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()
_lowerCAmelCase       =		logging.get_logger(__name__)
def 					_snake_case	(  __snake_case  ,     __snake_case=False						):
					_UpperCamelCase			     =     []
					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"
										_UpperCamelCase			     =     [(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 					_snake_case	(  __snake_case  ,     __snake_case  ,     __snake_case=False						):
					for i in range(config.num_hidden_layers						):
										if base_model:
															_UpperCamelCase			     =     ''''''
										else:
															_UpperCamelCase			     =     '''vit.'''
										# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
										_UpperCamelCase			     =     state_dict.pop(f"""blocks.{i}.attn.qkv.weight"""						)
										_UpperCamelCase			     =     state_dict.pop(f"""blocks.{i}.attn.qkv.bias"""						)
										# next, add query, keys and values (in that order) to the state dict
										_UpperCamelCase			     =     in_proj_weight[
										    : config.hidden_size, :
										]
										_UpperCamelCase			     =     in_proj_bias[: config.hidden_size]
										_UpperCamelCase			     =     in_proj_weight[
										    config.hidden_size : config.hidden_size * 2, :
										]
										_UpperCamelCase			     =     in_proj_bias[
										    config.hidden_size : config.hidden_size * 2
										]
										_UpperCamelCase			     =     in_proj_weight[
										    -config.hidden_size :, :
										]
										_UpperCamelCase			     =     in_proj_bias[-config.hidden_size :]
def 					_snake_case	(  __snake_case						):
					_UpperCamelCase			     =     ['''head.weight''', '''head.bias''']
					for k in ignore_keys:
										state_dict.pop(__snake_case  ,     __snake_case						)
def 					_snake_case	(  __snake_case  ,     __snake_case  ,     __snake_case						):
					_UpperCamelCase			     =     dct.pop(__snake_case						)
					_UpperCamelCase			     =     val
def 					_snake_case	(  ):
					_UpperCamelCase			     =     '''http://images.cocodataset.org/val2017/000000039769.jpg'''
					_UpperCamelCase			     =     Image.open(requests.get(__snake_case  ,     stream=__snake_case						).raw						)
					return im
@torch.no_grad()
def 					_snake_case	(  __snake_case  ,     __snake_case  ,     __snake_case=True						):
					_UpperCamelCase			     =     ViTConfig()
					# patch_size
					if model_name[-1] == "8":
										_UpperCamelCase			     =     8
					# set labels if required
					if not base_model:
										_UpperCamelCase			     =     1000
										_UpperCamelCase			     =     '''huggingface/label-files'''
										_UpperCamelCase			     =     '''imagenet-1k-id2label.json'''
										_UpperCamelCase			     =     json.load(open(hf_hub_download(__snake_case  ,     __snake_case  ,     repo_type='''dataset'''						)  ,     '''r'''						)						)
										_UpperCamelCase			     =     {int(__snake_case						): v for k, v in idalabel.items()}
										_UpperCamelCase			     =     idalabel
										_UpperCamelCase			     =     {v: k for k, v in idalabel.items()}
					# size of the architecture
					if model_name in ["dino_vits8", "dino_vits16"]:
										_UpperCamelCase			     =     384
										_UpperCamelCase			     =     1536
										_UpperCamelCase			     =     12
										_UpperCamelCase			     =     6

					# load original model from torch hub
					_UpperCamelCase			     =     torch.hub.load('''facebookresearch/dino:main'''  ,     __snake_case						)
					original_model.eval()

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

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

					# Check outputs on an image, prepared by ViTImageProcessor
					_UpperCamelCase			     =     ViTImageProcessor()
					_UpperCamelCase			     =     image_processor(images=prepare_img()  ,     return_tensors='''pt'''						)
					_UpperCamelCase			     =     encoding['''pixel_values''']
					_UpperCamelCase			     =     model(__snake_case						)

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

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


if __name__ == "__main__":
					_lowerCAmelCase       =		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)
					_lowerCAmelCase       =		parser.parse_args()
					convert_vit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.base_model)

from ...configuration_utils import PretrainedConfig
from ...utils import logging


_lowerCAmelCase       =		logging.get_logger(__name__)

_lowerCAmelCase       =		{
    "microsoft/trocr-base-handwritten": (
        "https://huggingface.co/microsoft/trocr-base-handwritten/resolve/main/config.json"
    ),
    # See all TrOCR models at https://huggingface.co/models?filter=trocr
}
class     lowerCAmelCase_     (       __lowercase ):
							UpperCAmelCase				   =     "trocr"
							UpperCAmelCase				   =     ["past_key_values"]
							UpperCAmelCase				   =     {
							    "num_attention_heads": "decoder_attention_heads",
							    "hidden_size": "d_model",
							    "num_hidden_layers": "decoder_layers",
							}







							def __init__(   self      :     List[str] ,			_A      :     Optional[Any]=5_0265 ,			_A      :     Optional[Any]=1024 ,			_A      :     Optional[Any]=12 ,			_A      :     Any=16 ,			_A      :     Any=4096 ,			_A      :     Optional[Any]="gelu" ,			_A      :     Union[str, Any]=512 ,			_A      :     Dict=0.1 ,			_A      :     List[str]=0.0 ,			_A      :     Optional[Any]=0.0 ,			_A      :     Union[str, Any]=2 ,			_A      :     Any=0.02 ,			_A      :     List[str]=0.0 ,			_A      :     List[str]=True ,			_A      :     str=False ,			_A      :     List[str]=True ,			_A      :     Optional[Any]=True ,			_A      :     Optional[int]=1 ,			_A      :     int=0 ,			_A      :     Any=2 ,			**_A      :     Optional[int] ,			):
												_UpperCamelCase			     =     vocab_size
												_UpperCamelCase			     =     d_model
												_UpperCamelCase			     =     decoder_layers
												_UpperCamelCase			     =     decoder_attention_heads
												_UpperCamelCase			     =     decoder_ffn_dim
												_UpperCamelCase			     =     activation_function
												_UpperCamelCase			     =     max_position_embeddings
												_UpperCamelCase			     =     dropout
												_UpperCamelCase			     =     attention_dropout
												_UpperCamelCase			     =     activation_dropout
												_UpperCamelCase			     =     init_std
												_UpperCamelCase			     =     decoder_layerdrop
												_UpperCamelCase			     =     use_cache
												_UpperCamelCase			     =     scale_embedding
												_UpperCamelCase			     =     use_learned_position_embeddings
												_UpperCamelCase			     =     layernorm_embedding

												super().__init__(
												    pad_token_id=_A ,			bos_token_id=_A ,			eos_token_id=_A ,			decoder_start_token_id=_A ,			**_A ,			)
