





'''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     __magic_name__							(				__UpperCAmelCase				,						__UpperCAmelCase				,						__UpperCAmelCase				,						__UpperCAmelCase				,						__UpperCAmelCase=True				,						__UpperCAmelCase="pt"   )    ->       List[Any]:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE   				=							{"""add_prefix_space""": True} if isinstance(__UpperCAmelCase				,						__UpperCAmelCase   ) and not line.startswith(""" """   ) else {}
   __SCREAMING_SNAKE_CASE   				=							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     __magic_name__							(				__UpperCAmelCase				,						__UpperCAmelCase				,						__UpperCAmelCase=None				,						)    ->       Tuple:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE   				=							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 __a			( _snake_case      ):





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

      '''simple docstring'''

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





   def __len__(       self					:      Optional[int]     ):

      '''simple docstring'''

      return len(self.src_lens     )





   def __getitem__(       self					:      Union[str, Any]					,lowerCamelCase					:      Any     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							index + 1  # linecache starts at 1
      __SCREAMING_SNAKE_CASE   				=							self.prefix + linecache.getline(str(self.src_file     )					,lowerCamelCase     ).rstrip("""\n"""     )
      __SCREAMING_SNAKE_CASE   				=							linecache.getline(str(self.tgt_file     )					,lowerCamelCase     ).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					,lowerCamelCase     ):
         source_line += self.tokenizer.eos_token
         tgt_line += self.tokenizer.eos_token

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

      __SCREAMING_SNAKE_CASE   				=							encode_line(lowerCamelCase					,lowerCamelCase					,self.max_source_length					,"""right"""     )
      __SCREAMING_SNAKE_CASE   				=							encode_line(lowerCamelCase					,lowerCamelCase					,self.max_target_length					,"""right"""     )

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





   @staticmethod
   def   UpperCAmelCase__  (       lowerCamelCase					:      List[str]     ):

      '''simple docstring'''

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





   def   UpperCAmelCase__  (       self					:      int					,lowerCamelCase					:      Union[str, Any]     ):

      '''simple docstring'''

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





a   	=    getLogger(__name__)

def     __magic_name__							(				__UpperCAmelCase   )    ->       Optional[Any]:

   '''simple docstring'''







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

def     __magic_name__							(				__UpperCAmelCase   )    ->       None:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE   				=							get_git_info()
   save_json(__UpperCAmelCase				,						os.path.join(__UpperCAmelCase				,						"""git_log.json"""   )   )

def     __magic_name__							(				__UpperCAmelCase				,						__UpperCAmelCase				,						__UpperCAmelCase=4				,						**__UpperCAmelCase   )    ->       Union[str, Any]:

   '''simple docstring'''







   with open(__UpperCAmelCase				,						"""w"""   ) as f:
      json.dump(__UpperCAmelCase				,						__UpperCAmelCase				,						indent=__UpperCAmelCase				,						**__UpperCAmelCase   )

def     __magic_name__							(				__UpperCAmelCase   )    ->       Dict:

   '''simple docstring'''







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

def     __magic_name__							(				)    ->       List[str]:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE   				=							git.Repo(search_parent_directories=__UpperCAmelCase   )
   __SCREAMING_SNAKE_CASE   				=							{
       """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     __magic_name__							(				__UpperCAmelCase				,						__UpperCAmelCase   )    ->       List:

   '''simple docstring'''







   return list(map(__UpperCAmelCase				,						__UpperCAmelCase   )   )

def     __magic_name__							(				__UpperCAmelCase				,						__UpperCAmelCase   )    ->       Tuple:

   '''simple docstring'''







   with open(__UpperCAmelCase				,						"""wb"""   ) as f:
      return pickle.dump(__UpperCAmelCase				,						__UpperCAmelCase   )

def     __magic_name__							(				__UpperCAmelCase   )    ->       str:

   '''simple docstring'''







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

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

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

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

   return white_space_fix(remove_articles(remove_punc(lower(__UpperCAmelCase   )   )   )   )

def     __magic_name__							(				__UpperCAmelCase				,						__UpperCAmelCase   )    ->       Tuple:

   '''simple docstring'''







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

def     __magic_name__							(				__UpperCAmelCase				,						__UpperCAmelCase   )    ->       str:

   '''simple docstring'''







   return normalize_answer(__UpperCAmelCase   ) == normalize_answer(__UpperCAmelCase   )

def     __magic_name__							(				__UpperCAmelCase				,						__UpperCAmelCase   )    ->       Dict:

   '''simple docstring'''







   assert len(__UpperCAmelCase   ) == len(__UpperCAmelCase   )
   __SCREAMING_SNAKE_CASE   				=							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     __magic_name__							(				__UpperCAmelCase   )    ->       Optional[Any]:

   '''simple docstring'''







   return model_prefix.startswith("""rag"""   )







def     __magic_name__							(				__UpperCAmelCase				,						__UpperCAmelCase				,						__UpperCAmelCase   )    ->       Tuple:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE   				=							{p: p for p in extra_params}
   # T5 models don't have `dropout` param, they have `dropout_rate` instead
   __SCREAMING_SNAKE_CASE   				=							"""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
         __SCREAMING_SNAKE_CASE   				=							p if hasattr(__UpperCAmelCase				,						__UpperCAmelCase   ) else equivalent_param[p]
         setattr(__UpperCAmelCase				,						__UpperCAmelCase				,						getattr(__UpperCAmelCase				,						__UpperCAmelCase   )   )
         delattr(__UpperCAmelCase				,						__UpperCAmelCase   )
   return hparams, config












'''simple docstring'''

import json
import sys
import tempfile
import unittest
from pathlib import Path

import transformers
from transformers import (
    CONFIG_MAPPING,
    IMAGE_PROCESSOR_MAPPING,
    AutoConfig,
    AutoImageProcessor,
    CLIPConfig,
    CLIPImageProcessor,
)
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER


sys.path.append(str(Path(__file__).parent.parent.parent.parent / "utils"))

from test_module.custom_configuration import CustomConfig  # noqa E402
from test_module.custom_image_processing import CustomImageProcessor  # noqa E402







class __a			( unittest.TestCase      ):





   def   UpperCAmelCase__  (       self					:      Any     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							0





   def   UpperCAmelCase__  (       self					:      str     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							AutoImageProcessor.from_pretrained("""openai/clip-vit-base-patch32"""     )
      self.assertIsInstance(lowerCamelCase					,lowerCamelCase     )





   def   UpperCAmelCase__  (       self					:      Dict     ):

      '''simple docstring'''

      with tempfile.TemporaryDirectory() as tmpdirname:
         __SCREAMING_SNAKE_CASE   				=							Path(lowerCamelCase     ) / """preprocessor_config.json"""
         __SCREAMING_SNAKE_CASE   				=							Path(lowerCamelCase     ) / """config.json"""
         json.dump(
             {"""image_processor_type""": """CLIPImageProcessor""", """processor_class""": """CLIPProcessor"""}					,open(lowerCamelCase					,"""w"""     )					,)
         json.dump({"""model_type""": """clip"""}					,open(lowerCamelCase					,"""w"""     )     )

         __SCREAMING_SNAKE_CASE   				=							AutoImageProcessor.from_pretrained(lowerCamelCase     )
         self.assertIsInstance(lowerCamelCase					,lowerCamelCase     )





   def   UpperCAmelCase__  (       self					:      Optional[Any]     ):

      '''simple docstring'''

      with tempfile.TemporaryDirectory() as tmpdirname:
         __SCREAMING_SNAKE_CASE   				=							Path(lowerCamelCase     ) / """preprocessor_config.json"""
         __SCREAMING_SNAKE_CASE   				=							Path(lowerCamelCase     ) / """config.json"""
         json.dump(
             {"""feature_extractor_type""": """CLIPFeatureExtractor""", """processor_class""": """CLIPProcessor"""}					,open(lowerCamelCase					,"""w"""     )					,)
         json.dump({"""model_type""": """clip"""}					,open(lowerCamelCase					,"""w"""     )     )

         __SCREAMING_SNAKE_CASE   				=							AutoImageProcessor.from_pretrained(lowerCamelCase     )
         self.assertIsInstance(lowerCamelCase					,lowerCamelCase     )





   def   UpperCAmelCase__  (       self					:      List[str]     ):

      '''simple docstring'''

      with tempfile.TemporaryDirectory() as tmpdirname:
         __SCREAMING_SNAKE_CASE   				=							CLIPConfig()

         # Create a dummy config file with image_proceesor_type
         __SCREAMING_SNAKE_CASE   				=							Path(lowerCamelCase     ) / """preprocessor_config.json"""
         __SCREAMING_SNAKE_CASE   				=							Path(lowerCamelCase     ) / """config.json"""
         json.dump(
             {"""image_processor_type""": """CLIPImageProcessor""", """processor_class""": """CLIPProcessor"""}					,open(lowerCamelCase					,"""w"""     )					,)
         json.dump({"""model_type""": """clip"""}					,open(lowerCamelCase					,"""w"""     )     )

         # remove image_processor_type to make sure config.json alone is enough to load image processor locally
         __SCREAMING_SNAKE_CASE   				=							AutoImageProcessor.from_pretrained(lowerCamelCase     ).to_dict()

         config_dict.pop("""image_processor_type"""     )
         __SCREAMING_SNAKE_CASE   				=							CLIPImageProcessor(**lowerCamelCase     )

         # save in new folder
         model_config.save_pretrained(lowerCamelCase     )
         config.save_pretrained(lowerCamelCase     )

         __SCREAMING_SNAKE_CASE   				=							AutoImageProcessor.from_pretrained(lowerCamelCase     )

         # make sure private variable is not incorrectly saved
         __SCREAMING_SNAKE_CASE   				=							json.loads(config.to_json_string()     )
         self.assertTrue("""_processor_class""" not in dict_as_saved     )

      self.assertIsInstance(lowerCamelCase					,lowerCamelCase     )





   def   UpperCAmelCase__  (       self					:      Union[str, Any]     ):

      '''simple docstring'''

      with tempfile.TemporaryDirectory() as tmpdirname:
         __SCREAMING_SNAKE_CASE   				=							Path(lowerCamelCase     ) / """preprocessor_config.json"""
         json.dump(
             {"""image_processor_type""": """CLIPImageProcessor""", """processor_class""": """CLIPProcessor"""}					,open(lowerCamelCase					,"""w"""     )					,)

         __SCREAMING_SNAKE_CASE   				=							AutoImageProcessor.from_pretrained(lowerCamelCase     )
         self.assertIsInstance(lowerCamelCase					,lowerCamelCase     )





   def   UpperCAmelCase__  (       self					:      Dict     ):

      '''simple docstring'''

      with self.assertRaisesRegex(
          lowerCamelCase					,"""clip-base is not a local folder and is not a valid model identifier"""     ):
         __SCREAMING_SNAKE_CASE   				=							AutoImageProcessor.from_pretrained("""clip-base"""     )





   def   UpperCAmelCase__  (       self					:      Any     ):

      '''simple docstring'''

      with self.assertRaisesRegex(
          lowerCamelCase					,r"""aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)"""     ):
         __SCREAMING_SNAKE_CASE   				=							AutoImageProcessor.from_pretrained(lowerCamelCase					,revision="""aaaaaa"""     )





   def   UpperCAmelCase__  (       self					:      List[Any]     ):

      '''simple docstring'''

      with self.assertRaisesRegex(
          lowerCamelCase					,"""hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json."""					,):
         __SCREAMING_SNAKE_CASE   				=							AutoImageProcessor.from_pretrained("""hf-internal-testing/config-no-model"""     )





   def   UpperCAmelCase__  (       self					:      Any     ):

      '''simple docstring'''

      with self.assertRaises(lowerCamelCase     ):
         __SCREAMING_SNAKE_CASE   				=							AutoImageProcessor.from_pretrained("""hf-internal-testing/test_dynamic_image_processor"""     )
      # If remote code is disabled, we can't load this config.
      with self.assertRaises(lowerCamelCase     ):
         __SCREAMING_SNAKE_CASE   				=							AutoImageProcessor.from_pretrained(
             """hf-internal-testing/test_dynamic_image_processor"""					,trust_remote_code=lowerCamelCase     )

      __SCREAMING_SNAKE_CASE   				=							AutoImageProcessor.from_pretrained(
          """hf-internal-testing/test_dynamic_image_processor"""					,trust_remote_code=lowerCamelCase     )
      self.assertEqual(image_processor.__class__.__name__					,"""NewImageProcessor"""     )

      # Test image processor can be reloaded.
      with tempfile.TemporaryDirectory() as tmp_dir:
         image_processor.save_pretrained(lowerCamelCase     )
         __SCREAMING_SNAKE_CASE   				=							AutoImageProcessor.from_pretrained(lowerCamelCase					,trust_remote_code=lowerCamelCase     )
      self.assertEqual(reloaded_image_processor.__class__.__name__					,"""NewImageProcessor"""     )





   def   UpperCAmelCase__  (       self					:      str     ):

      '''simple docstring'''

      try:
         AutoConfig.register("""custom"""					,lowerCamelCase     )
         AutoImageProcessor.register(lowerCamelCase					,lowerCamelCase     )
         # Trying to register something existing in the Transformers library will raise an error
         with self.assertRaises(lowerCamelCase     ):
            AutoImageProcessor.register(lowerCamelCase					,lowerCamelCase     )

         with tempfile.TemporaryDirectory() as tmpdirname:
            __SCREAMING_SNAKE_CASE   				=							Path(lowerCamelCase     ) / """preprocessor_config.json"""
            __SCREAMING_SNAKE_CASE   				=							Path(lowerCamelCase     ) / """config.json"""
            json.dump(
                {"""feature_extractor_type""": """CLIPFeatureExtractor""", """processor_class""": """CLIPProcessor"""}					,open(lowerCamelCase					,"""w"""     )					,)
            json.dump({"""model_type""": """clip"""}					,open(lowerCamelCase					,"""w"""     )     )

            __SCREAMING_SNAKE_CASE   				=							CustomImageProcessor.from_pretrained(lowerCamelCase     )

         # Now that the config is registered, it can be used as any other config with the auto-API
         with tempfile.TemporaryDirectory() as tmp_dir:
            image_processor.save_pretrained(lowerCamelCase     )
            __SCREAMING_SNAKE_CASE   				=							AutoImageProcessor.from_pretrained(lowerCamelCase     )
            self.assertIsInstance(lowerCamelCase					,lowerCamelCase     )

      finally:
         if "custom" in CONFIG_MAPPING._extra_content:
            del CONFIG_MAPPING._extra_content["custom"]
         if CustomConfig in IMAGE_PROCESSOR_MAPPING._extra_content:
            del IMAGE_PROCESSOR_MAPPING._extra_content[CustomConfig]





   def   UpperCAmelCase__  (       self					:      str     ):

      '''simple docstring'''

      class __a			( _snake_case      ):
         __UpperCamelCase :			int						   =						True

      try:
         AutoConfig.register("""custom"""					,lowerCamelCase     )
         AutoImageProcessor.register(lowerCamelCase					,lowerCamelCase     )
         # If remote code is not set, the default is to use local
         __SCREAMING_SNAKE_CASE   				=							AutoImageProcessor.from_pretrained("""hf-internal-testing/test_dynamic_image_processor"""     )
         self.assertEqual(image_processor.__class__.__name__					,"""NewImageProcessor"""     )
         self.assertTrue(image_processor.is_local     )

         # If remote code is disabled, we load the local one.
         __SCREAMING_SNAKE_CASE   				=							AutoImageProcessor.from_pretrained(
             """hf-internal-testing/test_dynamic_image_processor"""					,trust_remote_code=lowerCamelCase     )
         self.assertEqual(image_processor.__class__.__name__					,"""NewImageProcessor"""     )
         self.assertTrue(image_processor.is_local     )

         # If remote is enabled, we load from the Hub
         __SCREAMING_SNAKE_CASE   				=							AutoImageProcessor.from_pretrained(
             """hf-internal-testing/test_dynamic_image_processor"""					,trust_remote_code=lowerCamelCase     )
         self.assertEqual(image_processor.__class__.__name__					,"""NewImageProcessor"""     )
         self.assertTrue(not hasattr(lowerCamelCase					,"""is_local"""     )     )

      finally:
         if "custom" in CONFIG_MAPPING._extra_content:
            del CONFIG_MAPPING._extra_content["custom"]
         if CustomConfig in IMAGE_PROCESSOR_MAPPING._extra_content:
            del IMAGE_PROCESSOR_MAPPING._extra_content[CustomConfig]





