
'''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)

__lowerCAmelCase  							= logging.getLogger()
__lowerCAmelCase  							= torch.cuda.is_available()
__lowerCAmelCase  							= {
    "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       __SCREAMING_SNAKE_CASE   (						_SCREAMING_SNAKE_CASE							,      _SCREAMING_SNAKE_CASE   ):
							_snake_case     =						'''\n'''.join(snake_case_   )
							Path(snake_case_   ).open("""w"""   ).writelines(snake_case_   )


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


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






def       __SCREAMING_SNAKE_CASE   (						_SCREAMING_SNAKE_CASE   ):
							for split in ["train", "val", "test"]:
														_dump_articles(os.path.join(snake_case_							,      f"""{split}.source"""   )							,      snake_case_   )
														_dump_articles(os.path.join(snake_case_							,      f"""{split}.target"""   )							,      snake_case_   )
							return tmp_dir


class     _lowerCAmelCase					(					UpperCamelCase_   ):







							'''simple docstring'''

							@classmethod
							def  lowercase							(cls						)  ->     int:
														logging.disable(logging.CRITICAL						)  # remove noisy download output from tracebacks
														return cls

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

							def  lowercase							(self						)  ->     Dict:
														_snake_case     =						{'''student_encoder_layers''': 2, '''student_decoder_layers''': 1, '''no_teacher''': True}
														self._test_distiller_cli(__UpperCamelCase						)

							def  lowercase							(self						)  ->     Any:
														_snake_case     =						{
														    '''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''',
														}
														_snake_case     =						self._test_distiller_cli(__UpperCamelCase						,						check_contents=__UpperCamelCase						)

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

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

							def  lowercase							(self						)  ->     List[str]:
														_snake_case     =						AutoModelForSeqaSeqLM.from_pretrained(__UpperCamelCase						)
														_snake_case     =						model.dummy_inputs['''input_ids'''], model.dummy_inputs['''attention_mask''']
														_snake_case     =						torch.tensor([[0, 4, 8, 2], [0, 8, 2, 1]]						,						dtype=torch.long						,						device=model.device						)
														_snake_case     =						target_ids[:, :-1].contiguous()  # Why this line?
														_snake_case     =						target_ids[:, 1:].clone()  # why clone?
														_snake_case     =						model(
														    __UpperCamelCase						,						attention_mask=__UpperCamelCase						,						decoder_input_ids=__UpperCamelCase						,						labels=__UpperCamelCase						,						use_cache=__UpperCamelCase						).loss

														_snake_case     =						model(__UpperCamelCase						,						attention_mask=__UpperCamelCase						,						decoder_input_ids=__UpperCamelCase						,						use_cache=__UpperCamelCase						).logits

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

							def  lowercase							(self						)  ->     Tuple:
														_snake_case     =						{
														    '''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''',
														}
														_snake_case     =						self._test_distiller_cli(__UpperCamelCase						,						check_contents=__UpperCamelCase						)
														assert model.model.config.model_type == "mbart"

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

							def  lowercase							(self						)  ->     Tuple:
														_snake_case     =						{
														    '''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(__UpperCamelCase						)

							def  lowercase							(self						)  ->     Optional[Any]:
														_snake_case     =						{
														    '''teacher''': T5_TINY,
														    '''student''': T5_TINIER,
														    '''model_name_or_path''': T5_TINIER,
														    '''tokenizer_name''': T5_TINIER,
														}
														self._test_distiller_cli(__UpperCamelCase						)





							def  lowercase							(self						,						UpperCAmelCase						,						UpperCAmelCase=True						)  ->     List[str]:
														_snake_case     =						{
														    '''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(__UpperCamelCase						)
														_snake_case     =						CHEAP_ARGS.copy()
														_snake_case     =						make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()						)
														_snake_case     =						self.get_auto_remove_tmp_dir()

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

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

														_snake_case     =						load_json(model.metrics_save_path						)
														_snake_case     =						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}"""]						,						__UpperCamelCase						)
														_snake_case     =						int(args_d["""max_epochs"""] * (1 / args_d["""val_check_interval"""]) + 1						)
														self.assertEqual(len(metrics["""val"""]						)						,						__UpperCamelCase						)
														self.assertEqual(len(metrics["""test"""]						)						,						1						)
														return model


class     _lowerCAmelCase					(					UpperCamelCase_   ):







							'''simple docstring'''

							@parameterized.expand(
							    [T5_TINY, BART_TINY, MBART_TINY, MARIAN_TINY, FSMT_TINY]						,						)
							def  lowercase							(self						,						UpperCAmelCase						)  ->     str:
														_snake_case     =						CHEAP_ARGS.copy()
														_snake_case     =						'''translation''' if model in [MBART_TINY, MARIAN_TINY, FSMT_TINY] else '''summarization'''
														_snake_case     =						0.1 if task == '''translation''' else 0

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

														_snake_case     =						module.model.get_input_embeddings()
														assert not input_embeds.weight.requires_grad
														if model == T5_TINY:
																					_snake_case     =						module.model.lm_head
																					assert not lm_head.weight.requires_grad
																					assert (lm_head.weight == input_embeds.weight).all().item()
														elif model == FSMT_TINY:
																					_snake_case     =						module.model.model
																					_snake_case     =						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:
																					_snake_case     =						module.model.model
																					_snake_case     =						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

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

							def  lowercase							(self						)  ->     Union[str, Any]:
														_snake_case     =						CHEAP_ARGS.copy()

														_snake_case     =						'''summarization'''
														_snake_case     =						make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()						)

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

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

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





							def  lowercase							(self						)  ->     Optional[int]:
														_snake_case     =						CHEAP_ARGS.copy()

														_snake_case     =						'''summarization'''
														_snake_case     =						make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()						)

														_snake_case     =						BART_TINY
														_snake_case     =						self.get_auto_remove_tmp_dir()

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

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

														# --help test
														with pytest.raises(__UpperCamelCase						) as excinfo:
																					with CaptureStdout() as cs:
																												_snake_case     =						parser.parse_args(__UpperCamelCase						)
																					assert False, "--help is expected to sys.exit"
														assert excinfo.type == SystemExit
														_snake_case     =						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
														_snake_case     =						'''non_existing_scheduler'''
														_snake_case     =						{f"""--lr_scheduler={unsupported_param}"""}
														with pytest.raises(__UpperCamelCase						) as excinfo:
																					with CaptureStderr() as cs:
																												_snake_case     =						parser.parse_args(__UpperCamelCase						)
																					assert False, "invalid argument is expected to sys.exit"
														assert excinfo.type == SystemExit
														_snake_case     =						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
														_snake_case     =						'''cosine'''
														_snake_case     =						args_d.copy()
														_snake_case     =						supported_param
														_snake_case     =						argparse.Namespace(**__UpperCamelCase						)
														_snake_case     =						main(__UpperCamelCase						)
														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       __SCREAMING_SNAKE_CASE   (						_SCREAMING_SNAKE_CASE							,      _SCREAMING_SNAKE_CASE=False							,      _SCREAMING_SNAKE_CASE=False							,      _SCREAMING_SNAKE_CASE="data2vec."   ):
							_snake_case     =						"""backbone.""" if is_semantic else """"""

							_snake_case     =						[]
							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       __SCREAMING_SNAKE_CASE   (						_SCREAMING_SNAKE_CASE							,      _SCREAMING_SNAKE_CASE							,      _SCREAMING_SNAKE_CASE=False							,      _SCREAMING_SNAKE_CASE=False							,      _SCREAMING_SNAKE_CASE="data2vec_vision."   ):
							for i in range(config.num_hidden_layers   ):
														_snake_case     =						"""backbone.""" if is_semantic else """"""
														# queries, keys and values
														_snake_case     =						state_dict.pop(f"""{prefix}blocks.{i}.attn.qkv.weight"""   )
														_snake_case     =						state_dict.pop(f"""{prefix}blocks.{i}.attn.q_bias"""   )
														_snake_case     =						state_dict.pop(f"""{prefix}blocks.{i}.attn.v_bias"""   )

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

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

														_snake_case     =						gamma_a
														_snake_case     =						gamma_a

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

																					_snake_case     =						table
																					_snake_case     =						index






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

							return parser.parse_args()






def       __SCREAMING_SNAKE_CASE   (						_SCREAMING_SNAKE_CASE							,      _SCREAMING_SNAKE_CASE							,      _SCREAMING_SNAKE_CASE   ):
							def load_state_dict(_SCREAMING_SNAKE_CASE							,      _SCREAMING_SNAKE_CASE							,      _SCREAMING_SNAKE_CASE=""							,      _SCREAMING_SNAKE_CASE="relative_position_index"   ):
														_snake_case     =						[]
														_snake_case     =						[]
														_snake_case     =						[]
														# copy state_dict so _load_from_state_dict can modify it
														_snake_case     =						getattr(_SCREAMING_SNAKE_CASE							,      """_metadata"""							,      _SCREAMING_SNAKE_CASE   )
														_snake_case     =						state_dict.copy()
														if metadata is not None:
																					_snake_case     =						metadata

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

														load(_SCREAMING_SNAKE_CASE							,      prefix=_SCREAMING_SNAKE_CASE   )

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

														_snake_case     =						warn_missing_keys

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

							_snake_case     =						{
							    """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,
														    }   )

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

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

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

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

							load_state_dict(_SCREAMING_SNAKE_CASE							,      _SCREAMING_SNAKE_CASE							,      prefix=""""""   )

							return model






def       __SCREAMING_SNAKE_CASE   (						):
							_snake_case     =						get_args()

							_snake_case     =						"""ft1k""" in args.hf_checkpoint_name
							_snake_case     =						"""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
							_snake_case     =						DataaVecVisionConfig()
							if is_finetuned:
														_snake_case     =						True
														_snake_case     =						False
														_snake_case     =						True
														_snake_case     =						1000

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

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

							# 2. Load Beit model
							_snake_case     =						load_beit_model(_SCREAMING_SNAKE_CASE							,      _SCREAMING_SNAKE_CASE							,      _SCREAMING_SNAKE_CASE   )
							orig_model.eval()

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

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

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

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

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

							# 5. Forward HF Data2VecVision model
							with torch.no_grad():
														_snake_case     =						hf_model(_SCREAMING_SNAKE_CASE   )

							_snake_case     =						hf_model_output.logits if is_finetuned else hf_model_output.last_hidden_state

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

							print(f"""max_absolute_diff = {max_absolute_diff}"""   )
							_snake_case     =						torch.allclose(_SCREAMING_SNAKE_CASE							,      _SCREAMING_SNAKE_CASE							,      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"