
'''simple docstring'''





import collections
import inspect
import unittest

from transformers import SwinvaConfig
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, _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 SwinvaForImageClassification, SwinvaForMaskedImageModeling, SwinvaModel
  from transformers.models.swinva.modeling_swinva import SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
  from PIL import Image

  from transformers import AutoImageProcessor






class __A    :



 '''simple docstring'''







 def __init__(self				,     A				,     A=13				,     A=32				,     A=2				,     A=3				,     A=16				,     A=[1, 2, 1]				,     A=[2, 2, 4]				,     A=2				,     A=2.0				,     A=True				,     A=0.0				,     A=0.0				,     A=0.1				,     A="gelu"				,     A=False				,     A=True				,     A=0.02				,     A=1E-5				,     A=True				,     A=None				,     A=True				,     A=10				,     A=8				,     )					->  str:


      """simple docstring"""



      _a											=						parent
      _a											=						batch_size
      _a											=						image_size
      _a											=						patch_size
      _a											=						num_channels
      _a											=						embed_dim
      _a											=						depths
      _a											=						num_heads
      _a											=						window_size
      _a											=						mlp_ratio
      _a											=						qkv_bias
      _a											=						hidden_dropout_prob
      _a											=						attention_probs_dropout_prob
      _a											=						drop_path_rate
      _a											=						hidden_act
      _a											=						use_absolute_embeddings
      _a											=						patch_norm
      _a											=						layer_norm_eps
      _a											=						initializer_range
      _a											=						is_training
      _a											=						scope
      _a											=						use_labels
      _a											=						type_sequence_label_size
      _a											=						encoder_stride



 def       a__						(self )					->  List[str]:


      """simple docstring"""



      _a											=						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

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

      _a											=						self.get_config()

      return config, pixel_values, labels



 def       a__						(self )					->  str:


      """simple docstring"""



      return SwinvaConfig(
          image_size=self.image_size				,     patch_size=self.patch_size				,     num_channels=self.num_channels				,     embed_dim=self.embed_dim				,     depths=self.depths				,     num_heads=self.num_heads				,     window_size=self.window_size				,     mlp_ratio=self.mlp_ratio				,     qkv_bias=self.qkv_bias				,     hidden_dropout_prob=self.hidden_dropout_prob				,     attention_probs_dropout_prob=self.attention_probs_dropout_prob				,     drop_path_rate=self.drop_path_rate				,     hidden_act=self.hidden_act				,     use_absolute_embeddings=self.use_absolute_embeddings				,     path_norm=self.patch_norm				,     layer_norm_eps=self.layer_norm_eps				,     initializer_range=self.initializer_range				,     encoder_stride=self.encoder_stride				,     )



 def       a__						(self				,     A				,     A				,     A )					->  Tuple:


      """simple docstring"""



      _a											=						SwinvaModel(config=A )
      model.to(A )
      model.eval()
      _a											=						model(A )

      _a											=						((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths ) - 1))
      _a											=						int(config.embed_dim * 2 ** (len(config.depths ) - 1) )

      self.parent.assertEqual(result.last_hidden_state.shape				,     (self.batch_size, expected_seq_len, expected_dim) )



 def       a__						(self				,     A				,     A				,     A )					->  Union[str, Any]:


      """simple docstring"""



      _a											=						SwinvaForMaskedImageModeling(config=A )
      model.to(A )
      model.eval()
      _a											=						model(A )
      self.parent.assertEqual(
          result.logits.shape				,     (self.batch_size, self.num_channels, self.image_size, self.image_size) )

      # test greyscale images
      _a											=						1
      _a											=						SwinvaForMaskedImageModeling(A )
      model.to(A )
      model.eval()

      _a											=						floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
      _a											=						model(A )
      self.parent.assertEqual(result.logits.shape				,     (self.batch_size, 1, self.image_size, self.image_size) )



 def       a__						(self				,     A				,     A				,     A )					->  str:


      """simple docstring"""



      _a											=						self.type_sequence_label_size
      _a											=						SwinvaForImageClassification(A )
      model.to(A )
      model.eval()
      _a											=						model(A				,     labels=A )
      self.parent.assertEqual(result.logits.shape				,     (self.batch_size, self.type_sequence_label_size) )



 def       a__						(self )					->  Dict:


      """simple docstring"""



      _a											=						self.prepare_config_and_inputs()
      _a						,					_a						,					_a											=						config_and_inputs
      _a											=						{'''pixel_values''': pixel_values}
      return config, inputs_dict






@require_torch
class __A    (						A     ,     A     ,     unittest.TestCase		):



 '''simple docstring'''







 __lowerCamelCase					: str    = (
     (SwinvaModel, SwinvaForImageClassification, SwinvaForMaskedImageModeling) if is_torch_available() else ()
 )
 __lowerCamelCase					: Optional[int]    = (
     {'feature-extraction': SwinvaModel, 'image-classification': SwinvaForImageClassification}
     if is_torch_available()
     else {}
 )

 __lowerCamelCase					: List[str]    = False
 __lowerCamelCase					: Any    = False
 __lowerCamelCase					: Union[str, Any]    = False
 __lowerCamelCase					: Union[str, Any]    = False



 def       a__						(self )					->  Any:


      """simple docstring"""



      _a											=						SwinvaModelTester(self )
      _a											=						ConfigTester(self				,     config_class=A				,     embed_dim=37 )



 def       a__						(self )					->  Any:


      """simple docstring"""



      self.config_tester.create_and_test_config_to_json_string()
      self.config_tester.create_and_test_config_to_json_file()
      self.config_tester.create_and_test_config_from_and_save_pretrained()
      self.config_tester.create_and_test_config_with_num_labels()
      self.config_tester.check_config_can_be_init_without_params()
      self.config_tester.check_config_arguments_init()



 def       a__						(self )					->  Union[str, Any]:


      """simple docstring"""



      _a											=						self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*A )



 @unittest.skip(reason='''Got `CUDA error: misaligned address` with PyTorch 2.0.0.''' )
 def       a__						(self )					->  Optional[Any]:


      """simple docstring"""



      pass



 @unittest.skip(reason='''Swinv2 does not use inputs_embeds''' )
 def       a__						(self )					->  Any:


      """simple docstring"""



      pass



 def       a__						(self )					->  str:


      """simple docstring"""



      _a						,					_a											=						self.model_tester.prepare_config_and_inputs_for_common()

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



 def       a__						(self )					->  str:


      """simple docstring"""



      _a						,					_a											=						self.model_tester.prepare_config_and_inputs_for_common()

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

           _a											=						['''pixel_values''']
           self.assertListEqual(arg_names[:1]				,     A )



 def       a__						(self )					->  str:


      """simple docstring"""



      _a						,					_a											=						self.model_tester.prepare_config_and_inputs_for_common()
      _a											=						True

      for model_class in self.all_model_classes:
           _a											=						True
           _a											=						False
           _a											=						True
           _a											=						model_class(A )
           model.to(A )
           model.eval()
           with torch.no_grad():
                _a											=						model(**self._prepare_for_class(A				,     A ) )
           _a											=						outputs.attentions
           _a											=						len(self.model_tester.depths )
           self.assertEqual(len(A )				,     A )

           # check that output_attentions also work using config
           del inputs_dict["output_attentions"]
           _a											=						True
           _a											=						config.window_size**2
           _a											=						model_class(A )
           model.to(A )
           model.eval()
           with torch.no_grad():
                _a											=						model(**self._prepare_for_class(A				,     A ) )
           _a											=						outputs.attentions
           self.assertEqual(len(A )				,     A )

           self.assertListEqual(
               list(attentions[0].shape[-3:] )				,     [self.model_tester.num_heads[0], window_size_squared, window_size_squared]				,     )
           _a											=						len(A )

           # Check attention is always last and order is fine
           _a											=						True
           _a											=						True
           _a											=						model_class(A )
           model.to(A )
           model.eval()
           with torch.no_grad():
                _a											=						model(**self._prepare_for_class(A				,     A ) )

           if hasattr(self.model_tester				,     '''num_hidden_states_types''' ):
                _a											=						self.model_tester.num_hidden_states_types
           else:
                # also another +1 for reshaped_hidden_states
                _a											=						2
           self.assertEqual(out_len + added_hidden_states				,     len(A ) )

           _a											=						outputs.attentions

           self.assertEqual(len(A )				,     A )

           self.assertListEqual(
               list(self_attentions[0].shape[-3:] )				,     [self.model_tester.num_heads[0], window_size_squared, window_size_squared]				,     )



 def       a__						(self				,     A				,     A				,     A				,     A )					->  Optional[int]:


      """simple docstring"""



      _a											=						model_class(A )
      model.to(A )
      model.eval()

      with torch.no_grad():
           _a											=						model(**self._prepare_for_class(A				,     A ) )

      _a											=						outputs.hidden_states

      _a											=						getattr(
          self.model_tester				,     '''expected_num_hidden_layers'''				,     len(self.model_tester.depths ) + 1 )
      self.assertEqual(len(A )				,     A )

      # Swinv2 has a different seq_length
      _a											=						(
          config.patch_size
          if isinstance(config.patch_size				,     collections.abc.Iterable )
          else (config.patch_size, config.patch_size)
      )

      _a											=						(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

      self.assertListEqual(
          list(hidden_states[0].shape[-2:] )				,     [num_patches, self.model_tester.embed_dim]				,     )

      _a											=						outputs.reshaped_hidden_states
      self.assertEqual(len(A )				,     A )

      _a						,					_a						,					_a						,					_a											=						reshaped_hidden_states[0].shape
      _a											=						(
          reshaped_hidden_states[0].view(A				,     A				,     height * width ).permute(0				,     2				,     1 )
      )
      self.assertListEqual(
          list(reshaped_hidden_states.shape[-2:] )				,     [num_patches, self.model_tester.embed_dim]				,     )



 def       a__						(self )					->  Union[str, Any]:


      """simple docstring"""



      _a						,					_a											=						self.model_tester.prepare_config_and_inputs_for_common()

      _a											=						(
          self.model_tester.image_size
          if isinstance(self.model_tester.image_size				,     collections.abc.Iterable )
          else (self.model_tester.image_size, self.model_tester.image_size)
      )

      for model_class in self.all_model_classes:
           _a											=						True
           self.check_hidden_states_output(A				,     A				,     A				,     A )

           # check that output_hidden_states also work using config
           del inputs_dict["output_hidden_states"]
           _a											=						True

           self.check_hidden_states_output(A				,     A				,     A				,     A )



 def       a__						(self )					->  int:


      """simple docstring"""



      _a						,					_a											=						self.model_tester.prepare_config_and_inputs_for_common()
      _a											=						3

      _a											=						(
          self.model_tester.image_size
          if isinstance(self.model_tester.image_size				,     collections.abc.Iterable )
          else (self.model_tester.image_size, self.model_tester.image_size)
      )
      _a											=						(
          config.patch_size
          if isinstance(config.patch_size				,     collections.abc.Iterable )
          else (config.patch_size, config.patch_size)
      )

      _a											=						image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
      _a											=						image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

      for model_class in self.all_model_classes:
           _a											=						True
           self.check_hidden_states_output(A				,     A				,     A				,     (padded_height, padded_width) )

           # check that output_hidden_states also work using config
           del inputs_dict["output_hidden_states"]
           _a											=						True
           self.check_hidden_states_output(A				,     A				,     A				,     (padded_height, padded_width) )



 def       a__						(self )					->  List[str]:


      """simple docstring"""



      _a											=						self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_masked_image_modeling(*A )



 def       a__						(self )					->  Tuple:


      """simple docstring"""



      _a											=						self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_image_classification(*A )



 @slow
 def       a__						(self )					->  int:


      """simple docstring"""



      for model_name in SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           _a											=						SwinvaModel.from_pretrained(A )
           self.assertIsNotNone(A )



 def       a__						(self )					->  Any:


      """simple docstring"""



      _a						,					_a											=						self.model_tester.prepare_config_and_inputs_for_common()

      _a											=						_config_zero_init(A )
      for model_class in self.all_model_classes:
           _a											=						model_class(config=A )
           for name, param in model.named_parameters():
                if "embeddings" not in name and "logit_scale" not in name and 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'''				,     )






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



 '''simple docstring'''







 @cached_property
 def       a__						(self )					->  Optional[int]:


      """simple docstring"""



      return (
          AutoImageProcessor.from_pretrained('''microsoft/swinv2-tiny-patch4-window8-256''' )
          if is_vision_available()
          else None
      )



 @slow
 def       a__						(self )					->  List[Any]:


      """simple docstring"""



      _a											=						SwinvaForImageClassification.from_pretrained('''microsoft/swinv2-tiny-patch4-window8-256''' ).to(
          A )
      _a											=						self.default_image_processor

      _a											=						Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
      _a											=						image_processor(images=A				,     return_tensors='''pt''' ).to(A )

      # forward pass
      with torch.no_grad():
           _a											=						model(**A )

      # verify the logits
      _a											=						torch.Size((1, 1_000) )
      self.assertEqual(outputs.logits.shape				,     A )
      _a											=						torch.tensor([-0.3947, -0.4306, 0.0026] ).to(A )
      self.assertTrue(torch.allclose(outputs.logits[0, :3]				,     A				,     atol=1E-4 ) )








'''simple docstring'''





import datasets

from .nmt_bleu import compute_bleu  # From: https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py


lowercase_	 =						"\\n@INPROCEEDINGS{Papineni02bleu:a,\n    author = {Kishore Papineni and Salim Roukos and Todd Ward and Wei-jing Zhu},\n    title = {BLEU: a Method for Automatic Evaluation of Machine Translation},\n    booktitle = {},\n    year = {2002},\n    pages = {311--318}\n}\n@inproceedings{lin-och-2004-orange,\n    title = \"{ORANGE}: a Method for Evaluating Automatic Evaluation Metrics for Machine Translation\",\n    author = \"Lin, Chin-Yew  and\n      Och, Franz Josef\",\n    booktitle = \"{COLING} 2004: Proceedings of the 20th International Conference on Computational Linguistics\",\n    month = \"aug 23{--}aug 27\",\n    year = \"2004\",\n    address = \"Geneva, Switzerland\",\n    publisher = \"COLING\",\n    url = \"https://www.aclweb.org/anthology/C04-1072\",\n    pages = \"501--507\",\n}\n"

lowercase_	 =						"\\nBLEU (bilingual evaluation understudy) is an algorithm for evaluating the quality of text which has been machine-translated from one natural language to another.\nQuality is considered to be the correspondence between a machine's output and that of a human: \"the closer a machine translation is to a professional human translation,\nthe better it is\" – this is the central idea behind BLEU. BLEU was one of the first metrics to claim a high correlation with human judgements of quality, and\nremains one of the most popular automated and inexpensive metrics.\n\nScores are calculated for individual translated segments—generally sentences—by comparing them with a set of good quality reference translations.\nThose scores are then averaged over the whole corpus to reach an estimate of the translation's overall quality. Intelligibility or grammatical correctness\nare not taken into account[citation needed].\n\nBLEU's output is always a number between 0 and 1. This value indicates how similar the candidate text is to the reference texts, with values closer to 1\nrepresenting more similar texts. Few human translations will attain a score of 1, since this would indicate that the candidate is identical to one of the\nreference translations. For this reason, it is not necessary to attain a score of 1. Because there are more opportunities to match, adding additional\nreference translations will increase the BLEU score.\n"

lowercase_	 =						"\nComputes BLEU score of translated segments against one or more references.\nArgs:\n    predictions: list of translations to score.\n        Each translation should be tokenized into a list of tokens.\n    references: list of lists of references for each translation.\n        Each reference should be tokenized into a list of tokens.\n    max_order: Maximum n-gram order to use when computing BLEU score.\n    smooth: Whether or not to apply Lin et al. 2004 smoothing.\nReturns:\n    'bleu': bleu score,\n    'precisions': geometric mean of n-gram precisions,\n    'brevity_penalty': brevity penalty,\n    'length_ratio': ratio of lengths,\n    'translation_length': translation_length,\n    'reference_length': reference_length\nExamples:\n\n    >>> predictions = [\n    ...     [\"hello\", \"there\", \"general\", \"kenobi\"],                             # tokenized prediction of the first sample\n    ...     [\"foo\", \"bar\", \"foobar\"]                                             # tokenized prediction of the second sample\n    ... ]\n    >>> references = [\n    ...     [[\"hello\", \"there\", \"general\", \"kenobi\"], [\"hello\", \"there\", \"!\"]],  # tokenized references for the first sample (2 references)\n    ...     [[\"foo\", \"bar\", \"foobar\"]]                                           # tokenized references for the second sample (1 reference)\n    ... ]\n    >>> bleu = datasets.load_metric(\"bleu\")\n    >>> results = bleu.compute(predictions=predictions, references=references)\n    >>> print(results[\"bleu\"])\n    1.0\n"






@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION     ,     _KWARGS_DESCRIPTION		)
class __A    (						datasets.Metric		):



 '''simple docstring'''







 def       a__						(self )					->  Union[str, Any]:


      """simple docstring"""



      return datasets.MetricInfo(
          description=_DESCRIPTION				,     citation=_CITATION				,     inputs_description=_KWARGS_DESCRIPTION				,     features=datasets.Features(
              {
                  '''predictions''': datasets.Sequence(datasets.Value('''string'''				,     id='''token''' )				,     id='''sequence''' ),
                  '''references''': datasets.Sequence(
                      datasets.Sequence(datasets.Value('''string'''				,     id='''token''' )				,     id='''sequence''' )				,     id='''references''' ),
              } )				,     codebase_urls=['''https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py''']				,     reference_urls=[
              '''https://en.wikipedia.org/wiki/BLEU''',
              '''https://towardsdatascience.com/evaluating-text-output-in-nlp-bleu-at-your-own-risk-e8609665a213''',
          ]				,     )



 def       a__						(self				,     A				,     A				,     A=4				,     A=False )					->  Tuple:


      """simple docstring"""



      _a											=						compute_bleu(
          reference_corpus=A				,     translation_corpus=A				,     max_order=A				,     smooth=A )
      ((_a)						,					(_a)						,					(_a)						,					(_a)						,					(_a)						,					(_a))											=						score
      return {
          "bleu": bleu,
          "precisions": precisions,
          "brevity_penalty": bp,
          "length_ratio": ratio,
          "translation_length": translation_length,
          "reference_length": reference_length,
      }



