






from argparse import ArgumentParser, Namespace

from ..utils import logging
from . import BaseTransformersCLICommand






def   a			(						lowerCamelCase_				):






				'''simple docstring'''
				return ConvertCommand(
				    args.model_type		,   args.tf_checkpoint		,   args.pytorch_dump_output		,   args.config		,   args.finetuning_task_name				)


A__	:			int			   = '\ntransformers can only be used from the commandline to convert TensorFlow models in PyTorch, In that case, it requires\nTensorFlow to be installed. Please see https://www.tensorflow.org/install/ for installation instructions.\n'




class       _UpperCAmelCase					(       A__	):

				"""simple docstring"""




				@staticmethod
				def 				lowercase__   (     lowerCamelCase						:    ArgumentParser  ):

								'''simple docstring'''
								lowercase__    =			parser.add_parser(
								    '''convert''',  help='''CLI tool to run convert model from original author checkpoints to Transformers PyTorch checkpoints.''',  )
								train_parser.add_argument('''--model_type''',  type=lowerCamelCase,  required=lowerCamelCase,  help='''Model\'s type.'''  )
								train_parser.add_argument(
								    '''--tf_checkpoint''',  type=lowerCamelCase,  required=lowerCamelCase,  help='''TensorFlow checkpoint path or folder.'''  )
								train_parser.add_argument(
								    '''--pytorch_dump_output''',  type=lowerCamelCase,  required=lowerCamelCase,  help='''Path to the PyTorch saved model output.'''  )
								train_parser.add_argument('''--config''',  type=lowerCamelCase,  default='''''',  help='''Configuration file path or folder.'''  )
								train_parser.add_argument(
								    '''--finetuning_task_name''',  type=lowerCamelCase,  default=lowerCamelCase,  help='''Optional fine-tuning task name if the TF model was a finetuned model.''',  )
								train_parser.set_defaults(func=lowerCamelCase  )




				def __init__(     self						:    Optional[int],  lowerCamelCase						:    str,  lowerCamelCase						:    str,  lowerCamelCase						:    str,  lowerCamelCase						:    str,  lowerCamelCase						:    str,  *lowerCamelCase						:    Union[str, Any],  ):

								'''simple docstring'''
								lowercase__    =			logging.get_logger('''transformers-cli/converting'''  )

								self._logger.info(F"""Loading model {model_type}"""  )
								lowercase__    =			model_type
								lowercase__    =			tf_checkpoint
								lowercase__    =			pytorch_dump_output
								lowercase__    =			config
								lowercase__    =			finetuning_task_name




				def 				lowercase__   (     self						:    Tuple  ):

								'''simple docstring'''
								if self._model_type == "albert":
												try:
																from ..models.albert.convert_albert_original_tf_checkpoint_to_pytorch import (
																    convert_tf_checkpoint_to_pytorch,
																)
												except ImportError:
																raise ImportError(lowerCamelCase  )

												convert_tf_checkpoint_to_pytorch(self._tf_checkpoint,  self._config,  self._pytorch_dump_output  )
								elif self._model_type == "bert":
												try:
																from ..models.bert.convert_bert_original_tf_checkpoint_to_pytorch import (
																    convert_tf_checkpoint_to_pytorch,
																)
												except ImportError:
																raise ImportError(lowerCamelCase  )

												convert_tf_checkpoint_to_pytorch(self._tf_checkpoint,  self._config,  self._pytorch_dump_output  )
								elif self._model_type == "funnel":
												try:
																from ..models.funnel.convert_funnel_original_tf_checkpoint_to_pytorch import (
																    convert_tf_checkpoint_to_pytorch,
																)
												except ImportError:
																raise ImportError(lowerCamelCase  )

												convert_tf_checkpoint_to_pytorch(self._tf_checkpoint,  self._config,  self._pytorch_dump_output  )
								elif self._model_type == "t5":
												try:
																from ..models.ta.convert_ta_original_tf_checkpoint_to_pytorch import convert_tf_checkpoint_to_pytorch
												except ImportError:
																raise ImportError(lowerCamelCase  )

												convert_tf_checkpoint_to_pytorch(self._tf_checkpoint,  self._config,  self._pytorch_dump_output  )
								elif self._model_type == "gpt":
												from ..models.openai.convert_openai_original_tf_checkpoint_to_pytorch import (
												    convert_openai_checkpoint_to_pytorch,
												)

												convert_openai_checkpoint_to_pytorch(self._tf_checkpoint,  self._config,  self._pytorch_dump_output  )
								elif self._model_type == "transfo_xl":
												try:
																from ..models.transfo_xl.convert_transfo_xl_original_tf_checkpoint_to_pytorch import (
																    convert_transfo_xl_checkpoint_to_pytorch,
																)
												except ImportError:
																raise ImportError(lowerCamelCase  )

												if "ckpt" in self._tf_checkpoint.lower():
																lowercase__    =			self._tf_checkpoint
																lowercase__    =			''''''
												else:
																lowercase__    =			self._tf_checkpoint
																lowercase__    =			''''''
												convert_transfo_xl_checkpoint_to_pytorch(
												    lowerCamelCase,  self._config,  self._pytorch_dump_output,  lowerCamelCase  )
								elif self._model_type == "gpt2":
												try:
																from ..models.gpta.convert_gpta_original_tf_checkpoint_to_pytorch import (
																    convert_gpta_checkpoint_to_pytorch,
																)
												except ImportError:
																raise ImportError(lowerCamelCase  )

												convert_gpta_checkpoint_to_pytorch(self._tf_checkpoint,  self._config,  self._pytorch_dump_output  )
								elif self._model_type == "xlnet":
												try:
																from ..models.xlnet.convert_xlnet_original_tf_checkpoint_to_pytorch import (
																    convert_xlnet_checkpoint_to_pytorch,
																)
												except ImportError:
																raise ImportError(lowerCamelCase  )

												convert_xlnet_checkpoint_to_pytorch(
												    self._tf_checkpoint,  self._config,  self._pytorch_dump_output,  self._finetuning_task_name  )
								elif self._model_type == "xlm":
												from ..models.xlm.convert_xlm_original_pytorch_checkpoint_to_pytorch import (
												    convert_xlm_checkpoint_to_pytorch,
												)

												convert_xlm_checkpoint_to_pytorch(self._tf_checkpoint,  self._pytorch_dump_output  )
								elif self._model_type == "lxmert":
												from ..models.lxmert.convert_lxmert_original_tf_checkpoint_to_pytorch import (
												    convert_lxmert_checkpoint_to_pytorch,
												)

												convert_lxmert_checkpoint_to_pytorch(self._tf_checkpoint,  self._pytorch_dump_output  )
								elif self._model_type == "rembert":
												from ..models.rembert.convert_rembert_tf_checkpoint_to_pytorch import (
												    convert_rembert_tf_checkpoint_to_pytorch,
												)

												convert_rembert_tf_checkpoint_to_pytorch(self._tf_checkpoint,  self._config,  self._pytorch_dump_output  )
								else:
												raise ValueError(
												    '''--model_type should be selected in the list [bert, gpt, gpt2, t5, transfo_xl, xlnet, xlm, lxmert]'''  )












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


logging.set_verbosity_info()
A__	:			Optional[Any]			   = logging.get_logger(__name__)






def   a			(						lowerCamelCase_		,   lowerCamelCase_=False				):






				'''simple docstring'''
				lowercase__    =			[]
				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'''),
								        ('''pre_logits.fc.weight''', '''pooler.dense.weight'''),
								        ('''pre_logits.fc.bias''', '''pooler.dense.bias'''),
								    ]				)

								# if just the base model, we should remove "vit" from all keys that start with "vit"
								lowercase__    =			[(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			(						lowerCamelCase_		,   lowerCamelCase_		,   lowerCamelCase_=False				):






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






def   a			(						lowerCamelCase_				):






				'''simple docstring'''
				lowercase__    =			['''head.weight''', '''head.bias''']
				for k in ignore_keys:
								state_dict.pop(lowerCamelCase_		,   lowerCamelCase_				)






def   a			(						lowerCamelCase_		,   lowerCamelCase_		,   lowerCamelCase_				):






				'''simple docstring'''
				lowercase__    =			dct.pop(lowerCamelCase_				)
				lowercase__    =			val






def   a			(						):






				'''simple docstring'''
				lowercase__    =			'''http://images.cocodataset.org/val2017/000000039769.jpg'''
				lowercase__    =			Image.open(requests.get(lowerCamelCase_		,   stream=lowerCamelCase_				).raw				)
				return im






@torch.no_grad()
def   a			(						lowerCamelCase_		,   lowerCamelCase_				):






				'''simple docstring'''
				lowercase__    =			ViTConfig()
				lowercase__    =			False
				# dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
				if vit_name[-5:] == "in21k":
								lowercase__    =			True
								lowercase__    =			int(vit_name[-12:-10]				)
								lowercase__    =			int(vit_name[-9:-6]				)
				else:
								lowercase__    =			1000
								lowercase__    =			'''huggingface/label-files'''
								lowercase__    =			'''imagenet-1k-id2label.json'''
								lowercase__    =			json.load(open(hf_hub_download(lowerCamelCase_		,   lowerCamelCase_		,   repo_type='''dataset'''				)		,   '''r'''				)				)
								lowercase__    =			{int(lowerCamelCase_				): v for k, v in idalabel.items()}
								lowercase__    =			idalabel
								lowercase__    =			{v: k for k, v in idalabel.items()}
								lowercase__    =			int(vit_name[-6:-4]				)
								lowercase__    =			int(vit_name[-3:]				)
				# size of the architecture
				if "deit" in vit_name:
								if vit_name[9:].startswith('''tiny'''				):
												lowercase__    =			192
												lowercase__    =			768
												lowercase__    =			12
												lowercase__    =			3
								elif vit_name[9:].startswith('''small'''				):
												lowercase__    =			384
												lowercase__    =			1536
												lowercase__    =			12
												lowercase__    =			6
								else:
												pass
				else:
								if vit_name[4:].startswith('''small'''				):
												lowercase__    =			768
												lowercase__    =			2304
												lowercase__    =			8
												lowercase__    =			8
								elif vit_name[4:].startswith('''base'''				):
												pass
								elif vit_name[4:].startswith('''large'''				):
												lowercase__    =			1024
												lowercase__    =			4096
												lowercase__    =			24
												lowercase__    =			16
								elif vit_name[4:].startswith('''huge'''				):
												lowercase__    =			1280
												lowercase__    =			5120
												lowercase__    =			32
												lowercase__    =			16

    # load original model from timm
				lowercase__    =			timm.create_model(lowerCamelCase_		,   pretrained=lowerCamelCase_				)
				timm_model.eval()

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

				# load HuggingFace model
				if vit_name[-5:] == "in21k":
								lowercase__    =			ViTModel(lowerCamelCase_				).eval()
				else:
								lowercase__    =			ViTForImageClassification(lowerCamelCase_				).eval()
				model.load_state_dict(lowerCamelCase_				)

				# Check outputs on an image, prepared by ViTImageProcessor/DeiTImageProcessor
				if "deit" in vit_name:
								lowercase__    =			DeiTImageProcessor(size=config.image_size				)
				else:
								lowercase__    =			ViTImageProcessor(size=config.image_size				)
				lowercase__    =			image_processor(images=prepare_img()		,   return_tensors='''pt'''				)
				lowercase__    =			encoding['''pixel_values''']
				lowercase__    =			model(lowerCamelCase_				)

				if base_model:
								lowercase__    =			timm_model.forward_features(lowerCamelCase_				)
								assert timm_pooled_output.shape == outputs.pooler_output.shape
								assert torch.allclose(lowerCamelCase_		,   outputs.pooler_output		,   atol=1e-3				)
				else:
								lowercase__    =			timm_model(lowerCamelCase_				)
								assert timm_logits.shape == outputs.logits.shape
								assert torch.allclose(lowerCamelCase_		,   outputs.logits		,   atol=1e-3				)

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


if __name__ == "__main__":
							A__	:			int			   = argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    '--vit_name',
							    default='vit_base_patch16_224',
							    type=str,
							    help='Name of the ViT 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__	:			str			   = parser.parse_args()
							convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path)




