


import inspect
import math
import tempfile
import unittest

import numpy as np

from transformers import ViTMAEConfig
from transformers.testing_utils import require_torch, 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, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch
    from torch import nn

    from transformers import ViTMAEForPreTraining, ViTMAEModel
    from transformers.models.vit.modeling_vit import VIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
    from PIL import Image

    from transformers import ViTImageProcessor







class 			_lowercase :


       """simple docstring"""




       def __init__(   self							:			int					,      __lowerCamelCase							:			Any					,      __lowerCamelCase							:			List[str]=13					,      __lowerCamelCase							:			Optional[int]=30					,      __lowerCamelCase							:			str=2					,      __lowerCamelCase							:			Optional[int]=3					,      __lowerCamelCase							:			List[str]=True					,      __lowerCamelCase							:			List[Any]=True					,      __lowerCamelCase							:			Tuple=32					,      __lowerCamelCase							:			Optional[int]=5					,      __lowerCamelCase							:			int=4					,      __lowerCamelCase							:			Optional[Any]=37					,      __lowerCamelCase							:			Any="gelu"					,      __lowerCamelCase							:			Dict=0.1					,      __lowerCamelCase							:			Optional[int]=0.1					,      __lowerCamelCase							:			Optional[int]=10					,      __lowerCamelCase							:			Optional[Any]=0.0_2					,      __lowerCamelCase							:			Any=3					,      __lowerCamelCase							:			Any=0.6					,      __lowerCamelCase							:			int=None					,      ):


             '''simple docstring'''



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

             # in ViTMAE, the expected sequence length = (num_patches + 1) * (1 - config.mask_ratio), rounded above
             # (we add 1 for the [CLS] token)
             lowerCamelCase__		:				List[str]    =  (image_size // patch_size) ** 2
             lowerCamelCase__		:				Dict    =  int(math.ceil((1 - mask_ratio) * (num_patches + 1)       )       )





       def 						lowerCAmelCase     (   self							:			Dict       ):


             '''simple docstring'''



             lowerCamelCase__		:				Tuple    =  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )

             lowerCamelCase__		:				str    =  None
             if self.use_labels:
                   lowerCamelCase__		:				Tuple    =  ids_tensor([self.batch_size]					,      self.type_sequence_label_size       )

             lowerCamelCase__		:				Optional[Any]    =  self.get_config()

             return config, pixel_values, labels





       def 						lowerCAmelCase     (   self							:			int       ):


             '''simple docstring'''



             return ViTMAEConfig(
                 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=__lowerCamelCase					,      initializer_range=self.initializer_range					,      mask_ratio=self.mask_ratio					,      )





       def 						lowerCAmelCase     (   self							:			List[str]					,      __lowerCamelCase							:			Union[str, Any]					,      __lowerCamelCase							:			List[str]					,      __lowerCamelCase							:			List[Any]       ):


             '''simple docstring'''



             lowerCamelCase__		:				Dict    =  ViTMAEModel(config=__lowerCamelCase       )
             model.to(__lowerCamelCase       )
             model.eval()
             lowerCamelCase__		:				Any    =  model(__lowerCamelCase       )
             self.parent.assertEqual(result.last_hidden_state.shape					,      (self.batch_size, self.seq_length, self.hidden_size)       )





       def 						lowerCAmelCase     (   self							:			int					,      __lowerCamelCase							:			List[Any]					,      __lowerCamelCase							:			Dict					,      __lowerCamelCase							:			Optional[int]       ):


             '''simple docstring'''



             lowerCamelCase__		:				int    =  ViTMAEForPreTraining(__lowerCamelCase       )
             model.to(__lowerCamelCase       )
             model.eval()
             lowerCamelCase__		:				Any    =  model(__lowerCamelCase       )
             lowerCamelCase__		:				List[Any]    =  (self.image_size // self.patch_size) ** 2
             lowerCamelCase__		:				List[Any]    =  self.patch_size**2 * self.num_channels
             self.parent.assertEqual(result.logits.shape					,      (self.batch_size, num_patches, expected_num_channels)       )

             # test greyscale images
             lowerCamelCase__		:				str    =  1
             lowerCamelCase__		:				Tuple    =  ViTMAEForPreTraining(__lowerCamelCase       )
             model.to(__lowerCamelCase       )
             model.eval()
             lowerCamelCase__		:				List[str]    =  floats_tensor([self.batch_size, 1, self.image_size, self.image_size]       )
             lowerCamelCase__		:				List[Any]    =  model(__lowerCamelCase       )
             lowerCamelCase__		:				Tuple    =  self.patch_size**2
             self.parent.assertEqual(result.logits.shape					,      (self.batch_size, num_patches, expected_num_channels)       )





       def 						lowerCAmelCase     (   self							:			Dict       ):


             '''simple docstring'''



             lowerCamelCase__		:				str    =  self.prepare_config_and_inputs()
             lowerCamelCase__    ,   lowerCamelCase__    ,   lowerCamelCase__		:				List[Any]    =  config_and_inputs
             lowerCamelCase__		:				Dict    =  {"pixel_values": pixel_values}
             return config, inputs_dict







@require_torch
class 			_lowercase (							lowercase__							,  lowercase__							,  unittest.TestCase):


       """simple docstring"""




       A__											=			(ViTMAEModel, ViTMAEForPreTraining) if is_torch_available() else ()
       A__											=			{"feature-extraction": ViTMAEModel} if is_torch_available() else {}

       A__											=			False
       A__											=			False
       A__											=			False
       A__											=			False





       def 						lowerCAmelCase     (   self							:			str       ):


             '''simple docstring'''



             lowerCamelCase__		:				Any    =  ViTMAEModelTester(self       )
             lowerCamelCase__		:				Optional[int]    =  ConfigTester(self					,      config_class=__lowerCamelCase					,      has_text_modality=__lowerCamelCase					,      hidden_size=37       )





       def 						lowerCAmelCase     (   self							:			Dict       ):


             '''simple docstring'''



             self.config_tester.run_common_tests()





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


             '''simple docstring'''



             pass





       def 						lowerCAmelCase     (   self							:			Any       ):


             '''simple docstring'''



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

             for model_class in self.all_model_classes:
                   lowerCamelCase__		:				Optional[int]    =  model_class(__lowerCamelCase       )
                   self.assertIsInstance(model.get_input_embeddings()					,      (nn.Module)       )
                   lowerCamelCase__		:				Optional[Any]    =  model.get_output_embeddings()
                   self.assertTrue(x is None or isinstance(__lowerCamelCase					,      nn.Linear       )       )





       def 						lowerCAmelCase     (   self							:			Dict       ):


             '''simple docstring'''



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

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

                   lowerCamelCase__		:				Union[str, Any]    =  ["pixel_values"]
                   self.assertListEqual(arg_names[:1]					,      __lowerCamelCase       )





       def 						lowerCAmelCase     (   self							:			Any       ):


             '''simple docstring'''



             lowerCamelCase__		:				Dict    =  self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_model(*__lowerCamelCase       )





       def 						lowerCAmelCase     (   self							:			Optional[Any]       ):


             '''simple docstring'''



             lowerCamelCase__		:				Optional[Any]    =  self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_pretraining(*__lowerCamelCase       )





       def 						lowerCAmelCase     (   self							:			Union[str, Any]					,      __lowerCamelCase							:			Tuple					,      __lowerCamelCase							:			int					,      __lowerCamelCase							:			Tuple       ):


             '''simple docstring'''



             np.random.seed(2       )

             lowerCamelCase__		:				Optional[Any]    =  int((pt_model.config.image_size // pt_model.config.patch_size) ** 2       )
             lowerCamelCase__		:				Optional[int]    =  np.random.uniform(size=(self.model_tester.batch_size, num_patches)       )
             lowerCamelCase__		:				Tuple    =  torch.from_numpy(__lowerCamelCase       )

             # Add `noise` argument.
             # PT inputs will be prepared in `super().check_pt_tf_models()` with this added `noise` argument
             lowerCamelCase__		:				Union[str, Any]    =  pt_noise

             super().check_pt_tf_models(__lowerCamelCase					,      __lowerCamelCase					,      __lowerCamelCase       )





       def 						lowerCAmelCase     (   self							:			int       ):


             '''simple docstring'''



             lowerCamelCase__    ,   lowerCamelCase__		:				str    =  self.model_tester.prepare_config_and_inputs_for_common()

             for model_class in self.all_model_classes:
                   lowerCamelCase__		:				Tuple    =  model_class(__lowerCamelCase       )
                   model.to(__lowerCamelCase       )
                   model.eval()
                   # make random mask reproducible
                   torch.manual_seed(2       )
                   with torch.no_grad():
                         lowerCamelCase__		:				Any    =  model(**self._prepare_for_class(__lowerCamelCase					,      __lowerCamelCase       )       )

                   lowerCamelCase__		:				List[str]    =  outputs[0].cpu().numpy()
                   lowerCamelCase__		:				Tuple    =  0

                   with tempfile.TemporaryDirectory() as tmpdirname:
                         model.save_pretrained(__lowerCamelCase       )
                         lowerCamelCase__		:				int    =  model_class.from_pretrained(__lowerCamelCase       )
                         model.to(__lowerCamelCase       )
                         # make random mask reproducible
                         torch.manual_seed(2       )
                         with torch.no_grad():
                               lowerCamelCase__		:				str    =  model(**self._prepare_for_class(__lowerCamelCase					,      __lowerCamelCase       )       )

                         # Make sure we don't have nans
                         lowerCamelCase__		:				Any    =  after_outputs[0].cpu().numpy()
                         lowerCamelCase__		:				Optional[int]    =  0
                         lowerCamelCase__		:				List[Any]    =  np.amax(np.abs(out_a - out_a       )       )
                         self.assertLessEqual(__lowerCamelCase					,      1E-5       )





       @unittest.skip(
           reason="ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n    to get deterministic results."       )
       def 						lowerCAmelCase     (   self							:			List[Any]       ):


             '''simple docstring'''



             pass





       @unittest.skip(
           reason="ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n    to get deterministic results."       )
       def 						lowerCAmelCase     (   self							:			Any       ):


             '''simple docstring'''



             pass





       @unittest.skip(
           reason="ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n    to get deterministic results."       )
       def 						lowerCAmelCase     (   self							:			Dict       ):


             '''simple docstring'''



             pass





       @unittest.skip(reason="ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load"       )
       def 						lowerCAmelCase     (   self							:			str       ):


             '''simple docstring'''



             pass





       @unittest.skip("Will be fixed soon by reducing the size of the model used for common tests."       )
       def 						lowerCAmelCase     (   self							:			int       ):


             '''simple docstring'''



             pass





       @slow
       def 						lowerCAmelCase     (   self							:			str       ):


             '''simple docstring'''



             for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                   lowerCamelCase__		:				str    =  ViTMAEModel.from_pretrained(__lowerCamelCase       )
                   self.assertIsNotNone(__lowerCamelCase       )



def       lowercase_		(		):

      """simple docstring"""




      lowerCamelCase__		:				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 						lowerCAmelCase     (   self							:			List[Any]       ):


             '''simple docstring'''



             return ViTImageProcessor.from_pretrained("facebook/vit-mae-base"       ) if is_vision_available() else None





       @slow
       def 						lowerCAmelCase     (   self							:			List[str]       ):


             '''simple docstring'''



             np.random.seed(2       )

             lowerCamelCase__		:				Any    =  ViTMAEForPreTraining.from_pretrained("facebook/vit-mae-base"       ).to(__lowerCamelCase       )

             lowerCamelCase__		:				Optional[int]    =  self.default_image_processor
             lowerCamelCase__		:				List[Any]    =  prepare_img()
             lowerCamelCase__		:				str    =  image_processor(images=__lowerCamelCase					,      return_tensors="pt"       ).to(__lowerCamelCase       )

             # prepare a noise vector that will be also used for testing the TF model
             # (this way we can ensure that the PT and TF models operate on the same inputs)
             lowerCamelCase__		:				Union[str, Any]    =  ViTMAEConfig()
             lowerCamelCase__		:				str    =  int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2       )
             lowerCamelCase__		:				List[str]    =  np.random.uniform(size=(1, num_patches)       )

             # forward pass
             with torch.no_grad():
                   lowerCamelCase__		:				List[Any]    =  model(**__lowerCamelCase					,      noise=torch.from_numpy(__lowerCamelCase       ).to(device=__lowerCamelCase       )       )

             # verify the logits
             lowerCamelCase__		:				List[Any]    =  torch.Size((1, 196, 768)       )
             self.assertEqual(outputs.logits.shape					,      __lowerCamelCase       )

             lowerCamelCase__		:				Tuple    =  torch.tensor(
                 [[-0.0_5_4_8, -1.7_0_2_3, -0.9_3_2_5], [0.3_7_2_1, -0.5_6_7_0, -0.2_2_3_3], [0.8_2_3_5, -1.3_8_7_8, -0.3_5_2_4]]       )

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






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      :    Optional[int]		=					[
    "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      :    List[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 .",
    "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       lowercase_		(		):

      """simple docstring"""




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

def       lowercase_		(		):

      """simple docstring"""




      lowerCamelCase__		:				Any    =  "rougeLsum"
      lowerCamelCase__		:				List[str]    =  calculate_rouge(_A    ,					_A    ,					newline_sep=_A    ,					rouge_keys=[k]						)[k]
      lowerCamelCase__		:				str    =  calculate_rouge(_A    ,					_A    ,					newline_sep=_A    ,					rouge_keys=[k]						)[k]
      assert score > score_no_sep

def       lowercase_		(		):

      """simple docstring"""




      lowerCamelCase__		:				int    =  ["rouge1", "rouge2", "rougeL"]
      lowerCamelCase__		:				Union[str, Any]    =  calculate_rouge(_A    ,					_A    ,					newline_sep=_A    ,					rouge_keys=_A						)
      lowerCamelCase__		:				Any    =  calculate_rouge(_A    ,					_A    ,					newline_sep=_A    ,					rouge_keys=_A						)
      assert score_sep == score_no_sep

def       lowercase_		(		):

      """simple docstring"""




      lowerCamelCase__		:				Optional[Any]    =  [
          "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 .",
      ]
      lowerCamelCase__		:				Tuple    =  [
          "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(_A    ,					_A    ,					newline_sep=_A						) == calculate_rouge(_A    ,					_A    ,					newline_sep=_A						)

def       lowercase_		(		):

      """simple docstring"""




      lowerCamelCase__		:				List[str]    =  [
          "\" \"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\" "
      ]
      lowerCamelCase__		:				str    =  [
          " 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 ."
      ]

      lowerCamelCase__		:				Union[str, Any]    =  calculate_rouge(_A    ,					_A    ,					rouge_keys=["rougeLsum"]    ,					newline_sep=_A						)["rougeLsum"]
      lowerCamelCase__		:				List[str]    =  calculate_rouge(_A    ,					_A    ,					rouge_keys=["rougeLsum"]						)["rougeLsum"]
      assert new_score > prev_score


def       lowercase_		(		):

      """simple docstring"""




      lowerCamelCase__		:				Tuple    =  Path("examples/seq2seq/test_data/wmt_en_ro"						)
      lowerCamelCase__		:				Any    =  calculate_rouge_path(data_dir.joinpath("test.source"						)    ,					data_dir.joinpath("test.target"						)						)
      assert isinstance(_A    ,					_A						)
      lowerCamelCase__		:				str    =  calculate_rouge_path(
          data_dir.joinpath("test.source"						)    ,					data_dir.joinpath("test.target"						)    ,					bootstrap_aggregation=_A						)
      assert isinstance(_A    ,					_A						)


