lowerCamelCase )
        A__											=						model(**__lowerCamelCase )
        self.assertTrue(outputs.loss is not None )



 def 				UpperCamelCase			(							self ):
        A__					,	A__											=						self.model_tester.prepare_config_and_inputs_for_common()
        self.model_tester.create_and_check_maskaformer_model(__lowerCamelCase,**__lowerCamelCase,output_hidden_states=__lowerCamelCase )



 def 				UpperCamelCase			(							self ):
        A__					,	A__											=						self.model_tester.prepare_config_and_inputs_for_common()

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



 def 				UpperCamelCase			(							self ):
        if not self.model_tester.is_training:
               return

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

        A__											=						model_class(__lowerCamelCase )
        model.to(__lowerCamelCase )
        model.train()

        A__											=						model(__lowerCamelCase,mask_labels=__lowerCamelCase,class_labels=__lowerCamelCase ).loss
        loss.backward()




 def 				UpperCamelCase			(							self ):
        A__											=						self.all_model_classes[1]
        A__					,	A__					,	A__					,	A__					,	A__											=						self.model_tester.prepare_config_and_inputs()
        A__											=						True
        A__											=						True

        A__											=						model_class(__lowerCamelCase ).to(__lowerCamelCase )
        model.train()

        A__											=						model(__lowerCamelCase,mask_labels=__lowerCamelCase,class_labels=__lowerCamelCase )

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

        A__											=						outputs.pixel_decoder_hidden_states[0]
        pixel_decoder_hidden_states.retain_grad()

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

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

        outputs.loss.backward(retain_graph=__lowerCamelCase )

        self.assertIsNotNone(encoder_hidden_states.grad )
        self.assertIsNotNone(pixel_decoder_hidden_states.grad )
        self.assertIsNotNone(transformer_decoder_hidden_states.grad )
        self.assertIsNotNone(attentions.grad )
a__:      str									=		1e-4


def 	UpperCamelCase__(       )->Union[str, Any]:
       A__											=						Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''  )
       return image




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



 @cached_property
 def 				UpperCamelCase			(							self ):
        return "facebook/mask2former-swin-small-coco-instance"



 @cached_property
 def 				UpperCamelCase			(							self ):
        return MaskaFormerImageProcessor.from_pretrained(self.model_checkpoints ) if is_vision_available() else None



 def 				UpperCamelCase			(							self ):
        A__											=						MaskaFormerModel.from_pretrained(self.model_checkpoints ).to(__lowerCamelCase )
        A__											=						self.default_image_processor
        A__											=						prepare_img()
        A__											=						image_processor(__lowerCamelCase,return_tensors='''pt''' ).to(__lowerCamelCase )
        A__											=						inputs['''pixel_values'''].shape
        # check size is divisible by 32
        self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0 )
        # check size
        self.assertEqual(__lowerCamelCase,(1, 3, 384, 384) )

        with torch.no_grad():
               A__											=						model(**__lowerCamelCase )

        A__											=						torch.tensor(
            [[-0.2790, -1.0717, -1.1668], [-0.5128, -0.3128, -0.4987], [-0.5832, 0.1971, -0.0197]] ).to(__lowerCamelCase )
        self.assertTrue(
            torch.allclose(
                outputs.encoder_last_hidden_state[0, 0, :3, :3],__lowerCamelCase,atol=__lowerCamelCase ) )

        A__											=						torch.tensor(
            [[0.8973, 1.1847, 1.1776], [1.1934, 1.5040, 1.5128], [1.1153, 1.4486, 1.4951]] ).to(__lowerCamelCase )
        self.assertTrue(
            torch.allclose(
                outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3],__lowerCamelCase,atol=__lowerCamelCase ) )

        A__											=						torch.tensor(
            [[2.1152, 1.7000, -0.8603], [1.5808, 1.8004, -0.9353], [1.6043, 1.7495, -0.5999]] ).to(__lowerCamelCase )
        self.assertTrue(
            torch.allclose(
                outputs.transformer_decoder_last_hidden_state[0, :3, :3],__lowerCamelCase,atol=__lowerCamelCase ) )



 def 				UpperCamelCase			(							self ):
        A__											=						MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints ).to(__lowerCamelCase ).eval()
        A__											=						self.default_image_processor
        A__											=						prepare_img()
        A__											=						image_processor(__lowerCamelCase,return_tensors='''pt''' ).to(__lowerCamelCase )
        A__											=						inputs['''pixel_values'''].shape
        # check size is divisible by 32
        self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0 )
        # check size
        self.assertEqual(__lowerCamelCase,(1, 3, 384, 384) )

        with torch.no_grad():
               A__											=						model(**__lowerCamelCase )
        # masks_queries_logits
        A__											=						outputs.masks_queries_logits
        self.assertEqual(
            masks_queries_logits.shape,(1, model.config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4) )
        A__											=						[
            [-8.7839, -9.0056, -8.8121],
            [-7.4104, -7.0313, -6.5401],
            [-6.6105, -6.3427, -6.4675],
        ]
        A__											=						torch.tensor(__lowerCamelCase ).to(__lowerCamelCase )
        self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3],__lowerCamelCase,atol=__lowerCamelCase ) )
        # class_queries_logits
        A__											=						outputs.class_queries_logits
        self.assertEqual(class_queries_logits.shape,(1, model.config.num_queries, model.config.num_labels + 1) )
        A__											=						torch.tensor(
            [
                [1.8324, -8.0835, -4.1922],
                [0.8450, -9.0050, -3.6053],
                [0.3045, -7.7293, -3.0275],
            ] ).to(__lowerCamelCase )
        self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3],__lowerCamelCase,atol=__lowerCamelCase ) )




 def 				UpperCamelCase			(							self ):
        A__											=						MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints ).to(__lowerCamelCase ).eval()
        A__											=						self.default_image_processor

        A__											=						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''',)

        A__											=						inputs['''pixel_values'''].to(__lowerCamelCase )
        A__											=						[el.to(__lowerCamelCase ) for el in inputs['''mask_labels''']]
        A__											=						[el.to(__lowerCamelCase ) for el in inputs['''class_labels''']]

        with torch.no_grad():
               A__											=						model(**__lowerCamelCase )

        self.assertTrue(outputs.loss is not None )





