

import os

import jsonlines
import numpy as np
from tqdm import tqdm


lowercase_									=   2_0_4_8
lowercase_									=   4_0_9_6
lowercase_									=   4_2
lowercase_									=   os.environ.pop('PROCESS_TRAIN', 'false')
lowercase_									=   {'null': 0, 'short': 1, 'long': 2, 'yes': 3, 'no': 4}
def      a      (			A__	:   int     )    ->      Union[str, Any]:





      """simple docstring"""



      def choose_first(A__	:   Dict      ,   A__	:   int=False     ):
            assert isinstance(_lowerCAmelCase      ,   _lowerCAmelCase     )
            if len(_lowerCAmelCase     ) == 1:
                  _lowercase	       =answer[0]
                  return {k: [answer[k]] for k in answer} if is_long_answer else answer
            for a in answer:
                  if is_long_answer:
                        _lowercase	       ={k: [a[k]] for k in a}
                  if len(a['start_token']     ) > 0:
                        break
            return a

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

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

      _lowercase	       =["""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      a      (			A__	:   Dict      ,   A__	:   List[Any]=False     )    ->      List[Any]:





      """simple docstring"""



      _lowercase	       =_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
            _lowercase	       =example["""document"""]["""tokens"""]
            _lowercase	       =[]
            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

      _lowercase	       =["""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

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

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

      # checking above code
      if assertion:
            _lowercase	       =doc["""is_html"""][answer["""start_token"""] : answer["""end_token"""]]
            _lowercase	       =doc["""token"""][answer["""start_token"""] : answer["""end_token"""]]
            _lowercase	       =""" """.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      a      (			A__	:   Tuple      ,   A__	:   List[Any]      ,   A__	:   str=2048      ,   A__	:   int=4096      ,   A__	:   List[str]=True     )    ->      Optional[Any]:





      """simple docstring"""



      _lowercase	       =get_context_and_ans(_lowerCAmelCase      ,   assertion=_lowerCAmelCase     )
      _lowercase	       =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"],
                },
            }

      _lowercase	       =tokenizer(example['question']['text']      ,   out['context']     ).input_ids
      _lowercase	       =input_ids.index(tokenizer.sep_token_id     ) + 1

      # return yes/no
      if answer["category"][0] in ["yes", "no"]:  # category is list with one element
            _lowercase	       =[]
            _lowercase	       =[]
            _lowercase	       =input_ids[:q_len]
            _lowercase	       =range(_lowerCAmelCase      ,   len(_lowerCAmelCase     )      ,   max_length - doc_stride     )
            for i in doc_start_indices:
                  _lowercase	       =i + max_length - q_len
                  _lowercase	       =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,
                },
            }

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

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

      if assertion:
            _lowercase	       =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"],
                },
            }

      _lowercase	       =input_ids[:q_len]
      _lowercase	       =range(_lowerCAmelCase      ,   len(_lowerCAmelCase     )      ,   max_length - doc_stride     )

      _lowercase	       =[]
      _lowercase	       =[]
      _lowercase	       =[]
      _lowercase	       =[]  # null, yes, no, long, short
      for i in doc_start_indices:
            _lowercase	       =i + max_length - q_len
            _lowercase	       =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:
                  _lowercase	       =start_token - i + q_len
                  _lowercase	       =end_token - i + q_len
                  answers_category.append(answer['category'][0]     )  # ["short"] -> "short"
            else:
                  _lowercase	       =-100
                  _lowercase	       =-100
                  answers_category.append('null'     )
            _lowercase	       =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      a      (			A__	:   Optional[int]      ,   A__	:   Union[str, Any]      ,   A__	:   List[str]=2048      ,   A__	:   Union[str, Any]=4096      ,   A__	:   List[str]=False     )    ->      Optional[Any]:





      """simple docstring"""



      _lowercase	       =get_strided_contexts_and_ans(
          _lowerCAmelCase      ,   _lowerCAmelCase      ,   doc_stride=_lowerCAmelCase      ,   max_length=_lowerCAmelCase      ,   assertion=_lowerCAmelCase      ,   )

      return example
def      a      (			A__	:   Optional[Any]      ,   A__	:   Any     )    ->      Union[str, Any]:





      """simple docstring"""



      with jsonlines.open(_lowerCAmelCase      ,   'a'     ) as writer:
            for example in tqdm(_lowerCAmelCase      ,   total=len(_lowerCAmelCase     )      ,   desc='Saving samples ... '     ):
                  _lowercase	       =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

  lowercase_									=   load_dataset('natural_questions')
  lowercase_									=   BigBirdTokenizer.from_pretrained('google/bigbird-roberta-base')

  lowercase_									=   data['train' if PROCESS_TRAIN == 'true' else 'validation']

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

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







'''simple docstring'''





import argparse
import json

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

from transformers import AutoImageProcessor, SwinConfig, SwinForImageClassification





def 						__snake_case(				_lowerCAmelCase		)		-> List[Any]:
      snake_case__  :    Dict    						=      SwinConfig()
      snake_case__  :    Optional[Any]    						=      swin_name.split("""_"""		)

      snake_case__  :    Any    						=      name_split[1]
      snake_case__  :    List[Any]    						=      int(name_split[4]		)
      snake_case__  :    int    						=      int(name_split[3][-1]		)

      if model_size == "tiny":
            snake_case__  :    List[Any]    						=      96
            snake_case__  :    int    						=      (2, 2, 6, 2)
            snake_case__  :    int    						=      (3, 6, 12, 24)
      elif model_size == "small":
            snake_case__  :    Union[str, Any]    						=      96
            snake_case__  :    Optional[Any]    						=      (2, 2, 18, 2)
            snake_case__  :    str    						=      (3, 6, 12, 24)
      elif model_size == "base":
            snake_case__  :    Dict    						=      128
            snake_case__  :    str    						=      (2, 2, 18, 2)
            snake_case__  :    Dict    						=      (4, 8, 16, 32)
      else:
            snake_case__  :    List[str]    						=      192
            snake_case__  :    str    						=      (2, 2, 18, 2)
            snake_case__  :    List[Any]    						=      (6, 12, 24, 48)

      if "in22k" in swin_name:
            snake_case__  :    str    						=      21_841
      else:
            snake_case__  :    List[str]    						=      1_000
            snake_case__  :    int    						=      """huggingface/label-files"""
            snake_case__  :    Any    						=      """imagenet-1k-id2label.json"""
            snake_case__  :    List[Any]    						=      json.load(open(hf_hub_download(_lowerCAmelCase     ,   _lowerCAmelCase     ,   repo_type="""dataset"""		)     ,   """r"""		)		)
            snake_case__  :    Dict    						=      {int(_lowerCAmelCase		): v for k, v in idalabel.items()}
            snake_case__  :    Optional[int]    						=      idalabel
            snake_case__  :    List[Any]    						=      {v: k for k, v in idalabel.items()}

      snake_case__  :    List[Any]    						=      img_size
      snake_case__  :    Dict    						=      num_classes
      snake_case__  :    Dict    						=      embed_dim
      snake_case__  :    Optional[int]    						=      depths
      snake_case__  :    int    						=      num_heads
      snake_case__  :    Optional[int]    						=      window_size

      return config





def 						__snake_case(				_lowerCAmelCase		)		-> Dict:
      if "patch_embed.proj" in name:
            snake_case__  :    List[str]    						=      name.replace("""patch_embed.proj"""     ,   """embeddings.patch_embeddings.projection"""		)
      if "patch_embed.norm" in name:
            snake_case__  :    int    						=      name.replace("""patch_embed.norm"""     ,   """embeddings.norm"""		)
      if "layers" in name:
            snake_case__  :    str    						=      """encoder.""" + name
      if "attn.proj" in name:
            snake_case__  :    List[str]    						=      name.replace("""attn.proj"""     ,   """attention.output.dense"""		)
      if "attn" in name:
            snake_case__  :    Tuple    						=      name.replace("""attn"""     ,   """attention.self"""		)
      if "norm1" in name:
            snake_case__  :    List[str]    						=      name.replace("""norm1"""     ,   """layernorm_before"""		)
      if "norm2" in name:
            snake_case__  :    Optional[Any]    						=      name.replace("""norm2"""     ,   """layernorm_after"""		)
      if "mlp.fc1" in name:
            snake_case__  :    Union[str, Any]    						=      name.replace("""mlp.fc1"""     ,   """intermediate.dense"""		)
      if "mlp.fc2" in name:
            snake_case__  :    Dict    						=      name.replace("""mlp.fc2"""     ,   """output.dense"""		)

      if name == "norm.weight":
            snake_case__  :    Tuple    						=      """layernorm.weight"""
      if name == "norm.bias":
            snake_case__  :    Union[str, Any]    						=      """layernorm.bias"""

      if "head" in name:
            snake_case__  :    Optional[int]    						=      name.replace("""head"""     ,   """classifier"""		)
      else:
            snake_case__  :    List[str]    						=      """swin.""" + name

      return name





def 						__snake_case(				_lowerCAmelCase     ,   _lowerCAmelCase		)		-> Optional[Any]:
      for key in orig_state_dict.copy().keys():
            snake_case__  :    Optional[int]    						=      orig_state_dict.pop(_lowerCAmelCase		)

            if "mask" in key:
                  continue
            elif "qkv" in key:
                  snake_case__  :    Dict    						=      key.split("""."""		)
                  snake_case__  :    Optional[int]    						=      int(key_split[1]		)
                  snake_case__  :    Union[str, Any]    						=      int(key_split[3]		)
                  snake_case__  :    List[Any]    						=      model.swin.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

                  if "weight" in key:
                        snake_case__  :    Optional[Any]    						=      val[:dim, :]
                        snake_case__  :    Tuple    						=      val[
                            dim : dim * 2, :
                        ]
                        snake_case__  :    Dict    						=      val[-dim:, :]
                  else:
                        snake_case__  :    Tuple    						=      val[
                            :dim
                        ]
                        snake_case__  :    int    						=      val[
                            dim : dim * 2
                        ]
                        snake_case__  :    int    						=      val[
                            -dim:
                        ]
            else:
                  snake_case__  :    Union[str, Any]    						=      val

      return orig_state_dict





def 						__snake_case(				_lowerCAmelCase     ,   _lowerCAmelCase		)		-> int:
      snake_case__  :    Optional[int]    						=      timm.create_model(_lowerCAmelCase     ,   pretrained=_lowerCAmelCase		)
      timm_model.eval()

      snake_case__  :    Optional[int]    						=      get_swin_config(_lowerCAmelCase		)
      snake_case__  :    Optional[Any]    						=      SwinForImageClassification(_lowerCAmelCase		)
      model.eval()

      snake_case__  :    str    						=      convert_state_dict(timm_model.state_dict()     ,   _lowerCAmelCase		)
      model.load_state_dict(_lowerCAmelCase		)

      snake_case__  :    int    						=      """http://images.cocodataset.org/val2017/000000039769.jpg"""

      snake_case__  :    Dict    						=      AutoImageProcessor.from_pretrained("""microsoft/{}""".format(swin_name.replace("""_"""     ,   """-"""		)		)		)
      snake_case__  :    Dict    						=      Image.open(requests.get(_lowerCAmelCase     ,   stream=_lowerCAmelCase		).raw		)
      snake_case__  :    Optional[int]    						=      image_processor(images=_lowerCAmelCase     ,   return_tensors="""pt"""		)

      snake_case__  :    Optional[Any]    						=      timm_model(inputs["""pixel_values"""]		)
      snake_case__  :    str    						=      model(**_lowerCAmelCase		).logits

      assert torch.allclose(_lowerCAmelCase     ,   _lowerCAmelCase     ,   atol=1e-3		)

      print(f"Saving model {swin_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 __name__ == "__main__":
       __a          =		argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           "--swin_name",
           default="swin_tiny_patch4_window7_224",
           type=str,
           help="Name of the Swin timm model you'd like to convert.",
       )
       parser.add_argument(
           "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
       )

       __a          =		parser.parse_args()
       convert_swin_checkpoint(args.swin_name, args.pytorch_dump_folder_path)
