'''simple docstring'''






import datasets

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


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

SCREAMING_SNAKE_CASE_						 =      """\
BLEU (bilingual evaluation understudy) is an algorithm for evaluating the quality of text which has been machine-translated from one natural language to another.
Quality 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,
the 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
remains one of the most popular automated and inexpensive metrics.

Scores are calculated for individual translated segments—generally sentences—by comparing them with a set of good quality reference translations.
Those scores are then averaged over the whole corpus to reach an estimate of the translation's overall quality. Intelligibility or grammatical correctness
are not taken into account[citation needed].

BLEU'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
representing 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
reference translations. For this reason, it is not necessary to attain a score of 1. Because there are more opportunities to match, adding additional
reference translations will increase the BLEU score.
"""

SCREAMING_SNAKE_CASE_						 =      """
Computes BLEU score of translated segments against one or more references.
Args:
    predictions: list of translations to score.
        Each translation should be tokenized into a list of tokens.
    references: list of lists of references for each translation.
        Each reference should be tokenized into a list of tokens.
    max_order: Maximum n-gram order to use when computing BLEU score.
    smooth: Whether or not to apply Lin et al. 2004 smoothing.
Returns:
    'bleu': bleu score,
    'precisions': geometric mean of n-gram precisions,
    'brevity_penalty': brevity penalty,
    'length_ratio': ratio of lengths,
    'translation_length': translation_length,
    'reference_length': reference_length
Examples:

    >>> predictions = [
    ...     [\"hello\", \"there\", \"general\", \"kenobi\"],                             # tokenized prediction of the first sample
    ...     [\"foo\", \"bar\", \"foobar\"]                                             # tokenized prediction of the second sample
    ... ]
    >>> references = [
    ...     [[\"hello\", \"there\", \"general\", \"kenobi\"], [\"hello\", \"there\", \"!\"]],  # tokenized references for the first sample (2 references)
    ...     [[\"foo\", \"bar\", \"foobar\"]]                                           # tokenized references for the second sample (1 reference)
    ... ]
    >>> bleu = datasets.load_metric(\"bleu\")
    >>> results = bleu.compute(predictions=predictions, references=references)
    >>> print(results[\"bleu\"])
    1.0
"""




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



  """simple docstring"""







  def  lowercase_	(				self       ):

    '''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  lowercase_	(				self ,     snake_case_ ,     snake_case_ ,     snake_case_=4 ,     snake_case_=False       ):

    '''simple docstring'''







    __UpperCAmelCase:       Optional[int]		=			compute_bleu(
        reference_corpus=lowerCamelCase__ ,     translation_corpus=lowerCamelCase__ ,     max_order=lowerCamelCase__ ,     smooth=lowerCamelCase__       )
    (__UpperCAmelCase):       Optional[Any]		=			score
    return {
        "bleu": bleu,
        "precisions": precisions,
        "brevity_penalty": bp,
        "length_ratio": ratio,
        "translation_length": translation_length,
        "reference_length": reference_length,
    }
import inspect
import unittest
import warnings
from math import ceil, floor

from transformers import LevitConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

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 transformers import (
     MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
     MODEL_MAPPING,
     LevitForImageClassification,
     LevitForImageClassificationWithTeacher,
     LevitModel,
 )
 from transformers.models.levit.modeling_levit import LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
 from PIL import Image

 from transformers import LevitImageProcessor

class 					_lowercase    (     UpperCAmelCase__			):






    '''simple docstring'''




    def      _a    (    self ):
       lowerCAmelCase_:							Optional[int]       =	self.config_class(**self.inputs_dict )
       self.parent.assertTrue(hasattr(lowerCamelCase__       ,						"hidden_sizes" ) )
       self.parent.assertTrue(hasattr(lowerCamelCase__       ,						"num_attention_heads" ) )





class 					_lowercase    :






    '''simple docstring'''




    def __init__(    self       ,						lowerCamelCase__       ,						lowerCamelCase__=13       ,						lowerCamelCase__=64       ,						lowerCamelCase__=3       ,						lowerCamelCase__=3       ,						lowerCamelCase__=2       ,						lowerCamelCase__=1       ,						lowerCamelCase__=16       ,						lowerCamelCase__=[128, 256, 384]       ,						lowerCamelCase__=[4, 6, 8]       ,						lowerCamelCase__=[2, 3, 4]       ,						lowerCamelCase__=[16, 16, 16]       ,						lowerCamelCase__=0       ,						lowerCamelCase__=[2, 2, 2]       ,						lowerCamelCase__=[2, 2, 2]       ,						lowerCamelCase__=0.0_2       ,						lowerCamelCase__=True       ,						lowerCamelCase__=True       ,						lowerCamelCase__=2       ,						):
       lowerCAmelCase_:							int       =	parent
       lowerCAmelCase_:							Tuple       =	batch_size
       lowerCAmelCase_:							List[str]       =	image_size
       lowerCAmelCase_:							Tuple       =	num_channels
       lowerCAmelCase_:							Optional[int]       =	kernel_size
       lowerCAmelCase_:							int       =	stride
       lowerCAmelCase_:							Optional[int]       =	padding
       lowerCAmelCase_:							Tuple       =	hidden_sizes
       lowerCAmelCase_:							Union[str, Any]       =	num_attention_heads
       lowerCAmelCase_:							Tuple       =	depths
       lowerCAmelCase_:							Optional[int]       =	key_dim
       lowerCAmelCase_:							Optional[Any]       =	drop_path_rate
       lowerCAmelCase_:							List[str]       =	patch_size
       lowerCAmelCase_:							Any       =	attention_ratio
       lowerCAmelCase_:							Tuple       =	mlp_ratio
       lowerCAmelCase_:							Any       =	initializer_range
       lowerCAmelCase_:							List[str]       =	[
           ["Subsample", key_dim[0], hidden_sizes[0] // key_dim[0], 4, 2, 2],
           ["Subsample", key_dim[0], hidden_sizes[1] // key_dim[0], 4, 2, 2],
       ]
       lowerCAmelCase_:							Any       =	is_training
       lowerCAmelCase_:							Optional[int]       =	use_labels
       lowerCAmelCase_:							Dict       =	num_labels
       lowerCAmelCase_:							Any       =	initializer_range




    def      _a    (    self ):
       lowerCAmelCase_:							Union[str, Any]       =	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

       lowerCAmelCase_:							Tuple       =	None
       if self.use_labels:
          lowerCAmelCase_:							int       =	ids_tensor([self.batch_size]       ,						self.num_labels )

       lowerCAmelCase_:							Tuple       =	self.get_config()
       return config, pixel_values, labels




    def      _a    (    self ):
       return LevitConfig(
           image_size=self.image_size       ,						num_channels=self.num_channels       ,						kernel_size=self.kernel_size       ,						stride=self.stride       ,						padding=self.padding       ,						patch_size=self.patch_size       ,						hidden_sizes=self.hidden_sizes       ,						num_attention_heads=self.num_attention_heads       ,						depths=self.depths       ,						key_dim=self.key_dim       ,						drop_path_rate=self.drop_path_rate       ,						mlp_ratio=self.mlp_ratio       ,						attention_ratio=self.attention_ratio       ,						initializer_range=self.initializer_range       ,						down_ops=self.down_ops       ,						)




    def      _a    (    self       ,						lowerCamelCase__       ,						lowerCamelCase__       ,						lowerCamelCase__ ):
       lowerCAmelCase_:							int       =	LevitModel(config=lowerCamelCase__ )
       model.to(lowerCamelCase__ )
       model.eval()
       lowerCAmelCase_:							Optional[Any]       =	model(lowerCamelCase__ )
       lowerCAmelCase_:							Tuple       =	(self.image_size, self.image_size)
       lowerCAmelCase_  ,	lowerCAmelCase_:							Optional[int]       =	image_size[0], image_size[1]
       for _ in range(4 ):
          lowerCAmelCase_:							Union[str, Any]       =	floor(((height + 2 * self.padding - self.kernel_size) / self.stride) + 1 )
          lowerCAmelCase_:							str       =	floor(((width + 2 * self.padding - self.kernel_size) / self.stride) + 1 )
       self.parent.assertEqual(
           result.last_hidden_state.shape       ,						(self.batch_size, ceil(height / 4 ) * ceil(width / 4 ), self.hidden_sizes[-1])       ,						)




    def      _a    (    self       ,						lowerCamelCase__       ,						lowerCamelCase__       ,						lowerCamelCase__ ):
       lowerCAmelCase_:							Tuple       =	self.num_labels
       lowerCAmelCase_:							str       =	LevitForImageClassification(lowerCamelCase__ )
       model.to(lowerCamelCase__ )
       model.eval()
       lowerCAmelCase_:							Optional[Any]       =	model(lowerCamelCase__       ,						labels=lowerCamelCase__ )
       self.parent.assertEqual(result.logits.shape       ,						(self.batch_size, self.num_labels) )




    def      _a    (    self ):
       lowerCAmelCase_:							Union[str, Any]       =	self.prepare_config_and_inputs()
       lowerCAmelCase_  ,	lowerCAmelCase_  ,	lowerCAmelCase_:							Dict       =	config_and_inputs
       lowerCAmelCase_:							Tuple       =	{"pixel_values": pixel_values}
       return config, inputs_dict





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






    '''simple docstring'''
    SCREAMING_SNAKE_CASE:				Any          =    (
        (LevitModel, LevitForImageClassification, LevitForImageClassificationWithTeacher)
        if is_torch_available()
        else ()
    )
    SCREAMING_SNAKE_CASE:				int          =    (
        {
            'feature-extraction': LevitModel,
            'image-classification': (LevitForImageClassification, LevitForImageClassificationWithTeacher),
        }
        if is_torch_available()
        else {}
    )

    SCREAMING_SNAKE_CASE:				Optional[int]          =    False
    SCREAMING_SNAKE_CASE:				Optional[Any]          =    False
    SCREAMING_SNAKE_CASE:				str          =    False
    SCREAMING_SNAKE_CASE:				str          =    False
    SCREAMING_SNAKE_CASE:				List[Any]          =    False




    def      _a    (    self ):
       lowerCAmelCase_:							List[str]       =	LevitModelTester(self )
       lowerCAmelCase_:							Union[str, Any]       =	ConfigTester(self       ,						config_class=lowerCamelCase__       ,						has_text_modality=lowerCamelCase__       ,						hidden_size=37 )




    def      _a    (    self ):
       self.create_and_test_config_common_properties()
       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 ):
       return




    @unittest.skip(reason="Levit does not use inputs_embeds" )
    def      _a    (    self ):
       pass




    @unittest.skip(reason="Levit does not support input and output embeddings" )
    def      _a    (    self ):
       pass




    @unittest.skip(reason="Levit does not output attentions" )
    def      _a    (    self ):
       pass




    def      _a    (    self ):
       lowerCAmelCase_  ,	lowerCAmelCase_:							Optional[Any]       =	self.model_tester.prepare_config_and_inputs_for_common()

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

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




    def      _a    (    self ):
       def check_hidden_states_output(lowerCamelCase__       ,						lowerCamelCase__       ,						lowerCamelCase__ ):
          lowerCAmelCase_:							int       =	model_class(lowerCamelCase__ )
          model.to(lowerCamelCase__ )
          model.eval()

          with torch.no_grad():
             lowerCAmelCase_:							Dict       =	model(**self._prepare_for_class(lowerCamelCase__       ,						lowerCamelCase__ ) )

          lowerCAmelCase_:							str       =	outputs.hidden_states

          lowerCAmelCase_:							List[Any]       =	len(self.model_tester.depths ) + 1
          self.assertEqual(len(lowerCamelCase__ )       ,						lowerCamelCase__ )

          lowerCAmelCase_:							List[Any]       =	(self.model_tester.image_size, self.model_tester.image_size)
          lowerCAmelCase_  ,	lowerCAmelCase_:							int       =	image_size[0], image_size[1]
          for _ in range(4 ):
             lowerCAmelCase_:							Tuple       =	floor(
                 (
                     (height + 2 * self.model_tester.padding - self.model_tester.kernel_size)
                     / self.model_tester.stride
                 )
                 + 1 )
             lowerCAmelCase_:							Optional[int]       =	floor(
                 (
                     (width + 2 * self.model_tester.padding - self.model_tester.kernel_size)
                     / self.model_tester.stride
                 )
                 + 1 )
          # verify the first hidden states (first block)
          self.assertListEqual(
              list(hidden_states[0].shape[-2:] )       ,						[
                  height * width,
                  self.model_tester.hidden_sizes[0],
              ]       ,						)

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

       for model_class in self.all_model_classes:
          lowerCAmelCase_:							int       =	True
          check_hidden_states_output(lowerCamelCase__       ,						lowerCamelCase__       ,						lowerCamelCase__ )

          # check that output_hidden_states also work using config
          del inputs_dict["output_hidden_states"]
          lowerCAmelCase_:							Union[str, Any]       =	True

          check_hidden_states_output(lowerCamelCase__       ,						lowerCamelCase__       ,						lowerCamelCase__ )




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




    def      _a    (    self       ,						lowerCamelCase__       ,						lowerCamelCase__       ,						lowerCamelCase__=False ):
       lowerCAmelCase_:							List[str]       =	super()._prepare_for_class(lowerCamelCase__       ,						lowerCamelCase__       ,						return_labels=lowerCamelCase__ )

       if return_labels:
          if model_class.__name__ == "LevitForImageClassificationWithTeacher":
             del inputs_dict["labels"]

       return inputs_dict




    def      _a    (    self ):
       lowerCAmelCase_:							Union[str, Any]       =	self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*lowerCamelCase__ )




    def      _a    (    self ):
       lowerCAmelCase_:							str       =	self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*lowerCamelCase__ )




    def      _a    (    self ):
       if not self.model_tester.is_training:
          return

       lowerCAmelCase_  ,	lowerCAmelCase_:							Tuple       =	self.model_tester.prepare_config_and_inputs_for_common()
       lowerCAmelCase_:							int       =	True

       for model_class in self.all_model_classes:
          # LevitForImageClassificationWithTeacher supports inference-only
          if (
              model_class in get_values(lowerCamelCase__ )
              or model_class.__name__ == "LevitForImageClassificationWithTeacher"
          ):
             continue
          lowerCAmelCase_:							Tuple       =	model_class(lowerCamelCase__ )
          model.to(lowerCamelCase__ )
          model.train()
          lowerCAmelCase_:							Optional[int]       =	self._prepare_for_class(lowerCamelCase__       ,						lowerCamelCase__       ,						return_labels=lowerCamelCase__ )
          lowerCAmelCase_:							Tuple       =	model(**lowerCamelCase__ ).loss
          loss.backward()




    def      _a    (    self ):
       lowerCAmelCase_  ,	lowerCAmelCase_:							List[str]       =	self.model_tester.prepare_config_and_inputs_for_common()
       if not self.model_tester.is_training:
          return

       lowerCAmelCase_:							Any       =	False
       lowerCAmelCase_:							Tuple       =	True

       for model_class in self.all_model_classes:
          if model_class in get_values(lowerCamelCase__ ) or not model_class.supports_gradient_checkpointing:
             continue
          # LevitForImageClassificationWithTeacher supports inference-only
          if model_class.__name__ == "LevitForImageClassificationWithTeacher":
             continue
          lowerCAmelCase_:							Any       =	model_class(lowerCamelCase__ )
          model.gradient_checkpointing_enable()
          model.to(lowerCamelCase__ )
          model.train()
          lowerCAmelCase_:							str       =	self._prepare_for_class(lowerCamelCase__       ,						lowerCamelCase__       ,						return_labels=lowerCamelCase__ )
          lowerCAmelCase_:							List[str]       =	model(**lowerCamelCase__ ).loss
          loss.backward()




    def      _a    (    self ):
       lowerCAmelCase_  ,	lowerCAmelCase_:							int       =	self.model_tester.prepare_config_and_inputs_for_common()

       lowerCAmelCase_:							Optional[int]       =	[
           {"title": "multi_label_classification", "num_labels": 2, "dtype": torch.float},
           {"title": "single_label_classification", "num_labels": 1, "dtype": torch.long},
           {"title": "regression", "num_labels": 1, "dtype": torch.float},
       ]

       for model_class in self.all_model_classes:
          if (
              model_class
              not in [
                  *get_values(lowerCamelCase__ ),
              ]
              or model_class.__name__ == "LevitForImageClassificationWithTeacher"
          ):
             continue

          for problem_type in problem_types:
             with self.subTest(msg=F'''Testing {model_class} with {problem_type["title"]}''' ):
                lowerCAmelCase_:							Tuple       =	problem_type["title"]
                lowerCAmelCase_:							Optional[Any]       =	problem_type["num_labels"]

                lowerCAmelCase_:							List[str]       =	model_class(lowerCamelCase__ )
                model.to(lowerCamelCase__ )
                model.train()

                lowerCAmelCase_:							List[Any]       =	self._prepare_for_class(lowerCamelCase__       ,						lowerCamelCase__       ,						return_labels=lowerCamelCase__ )

                if problem_type["num_labels"] > 1:
                   lowerCAmelCase_:							List[str]       =	inputs["labels"].unsqueeze(1 ).repeat(1       ,						problem_type["num_labels"] )

                lowerCAmelCase_:							List[Any]       =	inputs["labels"].to(problem_type["dtype"] )

                # This tests that we do not trigger the warning form PyTorch "Using a target size that is different
                # to the input size. This will likely lead to incorrect results due to broadcasting. Please ensure
                # they have the same size." which is a symptom something in wrong for the regression problem.
                # See https://github.com/huggingface/transformers/issues/11780
                with warnings.catch_warnings(record=lowerCamelCase__ ) as warning_list:
                   lowerCAmelCase_:							Dict       =	model(**lowerCamelCase__ ).loss
                for w in warning_list:
                   if "Using a target size that is different to the input size" in str(w.message ):
                      raise ValueError(
                          F'''Something is going wrong in the regression problem: intercepted {w.message}''' )

                loss.backward()




    @slow
    def      _a    (    self ):
       for model_name in LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
          lowerCAmelCase_:							Optional[int]       =	LevitModel.from_pretrained(lowerCamelCase__ )
          self.assertIsNotNone(lowerCamelCase__ )





def 		snake_case__	(		):
   lowerCAmelCase_:							Optional[int]       =	Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"					)
   return image







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






    '''simple docstring'''




    @cached_property
    def      _a    (    self ):
       return LevitImageProcessor.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0] )




    @slow
    def      _a    (    self ):
       lowerCAmelCase_:							Any       =	LevitForImageClassificationWithTeacher.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0] ).to(
           lowerCamelCase__ )

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

       # forward pass
       with torch.no_grad():
          lowerCAmelCase_:							Tuple       =	model(**lowerCamelCase__ )

       # verify the logits
       lowerCAmelCase_:							str       =	torch.Size((1, 1_000) )
       self.assertEqual(outputs.logits.shape       ,						lowerCamelCase__ )

       lowerCAmelCase_:							Union[str, Any]       =	torch.tensor([1.0_4_4_8, -0.3_7_4_5, -1.8_3_1_7] ).to(lowerCamelCase__ )

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