






"""simple docstring"""





import argparse

import torch
from clip import load

from transformers import CLIPConfig, CLIPModel







def 	lowerCAmelCase_(			SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE       )       ->						List[str]:
    """simple docstring"""

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

    UpperCamelCase__								=     pt_attn_layer.out_proj.weight
    UpperCamelCase__								=     pt_attn_layer.out_proj.bias

    UpperCamelCase__								=     q_proj
    UpperCamelCase__								=     q_proj_bias

    UpperCamelCase__								=     k_proj
    UpperCamelCase__								=     k_proj_bias

    UpperCamelCase__								=     v_proj
    UpperCamelCase__								=     v_proj_bias

    UpperCamelCase__								=     out_proj_weights
    UpperCamelCase__								=     out_proj_bias







def 	lowerCAmelCase_(			SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE       )       ->						Dict:
    """simple docstring"""

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







def 	lowerCAmelCase_(			SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE       )       ->						List[Any]:
    """simple docstring"""

    UpperCamelCase__								=     pt_linear.weight
    UpperCamelCase__								=     pt_linear.bias







def 	lowerCAmelCase_(			SCREAMING_SNAKE_CASE			,						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 	lowerCAmelCase_(			SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE       )       ->						Dict:
    """simple docstring"""

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







def 	lowerCAmelCase_(			SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE       )       ->						Any:
    """simple docstring"""

    UpperCamelCase__								=     pt_model.token_embedding.weight
    UpperCamelCase__								=     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 	lowerCAmelCase_(			SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE       )       ->						Optional[Any]:
    """simple docstring"""

    UpperCamelCase__								=     pt_model.text_projection.data.T

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







def 	lowerCAmelCase_(			SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE       )       ->						Union[str, Any]:
    """simple docstring"""

    UpperCamelCase__								=     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
    UpperCamelCase__								=     pt_model.visual.conva.weight.data
    UpperCamelCase__								=     pt_model.visual.class_embedding
    UpperCamelCase__								=     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 	lowerCAmelCase_(			SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE=None       )       ->						List[Any]:
    """simple docstring"""

    if config_path is not None:
        UpperCamelCase__								=     CLIPConfig.from_pretrained(SCREAMING_SNAKE_CASE       )
    else:
        UpperCamelCase__								=     CLIPConfig(projection_dim=5_12			,						text_config={}			,						vision_config={}       )

    UpperCamelCase__								=     CLIPModel(SCREAMING_SNAKE_CASE       ).eval()

    UpperCamelCase__							,    UpperCamelCase__								=     load(SCREAMING_SNAKE_CASE			,						device='cpu'			,						jit=SCREAMING_SNAKE_CASE       )
    UpperCamelCase__								=     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       )
    UpperCamelCase__								=     pt_model.logit_scale

    UpperCamelCase__								=     torch.arange(0			,						77       ).unsqueeze(0       )
    UpperCamelCase__								=     torch.randn(1			,						3			,						2_24			,						2_24       )

    UpperCamelCase__								=     hf_model(input_ids=SCREAMING_SNAKE_CASE			,						pixel_values=SCREAMING_SNAKE_CASE			,						return_dict=SCREAMING_SNAKE_CASE       )
    UpperCamelCase__								=     hf_outputs.logits_per_image
    UpperCamelCase__								=     hf_outputs.logits_per_text
    UpperCamelCase__							,    UpperCamelCase__								=     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__":
  A__       :			List[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""")
  A__       :			List[Any]=						parser.parse_args()

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












"""simple docstring"""





from __future__ import annotations

from PIL import Image

# Define the first generation of cells
# fmt: off
A__       :			str=						[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,
          0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]







def 	lowerCAmelCase_(			SCREAMING_SNAKE_CASE       )       ->						list[int]:
    """simple docstring"""

    return [int(SCREAMING_SNAKE_CASE       ) for c in F'{ruleset:08}'[:8]]







def 	lowerCAmelCase_(			SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE       )       ->						list[int]:
    """simple docstring"""

    UpperCamelCase__								=     len(cells[0]       )  # 31
    UpperCamelCase__								=     []
    for i in range(SCREAMING_SNAKE_CASE       ):
        # Get the neighbors of each cell
        # Handle neighbours outside bounds by using 0 as their value
        UpperCamelCase__								=     0 if i == 0 else cells[time][i - 1]
        UpperCamelCase__								=     0 if i == population - 1 else cells[time][i + 1]
        # Define a new cell and add it to the new generation
        UpperCamelCase__								=     7 - int(F'{left_neighbor}{cells[time][i]}{right_neighbor}'			,						2       )
        next_generation.append(rule[situation]       )
    return next_generation







def 	lowerCAmelCase_(			SCREAMING_SNAKE_CASE       )       ->						Image.Image:
    """simple docstring"""

    UpperCamelCase__								=     Image.new('RGB'			,						(len(cells[0]       ), len(SCREAMING_SNAKE_CASE       ))       )
    UpperCamelCase__								=     img.load()
    # Generates image
    for w in range(img.width       ):
        for h in range(img.height       ):
            UpperCamelCase__								=     2_55 - int(2_55 * cells[h][w]       )
            UpperCamelCase__								=     (color, color, color)
    return img


if __name__ == "__main__":
  A__       :			Optional[int]=						bin(int(input("""Rule:\n""").strip()))[2:]
  A__       :			str=						format_ruleset(int(rule_num))
  for time in range(16):
    CELLS.append(new_generation(CELLS, rule, time))
  A__       :			Tuple=						generate_image(CELLS)
  # Uncomment to save the image
  # img.save(f"rule_{rule_num}.png")
  img.show()




