






'''simple docstring'''

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


UpperCAmelCase__						:Tuple          =			get_tests_dir("""fixtures/test_sentencepiece.model""")







@require_sentencepiece
class 	SCREAMING_SNAKE_CASE	(      lowerCAmelCase_    ,   unittest.TestCase  ):
       snake_case__    :     Optional[int]   		=			XLMProphetNetTokenizer
       snake_case__    :     Any   		=			False
       snake_case__    :     Optional[Any]   		=			True




       def 					a_					(					self      : List[Any]					):


            """simple docstring"""







            super().setUp()

            # We have a SentencePiece fixture for testing
            __lowerCamelCase						:       Union[str, Any]				   =				XLMProphetNetTokenizer(A__	,    keep_accents=A__					)
            tokenizer.save_pretrained(self.tmpdirname					)




       def 					a_					(					self      : Any					):


            """simple docstring"""







            __lowerCamelCase						:       Dict				   =				"""[PAD]"""
            __lowerCamelCase						:       Tuple				   =				0

            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      : Dict					):


            """simple docstring"""







            __lowerCamelCase						:       Union[str, Any]				   =				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(A__					)	,    1012					)




       def 					a_					(					self      : Dict					):


            """simple docstring"""







            self.assertEqual(self.get_tokenizer().vocab_size	,    1012					)




       def 					a_					(					self      : str					):


            """simple docstring"""







            __lowerCamelCase						:       List[Any]				   =				XLMProphetNetTokenizer(A__	,    keep_accents=A__					)

            __lowerCamelCase						:       int				   =				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 [285, 46, 10, 170, 382]]	,    )

            __lowerCamelCase						:       str				   =				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						:       List[Any]				   =				tokenizer.convert_tokens_to_ids(A__					)
            self.assertListEqual(
                A__	,    [
                    value + tokenizer.fairseq_offset
                    for value in [8, 21, 84, 55, 24, 19, 7, -9, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, -9, 4]
                ]	,    )

            __lowerCamelCase						:       str				   =				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]""",
                    """.""",
                ]	,    )




       @cached_property
       def 					a_					(					self      : List[Any]					):


            """simple docstring"""







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




       @slow
       def 					a_					(					self      : List[Any]					):


            """simple docstring"""







            __lowerCamelCase						:       Tuple				   =				"""Hello World!"""
            __lowerCamelCase						:       Tuple				   =				[35389, 6672, 49, 2]
            self.assertListEqual(A__	,    self.big_tokenizer.encode(A__					)					)




       @slow
       def 					a_					(					self      : int					):


            """simple docstring"""







            __lowerCamelCase						:       List[str]				   =				{"""input_ids""": [[11073, 82783, 18, 26, 82783, 549, 51540, 248, 17209, 1301, 217, 20, 215186, 1325, 147, 17209, 1301, 217, 20, 56370, 53, 122020, 20, 16477, 27, 87355, 4548, 20, 4728, 78392, 17, 159969, 18, 26, 24491, 629, 15, 538, 22704, 5439, 15, 2788, 24491, 9885, 15, 43534, 605, 15, 814, 18403, 33200, 29, 15, 43534, 24458, 12410, 111, 24966, 83669, 9637, 144068, 26, 850, 22346, 27, 147, 24966, 83669, 83490, 26, 39113, 735, 27, 689, 656, 2800, 1339, 4600, 53, 122020, 115785, 34, 816, 1339, 46887, 18, 147, 53905, 1951, 42238, 41170, 17732, 834, 436, 15, 27523, 98733, 217, 147, 5542, 4981, 930, 17347, 16, 2], [20091, 629, 94, 82786, 58, 490, 20, 1528, 84, 53905, 344, 80592, 110128, 18822, 5267, 1306, 62, 152537, 308, 7997, 401, 124427, 549, 35442, 225, 109, 15055, 25748, 147, 7119, 43712, 34, 767, 135366, 18, 16, 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], [592, 63784, 119466, 17, 147808, 88214, 18, 656, 81, 32, 3296, 10280, 16, 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=A__	,    model_name="""microsoft/xprophetnet-large-wiki100-cased"""	,    revision="""1acad1643ddd54a44df6a1b797ada8373685d90e"""	,    )











'''simple docstring'''

import gc
import unittest

import numpy as np
import torch

from diffusers import StableDiffusionKDiffusionPipeline
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()







@slow
@require_torch_gpu
class 	SCREAMING_SNAKE_CASE	(      unittest.TestCase  ):




       def 					a_					(					self      : Union[str, Any]					):


            """simple docstring"""







            super().tearDown()
            gc.collect()
            torch.cuda.empty_cache()




       def 					a_					(					self      : Optional[int]					):


            """simple docstring"""







            __lowerCamelCase						:       Dict				   =				StableDiffusionKDiffusionPipeline.from_pretrained("""CompVis/stable-diffusion-v1-4"""					)
            __lowerCamelCase						:       Tuple				   =				sd_pipe.to(A__					)
            sd_pipe.set_progress_bar_config(disable=A__					)

            sd_pipe.set_scheduler("""sample_euler"""					)

            __lowerCamelCase						:       Any				   =				"""A painting of a squirrel eating a burger"""
            __lowerCamelCase						:       List[Any]				   =				torch.manual_seed(0					)
            __lowerCamelCase						:       Dict				   =				sd_pipe([prompt]	,    generator=A__	,    guidance_scale=9.0	,    num_inference_steps=20	,    output_type="""np"""					)

            __lowerCamelCase						:       Tuple				   =				output.images

            __lowerCamelCase						:       int				   =				image[0, -3:, -3:, -1]

            assert image.shape == (1, 512, 512, 3)
            __lowerCamelCase						:       List[str]				   =				np.array([0.0447, 0.0492, 0.0468, 0.0408, 0.0383, 0.0408, 0.0354, 0.0380, 0.0339]					)

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




       def 					a_					(					self      : Dict					):


            """simple docstring"""







            __lowerCamelCase						:       Any				   =				StableDiffusionKDiffusionPipeline.from_pretrained("""stabilityai/stable-diffusion-2-1-base"""					)
            __lowerCamelCase						:       int				   =				sd_pipe.to(A__					)
            sd_pipe.set_progress_bar_config(disable=A__					)

            sd_pipe.set_scheduler("""sample_euler"""					)

            __lowerCamelCase						:       List[Any]				   =				"""A painting of a squirrel eating a burger"""
            __lowerCamelCase						:       Union[str, Any]				   =				torch.manual_seed(0					)
            __lowerCamelCase						:       int				   =				sd_pipe([prompt]	,    generator=A__	,    guidance_scale=9.0	,    num_inference_steps=20	,    output_type="""np"""					)

            __lowerCamelCase						:       List[str]				   =				output.images

            __lowerCamelCase						:       Optional[int]				   =				image[0, -3:, -3:, -1]

            assert image.shape == (1, 512, 512, 3)
            __lowerCamelCase						:       Optional[int]				   =				np.array([0.1237, 0.1320, 0.1438, 0.1359, 0.1390, 0.1132, 0.1277, 0.1175, 0.1112]					)

            assert np.abs(image_slice.flatten() - expected_slice					).max() < 5e-1




       def 					a_					(					self      : str					):


            """simple docstring"""







            __lowerCamelCase						:       List[str]				   =				StableDiffusionKDiffusionPipeline.from_pretrained("""stabilityai/stable-diffusion-2-1-base"""					)
            __lowerCamelCase						:       List[str]				   =				sd_pipe.to(A__					)
            sd_pipe.set_progress_bar_config(disable=A__					)

            sd_pipe.set_scheduler("""sample_dpmpp_2m"""					)

            __lowerCamelCase						:       int				   =				"""A painting of a squirrel eating a burger"""
            __lowerCamelCase						:       Tuple				   =				torch.manual_seed(0					)
            __lowerCamelCase						:       Union[str, Any]				   =				sd_pipe(
                [prompt]	,    generator=A__	,    guidance_scale=7.5	,    num_inference_steps=15	,    output_type="""np"""	,    use_karras_sigmas=A__	,    )

            __lowerCamelCase						:       int				   =				output.images

            __lowerCamelCase						:       Any				   =				image[0, -3:, -3:, -1]

            assert image.shape == (1, 512, 512, 3)
            __lowerCamelCase						:       int				   =				np.array(
                [0.1138_1689, 0.1211_2921, 0.138_9457, 0.1254_9606, 0.124_4964, 0.1083_1517, 0.1156_2866, 0.1086_7816, 0.1049_9048]					)

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



