


import os

import jsonlines
import numpy as np
from tqdm import tqdm


UpperCAmelCase_   :				Any						      = 2048
UpperCAmelCase_   :				int						      = 4096
UpperCAmelCase_   :				List[str]						      = 42
UpperCAmelCase_   :				Optional[Any]						      = os.environ.pop('PROCESS_TRAIN', 'false')
UpperCAmelCase_   :				Dict						      = {'null': 0, 'short': 1, 'long': 2, 'yes': 3, 'no': 4}







def  SCREAMING_SNAKE_CASE_		(    __A				:    Dict       )			->	int:

       """simple docstring"""

       def choose_first(__A				:    Optional[int]						,					__A				:    Optional[int]=False       ):
              assert isinstance(__A						,					__A       )
              if len(__A       ) == 1:
                     a_      :	Union[str, Any]							=						answer[0]
                     return {k: [answer[k]] for k in answer} if is_long_answer else answer
              for a in answer:
                     if is_long_answer:
                            a_      :	Dict							=						{k: [a[k]] for k in a}
                     if len(a['start_token']       ) > 0:
                            break
              return a

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

       # disregard some samples
       if len(answer['start_token']       ) > 1 or answer["start_token"] == answer["end_token"]:
              a_      :	int							=						True
       else:
              a_      :	Optional[Any]							=						False

       a_      :	List[str]							=						['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  SCREAMING_SNAKE_CASE_		(    __A				:    int						,					__A				:    Tuple=False       )			->	List[Any]:

       """simple docstring"""

       a_      :	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
              a_      :	List[str]							=						example['document']['tokens']
              a_      :	Optional[int]							=						[]
              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_00,  # ignore index in cross-entropy
                      "end_token": -1_00,  # 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

       a_      :	Optional[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

       a_      :	Tuple							=						example['document']['tokens']
       a_      :	Union[str, Any]							=						answer['start_token']
       a_      :	Union[str, Any]							=						answer['end_token']

       a_      :	List[str]							=						[]
       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
       a_      :	Optional[Any]							=						' '.join(context[start_token:end_token]       )

       # checking above code
       if assertion:
              a_      :	int							=						doc['is_html'][answer['start_token'] : answer['end_token']]
              a_      :	Tuple							=						doc['token'][answer['start_token'] : answer['end_token']]
              a_      :	List[str]							=						' '.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  SCREAMING_SNAKE_CASE_		(    __A				:    Optional[Any]						,					__A				:    int						,					__A				:    List[Any]=20_48						,					__A				:    Dict=40_96						,					__A				:    Optional[int]=True       )			->	List[Any]:

       """simple docstring"""

       a_      :	List[Any]							=						get_context_and_ans(__A						,					assertion=__A       )
       a_      :	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"],
                  },
              }

       a_      :	Optional[int]							=						tokenizer(example['question']['text']						,					out['context']       ).input_ids
       a_      :	List[str]							=						input_ids.index(tokenizer.sep_token_id       ) + 1

       # return yes/no
       if answer["category"][0] in ["yes", "no"]:  # category is list with one element
              a_      :	Any							=						[]
              a_      :	List[Any]							=						[]
              a_      :	int							=						input_ids[:q_len]
              a_      :	Optional[Any]							=						range(__A						,					len(__A       )						,					max_length - doc_stride       )
              for i in doc_start_indices:
                     a_      :	str							=						i + max_length - q_len
                     a_      :	Tuple							=						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_00] * len(__A       ),
                      "end_token": [-1_00] * len(__A       ),
                      "category": category,
                  },
              }

       a_      :	str							=						out['context'].split()
       a_      :	List[Any]							=						splitted_context[answer['end_token']]
       a_      :	int							=						len(
           tokenizer(
               ' '.join(splitted_context[: answer['start_token']]       )						,					add_special_tokens=__A						,					).input_ids       )
       a_      :	str							=						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
       a_      :	Optional[int]							=						len(tokenizer(__A						,					add_special_tokens=__A       ).input_ids       )
       if num_sub_tokens > 1:
              answer["end_token"] += num_sub_tokens - 1

       a_      :	List[str]							=						input_ids[answer['start_token'] : answer['end_token'] + 1]  # right & left are inclusive
       a_      :	List[Any]							=						answer['start_token']
       a_      :	Tuple							=						answer['end_token']

       if assertion:
              a_      :	List[Any]							=						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"],
                  },
              }

       a_      :	Union[str, Any]							=						input_ids[:q_len]
       a_      :	Tuple							=						range(__A						,					len(__A       )						,					max_length - doc_stride       )

       a_      :	str							=						[]
       a_      :	Dict							=						[]
       a_      :	Dict							=						[]
       a_      :	Optional[int]							=						[]  # null, yes, no, long, short
       for i in doc_start_indices:
              a_      :	Tuple							=						i + max_length - q_len
              a_      :	Dict							=						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:
                     a_      :	List[str]							=						start_token - i + q_len
                     a_      :	Optional[Any]							=						end_token - i + q_len
                     answers_category.append(answer['category'][0]       )  # ["short"] -> "short"
              else:
                     a_      :	Any							=						-1_00
                     a_      :	Any							=						-1_00
                     answers_category.append('null'       )
              a_      :	Tuple							=						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  SCREAMING_SNAKE_CASE_		(    __A				:    Optional[int]						,					__A				:    int						,					__A				:    Optional[Any]=20_48						,					__A				:    Union[str, Any]=40_96						,					__A				:    Any=False       )			->	Optional[Any]:

       """simple docstring"""

       a_      :	List[Any]							=						get_strided_contexts_and_ans(
           __A						,					__A						,					doc_stride=__A						,					max_length=__A						,					assertion=__A						,					)

       return example







def  SCREAMING_SNAKE_CASE_		(    __A				:    Dict						,					__A				:    int       )			->	str:

       """simple docstring"""

       with jsonlines.open(__A						,					'a'       ) as writer:
              for example in tqdm(__A						,					total=len(__A       )						,					desc='Saving samples ... '       ):
                     a_      :	Union[str, Any]							=						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_   :				List[Any]						      = load_dataset('natural_questions')
   UpperCAmelCase_   :				Union[str, Any]						      = BigBirdTokenizer.from_pretrained('google/bigbird-roberta-base')

   UpperCAmelCase_   :				str						      = data['train' if PROCESS_TRAIN == 'true' else 'validation']

   UpperCAmelCase_   :				Tuple						      = {
       'tokenizer': tokenizer,
       'doc_stride': DOC_STRIDE,
       'max_length': MAX_LENGTH,
       'assertion': False,
   }
   UpperCAmelCase_   :				List[str]						      = data.map(prepare_inputs, fn_kwargs=fn_kwargs)
   UpperCAmelCase_   :				Dict						      = data.remove_columns(['annotations', 'document', 'id', 'question'])
   print(data)

   np.random.seed(SEED)
   UpperCAmelCase_   :				Dict						      = '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 DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest
class 				SCREAMING_SNAKE_CASE__						(       lowercase__    ):
      snake_case__       :  Optional[Any]       =		(DDIMParallelScheduler,)
      snake_case__       :  Union[str, Any]       =		(('''eta''', 0.0), ('''num_inference_steps''', 50))
      def  SCREAMING_SNAKE_CASE							( self       : str  ,							**SCREAMING_SNAKE_CASE__       : List[Any]  )	->    List[Any]:
             a_      :	Union[str, Any]							=						{
                 'num_train_timesteps': 1_0_0_0,
                 'beta_start': 0.0001,
                 'beta_end': 0.02,
                 'beta_schedule': 'linear',
                 'clip_sample': True,
             }

             config.update(**SCREAMING_SNAKE_CASE__  )
             return config
      def  SCREAMING_SNAKE_CASE							( self       : str  ,							**SCREAMING_SNAKE_CASE__       : Tuple  )	->    int:
             a_      :	Any							=						self.scheduler_classes[0]
             a_      :	str							=						self.get_scheduler_config(**SCREAMING_SNAKE_CASE__  )
             a_      :	Optional[Any]							=						scheduler_class(**SCREAMING_SNAKE_CASE__  )

             a_							,	a_      :	Optional[Any]							=						1_0, 0.0

             a_      :	str							=						self.dummy_model()
             a_      :	Dict							=						self.dummy_sample_deter

             scheduler.set_timesteps(SCREAMING_SNAKE_CASE__  )

             for t in scheduler.timesteps:
                    a_      :	List[Any]							=						model(SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  )
                    a_      :	Optional[Any]							=						scheduler.step(SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  ).prev_sample

             return sample
      def  SCREAMING_SNAKE_CASE							( self       : Optional[int]  )	->    Any:
             for timesteps in [1_0_0, 5_0_0, 1_0_0_0]:
                    self.check_over_configs(num_train_timesteps=SCREAMING_SNAKE_CASE__  )
      def  SCREAMING_SNAKE_CASE							( self       : Dict  )	->    List[Any]:
             for steps_offset in [0, 1]:
                    self.check_over_configs(steps_offset=SCREAMING_SNAKE_CASE__  )

             a_      :	Tuple							=						self.scheduler_classes[0]
             a_      :	int							=						self.get_scheduler_config(steps_offset=1  )
             a_      :	str							=						scheduler_class(**SCREAMING_SNAKE_CASE__  )
             scheduler.set_timesteps(5  )
             assert torch.equal(scheduler.timesteps  ,							torch.LongTensor([8_0_1, 6_0_1, 4_0_1, 2_0_1, 1]  )  )
      def  SCREAMING_SNAKE_CASE							( self       : Optional[int]  )	->    List[str]:
             for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1]  ,							[0.002, 0.02, 0.2, 2]  ):
                    self.check_over_configs(beta_start=SCREAMING_SNAKE_CASE__  ,							beta_end=SCREAMING_SNAKE_CASE__  )
      def  SCREAMING_SNAKE_CASE							( self       : Optional[Any]  )	->    List[str]:
             for schedule in ["linear", "squaredcos_cap_v2"]:
                    self.check_over_configs(beta_schedule=SCREAMING_SNAKE_CASE__  )
      def  SCREAMING_SNAKE_CASE							( self       : Dict  )	->    Dict:
             for prediction_type in ["epsilon", "v_prediction"]:
                    self.check_over_configs(prediction_type=SCREAMING_SNAKE_CASE__  )
      def  SCREAMING_SNAKE_CASE							( self       : Optional[int]  )	->    Tuple:
             for clip_sample in [True, False]:
                    self.check_over_configs(clip_sample=SCREAMING_SNAKE_CASE__  )
      def  SCREAMING_SNAKE_CASE							( self       : str  )	->    Union[str, Any]:
             for timestep_spacing in ["trailing", "leading"]:
                    self.check_over_configs(timestep_spacing=SCREAMING_SNAKE_CASE__  )
      def  SCREAMING_SNAKE_CASE							( self       : str  )	->    int:
             for rescale_betas_zero_snr in [True, False]:
                    self.check_over_configs(rescale_betas_zero_snr=SCREAMING_SNAKE_CASE__  )
      def  SCREAMING_SNAKE_CASE							( self       : int  )	->    Tuple:
             self.check_over_configs(thresholding=SCREAMING_SNAKE_CASE__  )
             for threshold in [0.5, 1.0, 2.0]:
                    for prediction_type in ["epsilon", "v_prediction"]:
                           self.check_over_configs(
                               thresholding=SCREAMING_SNAKE_CASE__  ,							prediction_type=SCREAMING_SNAKE_CASE__  ,							sample_max_value=SCREAMING_SNAKE_CASE__  ,							)
      def  SCREAMING_SNAKE_CASE							( self       : Any  )	->    Tuple:
             for t in [1, 1_0, 4_9]:
                    self.check_over_forward(time_step=SCREAMING_SNAKE_CASE__  )
      def  SCREAMING_SNAKE_CASE							( self       : Dict  )	->    Dict:
             for t, num_inference_steps in zip([1, 1_0, 5_0]  ,							[1_0, 5_0, 5_0_0]  ):
                    self.check_over_forward(time_step=SCREAMING_SNAKE_CASE__  ,							num_inference_steps=SCREAMING_SNAKE_CASE__  )
      def  SCREAMING_SNAKE_CASE							( self       : Dict  )	->    Optional[Any]:
             for t, eta in zip([1, 1_0, 4_9]  ,							[0.0, 0.5, 1.0]  ):
                    self.check_over_forward(time_step=SCREAMING_SNAKE_CASE__  ,							eta=SCREAMING_SNAKE_CASE__  )
      def  SCREAMING_SNAKE_CASE							( self       : Dict  )	->    Tuple:
             a_      :	Union[str, Any]							=						self.scheduler_classes[0]
             a_      :	List[Any]							=						self.get_scheduler_config()
             a_      :	Dict							=						scheduler_class(**SCREAMING_SNAKE_CASE__  )

             assert torch.sum(torch.abs(scheduler._get_variance(0  ,							0  ) - 0.0  )  ) < 1E-5
             assert torch.sum(torch.abs(scheduler._get_variance(4_2_0  ,							4_0_0  ) - 0.14771  )  ) < 1E-5
             assert torch.sum(torch.abs(scheduler._get_variance(9_8_0  ,							9_6_0  ) - 0.32460  )  ) < 1E-5
             assert torch.sum(torch.abs(scheduler._get_variance(0  ,							0  ) - 0.0  )  ) < 1E-5
             assert torch.sum(torch.abs(scheduler._get_variance(4_8_7  ,							4_8_6  ) - 0.00979  )  ) < 1E-5
             assert torch.sum(torch.abs(scheduler._get_variance(9_9_9  ,							9_9_8  ) - 0.02  )  ) < 1E-5
      def  SCREAMING_SNAKE_CASE							( self       : Union[str, Any]  )	->    Dict:
             a_      :	Dict							=						self.scheduler_classes[0]
             a_      :	int							=						self.get_scheduler_config()
             a_      :	List[str]							=						scheduler_class(**SCREAMING_SNAKE_CASE__  )

             a_							,	a_      :	int							=						1_0, 0.0
             scheduler.set_timesteps(SCREAMING_SNAKE_CASE__  )

             a_      :	Union[str, Any]							=						self.dummy_model()
             a_      :	Optional[int]							=						self.dummy_sample_deter
             a_      :	Optional[Any]							=						self.dummy_sample_deter + 0.1
             a_      :	Any							=						self.dummy_sample_deter - 0.1

             a_      :	List[Any]							=						samplea.shape[0]
             a_      :	List[str]							=						torch.stack([samplea, samplea, samplea]  ,							dim=0  )
             a_      :	Union[str, Any]							=						torch.arange(SCREAMING_SNAKE_CASE__  )[0:3, None].repeat(1  ,							SCREAMING_SNAKE_CASE__  )

             a_      :	int							=						model(samples.flatten(0  ,							1  )  ,							timesteps.flatten(0  ,							1  )  )
             a_      :	Optional[Any]							=						scheduler.batch_step_no_noise(SCREAMING_SNAKE_CASE__  ,							timesteps.flatten(0  ,							1  )  ,							samples.flatten(0  ,							1  )  ,							SCREAMING_SNAKE_CASE__  )

             a_      :	Optional[int]							=						torch.sum(torch.abs(SCREAMING_SNAKE_CASE__  )  )
             a_      :	Optional[int]							=						torch.mean(torch.abs(SCREAMING_SNAKE_CASE__  )  )

             assert abs(result_sum.item() - 1147.7904  ) < 1E-2
             assert abs(result_mean.item() - 0.4982  ) < 1E-3
      def  SCREAMING_SNAKE_CASE							( self       : Optional[Any]  )	->    Tuple:
             a_      :	Tuple							=						self.full_loop()

             a_      :	List[str]							=						torch.sum(torch.abs(SCREAMING_SNAKE_CASE__  )  )
             a_      :	Any							=						torch.mean(torch.abs(SCREAMING_SNAKE_CASE__  )  )

             assert abs(result_sum.item() - 172.0067  ) < 1E-2
             assert abs(result_mean.item() - 0.223967  ) < 1E-3
      def  SCREAMING_SNAKE_CASE							( self       : int  )	->    List[str]:
             a_      :	Optional[int]							=						self.full_loop(prediction_type='v_prediction'  )

             a_      :	str							=						torch.sum(torch.abs(SCREAMING_SNAKE_CASE__  )  )
             a_      :	Union[str, Any]							=						torch.mean(torch.abs(SCREAMING_SNAKE_CASE__  )  )

             assert abs(result_sum.item() - 52.5302  ) < 1E-2
             assert abs(result_mean.item() - 0.0684  ) < 1E-3
      def  SCREAMING_SNAKE_CASE							( self       : str  )	->    Optional[Any]:
             # We specify different beta, so that the first alpha is 0.99
             a_      :	Dict							=						self.full_loop(set_alpha_to_one=SCREAMING_SNAKE_CASE__  ,							beta_start=0.01  )
             a_      :	int							=						torch.sum(torch.abs(SCREAMING_SNAKE_CASE__  )  )
             a_      :	str							=						torch.mean(torch.abs(SCREAMING_SNAKE_CASE__  )  )

             assert abs(result_sum.item() - 149.8295  ) < 1E-2
             assert abs(result_mean.item() - 0.1951  ) < 1E-3

      def  SCREAMING_SNAKE_CASE							( self       : Any  )	->    Union[str, Any]:
             # We specify different beta, so that the first alpha is 0.99
             a_      :	Tuple							=						self.full_loop(set_alpha_to_one=SCREAMING_SNAKE_CASE__  ,							beta_start=0.01  )
             a_      :	Dict							=						torch.sum(torch.abs(SCREAMING_SNAKE_CASE__  )  )
             a_      :	str							=						torch.mean(torch.abs(SCREAMING_SNAKE_CASE__  )  )

             assert abs(result_sum.item() - 149.0784  ) < 1E-2
             assert abs(result_mean.item() - 0.1941  ) < 1E-3


