





'''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


__lowerCamelCase : Optional[int]											=			[
    "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.",
]

__lowerCamelCase : Dict											=			[
    "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 							UpperCAmelCase_    (	):

				"""simple docstring"""






				lowercase         = calculate_rouge(lowerCAmelCase_		,							lowerCAmelCase_		,							bootstrap_aggregation=lowerCAmelCase_		,							rouge_keys=["rouge2", "rougeL"]							)
				assert isinstance(lowerCAmelCase_		,							lowerCAmelCase_							)
				lowercase         = calculate_rouge(lowerCAmelCase_		,							lowerCAmelCase_		,							bootstrap_aggregation=lowerCAmelCase_		,							rouge_keys=["rouge2"]							)
				assert (
				    pd.DataFrame(no_aggregation["rouge2"]							).fmeasure.mean()
				    == pd.DataFrame(no_aggregation_just_ra["rouge2"]							).fmeasure.mean()
				)







def 							UpperCAmelCase_    (	):

				"""simple docstring"""






				lowercase         = "rougeLsum"
				lowercase         = calculate_rouge(lowerCAmelCase_		,							lowerCAmelCase_		,							newline_sep=lowerCAmelCase_		,							rouge_keys=[k]							)[k]
				lowercase         = calculate_rouge(lowerCAmelCase_		,							lowerCAmelCase_		,							newline_sep=lowerCAmelCase_		,							rouge_keys=[k]							)[k]
				assert score > score_no_sep







def 							UpperCAmelCase_    (	):

				"""simple docstring"""






				lowercase         = ["rouge1", "rouge2", "rougeL"]
				lowercase         = calculate_rouge(lowerCAmelCase_		,							lowerCAmelCase_		,							newline_sep=lowerCAmelCase_		,							rouge_keys=lowerCAmelCase_							)
				lowercase         = calculate_rouge(lowerCAmelCase_		,							lowerCAmelCase_		,							newline_sep=lowerCAmelCase_		,							rouge_keys=lowerCAmelCase_							)
				assert score_sep == score_no_sep







def 							UpperCAmelCase_    (	):

				"""simple docstring"""






				lowercase         = [
				    "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 .",
				]
				lowercase         = [
				    "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(lowerCAmelCase_		,							lowerCAmelCase_		,							newline_sep=lowerCAmelCase_							) == calculate_rouge(lowerCAmelCase_		,							lowerCAmelCase_		,							newline_sep=lowerCAmelCase_							)







def 							UpperCAmelCase_    (	):

				"""simple docstring"""






				lowercase         = [
				    "\" \"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\" "
				]
				lowercase         = [
				    " 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 ."
				]

				lowercase         = calculate_rouge(lowerCAmelCase_		,							lowerCAmelCase_		,							rouge_keys=["rougeLsum"]		,							newline_sep=lowerCAmelCase_							)["rougeLsum"]
				lowercase         = calculate_rouge(lowerCAmelCase_		,							lowerCAmelCase_		,							rouge_keys=["rougeLsum"]							)["rougeLsum"]
				assert new_score > prev_score







def 							UpperCAmelCase_    (	):

				"""simple docstring"""






				lowercase         = Path("examples/seq2seq/test_data/wmt_en_ro"							)
				lowercase         = calculate_rouge_path(data_dir.joinpath("test.source"							)		,							data_dir.joinpath("test.target"							)							)
				assert isinstance(lowerCAmelCase_		,							lowerCAmelCase_							)
				lowercase         = calculate_rouge_path(
				    data_dir.joinpath("test.source"							)		,							data_dir.joinpath("test.target"							)		,							bootstrap_aggregation=lowerCAmelCase_							)
				assert isinstance(lowerCAmelCase_		,							lowerCAmelCase_							)











'''simple docstring'''

import inspect
import unittest

from transformers import RegNetConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
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 torch import nn

			from transformers import RegNetForImageClassification, RegNetModel
			from transformers.models.regnet.modeling_regnet import REGNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
			from PIL import Image

			from transformers import AutoImageProcessor

class       UpperCAmelCase :




		def __init__(self      :	Dict							,			A__      :	str							,			A__      :	int=3							,			A__      :	Dict=3_2							,			A__      :	str=3							,			A__      :	str=1_0							,			A__      :	Optional[int]=[1_0, 2_0, 3_0, 4_0]							,			A__      :	Tuple=[1, 1, 2, 1]							,			A__      :	int=True							,			A__      :	List[Any]=True							,			A__      :	List[Any]="relu"							,			A__      :	Any=3							,			A__      :	Any=None							,			)					-> Tuple:
						lowercase         = parent
						lowercase         = batch_size
						lowercase         = image_size
						lowercase         = num_channels
						lowercase         = embeddings_size
						lowercase         = hidden_sizes
						lowercase         = depths
						lowercase         = is_training
						lowercase         = use_labels
						lowercase         = hidden_act
						lowercase         = num_labels
						lowercase         = scope
						lowercase         = len(A__				)




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

						lowercase         = None
						if self.use_labels:
										lowercase         = ids_tensor([self.batch_size]							,			self.num_labels				)

						lowercase         = self.get_config()

						return config, pixel_values, labels




		def 				UpperCAmelCase__   (self      :	Optional[Any]				)					-> 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							,			)




		def 				UpperCAmelCase__   (self      :	Dict							,			A__      :	Union[str, Any]							,			A__      :	Optional[int]							,			A__      :	Dict				)					-> str:
						lowercase         = RegNetModel(config=A__				)
						model.to(A__				)
						model.eval()
						lowercase         = model(A__				)
						# expected last hidden states: B, C, H // 32, W // 32
						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 				UpperCAmelCase__   (self      :	List[str]							,			A__      :	List[str]							,			A__      :	Union[str, Any]							,			A__      :	str				)					-> Dict:
						lowercase         = self.num_labels
						lowercase         = RegNetForImageClassification(A__				)
						model.to(A__				)
						model.eval()
						lowercase         = model(A__							,			labels=A__				)
						self.parent.assertEqual(result.logits.shape							,			(self.batch_size, self.num_labels)				)





		def 				UpperCAmelCase__   (self      :	Any				)					-> Union[str, Any]:
						lowercase         = self.prepare_config_and_inputs()
						lowercase   ,							lowercase   ,							lowercase         = config_and_inputs
						lowercase         = {"pixel_values": pixel_values}
						return config, inputs_dict




@require_torch
class       UpperCAmelCase (						_lowercase			,       _lowercase			,       unittest.TestCase  ):
		UpperCAmelCase					:	str 			=		(RegNetModel, RegNetForImageClassification) if is_torch_available() else ()
		UpperCAmelCase					:	Dict 			=		(
		    {'''feature-extraction''': RegNetModel, '''image-classification''': RegNetForImageClassification}
		    if is_torch_available()
		    else {}
		)

		UpperCAmelCase					:	Dict 			=		False
		UpperCAmelCase					:	int 			=		False
		UpperCAmelCase					:	Tuple 			=		False
		UpperCAmelCase					:	Tuple 			=		False




		def 				UpperCAmelCase__   (self      :	Optional[int]				)					-> Tuple:
						lowercase         = RegNetModelTester(self				)
						lowercase         = ConfigTester(self							,			config_class=A__							,			has_text_modality=A__				)




		def 				UpperCAmelCase__   (self      :	List[Any]				)					-> str:
						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      :	Optional[Any]				)					-> int:
						return




		@unittest.skip(reason="RegNet does not use inputs_embeds"				)
		def 				UpperCAmelCase__   (self      :	int				)					-> Optional[int]:
						pass




		@unittest.skip(reason="RegNet does not support input and output embeddings"				)
		def 				UpperCAmelCase__   (self      :	List[Any]				)					-> Optional[Any]:
						pass




		def 				UpperCAmelCase__   (self      :	Any				)					-> str:
						lowercase   ,							lowercase         = self.model_tester.prepare_config_and_inputs_for_common()

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

										lowercase         = ["pixel_values"]
										self.assertListEqual(arg_names[:1]							,			A__				)




		def 				UpperCAmelCase__   (self      :	List[Any]				)					-> Union[str, Any]:
						lowercase         = self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*A__				)




		def 				UpperCAmelCase__   (self      :	List[str]				)					-> Tuple:
						lowercase   ,							lowercase         = self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
										lowercase         = model_class(config=A__				)
										for name, module in model.named_modules():
														if isinstance(A__							,			(nn.BatchNormad, nn.GroupNorm)				):
																		self.assertTrue(
																		    torch.all(module.weight == 1				)							,			msg=f'Parameter {name} of model {model_class} seems not properly initialized'							,			)
																		self.assertTrue(
																		    torch.all(module.bias == 0				)							,			msg=f'Parameter {name} of model {model_class} seems not properly initialized'							,			)




		def 				UpperCAmelCase__   (self      :	Optional[Any]				)					-> List[str]:
						def check_hidden_states_output(A__      :	Optional[Any]							,			A__      :	List[str]							,			A__      :	Tuple				):
										lowercase         = model_class(A__				)
										model.to(A__				)
										model.eval()

										with torch.no_grad():
														lowercase         = model(**self._prepare_for_class(A__							,			A__				)				)

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

										lowercase         = self.model_tester.num_stages
										self.assertEqual(len(A__				)							,			expected_num_stages + 1				)

										# RegNet's feature maps are of shape (batch_size, num_channels, height, width)
										self.assertListEqual(
										    list(hidden_states[0].shape[-2:]				)							,			[self.model_tester.image_size // 2, self.model_tester.image_size // 2]							,			)

						lowercase   ,							lowercase         = self.model_tester.prepare_config_and_inputs_for_common()
						lowercase         = ["basic", "bottleneck"]
						for model_class in self.all_model_classes:
										for layer_type in layers_type:
														lowercase         = layer_type
														lowercase         = True
														check_hidden_states_output(A__							,			A__							,			A__				)

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

														check_hidden_states_output(A__							,			A__							,			A__				)




		def 				UpperCAmelCase__   (self      :	List[Any]				)					-> Tuple:
						lowercase         = self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_image_classification(*A__				)





		@slow
		def 				UpperCAmelCase__   (self      :	Tuple				)					-> Any:
						for model_name in REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
										lowercase         = RegNetModel.from_pretrained(A__				)
										self.assertIsNotNone(A__				)







def 							UpperCAmelCase_    (	):

				"""simple docstring"""






				lowercase         = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"							)
				return image


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




		@cached_property
		def 				UpperCAmelCase__   (self      :	Optional[int]				)					-> Union[str, Any]:
						return (
						    AutoImageProcessor.from_pretrained(REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]				)
						    if is_vision_available()
						    else None
						)





		@slow
		def 				UpperCAmelCase__   (self      :	List[str]				)					-> int:
						lowercase         = RegNetForImageClassification.from_pretrained(REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]				).to(A__				)

						lowercase         = self.default_image_processor
						lowercase         = prepare_img()
						lowercase         = image_processor(images=A__							,			return_tensors="pt"				).to(A__				)

						# forward pass
						with torch.no_grad():
										lowercase         = model(**A__				)

						# verify the logits
						lowercase         = torch.Size((1, 1_0_0_0)				)
						self.assertEqual(outputs.logits.shape							,			A__				)

						lowercase         = torch.tensor([-0.4_1_8_0, -1.5_0_5_1, -3.4_8_3_6]				).to(A__				)

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




