




import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    MobileViTConfig,
    MobileViTForImageClassification,
    MobileViTForSemanticSegmentation,
    MobileViTImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
UpperCAmelCase_		:					List[str]          =  logging.get_logger(__name__)




def 		lowerCAmelCase_ (						lowerCamelCase ):
				__magic_name__						:						Tuple										=MobileViTConfig()

				# size of the architecture
				if "mobilevit_s" in mobilevit_name:
								__magic_name__						:						Any										=[144, 192, 240]
								__magic_name__						:						Union[str, Any]										=[16, 32, 64, 96, 128, 160, 640]
				elif "mobilevit_xs" in mobilevit_name:
								__magic_name__						:						Dict										=[96, 120, 144]
								__magic_name__						:						str										=[16, 32, 48, 64, 80, 96, 384]
				elif "mobilevit_xxs" in mobilevit_name:
								__magic_name__						:						List[str]										=[64, 80, 96]
								__magic_name__						:						List[str]										=[16, 16, 24, 48, 64, 80, 320]
								__magic_name__						:						Optional[int]										=0.0_5
								__magic_name__						:						int										=2.0

				if mobilevit_name.startswith("""deeplabv3_""" ):
								__magic_name__						:						int										=512
								__magic_name__						:						Dict										=16
								__magic_name__						:						Union[str, Any]										=21
								__magic_name__						:						int										="""pascal-voc-id2label.json"""
				else:
								__magic_name__						:						Union[str, Any]										=1000
								__magic_name__						:						List[Any]										="""imagenet-1k-id2label.json"""

				__magic_name__						:						List[Any]										="""huggingface/label-files"""
				__magic_name__						:						Dict										=json.load(open(hf_hub_download(lowerCamelCase						,  lowerCamelCase						,  repo_type="""dataset""" )						,  """r""" ) )
				__magic_name__						:						Any										={int(lowerCamelCase ): v for k, v in idalabel.items()}
				__magic_name__						:						int										=idalabel
				__magic_name__						:						Any										={v: k for k, v in idalabel.items()}

				return config




def 		lowerCAmelCase_ (						lowerCamelCase						,  lowerCamelCase=False ):
				for i in range(1						,  6 ):
								if F"layer_{i}." in name:
												__magic_name__						:						Dict										=name.replace(F"layer_{i}."						,  F"encoder.layer.{i - 1}." )

				if "conv_1." in name:
								__magic_name__						:						List[Any]										=name.replace("""conv_1."""						,  """conv_stem.""" )
				if ".block." in name:
								__magic_name__						:						int										=name.replace(""".block."""						,  """.""" )

				if "exp_1x1" in name:
								__magic_name__						:						str										=name.replace("""exp_1x1"""						,  """expand_1x1""" )
				if "red_1x1" in name:
								__magic_name__						:						Dict										=name.replace("""red_1x1"""						,  """reduce_1x1""" )
				if ".local_rep.conv_3x3." in name:
								__magic_name__						:						List[str]										=name.replace(""".local_rep.conv_3x3."""						,  """.conv_kxk.""" )
				if ".local_rep.conv_1x1." in name:
								__magic_name__						:						Optional[Any]										=name.replace(""".local_rep.conv_1x1."""						,  """.conv_1x1.""" )
				if ".norm." in name:
								__magic_name__						:						Tuple										=name.replace(""".norm."""						,  """.normalization.""" )
				if ".conv." in name:
								__magic_name__						:						Any										=name.replace(""".conv."""						,  """.convolution.""" )
				if ".conv_proj." in name:
								__magic_name__						:						Optional[Any]										=name.replace(""".conv_proj."""						,  """.conv_projection.""" )

				for i in range(0						,  2 ):
								for j in range(0						,  4 ):
												if F".{i}.{j}." in name:
																__magic_name__						:						int										=name.replace(F".{i}.{j}."						,  F".{i}.layer.{j}." )

				for i in range(2						,  6 ):
								for j in range(0						,  4 ):
												if F".{i}.{j}." in name:
																__magic_name__						:						Dict										=name.replace(F".{i}.{j}."						,  F".{i}." )
																if "expand_1x1" in name:
																				__magic_name__						:						Optional[int]										=name.replace("""expand_1x1"""						,  """downsampling_layer.expand_1x1""" )
																if "conv_3x3" in name:
																				__magic_name__						:						Optional[int]										=name.replace("""conv_3x3"""						,  """downsampling_layer.conv_3x3""" )
																if "reduce_1x1" in name:
																				__magic_name__						:						Any										=name.replace("""reduce_1x1"""						,  """downsampling_layer.reduce_1x1""" )

				for i in range(2						,  5 ):
								if F".global_rep.{i}.weight" in name:
												__magic_name__						:						Dict										=name.replace(F".global_rep.{i}.weight"						,  """.layernorm.weight""" )
								if F".global_rep.{i}.bias" in name:
												__magic_name__						:						Tuple										=name.replace(F".global_rep.{i}.bias"						,  """.layernorm.bias""" )

				if ".global_rep." in name:
								__magic_name__						:						List[str]										=name.replace(""".global_rep."""						,  """.transformer.""" )
				if ".pre_norm_mha.0." in name:
								__magic_name__						:						int										=name.replace(""".pre_norm_mha.0."""						,  """.layernorm_before.""" )
				if ".pre_norm_mha.1.out_proj." in name:
								__magic_name__						:						Optional[Any]										=name.replace(""".pre_norm_mha.1.out_proj."""						,  """.attention.output.dense.""" )
				if ".pre_norm_ffn.0." in name:
								__magic_name__						:						List[str]										=name.replace(""".pre_norm_ffn.0."""						,  """.layernorm_after.""" )
				if ".pre_norm_ffn.1." in name:
								__magic_name__						:						Any										=name.replace(""".pre_norm_ffn.1."""						,  """.intermediate.dense.""" )
				if ".pre_norm_ffn.4." in name:
								__magic_name__						:						Optional[int]										=name.replace(""".pre_norm_ffn.4."""						,  """.output.dense.""" )
				if ".transformer." in name:
								__magic_name__						:						int										=name.replace(""".transformer."""						,  """.transformer.layer.""" )

				if ".aspp_layer." in name:
								__magic_name__						:						List[Any]										=name.replace(""".aspp_layer."""						,  """.""" )
				if ".aspp_pool." in name:
								__magic_name__						:						List[Any]										=name.replace(""".aspp_pool."""						,  """.""" )
				if "seg_head." in name:
								__magic_name__						:						Tuple										=name.replace("""seg_head."""						,  """segmentation_head.""" )
				if "segmentation_head.classifier.classifier." in name:
								__magic_name__						:						Dict										=name.replace("""segmentation_head.classifier.classifier."""						,  """segmentation_head.classifier.""" )

				if "classifier.fc." in name:
								__magic_name__						:						Tuple										=name.replace("""classifier.fc."""						,  """classifier.""" )
				elif (not base_model) and ("segmentation_head." not in name):
								__magic_name__						:						Any										="""mobilevit.""" + name

				return name




def 		lowerCAmelCase_ (						lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase=False ):
				if base_model:
								__magic_name__						:						Any										=""""""
				else:
								__magic_name__						:						Tuple										="""mobilevit."""

				for key in orig_state_dict.copy().keys():
								__magic_name__						:						Dict										=orig_state_dict.pop(lowerCamelCase )

								if key[:8] == "encoder.":
												__magic_name__						:						Optional[int]										=key[8:]

								if "qkv" in key:
												__magic_name__						:						Tuple										=key.split(""".""" )
												__magic_name__						:						Dict										=int(key_split[0][6:] ) - 1
												__magic_name__						:						Dict										=int(key_split[3] )
												__magic_name__						:						Optional[int]										=model.get_submodule(F"{model_prefix}encoder.layer.{layer_num}" )
												__magic_name__						:						List[Any]										=layer.transformer.layer[transformer_num].attention.attention.all_head_size
												__magic_name__						:						Tuple										=(
												    F"{model_prefix}encoder.layer.{layer_num}.transformer.layer.{transformer_num}.attention.attention."
												)
												if "weight" in key:
																__magic_name__						:						Optional[int]										=val[:dim, :]
																__magic_name__						:						Tuple										=val[dim : dim * 2, :]
																__magic_name__						:						List[str]										=val[-dim:, :]
												else:
																__magic_name__						:						Tuple										=val[:dim]
																__magic_name__						:						str										=val[dim : dim * 2]
																__magic_name__						:						int										=val[-dim:]
								else:
												__magic_name__						:						Optional[int]										=val

				return orig_state_dict




def 		lowerCAmelCase_ (						):
				__magic_name__						:						List[Any]										="""http://images.cocodataset.org/val2017/000000039769.jpg"""
				__magic_name__						:						str										=Image.open(requests.get(lowerCamelCase						,  stream=lowerCamelCase ).raw )
				return im




@torch.no_grad()
def 		lowerCAmelCase_ (						lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase=False ):
				__magic_name__						:						Optional[int]										=get_mobilevit_config(lowerCamelCase )

				# load original state_dict
				__magic_name__						:						List[Any]										=torch.load(lowerCamelCase						,  map_location="""cpu""" )

				# load 🤗 model
				if mobilevit_name.startswith("""deeplabv3_""" ):
								__magic_name__						:						Union[str, Any]										=MobileViTForSemanticSegmentation(lowerCamelCase ).eval()
				else:
								__magic_name__						:						Tuple										=MobileViTForImageClassification(lowerCamelCase ).eval()

				__magic_name__						:						Dict										=convert_state_dict(lowerCamelCase						,  lowerCamelCase )
				model.load_state_dict(lowerCamelCase )

				# Check outputs on an image, prepared by MobileViTImageProcessor
				__magic_name__						:						Any										=MobileViTImageProcessor(crop_size=config.image_size						,  size=config.image_size + 32 )
				__magic_name__						:						Optional[int]										=image_processor(images=prepare_img()						,  return_tensors="""pt""" )
				__magic_name__						:						List[Any]										=model(**lowerCamelCase )
				__magic_name__						:						Union[str, Any]										=outputs.logits

				if mobilevit_name.startswith("""deeplabv3_""" ):
								assert logits.shape == (1, 21, 32, 32)

								if mobilevit_name == "deeplabv3_mobilevit_s":
												__magic_name__						:						Any										=torch.tensor(
												    [
												        [[6.2_0_6_5, 6.1_2_9_2, 6.2_0_7_0], [6.1_0_7_9, 6.1_2_5_4, 6.1_7_4_7], [6.0_0_4_2, 6.1_0_7_1, 6.1_0_3_4]],
												        [[-6.9_2_5_3, -6.8_6_5_3, -7.0_3_9_8], [-7.3_2_1_8, -7.3_9_8_3, -7.3_6_7_0], [-7.1_9_6_1, -7.2_4_8_2, -7.1_5_6_9]],
												        [[-4.4_7_2_3, -4.4_3_4_8, -4.3_7_6_9], [-5.3_6_2_9, -5.4_6_3_2, -5.4_5_9_8], [-5.1_5_8_7, -5.3_4_0_2, -5.5_0_5_9]],
												    ] )
								elif mobilevit_name == "deeplabv3_mobilevit_xs":
												__magic_name__						:						Dict										=torch.tensor(
												    [
												        [[5.4_4_4_9, 5.5_7_3_3, 5.6_3_1_4], [5.1_8_1_5, 5.3_9_3_0, 5.5_9_6_3], [5.1_6_5_6, 5.4_3_3_3, 5.4_8_5_3]],
												        [[-9.4_4_2_3, -9.7_7_6_6, -9.6_7_1_4], [-9.1_5_8_1, -9.5_7_2_0, -9.5_5_1_9], [-9.1_0_0_6, -9.6_4_5_8, -9.5_7_0_3]],
												        [[-7.7_7_2_1, -7.3_7_1_6, -7.1_5_8_3], [-8.4_5_9_9, -8.0_6_2_4, -7.7_9_4_4], [-8.4_1_7_2, -7.8_3_6_6, -7.5_0_2_5]],
												    ] )
								elif mobilevit_name == "deeplabv3_mobilevit_xxs":
												__magic_name__						:						Dict										=torch.tensor(
												    [
												        [[6.9_8_1_1, 6.9_7_4_3, 7.3_1_2_3], [7.1_7_7_7, 7.1_9_3_1, 7.3_9_3_8], [7.5_6_3_3, 7.8_0_5_0, 7.8_9_0_1]],
												        [[-1_0.5_5_3_6, -1_0.2_3_3_2, -1_0.2_9_2_4], [-1_0.2_3_3_6, -9.8_6_2_4, -9.5_9_6_4], [-1_0.8_8_4_0, -1_0.8_1_5_8, -1_0.6_6_5_9]],
												        [[-3.4_9_3_8, -3.0_6_3_1, -2.8_6_2_0], [-3.4_2_0_5, -2.8_1_3_5, -2.6_8_7_5], [-3.4_1_7_9, -2.7_9_4_5, -2.8_7_5_0]],
												    ] )
								else:
												raise ValueError(F"Unknown mobilevit_name: {mobilevit_name}" )

								assert torch.allclose(logits[0, :3, :3, :3]						,  lowerCamelCase						,  atol=1E-4 )
				else:
								assert logits.shape == (1, 1000)

								if mobilevit_name == "mobilevit_s":
												__magic_name__						:						List[str]										=torch.tensor([-0.9_8_6_6, 0.2_3_9_2, -1.1_2_4_1] )
								elif mobilevit_name == "mobilevit_xs":
												__magic_name__						:						Dict										=torch.tensor([-2.4_7_6_1, -0.9_3_9_9, -1.9_5_8_7] )
								elif mobilevit_name == "mobilevit_xxs":
												__magic_name__						:						Optional[Any]										=torch.tensor([-1.9_3_6_4, -1.2_3_2_7, -0.4_6_5_3] )
								else:
												raise ValueError(F"Unknown mobilevit_name: {mobilevit_name}" )

								assert torch.allclose(logits[0, :3]						,  lowerCamelCase						,  atol=1E-4 )

				Path(lowerCamelCase ).mkdir(exist_ok=lowerCamelCase )
				print(F"Saving model {mobilevit_name} to {pytorch_dump_folder_path}" )
				model.save_pretrained(lowerCamelCase )
				print(F"Saving image processor to {pytorch_dump_folder_path}" )
				image_processor.save_pretrained(lowerCamelCase )

				if push_to_hub:
								__magic_name__						:						List[Any]										={
								    """mobilevit_s""": """mobilevit-small""",
								    """mobilevit_xs""": """mobilevit-x-small""",
								    """mobilevit_xxs""": """mobilevit-xx-small""",
								    """deeplabv3_mobilevit_s""": """deeplabv3-mobilevit-small""",
								    """deeplabv3_mobilevit_xs""": """deeplabv3-mobilevit-x-small""",
								    """deeplabv3_mobilevit_xxs""": """deeplabv3-mobilevit-xx-small""",
								}

								print("""Pushing to the hub...""" )
								__magic_name__						:						str										=model_mapping[mobilevit_name]
								image_processor.push_to_hub(lowerCamelCase						,  organization="""apple""" )
								model.push_to_hub(lowerCamelCase						,  organization="""apple""" )


if __name__ == "__main__":
							UpperCAmelCase_		:					Tuple          =  argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    "--mobilevit_name",
							    default="mobilevit_s",
							    type=str,
							    help=(
							        "Name of the MobileViT model you'd like to convert. Should be one of 'mobilevit_s', 'mobilevit_xs',"
							        " 'mobilevit_xxs', 'deeplabv3_mobilevit_s', 'deeplabv3_mobilevit_xs', 'deeplabv3_mobilevit_xxs'."
							    ),
							)
							parser.add_argument(
							    "--checkpoint_path", required=True, type=str, help="Path to the original state dict (.pt file)."
							)
							parser.add_argument(
							    "--pytorch_dump_folder_path", required=True, type=str, help="Path to the output PyTorch model directory."
							)
							parser.add_argument(
							    "--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub."
							)

							UpperCAmelCase_		:					int          =  parser.parse_args()
							convert_movilevit_checkpoint(
							    args.mobilevit_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
							)










import os

import jsonlines
import numpy as np
from tqdm import tqdm


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




def 		lowerCAmelCase_ (						lowerCamelCase ):
				def choose_first(lowerCamelCase						,  lowerCamelCase=False ):
								assert isinstance(lowerCamelCase						,  lowerCamelCase )
								if len(lowerCamelCase ) == 1:
												__magic_name__						:						List[str]										=answer[0]
												return {k: [answer[k]] for k in answer} if is_long_answer else answer
								for a in answer:
												if is_long_answer:
																__magic_name__						:						Tuple										={k: [a[k]] for k in a}
												if len(a["""start_token"""] ) > 0:
																break
								return a

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

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

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

				return answer




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

				__magic_name__						:						Dict										=["""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

				__magic_name__						:						Tuple										=example["""document"""]["""tokens"""]
				__magic_name__						:						Optional[int]										=answer["""start_token"""]
				__magic_name__						:						List[Any]										=answer["""end_token"""]

				__magic_name__						:						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
				__magic_name__						:						Optional[int]										=""" """.join(context[start_token:end_token] )

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

				return {
				    "context": " ".join(lowerCamelCase ),
				    "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_ (						lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase=2048						,  lowerCamelCase=4096						,  lowerCamelCase=True ):
				# overlap will be of doc_stride - q_len

				__magic_name__						:						Any										=get_context_and_ans(lowerCamelCase						,  assertion=lowerCamelCase )
				__magic_name__						:						Union[str, 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"],
								    },
								}

				__magic_name__						:						List[Any]										=tokenizer(example["""question"""]["""text"""]						,  out["""context"""] ).input_ids
				__magic_name__						:						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
								__magic_name__						:						List[str]										=[]
								__magic_name__						:						int										=[]
								__magic_name__						:						List[str]										=input_ids[:q_len]
								__magic_name__						:						Dict										=range(lowerCamelCase						,  len(lowerCamelCase )						,  max_length - doc_stride )
								for i in doc_start_indices:
												__magic_name__						:						List[Any]										=i + max_length - q_len
												__magic_name__						:						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": [-100] * len(lowerCamelCase ),
								        "end_token": [-100] * len(lowerCamelCase ),
								        "category": category,
								    },
								}

				__magic_name__						:						int										=out["""context"""].split()
				__magic_name__						:						Any										=splitted_context[answer["""end_token"""]]
				__magic_name__						:						str										=len(
				    tokenizer(
				        """ """.join(splitted_context[: answer["""start_token"""]] )						,  add_special_tokens=lowerCamelCase						,  ).input_ids )
				__magic_name__						:						Optional[int]										=len(
				    tokenizer(""" """.join(splitted_context[: answer["""end_token"""]] )						,  add_special_tokens=lowerCamelCase ).input_ids )

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

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

				__magic_name__						:						str										=input_ids[answer["""start_token"""] : answer["""end_token"""] + 1]  # right & left are inclusive
				__magic_name__						:						Dict										=answer["""start_token"""]
				__magic_name__						:						int										=answer["""end_token"""]

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

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

				__magic_name__						:						Any										=input_ids[:q_len]
				__magic_name__						:						Union[str, Any]										=range(lowerCamelCase						,  len(lowerCamelCase )						,  max_length - doc_stride )

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

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

				return example




def 		lowerCAmelCase_ (						lowerCamelCase						,  lowerCamelCase ):
				with jsonlines.open(lowerCamelCase						,  """a""" ) as writer:
								for example in tqdm(lowerCamelCase						,  total=len(lowerCamelCase )						,  desc="""Saving samples ... """ ):
												__magic_name__						:						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

							UpperCAmelCase_		:					Optional[int]          =  load_dataset("natural_questions")
							UpperCAmelCase_		:					Optional[int]          =  BigBirdTokenizer.from_pretrained("google/bigbird-roberta-base")

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

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

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




