import os

import jsonlines
import numpy as np
from tqdm import tqdm


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





def 	__lowerCamelCase							(UpperCAmelCase__     :							Union[str, Any]  ):
     def choose_first(UpperCAmelCase__     :							Union[str, Any]      , UpperCAmelCase__     :							str=False  ):
          assert isinstance(UpperCAmelCase__      , UpperCAmelCase__  )
          if len(UpperCAmelCase__  ) == 1:
               SCREAMING_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:
                    SCREAMING_SNAKE_CASE    			=       {k: [a[k]] for k in a}
               if len(a["start_token"]  ) > 0:
                    break
          return a

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

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

     SCREAMING_SNAKE_CASE    			=       ["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 	__lowerCamelCase							(UpperCAmelCase__     :							Tuple      , UpperCAmelCase__     :							str=False  ):
     SCREAMING_SNAKE_CASE    			=       _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
          SCREAMING_SNAKE_CASE    			=       example["document"]["tokens"]
          SCREAMING_SNAKE_CASE    			=       []
          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": -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

     SCREAMING_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

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

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

     # checking above code
     if assertion:
          SCREAMING_SNAKE_CASE    			=       doc["is_html"][answer["start_token"] : answer["end_token"]]
          SCREAMING_SNAKE_CASE    			=       doc["token"][answer["start_token"] : answer["end_token"]]
          SCREAMING_SNAKE_CASE    			=       " ".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 	__lowerCamelCase							(UpperCAmelCase__     :							Union[str, Any]      , UpperCAmelCase__     :							List[Any]      , UpperCAmelCase__     :							Optional[Any]=2_0_4_8      , UpperCAmelCase__     :							List[Any]=4_0_9_6      , UpperCAmelCase__     :							Tuple=True  ):
     # overlap will be of doc_stride - q_len

     SCREAMING_SNAKE_CASE    			=       get_context_and_ans(UpperCAmelCase__      , assertion=UpperCAmelCase__  )
     SCREAMING_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"],
              },
          }

     SCREAMING_SNAKE_CASE    			=       tokenizer(example["question"]["text"]      , out["context"]  ).input_ids
     SCREAMING_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
          SCREAMING_SNAKE_CASE    			=       []
          SCREAMING_SNAKE_CASE    			=       []
          SCREAMING_SNAKE_CASE    			=       input_ids[:q_len]
          SCREAMING_SNAKE_CASE    			=       range(UpperCAmelCase__      , len(UpperCAmelCase__  )      , max_length - doc_stride  )
          for i in doc_start_indices:
               SCREAMING_SNAKE_CASE    			=       i + max_length - q_len
               SCREAMING_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": [-1_0_0] * len(UpperCAmelCase__  ),
                  "end_token": [-1_0_0] * len(UpperCAmelCase__  ),
                  "category": category,
              },
          }

     SCREAMING_SNAKE_CASE    			=       out["context"].split()
     SCREAMING_SNAKE_CASE    			=       splitted_context[answer["end_token"]]
     SCREAMING_SNAKE_CASE    			=       len(
         tokenizer(
             " ".join(splitted_context[: answer["start_token"]]  )      , add_special_tokens=UpperCAmelCase__      , ).input_ids  )
     SCREAMING_SNAKE_CASE    			=       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
     SCREAMING_SNAKE_CASE    			=       len(tokenizer(UpperCAmelCase__      , add_special_tokens=UpperCAmelCase__  ).input_ids  )
     if num_sub_tokens > 1:
          answer["end_token"] += num_sub_tokens - 1

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

     if assertion:
          SCREAMING_SNAKE_CASE    			=       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"],
              },
          }

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

     SCREAMING_SNAKE_CASE    			=       []
     SCREAMING_SNAKE_CASE    			=       []
     SCREAMING_SNAKE_CASE    			=       []
     SCREAMING_SNAKE_CASE    			=       []  # null, yes, no, long, short
     for i in doc_start_indices:
          SCREAMING_SNAKE_CASE    			=       i + max_length - q_len
          SCREAMING_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:
               SCREAMING_SNAKE_CASE    			=       start_token - i + q_len
               SCREAMING_SNAKE_CASE    			=       end_token - i + q_len
               answers_category.append(answer["category"][0]  )  # ["short"] -> "short"
          else:
               SCREAMING_SNAKE_CASE    			=       -1_0_0
               SCREAMING_SNAKE_CASE    			=       -1_0_0
               answers_category.append("null"  )
          SCREAMING_SNAKE_CASE    			=       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 	__lowerCamelCase							(UpperCAmelCase__     :							List[str]      , UpperCAmelCase__     :							Optional[Any]      , UpperCAmelCase__     :							Optional[int]=2_0_4_8      , UpperCAmelCase__     :							Dict=4_0_9_6      , UpperCAmelCase__     :							Optional[int]=False  ):
     SCREAMING_SNAKE_CASE    			=       get_strided_contexts_and_ans(
         UpperCAmelCase__      , UpperCAmelCase__      , doc_stride=UpperCAmelCase__      , max_length=UpperCAmelCase__      , assertion=UpperCAmelCase__      , )

     return example





def 	__lowerCamelCase							(UpperCAmelCase__     :							int      , UpperCAmelCase__     :							str  ):
     with jsonlines.open(UpperCAmelCase__      , "a"  ) as writer:
          for example in tqdm(UpperCAmelCase__      , total=len(UpperCAmelCase__  )      , desc="Saving samples ... "  ):
               SCREAMING_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

       _lowerCamelCase					:   str							=		load_dataset('''natural_questions''')
       _lowerCamelCase					:   Optional[int]							=		BigBirdTokenizer.from_pretrained('''google/bigbird-roberta-base''')

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

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

       np.random.seed(SEED)
       _lowerCamelCase					:   Union[str, Any]							=		'''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 KDPMaDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest


class 						lowercase    (     a     ):
							lowercase__						:       Tuple								=							(KDPMaDiscreteScheduler,)
							lowercase__						:       Optional[int]								=							10




							def 	__snake_case(						self   :					Optional[Any]	,							**_UpperCamelCase   :					List[Any]      )					->				Tuple:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       {
												    "num_train_timesteps": 1_100,
												    "beta_start": 0.0_0_0_1,
												    "beta_end": 0.0_2,
												    "beta_schedule": "linear",
												}

												config.update(**_UpperCamelCase      )
												return config




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



												'''simple docstring'''

												for timesteps in [10, 50, 100, 1_000]:
																	self.check_over_configs(num_train_timesteps=_UpperCamelCase      )




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



												'''simple docstring'''

												for beta_start, beta_end in zip([0.0_0_0_0_1, 0.0_0_0_1, 0.0_0_1]	,							[0.0_0_0_2, 0.0_0_2, 0.0_2]      ):
																	self.check_over_configs(beta_start=_UpperCamelCase	,							beta_end=_UpperCamelCase      )




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



												'''simple docstring'''

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




							def 	__snake_case(						self   :					Optional[int]      )					->				int:



												'''simple docstring'''

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




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



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       self.scheduler_classes[0]
												SCREAMING_SNAKE_CASE    			=       self.get_scheduler_config(prediction_type="v_prediction"      )
												SCREAMING_SNAKE_CASE    			=       scheduler_class(**_UpperCamelCase      )

												scheduler.set_timesteps(self.num_inference_steps      )

												SCREAMING_SNAKE_CASE    			=       self.dummy_model()
												SCREAMING_SNAKE_CASE    			=       self.dummy_sample_deter * scheduler.init_noise_sigma
												SCREAMING_SNAKE_CASE    			=       sample.to(_UpperCamelCase      )

												for i, t in enumerate(scheduler.timesteps      ):
																	SCREAMING_SNAKE_CASE    			=       scheduler.scale_model_input(_UpperCamelCase	,							_UpperCamelCase      )

																	SCREAMING_SNAKE_CASE    			=       model(_UpperCamelCase	,							_UpperCamelCase      )

																	SCREAMING_SNAKE_CASE    			=       scheduler.step(_UpperCamelCase	,							_UpperCamelCase	,							_UpperCamelCase      )
																	SCREAMING_SNAKE_CASE    			=       output.prev_sample

												SCREAMING_SNAKE_CASE    			=       torch.sum(torch.abs(_UpperCamelCase      )      )
												SCREAMING_SNAKE_CASE    			=       torch.mean(torch.abs(_UpperCamelCase      )      )

												if torch_device in ["cpu", "mps"]:
																	assert abs(result_sum.item() - 4.6_934e-07      ) < 1e-2
																	assert abs(result_mean.item() - 6.1_112e-10      ) < 1e-3
												else:
																	# CUDA
																	assert abs(result_sum.item() - 4.693_428_650_170_972e-07      ) < 1e-2
																	assert abs(result_mean.item() - 0.0_0_0_2      ) < 1e-3




							def 	__snake_case(						self   :					Dict      )					->				int:



												'''simple docstring'''

												if torch_device == "mps":
																	return
												SCREAMING_SNAKE_CASE    			=       self.scheduler_classes[0]
												SCREAMING_SNAKE_CASE    			=       self.get_scheduler_config()
												SCREAMING_SNAKE_CASE    			=       scheduler_class(**_UpperCamelCase      )

												scheduler.set_timesteps(self.num_inference_steps      )

												SCREAMING_SNAKE_CASE    			=       self.dummy_model()
												SCREAMING_SNAKE_CASE    			=       self.dummy_sample_deter * scheduler.init_noise_sigma
												SCREAMING_SNAKE_CASE    			=       sample.to(_UpperCamelCase      )

												for i, t in enumerate(scheduler.timesteps      ):
																	SCREAMING_SNAKE_CASE    			=       scheduler.scale_model_input(_UpperCamelCase	,							_UpperCamelCase      )

																	SCREAMING_SNAKE_CASE    			=       model(_UpperCamelCase	,							_UpperCamelCase      )

																	SCREAMING_SNAKE_CASE    			=       scheduler.step(_UpperCamelCase	,							_UpperCamelCase	,							_UpperCamelCase      )
																	SCREAMING_SNAKE_CASE    			=       output.prev_sample

												SCREAMING_SNAKE_CASE    			=       torch.sum(torch.abs(_UpperCamelCase      )      )
												SCREAMING_SNAKE_CASE    			=       torch.mean(torch.abs(_UpperCamelCase      )      )

												if torch_device in ["cpu", "mps"]:
																	assert abs(result_sum.item() - 2_0.4_1_2_5      ) < 1e-2
																	assert abs(result_mean.item() - 0.0_2_6_6      ) < 1e-3
												else:
																	# CUDA
																	assert abs(result_sum.item() - 2_0.4_1_2_5      ) < 1e-2
																	assert abs(result_mean.item() - 0.0_2_6_6      ) < 1e-3





							def 	__snake_case(						self   :					Tuple      )					->				str:



												'''simple docstring'''

												if torch_device == "mps":
																	return
												SCREAMING_SNAKE_CASE    			=       self.scheduler_classes[0]
												SCREAMING_SNAKE_CASE    			=       self.get_scheduler_config()
												SCREAMING_SNAKE_CASE    			=       scheduler_class(**_UpperCamelCase      )

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

												SCREAMING_SNAKE_CASE    			=       self.dummy_model()
												SCREAMING_SNAKE_CASE    			=       self.dummy_sample_deter.to(_UpperCamelCase      ) * scheduler.init_noise_sigma

												for t in scheduler.timesteps:
																	SCREAMING_SNAKE_CASE    			=       scheduler.scale_model_input(_UpperCamelCase	,							_UpperCamelCase      )

																	SCREAMING_SNAKE_CASE    			=       model(_UpperCamelCase	,							_UpperCamelCase      )

																	SCREAMING_SNAKE_CASE    			=       scheduler.step(_UpperCamelCase	,							_UpperCamelCase	,							_UpperCamelCase      )
																	SCREAMING_SNAKE_CASE    			=       output.prev_sample

												SCREAMING_SNAKE_CASE    			=       torch.sum(torch.abs(_UpperCamelCase      )      )
												SCREAMING_SNAKE_CASE    			=       torch.mean(torch.abs(_UpperCamelCase      )      )

												if str(_UpperCamelCase      ).startswith("cpu"      ):
																	# The following sum varies between 148 and 156 on mps. Why?
																	assert abs(result_sum.item() - 2_0.4_1_2_5      ) < 1e-2
																	assert abs(result_mean.item() - 0.0_2_6_6      ) < 1e-3
												else:
																	# CUDA
																	assert abs(result_sum.item() - 2_0.4_1_2_5      ) < 1e-2
																	assert abs(result_mean.item() - 0.0_2_6_6      ) < 1e-3




