


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   :    int     =		get_tests_dir("fixtures/test_sentencepiece.model")







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



		"""simple docstring"""

		UpperCamelCase     :					str			       =		XLMRobertaTokenizer
		UpperCamelCase     :					Dict			       =		XLMRobertaTokenizerFast
		UpperCamelCase     :					str			       =		True
		UpperCamelCase     :					List[Any]			       =		True







		def 					__A   ( self	)  -> Dict:







					'''simple docstring'''







					super().setUp()

					# We have a SentencePiece fixture for testing
					lowerCamelCase    		=  XLMRobertaTokenizer(A		,      keep_accents=A	)
					tokenizer.save_pretrained(self.tmpdirname	)







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







					'''simple docstring'''







					lowerCamelCase    		=  """<pad>"""
					lowerCamelCase    		=  1

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







		def 					__A   ( self	)  -> str:







					'''simple docstring'''







					lowerCamelCase    		=  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(A	)		,      10_02	)







		def 					__A   ( self	)  -> List[str]:







					'''simple docstring'''







					self.assertEqual(self.get_tokenizer().vocab_size		,      10_02	)







		def 					__A   ( self	)  -> str:







					'''simple docstring'''







					lowerCamelCase    		=  XLMRobertaTokenizer(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]
					        #                                       ^ unk: 2 + 1 = 3                  unk: 2 + 1 = 3 ^
					    ]		,      )

					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 					__A   ( self	)  -> List[Any]:







					'''simple docstring'''







					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

					lowerCamelCase    		=  (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})'	):
											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	)	)
														# self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
														# self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))

											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	)







		@cached_property
		def 					__A   ( self	)  -> Dict:







					'''simple docstring'''







					return XLMRobertaTokenizer.from_pretrained("""xlm-roberta-base"""	)







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







					'''simple docstring'''







					with tempfile.NamedTemporaryFile() as f:
								shutil.copyfile(A		,      f.name	)
								lowerCamelCase    		=  XLMRobertaTokenizer(f.name		,      keep_accents=A	)
								lowerCamelCase    		=  pickle.dumps(A	)
					pickle.loads(A	)







		def 					__A   ( self	)  -> List[str]:







					'''simple docstring'''







					if not self.test_rust_tokenizer:
								return

					lowerCamelCase    		=  self.get_tokenizer()
					lowerCamelCase    		=  self.get_rust_tokenizer()

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

					lowerCamelCase    		=  tokenizer.tokenize(A	)
					lowerCamelCase    		=  rust_tokenizer.tokenize(A	)
					self.assertListEqual(A		,      A	)

					lowerCamelCase    		=  tokenizer.encode(A		,      add_special_tokens=A	)
					lowerCamelCase    		=  rust_tokenizer.encode(A		,      add_special_tokens=A	)
					self.assertListEqual(A		,      A	)

					lowerCamelCase    		=  self.get_rust_tokenizer()
					lowerCamelCase    		=  tokenizer.encode(A	)
					lowerCamelCase    		=  rust_tokenizer.encode(A	)
					self.assertListEqual(A		,      A	)







		@slow
		def 					__A   ( self	)  -> List[str]:







					'''simple docstring'''







					lowerCamelCase    		=  """Hello World!"""
					lowerCamelCase    		=  [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(A		,      self.big_tokenizer.encode(A	)	)







		@slow
		def 					__A   ( self	)  -> Dict:







					'''simple docstring'''







					lowerCamelCase    		=  (
					    """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"""
					)
					lowerCamelCase    		=  [
					    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(A		,      self.big_tokenizer.encode(A	)	)







		@slow
		def 					__A   ( self	)  -> Optional[Any]:







					'''simple docstring'''







					lowerCamelCase    		=  {"""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=A		,      model_name="""xlm-roberta-base"""		,      revision="""d9d8a8ea5eb94b1c6654ae9249df7793cd2933d3"""		,      )





# limitations under the License.


from typing import Optional, Tuple, Union

import torch

from diffusers import DiffusionPipeline, ImagePipelineOutput







class        __lowercase       (							a_  ):



		"""simple docstring"""

		def __init__( self		,      A		,      A	)  -> List[Any]:







					'''simple docstring'''







					super().__init__()
					self.register_modules(unet=A		,      scheduler=A	)







		@torch.no_grad()
		def __call__( self		,      A = 1		,      A = None		,      A = 50		,      A = "pil"		,      A = True		,      **A		,      )  -> Union[ImagePipelineOutput, Tuple]:







					'''simple docstring'''







					lowerCamelCase    		=  torch.randn(
					    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)		,      generator=A		,      )
					lowerCamelCase    		=  image.to(self.device	)

					# set step values
					self.scheduler.set_timesteps(A	)

					for t in self.progress_bar(self.scheduler.timesteps	):
								# 1. predict noise model_output
								lowerCamelCase    		=  self.unet(A		,      A	).sample

								# 2. predict previous mean of image x_t-1 and add variance depending on eta
								# eta corresponds to η in paper and should be between [0, 1]
								# do x_t -> x_t-1
								lowerCamelCase    		=  self.scheduler.step(A		,      A		,      A	).prev_sample

					lowerCamelCase    		=  (image / 2 + 0.5).clamp(0		,      1	)
					lowerCamelCase    		=  image.cpu().permute(0		,      2		,      3		,      1	).numpy()
					if output_type == "pil":
								lowerCamelCase    		=  self.numpy_to_pil(A	)

					if not return_dict:
								return (image,), "This is a local test"

					return ImagePipelineOutput(images=A	), "This is a local test"

