import os

import jsonlines
import numpy as np
from tqdm import tqdm


_SCREAMING_SNAKE_CASE      						=	2_0_4_8
_SCREAMING_SNAKE_CASE      						=	4_0_9_6
_SCREAMING_SNAKE_CASE      						=	4_2
_SCREAMING_SNAKE_CASE      						=	os.environ.pop("""PROCESS_TRAIN""", """false""")
_SCREAMING_SNAKE_CASE      						=	{"""null""": 0, """short""": 1, """long""": 2, """yes""": 3, """no""": 4}





def        lowercase(     UpperCamelCase_    )						->     int:







 '''simple docstring'''
 def choose_first(UpperCamelCase_				, UpperCamelCase_=False    ):
  assert isinstance(lowercase__				, lowercase__    )
  if len(lowercase__    ) == 1:
   UpperCamelCase          =	answer[0]
   return {k: [answer[k]] for k in answer} if is_long_answer else answer
  for a in answer:
   if is_long_answer:
    UpperCamelCase          =	{k: [a[k]] for k in a}
   if len(a["""start_token"""]    ) > 0:
    break
  return a

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

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

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

 return answer





def        lowercase(     UpperCamelCase_				, UpperCamelCase_=False    )						->     Optional[int]:







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

 UpperCamelCase          =	['''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

 UpperCamelCase          =	example['''document''']['''tokens''']
 UpperCamelCase          =	answer['''start_token''']
 UpperCamelCase          =	answer['''end_token''']

 UpperCamelCase          =	[]
 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
 UpperCamelCase          =	''' '''.join(context[start_token:end_token]    )

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

 return {
     "context": " ".join(lowercase__    ),
     "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    )						->     Dict:







 '''simple docstring'''
 # overlap will be of doc_stride - q_len

 UpperCamelCase          =	get_context_and_ans(lowercase__				, assertion=lowercase__    )
 UpperCamelCase          =	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"],
      },
  }

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

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

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

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

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

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

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

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

 UpperCamelCase          =	input_ids[:q_len]
 UpperCamelCase          =	range(lowercase__				, len(lowercase__    )				, max_length - doc_stride    )

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

  answers_start_token.append(lowercase__    )
  answers_end_token.append(lowercase__    )
  if assertion:
   if new != old and new != [tokenizer.cls_token_id]:
    print("""ISSUE in strided for ID:"""				, example["""id"""]    )
    print("""New:"""				, tokenizer.decode(lowercase__    )    )
    print("""Old:"""				, tokenizer.decode(lowercase__    )				, 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    )						->     List[Any]:







 '''simple docstring'''
 UpperCamelCase          =	get_strided_contexts_and_ans(
     lowercase__				, lowercase__				, doc_stride=lowercase__				, max_length=lowercase__				, assertion=lowercase__				, )

 return example





def        lowercase(     UpperCamelCase_				, UpperCamelCase_    )						->     str:







 '''simple docstring'''
 with jsonlines.open(lowercase__				, """a"""    ) as writer:
  for example in tqdm(lowercase__				, total=len(lowercase__    )				, desc="""Saving samples ... """    ):
   UpperCamelCase          =	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      						=	load_dataset("""natural_questions""")
     _SCREAMING_SNAKE_CASE      						=	BigBirdTokenizer.from_pretrained("""google/bigbird-roberta-base""")

     _SCREAMING_SNAKE_CASE      						=	data["""train""" if PROCESS_TRAIN == """true""" else """validation"""]

     _SCREAMING_SNAKE_CASE      						=	{
         """tokenizer""": tokenizer,
         """doc_stride""": DOC_STRIDE,
         """max_length""": MAX_LENGTH,
         """assertion""": False,
     }
     _SCREAMING_SNAKE_CASE      						=	data.map(prepare_inputs, fn_kwargs=fn_kwargs)
     _SCREAMING_SNAKE_CASE      						=	data.remove_columns(["""annotations""", """document""", """id""", """question"""])
     print(data)

     np.random.seed(SEED)
     _SCREAMING_SNAKE_CASE      						=	"""nq-training.jsonl""" if PROCESS_TRAIN == """true""" else """nq-validation.jsonl"""
     save_to_disk(data, file_name=cache_file_name)



from dataclasses import dataclass
from typing import Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin







@dataclass
class   __lowercase      (_UpperCAmelCase ):
 _UpperCamelCase			  =  42
 _UpperCamelCase			  =  42
 _UpperCamelCase			  =  None







class   __lowercase      (_UpperCAmelCase		,   _UpperCAmelCase ):
 _UpperCamelCase			  =  2
 @register_to_config
 def __init__(							self      ,							A_ = 0.02      ,							A_ = 100      ,							A_ = 1.007      ,							A_ = 80      ,							A_ = 0.05      ,							A_ = 50      ,							)					->int:



       '''simple docstring'''





       __lowerCAmelCase					:						Optional[int]					=					sigma_max

       # setable values
       __lowerCAmelCase					:						int					=					None
       __lowerCAmelCase					:						np.IntTensor					=					None
       __lowerCAmelCase					:						torch.FloatTensor					=					None  # sigma(t_i)
 def 	UpperCamelCase__  (							self      ,							A_      ,							A_ = None					)					->torch.FloatTensor:



       '''simple docstring'''





       return sample
 def 	UpperCamelCase__  (							self      ,							A_      ,							A_ = None					)					->List[str]:



       '''simple docstring'''





       __lowerCAmelCase					:						str					=					num_inference_steps
       __lowerCAmelCase					:						Dict					=					np.arange(0      ,							self.num_inference_steps					)[::-1].copy()
       __lowerCAmelCase					:						Optional[Any]					=					torch.from_numpy(A_					).to(A_					)
       __lowerCAmelCase					:						Tuple					=					[
           (
               self.config.sigma_max**2
               * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
           )
           for i in self.timesteps
       ]
       __lowerCAmelCase					:						Optional[int]					=					torch.tensor(A_      ,							dtype=torch.floataa      ,							device=A_					)
 def 	UpperCamelCase__  (							self      ,							A_      ,							A_      ,							A_ = None					)					->Tuple[torch.FloatTensor, float]:



       '''simple docstring'''





       if self.config.s_min <= sigma <= self.config.s_max:
             __lowerCAmelCase					:						List[str]					=					min(self.config.s_churn / self.num_inference_steps      ,							2**0.5 - 1					)
       else:
             __lowerCAmelCase					:						List[str]					=					0

       # sample eps ~ N(0, S_noise^2 * I)
       __lowerCAmelCase					:						int					=					self.config.s_noise * randn_tensor(sample.shape      ,							generator=A_					).to(sample.device					)
       __lowerCAmelCase					:						str					=					sigma + gamma * sigma
       __lowerCAmelCase					:						Any					=					sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

       return sample_hat, sigma_hat
 def 	UpperCamelCase__  (							self      ,							A_      ,							A_      ,							A_      ,							A_      ,							A_ = True      ,							)					->Union[KarrasVeOutput, Tuple]:



       '''simple docstring'''





       __lowerCAmelCase					:						Union[str, Any]					=					sample_hat + sigma_hat * model_output
       __lowerCAmelCase					:						int					=					(sample_hat - pred_original_sample) / sigma_hat
       __lowerCAmelCase					:						Tuple					=					sample_hat + (sigma_prev - sigma_hat) * derivative

       if not return_dict:
             return (sample_prev, derivative)

       return KarrasVeOutput(
           prev_sample=A_      ,							derivative=A_      ,							pred_original_sample=A_					)
 def 	UpperCamelCase__  (							self      ,							A_      ,							A_      ,							A_      ,							A_      ,							A_      ,							A_      ,							A_ = True      ,							)					->Union[KarrasVeOutput, Tuple]:



       '''simple docstring'''





       __lowerCAmelCase					:						str					=					sample_prev + sigma_prev * model_output
       __lowerCAmelCase					:						List[Any]					=					(sample_prev - pred_original_sample) / sigma_prev
       __lowerCAmelCase					:						Any					=					sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)

       if not return_dict:
             return (sample_prev, derivative)

       return KarrasVeOutput(
           prev_sample=A_      ,							derivative=A_      ,							pred_original_sample=A_					)





 def 	UpperCamelCase__  (							self      ,							A_      ,							A_      ,							A_					)					->Any:



       '''simple docstring'''





       raise NotImplementedError()


