




"""simple docstring"""


from collections import defaultdict
from pathlib import Path

import pandas as pd
from rouge_cli import calculate_rouge_path

from utils import calculate_rouge


A_      			=       [
    'Prosecutor: "No videos were used in the crash investigation" German papers say they saw a cell phone video of the'
    ' final seconds on board Flight 9525. The Germanwings co-pilot says he had a "previous episode of severe'
    ' depression\" German airline confirms it knew of Andreas Lubitz\'s depression years before he took control.',
    'The Palestinian Authority officially becomes the 123rd member of the International Criminal Court. The formal'
    ' accession was marked with a ceremony at The Hague, in the Netherlands. The Palestinians signed the ICC\'s'
    ' founding Rome Statute in January. Israel and the United States opposed the Palestinians\' efforts to join the'
    ' body.',
    'Amnesty International releases its annual report on the death penalty. The report catalogs the use of'
    ' state-sanctioned killing as a punitive measure across the globe. At least 607 people were executed around the'
    ' world in 2014, compared to 778 in 2013. The U.S. remains one of the worst offenders for imposing capital'
    ' punishment.',
]

A_      			=       [
    'Marseille prosecutor says "so far no videos were used in the crash investigation" despite media reports .'
    ' Journalists at Bild and Paris Match are "very confident" the video clip is real, an editor says . Andreas Lubitz'
    ' had informed his Lufthansa training school of an episode of severe depression, airline says .',
    'Membership gives the ICC jurisdiction over alleged crimes committed in Palestinian territories since last June .'
    ' Israel and the United States opposed the move, which could open the door to war crimes investigations against'
    ' Israelis .',
    'Amnesty\'s annual death penalty report catalogs encouraging signs, but setbacks in numbers of those sentenced to'
    ' death . Organization claims that governments around the world are using the threat of terrorism to advance'
    ' executions . The number of executions worldwide has gone down by almost 22% compared with 2013, but death'
    ' sentences up by 28% .',
]


def 					UpperCAmelCase__ ():
  """simple docstring"""

  _snake_case							:	Optional[Any]			=						calculate_rouge(__lowerCAmelCase	,     __lowerCAmelCase	,     bootstrap_aggregation=__lowerCAmelCase	,     rouge_keys=["""rouge2""", """rougeL"""]					)
  assert isinstance(__lowerCAmelCase	,     __lowerCAmelCase					)
  _snake_case							:	Dict			=						calculate_rouge(__lowerCAmelCase	,     __lowerCAmelCase	,     bootstrap_aggregation=__lowerCAmelCase	,     rouge_keys=["""rouge2"""]					)
  assert (
      pd.DataFrame(no_aggregation["""rouge2"""]					).fmeasure.mean()
      == pd.DataFrame(no_aggregation_just_ra["""rouge2"""]					).fmeasure.mean()
  )


def 					UpperCAmelCase__ ():
  """simple docstring"""

  _snake_case							:	int			=						"""rougeLsum"""
  _snake_case							:	Union[str, Any]			=						calculate_rouge(__lowerCAmelCase	,     __lowerCAmelCase	,     newline_sep=__lowerCAmelCase	,     rouge_keys=[k]					)[k]
  _snake_case							:	Dict			=						calculate_rouge(__lowerCAmelCase	,     __lowerCAmelCase	,     newline_sep=__lowerCAmelCase	,     rouge_keys=[k]					)[k]
  assert score > score_no_sep


def 					UpperCAmelCase__ ():
  """simple docstring"""

  _snake_case							:	Any			=						["""rouge1""", """rouge2""", """rougeL"""]
  _snake_case							:	Optional[int]			=						calculate_rouge(__lowerCAmelCase	,     __lowerCAmelCase	,     newline_sep=__lowerCAmelCase	,     rouge_keys=__lowerCAmelCase					)
  _snake_case							:	Optional[int]			=						calculate_rouge(__lowerCAmelCase	,     __lowerCAmelCase	,     newline_sep=__lowerCAmelCase	,     rouge_keys=__lowerCAmelCase					)
  assert score_sep == score_no_sep


def 					UpperCAmelCase__ ():
  """simple docstring"""

  _snake_case							:	Optional[int]			=						[
      """Her older sister, Margot Frank, died in 1945, a month earlier than previously thought.""",
      """Marseille prosecutor says \"so far no videos were used in the crash investigation\" despite media reports .""",
  ]
  _snake_case							:	Dict			=						[
      """Margot Frank, died in 1945, a month earlier than previously thought.""",
      """Prosecutor: \"No videos were used in the crash investigation\" German papers say they saw a cell phone video of"""
      """ the final seconds on board Flight 9525.""",
  ]
  assert calculate_rouge(__lowerCAmelCase	,     __lowerCAmelCase	,     newline_sep=__lowerCAmelCase					) == calculate_rouge(__lowerCAmelCase	,     __lowerCAmelCase	,     newline_sep=__lowerCAmelCase					)


def 					UpperCAmelCase__ ():
  """simple docstring"""

  _snake_case							:	Union[str, Any]			=						[
      """\" \"a person who has such a video needs to immediately give it to the investigators,\" prosecutor says .<n> \"it is a very disturbing scene,\" editor-in-chief of bild online tells \"erin burnett: outfront\" """
  ]
  _snake_case							:	Union[str, Any]			=						[
      """ Marseille prosecutor says \"so far no videos were used in the crash investigation\" despite media reports . Journalists at Bild and Paris Match are \"very confident\" the video clip is real, an editor says . Andreas Lubitz had informed his Lufthansa training school of an episode of severe depression, airline says ."""
  ]

  _snake_case							:	str			=						calculate_rouge(__lowerCAmelCase	,     __lowerCAmelCase	,     rouge_keys=["""rougeLsum"""]	,     newline_sep=__lowerCAmelCase					)["""rougeLsum"""]
  _snake_case							:	Union[str, Any]			=						calculate_rouge(__lowerCAmelCase	,     __lowerCAmelCase	,     rouge_keys=["""rougeLsum"""]					)["""rougeLsum"""]
  assert new_score > prev_score






def 					UpperCAmelCase__ ():
  """simple docstring"""

  _snake_case							:	Tuple			=						Path("""examples/seq2seq/test_data/wmt_en_ro"""					)
  _snake_case							:	Union[str, Any]			=						calculate_rouge_path(data_dir.joinpath("""test.source"""					)	,     data_dir.joinpath("""test.target"""					)					)
  assert isinstance(__lowerCAmelCase	,     __lowerCAmelCase					)
  _snake_case							:	Tuple			=						calculate_rouge_path(
      data_dir.joinpath("""test.source"""					)	,     data_dir.joinpath("""test.target"""					)	,     bootstrap_aggregation=__lowerCAmelCase					)
  assert isinstance(__lowerCAmelCase	,     __lowerCAmelCase					)








"""simple docstring"""


import inspect
import unittest

from datasets import load_dataset
from packaging import version

from transformers import BeitConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
     import torch
     from torch import nn

     from transformers import (
         MODEL_MAPPING,
         BeitForImageClassification,
         BeitForMaskedImageModeling,
         BeitForSemanticSegmentation,
         BeitModel,
     )
     from transformers.models.beit.modeling_beit import BEIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
     import PIL
     from PIL import Image

     from transformers import BeitImageProcessor






class       lowercase:







 '''simple docstring'''





 def __init__(       self:	Optional[Any],   a_:	Union[str, Any],   a_:	int=100,   a_:	int=13,   a_:	List[Any]=30,   a_:	str=2,   a_:	Optional[Any]=3,   a_:	Optional[int]=True,   a_:	Any=True,   a_:	Optional[Any]=32,   a_:	Tuple=4,   a_:	str=4,   a_:	List[Any]=37,   a_:	List[str]="gelu",   a_:	str=0.1,   a_:	Optional[int]=0.1,   a_:	Any=10,   a_:	List[str]=0.02,   a_:	Dict=3,   a_:	str=None,   a_:	Optional[int]=[0, 1, 2, 3],   ):




   '''simple docstring'''



   _snake_case							:	Optional[int]			=						parent
   _snake_case							:	Optional[Any]			=						100
   _snake_case							:	Any			=						batch_size
   _snake_case							:	List[Any]			=						image_size
   _snake_case							:	Optional[Any]			=						patch_size
   _snake_case							:	str			=						num_channels
   _snake_case							:	Tuple			=						is_training
   _snake_case							:	Tuple			=						use_labels
   _snake_case							:	Any			=						hidden_size
   _snake_case							:	Optional[int]			=						num_hidden_layers
   _snake_case							:	List[str]			=						num_attention_heads
   _snake_case							:	Union[str, Any]			=						intermediate_size
   _snake_case							:	Dict			=						hidden_act
   _snake_case							:	str			=						hidden_dropout_prob
   _snake_case							:	Optional[int]			=						attention_probs_dropout_prob
   _snake_case							:	Optional[Any]			=						type_sequence_label_size
   _snake_case							:	Any			=						initializer_range
   _snake_case							:	List[str]			=						scope
   _snake_case							:	int			=						out_indices
   _snake_case							:	Optional[Any]			=						num_labels

   # in BeiT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
   _snake_case							:	Dict			=						(image_size // patch_size) ** 2
   _snake_case							:	str			=						num_patches + 1


 def 				UpperCamelCase_     (       self:	Any	):




   '''simple docstring'''



   _snake_case							:	int			=						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]	)

   _snake_case							:	List[Any]			=						None
   _snake_case							:	Tuple			=						None
   if self.use_labels:
     _snake_case							:	str			=						ids_tensor([self.batch_size],   self.type_sequence_label_size	)
     _snake_case							:	List[str]			=						ids_tensor([self.batch_size, self.image_size, self.image_size],   self.num_labels	)

   _snake_case							:	List[str]			=						self.get_config()

   return config, pixel_values, labels, pixel_labels


 def 				UpperCamelCase_     (       self:	List[Any]	):




   '''simple docstring'''



   return BeitConfig(
       vocab_size=self.vocab_size,   image_size=self.image_size,   patch_size=self.patch_size,   num_channels=self.num_channels,   hidden_size=self.hidden_size,   num_hidden_layers=self.num_hidden_layers,   num_attention_heads=self.num_attention_heads,   intermediate_size=self.intermediate_size,   hidden_act=self.hidden_act,   hidden_dropout_prob=self.hidden_dropout_prob,   attention_probs_dropout_prob=self.attention_probs_dropout_prob,   is_decoder=a_,   initializer_range=self.initializer_range,   out_indices=self.out_indices,   )


 def 				UpperCamelCase_     (       self:	List[Any],   a_:	List[Any],   a_:	Any,   a_:	Optional[Any],   a_:	List[str]	):




   '''simple docstring'''



   _snake_case							:	str			=						BeitModel(config=a_	)
   model.to(a_	)
   model.eval()
   _snake_case							:	Dict			=						model(a_	)
   self.parent.assertEqual(result.last_hidden_state.shape,   (self.batch_size, self.seq_length, self.hidden_size)	)


 def 				UpperCamelCase_     (       self:	str,   a_:	List[Any],   a_:	Optional[Any],   a_:	Optional[int],   a_:	List[Any]	):




   '''simple docstring'''



   _snake_case							:	List[str]			=						BeitForMaskedImageModeling(config=a_	)
   model.to(a_	)
   model.eval()
   _snake_case							:	Union[str, Any]			=						model(a_	)
   self.parent.assertEqual(result.logits.shape,   (self.batch_size, self.seq_length - 1, self.vocab_size)	)


 def 				UpperCamelCase_     (       self:	Any,   a_:	List[str],   a_:	Any,   a_:	List[Any],   a_:	Optional[Any]	):




   '''simple docstring'''



   _snake_case							:	Any			=						self.type_sequence_label_size
   _snake_case							:	Any			=						BeitForImageClassification(a_	)
   model.to(a_	)
   model.eval()
   _snake_case							:	List[Any]			=						model(a_,   labels=a_	)
   self.parent.assertEqual(result.logits.shape,   (self.batch_size, self.type_sequence_label_size)	)

   # test greyscale images
   _snake_case							:	Any			=						1
   _snake_case							:	str			=						BeitForImageClassification(a_	)
   model.to(a_	)
   model.eval()

   _snake_case							:	Dict			=						floats_tensor([self.batch_size, 1, self.image_size, self.image_size]	)
   _snake_case							:	Optional[Any]			=						model(a_,   labels=a_	)
   self.parent.assertEqual(result.logits.shape,   (self.batch_size, self.type_sequence_label_size)	)


 def 				UpperCamelCase_     (       self:	List[Any],   a_:	Optional[int],   a_:	List[Any],   a_:	str,   a_:	int	):




   '''simple docstring'''



   _snake_case							:	List[str]			=						self.num_labels
   _snake_case							:	List[Any]			=						BeitForSemanticSegmentation(a_	)
   model.to(a_	)
   model.eval()
   _snake_case							:	List[str]			=						model(a_	)
   self.parent.assertEqual(
       result.logits.shape,   (self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)	)
   _snake_case							:	str			=						model(a_,   labels=a_	)
   self.parent.assertEqual(
       result.logits.shape,   (self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)	)


 def 				UpperCamelCase_     (       self:	List[str]	):




   '''simple docstring'''



   _snake_case							:	Tuple			=						self.prepare_config_and_inputs()
   _snake_case	,      _snake_case	,      _snake_case	,      _snake_case							:	Any			=						config_and_inputs
   _snake_case							:	Optional[int]			=						{"""pixel_values""": pixel_values}
   return config, inputs_dict







@require_torch
class       lowercase(  __a ,    __a ,    unittest.TestCase				):







 '''simple docstring'''





 lowercase__				      =      (
     (BeitModel, BeitForImageClassification, BeitForMaskedImageModeling, BeitForSemanticSegmentation)
     if is_torch_available()
     else ()
 )
 lowercase__				      =      (
     {
         "feature-extraction": BeitModel,
         "image-classification": BeitForImageClassification,
         "image-segmentation": BeitForSemanticSegmentation,
     }
     if is_torch_available()
     else {}
 )

 lowercase__				      =      False
 lowercase__				      =      False
 lowercase__				      =      False


 def 				UpperCamelCase_     (       self:	Union[str, Any]	):




   '''simple docstring'''



   _snake_case							:	Dict			=						BeitModelTester(self	)
   _snake_case							:	int			=						ConfigTester(self,   config_class=a_,   has_text_modality=a_,   hidden_size=37	)


 def 				UpperCamelCase_     (       self:	str	):




   '''simple docstring'''



   self.config_tester.run_common_tests()


 @unittest.skip(reason="""BEiT does not use inputs_embeds"""	)
 def 				UpperCamelCase_     (       self:	Optional[int]	):




   '''simple docstring'''



   pass


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




   '''simple docstring'''



   pass


 def 				UpperCamelCase_     (       self:	List[Any]	):




   '''simple docstring'''



   _snake_case	,      _snake_case							:	Any			=						self.model_tester.prepare_config_and_inputs_for_common()

   for model_class in self.all_model_classes:
     _snake_case							:	List[str]			=						model_class(a_	)
     self.assertIsInstance(model.get_input_embeddings(),   (nn.Module)	)
     _snake_case							:	Tuple			=						model.get_output_embeddings()
     self.assertTrue(x is None or isinstance(a_,   nn.Linear	)	)


 def 				UpperCamelCase_     (       self:	Optional[Any]	):




   '''simple docstring'''



   _snake_case	,      _snake_case							:	Optional[Any]			=						self.model_tester.prepare_config_and_inputs_for_common()

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

     _snake_case							:	List[Any]			=						["""pixel_values"""]
     self.assertListEqual(arg_names[:1],   a_	)


 def 				UpperCamelCase_     (       self:	List[str]	):




   '''simple docstring'''



   _snake_case							:	Optional[int]			=						self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_model(*a_	)


 def 				UpperCamelCase_     (       self:	int	):




   '''simple docstring'''



   _snake_case							:	Optional[Any]			=						self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_masked_lm(*a_	)


 def 				UpperCamelCase_     (       self:	Dict	):




   '''simple docstring'''



   _snake_case							:	Dict			=						self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_image_classification(*a_	)


 def 				UpperCamelCase_     (       self:	Optional[int]	):




   '''simple docstring'''



   _snake_case							:	List[Any]			=						self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_semantic_segmentation(*a_	)


 def 				UpperCamelCase_     (       self:	int	):




   '''simple docstring'''



   if not self.model_tester.is_training:
     return

   _snake_case	,      _snake_case							:	List[str]			=						self.model_tester.prepare_config_and_inputs_for_common()
   _snake_case							:	Any			=						True

   for model_class in self.all_model_classes:
     # we don't test BeitForMaskedImageModeling
     if model_class in [*get_values(a_	), BeitForMaskedImageModeling]:
       continue

     _snake_case							:	List[Any]			=						model_class(a_	)
     model.to(a_	)
     model.train()
     _snake_case							:	Dict			=						self._prepare_for_class(a_,   a_,   return_labels=a_	)
     _snake_case							:	List[Any]			=						model(**a_	).loss
     loss.backward()


 def 				UpperCamelCase_     (       self:	Any	):




   '''simple docstring'''



   _snake_case	,      _snake_case							:	List[Any]			=						self.model_tester.prepare_config_and_inputs_for_common()
   if not self.model_tester.is_training:
     return

   _snake_case							:	Dict			=						False
   _snake_case							:	Optional[Any]			=						True

   for model_class in self.all_model_classes:
     # we don't test BeitForMaskedImageModeling
     if (
         model_class in [*get_values(a_	), BeitForMaskedImageModeling]
         or not model_class.supports_gradient_checkpointing
     ):
       continue

     _snake_case							:	Any			=						model_class(a_	)
     model.gradient_checkpointing_enable()
     model.to(a_	)
     model.train()
     _snake_case							:	Any			=						self._prepare_for_class(a_,   a_,   return_labels=a_	)
     _snake_case							:	int			=						model(**a_	).loss
     loss.backward()


 def 				UpperCamelCase_     (       self:	Tuple	):




   '''simple docstring'''



   _snake_case	,      _snake_case							:	List[str]			=						self.model_tester.prepare_config_and_inputs_for_common()

   _snake_case							:	int			=						_config_zero_init(a_	)
   for model_class in self.all_model_classes:
     _snake_case							:	Tuple			=						model_class(config=a_	)
     for name, param in model.named_parameters():
       # we skip lambda parameters as these require special initial values
       # determined by config.layer_scale_init_value
       if "lambda" in name:
         continue
       if param.requires_grad:
         self.assertIn(
             ((param.data.mean() * 1E9).round() / 1E9).item(),   [0.0, 1.0],   msg=f"Parameter {name} of model {model_class} seems not properly initialized",   )


 @slow
 def 				UpperCamelCase_     (       self:	int	):




   '''simple docstring'''



   for model_name in BEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
     _snake_case							:	Optional[int]			=						BeitModel.from_pretrained(a_	)
     self.assertIsNotNone(a_	)







def       UpperCAmelCase__ ():
  """simple docstring"""

  _snake_case							:	Union[str, Any]			=						Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""					)
  return image






@require_torch
@require_vision
class       lowercase(  unittest.TestCase				):







 '''simple docstring'''





 @cached_property
 def 				UpperCamelCase_     (       self:	Dict	):




   '''simple docstring'''



   return BeitImageProcessor.from_pretrained("""microsoft/beit-base-patch16-224"""	) if is_vision_available() else None


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




   '''simple docstring'''



   _snake_case							:	str			=						BeitForMaskedImageModeling.from_pretrained("""microsoft/beit-base-patch16-224-pt22k"""	).to(a_	)

   _snake_case							:	Dict			=						self.default_image_processor
   _snake_case							:	Dict			=						prepare_img()
   _snake_case							:	List[str]			=						image_processor(images=a_,   return_tensors="""pt"""	).pixel_values.to(a_	)

   # prepare bool_masked_pos
   _snake_case							:	Optional[int]			=						torch.ones((1, 196),   dtype=torch.bool	).to(a_	)

   # forward pass
   with torch.no_grad():
     _snake_case							:	int			=						model(pixel_values=a_,   bool_masked_pos=a_	)
   _snake_case							:	Dict			=						outputs.logits

   # verify the logits
   _snake_case							:	Optional[int]			=						torch.Size((1, 196, 8_192)	)
   self.assertEqual(logits.shape,   a_	)

   _snake_case							:	Optional[Any]			=						torch.tensor(
       [[-3.2_437, 0.5_072, -13.9_174], [-3.2_456, 0.4_948, -13.9_401], [-3.2_033, 0.5_121, -13.8_550]]	).to(a_	)

   self.assertTrue(torch.allclose(logits[bool_masked_pos][:3, :3],   a_,   atol=1E-2	)	)


 @slow
 def 				UpperCamelCase_     (       self:	Optional[int]	):




   '''simple docstring'''



   _snake_case							:	Dict			=						BeitForImageClassification.from_pretrained("""microsoft/beit-base-patch16-224"""	).to(a_	)

   _snake_case							:	List[Any]			=						self.default_image_processor
   _snake_case							:	Any			=						prepare_img()
   _snake_case							:	Any			=						image_processor(images=a_,   return_tensors="""pt"""	).to(a_	)

   # forward pass
   with torch.no_grad():
     _snake_case							:	int			=						model(**a_	)
   _snake_case							:	Optional[int]			=						outputs.logits

   # verify the logits
   _snake_case							:	Tuple			=						torch.Size((1, 1_000)	)
   self.assertEqual(logits.shape,   a_	)

   _snake_case							:	Any			=						torch.tensor([-1.2_385, -1.0_987, -1.0_108]	).to(a_	)

   self.assertTrue(torch.allclose(logits[0, :3],   a_,   atol=1E-4	)	)

   _snake_case							:	str			=						281
   self.assertEqual(logits.argmax(-1	).item(),   a_	)


 @slow
 def 				UpperCamelCase_     (       self:	Dict	):




   '''simple docstring'''



   _snake_case							:	int			=						BeitForImageClassification.from_pretrained("""microsoft/beit-large-patch16-224-pt22k-ft22k"""	).to(
       a_	)

   _snake_case							:	int			=						self.default_image_processor
   _snake_case							:	Optional[Any]			=						prepare_img()
   _snake_case							:	Union[str, Any]			=						image_processor(images=a_,   return_tensors="""pt"""	).to(a_	)

   # forward pass
   with torch.no_grad():
     _snake_case							:	Union[str, Any]			=						model(**a_	)
   _snake_case							:	Dict			=						outputs.logits

   # verify the logits
   _snake_case							:	Tuple			=						torch.Size((1, 21_841)	)
   self.assertEqual(logits.shape,   a_	)

   _snake_case							:	Optional[int]			=						torch.tensor([1.6_881, -0.2_787, 0.5_901]	).to(a_	)

   self.assertTrue(torch.allclose(logits[0, :3],   a_,   atol=1E-4	)	)

   _snake_case							:	List[str]			=						2_396
   self.assertEqual(logits.argmax(-1	).item(),   a_	)


 @slow
 def 				UpperCamelCase_     (       self:	Dict	):




   '''simple docstring'''



   _snake_case							:	List[str]			=						BeitForSemanticSegmentation.from_pretrained("""microsoft/beit-base-finetuned-ade-640-640"""	)
   _snake_case							:	int			=						model.to(a_	)

   _snake_case							:	List[str]			=						BeitImageProcessor(do_resize=a_,   size=640,   do_center_crop=a_	)

   _snake_case							:	Optional[int]			=						load_dataset("""hf-internal-testing/fixtures_ade20k""",   split="""test"""	)
   _snake_case							:	Union[str, Any]			=						Image.open(ds[0]["""file"""]	)
   _snake_case							:	List[Any]			=						image_processor(images=a_,   return_tensors="""pt"""	).to(a_	)

   # forward pass
   with torch.no_grad():
     _snake_case							:	Optional[int]			=						model(**a_	)
   _snake_case							:	Union[str, Any]			=						outputs.logits

   # verify the logits
   _snake_case							:	List[str]			=						torch.Size((1, 150, 160, 160)	)
   self.assertEqual(logits.shape,   a_	)

   _snake_case							:	Optional[int]			=						version.parse(PIL.__version__	) < version.parse("""9.0.0"""	)

   if is_pillow_less_than_a:
     _snake_case							:	Any			=						torch.tensor(
         [
             [[-4.9_225, -2.3_954, -3.0_522], [-2.8_822, -1.0_046, -1.7_561], [-2.9_549, -1.3_228, -2.1_347]],
             [[-5.8_168, -3.4_129, -4.0_778], [-3.8_651, -2.2_214, -3.0_277], [-3.8_356, -2.4_643, -3.3_535]],
             [[-0.0_078, 3.9_952, 4.0_754], [2.9_856, 4.6_944, 5.0_035], [3.2_413, 4.7_813, 4.9_969]],
         ],   device=a_,   )
   else:
     _snake_case							:	Optional[Any]			=						torch.tensor(
         [
             [[-4.8_960, -2.3_688, -3.0_355], [-2.8_478, -0.9_836, -1.7_418], [-2.9_449, -1.3_332, -2.1_456]],
             [[-5.8_081, -3.4_124, -4.1_006], [-3.8_561, -2.2_081, -3.0_323], [-3.8_365, -2.4_601, -3.3_669]],
             [[-0.0_309, 3.9_868, 4.0_540], [2.9_640, 4.6_877, 4.9_976], [3.2_081, 4.7_690, 4.9_942]],
         ],   device=a_,   )

   self.assertTrue(torch.allclose(logits[0, :3, :3, :3],   a_,   atol=1E-4	)	)


 @slow
 def 				UpperCamelCase_     (       self:	Optional[Any]	):




   '''simple docstring'''



   _snake_case							:	int			=						BeitForSemanticSegmentation.from_pretrained("""microsoft/beit-base-finetuned-ade-640-640"""	)
   _snake_case							:	List[Any]			=						model.to(a_	)

   _snake_case							:	Tuple			=						BeitImageProcessor(do_resize=a_,   size=640,   do_center_crop=a_	)

   _snake_case							:	Union[str, Any]			=						load_dataset("""hf-internal-testing/fixtures_ade20k""",   split="""test"""	)
   _snake_case							:	str			=						Image.open(ds[0]["""file"""]	)
   _snake_case							:	Tuple			=						image_processor(images=a_,   return_tensors="""pt"""	).to(a_	)

   # forward pass
   with torch.no_grad():
     _snake_case							:	Optional[int]			=						model(**a_	)

   _snake_case							:	Union[str, Any]			=						outputs.logits.detach().cpu()

   _snake_case							:	Union[str, Any]			=						image_processor.post_process_semantic_segmentation(outputs=a_,   target_sizes=[(500, 300)]	)
   _snake_case							:	Optional[int]			=						torch.Size((500, 300)	)
   self.assertEqual(segmentation[0].shape,   a_	)

   _snake_case							:	List[str]			=						image_processor.post_process_semantic_segmentation(outputs=a_	)
   _snake_case							:	List[str]			=						torch.Size((160, 160)	)
   self.assertEqual(segmentation[0].shape,   a_	)


