


import os

import jsonlines
import numpy as np
from tqdm import tqdm


SCREAMING_SNAKE_CASE						:Optional[int]			=  20_48
SCREAMING_SNAKE_CASE						:Optional[int]			=  40_96
SCREAMING_SNAKE_CASE						:int			=  42
SCREAMING_SNAKE_CASE						:List[Any]			=  os.environ.pop('''PROCESS_TRAIN''', '''false''')
SCREAMING_SNAKE_CASE						:List[str]			=  {'''null''': 0, '''short''': 1, '''long''': 2, '''yes''': 3, '''no''': 4}



def       _lowerCAmelCase    (				lowerCAmelCase_				:Optional[int]				)->Optional[Any]:
   '''simple docstring'''






   def choose_first(lowerCAmelCase_				:List[str]		,    lowerCAmelCase_				:Optional[int]=False				):
      assert isinstance(lowerCAmelCase_		,    lowerCAmelCase_				)
      if len(lowerCAmelCase_				) == 1:
         snake_case_            =       answer[0]
         return {k: [answer[k]] for k in answer} if is_long_answer else answer
      for a in answer:
         if is_long_answer:
            snake_case_            =       {k: [a[k]] for k in a}
         if len(a["start_token"]				) > 0:
            break
      return a

   snake_case_            =       {"id": example["id"]}
   snake_case_            =       example["annotations"]
   snake_case_            =       annotation["yes_no_answer"]
   if 0 in yes_no_answer or 1 in yes_no_answer:
      snake_case_            =       ["yes"] if 1 in yes_no_answer else ["no"]
      snake_case_            =       snake_case_            =       []
      snake_case_            =       snake_case_            =       []
      snake_case_            =       ["<cls>"]
   else:
      snake_case_            =       ["short"]
      snake_case_            =       choose_first(annotation["short_answers"]				)
      if len(out["start_token"]				) == 0:
         # answer will be long if short is not available
         snake_case_            =       ["long"]
         snake_case_            =       choose_first(annotation["long_answer"]		,    is_long_answer=lowerCAmelCase_				)
         snake_case_            =       []
      answer.update(lowerCAmelCase_				)

   # disregard some samples
   if len(answer["start_token"]				) > 1 or answer["start_token"] == answer["end_token"]:
      snake_case_            =       True
   else:
      snake_case_            =       False

   snake_case_            =       ["start_token", "end_token", "start_byte", "end_byte", "text"]
   if not all(isinstance(answer[k]		,    lowerCAmelCase_				) for k in cols				):
      raise ValueError("Issue in ID"		,    example["id"]				)

   return answer



def       _lowerCAmelCase    (				lowerCAmelCase_				:Dict		,    lowerCAmelCase_				:Tuple=False				)->str:
   '''simple docstring'''






   snake_case_            =       _get_single_answer(lowerCAmelCase_				)
   # bytes are of no use
   del answer["start_byte"]
   del answer["end_byte"]

   # handle yes_no answers explicitly
   if answer["category"][0] in ["yes", "no"]:  # category is list with one element
      snake_case_            =       example["document"]["tokens"]
      snake_case_            =       []
      for i in range(len(doc["token"]				)				):
         if not doc["is_html"][i]:
            context.append(doc["token"][i]				)
      return {
          "context": " ".join(lowerCAmelCase_				),
          "answer": {
              "start_token": -100,  # ignore index in cross-entropy
              "end_token": -100,  # ignore index in cross-entropy
              "category": answer["category"],
              "span": answer["category"],  # extra
          },
      }

   # later, help in removing all no answers
   if answer["start_token"] == [-1]:
      return {
          "context": "None",
          "answer": {
              "start_token": -1,
              "end_token": -1,
              "category": "null",
              "span": "None",  # extra
          },
      }

   # handling normal samples

   snake_case_            =       ["start_token", "end_token"]
   answer.update({k: answer[k][0] if len(answer[k]				) > 0 else answer[k] for k in cols}				)  # e.g. [10] == 10

   snake_case_            =       example["document"]["tokens"]
   snake_case_            =       answer["start_token"]
   snake_case_            =       answer["end_token"]

   snake_case_            =       []
   for i in range(len(doc["token"]				)				):
      if not doc["is_html"][i]:
         context.append(doc["token"][i]				)
      else:
         if answer["start_token"] > i:
            start_token -= 1
         if answer["end_token"] > i:
            end_token -= 1
   snake_case_            =       " ".join(context[start_token:end_token]				)

   # checking above code
   if assertion:
      snake_case_            =       doc["is_html"][answer["start_token"] : answer["end_token"]]
      snake_case_            =       doc["token"][answer["start_token"] : answer["end_token"]]
      snake_case_            =       " ".join([old[i] for i in range(len(lowerCAmelCase_				)				) if not is_html[i]]				)
      if new != old:
         print("ID:"		,    example["id"]				)
         print("New:"		,    lowerCAmelCase_		,    end="\n"				)
         print("Old:"		,    lowerCAmelCase_		,    end="\n\n"				)

   return {
       "context": " ".join(lowerCAmelCase_				),
       "answer": {
           "start_token": start_token,
           "end_token": end_token - 1,  # this makes it inclusive
           "category": answer["category"],  # either long or short
           "span": new,  # extra
       },
   }



def       _lowerCAmelCase    (				lowerCAmelCase_				:str		,    lowerCAmelCase_				:str		,    lowerCAmelCase_				:str=2_048		,    lowerCAmelCase_				:Optional[Any]=4_096		,    lowerCAmelCase_				:Dict=True				)->str:
   '''simple docstring'''






   snake_case_            =       get_context_and_ans(lowerCAmelCase_		,    assertion=lowerCAmelCase_				)
   snake_case_            =       out["answer"]

   # later, removing these samples
   if answer["start_token"] == -1:
      return {
          "example_id": example["id"],
          "input_ids": [[-1]],
          "labels": {
              "start_token": [-1],
              "end_token": [-1],
              "category": ["null"],
          },
      }

   snake_case_            =       tokenizer(example["question"]["text"]		,    out["context"]				).input_ids
   snake_case_            =       input_ids.index(tokenizer.sep_token_id				) + 1

   # return yes/no
   if answer["category"][0] in ["yes", "no"]:  # category is list with one element
      snake_case_            =       []
      snake_case_            =       []
      snake_case_            =       input_ids[:q_len]
      snake_case_            =       range(lowerCAmelCase_		,    len(lowerCAmelCase_				)		,    max_length - doc_stride				)
      for i in doc_start_indices:
         snake_case_            =       i + max_length - q_len
         snake_case_            =       input_ids[i:end_index]
         inputs.append(q_indices + slice				)
         category.append(answer["category"][0]				)
         if slice[-1] == tokenizer.sep_token_id:
            break

      return {
          "example_id": example["id"],
          "input_ids": inputs,
          "labels": {
              "start_token": [-100] * len(lowerCAmelCase_				),
              "end_token": [-100] * len(lowerCAmelCase_				),
              "category": category,
          },
      }

   snake_case_            =       out["context"].split()
   snake_case_            =       splitted_context[answer["end_token"]]
   snake_case_            =       len(
       tokenizer(
           " ".join(splitted_context[: answer["start_token"]]				)		,    add_special_tokens=lowerCAmelCase_		,    ).input_ids				)
   snake_case_            =       len(
       tokenizer(" ".join(splitted_context[: answer["end_token"]]				)		,    add_special_tokens=lowerCAmelCase_				).input_ids				)

   answer["start_token"] += q_len
   answer["end_token"] += q_len

   # fixing end token
   snake_case_            =       len(tokenizer(lowerCAmelCase_		,    add_special_tokens=lowerCAmelCase_				).input_ids				)
   if num_sub_tokens > 1:
      answer["end_token"] += num_sub_tokens - 1

   snake_case_            =       input_ids[answer["start_token"] : answer["end_token"] + 1]  # right & left are inclusive
   snake_case_            =       answer["start_token"]
   snake_case_            =       answer["end_token"]

   if assertion:
      snake_case_            =       tokenizer.decode(lowerCAmelCase_				)
      if answer["span"] != new:
         print("ISSUE IN TOKENIZATION"				)
         print("OLD:"		,    answer["span"]				)
         print("NEW:"		,    lowerCAmelCase_		,    end="\n\n"				)

   if len(lowerCAmelCase_				) <= max_length:
      return {
          "example_id": example["id"],
          "input_ids": [input_ids],
          "labels": {
              "start_token": [answer["start_token"]],
              "end_token": [answer["end_token"]],
              "category": answer["category"],
          },
      }

   snake_case_            =       input_ids[:q_len]
   snake_case_            =       range(lowerCAmelCase_		,    len(lowerCAmelCase_				)		,    max_length - doc_stride				)

   snake_case_            =       []
   snake_case_            =       []
   snake_case_            =       []
   snake_case_            =       []  # null, yes, no, long, short
   for i in doc_start_indices:
      snake_case_            =       i + max_length - q_len
      snake_case_            =       input_ids[i:end_index]
      inputs.append(q_indices + slice				)
      assert len(inputs[-1]				) <= max_length, "Issue in truncating length"

      if start_token >= i and end_token <= end_index - 1:
         snake_case_            =       start_token - i + q_len
         snake_case_            =       end_token - i + q_len
         answers_category.append(answer["category"][0]				)  # ["short"] -> "short"
      else:
         snake_case_            =       -100
         snake_case_            =       -100
         answers_category.append("null"				)
      snake_case_            =       inputs[-1][start_token : end_token + 1]

      answers_start_token.append(lowerCAmelCase_				)
      answers_end_token.append(lowerCAmelCase_				)
      if assertion:
         if new != old and new != [tokenizer.cls_token_id]:
            print("ISSUE in strided for ID:"		,    example["id"]				)
            print("New:"		,    tokenizer.decode(lowerCAmelCase_				)				)
            print("Old:"		,    tokenizer.decode(lowerCAmelCase_				)		,    end="\n\n"				)
      if slice[-1] == tokenizer.sep_token_id:
         break

   return {
       "example_id": example["id"],
       "input_ids": inputs,
       "labels": {
           "start_token": answers_start_token,
           "end_token": answers_end_token,
           "category": answers_category,
       },
   }



def       _lowerCAmelCase    (				lowerCAmelCase_				:Optional[int]		,    lowerCAmelCase_				:int		,    lowerCAmelCase_				:Union[str, Any]=2_048		,    lowerCAmelCase_				:Any=4_096		,    lowerCAmelCase_				:str=False				)->int:
   '''simple docstring'''






   snake_case_            =       get_strided_contexts_and_ans(
       lowerCAmelCase_		,    lowerCAmelCase_		,    doc_stride=lowerCAmelCase_		,    max_length=lowerCAmelCase_		,    assertion=lowerCAmelCase_		,    )

   return example



def       _lowerCAmelCase    (				lowerCAmelCase_				:Union[str, Any]		,    lowerCAmelCase_				:Optional[int]				)->Tuple:
   '''simple docstring'''






   with jsonlines.open(lowerCAmelCase_		,    "a"				) as writer:
      for example in tqdm(lowerCAmelCase_		,    total=len(lowerCAmelCase_				)		,    desc="Saving samples ... "				):
         snake_case_            =       example["labels"]
         for ids, start, end, cat in zip(
             example["input_ids"]		,    labels["start_token"]		,    labels["end_token"]		,    labels["category"]		,    ):
            if start == -1 and end == -1:
               continue  # leave waste samples with no answer
            if cat == "null" and np.random.rand() < 0.6:
               continue  # removing 50 % samples
            writer.write(
                {
                    "input_ids": ids,
                    "start_token": start,
                    "end_token": end,
                    "category": CATEGORY_MAPPING[cat],
                }				)


if __name__ == "__main__":
   from datasets import load_dataset

   from transformers import BigBirdTokenizer

   SCREAMING_SNAKE_CASE						:Any			=  load_dataset('''natural_questions''')
   SCREAMING_SNAKE_CASE						:str			=  BigBirdTokenizer.from_pretrained('''google/bigbird-roberta-base''')

   SCREAMING_SNAKE_CASE						:int			=  data['''train''' if PROCESS_TRAIN == '''true''' else '''validation''']

   SCREAMING_SNAKE_CASE						:Tuple			=  {
       '''tokenizer''': tokenizer,
       '''doc_stride''': DOC_STRIDE,
       '''max_length''': MAX_LENGTH,
       '''assertion''': False,
   }
   SCREAMING_SNAKE_CASE						:Optional[int]			=  data.map(prepare_inputs, fn_kwargs=fn_kwargs)
   SCREAMING_SNAKE_CASE						:Any			=  data.remove_columns(['''annotations''', '''document''', '''id''', '''question'''])
   print(data)

   np.random.seed(SEED)
   SCREAMING_SNAKE_CASE						:Any			=  '''nq-training.jsonl''' if PROCESS_TRAIN == '''true''' else '''nq-validation.jsonl'''
   save_to_disk(data, file_name=cache_file_name)








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      __lowerCAmelCase  (			unittest.TestCase       ):






  """simple docstring"""





  def __init__(  self							:							Union[str, Any]     ,					_lowerCAmelCase							:							List[Any]     ,					_lowerCAmelCase							:							List[Any]=7     ,					_lowerCAmelCase							:							Any=3     ,					_lowerCAmelCase							:							Dict=1_8     ,					_lowerCAmelCase							:							Tuple=3_0     ,					_lowerCAmelCase							:							Optional[int]=4_0_0     ,					_lowerCAmelCase							:							Optional[int]=True     ,					_lowerCAmelCase							:							Any=None     ,					_lowerCAmelCase							:							List[Any]=True     ,					) ->			Dict:




     """simple docstring"""
     snake_case_            =       size if size is not None else {"height": 1_8, "width": 1_8}
     snake_case_            =       parent
     snake_case_            =       batch_size
     snake_case_            =       num_channels
     snake_case_            =       image_size
     snake_case_            =       min_resolution
     snake_case_            =       max_resolution
     snake_case_            =       do_resize
     snake_case_            =       size
     snake_case_            =       do_normalize



  def        lowerCAmelCase__	(  self							:							str	) ->			Optional[int]:




     """simple docstring"""
     return {
         # here we create 2 clusters for the sake of simplicity
         "clusters": np.asarray(
             [
                 [0.8_866_443_634_033_203, 0.6_618_829_369_544_983, 0.3_891_746_401_786_804],
                 [-0.6_042_559_146_881_104, -0.02_295_008_860_528_469, 0.5_423_797_369_003_296],
             ]	),
         "do_resize": self.do_resize,
         "size": self.size,
         "do_normalize": self.do_normalize,
     }






@require_torch
@require_vision
class      __lowerCAmelCase  (			a			,	unittest.TestCase       ):






  """simple docstring"""





  _SCREAMING_SNAKE_CASE		     =							ImageGPTImageProcessor if is_vision_available() else None



  def        lowerCAmelCase__	(  self							:							Union[str, Any]	) ->			List[Any]:




     """simple docstring"""
     snake_case_            =       ImageGPTImageProcessingTester(self	)



  @property
  def        lowerCAmelCase__	(  self							:							str	) ->			Union[str, Any]:




     """simple docstring"""
     return self.image_processor_tester.prepare_image_processor_dict()



  def        lowerCAmelCase__	(  self							:							List[Any]	) ->			int:




     """simple docstring"""
     snake_case_            =       self.image_processing_class(**self.image_processor_dict	)
     self.assertTrue(hasattr(_lowerCAmelCase     ,					"clusters"	)	)
     self.assertTrue(hasattr(_lowerCAmelCase     ,					"do_resize"	)	)
     self.assertTrue(hasattr(_lowerCAmelCase     ,					"size"	)	)
     self.assertTrue(hasattr(_lowerCAmelCase     ,					"do_normalize"	)	)



  def        lowerCAmelCase__	(  self							:							int	) ->			Any:




     """simple docstring"""
     snake_case_            =       self.image_processing_class.from_dict(self.image_processor_dict	)
     self.assertEqual(image_processor.size     ,					{"height": 1_8, "width": 1_8}	)

     snake_case_            =       self.image_processing_class.from_dict(self.image_processor_dict     ,					size=4_2	)
     self.assertEqual(image_processor.size     ,					{"height": 4_2, "width": 4_2}	)



  def        lowerCAmelCase__	(  self							:							Tuple	) ->			Any:




     """simple docstring"""
     snake_case_            =       self.image_processing_class(**self.image_processor_dict	)
     snake_case_            =       json.loads(image_processor.to_json_string()	)
     for key, value in self.image_processor_dict.items():
        if key == "clusters":
           self.assertTrue(np.array_equal(_lowerCAmelCase     ,					obj[key]	)	)
        else:
           self.assertEqual(obj[key]     ,					_lowerCAmelCase	)



  def        lowerCAmelCase__	(  self							:							int	) ->			Optional[Any]:




     """simple docstring"""
     snake_case_            =       self.image_processing_class(**self.image_processor_dict	)

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

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



  def        lowerCAmelCase__	(  self							:							Optional[int]	) ->			Union[str, Any]:




     """simple docstring"""
     snake_case_            =       self.image_processing_class(**self.image_processor_dict	)

     with tempfile.TemporaryDirectory() as tmpdirname:
        image_processor_first.save_pretrained(_lowerCAmelCase	)
        snake_case_            =       self.image_processing_class.from_pretrained(_lowerCAmelCase	).to_dict()

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



  @unittest.skip("ImageGPT requires clusters at initialization"	)
  def        lowerCAmelCase__	(  self							:							List[str]	) ->			int:




     """simple docstring"""
     pass






def       _lowerCAmelCase    (				)->Optional[Any]:
   '''simple docstring'''






   snake_case_            =       load_dataset("hf-internal-testing/fixtures_image_utils"		,    split="test"				)

   snake_case_            =       Image.open(dataset[4]["file"]				)
   snake_case_            =       Image.open(dataset[5]["file"]				)

   snake_case_            =       [imagea, imagea]

   return images



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






  """simple docstring"""





  @slow
  def        lowerCAmelCase__	(  self							:							List[str]	) ->			int:




     """simple docstring"""
     snake_case_            =       ImageGPTImageProcessor.from_pretrained("openai/imagegpt-small"	)

     snake_case_            =       prepare_images()

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

     self.assertIsInstance(encoding.input_ids     ,					torch.LongTensor	)
     self.assertEqual(encoding.input_ids.shape     ,					(1, 1_0_2_4)	)

     snake_case_            =       [3_0_6, 1_9_1, 1_9_1]
     self.assertEqual(encoding.input_ids[0, :3].tolist()     ,					_lowerCAmelCase	)

     # test batched
     snake_case_            =       image_processing(_lowerCAmelCase     ,					return_tensors="pt"	)

     self.assertIsInstance(encoding.input_ids     ,					torch.LongTensor	)
     self.assertEqual(encoding.input_ids.shape     ,					(2, 1_0_2_4)	)

     snake_case_            =       [3_0_3, 1_3, 1_3]
     self.assertEqual(encoding.input_ids[1, -3:].tolist()     ,					_lowerCAmelCase	)




