






import argparse

import requests
import torch
from PIL import Image

from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel



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

				return name



def  a__ (__lowercase							:Dict					,  __lowercase							:Tuple   )		-> Dict:
				for key in orig_state_dict.copy().keys():
								_A	:				str   =      orig_state_dict.pop(__lowercase   )

								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
												_A	:				Any   =      key.split('''.'''   )
												_A  ,				_A	:				List[str]   =      int(key_split[2]   ), int(key_split[4]   )
												_A	:				Dict   =      config.vision_config.hidden_size
												if "weight" in key:
																_A	:				Tuple   =      val[:dim, :]
																_A	:				Any   =      val[dim : dim * 2, :]
																_A	:				Dict   =      val[-dim:, :]
												else:
																_A	:				str   =      val[:dim]
																_A	:				Optional[Any]   =      val[dim : dim * 2]
																_A	:				List[str]   =      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
												_A	:				Any   =      key.split('''.'''   )
												_A	:				List[Any]   =      int(key_split[3]   )
												_A	:				Optional[Any]   =      config.text_config.hidden_size
												if "weight" in key:
																_A	:				Dict   =      val[:dim, :]
																_A	:				Tuple   =      val[
																    dim : dim * 2, :
																]
																_A	:				Tuple   =      val[-dim:, :]
												else:
																_A	:				Any   =      val[:dim]
																_A	:				List[Any]   =      val[dim : dim * 2]
																_A	:				Dict   =      val[-dim:]
								else:
												_A	:				Optional[int]   =      rename_key(__lowercase   )
												# 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
												):
																_A	:				str   =      val.squeeze_()
												else:
																_A	:				int   =      val

				return orig_state_dict



def  a__ ()		-> Optional[Any]:
				_A	:				str   =      '''http://images.cocodataset.org/val2017/000000039769.jpg'''
				_A	:				Optional[int]   =      Image.open(requests.get(__lowercase					,  stream=__lowercase   ).raw   )
				return im



@torch.no_grad()
def  a__ (__lowercase							:List[Any]					,  __lowercase							:List[Any]					,  __lowercase							:Optional[int]="groupvit-gcc-yfcc"					,  __lowercase							:int=False   )		-> Any:
				_A	:				List[Any]   =      GroupViTConfig()
				_A	:				int   =      GroupViTModel(__lowercase   ).eval()

				_A	:				List[str]   =      torch.load(__lowercase					,  map_location='''cpu'''   )['''model''']
				_A	:				int   =      convert_state_dict(__lowercase					,  __lowercase   )
				_A  ,				_A	:				Optional[int]   =      model.load_state_dict(__lowercase					,  strict=__lowercase   )
				assert missing_keys == ["text_model.embeddings.position_ids"]
				assert (unexpected_keys == ["multi_label_logit_scale"]) or (len(__lowercase   ) == 0)

				# verify result
				_A	:				str   =      CLIPProcessor.from_pretrained('''openai/clip-vit-base-patch32'''   )
				_A	:				Optional[Any]   =      prepare_img()
				_A	:				Tuple   =      processor(text=['''a photo of a cat''', '''a photo of a dog''']					,  images=__lowercase					,  padding=__lowercase					,  return_tensors='''pt'''   )

				with torch.no_grad():
								_A	:				List[str]   =      model(**__lowercase   )

				if model_name == "groupvit-gcc-yfcc":
								_A	:				str   =      torch.tensor([[13.3523, 6.3629]]   )
				elif model_name == "groupvit-gcc-redcaps":
								_A	:				List[Any]   =      torch.tensor([[16.1873, 8.6230]]   )
				else:
								raise ValueError(f"""Model name {model_name} not supported."""   )
				assert torch.allclose(outputs.logits_per_image					,  __lowercase					,  atol=1e-3   )

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

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


if __name__ == "__main__":
	_UpperCamelCase			:	List[Any]								=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`.',
	)
	_UpperCamelCase			:	List[str]								=parser.parse_args()

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











from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_UpperCamelCase			:	Dict								={
    'configuration_albert': ['ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'AlbertConfig', 'AlbertOnnxConfig'],
}

try:
	if not is_sentencepiece_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_UpperCamelCase			:	List[Any]								=['AlbertTokenizer']

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_UpperCamelCase			:	List[Any]								=['AlbertTokenizerFast']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_UpperCamelCase			:	Optional[int]								=[
	    'ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'AlbertForMaskedLM',
	    'AlbertForMultipleChoice',
	    'AlbertForPreTraining',
	    'AlbertForQuestionAnswering',
	    'AlbertForSequenceClassification',
	    'AlbertForTokenClassification',
	    'AlbertModel',
	    'AlbertPreTrainedModel',
	    'load_tf_weights_in_albert',
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_UpperCamelCase			:	List[Any]								=[
	    'TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'TFAlbertForMaskedLM',
	    'TFAlbertForMultipleChoice',
	    'TFAlbertForPreTraining',
	    'TFAlbertForQuestionAnswering',
	    'TFAlbertForSequenceClassification',
	    'TFAlbertForTokenClassification',
	    'TFAlbertMainLayer',
	    'TFAlbertModel',
	    'TFAlbertPreTrainedModel',
	]

try:
	if not is_flax_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_UpperCamelCase			:	Any								=[
	    'FlaxAlbertForMaskedLM',
	    'FlaxAlbertForMultipleChoice',
	    'FlaxAlbertForPreTraining',
	    'FlaxAlbertForQuestionAnswering',
	    'FlaxAlbertForSequenceClassification',
	    'FlaxAlbertForTokenClassification',
	    'FlaxAlbertModel',
	    'FlaxAlbertPreTrainedModel',
	]

if TYPE_CHECKING:
	from .configuration_albert import ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, AlbertConfig, AlbertOnnxConfig

	try:
		if not is_sentencepiece_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_albert import AlbertTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_albert_fast import AlbertTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_albert import (
		    ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    AlbertForMaskedLM,
		    AlbertForMultipleChoice,
		    AlbertForPreTraining,
		    AlbertForQuestionAnswering,
		    AlbertForSequenceClassification,
		    AlbertForTokenClassification,
		    AlbertModel,
		    AlbertPreTrainedModel,
		    load_tf_weights_in_albert,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_albert import (
		    TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFAlbertForMaskedLM,
		    TFAlbertForMultipleChoice,
		    TFAlbertForPreTraining,
		    TFAlbertForQuestionAnswering,
		    TFAlbertForSequenceClassification,
		    TFAlbertForTokenClassification,
		    TFAlbertMainLayer,
		    TFAlbertModel,
		    TFAlbertPreTrainedModel,
		)

	try:
		if not is_flax_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_flax_albert import (
		    FlaxAlbertForMaskedLM,
		    FlaxAlbertForMultipleChoice,
		    FlaxAlbertForPreTraining,
		    FlaxAlbertForQuestionAnswering,
		    FlaxAlbertForSequenceClassification,
		    FlaxAlbertForTokenClassification,
		    FlaxAlbertModel,
		    FlaxAlbertPreTrainedModel,
		)
else:
	import sys

	_UpperCamelCase			:	str								=_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



