


'''simple docstring'''



import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List

import git
import torch
from torch.utils.data import Dataset

from transformers import BartTokenizer, RagTokenizer, TaTokenizer



def 			UpperCAmelCase__					(	UpperCAmelCase_    :				Optional[int]      ,    UpperCAmelCase_    :				Dict      ,    UpperCAmelCase_    :				Union[str, Any]      ,    UpperCAmelCase_    :				Any      ,    UpperCAmelCase_    :				List[Any]=True      ,    UpperCAmelCase_    :				int="pt"							)     ->		Optional[Any]:
     __lowerCamelCase   :	Optional[int]								=		{'add_prefix_space': True} if isinstance(UpperCAmelCase_      ,    UpperCAmelCase_							) and not line.startswith(' '							) else {}
     __lowerCamelCase   :	Tuple								=		padding_side
     return tokenizer(
         [line]      ,    max_length=UpperCAmelCase_      ,    padding='max_length' if pad_to_max_length else None      ,    truncation=UpperCAmelCase_      ,    return_tensors=UpperCAmelCase_      ,    add_special_tokens=UpperCAmelCase_      ,    **UpperCAmelCase_      ,    )



def 			UpperCAmelCase__					(	UpperCAmelCase_    :				str      ,    UpperCAmelCase_    :				Dict      ,    UpperCAmelCase_    :				Any=None      ,    )     ->		Optional[Any]:
     __lowerCamelCase   :	int								=		input_ids.ne(UpperCAmelCase_							).any(dim=0							)
     if attention_mask is None:
          return input_ids[:, keep_column_mask]
     else:
          return (input_ids[:, keep_column_mask], attention_mask[:, keep_column_mask])





class    UpperCAmelCase_  (_UpperCAmelCase  ):





    """simple docstring"""






    def __init__(							self			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_="train"			,     SCREAMING_SNAKE_CASE_=None			,     SCREAMING_SNAKE_CASE_=None			,     SCREAMING_SNAKE_CASE_=None			,     SCREAMING_SNAKE_CASE_=""			,     )				->     List[str]:
         super().__init__()
         __lowerCamelCase   :	List[Any]								=		Path(SCREAMING_SNAKE_CASE_	).joinpath(type_path + '.source'	)
         __lowerCamelCase   :	int								=		Path(SCREAMING_SNAKE_CASE_	).joinpath(type_path + '.target'	)
         __lowerCamelCase   :	List[Any]								=		self.get_char_lens(self.src_file	)
         __lowerCamelCase   :	Optional[int]								=		max_source_length
         __lowerCamelCase   :	Optional[int]								=		max_target_length
         assert min(self.src_lens	) > 0, f'found empty line in {self.src_file}'
         __lowerCamelCase   :	Optional[Any]								=		tokenizer
         __lowerCamelCase   :	Dict								=		prefix
         if n_obs is not None:
              __lowerCamelCase   :	Optional[Any]								=		self.src_lens[:n_obs]
         __lowerCamelCase   :	List[Any]								=		src_lang
         __lowerCamelCase   :	int								=		tgt_lang







    def __len__(							self	)				->     List[str]:
         return len(self.src_lens	)







    def __getitem__(							self			,     SCREAMING_SNAKE_CASE_	)				->     Dict[str, torch.Tensor]:
         __lowerCamelCase   :	Union[str, Any]								=		index + 1  # linecache starts at 1
         __lowerCamelCase   :	Any								=		self.prefix + linecache.getline(str(self.src_file	)			,     SCREAMING_SNAKE_CASE_	).rstrip('\n'	)
         __lowerCamelCase   :	Optional[int]								=		linecache.getline(str(self.tgt_file	)			,     SCREAMING_SNAKE_CASE_	).rstrip('\n'	)
         assert source_line, f'empty source line for index {index}'
         assert tgt_line, f'empty tgt line for index {index}'

         # Need to add eos token manually for T5
         if isinstance(self.tokenizer			,     SCREAMING_SNAKE_CASE_	):
              source_line += self.tokenizer.eos_token
              tgt_line += self.tokenizer.eos_token

         # Pad source and target to the right
         __lowerCamelCase   :	Dict								=		(
             self.tokenizer.question_encoder if isinstance(self.tokenizer			,     SCREAMING_SNAKE_CASE_	) else self.tokenizer
         )
         __lowerCamelCase   :	str								=		self.tokenizer.generator if isinstance(self.tokenizer			,     SCREAMING_SNAKE_CASE_	) else self.tokenizer

         __lowerCamelCase   :	Union[str, Any]								=		encode_line(SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_			,     self.max_source_length			,     'right'	)
         __lowerCamelCase   :	List[Any]								=		encode_line(SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_			,     self.max_target_length			,     'right'	)

         __lowerCamelCase   :	Tuple								=		source_inputs['input_ids'].squeeze()
         __lowerCamelCase   :	int								=		target_inputs['input_ids'].squeeze()
         __lowerCamelCase   :	str								=		source_inputs['attention_mask'].squeeze()
         return {
             "input_ids": source_ids,
             "attention_mask": src_mask,
             "decoder_input_ids": target_ids,
         }







    @staticmethod
    def       lowercase_				(							SCREAMING_SNAKE_CASE_	)				->     Optional[int]:
         return [len(SCREAMING_SNAKE_CASE_	) for x in Path(SCREAMING_SNAKE_CASE_	).open().readlines()]







    def       lowercase_				(							self			,     SCREAMING_SNAKE_CASE_	)				->     Dict[str, torch.Tensor]:
         __lowerCamelCase   :	Any								=		torch.stack([x['input_ids'] for x in batch]	)
         __lowerCamelCase   :	Dict								=		torch.stack([x['attention_mask'] for x in batch]	)
         __lowerCamelCase   :	Optional[int]								=		torch.stack([x['decoder_input_ids'] for x in batch]	)
         __lowerCamelCase   :	Optional[Any]								=		(
             self.tokenizer.generator.pad_token_id
             if isinstance(self.tokenizer			,     SCREAMING_SNAKE_CASE_	)
             else self.tokenizer.pad_token_id
         )
         __lowerCamelCase   :	Tuple								=		(
             self.tokenizer.question_encoder.pad_token_id
             if isinstance(self.tokenizer			,     SCREAMING_SNAKE_CASE_	)
             else self.tokenizer.pad_token_id
         )
         __lowerCamelCase   :	Optional[Any]								=		trim_batch(SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_	)
         __lowerCamelCase				,       __lowerCamelCase   :	Optional[int]								=		trim_batch(SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_			,     attention_mask=SCREAMING_SNAKE_CASE_	)
         __lowerCamelCase   :	Dict								=		{
             'input_ids': source_ids,
             'attention_mask': source_mask,
             'decoder_input_ids': y,
         }
         return batch







A__		:					List[Any]												=  getLogger(__name__)



def 			UpperCAmelCase__					(	UpperCAmelCase_    :				List[List]							)     ->		List[Any]:
     return list(itertools.chain.from_iterable(UpperCAmelCase_							)							)



def 			UpperCAmelCase__					(	UpperCAmelCase_    :				str							)     ->		None:
     __lowerCamelCase   :	Optional[int]								=		get_git_info()
     save_json(UpperCAmelCase_      ,    os.path.join(UpperCAmelCase_      ,    'git_log.json'							)							)



def 			UpperCAmelCase__					(	UpperCAmelCase_    :				List[str]      ,    UpperCAmelCase_    :				Optional[int]      ,    UpperCAmelCase_    :				Union[str, Any]=4      ,    **UpperCAmelCase_    :				Union[str, Any]							)     ->		Tuple:
     with open(UpperCAmelCase_      ,    'w'							) as f:
          json.dump(UpperCAmelCase_      ,    UpperCAmelCase_      ,    indent=UpperCAmelCase_      ,    **UpperCAmelCase_							)



def 			UpperCAmelCase__					(	UpperCAmelCase_    :				Optional[Any]							)     ->		List[Any]:
     with open(UpperCAmelCase_							) as f:
          return json.load(UpperCAmelCase_							)



def 			UpperCAmelCase__					(	)     ->		str:
     __lowerCamelCase   :	List[Any]								=		git.Repo(search_parent_directories=UpperCAmelCase_							)
     __lowerCamelCase   :	Optional[Any]								=		{
         'repo_id': str(UpperCAmelCase_							),
         'repo_sha': str(repo.head.object.hexsha							),
         'repo_branch': str(repo.active_branch							),
         'hostname': str(socket.gethostname()							),
     }
     return repo_infos



def 			UpperCAmelCase__					(	UpperCAmelCase_    :				Callable      ,    UpperCAmelCase_    :				Iterable							)     ->		List:
     return list(map(UpperCAmelCase_      ,    UpperCAmelCase_							)							)



def 			UpperCAmelCase__					(	UpperCAmelCase_    :				List[Any]      ,    UpperCAmelCase_    :				List[str]							)     ->		Dict:
     with open(UpperCAmelCase_      ,    'wb'							) as f:
          return pickle.dump(UpperCAmelCase_      ,    UpperCAmelCase_							)



def 			UpperCAmelCase__					(	UpperCAmelCase_    :				Any							)     ->		List[Any]:

     def remove_articles(UpperCAmelCase_    :				Optional[Any]							):
          return re.sub(R'\b(a|an|the)\b'      ,    ' '      ,    UpperCAmelCase_							)

     def white_space_fix(UpperCAmelCase_    :				List[Any]							):
          return " ".join(text.split()							)

     def remove_punc(UpperCAmelCase_    :				str							):
          __lowerCamelCase   :	Optional[int]								=		set(string.punctuation							)
          return "".join(ch for ch in text if ch not in exclude							)

     def lower(UpperCAmelCase_    :				Dict							):
          return text.lower()

     return white_space_fix(remove_articles(remove_punc(lower(UpperCAmelCase_							)							)							)							)



def 			UpperCAmelCase__					(	UpperCAmelCase_    :				Union[str, Any]      ,    UpperCAmelCase_    :				Tuple							)     ->		List[Any]:
     __lowerCamelCase   :	str								=		normalize_answer(UpperCAmelCase_							).split()
     __lowerCamelCase   :	Optional[int]								=		normalize_answer(UpperCAmelCase_							).split()
     __lowerCamelCase   :	int								=		Counter(UpperCAmelCase_							) & Counter(UpperCAmelCase_							)
     __lowerCamelCase   :	List[Any]								=		sum(common.values()							)
     if num_same == 0:
          return 0
     __lowerCamelCase   :	List[str]								=		1.0 * num_same / len(UpperCAmelCase_							)
     __lowerCamelCase   :	Union[str, Any]								=		1.0 * num_same / len(UpperCAmelCase_							)
     __lowerCamelCase   :	str								=		(2 * precision * recall) / (precision + recall)
     return fa



def 			UpperCAmelCase__					(	UpperCAmelCase_    :				Tuple      ,    UpperCAmelCase_    :				Union[str, Any]							)     ->		List[str]:
     return normalize_answer(UpperCAmelCase_							) == normalize_answer(UpperCAmelCase_							)



def 			UpperCAmelCase__					(	UpperCAmelCase_    :				List[str]      ,    UpperCAmelCase_    :				List[str]							)     ->		Dict:
     assert len(UpperCAmelCase_							) == len(UpperCAmelCase_							)
     __lowerCamelCase   :	Tuple								=		0
     for hypo, pred in zip(UpperCAmelCase_      ,    UpperCAmelCase_							):
          em += exact_match_score(UpperCAmelCase_      ,    UpperCAmelCase_							)
     if len(UpperCAmelCase_							) > 0:
          em /= len(UpperCAmelCase_							)
     return {"em": em}



def 			UpperCAmelCase__					(	UpperCAmelCase_    :				Tuple							)     ->		Tuple:
     return model_prefix.startswith('rag'							)



def 			UpperCAmelCase__					(	UpperCAmelCase_    :				Union[str, Any]      ,    UpperCAmelCase_    :				List[str]      ,    UpperCAmelCase_    :				Optional[int]							)     ->		Optional[int]:
     __lowerCamelCase   :	Optional[int]								=		{p: p for p in extra_params}
     # T5 models don't have `dropout` param, they have `dropout_rate` instead
     __lowerCamelCase   :	List[Any]								=		'dropout_rate'
     for p in extra_params:
          if getattr(UpperCAmelCase_      ,    UpperCAmelCase_      ,    UpperCAmelCase_							):
               if not hasattr(UpperCAmelCase_      ,    UpperCAmelCase_							) and not hasattr(UpperCAmelCase_      ,    equivalent_param[p]							):
                    logger.info('config doesn\'t have a `{}` attribute'.format(UpperCAmelCase_							)							)
                    delattr(UpperCAmelCase_      ,    UpperCAmelCase_							)
                    continue
               __lowerCamelCase   :	int								=		p if hasattr(UpperCAmelCase_      ,    UpperCAmelCase_							) else equivalent_param[p]
               setattr(UpperCAmelCase_      ,    UpperCAmelCase_      ,    getattr(UpperCAmelCase_      ,    UpperCAmelCase_							)							)
               delattr(UpperCAmelCase_      ,    UpperCAmelCase_							)
     return hparams, config










'''simple docstring'''



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    UpperCAmelCase_  (_UpperCAmelCase					,		unittest.TestCase  ):





    """simple docstring"""






    pass







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





    """simple docstring"""






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







    @property
    def       lowercase_				(							self	)				->     Dict:
         __lowerCamelCase   :	int								=		ort.SessionOptions()
         __lowerCamelCase   :	Optional[Any]								=		False
         return options







    def       lowercase_				(							self	)				->     Union[str, Any]:
         __lowerCamelCase   :	Any								=		load_image(
             'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
             '/in_paint/overture-creations-5sI6fQgYIuo.png'	)
         __lowerCamelCase   :	Any								=		load_image(
             'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
             '/in_paint/overture-creations-5sI6fQgYIuo_mask.png'	)
         __lowerCamelCase   :	Any								=		OnnxStableDiffusionInpaintPipeline.from_pretrained(
             'runwayml/stable-diffusion-inpainting'			,     revision='onnx'			,     safety_checker=SCREAMING_SNAKE_CASE_			,     feature_extractor=SCREAMING_SNAKE_CASE_			,     provider=self.gpu_provider			,     sess_options=self.gpu_options			,     )
         pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_	)

         __lowerCamelCase   :	Tuple								=		'A red cat sitting on a park bench'

         __lowerCamelCase   :	Union[str, Any]								=		np.random.RandomState(0	)
         __lowerCamelCase   :	Union[str, Any]								=		pipe(
             prompt=SCREAMING_SNAKE_CASE_			,     image=SCREAMING_SNAKE_CASE_			,     mask_image=SCREAMING_SNAKE_CASE_			,     guidance_scale=7.5			,     num_inference_steps=10			,     generator=SCREAMING_SNAKE_CASE_			,     output_type='np'			,     )
         __lowerCamelCase   :	Optional[Any]								=		output.images
         __lowerCamelCase   :	Tuple								=		images[0, 2_55:2_58, 2_55:2_58, -1]

         assert images.shape == (1, 5_12, 5_12, 3)
         __lowerCamelCase   :	Union[str, Any]								=		np.array([0.2_5_1_4, 0.3_0_0_7, 0.3_5_1_7, 0.1_7_9_0, 0.2_3_8_2, 0.3_1_6_7, 0.1_9_4_4, 0.2_2_7_3, 0.2_4_6_4]	)

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







    def       lowercase_				(							self	)				->     Optional[Any]:
         __lowerCamelCase   :	Union[str, Any]								=		load_image(
             'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
             '/in_paint/overture-creations-5sI6fQgYIuo.png'	)
         __lowerCamelCase   :	List[str]								=		load_image(
             'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
             '/in_paint/overture-creations-5sI6fQgYIuo_mask.png'	)
         __lowerCamelCase   :	Dict								=		LMSDiscreteScheduler.from_pretrained(
             'runwayml/stable-diffusion-inpainting'			,     subfolder='scheduler'			,     revision='onnx'	)
         __lowerCamelCase   :	Tuple								=		OnnxStableDiffusionInpaintPipeline.from_pretrained(
             'runwayml/stable-diffusion-inpainting'			,     revision='onnx'			,     scheduler=SCREAMING_SNAKE_CASE_			,     safety_checker=SCREAMING_SNAKE_CASE_			,     feature_extractor=SCREAMING_SNAKE_CASE_			,     provider=self.gpu_provider			,     sess_options=self.gpu_options			,     )
         pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_	)

         __lowerCamelCase   :	str								=		'A red cat sitting on a park bench'

         __lowerCamelCase   :	Tuple								=		np.random.RandomState(0	)
         __lowerCamelCase   :	Optional[int]								=		pipe(
             prompt=SCREAMING_SNAKE_CASE_			,     image=SCREAMING_SNAKE_CASE_			,     mask_image=SCREAMING_SNAKE_CASE_			,     guidance_scale=7.5			,     num_inference_steps=20			,     generator=SCREAMING_SNAKE_CASE_			,     output_type='np'			,     )
         __lowerCamelCase   :	List[str]								=		output.images
         __lowerCamelCase   :	Dict								=		images[0, 2_55:2_58, 2_55:2_58, -1]

         assert images.shape == (1, 5_12, 5_12, 3)
         __lowerCamelCase   :	Tuple								=		np.array([0.0_0_8_6, 0.0_0_7_7, 0.0_0_8_3, 0.0_0_9_3, 0.0_1_0_7, 0.0_1_3_9, 0.0_0_9_4, 0.0_0_9_7, 0.0_1_2_5]	)

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






