





"""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()
_UpperCamelCase					      =   logging.get_logger(__name__)



def 					lowerCAmelCase_      (   SCREAMING_SNAKE_CASE							:				Dict     ,    SCREAMING_SNAKE_CASE							:				Tuple=False ):


							'''simple docstring'''







							__lowerCamelCase   :   Optional[Any]       =[]
							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"
														__lowerCamelCase   :   List[str]       =[(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 					lowerCAmelCase_      (   SCREAMING_SNAKE_CASE							:				Tuple     ,    SCREAMING_SNAKE_CASE							:				str     ,    SCREAMING_SNAKE_CASE							:				Any=False ):


							'''simple docstring'''







							for i in range(config.num_hidden_layers ):
														if base_model:
																					__lowerCamelCase   :   str       =''''''
														else:
																					__lowerCamelCase   :   Any       ='''vit.'''
														# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
														__lowerCamelCase   :   str       =state_dict.pop(F'blocks.{i}.attn.qkv.weight' )
														__lowerCamelCase   :   Optional[Any]       =state_dict.pop(F'blocks.{i}.attn.qkv.bias' )
														# next, add query, keys and values (in that order) to the state dict
														__lowerCamelCase   :   Optional[int]       =in_proj_weight[
														    : config.hidden_size, :
														]
														__lowerCamelCase   :   List[Any]       =in_proj_bias[: config.hidden_size]
														__lowerCamelCase   :   Optional[int]       =in_proj_weight[
														    config.hidden_size : config.hidden_size * 2, :
														]
														__lowerCamelCase   :   Tuple       =in_proj_bias[
														    config.hidden_size : config.hidden_size * 2
														]
														__lowerCamelCase   :   Union[str, Any]       =in_proj_weight[
														    -config.hidden_size :, :
														]
														__lowerCamelCase   :   Optional[int]       =in_proj_bias[-config.hidden_size :]



def 					lowerCAmelCase_      (   SCREAMING_SNAKE_CASE							:				Tuple ):


							'''simple docstring'''







							__lowerCamelCase   :   int       =['''head.weight''', '''head.bias''']
							for k in ignore_keys:
														state_dict.pop(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE )



def 					lowerCAmelCase_      (   SCREAMING_SNAKE_CASE							:				Tuple     ,    SCREAMING_SNAKE_CASE							:				Union[str, Any]     ,    SCREAMING_SNAKE_CASE							:				Any ):


							'''simple docstring'''







							__lowerCamelCase   :   List[Any]       =dct.pop(SCREAMING_SNAKE_CASE )
							__lowerCamelCase   :   int       =val



def 					lowerCAmelCase_      (   ):


							'''simple docstring'''







							__lowerCamelCase   :   Dict       ='''http://images.cocodataset.org/val2017/000000039769.jpg'''
							__lowerCamelCase   :   str       =Image.open(requests.get(SCREAMING_SNAKE_CASE     ,    stream=SCREAMING_SNAKE_CASE ).raw )
							return im



@torch.no_grad()
def 					lowerCAmelCase_      (   SCREAMING_SNAKE_CASE							:				Optional[int]     ,    SCREAMING_SNAKE_CASE							:				Optional[Any]     ,    SCREAMING_SNAKE_CASE							:				List[str]=True ):


							'''simple docstring'''







							__lowerCamelCase   :   Optional[Any]       =ViTConfig()
							# patch_size
							if model_name[-1] == "8":
														__lowerCamelCase   :   List[Any]       =8
							# set labels if required
							if not base_model:
														__lowerCamelCase   :   Optional[Any]       =1000
														__lowerCamelCase   :   List[Any]       ='''huggingface/label-files'''
														__lowerCamelCase   :   Dict       ='''imagenet-1k-id2label.json'''
														__lowerCamelCase   :   Union[str, Any]       =json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE     ,    repo_type='''dataset''' )     ,    '''r''' ) )
														__lowerCamelCase   :   List[Any]       ={int(SCREAMING_SNAKE_CASE ): v for k, v in idalabel.items()}
														__lowerCamelCase   :   Optional[int]       =idalabel
														__lowerCamelCase   :   Tuple       ={v: k for k, v in idalabel.items()}
							# size of the architecture
							if model_name in ["dino_vits8", "dino_vits16"]:
														__lowerCamelCase   :   Tuple       =384
														__lowerCamelCase   :   Optional[int]       =1536
														__lowerCamelCase   :   Union[str, Any]       =12
														__lowerCamelCase   :   Tuple       =6

							# load original model from torch hub
							__lowerCamelCase   :   Union[str, Any]       =torch.hub.load('''facebookresearch/dino:main'''     ,    SCREAMING_SNAKE_CASE )
							original_model.eval()

							# load state_dict of original model, remove and rename some keys
							__lowerCamelCase   :   List[Any]       =original_model.state_dict()
							if base_model:
														remove_classification_head_(SCREAMING_SNAKE_CASE )
							__lowerCamelCase   :   Optional[int]       =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:
														__lowerCamelCase   :   Union[str, Any]       =ViTModel(SCREAMING_SNAKE_CASE     ,    add_pooling_layer=SCREAMING_SNAKE_CASE ).eval()
							else:
														__lowerCamelCase   :   Any       =ViTForImageClassification(SCREAMING_SNAKE_CASE ).eval()
							model.load_state_dict(SCREAMING_SNAKE_CASE )

							# Check outputs on an image, prepared by ViTImageProcessor
							__lowerCamelCase   :   int       =ViTImageProcessor()
							__lowerCamelCase   :   Tuple       =image_processor(images=prepare_img()     ,    return_tensors='''pt''' )
							__lowerCamelCase   :   Optional[int]       =encoding['''pixel_values''']
							__lowerCamelCase   :   str       =model(SCREAMING_SNAKE_CASE )

							if base_model:
														__lowerCamelCase   :   Tuple       =original_model(SCREAMING_SNAKE_CASE )
														assert torch.allclose(SCREAMING_SNAKE_CASE     ,    outputs.last_hidden_state[:, 0, :]     ,    atol=1E-1 )
							else:
														__lowerCamelCase   :   Tuple       =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__":
							_UpperCamelCase					      =   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)
							_UpperCamelCase					      =   parser.parse_args()
							convert_vit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.base_model)











"""simple docstring"""





from dataclasses import dataclass, field
from typing import Optional
@dataclass
class 		SCREAMING_SNAKE_CASE_				:
				"""simple docstring"""
				__snake_case :							Optional[str]									=		field(
				    default="""codeparrot/codeparrot"""							,	metadata={"""help""": """Model name or path of model to be trained."""} )
				__snake_case :							Optional[str]									=		field(
				    default="""./"""							,	metadata={"""help""": """Save dir where model repo is cloned and models updates are saved to."""} )
				__snake_case :							Optional[str]									=		field(
				    default="""codeparrot/codeparrot-clean-train"""							,	metadata={"""help""": """Name or path of training dataset."""} )
				__snake_case :							Optional[str]									=		field(
				    default="""codeparrot/codeparrot-clean-valid"""							,	metadata={"""help""": """Name or path of validation dataset."""} )
				__snake_case :							Optional[int]									=		field(default=2							,	metadata={"""help""": """Batch size for training."""} )
				__snake_case :							Optional[int]									=		field(default=2							,	metadata={"""help""": """Batch size for evaluation."""} )
				__snake_case :							Optional[float]									=		field(default=0.1							,	metadata={"""help""": """Value of weight decay."""} )
				__snake_case :							Optional[int]									=		field(
				    default=1_00_00							,	metadata={"""help""": """Size of buffer used to shuffle streaming dataset."""} )
				__snake_case :							Optional[float]									=		field(default=2e-4							,	metadata={"""help""": """Learning rate fo training."""} )
				__snake_case :							Optional[str]									=		field(default="""cosine"""							,	metadata={"""help""": """Learning rate."""} )
				__snake_case :							Optional[int]									=		field(
				    default=7_50							,	metadata={"""help""": """Number of warmup steps in the learning rate schedule."""} )
				__snake_case :							Optional[int]									=		field(
				    default=16							,	metadata={"""help""": """Number of gradient accumulation steps."""} )
				__snake_case :							Optional[bool]									=		field(
				    default=snake_case__							,	metadata={"""help""": """Use gradient checkpointing to reduce memory footprint."""} )
				__snake_case :							Optional[int]									=		field(default=5_00_00							,	metadata={"""help""": """Maximum number of training steps."""} )
				__snake_case :							Optional[int]									=		field(
				    default=-1							,	metadata={"""help""": """Maximum number of evaluation steps. If -1 the full dataset is evaluated."""} )
				__snake_case :							Optional[int]									=		field(default=10_24							,	metadata={"""help""": """Sequence lengths used for training."""} )
				__snake_case :							Optional[int]									=		field(default=1							,	metadata={"""help""": """Training seed."""} )
				__snake_case :							Optional[int]									=		field(
				    default=10_24							,	metadata={"""help""": """Interval to save checkpoints. Measured as number of forward passes not training steps."""}							,	)
				__snake_case :							Optional[str]									=		field(
				    default=snake_case__							,	metadata={"""help""": """States path if the training should continue from a checkpoint folder."""} )
				__snake_case :							Optional[bool]									=		field(default=snake_case__							,	metadata={"""help""": """If True the data is pretokenized."""} )

@dataclass
class 		SCREAMING_SNAKE_CASE_				:
				"""simple docstring"""
				__snake_case :							Optional[str]									=		field(
				    default="""codeparrot/codeparrot"""							,	metadata={"""help""": """Model name or path of model to be evaluated."""} )
				__snake_case :							Optional[str]									=		field(
				    default="""codeparrot/codeparrot-clean-valid"""							,	metadata={"""help""": """Name or path of validation dataset."""} )
				__snake_case :							Optional[int]									=		field(default=2							,	metadata={"""help""": """Batch size used for evaluation."""} )
				__snake_case :							Optional[int]									=		field(
				    default=-1							,	metadata={"""help""": """Maximum number of evaluation steps. If -1 the full dataset is evaluated."""} )
				__snake_case :							Optional[int]									=		field(default=10_24							,	metadata={"""help""": """Length of sequences to be evaluated."""} )
				__snake_case :							Optional[int]									=		field(default=1							,	metadata={"""help""": """Random seed used for evaluation."""} )

@dataclass
class 		SCREAMING_SNAKE_CASE_				:
				"""simple docstring"""
				__snake_case :							Optional[str]									=		field(
				    default="""codeparrot/codeparrot"""							,	metadata={"""help""": """Model name or path of model to be evaluated."""} )
				__snake_case :							Optional[int]									=		field(default=snake_case__							,	metadata={"""help""": """Number of workers used for code evaluation."""} )
				__snake_case :							Optional[int]									=		field(
				    default=snake_case__							,	metadata={"""help""": """The number of human-eval tasks to run. If not included all tasks are evaluated."""}							,	)
				__snake_case :							Optional[bool]									=		field(
				    default=snake_case__							,	metadata={"""help""": """Sample from the language model's output distribution."""} )
				__snake_case :							Optional[float]									=		field(default=0.2							,	metadata={"""help""": """Sampling temperature used for generation."""} )
				__snake_case :							Optional[int]									=		field(default=2_56							,	metadata={"""help""": """Maximum number of newly generated tokens."""} )
				__snake_case :							Optional[int]									=		field(default=0							,	metadata={"""help""": """Top-k parameter used for generation."""} )
				__snake_case :							Optional[float]									=		field(default=0.95							,	metadata={"""help""": """Top-p parameter used for nucleus sampling."""} )
				__snake_case :							Optional[int]									=		field(default=10							,	metadata={"""help""": """Number of generations to run in parallel."""} )
				__snake_case :							Optional[int]									=		field(
				    default=2_00							,	metadata={"""help""": """Number of completions to generate for each sample."""} )
				__snake_case :							Optional[int]									=		field(default=1							,	metadata={"""help""": """Random seed used for evaluation."""} )
				__snake_case :							Optional[str]									=		field(
				    default="""eval_results.json"""							,	metadata={"""help""": """Random seed used for evaluation."""} )
				__snake_case :							Optional[str]									=		field(
				    default="""0"""							,	metadata={"""help""": """Allow `code_eval` to execute Python code on machine"""} )
				__snake_case :							Optional[int]									=		field(
				    default=-1							,	metadata={
				        """help""": (
				            """Determine which device to run the `text-generation` Pipeline on. -1 is CPU and any zero or positive"""
				            """ number corresponds to which GPU device id to run on."""
				        )
				    }							,	)

@dataclass
class 		SCREAMING_SNAKE_CASE_				:
				"""simple docstring"""
				__snake_case :							Optional[int]									=		field(
				    default=snake_case__							,	metadata={
				        """help""": """The number of CPU cores to use for parallel preprocessing. Default uses the maximum available."""
				    }							,	)
				__snake_case :							Optional[str]									=		field(
				    default="""transformersbook/codeparrot"""							,	metadata={"""help""": """Folder or name of dataset to process."""} )
				__snake_case :							Optional[str]									=		field(
				    default="""codeparrot-clean"""							,	metadata={"""help""": """Folder to save processed processed dataset."""} )
				__snake_case :							Optional[int]									=		field(
				    default=10_00_00							,	metadata={"""help""": """Number of files to save per JSON output file."""} )
				__snake_case :							Optional[str]									=		field(default="""content"""							,	metadata={"""help""": """Column containing text data to process."""} )
				__snake_case :							Optional[float]									=		field(
				    default=10_00							,	metadata={"""help""": """Maximum line length in file, otherwise file is filtered."""} )
				__snake_case :							Optional[float]									=		field(
				    default=1_00							,	metadata={"""help""": """Maximum mean line length in file, otherwise file is filtered."""} )
				__snake_case :							Optional[float]									=		field(
				    default=0.25							,	metadata={"""help""": """Maximum fraction of non-alphanumeric characters, otherwise file is filtered."""} )
				__snake_case :							Optional[float]									=		field(
				    default=1.5							,	metadata={"""help""": """Minimum character token ratio for the file, otherwise file is filtered."""} )
				__snake_case :							Optional[float]									=		field(
				    default=0.7							,	metadata={"""help""": """Probability for filtering config, test and uncommon files."""} )
				__snake_case :							Optional[str]									=		field(
				    default="""codeparrot/codeparrot"""							,	metadata={"""help""": """Name or path to the tokenizer."""}							,	)
				__snake_case :							Optional[bool]									=		field(
				    default=snake_case__							,	metadata={"""help""": """If True, near-duplicate samples are removed."""} )
				__snake_case :							Optional[float]									=		field(
				    default=0.85							,	metadata={"""help""": """Jaccard threshold for near-duplicate samples."""} )

@dataclass
class 		SCREAMING_SNAKE_CASE_				:
				"""simple docstring"""
				__snake_case :							Optional[str]									=		field(
				    default="""gpt2"""							,	metadata={"""help""": """Base tokenizer to build new tokenizer from."""} )
				__snake_case :							Optional[str]									=		field(
				    default="""transformersbook/codeparrot-train"""							,	metadata={"""help""": """Dataset to train tokenizer on."""} )
				__snake_case :							Optional[str]									=		field(default="""content"""							,	metadata={"""help""": """Column containing text data to process."""} )
				__snake_case :							Optional[int]									=		field(default=20_00_00							,	metadata={"""help""": """Number of examples to train tokenizer on."""} )
				__snake_case :							Optional[int]									=		field(
				    default=3_27_68							,	metadata={"""help""": """Number of examples to train the tokenizer on."""} )
				__snake_case :							Optional[str]									=		field(default="""codeparrot"""							,	metadata={"""help""": """Name of new tokenizer."""} )
				__snake_case :							Optional[bool]									=		field(default=snake_case__							,	metadata={"""help""": """Push saved tokenizer to the hub."""} )

@dataclass
class 		SCREAMING_SNAKE_CASE_				:
				"""simple docstring"""
				__snake_case :							Optional[str]									=		field(
				    default="""codeparrot/codeparrot"""							,	metadata={"""help""": """Name or path to the tokenizer."""} )
				__snake_case :							Optional[str]									=		field(
				    default="""codeparrot/codeparrot-clean-train"""							,	metadata={"""help""": """Name or path to the dataset to pretokenize."""} )
				__snake_case :							Optional[str]									=		field(
				    default="""tokenized-codeparrot-train"""							,	metadata={"""help""": """Repo name of the pretokenized data."""} )
				__snake_case :							Optional[int]									=		field(default=snake_case__							,	metadata={"""help""": """Number of workers used for code evaluation."""} )

@dataclass
class 		SCREAMING_SNAKE_CASE_				:
				"""simple docstring"""
				__snake_case :							Optional[str]									=		field(
				    default="""gpt2-large"""							,	metadata={"""help""": """Configuration to use for model initialization."""} )
				__snake_case :							Optional[str]									=		field(
				    default="""codeparrot/codeparrot"""							,	metadata={"""help""": """Tokenizer attached to model."""} )
				__snake_case :							Optional[str]									=		field(default="""codeparrot"""							,	metadata={"""help""": """Name of the created model."""} )
				__snake_case :							Optional[bool]									=		field(default=snake_case__							,	metadata={"""help""": """Push saved tokenizer to the hub."""} )




