

'''simple docstring'''

import inspect
import unittest

import numpy as np

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

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


if is_torch_available():
    import torch

    from transformers import MaskaFormerForUniversalSegmentation, MaskaFormerModel

    if is_vision_available():
        from transformers import MaskaFormerImageProcessor

if is_vision_available():
    from PIL import Image





class 							_snake_case :
   def __init__( self			:      Optional[int]     ,SCREAMING_SNAKE_CASE__			:      Tuple     ,SCREAMING_SNAKE_CASE__			:      List[str]=2     ,SCREAMING_SNAKE_CASE__			:      Optional[Any]=True     ,SCREAMING_SNAKE_CASE__			:      List[Any]=False     ,SCREAMING_SNAKE_CASE__			:      Union[str, Any]=10     ,SCREAMING_SNAKE_CASE__			:      Any=3     ,SCREAMING_SNAKE_CASE__			:      Union[str, Any]=32 * 8     ,SCREAMING_SNAKE_CASE__			:      Tuple=32 * 8     ,SCREAMING_SNAKE_CASE__			:      Union[str, Any]=4     ,SCREAMING_SNAKE_CASE__			:      Optional[Any]=64     ,):
     SCREAMING_SNAKE_CASE:List[str]						      =					parent
     SCREAMING_SNAKE_CASE:Optional[Any]						      =					batch_size
     SCREAMING_SNAKE_CASE:Optional[int]						      =					is_training
     SCREAMING_SNAKE_CASE:Optional[Any]						      =					use_auxiliary_loss
     SCREAMING_SNAKE_CASE:Optional[int]						      =					num_queries
     SCREAMING_SNAKE_CASE:Optional[int]						      =					num_channels
     SCREAMING_SNAKE_CASE:List[Any]						      =					min_size
     SCREAMING_SNAKE_CASE:Tuple						      =					max_size
     SCREAMING_SNAKE_CASE:str						      =					num_labels
     SCREAMING_SNAKE_CASE:List[str]						      =					hidden_dim
     SCREAMING_SNAKE_CASE:Dict						      =					hidden_dim
   def     __UpperCamelCase						( self			:      Dict							):
     SCREAMING_SNAKE_CASE:List[Any]						      =					floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size]							).to(
         SCREAMING_SNAKE_CASE__							)

     SCREAMING_SNAKE_CASE:int						      =					torch.ones([self.batch_size, self.min_size, self.max_size]     ,device=SCREAMING_SNAKE_CASE__							)

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

     SCREAMING_SNAKE_CASE:str						      =					self.get_config()
     return config, pixel_values, pixel_mask, mask_labels, class_labels
   def     __UpperCamelCase						( self			:      Union[str, Any]							):
     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					MaskaFormerConfig(
         hidden_size=self.hidden_dim     ,)
     SCREAMING_SNAKE_CASE:Tuple						      =					self.num_queries
     SCREAMING_SNAKE_CASE:Optional[Any]						      =					self.num_labels

     SCREAMING_SNAKE_CASE:int						      =					[1, 1, 1, 1]
     SCREAMING_SNAKE_CASE:Any						      =					self.num_channels

     SCREAMING_SNAKE_CASE:List[str]						      =					64
     SCREAMING_SNAKE_CASE:Any						      =					128
     SCREAMING_SNAKE_CASE:Optional[Any]						      =					self.hidden_dim
     SCREAMING_SNAKE_CASE:Optional[Any]						      =					self.hidden_dim
     SCREAMING_SNAKE_CASE:str						      =					self.hidden_dim
     return config
   def     __UpperCamelCase						( self			:      Any							):
     SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE:int						      =					self.prepare_config_and_inputs()
     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					{"pixel_values": pixel_values, "pixel_mask": pixel_mask}
     return config, inputs_dict
   def     __UpperCamelCase						( self			:      Any     ,SCREAMING_SNAKE_CASE__			:      int     ,SCREAMING_SNAKE_CASE__			:      Any							):
     SCREAMING_SNAKE_CASE:Optional[int]						      =					output.encoder_hidden_states
     SCREAMING_SNAKE_CASE:Dict						      =					output.pixel_decoder_hidden_states
     SCREAMING_SNAKE_CASE:Tuple						      =					output.transformer_decoder_hidden_states

     self.parent.assertTrue(len(SCREAMING_SNAKE_CASE__							)     ,len(config.backbone_config.depths							)							)
     self.parent.assertTrue(len(SCREAMING_SNAKE_CASE__							)     ,len(config.backbone_config.depths							)							)
     self.parent.assertTrue(len(SCREAMING_SNAKE_CASE__							)     ,config.decoder_layers							)
   def     __UpperCamelCase						( self			:      Optional[int]     ,SCREAMING_SNAKE_CASE__			:      Optional[int]     ,SCREAMING_SNAKE_CASE__			:      Any     ,SCREAMING_SNAKE_CASE__			:      Optional[int]     ,SCREAMING_SNAKE_CASE__			:      Any=False							):
     with torch.no_grad():
       SCREAMING_SNAKE_CASE:Optional[int]						      =					MaskaFormerModel(config=SCREAMING_SNAKE_CASE__							)
       model.to(SCREAMING_SNAKE_CASE__							)
       model.eval()

       SCREAMING_SNAKE_CASE:Tuple						      =					model(pixel_values=SCREAMING_SNAKE_CASE__     ,pixel_mask=SCREAMING_SNAKE_CASE__							)
       SCREAMING_SNAKE_CASE:Union[str, Any]						      =					model(SCREAMING_SNAKE_CASE__     ,output_hidden_states=SCREAMING_SNAKE_CASE__							)

     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(SCREAMING_SNAKE_CASE__     ,SCREAMING_SNAKE_CASE__							)







   def     __UpperCamelCase						( self			:      Tuple     ,SCREAMING_SNAKE_CASE__			:      Tuple     ,SCREAMING_SNAKE_CASE__			:      Tuple     ,SCREAMING_SNAKE_CASE__			:      Dict     ,SCREAMING_SNAKE_CASE__			:      int     ,SCREAMING_SNAKE_CASE__			:      List[str]							):
     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					MaskaFormerForUniversalSegmentation(config=SCREAMING_SNAKE_CASE__							)
     model.to(SCREAMING_SNAKE_CASE__							)
     model.eval()

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

     with torch.no_grad():
       SCREAMING_SNAKE_CASE:Any						      =					model(pixel_values=SCREAMING_SNAKE_CASE__     ,pixel_mask=SCREAMING_SNAKE_CASE__							)
       SCREAMING_SNAKE_CASE:Optional[int]						      =					model(SCREAMING_SNAKE_CASE__							)

       comm_check_on_output(SCREAMING_SNAKE_CASE__							)

       SCREAMING_SNAKE_CASE:List[Any]						      =					model(
           pixel_values=SCREAMING_SNAKE_CASE__     ,pixel_mask=SCREAMING_SNAKE_CASE__     ,mask_labels=SCREAMING_SNAKE_CASE__     ,class_labels=SCREAMING_SNAKE_CASE__							)

     comm_check_on_output(SCREAMING_SNAKE_CASE__							)

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





@require_torch
class 							_snake_case (       _a	,  _a	,  unittest.TestCase						):
   _A       :     int       =					(MaskaFormerModel, MaskaFormerForUniversalSegmentation) if is_torch_available() else ()
   _A       :     List[Any]       =					{'''feature-extraction''': MaskaFormerModel} if is_torch_available() else {}

   _A       :     str       =					False
   _A       :     Dict       =					False
   _A       :     Union[str, Any]       =					False
   _A       :     Any       =					False
   def     __UpperCamelCase						( self			:      Optional[Any]							):
     SCREAMING_SNAKE_CASE:Optional[Any]						      =					MaskaFormerModelTester(self							)
     SCREAMING_SNAKE_CASE:List[str]						      =					ConfigTester(self     ,config_class=SCREAMING_SNAKE_CASE__     ,has_text_modality=SCREAMING_SNAKE_CASE__							)
   def     __UpperCamelCase						( self			:      List[str]							):
     self.config_tester.run_common_tests()
   def     __UpperCamelCase						( self			:      List[Any]							):
     SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE:Any						      =					self.model_tester.prepare_config_and_inputs_for_common()
     self.model_tester.create_and_check_maskaformer_model(SCREAMING_SNAKE_CASE__     ,**SCREAMING_SNAKE_CASE__     ,output_hidden_states=SCREAMING_SNAKE_CASE__							)
   def     __UpperCamelCase						( self			:      str							):
     SCREAMING_SNAKE_CASE:List[str]						      =					self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_maskaformer_instance_segmentation_head_model(*SCREAMING_SNAKE_CASE__							)
   @unittest.skip(reason="Mask2Former does not use inputs_embeds"							)
   def     __UpperCamelCase						( self			:      Any							):
     pass
   @unittest.skip(reason="Mask2Former does not have a get_input_embeddings method"							)
   def     __UpperCamelCase						( self			:      Union[str, Any]							):
     pass
   @unittest.skip(reason="Mask2Former is not a generative model"							)
   def     __UpperCamelCase						( self			:      List[Any]							):
     pass
   @unittest.skip(reason="Mask2Former does not use token embeddings"							)
   def     __UpperCamelCase						( self			:      Dict							):
     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			:      Optional[Any]							):
     pass
   @unittest.skip("Will be fixed soon by reducing the size of the model used for common tests."							)
   def     __UpperCamelCase						( self			:      Union[str, Any]							):
     pass
   def     __UpperCamelCase						( self			:      Tuple							):
     SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE:Tuple						      =					self.model_tester.prepare_config_and_inputs_for_common()

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

       SCREAMING_SNAKE_CASE:Tuple						      =					["pixel_values"]
       self.assertListEqual(arg_names[:1]     ,SCREAMING_SNAKE_CASE__							)
   @slow
   def     __UpperCamelCase						( self			:      Optional[int]							):
     for model_name in ["facebook/mask2former-swin-small-coco-instance"]:
       SCREAMING_SNAKE_CASE:int						      =					MaskaFormerModel.from_pretrained(SCREAMING_SNAKE_CASE__							)
       self.assertIsNotNone(SCREAMING_SNAKE_CASE__							)
   def     __UpperCamelCase						( self			:      List[str]							):
     SCREAMING_SNAKE_CASE:Dict						      =					(self.model_tester.min_size,) * 2
     SCREAMING_SNAKE_CASE:int						      =					{
         "pixel_values": torch.randn((2, 3, *size)     ,device=SCREAMING_SNAKE_CASE__							),
         "mask_labels": torch.randn((2, 10, *size)     ,device=SCREAMING_SNAKE_CASE__							),
         "class_labels": torch.zeros(2     ,10     ,device=SCREAMING_SNAKE_CASE__							).long(),
     }
     SCREAMING_SNAKE_CASE:List[str]						      =					self.model_tester.get_config()

     SCREAMING_SNAKE_CASE:Dict						      =					MaskaFormerForUniversalSegmentation(SCREAMING_SNAKE_CASE__							).to(SCREAMING_SNAKE_CASE__							)
     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					model(**SCREAMING_SNAKE_CASE__							)
     self.assertTrue(outputs.loss is not None							)
   def     __UpperCamelCase						( self			:      Tuple							):
     SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE:Tuple						      =					self.model_tester.prepare_config_and_inputs_for_common()
     self.model_tester.create_and_check_maskaformer_model(SCREAMING_SNAKE_CASE__     ,**SCREAMING_SNAKE_CASE__     ,output_hidden_states=SCREAMING_SNAKE_CASE__							)
   def     __UpperCamelCase						( self			:      int							):
     SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE:str						      =					self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
       SCREAMING_SNAKE_CASE:Dict						      =					model_class(SCREAMING_SNAKE_CASE__							).to(SCREAMING_SNAKE_CASE__							)
       SCREAMING_SNAKE_CASE:Any						      =					model(**SCREAMING_SNAKE_CASE__     ,output_attentions=SCREAMING_SNAKE_CASE__							)
       self.assertTrue(outputs.attentions is not None							)
   def     __UpperCamelCase						( self			:      Optional[Any]							):
     if not self.model_tester.is_training:
       return

     SCREAMING_SNAKE_CASE:Any						      =					self.all_model_classes[1]
     SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE:Dict						      =					self.model_tester.prepare_config_and_inputs()

     SCREAMING_SNAKE_CASE:int						      =					model_class(SCREAMING_SNAKE_CASE__							)
     model.to(SCREAMING_SNAKE_CASE__							)
     model.train()

     SCREAMING_SNAKE_CASE:Dict						      =					model(SCREAMING_SNAKE_CASE__     ,mask_labels=SCREAMING_SNAKE_CASE__     ,class_labels=SCREAMING_SNAKE_CASE__							).loss
     loss.backward()







   def     __UpperCamelCase						( self			:      Any							):
     SCREAMING_SNAKE_CASE:Optional[int]						      =					self.all_model_classes[1]
     SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE:Tuple						      =					self.model_tester.prepare_config_and_inputs()
     SCREAMING_SNAKE_CASE:List[Any]						      =					True
     SCREAMING_SNAKE_CASE:Dict						      =					True

     SCREAMING_SNAKE_CASE:List[Any]						      =					model_class(SCREAMING_SNAKE_CASE__							).to(SCREAMING_SNAKE_CASE__							)
     model.train()

     SCREAMING_SNAKE_CASE:Optional[Any]						      =					model(SCREAMING_SNAKE_CASE__     ,mask_labels=SCREAMING_SNAKE_CASE__     ,class_labels=SCREAMING_SNAKE_CASE__							)

     SCREAMING_SNAKE_CASE:List[str]						      =					outputs.encoder_hidden_states[0]
     encoder_hidden_states.retain_grad()

     SCREAMING_SNAKE_CASE:List[str]						      =					outputs.pixel_decoder_hidden_states[0]
     pixel_decoder_hidden_states.retain_grad()

     SCREAMING_SNAKE_CASE:Optional[Any]						      =					outputs.transformer_decoder_hidden_states[0]
     transformer_decoder_hidden_states.retain_grad()

     SCREAMING_SNAKE_CASE:Optional[int]						      =					outputs.attentions[0]
     attentions.retain_grad()

     outputs.loss.backward(retain_graph=SCREAMING_SNAKE_CASE__							)

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



A_       						=							1e-4





def    A_  ( ):
  SCREAMING_SNAKE_CASE:Optional[int]						      =					Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
  return image





@require_vision
@slow
class 							_snake_case (       unittest.TestCase						):
   @cached_property
   def     __UpperCamelCase						( self			:      Optional[Any]							):
     return "facebook/mask2former-swin-small-coco-instance"
   @cached_property
   def     __UpperCamelCase						( self			:      int							):
     return MaskaFormerImageProcessor.from_pretrained(self.model_checkpoints							) if is_vision_available() else None
   def     __UpperCamelCase						( self			:      Union[str, Any]							):
     SCREAMING_SNAKE_CASE:List[str]						      =					MaskaFormerModel.from_pretrained(self.model_checkpoints							).to(SCREAMING_SNAKE_CASE__							)
     SCREAMING_SNAKE_CASE:List[str]						      =					self.default_image_processor
     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					prepare_img()
     SCREAMING_SNAKE_CASE:List[str]						      =					image_processor(SCREAMING_SNAKE_CASE__     ,return_tensors="pt"							).to(SCREAMING_SNAKE_CASE__							)
     SCREAMING_SNAKE_CASE:Tuple						      =					inputs["pixel_values"].shape
     # check size is divisible by 32
     self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0							)
     # check size
     self.assertEqual(SCREAMING_SNAKE_CASE__     ,(1, 3, 384, 384)							)

     with torch.no_grad():
       SCREAMING_SNAKE_CASE:Optional[Any]						      =					model(**SCREAMING_SNAKE_CASE__							)

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

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

     SCREAMING_SNAKE_CASE:Tuple						      =					torch.tensor(
         [[2.1_152, 1.7_000, -0.8_603], [1.5_808, 1.8_004, -0.9_353], [1.6_043, 1.7_495, -0.5_999]]							).to(SCREAMING_SNAKE_CASE__							)
     self.assertTrue(
         torch.allclose(
             outputs.transformer_decoder_last_hidden_state[0, :3, :3]     ,SCREAMING_SNAKE_CASE__     ,atol=SCREAMING_SNAKE_CASE__							)							)
   def     __UpperCamelCase						( self			:      Any							):
     SCREAMING_SNAKE_CASE:List[str]						      =					MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints							).to(SCREAMING_SNAKE_CASE__							).eval()
     SCREAMING_SNAKE_CASE:Optional[int]						      =					self.default_image_processor
     SCREAMING_SNAKE_CASE:Tuple						      =					prepare_img()
     SCREAMING_SNAKE_CASE:int						      =					image_processor(SCREAMING_SNAKE_CASE__     ,return_tensors="pt"							).to(SCREAMING_SNAKE_CASE__							)
     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					inputs["pixel_values"].shape
     # check size is divisible by 32
     self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0							)
     # check size
     self.assertEqual(SCREAMING_SNAKE_CASE__     ,(1, 3, 384, 384)							)

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







   def     __UpperCamelCase						( self			:      Optional[int]							):
     SCREAMING_SNAKE_CASE:Optional[int]						      =					MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints							).to(SCREAMING_SNAKE_CASE__							).eval()
     SCREAMING_SNAKE_CASE:List[Any]						      =					self.default_image_processor

     SCREAMING_SNAKE_CASE:str						      =					image_processor(
         [np.zeros((3, 800, 1_333)							), np.zeros((3, 800, 1_333)							)]     ,segmentation_maps=[np.zeros((384, 384)							).astype(np.floataa							), np.zeros((384, 384)							).astype(np.floataa							)]     ,return_tensors="pt"     ,)

     SCREAMING_SNAKE_CASE:Any						      =					inputs["pixel_values"].to(SCREAMING_SNAKE_CASE__							)
     SCREAMING_SNAKE_CASE:Dict						      =					[el.to(SCREAMING_SNAKE_CASE__							) for el in inputs["mask_labels"]]
     SCREAMING_SNAKE_CASE:List[str]						      =					[el.to(SCREAMING_SNAKE_CASE__							) for el in inputs["class_labels"]]

     with torch.no_grad():
       SCREAMING_SNAKE_CASE:int						      =					model(**SCREAMING_SNAKE_CASE__							)

     self.assertTrue(outputs.loss is not None							)









'''simple docstring'''

import argparse
import random

import joblib
import numpy as np
import torch
from igf.igf import (
    SecondaryLearner,
    collect_objective_set,
    compute_perplexity,
    generate_datasets,
    load_gpta,
    recopy_gpta,
    set_seed,
    train_secondary_learner,
)
from torch.utils.data import DataLoader, RandomSampler

from transformers import GPTaLMHeadModel





def    A_  ( snake_case=32      ,       snake_case=10      ,       snake_case=100      ,       snake_case=1026      ,       snake_case=True      ,       snake_case="data/tokenized_stories_train_wikitext103.jbl"      ,       snake_case="igf_context_pairs.jbl"      ,       ):
  set_seed(3 )
  # generate train_data and objective_set
  SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE:Optional[int]						      =					generate_datasets(
      snake_case      ,       snake_case      ,       number=snake_case      ,       min_len=1026      ,       trim=snake_case )
  # keeps model same across runs
  set_seed(4 )
  # model, lm_optimizer, lm_scheduler = recopy_gpt2(model, device, max_steps) # store original model weights
  # can we train on GPU?
  SCREAMING_SNAKE_CASE:Tuple						      =					torch.device("cuda:0" if torch.cuda.is_available() else "cpu" )

  # load pretrained model
  SCREAMING_SNAKE_CASE:Optional[Any]						      =					load_gpta("gpt2" ).to(snake_case )
  print("computing perplexity on objective set" )
  SCREAMING_SNAKE_CASE:str						      =					compute_perplexity(snake_case      ,       snake_case      ,       snake_case ).item()
  print("perplexity on objective set:"      ,       snake_case )

  # collect igf pairs and save to file demo.jbl
  collect_objective_set(snake_case      ,       snake_case      ,       snake_case      ,       snake_case      ,       snake_case      ,       snake_case      ,       snake_case      ,       snake_case )

  # clean up, delete model and data we don't need anymore
  del model, train_data, objective_set
  torch.cuda.empty_cache()





def    A_  ( snake_case      ,       snake_case=15      ,       snake_case=128      ,       snake_case=100      ,       snake_case="igf_model.pt"      ,       ):
  set_seed(42 )

  # Load pre-trained model
  SCREAMING_SNAKE_CASE:int						      =					GPTaLMHeadModel.from_pretrained("gpt2" )

  # Initialize secondary learner to use embedding weights of model
  SCREAMING_SNAKE_CASE:Optional[Any]						      =					SecondaryLearner(snake_case )

  # Train secondary learner
  SCREAMING_SNAKE_CASE:int						      =					train_secondary_learner(
      snake_case      ,       snake_case      ,       max_epochs=snake_case      ,       batch_size=snake_case      ,       eval_freq=100      ,       igf_model_path=snake_case      ,       )

  del model, secondary_learner_train_data
  torch.cuda.empty_cache()

  return secondary_learner





def    A_  ( snake_case      ,       snake_case      ,       snake_case      ,       snake_case=32      ,       snake_case=1000      ,       snake_case=16      ,       snake_case=1.0      ,       snake_case=recopy_gpta      ,       snake_case=None      ,       snake_case=10      ,       snake_case="gpt2_finetuned.pt"      ,       ):
  SCREAMING_SNAKE_CASE:str						      =					torch.device("cuda:0" if torch.cuda.is_available() else "cpu" )
  SCREAMING_SNAKE_CASE:Dict						      =					RandomSampler(snake_case )
  SCREAMING_SNAKE_CASE:str						      =					DataLoader(snake_case      ,       sampler=snake_case )

  SCREAMING_SNAKE_CASE:str						      =					max_steps // (len(snake_case )) + 1
  SCREAMING_SNAKE_CASE:Tuple						      =					0
  SCREAMING_SNAKE_CASE:List[Any]						      =					torch.zeros((1, context_len)      ,       dtype=torch.long      ,       device=snake_case )
  SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE:int						      =					recopy_model(snake_case      ,       snake_case      ,       snake_case )

  model.train()
  if secondary_learner is not None:
    secondary_learner.to(snake_case )
    secondary_learner.eval()
  SCREAMING_SNAKE_CASE:List[Any]						      =					[]
  SCREAMING_SNAKE_CASE:Tuple						      =					0

  SCREAMING_SNAKE_CASE:Tuple						      =					[]
  SCREAMING_SNAKE_CASE:Tuple						      =					[]

  # Compute the performance of the transformer model at the beginning
  SCREAMING_SNAKE_CASE:int						      =					compute_perplexity(snake_case      ,       snake_case      ,       snake_case )
  test_perps.append(snake_case )
  print("Test perplexity, step"      ,       snake_case      ,       ":"      ,       snake_case )
  for epoch in range(int(snake_case ) ):
    for step, example in enumerate(snake_case ):
      torch.cuda.empty_cache()
      SCREAMING_SNAKE_CASE:str						      =					random.randint(0      ,       example.size(2 ) - context_len - 1 )
      SCREAMING_SNAKE_CASE:List[str]						      =					example[0, 0, start : start + context_len]
      lm_optimizer.zero_grad()
      SCREAMING_SNAKE_CASE:Any						      =					model(snake_case      ,       labels=snake_case )
      SCREAMING_SNAKE_CASE:List[Any]						      =					True

      if secondary_learner is not None:
        SCREAMING_SNAKE_CASE:List[str]						      =					secondary_learner.forward(
            torch.tensor(snake_case      ,       dtype=torch.long      ,       device=snake_case ).unsqueeze(0 ) )[0].item()
        observed_qs.append(float(snake_case ) )

        # Here we implement the simple non-constant threshold for the predicted IG(X) value
        # We will decay the selectivity of our secondary learner filter from
        # 1 standard deviation above average to 1 below average after 10 batches.

        if global_step == 10:
          SCREAMING_SNAKE_CASE:int						      =					-1
        if predicted_q < threshold:
          SCREAMING_SNAKE_CASE:Optional[int]						      =					False

            # If we passed the filter, add the context to the batch!
      if do_backprop:
        contexts.append(np.array(context.cpu() ) )
        SCREAMING_SNAKE_CASE:Union[str, Any]						      =					outputs[0]
        lm_loss.backward()
        examples += 1

      del outputs

      # Once the batch is filled with enough contexts, backprop on the batch.
      if examples == batch_size:
        torch.cuda.empty_cache()
        SCREAMING_SNAKE_CASE:int						      =					0
        # Do LM backprop
        torch.nn.utils.clip_grad_norm_(model.parameters()      ,       3.0 )
        lm_optimizer.step()
        lm_scheduler.step()  # Update learning rate schedule
        global_step += 1
        # Compute the performance of the transformer model at this batch
        if global_step % eval_interval == 0:
          SCREAMING_SNAKE_CASE:Optional[Any]						      =					compute_perplexity(snake_case      ,       snake_case      ,       snake_case )
          test_perps.append(snake_case )

          print("Test perplexity, step"      ,       snake_case      ,       ":"      ,       snake_case )
            # Break out of the loop after 60 batches
      if max_steps > 0 and global_step > 60:
        break
    if max_steps > 0 and global_step > 60:
      break

    # save finetuned transformer model
  torch.save(model.state_dict()      ,       snake_case )
  torch.cuda.empty_cache()
  # Do some cleaning up so we can reinitialize for the next run of this function
  del lm_optimizer
  del lm_scheduler
  return model





def    A_  ( ):
  SCREAMING_SNAKE_CASE:Optional[int]						      =					argparse.ArgumentParser(description="Fine-tune a transformer model with IGF on a language modeling task" )

  # Required parameters
  parser.add_argument(
      "--data_dir"      ,       default=snake_case      ,       type=snake_case      ,       required=snake_case      ,       help="The input data dir. Should contain data files for WikiText."      ,       )
  parser.add_argument(
      "--model_name_or_path"      ,       default=snake_case      ,       type=snake_case      ,       required=snake_case      ,       help="Path to pretrained model or model identifier from huggingface.co/models"      ,       )
  parser.add_argument(
      "--data_file"      ,       type=snake_case      ,       default=snake_case      ,       help=(
          "A jbl file containing tokenized data which can be split as objective dataset, "
          "train_dataset and test_dataset."
      )      ,       )

  parser.add_argument(
      "--igf_data_file"      ,       type=snake_case      ,       default=snake_case      ,       help="A jbl file containing the context and information gain pairs to train secondary learner."      ,       )

  parser.add_argument(
      "--output_dir"      ,       default=snake_case      ,       type=snake_case      ,       required=snake_case      ,       help="The output directory where the final fine-tuned model is stored."      ,       )

  parser.add_argument(
      "--tokenizer_name"      ,       default=snake_case      ,       type=snake_case      ,       help="Pretrained tokenizer name or path if not the same as model_name"      ,       )
  parser.add_argument("--seed"      ,       type=snake_case      ,       default=snake_case      ,       help="A seed for reproducible training." )

  parser.add_argument(
      "--context_len"      ,       default=32      ,       type=snake_case      ,       help=(
          "The maximum total input sequence length after tokenization. Sequences longer "
          "than this will be truncated, sequences shorter will be padded."
      )      ,       )

  parser.add_argument(
      "--size_objective_set"      ,       default=100      ,       type=snake_case      ,       help="number of articles that are long enough to be used as our objective set"      ,       )
  parser.add_argument(
      "--eval_freq"      ,       default=100      ,       type=snake_case      ,       help="secondary model evaluation is triggered at eval_freq" )

  parser.add_argument("--max_steps"      ,       default=1000      ,       type=snake_case      ,       help="To calculate training epochs" )

  parser.add_argument(
      "--secondary_learner_batch_size"      ,       default=128      ,       type=snake_case      ,       help="batch size of training data for secondary learner"      ,       )

  parser.add_argument(
      "--batch_size"      ,       default=16      ,       type=snake_case      ,       help="batch size of training data of language model(gpt2) " )

  parser.add_argument(
      "--eval_interval"      ,       default=10      ,       type=snake_case      ,       help=(
          "decay the selectivity of our secondary learner filter from"
          "1 standard deviation above average to 1 below average after 10 batches"
      )      ,       )

  parser.add_argument(
      "--number"      ,       default=100      ,       type=snake_case      ,       help="The number of examples split to be used as objective_set/test_data" )

  parser.add_argument(
      "--min_len"      ,       default=1026      ,       type=snake_case      ,       help="The minimum length of the article to be used as objective set" )

  parser.add_argument(
      "--secondary_learner_max_epochs"      ,       default=15      ,       type=snake_case      ,       help="number of epochs to train secondary learner" )

  parser.add_argument("--trim"      ,       default=snake_case      ,       type=snake_case      ,       help="truncate the example if it exceeds context length" )

  parser.add_argument(
      "--threshold"      ,       default=1.0      ,       type=snake_case      ,       help=(
          "The threshold value used by secondary learner to filter the train_data and allow only"
          " informative data as input to the model"
      )      ,       )

  parser.add_argument("--finetuned_model_name"      ,       default="gpt2_finetuned.pt"      ,       type=snake_case      ,       help="finetuned_model_name" )

  parser.add_argument(
      "--recopy_model"      ,       default=snake_case      ,       type=snake_case      ,       help="Reset the model to the original pretrained GPT-2 weights after each iteration"      ,       )

  # function calls
  # Collecting *n* pairs of context and information gain(X, IG(X)) for training the secondary learner
  generate_n_pairs(
      context_len=32      ,       max_steps=10      ,       size_objective_set=100      ,       min_len=1026      ,       trim=snake_case      ,       data_file="data/tokenized_stories_train_wikitext103.jbl"      ,       igf_data_file="igf_context_pairs.jbl"      ,       )

  # Load train data for secondary learner
  SCREAMING_SNAKE_CASE:Union[str, Any]						      =					joblib.load("data/IGF_values.jbl" )

  # Train secondary learner
  SCREAMING_SNAKE_CASE:Optional[int]						      =					training_secondary_learner(
      snake_case      ,       secondary_learner_max_epochs=15      ,       secondary_learner_batch_size=128      ,       eval_freq=100      ,       igf_model_path="igf_model.pt"      ,       )

  # load pretrained gpt2 model
  SCREAMING_SNAKE_CASE:Dict						      =					GPTaLMHeadModel.from_pretrained("gpt2" )
  set_seed(42 )

  # Generate train and test data to train and evaluate gpt2 model
  SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE:Optional[Any]						      =					generate_datasets(
      context_len=32      ,       file="data/tokenized_stories_train_wikitext103.jbl"      ,       number=100      ,       min_len=1026      ,       trim=snake_case )

  # fine-tuning of the gpt2 model using igf (Information Gain Filtration)
  finetune(
      snake_case      ,       snake_case      ,       snake_case      ,       context_len=32      ,       max_steps=1000      ,       batch_size=16      ,       threshold=1.0      ,       recopy_model=snake_case      ,       secondary_learner=snake_case      ,       eval_interval=10      ,       finetuned_model_name="gpt2_finetuned.pt"      ,       )


if __name__ == "__main__":
    main()






