





import argparse
import json
from collections import OrderedDict
from pathlib import Path

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

from transformers import PoolFormerConfig, PoolFormerForImageClassification, PoolFormerImageProcessor
from transformers.utils import logging


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


def 							SCREAMING_SNAKE_CASE__  (      __a				,	__a				,	__a				,	__a				):
			snake_case_						:     Optional[int]     =		original_name.split('.'				)[0]
			snake_case_						:     List[Any]     =		key.split('.'				)
			snake_case_						:     List[str]     =		int(key_list[key_list.index(__snake_case				) - 2]				)
			snake_case_						:     Any     =		int(key_list[key_list.index(__snake_case				) - 1]				)
			snake_case_						:     str     =		orig_block_num - offset

			snake_case_						:     int     =		key.replace(f"""{orig_block_num}.{layer_num}.{original_name}"""				,	f"""block.{new_block_num}.{layer_num}.{new_name}"""				)
			return key


def 							SCREAMING_SNAKE_CASE__  (      __a				):
			snake_case_						:     Any     =		OrderedDict()
			snake_case_						:     Optional[Any]     =		0, 0
			for key, value in state_dict.items():
						if key.startswith('network'				):
									snake_case_						:     int     =		key.replace('network'				,	'poolformer.encoder'				)
						if "proj" in key:
									# Works for the first embedding as well as the internal embedding layers
									if key.endswith('bias'				) and "patch_embed" not in key:
												patch_emb_offset += 1
									snake_case_						:     List[str]     =		key[: key.find('proj'				)]
									snake_case_						:     Any     =		key.replace(__snake_case				,	f"""patch_embeddings.{total_embed_found}."""				)
									snake_case_						:     Optional[Any]     =		key.replace('proj'				,	'projection'				)
									if key.endswith('bias'				):
												total_embed_found += 1
						if "patch_embeddings" in key:
									snake_case_						:     Any     =		"poolformer.encoder." + key
						if "mlp.fc1" in key:
									snake_case_						:     Dict     =		replace_key_with_offset(__snake_case				,	__snake_case				,	'mlp.fc1'				,	'output.conv1'				)
						if "mlp.fc2" in key:
									snake_case_						:     int     =		replace_key_with_offset(__snake_case				,	__snake_case				,	'mlp.fc2'				,	'output.conv2'				)
						if "norm1" in key:
									snake_case_						:     List[Any]     =		replace_key_with_offset(__snake_case				,	__snake_case				,	'norm1'				,	'before_norm'				)
						if "norm2" in key:
									snake_case_						:     Dict     =		replace_key_with_offset(__snake_case				,	__snake_case				,	'norm2'				,	'after_norm'				)
						if "layer_scale_1" in key:
									snake_case_						:     str     =		replace_key_with_offset(__snake_case				,	__snake_case				,	'layer_scale_1'				,	'layer_scale_1'				)
						if "layer_scale_2" in key:
									snake_case_						:     Union[str, Any]     =		replace_key_with_offset(__snake_case				,	__snake_case				,	'layer_scale_2'				,	'layer_scale_2'				)
						if "head" in key:
									snake_case_						:     str     =		key.replace('head'				,	'classifier'				)
						snake_case_						:     str     =		value
			return new_state_dict


def 							SCREAMING_SNAKE_CASE__  (      ):
			snake_case_						:     Dict     =		"http://images.cocodataset.org/val2017/000000039769.jpg"
			snake_case_						:     Any     =		Image.open(requests.get(__snake_case				,	stream=__snake_case				).raw				)

			return image


@torch.no_grad()
def 							SCREAMING_SNAKE_CASE__  (      __a				,	__a				,	__a				):
			snake_case_						:     Tuple     =		PoolFormerConfig()

			# set attributes based on model_name
			snake_case_						:     List[str]     =		"huggingface/label-files"
			snake_case_						:     List[Any]     =		model_name[-3:]
			snake_case_						:     List[Any]     =		10_00
			snake_case_						:     str     =		"imagenet-1k-id2label.json"
			snake_case_						:     List[str]     =		(1, 10_00)

			# set config attributes
			snake_case_						:     List[Any]     =		json.load(open(hf_hub_download(__snake_case				,	__snake_case				,	repo_type='dataset'				)				,	'r'				)				)
			snake_case_						:     Tuple     =		{int(__snake_case				): v for k, v in idalabel.items()}
			snake_case_						:     str     =		idalabel
			snake_case_						:     Any     =		{v: k for k, v in idalabel.items()}
			if size == "s12":
						snake_case_						:     Dict     =		[2, 2, 6, 2]
						snake_case_						:     Optional[Any]     =		[64, 1_28, 3_20, 5_12]
						snake_case_						:     List[Any]     =		4.0
						snake_case_						:     Union[str, Any]     =		0.9
			elif size == "s24":
						snake_case_						:     Tuple     =		[4, 4, 12, 4]
						snake_case_						:     str     =		[64, 1_28, 3_20, 5_12]
						snake_case_						:     Optional[int]     =		4.0
						snake_case_						:     int     =		0.9
			elif size == "s36":
						snake_case_						:     List[str]     =		[6, 6, 18, 6]
						snake_case_						:     List[str]     =		[64, 1_28, 3_20, 5_12]
						snake_case_						:     Any     =		4.0
						snake_case_						:     Optional[Any]     =		1E-6
						snake_case_						:     Optional[Any]     =		0.9
			elif size == "m36":
						snake_case_						:     Tuple     =		[6, 6, 18, 6]
						snake_case_						:     List[Any]     =		[96, 1_92, 3_84, 7_68]
						snake_case_						:     List[str]     =		4.0
						snake_case_						:     Optional[Any]     =		1E-6
						snake_case_						:     Union[str, Any]     =		0.95
			elif size == "m48":
						snake_case_						:     Union[str, Any]     =		[8, 8, 24, 8]
						snake_case_						:     List[str]     =		[96, 1_92, 3_84, 7_68]
						snake_case_						:     List[Any]     =		4.0
						snake_case_						:     Union[str, Any]     =		1E-6
						snake_case_						:     Optional[int]     =		0.95
			else:
						raise ValueError(f"""Size {size} not supported"""				)

			# load image processor
			snake_case_						:     Optional[int]     =		PoolFormerImageProcessor(crop_pct=__snake_case				)

			# Prepare image
			snake_case_						:     List[str]     =		prepare_img()
			snake_case_						:     int     =		image_processor(images=__snake_case				,	return_tensors='pt'				).pixel_values

			logger.info(f"""Converting model {model_name}..."""				)

			# load original state dict
			snake_case_						:     Optional[Any]     =		torch.load(__snake_case				,	map_location=torch.device('cpu'				)				)

			# rename keys
			snake_case_						:     Union[str, Any]     =		rename_keys(__snake_case				)

			# create HuggingFace model and load state dict
			snake_case_						:     List[str]     =		PoolFormerForImageClassification(__snake_case				)
			model.load_state_dict(__snake_case				)
			model.eval()

			# Define image processor
			snake_case_						:     Dict     =		PoolFormerImageProcessor(crop_pct=__snake_case				)
			snake_case_						:     Tuple     =		image_processor(images=prepare_img()				,	return_tensors='pt'				).pixel_values

			# forward pass
			snake_case_						:     Union[str, Any]     =		model(__snake_case				)
			snake_case_						:     List[Any]     =		outputs.logits

			# define expected logit slices for different models
			if size == "s12":
						snake_case_						:     int     =		torch.tensor([-0.3045, -0.6758, -0.4869]				)
			elif size == "s24":
						snake_case_						:     Any     =		torch.tensor([0.4402, -0.1374, -0.8045]				)
			elif size == "s36":
						snake_case_						:     Dict     =		torch.tensor([-0.6080, -0.5133, -0.5898]				)
			elif size == "m36":
						snake_case_						:     Dict     =		torch.tensor([0.3952, 0.2263, -1.2668]				)
			elif size == "m48":
						snake_case_						:     List[Any]     =		torch.tensor([0.1167, -0.0656, -0.3423]				)
			else:
						raise ValueError(f"""Size {size} not supported"""				)

			# verify logits
			assert logits.shape == expected_shape
			assert torch.allclose(logits[0, :3]				,	__snake_case				,	atol=1E-2				)

			# finally, save model and image processor
			logger.info(f"""Saving PyTorch model and image processor to {pytorch_dump_folder_path}..."""				)
			Path(__snake_case				).mkdir(exist_ok=__snake_case				)
			model.save_pretrained(__snake_case				)
			print(f"""Saving image processor to {pytorch_dump_folder_path}"""				)
			image_processor.save_pretrained(__snake_case				)


if __name__ == "__main__":
						_SCREAMING_SNAKE_CASE 		=						argparse.ArgumentParser()

						parser.add_argument(
						    """--model_name""",
						    default="""poolformer_s12""",
						    type=str,
						    help="""Name of the model you\'d like to convert.""",
						)
						parser.add_argument(
						    """--checkpoint_path""", default=None, type=str, help="""Path to the original PyTorch checkpoint (.pth file)."""
						)
						parser.add_argument(
						    """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the folder to output PyTorch model."""
						)
						_SCREAMING_SNAKE_CASE 		=						parser.parse_args()
						convert_poolformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)








import argparse

from transformers import (
    TapasConfig,
    TapasForMaskedLM,
    TapasForQuestionAnswering,
    TapasForSequenceClassification,
    TapasModel,
    TapasTokenizer,
    load_tf_weights_in_tapas,
)
from transformers.utils import logging


logging.set_verbosity_info()


def 							SCREAMING_SNAKE_CASE__  (      __a				,	__a				,	__a				,	__a				,	__a				):
	# Initialise PyTorch model.
	# If you want to convert a checkpoint that uses absolute position embeddings, make sure to set reset_position_index_per_cell of
	# TapasConfig to False.

	# initialize configuration from json file
	snake_case_						:     Optional[int]     =		TapasConfig.from_json_file(__a				)
	# set absolute/relative position embeddings parameter
	snake_case_						:     List[Any]     =		reset_position_index_per_cell

	# set remaining parameters of TapasConfig as well as the model based on the task
	if task == "SQA":
		snake_case_						:     int     =		TapasForQuestionAnswering(config=__a				)
	elif task == "WTQ":
		# run_task_main.py hparams
		snake_case_						:     Optional[int]     =		4
		snake_case_						:     List[str]     =		True
		# hparam_utils.py hparams
		snake_case_						:     Optional[Any]     =		0.664694
		snake_case_						:     Dict     =		0.207951
		snake_case_						:     Tuple     =		0.121194
		snake_case_						:     Dict     =		True
		snake_case_						:     int     =		True
		snake_case_						:     int     =		False
		snake_case_						:     str     =		0.0352513

		snake_case_						:     int     =		TapasForQuestionAnswering(config=__a				)
	elif task == "WIKISQL_SUPERVISED":
		# run_task_main.py hparams
		snake_case_						:     int     =		4
		snake_case_						:     Optional[int]     =		False
		# hparam_utils.py hparams
		snake_case_						:     str     =		36.4519
		snake_case_						:     Optional[Any]     =		0.903421
		snake_case_						:     List[Any]     =		222.088
		snake_case_						:     Optional[int]     =		True
		snake_case_						:     Optional[Any]     =		True
		snake_case_						:     str     =		True
		snake_case_						:     int     =		0.763141

		snake_case_						:     str     =		TapasForQuestionAnswering(config=__a				)
	elif task == "TABFACT":
		snake_case_						:     List[Any]     =		TapasForSequenceClassification(config=__a				)
	elif task == "MLM":
		snake_case_						:     Optional[int]     =		TapasForMaskedLM(config=__a				)
	elif task == "INTERMEDIATE_PRETRAINING":
		snake_case_						:     Tuple     =		TapasModel(config=__a				)
	else:
		raise ValueError(f"""Task {task} not supported."""				)

	print(f"""Building PyTorch model from configuration: {config}"""				)
	# Load weights from tf checkpoint
	load_tf_weights_in_tapas(__a				,	__a				,	__a				)

	# Save pytorch-model (weights and configuration)
	print(f"""Save PyTorch model to {pytorch_dump_path}"""				)
	model.save_pretrained(__a				)

	# Save tokenizer files
	print(f"""Save tokenizer files to {pytorch_dump_path}"""				)
	snake_case_						:     str     =		TapasTokenizer(vocab_file=tf_checkpoint_path[:-10] + 'vocab.txt'				,	model_max_length=5_12				)
	tokenizer.save_pretrained(__a				)

	print('Used relative position embeddings:'				,	model.config.reset_position_index_per_cell				)


if __name__ == "__main__":
						_SCREAMING_SNAKE_CASE 		=						argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    """--task""", default="""SQA""", type=str, help="""Model task for which to convert a checkpoint. Defaults to SQA."""
						)
						parser.add_argument(
						    """--reset_position_index_per_cell""",
						    default=False,
						    action="""store_true""",
						    help="""Whether to use relative position embeddings or not. Defaults to True.""",
						)
						parser.add_argument(
						    """--tf_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
						)
						parser.add_argument(
						    """--tapas_config_file""",
						    default=None,
						    type=str,
						    required=True,
						    help=(
						        """The config json file corresponding to the pre-trained TAPAS model. \n"""
						        """This specifies the model architecture."""
						    ),
						)
						parser.add_argument(
						    """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
						)
						_SCREAMING_SNAKE_CASE 		=						parser.parse_args()
						convert_tf_checkpoint_to_pytorch(
						    args.task,
						    args.reset_position_index_per_cell,
						    args.tf_checkpoint_path,
						    args.tapas_config_file,
						    args.pytorch_dump_path,
						)

