




'''simple docstring'''





from __future__ import annotations

import copy
import inspect
import json
import math
import os
import tempfile
import unittest
from importlib import import_module

import numpy as np

from transformers import ViTMAEConfig
from transformers.file_utils import cached_property, is_tf_available, is_vision_available
from transformers.testing_utils import require_tf, require_vision, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
   import tensorflow as tf

   from transformers import TFViTMAEForPreTraining, TFViTMAEModel


if is_vision_available():
   from PIL import Image

   from transformers import ViTImageProcessor

class        _a    :






     def __init__(			self ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE=13 ,_SCREAMING_SNAKE_CASE=30 ,_SCREAMING_SNAKE_CASE=2 ,_SCREAMING_SNAKE_CASE=3 ,_SCREAMING_SNAKE_CASE=True ,_SCREAMING_SNAKE_CASE=True ,_SCREAMING_SNAKE_CASE=32 ,_SCREAMING_SNAKE_CASE=2 ,_SCREAMING_SNAKE_CASE=4 ,_SCREAMING_SNAKE_CASE=37 ,_SCREAMING_SNAKE_CASE="gelu" ,_SCREAMING_SNAKE_CASE=0.1 ,_SCREAMING_SNAKE_CASE=0.1 ,_SCREAMING_SNAKE_CASE=10 ,_SCREAMING_SNAKE_CASE=0.0_2 ,_SCREAMING_SNAKE_CASE=3 ,_SCREAMING_SNAKE_CASE=0.6 ,_SCREAMING_SNAKE_CASE=None ,)							->      List[str]:
          _snake_case							=				parent
          _snake_case							=				batch_size
          _snake_case							=				image_size
          _snake_case							=				patch_size
          _snake_case							=				num_channels
          _snake_case							=				is_training
          _snake_case							=				use_labels
          _snake_case							=				hidden_size
          _snake_case							=				num_hidden_layers
          _snake_case							=				num_attention_heads
          _snake_case							=				intermediate_size
          _snake_case							=				hidden_act
          _snake_case							=				hidden_dropout_prob
          _snake_case							=				attention_probs_dropout_prob
          _snake_case							=				type_sequence_label_size
          _snake_case							=				initializer_range
          _snake_case							=				mask_ratio
          _snake_case							=				scope

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






     def     _lowercase     (			self   )							->      Optional[int]:
          _snake_case							=				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]   )

          _snake_case							=				None
          if self.use_labels:
               _snake_case							=				ids_tensor([self.batch_size] ,self.type_sequence_label_size   )

          _snake_case							=				self.get_config()

          return config, pixel_values, labels






     def     _lowercase     (			self   )							->      Tuple:
          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 ,decoder_hidden_size=self.hidden_size ,decoder_num_hidden_layers=self.num_hidden_layers ,decoder_num_attention_heads=self.num_attention_heads ,decoder_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=_SCREAMING_SNAKE_CASE ,initializer_range=self.initializer_range ,mask_ratio=self.mask_ratio ,)






     def     _lowercase     (			self ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   )							->      List[str]:
          _snake_case							=				TFViTMAEModel(config=_SCREAMING_SNAKE_CASE   )
          _snake_case							=				model(_SCREAMING_SNAKE_CASE ,training=_SCREAMING_SNAKE_CASE   )
          self.parent.assertEqual(result.last_hidden_state.shape ,(self.batch_size, self.seq_length, self.hidden_size)   )






     def     _lowercase     (			self ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   )							->      Optional[int]:
          _snake_case							=				TFViTMAEForPreTraining(_SCREAMING_SNAKE_CASE   )
          _snake_case							=				model(_SCREAMING_SNAKE_CASE ,training=_SCREAMING_SNAKE_CASE   )
          # expected sequence length = num_patches
          _snake_case							=				(self.image_size // self.patch_size) ** 2
          _snake_case							=				self.patch_size**2 * self.num_channels
          self.parent.assertEqual(result.logits.shape ,(self.batch_size, num_patches, expected_num_channels)   )

          # test greyscale images
          _snake_case							=				1
          _snake_case							=				TFViTMAEForPreTraining(_SCREAMING_SNAKE_CASE   )

          _snake_case							=				floats_tensor([self.batch_size, 1, self.image_size, self.image_size]   )
          _snake_case							=				model(_SCREAMING_SNAKE_CASE ,training=_SCREAMING_SNAKE_CASE   )
          _snake_case							=				self.patch_size**2
          self.parent.assertEqual(result.logits.shape ,(self.batch_size, num_patches, expected_num_channels)   )






     def     _lowercase     (			self   )							->      List[str]:
          _snake_case							=				self.prepare_config_and_inputs()
          ((_snake_case)							, (_snake_case)							, (_snake_case))							=				config_and_inputs
          _snake_case							=				{"pixel_values": pixel_values}
          return config, inputs_dict







@require_tf
class        _a    (						__lowerCAmelCase , __lowerCAmelCase , unittest.TestCase							):
     SCREAMING_SNAKE_CASE_							:   Tuple       =						(TFViTMAEModel, TFViTMAEForPreTraining) if is_tf_available() else ()
     SCREAMING_SNAKE_CASE_							:   str       =						{"""feature-extraction""": TFViTMAEModel} if is_tf_available() else {}

     SCREAMING_SNAKE_CASE_							:   int       =						False
     SCREAMING_SNAKE_CASE_							:   Any       =						False
     SCREAMING_SNAKE_CASE_							:   Optional[Any]       =						False
     SCREAMING_SNAKE_CASE_							:   Optional[Any]       =						False






     def     _lowercase     (			self   )							->      List[str]:
          _snake_case							=				TFViTMAEModelTester(self   )
          _snake_case							=				ConfigTester(self ,config_class=_SCREAMING_SNAKE_CASE ,has_text_modality=_SCREAMING_SNAKE_CASE ,hidden_size=37   )






     def     _lowercase     (			self   )							->      Optional[int]:
          self.config_tester.run_common_tests()






     @unittest.skip(reason="ViTMAE does not use inputs_embeds"   )
     def     _lowercase     (			self   )							->      Any:
          pass






     def     _lowercase     (			self   )							->      Optional[int]:
          _snake_case							, _snake_case							=				self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
               _snake_case							=				model_class(_SCREAMING_SNAKE_CASE   )
               self.assertIsInstance(model.get_input_embeddings() ,(tf.keras.layers.Layer)   )
               _snake_case							=				model.get_output_embeddings()
               self.assertTrue(x is None or isinstance(_SCREAMING_SNAKE_CASE ,tf.keras.layers.Layer   )   )






     def     _lowercase     (			self   )							->      Optional[Any]:
          _snake_case							, _snake_case							=				self.model_tester.prepare_config_and_inputs_for_common()

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

               _snake_case							=				["pixel_values"]
               self.assertListEqual(arg_names[:1] ,_SCREAMING_SNAKE_CASE   )






     def     _lowercase     (			self   )							->      Tuple:
          _snake_case							=				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE   )






     def     _lowercase     (			self   )							->      Any:
          _snake_case							=				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_pretraining(*_SCREAMING_SNAKE_CASE   )






     def     _lowercase     (			self   )							->      int:
          # make the mask reproducible
          np.random.seed(2   )

          _snake_case							, _snake_case							=				self.model_tester.prepare_config_and_inputs_for_common()

          _snake_case							=				int((config.image_size // config.patch_size) ** 2   )
          _snake_case							=				np.random.uniform(size=(self.model_tester.batch_size, num_patches)   )

          for model_class in self.all_model_classes:
               _snake_case							=				model_class(_SCREAMING_SNAKE_CASE   )
               _snake_case							=				self._prepare_for_class(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   )

               _snake_case							=				model(_SCREAMING_SNAKE_CASE ,noise=_SCREAMING_SNAKE_CASE   )

               _snake_case							=				copy.deepcopy(self._prepare_for_class(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   )   )
               _snake_case							=				model(**_SCREAMING_SNAKE_CASE ,noise=_SCREAMING_SNAKE_CASE   )
               _snake_case							=				outputs_dict[0].numpy()
               _snake_case							=				outputs_keywords[0].numpy()

               self.assertLess(np.sum(np.abs(output_dict - output_keywords   )   ) ,1e-6   )






     def     _lowercase     (			self   )							->      str:
          # make the mask reproducible
          np.random.seed(2   )

          _snake_case							, _snake_case							=				self.model_tester.prepare_config_and_inputs_for_common()

          _snake_case							=				int((config.image_size // config.patch_size) ** 2   )
          _snake_case							=				np.random.uniform(size=(self.model_tester.batch_size, num_patches)   )

          def prepare_numpy_arrays(_SCREAMING_SNAKE_CASE   ):
               _snake_case							=				{}
               for k, v in inputs_dict.items():
                    if tf.is_tensor(_SCREAMING_SNAKE_CASE   ):
                         _snake_case							=				v.numpy()
                    else:
                         _snake_case							=				np.array(_SCREAMING_SNAKE_CASE   )

               return inputs_np_dict

          for model_class in self.all_model_classes:
               _snake_case							=				model_class(_SCREAMING_SNAKE_CASE   )

               _snake_case							=				self._prepare_for_class(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   )
               _snake_case							=				prepare_numpy_arrays(_SCREAMING_SNAKE_CASE   )

               _snake_case							=				model(_SCREAMING_SNAKE_CASE ,noise=_SCREAMING_SNAKE_CASE   )
               _snake_case							=				model(**_SCREAMING_SNAKE_CASE ,noise=_SCREAMING_SNAKE_CASE   )
               self.assert_outputs_same(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   )






     def     _lowercase     (			self ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   )							->      Optional[Any]:
          # make masks reproducible
          np.random.seed(2   )

          _snake_case							=				int((tf_model.config.image_size // tf_model.config.patch_size) ** 2   )
          _snake_case							=				np.random.uniform(size=(self.model_tester.batch_size, num_patches)   )
          _snake_case							=				tf.constant(_SCREAMING_SNAKE_CASE   )

          # Add `noise` argument.
          # PT inputs will be prepared in `super().check_pt_tf_models()` with this added `noise` argument
          _snake_case							=				tf_noise

          super().check_pt_tf_models(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   )






     def     _lowercase     (			self   )							->      Optional[int]:
          # make mask reproducible
          np.random.seed(2   )

          _snake_case							, _snake_case							=				self.model_tester.prepare_config_and_inputs_for_common()

          _snake_case							=				{
              module_member
              for model_class in self.all_model_classes
              for module in (import_module(model_class.__module__   ),)
              for module_member_name in dir(_SCREAMING_SNAKE_CASE   )
              if module_member_name.endswith("MainLayer"   )
              # This condition is required, since `modeling_tf_clip.py` has 3 classes whose names end with `MainLayer`.
              and module_member_name[: -len("MainLayer"   )] == model_class.__name__[: -len("Model"   )]
              for module_member in (getattr(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   ),)
              if isinstance(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   )
              and tf.keras.layers.Layer in module_member.__bases__
              and getattr(_SCREAMING_SNAKE_CASE ,"_keras_serializable" ,_SCREAMING_SNAKE_CASE   )
          }

          _snake_case							=				int((config.image_size // config.patch_size) ** 2   )
          _snake_case							=				np.random.uniform(size=(self.model_tester.batch_size, num_patches)   )
          _snake_case							=				tf.convert_to_tensor(_SCREAMING_SNAKE_CASE   )
          inputs_dict.update({"noise": noise}   )

          for main_layer_class in tf_main_layer_classes:
               _snake_case							=				main_layer_class(_SCREAMING_SNAKE_CASE   )

               _snake_case							=				{
                   name: tf.keras.Input(tensor.shape[1:] ,dtype=tensor.dtype   ) for name, tensor in inputs_dict.items()
               }

               _snake_case							=				tf.keras.Model(_SCREAMING_SNAKE_CASE ,outputs=main_layer(_SCREAMING_SNAKE_CASE   )   )
               _snake_case							=				model(_SCREAMING_SNAKE_CASE   )

               with tempfile.TemporaryDirectory() as tmpdirname:
                    _snake_case							=				os.path.join(_SCREAMING_SNAKE_CASE ,"keras_model.h5"   )
                    model.save(_SCREAMING_SNAKE_CASE   )
                    _snake_case							=				tf.keras.models.load_model(
                        _SCREAMING_SNAKE_CASE ,custom_objects={main_layer_class.__name__: main_layer_class}   )
                    assert isinstance(_SCREAMING_SNAKE_CASE ,tf.keras.Model   )
                    _snake_case							=				model(_SCREAMING_SNAKE_CASE   )
                    self.assert_outputs_same(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   )






     @slow
     def     _lowercase     (			self   )							->      Tuple:
          # make mask reproducible
          np.random.seed(2   )

          _snake_case							, _snake_case							=				self.model_tester.prepare_config_and_inputs_for_common()

          _snake_case							=				int((config.image_size // config.patch_size) ** 2   )
          _snake_case							=				np.random.uniform(size=(self.model_tester.batch_size, num_patches)   )

          for model_class in self.all_model_classes:
               _snake_case							=				model_class(_SCREAMING_SNAKE_CASE   )
               _snake_case							=				self._prepare_for_class(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   )
               _snake_case							=				model(_SCREAMING_SNAKE_CASE ,noise=_SCREAMING_SNAKE_CASE   )

               if model_class.__name__ == "TFViTMAEModel":
                    _snake_case							=				outputs.last_hidden_state.numpy()
                    _snake_case							=				0
               else:
                    _snake_case							=				outputs.logits.numpy()
                    _snake_case							=				0

               with tempfile.TemporaryDirectory() as tmpdirname:
                    model.save_pretrained(_SCREAMING_SNAKE_CASE ,saved_model=_SCREAMING_SNAKE_CASE   )
                    _snake_case							=				model_class.from_pretrained(_SCREAMING_SNAKE_CASE   )
                    _snake_case							=				model(_SCREAMING_SNAKE_CASE ,noise=_SCREAMING_SNAKE_CASE   )

                    if model_class.__name__ == "TFViTMAEModel":
                         _snake_case							=				after_outputs["last_hidden_state"].numpy()
                         _snake_case							=				0
                    else:
                         _snake_case							=				after_outputs["logits"].numpy()
                         _snake_case							=				0

                    _snake_case							=				np.amax(np.abs(out_a - out_a   )   )
                    self.assertLessEqual(_SCREAMING_SNAKE_CASE ,1e-5   )






     def     _lowercase     (			self   )							->      Tuple:
          # make mask reproducible
          np.random.seed(2   )

          _snake_case							, _snake_case							=				self.model_tester.prepare_config_and_inputs_for_common()

          _snake_case							=				int((config.image_size // config.patch_size) ** 2   )
          _snake_case							=				np.random.uniform(size=(self.model_tester.batch_size, num_patches)   )

          for model_class in self.all_model_classes:
               _snake_case							=				model_class(_SCREAMING_SNAKE_CASE   )
               _snake_case							=				self._prepare_for_class(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   )

               _snake_case							=				model(_SCREAMING_SNAKE_CASE ,noise=_SCREAMING_SNAKE_CASE   )
               _snake_case							=				model.get_config()
               # make sure that returned config is jsonifiable, which is required by keras
               json.dumps(_SCREAMING_SNAKE_CASE   )
               _snake_case							=				model_class.from_config(model.get_config()   )
               # make sure it also accepts a normal config
               _snake_case							=				model_class.from_config(model.config   )
               _snake_case							=				new_model(_SCREAMING_SNAKE_CASE   )  # Build model
               new_model.set_weights(model.get_weights()   )
               _snake_case							=				new_model(_SCREAMING_SNAKE_CASE ,noise=_SCREAMING_SNAKE_CASE   )

               self.assert_outputs_same(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   )






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






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






     @slow
     def     _lowercase     (			self   )							->      List[str]:
          _snake_case							=				TFViTMAEModel.from_pretrained("google/vit-base-patch16-224"   )
          self.assertIsNotNone(_SCREAMING_SNAKE_CASE   )







def      __a     (       )						->			Dict:

     """simple docstring"""




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





@require_tf
@require_vision
class        _a    (						unittest.TestCase							):






     @cached_property
     def     _lowercase     (			self   )							->      Optional[Any]:
          return ViTImageProcessor.from_pretrained("facebook/vit-mae-base"   ) if is_vision_available() else None






     @slow
     def     _lowercase     (			self   )							->      str:
          # make random mask reproducible across the PT and TF model
          np.random.seed(2   )

          _snake_case							=				TFViTMAEForPreTraining.from_pretrained("facebook/vit-mae-base"   )

          _snake_case							=				self.default_image_processor
          _snake_case							=				prepare_img()
          _snake_case							=				image_processor(images=_SCREAMING_SNAKE_CASE ,return_tensors="tf"   )

          # 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)
          _snake_case							=				ViTMAEConfig()
          _snake_case							=				int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2   )
          _snake_case							=				np.random.uniform(size=(1, num_patches)   )

          # forward pass
          _snake_case							=				model(**_SCREAMING_SNAKE_CASE ,noise=_SCREAMING_SNAKE_CASE   )

          # verify the logits
          _snake_case							=				tf.convert_to_tensor([1, 196, 768]   )
          self.assertEqual(outputs.logits.shape ,_SCREAMING_SNAKE_CASE   )

          _snake_case							=				tf.convert_to_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]]   )

          tf.debugging.assert_near(outputs.logits[0, :3, :3] ,_SCREAMING_SNAKE_CASE ,atol=1e-4   )








'''simple docstring'''





import fire

from utils import calculate_rouge, save_json





def      __a     (       _UpperCamelCase:   Tuple ,						_UpperCamelCase:   Optional[int] ,						_UpperCamelCase:   Optional[int]=None ,						**_UpperCamelCase:   Any   )						->			Optional[Any]:

     """simple docstring"""




     _snake_case							=				[x.strip() for x in open(_UpperCamelCase   ).readlines()]
     _snake_case							=				[x.strip() for x in open(_UpperCamelCase   ).readlines()][: len(_UpperCamelCase   )]
     _snake_case							=				calculate_rouge(_UpperCamelCase ,						_UpperCamelCase ,						**_UpperCamelCase   )
     if save_path is not None:
          save_json(_UpperCamelCase ,						_UpperCamelCase ,						indent=_UpperCamelCase   )
     return metrics  # these print nicely


if __name__ == "__main__":
   fire.Fire(calculate_rouge_path)


