

import copy
import os
from typing import Union

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


lowerCamelCase      				=     logging.get_logger(__name__)

lowerCamelCase      				=     {
    '''google/pix2struct-textcaps-base''': (
        '''https://huggingface.co/google/pix2struct-textcaps-base/resolve/main/config.json'''
    ),
}





class     snake_case_			(				UpperCamelCase__					):


							"""simple docstring"""





							__UpperCAmelCase       ="""pix2struct_text_model"""
							__UpperCAmelCase       =["""past_key_values"""]
							__UpperCAmelCase       ={
							    """hidden_size""": """hidden_size""",
							    """num_attention_heads""": """num_heads""",
							    """num_hidden_layers""": """num_layers""",
							}




							def __init__( self					,						_A=5_0_2_4_4					,						_A=7_6_8					,						_A=6_4					,						_A=2_0_4_8					,						_A=1_2					,						_A=1_2					,						_A=3_2					,						_A=1_2_8					,						_A=0.1					,						_A=1e-6					,						_A=1.0					,						_A="gelu_new"					,						_A=0					,						_A=False					,						_A=0					,						_A=1					,						_A=False					,						_A=True					,						**_A					,						):
												__lowerCAmelCase					=							vocab_size
												__lowerCAmelCase					=							hidden_size
												__lowerCAmelCase					=							d_kv
												__lowerCAmelCase					=							d_ff
												__lowerCAmelCase					=							num_layers
												__lowerCAmelCase					=							num_heads
												__lowerCAmelCase					=							relative_attention_num_buckets
												__lowerCAmelCase					=							relative_attention_max_distance
												__lowerCAmelCase					=							dropout_rate
												__lowerCAmelCase					=							layer_norm_epsilon
												__lowerCAmelCase					=							initializer_factor
												__lowerCAmelCase					=							use_cache

												__lowerCAmelCase					=							eos_token_id
												__lowerCAmelCase					=							decoder_start_token_id

												# for backwards compatibility
												__lowerCAmelCase					=							dense_act_fn

												super().__init__(
												    pad_token_id=__A					,						eos_token_id=__A					,						decoder_start_token_id=__A					,						tie_word_embeddings=__A					,						is_decoder=__A					,						**__A					,						)




							@classmethod
							def        A__  ( cls					,						_A					,						**_A							):
												cls._set_token_in_kwargs(__A							)

												__lowerCAmelCase,	__lowerCAmelCase					=							cls.get_config_dict(__A					,						**__A							)

												# get the text config dict if we are loading from Pix2StructConfig
												if config_dict.get('model_type'							) == "pix2struct":
																	__lowerCAmelCase					=							config_dict['text_config']

												if "model_type" in config_dict and hasattr(cls					,						'model_type'							) and config_dict["model_type"] != cls.model_type:
																	logger.warning(
																	    F"""You are using a model of type {config_dict['model_type']} to instantiate a model of type """
																	    F"""{cls.model_type}. This is not supported for all configurations of models and can yield errors."""							)

												return cls.from_dict(__A					,						**__A							)





class     snake_case_			(				UpperCamelCase__					):


							"""simple docstring"""





							__UpperCAmelCase       ="""pix2struct_vision_model"""




							def __init__( self					,						_A=7_6_8					,						_A=7_6_8					,						_A=2_0_4_8					,						_A=6_4					,						_A=1_2					,						_A=1_2					,						_A="gelu_new"					,						_A=1e-6					,						_A=0.0					,						_A=0.0					,						_A=1e-1_0					,						_A=1.0					,						_A=4_0_9_6					,						_A=3_2					,						_A=1_2_8					,						**_A					,						):
												super().__init__(**__A							)

												__lowerCAmelCase					=							hidden_size
												__lowerCAmelCase					=							patch_embed_hidden_size
												__lowerCAmelCase					=							d_ff
												__lowerCAmelCase					=							dropout_rate
												__lowerCAmelCase					=							num_hidden_layers
												__lowerCAmelCase					=							num_attention_heads
												__lowerCAmelCase					=							initializer_range
												__lowerCAmelCase					=							initializer_factor
												__lowerCAmelCase					=							attention_dropout
												__lowerCAmelCase					=							layer_norm_eps
												__lowerCAmelCase					=							dense_act_fn
												__lowerCAmelCase					=							seq_len
												__lowerCAmelCase					=							relative_attention_num_buckets
												__lowerCAmelCase					=							relative_attention_max_distance
												__lowerCAmelCase					=							d_kv




							@classmethod
							def        A__  ( cls					,						_A					,						**_A							):
												cls._set_token_in_kwargs(__A							)

												__lowerCAmelCase,	__lowerCAmelCase					=							cls.get_config_dict(__A					,						**__A							)

												# get the vision config dict if we are loading from Pix2StructConfig
												if config_dict.get('model_type'							) == "pix2struct":
																	__lowerCAmelCase					=							config_dict['vision_config']

												if "model_type" in config_dict and hasattr(cls					,						'model_type'							) and config_dict["model_type"] != cls.model_type:
																	logger.warning(
																	    F"""You are using a model of type {config_dict['model_type']} to instantiate a model of type """
																	    F"""{cls.model_type}. This is not supported for all configurations of models and can yield errors."""							)

												return cls.from_dict(__A					,						**__A							)





class     snake_case_			(				UpperCamelCase__					):


							"""simple docstring"""





							__UpperCAmelCase       ="""pix2struct"""
							__UpperCAmelCase       =True




							def __init__( self					,						_A=None					,						_A=None					,						_A=1.0					,						_A=0.02					,						_A=False					,						_A=False					,						_A=True					,						**_A					,						):
												super().__init__(tie_word_embeddings=__A					,						is_encoder_decoder=__A					,						**__A							)

												if text_config is None:
																	__lowerCAmelCase					=							{}
																	logger.info('text_config is None. Initializing the Pix2StructTextConfig with default values.'							)

												if vision_config is None:
																	__lowerCAmelCase					=							{}
																	logger.info('vision_config is None. Initializing the Pix2StructVisionConfig with default values.'							)

												__lowerCAmelCase					=							PixaStructTextConfig(**__A							)
												__lowerCAmelCase					=							PixaStructVisionConfig(**__A							)

												__lowerCAmelCase					=							self.text_config.decoder_start_token_id
												__lowerCAmelCase					=							self.text_config.pad_token_id
												__lowerCAmelCase					=							self.text_config.eos_token_id

												__lowerCAmelCase					=							initializer_factor
												__lowerCAmelCase					=							initializer_range

												__lowerCAmelCase					=							self.initializer_range
												__lowerCAmelCase					=							self.initializer_range

												__lowerCAmelCase					=							is_vqa




							@classmethod
							def        A__  ( cls					,						_A					,						_A					,						**_A							):
												return cls(text_config=text_config.to_dict()					,						vision_config=vision_config.to_dict()					,						**__A							)




							def        A__  ( self							):
												__lowerCAmelCase					=							copy.deepcopy(self.__dict__							)
												__lowerCAmelCase					=							self.text_config.to_dict()
												__lowerCAmelCase					=							self.vision_config.to_dict()
												__lowerCAmelCase					=							self.__class__.model_type
												return output









import argparse

import requests
import torch
from PIL import Image

from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel


def        __lowercase    (  UpperCAmelCase__      ):

					"""simple docstring"""






					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        __lowercase    (  UpperCAmelCase__		,					UpperCAmelCase__      ):

					"""simple docstring"""






					for key in orig_state_dict.copy().keys():
										__lowerCAmelCase					=							orig_state_dict.pop(UpperCAmelCase__      )

										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(UpperCAmelCase__      )
															# 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        __lowercase    (  ):

					"""simple docstring"""






					__lowerCAmelCase					=							'http://images.cocodataset.org/val2017/000000039769.jpg'
					__lowerCAmelCase					=							Image.open(requests.get(UpperCAmelCase__		,					stream=UpperCAmelCase__      ).raw      )
					return im


@torch.no_grad()
def        __lowercase    (  UpperCAmelCase__		,					UpperCAmelCase__		,					UpperCAmelCase__="groupvit-gcc-yfcc"		,					UpperCAmelCase__=False      ):

					"""simple docstring"""






					__lowerCAmelCase					=							GroupViTConfig()
					__lowerCAmelCase					=							GroupViTModel(UpperCAmelCase__      ).eval()

					__lowerCAmelCase					=							torch.load(UpperCAmelCase__		,					map_location='cpu'      )['model']
					__lowerCAmelCase					=							convert_state_dict(UpperCAmelCase__		,					UpperCAmelCase__      )
					__lowerCAmelCase,	__lowerCAmelCase					=							model.load_state_dict(UpperCAmelCase__		,					strict=UpperCAmelCase__      )
					assert missing_keys == ["text_model.embeddings.position_ids"]
					assert (unexpected_keys == ["multi_label_logit_scale"]) or (len(UpperCAmelCase__      ) == 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=UpperCAmelCase__		,					padding=UpperCAmelCase__		,					return_tensors='pt'      )

					with torch.no_grad():
										__lowerCAmelCase					=							model(**UpperCAmelCase__      )

					if model_name == "groupvit-gcc-yfcc":
										__lowerCAmelCase					=							torch.tensor([[13.3523, 6.3629]]      )
					elif model_name == "groupvit-gcc-redcaps":
										__lowerCAmelCase					=							torch.tensor([[16.1873, 8.6230]]      )
					else:
										raise ValueError(F"""Model name {model_name} not supported."""      )
					assert torch.allclose(outputs.logits_per_image		,					UpperCAmelCase__		,					atol=1E-3      )

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

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


if __name__ == "__main__":
		lowerCamelCase      				=     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`.''',
		)
		lowerCamelCase      				=     parser.parse_args()

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






