



import os

import jsonlines
import numpy as np
from tqdm import tqdm


a_						:     List[str]	     =		2_0_4_8
a_						:     str	     =		4_0_9_6
a_						:     Any	     =		4_2
a_						:     Dict	     =		os.environ.pop('PROCESS_TRAIN', 'false')
a_						:     int	     =		{'null': 0, 'short': 1, 'long': 2, 'yes': 3, 'no': 4}

def     __lowercase(	UpperCAmelCase__ ):


      """simple docstring"""



      def choose_first(UpperCAmelCase__   ,   UpperCAmelCase__=False ):
            assert isinstance(UpperCAmelCase__   ,   UpperCAmelCase__ )
            if len(UpperCAmelCase__ ) == 1:
                  lowerCamelCase								=		answer[0]
                  return {k: [answer[k]] for k in answer} if is_long_answer else answer
            for a in answer:
                  if is_long_answer:
                        lowerCamelCase								=		{k: [a[k]] for k in a}
                  if len(a["start_token"] ) > 0:
                        break
            return a

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

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

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

      return answer

def     __lowercase(	UpperCAmelCase__   ,   UpperCAmelCase__=False ):


      """simple docstring"""



      lowerCamelCase								=		_get_single_answer(UpperCAmelCase__ )
      # 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
            lowerCamelCase								=		example["document"]["tokens"]
            lowerCamelCase								=		[]
            for i in range(len(doc["token"] ) ):
                  if not doc["is_html"][i]:
                        context.append(doc["token"][i] )
            return {
                "context": " ".join(UpperCAmelCase__ ),
                "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

      lowerCamelCase								=		["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

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

      lowerCamelCase								=		[]
      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
      lowerCamelCase								=		" ".join(context[start_token:end_token] )

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

      return {
          "context": " ".join(UpperCAmelCase__ ),
          "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     __lowercase(	UpperCAmelCase__   ,   UpperCAmelCase__   ,   UpperCAmelCase__=2048   ,   UpperCAmelCase__=4096   ,   UpperCAmelCase__=True ):


      """simple docstring"""



      lowerCamelCase								=		get_context_and_ans(UpperCAmelCase__   ,   assertion=UpperCAmelCase__ )
      lowerCamelCase								=		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"],
                },
            }

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

      # return yes/no
      if answer["category"][0] in ["yes", "no"]:  # category is list with one element
            lowerCamelCase								=		[]
            lowerCamelCase								=		[]
            lowerCamelCase								=		input_ids[:q_len]
            lowerCamelCase								=		range(UpperCAmelCase__   ,   len(UpperCAmelCase__ )   ,   max_length - doc_stride )
            for i in doc_start_indices:
                  lowerCamelCase								=		i + max_length - q_len
                  lowerCamelCase								=		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(UpperCAmelCase__ ),
                    "end_token": [-100] * len(UpperCAmelCase__ ),
                    "category": category,
                },
            }

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

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

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

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

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

      if len(UpperCAmelCase__ ) <= 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"],
                },
            }

      lowerCamelCase								=		input_ids[:q_len]
      lowerCamelCase								=		range(UpperCAmelCase__   ,   len(UpperCAmelCase__ )   ,   max_length - doc_stride )

      lowerCamelCase								=		[]
      lowerCamelCase								=		[]
      lowerCamelCase								=		[]
      lowerCamelCase								=		[]  # null, yes, no, long, short
      for i in doc_start_indices:
            lowerCamelCase								=		i + max_length - q_len
            lowerCamelCase								=		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:
                  lowerCamelCase								=		start_token - i + q_len
                  lowerCamelCase								=		end_token - i + q_len
                  answers_category.append(answer["category"][0] )  # ["short"] -> "short"
            else:
                  lowerCamelCase								=		-100
                  lowerCamelCase								=		-100
                  answers_category.append("null" )
            lowerCamelCase								=		inputs[-1][start_token : end_token + 1]

            answers_start_token.append(UpperCAmelCase__ )
            answers_end_token.append(UpperCAmelCase__ )
            if assertion:
                  if new != old and new != [tokenizer.cls_token_id]:
                        print("ISSUE in strided for ID:"   ,   example["id"] )
                        print("New:"   ,   tokenizer.decode(UpperCAmelCase__ ) )
                        print("Old:"   ,   tokenizer.decode(UpperCAmelCase__ )   ,   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     __lowercase(	UpperCAmelCase__   ,   UpperCAmelCase__   ,   UpperCAmelCase__=2048   ,   UpperCAmelCase__=4096   ,   UpperCAmelCase__=False ):


      """simple docstring"""



      lowerCamelCase								=		get_strided_contexts_and_ans(
          UpperCAmelCase__   ,   UpperCAmelCase__   ,   doc_stride=UpperCAmelCase__   ,   max_length=UpperCAmelCase__   ,   assertion=UpperCAmelCase__   ,   )

      return example

def     __lowercase(	UpperCAmelCase__   ,   UpperCAmelCase__ ):


      """simple docstring"""



      with jsonlines.open(UpperCAmelCase__   ,   "a" ) as writer:
            for example in tqdm(UpperCAmelCase__   ,   total=len(UpperCAmelCase__ )   ,   desc="Saving samples ... " ):
                  lowerCamelCase								=		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

     a_						:     Dict	     =		load_dataset('natural_questions')
     a_						:     List[Any]	     =		BigBirdTokenizer.from_pretrained('google/bigbird-roberta-base')

     a_						:     Optional[Any]	     =		data['train' if PROCESS_TRAIN == 'true' else 'validation']

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

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




from tempfile import TemporaryDirectory
from unittest import TestCase
from unittest.mock import MagicMock, patch

from transformers import AutoModel, TFAutoModel
from transformers.onnx import FeaturesManager
from transformers.testing_utils import SMALL_MODEL_IDENTIFIER, require_tf, require_torch





@require_torch
@require_tf
class    lowerCamelCase__  (						UpperCAmelCase_):
     """simple docstring"""



     def 						_a      (self    ):






           '''simple docstring'''



           lowerCamelCase								=		SMALL_MODEL_IDENTIFIER
           lowerCamelCase								=		"pt"
           lowerCamelCase								=		"tf"







     def 						_a      (self				,			__a    ):






           '''simple docstring'''



           lowerCamelCase								=		AutoModel.from_pretrained(self.test_model    )
           model_pt.save_pretrained(__a    )







     def 						_a      (self				,			__a    ):






           '''simple docstring'''



           lowerCamelCase								=		TFAutoModel.from_pretrained(self.test_model				,			from_pt=__a    )
           model_tf.save_pretrained(__a    )







     def 						_a      (self    ):






           '''simple docstring'''



           lowerCamelCase								=		"mock_framework"

           # Framework provided - return whatever the user provides
           lowerCamelCase								=		FeaturesManager.determine_framework(self.test_model				,			__a    )
           self.assertEqual(__a				,			__a    )

           # Local checkpoint and framework provided - return provided framework
           # PyTorch checkpoint
           with TemporaryDirectory() as local_pt_ckpt:
                 self._setup_pt_ckpt(__a    )
                 lowerCamelCase								=		FeaturesManager.determine_framework(__a				,			__a    )
                 self.assertEqual(__a				,			__a    )

           # TensorFlow checkpoint
           with TemporaryDirectory() as local_tf_ckpt:
                 self._setup_tf_ckpt(__a    )
                 lowerCamelCase								=		FeaturesManager.determine_framework(__a				,			__a    )
                 self.assertEqual(__a				,			__a    )







     def 						_a      (self    ):






           '''simple docstring'''



           with TemporaryDirectory() as local_pt_ckpt:
                 self._setup_pt_ckpt(__a    )
                 lowerCamelCase								=		FeaturesManager.determine_framework(__a    )
                 self.assertEqual(__a				,			self.framework_pt    )

           # TensorFlow checkpoint
           with TemporaryDirectory() as local_tf_ckpt:
                 self._setup_tf_ckpt(__a    )
                 lowerCamelCase								=		FeaturesManager.determine_framework(__a    )
                 self.assertEqual(__a				,			self.framework_tf    )

           # Invalid local checkpoint
           with TemporaryDirectory() as local_invalid_ckpt:
                 with self.assertRaises(__a    ):
                       lowerCamelCase								=		FeaturesManager.determine_framework(__a    )







     def 						_a      (self    ):






           '''simple docstring'''



           lowerCamelCase								=		MagicMock(return_value=__a    )
           with patch("transformers.onnx.features.is_tf_available"				,			__a    ):
                 lowerCamelCase								=		FeaturesManager.determine_framework(self.test_model    )
                 self.assertEqual(__a				,			self.framework_pt    )

           # PyTorch not in environment -> use TensorFlow
           lowerCamelCase								=		MagicMock(return_value=__a    )
           with patch("transformers.onnx.features.is_torch_available"				,			__a    ):
                 lowerCamelCase								=		FeaturesManager.determine_framework(self.test_model    )
                 self.assertEqual(__a				,			self.framework_tf    )

           # Both in environment -> use PyTorch
           lowerCamelCase								=		MagicMock(return_value=__a    )
           lowerCamelCase								=		MagicMock(return_value=__a    )
           with patch("transformers.onnx.features.is_tf_available"				,			__a    ), patch(
               "transformers.onnx.features.is_torch_available"				,			__a    ):
                 lowerCamelCase								=		FeaturesManager.determine_framework(self.test_model    )
                 self.assertEqual(__a				,			self.framework_pt    )

           # Both not in environment -> raise error
           lowerCamelCase								=		MagicMock(return_value=__a    )
           lowerCamelCase								=		MagicMock(return_value=__a    )
           with patch("transformers.onnx.features.is_tf_available"				,			__a    ), patch(
               "transformers.onnx.features.is_torch_available"				,			__a    ):
                 with self.assertRaises(__a    ):
                       lowerCamelCase								=		FeaturesManager.determine_framework(self.test_model    )