

import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
						import torch

if is_vision_available():
						from PIL import Image

						from transformers import DetrImageProcessor



class   __lowercase    (			unittest.TestCase     ):




			"""simple docstring"""







			def __init__(			self  :					List[str]       ,						lowerCAmelCase__  :					List[str]       ,						lowerCAmelCase__  :					List[str]=7       ,						lowerCAmelCase__  :					Optional[Any]=3       ,						lowerCAmelCase__  :					Any=30       ,						lowerCAmelCase__  :					Dict=400       ,						lowerCAmelCase__  :					Optional[int]=True       ,						lowerCAmelCase__  :					Optional[int]=None       ,						lowerCAmelCase__  :					List[Any]=True       ,						lowerCAmelCase__  :					Any=1 / 255       ,						lowerCAmelCase__  :					int=True       ,						lowerCAmelCase__  :					Dict=[0.5, 0.5, 0.5]       ,						lowerCAmelCase__  :					Optional[Any]=[0.5, 0.5, 0.5]       ,						lowerCAmelCase__  :					str=True       ,						):
									SCREAMING_SNAKE_CASE_:			List[Any]  		=	size if size is not None else {"shortest_edge": 18, "longest_edge": 1333}
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	parent
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	batch_size
									SCREAMING_SNAKE_CASE_:			Dict  		=	num_channels
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	min_resolution
									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	max_resolution
									SCREAMING_SNAKE_CASE_:			int  		=	do_resize
									SCREAMING_SNAKE_CASE_:			Tuple  		=	size
									SCREAMING_SNAKE_CASE_:			Dict  		=	do_rescale
									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	rescale_factor
									SCREAMING_SNAKE_CASE_:			Tuple  		=	do_normalize
									SCREAMING_SNAKE_CASE_:			List[str]  		=	image_mean
									SCREAMING_SNAKE_CASE_:			int  		=	image_std
									SCREAMING_SNAKE_CASE_:			str  		=	do_pad



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Optional[int]):
									return {
									    "do_resize": self.do_resize,
									    "size": self.size,
									    "do_rescale": self.do_rescale,
									    "rescale_factor": self.rescale_factor,
									    "do_normalize": self.do_normalize,
									    "image_mean": self.image_mean,
									    "image_std": self.image_std,
									    "do_pad": self.do_pad,
									}




			def 	_SCREAMING_SNAKE_CASE      (			self  :					Union[str, Any]       ,						lowerCAmelCase__  :					Tuple       ,						lowerCAmelCase__  :					List[str]=False):







									if not batched:
															SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	image_inputs[0]
															if isinstance(lowerCAmelCase__       ,						Image.Image):
																					SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_:			List[Any]  		=	image.size
															else:
																					SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_:			str  		=	image.shape[1], image.shape[2]
															if w < h:
																					SCREAMING_SNAKE_CASE_:			str  		=	int(self.size["shortest_edge"] * h / w)
																					SCREAMING_SNAKE_CASE_:			int  		=	self.size["shortest_edge"]
															elif w > h:
																					SCREAMING_SNAKE_CASE_:			int  		=	self.size["shortest_edge"]
																					SCREAMING_SNAKE_CASE_:			Dict  		=	int(self.size["shortest_edge"] * w / h)
															else:
																					SCREAMING_SNAKE_CASE_:			str  		=	self.size["shortest_edge"]
																					SCREAMING_SNAKE_CASE_:			List[str]  		=	self.size["shortest_edge"]

									else:
															SCREAMING_SNAKE_CASE_:			Dict  		=	[]
															for image in image_inputs:
																					SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_:			Any  		=	self.get_expected_values([image])
																					expected_values.append((expected_height, expected_width))
															SCREAMING_SNAKE_CASE_:			Tuple  		=	max(lowerCAmelCase__       ,						key=lambda lowerCAmelCase__: item[0])[0]
															SCREAMING_SNAKE_CASE_:			List[str]  		=	max(lowerCAmelCase__       ,						key=lambda lowerCAmelCase__: item[1])[1]

									return expected_height, expected_width







@require_torch
@require_vision
class   __lowercase    (			a_		,	unittest.TestCase     ):




			"""simple docstring"""







			_UpperCAmelCase       :							Optional[Any]      							=					DetrImageProcessor if is_vision_available() else None



			def 	_SCREAMING_SNAKE_CASE      (			self  :					str):
									SCREAMING_SNAKE_CASE_:			Any  		=	DetrImageProcessingTester(self)



			@property
			def 	_SCREAMING_SNAKE_CASE      (			self  :					List[str]):
									return self.image_processor_tester.prepare_image_processor_dict()



			def 	_SCREAMING_SNAKE_CASE      (			self  :					str):
									SCREAMING_SNAKE_CASE_:			Any  		=	self.image_processing_class(**self.image_processor_dict)
									self.assertTrue(hasattr(lowerCAmelCase__       ,						"image_mean"))
									self.assertTrue(hasattr(lowerCAmelCase__       ,						"image_std"))
									self.assertTrue(hasattr(lowerCAmelCase__       ,						"do_normalize"))
									self.assertTrue(hasattr(lowerCAmelCase__       ,						"do_rescale"))
									self.assertTrue(hasattr(lowerCAmelCase__       ,						"rescale_factor"))
									self.assertTrue(hasattr(lowerCAmelCase__       ,						"do_resize"))
									self.assertTrue(hasattr(lowerCAmelCase__       ,						"size"))
									self.assertTrue(hasattr(lowerCAmelCase__       ,						"do_pad"))



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Tuple):
									SCREAMING_SNAKE_CASE_:			Tuple  		=	self.image_processing_class.from_dict(self.image_processor_dict)
									self.assertEqual(image_processor.size       ,						{"shortest_edge": 18, "longest_edge": 1333})
									self.assertEqual(image_processor.do_pad       ,						lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_:			Tuple  		=	self.image_processing_class.from_dict(
									    self.image_processor_dict       ,						size=42       ,						max_size=84       ,						pad_and_return_pixel_mask=lowerCAmelCase__)
									self.assertEqual(image_processor.size       ,						{"shortest_edge": 42, "longest_edge": 84})
									self.assertEqual(image_processor.do_pad       ,						lowerCAmelCase__)



			def 	_SCREAMING_SNAKE_CASE      (			self  :					int):
									pass



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Optional[int]):
									SCREAMING_SNAKE_CASE_:			Tuple  		=	self.image_processing_class(**self.image_processor_dict)
									# create random PIL images
									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	prepare_image_inputs(self.image_processor_tester       ,						equal_resolution=lowerCAmelCase__)
									for image in image_inputs:
															self.assertIsInstance(lowerCAmelCase__       ,						Image.Image)

									# Test not batched input
									SCREAMING_SNAKE_CASE_:			List[str]  		=	image_processing(image_inputs[0]       ,						return_tensors="pt").pixel_values

									SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_:			Dict  		=	self.image_processor_tester.get_expected_values(lowerCAmelCase__)

									self.assertEqual(
									    encoded_images.shape       ,						(1, self.image_processor_tester.num_channels, expected_height, expected_width)       ,						)

									# Test batched
									SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_:			List[str]  		=	self.image_processor_tester.get_expected_values(lowerCAmelCase__       ,						batched=lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_:			List[str]  		=	image_processing(lowerCAmelCase__       ,						return_tensors="pt").pixel_values
									self.assertEqual(
									    encoded_images.shape       ,						(
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        expected_height,
									        expected_width,
									    )       ,						)



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Tuple):
									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	self.image_processing_class(**self.image_processor_dict)
									# create random numpy tensors
									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	prepare_image_inputs(self.image_processor_tester       ,						equal_resolution=lowerCAmelCase__       ,						numpify=lowerCAmelCase__)
									for image in image_inputs:
															self.assertIsInstance(lowerCAmelCase__       ,						np.ndarray)

									# Test not batched input
									SCREAMING_SNAKE_CASE_:			List[Any]  		=	image_processing(image_inputs[0]       ,						return_tensors="pt").pixel_values

									SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_:			Tuple  		=	self.image_processor_tester.get_expected_values(lowerCAmelCase__)

									self.assertEqual(
									    encoded_images.shape       ,						(1, self.image_processor_tester.num_channels, expected_height, expected_width)       ,						)

									# Test batched
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	image_processing(lowerCAmelCase__       ,						return_tensors="pt").pixel_values

									SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_:			Any  		=	self.image_processor_tester.get_expected_values(lowerCAmelCase__       ,						batched=lowerCAmelCase__)

									self.assertEqual(
									    encoded_images.shape       ,						(
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        expected_height,
									        expected_width,
									    )       ,						)



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Tuple):
									SCREAMING_SNAKE_CASE_:			str  		=	self.image_processing_class(**self.image_processor_dict)
									# create random PyTorch tensors
									SCREAMING_SNAKE_CASE_:			Tuple  		=	prepare_image_inputs(self.image_processor_tester       ,						equal_resolution=lowerCAmelCase__       ,						torchify=lowerCAmelCase__)
									for image in image_inputs:
															self.assertIsInstance(lowerCAmelCase__       ,						torch.Tensor)

									# Test not batched input
									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	image_processing(image_inputs[0]       ,						return_tensors="pt").pixel_values

									SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_:			Tuple  		=	self.image_processor_tester.get_expected_values(lowerCAmelCase__)

									self.assertEqual(
									    encoded_images.shape       ,						(1, self.image_processor_tester.num_channels, expected_height, expected_width)       ,						)

									# Test batched
									SCREAMING_SNAKE_CASE_:			Tuple  		=	image_processing(lowerCAmelCase__       ,						return_tensors="pt").pixel_values

									SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_:			str  		=	self.image_processor_tester.get_expected_values(lowerCAmelCase__       ,						batched=lowerCAmelCase__)

									self.assertEqual(
									    encoded_images.shape       ,						(
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        expected_height,
									        expected_width,
									    )       ,						)



			@slow
			def 	_SCREAMING_SNAKE_CASE      (			self  :					Optional[int]):
									SCREAMING_SNAKE_CASE_:			str  		=	Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
									with open("./tests/fixtures/tests_samples/COCO/coco_annotations.txt"       ,						"r") as f:
															SCREAMING_SNAKE_CASE_:			List[Any]  		=	json.loads(f.read())

									SCREAMING_SNAKE_CASE_:			str  		=	{"image_id": 3_9769, "annotations": target}

									# encode them
									SCREAMING_SNAKE_CASE_:			str  		=	DetrImageProcessor.from_pretrained("facebook/detr-resnet-50")
									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	image_processing(images=lowerCAmelCase__       ,						annotations=lowerCAmelCase__       ,						return_tensors="pt")

									# verify pixel values
									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	torch.Size([1, 3, 800, 1066])
									self.assertEqual(encoding["pixel_values"].shape       ,						lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_:			List[str]  		=	torch.tensor([0.2796, 0.3138, 0.3481])
									self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]       ,						lowerCAmelCase__       ,						atol=1E-4))

									# verify area
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	torch.tensor([5887.9600, 1_1250.2061, 48_9353.8438, 83_7122.7500, 14_7967.5156, 16_5732.3438])
									self.assertTrue(torch.allclose(encoding["labels"][0]["area"]       ,						lowerCAmelCase__))
									# verify boxes
									SCREAMING_SNAKE_CASE_:			Dict  		=	torch.Size([6, 4])
									self.assertEqual(encoding["labels"][0]["boxes"].shape       ,						lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	torch.tensor([0.5503, 0.2765, 0.0604, 0.2215])
									self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]       ,						lowerCAmelCase__       ,						atol=1E-3))
									# verify image_id
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	torch.tensor([3_9769])
									self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]       ,						lowerCAmelCase__))
									# verify is_crowd
									SCREAMING_SNAKE_CASE_:			str  		=	torch.tensor([0, 0, 0, 0, 0, 0])
									self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]       ,						lowerCAmelCase__))
									# verify class_labels
									SCREAMING_SNAKE_CASE_:			str  		=	torch.tensor([75, 75, 63, 65, 17, 17])
									self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]       ,						lowerCAmelCase__))
									# verify orig_size
									SCREAMING_SNAKE_CASE_:			Dict  		=	torch.tensor([480, 640])
									self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]       ,						lowerCAmelCase__))
									# verify size
									SCREAMING_SNAKE_CASE_:			Tuple  		=	torch.tensor([800, 1066])
									self.assertTrue(torch.allclose(encoding["labels"][0]["size"]       ,						lowerCAmelCase__))




			@slow
			def 	_SCREAMING_SNAKE_CASE      (			self  :					List[str]):
									SCREAMING_SNAKE_CASE_:			int  		=	Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
									with open("./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt"       ,						"r") as f:
															SCREAMING_SNAKE_CASE_:			Tuple  		=	json.loads(f.read())

									SCREAMING_SNAKE_CASE_:			int  		=	{"file_name": "000000039769.png", "image_id": 3_9769, "segments_info": target}

									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	pathlib.Path("./tests/fixtures/tests_samples/COCO/coco_panoptic")

									# encode them
									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	DetrImageProcessor.from_pretrained("facebook/detr-resnet-50-panoptic")
									SCREAMING_SNAKE_CASE_:			int  		=	image_processing(images=lowerCAmelCase__       ,						annotations=lowerCAmelCase__       ,						masks_path=lowerCAmelCase__       ,						return_tensors="pt")

									# verify pixel values
									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	torch.Size([1, 3, 800, 1066])
									self.assertEqual(encoding["pixel_values"].shape       ,						lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_:			str  		=	torch.tensor([0.2796, 0.3138, 0.3481])
									self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]       ,						lowerCAmelCase__       ,						atol=1E-4))

									# verify area
									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	torch.tensor([14_7979.6875, 16_5527.0469, 48_4638.5938, 1_1292.9375, 5879.6562, 7634.1147])
									self.assertTrue(torch.allclose(encoding["labels"][0]["area"]       ,						lowerCAmelCase__))
									# verify boxes
									SCREAMING_SNAKE_CASE_:			Tuple  		=	torch.Size([6, 4])
									self.assertEqual(encoding["labels"][0]["boxes"].shape       ,						lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	torch.tensor([0.2625, 0.5437, 0.4688, 0.8625])
									self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]       ,						lowerCAmelCase__       ,						atol=1E-3))
									# verify image_id
									SCREAMING_SNAKE_CASE_:			int  		=	torch.tensor([3_9769])
									self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]       ,						lowerCAmelCase__))
									# verify is_crowd
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	torch.tensor([0, 0, 0, 0, 0, 0])
									self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]       ,						lowerCAmelCase__))
									# verify class_labels
									SCREAMING_SNAKE_CASE_:			List[str]  		=	torch.tensor([17, 17, 63, 75, 75, 93])
									self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]       ,						lowerCAmelCase__))
									# verify masks
									SCREAMING_SNAKE_CASE_:			Any  		=	82_2873
									self.assertEqual(encoding["labels"][0]["masks"].sum().item()       ,						lowerCAmelCase__)
									# verify orig_size
									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	torch.tensor([480, 640])
									self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]       ,						lowerCAmelCase__))
									# verify size
									SCREAMING_SNAKE_CASE_:			Tuple  		=	torch.tensor([800, 1066])
									self.assertTrue(torch.allclose(encoding["labels"][0]["size"]       ,						lowerCAmelCase__))









import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, BatchEncoding, PLBartTokenizer, is_torch_available
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
)

from ...test_tokenization_common import TokenizerTesterMixin


lowerCAmelCase							:  Any            =						get_tests_dir("""fixtures/test_sentencepiece.model""")


if is_torch_available():
						from transformers.models.plbart.modeling_plbart import shift_tokens_right

lowerCAmelCase							:  Optional[Any]            =						50003
lowerCAmelCase							:  List[str]            =						50002



@require_sentencepiece
@require_tokenizers
class   __lowercase    (			UpperCAmelCase_		,	unittest.TestCase     ):




			"""simple docstring"""







			_UpperCAmelCase       :							int      							=					PLBartTokenizer
			_UpperCAmelCase       :							Any      							=					None
			_UpperCAmelCase       :							Optional[Any]      							=					False



			def 	_SCREAMING_SNAKE_CASE      (			self  :					str):
									super().setUp()

									# We have a SentencePiece fixture for testing
									SCREAMING_SNAKE_CASE_:			Tuple  		=	PLBartTokenizer(lowerCAmelCase__       ,						language_codes="base"       ,						keep_accents=lowerCAmelCase__)
									tokenizer.save_pretrained(self.tmpdirname)



			def 	_SCREAMING_SNAKE_CASE      (			self  :					str):
									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	PLBartTokenizer(lowerCAmelCase__       ,						language_codes="base"       ,						keep_accents=lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_:			Dict  		=	tokenizer.tokenize("This is a test")
									self.assertListEqual(lowerCAmelCase__       ,						["▁This", "▁is", "▁a", "▁t", "est"])

									self.assertListEqual(
									    tokenizer.convert_tokens_to_ids(lowerCAmelCase__)       ,						[value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]]       ,						)

									SCREAMING_SNAKE_CASE_:			List[Any]  		=	tokenizer.tokenize("I was born in 92000, and this is falsé.")
									self.assertListEqual(
									    lowerCAmelCase__       ,						[
									        SPIECE_UNDERLINE + "I",
									        SPIECE_UNDERLINE + "was",
									        SPIECE_UNDERLINE + "b",
									        "or",
									        "n",
									        SPIECE_UNDERLINE + "in",
									        SPIECE_UNDERLINE + "",
									        "9",
									        "2",
									        "0",
									        "0",
									        "0",
									        ",",
									        SPIECE_UNDERLINE + "and",
									        SPIECE_UNDERLINE + "this",
									        SPIECE_UNDERLINE + "is",
									        SPIECE_UNDERLINE + "f",
									        "al",
									        "s",
									        "é",
									        ".",
									    ]       ,						)
									SCREAMING_SNAKE_CASE_:			str  		=	tokenizer.convert_tokens_to_ids(lowerCAmelCase__)
									self.assertListEqual(
									    lowerCAmelCase__       ,						[
									        value + tokenizer.fairseq_offset
									        for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
									    ]       ,						)

									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	tokenizer.convert_ids_to_tokens(lowerCAmelCase__)
									self.assertListEqual(
									    lowerCAmelCase__       ,						[
									        SPIECE_UNDERLINE + "I",
									        SPIECE_UNDERLINE + "was",
									        SPIECE_UNDERLINE + "b",
									        "or",
									        "n",
									        SPIECE_UNDERLINE + "in",
									        SPIECE_UNDERLINE + "",
									        "<unk>",
									        "2",
									        "0",
									        "0",
									        "0",
									        ",",
									        SPIECE_UNDERLINE + "and",
									        SPIECE_UNDERLINE + "this",
									        SPIECE_UNDERLINE + "is",
									        SPIECE_UNDERLINE + "f",
									        "al",
									        "s",
									        "<unk>",
									        ".",
									    ]       ,						)

									SCREAMING_SNAKE_CASE_:			int  		=	tokenizer.vocab_size
									SCREAMING_SNAKE_CASE_:			Tuple  		=	[tokenizer.convert_ids_to_tokens(lowerCAmelCase__) for x in range(end - 4       ,						lowerCAmelCase__)]

									self.assertListEqual(lowerCAmelCase__       ,						["__java__", "__python__", "__en_XX__", "<mask>"])

									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	"java.lang.Exception, python.lang.Exception, javascript, php, ruby, go"
									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	tokenizer(lowerCAmelCase__).input_ids
									self.assertEqual(
									    tokenizer.decode(lowerCAmelCase__       ,						skip_special_tokens=lowerCAmelCase__       ,						clean_up_tokenization_spaces=lowerCAmelCase__)       ,						lowerCAmelCase__       ,						)




			def 	_SCREAMING_SNAKE_CASE      (			self  :					Union[str, Any]):
									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	PLBartTokenizer(lowerCAmelCase__       ,						language_codes="multi"       ,						keep_accents=lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_:			str  		=	tokenizer.tokenize("This is a test")
									self.assertListEqual(lowerCAmelCase__       ,						["▁This", "▁is", "▁a", "▁t", "est"])

									self.assertListEqual(
									    tokenizer.convert_tokens_to_ids(lowerCAmelCase__)       ,						[value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]]       ,						)

									SCREAMING_SNAKE_CASE_:			Tuple  		=	tokenizer.tokenize("I was born in 92000, and this is falsé.")
									self.assertListEqual(
									    lowerCAmelCase__       ,						[
									        SPIECE_UNDERLINE + "I",
									        SPIECE_UNDERLINE + "was",
									        SPIECE_UNDERLINE + "b",
									        "or",
									        "n",
									        SPIECE_UNDERLINE + "in",
									        SPIECE_UNDERLINE + "",
									        "9",
									        "2",
									        "0",
									        "0",
									        "0",
									        ",",
									        SPIECE_UNDERLINE + "and",
									        SPIECE_UNDERLINE + "this",
									        SPIECE_UNDERLINE + "is",
									        SPIECE_UNDERLINE + "f",
									        "al",
									        "s",
									        "é",
									        ".",
									    ]       ,						)
									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	tokenizer.convert_tokens_to_ids(lowerCAmelCase__)
									self.assertListEqual(
									    lowerCAmelCase__       ,						[
									        value + tokenizer.fairseq_offset
									        for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
									    ]       ,						)

									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	tokenizer.convert_ids_to_tokens(lowerCAmelCase__)
									self.assertListEqual(
									    lowerCAmelCase__       ,						[
									        SPIECE_UNDERLINE + "I",
									        SPIECE_UNDERLINE + "was",
									        SPIECE_UNDERLINE + "b",
									        "or",
									        "n",
									        SPIECE_UNDERLINE + "in",
									        SPIECE_UNDERLINE + "",
									        "<unk>",
									        "2",
									        "0",
									        "0",
									        "0",
									        ",",
									        SPIECE_UNDERLINE + "and",
									        SPIECE_UNDERLINE + "this",
									        SPIECE_UNDERLINE + "is",
									        SPIECE_UNDERLINE + "f",
									        "al",
									        "s",
									        "<unk>",
									        ".",
									    ]       ,						)
									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	tokenizer.vocab_size
									SCREAMING_SNAKE_CASE_:			int  		=	[tokenizer.convert_ids_to_tokens(lowerCAmelCase__) for x in range(end - 7       ,						lowerCAmelCase__)]

									self.assertListEqual(
									    lowerCAmelCase__       ,						["__java__", "__python__", "__en_XX__", "__javascript__", "__php__", "__ruby__", "__go__"])
									SCREAMING_SNAKE_CASE_:			str  		=	"java.lang.Exception, python.lang.Exception, javascript, php, ruby, go"
									SCREAMING_SNAKE_CASE_:			Tuple  		=	tokenizer(lowerCAmelCase__).input_ids
									self.assertEqual(
									    tokenizer.decode(lowerCAmelCase__       ,						skip_special_tokens=lowerCAmelCase__       ,						clean_up_tokenization_spaces=lowerCAmelCase__)       ,						lowerCAmelCase__       ,						)







@require_torch
@require_sentencepiece
@require_tokenizers
class   __lowercase    (			unittest.TestCase     ):




			"""simple docstring"""







			_UpperCAmelCase       :							Optional[Any]      							=					'''uclanlp/plbart-python-en_XX'''
			_UpperCAmelCase       :							List[str]      							=					[
			    '''def maximum(a,b,c):NEW_LINE_INDENTreturn max([a,b,c])''',
			    '''def sum(a,b,c):NEW_LINE_INDENTreturn sum([a,b,c])''',
			]
			_UpperCAmelCase       :							int      							=					[
			    '''Returns the maximum value of a b c.''',
			    '''Sums the values of a b c.''',
			]
			_UpperCAmelCase       :							Optional[Any]      							=					[
			    134,
			    5452,
			    3_3460,
			    3_3441,
			    3_3463,
			    3_3465,
			    3_3463,
			    3_3449,
			    988,
			    20,
			    3_3456,
			    19,
			    3_3456,
			    771,
			    39,
			    4258,
			    889,
			    3318,
			    3_3441,
			    3_3463,
			    3_3465,
			    3_3463,
			    3_3449,
			    2471,
			    2,
			    PYTHON_CODE,
			]



			@classmethod
			def 	_SCREAMING_SNAKE_CASE      (			cls  :					Dict):
									SCREAMING_SNAKE_CASE_:			PLBartTokenizer  		=	PLBartTokenizer.from_pretrained(
									    cls.checkpoint_name       ,						language_codes="base"       ,						src_lang="python"       ,						tgt_lang="en_XX")
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	1
									return cls



			def 	_SCREAMING_SNAKE_CASE      (			self  :					List[str]):
									self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["__java__"]       ,						5_0001)
									self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["__python__"]       ,						5_0002)
									self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["__en_XX__"]       ,						5_0003)



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Optional[Any]):
									SCREAMING_SNAKE_CASE_:			Dict  		=	self.tokenizer.batch_encode_plus(self.src_text).input_ids[0]
									self.assertListEqual(self.expected_src_tokens       ,						lowerCAmelCase__)



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Dict):
									self.assertIn(lowerCAmelCase__       ,						self.tokenizer.all_special_ids)
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	[EN_CODE, 9037, 3_3442, 57, 752, 153, 14, 56, 18, 9, 2]
									SCREAMING_SNAKE_CASE_:			int  		=	self.tokenizer.decode(lowerCAmelCase__       ,						skip_special_tokens=lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	self.tokenizer.decode(generated_ids[1:]       ,						skip_special_tokens=lowerCAmelCase__)
									self.assertEqual(lowerCAmelCase__       ,						lowerCAmelCase__)
									self.assertNotIn(self.tokenizer.eos_token       ,						lowerCAmelCase__)



			def 	_SCREAMING_SNAKE_CASE      (			self  :					str):
									SCREAMING_SNAKE_CASE_:			List[str]  		=	["def sum(a,b,c):NEW_LINE_INDENTreturn sum([a,b,c])" * 20]
									self.assertIsInstance(src_text[0]       ,						lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			List[Any]  		=	10
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	self.tokenizer(lowerCAmelCase__       ,						max_length=lowerCAmelCase__       ,						truncation=lowerCAmelCase__).input_ids[0]
									self.assertEqual(ids[-2]       ,						2)
									self.assertEqual(ids[-1]       ,						lowerCAmelCase__)
									self.assertEqual(len(lowerCAmelCase__)       ,						lowerCAmelCase__)



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Tuple):
									self.assertListEqual(self.tokenizer.convert_tokens_to_ids(["<mask>", "__java__"])       ,						[5_0004, 5_0001])



			def 	_SCREAMING_SNAKE_CASE      (			self  :					str):
									SCREAMING_SNAKE_CASE_:			List[Any]  		=	tempfile.mkdtemp()
									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	self.tokenizer.fairseq_tokens_to_ids
									self.tokenizer.save_pretrained(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			int  		=	PLBartTokenizer.from_pretrained(lowerCAmelCase__)
									self.assertDictEqual(new_tok.fairseq_tokens_to_ids       ,						lowerCAmelCase__)



			@require_torch
			def 	_SCREAMING_SNAKE_CASE      (			self  :					int):
									SCREAMING_SNAKE_CASE_:			List[Any]  		=	self.tokenizer(self.src_text       ,						text_target=self.tgt_text       ,						padding=lowerCAmelCase__       ,						return_tensors="pt")
									SCREAMING_SNAKE_CASE_:			Any  		=	shift_tokens_right(batch["labels"]       ,						self.tokenizer.pad_token_id)

									# fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
									self.assertEqual(batch.input_ids[1][-2:].tolist()       ,						[2, PYTHON_CODE])
									self.assertEqual(batch.decoder_input_ids[1][0]       ,						lowerCAmelCase__)
									self.assertEqual(batch.decoder_input_ids[1][-1]       ,						2)
									self.assertEqual(batch.labels[1][-2:].tolist()       ,						[2, EN_CODE])



			@require_torch
			def 	_SCREAMING_SNAKE_CASE      (			self  :					str):
									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	self.tokenizer(
									    self.src_text       ,						text_target=self.tgt_text       ,						padding=lowerCAmelCase__       ,						truncation=lowerCAmelCase__       ,						max_length=len(self.expected_src_tokens)       ,						return_tensors="pt"       ,						)
									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	shift_tokens_right(batch["labels"]       ,						self.tokenizer.pad_token_id)

									self.assertIsInstance(lowerCAmelCase__       ,						lowerCAmelCase__)

									self.assertEqual((2, 26)       ,						batch.input_ids.shape)
									self.assertEqual((2, 26)       ,						batch.attention_mask.shape)
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	batch.input_ids.tolist()[0]
									self.assertListEqual(self.expected_src_tokens       ,						lowerCAmelCase__)
									self.assertEqual(2       ,						batch.decoder_input_ids[0, -1])  # EOS
									# Test that special tokens are reset
									self.assertEqual(self.tokenizer.prefix_tokens       ,						[])
									self.assertEqual(self.tokenizer.suffix_tokens       ,						[self.tokenizer.eos_token_id, PYTHON_CODE])



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Optional[Any]):
									SCREAMING_SNAKE_CASE_:			List[Any]  		=	self.tokenizer(self.src_text       ,						padding=lowerCAmelCase__       ,						truncation=lowerCAmelCase__       ,						max_length=3       ,						return_tensors="pt")
									SCREAMING_SNAKE_CASE_:			Any  		=	self.tokenizer(
									    text_target=self.tgt_text       ,						padding=lowerCAmelCase__       ,						truncation=lowerCAmelCase__       ,						max_length=10       ,						return_tensors="pt")
									SCREAMING_SNAKE_CASE_:			List[Any]  		=	targets["input_ids"]
									SCREAMING_SNAKE_CASE_:			List[Any]  		=	shift_tokens_right(lowerCAmelCase__       ,						self.tokenizer.pad_token_id)

									self.assertEqual(batch.input_ids.shape[1]       ,						3)
									self.assertEqual(batch.decoder_input_ids.shape[1]       ,						10)




			@require_torch
			def 	_SCREAMING_SNAKE_CASE      (			self  :					int):
									SCREAMING_SNAKE_CASE_:			List[str]  		=	self.tokenizer._build_translation_inputs(
									    "A test"       ,						return_tensors="pt"       ,						src_lang="en_XX"       ,						tgt_lang="java")

									self.assertEqual(
									    nested_simplify(lowerCAmelCase__)       ,						{
									        # A, test, EOS, en_XX
									        "input_ids": [[150, 242, 2, 5_0003]],
									        "attention_mask": [[1, 1, 1, 1]],
									        # java
									        "forced_bos_token_id": 5_0001,
									    }       ,						)






