
import argparse

import requests
import torch
from PIL import Image

from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel



def      __lowerCAmelCase						(							UpperCAmelCase__				:      str       )  ->    Optional[int]:







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

							return name



def      __lowerCAmelCase						(							UpperCAmelCase__				:      Dict			,				UpperCAmelCase__				:      int       )  ->    Tuple:







							for key in orig_state_dict.copy().keys():
														lowerCamelCase_							  =					orig_state_dict.pop(__snake_case       )

														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
																					lowerCamelCase_							  =					key.split("""."""       )
																					lowerCamelCase_  ,   lowerCamelCase_							  =					int(key_split[2]       ), int(key_split[4]       )
																					lowerCamelCase_							  =					config.vision_config.hidden_size
																					if "weight" in key:
																												lowerCamelCase_							  =					val[:dim, :]
																												lowerCamelCase_							  =					val[dim : dim * 2, :]
																												lowerCamelCase_							  =					val[-dim:, :]
																					else:
																												lowerCamelCase_							  =					val[:dim]
																												lowerCamelCase_							  =					val[dim : dim * 2]
																												lowerCamelCase_							  =					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
																					lowerCamelCase_							  =					key.split("""."""       )
																					lowerCamelCase_							  =					int(key_split[3]       )
																					lowerCamelCase_							  =					config.text_config.hidden_size
																					if "weight" in key:
																												lowerCamelCase_							  =					val[:dim, :]
																												lowerCamelCase_							  =					val[
																												    dim : dim * 2, :
																												]
																												lowerCamelCase_							  =					val[-dim:, :]
																					else:
																												lowerCamelCase_							  =					val[:dim]
																												lowerCamelCase_							  =					val[dim : dim * 2]
																												lowerCamelCase_							  =					val[-dim:]
														else:
																					lowerCamelCase_							  =					rename_key(__snake_case       )
																					# 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
																					):
																												lowerCamelCase_							  =					val.squeeze_()
																					else:
																												lowerCamelCase_							  =					val

							return orig_state_dict



def      __lowerCAmelCase						(							)  ->    Tuple:
							lowerCamelCase_							  =					"""http://images.cocodataset.org/val2017/000000039769.jpg"""
							lowerCamelCase_							  =					Image.open(requests.get(__snake_case			,				stream=__snake_case       ).raw       )
							return im



@torch.no_grad()
def      __lowerCAmelCase						(							UpperCAmelCase__				:      Any			,				UpperCAmelCase__				:      Optional[Any]			,				UpperCAmelCase__				:      Tuple="groupvit-gcc-yfcc"			,				UpperCAmelCase__				:      str=False       )  ->    List[str]:
							lowerCamelCase_							  =					GroupViTConfig()
							lowerCamelCase_							  =					GroupViTModel(__snake_case       ).eval()

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

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

							with torch.no_grad():
														lowerCamelCase_							  =					model(**__snake_case       )

							if model_name == "groupvit-gcc-yfcc":
														lowerCamelCase_							  =					torch.tensor([[1_3.3_5_2_3, 6.3_6_2_9]]       )
							elif model_name == "groupvit-gcc-redcaps":
														lowerCamelCase_							  =					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			,				__snake_case			,				atol=1e-3       )

							processor.save_pretrained(__snake_case       )
							model.save_pretrained(__snake_case       )
							print("""Successfully saved processor and model to"""			,				__snake_case       )

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


if __name__ == "__main__":
		lowercase        =  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        =  parser.parse_args()

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







from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType





class       __A(				UpperCAmelCase       ):
					SCREAMING_SNAKE_CASE			    =    ['''image_processor''', '''tokenizer''']
					SCREAMING_SNAKE_CASE			    =    '''BlipImageProcessor'''
					SCREAMING_SNAKE_CASE			    =    '''AutoTokenizer'''

					def __init__(	self					:     Union[str, Any]   ,					__UpperCamelCase					:     List[Any]   ,					__UpperCamelCase					:     List[str]   ):
												lowerCamelCase_							  =					False
												super().__init__(__UpperCamelCase   ,					__UpperCamelCase   )
												lowerCamelCase_							  =					self.image_processor

					def __call__(	self					:     Any   ,					__UpperCamelCase					:     ImageInput = None   ,					__UpperCamelCase					:     Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None   ,					__UpperCamelCase					:     bool = True   ,					__UpperCamelCase					:     Union[bool, str, PaddingStrategy] = False   ,					__UpperCamelCase					:     Union[bool, str, TruncationStrategy] = None   ,					__UpperCamelCase					:     Optional[int] = None   ,					__UpperCamelCase					:     int = 0   ,					__UpperCamelCase					:     Optional[int] = None   ,					__UpperCamelCase					:     Optional[bool] = None   ,					__UpperCamelCase					:     bool = False   ,					__UpperCamelCase					:     bool = False   ,					__UpperCamelCase					:     bool = False   ,					__UpperCamelCase					:     bool = False   ,					__UpperCamelCase					:     bool = False   ,					__UpperCamelCase					:     bool = True   ,					__UpperCamelCase					:     Optional[Union[str, TensorType]] = None   ,					**__UpperCamelCase					:     Optional[Any]   ,					):
												if images is None and text is None:
																			raise ValueError("""You have to specify either images or text."""   )

												# Get only text
												if images is None:
																			lowerCamelCase_							  =					self.tokenizer
																			lowerCamelCase_							  =					self.tokenizer(
																			    text=__UpperCamelCase   ,					add_special_tokens=__UpperCamelCase   ,					padding=__UpperCamelCase   ,					truncation=__UpperCamelCase   ,					max_length=__UpperCamelCase   ,					stride=__UpperCamelCase   ,					pad_to_multiple_of=__UpperCamelCase   ,					return_attention_mask=__UpperCamelCase   ,					return_overflowing_tokens=__UpperCamelCase   ,					return_special_tokens_mask=__UpperCamelCase   ,					return_offsets_mapping=__UpperCamelCase   ,					return_token_type_ids=__UpperCamelCase   ,					return_length=__UpperCamelCase   ,					verbose=__UpperCamelCase   ,					return_tensors=__UpperCamelCase   ,					**__UpperCamelCase   ,					)
																			return text_encoding

												# add pixel_values
												lowerCamelCase_							  =					self.image_processor(__UpperCamelCase   ,					return_tensors=__UpperCamelCase   )

												if text is not None:
																			lowerCamelCase_							  =					self.tokenizer(
																			    text=__UpperCamelCase   ,					add_special_tokens=__UpperCamelCase   ,					padding=__UpperCamelCase   ,					truncation=__UpperCamelCase   ,					max_length=__UpperCamelCase   ,					stride=__UpperCamelCase   ,					pad_to_multiple_of=__UpperCamelCase   ,					return_attention_mask=__UpperCamelCase   ,					return_overflowing_tokens=__UpperCamelCase   ,					return_special_tokens_mask=__UpperCamelCase   ,					return_offsets_mapping=__UpperCamelCase   ,					return_token_type_ids=__UpperCamelCase   ,					return_length=__UpperCamelCase   ,					verbose=__UpperCamelCase   ,					return_tensors=__UpperCamelCase   ,					**__UpperCamelCase   ,					)
												else:
																			lowerCamelCase_							  =					None

												if text_encoding is not None:
																			encoding_image_processor.update(__UpperCamelCase   )

												return encoding_image_processor

					def    lowercase__      (	self					:     Any   ,					*__UpperCamelCase					:     Union[str, Any]   ,					**__UpperCamelCase					:     List[Any]   ):
												return self.tokenizer.batch_decode(*__UpperCamelCase   ,					**__UpperCamelCase   )

					def    lowercase__      (	self					:     List[str]   ,					*__UpperCamelCase					:     List[Any]   ,					**__UpperCamelCase					:     int   ):
												return self.tokenizer.decode(*__UpperCamelCase   ,					**__UpperCamelCase   )



					@property
					# Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
					def    lowercase__      (	self					:     Optional[int]   ):
												lowerCamelCase_							  =					self.tokenizer.model_input_names
												lowerCamelCase_							  =					self.image_processor.model_input_names
												return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names   )   )





