

import argparse
import shutil
import time
from json import JSONDecodeError
from logging import getLogger
from pathlib import Path
from typing import Dict, List

import torch
from torch.utils.data import DataLoader
from tqdm import tqdm

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import (
    SeqaSeqDataset,
    calculate_bleu,
    calculate_rouge,
    chunks,
    lmap,
    load_json,
    parse_numeric_n_bool_cl_kwargs,
    save_json,
    use_task_specific_params,
    write_txt_file,
)


__UpperCAmelCase								=			getLogger(__name__)





def      snake_case_				(__A					:						List[Any] ,       __A					:						str ,       __A					:						str ,       __A					:						int = 8 ,       __A					:						int = 1_0_2_4 ,       __A					:						Any="val" ,       __A					:						Union[str, Any]=None ,       __A					:						Tuple=False ,       __A					:						Any="summarization" ,       __A					:						Any=None ,       __A					:						int=1 ,       __A					:						Dict = None ,       __A					:						int="" ,       **__A					:						Optional[Any] ,       )      ->     Dict:
   __lowerCAmelCase  :      List[Any]    =						str(__A					)
   assert local_rank is not None
   torch.distributed.init_process_group(backend="""nccl""" ,       rank=__A					)

   __lowerCAmelCase  :      int    =						Path(__A					)
   __lowerCAmelCase  :      List[str]    =						save_dir.joinpath(f'''rank_{local_rank}_output.json'''					)
   torch.cuda.set_device(__A					)
   __lowerCAmelCase  :      List[str]    =						AutoModelForSeqaSeqLM.from_pretrained(__A					).cuda()
   if fpaa:
      __lowerCAmelCase  :      Dict    =						model.half()
   # determine if we need to increase num_beams
   use_task_specific_params(__A ,       __A					)  # update config with task specific params
   __lowerCAmelCase  :      Tuple    =						generate_kwargs.pop("""num_beams""" ,       model.config.num_beams					)  # AttributeError risk?
   if num_return_sequences > num_beams:
      __lowerCAmelCase  :      Tuple    =						num_return_sequences

   __lowerCAmelCase  :      List[str]    =						AutoTokenizer.from_pretrained(__A					)
   logger.info(f'''Inferred tokenizer type: {tokenizer.__class__}'''					)  # if this is wrong, check config.model_type.

   if max_source_length is None:
      __lowerCAmelCase  :      Optional[Any]    =						tokenizer.model_max_length
   if prefix is None:
      __lowerCAmelCase  :      Any    =						prefix or getattr(model.config ,       """prefix""" ,       """"""					) or """"""
   __lowerCAmelCase  :      List[Any]    =						SeqaSeqDataset(
       __A ,       __A ,       __A ,       max_target_length=1_0_2_4 ,       type_path=__A ,       n_obs=__A ,       prefix=__A ,       **__A ,       )
   # I set shuffle=True for a more accurate progress bar.
   # If all the longest samples are first, the prog bar estimate is too high at the beginning.
   __lowerCAmelCase  :      Any    =						ds.make_sortish_sampler(__A ,       distributed=__A ,       add_extra_examples=__A ,       shuffle=__A					)
   __lowerCAmelCase  :      int    =						DataLoader(__A ,       sampler=__A ,       batch_size=__A ,       collate_fn=ds.collate_fn					)
   __lowerCAmelCase  :      str    =						[]
   for batch in tqdm(__A					):
      __lowerCAmelCase  :      str    =						model.generate(
          input_ids=batch["""input_ids"""].to(model.device					) ,       attention_mask=batch["""attention_mask"""].to(model.device					) ,       num_return_sequences=__A ,       num_beams=__A ,       **__A ,       )
      __lowerCAmelCase  :      Dict    =						tokenizer.batch_decode(__A ,       skip_special_tokens=__A ,       clean_up_tokenization_spaces=__A					)
      __lowerCAmelCase  :      Any    =						batch["""ids"""]
      if num_return_sequences > 1:
         __lowerCAmelCase  :      int    =						chunks(__A ,       __A					)  # batch size chunks, each of size num_return_seq
      for i, pred in enumerate(__A					):
         results.append({"""pred""": pred, """id""": ids[i].item()}					)
   save_json(__A ,       __A					)
   return results, sampler.num_replicas





def      snake_case_				()      ->     List[Any]:
   __lowerCAmelCase  :      List[Any]    =						argparse.ArgumentParser(
       epilog="""Unspecified args like --num_beams=2 --decoder_start_token_id=4 are passed to model.generate"""					)
   parser.add_argument("""--data_dir""" ,       type=__A ,       help="""like cnn_dm/test.source"""					)
   parser.add_argument(
       """--model_name""" ,       type=__A ,       help="""like facebook/bart-large-cnn,t5-base, etc.""" ,       default="""sshleifer/distilbart-xsum-12-3""" ,       )
   parser.add_argument("""--save_dir""" ,       type=__A ,       help="""where to save""" ,       default="""tmp_gen"""					)
   parser.add_argument("""--max_source_length""" ,       type=__A ,       default=__A					)
   parser.add_argument(
       """--type_path""" ,       type=__A ,       default="""test""" ,       help="""which subset to evaluate typically train/val/test"""					)
   parser.add_argument("""--task""" ,       type=__A ,       default="""summarization""" ,       help="""used for task_specific_params + metrics"""					)
   parser.add_argument("""--bs""" ,       type=__A ,       default=8 ,       required=__A ,       help="""batch size"""					)
   parser.add_argument(
       """--local_rank""" ,       type=__A ,       default=-1 ,       required=__A ,       help="""should be passed by distributed.launch"""					)

   parser.add_argument(
       """--n_obs""" ,       type=__A ,       default=__A ,       required=__A ,       help="""How many observations. Defaults to all."""					)
   parser.add_argument(
       """--num_return_sequences""" ,       type=__A ,       default=1 ,       required=__A ,       help="""How many sequences to return"""					)
   parser.add_argument(
       """--sync_timeout""" ,       type=__A ,       default=6_0_0 ,       required=__A ,       help="""How long should master process wait for other processes to finish.""" ,       )
   parser.add_argument("""--src_lang""" ,       type=__A ,       default=__A ,       required=__A					)
   parser.add_argument("""--tgt_lang""" ,       type=__A ,       default=__A ,       required=__A					)
   parser.add_argument(
       """--prefix""" ,       type=__A ,       required=__A ,       default=__A ,       help="""will be added to the begininng of src examples"""					)
   parser.add_argument("""--fp16""" ,       action="""store_true"""					)
   parser.add_argument("""--debug""" ,       action="""store_true"""					)
   __lowerCAmelCase  :      int    =						time.time()
   __lowerCAmelCase   ,__lowerCAmelCase  :      int    =						parser.parse_known_args()
   __lowerCAmelCase  :      List[Any]    =						parse_numeric_n_bool_cl_kwargs(__A					)
   if generate_kwargs and args.local_rank <= 0:
      print(f'''parsed the following generate kwargs: {generate_kwargs}'''					)
   __lowerCAmelCase  :      List[Any]    =						Path(args.save_dir + """_tmp"""					)
   Path(__A					).mkdir(exist_ok=__A					)  # this handles locking.
   __lowerCAmelCase  :      int    =						list(json_save_dir.glob("""rank_*.json"""					)					)
   if intermediate_files:
      raise ValueError(f'''Found files at {json_save_dir} please move or remove them.'''					)
      # In theory, a node could finish and save before another node hits this. If this happens, we can address later.
   __lowerCAmelCase  :      List[Any]    =						{}
   if args.src_lang is not None:
      __lowerCAmelCase  :      List[Any]    =						args.src_lang
   if args.tgt_lang is not None:
      __lowerCAmelCase  :      List[str]    =						args.tgt_lang

   Path(args.save_dir					).mkdir(exist_ok=__A					)
   __lowerCAmelCase   ,__lowerCAmelCase  :      Any    =						eval_data_dir(
       args.data_dir ,       __A ,       args.model_name ,       type_path=args.type_path ,       bs=args.bs ,       fpaa=args.fpaa ,       task=args.task ,       local_rank=args.local_rank ,       n_obs=args.n_obs ,       max_source_length=args.max_source_length ,       num_return_sequences=args.num_return_sequences ,       prefix=args.prefix ,       dataset_kwargs=__A ,       **__A ,       )

   if args.local_rank <= 0:
      __lowerCAmelCase  :      List[Any]    =						Path(args.save_dir					)
      save_dir.mkdir(exist_ok=__A					)
      __lowerCAmelCase  :      Optional[Any]    =						gather_results_from_each_node(__A ,       __A ,       args.sync_timeout					)
      __lowerCAmelCase  :      List[str]    =						combine_partial_results(__A					)
      if args.num_return_sequences > 1:
         __lowerCAmelCase  :      List[str]    =						save_dir.joinpath("""pseudolabel_results.json"""					)
         print(f'''Saving aggregated results at {save_path}, intermediate in {json_save_dir}/'''					)
         save_json(__A ,       __A					)
         return
      __lowerCAmelCase  :      Union[str, Any]    =						Path(args.data_dir					).joinpath(args.type_path + """.target"""					)
      with open(__A					) as f:
         __lowerCAmelCase  :      int    =						[x.rstrip() for x in f.readlines()][: len(__A					)]

      # Calculate metrics, save metrics,  and save _generations.txt
      __lowerCAmelCase  :      List[str]    =						"""translation""" in args.task
      __lowerCAmelCase  :      List[str]    =						calculate_bleu if calc_bleu else calculate_rouge
      __lowerCAmelCase  :      Any    =						"""bleu""" if calc_bleu else """rouge"""
      __lowerCAmelCase  :      Dict    =						score_fn(__A ,       __A					)
      __lowerCAmelCase  :      int    =						len(__A					)
      __lowerCAmelCase  :      Optional[Any]    =						time.time() - start_time
      __lowerCAmelCase  :      Optional[Any]    =						round(runtime / metrics["""n_obs"""] ,       4					)
      __lowerCAmelCase  :      List[Any]    =						num_replicas
      # TODO(@stas00): add whatever metadata to metrics
      __lowerCAmelCase  :      str    =						save_dir.joinpath(f'''{args.type_path}_{metric_name}.json'''					)
      save_json(__A ,       __A ,       indent=__A					)
      print(__A					)
      write_txt_file(__A ,       save_dir.joinpath(f'''{args.type_path}_generations.txt'''					)					)
      if args.debug:
         write_txt_file(__A ,       save_dir.joinpath(f'''{args.type_path}.target'''					)					)
      else:
         shutil.rmtree(__A					)





def      snake_case_				(__A					:						Union[str, Any]					)      ->     List:
   __lowerCAmelCase  :      Union[str, Any]    =						[]
   for partial_result in partial_results:
      records.extend(__A					)
   __lowerCAmelCase  :      Any    =						sorted(__A ,       key=lambda __A					: x["id"]					)
   __lowerCAmelCase  :      Any    =						[x["""pred"""] for x in records]
   return preds





def      snake_case_				(__A					:						Optional[int] ,       __A					:						int ,       __A					:						Union[str, Any]					)      ->     List[Dict[str, List]]:
   # WAIT FOR lots of .json files
   __lowerCAmelCase  :      Optional[Any]    =						time.time()
   logger.info("""waiting for all nodes to finish"""					)
   __lowerCAmelCase  :      int    =						None
   while (time.time() - start_wait) < timeout:
      __lowerCAmelCase  :      int    =						list(save_dir.glob("""rank_*.json"""					)					)
      if len(__A					) < num_replicas:
         continue
      try:
         # make sure all json files are fully saved
         __lowerCAmelCase  :      Optional[int]    =						lmap(__A ,       __A					)
         return json_data
      except JSONDecodeError:
         continue
   else:
      raise TimeoutError("""Rank 0 gave up on waiting for other processes"""					)
   # Unreachable


if __name__ == "__main__":
   # Usage for MT:
   run_generate()





import argparse
from pathlib import Path

import requests
import torch
from PIL import Image

from transformers import (
    RobertaTokenizer,
    TrOCRConfig,
    TrOCRForCausalLM,
    TrOCRProcessor,
    VisionEncoderDecoderModel,
    ViTConfig,
    ViTImageProcessor,
    ViTModel,
)
from transformers.utils import logging


logging.set_verbosity_info()
__UpperCAmelCase								=			logging.get_logger(__name__)





def      snake_case_				(__A					:						Optional[int] ,       __A					:						Any					)      ->     Any:
   __lowerCAmelCase  :      Union[str, Any]    =						[]
   for i in range(encoder_config.num_hidden_layers					):
      # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
      rename_keys.append(
          (f'''encoder.deit.blocks.{i}.norm1.weight''', f'''encoder.encoder.layer.{i}.layernorm_before.weight''')					)
      rename_keys.append((f'''encoder.deit.blocks.{i}.norm1.bias''', f'''encoder.encoder.layer.{i}.layernorm_before.bias''')					)
      rename_keys.append(
          (f'''encoder.deit.blocks.{i}.attn.proj.weight''', f'''encoder.encoder.layer.{i}.attention.output.dense.weight''')					)
      rename_keys.append(
          (f'''encoder.deit.blocks.{i}.attn.proj.bias''', f'''encoder.encoder.layer.{i}.attention.output.dense.bias''')					)
      rename_keys.append(
          (f'''encoder.deit.blocks.{i}.norm2.weight''', f'''encoder.encoder.layer.{i}.layernorm_after.weight''')					)
      rename_keys.append((f'''encoder.deit.blocks.{i}.norm2.bias''', f'''encoder.encoder.layer.{i}.layernorm_after.bias''')					)
      rename_keys.append(
          (f'''encoder.deit.blocks.{i}.mlp.fc1.weight''', f'''encoder.encoder.layer.{i}.intermediate.dense.weight''')					)
      rename_keys.append(
          (f'''encoder.deit.blocks.{i}.mlp.fc1.bias''', f'''encoder.encoder.layer.{i}.intermediate.dense.bias''')					)
      rename_keys.append(
          (f'''encoder.deit.blocks.{i}.mlp.fc2.weight''', f'''encoder.encoder.layer.{i}.output.dense.weight''')					)
      rename_keys.append((f'''encoder.deit.blocks.{i}.mlp.fc2.bias''', f'''encoder.encoder.layer.{i}.output.dense.bias''')					)

   # cls token, position embeddings and patch embeddings of encoder
   rename_keys.extend(
       [
           ("""encoder.deit.cls_token""", """encoder.embeddings.cls_token"""),
           ("""encoder.deit.pos_embed""", """encoder.embeddings.position_embeddings"""),
           ("""encoder.deit.patch_embed.proj.weight""", """encoder.embeddings.patch_embeddings.projection.weight"""),
           ("""encoder.deit.patch_embed.proj.bias""", """encoder.embeddings.patch_embeddings.projection.bias"""),
           ("""encoder.deit.norm.weight""", """encoder.layernorm.weight"""),
           ("""encoder.deit.norm.bias""", """encoder.layernorm.bias"""),
       ]					)

   return rename_keys





def      snake_case_				(__A					:						List[str] ,       __A					:						str					)      ->     Optional[Any]:
   for i in range(encoder_config.num_hidden_layers					):
      # queries, keys and values (only weights, no biases)
      __lowerCAmelCase  :      Optional[Any]    =						state_dict.pop(f'''encoder.deit.blocks.{i}.attn.qkv.weight'''					)

      __lowerCAmelCase  :      Tuple    =						in_proj_weight[
          : encoder_config.hidden_size, :
      ]
      __lowerCAmelCase  :      str    =						in_proj_weight[
          encoder_config.hidden_size : encoder_config.hidden_size * 2, :
      ]
      __lowerCAmelCase  :      str    =						in_proj_weight[
          -encoder_config.hidden_size :, :
      ]





def      snake_case_				(__A					:						Union[str, Any] ,       __A					:						str ,       __A					:						Optional[Any]					)      ->     Optional[Any]:
   __lowerCAmelCase  :      Any    =						dct.pop(__A					)
   __lowerCAmelCase  :      str    =						val





def      snake_case_				(__A					:						int					)      ->     Tuple:
   if "handwritten" in checkpoint_url:
      __lowerCAmelCase  :      Tuple    =						"""https://fki.tic.heia-fr.ch/static/img/a01-122-02-00.jpg"""  # industry
      # url = "https://fki.tic.heia-fr.ch/static/img/a01-122-02-12.jpg" # have
      # url = "https://fki.tic.heia-fr.ch/static/img/a01-122-02-10.jpg" # let
      # url = "https://fki.tic.heia-fr.ch/static/img/a01-122-02.jpg"  #
      # url = "https://fki.tic.heia-fr.ch/static/img/a01-122.jpg"
   elif "printed" in checkpoint_url or "stage1" in checkpoint_url:
      __lowerCAmelCase  :      Optional[Any]    =						"""https://www.researchgate.net/profile/Dinh-Sang/publication/338099565/figure/fig8/AS:840413229350922@1577381536857/An-receipt-example-in-the-SROIE-2019-dataset_Q640.jpg"""
   __lowerCAmelCase  :      Dict    =						Image.open(requests.get(__A ,       stream=__A					).raw					).convert("""RGB"""					)
   return im





@torch.no_grad()
def      snake_case_				(__A					:						Any ,       __A					:						Union[str, Any]					)      ->     Optional[int]:
   __lowerCAmelCase  :      List[Any]    =						ViTConfig(image_size=3_8_4 ,       qkv_bias=__A					)
   __lowerCAmelCase  :      List[Any]    =						TrOCRConfig()

   # size of the architecture
   if "base" in checkpoint_url:
      __lowerCAmelCase  :      Union[str, Any]    =						7_6_8
   elif "large" in checkpoint_url:
      # use ViT-large encoder
      __lowerCAmelCase  :      Any    =						1_0_2_4
      __lowerCAmelCase  :      Any    =						4_0_9_6
      __lowerCAmelCase  :      Optional[int]    =						2_4
      __lowerCAmelCase  :      str    =						1_6
      __lowerCAmelCase  :      List[Any]    =						1_0_2_4
   else:
      raise ValueError("""Should either find 'base' or 'large' in checkpoint URL"""					)

   # the large-printed + stage1 checkpoints uses sinusoidal position embeddings, no layernorm afterwards
   if "large-printed" in checkpoint_url or "stage1" in checkpoint_url:
      __lowerCAmelCase  :      Tuple    =						False
      __lowerCAmelCase  :      Union[str, Any]    =						"""relu"""
      __lowerCAmelCase  :      List[Any]    =						1_0_2_4
      __lowerCAmelCase  :      Any    =						True
      __lowerCAmelCase  :      List[Any]    =						False
      __lowerCAmelCase  :      Dict    =						False

   # load HuggingFace model
   __lowerCAmelCase  :      Dict    =						ViTModel(__A ,       add_pooling_layer=__A					)
   __lowerCAmelCase  :      Union[str, Any]    =						TrOCRForCausalLM(__A					)
   __lowerCAmelCase  :      Any    =						VisionEncoderDecoderModel(encoder=__A ,       decoder=__A					)
   model.eval()

   # load state_dict of original model, rename some keys
   __lowerCAmelCase  :      Union[str, Any]    =						torch.hub.load_state_dict_from_url(__A ,       map_location="""cpu""" ,       check_hash=__A					)["""model"""]

   __lowerCAmelCase  :      Any    =						create_rename_keys(__A ,       __A					)
   for src, dest in rename_keys:
      rename_key(__A ,       __A ,       __A					)
   read_in_q_k_v(__A ,       __A					)

   # remove parameters we don't need
   del state_dict["encoder.deit.head.weight"]
   del state_dict["encoder.deit.head.bias"]
   del state_dict["decoder.version"]

   # add prefix to decoder keys
   for key, val in state_dict.copy().items():
      __lowerCAmelCase  :      Tuple    =						state_dict.pop(__A					)
      if key.startswith("""decoder"""					) and "output_projection" not in key:
         __lowerCAmelCase  :      str    =						val
      else:
         __lowerCAmelCase  :      Tuple    =						val

    # load state dict
   model.load_state_dict(__A					)

   # Check outputs on an image
   __lowerCAmelCase  :      List[Any]    =						ViTImageProcessor(size=encoder_config.image_size					)
   __lowerCAmelCase  :      List[str]    =						RobertaTokenizer.from_pretrained("""roberta-large"""					)
   __lowerCAmelCase  :      List[Any]    =						TrOCRProcessor(__A ,       __A					)

   __lowerCAmelCase  :      List[str]    =						processor(images=prepare_img(__A					) ,       return_tensors="""pt"""					).pixel_values

   # verify logits
   __lowerCAmelCase  :      List[str]    =						torch.tensor([[model.config.decoder.decoder_start_token_id]]					)
   __lowerCAmelCase  :      List[str]    =						model(pixel_values=__A ,       decoder_input_ids=__A					)
   __lowerCAmelCase  :      Optional[Any]    =						outputs.logits

   __lowerCAmelCase  :      Union[str, Any]    =						torch.Size([1, 1, 5_0_2_6_5]					)
   if "trocr-base-handwritten" in checkpoint_url:
      __lowerCAmelCase  :      Dict    =						torch.tensor(
          [-1.4502, -4.6683, -0.5347, -2.9291, 9.1435, -3.0571, 8.9764, 1.7560, 8.7358, -1.5311]					)
   elif "trocr-large-handwritten" in checkpoint_url:
      __lowerCAmelCase  :      List[Any]    =						torch.tensor(
          [-2.6437, -1.3129, -2.2596, -5.3455, 6.3539, 1.7604, 5.4991, 1.4702, 5.6113, 2.0170]					)
   elif "trocr-base-printed" in checkpoint_url:
      __lowerCAmelCase  :      Tuple    =						torch.tensor(
          [-5.6816, -5.8388, 1.1398, -6.9034, 6.8505, -2.4393, 1.2284, -1.0232, -1.9661, -3.9210]					)
   elif "trocr-large-printed" in checkpoint_url:
      __lowerCAmelCase  :      List[Any]    =						torch.tensor(
          [-6.0162, -7.0959, 4.4155, -5.1063, 7.0468, -3.1631, 2.6466, -0.3081, -0.8106, -1.7535]					)

   if "stage1" not in checkpoint_url:
      assert logits.shape == expected_shape, "Shape of logits not as expected"
      assert torch.allclose(logits[0, 0, :1_0] ,       __A ,       atol=1e-3					), "First elements of logits not as expected"

   Path(__A					).mkdir(exist_ok=__A					)
   print(f'''Saving model to {pytorch_dump_folder_path}'''					)
   model.save_pretrained(__A					)
   print(f'''Saving processor to {pytorch_dump_folder_path}'''					)
   processor.save_pretrained(__A					)


if __name__ == "__main__":
   __UpperCAmelCase								=			argparse.ArgumentParser()

   parser.add_argument(
       """--checkpoint_url""",
       default="""https://layoutlm.blob.core.windows.net/trocr/model_zoo/fairseq/trocr-base-handwritten.pt""",
       type=str,
       help="""URL to the original PyTorch checkpoint (.pth file).""",
   )
   parser.add_argument(
       """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the folder to output PyTorch model."""
   )
   __UpperCAmelCase								=			parser.parse_args()
   convert_tr_ocr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)


