





'''simple docstring'''


import json
import os
import tempfile
import unittest

import numpy as np
from datasets import load_dataset

from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin


if is_torch_available():
 import torch

if is_vision_available():
 from PIL import Image

 from transformers import ImageGPTImageProcessor
class       a_   (				unittest.TestCase							):





     def __init__( self		:					Optional[Any]	,      lowercase		:					List[Any]	,      lowercase		:					int=7	,      lowercase		:					List[str]=3	,      lowercase		:					List[str]=18	,      lowercase		:					str=30	,      lowercase		:					Any=400	,      lowercase		:					int=True	,      lowercase		:					str=None	,      lowercase		:					int=True	,      ):





        """simple docstring"""




        lowercase_			:Tuple    			= size if size is not None else {"height": 18, "width": 18}
        lowercase_			:Dict    			= parent
        lowercase_			:List[str]    			= batch_size
        lowercase_			:str    			= num_channels
        lowercase_			:str    			= image_size
        lowercase_			:str    			= min_resolution
        lowercase_			:List[Any]    			= max_resolution
        lowercase_			:int    			= do_resize
        lowercase_			:Optional[int]    			= size
        lowercase_			:Any    			= do_normalize







     def 							lowercase__      ( self		:					str						):





        """simple docstring"""




        return {
            # here we create 2 clusters for the sake of simplicity
            "clusters": np.asarray(
                [
                    [0.88_66_44_36_34_03_32_03, 0.66_18_82_93_69_54_49_83, 0.38_91_74_64_01_78_68_04],
                    [-0.60_42_55_91_46_88_11_04, -0.0_22_95_00_88_60_52_84_69, 0.54_23_79_73_69_00_32_96],
                ]						),
            "do_resize": self.do_resize,
            "size": self.size,
            "do_normalize": self.do_normalize,
        }







@require_torch
@require_vision
class       a_   (				_lowerCAmelCase	,						unittest.TestCase							):
     __A      =			ImageGPTImageProcessor if is_vision_available() else None





     def 							lowercase__      ( self		:					Any						):





        """simple docstring"""




        lowercase_			:List[Any]    			= ImageGPTImageProcessingTester(self						)





     @property
     def 							lowercase__      ( self		:					Optional[int]						):





        """simple docstring"""




        return self.image_processor_tester.prepare_image_processor_dict()





     def 							lowercase__      ( self		:					Optional[Any]						):





        """simple docstring"""




        lowercase_			:Any    			= self.image_processing_class(**self.image_processor_dict						)
        self.assertTrue(hasattr(lowercase	,      "clusters"						)						)
        self.assertTrue(hasattr(lowercase	,      "do_resize"						)						)
        self.assertTrue(hasattr(lowercase	,      "size"						)						)
        self.assertTrue(hasattr(lowercase	,      "do_normalize"						)						)





     def 							lowercase__      ( self		:					Optional[int]						):





        """simple docstring"""




        lowercase_			:Optional[Any]    			= self.image_processing_class.from_dict(self.image_processor_dict						)
        self.assertEqual(image_processor.size	,      {"height": 18, "width": 18}						)

        lowercase_			:Optional[int]    			= self.image_processing_class.from_dict(self.image_processor_dict	,      size=42						)
        self.assertEqual(image_processor.size	,      {"height": 42, "width": 42}						)





     def 							lowercase__      ( self		:					str						):





        """simple docstring"""




        lowercase_			:Any    			= self.image_processing_class(**self.image_processor_dict						)
        lowercase_			:List[str]    			= json.loads(image_processor.to_json_string()						)
        for key, value in self.image_processor_dict.items():
           if key == "clusters":
              self.assertTrue(np.array_equal(lowercase	,      obj[key]						)						)
           else:
              self.assertEqual(obj[key]	,      lowercase						)





     def 							lowercase__      ( self		:					Optional[Any]						):





        """simple docstring"""




        lowercase_			:str    			= self.image_processing_class(**self.image_processor_dict						)

        with tempfile.TemporaryDirectory() as tmpdirname:
           lowercase_			:List[str]    			= os.path.join(lowercase	,      "image_processor.json"						)
           image_processor_first.to_json_file(lowercase						)
           lowercase_			:Tuple    			= self.image_processing_class.from_json_file(lowercase						).to_dict()

        lowercase_			:Union[str, Any]    			= image_processor_first.to_dict()
        for key, value in image_processor_first.items():
           if key == "clusters":
              self.assertTrue(np.array_equal(lowercase	,      image_processor_second[key]						)						)
           else:
              self.assertEqual(image_processor_first[key]	,      lowercase						)





     def 							lowercase__      ( self		:					Dict						):





        """simple docstring"""




        lowercase_			:List[str]    			= self.image_processing_class(**self.image_processor_dict						)

        with tempfile.TemporaryDirectory() as tmpdirname:
           image_processor_first.save_pretrained(lowercase						)
           lowercase_			:Union[str, Any]    			= self.image_processing_class.from_pretrained(lowercase						).to_dict()

        lowercase_			:Optional[Any]    			= image_processor_first.to_dict()
        for key, value in image_processor_first.items():
           if key == "clusters":
              self.assertTrue(np.array_equal(lowercase	,      image_processor_second[key]						)						)
           else:
              self.assertEqual(image_processor_first[key]	,      lowercase						)







     @unittest.skip("ImageGPT requires clusters at initialization"						)
     def 							lowercase__      ( self		:					Any						):





        """simple docstring"""




        pass







def 							UpperCAmelCase_  (						):
   lowercase_			:Union[str, Any]    			= load_dataset("hf-internal-testing/fixtures_image_utils"	,split="test" )

   lowercase_			:Tuple    			= Image.open(dataset[4]["file"] )
   lowercase_			:Any    			= Image.open(dataset[5]["file"] )

   lowercase_			:Optional[Any]    			= [imagea, imagea]

   return images


@require_vision
@require_torch
class       a_   (				unittest.TestCase							):





     @slow
     def 							lowercase__      ( self		:					int						):





        """simple docstring"""




        lowercase_			:int    			= ImageGPTImageProcessor.from_pretrained("openai/imagegpt-small"						)

        lowercase_			:Tuple    			= prepare_images()

        # test non-batched
        lowercase_			:Tuple    			= image_processing(images[0]	,      return_tensors="pt"						)

        self.assertIsInstance(encoding.input_ids	,      torch.LongTensor						)
        self.assertEqual(encoding.input_ids.shape	,      (1, 1_024)						)

        lowercase_			:Optional[Any]    			= [306, 191, 191]
        self.assertEqual(encoding.input_ids[0, :3].tolist()	,      lowercase						)

        # test batched
        lowercase_			:Union[str, Any]    			= image_processing(lowercase	,      return_tensors="pt"						)

        self.assertIsInstance(encoding.input_ids	,      torch.LongTensor						)
        self.assertEqual(encoding.input_ids.shape	,      (2, 1_024)						)

        lowercase_			:int    			= [303, 13, 13]
        self.assertEqual(encoding.input_ids[1, -3:].tolist()	,      lowercase						)









'''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_  (						__lowerCamelCase					:					Optional[int]	,__lowerCamelCase					:					Any	,__lowerCamelCase					:					Any	,__lowerCamelCase					:					List[str]	,__lowerCamelCase					:					Dict=True	,__lowerCamelCase					:					List[Any]="pt" ):
   lowercase_			:Dict    			= {"add_prefix_space": True} if isinstance(__lowerCamelCase	,__lowerCamelCase ) and not line.startswith(" " ) else {}
   lowercase_			:str    			= padding_side
   return tokenizer(
       [line]	,max_length=__lowerCamelCase	,padding="max_length" if pad_to_max_length else None	,truncation=__lowerCamelCase	,return_tensors=__lowerCamelCase	,add_special_tokens=__lowerCamelCase	,**__lowerCamelCase	,)
def 							UpperCAmelCase_  (						__lowerCamelCase					:					List[str]	,__lowerCamelCase					:					Dict	,__lowerCamelCase					:					str=None	,):
   lowercase_			:Optional[int]    			= input_ids.ne(__lowerCamelCase ).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       a_   (				_lowerCAmelCase							):





     def __init__( self		:					Optional[int]	,      lowercase		:					Any	,      lowercase		:					Tuple	,      lowercase		:					Union[str, Any]	,      lowercase		:					Tuple	,      lowercase		:					str="train"	,      lowercase		:					Dict=None	,      lowercase		:					Tuple=None	,      lowercase		:					List[str]=None	,      lowercase		:					int=""	,      ):





        """simple docstring"""




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





     def __len__( self		:					Tuple						):





        """simple docstring"""




        return len(self.src_lens						)





     def __getitem__( self		:					str	,      lowercase		:					Dict						):





        """simple docstring"""




        lowercase_			:Tuple    			= index + 1  # linecache starts at 1
        lowercase_			:Optional[Any]    			= self.prefix + linecache.getline(str(self.src_file						)	,      lowercase						).rstrip("\n"						)
        lowercase_			:List[str]    			= linecache.getline(str(self.tgt_file						)	,      lowercase						).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	,      lowercase						):
           source_line += self.tokenizer.eos_token
           tgt_line += self.tokenizer.eos_token

        # Pad source and target to the right
        lowercase_			:List[str]    			= (
            self.tokenizer.question_encoder if isinstance(self.tokenizer	,      lowercase						) else self.tokenizer
        )
        lowercase_			:int    			= self.tokenizer.generator if isinstance(self.tokenizer	,      lowercase						) else self.tokenizer

        lowercase_			:List[str]    			= encode_line(lowercase	,      lowercase	,      self.max_source_length	,      "right"						)
        lowercase_			:Any    			= encode_line(lowercase	,      lowercase	,      self.max_target_length	,      "right"						)

        lowercase_			:Dict    			= source_inputs["input_ids"].squeeze()
        lowercase_			:Tuple    			= target_inputs["input_ids"].squeeze()
        lowercase_			:Optional[int]    			= source_inputs["attention_mask"].squeeze()
        return {
            "input_ids": source_ids,
            "attention_mask": src_mask,
            "decoder_input_ids": target_ids,
        }





     @staticmethod
     def 							lowercase__      ( lowercase		:					Union[str, Any]						):





        """simple docstring"""




        return [len(lowercase						) for x in Path(lowercase						).open().readlines()]







     def 							lowercase__      ( self		:					str	,      lowercase		:					List[Any]						):





        """simple docstring"""




        lowercase_			:Optional[int]    			= torch.stack([x["input_ids"] for x in batch]						)
        lowercase_			:Dict    			= torch.stack([x["attention_mask"] for x in batch]						)
        lowercase_			:List[str]    			= torch.stack([x["decoder_input_ids"] for x in batch]						)
        lowercase_			:Any    			= (
            self.tokenizer.generator.pad_token_id
            if isinstance(self.tokenizer	,      lowercase						)
            else self.tokenizer.pad_token_id
        )
        lowercase_			:str    			= (
            self.tokenizer.question_encoder.pad_token_id
            if isinstance(self.tokenizer	,      lowercase						)
            else self.tokenizer.pad_token_id
        )
        lowercase_			:Union[str, Any]    			= trim_batch(lowercase	,      lowercase						)
        lowercase_			,							lowercase_			:Optional[Any]    			= trim_batch(lowercase	,      lowercase	,      attention_mask=lowercase						)
        lowercase_			:Tuple    			= {
            "input_ids": source_ids,
            "attention_mask": source_mask,
            "decoder_input_ids": y,
        }
        return batch







lowerCAmelCase					:     List[str]												=getLogger(__name__)
def 							UpperCAmelCase_  (						__lowerCamelCase					:					List[List] ):
   return list(itertools.chain.from_iterable(__lowerCamelCase ) )
def 							UpperCAmelCase_  (						__lowerCamelCase					:					str ):
   lowercase_			:List[str]    			= get_git_info()
   save_json(__lowerCamelCase	,os.path.join(__lowerCamelCase	,"git_log.json" ) )
def 							UpperCAmelCase_  (						__lowerCamelCase					:					Any	,__lowerCamelCase					:					Any	,__lowerCamelCase					:					List[Any]=4	,**__lowerCamelCase					:					List[str] ):
   with open(__lowerCamelCase	,"w" ) as f:
      json.dump(__lowerCamelCase	,__lowerCamelCase	,indent=__lowerCamelCase	,**__lowerCamelCase )
def 							UpperCAmelCase_  (						__lowerCamelCase					:					Tuple ):
   with open(__lowerCamelCase ) as f:
      return json.load(__lowerCamelCase )
def 							UpperCAmelCase_  (						):
   lowercase_			:Dict    			= git.Repo(search_parent_directories=__lowerCamelCase )
   lowercase_			:List[str]    			= {
       "repo_id": str(__lowerCamelCase ),
       "repo_sha": str(repo.head.object.hexsha ),
       "repo_branch": str(repo.active_branch ),
       "hostname": str(socket.gethostname() ),
   }
   return repo_infos
def 							UpperCAmelCase_  (						__lowerCamelCase					:					Callable	,__lowerCamelCase					:					Iterable ):
   return list(map(__lowerCamelCase	,__lowerCamelCase ) )
def 							UpperCAmelCase_  (						__lowerCamelCase					:					Union[str, Any]	,__lowerCamelCase					:					List[str] ):
   with open(__lowerCamelCase	,"wb" ) as f:
      return pickle.dump(__lowerCamelCase	,__lowerCamelCase )
def 							UpperCAmelCase_  (						__lowerCamelCase					:					str ):

   def remove_articles(__lowerCamelCase					:					Optional[int] ):
      return re.sub(r"\b(a|an|the)\b"	," "	,__lowerCamelCase )

   def white_space_fix(__lowerCamelCase					:					Dict ):
      return " ".join(text.split() )

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

   def lower(__lowerCamelCase					:					List[Any] ):
      return text.lower()

   return white_space_fix(remove_articles(remove_punc(lower(__lowerCamelCase ) ) ) )
def 							UpperCAmelCase_  (						__lowerCamelCase					:					Optional[int]	,__lowerCamelCase					:					Optional[int] ):
   lowercase_			:Tuple    			= normalize_answer(__lowerCamelCase ).split()
   lowercase_			:Dict    			= normalize_answer(__lowerCamelCase ).split()
   lowercase_			:Tuple    			= Counter(__lowerCamelCase ) & Counter(__lowerCamelCase )
   lowercase_			:Tuple    			= sum(common.values() )
   if num_same == 0:
      return 0
   lowercase_			:Union[str, Any]    			= 1.0 * num_same / len(__lowerCamelCase )
   lowercase_			:List[Any]    			= 1.0 * num_same / len(__lowerCamelCase )
   lowercase_			:Tuple    			= (2 * precision * recall) / (precision + recall)
   return fa
def 							UpperCAmelCase_  (						__lowerCamelCase					:					int	,__lowerCamelCase					:					Union[str, Any] ):
   return normalize_answer(__lowerCamelCase ) == normalize_answer(__lowerCamelCase )
def 							UpperCAmelCase_  (						__lowerCamelCase					:					List[str]	,__lowerCamelCase					:					List[str] ):
   assert len(__lowerCamelCase ) == len(__lowerCamelCase )
   lowercase_			:Any    			= 0
   for hypo, pred in zip(__lowerCamelCase	,__lowerCamelCase ):
      em += exact_match_score(__lowerCamelCase	,__lowerCamelCase )
   if len(__lowerCamelCase ) > 0:
      em /= len(__lowerCamelCase )
   return {"em": em}
def 							UpperCAmelCase_  (						__lowerCamelCase					:					str ):
   return model_prefix.startswith("rag" )


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


