




import argparse
import json
from pathlib import Path

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

from transformers import DeiTConfig, DeiTForImageClassificationWithTeacher, DeiTImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
__A        =    logging.get_logger(__name__)







def 			lowerCamelCase_		(       UpperCamelCase__				:      str ,     UpperCamelCase__				:      int=False      )     ->       Optional[int]:





				"""simple docstring"""


				__lowerCamelCase		       =    []
				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"""deit.encoder.layer.{i}.layernorm_before.weight""")      )
								rename_keys.append((F"""blocks.{i}.norm1.bias""", F"""deit.encoder.layer.{i}.layernorm_before.bias""")      )
								rename_keys.append((F"""blocks.{i}.attn.proj.weight""", F"""deit.encoder.layer.{i}.attention.output.dense.weight""")      )
								rename_keys.append((F"""blocks.{i}.attn.proj.bias""", F"""deit.encoder.layer.{i}.attention.output.dense.bias""")      )
								rename_keys.append((F"""blocks.{i}.norm2.weight""", F"""deit.encoder.layer.{i}.layernorm_after.weight""")      )
								rename_keys.append((F"""blocks.{i}.norm2.bias""", F"""deit.encoder.layer.{i}.layernorm_after.bias""")      )
								rename_keys.append((F"""blocks.{i}.mlp.fc1.weight""", F"""deit.encoder.layer.{i}.intermediate.dense.weight""")      )
								rename_keys.append((F"""blocks.{i}.mlp.fc1.bias""", F"""deit.encoder.layer.{i}.intermediate.dense.bias""")      )
								rename_keys.append((F"""blocks.{i}.mlp.fc2.weight""", F"""deit.encoder.layer.{i}.output.dense.weight""")      )
								rename_keys.append((F"""blocks.{i}.mlp.fc2.bias""", F"""deit.encoder.layer.{i}.output.dense.bias""")      )

				# projection layer + position embeddings
				rename_keys.extend(
				    [
				        ('cls_token', 'deit.embeddings.cls_token'),
				        ('dist_token', 'deit.embeddings.distillation_token'),
				        ('patch_embed.proj.weight', 'deit.embeddings.patch_embeddings.projection.weight'),
				        ('patch_embed.proj.bias', 'deit.embeddings.patch_embeddings.projection.bias'),
				        ('pos_embed', 'deit.embeddings.position_embeddings'),
				    ]      )

				if base_model:
								# layernorm + pooler
								rename_keys.extend(
								    [
								        ('norm.weight', 'layernorm.weight'),
								        ('norm.bias', 'layernorm.bias'),
								        ('pre_logits.fc.weight', 'pooler.dense.weight'),
								        ('pre_logits.fc.bias', 'pooler.dense.bias'),
								    ]      )

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

				return rename_keys







def 			lowerCamelCase_		(       UpperCamelCase__				:      Optional[Any] ,     UpperCamelCase__				:      Tuple ,     UpperCamelCase__				:      Tuple=False      )     ->       Dict:





				"""simple docstring"""


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







def 			lowerCamelCase_		(       UpperCamelCase__				:      Tuple ,     UpperCamelCase__				:      Dict ,     UpperCamelCase__				:      Tuple      )     ->       Dict:





				"""simple docstring"""


				__lowerCamelCase		       =    dct.pop(UpperCamelCase__      )
				__lowerCamelCase		       =    val







def 			lowerCamelCase_		(       )     ->       List[str]:





				"""simple docstring"""


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







@torch.no_grad()
def 			lowerCamelCase_		(       UpperCamelCase__				:      int ,     UpperCamelCase__				:      Optional[int]      )     ->       Any:





				"""simple docstring"""


				__lowerCamelCase		       =    DeiTConfig()
				# all deit models have fine-tuned heads
				__lowerCamelCase		       =    False
				# dataset (fine-tuned on ImageNet 2012), patch_size and image_size
				__lowerCamelCase		       =    1000
				__lowerCamelCase		       =    'huggingface/label-files'
				__lowerCamelCase		       =    'imagenet-1k-id2label.json'
				__lowerCamelCase		       =    json.load(open(hf_hub_download(UpperCamelCase__ ,     UpperCamelCase__ ,     repo_type='dataset'      ) ,     'r'      )      )
				__lowerCamelCase		       =    {int(UpperCamelCase__      ): v for k, v in idalabel.items()}
				__lowerCamelCase		       =    idalabel
				__lowerCamelCase		       =    {v: k for k, v in idalabel.items()}
				__lowerCamelCase		       =    int(deit_name[-6:-4]      )
				__lowerCamelCase		       =    int(deit_name[-3:]      )
				# size of the architecture
				if deit_name[9:].startswith('tiny'      ):
								__lowerCamelCase		       =    192
								__lowerCamelCase		       =    768
								__lowerCamelCase		       =    12
								__lowerCamelCase		       =    3
				elif deit_name[9:].startswith('small'      ):
								__lowerCamelCase		       =    384
								__lowerCamelCase		       =    1536
								__lowerCamelCase		       =    12
								__lowerCamelCase		       =    6
				if deit_name[9:].startswith('base'      ):
								pass
				elif deit_name[4:].startswith('large'      ):
								__lowerCamelCase		       =    1024
								__lowerCamelCase		       =    4096
								__lowerCamelCase		       =    24
								__lowerCamelCase		       =    16

				# load original model from timm
				__lowerCamelCase		       =    timm.create_model(UpperCamelCase__ ,     pretrained=UpperCamelCase__      )
				timm_model.eval()

				# load state_dict of original model, remove and rename some keys
				__lowerCamelCase		       =    timm_model.state_dict()
				__lowerCamelCase		       =    create_rename_keys(UpperCamelCase__ ,     UpperCamelCase__      )
				for src, dest in rename_keys:
								rename_key(UpperCamelCase__ ,     UpperCamelCase__ ,     UpperCamelCase__      )
				read_in_q_k_v(UpperCamelCase__ ,     UpperCamelCase__ ,     UpperCamelCase__      )

				# load HuggingFace model
				__lowerCamelCase		       =    DeiTForImageClassificationWithTeacher(UpperCamelCase__      ).eval()
				model.load_state_dict(UpperCamelCase__      )

				# Check outputs on an image, prepared by DeiTImageProcessor
				__lowerCamelCase		       =    int(
				    (256 / 224) * config.image_size      )  # to maintain same ratio w.r.t. 224 images, see https://github.com/facebookresearch/deit/blob/ab5715372db8c6cad5740714b2216d55aeae052e/datasets.py#L103
				__lowerCamelCase		       =    DeiTImageProcessor(size=UpperCamelCase__ ,     crop_size=config.image_size      )
				__lowerCamelCase		       =    image_processor(images=prepare_img() ,     return_tensors='pt'      )
				__lowerCamelCase		       =    encoding['pixel_values']
				__lowerCamelCase		       =    model(UpperCamelCase__      )

				__lowerCamelCase		       =    timm_model(UpperCamelCase__      )
				assert timm_logits.shape == outputs.logits.shape
				assert torch.allclose(UpperCamelCase__ ,     outputs.logits ,     atol=1E-3      )

				Path(UpperCamelCase__      ).mkdir(exist_ok=UpperCamelCase__      )
				print(F"""Saving model {deit_name} to {pytorch_dump_folder_path}"""      )
				model.save_pretrained(UpperCamelCase__      )
				print(F"""Saving image processor to {pytorch_dump_folder_path}"""      )
				image_processor.save_pretrained(UpperCamelCase__      )


if __name__ == "__main__":
					__A        =    argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    "--deit_name",
					    default="vit_deit_base_distilled_patch16_224",
					    type=str,
					    help="Name of the DeiT timm model you'd like to convert.",
					)
					parser.add_argument(
					    "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
					)

					__A        =    parser.parse_args()
					convert_deit_checkpoint(args.deit_name, args.pytorch_dump_folder_path)








import argparse
import os
import pickle
import sys

import torch

from transformers import TransfoXLConfig, TransfoXLLMHeadModel, load_tf_weights_in_transfo_xl
from transformers.models.transfo_xl import tokenization_transfo_xl as data_utils
from transformers.models.transfo_xl.tokenization_transfo_xl import CORPUS_NAME, VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()

# We do this to be able to load python 2 datasets pickles
# See e.g. https://stackoverflow.com/questions/2121874/python-pickling-after-changing-a-modules-directory/2121918#2121918
__A        =    data_utils.TransfoXLTokenizer
__A        =    data_utils.TransfoXLCorpus
__A        =    data_utils
__A        =    data_utils







def 			lowerCamelCase_		(       UpperCamelCase__				:      List[str] ,     UpperCamelCase__				:      Optional[int] ,     UpperCamelCase__				:      Union[str, Any] ,     UpperCamelCase__				:      Optional[Any]      )     ->       Union[str, Any]:





				"""simple docstring"""


				if transfo_xl_dataset_file:
								# Convert a pre-processed corpus (see original TensorFlow repo)
								with open(UpperCamelCase__ ,     'rb'      ) as fp:
												__lowerCamelCase		       =    pickle.load(UpperCamelCase__ ,     encoding='latin1'      )
								# Save vocabulary and dataset cache as Dictionaries (should be better than pickles for the long-term)
								__lowerCamelCase		       =    pytorch_dump_folder_path + '/' + VOCAB_FILES_NAMES['pretrained_vocab_file']
								print(F"""Save vocabulary to {pytorch_vocab_dump_path}"""      )
								__lowerCamelCase		       =    corpus.vocab.__dict__
								torch.save(UpperCamelCase__ ,     UpperCamelCase__      )

								__lowerCamelCase		       =    corpus.__dict__
								corpus_dict_no_vocab.pop('vocab' ,     UpperCamelCase__      )
								__lowerCamelCase		       =    pytorch_dump_folder_path + '/' + CORPUS_NAME
								print(F"""Save dataset to {pytorch_dataset_dump_path}"""      )
								torch.save(UpperCamelCase__ ,     UpperCamelCase__      )

				if tf_checkpoint_path:
								# Convert a pre-trained TensorFlow model
								__lowerCamelCase		       =    os.path.abspath(UpperCamelCase__      )
								__lowerCamelCase		       =    os.path.abspath(UpperCamelCase__      )

								print(F"""Converting Transformer XL checkpoint from {tf_path} with config at {config_path}."""      )
								# Initialise PyTorch model
								if transfo_xl_config_file == "":
												__lowerCamelCase		       =    TransfoXLConfig()
								else:
												__lowerCamelCase		       =    TransfoXLConfig.from_json_file(UpperCamelCase__      )
								print(F"""Building PyTorch model from configuration: {config}"""      )
								__lowerCamelCase		       =    TransfoXLLMHeadModel(UpperCamelCase__      )

								__lowerCamelCase		       =    load_tf_weights_in_transfo_xl(UpperCamelCase__ ,     UpperCamelCase__ ,     UpperCamelCase__      )
								# Save pytorch-model
								__lowerCamelCase		       =    os.path.join(UpperCamelCase__ ,     UpperCamelCase__      )
								__lowerCamelCase		       =    os.path.join(UpperCamelCase__ ,     UpperCamelCase__      )
								print(F"""Save PyTorch model to {os.path.abspath(UpperCamelCase__      )}"""      )
								torch.save(model.state_dict() ,     UpperCamelCase__      )
								print(F"""Save configuration file to {os.path.abspath(UpperCamelCase__      )}"""      )
								with open(UpperCamelCase__ ,     'w' ,     encoding='utf-8'      ) as f:
												f.write(config.to_json_string()      )


if __name__ == "__main__":
					__A        =    argparse.ArgumentParser()
					parser.add_argument(
					    "--pytorch_dump_folder_path",
					    default=None,
					    type=str,
					    required=True,
					    help="Path to the folder to store the PyTorch model or dataset/vocab.",
					)
					parser.add_argument(
					    "--tf_checkpoint_path",
					    default="",
					    type=str,
					    help="An optional path to a TensorFlow checkpoint path to be converted.",
					)
					parser.add_argument(
					    "--transfo_xl_config_file",
					    default="",
					    type=str,
					    help=(
					        "An optional config json file corresponding to the pre-trained BERT model. \n"
					        "This specifies the model architecture."
					    ),
					)
					parser.add_argument(
					    "--transfo_xl_dataset_file",
					    default="",
					    type=str,
					    help="An optional dataset file to be converted in a vocabulary.",
					)
					__A        =    parser.parse_args()
					convert_transfo_xl_checkpoint_to_pytorch(
					    args.tf_checkpoint_path,
					    args.transfo_xl_config_file,
					    args.pytorch_dump_folder_path,
					    args.transfo_xl_dataset_file,
					)


