import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

from seqaseq_trainer import SeqaSeqTrainer
from seqaseq_training_args import SeqaSeqTrainingArguments

import transformers
from transformers import (
    AutoConfig,
    AutoModelForSeqaSeqLM,
    AutoTokenizer,
    HfArgumentParser,
    MBartTokenizer,
    MBartTokenizerFast,
    set_seed,
)
from transformers.trainer_utils import EvaluationStrategy, is_main_process
from transformers.training_args import ParallelMode
from utils import (
    SeqaSeqDataCollator,
    SeqaSeqDataset,
    assert_all_frozen,
    build_compute_metrics_fn,
    check_output_dir,
    freeze_embeds,
    freeze_params,
    lmap,
    save_json,
    use_task_specific_params,
    write_txt_file,
)


SCREAMING_SNAKE_CASE :	Union[str, Any]							     =		logging.getLogger(__name__)
@dataclass
class    A_ :
  _SCREAMING_SNAKE_CASE    				=			field(
      metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""} )
  _SCREAMING_SNAKE_CASE    				=			field(
      default=a_			,       metadata={"""help""": """Pretrained config name or path if not the same as model_name"""} )
  _SCREAMING_SNAKE_CASE    				=			field(
      default=a_			,       metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""} )
  _SCREAMING_SNAKE_CASE    				=			field(
      default=a_			,       metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""}			,       )
  _SCREAMING_SNAKE_CASE    				=			field(default=a_			,       metadata={"""help""": """Whether tp freeze the encoder."""} )
  _SCREAMING_SNAKE_CASE    				=			field(default=a_			,       metadata={"""help""": """Whether  to freeze the embeddings."""} )
@dataclass
class    A_ :
  _SCREAMING_SNAKE_CASE    				=			field(
      metadata={"""help""": """The input data dir. Should contain the .tsv files (or other data files) for the task."""} )
  _SCREAMING_SNAKE_CASE    				=			field(
      default="""summarization"""			,       metadata={"""help""": """Task name, summarization (or summarization_{dataset} for pegasus) or translation"""}			,       )
  _SCREAMING_SNAKE_CASE    				=			field(
      default=10_24			,       metadata={
          """help""": (
              """The maximum total input sequence length after tokenization. Sequences longer """
              """than this will be truncated, sequences shorter will be padded."""
          )
      }			,       )
  _SCREAMING_SNAKE_CASE    				=			field(
      default=1_28			,       metadata={
          """help""": (
              """The maximum total sequence length for target text after tokenization. Sequences longer """
              """than this will be truncated, sequences shorter will be padded."""
          )
      }			,       )
  _SCREAMING_SNAKE_CASE    				=			field(
      default=1_42			,       metadata={
          """help""": (
              """The maximum total sequence length for validation target text after tokenization. Sequences longer """
              """than this will be truncated, sequences shorter will be padded. """
              """This argument is also used to override the ``max_length`` param of ``model.generate``, which is used """
              """during ``evaluate`` and ``predict``."""
          )
      }			,       )
  _SCREAMING_SNAKE_CASE    				=			field(
      default=1_42			,       metadata={
          """help""": (
              """The maximum total sequence length for test target text after tokenization. Sequences longer """
              """than this will be truncated, sequences shorter will be padded."""
          )
      }			,       )
  _SCREAMING_SNAKE_CASE    				=			field(default=-1			,       metadata={"""help""": """# training examples. -1 means use all."""} )
  _SCREAMING_SNAKE_CASE    				=			field(default=-1			,       metadata={"""help""": """# validation examples. -1 means use all."""} )
  _SCREAMING_SNAKE_CASE    				=			field(default=-1			,       metadata={"""help""": """# test examples. -1 means use all."""} )
  _SCREAMING_SNAKE_CASE    				=			field(default=a_			,       metadata={"""help""": """Source language id for translation."""} )
  _SCREAMING_SNAKE_CASE    				=			field(default=a_			,       metadata={"""help""": """Target language id for translation."""} )
  _SCREAMING_SNAKE_CASE    				=			field(default=a_			,       metadata={"""help""": """# num_beams to use for evaluation."""} )
  _SCREAMING_SNAKE_CASE    				=			field(
      default=a_			,       metadata={"""help""": """If only pad tokens should be ignored. This assumes that `config.pad_token_id` is defined."""}			,       )



def   __A		(			_A							,				_A							,				_A					):



 """simple docstring"""


 logger.info(f"""***** {split} metrics *****"""					)
 for key in sorted(metrics.keys()					):
  logger.info(f"""  {key} = {metrics[key]}"""					)
 save_json(_A							,				os.path.join(_A							,				f"""{split}_results.json"""					)					)



def   __A		(			):



 """simple docstring"""


 __a       =  HfArgumentParser((ModelArguments, DataTrainingArguments, SeqaSeqTrainingArguments)					)

 if len(sys.argv					) == 2 and sys.argv[1].endswith(".json"					):
  # If we pass only one argument to the script and it's the path to a json file,
  # let's parse it to get our arguments.
  __a       ,     __a       ,     __a       =  parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]					)					)
 else:
  __a       ,     __a       ,     __a       =  parser.parse_args_into_dataclasses()

 check_output_dir(_A					)

 # Setup logging
 logging.basicConfig(
     format="%(asctime)s - %(levelname)s - %(name)s - %(message)s"							,				datefmt="%m/%d/%Y %H:%M:%S"							,				level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN							,				)
 logger.warning(
     "Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s"							,				training_args.local_rank							,				training_args.device							,				training_args.n_gpu							,				bool(training_args.parallel_mode == ParallelMode.DISTRIBUTED					)							,				training_args.fpaa							,				)
 transformers.utils.logging.enable_default_handler()
 transformers.utils.logging.enable_explicit_format()
 # Set the verbosity to info of the Transformers logger (on main process only):
 if is_main_process(training_args.local_rank					):
  transformers.utils.logging.set_verbosity_info()
 logger.info("Training/evaluation parameters %s"							,				_A					)

 # Set seed
 set_seed(training_args.seed					)

 # Load pretrained model and tokenizer
 #
 # Distributed training:
 # The .from_pretrained methods guarantee that only one local process can concurrently
 # download model & vocab.

 __a       =  AutoConfig.from_pretrained(
     model_args.config_name if model_args.config_name else model_args.model_name_or_path							,				cache_dir=model_args.cache_dir							,				)

 __a       =  ("encoder_layerdrop", "decoder_layerdrop", "dropout", "attention_dropout")
 for p in extra_model_params:
  if getattr(_A							,				_A							,				_A					):
   assert hasattr(_A							,				_A					), f"""({config.__class__.__name__}) doesn't have a `{p}` attribute"""
   setattr(_A							,				_A							,				getattr(_A							,				_A					)					)

 __a       =  AutoTokenizer.from_pretrained(
     model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path							,				cache_dir=model_args.cache_dir							,				)
 __a       =  AutoModelForSeqaSeqLM.from_pretrained(
     model_args.model_name_or_path							,				from_tf=".ckpt" in model_args.model_name_or_path							,				config=_A							,				cache_dir=model_args.cache_dir							,				)

 # use task specific params
 use_task_specific_params(_A							,				data_args.task					)

 # set num_beams for evaluation
 if data_args.eval_beams is None:
  __a       =  model.config.num_beams

 # set decoder_start_token_id for MBart
 if model.config.decoder_start_token_id is None and isinstance(_A							,				(MBartTokenizer, MBartTokenizerFast)					):
  assert (
      data_args.tgt_lang is not None and data_args.src_lang is not None
  ), "mBart requires --tgt_lang and --src_lang"
  if isinstance(_A							,				_A					):
   __a       =  tokenizer.lang_code_to_id[data_args.tgt_lang]
  else:
   __a       =  tokenizer.convert_tokens_to_ids(data_args.tgt_lang					)

 if model_args.freeze_embeds:
  freeze_embeds(_A					)
 if model_args.freeze_encoder:
  freeze_params(model.get_encoder()					)
  assert_all_frozen(model.get_encoder()					)

 __a       =  SeqaSeqDataset

 # Get datasets
 __a       =  (
     dataset_class(
         _A							,				type_path="train"							,				data_dir=data_args.data_dir							,				n_obs=data_args.n_train							,				max_target_length=data_args.max_target_length							,				max_source_length=data_args.max_source_length							,				prefix=model.config.prefix or ""							,				)
     if training_args.do_train
     else None
 )
 __a       =  (
     dataset_class(
         _A							,				type_path="val"							,				data_dir=data_args.data_dir							,				n_obs=data_args.n_val							,				max_target_length=data_args.val_max_target_length							,				max_source_length=data_args.max_source_length							,				prefix=model.config.prefix or ""							,				)
     if training_args.do_eval or training_args.evaluation_strategy != EvaluationStrategy.NO
     else None
 )
 __a       =  (
     dataset_class(
         _A							,				type_path="test"							,				data_dir=data_args.data_dir							,				n_obs=data_args.n_test							,				max_target_length=data_args.test_max_target_length							,				max_source_length=data_args.max_source_length							,				prefix=model.config.prefix or ""							,				)
     if training_args.do_predict
     else None
 )

 # Initialize our Trainer
 __a       =  (
     build_compute_metrics_fn(data_args.task							,				_A					) if training_args.predict_with_generate else None
 )
 __a       =  SeqaSeqTrainer(
     model=_A							,				args=_A							,				data_args=_A							,				train_dataset=_A							,				eval_dataset=_A							,				data_collator=SeqaSeqDataCollator(
         _A							,				_A							,				model.config.decoder_start_token_id							,				training_args.tpu_num_cores					)							,				compute_metrics=_A							,				tokenizer=_A							,				)

 __a       =  {}
 # Training
 if training_args.do_train:
  logger.info("*** Train ***"					)

  __a       =  trainer.train(
      model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path					) else None					)
  __a       =  train_result.metrics
  __a       =  data_args.n_train

  trainer.save_model()  # this also saves the tokenizer

  if trainer.is_world_process_zero():
   handle_metrics("train"							,				_A							,				training_args.output_dir					)
   all_metrics.update(_A					)

   # Need to save the state, since Trainer.save_model saves only the tokenizer with the model
   trainer.state.save_to_json(os.path.join(training_args.output_dir							,				"trainer_state.json"					)					)

   # For convenience, we also re-save the tokenizer to the same directory,
   # so that you can share your model easily on huggingface.co/models =)
   tokenizer.save_pretrained(training_args.output_dir					)

    # Evaluation
 if training_args.do_eval:
  logger.info("*** Evaluate ***"					)

  __a       =  trainer.evaluate(metric_key_prefix="val"					)
  __a       =  data_args.n_val
  __a       =  round(metrics["val_loss"]							,				4					)

  if trainer.is_world_process_zero():
   handle_metrics("val"							,				_A							,				training_args.output_dir					)
   all_metrics.update(_A					)

 if training_args.do_predict:
  logger.info("*** Predict ***"					)

  __a       =  trainer.predict(test_dataset=_A							,				metric_key_prefix="test"					)
  __a       =  test_output.metrics
  __a       =  data_args.n_test

  if trainer.is_world_process_zero():
   __a       =  round(metrics["test_loss"]							,				4					)
   handle_metrics("test"							,				_A							,				training_args.output_dir					)
   all_metrics.update(_A					)

   if training_args.predict_with_generate:
    __a       =  tokenizer.batch_decode(
        test_output.predictions							,				skip_special_tokens=_A							,				clean_up_tokenization_spaces=_A					)
    __a       =  lmap(str.strip							,				_A					)
    write_txt_file(_A							,				os.path.join(training_args.output_dir							,				"test_generations.txt"					)					)

 if trainer.is_world_process_zero():
  save_json(_A							,				os.path.join(training_args.output_dir							,				"all_results.json"					)					)

 return all_metrics



def   __A		(			_A					):



 """simple docstring"""


 main()


if __name__ == "__main__":
  main()



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 BeitConfig, BeitForImageClassification, BeitForMaskedImageModeling, BeitImageProcessor
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE :	str							     =		logging.get_logger(__name__)



def   __A		(			_A							,				_A=False							,				_A=False					):



 """simple docstring"""


 __a       =  "backbone." if is_semantic else ""

 __a       =  []
 for i in range(config.num_hidden_layers					):
  # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
  rename_keys.append((f"""{prefix}blocks.{i}.norm1.weight""", f"""beit.encoder.layer.{i}.layernorm_before.weight""")					)
  rename_keys.append((f"""{prefix}blocks.{i}.norm1.bias""", f"""beit.encoder.layer.{i}.layernorm_before.bias""")					)
  rename_keys.append(
      (f"""{prefix}blocks.{i}.attn.proj.weight""", f"""beit.encoder.layer.{i}.attention.output.dense.weight""")					)
  rename_keys.append(
      (f"""{prefix}blocks.{i}.attn.proj.bias""", f"""beit.encoder.layer.{i}.attention.output.dense.bias""")					)
  rename_keys.append((f"""{prefix}blocks.{i}.norm2.weight""", f"""beit.encoder.layer.{i}.layernorm_after.weight""")					)
  rename_keys.append((f"""{prefix}blocks.{i}.norm2.bias""", f"""beit.encoder.layer.{i}.layernorm_after.bias""")					)
  rename_keys.append((f"""{prefix}blocks.{i}.mlp.fc1.weight""", f"""beit.encoder.layer.{i}.intermediate.dense.weight""")					)
  rename_keys.append((f"""{prefix}blocks.{i}.mlp.fc1.bias""", f"""beit.encoder.layer.{i}.intermediate.dense.bias""")					)
  rename_keys.append((f"""{prefix}blocks.{i}.mlp.fc2.weight""", f"""beit.encoder.layer.{i}.output.dense.weight""")					)
  rename_keys.append((f"""{prefix}blocks.{i}.mlp.fc2.bias""", f"""beit.encoder.layer.{i}.output.dense.bias""")					)

 # projection layer + position embeddings
 rename_keys.extend(
     [
         (f"""{prefix}cls_token""", "beit.embeddings.cls_token"),
         (f"""{prefix}patch_embed.proj.weight""", "beit.embeddings.patch_embeddings.projection.weight"),
         (f"""{prefix}patch_embed.proj.bias""", "beit.embeddings.patch_embeddings.projection.bias"),
         (f"""{prefix}pos_embed""", "beit.embeddings.position_embeddings"),
     ]					)

 if has_lm_head:
  # mask token + layernorm
  rename_keys.extend(
      [
          ("mask_token", "beit.embeddings.mask_token"),
          ("norm.weight", "layernorm.weight"),
          ("norm.bias", "layernorm.bias"),
      ]					)
 else:
  # layernorm + classification head
  rename_keys.extend(
      [
          ("fc_norm.weight", "beit.pooler.layernorm.weight"),
          ("fc_norm.bias", "beit.pooler.layernorm.bias"),
          ("head.weight", "classifier.weight"),
          ("head.bias", "classifier.bias"),
      ]					)

 return rename_keys



def   __A		(			_A							,				_A							,				_A=False							,				_A=False					):



 """simple docstring"""


 for i in range(config.num_hidden_layers					):
  __a       =  "backbone." if is_semantic else ""
  # queries, keys and values
  __a       =  state_dict.pop(f"""{prefix}blocks.{i}.attn.qkv.weight"""					)
  __a       =  state_dict.pop(f"""{prefix}blocks.{i}.attn.q_bias"""					)
  __a       =  state_dict.pop(f"""{prefix}blocks.{i}.attn.v_bias"""					)

  __a       =  in_proj_weight[
      : config.hidden_size, :
  ]
  __a       =  q_bias
  __a       =  in_proj_weight[
      config.hidden_size : config.hidden_size * 2, :
  ]
  __a       =  in_proj_weight[
      -config.hidden_size :, :
  ]
  __a       =  v_bias

  # gamma_1 and gamma_2
  # we call them lambda because otherwise they are renamed when using .from_pretrained
  __a       =  state_dict.pop(f"""{prefix}blocks.{i}.gamma_1"""					)
  __a       =  state_dict.pop(f"""{prefix}blocks.{i}.gamma_2"""					)

  __a       =  gamma_a
  __a       =  gamma_a



def   __A		(			_A							,				_A							,				_A					):



 """simple docstring"""


 __a       =  dct.pop(_A					)
 __a       =  val



def   __A		(			):



 """simple docstring"""


 __a       =  "http://images.cocodataset.org/val2017/000000039769.jpg"
 __a       =  Image.open(requests.get(_A							,				stream=_A					).raw					)
 return im



@torch.no_grad()
def   __A		(			_A							,				_A							,				_A=False					):



 """simple docstring"""


 __a       =  False if "rvlcdip" in checkpoint_url else True
 __a       =  BeitConfig(use_absolute_position_embeddings=_A							,				use_mask_token=_A					)

 # size of the architecture
 if "large" in checkpoint_url or "dit-l" in checkpoint_url:
  __a       =  1024
  __a       =  4096
  __a       =  24
  __a       =  16

 # labels
 if "rvlcdip" in checkpoint_url:
  __a       =  16
  __a       =  "huggingface/label-files"
  __a       =  "rvlcdip-id2label.json"
  __a       =  json.load(open(hf_hub_download(_A							,				_A							,				repo_type="dataset"					)							,				"r"					)					)
  __a       =  {int(_A					): v for k, v in idalabel.items()}
  __a       =  idalabel
  __a       =  {v: k for k, v in idalabel.items()}

 # load state_dict of original model, remove and rename some keys
 __a       =  torch.hub.load_state_dict_from_url(_A							,				map_location="cpu"					)["model"]

 __a       =  create_rename_keys(_A							,				has_lm_head=_A					)
 for src, dest in rename_keys:
  rename_key(_A							,				_A							,				_A					)
 read_in_q_k_v(_A							,				_A							,				has_lm_head=_A					)

 # load HuggingFace model
 __a       =  BeitForMaskedImageModeling(_A					) if has_lm_head else BeitForImageClassification(_A					)
 model.eval()
 model.load_state_dict(_A					)

 # Check outputs on an image
 __a       =  BeitImageProcessor(
     size=config.image_size							,				resample=PILImageResampling.BILINEAR							,				do_center_crop=_A					)
 __a       =  prepare_img()

 __a       =  image_processor(images=_A							,				return_tensors="pt"					)
 __a       =  encoding["pixel_values"]

 __a       =  model(_A					)
 __a       =  outputs.logits

 # verify logits
 __a       =  [1, 16] if "rvlcdip" in checkpoint_url else [1, 196, 8192]
 assert logits.shape == torch.Size(_A					), "Shape 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 image processor to {pytorch_dump_folder_path}"""					)
 image_processor.save_pretrained(_A					)

 if push_to_hub:
  if has_lm_head:
   __a       =  "dit-base" if "base" in checkpoint_url else "dit-large"
  else:
   __a       =  "dit-base-finetuned-rvlcdip" if "dit-b" in checkpoint_url else "dit-large-finetuned-rvlcdip"
  image_processor.push_to_hub(
      repo_path_or_name=Path(_A							,				_A					)							,				organization="nielsr"							,				commit_message="Add image processor"							,				use_temp_dir=_A							,				)
  model.push_to_hub(
      repo_path_or_name=Path(_A							,				_A					)							,				organization="nielsr"							,				commit_message="Add model"							,				use_temp_dir=_A							,				)


if __name__ == "__main__":
  SCREAMING_SNAKE_CASE :	int							     =		argparse.ArgumentParser()

  parser.add_argument(
      """--checkpoint_url""",
      default="""https://layoutlm.blob.core.windows.net/dit/dit-pts/dit-base-224-p16-500k-62d53a.pth""",
      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."""
  )
  parser.add_argument(
      """--push_to_hub""",
      action="""store_true""",
  )
  SCREAMING_SNAKE_CASE :	Union[str, Any]							     =		parser.parse_args()
  convert_dit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)


