



'''simple docstring'''


import inspect
import unittest

from transformers import RegNetConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from transformers.utils import cached_property, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor


if is_flax_available():
   import jax
   import jax.numpy as jnp

   from transformers.models.regnet.modeling_flax_regnet import FlaxRegNetForImageClassification, FlaxRegNetModel

if is_vision_available():
   from PIL import Image

   from transformers import AutoImageProcessor

class  SCREAMING_SNAKE_CASE (  unittest.TestCase		):






    """simple docstring"""





    def __init__(  self      :					Optional[int]  ,						UpperCamelCase__      :					int  ,						UpperCamelCase__      :					Union[str, Any]=3  ,						UpperCamelCase__      :					Optional[Any]=3_2  ,						UpperCamelCase__      :					int=3  ,						UpperCamelCase__      :					Dict=1_0  ,						UpperCamelCase__      :					int=[1_0, 2_0, 3_0, 4_0]  ,						UpperCamelCase__      :					int=[1, 1, 2, 1]  ,						UpperCamelCase__      :					str=True  ,						UpperCamelCase__      :					int=True  ,						UpperCamelCase__      :					str="relu"  ,						UpperCamelCase__      :					int=3  ,						UpperCamelCase__      :					List[Any]=None  ,						):


       """simple docstring"""







       UpperCamelCase					=  parent
       UpperCamelCase					=  batch_size
       UpperCamelCase					=  image_size
       UpperCamelCase					=  num_channels
       UpperCamelCase					=  embeddings_size
       UpperCamelCase					=  hidden_sizes
       UpperCamelCase					=  depths
       UpperCamelCase					=  is_training
       UpperCamelCase					=  use_labels
       UpperCamelCase					=  hidden_act
       UpperCamelCase					=  num_labels
       UpperCamelCase					=  scope
       UpperCamelCase					=  len(UpperCamelCase__ )






    def  A       (  self      :					List[str] ):


       """simple docstring"""







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

       UpperCamelCase					=  self.get_config()

       return config, pixel_values






    def  A       (  self      :					Dict ):


       """simple docstring"""







       return RegNetConfig(
           num_channels=self.num_channels  ,						embeddings_size=self.embeddings_size  ,						hidden_sizes=self.hidden_sizes  ,						depths=self.depths  ,						hidden_act=self.hidden_act  ,						num_labels=self.num_labels  ,						image_size=self.image_size  ,						)






    def  A       (  self      :					Optional[int]  ,						UpperCamelCase__      :					Optional[int]  ,						UpperCamelCase__      :					Optional[int] ):


       """simple docstring"""







       UpperCamelCase					=  FlaxRegNetModel(config=UpperCamelCase__ )
       UpperCamelCase					=  model(UpperCamelCase__ )

       # Output shape (b, c, h, w)
       self.parent.assertEqual(
           result.last_hidden_state.shape  ,						(self.batch_size, self.hidden_sizes[-1], self.image_size // 3_2, self.image_size // 3_2)  ,						)






    def  A       (  self      :					Optional[int]  ,						UpperCamelCase__      :					List[Any]  ,						UpperCamelCase__      :					Optional[Any] ):


       """simple docstring"""







       UpperCamelCase					=  self.num_labels
       UpperCamelCase					=  FlaxRegNetForImageClassification(config=UpperCamelCase__ )
       UpperCamelCase					=  model(UpperCamelCase__ )
       self.parent.assertEqual(result.logits.shape  ,						(self.batch_size, self.num_labels) )






    def  A       (  self      :					int ):


       """simple docstring"""







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

@require_flax
class  SCREAMING_SNAKE_CASE (  _a   ,						unittest.TestCase		):






    """simple docstring"""





    _SCREAMING_SNAKE_CASE	  =  (FlaxRegNetModel, FlaxRegNetForImageClassification) if is_flax_available() else ()

    _SCREAMING_SNAKE_CASE	  =  False
    _SCREAMING_SNAKE_CASE	  =  False
    _SCREAMING_SNAKE_CASE	  =  False






    def  A       (  self      :					int ):


       """simple docstring"""







       UpperCamelCase					=  FlaxRegNetModelTester(self )
       UpperCamelCase					=  ConfigTester(self  ,						config_class=UpperCamelCase__  ,						has_text_modality=UpperCamelCase__ )






    def  A       (  self      :					str ):


       """simple docstring"""







       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      :					str ):


       """simple docstring"""







       return






    def  A       (  self      :					Optional[int] ):


       """simple docstring"""







       UpperCamelCase					=  self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*UpperCamelCase__ )






    def  A       (  self      :					int ):


       """simple docstring"""







       UpperCamelCase					=  self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*UpperCamelCase__ )






    @unittest.skip(reason='RegNet does not use inputs_embeds' )
    def  A       (  self      :					Union[str, Any] ):


       """simple docstring"""







       pass






    @unittest.skip(reason='RegNet does not support input and output embeddings' )
    def  A       (  self      :					str ):


       """simple docstring"""







       pass






    def  A       (  self      :					List[str] ):


       """simple docstring"""







       UpperCamelCase				,      UpperCamelCase					=  self.model_tester.prepare_config_and_inputs_for_common()

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

          UpperCamelCase					=  ['pixel_values']
          self.assertListEqual(arg_names[:1]  ,						UpperCamelCase__ )






    def  A       (  self      :					List[str] ):


       """simple docstring"""







       def check_hidden_states_output(UpperCamelCase__      :					Any  ,						UpperCamelCase__      :					int  ,						UpperCamelCase__      :					Tuple ):
          UpperCamelCase					=  model_class(UpperCamelCase__ )

          UpperCamelCase					=  model(**self._prepare_for_class(UpperCamelCase__  ,						UpperCamelCase__ ) )

          UpperCamelCase					=  outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

          UpperCamelCase					=  self.model_tester.num_stages
          self.assertEqual(len(UpperCamelCase__ )  ,						expected_num_stages + 1 )

       UpperCamelCase				,      UpperCamelCase					=  self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
          UpperCamelCase					=  True
          check_hidden_states_output(UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__ )

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

          check_hidden_states_output(UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__ )






    def  A       (  self      :					int ):


       """simple docstring"""







       UpperCamelCase				,      UpperCamelCase					=  self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
          with self.subTest(model_class.__name__ ):
             UpperCamelCase					=  self._prepare_for_class(UpperCamelCase__  ,						UpperCamelCase__ )
             UpperCamelCase					=  model_class(UpperCamelCase__ )

             @jax.jit
             def model_jitted(UpperCamelCase__      :					Union[str, Any]  ,						**UpperCamelCase__      :					List[str] ):
                return model(pixel_values=UpperCamelCase__  ,						**UpperCamelCase__ )

             with self.subTest('JIT Enabled' ):
                UpperCamelCase					=  model_jitted(**UpperCamelCase__ ).to_tuple()

             with self.subTest('JIT Disabled' ):
                with jax.disable_jit():
                   UpperCamelCase					=  model_jitted(**UpperCamelCase__ ).to_tuple()

             self.assertEqual(len(UpperCamelCase__ )  ,						len(UpperCamelCase__ ) )
             for jitted_output, output in zip(UpperCamelCase__  ,						UpperCamelCase__ ):
                self.assertEqual(jitted_output.shape  ,						output.shape )







def 				__lowerCamelCase				(   )       ->	List[Any]:






   """simple docstring"""
   UpperCamelCase					=  Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'    )
   return image

@require_flax
class  SCREAMING_SNAKE_CASE (  unittest.TestCase		):






    """simple docstring"""





    @cached_property
    def  A       (  self      :					Tuple ):


       """simple docstring"""







       return AutoImageProcessor.from_pretrained('facebook/regnet-y-040' ) if is_vision_available() else None






    @slow
    def  A       (  self      :					Tuple ):


       """simple docstring"""







       UpperCamelCase					=  FlaxRegNetForImageClassification.from_pretrained('facebook/regnet-y-040' )

       UpperCamelCase					=  self.default_image_processor
       UpperCamelCase					=  prepare_img()
       UpperCamelCase					=  image_processor(images=UpperCamelCase__  ,						return_tensors='np' )

       UpperCamelCase					=  model(**UpperCamelCase__ )

       # verify the logits
       UpperCamelCase					=  (1, 1_0_0_0)
       self.assertEqual(outputs.logits.shape  ,						UpperCamelCase__ )

       UpperCamelCase					=  jnp.array([-0.4_1_8_0, -1.5_0_5_1, -3.4_8_3_6] )

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






'''simple docstring'''


import absl  # noqa: F401 # Here to have a nice missing dependency error message early on
import nltk  # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy  # noqa: F401 # Here to have a nice missing dependency error message early on
import six  # noqa: F401 # Here to have a nice missing dependency error message early on
from rouge_score import rouge_scorer, scoring

import datasets


_lowerCamelCase	:       List[str]      	=	"\\n@inproceedings{lin-2004-rouge,\n    title = \"{ROUGE}: A Package for Automatic Evaluation of Summaries\",\n    author = \"Lin, Chin-Yew\",\n    booktitle = \"Text Summarization Branches Out\",\n    month = jul,\n    year = \"2004\",\n    address = \"Barcelona, Spain\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://www.aclweb.org/anthology/W04-1013\",\n    pages = \"74--81\",\n}\n"

_lowerCamelCase	:       Optional[int]      	=	"\\nROUGE, or Recall-Oriented Understudy for Gisting Evaluation, is a set of metrics and a software package used for\nevaluating automatic summarization and machine translation software in natural language processing.\nThe metrics compare an automatically produced summary or translation against a reference or a set of references (human-produced) summary or translation.\n\nNote that ROUGE is case insensitive, meaning that upper case letters are treated the same way as lower case letters.\n\nThis metrics is a wrapper around Google Research reimplementation of ROUGE:\nhttps://github.com/google-research/google-research/tree/master/rouge\n"

_lowerCamelCase	:       str      	=	"\nCalculates average rouge scores for a list of hypotheses and references\nArgs:\n    predictions: list of predictions to score. Each prediction\n        should be a string with tokens separated by spaces.\n    references: list of reference for each prediction. Each\n        reference should be a string with tokens separated by spaces.\n    rouge_types: A list of rouge types to calculate.\n        Valid names:\n        `\"rouge{n}\"` (e.g. `\"rouge1\"`, `\"rouge2\"`) where: {n} is the n-gram based scoring,\n        `\"rougeL\"`: Longest common subsequence based scoring.\n        `\"rougeLSum\"`: rougeLsum splits text using `\"\n\"`.\n        See details in https://github.com/huggingface/datasets/issues/617\n    use_stemmer: Bool indicating whether Porter stemmer should be used to strip word suffixes.\n    use_aggregator: Return aggregates if this is set to True\nReturns:\n    rouge1: rouge_1 (precision, recall, f1),\n    rouge2: rouge_2 (precision, recall, f1),\n    rougeL: rouge_l (precision, recall, f1),\n    rougeLsum: rouge_lsum (precision, recall, f1)\nExamples:\n\n    >>> rouge = datasets.load_metric('rouge')\n    >>> predictions = [\"hello there\", \"general kenobi\"]\n    >>> references = [\"hello there\", \"general kenobi\"]\n    >>> results = rouge.compute(predictions=predictions, references=references)\n    >>> print(list(results.keys()))\n    ['rouge1', 'rouge2', 'rougeL', 'rougeLsum']\n    >>> print(results[\"rouge1\"])\n    AggregateScore(low=Score(precision=1.0, recall=1.0, fmeasure=1.0), mid=Score(precision=1.0, recall=1.0, fmeasure=1.0), high=Score(precision=1.0, recall=1.0, fmeasure=1.0))\n    >>> print(results[\"rouge1\"].mid.fmeasure)\n    1.0\n"

@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION   ,						_KWARGS_DESCRIPTION		)
class  SCREAMING_SNAKE_CASE (  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.Value('string'  ,						id='sequence' ),
                   'references': datasets.Value('string'  ,						id='sequence' ),
               } )  ,						codebase_urls=['https://github.com/google-research/google-research/tree/master/rouge']  ,						reference_urls=[
               'https://en.wikipedia.org/wiki/ROUGE_(metric)',
               'https://github.com/google-research/google-research/tree/master/rouge',
           ]  ,						)






    def  A       (  self      :					Tuple  ,						UpperCamelCase__      :					Tuple  ,						UpperCamelCase__      :					Dict  ,						UpperCamelCase__      :					List[str]=None  ,						UpperCamelCase__      :					List[Any]=True  ,						UpperCamelCase__      :					Optional[Any]=False ):


       """simple docstring"""







       if rouge_types is None:
          UpperCamelCase					=  ['rouge1', 'rouge2', 'rougeL', 'rougeLsum']

       UpperCamelCase					=  rouge_scorer.RougeScorer(rouge_types=UpperCamelCase__  ,						use_stemmer=UpperCamelCase__ )
       if use_aggregator:
          UpperCamelCase					=  scoring.BootstrapAggregator()
       else:
          UpperCamelCase					=  []

       for ref, pred in zip(UpperCamelCase__  ,						UpperCamelCase__ ):
          UpperCamelCase					=  scorer.score(UpperCamelCase__  ,						UpperCamelCase__ )
          if use_aggregator:
             aggregator.add_scores(UpperCamelCase__ )
          else:
             scores.append(UpperCamelCase__ )

       if use_aggregator:
          UpperCamelCase					=  aggregator.aggregate()
       else:
          UpperCamelCase					=  {}
          for key in scores[0]:
             UpperCamelCase					=  [score[key] for score in scores]

       return result

