
import argparse

import torch
from clip import load

from transformers import CLIPConfig, CLIPModel
def       snake_case_	(       __SCREAMING_SNAKE_CASE      :				Union[str, Any]   ,   __SCREAMING_SNAKE_CASE      :				int							):



						"""simple docstring"""





						lowercase_			:  str									= pt_attn_layer.in_proj_weight.chunk(3   ,   dim=0							)
						lowercase_			:  Dict									= pt_attn_layer.in_proj_bias.chunk(3   ,   dim=0							)

						lowercase_			:  str									= pt_attn_layer.out_proj.weight
						lowercase_			:  List[Any]									= pt_attn_layer.out_proj.bias

						lowercase_			:  List[str]									= q_proj
						lowercase_			:  Dict									= q_proj_bias

						lowercase_			:  int									= k_proj
						lowercase_			:  int									= k_proj_bias

						lowercase_			:  int									= v_proj
						lowercase_			:  int									= v_proj_bias

						lowercase_			:  Optional[Any]									= out_proj_weights
						lowercase_			:  int									= out_proj_bias
def       snake_case_	(       __SCREAMING_SNAKE_CASE      :				List[Any]   ,   __SCREAMING_SNAKE_CASE      :				List[Any]							):



						"""simple docstring"""





						copy_linear(hf_mlp.fca   ,   pt_mlp.c_fc							)
						copy_linear(hf_mlp.fca   ,   pt_mlp.c_proj							)
def       snake_case_	(       __SCREAMING_SNAKE_CASE      :				Any   ,   __SCREAMING_SNAKE_CASE      :				List[Any]							):



						"""simple docstring"""





						lowercase_			:  Union[str, Any]									= pt_linear.weight
						lowercase_			:  Optional[Any]									= pt_linear.bias
def       snake_case_	(       __SCREAMING_SNAKE_CASE      :				List[Any]   ,   __SCREAMING_SNAKE_CASE      :				int							):



						"""simple docstring"""





						copy_linear(hf_layer.layer_norma   ,   pt_layer.ln_a							)
						copy_linear(hf_layer.layer_norma   ,   pt_layer.ln_a							)

						# copy MLP
						copy_mlp(hf_layer.mlp   ,   pt_layer.mlp							)

						# copy attn
						copy_attn_layer(hf_layer.self_attn   ,   pt_layer.attn							)
def       snake_case_	(       __SCREAMING_SNAKE_CASE      :				Union[str, Any]   ,   __SCREAMING_SNAKE_CASE      :				List[str]							):



						"""simple docstring"""





						for hf_layer, pt_layer in zip(__SCREAMING_SNAKE_CASE   ,   __SCREAMING_SNAKE_CASE							):
												copy_layer(__SCREAMING_SNAKE_CASE   ,   __SCREAMING_SNAKE_CASE							)
def       snake_case_	(       __SCREAMING_SNAKE_CASE      :				Any   ,   __SCREAMING_SNAKE_CASE      :				Optional[Any]							):



						"""simple docstring"""





						lowercase_			:  List[Any]									= pt_model.token_embedding.weight
						lowercase_			:  int									= pt_model.positional_embedding

						# copy layer norm
						copy_linear(hf_encoder.final_layer_norm   ,   pt_model.ln_final							)

						# copy hidden layers
						copy_layers(hf_encoder.encoder.layers   ,   pt_model.transformer.resblocks							)
def       snake_case_	(       __SCREAMING_SNAKE_CASE      :				List[str]   ,   __SCREAMING_SNAKE_CASE      :				Dict							):



						"""simple docstring"""





						lowercase_			:  Optional[Any]									= pt_model.text_projection.data.T

						# copy text encoder
						copy_encoder(hf_model.text_model   ,   __SCREAMING_SNAKE_CASE							)
def       snake_case_	(       __SCREAMING_SNAKE_CASE      :				Any   ,   __SCREAMING_SNAKE_CASE      :				Optional[Any]							):



						"""simple docstring"""





						lowercase_			:  List[Any]									= pt_model.visual.proj.data.T

						# copy layer norms
						copy_linear(hf_model.vision_model.pre_layrnorm   ,   pt_model.visual.ln_pre							)
						copy_linear(hf_model.vision_model.post_layernorm   ,   pt_model.visual.ln_post							)

						# copy embeds
						lowercase_			:  Tuple									= pt_model.visual.conva.weight.data
						lowercase_			:  Tuple									= pt_model.visual.class_embedding
						lowercase_			:  int									= pt_model.visual.positional_embedding.data

						# copy encoder
						copy_layers(hf_model.vision_model.encoder.layers   ,   pt_model.visual.transformer.resblocks							)
@torch.no_grad()
def       snake_case_	(       __SCREAMING_SNAKE_CASE      :				int   ,   __SCREAMING_SNAKE_CASE      :				Optional[int]   ,   __SCREAMING_SNAKE_CASE      :				Dict=None							):



						"""simple docstring"""





						if config_path is not None:
												lowercase_			:  Union[str, Any]									= CLIPConfig.from_pretrained(__SCREAMING_SNAKE_CASE							)
						else:
												lowercase_			:  List[Any]									= CLIPConfig(projection_dim=512   ,   text_config={}   ,   vision_config={}							)

						lowercase_			:  Optional[int]									= CLIPModel(__SCREAMING_SNAKE_CASE							).eval()

						lowercase_			:  List[str]									= load(__SCREAMING_SNAKE_CASE   ,   device='''cpu'''   ,   jit=__SCREAMING_SNAKE_CASE							)
						lowercase_			:  int									= pt_model.eval()

						copy_text_model_and_projection(__SCREAMING_SNAKE_CASE   ,   __SCREAMING_SNAKE_CASE							)
						copy_vison_model_and_projection(__SCREAMING_SNAKE_CASE   ,   __SCREAMING_SNAKE_CASE							)
						lowercase_			:  Union[str, Any]									= pt_model.logit_scale

						lowercase_			:  Optional[Any]									= torch.arange(0   ,   77							).unsqueeze(0							)
						lowercase_			:  Tuple									= torch.randn(1   ,   3   ,   224   ,   224							)

						lowercase_			:  Optional[int]									= hf_model(input_ids=__SCREAMING_SNAKE_CASE   ,   pixel_values=__SCREAMING_SNAKE_CASE   ,   return_dict=__SCREAMING_SNAKE_CASE							)
						lowercase_			:  Optional[int]									= hf_outputs.logits_per_image
						lowercase_			:  Any									= hf_outputs.logits_per_text
						lowercase_			:  List[str]									= pt_model(__SCREAMING_SNAKE_CASE   ,   __SCREAMING_SNAKE_CASE							)

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

						hf_model.save_pretrained(__SCREAMING_SNAKE_CASE							)


if __name__ == "__main__":
							_lowercase				:						int												= argparse.ArgumentParser()
							parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
							parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to fairseq checkpoint")
							parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
							_lowercase				:						Optional[int]												= parser.parse_args()

							convert_clip_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)





'''simple docstring'''







from typing import List

from ...processing_utils import ProcessorMixin
from ...utils import is_torch_available


if is_torch_available():
							import torch






class 				lowerCAmelCase__     (						lowerCamelCase_  ):
					lowerCAmelCase_       =  ['''image_processor''', '''tokenizer''']
					lowerCAmelCase_       =  '''OneFormerImageProcessor'''
					lowerCAmelCase_       =  ('''CLIPTokenizer''', '''CLIPTokenizerFast''')






					def __init__(     self       ,							__SCREAMING_SNAKE_CASE=None       ,							__SCREAMING_SNAKE_CASE=None       ,							__SCREAMING_SNAKE_CASE = 77       ,							__SCREAMING_SNAKE_CASE = 77       ,							**__SCREAMING_SNAKE_CASE	):


											"""simple docstring"""





											if image_processor is None:
																	raise ValueError('''You need to specify an `image_processor`.'''	)
											if tokenizer is None:
																	raise ValueError('''You need to specify a `tokenizer`.'''	)

											lowercase_			:  Dict									= max_seq_length
											lowercase_			:  Dict									= task_seq_length

											super().__init__(__SCREAMING_SNAKE_CASE       ,							__SCREAMING_SNAKE_CASE	)






					def 			_snake_case					(     self       ,							__SCREAMING_SNAKE_CASE=None       ,							__SCREAMING_SNAKE_CASE=77	):


											"""simple docstring"""





											if text_list is None:
																	raise ValueError('''tokens cannot be None.'''	)

											lowercase_			:  str									= self.tokenizer(__SCREAMING_SNAKE_CASE       ,							padding='''max_length'''       ,							max_length=__SCREAMING_SNAKE_CASE       ,							truncation=__SCREAMING_SNAKE_CASE	)

											lowercase_		,				lowercase_			:  List[str]									= tokens['''attention_mask'''], tokens['''input_ids''']

											lowercase_			:  List[str]									= []
											for attn_mask, input_id in zip(__SCREAMING_SNAKE_CASE       ,							__SCREAMING_SNAKE_CASE	):
																	lowercase_			:  Optional[Any]									= torch.tensor(__SCREAMING_SNAKE_CASE	) * torch.tensor(__SCREAMING_SNAKE_CASE	)
																	token_inputs.append(token.unsqueeze(0	)	)

											lowercase_			:  str									= torch.cat(__SCREAMING_SNAKE_CASE       ,							dim=0	)
											return token_inputs






					def __call__(     self       ,							__SCREAMING_SNAKE_CASE=None       ,							__SCREAMING_SNAKE_CASE=None       ,							__SCREAMING_SNAKE_CASE=None       ,							**__SCREAMING_SNAKE_CASE	):


											"""simple docstring"""





											if task_inputs is None:
																	raise ValueError('''You have to specify the task_input. Found None.'''	)
											elif images is None:
																	raise ValueError('''You have to specify the image. Found None.'''	)

											if not all(task in ['''semantic''', '''instance''', '''panoptic'''] for task in task_inputs	):
																	raise ValueError('''task_inputs must be semantic, instance, or panoptic.'''	)

											lowercase_			:  Optional[int]									= self.image_processor(__SCREAMING_SNAKE_CASE       ,							__SCREAMING_SNAKE_CASE       ,							__SCREAMING_SNAKE_CASE       ,							**__SCREAMING_SNAKE_CASE	)

											if isinstance(__SCREAMING_SNAKE_CASE       ,							__SCREAMING_SNAKE_CASE	):
																	lowercase_			:  List[str]									= [task_inputs]

											if isinstance(__SCREAMING_SNAKE_CASE       ,							__SCREAMING_SNAKE_CASE	) and all(isinstance(__SCREAMING_SNAKE_CASE       ,							__SCREAMING_SNAKE_CASE	) for task_input in task_inputs	):
																	lowercase_			:  List[str]									= []
																	for task in task_inputs:
																							lowercase_			:  Tuple									= F'''the task is {task}'''
																							task_token_inputs.append(__SCREAMING_SNAKE_CASE	)
																	lowercase_			:  Tuple									= self._preprocess_text(__SCREAMING_SNAKE_CASE       ,							max_length=self.task_seq_length	)
											else:
																	raise TypeError('''Task Inputs should be a string or a list of strings.'''	)

											if hasattr(__SCREAMING_SNAKE_CASE       ,							'''text_inputs'''	):
																	lowercase_			:  Any									= encoded_inputs.text_inputs

																	lowercase_			:  int									= []
																	for texts in texts_list:
																							lowercase_			:  Any									= self._preprocess_text(__SCREAMING_SNAKE_CASE       ,							max_length=self.max_seq_length	)
																							text_inputs.append(text_input_list.unsqueeze(0	)	)

																	lowercase_			:  Tuple									= torch.cat(__SCREAMING_SNAKE_CASE       ,							dim=0	)

											return encoded_inputs






					def 			_snake_case					(     self       ,							__SCREAMING_SNAKE_CASE=None       ,							__SCREAMING_SNAKE_CASE=None       ,							__SCREAMING_SNAKE_CASE=None       ,							**__SCREAMING_SNAKE_CASE	):


											"""simple docstring"""





											if task_inputs is None:
																	raise ValueError('''You have to specify the task_input. Found None.'''	)
											elif images is None:
																	raise ValueError('''You have to specify the image. Found None.'''	)

											if not all(task in ['''semantic''', '''instance''', '''panoptic'''] for task in task_inputs	):
																	raise ValueError('''task_inputs must be semantic, instance, or panoptic.'''	)

											lowercase_			:  Dict									= self.image_processor.encode_inputs(__SCREAMING_SNAKE_CASE       ,							__SCREAMING_SNAKE_CASE       ,							__SCREAMING_SNAKE_CASE       ,							**__SCREAMING_SNAKE_CASE	)

											if isinstance(__SCREAMING_SNAKE_CASE       ,							__SCREAMING_SNAKE_CASE	):
																	lowercase_			:  List[Any]									= [task_inputs]

											if isinstance(__SCREAMING_SNAKE_CASE       ,							__SCREAMING_SNAKE_CASE	) and all(isinstance(__SCREAMING_SNAKE_CASE       ,							__SCREAMING_SNAKE_CASE	) for task_input in task_inputs	):
																	lowercase_			:  int									= []
																	for task in task_inputs:
																							lowercase_			:  int									= F'''the task is {task}'''
																							task_token_inputs.append(__SCREAMING_SNAKE_CASE	)
																	lowercase_			:  str									= self._preprocess_text(__SCREAMING_SNAKE_CASE       ,							max_length=self.task_seq_length	)
											else:
																	raise TypeError('''Task Inputs should be a string or a list of strings.'''	)

											if hasattr(__SCREAMING_SNAKE_CASE       ,							'''text_inputs'''	):
																	lowercase_			:  Dict									= encoded_inputs.text_inputs

																	lowercase_			:  str									= []
																	for texts in texts_list:
																							lowercase_			:  List[str]									= self._preprocess_text(__SCREAMING_SNAKE_CASE       ,							max_length=self.max_seq_length	)
																							text_inputs.append(text_input_list.unsqueeze(0	)	)

																	lowercase_			:  Union[str, Any]									= torch.cat(__SCREAMING_SNAKE_CASE       ,							dim=0	)

											return encoded_inputs






					def 			_snake_case					(     self       ,							*__SCREAMING_SNAKE_CASE       ,							**__SCREAMING_SNAKE_CASE	):


											"""simple docstring"""





											return self.image_processor.post_process_semantic_segmentation(*__SCREAMING_SNAKE_CASE       ,							**__SCREAMING_SNAKE_CASE	)






					def 			_snake_case					(     self       ,							*__SCREAMING_SNAKE_CASE       ,							**__SCREAMING_SNAKE_CASE	):


											"""simple docstring"""





											return self.image_processor.post_process_instance_segmentation(*__SCREAMING_SNAKE_CASE       ,							**__SCREAMING_SNAKE_CASE	)






					def 			_snake_case					(     self       ,							*__SCREAMING_SNAKE_CASE       ,							**__SCREAMING_SNAKE_CASE	):


											"""simple docstring"""





											return self.image_processor.post_process_panoptic_segmentation(*__SCREAMING_SNAKE_CASE       ,							**__SCREAMING_SNAKE_CASE	)



