


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       lowercase_					(unittest.TestCase   ):





				def __init__(				self       ,  lowercase_       ,  lowercase_=3       ,  lowercase_=32       ,  lowercase_=3       ,  lowercase_=10       ,  lowercase_=[10, 20, 30, 40]       ,  lowercase_=[1, 1, 2, 1]       ,  lowercase_=True       ,  lowercase_=True       ,  lowercase_="relu"       ,  lowercase_=3       ,  lowercase_=None       ,  )  -> List[str]:
											a__      =parent
											a__      =batch_size
											a__      =image_size
											a__      =num_channels
											a__      =embeddings_size
											a__      =hidden_sizes
											a__      =depths
											a__      =is_training
											a__      =use_labels
											a__      =hidden_act
											a__      =num_labels
											a__      =scope
											a__      =len(lowercase_)





				def 						__UpperCamelCase    (				self)  -> List[str]:
											a__      =floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

											a__      =self.get_config()

											return config, pixel_values





				def 						__UpperCamelCase    (				self)  -> List[str]:
											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       ,  lowercase_       ,  lowercase_)  -> Any:
											a__      =FlaxRegNetModel(config=lowercase_)
											a__      =model(lowercase_)

											# 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       ,  lowercase_       ,  lowercase_)  -> str:
											a__      =self.num_labels
											a__      =FlaxRegNetForImageClassification(config=lowercase_)
											a__      =model(lowercase_)
											self.parent.assertEqual(result.logits.shape       ,  (self.batch_size, self.num_labels))





				def 						__UpperCamelCase    (				self)  -> Dict:
											a__      =self.prepare_config_and_inputs()
											a__    ,     a__      =config_and_inputs
											a__      ={'pixel_values': pixel_values}
											return config, inputs_dict







@require_flax
class       lowercase_					(lowercase__      ,					unittest.TestCase   ):
				snake_case	    =(FlaxRegNetModel, FlaxRegNetForImageClassification) if is_flax_available() else ()

				snake_case	    =False
				snake_case	    =False
				snake_case	    =False





				def 						__UpperCamelCase    (				self)  -> None:
											a__      =FlaxRegNetModelTester(self)
											a__      =ConfigTester(self       ,  config_class=lowercase_       ,  has_text_modality=lowercase_)





				def 						__UpperCamelCase    (				self)  -> List[Any]:
											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)  -> List[Any]:
											return





				def 						__UpperCamelCase    (				self)  -> Dict:
											a__      =self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*lowercase_)





				def 						__UpperCamelCase    (				self)  -> int:
											a__      =self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_image_classification(*lowercase_)





				@unittest.skip(reason='RegNet does not use inputs_embeds')
				def 						__UpperCamelCase    (				self)  -> int:
											pass





				@unittest.skip(reason='RegNet does not support input and output embeddings')
				def 						__UpperCamelCase    (				self)  -> Dict:
											pass





				def 						__UpperCamelCase    (				self)  -> Optional[int]:
											a__    ,     a__      =self.model_tester.prepare_config_and_inputs_for_common()

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

																		a__      =['pixel_values']
																		self.assertListEqual(arg_names[:1]       ,  lowercase_)





				def 						__UpperCamelCase    (				self)  -> Union[str, Any]:
											def check_hidden_states_output(lowercase_       ,  lowercase_       ,  lowercase_):
																		a__      =model_class(lowercase_)

																		a__      =model(**self._prepare_for_class(lowercase_       ,  lowercase_))

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

																		a__      =self.model_tester.num_stages
																		self.assertEqual(len(lowercase_)       ,  expected_num_stages + 1)

											a__    ,     a__      =self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
																		a__      =True
																		check_hidden_states_output(lowercase_       ,  lowercase_       ,  lowercase_)

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

																		check_hidden_states_output(lowercase_       ,  lowercase_       ,  lowercase_)





				def 						__UpperCamelCase    (				self)  -> List[str]:
											a__    ,     a__      =self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
																		with self.subTest(model_class.__name__):
																									a__      =self._prepare_for_class(lowercase_       ,  lowercase_)
																									a__      =model_class(lowercase_)

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

																									with self.subTest('JIT Enabled'):
																																a__      =model_jitted(**lowercase_).to_tuple()

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

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







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







@require_flax
class       lowercase_					(unittest.TestCase   ):





				@cached_property
				def 						__UpperCamelCase    (				self)  -> Optional[Any]:
											return AutoImageProcessor.from_pretrained('facebook/regnet-y-040') if is_vision_available() else None





				@slow
				def 						__UpperCamelCase    (				self)  -> List[Any]:
											a__      =FlaxRegNetForImageClassification.from_pretrained('facebook/regnet-y-040')

											a__      =self.default_image_processor
											a__      =prepare_img()
											a__      =image_processor(images=lowercase_       ,  return_tensors='np')

											a__      =model(**lowercase_)

											# verify the logits
											a__      =(1, 1000)
											self.assertEqual(outputs.logits.shape       ,  lowercase_)

											a__      =jnp.array([-0.41_80, -1.50_51, -3.48_36])

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





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:						Union[str, Any]   				=       '\\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:						Dict   				=       '\\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:						List[Any]   				=       '\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       lowercase_					(datasets.Metric   ):





				def 						__UpperCamelCase    (				self)  -> Optional[int]:
											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       ,  lowercase_       ,  lowercase_       ,  lowercase_=None       ,  lowercase_=True       ,  lowercase_=False)  -> Any:
											if rouge_types is None:
																		a__      =['rouge1', 'rouge2', 'rougeL', 'rougeLsum']

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

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

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

											return result

