



'''simple docstring'''





import unittest

import numpy as np

from transformers.testing_utils import is_flaky, 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 DonutImageProcessor


class 					snake_case			(unittest.TestCase ):



       def __init__(					self  ,UpperCAmelCase_  ,UpperCAmelCase_=7  ,UpperCAmelCase_=3  ,UpperCAmelCase_=18  ,UpperCAmelCase_=30  ,UpperCAmelCase_=400  ,UpperCAmelCase_=True  ,UpperCAmelCase_=None  ,UpperCAmelCase_=True  ,UpperCAmelCase_=False  ,UpperCAmelCase_=True  ,UpperCAmelCase_=True  ,UpperCAmelCase_=[0.5, 0.5, 0.5]  ,UpperCAmelCase_=[0.5, 0.5, 0.5]  ,)	->				Any:
           lowercase__									=    parent
           lowercase__									=    batch_size
           lowercase__									=    num_channels
           lowercase__									=    image_size
           lowercase__									=    min_resolution
           lowercase__									=    max_resolution
           lowercase__									=    do_resize
           lowercase__									=    size if size is not None else {"height": 18, "width": 20}
           lowercase__									=    do_thumbnail
           lowercase__									=    do_align_axis
           lowercase__									=    do_pad
           lowercase__									=    do_normalize
           lowercase__									=    image_mean
           lowercase__									=    image_std







       def 				_a   (					self   )	->				Dict:
           return {
               "do_resize": self.do_resize,
               "size": self.size,
               "do_thumbnail": self.do_thumbnail,
               "do_align_long_axis": self.do_align_axis,
               "do_pad": self.do_pad,
               "do_normalize": self.do_normalize,
               "image_mean": self.image_mean,
               "image_std": self.image_std,
           }





@require_torch
@require_vision
class 					snake_case			(UpperCamelCase		,   unittest.TestCase ):
       lowerCAmelCase__	:List[str]     =       DonutImageProcessor if is_vision_available() else None



       def 				_a   (					self   )	->				List[Any]:
           lowercase__									=    DonutImageProcessingTester(self   )



       @property
       def 				_a   (					self   )	->				Tuple:
           return self.image_processor_tester.prepare_image_processor_dict()



       def 				_a   (					self   )	->				Any:
           lowercase__									=    self.image_processing_class(**self.image_processor_dict   )
           self.assertTrue(hasattr(UpperCAmelCase_  ,"do_resize"   )   )
           self.assertTrue(hasattr(UpperCAmelCase_  ,"size"   )   )
           self.assertTrue(hasattr(UpperCAmelCase_  ,"do_thumbnail"   )   )
           self.assertTrue(hasattr(UpperCAmelCase_  ,"do_align_long_axis"   )   )
           self.assertTrue(hasattr(UpperCAmelCase_  ,"do_pad"   )   )
           self.assertTrue(hasattr(UpperCAmelCase_  ,"do_normalize"   )   )
           self.assertTrue(hasattr(UpperCAmelCase_  ,"image_mean"   )   )
           self.assertTrue(hasattr(UpperCAmelCase_  ,"image_std"   )   )



       def 				_a   (					self   )	->				Optional[int]:
           lowercase__									=    self.image_processing_class.from_dict(self.image_processor_dict   )
           self.assertEqual(image_processor.size  ,{"height": 18, "width": 20}   )

           lowercase__									=    self.image_processing_class.from_dict(self.image_processor_dict  ,size=42   )
           self.assertEqual(image_processor.size  ,{"height": 42, "width": 42}   )

           # Previous config had dimensions in (width, height) order
           lowercase__									=    self.image_processing_class.from_dict(self.image_processor_dict  ,size=(42, 84)   )
           self.assertEqual(image_processor.size  ,{"height": 84, "width": 42}   )



       def 				_a   (					self   )	->				Dict:
           pass



       @is_flaky()
       def 				_a   (					self   )	->				Optional[Any]:
           # Initialize image_processing
           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.size["height"],
                   self.image_processor_tester.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.size["height"],
                   self.image_processor_tester.size["width"],
               )  ,)



       @is_flaky()
       def 				_a   (					self   )	->				Dict:
           # Initialize image_processing
           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.size["height"],
                   self.image_processor_tester.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.size["height"],
                   self.image_processor_tester.size["width"],
               )  ,)







       @is_flaky()
       def 				_a   (					self   )	->				int:
           # Initialize image_processing
           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.size["height"],
                   self.image_processor_tester.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.size["height"],
                   self.image_processor_tester.size["width"],
               )  ,)









'''simple docstring'''





import pickle
import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, XGLMTokenizer, XGLMTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


SCREAMING_SNAKE_CASE__          =  get_tests_dir("fixtures/test_sentencepiece.model")


@require_sentencepiece
@require_tokenizers
class 					snake_case			(UpperCamelCase		,   unittest.TestCase ):
       lowerCAmelCase__	:List[Any]     =       XGLMTokenizer
       lowerCAmelCase__	:Tuple     =       XGLMTokenizerFast
       lowerCAmelCase__	:Any     =       True
       lowerCAmelCase__	:List[Any]     =       True



       def 				_a   (					self   )	->				Tuple:
           super().setUp()

           # We have a SentencePiece fixture for testing
           lowercase__									=    XGLMTokenizer(UpperCAmelCase_  ,keep_accents=UpperCAmelCase_   )
           tokenizer.save_pretrained(self.tmpdirname   )



       def 				_a   (					self   )	->				Tuple:
           lowercase__									=    "<pad>"
           lowercase__									=    1

           self.assertEqual(self.get_tokenizer()._convert_token_to_id(UpperCAmelCase_   )  ,UpperCAmelCase_   )
           self.assertEqual(self.get_tokenizer()._convert_id_to_token(UpperCAmelCase_   )  ,UpperCAmelCase_   )



       def 				_a   (					self   )	->				Dict:
           lowercase__									=    list(self.get_tokenizer().get_vocab().keys()   )

           self.assertEqual(vocab_keys[0]  ,"<s>"   )
           self.assertEqual(vocab_keys[1]  ,"<pad>"   )
           self.assertEqual(len(UpperCAmelCase_   )  ,1_008   )



       def 				_a   (					self   )	->				Union[str, Any]:
           self.assertEqual(self.get_tokenizer().vocab_size  ,1_008   )



       def 				_a   (					self   )	->				Dict:
           lowercase__									=    XGLMTokenizer(UpperCAmelCase_  ,keep_accents=UpperCAmelCase_   )

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

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

           lowercase__									=    tokenizer.tokenize("I was born in 92000, and this is falsé."   )
           self.assertListEqual(
               UpperCAmelCase_  ,[
                   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",
                   ".",
               ]  ,)
           lowercase__									=    tokenizer.convert_tokens_to_ids(UpperCAmelCase_   )
           self.assertListEqual(
               UpperCAmelCase_  ,[
                   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]
               ]  ,)

           lowercase__									=    tokenizer.convert_ids_to_tokens(UpperCAmelCase_   )
           self.assertListEqual(
               UpperCAmelCase_  ,[
                   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>",
                   ".",
               ]  ,)



       @cached_property
       def 				_a   (					self   )	->				Optional[int]:
           return XGLMTokenizer.from_pretrained("facebook/xglm-564M"   )



       def 				_a   (					self   )	->				int:
           with tempfile.NamedTemporaryFile() as f:
               shutil.copyfile(UpperCAmelCase_  ,f.name   )
               lowercase__									=    XGLMTokenizer(f.name  ,keep_accents=UpperCAmelCase_   )
               lowercase__									=    pickle.dumps(UpperCAmelCase_   )
           pickle.loads(UpperCAmelCase_   )



       def 				_a   (					self   )	->				Optional[int]:
           if not self.test_rust_tokenizer:
               return

           lowercase__									=    self.get_tokenizer()
           lowercase__									=    self.get_rust_tokenizer()

           lowercase__									=    "I was born in 92000, and this is falsé."

           lowercase__									=    tokenizer.tokenize(UpperCAmelCase_   )
           lowercase__									=    rust_tokenizer.tokenize(UpperCAmelCase_   )
           self.assertListEqual(UpperCAmelCase_  ,UpperCAmelCase_   )

           lowercase__									=    tokenizer.encode(UpperCAmelCase_  ,add_special_tokens=UpperCAmelCase_   )
           lowercase__									=    rust_tokenizer.encode(UpperCAmelCase_  ,add_special_tokens=UpperCAmelCase_   )
           self.assertListEqual(UpperCAmelCase_  ,UpperCAmelCase_   )

           lowercase__									=    self.get_rust_tokenizer()
           lowercase__									=    tokenizer.encode(UpperCAmelCase_   )
           lowercase__									=    rust_tokenizer.encode(UpperCAmelCase_   )
           self.assertListEqual(UpperCAmelCase_  ,UpperCAmelCase_   )



       @slow
       def 				_a   (					self   )	->				List[str]:
           lowercase__									=    "Hello World!"
           lowercase__									=    [2, 31_227, 4_447, 35]

           self.assertListEqual(UpperCAmelCase_  ,self.big_tokenizer.encode(UpperCAmelCase_   )   )



       @slow
       def 				_a   (					self   )	->				str:
           lowercase__									=    (
               "This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) \" [ ] ! : - . Also we will"
               " add words that should not exsist and be tokenized to unk, such as saoneuhaoesuth"
           )
           # fmt: off
           lowercase__									=    [2, 1_018, 67, 11, 1_988, 2_617, 5_631, 278, 11, 3_407, 48, 71_630, 28_085, 4, 3_234, 157, 13, 6, 5, 6, 4, 3_526, 768, 15, 659, 57, 298, 3_983, 864, 129, 21, 6, 5, 13_675, 377, 652, 7_580, 10_341, 155, 2_817, 422, 1_666, 7, 1_674, 53, 113, 202_277, 17_892, 33, 60, 87, 4, 3_234, 157, 61, 2_667, 52_376, 19, 88, 23, 735]
           # fmt: on

           self.assertListEqual(UpperCAmelCase_  ,self.big_tokenizer.encode(UpperCAmelCase_   )   )







       @slow
       def 				_a   (					self   )	->				str:
           # fmt: off
           lowercase__									=    {
               "input_ids": [[2, 108_825, 1_163, 15, 88_010, 473, 15_898, 157, 13_672, 1_857, 312, 8, 238_021, 1_163, 53, 13_672, 1_857, 312, 8, 53_283, 182_396, 8, 18_566, 16, 36_733, 4_101, 8, 230, 244_017, 122_553, 7, 15, 132_597, 4, 293, 12_511, 7_610, 4, 3_414, 132_597, 9, 4, 32_361, 362, 4, 734, 28_512, 32_569, 18, 4, 32_361, 26_096, 14_982, 73, 18_715, 21_433, 235_261, 15, 492, 12_427, 16, 53, 18_715, 21_433, 65_454, 15, 23_659, 563, 16, 278, 597, 2_843, 595, 7_931, 182_396, 64_186, 22, 886, 595, 132_981, 53, 25_540, 3_449, 43_982, 39_901, 5_951, 878, 330, 4, 27_694, 80_269, 312, 53, 6_517, 11_780, 611, 20_408, 5], [2, 6, 132_597, 67, 42_897, 33, 592, 8, 163_729, 25_540, 361, 136_997, 109_514, 173_230, 7, 501, 60, 102_913, 196, 5_631, 235, 63_243, 473, 6, 231_757, 74, 5_277, 7_905, 53, 3_095, 37_317, 22, 454, 183_874, 5], [2, 268, 31_298, 46_530, 6, 132_935, 43_831, 7, 597, 32, 24, 3_688, 9_865, 5]],
               "attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]
           }  # noqa: E501
           # fmt: on

           self.tokenizer_integration_test_util(
               expected_encoding=UpperCAmelCase_  ,model_name="facebook/xglm-564M"  ,padding=UpperCAmelCase_  ,)




