


"""simple docstring"""


import shutil
import tempfile
import unittest

from transformers import (
    SPIECE_UNDERLINE,
    AddedToken,
    BatchEncoding,
    NllbTokenizer,
    NllbTokenizerFast,
    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


A     :   Optional[Any]			    =			get_tests_dir("fixtures/test_sentencepiece.model")


if is_torch_available():
	from transformers.models.mam_aaa.modeling_mam_aaa import shift_tokens_right

A     :   Tuple			    =			2_5_6_0_4_7
A     :   Dict			    =			2_5_6_1_4_5
@require_sentencepiece
@require_tokenizers
class   _UpperCamelCase  (   lowerCAmelCase__       ,unittest.TestCase		):
							'''simple docstring'''







							__UpperCAmelCase						:    Dict				      =NllbTokenizer
							__UpperCAmelCase						:    str				      =NllbTokenizerFast
							__UpperCAmelCase						:    str				      =True
							__UpperCAmelCase						:    int				      =True
							__UpperCAmelCase						:    List[Any]				      ={}

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

										# We have a SentencePiece fixture for testing
										__lowerCAmelCase				    =			NllbTokenizer(__a    ,   keep_accents=__a	)
										tokenizer.save_pretrained(self.tmpdirname	)

							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			NllbTokenizer(__a    ,   keep_accents=__a	)

										__lowerCAmelCase				    =			tokenizer.tokenize("This is a test"	)
										self.assertListEqual(__a    ,   ["▁This", "▁is", "▁a", "▁t", "est"]	)

										self.assertListEqual(
										    tokenizer.convert_tokens_to_ids(__a	)    ,   [value + tokenizer.fairseq_offset for value in [2_85, 46, 10, 1_70, 3_82]]    ,   )

										__lowerCAmelCase				    =			tokenizer.tokenize("I was born in 92000, and this is falsé."	)
										self.assertListEqual(
										    __a    ,   [
										        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",
										        ".",
										    ]    ,   )
										__lowerCAmelCase				    =			tokenizer.convert_tokens_to_ids(__a	)
										self.assertListEqual(
										    __a    ,   [
										        value + tokenizer.fairseq_offset
										        for value in [8, 21, 84, 55, 24, 19, 7, 2, 6_02, 3_47, 3_47, 3_47, 3, 12, 66, 46, 72, 80, 6, 2, 4]
										    ]    ,   )

										__lowerCAmelCase				    =			tokenizer.convert_ids_to_tokens(__a	)
										self.assertListEqual(
										    __a    ,   [
										        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 			snake_case				(     self	):
										__lowerCAmelCase				    =			(self.rust_tokenizer_class, "hf-internal-testing/tiny-random-nllb", {})
										for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
													with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"	):
																__lowerCAmelCase				    =			self.rust_tokenizer_class.from_pretrained(__a    ,   **__a	)
																__lowerCAmelCase				    =			self.tokenizer_class.from_pretrained(__a    ,   **__a	)

																__lowerCAmelCase				    =			tempfile.mkdtemp()

																__lowerCAmelCase				    =			tokenizer_r.save_pretrained(__a	)
																__lowerCAmelCase				    =			tokenizer_p.save_pretrained(__a	)

																# 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	)	)
																__lowerCAmelCase				    =			tuple(f for f in tokenizer_r_files if "tokenizer.json" not in f	)
																self.assertSequenceEqual(__a    ,   __a	)

																# Checks everything loads correctly in the same way
																__lowerCAmelCase				    =			tokenizer_r.from_pretrained(__a	)
																__lowerCAmelCase				    =			tokenizer_p.from_pretrained(__a	)

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

																shutil.rmtree(__a	)

																# Save tokenizer rust, legacy_format=True
																__lowerCAmelCase				    =			tempfile.mkdtemp()

																__lowerCAmelCase				    =			tokenizer_r.save_pretrained(__a    ,   legacy_format=__a	)
																__lowerCAmelCase				    =			tokenizer_p.save_pretrained(__a	)

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

																# Checks everything loads correctly in the same way
																__lowerCAmelCase				    =			tokenizer_r.from_pretrained(__a	)
																__lowerCAmelCase				    =			tokenizer_p.from_pretrained(__a	)

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

																shutil.rmtree(__a	)

																# Save tokenizer rust, legacy_format=False
																__lowerCAmelCase				    =			tempfile.mkdtemp()

																__lowerCAmelCase				    =			tokenizer_r.save_pretrained(__a    ,   legacy_format=__a	)
																__lowerCAmelCase				    =			tokenizer_p.save_pretrained(__a	)

																# 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
																__lowerCAmelCase				    =			tokenizer_r.from_pretrained(__a	)
																__lowerCAmelCase				    =			tokenizer_p.from_pretrained(__a	)

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

																shutil.rmtree(__a	)

							@require_torch
							def 			snake_case				(     self	):
										if not self.test_seqaseq:
													return

										__lowerCAmelCase				    =			self.get_tokenizers()
										for tokenizer in tokenizers:
													with self.subTest(f"{tokenizer.__class__.__name__}"	):
																# Longer text that will definitely require truncation.
																__lowerCAmelCase				    =			[
																    " 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.",
																]
																__lowerCAmelCase				    =			[
																    "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.",
																]
																try:
																			__lowerCAmelCase				    =			tokenizer.prepare_seqaseq_batch(
																			    src_texts=__a    ,   tgt_texts=__a    ,   max_length=3    ,   max_target_length=10    ,   return_tensors="pt"    ,   src_lang="eng_Latn"    ,   tgt_lang="ron_Latn"    ,   )
																except NotImplementedError:
																			return
																self.assertEqual(batch.input_ids.shape[1]    ,   3	)
																self.assertEqual(batch.labels.shape[1]    ,   10	)
																# max_target_length will default to max_length if not specified
																__lowerCAmelCase				    =			tokenizer.prepare_seqaseq_batch(
																    __a    ,   tgt_texts=__a    ,   max_length=3    ,   return_tensors="pt"	)
																self.assertEqual(batch.input_ids.shape[1]    ,   3	)
																self.assertEqual(batch.labels.shape[1]    ,   3	)

																__lowerCAmelCase				    =			tokenizer.prepare_seqaseq_batch(
																    src_texts=__a    ,   max_length=3    ,   max_target_length=10    ,   return_tensors="pt"	)
																self.assertEqual(batch_encoder_only.input_ids.shape[1]    ,   3	)
																self.assertEqual(batch_encoder_only.attention_mask.shape[1]    ,   3	)
																self.assertNotIn("decoder_input_ids"    ,   __a	)

							@unittest.skip("Unfortunately way too slow to build a BPE with SentencePiece."	)
							def 			snake_case				(     self	):
										pass



							def 			snake_case				(     self	):
										for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
													with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"	):
																__lowerCAmelCase				    =			[AddedToken("<special>"    ,   lstrip=__a	)]

																__lowerCAmelCase				    =			self.rust_tokenizer_class.from_pretrained(
																    __a    ,   additional_special_tokens=__a    ,   **__a	)
																__lowerCAmelCase				    =			tokenizer_r.encode("Hey this is a <special> token"	)

																__lowerCAmelCase				    =			tokenizer_r.encode("<special>"    ,   add_special_tokens=__a	)[0]

																self.assertTrue(special_token_id in r_output	)

																if self.test_slow_tokenizer:
																			__lowerCAmelCase				    =			self.rust_tokenizer_class.from_pretrained(
																			    __a    ,   additional_special_tokens=__a    ,   **__a    ,   )
																			__lowerCAmelCase				    =			self.tokenizer_class.from_pretrained(
																			    __a    ,   additional_special_tokens=__a    ,   **__a	)

																			__lowerCAmelCase				    =			tokenizer_p.encode("Hey this is a <special> token"	)

																			__lowerCAmelCase				    =			tokenizer_cr.encode("Hey this is a <special> token"	)

																			self.assertEqual(__a    ,   __a	)
																			self.assertEqual(__a    ,   __a	)
																			self.assertTrue(special_token_id in p_output	)
																			self.assertTrue(special_token_id in cr_output	)
@require_torch
@require_sentencepiece
@require_tokenizers
class   _UpperCamelCase  (   unittest.TestCase		):
							'''simple docstring'''







							__UpperCAmelCase						:    Tuple				      ="""facebook/nllb-200-distilled-600M"""
							__UpperCAmelCase						:    Any				      =[
							    """ 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.""",
							]
							__UpperCAmelCase						:    List[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.""",
							]
							__UpperCAmelCase						:    str				      =[
							    2_5_6_0_4_7,
							    1_6_2_9_7,
							    1_3_4_4_0_8,
							    8_1_6_5,
							    2_4_8_0_6_6,
							    1_4_7_3_4,
							    9_5_0,
							    1_1_3_5,
							    1_0_5_7_2_1,
							    3_5_7_3,
							    8_3,
							    2_7_3_5_2,
							    1_0_8,
							    4_9_4_8_6,
							    2,
							]

							@classmethod
							def 			snake_case				(     cls	):
										__lowerCAmelCase				    =			NllbTokenizer.from_pretrained(
										    cls.checkpoint_name    ,   src_lang="eng_Latn"    ,   tgt_lang="ron_Latn"	)
										__lowerCAmelCase				    =			1
										return cls

							def 			snake_case				(     self	):
										self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["ace_Arab"]    ,   25_60_01	)
										self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["ace_Latn"]    ,   25_60_02	)
										self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["fra_Latn"]    ,   25_60_57	)

							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			self.tokenizer.batch_encode_plus(self.src_text	).input_ids[0]
										self.assertListEqual(self.expected_src_tokens    ,   __a	)

							def 			snake_case				(     self	):
										self.assertIn(__a    ,   self.tokenizer.all_special_ids	)
										# fmt: off
										__lowerCAmelCase				    =			[RO_CODE, 42_54, 9_80_68, 11_29_23, 3_90_72, 39_09, 7_13, 10_27_67, 26, 1_73_14, 3_56_42, 1_46_83, 3_31_18, 20_22, 6_69_87, 2, 25_60_47]
										# fmt: on

										__lowerCAmelCase				    =			self.tokenizer.decode(__a    ,   skip_special_tokens=__a	)
										__lowerCAmelCase				    =			self.tokenizer.decode(generated_ids[1:]    ,   skip_special_tokens=__a	)
										self.assertEqual(__a    ,   __a	)
										self.assertNotIn(self.tokenizer.eos_token    ,   __a	)

							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			["this is gunna be a long sentence " * 20]
										assert isinstance(src_text[0]    ,   __a	)
										__lowerCAmelCase				    =			10
										__lowerCAmelCase				    =			self.tokenizer(__a    ,   max_length=__a    ,   truncation=__a	).input_ids[0]
										self.assertEqual(ids[-1]    ,   2	)
										self.assertEqual(ids[0]    ,   __a	)
										self.assertEqual(len(__a	)    ,   __a	)

							def 			snake_case				(     self	):
										self.assertListEqual(self.tokenizer.convert_tokens_to_ids(["<mask>", "ar_AR"]	)    ,   [25_62_03, 3]	)

							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			tempfile.mkdtemp()
										__lowerCAmelCase				    =			self.tokenizer.fairseq_tokens_to_ids
										self.tokenizer.save_pretrained(__a	)
										__lowerCAmelCase				    =			NllbTokenizer.from_pretrained(__a	)
										self.assertDictEqual(new_tok.fairseq_tokens_to_ids    ,   __a	)

							@require_torch
							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			self.tokenizer(
										    self.src_text    ,   text_target=self.tgt_text    ,   padding=__a    ,   truncation=__a    ,   max_length=len(self.expected_src_tokens	)    ,   return_tensors="pt"    ,   )
										__lowerCAmelCase				    =			shift_tokens_right(
										    batch["labels"]    ,   self.tokenizer.pad_token_id    ,   self.tokenizer.lang_code_to_id["ron_Latn"]	)

										self.assertIsInstance(__a    ,   __a	)

										self.assertEqual((2, 15)    ,   batch.input_ids.shape	)
										self.assertEqual((2, 15)    ,   batch.attention_mask.shape	)
										__lowerCAmelCase				    =			batch.input_ids.tolist()[0]
										self.assertListEqual(self.expected_src_tokens    ,   __a	)
										self.assertEqual(__a    ,   batch.decoder_input_ids[0, 0]	)  # EOS
										# Test that special tokens are reset
										self.assertEqual(self.tokenizer.prefix_tokens    ,   [EN_CODE]	)
										self.assertEqual(self.tokenizer.suffix_tokens    ,   [self.tokenizer.eos_token_id]	)

							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			self.tokenizer(self.src_text    ,   padding=__a    ,   truncation=__a    ,   max_length=3    ,   return_tensors="pt"	)
										__lowerCAmelCase				    =			self.tokenizer(
										    text_target=self.tgt_text    ,   padding=__a    ,   truncation=__a    ,   max_length=10    ,   return_tensors="pt"	)
										__lowerCAmelCase				    =			targets["input_ids"]
										__lowerCAmelCase				    =			shift_tokens_right(
										    __a    ,   self.tokenizer.pad_token_id    ,   decoder_start_token_id=self.tokenizer.lang_code_to_id[self.tokenizer.tgt_lang]    ,   )

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

							@require_torch
							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			self.tokenizer._build_translation_inputs(
										    "A test"    ,   return_tensors="pt"    ,   src_lang="eng_Latn"    ,   tgt_lang="fra_Latn"	)

										self.assertEqual(
										    nested_simplify(__a	)    ,   {
										        # A, test, EOS, en_XX
										        "input_ids": [[25_60_47, 70, 73_56, 2]],
										        "attention_mask": [[1, 1, 1, 1]],
										        # ar_AR
										        "forced_bos_token_id": 25_60_57,
										    }    ,   )



							@require_torch
							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			True
										__lowerCAmelCase				    =			self.tokenizer(
										    "UN Chief says there is no military solution in Syria"    ,   src_lang="eng_Latn"    ,   tgt_lang="fra_Latn"	)
										self.assertEqual(
										    inputs.input_ids    ,   [1_62_97, 13_44_08, 2_56_53, 63_70, 2_48, 2_54, 10_39_29, 9_49_95, 1_08, 4_94_86, 2, 25_60_47]	)

										__lowerCAmelCase				    =			False
										__lowerCAmelCase				    =			self.tokenizer(
										    "UN Chief says there is no military solution in Syria"    ,   src_lang="eng_Latn"    ,   tgt_lang="fra_Latn"	)
										self.assertEqual(
										    inputs.input_ids    ,   [25_60_47, 1_62_97, 13_44_08, 2_56_53, 63_70, 2_48, 2_54, 10_39_29, 9_49_95, 1_08, 4_94_86, 2]	)








import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
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 LevitImageProcessor


class        _a							(			unittest.TestCase			):

							def __init__(							self:	Dict    ,						UpperCamelCase_:	Any    ,						UpperCamelCase_:	Dict=7    ,						UpperCamelCase_:	Optional[int]=3    ,						UpperCamelCase_:	List[Any]=18    ,						UpperCamelCase_:	Dict=30    ,						UpperCamelCase_:	Optional[int]=400    ,						UpperCamelCase_:	Optional[int]=True    ,						UpperCamelCase_:	Union[str, Any]=None    ,						UpperCamelCase_:	List[str]=True    ,						UpperCamelCase_:	Dict=None    ,						UpperCamelCase_:	Optional[int]=True    ,						UpperCamelCase_:	Tuple=[0.5, 0.5, 0.5]    ,						UpperCamelCase_:	List[str]=[0.5, 0.5, 0.5]    ,						)			->   int:





									"""simple docstring"""







									lowercase__            =      size if size is not None else {'''shortest_edge''': 18}
									lowercase__            =      crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
									lowercase__            =      parent
									lowercase__            =      batch_size
									lowercase__            =      num_channels
									lowercase__            =      image_size
									lowercase__            =      min_resolution
									lowercase__            =      max_resolution
									lowercase__            =      do_resize
									lowercase__            =      size
									lowercase__            =      do_center_crop
									lowercase__            =      crop_size
									lowercase__            =      do_normalize
									lowercase__            =      image_mean
									lowercase__            =      image_std


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





									"""simple docstring"""







									return {
									    "image_mean": self.image_mean,
									    "image_std": self.image_std,
									    "do_normalize": self.do_normalize,
									    "do_resize": self.do_resize,
									    "do_center_crop": self.do_center_crop,
									    "size": self.size,
									    "crop_size": self.crop_size,
									}


@require_torch
@require_vision
class        _a							(			UpperCamelCase__     ,	unittest.TestCase			):
							_lowercase	:			List[str]        = LevitImageProcessor if is_vision_available() else None

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





									"""simple docstring"""







									lowercase__            =      LevitImageProcessingTester(self							)

							@property
							def       lowerCamelCase_ (							self:	List[Any]							)			->   Any:





									"""simple docstring"""







									return self.image_processor_tester.prepare_image_processor_dict()

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





									"""simple docstring"""







									lowercase__            =      self.image_processing_class(**self.image_processor_dict							)
									self.assertTrue(hasattr(UpperCamelCase_    ,						'''image_mean'''							)							)
									self.assertTrue(hasattr(UpperCamelCase_    ,						'''image_std'''							)							)
									self.assertTrue(hasattr(UpperCamelCase_    ,						'''do_normalize'''							)							)
									self.assertTrue(hasattr(UpperCamelCase_    ,						'''do_resize'''							)							)
									self.assertTrue(hasattr(UpperCamelCase_    ,						'''do_center_crop'''							)							)
									self.assertTrue(hasattr(UpperCamelCase_    ,						'''size'''							)							)

							def       lowerCamelCase_ (							self:	str							)			->   Tuple:





									"""simple docstring"""







									lowercase__            =      self.image_processing_class.from_dict(self.image_processor_dict							)
									self.assertEqual(image_processor.size    ,						{'''shortest_edge''': 18}							)
									self.assertEqual(image_processor.crop_size    ,						{'''height''': 18, '''width''': 18}							)

									lowercase__            =      self.image_processing_class.from_dict(self.image_processor_dict    ,						size=42    ,						crop_size=84							)
									self.assertEqual(image_processor.size    ,						{'''shortest_edge''': 42}							)
									self.assertEqual(image_processor.crop_size    ,						{'''height''': 84, '''width''': 84}							)

							def       lowerCamelCase_ (							self:	List[str]							)			->   Dict:





									"""simple docstring"""







									pass

							def       lowerCamelCase_ (							self:	List[str]							)			->   int:





									"""simple docstring"""







									lowercase__            =      self.image_processing_class(**self.image_processor_dict							)
									# create random PIL images
									lowercase__            =      prepare_image_inputs(self.image_processor_tester    ,						equal_resolution=UpperCamelCase_							)
									for image in image_inputs:
											self.assertIsInstance(UpperCamelCase_    ,						Image.Image							)

									# Test not batched input
									lowercase__            =      image_processing(image_inputs[0]    ,						return_tensors='''pt'''							).pixel_values
									self.assertEqual(
									    encoded_images.shape    ,						(
									        1,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size['''height'''],
									        self.image_processor_tester.crop_size['''width'''],
									    )    ,						)

									# Test batched
									lowercase__            =      image_processing(UpperCamelCase_    ,						return_tensors='''pt'''							).pixel_values
									self.assertEqual(
									    encoded_images.shape    ,						(
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size['''height'''],
									        self.image_processor_tester.crop_size['''width'''],
									    )    ,						)

							def       lowerCamelCase_ (							self:	Union[str, Any]							)			->   Optional[int]:





									"""simple docstring"""







									lowercase__            =      self.image_processing_class(**self.image_processor_dict							)
									# create random numpy tensors
									lowercase__            =      prepare_image_inputs(self.image_processor_tester    ,						equal_resolution=UpperCamelCase_    ,						numpify=UpperCamelCase_							)
									for image in image_inputs:
											self.assertIsInstance(UpperCamelCase_    ,						np.ndarray							)

									# Test not batched input
									lowercase__            =      image_processing(image_inputs[0]    ,						return_tensors='''pt'''							).pixel_values
									self.assertEqual(
									    encoded_images.shape    ,						(
									        1,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size['''height'''],
									        self.image_processor_tester.crop_size['''width'''],
									    )    ,						)

									# Test batched
									lowercase__            =      image_processing(UpperCamelCase_    ,						return_tensors='''pt'''							).pixel_values
									self.assertEqual(
									    encoded_images.shape    ,						(
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size['''height'''],
									        self.image_processor_tester.crop_size['''width'''],
									    )    ,						)


							def       lowerCamelCase_ (							self:	Any							)			->   Optional[Any]:





									"""simple docstring"""







									lowercase__            =      self.image_processing_class(**self.image_processor_dict							)
									# create random PyTorch tensors
									lowercase__            =      prepare_image_inputs(self.image_processor_tester    ,						equal_resolution=UpperCamelCase_    ,						torchify=UpperCamelCase_							)
									for image in image_inputs:
											self.assertIsInstance(UpperCamelCase_    ,						torch.Tensor							)

									# Test not batched input
									lowercase__            =      image_processing(image_inputs[0]    ,						return_tensors='''pt'''							).pixel_values
									self.assertEqual(
									    encoded_images.shape    ,						(
									        1,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size['''height'''],
									        self.image_processor_tester.crop_size['''width'''],
									    )    ,						)

									# Test batched
									lowercase__            =      image_processing(UpperCamelCase_    ,						return_tensors='''pt'''							).pixel_values
									self.assertEqual(
									    encoded_images.shape    ,						(
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size['''height'''],
									        self.image_processor_tester.crop_size['''width'''],
									    )    ,						)


