





'''simple docstring'''




import argparse
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing the experiment tracking capability,
# and builds off the `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################

__SCREAMING_SNAKE_CASE     :str       =				16
__SCREAMING_SNAKE_CASE     :List[str]       =				32
def 	UpperCAmelCase_							(					__lowercase							:      Accelerator			,     __lowercase							:      int = 16       )		->       Optional[int]:


     '''simple docstring'''



     _UpperCAmelCase     							=   AutoTokenizer.from_pretrained("bert-base-cased"       )
     _UpperCAmelCase     							=   load_dataset("glue"			,     "mrpc"       )

     def tokenize_function(__lowercase							:      Optional[int]       ):
          # max_length=None => use the model max length (it's actually the default)
          _UpperCAmelCase     							=   tokenizer(examples["sentence1"]			,     examples["sentence2"]			,     truncation=__lowercase			,     max_length=__lowercase       )
          return outputs

     # Apply the method we just defined to all the examples in all the splits of the dataset
     # starting with the main process first:
     with accelerator.main_process_first():
          _UpperCAmelCase     							=   datasets.map(
              __lowercase			,     batched=__lowercase			,     remove_columns=["idx", "sentence1", "sentence2"]			,     )

     # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
     # transformers library
     _UpperCAmelCase     							=   tokenized_datasets.rename_column("label"			,     "labels"       )

     def collate_fn(__lowercase							:      Optional[Any]       ):
          # On TPU it's best to pad everything to the same length or training will be very slow.
          _UpperCAmelCase     							=   128 if accelerator.distributed_type == DistributedType.TPU else None
          # When using mixed precision we want round multiples of 8/16
          if accelerator.mixed_precision == "fp8":
               _UpperCAmelCase     							=   16
          elif accelerator.mixed_precision != "no":
               _UpperCAmelCase     							=   8
          else:
               _UpperCAmelCase     							=   None

          return tokenizer.pad(
              __lowercase			,     padding="longest"			,     max_length=__lowercase			,     pad_to_multiple_of=__lowercase			,     return_tensors="pt"			,     )

     # Instantiate dataloaders.
     _UpperCAmelCase     							=   DataLoader(
         tokenized_datasets["train"]			,     shuffle=__lowercase			,     collate_fn=__lowercase			,     batch_size=__lowercase       )
     _UpperCAmelCase     							=   DataLoader(
         tokenized_datasets["validation"]			,     shuffle=__lowercase			,     collate_fn=__lowercase			,     batch_size=__lowercase       )

     return train_dataloader, eval_dataloader


# For testing only
if os.environ.get('''TESTING_MOCKED_DATALOADERS''', None) == "1":
  from accelerate.test_utils.training import mocked_dataloaders

  __SCREAMING_SNAKE_CASE     :Optional[int]       =				mocked_dataloaders  # noqa: F811
def 	UpperCAmelCase_							(					__lowercase							:      Union[str, Any]			,     __lowercase							:      Tuple       )		->       int:


     '''simple docstring'''



     if os.environ.get("TESTING_MOCKED_DATALOADERS"			,     __lowercase       ) == "1":
          _UpperCAmelCase     							=   2
     # Initialize Accelerator

     # New Code #
     # We pass in "all" to `log_with` to grab all available trackers in the environment
     # Note: If using a custom `Tracker` class, should be passed in here such as:
     # >>> log_with = ["all", MyCustomTrackerClassInstance()]
     if args.with_tracking:
          _UpperCAmelCase     							=   Accelerator(
              cpu=args.cpu			,     mixed_precision=args.mixed_precision			,     log_with="all"			,     project_dir=args.project_dir       )
     else:
          _UpperCAmelCase     							=   Accelerator(cpu=args.cpu			,     mixed_precision=args.mixed_precision       )
     # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
     _UpperCAmelCase     							=   config["lr"]
     _UpperCAmelCase     							=   int(config["num_epochs"]       )
     _UpperCAmelCase     							=   int(config["seed"]       )
     _UpperCAmelCase     							=   int(config["batch_size"]       )
     set_seed(__lowercase       )

     _UpperCAmelCase							,  _UpperCAmelCase     							=   get_dataloaders(__lowercase			,     __lowercase       )
     _UpperCAmelCase     							=   evaluate.load("glue"			,     "mrpc"       )

     # If the batch size is too big we use gradient accumulation
     _UpperCAmelCase     							=   1
     if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
          _UpperCAmelCase     							=   batch_size // MAX_GPU_BATCH_SIZE
          _UpperCAmelCase     							=   MAX_GPU_BATCH_SIZE

     # Instantiate the model (we build the model here so that the seed also control new weights initialization)
     _UpperCAmelCase     							=   AutoModelForSequenceClassification.from_pretrained("bert-base-cased"			,     return_dict=__lowercase       )

     # We could avoid this line since the accelerator is set with `device_placement=True` (default value).
     # Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
     # creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
     _UpperCAmelCase     							=   model.to(accelerator.device       )

     # Instantiate optimizer
     _UpperCAmelCase     							=   AdamW(params=model.parameters()			,     lr=__lowercase       )

     # Instantiate scheduler
     _UpperCAmelCase     							=   get_linear_schedule_with_warmup(
         optimizer=__lowercase			,     num_warmup_steps=100			,     num_training_steps=(len(__lowercase       ) * num_epochs) // gradient_accumulation_steps			,     )

     # Prepare everything
     # There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
     # prepare method.
     _UpperCAmelCase							,  _UpperCAmelCase							,  _UpperCAmelCase							,  _UpperCAmelCase							,  _UpperCAmelCase     							=   accelerator.prepare(
         __lowercase			,     __lowercase			,     __lowercase			,     __lowercase			,     __lowercase       )

     # New Code #
     # We need to initialize the trackers we use. Overall configurations can also be stored
     if args.with_tracking:
          _UpperCAmelCase     							=   os.path.split(__lowercase       )[-1].split("."       )[0]
          accelerator.init_trackers(__lowercase			,     __lowercase       )

     # Now we train the model
     for epoch in range(__lowercase       ):
          model.train()
          # New Code #
          # For our tracking example, we will log the total loss of each epoch
          if args.with_tracking:
               _UpperCAmelCase     							=   0
          for step, batch in enumerate(__lowercase       ):
               # We could avoid this line since we set the accelerator with `device_placement=True`.
               batch.to(accelerator.device       )
               _UpperCAmelCase     							=   model(**__lowercase       )
               _UpperCAmelCase     							=   outputs.loss
               # New Code #
               if args.with_tracking:
                    total_loss += loss.detach().float()
               _UpperCAmelCase     							=   loss / gradient_accumulation_steps
               accelerator.backward(__lowercase       )
               if step % gradient_accumulation_steps == 0:
                    optimizer.step()
                    lr_scheduler.step()
                    optimizer.zero_grad()

          model.eval()
          for step, batch in enumerate(__lowercase       ):
               # We could avoid this line since we set the accelerator with `device_placement=True` (the default).
               batch.to(accelerator.device       )
               with torch.no_grad():
                    _UpperCAmelCase     							=   model(**__lowercase       )
               _UpperCAmelCase     							=   outputs.logits.argmax(dim=-1       )
               _UpperCAmelCase							,  _UpperCAmelCase     							=   accelerator.gather_for_metrics((predictions, batch["labels"])       )
               metric.add_batch(
                   predictions=__lowercase			,     references=__lowercase			,     )

          _UpperCAmelCase     							=   metric.compute()
          # Use accelerator.print to print only on the main process.
          accelerator.print(f'epoch {epoch}:'			,     __lowercase       )

          # New Code #
          # To actually log, we call `Accelerator.log`
          # The values passed can be of `str`, `int`, `float` or `dict` of `str` to `float`/`int`
          if args.with_tracking:
               accelerator.log(
                   {
                       "accuracy": eval_metric["accuracy"],
                       "f1": eval_metric["f1"],
                       "train_loss": total_loss.item() / len(__lowercase       ),
                       "epoch": epoch,
                   }			,     step=__lowercase			,     )

    # New Code #
    # When a run is finished, you should call `accelerator.end_training()`
    # to close all of the open trackers
     if args.with_tracking:
          accelerator.end_training()
def 	UpperCAmelCase_							(					)		->       int:


     '''simple docstring'''



     _UpperCAmelCase     							=   argparse.ArgumentParser(description="Simple example of training script."       )
     parser.add_argument(
         "--mixed_precision"			,     type=__lowercase			,     default=__lowercase			,     choices=["no", "fp16", "bf16", "fp8"]			,     help="Whether to use mixed precision. Choose"
         "between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."
         "and an Nvidia Ampere GPU."			,     )
     parser.add_argument("--cpu"			,     action="store_true"			,     help="If passed, will train on the CPU."       )
     parser.add_argument(
         "--with_tracking"			,     action="store_true"			,     help="Whether to load in all available experiment trackers from the environment and use them for logging."			,     )
     parser.add_argument(
         "--project_dir"			,     type=__lowercase			,     default="logs"			,     help="Location on where to store experiment tracking logs` and relevent project information"			,     )
     _UpperCAmelCase     							=   parser.parse_args()
     _UpperCAmelCase     							=   {"lr": 2E-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
     training_function(__lowercase			,     __lowercase       )


if __name__ == "__main__":
  main()











'''simple docstring'''




import inspect
import unittest

import numpy as np

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

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


if is_torch_available():
  import torch

  from transformers import MaskFormerForInstanceSegmentation, MaskFormerModel

  if is_vision_available():
    from transformers import MaskFormerImageProcessor

if is_vision_available():
  from PIL import Image







class   A_	:




 def __init__(					self       :    str	,			snake_case_       :    int	,			snake_case_       :    Union[str, Any]=2	,			snake_case_       :    List[Any]=True	,			snake_case_       :    str=False	,			snake_case_       :    str=1_0	,			snake_case_       :    str=3	,			snake_case_       :    Dict=3_2 * 4	,			snake_case_       :    Any=3_2 * 6	,			snake_case_       :    Optional[Any]=4	,			snake_case_       :    Optional[int]=3_2	,			):
      _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      lowercase	(					self       :    Union[str, Any]    ):
      _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      lowercase	(					self       :    List[Any]    ):
      return MaskFormerConfig.from_backbone_and_decoder_configs(
          backbone_config=SwinConfig(
              depths=[1, 1, 1, 1]	,			)	,			decoder_config=DetrConfig(
              decoder_ffn_dim=1_2_8	,			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      lowercase	(					self       :    Optional[Any]    ):
      _UpperCAmelCase							,  _UpperCAmelCase							,  _UpperCAmelCase							,  _UpperCAmelCase							,  _UpperCAmelCase     							=   self.prepare_config_and_inputs()
      _UpperCAmelCase     							=   {"pixel_values": pixel_values, "pixel_mask": pixel_mask}
      return config, inputs_dict




 def      lowercase	(					self       :    List[Any]	,			snake_case_       :    Optional[Any]	,			snake_case_       :    Optional[Any]    ):
      _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      lowercase	(					self       :    Tuple	,			snake_case_       :    str	,			snake_case_       :    Optional[int]	,			snake_case_       :    Any	,			snake_case_       :    Optional[Any]=False    ):
      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      lowercase	(					self       :    Any	,			snake_case_       :    List[str]	,			snake_case_       :    List[Any]	,			snake_case_       :    int	,			snake_case_       :    str	,			snake_case_       :    List[Any]    ):
      _UpperCAmelCase     							=   MaskFormerForInstanceSegmentation(config=snake_case_    )
      model.to(snake_case_    )
      model.eval()

      def comm_check_on_output(snake_case_       :    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():
           _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   A_	(  lowerCAmelCase_							,						lowerCAmelCase_							,						unittest.TestCase			):
 _lowerCamelCase  : Dict           =							(MaskFormerModel, MaskFormerForInstanceSegmentation) if is_torch_available() else ()
 _lowerCamelCase  : Tuple           =							(
     {"""feature-extraction""": MaskFormerModel, """image-segmentation""": MaskFormerForInstanceSegmentation}
     if is_torch_available()
     else {}
 )

 _lowerCamelCase  : Optional[Any]           =							False
 _lowerCamelCase  : Dict           =							False
 _lowerCamelCase  : Any           =							False
 _lowerCamelCase  : List[Any]           =							False




 def      lowercase	(					self       :    Optional[int]    ):
      _UpperCAmelCase     							=   MaskFormerModelTester(self    )
      _UpperCAmelCase     							=   ConfigTester(self	,			config_class=snake_case_	,			has_text_modality=snake_case_    )




 def      lowercase	(					self       :    Optional[Any]    ):
      self.config_tester.run_common_tests()




 def      lowercase	(					self       :    Union[str, Any]    ):
      _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      lowercase	(					self       :    int    ):
      _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      lowercase	(					self       :    Any    ):
      pass




 @unittest.skip(reason="MaskFormer does not have a get_input_embeddings method"    )
 def      lowercase	(					self       :    List[str]    ):
      pass




 @unittest.skip(reason="MaskFormer is not a generative model"    )
 def      lowercase	(					self       :    List[str]    ):
      pass




 @unittest.skip(reason="MaskFormer does not use token embeddings"    )
 def      lowercase	(					self       :    List[Any]    ):
      pass




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




 @unittest.skip("Will be fixed soon by reducing the size of the model used for common tests."    )
 def      lowercase	(					self       :    Union[str, Any]    ):
      pass




 def      lowercase	(					self       :    List[str]    ):
      _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      lowercase	(					self       :    Optional[int]    ):
      for model_name in ["facebook/maskformer-swin-small-coco"]:
           _UpperCAmelCase     							=   MaskFormerModel.from_pretrained(snake_case_    )
           self.assertIsNotNone(snake_case_    )




 def      lowercase	(					self       :    Optional[int]    ):
      _UpperCAmelCase     							=   (self.model_tester.min_size,) * 2
      _UpperCAmelCase     							=   {
          "pixel_values": torch.randn((2, 3, *size)	,			device=snake_case_    ),
          "mask_labels": torch.randn((2, 1_0, *size)	,			device=snake_case_    ),
          "class_labels": torch.zeros(2	,			1_0	,			device=snake_case_    ).long(),
      }

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




 def      lowercase	(					self       :    Dict    ):
      _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      lowercase	(					self       :    Any    ):
      _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      lowercase	(					self       :    int    ):
      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      lowercase	(					self       :    int    ):
      # only MaskFormerForInstanceSegmentation has the loss
      _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    )





__SCREAMING_SNAKE_CASE     :Dict       =				1e-4
def 	UpperCAmelCase_							(					)		->       List[str]:


     '''simple docstring'''



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







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




 @cached_property
 def      lowercase	(					self       :    Dict    ):
      return (
          MaskFormerImageProcessor.from_pretrained("facebook/maskformer-swin-small-coco"    )
          if is_vision_available()
          else None
      )




 def      lowercase	(					self       :    List[Any]    ):
      _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] % 3_2) == 0 and (inputs_shape[-2] % 3_2) == 0    )
      # check size
      self.assertEqual(snake_case_	,			(1, 3, 8_0_0, 1_0_8_8)    )

      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      lowercase	(					self       :    Tuple    ):
      _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] % 3_2) == 0 and (inputs_shape[-2] % 3_2) == 0    )
      # check size
      self.assertEqual(snake_case_	,			(1, 3, 8_0_0, 1_0_8_8)    )

      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(
          [
              [1.6_512e00, -5.2_572e00, -3.3_519e00],
              [3.6_169e-02, -5.9_025e00, -2.9_313e00],
              [1.0_766e-04, -7.7_630e00, -5.1_263e00],
          ]    ).to(snake_case_    )
      self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]	,			snake_case_	,			atol=snake_case_    )    )




 def      lowercase	(					self       :    int    ):
      _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] % 3_2) == 0 and (inputs_shape[-2] % 3_2) == 0    )
      # check size
      self.assertEqual(snake_case_	,			(1, 3, 8_0_0, 1_0_8_8)    )

      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      lowercase	(					self       :    List[Any]    ):
      _UpperCAmelCase     							=   (
          MaskFormerForInstanceSegmentation.from_pretrained("facebook/maskformer-swin-small-coco"    )
          .to(snake_case_    )
          .eval()
      )
      _UpperCAmelCase     							=   self.default_image_processor

      _UpperCAmelCase     							=   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"	,			)

      _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    )




