



'''simple docstring'''




import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartTokenizer, MBartTokenizerFast, 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							:Union[str, Any]       = get_tests_dir('''fixtures/test_sentencepiece.model''')


if is_torch_available():
						from transformers.models.mbart.modeling_mbart import shift_tokens_right

lowerCamelCase							:Dict       = 2_5_0_0_0_4
lowerCamelCase							:Dict       = 2_5_0_0_2_0







@require_sentencepiece
@require_tokenizers
class      _lowerCAmelCase							(      __UpperCAmelCase				,      unittest.TestCase			):
	__SCREAMING_SNAKE_CASE       :		int		      = MBartTokenizer
	__SCREAMING_SNAKE_CASE       :		Optional[Any]		      = MBartTokenizerFast
	__SCREAMING_SNAKE_CASE       :		int		      = True
	__SCREAMING_SNAKE_CASE       :		List[str]		      = True


	def    _a     (self							):
				super().setUp()

				# We have a SentencePiece fixture for testing
				A_     :						Dict     =					MBartTokenizer(lowercase	,				keep_accents=lowercase							)
				tokenizer.save_pretrained(self.tmpdirname							)


	def    _a     (self							):
				A_     :						Any     =					MBartTokenizer(lowercase	,				keep_accents=lowercase							)

				A_     :						int     =					tokenizer.tokenize("""This is a test"""							)
				self.assertListEqual(lowercase	,				["""▁This""", """▁is""", """▁a""", """▁t""", """est"""]							)

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

				A_     :						List[Any]     =					tokenizer.tokenize("""I was born in 92000, and this is falsé."""							)
				self.assertListEqual(
				    lowercase	,				[
				        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""",
				        """.""",
				    ]	,				)
				A_     :						Dict     =					tokenizer.convert_tokens_to_ids(lowercase							)
				self.assertListEqual(
				    lowercase	,				[
				        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]
				        #                                       ^ unk: 2 + 1 = 3                  unk: 2 + 1 = 3 ^
				    ]	,				)

				A_     :						List[str]     =					tokenizer.convert_ids_to_tokens(lowercase							)
				self.assertListEqual(
				    lowercase	,				[
				        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>""",
				        """.""",
				    ]	,				)


	def    _a     (self							):
				if not self.test_slow_tokenizer:
							# as we don't have a slow version, we can't compare the outputs between slow and fast versions
							return

				A_     :						Any     =					(self.rust_tokenizer_class, """hf-internal-testing/tiny-random-mbart""", {})
				for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
							with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})'							):
										A_     :						Union[str, Any]     =					self.rust_tokenizer_class.from_pretrained(lowercase	,				**lowercase							)
										A_     :						List[Any]     =					self.tokenizer_class.from_pretrained(lowercase	,				**lowercase							)

										A_     :						str     =					tempfile.mkdtemp()

										A_     :						Optional[Any]     =					tokenizer_r.save_pretrained(lowercase							)
										A_     :						Dict     =					tokenizer_p.save_pretrained(lowercase							)

										# Checks it save with the same files + the tokenizer.json file for the fast one
										self.assertTrue(any("""tokenizer.json""" in f for f in tokenizer_r_files							)							)
										A_     :						str     =					tuple(f for f in tokenizer_r_files if """tokenizer.json""" not in f							)
										self.assertSequenceEqual(lowercase	,				lowercase							)

										# Checks everything loads correctly in the same way
										A_     :						Optional[int]     =					tokenizer_r.from_pretrained(lowercase							)
										A_     :						Dict     =					tokenizer_p.from_pretrained(lowercase							)

										# Check special tokens are set accordingly on Rust and Python
										for key in tokenizer_pp.special_tokens_map:
													self.assertTrue(hasattr(lowercase	,				lowercase							)							)
													# self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
													# self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))

										shutil.rmtree(lowercase							)

										# Save tokenizer rust, legacy_format=True
										A_     :						Union[str, Any]     =					tempfile.mkdtemp()

										A_     :						Union[str, Any]     =					tokenizer_r.save_pretrained(lowercase	,				legacy_format=lowercase							)
										A_     :						Dict     =					tokenizer_p.save_pretrained(lowercase							)

										# Checks it save with the same files
										self.assertSequenceEqual(lowercase	,				lowercase							)

										# Checks everything loads correctly in the same way
										A_     :						Optional[int]     =					tokenizer_r.from_pretrained(lowercase							)
										A_     :						Any     =					tokenizer_p.from_pretrained(lowercase							)

										# Check special tokens are set accordingly on Rust and Python
										for key in tokenizer_pp.special_tokens_map:
													self.assertTrue(hasattr(lowercase	,				lowercase							)							)

										shutil.rmtree(lowercase							)

										# Save tokenizer rust, legacy_format=False
										A_     :						List[str]     =					tempfile.mkdtemp()

										A_     :						List[Any]     =					tokenizer_r.save_pretrained(lowercase	,				legacy_format=lowercase							)
										A_     :						List[Any]     =					tokenizer_p.save_pretrained(lowercase							)

										# Checks it saved the tokenizer.json file
										self.assertTrue(any("""tokenizer.json""" in f for f in tokenizer_r_files							)							)

										# Checks everything loads correctly in the same way
										A_     :						List[str]     =					tokenizer_r.from_pretrained(lowercase							)
										A_     :						List[str]     =					tokenizer_p.from_pretrained(lowercase							)

										# Check special tokens are set accordingly on Rust and Python
										for key in tokenizer_pp.special_tokens_map:
													self.assertTrue(hasattr(lowercase	,				lowercase							)							)

										shutil.rmtree(lowercase							)







@require_torch
@require_sentencepiece
@require_tokenizers
class      _lowerCAmelCase							(      unittest.TestCase			):
	__SCREAMING_SNAKE_CASE       :		Tuple		      = 'facebook/mbart-large-en-ro'
	__SCREAMING_SNAKE_CASE       :		Optional[int]		      = [
	    ' UN Chief Says There Is No Military Solution in Syria',
	    ' Secretary-General Ban Ki-moon says his response to Russia\'s stepped up military support for Syria is that "there is no military solution" to the nearly five-year conflict and more weapons will only worsen the violence and misery for millions of people.',
	]
	__SCREAMING_SNAKE_CASE       :		Union[str, Any]		      = [
	    'Şeful ONU declară că nu există o soluţie militară în Siria',
	    'Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al Rusiei'
	    ' pentru Siria este că "nu există o soluţie militară" la conflictul de aproape cinci ani şi că noi arme nu vor'
	    ' face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.',
	]
	__SCREAMING_SNAKE_CASE       :		Union[str, Any]		      = [8_274, 127_873, 25_916, 7, 8_622, 2_071, 438, 67_485, 53, 187_895, 23, 51_712, 2, EN_CODE]


	@classmethod
	def    _a     (cls							):
				A_     :						MBartTokenizer     =					MBartTokenizer.from_pretrained(
				    cls.checkpoint_name	,				src_lang="""en_XX"""	,				tgt_lang="""ro_RO"""							)
				A_     :						str     =					1
				return cls


	def    _a     (self							):
				self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""ar_AR"""]	,				250001							)
				self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""en_EN"""]	,				250004							)
				self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""ro_RO"""]	,				250020							)


	def    _a     (self							):
				A_     :						Any     =					self.tokenizer.batch_encode_plus(self.src_text							).input_ids[0]
				self.assertListEqual(self.expected_src_tokens	,				lowercase							)


	def    _a     (self							):
				self.assertIn(lowercase	,				self.tokenizer.all_special_ids							)
				A_     :						List[str]     =					[RO_CODE, 884, 9019, 96, 9, 916, 86792, 36, 18743, 15596, 5, 2]
				A_     :						Optional[Any]     =					self.tokenizer.decode(lowercase	,				skip_special_tokens=lowercase							)
				A_     :						str     =					self.tokenizer.decode(generated_ids[1:]	,				skip_special_tokens=lowercase							)
				self.assertEqual(lowercase	,				lowercase							)
				self.assertNotIn(self.tokenizer.eos_token	,				lowercase							)


	def    _a     (self							):
				A_     :						Optional[int]     =					["""this is gunna be a long sentence """ * 20]
				assert isinstance(src_text[0]	,				lowercase							)
				A_     :						str     =					10
				A_     :						List[str]     =					self.tokenizer(lowercase	,				max_length=lowercase	,				truncation=lowercase							).input_ids[0]
				self.assertEqual(ids[-2]	,				2							)
				self.assertEqual(ids[-1]	,				lowercase							)
				self.assertEqual(len(lowercase							)	,				lowercase							)


	def    _a     (self							):
				self.assertListEqual(self.tokenizer.convert_tokens_to_ids(["""<mask>""", """ar_AR"""]							)	,				[250026, 250001]							)


	def    _a     (self							):
				A_     :						int     =					tempfile.mkdtemp()
				A_     :						Any     =					self.tokenizer.fairseq_tokens_to_ids
				self.tokenizer.save_pretrained(lowercase							)
				A_     :						Optional[Any]     =					MBartTokenizer.from_pretrained(lowercase							)
				self.assertDictEqual(new_tok.fairseq_tokens_to_ids	,				lowercase							)


	@require_torch
	def    _a     (self							):
				A_     :						Dict     =					self.tokenizer(self.src_text	,				text_target=self.tgt_text	,				padding=lowercase	,				return_tensors="""pt"""							)
				A_     :						Optional[Any]     =					shift_tokens_right(batch["""labels"""]	,				self.tokenizer.pad_token_id							)

				# fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
				assert batch.input_ids[1][-2:].tolist() == [2, EN_CODE]
				assert batch.decoder_input_ids[1][0].tolist() == RO_CODE
				assert batch.decoder_input_ids[1][-1] == 2
				assert batch.labels[1][-2:].tolist() == [2, RO_CODE]


	@require_torch
	def    _a     (self							):
				A_     :						List[str]     =					self.tokenizer(
				    self.src_text	,				text_target=self.tgt_text	,				padding=lowercase	,				truncation=lowercase	,				max_length=len(self.expected_src_tokens							)	,				return_tensors="""pt"""	,				)

				A_     :						List[str]     =					shift_tokens_right(batch["""labels"""]	,				self.tokenizer.pad_token_id							)

				self.assertIsInstance(lowercase	,				lowercase							)

				self.assertEqual((2, 14)	,				batch.input_ids.shape							)
				self.assertEqual((2, 14)	,				batch.attention_mask.shape							)
				A_     :						Optional[int]     =					batch.input_ids.tolist()[0]
				self.assertListEqual(self.expected_src_tokens	,				lowercase							)
				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, EN_CODE]							)


	def    _a     (self							):
				A_     :						Union[str, Any]     =					self.tokenizer(self.src_text	,				padding=lowercase	,				truncation=lowercase	,				max_length=3	,				return_tensors="""pt"""							)
				A_     :						Any     =					self.tokenizer(
				    text_target=self.tgt_text	,				padding=lowercase	,				truncation=lowercase	,				max_length=10	,				return_tensors="""pt"""							)
				A_     :						Optional[Any]     =					targets["""input_ids"""]
				A_     :						Union[str, Any]     =					shift_tokens_right(lowercase	,				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    _a     (self							):
				A_     :						int     =					self.tokenizer._build_translation_inputs(
				    """A test"""	,				return_tensors="""pt"""	,				src_lang="""en_XX"""	,				tgt_lang="""ar_AR"""							)

				self.assertEqual(
				    nested_simplify(lowercase							)	,				{
				        # A, test, EOS, en_XX
				        """input_ids""": [[62, 3034, 2, 250004]],
				        """attention_mask""": [[1, 1, 1, 1]],
				        # ar_AR
				        """forced_bos_token_id""": 250001,
				    }	,				)




'''simple docstring'''




from typing import Dict, List, Optional, Tuple, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    flip_channel_order,
    get_resize_output_image_size,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, is_vision_available, logging


if is_vision_available():
						import PIL

if is_torch_available():
						import torch


lowerCamelCase							:str       = logging.get_logger(__name__)







class      _lowerCAmelCase							(      __UpperCAmelCase			):
	__SCREAMING_SNAKE_CASE       :		Tuple		      = ['pixel_values']


	def __init__(self	,				lowercase = True	,				lowercase = None	,				lowercase = PILImageResampling.BILINEAR	,				lowercase = True	,				lowercase = 1 / 255	,				lowercase = True	,				lowercase = None	,				lowercase = True	,				**lowercase	,				):
				super().__init__(**lowercase							)
				A_     :						Union[str, Any]     =					size if size is not None else {"""shortest_edge""": 224}
				A_     :						Union[str, Any]     =					get_size_dict(lowercase	,				default_to_square=lowercase							)
				A_     :						Optional[Any]     =					crop_size if crop_size is not None else {"""height""": 256, """width""": 256}
				A_     :						Tuple     =					get_size_dict(lowercase	,				param_name="""crop_size"""							)

				A_     :						List[Any]     =					do_resize
				A_     :						List[str]     =					size
				A_     :						Dict     =					resample
				A_     :						int     =					do_rescale
				A_     :						str     =					rescale_factor
				A_     :						Tuple     =					do_center_crop
				A_     :						Tuple     =					crop_size
				A_     :						List[str]     =					do_flip_channel_order


	def    _a     (self	,				lowercase	,				lowercase	,				lowercase = PIL.Image.BILINEAR	,				lowercase = None	,				**lowercase	,				):
				A_     :						Any     =					get_size_dict(lowercase	,				default_to_square=lowercase							)
				if "shortest_edge" not in size:
							raise ValueError(F'The `size` dictionary must contain the key `shortest_edge`. Got {size.keys()}'							)
				A_     :						Any     =					get_resize_output_image_size(lowercase	,				size=size["""shortest_edge"""]	,				default_to_square=lowercase							)
				return resize(lowercase	,				size=lowercase	,				resample=lowercase	,				data_format=lowercase	,				**lowercase							)


	def    _a     (self	,				lowercase	,				lowercase	,				lowercase = None	,				**lowercase	,				):
				A_     :						Tuple     =					get_size_dict(lowercase							)
				if "height" not in size or "width" not in size:
							raise ValueError(F'The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}'							)
				return center_crop(lowercase	,				size=(size["""height"""], size["""width"""])	,				data_format=lowercase	,				**lowercase							)


	def    _a     (self	,				lowercase	,				lowercase	,				lowercase = None	,				**lowercase	,				):
				return rescale(lowercase	,				scale=lowercase	,				data_format=lowercase	,				**lowercase							)


	def    _a     (self	,				lowercase	,				lowercase = None							):
				return flip_channel_order(lowercase	,				data_format=lowercase							)


	def    _a     (self	,				lowercase	,				lowercase = None	,				lowercase = None	,				lowercase = None	,				lowercase = None	,				lowercase = None	,				lowercase = None	,				lowercase = None	,				lowercase = None	,				lowercase = None	,				lowercase = ChannelDimension.FIRST	,				**lowercase	,				):
				A_     :						str     =					do_resize if do_resize is not None else self.do_resize
				A_     :						Optional[int]     =					resample if resample is not None else self.resample
				A_     :						str     =					do_rescale if do_rescale is not None else self.do_rescale
				A_     :						str     =					rescale_factor if rescale_factor is not None else self.rescale_factor
				A_     :						str     =					do_center_crop if do_center_crop is not None else self.do_center_crop
				A_     :						Dict     =					(
				    do_flip_channel_order if do_flip_channel_order is not None else self.do_flip_channel_order
				)

				A_     :						Union[str, Any]     =					size if size is not None else self.size
				A_     :						Dict     =					get_size_dict(lowercase	,				default_to_square=lowercase							)
				A_     :						Any     =					crop_size if crop_size is not None else self.crop_size
				A_     :						Union[str, Any]     =					get_size_dict(lowercase	,				param_name="""crop_size"""							)

				A_     :						Union[str, Any]     =					make_list_of_images(lowercase							)

				if not valid_images(lowercase							):
							raise ValueError(
							    """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
							    """torch.Tensor, tf.Tensor or jax.ndarray."""							)

				if do_resize and size is None:
							raise ValueError("""Size must be specified if do_resize is True."""							)

				if do_rescale and rescale_factor is None:
							raise ValueError("""Rescale factor must be specified if do_rescale is True."""							)

				if do_center_crop and crop_size is None:
							raise ValueError("""Crop size must be specified if do_center_crop is True."""							)

				# All transformations expect numpy arrays.
				A_     :						Optional[Any]     =					[to_numpy_array(lowercase							) for image in images]

				if do_resize:
							A_     :						List[Any]     =					[self.resize(image=lowercase	,				size=lowercase	,				resample=lowercase							) for image in images]

				if do_center_crop:
							A_     :						str     =					[self.center_crop(image=lowercase	,				size=lowercase							) for image in images]

				if do_rescale:
							A_     :						Dict     =					[self.rescale(image=lowercase	,				scale=lowercase							) for image in images]

				# the pretrained checkpoints assume images are BGR, not RGB
				if do_flip_channel_order:
							A_     :						List[str]     =					[self.flip_channel_order(image=lowercase							) for image in images]

				A_     :						List[str]     =					[to_channel_dimension_format(lowercase	,				lowercase							) for image in images]

				A_     :						str     =					{"""pixel_values""": images}
				return BatchFeature(data=lowercase	,				tensor_type=lowercase							)


	def    _a     (self	,				lowercase	,				lowercase = None							):
				A_     :						Optional[Any]     =					outputs.logits

				# Resize logits and compute semantic segmentation maps
				if target_sizes is not None:
							if len(lowercase							) != len(lowercase							):
										raise ValueError(
										    """Make sure that you pass in as many target sizes as the batch dimension of the logits"""							)

							if is_torch_tensor(lowercase							):
										A_     :						Dict     =					target_sizes.numpy()

							A_     :						Union[str, Any]     =					[]

							for idx in range(len(lowercase							)							):
										A_     :						Union[str, Any]     =					torch.nn.functional.interpolate(
										    logits[idx].unsqueeze(dim=0							)	,				size=target_sizes[idx]	,				mode="""bilinear"""	,				align_corners=lowercase							)
										A_     :						Any     =					resized_logits[0].argmax(dim=0							)
										semantic_segmentation.append(lowercase							)
				else:
							A_     :						str     =					logits.argmax(dim=1							)
							A_     :						Optional[Any]     =					[semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]							)]

				return semantic_segmentation