






'''simple docstring'''

import pickle
import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, XLMRobertaTokenizer, XLMRobertaTokenizerFast
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


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




@require_sentencepiece
@require_tokenizers
class      A     (   SCREAMING_SNAKE_CASE__, unittest.TestCase    ):






 """simple docstring"""





 __a      :    Dict			  =    XLMRobertaTokenizer
 __a      :    str			  =    XLMRobertaTokenizerFast
 __a      :    int			  =    True
 __a      :    str			  =    True
 def 	_UpperCAmelCase							(				self			):
       super().setUp()

       # We have a SentencePiece fixture for testing
       UpperCamelCase_					: str            =				XLMRobertaTokenizer(__lowerCAmelCase      ,  keep_accents=__lowerCAmelCase			)
       tokenizer.save_pretrained(self.tmpdirname			)
 def 	_UpperCAmelCase							(				self			):
       UpperCamelCase_					: Tuple            =				"""<pad>"""
       UpperCamelCase_					: int            =				1

       self.assertEqual(self.get_tokenizer()._convert_token_to_id(__lowerCAmelCase			)      ,  __lowerCAmelCase			)
       self.assertEqual(self.get_tokenizer()._convert_id_to_token(__lowerCAmelCase			)      ,  __lowerCAmelCase			)
 def 	_UpperCAmelCase							(				self			):
       UpperCamelCase_					: Tuple            =				list(self.get_tokenizer().get_vocab().keys()			)

       self.assertEqual(vocab_keys[0]      ,  """<s>"""			)
       self.assertEqual(vocab_keys[1]      ,  """<pad>"""			)
       self.assertEqual(vocab_keys[-1]      ,  """<mask>"""			)
       self.assertEqual(len(__lowerCAmelCase			)      ,  10_02			)
 def 	_UpperCAmelCase							(				self			):
       self.assertEqual(self.get_tokenizer().vocab_size      ,  10_02			)
 def 	_UpperCAmelCase							(				self			):
       UpperCamelCase_					: Optional[int]            =				XLMRobertaTokenizer(__lowerCAmelCase      ,  keep_accents=__lowerCAmelCase			)

       UpperCamelCase_					: List[Any]            =				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 [2_85, 46, 10, 1_70, 3_82]]      ,  )

       UpperCamelCase_					: int            =				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""",
               """.""",
           ]      ,  )
       UpperCamelCase_					: List[Any]            =				tokenizer.convert_tokens_to_ids(__lowerCAmelCase			)
       self.assertListEqual(
           __lowerCAmelCase      ,  [
               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]
               #                                       ^ unk: 2 + 1 = 3                  unk: 2 + 1 = 3 ^
           ]      ,  )

       UpperCamelCase_					: str            =				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>""",
               """.""",
           ]      ,  )
 def 	_UpperCAmelCase							(				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

       UpperCamelCase_					: Optional[Any]            =				(self.rust_tokenizer_class, """hf-internal-testing/tiny-xlm-roberta""", {})
       for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
             with self.subTest(F"{tokenizer.__class__.__name__} ({pretrained_name})"			):
                   UpperCamelCase_					: List[Any]            =				self.rust_tokenizer_class.from_pretrained(__lowerCAmelCase      ,  **__lowerCAmelCase			)
                   UpperCamelCase_					: Union[str, Any]            =				self.tokenizer_class.from_pretrained(__lowerCAmelCase      ,  **__lowerCAmelCase			)

                   UpperCamelCase_					: int            =				tempfile.mkdtemp()

                   UpperCamelCase_					: int            =				tokenizer_r.save_pretrained(__lowerCAmelCase			)
                   UpperCamelCase_					: Optional[Any]            =				tokenizer_p.save_pretrained(__lowerCAmelCase			)

                   # 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			)			)
                   UpperCamelCase_					: Optional[Any]            =				tuple(f for f in tokenizer_r_files if """tokenizer.json""" not in f			)
                   self.assertSequenceEqual(__lowerCAmelCase      ,  __lowerCAmelCase			)

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

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

                   shutil.rmtree(__lowerCAmelCase			)

                   # Save tokenizer rust, legacy_format=True
                   UpperCamelCase_					: str            =				tempfile.mkdtemp()

                   UpperCamelCase_					: List[str]            =				tokenizer_r.save_pretrained(__lowerCAmelCase      ,  legacy_format=__lowerCAmelCase			)
                   UpperCamelCase_					: str            =				tokenizer_p.save_pretrained(__lowerCAmelCase			)

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

                   # Checks everything loads correctly in the same way
                   UpperCamelCase_					: Any            =				tokenizer_r.from_pretrained(__lowerCAmelCase			)
                   UpperCamelCase_					: Dict            =				tokenizer_p.from_pretrained(__lowerCAmelCase			)

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

                   shutil.rmtree(__lowerCAmelCase			)

                   # Save tokenizer rust, legacy_format=False
                   UpperCamelCase_					: Tuple            =				tempfile.mkdtemp()

                   UpperCamelCase_					: List[str]            =				tokenizer_r.save_pretrained(__lowerCAmelCase      ,  legacy_format=__lowerCAmelCase			)
                   UpperCamelCase_					: int            =				tokenizer_p.save_pretrained(__lowerCAmelCase			)

                   # 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
                   UpperCamelCase_					: Union[str, Any]            =				tokenizer_r.from_pretrained(__lowerCAmelCase			)
                   UpperCamelCase_					: Optional[Any]            =				tokenizer_p.from_pretrained(__lowerCAmelCase			)

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

                   shutil.rmtree(__lowerCAmelCase			)
 @cached_property
 def 	_UpperCAmelCase							(				self			):
       return XLMRobertaTokenizer.from_pretrained("""xlm-roberta-base"""			)
 def 	_UpperCAmelCase							(				self			):
       with tempfile.NamedTemporaryFile() as f:
             shutil.copyfile(__lowerCAmelCase      ,  f.name			)
             UpperCamelCase_					: Any            =				XLMRobertaTokenizer(f.name      ,  keep_accents=__lowerCAmelCase			)
             UpperCamelCase_					: Union[str, Any]            =				pickle.dumps(__lowerCAmelCase			)
       pickle.loads(__lowerCAmelCase			)
 def 	_UpperCAmelCase							(				self			):
       if not self.test_rust_tokenizer:
             return

       UpperCamelCase_					: int            =				self.get_tokenizer()
       UpperCamelCase_					: Union[str, Any]            =				self.get_rust_tokenizer()

       UpperCamelCase_					: Dict            =				"""I was born in 92000, and this is falsé."""

       UpperCamelCase_					: Union[str, Any]            =				tokenizer.tokenize(__lowerCAmelCase			)
       UpperCamelCase_					: Any            =				rust_tokenizer.tokenize(__lowerCAmelCase			)
       self.assertListEqual(__lowerCAmelCase      ,  __lowerCAmelCase			)

       UpperCamelCase_					: str            =				tokenizer.encode(__lowerCAmelCase      ,  add_special_tokens=__lowerCAmelCase			)
       UpperCamelCase_					: str            =				rust_tokenizer.encode(__lowerCAmelCase      ,  add_special_tokens=__lowerCAmelCase			)
       self.assertListEqual(__lowerCAmelCase      ,  __lowerCAmelCase			)

       UpperCamelCase_					: Optional[int]            =				self.get_rust_tokenizer()
       UpperCamelCase_					: str            =				tokenizer.encode(__lowerCAmelCase			)
       UpperCamelCase_					: Optional[int]            =				rust_tokenizer.encode(__lowerCAmelCase			)
       self.assertListEqual(__lowerCAmelCase      ,  __lowerCAmelCase			)
 @slow
 def 	_UpperCAmelCase							(				self			):
       UpperCamelCase_					: Tuple            =				"""Hello World!"""
       UpperCamelCase_					: List[str]            =				[0, 3_53_78, 66_61, 38, 2]
       # xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')  # xlmr.large has same tokenizer
       # xlmr.eval()
       # xlmr.encode(symbols)

       self.assertListEqual(__lowerCAmelCase      ,  self.big_tokenizer.encode(__lowerCAmelCase			)			)
 @slow
 def 	_UpperCAmelCase							(				self			):
       UpperCamelCase_					: List[Any]            =				(
           """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"""
       )
       UpperCamelCase_					: str            =				[
           0,
           32_93,
           83,
           10,
           45_52,
           49_89,
           79_86,
           6_78,
           10,
           59_15,
           1_11,
           17_94_59,
           12_48_50,
           4,
           60_44,
           2_37,
           12,
           6,
           5,
           6,
           4,
           67_80,
           7_05,
           15,
           13_88,
           44,
           3_78,
           1_01_14,
           7_11,
           1_52,
           20,
           6,
           5,
           2_23_76,
           6_42,
           12_21,
           1_51_90,
           3_41_53,
           4_50,
           56_08,
           9_59,
           11_19,
           5_77_02,
           1_36,
           1_86,
           47,
           10_98,
           2_93_67,
           47,
           # 4426, # What fairseq tokenizes from "<unk>": "_<"
           # 3678, # What fairseq tokenizes from "<unk>": "unk"
           # 2740, # What fairseq tokenizes from "<unk>": ">"
           3,  # What we tokenize from "<unk>": "<unk>"
           6,  # Residue from the tokenization: an extra sentencepiece underline
           4,
           60_44,
           2_37,
           62_84,
           5_09_01,
           5_28,
           31,
           90,
           34,
           9_27,
           2,
       ]
       # xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')  # xlmr.large has same tokenizer
       # xlmr.eval()
       # xlmr.encode(symbols)

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





 @slow
 def 	_UpperCAmelCase							(				self			):
       # fmt: off
       UpperCamelCase_					: Optional[int]            =				{"""input_ids""": [[0, 1_10_62, 8_27_72, 7, 15, 8_27_72, 5_38, 5_15_29, 2_37, 1_71_98, 12_90, 2_06, 9, 21_51_75, 13_14, 1_36, 1_71_98, 12_90, 2_06, 9, 5_63_59, 42, 12_20_09, 9, 1_64_66, 16, 8_73_44, 45_37, 9, 47_17, 7_83_81, 6, 15_99_58, 7, 15, 2_44_80, 6_18, 4, 5_27, 2_26_93, 54_28, 4, 27_77, 2_44_80, 98_74, 4, 4_35_23, 5_94, 4, 8_03, 1_83_92, 3_31_89, 18, 4, 4_35_23, 2_44_47, 1_23_99, 1_00, 2_49_55, 8_36_58, 96_26, 14_40_57, 15, 8_39, 2_23_35, 16, 1_36, 2_49_55, 8_36_58, 8_34_79, 15, 3_91_02, 7_24, 16, 6_78, 6_45, 27_89, 13_28, 45_89, 42, 12_20_09, 11_57_74, 23, 8_05, 13_28, 4_68_76, 7, 1_36, 5_38_94, 19_40, 4_22_27, 4_11_59, 1_77_21, 8_23, 4_25, 4, 2_75_12, 9_87_22, 2_06, 1_36, 55_31, 49_70, 9_19, 1_73_36, 5, 2], [0, 2_00_80, 6_18, 83, 8_27_75, 47, 4_79, 9, 15_17, 73, 5_38_94, 3_33, 8_05_81, 11_01_17, 1_88_11, 52_56, 12_95, 51, 15_25_26, 2_97, 79_86, 3_90, 12_44_16, 5_38, 3_54_31, 2_14, 98, 1_50_44, 2_57_37, 1_36, 71_08, 4_37_01, 23, 7_56, 13_53_55, 7, 5, 2, 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], [0, 5_81, 6_37_73, 11_94_55, 6, 14_77_97, 8_82_03, 7, 6_45, 70, 21, 32_85, 1_02_69, 5, 2, 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]], """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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
       # fmt: on

       self.tokenizer_integration_test_util(
           expected_encoding=__lowerCAmelCase      ,  model_name="""xlm-roberta-base"""      ,  revision="""d9d8a8ea5eb94b1c6654ae9249df7793cd2933d3"""      ,  )














'''simple docstring'''

import unittest

import numpy as np
import torch

from diffusers import PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()




class      A     (   unittest.TestCase    ):






 """simple docstring"""





 @property
 def 	_UpperCAmelCase							(				self			):
       torch.manual_seed(0			)
       UpperCamelCase_					: int            =				UNetaDModel(
           block_out_channels=(32, 64)      ,  layers_per_block=2      ,  sample_size=32      ,  in_channels=3      ,  out_channels=3      ,  down_block_types=("""DownBlock2D""", """AttnDownBlock2D""")      ,  up_block_types=("""AttnUpBlock2D""", """UpBlock2D""")      ,  )
       return model





 def 	_UpperCAmelCase							(				self			):
       UpperCamelCase_					: Union[str, Any]            =				self.dummy_uncond_unet
       UpperCamelCase_					: List[str]            =				PNDMScheduler()

       UpperCamelCase_					: Dict            =				PNDMPipeline(unet=__lowerCAmelCase      ,  scheduler=__lowerCAmelCase			)
       pndm.to(__lowerCAmelCase			)
       pndm.set_progress_bar_config(disable=__lowerCAmelCase			)

       UpperCamelCase_					: Union[str, Any]            =				torch.manual_seed(0			)
       UpperCamelCase_					: Optional[int]            =				pndm(generator=__lowerCAmelCase      ,  num_inference_steps=20      ,  output_type="""numpy"""			).images

       UpperCamelCase_					: Optional[int]            =				torch.manual_seed(0			)
       UpperCamelCase_					: Tuple            =				pndm(generator=__lowerCAmelCase      ,  num_inference_steps=20      ,  output_type="""numpy"""      ,  return_dict=__lowerCAmelCase			)[0]

       UpperCamelCase_					: Union[str, Any]            =				image[0, -3:, -3:, -1]
       UpperCamelCase_					: int            =				image_from_tuple[0, -3:, -3:, -1]

       assert image.shape == (1, 32, 32, 3)
       UpperCamelCase_					: Optional[int]            =				np.array([1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0]			)

       assert np.abs(image_slice.flatten() - expected_slice			).max() < 1E-2
       assert np.abs(image_from_tuple_slice.flatten() - expected_slice			).max() < 1E-2






@slow
@require_torch
class      A     (   unittest.TestCase    ):






 """simple docstring"""





 def 	_UpperCAmelCase							(				self			):
       UpperCamelCase_					: Optional[Any]            =				"""google/ddpm-cifar10-32"""

       UpperCamelCase_					: List[str]            =				UNetaDModel.from_pretrained(__lowerCAmelCase			)
       UpperCamelCase_					: int            =				PNDMScheduler()

       UpperCamelCase_					: Any            =				PNDMPipeline(unet=__lowerCAmelCase      ,  scheduler=__lowerCAmelCase			)
       pndm.to(__lowerCAmelCase			)
       pndm.set_progress_bar_config(disable=__lowerCAmelCase			)
       UpperCamelCase_					: Optional[int]            =				torch.manual_seed(0			)
       UpperCamelCase_					: Optional[int]            =				pndm(generator=__lowerCAmelCase      ,  output_type="""numpy"""			).images

       UpperCamelCase_					: Tuple            =				image[0, -3:, -3:, -1]

       assert image.shape == (1, 32, 32, 3)
       UpperCamelCase_					: Union[str, Any]            =				np.array([0.15_64, 0.1_46_45, 0.14_06, 0.1_47_15, 0.1_24_25, 0.1_40_45, 0.1_31_15, 0.1_21_75, 0.1_25]			)

       assert np.abs(image_slice.flatten() - expected_slice			).max() < 1E-2






