



import json
import os
import unittest

from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES, XLMTokenizer
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin



class   UpperCAmelCase_    (       lowerCamelCase_   ,					unittest.TestCase):
							lowerCamelCase__   			=		XLMTokenizer
							lowerCamelCase__   			=		False







							def    snake_case__      (   self):

														'''simple docstring'''

														super().setUp()

														# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
														_lowerCAmelCase     :							Tuple    =    [
														    "l",
														    "o",
														    "w",
														    "e",
														    "r",
														    "s",
														    "t",
														    "i",
														    "d",
														    "n",
														    "w</w>",
														    "r</w>",
														    "t</w>",
														    "lo",
														    "low",
														    "er</w>",
														    "low</w>",
														    "lowest</w>",
														    "newer</w>",
														    "wider</w>",
														    "<unk>",
														]
														_lowerCAmelCase     :							str    =    dict(zip(lowerCAmelCase__,			range(len(lowerCAmelCase__))))
														_lowerCAmelCase     :							int    =    ["l o 123", "lo w 1456", "e r</w> 1789", ""]

														_lowerCAmelCase     :							Union[str, Any]    =    os.path.join(self.tmpdirname,			VOCAB_FILES_NAMES["vocab_file"])
														_lowerCAmelCase     :							Any    =    os.path.join(self.tmpdirname,			VOCAB_FILES_NAMES["merges_file"])
														with open(self.vocab_file,			"w") as fp:
																					fp.write(json.dumps(lowerCAmelCase__))
														with open(self.merges_file,			"w") as fp:
																					fp.write("\n".join(lowerCAmelCase__))







							def    snake_case__      (   self,			__a):

														'''simple docstring'''

														_lowerCAmelCase     :							Tuple    =    "lower newer"
														_lowerCAmelCase     :							Union[str, Any]    =    "lower newer"
														return input_text, output_text







							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							Union[str, Any]    =    XLMTokenizer(self.vocab_file,			self.merges_file)

														_lowerCAmelCase     :							str    =    "lower"
														_lowerCAmelCase     :							str    =    ["low", "er</w>"]
														_lowerCAmelCase     :							List[Any]    =    tokenizer.tokenize(lowerCAmelCase__)
														self.assertListEqual(lowerCAmelCase__,			lowerCAmelCase__)

														_lowerCAmelCase     :							List[Any]    =    tokens + ["<unk>"]
														_lowerCAmelCase     :							str    =    [14, 15, 20]
														self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCAmelCase__),			lowerCAmelCase__)







							@slow
							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							str    =    XLMTokenizer.from_pretrained("xlm-mlm-en-2048")

														_lowerCAmelCase     :							str    =    tokenizer.encode("sequence builders",			add_special_tokens=lowerCAmelCase__)
														_lowerCAmelCase     :							Dict    =    tokenizer.encode("multi-sequence build",			add_special_tokens=lowerCAmelCase__)

														_lowerCAmelCase     :							int    =    tokenizer.build_inputs_with_special_tokens(lowerCAmelCase__)
														_lowerCAmelCase     :							int    =    tokenizer.build_inputs_with_special_tokens(lowerCAmelCase__,			lowerCAmelCase__)

														assert encoded_sentence == [0] + text + [1]
														assert encoded_pair == [0] + text + [1] + text_a + [1]




"""simple docstring"""




import unittest

import numpy as np

from diffusers import OnnxStableDiffusionInpaintPipelineLegacy
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    load_numpy,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)


if is_onnx_available():
	import onnxruntime as ort
@nightly
@require_onnxruntime
@require_torch_gpu
class 					lowerCAmelCase	(					unittest.TestCase    ):




		'''simple docstring'''







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







		@property
		def 							__A						(	self				)     ->							List[str]:
								SCREAMING_SNAKE_CASE													=					ort.SessionOptions()
								SCREAMING_SNAKE_CASE													=					False
								return options







		def 							__A						(	self				)     ->							Dict:
								SCREAMING_SNAKE_CASE													=					load_image(
								    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
								    '/in_paint/overture-creations-5sI6fQgYIuo.png'				)
								SCREAMING_SNAKE_CASE													=					load_image(
								    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
								    '/in_paint/overture-creations-5sI6fQgYIuo_mask.png'				)
								SCREAMING_SNAKE_CASE													=					load_numpy(
								    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
								    '/in_paint/red_cat_sitting_on_a_park_bench_onnx.npy'				)

								# using the PNDM scheduler by default
								SCREAMING_SNAKE_CASE													=					OnnxStableDiffusionInpaintPipelineLegacy.from_pretrained(
								    'CompVis/stable-diffusion-v1-4'						,       revision='onnx'						,       safety_checker=lowerCAmelCase__						,       feature_extractor=lowerCAmelCase__						,       provider=self.gpu_provider						,       sess_options=self.gpu_options						,       )
								pipe.set_progress_bar_config(disable=lowerCAmelCase__				)

								SCREAMING_SNAKE_CASE													=					'A red cat sitting on a park bench'

								SCREAMING_SNAKE_CASE													=					np.random.RandomState(0				)
								SCREAMING_SNAKE_CASE													=					pipe(
								    prompt=lowerCAmelCase__						,       image=lowerCAmelCase__						,       mask_image=lowerCAmelCase__						,       strength=0.75						,       guidance_scale=7.5						,       num_inference_steps=15						,       generator=lowerCAmelCase__						,       output_type='np'						,       )

								SCREAMING_SNAKE_CASE													=					output.images[0]

								assert image.shape == (512, 512, 3)
								assert np.abs(expected_image - image				).max() < 1e-2


