import argparse

import torch
from clip import load

from transformers import CLIPConfig, CLIPModel






def    UpperCAmelCase__(						lowerCamelCase_							,	lowerCamelCase_  ):



			"""simple docstring"""





			_snake_case       , _snake_case       , _snake_case              =      pt_attn_layer.in_proj_weight.chunk(3							,	dim=0  )
			_snake_case       , _snake_case       , _snake_case              =      pt_attn_layer.in_proj_bias.chunk(3							,	dim=0  )

			_snake_case              =      pt_attn_layer.out_proj.weight
			_snake_case              =      pt_attn_layer.out_proj.bias

			_snake_case              =      q_proj
			_snake_case              =      q_proj_bias

			_snake_case              =      k_proj
			_snake_case              =      k_proj_bias

			_snake_case              =      v_proj
			_snake_case              =      v_proj_bias

			_snake_case              =      out_proj_weights
			_snake_case              =      out_proj_bias






def    UpperCAmelCase__(						lowerCamelCase_							,	lowerCamelCase_  ):



			"""simple docstring"""





			copy_linear(hf_mlp.fca							,	pt_mlp.c_fc  )
			copy_linear(hf_mlp.fca							,	pt_mlp.c_proj  )






def    UpperCAmelCase__(						lowerCamelCase_							,	lowerCamelCase_  ):



			"""simple docstring"""





			_snake_case              =      pt_linear.weight
			_snake_case              =      pt_linear.bias






def    UpperCAmelCase__(						lowerCamelCase_							,	lowerCamelCase_  ):



			"""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    UpperCAmelCase__(						lowerCamelCase_							,	lowerCamelCase_  ):



			"""simple docstring"""





			for hf_layer, pt_layer in zip(lowerCamelCase_							,	lowerCamelCase_  ):
						copy_layer(lowerCamelCase_							,	lowerCamelCase_  )






def    UpperCAmelCase__(						lowerCamelCase_							,	lowerCamelCase_  ):



			"""simple docstring"""





			_snake_case              =      pt_model.token_embedding.weight
			_snake_case              =      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    UpperCAmelCase__(						lowerCamelCase_							,	lowerCamelCase_  ):



			"""simple docstring"""





			_snake_case              =      pt_model.text_projection.data.T

			# copy text encoder
			copy_encoder(hf_model.text_model							,	lowerCamelCase_  )






def    UpperCAmelCase__(						lowerCamelCase_							,	lowerCamelCase_  ):



			"""simple docstring"""





			_snake_case              =      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
			_snake_case              =      pt_model.visual.conva.weight.data
			_snake_case              =      pt_model.visual.class_embedding
			_snake_case              =      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    UpperCAmelCase__(						lowerCamelCase_							,	lowerCamelCase_							,	lowerCamelCase_=None  ):



			"""simple docstring"""





			if config_path is not None:
						_snake_case              =      CLIPConfig.from_pretrained(lowerCamelCase_  )
			else:
						_snake_case              =      CLIPConfig(projection_dim=512							,	text_config={}							,	vision_config={}  )

			_snake_case              =      CLIPModel(lowerCamelCase_  ).eval()

			_snake_case       , _snake_case              =      load(lowerCamelCase_							,	device="cpu"							,	jit=lowerCamelCase_  )
			_snake_case              =      pt_model.eval()

			copy_text_model_and_projection(lowerCamelCase_							,	lowerCamelCase_  )
			copy_vison_model_and_projection(lowerCamelCase_							,	lowerCamelCase_  )
			_snake_case              =      pt_model.logit_scale

			_snake_case              =      torch.arange(0							,	77  ).unsqueeze(0  )
			_snake_case              =      torch.randn(1							,	3							,	224							,	224  )

			_snake_case              =      hf_model(input_ids=lowerCamelCase_							,	pixel_values=lowerCamelCase_							,	return_dict=lowerCamelCase_  )
			_snake_case              =      hf_outputs.logits_per_image
			_snake_case              =      hf_outputs.logits_per_text
			_snake_case       , _snake_case              =      pt_model(lowerCamelCase_							,	lowerCamelCase_  )

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

			hf_model.save_pretrained(lowerCamelCase_  )


if __name__ == "__main__":
			_SCREAMING_SNAKE_CASE:						str    	=       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''')
			_SCREAMING_SNAKE_CASE:						List[str]    	=       parser.parse_args()

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


def    UpperCAmelCase__(						lowerCamelCase_							,	lowerCamelCase_  ):



			"""simple docstring"""





			_snake_case              =      sorted(lowerCamelCase_							,	key=lambda lowerCamelCase_  : value[2]							,	reverse=lowerCamelCase_  )

			# Create a list of size equal to the maximum deadline
			# and initialize it with -1
			_snake_case              =      max(lowerCamelCase_							,	key=lambda lowerCamelCase_  : value[1]  )[1]
			_snake_case              =      [-1] * max_deadline

			# Finding the maximum profit and the count of jobs
			_snake_case              =      0
			_snake_case              =      0
			for job in jobs:
						# Find a free time slot for this job
						# (Note that we start from the last possible slot)
						for i in range(job[1] - 1							,	-1							,	-1  ):
									if time_slots[i] == -1:
												_snake_case              =      job[0]
												count += 1
												max_profit += job[2]
												break
			return [count, max_profit]


if __name__ == "__main__":
			import doctest

			doctest.testmod()

