"""simple docstring"""


import argparse
import logging
import os
import sys
import tempfile
from pathlib import Path

import lightning_base
import pytest
import pytorch_lightning as pl
import torch
from convert_pl_checkpoint_to_hf import convert_pl_to_hf
from distillation import distill_main
from finetune import SummarizationModule, main
from huggingface_hub import list_models
from parameterized import parameterized
from run_eval import generate_summaries_or_translations
from torch import nn

from transformers import AutoConfig, AutoModelForSeqaSeqLM
from transformers.testing_utils import CaptureStderr, CaptureStdout, TestCasePlus, require_torch_gpu, slow
from utils import label_smoothed_nll_loss, lmap, load_json


logging.basicConfig(level=logging.DEBUG)

__A       			=      logging.getLogger()
__A       			=      torch.cuda.is_available()
__A       			=      {
    """max_tokens_per_batch""": None,
    """supervise_forward""": True,
    """normalize_hidden""": True,
    """label_smoothing""": 0.2,
    """eval_max_gen_length""": None,
    """eval_beams""": 1,
    """val_metric""": """loss""",
    """save_top_k""": 1,
    """adafactor""": True,
    """early_stopping_patience""": 2,
    """logger_name""": """default""",
    """length_penalty""": 0.5,
    """cache_dir""": """""",
    """task""": """summarization""",
    """num_workers""": 2,
    """alpha_hid""": 0,
    """freeze_embeds""": True,
    """enc_only""": False,
    """tgt_suffix""": """""",
    """resume_from_checkpoint""": None,
    """sortish_sampler""": True,
    """student_decoder_layers""": 1,
    """val_check_interval""": 1.0,
    """output_dir""": """""",
    """fp16""": False,  # TODO(SS): set this to CUDA_AVAILABLE if ci installs apex or start using native amp
    """no_teacher""": False,
    """fp16_opt_level""": """O1""",
    """gpus""": 1 if CUDA_AVAILABLE else 0,
    """n_tpu_cores""": 0,
    """max_grad_norm""": 1.0,
    """do_train""": True,
    """do_predict""": True,
    """accumulate_grad_batches""": 1,
    """server_ip""": """""",
    """server_port""": """""",
    """seed""": 42,
    """model_name_or_path""": """sshleifer/bart-tiny-random""",
    """config_name""": """""",
    """tokenizer_name""": """facebook/bart-large""",
    """do_lower_case""": False,
    """learning_rate""": 0.3,
    """lr_scheduler""": """linear""",
    """weight_decay""": 0.0,
    """adam_epsilon""": 1E-08,
    """warmup_steps""": 0,
    """max_epochs""": 1,
    """train_batch_size""": 2,
    """eval_batch_size""": 2,
    """max_source_length""": 12,
    """max_target_length""": 12,
    """val_max_target_length""": 12,
    """test_max_target_length""": 12,
    """fast_dev_run""": False,
    """no_cache""": False,
    """n_train""": -1,
    """n_val""": -1,
    """n_test""": -1,
    """student_encoder_layers""": 1,
    """freeze_encoder""": False,
    """auto_scale_batch_size""": False,
    """overwrite_output_dir""": False,
    """student""": None,
}

def 						UpperCamelCase				(	_lowerCAmelCase   :							Path				, _lowerCAmelCase   :							list						):
    __a			      =						"""\n""".join(_lowerCAmelCase						)
    Path(_lowerCAmelCase						).open("""w"""						).writelines(_lowerCAmelCase						)


__A       			=      [""" Sam ate lunch today.""", """Sams lunch ingredients."""]
__A       			=      ["""A very interesting story about what I ate for lunch.""", """Avocado, celery, turkey, coffee"""]
__A       			=      """patrickvonplaten/t5-tiny-random"""
__A       			=      """sshleifer/t5-tinier-random"""
__A       			=      """sshleifer/bart-tiny-random"""
__A       			=      """sshleifer/tiny-mbart"""
__A       			=      """sshleifer/tiny-marian-en-de"""
__A       			=      """stas/tiny-wmt19-en-de"""


__A       			=      logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
logging.disable(logging.CRITICAL)  # remove noisy download output from tracebacks

def 						UpperCamelCase				(	_lowerCAmelCase   :							List[Any]						):
    for split in ["train", "val", "test"]:
        _dump_articles(os.path.join(_lowerCAmelCase				, f"""{split}.source"""						)				, _lowerCAmelCase						)
        _dump_articles(os.path.join(_lowerCAmelCase				, f"""{split}.target"""						)				, _lowerCAmelCase						)
    return tmp_dir






class 					a					(				A_							):



  @classmethod
  def        lowerCAmelCase_				(		cls   :    Optional[Any]     )      -> Optional[int]:
      logging.disable(logging.CRITICAL     )  # remove noisy download output from tracebacks
      return cls



  @slow
  @require_torch_gpu
  def        lowerCAmelCase_				(		self   :    List[str]     )      -> str:
      __a			      =						list_models()
      __a			      =						"""sshleifer"""
      __a			      =						[x.modelId for x in model_list if x.modelId.startswith(lowerCamelCase_     )]
      __a			      =						["""sshleifer/blenderbot-3B""", """sshleifer/blenderbot-90M"""]
      __a			      =						[]
      for m in model_ids:
          if m in allowed_to_be_broken:
              continue
          try:
              AutoConfig.from_pretrained(lowerCamelCase_     )
          except Exception:
              failures.append(lowerCamelCase_     )
      assert not failures, F"""The following models could not be loaded through AutoConfig: {failures}"""



  def        lowerCAmelCase_				(		self   :    Any     )      -> Dict:
      __a			      =						{"""student_encoder_layers""": 2, """student_decoder_layers""": 1, """no_teacher""": True}
      self._test_distiller_cli(lowerCamelCase_     )



  def        lowerCAmelCase_				(		self   :    Dict     )      -> Any:
      __a			      =						{
          """student_encoder_layers""": 2,
          """student_decoder_layers""": 1,
          """max_epochs""": 4,
          """val_check_interval""": 0.25,
          """alpha_hid""": 2.0,
          """model_name_or_path""": """IGNORE_THIS_IT_DOESNT_GET_USED""",
      }
      __a			      =						self._test_distiller_cli(lowerCamelCase_						,  check_contents=lowerCamelCase_     )

      __a			      =						list(Path(model.output_dir     ).glob("""*.ckpt"""     )     )
      self.assertEqual(1						,  len(lowerCamelCase_     )     )
      __a			      =						list(Path(model.output_dir     ).glob("""**/*.bin"""     )     )
      self.assertEqual(len(lowerCamelCase_     )						,  2     )
      __a			      =						lmap(str.strip						,  Path(model.hparams.data_dir     ).joinpath("""test.source"""     ).open().readlines()     )
      __a			      =						tempfile.mktemp()  # XXX: not being cleaned up
      generate_summaries_or_translations(lowerCamelCase_						,  lowerCamelCase_						,  str(model.output_dir / """best_tfmr"""     )     )
      self.assertTrue(Path(lowerCamelCase_     ).exists()     )

      __a			      =						self.get_auto_remove_tmp_dir()
      convert_pl_to_hf(ckpts[0]						,  transformer_ckpts[0].parent						,  lowerCamelCase_     )
      assert os.path.exists(os.path.join(lowerCamelCase_						,  """pytorch_model.bin"""     )     )



  def        lowerCAmelCase_				(		self   :    Optional[int]     )      -> Any:
      __a			      =						AutoModelForSeqaSeqLM.from_pretrained(lowerCamelCase_     )
      __a      ,  __a			      =						model.dummy_inputs["""input_ids"""], model.dummy_inputs["""attention_mask"""]
      __a			      =						torch.tensor([[0, 4, 8, 2], [0, 8, 2, 1]]						,  dtype=torch.long						,  device=model.device     )
      __a			      =						target_ids[:, :-1].contiguous()  # Why this line?
      __a			      =						target_ids[:, 1:].clone()  # why clone?
      __a			      =						model(
          lowerCamelCase_						,  attention_mask=lowerCamelCase_						,  decoder_input_ids=lowerCamelCase_						,  labels=lowerCamelCase_						,  use_cache=lowerCamelCase_     ).loss

      __a			      =						model(lowerCamelCase_						,  attention_mask=lowerCamelCase_						,  decoder_input_ids=lowerCamelCase_						,  use_cache=lowerCamelCase_     ).logits

      __a			      =						nn.functional.log_softmax(lowerCamelCase_						,  dim=-1     )
      __a      ,  __a			      =						label_smoothed_nll_loss(
          lowerCamelCase_						,  lowerCamelCase_						,  0.1						,  ignore_index=model.config.pad_token_id     )
      with self.assertRaises(lowerCamelCase_     ):
          # TODO: understand why this breaks
          self.assertEqual(lowerCamelCase_						,  lowerCamelCase_     )



  def        lowerCAmelCase_				(		self   :    str     )      -> int:
      __a			      =						{
          """student_encoder_layers""": 2,
          """student_decoder_layers""": 1,
          """num_train_epochs""": 4,
          """val_check_interval""": 0.25,
          """alpha_hid""": 2.0,
          """task""": """translation""",
          """model_name_or_path""": """IGNORE_THIS_IT_DOESNT_GET_USED""",
          """tokenizer_name""": MBART_TINY,
          """teacher""": MBART_TINY,
          """src_lang""": """en_XX""",
          """tgt_lang""": """ro_RO""",
      }
      __a			      =						self._test_distiller_cli(lowerCamelCase_						,  check_contents=lowerCamelCase_     )
      assert model.model.config.model_type == "mbart"

      __a			      =						list(Path(model.output_dir     ).glob("""*.ckpt"""     )     )
      self.assertEqual(1						,  len(lowerCamelCase_     )     )
      __a			      =						list(Path(model.output_dir     ).glob("""**/*.bin"""     )     )
      __a			      =						list(Path(model.output_dir     ).glob("""best_tfmr/*"""     )     )
      assert len(lowerCamelCase_     ) > 2
      self.assertEqual(len(lowerCamelCase_     )						,  2     )



  def        lowerCAmelCase_				(		self   :    Dict     )      -> Union[str, Any]:
      __a			      =						{
          """student_encoder_layers""": 1,
          """student_decoder_layers""": 1,
          """alpha_hid""": 2.0,
          """teacher""": T5_TINY,
          """model_name_or_path""": T5_TINY,
          """tokenizer_name""": T5_TINY,
      }
      self._test_distiller_cli(lowerCamelCase_     )



  def        lowerCAmelCase_				(		self   :    List[str]     )      -> List[str]:
      __a			      =						{
          """teacher""": T5_TINY,
          """student""": T5_TINIER,
          """model_name_or_path""": T5_TINIER,
          """tokenizer_name""": T5_TINIER,
      }
      self._test_distiller_cli(lowerCamelCase_     )



  def        lowerCAmelCase_				(		self   :    int						,  lowerCamelCase_   :    List[Any]						,  lowerCamelCase_   :    Union[str, Any]=True     )      -> Optional[Any]:
      __a			      =						{
          """label_smoothing""": 0.0,
          """early_stopping_patience""": -1,
          """train_batch_size""": 1,
          """eval_batch_size""": 2,
          """max_epochs""": 2,
          """alpha_mlm""": 0.2,
          """alpha_ce""": 0.8,
          """do_predict""": True,
          """model_name_or_path""": """sshleifer/tinier_bart""",
          """teacher""": CHEAP_ARGS["""model_name_or_path"""],
          """val_check_interval""": 0.5,
      }
      default_updates.update(lowerCamelCase_     )
      __a			      =						CHEAP_ARGS.copy()
      __a			      =						make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()     )
      __a			      =						self.get_auto_remove_tmp_dir()

      args_d.update(data_dir=lowerCamelCase_						,  output_dir=lowerCamelCase_						,  **lowerCamelCase_     )
      __a			      =						distill_main(argparse.Namespace(**lowerCamelCase_     )     )
      if not check_contents:
          return model
      __a			      =						os.listdir(lowerCamelCase_     )
      __a			      =						{os.path.basename(lowerCamelCase_     ) for p in contents}
      __a			      =						[p for p in contents if p.endswith("""ckpt"""     )]
      assert len(lowerCamelCase_     ) > 0

      self.assertIn("""test_generations.txt"""						,  lowerCamelCase_     )
      self.assertIn("""test_results.txt"""						,  lowerCamelCase_     )

      __a			      =						load_json(model.metrics_save_path     )
      __a			      =						metrics["""val"""][-1]
      self.assertGreaterEqual(last_step_stats["""val_avg_gen_time"""]						,  0.01     )
      self.assertGreaterEqual(1.0						,  last_step_stats["""val_avg_gen_time"""]     )
      self.assertIsInstance(last_step_stats[F"""val_avg_{model.val_metric}"""]						,  lowerCamelCase_     )
      __a			      =						int(args_d["""max_epochs"""] * (1 / args_d["""val_check_interval"""]) + 1     )
      self.assertEqual(len(metrics["""val"""]     )						,  lowerCamelCase_     )
      self.assertEqual(len(metrics["""test"""]     )						,  1     )
      return model






class 					a					(				A_							):



  @parameterized.expand(
      [T5_TINY, BART_TINY, MBART_TINY, MARIAN_TINY, FSMT_TINY]						,  )
  def        lowerCAmelCase_				(		self   :    List[Any]						,  lowerCamelCase_   :    Optional[Any]     )      -> Dict:
      __a			      =						CHEAP_ARGS.copy()
      __a			      =						"""translation""" if model in [MBART_TINY, MARIAN_TINY, FSMT_TINY] else """summarization"""
      __a			      =						0.1 if task == """translation""" else 0

      __a			      =						make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()     )
      __a			      =						self.get_auto_remove_tmp_dir()
      args_d.update(
          data_dir=lowerCamelCase_						,  model_name_or_path=lowerCamelCase_						,  tokenizer_name=lowerCamelCase_						,  train_batch_size=2						,  eval_batch_size=2						,  output_dir=lowerCamelCase_						,  do_predict=lowerCamelCase_						,  task=lowerCamelCase_						,  src_lang="""en_XX"""						,  tgt_lang="""ro_RO"""						,  freeze_encoder=lowerCamelCase_						,  freeze_embeds=lowerCamelCase_						,  )
      assert "n_train" in args_d
      __a			      =						argparse.Namespace(**lowerCamelCase_     )
      __a			      =						main(lowerCamelCase_     )

      __a			      =						module.model.get_input_embeddings()
      assert not input_embeds.weight.requires_grad
      if model == T5_TINY:
          __a			      =						module.model.lm_head
          assert not lm_head.weight.requires_grad
          assert (lm_head.weight == input_embeds.weight).all().item()
      elif model == FSMT_TINY:
          __a			      =						module.model.model
          __a			      =						fsmt.decoder.embed_positions
          assert not embed_pos.weight.requires_grad
          assert not fsmt.decoder.embed_tokens.weight.requires_grad
          # check that embeds are not the same
          assert fsmt.decoder.embed_tokens != fsmt.encoder.embed_tokens
      else:
          __a			      =						module.model.model
          __a			      =						bart.decoder.embed_positions
          assert not embed_pos.weight.requires_grad
          assert not bart.shared.weight.requires_grad
          # check that embeds are the same
          assert bart.decoder.embed_tokens == bart.encoder.embed_tokens
          assert bart.decoder.embed_tokens == bart.shared

      __a			      =						load_json(module.output_dir / """text_batch.json"""     )
      assert isinstance(lowerCamelCase_						,  lowerCamelCase_     )
      assert len(lowerCamelCase_     ) >= 4



  def        lowerCAmelCase_				(		self   :    Dict     )      -> Tuple:
      __a			      =						CHEAP_ARGS.copy()

      __a			      =						"""summarization"""
      __a			      =						make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()     )

      args_d.update(
          data_dir=lowerCamelCase_						,  tokenizer_name=lowerCamelCase_						,  train_batch_size=2						,  eval_batch_size=2						,  do_predict=lowerCamelCase_						,  task=lowerCamelCase_						,  src_lang="""en_XX"""						,  tgt_lang="""ro_RO"""						,  freeze_encoder=lowerCamelCase_						,  freeze_embeds=lowerCamelCase_						,  )

      # test models whose config includes the extra_model_args
      __a			      =						BART_TINY
      __a			      =						self.get_auto_remove_tmp_dir()
      __a			      =						args_d.copy()
      args_da.update(
          model_name_or_path=lowerCamelCase_						,  output_dir=lowerCamelCase_						,  )
      __a			      =						("""encoder_layerdrop""", """decoder_layerdrop""", """dropout""", """attention_dropout""")
      for p in extra_model_params:
          __a			      =						0.5
      __a			      =						argparse.Namespace(**lowerCamelCase_     )
      __a			      =						main(lowerCamelCase_     )
      for p in extra_model_params:
          assert getattr(model.config						,  lowerCamelCase_     ) == 0.5, F"""failed to override the model config for param {p}"""

      # test models whose config doesn't include the extra_model_args
      __a			      =						T5_TINY
      __a			      =						self.get_auto_remove_tmp_dir()
      __a			      =						args_d.copy()
      args_da.update(
          model_name_or_path=lowerCamelCase_						,  output_dir=lowerCamelCase_						,  )
      __a			      =						"""encoder_layerdrop"""
      __a			      =						0.5
      __a			      =						argparse.Namespace(**lowerCamelCase_     )
      with pytest.raises(lowerCamelCase_     ) as excinfo:
          __a			      =						main(lowerCamelCase_     )
      assert str(excinfo.value     ) == F"""model config doesn't have a `{unsupported_param}` attribute"""



  def        lowerCAmelCase_				(		self   :    int     )      -> int:
      __a			      =						CHEAP_ARGS.copy()

      __a			      =						"""summarization"""
      __a			      =						make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()     )

      __a			      =						BART_TINY
      __a			      =						self.get_auto_remove_tmp_dir()

      args_d.update(
          data_dir=lowerCamelCase_						,  model_name_or_path=lowerCamelCase_						,  output_dir=lowerCamelCase_						,  tokenizer_name=lowerCamelCase_						,  train_batch_size=2						,  eval_batch_size=2						,  do_predict=lowerCamelCase_						,  task=lowerCamelCase_						,  src_lang="""en_XX"""						,  tgt_lang="""ro_RO"""						,  freeze_encoder=lowerCamelCase_						,  freeze_embeds=lowerCamelCase_						,  )

      # emulate finetune.py
      __a			      =						argparse.ArgumentParser()
      __a			      =						pl.Trainer.add_argparse_args(lowerCamelCase_     )
      __a			      =						SummarizationModule.add_model_specific_args(lowerCamelCase_						,  os.getcwd()     )
      __a			      =						{"""--help""": True}

      # --help test
      with pytest.raises(lowerCamelCase_     ) as excinfo:
          with CaptureStdout() as cs:
              __a			      =						parser.parse_args(lowerCamelCase_     )
          assert False, "--help is expected to sys.exit"
      assert excinfo.type == SystemExit
      __a			      =						lightning_base.arg_to_scheduler_metavar
      assert expected in cs.out, "--help is expected to list the supported schedulers"

      # --lr_scheduler=non_existing_scheduler test
      __a			      =						"""non_existing_scheduler"""
      __a			      =						{F"""--lr_scheduler={unsupported_param}"""}
      with pytest.raises(lowerCamelCase_     ) as excinfo:
          with CaptureStderr() as cs:
              __a			      =						parser.parse_args(lowerCamelCase_     )
          assert False, "invalid argument is expected to sys.exit"
      assert excinfo.type == SystemExit
      __a			      =						F"""invalid choice: '{unsupported_param}'"""
      assert expected in cs.err, F"""should have bailed on invalid choice of scheduler {unsupported_param}"""

      # --lr_scheduler=existing_scheduler test
      __a			      =						"""cosine"""
      __a			      =						args_d.copy()
      __a			      =						supported_param
      __a			      =						argparse.Namespace(**lowerCamelCase_     )
      __a			      =						main(lowerCamelCase_     )
      assert (
          getattr(model.hparams						,  """lr_scheduler"""     ) == supported_param
      ), F"""lr_scheduler={supported_param} shouldn't fail"""

"""simple docstring"""


import argparse
import json

import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm.models import create_model

from transformers import (
    BeitImageProcessor,
    DataaVecVisionConfig,
    DataaVecVisionForImageClassification,
    DataaVecVisionModel,
)

def 						UpperCamelCase				(	_lowerCAmelCase   :							List[str]				, _lowerCAmelCase   :							Optional[int]=False				, _lowerCAmelCase   :							Union[str, Any]=False				, _lowerCAmelCase   :							Any="data2vec."						):
    __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"""{hf_prefix}encoder.layer.{i}.layernorm_before.weight""")						)
        rename_keys.append((f"""{prefix}blocks.{i}.norm1.bias""", f"""{hf_prefix}encoder.layer.{i}.layernorm_before.bias""")						)
        rename_keys.append(
            (f"""{prefix}blocks.{i}.attn.proj.weight""", f"""{hf_prefix}encoder.layer.{i}.attention.output.dense.weight""")						)
        rename_keys.append(
            (f"""{prefix}blocks.{i}.attn.proj.bias""", f"""{hf_prefix}encoder.layer.{i}.attention.output.dense.bias""")						)
        rename_keys.append(
            (f"""{prefix}blocks.{i}.norm2.weight""", f"""{hf_prefix}encoder.layer.{i}.layernorm_after.weight""")						)
        rename_keys.append((f"""{prefix}blocks.{i}.norm2.bias""", f"""{hf_prefix}encoder.layer.{i}.layernorm_after.bias""")						)
        rename_keys.append(
            (f"""{prefix}blocks.{i}.mlp.fc1.weight""", f"""{hf_prefix}encoder.layer.{i}.intermediate.dense.weight""")						)
        rename_keys.append(
            (f"""{prefix}blocks.{i}.mlp.fc1.bias""", f"""{hf_prefix}encoder.layer.{i}.intermediate.dense.bias""")						)
        rename_keys.append((f"""{prefix}blocks.{i}.mlp.fc2.weight""", f"""{hf_prefix}encoder.layer.{i}.output.dense.weight""")						)
        rename_keys.append((f"""{prefix}blocks.{i}.mlp.fc2.bias""", f"""{hf_prefix}encoder.layer.{i}.output.dense.bias""")						)

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

    if has_lm_head:
        # mask token + shared relative position bias + layernorm
        rename_keys.extend(
            [
                ("""mask_token""", f"""{hf_prefix}embeddings.mask_token"""),
                (
                    """rel_pos_bias.relative_position_bias_table""",
                    f"""{hf_prefix}encoder.relative_position_bias.relative_position_bias_table""",
                ),
                (
                    """rel_pos_bias.relative_position_index""",
                    f"""{hf_prefix}encoder.relative_position_bias.relative_position_index""",
                ),
                ("""norm.weight""", """layernorm.weight"""),
                ("""norm.bias""", """layernorm.bias"""),
            ]						)
    elif is_semantic:
        # semantic segmentation classification heads
        rename_keys.extend(
            [
                ("""decode_head.conv_seg.weight""", """decode_head.classifier.weight"""),
                ("""decode_head.conv_seg.bias""", """decode_head.classifier.bias"""),
                ("""auxiliary_head.conv_seg.weight""", """auxiliary_head.classifier.weight"""),
                ("""auxiliary_head.conv_seg.bias""", """auxiliary_head.classifier.bias"""),
            ]						)
    else:
        # layernorm + classification head
        rename_keys.extend(
            [
                ("""fc_norm.weight""", f"""{hf_prefix}pooler.layernorm.weight"""),
                ("""fc_norm.bias""", f"""{hf_prefix}pooler.layernorm.bias"""),
                ("""head.weight""", """classifier.weight"""),
                ("""head.bias""", """classifier.bias"""),
            ]						)

    return rename_keys

def 						UpperCamelCase				(	_lowerCAmelCase   :							str				, _lowerCAmelCase   :							Tuple				, _lowerCAmelCase   :							List[Any]=False				, _lowerCAmelCase   :							Optional[int]=False				, _lowerCAmelCase   :							Tuple="data2vec_vision."						):
    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

        # relative_position bias table + index
        if not has_lm_head:
            # each layer has its own relative position bias
            __a			      =						state_dict.pop(f"""{prefix}blocks.{i}.attn.relative_position_bias_table"""						)
            __a			      =						state_dict.pop(f"""{prefix}blocks.{i}.attn.relative_position_index"""						)

            __a			      =						table
            __a			      =						index

def 						UpperCamelCase				(	):
    __a			      =						argparse.ArgumentParser(
        """Convert Data2VecVision to HF for image classification and pretraining"""				, add_help=_lowerCAmelCase						)
    parser.add_argument("""--hf_checkpoint_name"""				, type=_lowerCAmelCase						)
    parser.add_argument("""--input_size"""				, default=224				, type=_lowerCAmelCase				, help="""images input size"""						)
    parser.add_argument("""--beit_checkpoint"""				, default=""""""				, help="""beit checkpoint"""						)

    return parser.parse_args()

def 						UpperCamelCase				(	_lowerCAmelCase   :							Optional[int]				, _lowerCAmelCase   :							Union[str, Any]				, _lowerCAmelCase   :							Union[str, Any]						):
    def load_state_dict(_lowerCAmelCase   :							Tuple				, _lowerCAmelCase   :							str				, _lowerCAmelCase   :							Union[str, Any]=""				, _lowerCAmelCase   :							Union[str, Any]="relative_position_index"						):
        __a			      =						[]
        __a			      =						[]
        __a			      =						[]
        # copy state_dict so _load_from_state_dict can modify it
        __a			      =						getattr(_lowerCAmelCase				, """_metadata"""				, _lowerCAmelCase						)
        __a			      =						state_dict.copy()
        if metadata is not None:
            __a			      =						metadata

        def load(_lowerCAmelCase   :							List[Any]				, _lowerCAmelCase   :							List[str]=""						):
            __a			      =						{} if metadata is None else metadata.get(prefix[:-1]				, {}						)
            module._load_from_state_dict(
                _lowerCAmelCase				, _lowerCAmelCase				, _lowerCAmelCase				, _lowerCAmelCase				, _lowerCAmelCase				, _lowerCAmelCase				, _lowerCAmelCase						)
            for name, child in module._modules.items():
                if child is not None:
                    load(_lowerCAmelCase				, prefix + name + """."""						)

        load(_lowerCAmelCase				, prefix=_lowerCAmelCase						)

        __a			      =						[]
        __a			      =						[]
        for key in missing_keys:
            __a			      =						True
            for ignore_key in ignore_missing.split("""|"""						):
                if ignore_key in key:
                    __a			      =						False
                    break
            if keep_flag:
                warn_missing_keys.append(_lowerCAmelCase						)
            else:
                ignore_missing_keys.append(_lowerCAmelCase						)

        __a			      =						warn_missing_keys

        if len(_lowerCAmelCase						) > 0:
            print(
                """Weights of {} not initialized from pretrained model: {}""".format(
                    model.__class__.__name__				, _lowerCAmelCase						)						)
        if len(_lowerCAmelCase						) > 0:
            print("""Weights from pretrained model not used in {}: {}""".format(model.__class__.__name__				, _lowerCAmelCase						)						)
        if len(_lowerCAmelCase						) > 0:
            print(
                """Ignored weights of {} not initialized from pretrained model: {}""".format(
                    model.__class__.__name__				, _lowerCAmelCase						)						)
        if len(_lowerCAmelCase						) > 0:
            print("""\n""".join(_lowerCAmelCase						)						)

    __a			      =						{
        """pretrained""": False,
        """use_shared_rel_pos_bias""": True,
        """use_abs_pos_emb""": False,
        """init_values""": 0.1,
    }

    if is_finetuned:
        model_kwargs.update(
            {
                """num_classes""": 1000,
                """use_mean_pooling""": True,
                """init_scale""": 0.001,
                """use_rel_pos_bias""": True,
            }						)

    __a			      =						create_model(
        """beit_large_patch16_224""" if is_large else """beit_base_patch16_224"""				, **_lowerCAmelCase				, )
    __a			      =						model.patch_embed.patch_size
    __a			      =						(args.input_size // patch_size[0], args.input_size // patch_size[1])
    __a			      =						torch.load(args.beit_checkpoint				, map_location="""cpu"""						)

    print(f"""Load ckpt from {args.beit_checkpoint}"""						)
    __a			      =						None
    for model_key in ("model", "module"):
        if model_key in checkpoint:
            __a			      =						checkpoint[model_key]
            print(f"""Load state_dict by model_key = {model_key}"""						)
            break

    __a			      =						list(checkpoint_model.keys()						)
    for key in all_keys:
        if "relative_position_index" in key:
            checkpoint_model.pop(_lowerCAmelCase						)

        if "relative_position_bias_table" in key:
            __a			      =						checkpoint_model[key]
            __a      ,  __a			      =						rel_pos_bias.size()
            __a      ,  __a			      =						model.state_dict()[key].size()
            __a			      =						model.patch_embed.patch_shape
            if dst_patch_shape[0] != dst_patch_shape[1]:
                raise NotImplementedError()

    load_state_dict(_lowerCAmelCase				, _lowerCAmelCase				, prefix=""""""						)

    return model

def 						UpperCamelCase				(	):
    __a			      =						get_args()

    __a			      =						"""ft1k""" in args.hf_checkpoint_name
    __a			      =						"""large""" in args.hf_checkpoint_name

    if is_finetuned:
        # To convert Beit's data2vec_vision to HF you need to copy
        # https://github.com/facebookresearch/data2vec_vision/blob/main/beit/modeling_finetune.py
        # into this folder.
        import modeling_finetune  # noqa: F401
    else:
        # To convert Beit's data2vec_vision to HF you need to copy
        # https://github.com/facebookresearch/data2vec_vision/blob/main/beit/modeling_cyclical.py
        # into this folder
        # IMPORTANT: Note that for now we've only converted the down-stream
        # model and not the full pretrained model. This means for the integration
        # test you need to add a `return x` after the following line:
        # https://github.com/facebookresearch/data2vec_vision/blob/af9a36349aaed59ae66e69b5dabeef2d62fdc5da/beit/modeling_cyclical.py#L197
        # to make the integration test pass.
        import modeling_cyclical  # noqa: F401

    # 1. Create model config
    __a			      =						DataaVecVisionConfig()
    if is_finetuned:
        __a			      =						True
        __a			      =						False
        __a			      =						True
        __a			      =						1000

        __a			      =						"""huggingface/label-files"""
        __a			      =						"""imagenet-1k-id2label.json"""
        __a			      =						json.load(open(hf_hub_download(_lowerCAmelCase				, _lowerCAmelCase				, repo_type="""dataset"""						)				, """r"""						)						)
        __a			      =						{int(_lowerCAmelCase						): v for k, v in idalabel.items()}
        __a			      =						idalabel
        __a			      =						{v: k for k, v in idalabel.items()}
    else:
        __a			      =						False
        __a			      =						True
        __a			      =						False

    if is_large:
        __a			      =						1024
        __a			      =						4096
        __a			      =						24
        __a			      =						16

    # 2. Load Beit model
    __a			      =						load_beit_model(_lowerCAmelCase				, _lowerCAmelCase				, _lowerCAmelCase						)
    orig_model.eval()

    # 3. Forward Beit model
    __a			      =						BeitImageProcessor(size=config.image_size				, do_center_crop=_lowerCAmelCase						)
    __a			      =						Image.open("""../../../../tests/fixtures/tests_samples/COCO/000000039769.png"""						)
    __a			      =						image_processor(images=_lowerCAmelCase				, return_tensors="""pt"""						)
    __a			      =						encoding["""pixel_values"""]

    __a			      =						(pixel_values,) if is_finetuned else (pixel_values, None)
    with torch.no_grad():
        __a			      =						orig_model(*_lowerCAmelCase						)

    # 4. Load HF Data2VecVision model
    if is_finetuned:
        __a			      =						DataaVecVisionForImageClassification(_lowerCAmelCase						)
        hf_model.eval()
        __a			      =						False
        __a			      =						"""data2vec_vision."""
    else:
        __a			      =						DataaVecVisionModel(_lowerCAmelCase						)
        hf_model.eval()
        __a			      =						True
        __a			      =						""""""

    __a			      =						create_rename_keys(_lowerCAmelCase				, hf_prefix=_lowerCAmelCase				, has_lm_head=_lowerCAmelCase						)
    __a			      =						orig_model.state_dict()
    for src, dest in rename_keys:
        __a			      =						state_dict.pop(_lowerCAmelCase						)
        __a			      =						val

    read_in_q_k_v(_lowerCAmelCase				, _lowerCAmelCase				, hf_prefix=_lowerCAmelCase				, has_lm_head=_lowerCAmelCase						)
    __a      ,  __a			      =						hf_model.load_state_dict(_lowerCAmelCase				, strict=_lowerCAmelCase						)
    print("""HF missing"""				, _lowerCAmelCase						)
    print("""HF unexpected_keys"""				, _lowerCAmelCase						)

    # 5. Forward HF Data2VecVision model
    with torch.no_grad():
        __a			      =						hf_model(_lowerCAmelCase						)

    __a			      =						hf_model_output.logits if is_finetuned else hf_model_output.last_hidden_state

    # 6. Compare
    __a			      =						torch.max(torch.abs(hf_output - orig_model_output						)						).item()

    print(f"""max_absolute_diff = {max_absolute_diff}"""						)
    __a			      =						torch.allclose(_lowerCAmelCase				, _lowerCAmelCase				, atol=1E-3						)
    print("""Do both models output the same tensors?"""				, """🔥""" if success else """💩"""						)
    if not success:
        raise Exception("""Something went wRoNg"""						)

    # 7. Save
    print(f"""Saving to {args.hf_checkpoint_name}"""						)
    hf_model.save_pretrained(args.hf_checkpoint_name						)
    image_processor.save_pretrained(args.hf_checkpoint_name						)


if __name__ == "__main__":
 main()
 # Run the following to convert checkpoints
 #  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
 #          --beit_checkpoint ./pretrained_base.pt \
 #          --hf_checkpoint_name "./data2vec-vision-base"
 #  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
 #          --beit_checkpoint ./finetuned_base.pt \
 #          --hf_checkpoint_name "./data2vec-vision-base-ft1k"
 #  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
 #          --beit_checkpoint ./pretrained_large.pt \
 #          --hf_checkpoint_name "./data2vec-vision-large"
 #  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
 #          --beit_checkpoint ./finetuned_large.pt \
 #          --hf_checkpoint_name "./data2vec-vision-large-ft1k"
