
"""simple docstring"""

from collections import defaultdict
from pathlib import Path

import pandas as pd
from rouge_cli import calculate_rouge_path

from utils import calculate_rouge


SCREAMING_SNAKE_CASE_    					=						[
    '''Prosecutor: "No videos were used in the crash investigation" German papers say they saw a cell phone video of the'''
    ''' final seconds on board Flight 9525. The Germanwings co-pilot says he had a "previous episode of severe'''
    ''' depression\" German airline confirms it knew of Andreas Lubitz\'s depression years before he took control.''',
    '''The Palestinian Authority officially becomes the 123rd member of the International Criminal Court. The formal'''
    ''' accession was marked with a ceremony at The Hague, in the Netherlands. The Palestinians signed the ICC\'s'''
    ''' founding Rome Statute in January. Israel and the United States opposed the Palestinians\' efforts to join the'''
    ''' body.''',
    '''Amnesty International releases its annual report on the death penalty. The report catalogs the use of'''
    ''' state-sanctioned killing as a punitive measure across the globe. At least 607 people were executed around the'''
    ''' world in 2014, compared to 778 in 2013. The U.S. remains one of the worst offenders for imposing capital'''
    ''' punishment.''',
]

SCREAMING_SNAKE_CASE_    					=						[
    '''Marseille prosecutor says "so far no videos were used in the crash investigation" despite media reports .'''
    ''' Journalists at Bild and Paris Match are "very confident" the video clip is real, an editor says . Andreas Lubitz'''
    ''' had informed his Lufthansa training school of an episode of severe depression, airline says .''',
    '''Membership gives the ICC jurisdiction over alleged crimes committed in Palestinian territories since last June .'''
    ''' Israel and the United States opposed the move, which could open the door to war crimes investigations against'''
    ''' Israelis .''',
    '''Amnesty\'s annual death penalty report catalogs encouraging signs, but setbacks in numbers of those sentenced to'''
    ''' death . Organization claims that governments around the world are using the threat of terrorism to advance'''
    ''' executions . The number of executions worldwide has gone down by almost 22% compared with 2013, but death'''
    ''' sentences up by 28% .''',
]




def     lowercase__						(					) ->      Dict:






    """simple docstring"""




    UpperCAmelCase									=					calculate_rouge(lowercase__ ,				lowercase__ ,				bootstrap_aggregation=lowercase__ ,				rouge_keys=['rouge2', 'rougeL'] )
    assert isinstance(lowercase__ ,				lowercase__ )
    UpperCAmelCase									=					calculate_rouge(lowercase__ ,				lowercase__ ,				bootstrap_aggregation=lowercase__ ,				rouge_keys=['rouge2'] )
    assert (
        pd.DataFrame(no_aggregation['rouge2'] ).fmeasure.mean()
        == pd.DataFrame(no_aggregation_just_ra['rouge2'] ).fmeasure.mean()
    )




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






    """simple docstring"""




    UpperCAmelCase									=					'rougeLsum'
    UpperCAmelCase									=					calculate_rouge(lowercase__ ,				lowercase__ ,				newline_sep=lowercase__ ,				rouge_keys=[k] )[k]
    UpperCAmelCase									=					calculate_rouge(lowercase__ ,				lowercase__ ,				newline_sep=lowercase__ ,				rouge_keys=[k] )[k]
    assert score > score_no_sep




def     lowercase__						(					) ->      Optional[int]:






    """simple docstring"""




    UpperCAmelCase									=					['rouge1', 'rouge2', 'rougeL']
    UpperCAmelCase									=					calculate_rouge(lowercase__ ,				lowercase__ ,				newline_sep=lowercase__ ,				rouge_keys=lowercase__ )
    UpperCAmelCase									=					calculate_rouge(lowercase__ ,				lowercase__ ,				newline_sep=lowercase__ ,				rouge_keys=lowercase__ )
    assert score_sep == score_no_sep




def     lowercase__						(					) ->      Any:






    """simple docstring"""




    UpperCAmelCase									=					[
        'Her older sister, Margot Frank, died in 1945, a month earlier than previously thought.',
        'Marseille prosecutor says \"so far no videos were used in the crash investigation\" despite media reports .',
    ]
    UpperCAmelCase									=					[
        'Margot Frank, died in 1945, a month earlier than previously thought.',
        'Prosecutor: \"No videos were used in the crash investigation\" German papers say they saw a cell phone video of'
        ' the final seconds on board Flight 9525.',
    ]
    assert calculate_rouge(lowercase__ ,				lowercase__ ,				newline_sep=lowercase__ ) == calculate_rouge(lowercase__ ,				lowercase__ ,				newline_sep=lowercase__ )




def     lowercase__						(					) ->      Tuple:






    """simple docstring"""




    UpperCAmelCase									=					[
        '\" \"a person who has such a video needs to immediately give it to the investigators,\" prosecutor says .<n> \"it is a very disturbing scene,\" editor-in-chief of bild online tells \"erin burnett: outfront\" '
    ]
    UpperCAmelCase									=					[
        ' Marseille prosecutor says \"so far no videos were used in the crash investigation\" despite media reports . Journalists at Bild and Paris Match are \"very confident\" the video clip is real, an editor says . Andreas Lubitz had informed his Lufthansa training school of an episode of severe depression, airline says .'
    ]

    UpperCAmelCase									=					calculate_rouge(lowercase__ ,				lowercase__ ,				rouge_keys=['rougeLsum'] ,				newline_sep=lowercase__ )['rougeLsum']
    UpperCAmelCase									=					calculate_rouge(lowercase__ ,				lowercase__ ,				rouge_keys=['rougeLsum'] )['rougeLsum']
    assert new_score > prev_score







def     lowercase__						(					) ->      Any:






    """simple docstring"""




    UpperCAmelCase									=					Path('examples/seq2seq/test_data/wmt_en_ro' )
    UpperCAmelCase									=					calculate_rouge_path(data_dir.joinpath('test.source' ) ,				data_dir.joinpath('test.target' ) )
    assert isinstance(lowercase__ ,				lowercase__ )
    UpperCAmelCase									=					calculate_rouge_path(
        data_dir.joinpath('test.source' ) ,				data_dir.joinpath('test.target' ) ,				bootstrap_aggregation=lowercase__ )
    assert isinstance(lowercase__ ,				lowercase__ )





"""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      _UpperCAmelCase							(		unittest.TestCase							):




      def __init__(							self   ,       lowercase_   ,       lowercase_=3   ,       lowercase_=3_2   ,       lowercase_=3   ,       lowercase_=1_0   ,       lowercase_=[1_0, 2_0, 3_0, 4_0]   ,       lowercase_=[1, 1, 2, 1]   ,       lowercase_=True   ,       lowercase_=True   ,       lowercase_="relu"   ,       lowercase_=3   ,       lowercase_=None   ,       )						->					str:
          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(lowercase_							)




      def     a_      (							self							)						->					Optional[int]:
          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							)						->					Optional[Any]:
          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   ,       lowercase_   ,       lowercase_							)						->					List[Any]:
          UpperCAmelCase									=					FlaxRegNetModel(config=lowercase_							)
          UpperCAmelCase									=					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 // 3_2, self.image_size // 3_2)   ,       )




      def     a_      (							self   ,       lowercase_   ,       lowercase_							)						->					Union[str, Any]:
          UpperCAmelCase									=					self.num_labels
          UpperCAmelCase									=					FlaxRegNetForImageClassification(config=lowercase_							)
          UpperCAmelCase									=					model(lowercase_							)
          self.parent.assertEqual(result.logits.shape   ,       (self.batch_size, self.num_labels)							)




      def     a_      (							self							)						->					List[Any]:
          UpperCAmelCase									=					self.prepare_config_and_inputs()
          UpperCAmelCase			,							UpperCAmelCase									=					config_and_inputs
          UpperCAmelCase									=					{'pixel_values': pixel_values}
          return config, inputs_dict

@require_flax
class      _UpperCAmelCase							(		SCREAMING_SNAKE_CASE_     ,			unittest.TestCase							):
      __SCREAMING_SNAKE_CASE   :							Tuple							      =	(FlaxRegNetModel, FlaxRegNetForImageClassification) if is_flax_available() else ()

      __SCREAMING_SNAKE_CASE   :							List[Any]							      =	False
      __SCREAMING_SNAKE_CASE   :							Optional[int]							      =	False
      __SCREAMING_SNAKE_CASE   :							Tuple							      =	False




      def     a_      (							self							)						->					None:
          UpperCAmelCase									=					FlaxRegNetModelTester(self							)
          UpperCAmelCase									=					ConfigTester(self   ,       config_class=lowercase_   ,       has_text_modality=lowercase_							)




      def     a_      (							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     a_      (							self							)						->					Optional[Any]:
          return




      def     a_      (							self							)						->					Any:
          UpperCAmelCase									=					self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*lowercase_							)




      def     a_      (							self							)						->					List[str]:
          UpperCAmelCase									=					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     a_      (							self							)						->					List[str]:
          pass




      @unittest.skip(reason='RegNet does not support input and output embeddings'							)
      def     a_      (							self							)						->					Optional[int]:
          pass




      def     a_      (							self							)						->					Dict:
          UpperCAmelCase			,							UpperCAmelCase									=					self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
              UpperCAmelCase									=					model_class(lowercase_							)
              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]   ,       lowercase_							)




      def     a_      (							self							)						->					int:
          def check_hidden_states_output(lowercase_   ,       lowercase_   ,       lowercase_							):
              UpperCAmelCase									=					model_class(lowercase_							)

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

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

              UpperCAmelCase									=					self.model_tester.num_stages
              self.assertEqual(len(lowercase_							)   ,       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(lowercase_   ,       lowercase_   ,       lowercase_							)

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

              check_hidden_states_output(lowercase_   ,       lowercase_   ,       lowercase_							)




      def     a_      (							self							)						->					List[Any]:
          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(lowercase_   ,       lowercase_							)
                  UpperCAmelCase									=					model_class(lowercase_							)

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

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

                  with self.subTest('JIT Disabled'							):
                      with jax.disable_jit():
                          UpperCAmelCase									=					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__						(					) ->      Tuple:






    """simple docstring"""




    UpperCAmelCase									=					Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
    return image







@require_flax
class      _UpperCAmelCase							(		unittest.TestCase							):




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




      @slow
      def     a_      (							self							)						->					Union[str, Any]:
          UpperCAmelCase									=					FlaxRegNetForImageClassification.from_pretrained('facebook/regnet-y-040'							)

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

          UpperCAmelCase									=					model(**lowercase_							)

          # verify the logits
          UpperCAmelCase									=					(1, 1_0_0_0)
          self.assertEqual(outputs.logits.shape   ,       lowercase_							)

          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]   ,       lowercase_   ,       atol=1E-4							)							)



