

import os

import jsonlines
import numpy as np
from tqdm import tqdm


__UpperCAmelCase								=			2_048
__UpperCAmelCase								=			4_096
__UpperCAmelCase								=			42
__UpperCAmelCase								=			os.environ.pop("""PROCESS_TRAIN""", """false""")
__UpperCAmelCase								=			{"""null""": 0, """short""": 1, """long""": 2, """yes""": 3, """no""": 4}





def      snake_case_				(__A					:						str					)      ->     List[Any]:
   def choose_first(__A					:						str ,       __A					:						Union[str, Any]=False					):
      assert isinstance(__A ,       __A					)
      if len(__A					) == 1:
         __lowerCAmelCase  :      Dict    =						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  :      Any    =						{k: [a[k]] for k in a}
         if len(a["""start_token"""]					) > 0:
            break
      return a

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

   # disregard some samples
   if len(answer["""start_token"""]					) > 1 or answer["start_token"] == answer["end_token"]:
      __lowerCAmelCase  :      Union[str, Any]    =						True
   else:
      __lowerCAmelCase  :      Union[str, Any]    =						False

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

   return answer





def      snake_case_				(__A					:						List[str] ,       __A					:						Union[str, Any]=False					)      ->     Any:
   __lowerCAmelCase  :      List[Any]    =						_get_single_answer(__A					)
   # 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  :      Optional[Any]    =						example["""document"""]["""tokens"""]
      __lowerCAmelCase  :      List[Any]    =						[]
      for i in range(len(doc["""token"""]					)					):
         if not doc["is_html"][i]:
            context.append(doc["""token"""][i]					)
      return {
          "context": " ".join(__A					),
          "answer": {
              "start_token": -1_0_0,  # ignore index in cross-entropy
              "end_token": -1_0_0,  # 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  :      Union[str, Any]    =						["""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  :      Union[str, Any]    =						example["""document"""]["""tokens"""]
   __lowerCAmelCase  :      Any    =						answer["""start_token"""]
   __lowerCAmelCase  :      Optional[Any]    =						answer["""end_token"""]

   __lowerCAmelCase  :      Dict    =						[]
   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  :      Any    =						""" """.join(context[start_token:end_token]					)

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

   return {
       "context": " ".join(__A					),
       "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      snake_case_				(__A					:						Dict ,       __A					:						Optional[int] ,       __A					:						Optional[int]=2_0_4_8 ,       __A					:						List[str]=4_0_9_6 ,       __A					:						List[Any]=True					)      ->     Any:
   # overlap will be of doc_stride - q_len

   __lowerCAmelCase  :      Any    =						get_context_and_ans(__A ,       assertion=__A					)
   __lowerCAmelCase  :      Any    =						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  :      Optional[int]    =						tokenizer(example["""question"""]["""text"""] ,       out["""context"""]					).input_ids
   __lowerCAmelCase  :      int    =						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  :      Optional[int]    =						[]
      __lowerCAmelCase  :      Optional[Any]    =						[]
      __lowerCAmelCase  :      Dict    =						input_ids[:q_len]
      __lowerCAmelCase  :      List[str]    =						range(__A ,       len(__A					) ,       max_length - doc_stride					)
      for i in doc_start_indices:
         __lowerCAmelCase  :      int    =						i + max_length - q_len
         __lowerCAmelCase  :      Optional[Any]    =						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": [-1_0_0] * len(__A					),
              "end_token": [-1_0_0] * len(__A					),
              "category": category,
          },
      }

   __lowerCAmelCase  :      Optional[int]    =						out["""context"""].split()
   __lowerCAmelCase  :      Dict    =						splitted_context[answer["""end_token"""]]
   __lowerCAmelCase  :      Dict    =						len(
       tokenizer(
           """ """.join(splitted_context[: answer["""start_token"""]]					) ,       add_special_tokens=__A ,       ).input_ids					)
   __lowerCAmelCase  :      int    =						len(
       tokenizer(""" """.join(splitted_context[: answer["""end_token"""]]					) ,       add_special_tokens=__A					).input_ids					)

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

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

   __lowerCAmelCase  :      Dict    =						input_ids[answer["""start_token"""] : answer["""end_token"""] + 1]  # right & left are inclusive
   __lowerCAmelCase  :      List[str]    =						answer["""start_token"""]
   __lowerCAmelCase  :      Optional[Any]    =						answer["""end_token"""]

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

   if len(__A					) <= 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  :      Any    =						input_ids[:q_len]
   __lowerCAmelCase  :      int    =						range(__A ,       len(__A					) ,       max_length - doc_stride					)

   __lowerCAmelCase  :      str    =						[]
   __lowerCAmelCase  :      Tuple    =						[]
   __lowerCAmelCase  :      Optional[int]    =						[]
   __lowerCAmelCase  :      Optional[int]    =						[]  # null, yes, no, long, short
   for i in doc_start_indices:
      __lowerCAmelCase  :      List[Any]    =						i + max_length - q_len
      __lowerCAmelCase  :      List[Any]    =						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  :      Union[str, Any]    =						start_token - i + q_len
         __lowerCAmelCase  :      Optional[Any]    =						end_token - i + q_len
         answers_category.append(answer["""category"""][0]					)  # ["short"] -> "short"
      else:
         __lowerCAmelCase  :      List[Any]    =						-1_0_0
         __lowerCAmelCase  :      List[str]    =						-1_0_0
         answers_category.append("""null"""					)
      __lowerCAmelCase  :      List[Any]    =						inputs[-1][start_token : end_token + 1]

      answers_start_token.append(__A					)
      answers_end_token.append(__A					)
      if assertion:
         if new != old and new != [tokenizer.cls_token_id]:
            print("""ISSUE in strided for ID:""" ,       example["""id"""]					)
            print("""New:""" ,       tokenizer.decode(__A					)					)
            print("""Old:""" ,       tokenizer.decode(__A					) ,       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      snake_case_				(__A					:						Optional[Any] ,       __A					:						List[str] ,       __A					:						Tuple=2_0_4_8 ,       __A					:						Any=4_0_9_6 ,       __A					:						Any=False					)      ->     Any:
   __lowerCAmelCase  :      int    =						get_strided_contexts_and_ans(
       __A ,       __A ,       doc_stride=__A ,       max_length=__A ,       assertion=__A ,       )

   return example





def      snake_case_				(__A					:						List[Any] ,       __A					:						str					)      ->     List[Any]:
   with jsonlines.open(__A ,       """a"""					) as writer:
      for example in tqdm(__A ,       total=len(__A					) ,       desc="""Saving samples ... """					):
         __lowerCAmelCase  :      List[str]    =						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

   __UpperCAmelCase								=			load_dataset("""natural_questions""")
   __UpperCAmelCase								=			BigBirdTokenizer.from_pretrained("""google/bigbird-roberta-base""")

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

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

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





import torch

from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest



class SCREAMING_SNAKE_CASE     (  a_			):
  """simple docstring"""






  lowerCamelCase   :      Dict										=(EulerDiscreteScheduler,)
  lowerCamelCase   :      Dict										=10







  def 	SCREAMING_SNAKE_CASE   (      self			:   Optional[int]	,							**lowerCAmelCase			:   Union[str, Any]   )							->     List[str]:






     """simple docstring"""







     __lowerCAmelCase  :      Dict    =						{
         """num_train_timesteps""": 11_00,
         """beta_start""": 0.0001,
         """beta_end""": 0.02,
         """beta_schedule""": """linear""",
     }

     config.update(**lowerCAmelCase   )
     return config







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






     """simple docstring"""







     for timesteps in [10, 50, 1_00, 10_00]:
        self.check_over_configs(num_train_timesteps=lowerCAmelCase   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   Tuple   )							->     Tuple:






     """simple docstring"""







     for beta_start, beta_end in zip([0.0_0001, 0.0001, 0.001]	,							[0.0002, 0.002, 0.02]   ):
        self.check_over_configs(beta_start=lowerCAmelCase	,							beta_end=lowerCAmelCase   )







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






     """simple docstring"""







     for schedule in ["linear", "scaled_linear"]:
        self.check_over_configs(beta_schedule=lowerCAmelCase   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   Any   )							->     List[Any]:






     """simple docstring"""







     for prediction_type in ["epsilon", "v_prediction"]:
        self.check_over_configs(prediction_type=lowerCAmelCase   )







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






     """simple docstring"""







     __lowerCAmelCase  :      Any    =						self.scheduler_classes[0]
     __lowerCAmelCase  :      int    =						self.get_scheduler_config()
     __lowerCAmelCase  :      Any    =						scheduler_class(**lowerCAmelCase   )

     scheduler.set_timesteps(self.num_inference_steps   )

     __lowerCAmelCase  :      str    =						torch.manual_seed(0   )

     __lowerCAmelCase  :      List[Any]    =						self.dummy_model()
     __lowerCAmelCase  :      Dict    =						self.dummy_sample_deter * scheduler.init_noise_sigma
     __lowerCAmelCase  :      int    =						sample.to(lowerCAmelCase   )

     for i, t in enumerate(scheduler.timesteps   ):
        __lowerCAmelCase  :      Tuple    =						scheduler.scale_model_input(lowerCAmelCase	,							lowerCAmelCase   )

        __lowerCAmelCase  :      Dict    =						model(lowerCAmelCase	,							lowerCAmelCase   )

        __lowerCAmelCase  :      str    =						scheduler.step(lowerCAmelCase	,							lowerCAmelCase	,							lowerCAmelCase	,							generator=lowerCAmelCase   )
        __lowerCAmelCase  :      Optional[int]    =						output.prev_sample

     __lowerCAmelCase  :      str    =						torch.sum(torch.abs(lowerCAmelCase   )   )
     __lowerCAmelCase  :      Optional[int]    =						torch.mean(torch.abs(lowerCAmelCase   )   )

     assert abs(result_sum.item() - 10.0807   ) < 1e-2
     assert abs(result_mean.item() - 0.0131   ) < 1e-3







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






     """simple docstring"""







     __lowerCAmelCase  :      List[Any]    =						self.scheduler_classes[0]
     __lowerCAmelCase  :      List[Any]    =						self.get_scheduler_config(prediction_type="""v_prediction"""   )
     __lowerCAmelCase  :      List[str]    =						scheduler_class(**lowerCAmelCase   )

     scheduler.set_timesteps(self.num_inference_steps   )

     __lowerCAmelCase  :      Any    =						torch.manual_seed(0   )

     __lowerCAmelCase  :      int    =						self.dummy_model()
     __lowerCAmelCase  :      List[str]    =						self.dummy_sample_deter * scheduler.init_noise_sigma
     __lowerCAmelCase  :      List[Any]    =						sample.to(lowerCAmelCase   )

     for i, t in enumerate(scheduler.timesteps   ):
        __lowerCAmelCase  :      Any    =						scheduler.scale_model_input(lowerCAmelCase	,							lowerCAmelCase   )

        __lowerCAmelCase  :      Any    =						model(lowerCAmelCase	,							lowerCAmelCase   )

        __lowerCAmelCase  :      Dict    =						scheduler.step(lowerCAmelCase	,							lowerCAmelCase	,							lowerCAmelCase	,							generator=lowerCAmelCase   )
        __lowerCAmelCase  :      Dict    =						output.prev_sample

     __lowerCAmelCase  :      List[str]    =						torch.sum(torch.abs(lowerCAmelCase   )   )
     __lowerCAmelCase  :      Optional[int]    =						torch.mean(torch.abs(lowerCAmelCase   )   )

     assert abs(result_sum.item() - 0.0002   ) < 1e-2
     assert abs(result_mean.item() - 2.2676e-06   ) < 1e-3







  def 	SCREAMING_SNAKE_CASE   (      self			:   Tuple   )							->     Dict:






     """simple docstring"""







     __lowerCAmelCase  :      List[Any]    =						self.scheduler_classes[0]
     __lowerCAmelCase  :      Dict    =						self.get_scheduler_config()
     __lowerCAmelCase  :      Optional[int]    =						scheduler_class(**lowerCAmelCase   )

     scheduler.set_timesteps(self.num_inference_steps	,							device=lowerCAmelCase   )

     __lowerCAmelCase  :      Union[str, Any]    =						torch.manual_seed(0   )

     __lowerCAmelCase  :      Dict    =						self.dummy_model()
     __lowerCAmelCase  :      Dict    =						self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
     __lowerCAmelCase  :      Dict    =						sample.to(lowerCAmelCase   )

     for t in scheduler.timesteps:
        __lowerCAmelCase  :      Union[str, Any]    =						scheduler.scale_model_input(lowerCAmelCase	,							lowerCAmelCase   )

        __lowerCAmelCase  :      List[str]    =						model(lowerCAmelCase	,							lowerCAmelCase   )

        __lowerCAmelCase  :      int    =						scheduler.step(lowerCAmelCase	,							lowerCAmelCase	,							lowerCAmelCase	,							generator=lowerCAmelCase   )
        __lowerCAmelCase  :      Any    =						output.prev_sample

     __lowerCAmelCase  :      int    =						torch.sum(torch.abs(lowerCAmelCase   )   )
     __lowerCAmelCase  :      str    =						torch.mean(torch.abs(lowerCAmelCase   )   )

     assert abs(result_sum.item() - 10.0807   ) < 1e-2
     assert abs(result_mean.item() - 0.0131   ) < 1e-3







  def 	SCREAMING_SNAKE_CASE   (      self			:   Dict   )							->     str:






     """simple docstring"""







     __lowerCAmelCase  :      Optional[int]    =						self.scheduler_classes[0]
     __lowerCAmelCase  :      Optional[int]    =						self.get_scheduler_config()
     __lowerCAmelCase  :      List[Any]    =						scheduler_class(**lowerCAmelCase	,							use_karras_sigmas=lowerCAmelCase   )

     scheduler.set_timesteps(self.num_inference_steps	,							device=lowerCAmelCase   )

     __lowerCAmelCase  :      str    =						torch.manual_seed(0   )

     __lowerCAmelCase  :      str    =						self.dummy_model()
     __lowerCAmelCase  :      str    =						self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
     __lowerCAmelCase  :      int    =						sample.to(lowerCAmelCase   )

     for t in scheduler.timesteps:
        __lowerCAmelCase  :      int    =						scheduler.scale_model_input(lowerCAmelCase	,							lowerCAmelCase   )

        __lowerCAmelCase  :      Dict    =						model(lowerCAmelCase	,							lowerCAmelCase   )

        __lowerCAmelCase  :      Tuple    =						scheduler.step(lowerCAmelCase	,							lowerCAmelCase	,							lowerCAmelCase	,							generator=lowerCAmelCase   )
        __lowerCAmelCase  :      List[Any]    =						output.prev_sample

     __lowerCAmelCase  :      Tuple    =						torch.sum(torch.abs(lowerCAmelCase   )   )
     __lowerCAmelCase  :      Any    =						torch.mean(torch.abs(lowerCAmelCase   )   )

     assert abs(result_sum.item() - 124.52_2994_9951_1719   ) < 1e-2
     assert abs(result_mean.item() - 0.1_6213_9326_3339_9963   ) < 1e-3


