
"""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_     (						lowerCamelCase_						,					lowerCamelCase_=False						,					lowerCamelCase_=False						,					lowerCamelCase_="data2vec."			)  ->      Tuple:


					'''simple docstring'''
					__a:    List[Any]		     =       'backbone.' if is_semantic else ''

					__a:    Optional[int]		     =       []
					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_     (						lowerCamelCase_						,					lowerCamelCase_						,					lowerCamelCase_=False						,					lowerCamelCase_=False						,					lowerCamelCase_="data2vec_vision."			)  ->      int:


					'''simple docstring'''
					for i in range(config.num_hidden_layers			):
										__a:    Union[str, Any]		     =       'backbone.' if is_semantic else ''
										# queries, keys and values
										__a:    Tuple		     =       state_dict.pop(f"""{prefix}blocks.{i}.attn.qkv.weight"""			)
										__a:    Dict		     =       state_dict.pop(f"""{prefix}blocks.{i}.attn.q_bias"""			)
										__a:    str		     =       state_dict.pop(f"""{prefix}blocks.{i}.attn.v_bias"""			)

										__a:    Optional[int]		     =       in_proj_weight[
										    : config.hidden_size, :
										]
										__a:    List[Any]		     =       q_bias
										__a:    List[Any]		     =       in_proj_weight[
										    config.hidden_size : config.hidden_size * 2, :
										]
										__a:    Union[str, Any]		     =       in_proj_weight[
										    -config.hidden_size :, :
										]
										__a:    Optional[int]		     =       v_bias

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

										__a:    Tuple		     =       gamma_a
										__a:    Optional[Any]		     =       gamma_a

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

															__a:    Optional[Any]		     =       table
															__a:    Optional[Any]		     =       index


def      lowerCamelCase_     (						)  ->      Any:


					'''simple docstring'''
					__a:    int		     =       argparse.ArgumentParser(
					    """Convert Data2VecVision to HF for image classification and pretraining"""						,					add_help=lowercase__			)
					parser.add_argument("""--hf_checkpoint_name"""						,					type=lowercase__			)
					parser.add_argument("""--input_size"""						,					default=2_24						,					type=lowercase__						,					help="""images input size"""			)
					parser.add_argument("""--beit_checkpoint"""						,					default=""""""						,					help="""beit checkpoint"""			)

					return parser.parse_args()


def      lowerCamelCase_     (						lowerCamelCase_						,					lowerCamelCase_						,					lowerCamelCase_			)  ->      str:


					'''simple docstring'''
					def load_state_dict(lowerCamelCase_						,					lowerCamelCase_						,					lowerCamelCase_=""						,					lowerCamelCase_="relative_position_index"			):
										__a:    List[Any]		     =       []
										__a:    Any		     =       []
										__a:    Tuple		     =       []
										# copy state_dict so _load_from_state_dict can modify it
										__a:    Any		     =       getattr(lowercase__						,					"""_metadata"""						,					lowercase__			)
										__a:    Any		     =       state_dict.copy()
										if metadata is not None:
															__a:    Tuple		     =       metadata

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

										load(lowercase__						,					prefix=lowercase__			)

										__a:    Any		     =       []
										__a:    Union[str, Any]		     =       []
										for key in missing_keys:
															__a:    int		     =       True
															for ignore_key in ignore_missing.split("""|"""			):
																				if ignore_key in key:
																									__a:    str		     =       False
																									break
															if keep_flag:
																				warn_missing_keys.append(lowercase__			)
															else:
																				ignore_missing_keys.append(lowercase__			)

										__a:    int		     =       warn_missing_keys

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

					__a:    Dict		     =       {
					    '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""": 10_00,
										        """use_mean_pooling""": True,
										        """init_scale""": 0.001,
										        """use_rel_pos_bias""": True,
										    }			)

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

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

					__a:    Optional[Any]		     =       list(checkpoint_model.keys()			)
					for key in all_keys:
										if "relative_position_index" in key:
															checkpoint_model.pop(lowercase__			)

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

					load_state_dict(lowercase__						,					lowercase__						,					prefix=""""""			)

					return model


def      lowerCamelCase_     (						)  ->      Optional[int]:


					'''simple docstring'''
					__a:    int		     =       get_args()

					__a:    Dict		     =       'ft1k' in args.hf_checkpoint_name
					__a:    Any		     =       '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:    List[str]		     =       DataaVecVisionConfig()
					if is_finetuned:
										__a:    Dict		     =       True
										__a:    Union[str, Any]		     =       False
										__a:    List[str]		     =       True
										__a:    List[Any]		     =       10_00

										__a:    Optional[Any]		     =       'huggingface/label-files'
										__a:    int		     =       'imagenet-1k-id2label.json'
										__a:    Optional[int]		     =       json.load(open(hf_hub_download(lowercase__						,					lowercase__						,					repo_type="""dataset"""			)						,					"""r"""			)			)
										__a:    Tuple		     =       {int(lowercase__			): v for k, v in idalabel.items()}
										__a:    int		     =       idalabel
										__a:    List[str]		     =       {v: k for k, v in idalabel.items()}
					else:
										__a:    Any		     =       False
										__a:    str		     =       True
										__a:    List[Any]		     =       False

					if is_large:
										__a:    Optional[int]		     =       10_24
										__a:    Optional[int]		     =       40_96
										__a:    Optional[Any]		     =       24
										__a:    List[Any]		     =       16

					# 2. Load Beit model
					__a:    Any		     =       load_beit_model(lowercase__						,					lowercase__						,					lowercase__			)
					orig_model.eval()

					# 3. Forward Beit model
					__a:    Any		     =       BeitImageProcessor(size=config.image_size						,					do_center_crop=lowercase__			)
					__a:    List[str]		     =       Image.open("""../../../../tests/fixtures/tests_samples/COCO/000000039769.png"""			)
					__a:    Optional[Any]		     =       image_processor(images=lowercase__						,					return_tensors="""pt"""			)
					__a:    int		     =       encoding['pixel_values']

					__a:    Tuple		     =       (pixel_values,) if is_finetuned else (pixel_values, None)
					with torch.no_grad():
										__a:    Union[str, Any]		     =       orig_model(*lowercase__			)

					# 4. Load HF Data2VecVision model
					if is_finetuned:
										__a:    Optional[Any]		     =       DataaVecVisionForImageClassification(lowercase__			)
										hf_model.eval()
										__a:    Tuple		     =       False
										__a:    Dict		     =       'data2vec_vision.'
					else:
										__a:    Union[str, Any]		     =       DataaVecVisionModel(lowercase__			)
										hf_model.eval()
										__a:    Union[str, Any]		     =       True
										__a:    List[str]		     =       ''

					__a:    Optional[Any]		     =       create_rename_keys(lowercase__						,					hf_prefix=lowercase__						,					has_lm_head=lowercase__			)
					__a:    Any		     =       orig_model.state_dict()
					for src, dest in rename_keys:
										__a:    Dict		     =       state_dict.pop(lowercase__			)
										__a:    Any		     =       val

					read_in_q_k_v(lowercase__						,					lowercase__						,					hf_prefix=lowercase__						,					has_lm_head=lowercase__			)
					__a:    Optional[Any]		     =       hf_model.load_state_dict(lowercase__						,					strict=lowercase__			)
					print("""HF missing"""						,					lowercase__			)
					print("""HF unexpected_keys"""						,					lowercase__			)

					# 5. Forward HF Data2VecVision model
					with torch.no_grad():
										__a:    Union[str, Any]		     =       hf_model(lowercase__			)

					__a:    str		     =       hf_model_output.logits if is_finetuned else hf_model_output.last_hidden_state

					# 6. Compare
					__a:    List[str]		     =       torch.max(torch.abs(hf_output - orig_model_output			)			).item()

					print(f"""max_absolute_diff = {max_absolute_diff}"""			)
					__a:    str		     =       torch.allclose(lowercase__						,					lowercase__						,					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"







'''simple docstring'''






import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
		from .tokenization_xlm_roberta import XLMRobertaTokenizer
else:
		A :			Optional[Any]							   =      None


A :			Tuple							   =      logging.get_logger(__name__)

A :			Optional[int]							   =      {'vocab_file': 'sentencepiece.bpe.model', 'tokenizer_file': 'tokenizer.json'}

A :			Tuple							   =      {
    'vocab_file': {
        'xlm-roberta-base': 'https://huggingface.co/xlm-roberta-base/resolve/main/sentencepiece.bpe.model',
        'xlm-roberta-large': 'https://huggingface.co/xlm-roberta-large/resolve/main/sentencepiece.bpe.model',
        'xlm-roberta-large-finetuned-conll02-dutch': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/sentencepiece.bpe.model'
        ),
        'xlm-roberta-large-finetuned-conll02-spanish': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/sentencepiece.bpe.model'
        ),
        'xlm-roberta-large-finetuned-conll03-english': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/sentencepiece.bpe.model'
        ),
        'xlm-roberta-large-finetuned-conll03-german': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/sentencepiece.bpe.model'
        ),
    },
    'tokenizer_file': {
        'xlm-roberta-base': 'https://huggingface.co/xlm-roberta-base/resolve/main/tokenizer.json',
        'xlm-roberta-large': 'https://huggingface.co/xlm-roberta-large/resolve/main/tokenizer.json',
        'xlm-roberta-large-finetuned-conll02-dutch': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/tokenizer.json'
        ),
        'xlm-roberta-large-finetuned-conll02-spanish': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/tokenizer.json'
        ),
        'xlm-roberta-large-finetuned-conll03-english': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/tokenizer.json'
        ),
        'xlm-roberta-large-finetuned-conll03-german': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/tokenizer.json'
        ),
    },
}

A :			int							   =      {
    'xlm-roberta-base': 512,
    'xlm-roberta-large': 512,
    'xlm-roberta-large-finetuned-conll02-dutch': 512,
    'xlm-roberta-large-finetuned-conll02-spanish': 512,
    'xlm-roberta-large-finetuned-conll03-english': 512,
    'xlm-roberta-large-finetuned-conll03-german': 512,
}







class       lowerCamelCase       (    __UpperCAmelCase	):
					_SCREAMING_SNAKE_CASE				 =     VOCAB_FILES_NAMES
					_SCREAMING_SNAKE_CASE				 =     PRETRAINED_VOCAB_FILES_MAP
					_SCREAMING_SNAKE_CASE				 =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					_SCREAMING_SNAKE_CASE				 =     ["input_ids", "attention_mask"]
					_SCREAMING_SNAKE_CASE				 =     XLMRobertaTokenizer


					def __init__(       self						:	Any							,  __snake_case						:	Tuple=None							,  __snake_case						:	List[Any]=None							,  __snake_case						:	List[str]="<s>"							,  __snake_case						:	int="</s>"							,  __snake_case						:	str="</s>"							,  __snake_case						:	Any="<s>"							,  __snake_case						:	int="<unk>"							,  __snake_case						:	Optional[Any]="<pad>"							,  __snake_case						:	Optional[int]="<mask>"							,  **__snake_case						:	List[str]							,  ):





											'''simple docstring'''





											_snake_case: Optional[Any]	       =		AddedToken(__snake_case							,  lstrip=__snake_case							,  rstrip=__snake_case							) if isinstance(__snake_case							,  __snake_case							) else mask_token

											super().__init__(
											    __snake_case							,  tokenizer_file=__snake_case							,  bos_token=__snake_case							,  eos_token=__snake_case							,  sep_token=__snake_case							,  cls_token=__snake_case							,  unk_token=__snake_case							,  pad_token=__snake_case							,  mask_token=__snake_case							,  **__snake_case							,  )

											_snake_case: int	       =		vocab_file
											_snake_case: List[Any]	       =		False if not self.vocab_file else True


					def  SCREAMING_SNAKE_CASE_		(       self						:	Tuple							,  __snake_case						:	List[int]							,  __snake_case						:	Optional[List[int]] = None							):





											'''simple docstring'''





											if token_ids_a is None:
																	return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
											_snake_case: Union[str, Any]	       =		[self.cls_token_id]
											_snake_case: int	       =		[self.sep_token_id]
											return cls + token_ids_a + sep + sep + token_ids_a + sep


					def  SCREAMING_SNAKE_CASE_		(       self						:	Union[str, Any]							,  __snake_case						:	List[int]							,  __snake_case						:	Optional[List[int]] = None							):





											'''simple docstring'''





											_snake_case: Union[str, Any]	       =		[self.sep_token_id]
											_snake_case: List[str]	       =		[self.cls_token_id]

											if token_ids_a is None:
																	return len(cls + token_ids_a + sep							) * [0]
											return len(cls + token_ids_a + sep + sep + token_ids_a + sep							) * [0]




					def  SCREAMING_SNAKE_CASE_		(       self						:	Any							,  __snake_case						:	str							,  __snake_case						:	Optional[str] = None							):





											'''simple docstring'''





											if not self.can_save_slow_tokenizer:
																	raise ValueError(
																	    'Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '
																	    'tokenizer.'							)

											if not os.path.isdir(__snake_case							):
																	logger.error(f'''Vocabulary path ({save_directory}) should be a directory.'''							)
																	return
											_snake_case: Optional[int]	       =		os.path.join(
											    __snake_case							,  (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']							)

											if os.path.abspath(self.vocab_file							) != os.path.abspath(__snake_case							):
																	copyfile(self.vocab_file							,  __snake_case							)

											return (out_vocab_file,)





