



'''simple docstring'''







import argparse
import json
import logging
import os
import sys
from unittest import skip
from unittest.mock import patch

import tensorflow as tf

from transformers.testing_utils import TestCasePlus, get_gpu_count, slow


SCREAMING_SNAKE_CASE__     : Optional[int]				=   [
    os.path.join(os.path.dirname(__file__), dirname)
    for dirname in [
        """text-generation""",
        """text-classification""",
        """token-classification""",
        """language-modeling""",
        """multiple-choice""",
        """question-answering""",
        """summarization""",
        """translation""",
        """image-classification""",
    ]
]
sys.path.extend(SRC_DIRS)


if SRC_DIRS is not None:
							import run_clm
							import run_image_classification
							import run_mlm
							import run_ner
							import run_qa as run_squad
							import run_summarization
							import run_swag
							import run_text_classification
							import run_translation


logging.basicConfig(level=logging.DEBUG)

SCREAMING_SNAKE_CASE__     : Optional[int]				=   logging.getLogger()







def  SCREAMING_SNAKE_CASE__ (				):
		SCREAMING_SNAKE_CASE_						:Any										=      argparse.ArgumentParser()
		parser.add_argument('-f'     )
		SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      parser.parse_args()
		return args.f







def  SCREAMING_SNAKE_CASE__ (				SCREAMING_SNAKE_CASE     ):
		SCREAMING_SNAKE_CASE_						:List[str]										=      {}
		SCREAMING_SNAKE_CASE_						:int										=      os.path.join(SCREAMING_SNAKE_CASE ,		'all_results.json'     )
		if os.path.exists(SCREAMING_SNAKE_CASE     ):
				with open(SCREAMING_SNAKE_CASE ,		'r'     ) as f:
						SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      json.load(SCREAMING_SNAKE_CASE     )
		else:
				raise ValueError(F'can\'t find {path}'     )
		return results







def  SCREAMING_SNAKE_CASE__ (				):
		return bool(tf.config.list_physical_devices('GPU'     )     )


SCREAMING_SNAKE_CASE__     : Optional[int]				=   logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)





class 				__lowerCAmelCase(							lowerCAmelCase__      ):






			@skip('Skipping until shape inference for to_tf_dataset PR is merged.'	)
			def 				_lowercase	(   self  :							Tuple	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:Tuple										=      self.get_auto_remove_tmp_dir()
					SCREAMING_SNAKE_CASE_						:List[str]										=      f'\n            run_text_classification.py\n            --model_name_or_path distilbert-base-uncased\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --train_file ./tests/fixtures/tests_samples/MRPC/train.csv\n            --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv\n            --do_train\n            --do_eval\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --learning_rate=1e-4\n            --max_steps=10\n            --warmup_steps=2\n            --seed=42\n            --max_seq_length=128\n            '.split()

					if is_cuda_available():
							testargs.append('--fp16'	)

					with patch.object(SCREAMING_SNAKE_CASE      ,       'argv'      ,       SCREAMING_SNAKE_CASE	):
							run_text_classification.main()
							# Reset the mixed precision policy so we don't break other tests
							tf.keras.mixed_precision.set_global_policy('float32'	)
							SCREAMING_SNAKE_CASE_						:Optional[int]										=      get_results(SCREAMING_SNAKE_CASE	)
							self.assertGreaterEqual(result['eval_accuracy']      ,       0.75	)






			def 				_lowercase	(   self  :							str	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:List[str]										=      self.get_auto_remove_tmp_dir()
					SCREAMING_SNAKE_CASE_						:str										=      f'\n            run_clm.py\n            --model_name_or_path distilgpt2\n            --train_file ./tests/fixtures/sample_text.txt\n            --validation_file ./tests/fixtures/sample_text.txt\n            --do_train\n            --do_eval\n            --block_size 128\n            --per_device_train_batch_size 2\n            --per_device_eval_batch_size 1\n            --num_train_epochs 2\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            '.split()

					if len(tf.config.list_physical_devices('GPU'	)	) > 1:
							# Skipping because there are not enough batches to train the model + would need a drop_last to work.
							return

					with patch.object(SCREAMING_SNAKE_CASE      ,       'argv'      ,       SCREAMING_SNAKE_CASE	):
							run_clm.main()
							SCREAMING_SNAKE_CASE_						:Any										=      get_results(SCREAMING_SNAKE_CASE	)
							self.assertLess(result['eval_perplexity']      ,       100	)






			def 				_lowercase	(   self  :							int	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:Optional[int]										=      self.get_auto_remove_tmp_dir()
					SCREAMING_SNAKE_CASE_						:int										=      f'\n            run_mlm.py\n            --model_name_or_path distilroberta-base\n            --train_file ./tests/fixtures/sample_text.txt\n            --validation_file ./tests/fixtures/sample_text.txt\n            --max_seq_length 64\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --do_train\n            --do_eval\n            --prediction_loss_only\n            --num_train_epochs=1\n            --learning_rate=1e-4\n        '.split()

					with patch.object(SCREAMING_SNAKE_CASE      ,       'argv'      ,       SCREAMING_SNAKE_CASE	):
							run_mlm.main()
							SCREAMING_SNAKE_CASE_						:Optional[Any]										=      get_results(SCREAMING_SNAKE_CASE	)
							self.assertLess(result['eval_perplexity']      ,       42	)






			def 				_lowercase	(   self  :							Optional[Any]	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:List[str]										=      7 if get_gpu_count() > 1 else 2

					SCREAMING_SNAKE_CASE_						:Tuple										=      self.get_auto_remove_tmp_dir()
					SCREAMING_SNAKE_CASE_						:str										=      f'\n            run_ner.py\n            --model_name_or_path bert-base-uncased\n            --train_file tests/fixtures/tests_samples/conll/sample.json\n            --validation_file tests/fixtures/tests_samples/conll/sample.json\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --do_train\n            --do_eval\n            --warmup_steps=2\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=2\n            --num_train_epochs={epochs}\n            --seed 7\n        '.split()

					with patch.object(SCREAMING_SNAKE_CASE      ,       'argv'      ,       SCREAMING_SNAKE_CASE	):
							run_ner.main()
							SCREAMING_SNAKE_CASE_						:Optional[Any]										=      get_results(SCREAMING_SNAKE_CASE	)
							self.assertGreaterEqual(result['accuracy']      ,       0.75	)






			def 				_lowercase	(   self  :							Tuple	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:Tuple										=      self.get_auto_remove_tmp_dir()
					SCREAMING_SNAKE_CASE_						:Any										=      f'\n            run_qa.py\n            --model_name_or_path bert-base-uncased\n            --version_2_with_negative\n            --train_file tests/fixtures/tests_samples/SQUAD/sample.json\n            --validation_file tests/fixtures/tests_samples/SQUAD/sample.json\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --max_steps=10\n            --warmup_steps=2\n            --do_train\n            --do_eval\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n        '.split()

					with patch.object(SCREAMING_SNAKE_CASE      ,       'argv'      ,       SCREAMING_SNAKE_CASE	):
							run_squad.main()
							SCREAMING_SNAKE_CASE_						:Optional[Any]										=      get_results(SCREAMING_SNAKE_CASE	)
							self.assertGreaterEqual(result['f1']      ,       30	)
							self.assertGreaterEqual(result['exact']      ,       30	)






			def 				_lowercase	(   self  :							Union[str, Any]	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:Dict										=      self.get_auto_remove_tmp_dir()
					SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      f'\n            run_swag.py\n            --model_name_or_path bert-base-uncased\n            --train_file tests/fixtures/tests_samples/swag/sample.json\n            --validation_file tests/fixtures/tests_samples/swag/sample.json\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --max_steps=20\n            --warmup_steps=2\n            --do_train\n            --do_eval\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n        '.split()

					with patch.object(SCREAMING_SNAKE_CASE      ,       'argv'      ,       SCREAMING_SNAKE_CASE	):
							run_swag.main()
							SCREAMING_SNAKE_CASE_						:int										=      get_results(SCREAMING_SNAKE_CASE	)
							self.assertGreaterEqual(result['val_accuracy']      ,       0.8	)






			@slow
			def 				_lowercase	(   self  :							List[Any]	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:Any										=      self.get_auto_remove_tmp_dir()
					SCREAMING_SNAKE_CASE_						:Optional[int]										=      f'\n            run_summarization.py\n            --model_name_or_path t5-small\n            --train_file tests/fixtures/tests_samples/xsum/sample.json\n            --validation_file tests/fixtures/tests_samples/xsum/sample.json\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --max_steps=50\n            --warmup_steps=8\n            --do_train\n            --do_eval\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n        '.split()

					with patch.object(SCREAMING_SNAKE_CASE      ,       'argv'      ,       SCREAMING_SNAKE_CASE	):
							run_summarization.main()
							SCREAMING_SNAKE_CASE_						:Dict										=      get_results(SCREAMING_SNAKE_CASE	)
							self.assertGreaterEqual(result['rouge1']      ,       10	)
							self.assertGreaterEqual(result['rouge2']      ,       2	)
							self.assertGreaterEqual(result['rougeL']      ,       7	)
							self.assertGreaterEqual(result['rougeLsum']      ,       7	)






			@slow
			def 				_lowercase	(   self  :							Optional[Any]	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:Dict										=      self.get_auto_remove_tmp_dir()
					SCREAMING_SNAKE_CASE_						:List[Any]										=      f'\n            run_translation.py\n            --model_name_or_path Rocketknight1/student_marian_en_ro_6_1\n            --source_lang en\n            --target_lang ro\n            --train_file tests/fixtures/tests_samples/wmt16/sample.json\n            --validation_file tests/fixtures/tests_samples/wmt16/sample.json\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --warmup_steps=8\n            --do_train\n            --do_eval\n            --learning_rate=3e-3\n            --num_train_epochs 12\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --source_lang en_XX\n            --target_lang ro_RO\n        '.split()

					with patch.object(SCREAMING_SNAKE_CASE      ,       'argv'      ,       SCREAMING_SNAKE_CASE	):
							run_translation.main()
							SCREAMING_SNAKE_CASE_						:str										=      get_results(SCREAMING_SNAKE_CASE	)
							self.assertGreaterEqual(result['bleu']      ,       30	)






			def 				_lowercase	(   self  :							Optional[int]	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:List[str]										=      self.get_auto_remove_tmp_dir()
					SCREAMING_SNAKE_CASE_						:Any										=      f'\n            run_image_classification.py\n            --dataset_name hf-internal-testing/cats_vs_dogs_sample\n            --model_name_or_path microsoft/resnet-18\n            --do_train\n            --do_eval\n            --learning_rate 1e-4\n            --per_device_train_batch_size 2\n            --per_device_eval_batch_size 1\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --dataloader_num_workers 16\n            --num_train_epochs 2\n            --train_val_split 0.1\n            --seed 42\n            --ignore_mismatched_sizes True\n            '.split()

					with patch.object(SCREAMING_SNAKE_CASE      ,       'argv'      ,       SCREAMING_SNAKE_CASE	):
							run_image_classification.main()
							SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      get_results(SCREAMING_SNAKE_CASE	)
							self.assertGreaterEqual(result['accuracy']      ,       0.7	)








"""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     lowerCamelCase_					(	__A	:		List[str]      ,						__A	:		Optional[Any]=False      ,						__A	:		Optional[Any]=False      ,						__A	:		Any="data2vec."      ):
					lowerCamelCase__					='backbone.' if is_semantic else ''

					lowerCamelCase__					=[]
					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     lowerCamelCase_					(	__A	:		Tuple      ,						__A	:		Tuple      ,						__A	:		Dict=False      ,						__A	:		List[Any]=False      ,						__A	:		Tuple="data2vec_vision."      ):
					for i in range(config.num_hidden_layers      ):
										lowerCamelCase__					='backbone.' if is_semantic else ''
										# queries, keys and values
										lowerCamelCase__					=state_dict.pop(f"{prefix}blocks.{i}.attn.qkv.weight"      )
										lowerCamelCase__					=state_dict.pop(f"{prefix}blocks.{i}.attn.q_bias"      )
										lowerCamelCase__					=state_dict.pop(f"{prefix}blocks.{i}.attn.v_bias"      )

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

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

										lowerCamelCase__					=gamma_a
										lowerCamelCase__					=gamma_a

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

															lowerCamelCase__					=table
															lowerCamelCase__					=index




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

					return parser.parse_args()




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

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

										load(__A      ,						prefix=__A      )

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

										lowerCamelCase__					=warn_missing_keys

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

					lowerCamelCase__					={
					    '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': 0.0_0_1,
										        'use_rel_pos_bias': True,
										    }      )

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

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

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

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

					load_state_dict(__A      ,						__A      ,						prefix=''      )

					return model




def     lowerCamelCase_					(	):
					lowerCamelCase__					=get_args()

					lowerCamelCase__					='ft1k' in args.hf_checkpoint_name
					lowerCamelCase__					='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
					lowerCamelCase__					=DataaVecVisionConfig()
					if is_finetuned:
										lowerCamelCase__					=True
										lowerCamelCase__					=False
										lowerCamelCase__					=True
										lowerCamelCase__					=1000

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

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

					# 2. Load Beit model
					lowerCamelCase__					=load_beit_model(__A      ,						__A      ,						__A      )
					orig_model.eval()

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

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

					# 4. Load HF Data2VecVision model
					if is_finetuned:
										lowerCamelCase__					=DataaVecVisionForImageClassification(__A      )
										hf_model.eval()
										lowerCamelCase__					=False
										lowerCamelCase__					='data2vec_vision.'
					else:
										lowerCamelCase__					=DataaVecVisionModel(__A      )
										hf_model.eval()
										lowerCamelCase__					=True
										lowerCamelCase__					=''

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

					read_in_q_k_v(__A      ,						__A      ,						hf_prefix=__A      ,						has_lm_head=__A      )
					lowerCamelCase__					,  lowerCamelCase__					=hf_model.load_state_dict(__A      ,						strict=__A      )
					print('HF missing'      ,						__A      )
					print('HF unexpected_keys'      ,						__A      )

					# 5. Forward HF Data2VecVision model
					with torch.no_grad():
										lowerCamelCase__					=hf_model(__A      )

					lowerCamelCase__					=hf_model_output.logits if is_finetuned else hf_model_output.last_hidden_state

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

					print(f"max_absolute_diff = {max_absolute_diff}"      )
					lowerCamelCase__					=torch.allclose(__A      ,						__A      ,						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"