




import unittest

from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow

from ...test_tokenization_common import TokenizerTesterMixin







@require_tokenizers
@require_sentencepiece
@slow  # see https://github.com/huggingface/transformers/issues/11457
class __snake_case			(			a ,  unittest.TestCase		):
   UpperCAmelCase__  :    Dict          =  BarthezTokenizer
   UpperCAmelCase__  :    Optional[Any]          =  BarthezTokenizerFast
   UpperCAmelCase__  :    Union[str, Any]          =  True
   UpperCAmelCase__  :    Optional[Any]          =  True







   def lowerCamelCase						(      self		:						Optional[int]):

        """simple docstring"""


        super().setUp()

        UpperCAmelCase_          =							BarthezTokenizerFast.from_pretrained('''moussaKam/mbarthez''')
        tokenizer.save_pretrained(self.tmpdirname)
        tokenizer.save_pretrained(self.tmpdirname					,						legacy_format=_snake_case)
        UpperCAmelCase_          =							tokenizer







   def lowerCamelCase						(      self		:						str):

        """simple docstring"""


        UpperCAmelCase_          =							'''<pad>'''
        UpperCAmelCase_          =							1

        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		:						Any):

        """simple docstring"""


        UpperCAmelCase_          =							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(_snake_case)					,						101122)







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

        """simple docstring"""


        self.assertEqual(self.get_tokenizer().vocab_size					,						101122)







   @require_torch
   def lowerCamelCase						(      self		:						Optional[Any]):

        """simple docstring"""


        UpperCAmelCase_          =							['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
        UpperCAmelCase_          =							[0, 57, 3018, 70307, 91, 2]

        UpperCAmelCase_          =							self.tokenizer(
            _snake_case					,						max_length=len(_snake_case)					,						padding=_snake_case					,						truncation=_snake_case					,						return_tensors='''pt''')
        self.assertIsInstance(_snake_case					,						_snake_case)

        self.assertEqual((2, 6)					,						batch.input_ids.shape)
        self.assertEqual((2, 6)					,						batch.attention_mask.shape)
        UpperCAmelCase_          =							batch.input_ids.tolist()[0]
        self.assertListEqual(_snake_case					,						_snake_case)







   def lowerCamelCase						(      self		:						Optional[Any]):

        """simple docstring"""


        if not self.test_rust_tokenizer:
             return

        UpperCAmelCase_          =							self.get_tokenizer()
        UpperCAmelCase_          =							self.get_rust_tokenizer()

        UpperCAmelCase_          =							'''I was born in 92000, and this is falsé.'''

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

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

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







   @slow
   def lowerCamelCase						(      self		:						List[str]):

        """simple docstring"""


        UpperCAmelCase_          =							{'''input_ids''': [[0, 490, 14328, 4507, 354, 47, 43669, 95, 25, 78117, 20215, 19779, 190, 22, 400, 4, 35343, 80310, 603, 86, 24937, 105, 33438, 94762, 196, 39642, 7, 15, 15933, 173, 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], [0, 10534, 87, 25, 66, 3358, 196, 55289, 8, 82961, 81, 2204, 75203, 7, 15, 763, 12956, 216, 178, 14328, 9595, 1377, 69693, 7, 448, 71021, 196, 18106, 1437, 13974, 108, 9083, 4, 49315, 7, 39, 86, 1326, 2793, 46333, 4, 448, 196, 74588, 7, 49315, 7, 39, 21, 822, 38470, 74, 21, 66723, 62480, 8, 22050, 5, 2]], '''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, 0, 0, 0, 0, 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, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 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

        # moussaKam/mbarthez is a french model. So we also use french texts.
        UpperCAmelCase_          =							[
            '''Le transformeur est un modèle d\'apprentissage profond introduit en 2017, '''
            '''utilisé principalement dans le domaine du traitement automatique des langues (TAL).''',
            '''À l\'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus '''
            '''pour gérer des données séquentielles, telles que le langage naturel, pour des tâches '''
            '''telles que la traduction et la synthèse de texte.''',
        ]

        self.tokenizer_integration_test_util(
            expected_encoding=_snake_case					,						model_name='''moussaKam/mbarthez'''					,						revision='''c2e4ecbca5e3cd2c37fe1ac285ca4fbdf1366fb6'''					,						sequences=_snake_case					,						)










import random
import unittest

import numpy as np
import torch

from diffusers import (
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    OnnxStableDiffusionUpscalePipeline,
    PNDMScheduler,
)
from diffusers.utils import floats_tensor
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)

from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin


if is_onnx_available():
 import onnxruntime as ort






class _lowerCamelCase(						_a, unittest.TestCase   ):
       # TODO: is there an appropriate internal test set?
       lowercase_ :	int     =			"""ssube/stable-diffusion-x4-upscaler-onnx"""
       def 			UpperCamelCase     (					self,      lowerCamelCase=0)       ->   Union[str, Any]:




           """simple docstring"""

           _lowercase       :							Dict      		=		floats_tensor((1, 3, 1_28, 1_28),      rng=random.Random(lowerCamelCase))
           _lowercase       :							Union[str, Any]      		=		torch.manual_seed(lowerCamelCase)
           _lowercase       :							Optional[Any]      		=		{
               'prompt': 'A painting of a squirrel eating a burger',
               'image': image,
               'generator': generator,
               'num_inference_steps': 3,
               'guidance_scale': 7.5,
               'output_type': 'numpy',
           }
           return inputs
       def 			UpperCamelCase     (					self)       ->   Tuple:




           """simple docstring"""

           _lowercase       :							Tuple      		=		OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint,      provider='CPUExecutionProvider')
           pipe.set_progress_bar_config(disable=lowerCamelCase)

           _lowercase       :							Dict      		=		self.get_dummy_inputs()
           _lowercase       :							Optional[int]      		=		pipe(**lowerCamelCase).images
           _lowercase       :							Optional[int]      		=		image[0, -3:, -3:, -1].flatten()

           # started as 128, should now be 512
           assert image.shape == (1, 5_12, 5_12, 3)
           _lowercase       :							Union[str, Any]      		=		np.array(
               [0.6_9_7_4_7_8_2, 0.6_8_9_0_2_0_9_3, 0.7_0_1_3_5_8_8_5, 0.7_5_8_3_6_1_8, 0.7_8_0_4_5_4_5, 0.7_8_5_4_9_1_2, 0.7_8_6_6_7_4_2_6, 0.7_8_7_4_3_8_6_3, 0.7_8_0_7_0_2_2_3])
           assert np.abs(image_slice - expected_slice).max() < 1E-1
       def 			UpperCamelCase     (					self)       ->   Union[str, Any]:




           """simple docstring"""

           _lowercase       :							int      		=		OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint,      provider='CPUExecutionProvider')
           _lowercase       :							str      		=		PNDMScheduler.from_config(pipe.scheduler.config,      skip_prk_steps=lowerCamelCase)
           pipe.set_progress_bar_config(disable=lowerCamelCase)

           _lowercase       :							List[str]      		=		self.get_dummy_inputs()
           _lowercase       :							List[Any]      		=		pipe(**lowerCamelCase).images
           _lowercase       :							str      		=		image[0, -3:, -3:, -1]

           assert image.shape == (1, 5_12, 5_12, 3)
           _lowercase       :							int      		=		np.array(
               [0.6_8_9_8_8_9_2, 0.5_9_2_4_0_5_5_6, 0.5_2_4_9_9_5_2_7, 0.5_8_8_6_6_2_1_5, 0.5_2_2_5_8_2_3_5, 0.5_2_5_7_2_7_1_5, 0.6_2_4_1_4_4_7_3, 0.6_1_7_4_3_8_7, 0.6_2_1_4_9_6_4])
           assert np.abs(image_slice.flatten() - expected_slice).max() < 1E-1
       def 			UpperCamelCase     (					self)       ->   Optional[Any]:




           """simple docstring"""

           _lowercase       :							Dict      		=		OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint,      provider='CPUExecutionProvider')
           _lowercase       :							Optional[int]      		=		DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
           pipe.set_progress_bar_config(disable=lowerCamelCase)

           _lowercase       :							Optional[int]      		=		self.get_dummy_inputs()
           _lowercase       :							Union[str, Any]      		=		pipe(**lowerCamelCase).images
           _lowercase       :							str      		=		image[0, -3:, -3:, -1]

           assert image.shape == (1, 5_12, 5_12, 3)
           _lowercase       :							Optional[int]      		=		np.array(
               [0.7_6_5_9_2_7_8, 0.7_6_4_3_7_6_6_4, 0.7_5_5_7_9_1_0_7, 0.7_6_9_1_1_1_6, 0.7_7_6_6_6_9_8_6, 0.7_7_2_7_6_7_2, 0.7_7_5_8_6_6_4, 0.7_8_1_2_2_2_6, 0.7_6_9_4_2_5_1_5])

           assert np.abs(image_slice.flatten() - expected_slice).max() < 1E-1
       def 			UpperCamelCase     (					self)       ->   Optional[int]:




           """simple docstring"""

           _lowercase       :							List[str]      		=		OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint,      provider='CPUExecutionProvider')
           _lowercase       :							List[str]      		=		EulerDiscreteScheduler.from_config(pipe.scheduler.config)
           pipe.set_progress_bar_config(disable=lowerCamelCase)

           _lowercase       :							Dict      		=		self.get_dummy_inputs()
           _lowercase       :							Optional[Any]      		=		pipe(**lowerCamelCase).images
           _lowercase       :							Optional[int]      		=		image[0, -3:, -3:, -1]

           assert image.shape == (1, 5_12, 5_12, 3)
           _lowercase       :							Union[str, Any]      		=		np.array(
               [0.6_9_7_4_7_8_2, 0.6_8_9_0_2_0_9_3, 0.7_0_1_3_5_8_8_5, 0.7_5_8_3_6_1_8, 0.7_8_0_4_5_4_5, 0.7_8_5_4_9_1_2, 0.7_8_6_6_7_4_2_6, 0.7_8_7_4_3_8_6_3, 0.7_8_0_7_0_2_2_3])
           assert np.abs(image_slice.flatten() - expected_slice).max() < 1E-1




       def 			UpperCamelCase     (					self)       ->   Dict:




           """simple docstring"""

           _lowercase       :							Tuple      		=		OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint,      provider='CPUExecutionProvider')
           _lowercase       :							Any      		=		EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)
           pipe.set_progress_bar_config(disable=lowerCamelCase)

           _lowercase       :							Any      		=		self.get_dummy_inputs()
           _lowercase       :							List[str]      		=		pipe(**lowerCamelCase).images
           _lowercase       :							Any      		=		image[0, -3:, -3:, -1]

           assert image.shape == (1, 5_12, 5_12, 3)
           _lowercase       :							Union[str, Any]      		=		np.array(
               [0.7_7_4_2_4_4_9_6, 0.7_7_3_6_0_1, 0.7_6_4_5_2_8_8, 0.7_7_6_9_5_9_8, 0.7_7_7_2_7_3_9, 0.7_7_3_8_6_8_8, 0.7_8_1_8_7_2_3_3, 0.7_7_8_7_9_5_8_4, 0.7_6_7_0_4_3])

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






@nightly
@require_onnxruntime
@require_torch_gpu
class _lowerCamelCase(						unittest.TestCase   ):
       @property
       def 			UpperCamelCase     (					self)       ->   List[Any]:




           """simple docstring"""

           return (
               "CUDAExecutionProvider",
               {
                   "gpu_mem_limit": "15000000000",  # 15GB
                   "arena_extend_strategy": "kSameAsRequested",
               },
           )
       @property
       def 			UpperCamelCase     (					self)       ->   Any:




           """simple docstring"""

           _lowercase       :							Union[str, Any]      		=		ort.SessionOptions()
           _lowercase       :							str      		=		False
           return options
       def 			UpperCamelCase     (					self)       ->   Any:




           """simple docstring"""

           _lowercase       :							Optional[int]      		=		load_image(
               'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
               '/img2img/sketch-mountains-input.jpg')
           _lowercase       :							int      		=		init_image.resize((1_28, 1_28))
           # using the PNDM scheduler by default
           _lowercase       :							Tuple      		=		OnnxStableDiffusionUpscalePipeline.from_pretrained(
               'ssube/stable-diffusion-x4-upscaler-onnx',      provider=self.gpu_provider,      sess_options=self.gpu_options,      )
           pipe.set_progress_bar_config(disable=lowerCamelCase)

           _lowercase       :							Optional[int]      		=		'A fantasy landscape, trending on artstation'

           _lowercase       :							List[Any]      		=		torch.manual_seed(0)
           _lowercase       :							str      		=		pipe(
               prompt=lowerCamelCase,      image=lowerCamelCase,      guidance_scale=7.5,      num_inference_steps=10,      generator=lowerCamelCase,      output_type='np',      )
           _lowercase       :							List[Any]      		=		output.images
           _lowercase       :							List[Any]      		=		images[0, 2_55:2_58, 3_83:3_86, -1]

           assert images.shape == (1, 5_12, 5_12, 3)
           _lowercase       :							List[Any]      		=		np.array([0.4_8_8_3, 0.4_9_4_7, 0.4_9_8_0, 0.4_9_7_5, 0.4_9_8_2, 0.4_9_8_0, 0.5_0_0_0, 0.5_0_0_6, 0.4_9_7_2])
           # TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

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




       def 			UpperCamelCase     (					self)       ->   Any:




           """simple docstring"""

           _lowercase       :							Optional[int]      		=		load_image(
               'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
               '/img2img/sketch-mountains-input.jpg')
           _lowercase       :							int      		=		init_image.resize((1_28, 1_28))
           _lowercase       :							str      		=		LMSDiscreteScheduler.from_pretrained(
               'ssube/stable-diffusion-x4-upscaler-onnx',      subfolder='scheduler')
           _lowercase       :							Dict      		=		OnnxStableDiffusionUpscalePipeline.from_pretrained(
               'ssube/stable-diffusion-x4-upscaler-onnx',      scheduler=lowerCamelCase,      provider=self.gpu_provider,      sess_options=self.gpu_options,      )
           pipe.set_progress_bar_config(disable=lowerCamelCase)

           _lowercase       :							Optional[int]      		=		'A fantasy landscape, trending on artstation'

           _lowercase       :							List[Any]      		=		torch.manual_seed(0)
           _lowercase       :							str      		=		pipe(
               prompt=lowerCamelCase,      image=lowerCamelCase,      guidance_scale=7.5,      num_inference_steps=20,      generator=lowerCamelCase,      output_type='np',      )
           _lowercase       :							str      		=		output.images
           _lowercase       :							str      		=		images[0, 2_55:2_58, 3_83:3_86, -1]

           assert images.shape == (1, 5_12, 5_12, 3)
           _lowercase       :							Union[str, Any]      		=		np.array(
               [0.5_0_1_7_3_7_5_3, 0.5_0_2_2_3_3_5_6, 0.5_0_2_0_3_9, 0.5_0_2_3_3_0_3_6, 0.5_0_2_3_7_2_5, 0.5_0_2_2_6_0_1, 0.5_0_1_8_7_5_8, 0.5_0_2_3_4_0_8_5, 0.5_0_2_4_1_5_6_6])
           # TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

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


