
'''simple docstring'''





import argparse
import json

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

from transformers import AutoImageProcessor, SwinConfig, SwinForImageClassification





def 				snake_case							(						UpperCAmelCase		)->	Any:







      """simple docstring"""
      __A						=	SwinConfig()
      __A						=	swin_name.split('_'		)

      __A						=	name_split[1]
      __A						=	int(name_split[4]		)
      __A						=	int(name_split[3][-1]		)

      if model_size == "tiny":
            __A						=	9_6
            __A						=	(2, 2, 6, 2)
            __A						=	(3, 6, 1_2, 2_4)
      elif model_size == "small":
            __A						=	9_6
            __A						=	(2, 2, 1_8, 2)
            __A						=	(3, 6, 1_2, 2_4)
      elif model_size == "base":
            __A						=	1_2_8
            __A						=	(2, 2, 1_8, 2)
            __A						=	(4, 8, 1_6, 3_2)
      else:
            __A						=	1_9_2
            __A						=	(2, 2, 1_8, 2)
            __A						=	(6, 1_2, 2_4, 4_8)

      if "in22k" in swin_name:
            __A						=	2_1_8_4_1
      else:
            __A						=	1_0_0_0
            __A						=	"""huggingface/label-files"""
            __A						=	"""imagenet-1k-id2label.json"""
            __A						=	json.load(open(hf_hub_download(lowerCAmelCase_					,     lowerCAmelCase_					,     repo_type='dataset'		)					,     'r'		)		)
            __A						=	{int(lowerCAmelCase_		): v for k, v in idalabel.items()}
            __A						=	idalabel
            __A						=	{v: k for k, v in idalabel.items()}

      __A						=	img_size
      __A						=	num_classes
      __A						=	embed_dim
      __A						=	depths
      __A						=	num_heads
      __A						=	window_size

      return config





def 				snake_case							(						UpperCAmelCase		)->	List[Any]:







      """simple docstring"""
      if "patch_embed.proj" in name:
            __A						=	name.replace('patch_embed.proj'					,     'embeddings.patch_embeddings.projection'		)
      if "patch_embed.norm" in name:
            __A						=	name.replace('patch_embed.norm'					,     'embeddings.norm'		)
      if "layers" in name:
            __A						=	"""encoder.""" + name
      if "attn.proj" in name:
            __A						=	name.replace('attn.proj'					,     'attention.output.dense'		)
      if "attn" in name:
            __A						=	name.replace('attn'					,     'attention.self'		)
      if "norm1" in name:
            __A						=	name.replace('norm1'					,     'layernorm_before'		)
      if "norm2" in name:
            __A						=	name.replace('norm2'					,     'layernorm_after'		)
      if "mlp.fc1" in name:
            __A						=	name.replace('mlp.fc1'					,     'intermediate.dense'		)
      if "mlp.fc2" in name:
            __A						=	name.replace('mlp.fc2'					,     'output.dense'		)

      if name == "norm.weight":
            __A						=	"""layernorm.weight"""
      if name == "norm.bias":
            __A						=	"""layernorm.bias"""

      if "head" in name:
            __A						=	name.replace('head'					,     'classifier'		)
      else:
            __A						=	"""swin.""" + name

      return name





def 				snake_case							(						UpperCAmelCase					,     UpperCAmelCase		)->	Union[str, Any]:







      """simple docstring"""
      for key in orig_state_dict.copy().keys():
            __A						=	orig_state_dict.pop(lowerCAmelCase_		)

            if "mask" in key:
                  continue
            elif "qkv" in key:
                  __A						=	key.split('.'		)
                  __A						=	int(key_split[1]		)
                  __A						=	int(key_split[3]		)
                  __A						=	model.swin.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

                  if "weight" in key:
                        __A						=	val[:dim, :]
                        __A						=	val[
                            dim : dim * 2, :
                        ]
                        __A						=	val[-dim:, :]
                  else:
                        __A						=	val[
                            :dim
                        ]
                        __A						=	val[
                            dim : dim * 2
                        ]
                        __A						=	val[
                            -dim:
                        ]
            else:
                  __A						=	val

      return orig_state_dict





def 				snake_case							(						UpperCAmelCase					,     UpperCAmelCase		)->	Any:







      """simple docstring"""
      __A						=	timm.create_model(lowerCAmelCase_					,     pretrained=lowerCAmelCase_		)
      timm_model.eval()

      __A						=	get_swin_config(lowerCAmelCase_		)
      __A						=	SwinForImageClassification(lowerCAmelCase_		)
      model.eval()

      __A						=	convert_state_dict(timm_model.state_dict()					,     lowerCAmelCase_		)
      model.load_state_dict(lowerCAmelCase_		)

      __A						=	"""http://images.cocodataset.org/val2017/000000039769.jpg"""

      __A						=	AutoImageProcessor.from_pretrained('microsoft/{}'.format(swin_name.replace('_'					,     '-'		)		)		)
      __A						=	Image.open(requests.get(lowerCAmelCase_					,     stream=lowerCAmelCase_		).raw		)
      __A						=	image_processor(images=lowerCAmelCase_					,     return_tensors='pt'		)

      __A						=	timm_model(inputs['pixel_values']		)
      __A						=	model(**lowerCAmelCase_		).logits

      assert torch.allclose(lowerCAmelCase_					,     lowerCAmelCase_					,     atol=1e-3		)

      print(f'Saving model {swin_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__						:      str   				=				argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     "--swin_name",
     default="swin_tiny_patch4_window7_224",
     type=str,
     help="Name of the Swin 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__						:      Dict   				=				parser.parse_args()
 convert_swin_checkpoint(args.swin_name, 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 					snake_case_						(   lowerCAmelCase_				:  List[Any]				,				lowerCAmelCase_				:  Dict				,				lowerCAmelCase_				:  Any				,				lowerCAmelCase_				:  Tuple				,				lowerCAmelCase_				:  int					):
							# 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
							__lowercase					:							Optional[int]			=   TapasConfig.from_json_file(lowerCAmelCase_					)
							# set absolute/relative position embeddings parameter
							__lowercase					:							Optional[Any]			=   reset_position_index_per_cell

							# set remaining parameters of TapasConfig as well as the model based on the task
							if task == "SQA":
														__lowercase					:							Union[str, Any]			=   TapasForQuestionAnswering(config=lowerCAmelCase_					)
							elif task == "WTQ":
														# run_task_main.py hparams
														__lowercase					:							List[Any]			=   4
														__lowercase					:							Union[str, Any]			=   True
														# hparam_utils.py hparams
														__lowercase					:							Any			=   0.664_694
														__lowercase					:							Tuple			=   0.207_951
														__lowercase					:							Dict			=   0.121_194
														__lowercase					:							List[str]			=   True
														__lowercase					:							str			=   True
														__lowercase					:							Dict			=   False
														__lowercase					:							Tuple			=   0.0_352_513

														__lowercase					:							List[Any]			=   TapasForQuestionAnswering(config=lowerCAmelCase_					)
							elif task == "WIKISQL_SUPERVISED":
														# run_task_main.py hparams
														__lowercase					:							Optional[int]			=   4
														__lowercase					:							int			=   False
														# hparam_utils.py hparams
														__lowercase					:							Tuple			=   36.4_519
														__lowercase					:							str			=   0.903_421
														__lowercase					:							List[Any]			=   222.088
														__lowercase					:							Union[str, Any]			=   True
														__lowercase					:							Tuple			=   True
														__lowercase					:							Union[str, Any]			=   True
														__lowercase					:							Optional[Any]			=   0.763_141

														__lowercase					:							str			=   TapasForQuestionAnswering(config=lowerCAmelCase_					)
							elif task == "TABFACT":
														__lowercase					:							List[Any]			=   TapasForSequenceClassification(config=lowerCAmelCase_					)
							elif task == "MLM":
														__lowercase					:							Optional[int]			=   TapasForMaskedLM(config=lowerCAmelCase_					)
							elif task == "INTERMEDIATE_PRETRAINING":
														__lowercase					:							Dict			=   TapasModel(config=lowerCAmelCase_					)
							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(lowerCAmelCase_				,				lowerCAmelCase_				,				lowerCAmelCase_					)

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

							# Save tokenizer files
							print(F"Save tokenizer files to {pytorch_dump_path}"					)
							__lowercase					:							Any			=   TapasTokenizer(vocab_file=tf_checkpoint_path[:-10] + """vocab.txt"""				,				model_max_length=512					)
							tokenizer.save_pretrained(lowerCAmelCase_					)

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


if __name__ == "__main__":
	lowerCamelCase       :       List[Any]        =				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.'''
	)
	lowerCamelCase       :       int        =				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,
	)