
import os
from typing import List, Optional, Union

from ...tokenization_utils import PreTrainedTokenizer
from ...tokenization_utils_base import AddedToken
from ...utils import logging


A     :	Optional[int]    = logging.get_logger(__name__)

A     :	Dict    = {'vocab_file': 'vocab.txt'}

A     :	Union[str, Any]    = {
    'vocab_file': {
        'facebook/esm2_t6_8M_UR50D': 'https://huggingface.co/facebook/esm2_t6_8M_UR50D/resolve/main/vocab.txt',
        'facebook/esm2_t12_35M_UR50D': 'https://huggingface.co/facebook/esm2_t12_35M_UR50D/resolve/main/vocab.txt',
    },
}

A     :	int    = {
    'facebook/esm2_t6_8M_UR50D': 1_0_2_4,
    'facebook/esm2_t12_35M_UR50D': 1_0_2_4,
}


def     __lowerCAmelCase			(			a__  )       ->  Dict:
				with open(a__						,    '''r'''  ) as f:
								__a										=    f.read().splitlines()
								return [l.strip() for l in lines]







class 			__A( a			):
				snake_case_						   =    VOCAB_FILES_NAMES
				snake_case_						   =    PRETRAINED_VOCAB_FILES_MAP
				snake_case_						   =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				snake_case_						   =    ['''input_ids''', '''attention_mask''']




				def __init__(     self		,     _snake_case		,     _snake_case="<unk>"		,     _snake_case="<cls>"		,     _snake_case="<pad>"		,     _snake_case="<mask>"		,     _snake_case="<eos>"		,     **_snake_case		,     )    ->      str:


								'''simple docstring'''




								super().__init__(**_snake_case				)
								__a										=    load_vocab_file(_snake_case				)
								__a										=    dict(enumerate(self.all_tokens				)				)
								__a										=    {tok: ind for ind, tok in enumerate(self.all_tokens				)}
								__a										=    unk_token
								__a										=    cls_token
								__a										=    pad_token
								__a										=    mask_token
								__a										=    eos_token
								__a										=    self.all_tokens
								self._create_trie(self.unique_no_split_tokens				)




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case				)    ->      str:


								'''simple docstring'''




								return self._id_to_token.get(_snake_case		,     self.unk_token				)




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case				)    ->      int:


								'''simple docstring'''




								return self._token_to_id.get(_snake_case		,     self._token_to_id.get(self.unk_token				)				)




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case		,     **_snake_case				)    ->      Optional[int]:


								'''simple docstring'''




								return text.split()




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case=False				)    ->      str:


								'''simple docstring'''




								return len(self._id_to_token				)




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


								'''simple docstring'''




								return {token: i for i, token in enumerate(self.all_tokens				)}




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case				)    ->      int:


								'''simple docstring'''




								return self._token_to_id.get(_snake_case		,     self._token_to_id.get(self.unk_token				)				)




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case				)    ->      str:


								'''simple docstring'''




								return self._id_to_token.get(_snake_case		,     self.unk_token				)




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case		,     _snake_case = None				)    ->      List[int]:


								'''simple docstring'''




								__a										=    [self.cls_token_id]
								__a										=    [self.eos_token_id]  # No sep token in ESM vocabulary
								if token_ids_a is None:
												if self.eos_token_id is None:
																return cls + token_ids_a
												else:
																return cls + token_ids_a + sep
								elif self.eos_token_id is None:
												raise ValueError('''Cannot tokenize multiple sequences when EOS token is not set!'''				)
								return cls + token_ids_a + sep + token_ids_a + sep  # Multiple inputs always have an EOS token




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case		,     _snake_case = None		,     _snake_case = False				)    ->      List[int]:


								'''simple docstring'''




								if already_has_special_tokens:
												if token_ids_a is not None:
																raise ValueError(
																    '''You should not supply a second sequence if the provided sequence of '''
																    '''ids is already formatted with special tokens for the model.'''				)

												return [1 if token in self.all_special_ids else 0 for token in token_ids_a]
								__a										=    [1] + ([0] * len(_snake_case				)) + [1]
								if token_ids_a is not None:
												mask += [0] * len(_snake_case				) + [1]
								return mask




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case		,     _snake_case				)    ->      Optional[Any]:


								'''simple docstring'''




								__a										=    os.path.join(_snake_case		,     (filename_prefix + '''-''' if filename_prefix else '''''') + '''vocab.txt'''				)
								with open(_snake_case		,     '''w'''				) as f:
												f.write('''\n'''.join(self.all_tokens				)				)
								return (vocab_file,)




				@property
				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      int:


								'''simple docstring'''




								return self.get_vocab_size(with_added_tokens=_snake_case				)




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case		,     _snake_case = False				)    ->      int:


								'''simple docstring'''




								return super()._add_tokens(_snake_case		,     special_tokens=_snake_case				)

import unittest

import numpy as np

from diffusers import LMSDiscreteScheduler, OnnxStableDiffusionInpaintPipeline
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 			__A( a  ,   unittest.TestCase			):
				# FIXME: add fast tests
				pass







@nightly
@require_onnxruntime
@require_torch_gpu
class 			__A( unittest.TestCase			):




				@property
				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      List[str]:


								'''simple docstring'''




								return (
								    "CUDAExecutionProvider",
								    {
								        "gpu_mem_limit": "15000000000",  # 15GB
								        "arena_extend_strategy": "kSameAsRequested",
								    },
								)




				@property
				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Union[str, Any]:


								'''simple docstring'''




								__a										=    ort.SessionOptions()
								__a										=    False
								return options




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


								'''simple docstring'''




								__a										=    load_image(
								    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
								    '''/in_paint/overture-creations-5sI6fQgYIuo.png'''				)
								__a										=    load_image(
								    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
								    '''/in_paint/overture-creations-5sI6fQgYIuo_mask.png'''				)
								__a										=    OnnxStableDiffusionInpaintPipeline.from_pretrained(
								    '''runwayml/stable-diffusion-inpainting'''		,     revision='''onnx'''		,     safety_checker=_snake_case		,     feature_extractor=_snake_case		,     provider=self.gpu_provider		,     sess_options=self.gpu_options		,     )
								pipe.set_progress_bar_config(disable=_snake_case				)

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

								__a										=    np.random.RandomState(0				)
								__a										=    pipe(
								    prompt=_snake_case		,     image=_snake_case		,     mask_image=_snake_case		,     guidance_scale=7.5		,     num_inference_steps=10		,     generator=_snake_case		,     output_type='''np'''		,     )
								__a										=    output.images
								__a										=    images[0, 255:258, 255:258, -1]

								assert images.shape == (1, 512, 512, 3)
								__a										=    np.array([0.2514, 0.3007, 0.3517, 0.1790, 0.2382, 0.3167, 0.1944, 0.2273, 0.2464]				)

								assert np.abs(image_slice.flatten() - expected_slice				).max() < 1E-3




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Tuple:


								'''simple docstring'''




								__a										=    load_image(
								    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
								    '''/in_paint/overture-creations-5sI6fQgYIuo.png'''				)
								__a										=    load_image(
								    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
								    '''/in_paint/overture-creations-5sI6fQgYIuo_mask.png'''				)
								__a										=    LMSDiscreteScheduler.from_pretrained(
								    '''runwayml/stable-diffusion-inpainting'''		,     subfolder='''scheduler'''		,     revision='''onnx'''				)
								__a										=    OnnxStableDiffusionInpaintPipeline.from_pretrained(
								    '''runwayml/stable-diffusion-inpainting'''		,     revision='''onnx'''		,     scheduler=_snake_case		,     safety_checker=_snake_case		,     feature_extractor=_snake_case		,     provider=self.gpu_provider		,     sess_options=self.gpu_options		,     )
								pipe.set_progress_bar_config(disable=_snake_case				)

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

								__a										=    np.random.RandomState(0				)
								__a										=    pipe(
								    prompt=_snake_case		,     image=_snake_case		,     mask_image=_snake_case		,     guidance_scale=7.5		,     num_inference_steps=20		,     generator=_snake_case		,     output_type='''np'''		,     )
								__a										=    output.images
								__a										=    images[0, 255:258, 255:258, -1]

								assert images.shape == (1, 512, 512, 3)
								__a										=    np.array([0.0086, 0.0077, 0.0083, 0.0093, 0.0107, 0.0139, 0.0094, 0.0097, 0.0125]				)

								assert np.abs(image_slice.flatten() - expected_slice				).max() < 1E-3