


from collections import defaultdict
from pathlib import Path

import pandas as pd
from rouge_cli import calculate_rouge_path

from utils import calculate_rouge


A							:					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.',
]

A							:					Any     						=     [
    '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 	_lowerCAmelCase       (    )						->	Optional[Any]:


						'''simple docstring'''






						__snake_case				=		calculate_rouge(_lowerCAmelCase      ,	_lowerCAmelCase      ,	bootstrap_aggregation=_lowerCAmelCase      ,	rouge_keys=["rouge2", "rougeL"] )
						assert isinstance(_lowerCAmelCase      ,	_lowerCAmelCase )
						__snake_case				=		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 	_lowerCAmelCase       (    )						->	Tuple:


						'''simple docstring'''






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





def 	_lowerCAmelCase       (    )						->	Any:


						'''simple docstring'''






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





def 	_lowerCAmelCase       (    )						->	List[Any]:


						'''simple docstring'''






						__snake_case				=		[
						    "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 .",
						]
						__snake_case				=		[
						    "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 	_lowerCAmelCase       (    )						->	List[Any]:


						'''simple docstring'''






						__snake_case				=		[
						    "\" \"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\" "
						]
						__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 ."
						]

						__snake_case				=		calculate_rouge(_lowerCAmelCase      ,	_lowerCAmelCase      ,	rouge_keys=["rougeLsum"]      ,	newline_sep=_lowerCAmelCase )["rougeLsum"]
						__snake_case				=		calculate_rouge(_lowerCAmelCase      ,	_lowerCAmelCase      ,	rouge_keys=["rougeLsum"] )["rougeLsum"]
						assert new_score > prev_score





def 	_lowerCAmelCase       (    )						->	List[Any]:


						'''simple docstring'''






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









import inspect
import unittest

from transformers import ViTHybridConfig
from transformers.testing_utils import require_accelerate, require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
	import torch
	from torch import nn

	from transformers import ViTHybridForImageClassification, ViTHybridImageProcessor, ViTHybridModel
	from transformers.models.vit_hybrid.modeling_vit_hybrid import VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
	from PIL import Image






class     UpperCamelCase:


					def __init__(						self	:		List[Any]     ,    SCREAMING_SNAKE_CASE	:		Union[str, Any]     ,    SCREAMING_SNAKE_CASE	:		List[Any]=1_3     ,    SCREAMING_SNAKE_CASE	:		Optional[Any]=6_4     ,    SCREAMING_SNAKE_CASE	:		Union[str, Any]=2     ,    SCREAMING_SNAKE_CASE	:		Union[str, Any]=3     ,    SCREAMING_SNAKE_CASE	:		List[str]=True     ,    SCREAMING_SNAKE_CASE	:		List[Any]=True     ,    SCREAMING_SNAKE_CASE	:		Any=3_2     ,    SCREAMING_SNAKE_CASE	:		Any=5     ,    SCREAMING_SNAKE_CASE	:		List[Any]=4     ,    SCREAMING_SNAKE_CASE	:		Optional[int]=3_7     ,    SCREAMING_SNAKE_CASE	:		Union[str, Any]="gelu"     ,    SCREAMING_SNAKE_CASE	:		Dict=0.1     ,    SCREAMING_SNAKE_CASE	:		Optional[Any]=0.1     ,    SCREAMING_SNAKE_CASE	:		Optional[int]=1_0     ,    SCREAMING_SNAKE_CASE	:		int=0.02     ,    SCREAMING_SNAKE_CASE	:		Dict=[1, 1_6, 4, 4]     ,    SCREAMING_SNAKE_CASE	:		List[str]=None     ,    )			->	int:




											'''simple docstring'''

											__snake_case				=		parent
											__snake_case				=		batch_size
											__snake_case				=		image_size
											__snake_case				=		patch_size
											__snake_case				=		num_channels
											__snake_case				=		is_training
											__snake_case				=		use_labels
											__snake_case				=		hidden_size
											__snake_case				=		num_hidden_layers
											__snake_case				=		num_attention_heads
											__snake_case				=		intermediate_size
											__snake_case				=		hidden_act
											__snake_case				=		hidden_dropout_prob
											__snake_case				=		attention_probs_dropout_prob
											__snake_case				=		type_sequence_label_size
											__snake_case				=		initializer_range
											__snake_case				=		scope
											__snake_case				=		backbone_featmap_shape

											# in ViT hybrid, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
											# the number of patches is based on the feature map of the backbone, which by default uses an output stride
											# of 32, which means that the feature map has a spatial resolution of 1/32 of the input image size
											__snake_case				=		(self.image_size // 3_2) ** 2
											__snake_case				=		num_patches + 1


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Optional[int]						)			->	List[Any]:




											'''simple docstring'''

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

											__snake_case				=		None
											if self.use_labels:
																	__snake_case				=		ids_tensor([self.batch_size]     ,    self.type_sequence_label_size						)

											__snake_case				=		self.get_config()

											return config, pixel_values, labels


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Optional[Any]						)			->	int:




											'''simple docstring'''

											__snake_case				=		{
											    "global_padding": "same",
											    "layer_type": "bottleneck",
											    "depths": [3, 4, 9],
											    "out_features": ["stage1", "stage2", "stage3"],
											    "embedding_dynamic_padding": True,
											    "hidden_sizes": [4, 8, 1_6, 3_2],
											    "num_groups": 2,
											}

											return ViTHybridConfig(
											    image_size=self.image_size     ,    patch_size=self.patch_size     ,    num_channels=self.num_channels     ,    hidden_size=self.hidden_size     ,    num_hidden_layers=self.num_hidden_layers     ,    num_attention_heads=self.num_attention_heads     ,    intermediate_size=self.intermediate_size     ,    hidden_act=self.hidden_act     ,    hidden_dropout_prob=self.hidden_dropout_prob     ,    attention_probs_dropout_prob=self.attention_probs_dropout_prob     ,    is_decoder=SCREAMING_SNAKE_CASE     ,    initializer_range=self.initializer_range     ,    backbone_featmap_shape=self.backbone_featmap_shape     ,    backbone_config=SCREAMING_SNAKE_CASE     ,    )


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Any     ,    SCREAMING_SNAKE_CASE	:		Optional[Any]     ,    SCREAMING_SNAKE_CASE	:		Optional[int]     ,    SCREAMING_SNAKE_CASE	:		str						)			->	int:




											'''simple docstring'''

											__snake_case				=		ViTHybridModel(config=SCREAMING_SNAKE_CASE						)
											model.to(SCREAMING_SNAKE_CASE						)
											model.eval()
											__snake_case				=		model(SCREAMING_SNAKE_CASE						)
											self.parent.assertEqual(result.last_hidden_state.shape     ,    (self.batch_size, self.seq_length, self.hidden_size)						)


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Tuple     ,    SCREAMING_SNAKE_CASE	:		Tuple     ,    SCREAMING_SNAKE_CASE	:		List[Any]     ,    SCREAMING_SNAKE_CASE	:		List[str]						)			->	List[Any]:




											'''simple docstring'''

											__snake_case				=		self.type_sequence_label_size
											__snake_case				=		ViTHybridForImageClassification(SCREAMING_SNAKE_CASE						)
											model.to(SCREAMING_SNAKE_CASE						)
											model.eval()
											__snake_case				=		model(SCREAMING_SNAKE_CASE     ,    labels=SCREAMING_SNAKE_CASE						)
											self.parent.assertEqual(result.logits.shape     ,    (self.batch_size, self.type_sequence_label_size)						)






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




											'''simple docstring'''

											__snake_case				=		self.prepare_config_and_inputs()
											__snake_case		,					__snake_case		,					__snake_case				=		config_and_inputs
											__snake_case				=		{"pixel_values": pixel_values}
											return config, inputs_dict






@require_torch
class     UpperCamelCase(  _a     ,							_a     ,							unittest.TestCase   ):
					snake_case_		:				Union[str, Any]										=	(ViTHybridModel, ViTHybridForImageClassification) if is_torch_available() else ()
					snake_case_		:				str										=	(
					    {"""feature-extraction""": ViTHybridModel, """image-classification""": ViTHybridForImageClassification}
					    if is_torch_available()
					    else {}
					)
					snake_case_		:				Tuple										=	False
					snake_case_		:				Optional[Any]										=	False
					snake_case_		:				Dict										=	False


					def 							SCREAMING_SNAKE_CASE_  (						self	:		List[Any]						)			->	int:




											'''simple docstring'''

											__snake_case				=		ViTHybridModelTester(self						)
											__snake_case				=		ConfigTester(self     ,    config_class=SCREAMING_SNAKE_CASE     ,    has_text_modality=SCREAMING_SNAKE_CASE     ,    hidden_size=3_7						)


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Tuple						)			->	List[Any]:




											'''simple docstring'''

											self.config_tester.run_common_tests()


					@unittest.skip(reason="ViT does not use inputs_embeds"						)
					def 							SCREAMING_SNAKE_CASE_  (						self	:		Optional[int]						)			->	List[Any]:




											'''simple docstring'''

											pass


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Optional[Any]						)			->	int:




											'''simple docstring'''

											__snake_case		,					__snake_case				=		self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
																	__snake_case				=		model_class(SCREAMING_SNAKE_CASE						)
																	self.assertIsInstance(model.get_input_embeddings()     ,    (nn.Module)						)
																	__snake_case				=		model.get_output_embeddings()
																	self.assertTrue(x is None or isinstance(SCREAMING_SNAKE_CASE     ,    nn.Linear						)						)


					def 							SCREAMING_SNAKE_CASE_  (						self	:		str						)			->	Optional[Any]:




											'''simple docstring'''

											__snake_case		,					__snake_case				=		self.model_tester.prepare_config_and_inputs_for_common()

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

																	__snake_case				=		["pixel_values"]
																	self.assertListEqual(arg_names[:1]     ,    SCREAMING_SNAKE_CASE						)


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Any						)			->	Dict:




											'''simple docstring'''

											__snake_case				=		self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE						)


					def 							SCREAMING_SNAKE_CASE_  (						self	:		int						)			->	Optional[Any]:




											'''simple docstring'''

											__snake_case				=		self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_image_classification(*SCREAMING_SNAKE_CASE						)


					def 							SCREAMING_SNAKE_CASE_  (						self	:		List[str]						)			->	List[str]:




											'''simple docstring'''

											__snake_case		,					__snake_case				=		self.model_tester.prepare_config_and_inputs_for_common()

											__snake_case				=		_config_zero_init(SCREAMING_SNAKE_CASE						)
											for model_class in self.all_model_classes:
																	__snake_case				=		model_class(config=SCREAMING_SNAKE_CASE						)
																	# Skip the check for the backbone
																	for name, module in model.named_modules():
																							if module.__class__.__name__ == "ViTHybridPatchEmbeddings":
																													__snake_case				=		[f'''{name}.{key}''' for key in module.state_dict().keys()]
																													break

																	for name, param in model.named_parameters():
																							if param.requires_grad:
																													if name in backbone_params:
																																			continue
																													self.assertIn(
																													    ((param.data.mean() * 1e9).round() / 1e9).item()     ,    [0.0, 1.0]     ,    msg=f'''Parameter {name} of model {model_class} seems not properly initialized'''     ,    )






					@slow
					def 							SCREAMING_SNAKE_CASE_  (						self	:		int						)			->	Tuple:




											'''simple docstring'''

											for model_name in VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																	__snake_case				=		ViTHybridModel.from_pretrained(SCREAMING_SNAKE_CASE						)
																	self.assertIsNotNone(SCREAMING_SNAKE_CASE						)





def 	_lowerCAmelCase       (    )						->	List[str]:


						'''simple docstring'''






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






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


					@cached_property
					def 							SCREAMING_SNAKE_CASE_  (						self	:		List[str]						)			->	int:




											'''simple docstring'''

											return (
											    ViTHybridImageProcessor.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0]						)
											    if is_vision_available()
											    else None
											)


					@slow
					def 							SCREAMING_SNAKE_CASE_  (						self	:		int						)			->	str:




											'''simple docstring'''

											__snake_case				=		ViTHybridForImageClassification.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0]						).to(
											    SCREAMING_SNAKE_CASE						)

											__snake_case				=		self.default_image_processor
											__snake_case				=		prepare_img()
											__snake_case				=		image_processor(images=SCREAMING_SNAKE_CASE     ,    return_tensors="pt"						).to(SCREAMING_SNAKE_CASE						)

											# forward pass
											with torch.no_grad():
																	__snake_case				=		model(**SCREAMING_SNAKE_CASE						)

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

											__snake_case				=		torch.tensor([-1.9090, -0.4993, -0.2389]						).to(SCREAMING_SNAKE_CASE						)

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






					@slow
					@require_accelerate
					def 							SCREAMING_SNAKE_CASE_  (						self	:		List[str]						)			->	Optional[int]:




											'''simple docstring'''

											__snake_case				=		ViTHybridImageProcessor.from_pretrained("google/vit-hybrid-base-bit-384"						)
											__snake_case				=		ViTHybridForImageClassification.from_pretrained("google/vit-hybrid-base-bit-384"     ,    device_map="auto"						)

											__snake_case				=		prepare_img()

											__snake_case				=		image_processor(images=SCREAMING_SNAKE_CASE     ,    return_tensors="pt"						)
											__snake_case				=		model(**SCREAMING_SNAKE_CASE						)
											__snake_case				=		outputs.logits
											# model predicts one of the 1000 ImageNet classes
											__snake_case				=		logits.argmax(-1						).item()

											self.assertTrue(model.config.idalabel[predicted_class_idx]     ,    "tabby, tabby cat"						)





