






import argparse

import torch
from clip import load

from transformers import CLIPConfig, CLIPModel





def 		A_			(		lowercase_					,       lowercase_	)							->str:







 """simple docstring"""

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

 SCREAMING_SNAKE_CASE							  =						pt_attn_layer.out_proj.weight
 SCREAMING_SNAKE_CASE							  =						pt_attn_layer.out_proj.bias

 SCREAMING_SNAKE_CASE							  =						q_proj
 SCREAMING_SNAKE_CASE							  =						q_proj_bias

 SCREAMING_SNAKE_CASE							  =						k_proj
 SCREAMING_SNAKE_CASE							  =						k_proj_bias

 SCREAMING_SNAKE_CASE							  =						v_proj
 SCREAMING_SNAKE_CASE							  =						v_proj_bias

 SCREAMING_SNAKE_CASE							  =						out_proj_weights
 SCREAMING_SNAKE_CASE							  =						out_proj_bias





def 		A_			(		lowercase_					,       lowercase_	)							->int:







 """simple docstring"""

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





def 		A_			(		lowercase_					,       lowercase_	)							->Optional[Any]:







 """simple docstring"""

 SCREAMING_SNAKE_CASE							  =						pt_linear.weight
 SCREAMING_SNAKE_CASE							  =						pt_linear.bias





def 		A_			(		lowercase_					,       lowercase_	)							->Dict:







 """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 		A_			(		lowercase_					,       lowercase_	)							->int:







 """simple docstring"""

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





def 		A_			(		lowercase_					,       lowercase_	)							->Union[str, Any]:







 """simple docstring"""

 SCREAMING_SNAKE_CASE							  =						pt_model.token_embedding.weight
 SCREAMING_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 		A_			(		lowercase_					,       lowercase_	)							->List[Any]:







 """simple docstring"""

 SCREAMING_SNAKE_CASE							  =						pt_model.text_projection.data.T

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





def 		A_			(		lowercase_					,       lowercase_	)							->List[str]:







 """simple docstring"""

 SCREAMING_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
 SCREAMING_SNAKE_CASE							  =						pt_model.visual.conva.weight.data
 SCREAMING_SNAKE_CASE							  =						pt_model.visual.class_embedding
 SCREAMING_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 		A_			(		lowercase_					,       lowercase_					,       lowercase_=None	)							->Dict:







 """simple docstring"""

 if config_path is not None:
  SCREAMING_SNAKE_CASE							  =						CLIPConfig.from_pretrained(lowercase_	)
 else:
  SCREAMING_SNAKE_CASE							  =						CLIPConfig(projection_dim=5_1_2					,       text_config={}					,       vision_config={}	)

 SCREAMING_SNAKE_CASE							  =						CLIPModel(lowercase_	).eval()

 SCREAMING_SNAKE_CASE  , SCREAMING_SNAKE_CASE							  =						load(lowercase_					,       device='cpu'					,       jit=lowercase_	)
 SCREAMING_SNAKE_CASE							  =						pt_model.eval()

 copy_text_model_and_projection(lowercase_					,       lowercase_	)
 copy_vison_model_and_projection(lowercase_					,       lowercase_	)
 SCREAMING_SNAKE_CASE							  =						pt_model.logit_scale

 SCREAMING_SNAKE_CASE							  =						torch.arange(0					,       7_7	).unsqueeze(0	)
 SCREAMING_SNAKE_CASE							  =						torch.randn(1					,       3					,       2_2_4					,       2_2_4	)

 SCREAMING_SNAKE_CASE							  =						hf_model(input_ids=lowercase_					,       pixel_values=lowercase_					,       return_dict=lowercase_	)
 SCREAMING_SNAKE_CASE							  =						hf_outputs.logits_per_image
 SCREAMING_SNAKE_CASE							  =						hf_outputs.logits_per_text
 SCREAMING_SNAKE_CASE  , SCREAMING_SNAKE_CASE							  =						pt_model(lowercase_					,       lowercase_	)

 assert torch.allclose(lowercase_					,       lowercase_					,       atol=1e-3	)
 assert torch.allclose(lowercase_					,       lowercase_					,       atol=1e-3	)

 hf_model.save_pretrained(lowercase_	)


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

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












from datetime import date

import requests

__UpperCAmelCase      				=							"https://api.carbonintensity.org.uk/intensity"





def 		A_			(		)							->str:







 """simple docstring"""

 SCREAMING_SNAKE_CASE							  =						requests.get(lowercase_	).json()['data'][0]
 return last_half_hour["intensity"]["actual"]





def 		A_			(		lowercase_					,       lowercase_	)							->list:







 """simple docstring"""

 return requests.get(f'''{BASE_URL}/{start}/{end}'''	).json()["data"]


if __name__ == "__main__":
      for entry in fetch_from_to(start=date(2_0_2_0, 1_0, 1), end=date(2_0_2_0, 1_0, 3)):
            print("from {from} to {to}: {intensity[actual]}".format(**entry))
      print(f'{fetch_last_half_hour() = }')




