



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     A__   (  lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase=True       ,      lowerCamelCase="pt"  )					->		List[str]:
				UpperCamelCase_:							Optional[int]		=  {"""add_prefix_space""": True} if isinstance(lowerCamelCase       ,      lowerCamelCase  ) and not line.startswith(""" """  ) else {}
				UpperCamelCase_:							int		=  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     A__   (  lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase=None       ,      )					->		Optional[int]:
				UpperCamelCase_:							Any		=  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     _UpperCamelCase			(   _A					):






			'''simple docstring'''

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

			def __len__(    self	:    Union[str, Any] ):
							return len(self.src_lens )

			def __getitem__(    self	:    Tuple  ,		snake_case_	:    List[str] ):
							UpperCamelCase_:							int		=  index + 1  # linecache starts at 1
							UpperCamelCase_:							List[str]		=  self.prefix + linecache.getline(str(self.src_file )  ,		snake_case_ ).rstrip("""\n""" )
							UpperCamelCase_:							Tuple		=  linecache.getline(str(self.tgt_file )  ,		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  ,		snake_case_ ):
											source_line += self.tokenizer.eos_token
											tgt_line += self.tokenizer.eos_token

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

							UpperCamelCase_:							Any		=  encode_line(snake_case_  ,		snake_case_  ,		self.max_source_length  ,		"""right""" )
							UpperCamelCase_:							Tuple		=  encode_line(snake_case_  ,		snake_case_  ,		self.max_target_length  ,		"""right""" )

							UpperCamelCase_:							List[Any]		=  source_inputs["""input_ids"""].squeeze()
							UpperCamelCase_:							Optional[Any]		=  target_inputs["""input_ids"""].squeeze()
							UpperCamelCase_:							Tuple		=  source_inputs["""attention_mask"""].squeeze()
							return {
							    "input_ids": source_ids,
							    "attention_mask": src_mask,
							    "decoder_input_ids": target_ids,
							}

			@staticmethod
			def  lowerCAmelCase__		(    snake_case_	:    List[str] ):
							return [len(snake_case_ ) for x in Path(snake_case_ ).open().readlines()]




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




lowerCamelCase_  :	List[str] 				=					getLogger(__name__)







def     A__   (  lowerCamelCase  )					->		Union[str, Any]:
				return list(itertools.chain.from_iterable(lowerCamelCase  )  )







def     A__   (  lowerCamelCase  )					->		None:
				UpperCamelCase_:							int		=  get_git_info()
				save_json(lowerCamelCase       ,      os.path.join(lowerCamelCase       ,      """git_log.json"""  )  )







def     A__   (  lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase=4       ,      **lowerCamelCase  )					->		Optional[int]:
				with open(lowerCamelCase       ,      """w"""  ) as f:
								json.dump(lowerCamelCase       ,      lowerCamelCase       ,      indent=lowerCamelCase       ,      **lowerCamelCase  )







def     A__   (  lowerCamelCase  )					->		str:
				with open(lowerCamelCase  ) as f:
								return json.load(lowerCamelCase  )







def     A__   (  )					->		Dict:
				UpperCamelCase_:							List[str]		=  git.Repo(search_parent_directories=lowerCamelCase  )
				UpperCamelCase_:							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     A__   (  lowerCamelCase       ,      lowerCamelCase  )					->		List:
				return list(map(lowerCamelCase       ,      lowerCamelCase  )  )







def     A__   (  lowerCamelCase       ,      lowerCamelCase  )					->		Dict:
				with open(lowerCamelCase       ,      """wb"""  ) as f:
								return pickle.dump(lowerCamelCase       ,      lowerCamelCase  )







def     A__   (  lowerCamelCase  )					->		Optional[int]:

				def remove_articles(lowerCamelCase  ):
								return re.sub(r"""\b(a|an|the)\b"""       ,      """ """       ,      lowerCamelCase  )

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

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

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

				return white_space_fix(remove_articles(remove_punc(lower(lowerCamelCase  )  )  )  )







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







def     A__   (  lowerCamelCase       ,      lowerCamelCase  )					->		List[Any]:
				return normalize_answer(lowerCamelCase  ) == normalize_answer(lowerCamelCase  )







def     A__   (  lowerCamelCase       ,      lowerCamelCase  )					->		Dict:
				assert len(lowerCamelCase  ) == len(lowerCamelCase  )
				UpperCamelCase_:							Tuple		=  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     A__   (  lowerCamelCase  )					->		Dict:
				return model_prefix.startswith("""rag"""  )







def     A__   (  lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase  )					->		Optional[Any]:
				UpperCamelCase_:							List[Any]		=  {p: p for p in extra_params}
				# T5 models don't have `dropout` param, they have `dropout_rate` instead
				UpperCamelCase_:							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
												UpperCamelCase_:							Union[str, Any]		=  p if hasattr(lowerCamelCase       ,      lowerCamelCase  ) else equivalent_param[p]
												setattr(lowerCamelCase       ,      lowerCamelCase       ,      getattr(lowerCamelCase       ,      lowerCamelCase  )  )
												delattr(lowerCamelCase       ,      lowerCamelCase  )
				return hparams, config








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     _UpperCamelCase			(   unittest.TestCase					):






			'''simple docstring'''

			def __init__(    self	:    Optional[int]  ,		snake_case_	:    Dict  ,		snake_case_	:    Tuple=7  ,		snake_case_	:    Optional[Any]=3  ,		snake_case_	:    Dict=18  ,		snake_case_	:    Dict=30  ,		snake_case_	:    Union[str, Any]=400  ,		snake_case_	:    List[Any]=True  ,		snake_case_	:    Any=None  ,		snake_case_	:    List[str]=True  ,		):
							UpperCamelCase_:							Dict		=  size if size is not None else {"""height""": 18, """width""": 18}
							UpperCamelCase_:							Union[str, Any]		=  parent
							UpperCamelCase_:							Tuple		=  batch_size
							UpperCamelCase_:							List[str]		=  num_channels
							UpperCamelCase_:							Optional[int]		=  image_size
							UpperCamelCase_:							Dict		=  min_resolution
							UpperCamelCase_:							Optional[int]		=  max_resolution
							UpperCamelCase_:							str		=  do_resize
							UpperCamelCase_:							Tuple		=  size
							UpperCamelCase_:							Dict		=  do_normalize




			def  lowerCAmelCase__		(    self	:    str ):
							return {
							    # here we create 2 clusters for the sake of simplicity
							    "clusters": np.asarray(
							        [
							            [0.8866_4436_3403_3203, 0.6618_8293_6954_4983, 0.3891_7464_0178_6804],
							            [-0.6042_5591_4688_1104, -0.0_2295_0088_6052_8469, 0.5423_7973_6900_3296],
							        ] ),
							    "do_resize": self.do_resize,
							    "size": self.size,
							    "do_normalize": self.do_normalize,
							}




@require_torch
@require_vision
class     _UpperCamelCase			(   _A       ,    unittest.TestCase					):






			'''simple docstring'''

			__UpperCamelCase :  Optional[int]    =	ImageGPTImageProcessor if is_vision_available() else None

			def  lowerCAmelCase__		(    self	:    List[str] ):
							UpperCamelCase_:							Any		=  ImageGPTImageProcessingTester(self )

			@property
			def  lowerCAmelCase__		(    self	:    Optional[int] ):
							return self.image_processor_tester.prepare_image_processor_dict()

			def  lowerCAmelCase__		(    self	:    Optional[int] ):
							UpperCamelCase_:							List[Any]		=  self.image_processing_class(**self.image_processor_dict )
							self.assertTrue(hasattr(snake_case_  ,		"""clusters""" ) )
							self.assertTrue(hasattr(snake_case_  ,		"""do_resize""" ) )
							self.assertTrue(hasattr(snake_case_  ,		"""size""" ) )
							self.assertTrue(hasattr(snake_case_  ,		"""do_normalize""" ) )

			def  lowerCAmelCase__		(    self	:    int ):
							UpperCamelCase_:							List[Any]		=  self.image_processing_class.from_dict(self.image_processor_dict )
							self.assertEqual(image_processor.size  ,		{"""height""": 18, """width""": 18} )

							UpperCamelCase_:							Tuple		=  self.image_processing_class.from_dict(self.image_processor_dict  ,		size=42 )
							self.assertEqual(image_processor.size  ,		{"""height""": 42, """width""": 42} )

			def  lowerCAmelCase__		(    self	:    Optional[Any] ):
							UpperCamelCase_:							int		=  self.image_processing_class(**self.image_processor_dict )
							UpperCamelCase_:							Optional[int]		=  json.loads(image_processor.to_json_string() )
							for key, value in self.image_processor_dict.items():
											if key == "clusters":
															self.assertTrue(np.array_equal(snake_case_  ,		obj[key] ) )
											else:
															self.assertEqual(obj[key]  ,		snake_case_ )

			def  lowerCAmelCase__		(    self	:    List[Any] ):
							UpperCamelCase_:							Dict		=  self.image_processing_class(**self.image_processor_dict )

							with tempfile.TemporaryDirectory() as tmpdirname:
											UpperCamelCase_:							int		=  os.path.join(snake_case_  ,		"""image_processor.json""" )
											image_processor_first.to_json_file(snake_case_ )
											UpperCamelCase_:							Any		=  self.image_processing_class.from_json_file(snake_case_ ).to_dict()

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

			def  lowerCAmelCase__		(    self	:    Union[str, Any] ):
							UpperCamelCase_:							Optional[Any]		=  self.image_processing_class(**self.image_processor_dict )

							with tempfile.TemporaryDirectory() as tmpdirname:
											image_processor_first.save_pretrained(snake_case_ )
											UpperCamelCase_:							Optional[int]		=  self.image_processing_class.from_pretrained(snake_case_ ).to_dict()

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




			@unittest.skip("""ImageGPT requires clusters at initialization""" )
			def  lowerCAmelCase__		(    self	:    List[Any] ):
							pass







def     A__   (  )					->		Optional[int]:
				UpperCamelCase_:							Optional[int]		=  load_dataset("""hf-internal-testing/fixtures_image_utils"""       ,      split="""test"""  )

				UpperCamelCase_:							Tuple		=  Image.open(dataset[4]["""file"""]  )
				UpperCamelCase_:							Union[str, Any]		=  Image.open(dataset[5]["""file"""]  )

				UpperCamelCase_:							List[str]		=  [imagea, imagea]

				return images





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






			'''simple docstring'''

			@slow
			def  lowerCAmelCase__		(    self	:    List[str] ):
							UpperCamelCase_:							List[Any]		=  ImageGPTImageProcessor.from_pretrained("""openai/imagegpt-small""" )

							UpperCamelCase_:							List[str]		=  prepare_images()

							# test non-batched
							UpperCamelCase_:							List[str]		=  image_processing(images[0]  ,		return_tensors="""pt""" )

							self.assertIsInstance(encoding.input_ids  ,		torch.LongTensor )
							self.assertEqual(encoding.input_ids.shape  ,		(1, 1024) )

							UpperCamelCase_:							Union[str, Any]		=  [306, 191, 191]
							self.assertEqual(encoding.input_ids[0, :3].tolist()  ,		snake_case_ )

							# test batched
							UpperCamelCase_:							Optional[int]		=  image_processing(snake_case_  ,		return_tensors="""pt""" )

							self.assertIsInstance(encoding.input_ids  ,		torch.LongTensor )
							self.assertEqual(encoding.input_ids.shape  ,		(2, 1024) )

							UpperCamelCase_:							str		=  [303, 13, 13]
							self.assertEqual(encoding.input_ids[1, -3:].tolist()  ,		snake_case_ )






