
import unittest

from transformers.models.xlm_prophetnet.tokenization_xlm_prophetnet import SPIECE_UNDERLINE, XLMProphetNetTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


_A		     =    get_tests_dir('''fixtures/test_sentencepiece.model''')

@require_sentencepiece
class 						lowercase_				(				A__							,		unittest.TestCase ):
 A__  : Any      					=			XLMProphetNetTokenizer
 A__  : Any      					=			False
 A__  : List[Any]      					=			True

 def lowerCamelCase_ (					self   ):




        """simple docstring"""


        super().setUp()

        # We have a SentencePiece fixture for testing
        UpperCamelCase_											=				XLMProphetNetTokenizer(__snake_case   ,		keep_accents=__snake_case   )
        tokenizer.save_pretrained(self.tmpdirname   )

 def lowerCamelCase_ (					self   ):




        """simple docstring"""


        UpperCamelCase_											=				'''[PAD]'''
        UpperCamelCase_											=				0

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

 def lowerCamelCase_ (					self   ):




        """simple docstring"""


        UpperCamelCase_											=				list(self.get_tokenizer().get_vocab().keys()   )

        self.assertEqual(vocab_keys[0]   ,		"""[PAD]"""   )
        self.assertEqual(vocab_keys[1]   ,		"""[CLS]"""   )
        self.assertEqual(vocab_keys[-1]   ,		"""j"""   )
        self.assertEqual(len(__snake_case   )   ,		1_0_1_2   )

 def lowerCamelCase_ (					self   ):




        """simple docstring"""


        self.assertEqual(self.get_tokenizer().vocab_size   ,		1_0_1_2   )

 def lowerCamelCase_ (					self   ):




        """simple docstring"""


        UpperCamelCase_											=				XLMProphetNetTokenizer(__snake_case   ,		keep_accents=__snake_case   )

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

        self.assertListEqual(
            tokenizer.convert_tokens_to_ids(__snake_case   )   ,		[value + tokenizer.fairseq_offset for value in [2_8_5, 4_6, 1_0, 1_7_0, 3_8_2]]   ,		)

        UpperCamelCase_											=				tokenizer.tokenize("""I was born in 92000, and this is falsé."""   )
        self.assertListEqual(
            __snake_case   ,		[
                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_											=				tokenizer.convert_tokens_to_ids(__snake_case   )
        self.assertListEqual(
            __snake_case   ,		[
                value + tokenizer.fairseq_offset
                for value in [8, 2_1, 8_4, 5_5, 2_4, 1_9, 7, -9, 6_0_2, 3_4_7, 3_4_7, 3_4_7, 3, 1_2, 6_6, 4_6, 7_2, 8_0, 6, -9, 4]
            ]   ,		)

        UpperCamelCase_											=				tokenizer.convert_ids_to_tokens(__snake_case   )
        self.assertListEqual(
            __snake_case   ,		[
                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 lowerCamelCase_ (					self   ):




        """simple docstring"""


        return XLMProphetNetTokenizer.from_pretrained("""microsoft/xprophetnet-large-wiki100-cased"""   )

 @slow
 def lowerCamelCase_ (					self   ):




        """simple docstring"""


        UpperCamelCase_											=				'''Hello World!'''
        UpperCamelCase_											=				[3_5_3_8_9, 6_6_7_2, 4_9, 2]
        self.assertListEqual(__snake_case   ,		self.big_tokenizer.encode(__snake_case   )   )

 @slow
 def lowerCamelCase_ (					self   ):




        """simple docstring"""


        UpperCamelCase_											=				{'''input_ids''': [[1_1_0_7_3, 8_2_7_8_3, 1_8, 2_6, 8_2_7_8_3, 5_4_9, 5_1_5_4_0, 2_4_8, 1_7_2_0_9, 1_3_0_1, 2_1_7, 2_0, 2_1_5_1_8_6, 1_3_2_5, 1_4_7, 1_7_2_0_9, 1_3_0_1, 2_1_7, 2_0, 5_6_3_7_0, 5_3, 1_2_2_0_2_0, 2_0, 1_6_4_7_7, 2_7, 8_7_3_5_5, 4_5_4_8, 2_0, 4_7_2_8, 7_8_3_9_2, 1_7, 1_5_9_9_6_9, 1_8, 2_6, 2_4_4_9_1, 6_2_9, 1_5, 5_3_8, 2_2_7_0_4, 5_4_3_9, 1_5, 2_7_8_8, 2_4_4_9_1, 9_8_8_5, 1_5, 4_3_5_3_4, 6_0_5, 1_5, 8_1_4, 1_8_4_0_3, 3_3_2_0_0, 2_9, 1_5, 4_3_5_3_4, 2_4_4_5_8, 1_2_4_1_0, 1_1_1, 2_4_9_6_6, 8_3_6_6_9, 9_6_3_7, 1_4_4_0_6_8, 2_6, 8_5_0, 2_2_3_4_6, 2_7, 1_4_7, 2_4_9_6_6, 8_3_6_6_9, 8_3_4_9_0, 2_6, 3_9_1_1_3, 7_3_5, 2_7, 6_8_9, 6_5_6, 2_8_0_0, 1_3_3_9, 4_6_0_0, 5_3, 1_2_2_0_2_0, 1_1_5_7_8_5, 3_4, 8_1_6, 1_3_3_9, 4_6_8_8_7, 1_8, 1_4_7, 5_3_9_0_5, 1_9_5_1, 4_2_2_3_8, 4_1_1_7_0, 1_7_7_3_2, 8_3_4, 4_3_6, 1_5, 2_7_5_2_3, 9_8_7_3_3, 2_1_7, 1_4_7, 5_5_4_2, 4_9_8_1, 9_3_0, 1_7_3_4_7, 1_6, 2], [2_0_0_9_1, 6_2_9, 9_4, 8_2_7_8_6, 5_8, 4_9_0, 2_0, 1_5_2_8, 8_4, 5_3_9_0_5, 3_4_4, 8_0_5_9_2, 1_1_0_1_2_8, 1_8_8_2_2, 5_2_6_7, 1_3_0_6, 6_2, 1_5_2_5_3_7, 3_0_8, 7_9_9_7, 4_0_1, 1_2_4_4_2_7, 5_4_9, 3_5_4_4_2, 2_2_5, 1_0_9, 1_5_0_5_5, 2_5_7_4_8, 1_4_7, 7_1_1_9, 4_3_7_1_2, 3_4, 7_6_7, 1_3_5_3_6_6, 1_8, 1_6, 2, 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], [5_9_2, 6_3_7_8_4, 1_1_9_4_6_6, 1_7, 1_4_7_8_0_8, 8_8_2_1_4, 1_8, 6_5_6, 8_1, 3_2, 3_2_9_6, 1_0_2_8_0, 1_6, 2, 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]], '''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, 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, 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=__snake_case   ,		model_name="""microsoft/xprophetnet-large-wiki100-cased"""   ,		revision="""1acad1643ddd54a44df6a1b797ada8373685d90e"""   ,		)








'''simple docstring'''






from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput

class 				SCREAMING_SNAKE_CASE(							A__			):







	"""simple docstring"""






	lowerCamelCase__			 =       42
	lowerCamelCase__			 =       42







	def __init__(						self			:     Union[str, Any]		,			__snake_case			:     UNetaDModel		,			__snake_case			:     ScoreSdeVeScheduler		)			->		int:
								super().__init__()
								self.register_modules(unet=__snake_case		,			scheduler=__snake_case		)







	@torch.no_grad()
	def __call__(						self			:     Optional[int]		,			__snake_case			:     int = 1		,			__snake_case			:     int = 2000		,			__snake_case			:     Optional[Union[torch.Generator, List[torch.Generator]]] = None		,			__snake_case			:     Optional[str] = "pil"		,			__snake_case			:     bool = True		,			**__snake_case			:     Optional[int]		,			)			->		Union[ImagePipelineOutput, Tuple]:
								UpperCAmelCase :       str 							=		self.unet.config.sample_size
								UpperCAmelCase :       Union[str, Any] 							=		(batch_size, 3, img_size, img_size)

								UpperCAmelCase :       int 							=		self.unet

								UpperCAmelCase :       Any 							=		randn_tensor(__snake_case		,			generator=__snake_case		) * self.scheduler.init_noise_sigma
								UpperCAmelCase :       List[Any] 							=		sample.to(self.device		)

								self.scheduler.set_timesteps(__snake_case		)
								self.scheduler.set_sigmas(__snake_case		)

								for i, t in enumerate(self.progress_bar(self.scheduler.timesteps		)		):
															UpperCAmelCase :       Any 							=		self.scheduler.sigmas[i] * torch.ones(shape[0]		,			device=self.device		)

															# correction step
															for _ in range(self.scheduler.config.correct_steps		):
																						UpperCAmelCase :       Union[str, Any] 							=		self.unet(__snake_case		,			__snake_case		).sample
																						UpperCAmelCase :       Optional[Any] 							=		self.scheduler.step_correct(__snake_case		,			__snake_case		,			generator=__snake_case		).prev_sample

															# prediction step
															UpperCAmelCase :       Optional[Any] 							=		model(__snake_case		,			__snake_case		).sample
															UpperCAmelCase :       List[str] 							=		self.scheduler.step_pred(__snake_case		,			__snake_case		,			__snake_case		,			generator=__snake_case		)

															UpperCAmelCase							,		UpperCAmelCase :       Optional[Any] 							=		output.prev_sample, output.prev_sample_mean

								UpperCAmelCase :       int 							=		sample_mean.clamp(0		,			1		)
								UpperCAmelCase :       Union[str, Any] 							=		sample.cpu().permute(0		,			2		,			3		,			1		).numpy()
								if output_type == "pil":
															UpperCAmelCase :       Optional[Any] 							=		self.numpy_to_pil(__snake_case		)

								if not return_dict:
															return (sample,)

								return ImagePipelineOutput(images=__snake_case		)





