


"""simple docstring"""
import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging


if TYPE_CHECKING:
				from transformers.pipelines.conversational import Conversation


__SCREAMING_SNAKE_CASE							:      str						=      logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE							:      int						=      {'tokenizer_file': 'tokenizer.json'}

__SCREAMING_SNAKE_CASE							:      Optional[Any]						=      {
    'tokenizer_file': {
        'bigscience/tokenizer': 'https://huggingface.co/bigscience/tokenizer/blob/main/tokenizer.json',
        'bigscience/bloom-560m': 'https://huggingface.co/bigscience/bloom-560m/blob/main/tokenizer.json',
        'bigscience/bloom-1b1': 'https://huggingface.co/bigscience/bloom-1b1/blob/main/tokenizer.json',
        'bigscience/bloom-1b7': 'https://huggingface.co/bigscience/bloom-1b7/blob/main/tokenizer.json',
        'bigscience/bloom-3b': 'https://huggingface.co/bigscience/bloom-3b/blob/main/tokenizer.json',
        'bigscience/bloom-7b1': 'https://huggingface.co/bigscience/bloom-7b1/blob/main/tokenizer.json',
        'bigscience/bloom': 'https://huggingface.co/bigscience/bloom/blob/main/tokenizer.json',
    },
}





class        __A       (snake_case__):





							'''simple docstring'''

							__lowercase:						Dict							 =      VOCAB_FILES_NAMES
							__lowercase:						Optional[int]							 =      PRETRAINED_VOCAB_FILES_MAP
							__lowercase:						Optional[Any]							 =      ["""input_ids""", """attention_mask"""]
							__lowercase:						Any							 =      None



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



														"""simple docstring"""



														super().__init__(
														    UpperCAmelCase_						,    UpperCAmelCase_						,    tokenizer_file=UpperCAmelCase_						,    unk_token=UpperCAmelCase_						,    bos_token=UpperCAmelCase_						,    eos_token=UpperCAmelCase_						,    pad_token=UpperCAmelCase_						,    add_prefix_space=UpperCAmelCase_						,    clean_up_tokenization_spaces=UpperCAmelCase_						,    **UpperCAmelCase_						,    )
														snake_case_          =						json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()    )
														if pre_tok_state.get("""add_prefix_space"""						,    UpperCAmelCase_    ) != add_prefix_space:
																					snake_case_          =						getattr(UpperCAmelCase_						,    pre_tok_state.pop("""type"""    )    )
																					snake_case_          =						add_prefix_space
																					snake_case_          =						pre_tok_class(**UpperCAmelCase_    )

														snake_case_          =						add_prefix_space



							def 						lowerCAmelCase    (			self							: List[Any]						,    *UpperCAmelCase_							: Any						,    **UpperCAmelCase_							: Any    )   ->BatchEncoding:



														"""simple docstring"""



														snake_case_          =						kwargs.get("""is_split_into_words"""						,    UpperCAmelCase_    )
														if not (self.add_prefix_space or not is_split_into_words):
																					raise Exception(
																					    F"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True to use it with"""
																					    """ pretokenized inputs."""    )

														return super()._batch_encode_plus(*UpperCAmelCase_						,    **UpperCAmelCase_    )



							def 						lowerCAmelCase    (			self							: Any						,    *UpperCAmelCase_							: Optional[Any]						,    **UpperCAmelCase_							: Union[str, Any]    )   ->BatchEncoding:



														"""simple docstring"""



														snake_case_          =						kwargs.get("""is_split_into_words"""						,    UpperCAmelCase_    )

														if not (self.add_prefix_space or not is_split_into_words):
																					raise Exception(
																					    F"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True to use it with"""
																					    """ pretokenized inputs."""    )

														return super()._encode_plus(*UpperCAmelCase_						,    **UpperCAmelCase_    )



							def 						lowerCAmelCase    (			self							: int						,    UpperCAmelCase_							: str						,    UpperCAmelCase_							: Optional[str] = None    )   ->Tuple[str]:



														"""simple docstring"""



														snake_case_          =						self._tokenizer.model.save(UpperCAmelCase_						,    name=UpperCAmelCase_    )
														return tuple(UpperCAmelCase_    )



							def 						lowerCAmelCase    (			self							: List[Any]						,    UpperCAmelCase_							: "Conversation"    )   ->List[int]:



														"""simple docstring"""



														snake_case_          =						[]
														for is_user, text in conversation.iter_texts():
																					input_ids.extend(self.encode(UpperCAmelCase_						,    add_special_tokens=UpperCAmelCase_    ) + [self.eos_token_id]    )

														if len(UpperCAmelCase_    ) > self.model_max_length:
																					snake_case_          =						input_ids[-self.model_max_length :]
														return input_ids







"""simple docstring"""
import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import logging


logging.set_verbosity_info()
__SCREAMING_SNAKE_CASE							:      List[str]						=      logging.get_logger(__name__)







def   _a			( _SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE=False			)      ->			int:
							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"""blocks.{i}.norm1.weight""", f"""vit.encoder.layer.{i}.layernorm_before.weight""")			)
														rename_keys.append((f"""blocks.{i}.norm1.bias""", f"""vit.encoder.layer.{i}.layernorm_before.bias""")			)
														rename_keys.append((f"""blocks.{i}.attn.proj.weight""", f"""vit.encoder.layer.{i}.attention.output.dense.weight""")			)
														rename_keys.append((f"""blocks.{i}.attn.proj.bias""", f"""vit.encoder.layer.{i}.attention.output.dense.bias""")			)
														rename_keys.append((f"""blocks.{i}.norm2.weight""", f"""vit.encoder.layer.{i}.layernorm_after.weight""")			)
														rename_keys.append((f"""blocks.{i}.norm2.bias""", f"""vit.encoder.layer.{i}.layernorm_after.bias""")			)
														rename_keys.append((f"""blocks.{i}.mlp.fc1.weight""", f"""vit.encoder.layer.{i}.intermediate.dense.weight""")			)
														rename_keys.append((f"""blocks.{i}.mlp.fc1.bias""", f"""vit.encoder.layer.{i}.intermediate.dense.bias""")			)
														rename_keys.append((f"""blocks.{i}.mlp.fc2.weight""", f"""vit.encoder.layer.{i}.output.dense.weight""")			)
														rename_keys.append((f"""blocks.{i}.mlp.fc2.bias""", f"""vit.encoder.layer.{i}.output.dense.bias""")			)

							# projection layer + position embeddings
							rename_keys.extend(
							    [
							        ("""cls_token""", """vit.embeddings.cls_token"""),
							        ("""patch_embed.proj.weight""", """vit.embeddings.patch_embeddings.projection.weight"""),
							        ("""patch_embed.proj.bias""", """vit.embeddings.patch_embeddings.projection.bias"""),
							        ("""pos_embed""", """vit.embeddings.position_embeddings"""),
							    ]			)

							if base_model:
														# layernorm + pooler
														rename_keys.extend(
														    [
														        ("""norm.weight""", """layernorm.weight"""),
														        ("""norm.bias""", """layernorm.bias"""),
														    ]			)

														# if just the base model, we should remove "vit" from all keys that start with "vit"
														snake_case_          =						[(pair[0], pair[1][4:]) if pair[1].startswith("""vit"""			) else pair for pair in rename_keys]
							else:
														# layernorm + classification head
														rename_keys.extend(
														    [
														        ("""norm.weight""", """vit.layernorm.weight"""),
														        ("""norm.bias""", """vit.layernorm.bias"""),
														        ("""head.weight""", """classifier.weight"""),
														        ("""head.bias""", """classifier.bias"""),
														    ]			)

							return rename_keys







def   _a			( _SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE=False			)      ->			List[Any]:
							for i in range(config.num_hidden_layers			):
														if base_model:
																					snake_case_          =						""""""
														else:
																					snake_case_          =						"""vit."""
														# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
														snake_case_          =						state_dict.pop(f"""blocks.{i}.attn.qkv.weight"""			)
														snake_case_          =						state_dict.pop(f"""blocks.{i}.attn.qkv.bias"""			)
														# next, add query, keys and values (in that order) to the state dict
														snake_case_          =						in_proj_weight[
														    : config.hidden_size, :
														]
														snake_case_          =						in_proj_bias[: config.hidden_size]
														snake_case_          =						in_proj_weight[
														    config.hidden_size : config.hidden_size * 2, :
														]
														snake_case_          =						in_proj_bias[
														    config.hidden_size : config.hidden_size * 2
														]
														snake_case_          =						in_proj_weight[
														    -config.hidden_size :, :
														]
														snake_case_          =						in_proj_bias[-config.hidden_size :]







def   _a			( _SCREAMING_SNAKE_CASE			)      ->			Union[str, Any]:
							snake_case_          =						["""head.weight""", """head.bias"""]
							for k in ignore_keys:
														state_dict.pop(_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			)







def   _a			( _SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			)      ->			int:
							snake_case_          =						dct.pop(_SCREAMING_SNAKE_CASE			)
							snake_case_          =						val







def   _a			( )      ->			Dict:
							snake_case_          =						"""http://images.cocodataset.org/val2017/000000039769.jpg"""
							snake_case_          =						Image.open(requests.get(_SCREAMING_SNAKE_CASE			,	stream=_SCREAMING_SNAKE_CASE			).raw			)
							return im







@torch.no_grad()
def   _a			( _SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE=True			)      ->			Any:
							snake_case_          =						ViTConfig()
							# patch_size
							if model_name[-1] == "8":
														snake_case_          =						8
							# set labels if required
							if not base_model:
														snake_case_          =						1_000
														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()}
							# size of the architecture
							if model_name in ["dino_vits8", "dino_vits16"]:
														snake_case_          =						384
														snake_case_          =						1_536
														snake_case_          =						12
														snake_case_          =						6

							# load original model from torch hub
							snake_case_          =						torch.hub.load("""facebookresearch/dino:main"""			,	_SCREAMING_SNAKE_CASE			)
							original_model.eval()

							# load state_dict of original model, remove and rename some keys
							snake_case_          =						original_model.state_dict()
							if base_model:
														remove_classification_head_(_SCREAMING_SNAKE_CASE			)
							snake_case_          =						create_rename_keys(_SCREAMING_SNAKE_CASE			,	base_model=_SCREAMING_SNAKE_CASE			)
							for src, dest in rename_keys:
														rename_key(_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			)
							read_in_q_k_v(_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			)

							# load HuggingFace model
							if base_model:
														snake_case_          =						ViTModel(_SCREAMING_SNAKE_CASE			,	add_pooling_layer=_SCREAMING_SNAKE_CASE			).eval()
							else:
														snake_case_          =						ViTForImageClassification(_SCREAMING_SNAKE_CASE			).eval()
							model.load_state_dict(_SCREAMING_SNAKE_CASE			)

							# Check outputs on an image, prepared by ViTImageProcessor
							snake_case_          =						ViTImageProcessor()
							snake_case_          =						image_processor(images=prepare_img()			,	return_tensors="""pt"""			)
							snake_case_          =						encoding["""pixel_values"""]
							snake_case_          =						model(_SCREAMING_SNAKE_CASE			)

							if base_model:
														snake_case_          =						original_model(_SCREAMING_SNAKE_CASE			)
														assert torch.allclose(_SCREAMING_SNAKE_CASE			,	outputs.last_hidden_state[:, 0, :]			,	atol=1E-1			)
							else:
														snake_case_          =						original_model(_SCREAMING_SNAKE_CASE			)
														assert logits.shape == outputs.logits.shape
														assert torch.allclose(_SCREAMING_SNAKE_CASE			,	outputs.logits			,	atol=1E-3			)

							Path(_SCREAMING_SNAKE_CASE			).mkdir(exist_ok=_SCREAMING_SNAKE_CASE			)
							print(f"""Saving model {model_name} to {pytorch_dump_folder_path}"""			)
							model.save_pretrained(_SCREAMING_SNAKE_CASE			)
							print(f"""Saving image processor to {pytorch_dump_folder_path}"""			)
							image_processor.save_pretrained(_SCREAMING_SNAKE_CASE			)


if __name__ == "__main__":
				__SCREAMING_SNAKE_CASE							:      Optional[int]						=      argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    '--model_name',
				    default='dino_vitb16',
				    type=str,
				    help='Name of the model trained with DINO you\'d like to convert.',
				)
				parser.add_argument(
				    '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
				)
				parser.add_argument(
				    '--base_model',
				    action='store_true',
				    help='Whether to only convert the base model (no projection head weights).',
				)

				parser.set_defaults(base_model=True)
				__SCREAMING_SNAKE_CASE							:      int						=      parser.parse_args()
				convert_vit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.base_model)



