


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 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE=True , SCREAMING_SNAKE_CASE="pt" )->  str:




      """simple docstring"""

      snake_case_	=				{'''add_prefix_space''': True} if isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) and not line.startswith(''' ''' ) else {}
      snake_case_	=				padding_side
      return tokenizer(
          [line] , max_length=SCREAMING_SNAKE_CASE , padding='''max_length''' if pad_to_max_length else None , truncation=SCREAMING_SNAKE_CASE , return_tensors=SCREAMING_SNAKE_CASE , add_special_tokens=SCREAMING_SNAKE_CASE , **SCREAMING_SNAKE_CASE , )






def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE=None , )->  List[str]:




      """simple docstring"""

      snake_case_	=				input_ids.ne(SCREAMING_SNAKE_CASE ).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 lowerCAmelCase_						(			lowerCamelCase__	):

 '''simple docstring'''

 def __init__(     self		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase="train"		,				_UpperCAmelCase=None		,				_UpperCAmelCase=None		,				_UpperCAmelCase=None		,				_UpperCAmelCase=""		,				):
       super().__init__()
       snake_case_	=				Path(_UpperCAmelCase ).joinpath(type_path + '''.source''' )
       snake_case_	=				Path(_UpperCAmelCase ).joinpath(type_path + '''.target''' )
       snake_case_	=				self.get_char_lens(self.src_file )
       snake_case_	=				max_source_length
       snake_case_	=				max_target_length
       assert min(self.src_lens ) > 0, F'''found empty line in {self.src_file}'''
       snake_case_	=				tokenizer
       snake_case_	=				prefix
       if n_obs is not None:
             snake_case_	=				self.src_lens[:n_obs]
       snake_case_	=				src_lang
       snake_case_	=				tgt_lang



 def __len__(     self ):
       return len(self.src_lens )



 def __getitem__(     self		,				_UpperCAmelCase ):
       snake_case_	=				index + 1  # linecache starts at 1
       snake_case_	=				self.prefix + linecache.getline(str(self.src_file )		,				_UpperCAmelCase ).rstrip('''\n''' )
       snake_case_	=				linecache.getline(str(self.tgt_file )		,				_UpperCAmelCase ).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		,				_UpperCAmelCase ):
             source_line += self.tokenizer.eos_token
             tgt_line += self.tokenizer.eos_token

       # Pad source and target to the right
       snake_case_	=				(
           self.tokenizer.question_encoder if isinstance(self.tokenizer		,				_UpperCAmelCase ) else self.tokenizer
       )
       snake_case_	=				self.tokenizer.generator if isinstance(self.tokenizer		,				_UpperCAmelCase ) else self.tokenizer

       snake_case_	=				encode_line(_UpperCAmelCase		,				_UpperCAmelCase		,				self.max_source_length		,				'''right''' )
       snake_case_	=				encode_line(_UpperCAmelCase		,				_UpperCAmelCase		,				self.max_target_length		,				'''right''' )

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



 @staticmethod
 def 						UpperCamelCase__					(     _UpperCAmelCase ):
       return [len(_UpperCAmelCase ) for x in Path(_UpperCAmelCase ).open().readlines()]





 def 						UpperCamelCase__					(     self		,				_UpperCAmelCase ):
       snake_case_	=				torch.stack([x['''input_ids'''] for x in batch] )
       snake_case_	=				torch.stack([x['''attention_mask'''] for x in batch] )
       snake_case_	=				torch.stack([x['''decoder_input_ids'''] for x in batch] )
       snake_case_	=				(
           self.tokenizer.generator.pad_token_id
           if isinstance(self.tokenizer		,				_UpperCAmelCase )
           else self.tokenizer.pad_token_id
       )
       snake_case_	=				(
           self.tokenizer.question_encoder.pad_token_id
           if isinstance(self.tokenizer		,				_UpperCAmelCase )
           else self.tokenizer.pad_token_id
       )
       snake_case_	=				trim_batch(_UpperCAmelCase		,				_UpperCAmelCase )
       snake_case_	,   snake_case_	=				trim_batch(_UpperCAmelCase		,				_UpperCAmelCase		,				attention_mask=_UpperCAmelCase )
       snake_case_	=				{
           '''input_ids''': source_ids,
           '''attention_mask''': source_mask,
           '''decoder_input_ids''': y,
       }
       return batch





UpperCAmelCase      =   getLogger(__name__)






def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE )->  int:




      """simple docstring"""

      return list(itertools.chain.from_iterable(SCREAMING_SNAKE_CASE ) )






def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE )->  None:




      """simple docstring"""

      snake_case_	=				get_git_info()
      save_json(SCREAMING_SNAKE_CASE , os.path.join(SCREAMING_SNAKE_CASE , '''git_log.json''' ) )






def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE=4 , **SCREAMING_SNAKE_CASE )->  List[str]:




      """simple docstring"""

      with open(SCREAMING_SNAKE_CASE , '''w''' ) as f:
            json.dump(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , indent=SCREAMING_SNAKE_CASE , **SCREAMING_SNAKE_CASE )






def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE )->  int:




      """simple docstring"""

      with open(SCREAMING_SNAKE_CASE ) as f:
            return json.load(SCREAMING_SNAKE_CASE )






def 	__lowerCAmelCase   ()->  Optional[int]:




      """simple docstring"""

      snake_case_	=				git.Repo(search_parent_directories=SCREAMING_SNAKE_CASE )
      snake_case_	=				{
          '''repo_id''': str(SCREAMING_SNAKE_CASE ),
          '''repo_sha''': str(repo.head.object.hexsha ),
          '''repo_branch''': str(repo.active_branch ),
          '''hostname''': str(socket.gethostname() ),
      }
      return repo_infos






def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )->  List:




      """simple docstring"""

      return list(map(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) )






def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )->  Optional[Any]:




      """simple docstring"""

      with open(SCREAMING_SNAKE_CASE , '''wb''' ) as f:
            return pickle.dump(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )






def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE )->  int:




      """simple docstring"""

      def remove_articles(SCREAMING_SNAKE_CASE ):
            return re.sub(R'''\b(a|an|the)\b''' , ''' ''' , SCREAMING_SNAKE_CASE )

      def white_space_fix(SCREAMING_SNAKE_CASE ):
            return " ".join(text.split() )

      def remove_punc(SCREAMING_SNAKE_CASE ):
            snake_case_	=				set(string.punctuation )
            return "".join(ch for ch in text if ch not in exclude )

      def lower(SCREAMING_SNAKE_CASE ):
            return text.lower()

      return white_space_fix(remove_articles(remove_punc(lower(SCREAMING_SNAKE_CASE ) ) ) )






def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )->  Union[str, Any]:




      """simple docstring"""

      snake_case_	=				normalize_answer(SCREAMING_SNAKE_CASE ).split()
      snake_case_	=				normalize_answer(SCREAMING_SNAKE_CASE ).split()
      snake_case_	=				Counter(SCREAMING_SNAKE_CASE ) & Counter(SCREAMING_SNAKE_CASE )
      snake_case_	=				sum(common.values() )
      if num_same == 0:
            return 0
      snake_case_	=				1.0 * num_same / len(SCREAMING_SNAKE_CASE )
      snake_case_	=				1.0 * num_same / len(SCREAMING_SNAKE_CASE )
      snake_case_	=				(2 * precision * recall) / (precision + recall)
      return fa






def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )->  int:




      """simple docstring"""

      return normalize_answer(SCREAMING_SNAKE_CASE ) == normalize_answer(SCREAMING_SNAKE_CASE )






def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )->  Dict:




      """simple docstring"""

      assert len(SCREAMING_SNAKE_CASE ) == len(SCREAMING_SNAKE_CASE )
      snake_case_	=				0
      for hypo, pred in zip(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
            em += exact_match_score(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
      if len(SCREAMING_SNAKE_CASE ) > 0:
            em /= len(SCREAMING_SNAKE_CASE )
      return {"em": em}






def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE )->  Optional[int]:




      """simple docstring"""

      return model_prefix.startswith('''rag''' )






def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )->  Any:




      """simple docstring"""

      snake_case_	=				{p: p for p in extra_params}
      # T5 models don't have `dropout` param, they have `dropout_rate` instead
      snake_case_	=				'''dropout_rate'''
      for p in extra_params:
            if getattr(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
                  if not hasattr(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) and not hasattr(SCREAMING_SNAKE_CASE , equivalent_param[p] ):
                        logger.info('''config doesn\'t have a `{}` attribute'''.format(SCREAMING_SNAKE_CASE ) )
                        delattr(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
                        continue
                  snake_case_	=				p if hasattr(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) else equivalent_param[p]
                  setattr(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , getattr(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) )
                  delattr(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
      return hparams, config



import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, PNDMScheduler, StableDiffusionInpaintPipeline, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow

from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()







class lowerCAmelCase_						(			lowerCamelCase__ ,						lowerCamelCase__ ,						lowerCamelCase__ ,						unittest.TestCase	):

 '''simple docstring'''

 __snake_case												=  StableDiffusionInpaintPipeline
 __snake_case												=  TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
 __snake_case												=  TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
 __snake_case												=  frozenset(
     []	)  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
 __snake_case												=  frozenset([]	)



 def 						UpperCamelCase__					(     self ):
       torch.manual_seed(0 )
       snake_case_	=				UNetaDConditionModel(
           block_out_channels=(32, 64)		,				layers_per_block=2		,				sample_size=32		,				in_channels=9		,				out_channels=4		,				down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')		,				up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')		,				cross_attention_dim=32		,				attention_head_dim=(2, 4)		,				use_linear_projection=_UpperCAmelCase		,				)
       snake_case_	=				PNDMScheduler(skip_prk_steps=_UpperCAmelCase )
       torch.manual_seed(0 )
       snake_case_	=				AutoencoderKL(
           block_out_channels=[32, 64]		,				in_channels=3		,				out_channels=3		,				down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']		,				up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']		,				latent_channels=4		,				sample_size=1_28		,				)
       torch.manual_seed(0 )
       snake_case_	=				CLIPTextConfig(
           bos_token_id=0		,				eos_token_id=2		,				hidden_size=32		,				intermediate_size=37		,				layer_norm_eps=1E-05		,				num_attention_heads=4		,				num_hidden_layers=5		,				pad_token_id=1		,				vocab_size=10_00		,				hidden_act='''gelu'''		,				projection_dim=5_12		,				)
       snake_case_	=				CLIPTextModel(_UpperCAmelCase )
       snake_case_	=				CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''' )

       snake_case_	=				{
           '''unet''': unet,
           '''scheduler''': scheduler,
           '''vae''': vae,
           '''text_encoder''': text_encoder,
           '''tokenizer''': tokenizer,
           '''safety_checker''': None,
           '''feature_extractor''': None,
       }
       return components



 def 						UpperCamelCase__					(     self		,				_UpperCAmelCase		,				_UpperCAmelCase=0 ):
       # TODO: use tensor inputs instead of PIL, this is here just to leave the old expected_slices untouched
       snake_case_	=				floats_tensor((1, 3, 32, 32)		,				rng=random.Random(_UpperCAmelCase ) ).to(_UpperCAmelCase )
       snake_case_	=				image.cpu().permute(0		,				2		,				3		,				1 )[0]
       snake_case_	=				Image.fromarray(np.uinta(_UpperCAmelCase ) ).convert('''RGB''' ).resize((64, 64) )
       snake_case_	=				Image.fromarray(np.uinta(image + 4 ) ).convert('''RGB''' ).resize((64, 64) )
       if str(_UpperCAmelCase ).startswith('''mps''' ):
             snake_case_	=				torch.manual_seed(_UpperCAmelCase )
       else:
             snake_case_	=				torch.Generator(device=_UpperCAmelCase ).manual_seed(_UpperCAmelCase )
       snake_case_	=				{
           '''prompt''': '''A painting of a squirrel eating a burger''',
           '''image''': init_image,
           '''mask_image''': mask_image,
           '''generator''': generator,
           '''num_inference_steps''': 2,
           '''guidance_scale''': 6.0,
           '''output_type''': '''numpy''',
       }
       return inputs



 def 						UpperCamelCase__					(     self ):
       snake_case_	=				'''cpu'''  # ensure determinism for the device-dependent torch.Generator
       snake_case_	=				self.get_dummy_components()
       snake_case_	=				StableDiffusionInpaintPipeline(**_UpperCAmelCase )
       snake_case_	=				sd_pipe.to(_UpperCAmelCase )
       sd_pipe.set_progress_bar_config(disable=_UpperCAmelCase )

       snake_case_	=				self.get_dummy_inputs(_UpperCAmelCase )
       snake_case_	=				sd_pipe(**_UpperCAmelCase ).images
       snake_case_	=				image[0, -3:, -3:, -1]

       assert image.shape == (1, 64, 64, 3)
       snake_case_	=				np.array([0.4_727, 0.5_735, 0.3_941, 0.5_446, 0.5_926, 0.4_394, 0.5_062, 0.4_654, 0.4_476] )

       assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2





 def 						UpperCamelCase__					(     self ):
       super().test_inference_batch_single_identical(expected_max_diff=3E-3 )







@slow
@require_torch_gpu
class lowerCAmelCase_						(			unittest.TestCase	):

 '''simple docstring'''

 def 						UpperCamelCase__					(     self ):
       # clean up the VRAM after each test
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()



 def 						UpperCamelCase__					(     self ):
       snake_case_	=				load_image(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
           '''/sd2-inpaint/init_image.png''' )
       snake_case_	=				load_image(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png''' )
       snake_case_	=				load_numpy(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint'''
           '''/yellow_cat_sitting_on_a_park_bench.npy''' )

       snake_case_	=				'''stabilityai/stable-diffusion-2-inpainting'''
       snake_case_	=				StableDiffusionInpaintPipeline.from_pretrained(_UpperCAmelCase		,				safety_checker=_UpperCAmelCase )
       pipe.to(_UpperCAmelCase )
       pipe.set_progress_bar_config(disable=_UpperCAmelCase )
       pipe.enable_attention_slicing()

       snake_case_	=				'''Face of a yellow cat, high resolution, sitting on a park bench'''

       snake_case_	=				torch.manual_seed(0 )
       snake_case_	=				pipe(
           prompt=_UpperCAmelCase		,				image=_UpperCAmelCase		,				mask_image=_UpperCAmelCase		,				generator=_UpperCAmelCase		,				output_type='''np'''		,				)
       snake_case_	=				output.images[0]

       assert image.shape == (5_12, 5_12, 3)
       assert np.abs(expected_image - image ).max() < 9E-3



 def 						UpperCamelCase__					(     self ):
       snake_case_	=				load_image(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
           '''/sd2-inpaint/init_image.png''' )
       snake_case_	=				load_image(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png''' )
       snake_case_	=				load_numpy(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint'''
           '''/yellow_cat_sitting_on_a_park_bench_fp16.npy''' )

       snake_case_	=				'''stabilityai/stable-diffusion-2-inpainting'''
       snake_case_	=				StableDiffusionInpaintPipeline.from_pretrained(
           _UpperCAmelCase		,				torch_dtype=torch.floataa		,				safety_checker=_UpperCAmelCase		,				)
       pipe.to(_UpperCAmelCase )
       pipe.set_progress_bar_config(disable=_UpperCAmelCase )
       pipe.enable_attention_slicing()

       snake_case_	=				'''Face of a yellow cat, high resolution, sitting on a park bench'''

       snake_case_	=				torch.manual_seed(0 )
       snake_case_	=				pipe(
           prompt=_UpperCAmelCase		,				image=_UpperCAmelCase		,				mask_image=_UpperCAmelCase		,				generator=_UpperCAmelCase		,				output_type='''np'''		,				)
       snake_case_	=				output.images[0]

       assert image.shape == (5_12, 5_12, 3)
       assert np.abs(expected_image - image ).max() < 5E-1





 def 						UpperCamelCase__					(     self ):
       torch.cuda.empty_cache()
       torch.cuda.reset_max_memory_allocated()
       torch.cuda.reset_peak_memory_stats()

       snake_case_	=				load_image(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
           '''/sd2-inpaint/init_image.png''' )
       snake_case_	=				load_image(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png''' )

       snake_case_	=				'''stabilityai/stable-diffusion-2-inpainting'''
       snake_case_	=				PNDMScheduler.from_pretrained(_UpperCAmelCase		,				subfolder='''scheduler''' )
       snake_case_	=				StableDiffusionInpaintPipeline.from_pretrained(
           _UpperCAmelCase		,				safety_checker=_UpperCAmelCase		,				scheduler=_UpperCAmelCase		,				torch_dtype=torch.floataa		,				)
       pipe.to(_UpperCAmelCase )
       pipe.set_progress_bar_config(disable=_UpperCAmelCase )
       pipe.enable_attention_slicing(1 )
       pipe.enable_sequential_cpu_offload()

       snake_case_	=				'''Face of a yellow cat, high resolution, sitting on a park bench'''

       snake_case_	=				torch.manual_seed(0 )
       snake_case_	=				pipe(
           prompt=_UpperCAmelCase		,				image=_UpperCAmelCase		,				mask_image=_UpperCAmelCase		,				generator=_UpperCAmelCase		,				num_inference_steps=2		,				output_type='''np'''		,				)

       snake_case_	=				torch.cuda.max_memory_allocated()
       # make sure that less than 2.65 GB is allocated
       assert mem_bytes < 2.65 * 10**9