
'''simple docstring'''







import argparse

import requests
import torch
from PIL import Image

from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel







def 				SCREAMING_SNAKE_CASE__	(   __A	)       ->	Optional[Any]:
	# vision encoder
	if "img_encoder.pos_embed" in name:
		_snake_case   						=		name.replace('img_encoder.pos_embed'			,				'vision_model.embeddings.position_embeddings'	)
	if "img_encoder.patch_embed.proj" in name:
		_snake_case   						=		name.replace('img_encoder.patch_embed.proj'			,				'vision_model.embeddings.patch_embeddings.projection'	)
	if "img_encoder.patch_embed.norm" in name:
		_snake_case   						=		name.replace('img_encoder.patch_embed.norm'			,				'vision_model.embeddings.layernorm'	)
	if "img_encoder.layers" in name:
		_snake_case   						=		name.replace('img_encoder.layers'			,				'vision_model.encoder.stages'	)
	if "blocks" in name and "res" not in name:
		_snake_case   						=		name.replace('blocks'			,				'layers'	)
	if "attn" in name and "pre_assign" not in name:
		_snake_case   						=		name.replace('attn'			,				'self_attn'	)
	if "proj" in name and "self_attn" in name and "text" not in name:
		_snake_case   						=		name.replace('proj'			,				'out_proj'	)
	if "pre_assign_attn.attn.proj" in name:
		_snake_case   						=		name.replace('pre_assign_attn.attn.proj'			,				'pre_assign_attn.attn.out_proj'	)
	if "norm1" in name:
		_snake_case   						=		name.replace('norm1'			,				'layer_norm1'	)
	if "norm2" in name and "pre_assign" not in name:
		_snake_case   						=		name.replace('norm2'			,				'layer_norm2'	)
	if "img_encoder.norm" in name:
		_snake_case   						=		name.replace('img_encoder.norm'			,				'vision_model.layernorm'	)
	# text encoder
	if "text_encoder.token_embedding" in name:
		_snake_case   						=		name.replace('text_encoder.token_embedding'			,				'text_model.embeddings.token_embedding'	)
	if "text_encoder.positional_embedding" in name:
		_snake_case   						=		name.replace('text_encoder.positional_embedding'			,				'text_model.embeddings.position_embedding.weight'	)
	if "text_encoder.transformer.resblocks." in name:
		_snake_case   						=		name.replace('text_encoder.transformer.resblocks.'			,				'text_model.encoder.layers.'	)
	if "ln_1" in name:
		_snake_case   						=		name.replace('ln_1'			,				'layer_norm1'	)
	if "ln_2" in name:
		_snake_case   						=		name.replace('ln_2'			,				'layer_norm2'	)
	if "c_fc" in name:
		_snake_case   						=		name.replace('c_fc'			,				'fc1'	)
	if "c_proj" in name:
		_snake_case   						=		name.replace('c_proj'			,				'fc2'	)
	if "text_encoder" in name:
		_snake_case   						=		name.replace('text_encoder'			,				'text_model'	)
	if "ln_final" in name:
		_snake_case   						=		name.replace('ln_final'			,				'final_layer_norm'	)
	# projection layers
	if "img_projector.linear_hidden." in name:
		_snake_case   						=		name.replace('img_projector.linear_hidden.'			,				'visual_projection.'	)
	if "img_projector.linear_out." in name:
		_snake_case   						=		name.replace('img_projector.linear_out.'			,				'visual_projection.3.'	)
	if "text_projector.linear_hidden" in name:
		_snake_case   						=		name.replace('text_projector.linear_hidden'			,				'text_projection'	)
	if "text_projector.linear_out" in name:
		_snake_case   						=		name.replace('text_projector.linear_out'			,				'text_projection.3'	)

	return name







def 				SCREAMING_SNAKE_CASE__	(   __A			,				__A	)       ->	str:
	for key in orig_state_dict.copy().keys():
		_snake_case   						=		orig_state_dict.pop(__A	)

		if "qkv" in key:
			# weights and biases of the key, value and query projections of vision encoder's attention layers require special treatment:
			# we need to split them up into separate matrices/vectors
			_snake_case   						=		key.split('.'	)
			_snake_case						, _snake_case   						=		int(key_split[2]	), int(key_split[4]	)
			_snake_case   						=		config.vision_config.hidden_size
			if "weight" in key:
				_snake_case   						=		val[:dim, :]
				_snake_case   						=		val[dim : dim * 2, :]
				_snake_case   						=		val[-dim:, :]
			else:
				_snake_case   						=		val[:dim]
				_snake_case   						=		val[dim : dim * 2]
				_snake_case   						=		val[-dim:]
		elif "in_proj" in key:
			# weights and biases of the key, value and query projections of text encoder's attention layers require special treatment:
			# we need to split them up into separate matrices/vectors
			_snake_case   						=		key.split('.'	)
			_snake_case   						=		int(key_split[3]	)
			_snake_case   						=		config.text_config.hidden_size
			if "weight" in key:
				_snake_case   						=		val[:dim, :]
				_snake_case   						=		val[
				    dim : dim * 2, :
				]
				_snake_case   						=		val[-dim:, :]
			else:
				_snake_case   						=		val[:dim]
				_snake_case   						=		val[dim : dim * 2]
				_snake_case   						=		val[-dim:]
		else:
			_snake_case   						=		rename_key(__A	)
			# squeeze if necessary
			if (
			    "text_projection.0" in new_name
			    or "text_projection.3" in new_name
			    or "visual_projection.0" in new_name
			    or "visual_projection.3" in new_name
			):
				_snake_case   						=		val.squeeze_()
			else:
				_snake_case   						=		val

	return orig_state_dict







def 				SCREAMING_SNAKE_CASE__	(   )       ->	int:
	_snake_case   						=		'http://images.cocodataset.org/val2017/000000039769.jpg'
	_snake_case   						=		Image.open(requests.get(__A			,				stream=__A	).raw	)
	return im







@torch.no_grad()
def 				SCREAMING_SNAKE_CASE__	(   __A			,				__A			,				__A="groupvit-gcc-yfcc"			,				__A=False	)       ->	Dict:
	_snake_case   						=		GroupViTConfig()
	_snake_case   						=		GroupViTModel(__A	).eval()

	_snake_case   						=		torch.load(__A			,				map_location='cpu'	)['model']
	_snake_case   						=		convert_state_dict(__A			,				__A	)
	_snake_case						, _snake_case   						=		model.load_state_dict(__A			,				strict=__A	)
	assert missing_keys == ["text_model.embeddings.position_ids"]
	assert (unexpected_keys == ["multi_label_logit_scale"]) or (len(__A	) == 0)

	# verify result
	_snake_case   						=		CLIPProcessor.from_pretrained('openai/clip-vit-base-patch32'	)
	_snake_case   						=		prepare_img()
	_snake_case   						=		processor(text=['a photo of a cat', 'a photo of a dog']			,				images=__A			,				padding=__A			,				return_tensors='pt'	)

	with torch.no_grad():
		_snake_case   						=		model(**__A	)

	if model_name == "groupvit-gcc-yfcc":
		_snake_case   						=		torch.tensor([[1_3.3_5_2_3, 6.3_6_2_9]]	)
	elif model_name == "groupvit-gcc-redcaps":
		_snake_case   						=		torch.tensor([[1_6.1_8_7_3, 8.6_2_3_0]]	)
	else:
		raise ValueError(F'Model name {model_name} not supported.'	)
	assert torch.allclose(outputs.logits_per_image			,				__A			,				atol=1e-3	)

	processor.save_pretrained(__A	)
	model.save_pretrained(__A	)
	print('Successfully saved processor and model to'			,				__A	)

	if push_to_hub:
		print('Pushing to the hub...'	)
		processor.push_to_hub(__A			,				organization='nielsr'	)
		model.push_to_hub(__A			,				organization='nielsr'	)


if __name__ == "__main__":
					lowercase    :							Tuple      =					argparse.ArgumentParser()
					parser.add_argument(
					    "--pytorch_dump_folder_path", default=None, type=str, help="Path to dump the processor and PyTorch model."
					)
					parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to GroupViT checkpoint")
					parser.add_argument(
					    "--model_name",
					    default="groupvit-gccy-fcc",
					    type=str,
					    help="Name of the model. Expecting either 'groupvit-gcc-yfcc' or 'groupvit-gcc-redcaps'",
					)
					parser.add_argument(
					    "--push_to_hub",
					    action="store_true",
					    help="Whether or not to push the converted model and processor to the 🤗 hub using the provided `model_name`.",
					)
					lowercase    :							int      =					parser.parse_args()

					convert_groupvit_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub)



'''simple docstring'''







import warnings

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


lowercase    :							int      =					logging.get_logger(__name__)

lowercase    :							Union[str, Any]      =					{
    "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 						__UpperCAmelCase      (					_lowerCamelCase						):
							__lowercase      		=       """xlnet"""
							__lowercase      		=       ["""mems"""]
							__lowercase      		=       {
							    """n_token""": """vocab_size""",  # Backward compatibility
							    """hidden_size""": """d_model""",
							    """num_attention_heads""": """n_head""",
							    """num_hidden_layers""": """n_layer""",
							}


							def __init__(       self    ,   lowerCAmelCase_=3_20_00    ,   lowerCAmelCase_=10_24    ,   lowerCAmelCase_=24    ,   lowerCAmelCase_=16    ,   lowerCAmelCase_=40_96    ,   lowerCAmelCase_="gelu"    ,   lowerCAmelCase_=True    ,   lowerCAmelCase_="bi"    ,   lowerCAmelCase_=0.02    ,   lowerCAmelCase_=1E-12    ,   lowerCAmelCase_=0.1    ,   lowerCAmelCase_=5_12    ,   lowerCAmelCase_=None    ,   lowerCAmelCase_=True    ,   lowerCAmelCase_=False    ,   lowerCAmelCase_=False    ,   lowerCAmelCase_=-1    ,   lowerCAmelCase_=False    ,   lowerCAmelCase_="last"    ,   lowerCAmelCase_=True    ,   lowerCAmelCase_="tanh"    ,   lowerCAmelCase_=0.1    ,   lowerCAmelCase_=5    ,   lowerCAmelCase_=5    ,   lowerCAmelCase_=5    ,   lowerCAmelCase_=1    ,   lowerCAmelCase_=2    ,   **lowerCAmelCase_    ,   ):





								"""simple docstring"""






								_snake_case   						=		vocab_size
								_snake_case   						=		d_model
								_snake_case   						=		n_layer
								_snake_case   						=		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})'  )
								_snake_case   						=		d_model // n_head
								_snake_case   						=		ff_activation
								_snake_case   						=		d_inner
								_snake_case   						=		untie_r
								_snake_case   						=		attn_type

								_snake_case   						=		initializer_range
								_snake_case   						=		layer_norm_eps

								_snake_case   						=		dropout
								_snake_case   						=		mem_len
								_snake_case   						=		reuse_len
								_snake_case   						=		bi_data
								_snake_case   						=		clamp_len
								_snake_case   						=		same_length

								_snake_case   						=		summary_type
								_snake_case   						=		summary_use_proj
								_snake_case   						=		summary_activation
								_snake_case   						=		summary_last_dropout
								_snake_case   						=		start_n_top
								_snake_case   						=		end_n_top

								_snake_case   						=		bos_token_id
								_snake_case   						=		pad_token_id
								_snake_case   						=		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.'    ,   lowerCAmelCase_    ,   )
									_snake_case   						=		kwargs['use_cache']

								_snake_case   						=		use_mems_eval
								_snake_case   						=		use_mems_train
								super().__init__(pad_token_id=lowerCAmelCase_    ,   bos_token_id=lowerCAmelCase_    ,   eos_token_id=lowerCAmelCase_    ,   **lowerCAmelCase_  )


							@property
							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								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  lowerCamelCase				(       self    ,   lowerCAmelCase_  ):





								"""simple docstring"""






								raise NotImplementedError(
								    F'The model {self.model_type} is one of the few models that has no sequence length limit.'  )

