
'''simple docstring'''






import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer
from ...utils import logging


A			      =      logging.get_logger(__name__)

A			      =      """▁"""

A			      =      {"""vocab_file""": """sentencepiece.bpe.model"""}

A			      =      {
    """vocab_file""": {
        """facebook/mbart-large-en-ro""": (
            """https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/sentencepiece.bpe.model"""
        ),
        """facebook/mbart-large-cc25""": (
            """https://huggingface.co/facebook/mbart-large-cc25/resolve/main/sentencepiece.bpe.model"""
        ),
    }
}

A			      =      {
    """facebook/mbart-large-en-ro""": 1024,
    """facebook/mbart-large-cc25""": 1024,
}

# fmt: off
A			      =      ["""ar_AR""", """cs_CZ""", """de_DE""", """en_XX""", """es_XX""", """et_EE""", """fi_FI""", """fr_XX""", """gu_IN""", """hi_IN""", """it_IT""", """ja_XX""", """kk_KZ""", """ko_KR""", """lt_LT""", """lv_LV""", """my_MM""", """ne_NP""", """nl_XX""", """ro_RO""", """ru_RU""", """si_LK""", """tr_TR""", """vi_VN""", """zh_CN"""]




class     __snake_case		(   __snake_case):
					_lowerCAmelCase			     =	VOCAB_FILES_NAMES
					_lowerCAmelCase			     =	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					_lowerCAmelCase			     =	PRETRAINED_VOCAB_FILES_MAP
					_lowerCAmelCase			     =	['input_ids', 'attention_mask']

					_lowerCAmelCase			     =	[]
					_lowerCAmelCase			     =	[]
					def __init__(     self,    A,    A="<s>",    A="</s>",    A="</s>",    A="<s>",    A="<unk>",    A="<pad>",    A="<mask>",    A=None,    A=None,    A=None,    A = None,    A=None,    **A,    ):

									"""simple docstring"""


									lowerCamelCase					:			int									= AddedToken(A_,    lstrip=A_,    rstrip=A_      ) if isinstance(A_,    A_      ) else mask_token

									lowerCamelCase					:			Optional[int]									= {} if sp_model_kwargs is None else sp_model_kwargs

									super().__init__(
									    bos_token=A_,    eos_token=A_,    unk_token=A_,    sep_token=A_,    cls_token=A_,    pad_token=A_,    mask_token=A_,    tokenizer_file=A_,    src_lang=A_,    tgt_lang=A_,    additional_special_tokens=A_,    sp_model_kwargs=self.sp_model_kwargs,    **A_,    )

									lowerCamelCase					:			Optional[int]									= spm.SentencePieceProcessor(**self.sp_model_kwargs      )
									self.sp_model.Load(str(A_      )      )
									lowerCamelCase					:			Optional[Any]									= vocab_file

									# Original fairseq vocab and spm vocab must be "aligned":
									# Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
									# -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
									# fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
									# spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

									# Mimic fairseq token-to-id alignment for the first 4 token
									lowerCamelCase					:			Optional[int]									= {"<s>": 0, "<pad>": 1, "</s>": 2, "<unk>": 3}

									# The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
									lowerCamelCase					:			Any									= 1

									lowerCamelCase					:			int									= len(self.sp_model      )
									lowerCamelCase					:			str									= {
									    code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(A_      )
									}
									lowerCamelCase					:			List[str]									= {v: k for k, v in self.lang_code_to_id.items()}
									lowerCamelCase					:			Union[str, Any]									= len(self.sp_model      ) + len(self.lang_code_to_id      ) + self.fairseq_offset

									self.fairseq_tokens_to_ids.update(self.lang_code_to_id      )
									lowerCamelCase					:			Union[str, Any]									= {v: k for k, v in self.fairseq_tokens_to_ids.items()}
									lowerCamelCase					:			Any									= list(self.lang_code_to_id.keys()      )

									if additional_special_tokens is not None:
													# Only add those special tokens if they are not already there.
													self._additional_special_tokens.extend(
													    [t for t in additional_special_tokens if t not in self._additional_special_tokens]      )

									lowerCamelCase					:			int									= src_lang if src_lang is not None else "en_XX"
									lowerCamelCase					:			str									= self.lang_code_to_id[self._src_lang]
									lowerCamelCase					:			Optional[Any]									= tgt_lang
									self.set_src_lang_special_tokens(self._src_lang      )
					def __getstate__(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			Dict									= self.__dict__.copy()
									lowerCamelCase					:			Tuple									= None
									lowerCamelCase					:			str									= self.sp_model.serialized_model_proto()
									return state
					def __setstate__(     self,    A      ):

									"""simple docstring"""


									lowerCamelCase					:			int									= d

									# for backward compatibility
									if not hasattr(self,    'sp_model_kwargs'      ):
													lowerCamelCase					:			Optional[int]									= {}

									lowerCamelCase					:			Tuple									= spm.SentencePieceProcessor(**self.sp_model_kwargs      )
									self.sp_model.LoadFromSerializedProto(self.sp_model_proto      )
					@property
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									return len(self.sp_model      ) + len(self.lang_code_to_id      ) + self.fairseq_offset + 1  # Plus 1 for the mask token
					@property
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									return self._src_lang
					@src_lang.setter
					def     UpperCAmelCase_							(     self,    A      ):

									"""simple docstring"""


									lowerCamelCase					:			Dict									= new_src_lang
									self.set_src_lang_special_tokens(self._src_lang      )
					def     UpperCAmelCase_							(     self,    A,    A = None,    A = False      ):

									"""simple docstring"""


									if already_has_special_tokens:
													return super().get_special_tokens_mask(
													    token_ids_a=A_,    token_ids_a=A_,    already_has_special_tokens=A_      )

									lowerCamelCase					:			int									= [1] * len(self.prefix_tokens      )
									lowerCamelCase					:			List[Any]									= [1] * len(self.suffix_tokens      )
									if token_ids_a is None:
													return prefix_ones + ([0] * len(A_      )) + suffix_ones
									return prefix_ones + ([0] * len(A_      )) + ([0] * len(A_      )) + suffix_ones
					def     UpperCAmelCase_							(     self,    A,    A = None      ):

									"""simple docstring"""


									if token_ids_a is None:
													return self.prefix_tokens + token_ids_a + self.suffix_tokens
									# We don't expect to process pairs, but leave the pair logic for API consistency
									return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens
					def     UpperCAmelCase_							(     self,    A,    A = None      ):

									"""simple docstring"""


									lowerCamelCase					:			Dict									= [self.sep_token_id]
									lowerCamelCase					:			Union[str, Any]									= [self.cls_token_id]

									if token_ids_a is None:
													return len(cls + token_ids_a + sep      ) * [0]
									return len(cls + token_ids_a + sep + sep + token_ids_a + sep      ) * [0]
					def     UpperCAmelCase_							(     self,    A,    A,    A,    A,    **A      ):

									"""simple docstring"""


									if src_lang is None or tgt_lang is None:
													raise ValueError('Translation requires a `src_lang` and a `tgt_lang` for this model'      )
									lowerCamelCase					:			Tuple									= src_lang
									lowerCamelCase					:			Tuple									= self(A_,    add_special_tokens=A_,    return_tensors=A_,    **A_      )
									lowerCamelCase					:			Optional[int]									= self.convert_tokens_to_ids(A_      )
									lowerCamelCase					:			Dict									= tgt_lang_id
									return inputs
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			Tuple									= {self.convert_ids_to_tokens(A_      ): i for i in range(self.vocab_size      )}
									vocab.update(self.added_tokens_encoder      )
									return vocab
					def     UpperCAmelCase_							(     self,    A      ):

									"""simple docstring"""


									return self.sp_model.encode(A_,    out_type=A_      )
					def     UpperCAmelCase_							(     self,    A      ):

									"""simple docstring"""


									if token in self.fairseq_tokens_to_ids:
													return self.fairseq_tokens_to_ids[token]
									lowerCamelCase					:			Optional[Any]									= self.sp_model.PieceToId(A_      )

									# Need to return unknown token if the SP model returned 0
									return spm_id + self.fairseq_offset if spm_id else self.unk_token_id
					def     UpperCAmelCase_							(     self,    A      ):

									"""simple docstring"""


									if index in self.fairseq_ids_to_tokens:
													return self.fairseq_ids_to_tokens[index]
									return self.sp_model.IdToPiece(index - self.fairseq_offset      )
					def     UpperCAmelCase_							(     self,    A      ):

									"""simple docstring"""


									lowerCamelCase					:			Dict									= "".join(A_      ).replace(A_,    ' '      ).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					:			List[str]									= os.path.join(
									    A_,    (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']      )

									if os.path.abspath(self.vocab_file      ) != os.path.abspath(A_      ) and os.path.isfile(self.vocab_file      ):
													copyfile(self.vocab_file,    A_      )
									elif not os.path.isfile(self.vocab_file      ):
													with open(A_,    'wb'      ) as fi:
																	lowerCamelCase					:			str									= self.sp_model.serialized_model_proto()
																	fi.write(A_      )

									return (out_vocab_file,)
					def     UpperCAmelCase_							(     self,    A,    A = "en_XX",    A = None,    A = "ro_RO",    **A,    ):

									"""simple docstring"""


									lowerCamelCase					:			str									= src_lang
									lowerCamelCase					:			Dict									= tgt_lang
									return super().prepare_seqaseq_batch(A_,    A_,    **A_      )
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									return self.set_src_lang_special_tokens(self.src_lang      )
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									return self.set_tgt_lang_special_tokens(self.tgt_lang      )
					def     UpperCAmelCase_							(     self,    A      ):

									"""simple docstring"""


									lowerCamelCase					:			List[str]									= self.lang_code_to_id[src_lang]
									lowerCamelCase					:			List[str]									= []
									lowerCamelCase					:			Optional[Any]									= [self.eos_token_id, self.cur_lang_code]




					def     UpperCAmelCase_							(     self,    A      ):

									"""simple docstring"""


									lowerCamelCase					:			List[Any]									= self.lang_code_to_id[lang]
									lowerCamelCase					:			Any									= []
									lowerCamelCase					:			Dict									= [self.eos_token_id, self.cur_lang_code]


'''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
