



'''simple docstring'''






import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union

import sentencepiece

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


lowerCAmelCase:      List[Any]		 =		logging.get_logger(__name__)

lowerCAmelCase:      Optional[Any]		 =		'▁'

lowerCAmelCase:      Any		 =		{
    'vocab_file': 'vocab.json',
    'spm_file': 'sentencepiece.bpe.model',
    'tokenizer_config_file': 'tokenizer_config.json',
}

lowerCAmelCase:      Optional[int]		 =		{
    'vocab_file': {
        'facebook/m2m100_418M': 'https://huggingface.co/facebook/m2m100_418M/resolve/main/vocab.json',
        'facebook/m2m100_1.2B': 'https://huggingface.co/facebook/m2m100_1.2B/resolve/main/vocab.json',
    },
    'spm_file': {
        'facebook/m2m100_418M': 'https://huggingface.co/facebook/m2m100_418M/resolve/main/sentencepiece.bpe.model',
        'facebook/m2m100_1.2B': 'https://huggingface.co/facebook/m2m100_1.2B/resolve/main/sentencepiece.bpe.model',
    },
    'tokenizer_config_file': {
        'facebook/m2m100_418M': 'https://huggingface.co/facebook/m2m100_418M/resolve/main/tokenizer_config.json',
        'facebook/m2m100_1.2B': 'https://huggingface.co/facebook/m2m100_1.2B/resolve/main/tokenizer_config.json',
    },
}

lowerCAmelCase:      int		 =		{
    'facebook/m2m100_418M': 1_0_2_4,
}

# fmt: off
lowerCAmelCase:      Dict		 =		{
    'm2m100': ['af', 'am', 'ar', 'ast', 'az', 'ba', 'be', 'bg', 'bn', 'br', 'bs', 'ca', 'ceb', 'cs', 'cy', 'da', 'de', 'el', 'en', 'es', 'et', 'fa', 'ff', 'fi', 'fr', 'fy', 'ga', 'gd', 'gl', 'gu', 'ha', 'he', 'hi', 'hr', 'ht', 'hu', 'hy', 'id', 'ig', 'ilo', 'is', 'it', 'ja', 'jv', 'ka', 'kk', 'km', 'kn', 'ko', 'lb', 'lg', 'ln', 'lo', 'lt', 'lv', 'mg', 'mk', 'ml', 'mn', 'mr', 'ms', 'my', 'ne', 'nl', 'no', 'ns', 'oc', 'or', 'pa', 'pl', 'ps', 'pt', 'ro', 'ru', 'sd', 'si', 'sk', 'sl', 'so', 'sq', 'sr', 'ss', 'su', 'sv', 'sw', 'ta', 'th', 'tl', 'tn', 'tr', 'uk', 'ur', 'uz', 'vi', 'wo', 'xh', 'yi', 'yo', 'zh', 'zu'],
    'wmt21': ['en', 'ha', 'is', 'ja', 'cs', 'ru', 'zh', 'de']
}

class 							a__(      lowerCamelCase__			):
    lowercase__				=					VOCAB_FILES_NAMES
    lowercase__				=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    lowercase__				=					PRETRAINED_VOCAB_FILES_MAP
    lowercase__				=					["""input_ids""", """attention_mask"""]

    lowercase__				=					[]
    lowercase__				=					[]







    def __init__(   self				:						Any		,    __snake_case				:						str		,    __snake_case				:						Union[str, Any]		,    __snake_case				:						Union[str, Any]=None		,    __snake_case				:						Tuple=None		,    __snake_case				:						str="<s>"		,    __snake_case				:						str="</s>"		,    __snake_case				:						Union[str, Any]="</s>"		,    __snake_case				:						List[Any]="<pad>"		,    __snake_case				:						Dict="<unk>"		,    __snake_case				:						Optional[int]="m2m100"		,    __snake_case				:						Optional[Dict[str, Any]] = None		,    __snake_case				:						str=8		,    **__snake_case				:						Tuple		,    ):
      a		:      int       =  {} if sp_model_kwargs is None else sp_model_kwargs

      a		:      str       =  language_codes
      a		:      Optional[Any]       =  FAIRSEQ_LANGUAGE_CODES[language_codes]
      a		:      Optional[int]       =  {lang_code: F"""__{lang_code}__""" for lang_code in fairseq_language_code}

      a		:      Any       =  kwargs.get('additional_special_tokens'		,    []			)
      kwargs["additional_special_tokens"] += [
          self.get_lang_token(__snake_case			)
          for lang_code in fairseq_language_code
          if self.get_lang_token(__snake_case			) not in kwargs["additional_special_tokens"]
      ]

      super().__init__(
          src_lang=__snake_case		,    tgt_lang=__snake_case		,    bos_token=__snake_case		,    eos_token=__snake_case		,    sep_token=__snake_case		,    unk_token=__snake_case		,    pad_token=__snake_case		,    language_codes=__snake_case		,    sp_model_kwargs=self.sp_model_kwargs		,    num_madeup_words=__snake_case		,    **__snake_case		,    )

      a		:      int       =  vocab_file
      a		:      Any       =  load_json(__snake_case			)
      a		:      List[Any]       =  {v: k for k, v in self.encoder.items()}
      a		:      Union[str, Any]       =  spm_file
      a		:      Dict       =  load_spm(__snake_case		,    self.sp_model_kwargs			)

      a		:      str       =  len(self.encoder			)

      a		:      List[str]       =  {
          self.get_lang_token(__snake_case			): self.encoder_size + i for i, lang_code in enumerate(__snake_case			)
      }
      a		:      str       =  {lang_code: self.encoder_size + i for i, lang_code in enumerate(__snake_case			)}
      a		:      Union[str, Any]       =  {v: k for k, v in self.lang_token_to_id.items()}

      a		:      int       =  src_lang if src_lang is not None else 'en'
      a		:      List[str]       =  tgt_lang
      a		:      Tuple       =  self.get_lang_id(self._src_lang			)
      self.set_src_lang_special_tokens(self._src_lang			)

      a		:      Tuple       =  num_madeup_words







    @property
    def     lowercase_ (   self				:						int			):
      return len(self.encoder			) + len(self.lang_token_to_id			)







    @property
    def     lowercase_ (   self				:						Tuple			):
      return self._src_lang







    @src_lang.setter
    def     lowercase_ (   self				:						Tuple		,    __snake_case				:						str			):
      a		:      Dict       =  new_src_lang
      self.set_src_lang_special_tokens(self._src_lang			)







    def     lowercase_ (   self				:						Dict		,    __snake_case				:						str			):
      return self.sp_model.encode(__snake_case		,    out_type=__snake_case			)







    def     lowercase_ (   self				:						Dict		,    __snake_case				:						Any			):
      if token in self.lang_token_to_id:
        return self.lang_token_to_id[token]
      return self.encoder.get(__snake_case		,    self.encoder[self.unk_token]			)







    def     lowercase_ (   self				:						Optional[int]		,    __snake_case				:						int			):
      if index in self.id_to_lang_token:
        return self.id_to_lang_token[index]
      return self.decoder.get(__snake_case		,    self.unk_token			)







    def     lowercase_ (   self				:						Tuple		,    __snake_case				:						Dict			):
      a		:      Dict       =  []
      a		:      Optional[Any]       =  ''
      for token in tokens:
        # make sure that special tokens are not decoded using sentencepiece model
        if token in self.all_special_tokens:
          out_string += self.sp_model.decode(__snake_case			) + token
          a		:      int       =  []
        else:
          current_sub_tokens.append(__snake_case			)
      out_string += self.sp_model.decode(__snake_case			)
      return out_string.strip()







    def     lowercase_ (   self				:						Optional[Any]		,    __snake_case				:						List[int]		,    __snake_case				:						Optional[List[int]] = None		,    __snake_case				:						bool = False			):

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

      a		:      Optional[int]       =  [1] * len(self.prefix_tokens			)
      a		:      Optional[int]       =  [1] * len(self.suffix_tokens			)
      if token_ids_a is None:
        return prefix_ones + ([0] * len(__snake_case			)) + suffix_ones
      return prefix_ones + ([0] * len(__snake_case			)) + ([0] * len(__snake_case			)) + suffix_ones







    def     lowercase_ (   self				:						Dict		,    __snake_case				:						List[int]		,    __snake_case				:						Optional[List[int]] = None			):
      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     lowercase_ (   self				:						Tuple			):
      a		:      Optional[Any]       =  {self.convert_ids_to_tokens(__snake_case			): i for i in range(self.vocab_size			)}
      vocab.update(self.added_tokens_encoder			)
      return vocab







    def __getstate__(   self				:						Optional[int]			):
      a		:      Optional[int]       =  self.__dict__.copy()
      a		:      List[str]       =  None
      return state







    def __setstate__(   self				:						Any		,    __snake_case				:						Dict			):
      a		:      Tuple       =  d

      # for backward compatibility
      if not hasattr(self		,    'sp_model_kwargs'			):
        a		:      Any       =  {}

      a		:      Any       =  load_spm(self.spm_file		,    self.sp_model_kwargs			)







    def     lowercase_ (   self				:						int		,    __snake_case				:						str		,    __snake_case				:						Optional[str] = None			):
      a		:      List[str]       =  Path(__snake_case			)
      if not save_dir.is_dir():
        raise OSError(F"""{save_directory} should be a directory"""			)
      a		:      List[str]       =  save_dir / (
          (filename_prefix + '-' if filename_prefix else '') + self.vocab_files_names['vocab_file']
      )
      a		:      List[str]       =  save_dir / (
          (filename_prefix + '-' if filename_prefix else '') + self.vocab_files_names['spm_file']
      )

      save_json(self.encoder		,    __snake_case			)

      if os.path.abspath(self.spm_file			) != os.path.abspath(__snake_case			) and os.path.isfile(self.spm_file			):
        copyfile(self.spm_file		,    __snake_case			)
      elif not os.path.isfile(self.spm_file			):
        with open(__snake_case		,    'wb'			) as fi:
          a		:      Optional[Any]       =  self.sp_model.serialized_model_proto()
          fi.write(__snake_case			)

      return (str(__snake_case			), str(__snake_case			))







    def     lowercase_ (   self				:						Union[str, Any]		,    __snake_case				:						List[str]		,    __snake_case				:						str = "en"		,    __snake_case				:						Optional[List[str]] = None		,    __snake_case				:						str = "ro"		,    **__snake_case				:						List[Any]		,    ):
      a		:      List[str]       =  src_lang
      a		:      List[str]       =  tgt_lang
      self.set_src_lang_special_tokens(self.src_lang			)
      return super().prepare_seqaseq_batch(__snake_case		,    __snake_case		,    **__snake_case			)







    def     lowercase_ (   self				:						str		,    __snake_case				:						Any		,    __snake_case				:						Optional[str]		,    __snake_case				:						Optional[str]		,    **__snake_case				:						Tuple			):
      if src_lang is None or tgt_lang is None:
        raise ValueError('Translation requires a `src_lang` and a `tgt_lang` for this model'			)
      a		:      Tuple       =  src_lang
      a		:      Optional[Any]       =  self(__snake_case		,    add_special_tokens=__snake_case		,    **__snake_case			)
      a		:      Union[str, Any]       =  self.get_lang_id(__snake_case			)
      a		:      List[str]       =  tgt_lang_id
      return inputs







    def     lowercase_ (   self				:						Tuple			):
      self.set_src_lang_special_tokens(self.src_lang			)







    def     lowercase_ (   self				:						Optional[Any]			):
      self.set_tgt_lang_special_tokens(self.tgt_lang			)







    def     lowercase_ (   self				:						Tuple		,    __snake_case				:						str			):
      a		:      Dict       =  self.get_lang_token(__snake_case			)
      a		:      Optional[int]       =  self.lang_token_to_id[lang_token]
      a		:      Optional[int]       =  [self.cur_lang_id]
      a		:      Optional[Any]       =  [self.eos_token_id]







    def     lowercase_ (   self				:						List[Any]		,    __snake_case				:						str			):
      a		:      Union[str, Any]       =  self.get_lang_token(__snake_case			)
      a		:      Optional[int]       =  self.lang_token_to_id[lang_token]
      a		:      Any       =  [self.cur_lang_id]
      a		:      int       =  [self.eos_token_id]







    def     lowercase_ (   self				:						int		,    __snake_case				:						str			):
      return self.lang_code_to_token[lang]







    def     lowercase_ (   self				:						Optional[int]		,    __snake_case				:						str			):
      a		:      Optional[int]       =  self.get_lang_token(__snake_case			)
      return self.lang_token_to_id[lang_token]
def    lowerCamelCase__ (		_A				,    _A       ):
  a		:      Optional[int]       =  sentencepiece.SentencePieceProcessor(**_A       )
  spm.Load(str(_A       )       )
  return spm
def    lowerCamelCase__ (		_A       ):
  with open(_A				,    'r'       ) as f:
    return json.load(_A       )

def    lowerCamelCase__ (		_A				,    _A       ):
  with open(_A				,    'w'       ) as f:
    json.dump(_A				,    _A				,    indent=2       )




'''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 							a__(      lowerCamelCase__	,				unittest.TestCase			):
    lowercase__				=					"""hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline"""







    def     lowercase_ (   self				:						Dict		,    __snake_case				:						Union[str, Any]=0			):
      a		:      List[str]       =  floats_tensor((1, 3, 1_28, 1_28)		,    rng=random.Random(__snake_case			)			)
      a		:      Optional[Any]       =  np.random.RandomState(__snake_case			)
      a		:      List[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     lowercase_ (   self				:						Union[str, Any]			):
      a		:      Dict       =  OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint		,    provider='CPUExecutionProvider'			)
      pipe.set_progress_bar_config(disable=__snake_case			)

      a		:      Any       =  self.get_dummy_inputs()
      a		:      int       =  pipe(**__snake_case			).images
      a		:      List[Any]       =  image[0, -3:, -3:, -1].flatten()

      assert image.shape == (1, 1_28, 1_28, 3)
      a		:      Dict       =  np.array([0.69643, 0.58484, 0.50314, 0.58760, 0.55368, 0.59643, 0.51529, 0.41217, 0.49087]			)
      assert np.abs(image_slice - expected_slice			).max() < 1e-1







    def     lowercase_ (   self				:						str			):
      a		:      List[Any]       =  OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint		,    provider='CPUExecutionProvider'			)
      a		:      Dict       =  PNDMScheduler.from_config(pipe.scheduler.config		,    skip_prk_steps=__snake_case			)
      pipe.set_progress_bar_config(disable=__snake_case			)

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

      assert image.shape == (1, 1_28, 1_28, 3)
      a		:      int       =  np.array([0.61737, 0.54642, 0.53183, 0.54465, 0.52742, 0.60525, 0.49969, 0.40655, 0.48154]			)

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







    def     lowercase_ (   self				:						List[str]			):
      a		:      Optional[int]       =  OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint		,    provider='CPUExecutionProvider'			)
      a		:      List[Any]       =  LMSDiscreteScheduler.from_config(pipe.scheduler.config			)
      pipe.set_progress_bar_config(disable=__snake_case			)

      # warmup pass to apply optimizations
      a		:      Any       =  pipe(**self.get_dummy_inputs()			)

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

      assert image.shape == (1, 1_28, 1_28, 3)
      a		:      Any       =  np.array([0.52761, 0.59977, 0.49033, 0.49619, 0.54282, 0.50311, 0.47600, 0.40918, 0.45203]			)

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







    def     lowercase_ (   self				:						List[Any]			):
      a		:      Dict       =  OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint		,    provider='CPUExecutionProvider'			)
      a		:      Any       =  EulerDiscreteScheduler.from_config(pipe.scheduler.config			)
      pipe.set_progress_bar_config(disable=__snake_case			)

      a		:      List[str]       =  self.get_dummy_inputs()
      a		:      Optional[int]       =  pipe(**__snake_case			).images
      a		:      Any       =  image[0, -3:, -3:, -1]

      assert image.shape == (1, 1_28, 1_28, 3)
      a		:      Dict       =  np.array([0.52911, 0.60004, 0.49229, 0.49805, 0.54502, 0.50680, 0.47777, 0.41028, 0.45304]			)

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







    def     lowercase_ (   self				:						Tuple			):
      a		:      Any       =  OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint		,    provider='CPUExecutionProvider'			)
      a		:      Optional[Any]       =  EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config			)
      pipe.set_progress_bar_config(disable=__snake_case			)

      a		:      Dict       =  self.get_dummy_inputs()
      a		:      Union[str, Any]       =  pipe(**__snake_case			).images
      a		:      int       =  image[0, -3:, -3:, -1]

      assert image.shape == (1, 1_28, 1_28, 3)
      a		:      int       =  np.array([0.52911, 0.60004, 0.49229, 0.49805, 0.54502, 0.50680, 0.47777, 0.41028, 0.45304]			)

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







    def     lowercase_ (   self				:						Optional[Any]			):
      a		:      Optional[int]       =  OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint		,    provider='CPUExecutionProvider'			)
      a		:      Optional[int]       =  DPMSolverMultistepScheduler.from_config(pipe.scheduler.config			)
      pipe.set_progress_bar_config(disable=__snake_case			)

      a		:      Tuple       =  self.get_dummy_inputs()
      a		:      Optional[Any]       =  pipe(**__snake_case			).images
      a		:      str       =  image[0, -3:, -3:, -1]

      assert image.shape == (1, 1_28, 1_28, 3)
      a		:      Optional[int]       =  np.array([0.65331, 0.58277, 0.48204, 0.56059, 0.53665, 0.56235, 0.50969, 0.40009, 0.46552]			)

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

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







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







    @property
    def     lowercase_ (   self				:						Dict			):
      a		:      Tuple       =  ort.SessionOptions()
      a		:      Optional[Any]       =  False
      return options







    def     lowercase_ (   self				:						List[str]			):
      a		:      Dict       =  load_image(
          'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
          '/img2img/sketch-mountains-input.jpg'			)
      a		:      Optional[int]       =  init_image.resize((7_68, 5_12)			)
      # using the PNDM scheduler by default
      a		:      Any       =  OnnxStableDiffusionImgaImgPipeline.from_pretrained(
          'CompVis/stable-diffusion-v1-4'		,    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		:      Union[str, Any]       =  'A fantasy landscape, trending on artstation'

      a		:      Dict       =  np.random.RandomState(0			)
      a		:      Optional[int]       =  pipe(
          prompt=__snake_case		,    image=__snake_case		,    strength=0.75		,    guidance_scale=7.5		,    num_inference_steps=10		,    generator=__snake_case		,    output_type='np'		,    )
      a		:      str       =  output.images
      a		:      Optional[int]       =  images[0, 2_55:2_58, 3_83:3_86, -1]

      assert images.shape == (1, 5_12, 7_68, 3)
      a		:      List[str]       =  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     lowercase_ (   self				:						str			):
      a		:      Optional[Any]       =  load_image(
          'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
          '/img2img/sketch-mountains-input.jpg'			)
      a		:      List[str]       =  init_image.resize((7_68, 5_12)			)
      a		:      Union[str, Any]       =  LMSDiscreteScheduler.from_pretrained(
          'runwayml/stable-diffusion-v1-5'		,    subfolder='scheduler'		,    revision='onnx'			)
      a		:      Any       =  OnnxStableDiffusionImgaImgPipeline.from_pretrained(
          'runwayml/stable-diffusion-v1-5'		,    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		:      Optional[int]       =  'A fantasy landscape, trending on artstation'

      a		:      str       =  np.random.RandomState(0			)
      a		:      List[str]       =  pipe(
          prompt=__snake_case		,    image=__snake_case		,    strength=0.75		,    guidance_scale=7.5		,    num_inference_steps=20		,    generator=__snake_case		,    output_type='np'		,    )
      a		:      str       =  output.images
      a		:      Tuple       =  images[0, 2_55:2_58, 3_83:3_86, -1]

      assert images.shape == (1, 5_12, 7_68, 3)
      a		:      Union[str, Any]       =  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