






import os

import jsonlines
import numpy as np
from tqdm import tqdm


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







def 					SCREAMING_SNAKE_CASE    (			snake_case_ :							Optional[Any]       ):
	def choose_first(snake_case_ :							Dict					,  snake_case_ :							str=False       ):
		assert isinstance(snake_case_					,  snake_case_       )
		if len(snake_case_       ) == 1:
			snake_case__			:				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:
				snake_case__			:				Optional[Any]       =  {k: [a[k]] for k in a}
			if len(a["start_token"]       ) > 0:
				break
		return a

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

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

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

	return answer







def 					SCREAMING_SNAKE_CASE    (			snake_case_ :							Any					,  snake_case_ :							Optional[Any]=False       ):
	snake_case__			:				List[str]       =  _get_single_answer(snake_case_       )
	# 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
		snake_case__			:				Optional[int]       =  example["""document"""]["""tokens"""]
		snake_case__			:				List[Any]       =  []
		for i in range(len(doc["token"]       )       ):
			if not doc["is_html"][i]:
				context.append(doc["token"][i]       )
		return {
		    "context": " ".join(snake_case_       ),
		    "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

	snake_case__			:				str       =  ["""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

	snake_case__			:				Dict       =  example["""document"""]["""tokens"""]
	snake_case__			:				List[str]       =  answer["""start_token"""]
	snake_case__			:				Optional[int]       =  answer["""end_token"""]

	snake_case__			:				Optional[Any]       =  []
	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
	snake_case__			:				Optional[Any]       =  """ """.join(context[start_token:end_token]       )

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

	return {
	    "context": " ".join(snake_case_       ),
	    "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    (			snake_case_ :							Dict					,  snake_case_ :							Optional[Any]					,  snake_case_ :							Tuple=2048					,  snake_case_ :							str=4096					,  snake_case_ :							Optional[int]=True       ):
	snake_case__			:				List[Any]       =  get_context_and_ans(snake_case_					,  assertion=snake_case_       )
	snake_case__			:				Optional[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"],
		    },
		}

	snake_case__			:				Optional[Any]       =  tokenizer(example["question"]["text"]					,  out["context"]       ).input_ids
	snake_case__			:				Dict       =  input_ids.index(tokenizer.sep_token_id       ) + 1

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

	snake_case__			:				int       =  out["""context"""].split()
	snake_case__			:				Tuple       =  splitted_context[answer["""end_token"""]]
	snake_case__			:				List[Any]       =  len(
	    tokenizer(
	        " ".join(splitted_context[: answer["start_token"]]       )					,  add_special_tokens=snake_case_					,  ).input_ids       )
	snake_case__			:				List[str]       =  len(
	    tokenizer(" ".join(splitted_context[: answer["end_token"]]       )					,  add_special_tokens=snake_case_       ).input_ids       )

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

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

	snake_case__			:				Tuple       =  input_ids[answer["""start_token"""] : answer["""end_token"""] + 1]  # right & left are inclusive
	snake_case__			:				Any       =  answer["""start_token"""]
	snake_case__			:				Optional[Any]       =  answer["""end_token"""]

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

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

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

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

		answers_start_token.append(snake_case_       )
		answers_end_token.append(snake_case_       )
		if assertion:
			if new != old and new != [tokenizer.cls_token_id]:
				print("ISSUE in strided for ID:"					,  example["id"]       )
				print("New:"					,  tokenizer.decode(snake_case_       )       )
				print("Old:"					,  tokenizer.decode(snake_case_       )					,  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    (			snake_case_ :							Optional[Any]					,  snake_case_ :							List[str]					,  snake_case_ :							str=2048					,  snake_case_ :							List[Any]=4096					,  snake_case_ :							Any=False       ):
	snake_case__			:				Optional[Any]       =  get_strided_contexts_and_ans(
	    snake_case_					,  snake_case_					,  doc_stride=snake_case_					,  max_length=snake_case_					,  assertion=snake_case_					,  )

	return example







def 					SCREAMING_SNAKE_CASE    (			snake_case_ :							Optional[int]					,  snake_case_ :							Optional[int]       ):
	with jsonlines.open(snake_case_					,  "a"       ) as writer:
		for example in tqdm(snake_case_					,  total=len(snake_case_       )					,  desc="Saving samples ... "       ):
			snake_case__			:				int       =  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 :      Union[str, Any]										=				load_dataset("""natural_questions""")
	__lowerCamelCase :      List[str]										=				BigBirdTokenizer.from_pretrained("""google/bigbird-roberta-base""")

	__lowerCamelCase :      Dict										=				data['''train''' if PROCESS_TRAIN == '''true''' else '''validation''']

	__lowerCamelCase :      Tuple										=				{
	    '''tokenizer''': tokenizer,
	    '''doc_stride''': DOC_STRIDE,
	    '''max_length''': MAX_LENGTH,
	    '''assertion''': False,
	}
	__lowerCamelCase :      List[Any]										=				data.map(prepare_inputs, fn_kwargs=fn_kwargs)
	__lowerCamelCase :      int										=				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)











# limitations under the License.

# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works

from .pipelines import DiffusionPipeline, ImagePipelineOutput  # noqa: F401
from .utils import deprecate


deprecate(
    """pipelines_utils""",
    """0.22.0""",
    """Importing `DiffusionPipeline` or `ImagePipelineOutput` from diffusers.pipeline_utils is deprecated. Please import from diffusers.pipelines.pipeline_utils instead.""",
    standard_warn=False,
    stacklevel=3,
)



