





"""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 						__snake_case   (						unittest.TestCase	):





 """simple docstring"""


 def __init__(       self   ,		__lowerCamelCase   ,		__lowerCamelCase=3   ,		__lowerCamelCase=32   ,		__lowerCamelCase=3   ,		__lowerCamelCase=10   ,		__lowerCamelCase=[10, 20, 30, 40]   ,		__lowerCamelCase=[1, 1, 2, 1]   ,		__lowerCamelCase=True   ,		__lowerCamelCase=True   ,		__lowerCamelCase="relu"   ,		__lowerCamelCase=3   ,		__lowerCamelCase=None   ,		):



     '''simple docstring'''




     __A       :		str							=	parent
     __A       :		List[str]							=	batch_size
     __A       :		Any							=	image_size
     __A       :		Union[str, Any]							=	num_channels
     __A       :		List[Any]							=	embeddings_size
     __A       :		int							=	hidden_sizes
     __A       :		List[str]							=	depths
     __A       :		int							=	is_training
     __A       :		Optional[int]							=	use_labels
     __A       :		Any							=	hidden_act
     __A       :		str							=	num_labels
     __A       :		Union[str, Any]							=	scope
     __A       :		Dict							=	len(__lowerCamelCase    )

 def    UpperCamelCase__(       self    ):



     '''simple docstring'''




     __A       :		str							=	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]    )

     __A       :		Optional[int]							=	self.get_config()

     return config, pixel_values

 def    UpperCamelCase__(       self    ):



     '''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    UpperCamelCase__(       self   ,		__lowerCamelCase   ,		__lowerCamelCase    ):



     '''simple docstring'''




     __A       :		List[str]							=	FlaxRegNetModel(config=__lowerCamelCase    )
     __A       :		List[str]							=	model(__lowerCamelCase    )

     # Output shape (b, c, h, w)
     self.parent.assertEqual(
         result.last_hidden_state.shape   ,		(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)   ,		)

 def    UpperCamelCase__(       self   ,		__lowerCamelCase   ,		__lowerCamelCase    ):



     '''simple docstring'''




     __A       :		str							=	self.num_labels
     __A       :		Optional[Any]							=	FlaxRegNetForImageClassification(config=__lowerCamelCase    )
     __A       :		Optional[Any]							=	model(__lowerCamelCase    )
     self.parent.assertEqual(result.logits.shape   ,		(self.batch_size, self.num_labels)    )







 def    UpperCamelCase__(       self    ):



     '''simple docstring'''




     __A       :		Dict							=	self.prepare_config_and_inputs()
     __A					,			__A       :		List[Any]							=	config_and_inputs
     __A       :		Dict							=	{'''pixel_values''': pixel_values}
     return config, inputs_dict






@require_flax
class 						__snake_case   (						SCREAMING_SNAKE_CASE__				,				unittest.TestCase	):





 """simple docstring"""


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

 _lowerCamelCase								=	False
 _lowerCamelCase								=	False
 _lowerCamelCase								=	False

 def    UpperCamelCase__(       self    ):



     '''simple docstring'''




     __A       :		Optional[Any]							=	FlaxRegNetModelTester(self    )
     __A       :		Optional[int]							=	ConfigTester(self   ,		config_class=__lowerCamelCase   ,		has_text_modality=__lowerCamelCase    )

 def    UpperCamelCase__(       self    ):



     '''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    UpperCamelCase__(       self    ):



     '''simple docstring'''




     return

 def    UpperCamelCase__(       self    ):



     '''simple docstring'''




     __A       :		Dict							=	self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*__lowerCamelCase    )

 def    UpperCamelCase__(       self    ):



     '''simple docstring'''




     __A       :		Optional[Any]							=	self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_image_classification(*__lowerCamelCase    )

 @unittest.skip(reason='''RegNet does not use inputs_embeds'''    )
 def    UpperCamelCase__(       self    ):



     '''simple docstring'''




     pass

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



     '''simple docstring'''




     pass

 def    UpperCamelCase__(       self    ):



     '''simple docstring'''




     __A					,			__A       :		Any							=	self.model_tester.prepare_config_and_inputs_for_common()

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

         __A       :		Optional[int]							=	['''pixel_values''']
         self.assertListEqual(arg_names[:1]   ,		__lowerCamelCase    )

 def    UpperCamelCase__(       self    ):



     '''simple docstring'''




     def check_hidden_states_output(__lowerCamelCase   ,		__lowerCamelCase   ,		__lowerCamelCase    ):
         __A       :		str							=	model_class(__lowerCamelCase    )

         __A       :		Optional[Any]							=	model(**self._prepare_for_class(__lowerCamelCase   ,		__lowerCamelCase    )    )

         __A       :		Optional[Any]							=	outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

         __A       :		List[Any]							=	self.model_tester.num_stages
         self.assertEqual(len(__lowerCamelCase    )   ,		expected_num_stages + 1    )

     __A					,			__A       :		Any							=	self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
         __A       :		Union[str, Any]							=	True
         check_hidden_states_output(__lowerCamelCase   ,		__lowerCamelCase   ,		__lowerCamelCase    )

         # check that output_hidden_states also work using config
         del inputs_dict["output_hidden_states"]
         __A       :		Optional[int]							=	True

         check_hidden_states_output(__lowerCamelCase   ,		__lowerCamelCase   ,		__lowerCamelCase    )







 def    UpperCamelCase__(       self    ):



     '''simple docstring'''




     __A					,			__A       :		List[str]							=	self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
         with self.subTest(model_class.__name__    ):
             __A       :		Dict							=	self._prepare_for_class(__lowerCamelCase   ,		__lowerCamelCase    )
             __A       :		Union[str, Any]							=	model_class(__lowerCamelCase    )

             @jax.jit
             def model_jitted(__lowerCamelCase   ,		**__lowerCamelCase    ):
                 return model(pixel_values=__lowerCamelCase   ,		**__lowerCamelCase    )

             with self.subTest('''JIT Enabled'''    ):
                 __A       :		List[Any]							=	model_jitted(**__lowerCamelCase    ).to_tuple()

             with self.subTest('''JIT Disabled'''    ):
                 with jax.disable_jit():
                     __A       :		List[str]							=	model_jitted(**__lowerCamelCase    ).to_tuple()

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







def 						__lowercase	(  )  ->Optional[Any]:



    '''simple docstring'''







    __A       :		Optional[int]							=	Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''		)
    return image






@require_flax
class 						__snake_case   (						unittest.TestCase	):





 """simple docstring"""


 @cached_property
 def    UpperCamelCase__(       self    ):



     '''simple docstring'''




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







 @slow
 def    UpperCamelCase__(       self    ):



     '''simple docstring'''




     __A       :		Union[str, Any]							=	FlaxRegNetForImageClassification.from_pretrained('''facebook/regnet-y-040'''    )

     __A       :		Union[str, Any]							=	self.default_image_processor
     __A       :		Union[str, Any]							=	prepare_img()
     __A       :		Dict							=	image_processor(images=__lowerCamelCase   ,		return_tensors='''np'''    )

     __A       :		Union[str, Any]							=	model(**__lowerCamelCase    )

     # verify the logits
     __A       :		Dict							=	(1, 1000)
     self.assertEqual(outputs.logits.shape   ,		__lowerCamelCase    )

     __A       :		Optional[int]							=	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]   ,		__lowerCamelCase   ,		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


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

a_					=     """\
ROUGE, or Recall-Oriented Understudy for Gisting Evaluation, is a set of metrics and a software package used for
evaluating automatic summarization and machine translation software in natural language processing.
The metrics compare an automatically produced summary or translation against a reference or a set of references (human-produced) summary or translation.

Note that ROUGE is case insensitive, meaning that upper case letters are treated the same way as lower case letters.

This metrics is a wrapper around Google Research reimplementation of ROUGE:
https://github.com/google-research/google-research/tree/master/rouge
"""

a_					=     """
Calculates average rouge scores for a list of hypotheses and references
Args:
    predictions: list of predictions to score. Each prediction
        should be a string with tokens separated by spaces.
    references: list of reference for each prediction. Each
        reference should be a string with tokens separated by spaces.
    rouge_types: A list of rouge types to calculate.
        Valid names:
        `\"rouge{n}\"` (e.g. `\"rouge1\"`, `\"rouge2\"`) where: {n} is the n-gram based scoring,
        `\"rougeL\"`: Longest common subsequence based scoring.
        `\"rougeLSum\"`: rougeLsum splits text using `\"\n\"`.
        See details in https://github.com/huggingface/datasets/issues/617
    use_stemmer: Bool indicating whether Porter stemmer should be used to strip word suffixes.
    use_aggregator: Return aggregates if this is set to True
Returns:
    rouge1: rouge_1 (precision, recall, f1),
    rouge2: rouge_2 (precision, recall, f1),
    rougeL: rouge_l (precision, recall, f1),
    rougeLsum: rouge_lsum (precision, recall, f1)
Examples:

    >>> rouge = datasets.load_metric('rouge')
    >>> predictions = [\"hello there\", \"general kenobi\"]
    >>> references = [\"hello there\", \"general kenobi\"]
    >>> results = rouge.compute(predictions=predictions, references=references)
    >>> print(list(results.keys()))
    ['rouge1', 'rouge2', 'rougeL', 'rougeLsum']
    >>> print(results[\"rouge1\"])
    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))
    >>> print(results[\"rouge1\"].mid.fmeasure)
    1.0
"""






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





 """simple docstring"""


 def    UpperCamelCase__(       self    ):



     '''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    UpperCamelCase__(       self   ,		__lowerCamelCase   ,		__lowerCamelCase   ,		__lowerCamelCase=None   ,		__lowerCamelCase=True   ,		__lowerCamelCase=False    ):



     '''simple docstring'''




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

     __A       :		Dict							=	rouge_scorer.RougeScorer(rouge_types=__lowerCamelCase   ,		use_stemmer=__lowerCamelCase    )
     if use_aggregator:
         __A       :		Dict							=	scoring.BootstrapAggregator()
     else:
         __A       :		Optional[Any]							=	[]

     for ref, pred in zip(__lowerCamelCase   ,		__lowerCamelCase    ):
         __A       :		List[Any]							=	scorer.score(__lowerCamelCase   ,		__lowerCamelCase    )
         if use_aggregator:
             aggregator.add_scores(__lowerCamelCase    )
         else:
             scores.append(__lowerCamelCase    )

     if use_aggregator:
         __A       :		Optional[Any]							=	aggregator.aggregate()
     else:
         __A       :		List[Any]							=	{}
         for key in scores[0]:
             __A       :		Tuple							=	[score[key] for score in scores]

     return result




