


'''simple docstring'''




import inspect
import unittest

from transformers import ViTConfig
from transformers.testing_utils import (
    require_accelerate,
    require_torch,
    require_torch_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, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
       import torch
       from torch import nn

       from transformers import ViTForImageClassification, ViTForMaskedImageModeling, ViTModel
       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 		UpperCAmelCase_      :





     def __init__( self       :      Union[str, Any]    ,      UpperCAmelCase__       :      List[str]    ,      UpperCAmelCase__       :      Optional[int]=1_3    ,      UpperCAmelCase__       :      Optional[int]=3_0    ,      UpperCAmelCase__       :      Dict=2    ,      UpperCAmelCase__       :      Dict=3    ,      UpperCAmelCase__       :      Any=True    ,      UpperCAmelCase__       :      List[str]=True    ,      UpperCAmelCase__       :      str=3_2    ,      UpperCAmelCase__       :      List[str]=5    ,      UpperCAmelCase__       :      Optional[int]=4    ,      UpperCAmelCase__       :      List[str]=3_7    ,      UpperCAmelCase__       :      Dict="gelu"    ,      UpperCAmelCase__       :      str=0.1    ,      UpperCAmelCase__       :      List[Any]=0.1    ,      UpperCAmelCase__       :      Tuple=1_0    ,      UpperCAmelCase__       :      int=0.02    ,      UpperCAmelCase__       :      Union[str, Any]=None    ,      UpperCAmelCase__       :      Tuple=2    ,      )				->  Tuple:
        lowerCAmelCase     =			parent
        lowerCAmelCase     =			batch_size
        lowerCAmelCase     =			image_size
        lowerCAmelCase     =			patch_size
        lowerCAmelCase     =			num_channels
        lowerCAmelCase     =			is_training
        lowerCAmelCase     =			use_labels
        lowerCAmelCase     =			hidden_size
        lowerCAmelCase     =			num_hidden_layers
        lowerCAmelCase     =			num_attention_heads
        lowerCAmelCase     =			intermediate_size
        lowerCAmelCase     =			hidden_act
        lowerCAmelCase     =			hidden_dropout_prob
        lowerCAmelCase     =			attention_probs_dropout_prob
        lowerCAmelCase     =			type_sequence_label_size
        lowerCAmelCase     =			initializer_range
        lowerCAmelCase     =			scope
        lowerCAmelCase     =			encoder_stride

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





     def 							__UpperCAmelCase      ( self       :      Optional[int]    )				->  Union[str, Any]:
        lowerCAmelCase     =			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]    )

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

        lowerCAmelCase     =			self.get_config()

        return config, pixel_values, labels





     def 							__UpperCAmelCase      ( self       :      Tuple    )				->  Optional[int]:
        return ViTConfig(
            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=UpperCAmelCase__    ,      initializer_range=self.initializer_range    ,      encoder_stride=self.encoder_stride    ,      )





     def 							__UpperCAmelCase      ( self       :      Optional[int]    ,      UpperCAmelCase__       :      Union[str, Any]    ,      UpperCAmelCase__       :      Dict    ,      UpperCAmelCase__       :      int    )				->  Optional[Any]:
        lowerCAmelCase     =			ViTModel(config=UpperCAmelCase__    )
        model.to(UpperCAmelCase__    )
        model.eval()
        lowerCAmelCase     =			model(UpperCAmelCase__    )
        self.parent.assertEqual(result.last_hidden_state.shape    ,      (self.batch_size, self.seq_length, self.hidden_size)    )





     def 							__UpperCAmelCase      ( self       :      Any    ,      UpperCAmelCase__       :      Optional[int]    ,      UpperCAmelCase__       :      List[str]    ,      UpperCAmelCase__       :      List[Any]    )				->  Union[str, Any]:
        lowerCAmelCase     =			ViTForMaskedImageModeling(config=UpperCAmelCase__    )
        model.to(UpperCAmelCase__    )
        model.eval()
        lowerCAmelCase     =			model(UpperCAmelCase__    )
        self.parent.assertEqual(
            result.reconstruction.shape    ,      (self.batch_size, self.num_channels, self.image_size, self.image_size)    )

        # test greyscale images
        lowerCAmelCase     =			1
        lowerCAmelCase     =			ViTForMaskedImageModeling(UpperCAmelCase__    )
        model.to(UpperCAmelCase__    )
        model.eval()

        lowerCAmelCase     =			floats_tensor([self.batch_size, 1, self.image_size, self.image_size]    )
        lowerCAmelCase     =			model(UpperCAmelCase__    )
        self.parent.assertEqual(result.reconstruction.shape    ,      (self.batch_size, 1, self.image_size, self.image_size)    )





     def 							__UpperCAmelCase      ( self       :      Optional[Any]    ,      UpperCAmelCase__       :      Tuple    ,      UpperCAmelCase__       :      List[str]    ,      UpperCAmelCase__       :      str    )				->  Tuple:
        lowerCAmelCase     =			self.type_sequence_label_size
        lowerCAmelCase     =			ViTForImageClassification(UpperCAmelCase__    )
        model.to(UpperCAmelCase__    )
        model.eval()
        lowerCAmelCase     =			model(UpperCAmelCase__    ,      labels=UpperCAmelCase__    )
        self.parent.assertEqual(result.logits.shape    ,      (self.batch_size, self.type_sequence_label_size)    )

        # test greyscale images
        lowerCAmelCase     =			1
        lowerCAmelCase     =			ViTForImageClassification(UpperCAmelCase__    )
        model.to(UpperCAmelCase__    )
        model.eval()

        lowerCAmelCase     =			floats_tensor([self.batch_size, 1, self.image_size, self.image_size]    )
        lowerCAmelCase     =			model(UpperCAmelCase__    )
        self.parent.assertEqual(result.logits.shape    ,      (self.batch_size, self.type_sequence_label_size)    )





     def 							__UpperCAmelCase      ( self       :      List[str]    )				->  Optional[Any]:
        lowerCAmelCase     =			self.prepare_config_and_inputs()
        (
            (
            lowerCAmelCase
        )     ,  (
            lowerCAmelCase
        )     ,  (
            lowerCAmelCase
        )     ,  
        )     =			config_and_inputs
        lowerCAmelCase     =			{'pixel_values': pixel_values}
        return config, inputs_dict




@require_torch
class 		UpperCAmelCase_      (  __lowercase       ,	__lowercase       ,	unittest.TestCase		):
     lowerCamelCase				:	Optional[Any]		=     (
         (
             ViTModel,
             ViTForImageClassification,
             ViTForMaskedImageModeling,
         )
         if is_torch_available()
         else ()
     )
     lowerCamelCase				:	Union[str, Any]		=     (
         {'''feature-extraction''': ViTModel, '''image-classification''': ViTForImageClassification}
         if is_torch_available()
         else {}
     )
     lowerCamelCase				:	int		=     True

     lowerCamelCase				:	str		=     False
     lowerCamelCase				:	List[str]		=     False
     lowerCamelCase				:	Optional[int]		=     False





     def 							__UpperCAmelCase      ( self       :      Optional[Any]    )				->  List[str]:
        lowerCAmelCase     =			ViTModelTester(self    )
        lowerCAmelCase     =			ConfigTester(self    ,      config_class=UpperCAmelCase__    ,      has_text_modality=UpperCAmelCase__    ,      hidden_size=3_7    )





     def 							__UpperCAmelCase      ( self       :      str    )				->  str:
        self.config_tester.run_common_tests()





     @unittest.skip(reason='ViT does not use inputs_embeds'    )
     def 							__UpperCAmelCase      ( self       :      List[str]    )				->  List[Any]:
        pass





     def 							__UpperCAmelCase      ( self       :      Union[str, Any]    )				->  List[Any]:
        lowerCAmelCase     ,  lowerCAmelCase     =			self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
           lowerCAmelCase     =			model_class(UpperCAmelCase__    )
           self.assertIsInstance(model.get_input_embeddings()    ,      (nn.Module)    )
           lowerCAmelCase     =			model.get_output_embeddings()
           self.assertTrue(x is None or isinstance(UpperCAmelCase__    ,      nn.Linear    )    )





     def 							__UpperCAmelCase      ( self       :      List[str]    )				->  List[Any]:
        lowerCAmelCase     ,  lowerCAmelCase     =			self.model_tester.prepare_config_and_inputs_for_common()

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

           lowerCAmelCase     =			['pixel_values']
           self.assertListEqual(arg_names[:1]    ,      UpperCAmelCase__    )





     def 							__UpperCAmelCase      ( self       :      int    )				->  Any:
        lowerCAmelCase     =			self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*UpperCAmelCase__    )





     def 							__UpperCAmelCase      ( self       :      Any    )				->  Any:
        lowerCAmelCase     =			self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_masked_image_modeling(*UpperCAmelCase__    )





     def 							__UpperCAmelCase      ( self       :      Optional[Any]    )				->  Tuple:
        lowerCAmelCase     =			self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*UpperCAmelCase__    )





     @slow
     def 							__UpperCAmelCase      ( self       :      List[str]    )				->  Optional[int]:
        for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           lowerCAmelCase     =			ViTModel.from_pretrained(UpperCAmelCase__    )
           self.assertIsNotNone(UpperCAmelCase__    )





def 	a_					( ):
   lowerCAmelCase     =			Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'				)
   return image




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





     @cached_property
     def 							__UpperCAmelCase      ( self       :      Optional[Any]    )				->  List[Any]:
        return ViTImageProcessor.from_pretrained('google/vit-base-patch16-224'    ) if is_vision_available() else None





     @slow
     def 							__UpperCAmelCase      ( self       :      Dict    )				->  Union[str, Any]:
        lowerCAmelCase     =			ViTForImageClassification.from_pretrained('google/vit-base-patch16-224'    ).to(UpperCAmelCase__    )

        lowerCAmelCase     =			self.default_image_processor
        lowerCAmelCase     =			prepare_img()
        lowerCAmelCase     =			image_processor(images=UpperCAmelCase__    ,      return_tensors='pt'    ).to(UpperCAmelCase__    )

        # forward pass
        with torch.no_grad():
           lowerCAmelCase     =			model(**UpperCAmelCase__    )

        # verify the logits
        lowerCAmelCase     =			torch.Size((1, 1_0_0_0)    )
        self.assertEqual(outputs.logits.shape    ,      UpperCAmelCase__    )

        lowerCAmelCase     =			torch.tensor([-0.2_744, 0.8_215, -0.0_836]    ).to(UpperCAmelCase__    )

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





     @slow
     def 							__UpperCAmelCase      ( self       :      int    )				->  List[Any]:
        # ViT models have an `interpolate_pos_encoding` argument in their forward method,
        # allowing to interpolate the pre-trained position embeddings in order to use
        # the model on higher resolutions. The DINO model by Facebook AI leverages this
        # to visualize self-attention on higher resolution images.
        lowerCAmelCase     =			ViTModel.from_pretrained('facebook/dino-vits8'    ).to(UpperCAmelCase__    )

        lowerCAmelCase     =			ViTImageProcessor.from_pretrained('facebook/dino-vits8'    ,      size=4_8_0    )
        lowerCAmelCase     =			prepare_img()
        lowerCAmelCase     =			image_processor(images=UpperCAmelCase__    ,      return_tensors='pt'    )
        lowerCAmelCase     =			inputs.pixel_values.to(UpperCAmelCase__    )

        # forward pass
        with torch.no_grad():
           lowerCAmelCase     =			model(UpperCAmelCase__    ,      interpolate_pos_encoding=UpperCAmelCase__    )

        # verify the logits
        lowerCAmelCase     =			torch.Size((1, 3_6_0_1, 3_8_4)    )
        self.assertEqual(outputs.last_hidden_state.shape    ,      UpperCAmelCase__    )

        lowerCAmelCase     =			torch.tensor(
            [[4.2_340, 4.3_906, -6.6_692], [4.5_463, 1.8_928, -6.7_257], [4.4_429, 0.8_496, -5.8_585]]    ).to(UpperCAmelCase__    )

        self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3]    ,      UpperCAmelCase__    ,      atol=1E-4    )    )





     @slow
     @require_accelerate
     @require_torch_gpu
     def 							__UpperCAmelCase      ( self       :      Any    )				->  Optional[int]:
        lowerCAmelCase     =			ViTModel.from_pretrained('facebook/dino-vits8'    ,      torch_dtype=torch.floataa    ,      device_map='auto'    )
        lowerCAmelCase     =			self.default_image_processor

        lowerCAmelCase     =			prepare_img()
        lowerCAmelCase     =			image_processor(images=UpperCAmelCase__    ,      return_tensors='pt'    )
        lowerCAmelCase     =			inputs.pixel_values.to(UpperCAmelCase__    )

        # forward pass to make sure inference works in fp16
        with torch.no_grad():
           lowerCAmelCase     =			model(UpperCAmelCase__    )













"""simple docstring"""



import logging
import os
import sys
from pathlib import Path
from unittest.mock import patch

from parameterized import parameterized
from run_eval import run_generate
from run_eval_search import run_search

from transformers.testing_utils import CaptureStdout, TestCasePlus, slow
from utils import ROUGE_KEYS


logging.basicConfig(level=logging.DEBUG)
_UpperCamelCase		:  Tuple						=		logging.getLogger()
def 						_SCREAMING_SNAKE_CASE (    __snake_case			:  Path		,					__snake_case			:  list    ):





  '''simple docstring'''





  lowercase							    =       '\n'.join(__snake_case    )
  Path(__snake_case    ).open('w'    ).writelines(__snake_case    )


_UpperCamelCase		:  Union[str, Any]						=		'patrickvonplaten/t5-tiny-random'
_UpperCamelCase		:  Union[str, Any]						=		'sshleifer/bart-tiny-random'
_UpperCamelCase		:  Tuple						=		'sshleifer/tiny-mbart'

_UpperCamelCase		:  Union[str, Any]						=		logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
logging.disable(logging.CRITICAL)  # remove noisy download output from tracebacks



class        a     (   a_	):







    def 			UpperCamelCase_      (			self							,		_lowerCamelCase       ):
      lowercase							    =       Path(self.get_auto_remove_tmp_dir()       ) / 'utest_input.source'
      lowercase							    =       input_file_name.parent / 'utest_output.txt'
      assert not output_file_name.exists()
      lowercase							    =       [' New York (CNN)When Liana Barrientos was 23 years old, she got married in Westchester County.']
      _dump_articles(_lowerCamelCase							,		_lowerCamelCase       )

      lowercase							    =       str(Path(self.get_auto_remove_tmp_dir()       ) / 'scores.json'       )
      lowercase							    =       'translation_en_to_de' if model == T5_TINY else 'summarization'
      lowercase							    =       F'\n            run_eval_search.py\n            {model}\n            {input_file_name}\n            {output_file_name}\n            --score_path {score_path}\n            --task {task}\n            --num_beams 2\n            --length_penalty 2.0\n            '.split()

      with patch.object(_lowerCamelCase							,		'argv'							,		_lowerCamelCase       ):
        run_generate()
        assert Path(_lowerCamelCase       ).exists()
        # os.remove(Path(output_file_name))







    def 			UpperCamelCase_      (			self       ):
      self.run_eval_tester(_lowerCamelCase       )







    @parameterized.expand([BART_TINY, MBART_TINY]       )
    @slow
    def 			UpperCamelCase_      (			self							,		_lowerCamelCase       ):
      self.run_eval_tester(_lowerCamelCase       )







    @parameterized.expand([T5_TINY, MBART_TINY]       )
    @slow
    def 			UpperCamelCase_      (			self							,		_lowerCamelCase       ):
      lowercase							    =       Path(self.get_auto_remove_tmp_dir()       ) / 'utest_input.source'
      lowercase							    =       input_file_name.parent / 'utest_output.txt'
      assert not output_file_name.exists()

      lowercase							    =       {
          'en': ['Machine learning is great, isn\'t it?', 'I like to eat bananas', 'Tomorrow is another great day!'],
          'de': [
              'Maschinelles Lernen ist großartig, oder?',
              'Ich esse gerne Bananen',
              'Morgen ist wieder ein toller Tag!',
          ],
      }

      lowercase							    =       Path(self.get_auto_remove_tmp_dir()       )
      lowercase							    =       str(tmp_dir / 'scores.json'       )
      lowercase							    =       str(tmp_dir / 'val.target'       )
      _dump_articles(_lowerCamelCase							,		text['en']       )
      _dump_articles(_lowerCamelCase							,		text['de']       )
      lowercase							    =       'translation_en_to_de' if model == T5_TINY else 'summarization'
      lowercase							    =       F'\n            run_eval_search.py\n            {model}\n            {str(_lowerCamelCase       )}\n            {str(_lowerCamelCase       )}\n            --score_path {score_path}\n            --reference_path {reference_path}\n            --task {task}\n            '.split()
      testargs.extend(['--search', 'num_beams=1:2 length_penalty=0.9:1.0']       )

      with patch.object(_lowerCamelCase							,		'argv'							,		_lowerCamelCase       ):
        with CaptureStdout() as cs:
          run_search()
        lowercase							    =       [' num_beams | length_penalty', model, 'Best score args']
        lowercase							    =       ['Info']
        if "translation" in task:
          expected_strings.append('bleu'       )
        else:
          expected_strings.extend(_lowerCamelCase       )
        for w in expected_strings:
          assert w in cs.out
        for w in un_expected_strings:
          assert w not in cs.out
        assert Path(_lowerCamelCase       ).exists()
        os.remove(Path(_lowerCamelCase       )       )


