
'''simple docstring'''






import json
import os
from typing import Dict, List, Optional, Tuple

import regex as re

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


A			      =      logging.get_logger(__name__)


A			      =      {
    'vocab_file': 'vocab.json',
    'merges_file': 'merges.txt',
    'tokenizer_config_file': 'tokenizer_config.json',
}

A			      =      {
    'vocab_file': {
        'facebook/blenderbot_small-90M': 'https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/vocab.json'
    },
    'merges_file': {
        'facebook/blenderbot_small-90M': 'https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/merges.txt'
    },
    'tokenizer_config_file': {
        'facebook/blenderbot_small-90M': (
            'https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/tokenizer_config.json'
        )
    },
}

A			      =      {'facebook/blenderbot_small-90M': 512}





def    UpperCAmelCase							(  UpperCAmelCase__							:							List[Any]):
				lowerCamelCase					:			Optional[int]									= set()
				lowerCamelCase					:			Optional[int]									= word[0]
				for char in word[1:]:
								pairs.add((prev_char, char))
								lowerCamelCase					:			Tuple									= char

				lowerCamelCase					:			Dict									= set(_lowerCamelCase)
				return pairs




class     __snake_case		(   a__):
					_lowerCAmelCase			     =	VOCAB_FILES_NAMES
					_lowerCAmelCase			     =	PRETRAINED_VOCAB_FILES_MAP
					_lowerCAmelCase			     =	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					_lowerCAmelCase			     =	['''input_ids''', '''attention_mask''']
					def __init__(     self,    A,    A,    A="__start__",    A="__end__",    A="__unk__",    A="__null__",    **A,    ):

									"""simple docstring"""


									super().__init__(unk_token=_A,    bos_token=_A,    eos_token=_A,    pad_token=_A,    **_A      )

									with open(_A,    encoding='utf-8'      ) as vocab_handle:
													lowerCamelCase					:			Union[str, Any]									= json.load(_A      )
									lowerCamelCase					:			int									= {v: k for k, v in self.encoder.items()}
									with open(_A,    encoding='utf-8'      ) as merges_handle:
													lowerCamelCase					:			Optional[int]									= merges_handle.read().split('\n'      )[1:-1]
									lowerCamelCase					:			Optional[int]									= [tuple(merge.split()      ) for merge in merges]
									lowerCamelCase					:			Union[str, Any]									= dict(zip(_A,    range(len(_A      )      )      )      )
									lowerCamelCase					:			Dict									= {}
					@property
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									return len(self.encoder      )
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									return dict(self.encoder,    **self.added_tokens_encoder      )
					def     UpperCAmelCase_							(     self,    A      ):

									"""simple docstring"""


									if token in self.cache:
													return self.cache[token]
									lowerCamelCase					:			Union[str, Any]									= re.sub('([.,!?()])',    r' \1',    _A      )
									lowerCamelCase					:			Any									= re.sub('(\')',    r' \1 ',    _A      )
									lowerCamelCase					:			Tuple									= re.sub(r'\s{2,}',    ' ',    _A      )
									if "\n" in token:
													lowerCamelCase					:			Optional[Any]									= token.replace('\n',    ' __newln__'      )

									lowerCamelCase					:			Optional[Any]									= token.split(' '      )
									lowerCamelCase					:			List[str]									= []
									for token in tokens:
													if not len(_A      ):
																	continue

													lowerCamelCase					:			str									= token.lower()
													lowerCamelCase					:			Optional[Any]									= tuple(_A      )
													lowerCamelCase					:			Union[str, Any]									= tuple(list(word[:-1]      ) + [word[-1] + '</w>']      )
													lowerCamelCase					:			str									= get_pairs(_A      )

													if not pairs:
																	words.append(_A      )
																	continue

													while True:
																	lowerCamelCase					:			str									= min(_A,    key=lambda A      : self.bpe_ranks.get(_A,    float('inf'      )      )      )
																	if bigram not in self.bpe_ranks:
																					break
																	lowerCamelCase					:			Tuple									= bigram
																	lowerCamelCase					:			List[Any]									= []
																	lowerCamelCase					:			Any									= 0

																	while i < len(_A      ):
																					try:
																									lowerCamelCase					:			str									= word.index(_A,    _A      )
																									new_word.extend(word[i:j]      )
																									lowerCamelCase					:			Union[str, Any]									= j
																					except ValueError:
																									new_word.extend(word[i:]      )
																									break

																					if word[i] == first and i < len(_A      ) - 1 and word[i + 1] == second:
																									new_word.append(first + second      )
																									i += 2
																					else:
																									new_word.append(word[i]      )
																									i += 1
																	lowerCamelCase					:			Optional[int]									= tuple(_A      )
																	lowerCamelCase					:			str									= new_word
																	if len(_A      ) == 1:
																					break
																	else:
																					lowerCamelCase					:			List[Any]									= get_pairs(_A      )
													lowerCamelCase					:			Any									= '@@ '.join(_A      )
													lowerCamelCase					:			List[str]									= word[:-4]

													lowerCamelCase					:			Any									= word
													words.append(_A      )
									return " ".join(_A      )
					def     UpperCAmelCase_							(     self,    A      ):

									"""simple docstring"""


									lowerCamelCase					:			Optional[Any]									= []

									lowerCamelCase					:			int									= re.findall(r'\S+\n?',    _A      )

									for token in words:
													split_tokens.extend(list(self.bpe(_A      ).split(' '      )      )      )
									return split_tokens
					def     UpperCAmelCase_							(     self,    A      ):

									"""simple docstring"""


									lowerCamelCase					:			Any									= token.lower()
									return self.encoder.get(_A,    self.encoder.get(self.unk_token      )      )
					def     UpperCAmelCase_							(     self,    A      ):

									"""simple docstring"""


									return self.decoder.get(_A,    self.unk_token      )
					def     UpperCAmelCase_							(     self,    A      ):

									"""simple docstring"""


									lowerCamelCase					:			Optional[Any]									= ' '.join(_A      ).replace('@@ ',    ''      ).strip()
									return out_string




					def     UpperCAmelCase_							(     self,    A,    A = None      ):

									"""simple docstring"""


									if not os.path.isdir(_A      ):
													logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''      )
													return
									lowerCamelCase					:			Dict									= os.path.join(
									    _A,    (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']      )
									lowerCamelCase					:			Tuple									= os.path.join(
									    _A,    (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['merges_file']      )

									with open(_A,    'w',    encoding='utf-8'      ) as f:
													f.write(json.dumps(self.encoder,    indent=2,    sort_keys=_A,    ensure_ascii=_A      ) + '\n'      )

									lowerCamelCase					:			Tuple									= 0
									with open(_A,    'w',    encoding='utf-8'      ) as writer:
													writer.write('#version: 0.2\n'      )
													for bpe_tokens, token_index in sorted(self.bpe_ranks.items(),    key=lambda A      : kv[1]      ):
																	if index != token_index:
																					logger.warning(
																					    F'''Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'''
																					    ' Please check that the tokenizer is not corrupted!'      )
																					lowerCamelCase					:			Dict									= token_index
																	writer.write(' '.join(_A      ) + '\n'      )
																	index += 1

									return vocab_file, merge_file


'''simple docstring'''






import random
import unittest

import numpy as np

from diffusers import (
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    OnnxStableDiffusionImgaImgPipeline,
    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     __snake_case		(   a__    ,   unittest.TestCase):
					_lowerCAmelCase			     =	'''hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline'''
					def     UpperCAmelCase_							(     self,    A=0      ):

									"""simple docstring"""


									lowerCamelCase					:			List[Any]									= floats_tensor((1, 3, 128, 128),    rng=random.Random(A      )      )
									lowerCamelCase					:			Tuple									= np.random.RandomState(A      )
									lowerCamelCase					:			str									= {
									    'prompt': 'A painting of a squirrel eating a burger',
									    'image': image,
									    'generator': generator,
									    'num_inference_steps': 3,
									    'strength': 0.75,
									    'guidance_scale': 7.5,
									    'output_type': 'numpy',
									}
									return inputs
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			Union[str, Any]									= OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint,    provider='CPUExecutionProvider'      )
									pipe.set_progress_bar_config(disable=A      )

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

									assert image.shape == (1, 128, 128, 3)
									lowerCamelCase					:			Union[str, Any]									= np.array([0.6_9643, 0.5_8484, 0.5_0314, 0.5_8760, 0.5_5368, 0.5_9643, 0.5_1529, 0.4_1217, 0.4_9087]      )
									assert np.abs(image_slice - expected_slice      ).max() < 1e-1
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			str									= OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint,    provider='CPUExecutionProvider'      )
									lowerCamelCase					:			Any									= PNDMScheduler.from_config(pipe.scheduler.config,    skip_prk_steps=A      )
									pipe.set_progress_bar_config(disable=A      )

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

									assert image.shape == (1, 128, 128, 3)
									lowerCamelCase					:			Union[str, Any]									= np.array([0.6_1737, 0.5_4642, 0.5_3183, 0.5_4465, 0.5_2742, 0.6_0525, 0.4_9969, 0.4_0655, 0.4_8154]      )

									assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1e-1
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			Optional[Any]									= OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint,    provider='CPUExecutionProvider'      )
									lowerCamelCase					:			Optional[int]									= LMSDiscreteScheduler.from_config(pipe.scheduler.config      )
									pipe.set_progress_bar_config(disable=A      )

									# warmup pass to apply optimizations
									lowerCamelCase					:			Union[str, Any]									= pipe(**self.get_dummy_inputs()      )

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

									assert image.shape == (1, 128, 128, 3)
									lowerCamelCase					:			Dict									= np.array([0.5_2761, 0.5_9977, 0.4_9033, 0.4_9619, 0.5_4282, 0.5_0311, 0.4_7600, 0.4_0918, 0.4_5203]      )

									assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1e-1
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			Any									= OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint,    provider='CPUExecutionProvider'      )
									lowerCamelCase					:			int									= EulerDiscreteScheduler.from_config(pipe.scheduler.config      )
									pipe.set_progress_bar_config(disable=A      )

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

									assert image.shape == (1, 128, 128, 3)
									lowerCamelCase					:			List[str]									= np.array([0.5_2911, 0.6_0004, 0.4_9229, 0.4_9805, 0.5_4502, 0.5_0680, 0.4_7777, 0.4_1028, 0.4_5304]      )

									assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1e-1
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			int									= OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint,    provider='CPUExecutionProvider'      )
									lowerCamelCase					:			Union[str, Any]									= EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config      )
									pipe.set_progress_bar_config(disable=A      )

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

									assert image.shape == (1, 128, 128, 3)
									lowerCamelCase					:			Optional[int]									= np.array([0.5_2911, 0.6_0004, 0.4_9229, 0.4_9805, 0.5_4502, 0.5_0680, 0.4_7777, 0.4_1028, 0.4_5304]      )

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




					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			Union[str, Any]									= OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint,    provider='CPUExecutionProvider'      )
									lowerCamelCase					:			Any									= DPMSolverMultistepScheduler.from_config(pipe.scheduler.config      )
									pipe.set_progress_bar_config(disable=A      )

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

									assert image.shape == (1, 128, 128, 3)
									lowerCamelCase					:			List[Any]									= np.array([0.6_5331, 0.5_8277, 0.4_8204, 0.5_6059, 0.5_3665, 0.5_6235, 0.5_0969, 0.4_0009, 0.4_6552]      )

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





@nightly
@require_onnxruntime
@require_torch_gpu
class     __snake_case		(   unittest.TestCase):
					@property
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


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

									"""simple docstring"""


									lowerCamelCase					:			Optional[int]									= ort.SessionOptions()
									lowerCamelCase					:			Union[str, Any]									= False
									return options
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			Tuple									= load_image(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
									    '/img2img/sketch-mountains-input.jpg'      )
									lowerCamelCase					:			int									= init_image.resize((768, 512)      )
									# using the PNDM scheduler by default
									lowerCamelCase					:			Union[str, Any]									= OnnxStableDiffusionImgaImgPipeline.from_pretrained(
									    'CompVis/stable-diffusion-v1-4',    revision='onnx',    safety_checker=A,    feature_extractor=A,    provider=self.gpu_provider,    sess_options=self.gpu_options,    )
									pipe.set_progress_bar_config(disable=A      )

									lowerCamelCase					:			Optional[Any]									= 'A fantasy landscape, trending on artstation'

									lowerCamelCase					:			Dict									= np.random.RandomState(0      )
									lowerCamelCase					:			Any									= pipe(
									    prompt=A,    image=A,    strength=0.75,    guidance_scale=7.5,    num_inference_steps=10,    generator=A,    output_type='np',    )
									lowerCamelCase					:			Union[str, Any]									= output.images
									lowerCamelCase					:			Union[str, Any]									= images[0, 255:258, 383:386, -1]

									assert images.shape == (1, 512, 768, 3)
									lowerCamelCase					:			List[Any]									= np.array([0.4909, 0.5059, 0.5372, 0.4623, 0.4876, 0.5049, 0.4820, 0.4956, 0.5019]      )
									# 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      ):

									"""simple docstring"""


									lowerCamelCase					:			List[Any]									= load_image(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
									    '/img2img/sketch-mountains-input.jpg'      )
									lowerCamelCase					:			Any									= init_image.resize((768, 512)      )
									lowerCamelCase					:			List[Any]									= LMSDiscreteScheduler.from_pretrained(
									    'runwayml/stable-diffusion-v1-5',    subfolder='scheduler',    revision='onnx'      )
									lowerCamelCase					:			Dict									= OnnxStableDiffusionImgaImgPipeline.from_pretrained(
									    'runwayml/stable-diffusion-v1-5',    revision='onnx',    scheduler=A,    safety_checker=A,    feature_extractor=A,    provider=self.gpu_provider,    sess_options=self.gpu_options,    )
									pipe.set_progress_bar_config(disable=A      )

									lowerCamelCase					:			Union[str, Any]									= 'A fantasy landscape, trending on artstation'

									lowerCamelCase					:			List[Any]									= np.random.RandomState(0      )
									lowerCamelCase					:			Tuple									= pipe(
									    prompt=A,    image=A,    strength=0.75,    guidance_scale=7.5,    num_inference_steps=20,    generator=A,    output_type='np',    )
									lowerCamelCase					:			List[Any]									= output.images
									lowerCamelCase					:			Union[str, Any]									= images[0, 255:258, 383:386, -1]

									assert images.shape == (1, 512, 768, 3)
									lowerCamelCase					:			int									= np.array([0.8043, 0.926, 0.9581, 0.8119, 0.8954, 0.913, 0.7209, 0.7463, 0.7431]      )
									# TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

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