
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 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE							):
	lowercase       =    SwinConfig()
	lowercase       =    swin_name.split('_'							)

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

	if model_size == "tiny":
		lowercase       =    96
		lowercase       =    (2, 2, 6, 2)
		lowercase       =    (3, 6, 12, 24)
	elif model_size == "small":
		lowercase       =    96
		lowercase       =    (2, 2, 18, 2)
		lowercase       =    (3, 6, 12, 24)
	elif model_size == "base":
		lowercase       =    128
		lowercase       =    (2, 2, 18, 2)
		lowercase       =    (4, 8, 16, 32)
	else:
		lowercase       =    192
		lowercase       =    (2, 2, 18, 2)
		lowercase       =    (6, 12, 24, 48)

	if "in22k" in swin_name:
		lowercase       =    2_1841
	else:
		lowercase       =    1000
		lowercase       =    'huggingface/label-files'
		lowercase       =    'imagenet-1k-id2label.json'
		lowercase       =    json.load(open(hf_hub_download(__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							repo_type='dataset'							) ,							'r'							)							)
		lowercase       =    {int(__SCREAMING_SNAKE_CASE							): v for k, v in idalabel.items()}
		lowercase       =    idalabel
		lowercase       =    {v: k for k, v in idalabel.items()}

	lowercase       =    img_size
	lowercase       =    num_classes
	lowercase       =    embed_dim
	lowercase       =    depths
	lowercase       =    num_heads
	lowercase       =    window_size

	return config






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

	if name == "norm.weight":
		lowercase       =    'layernorm.weight'
	if name == "norm.bias":
		lowercase       =    'layernorm.bias'

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

	return name






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE							):
	for key in orig_state_dict.copy().keys():
		lowercase       =    orig_state_dict.pop(__SCREAMING_SNAKE_CASE							)

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

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

	return orig_state_dict






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE							):
	lowercase       =    timm.create_model(__SCREAMING_SNAKE_CASE ,							pretrained=__SCREAMING_SNAKE_CASE							)
	timm_model.eval()

	lowercase       =    get_swin_config(__SCREAMING_SNAKE_CASE							)
	lowercase       =    SwinForImageClassification(__SCREAMING_SNAKE_CASE							)
	model.eval()

	lowercase       =    convert_state_dict(timm_model.state_dict() ,							__SCREAMING_SNAKE_CASE							)
	model.load_state_dict(__SCREAMING_SNAKE_CASE							)

	lowercase       =    'http://images.cocodataset.org/val2017/000000039769.jpg'

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

	lowercase       =    timm_model(inputs['pixel_values']							)
	lowercase       =    model(**__SCREAMING_SNAKE_CASE							).logits

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

	print(F'''Saving model {swin_name} to {pytorch_dump_folder_path}'''							)
	model.save_pretrained(__SCREAMING_SNAKE_CASE							)

	print(F'''Saving image processor to {pytorch_dump_folder_path}'''							)
	image_processor.save_pretrained(__SCREAMING_SNAKE_CASE							)


if __name__ == "__main__":
	UpperCAmelCase	      = 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.'''
	)

	UpperCAmelCase	      = parser.parse_args()
	convert_swin_checkpoint(args.swin_name, args.pytorch_dump_folder_path)



import warnings

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


UpperCAmelCase	      = logging.get_logger(__name__)

UpperCAmelCase	      = {
    '''xlnet-base-cased''': '''https://huggingface.co/xlnet-base-cased/resolve/main/config.json''',
    '''xlnet-large-cased''': '''https://huggingface.co/xlnet-large-cased/resolve/main/config.json''',
}







class        A_   (			__lowerCamelCase   ):




	'''simple docstring'''


	_UpperCamelCase     :     Tuple							 =       """xlnet"""
	_UpperCamelCase     :     Optional[Any]							 =       ["""mems"""]
	_UpperCamelCase     :     Tuple							 =       {
	    """n_token""": """vocab_size""",  # Backward compatibility
	    """hidden_size""": """d_model""",
	    """num_attention_heads""": """n_head""",
	    """num_hidden_layers""": """n_layer""",
	}





	def __init__(       self		,    snake_case=3_2000		,    snake_case=1024		,    snake_case=24		,    snake_case=16		,    snake_case=4096		,    snake_case="gelu"		,    snake_case=True		,    snake_case="bi"		,    snake_case=0.02		,    snake_case=1E-12		,    snake_case=0.1		,    snake_case=512		,    snake_case=None		,    snake_case=True		,    snake_case=False		,    snake_case=False		,    snake_case=-1		,    snake_case=False		,    snake_case="last"		,    snake_case=True		,    snake_case="tanh"		,    snake_case=0.1		,    snake_case=5		,    snake_case=5		,    snake_case=5		,    snake_case=1		,    snake_case=2		,    **snake_case		,    ):
		lowercase       =    vocab_size
		lowercase       =    d_model
		lowercase       =    n_layer
		lowercase       =    n_head
		if d_model % n_head != 0:
			raise ValueError(F'''\'d_model % n_head\' ({d_model % n_head}) should be equal to 0'''       )
		if "d_head" in kwargs:
			if kwargs["d_head"] != d_model // n_head:
				raise ValueError(
				    F'''`d_head` ({kwargs['d_head']}) should be equal to `d_model // n_head` ({d_model // n_head})'''       )
		lowercase       =    d_model // n_head
		lowercase       =    ff_activation
		lowercase       =    d_inner
		lowercase       =    untie_r
		lowercase       =    attn_type

		lowercase       =    initializer_range
		lowercase       =    layer_norm_eps

		lowercase       =    dropout
		lowercase       =    mem_len
		lowercase       =    reuse_len
		lowercase       =    bi_data
		lowercase       =    clamp_len
		lowercase       =    same_length

		lowercase       =    summary_type
		lowercase       =    summary_use_proj
		lowercase       =    summary_activation
		lowercase       =    summary_last_dropout
		lowercase       =    start_n_top
		lowercase       =    end_n_top

		lowercase       =    bos_token_id
		lowercase       =    pad_token_id
		lowercase       =    eos_token_id

		if "use_cache" in kwargs:
			warnings.warn(
			    'The `use_cache` argument is deprecated and will be removed in a future version, use `use_mems_eval`'
			    ' instead.'		,    snake_case		,    )
			lowercase       =    kwargs['use_cache']

		lowercase       =    use_mems_eval
		lowercase       =    use_mems_train
		super().__init__(pad_token_id=snake_case		,    bos_token_id=snake_case		,    eos_token_id=snake_case		,    **snake_case       )





	@property
	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		logger.info(F'''The model {self.model_type} is one of the few models that has no sequence length limit.'''       )
		return -1





	@max_position_embeddings.setter
	def 	SCREAMING_SNAKE_CASE__						(       self		,    snake_case       ):
		# Message copied from Transformer-XL documentation
		raise NotImplementedError(
		    F'''The model {self.model_type} is one of the few models that has no sequence length limit.'''       )

